Code
knitr::opts_chunk$set(cache = TRUE)Hooded Plover Demography
Nest and Chick Survival Modelling
Prerequisites
R packages
- The following packages are needed for analysis and can be easily installed from CRAN or GitHub by running the following code chunk:
Code
# a vector of all the packages needed in the project
packages_required_in_project <- c("tidyverse",
"readxl",
"RMark",
"RColorBrewer",
"patchwork",
"mapview",
"lubridate",
"extrafont",
"here",
"DT",
"leaflet",
"sf",
"leafpop",
"tsibble",
"corrplot",
"gghalves",
"gam",
"pscl",
"gamlss",
"gt",
"lme4",
"timeDate",
"circular",
"openxlsx",
"fuzzyjoin")
# of the required packages, check if some need to be installed
new.packages <-
packages_required_in_project[!(packages_required_in_project %in%
installed.packages()[,"Package"])]
# install all packages that are not locally available
if(length(new.packages)) install.packages(new.packages)
# load all the packages into the current R session
lapply(packages_required_in_project, require, character.only = TRUE)
# set the home directory to where the project is locally based (i.e., to find
# the relevant datasets to import, etc.
here::set_here()Plotting themes
- The following plotting themes, colors, and typefaces are used throughout the project:
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# Find fonts from computer that you want. Use regular expressions to do this
# For example, load all fonts that are 'verdana' or 'Verdana'
extrafont::font_import(pattern = "[V/v]erdana", prompt = FALSE)
# check which fonts were loaded
extrafont::fonts()
extrafont::fonttable()
extrafont::loadfonts() # load these into R
# define the plotting theme to be used in subsequent ggplots
luke_theme <-
theme_bw() +
theme(
text = element_text(family = "Verdana"),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8),
axis.title.x = element_text(size = 10),
axis.text.x = element_text(size = 8),
axis.title.y = element_text(size = 10),
axis.text.y = element_text(size = 8),
strip.text = element_text(size = 10),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.ticks = element_line(size = 0.5, colour = "grey40"),
axis.ticks.length = unit(0.2, "cm"),
panel.border = element_rect(linetype = "solid", colour = "grey"),
legend.position = c(0.1, 0.9)
)
region_names <- c(
'FP' = "Fleurieu Peninsula",
'MP' = "Mornington Peninsula",
'BSC' = "Bellarine / Surf Coast",
'PI' = "Phillip Island",
'BC' = "Bass Coast" )
# set mapview to show satellite imagery
mapviewOptions(basemaps = c("Esri.WorldImagery"))Project-specific Functions
The following custom functions are used to
%!in%
This function simply does the opposite of %in%, which is used to test if elements on the left-hand side are members of the set defined by the right-hand side. %!in% returns a logical vector indicating whether each element on the left-hand side is not present in the right-hand side.
Code
`%!in%` = Negate(`%in%`)nest_import()
This function imports the HOPL nest survival data stored as Excel sheets into R, wrangles it into a single dataframe, and prepares it for subsequent analysis (e.g., specifies relevent date columns, etc.)
arguments:
year_1: first calender year of the focal data sheet (e.g., 2002)year_2: second calender year of the focal data set (i.e., always year_1 + 1)file_name: name of the Excel sheet to import data fromsite: site that the data describes (MP, FP, BSC, BC or PI)extra_text: the extra text associated with each sheet in the Excel file (i.e., besides from the year)first_found_date_col: the number of the column in the sheets that correspond to the first found datelast_alive_date_col: the number of the column in the sheets that correspond to the last alive datelast_checked_col: the number of the column in the sheets that correspond to the last checked date
Code
nest_import <-
function(year_1, year_2, file_name, site, extra_text = NULL, Nest_ID_col_name = "Nest ID_2",
first_found_date_col, last_alive_date_col, last_checked_col) {
if(is.null(extra_text)){
file <-
read_excel(paste0("data/", file_name),
sheet = paste0(site, " ", year_1, "_", str_sub(year_2, 3, 4)),
col_types = "text", na = "n/a")
}
else{
file <-
read_excel(paste0("data/", file_name),
sheet = paste0(site, " ", year_1, "_", str_sub(year_2, 3, 4), extra_text),
col_types = "text", na = "n/a")
}
file %>%
# simplify column names
rename(first_found = first_found_date_col,
last_alive = last_alive_date_col,
last_checked = last_checked_col,
Fate = `Hatch?`,
season = Season,
# site = Site,
site = `Suggested update to site name`,
nest_ID = Nest_ID_col_name,
nest_hab = `Nest habitat`,
management_status = `Nest managed?`,
management_type = `Nest management type`,
nest_lat = `Nest latitude`,
nest_lon = `Nest longitude`,
chick_1st_ob = which(names(file) %in% c("Date chicks sighted", "Chicks first obsv date"))[1],
clutch_comp = which(names(file) %in% c("Known clutch complete date", "Known laying date")),
state_1st_ob = `Age stage first observed`,
chick_management_status = which(names(file) %in% c("Chick manged?", "Chick managed?")),
chick_management_type = `Chick management type`) %>%
# consolidate columns
dplyr::select(season, site, nest_ID, first_found, last_alive, last_checked, Fate, nest_hab,
management_status, management_type, nest_lat, nest_lon,
chick_1st_ob, clutch_comp, state_1st_ob,
chick_management_status, chick_management_type) %>%
# wrangle: clean up Fate column for consistency
mutate(Fate = ifelse(Fate == "?", "Unk", as.character(Fate))) %>%
mutate(Fate = toupper(Fate)) %>%
# remove rows that have either an NA or numbers in the first_found column
filter(is.na(first_found) | grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", first_found)) %>%
# clean up the last_checked and last_alive columns so that non-number data are dropped to NA
mutate(last_checked = ifelse(!is.na(last_checked) &
!grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_checked),
NA, last_checked)) %>%
mutate(last_alive = ifelse(!is.na(last_alive) &
!grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_alive),
NA, last_alive))%>%
# wrangle: if date last alive is "Unk." make it "NA"
mutate(last_alive = ifelse(str_detect(last_alive, "Unk."), NA, last_alive),
# change Fate to 1 or 0 (1 = failed, 0 = hatched, NA = unknown)
Fate = ifelse(Fate == "Y", "0",
ifelse(Fate == "N", "1",
ifelse(Fate == "UNK", NA, "XXX")))) %>%
# remove nests without Fate information
filter(Fate %in% c("0", "1")) %>%
# classify the chick_1st_ob and clutch_comp columns as.Date()
mutate(chick_1st_ob = case_when(
# Excel serial dates stored as numbers
str_detect(chick_1st_ob, "^[0-9]+$") ~ as.Date(as.numeric(chick_1st_ob), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
),
clutch_comp = case_when(
# Excel serial dates stored as numbers
str_detect(clutch_comp, "^[0-9]+$") ~ as.Date(as.numeric(clutch_comp), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
)) %>%
# if the clutch completion date is NA, then estimate it as 35 days before estimated hatch date - 28 days
# using the date of first chick observation and the size of the chicks when seen
mutate(clutch_comp = ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3 - 28,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10 - 28,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19 - 28,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30 - 28,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35 - 28,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob - 28, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob), NA, NA)) %>% as.Date()) %>%
# specify the last_alive2 date for hatched nests as the estimated hatch date
# using the date of first chick observation and the size of the chicks when seen
mutate(last_alive2 = ifelse(Fate == "0" & !is.na(clutch_comp) & is.na(last_alive), clutch_comp + 28,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob) & is.na(last_alive),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob) & is.na(last_alive), NA, NA))) %>% as.Date()) %>%
# clean up the last_alive date given the new last_alive2 date extracted above
mutate(last_alive = ifelse(!is.na(last_alive2),
paste0(str_sub(as.character(last_alive2), 9, 10),
str_sub(as.character(last_alive2), 6, 7),
str_sub(as.character(last_alive2), 1, 4)), last_alive)) %>%
mutate(
# wrangle: if last_alive has a date and last_checked is NA, then change
# last_checked to the date in last_alive
last_checked = ifelse(!is.na(last_alive) & is.na(last_checked),
last_alive,
# if both last_alive and last_checked is "NA", then
# change last_checked to the first_found date
ifelse(is.na(last_alive) & is.na(last_checked),
first_found,
last_checked))) %>%
mutate(
# wrangle: if last_alive is NA and the nest hatched and last_checked has a
# date, then specify last_alive as the date from last_checked
last_alive = ifelse(is.na(last_alive) & Fate == "0" & !is.na(last_checked),
last_checked,
# if the last_alive is NA and the nest failed and
# last_checked has a date, then specify last_alive as the
# date from first_found
ifelse(is.na(last_alive) & Fate == "1" & !is.na(last_checked),
first_found,
last_alive))) %>%
# only keep rows that have 8 characters in the first_found, last_alive, and last_checked
filter(nchar(first_found) == 8 & nchar(last_alive) == 8 & nchar(last_checked) == 8) %>%
# specify date columns as a date string
mutate(first_found2 = as.Date(paste(str_sub(first_found, 5, 8),
str_sub(first_found, 3, 4),
str_sub(first_found, 1, 2), sep = "-")),
last_alive2 = as.Date(paste(str_sub(last_alive, 5, 8),
str_sub(last_alive, 3, 4),
str_sub(last_alive, 1, 2), sep = "-")),
last_checked2 = as.Date(paste(str_sub(last_checked, 5, 8),
str_sub(last_checked, 3, 4),
str_sub(last_checked, 1, 2), sep = "-"))) %>%
# if the found date is > days before the last alive date, and the clutch
# completion date is not NA and is < 40 days before the last alive date
# then replace the found date with the clutch completion date
mutate(first_found2 = ifelse(!is.na(clutch_comp) & ((last_alive2 - first_found2) > 40 | (last_alive2 - first_found2) < 0)
& ((last_alive2 - clutch_comp) < 40 | (last_alive2 - clutch_comp) > 0),
clutch_comp, first_found2) %>% as.Date()) %>%
# if last checked date is before last alive date, then change it to the
# last alive date, if not then leave as is
mutate(last_checked2 = ifelse(!is.na(last_alive2) & !is.na(last_checked2) & last_checked2 < last_alive2,
last_alive2, last_checked2) %>% as.Date()) %>%
# # julian dates
# mutate(FirstFound = ((as.integer(format(first_found2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(first_found2 + 180, "%j")),
# LastPresent = ((as.integer(format(last_alive2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(last_alive2 + 180, "%j")),
# LastChecked = ((as.integer(format(last_checked2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365)) %>% #as.numeric(format(last_checked2 + 180, "%j"))) %>%
# julian dates
mutate(FirstFound = as.numeric(format(first_found2 + 180, "%j")),
LastPresent = as.numeric(format(last_alive2 + 180, "%j")),
LastChecked = as.numeric(format(last_checked2 + 180, "%j"))) %>%
# remove all nests that have unknown fate
# filter(!is.na(Fate)) %>%
# clean up the management_type column
mutate(management_type = tolower(management_type)) %>%
mutate(management_type = str_replace(management_type, "acess", "access")) %>%
mutate(management_type = str_replace(management_type, "and", ",")) %>%
mutate(management_type = str_replace(management_type, "temporary", "")) %>%
mutate(management_type = str_replace_all(management_type, " ", "")) %>%
mutate(management_type = str_replace_all(management_type, "shelters", "")) %>%
mutate(management_type = str_replace_all(management_type, "banners", "")) %>%
mutate(management_type = str_replace_all(management_type, ",,", ",")) %>%
mutate(sign_access = ifelse(str_detect(management_type, "signaccess"), 1, 0)) %>%
mutate(perm_fence = ifelse(str_detect(management_type, "permanentfence") | str_detect(management_type, "ringlockfence"), 1, 0)) %>%
mutate(sign_nest = ifelse(str_detect(management_type, "signnest"), 1, 0)) %>%
mutate(rope_fence = ifelse(str_detect(management_type, "ropefence"), 1, 0)) %>%
mutate(wardens = ifelse(str_detect(management_type, "wardens"), 1, 0)) %>%
mutate(none = ifelse(str_detect(management_type, "none"), 1,
ifelse(is.na(management_type) & management_status == "N", 1, 0))) %>%
mutate(other = ifelse(str_detect(management_type, "other"), 1, 0)) %>%
mutate(nest_hab = tolower(nest_hab)) %>%
# Level 4 is Sign Nest, Rope fence, Wardens
mutate(management_level = ifelse(rope_fence == 1 & wardens == 1, 4,
# Level 3 is Sign Nest, Rope fence
ifelse(rope_fence == 1, 3,
# Level 2 is Sign Nest
ifelse(sign_nest == 1, 2,
# Level 1 is Sign Access or Permanent fence (but only if nest in dune)
ifelse((perm_fence == 1 & nest_hab == "dune") | sign_access == 1, 1,
ifelse(none == 1, 0, NA))))))
}Importing and checking nest survival data
Mornington Peninsula
Flag potential issues in data
First we import the data and run a few checks to assess if there are any rows with the following issues:
found date is not 8 characters
last seen alive date is not 8 characters
last checked date is not 8 characters
found date missing
last seen alive date missing
last checked date missing
Nest managed? is not Y or N
Nest habitat is not Beach, Dune, Foredune/face, or Estuary/spit
Management type is not sufficient for making levels
Double check dates because incubation time greater than 35 days
Found date is after Last Alive date (should be greater or equal)
Found date is after Last Checked date (should be greater or equal)
Last Checked date is before Last Alive date (should be greater or equal)
Code
MP_nest_data_issues <-
suppressMessages(
bind_rows(
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2023", "_", str_sub("2024", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2022", "_", str_sub("2023", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2021", "_", str_sub("2022", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2020", "_", str_sub("2021", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2019", "_", str_sub("2020", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2018", "_", str_sub("2019", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2017", "_", str_sub("2018", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2016", "_", str_sub("2017", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2015", "_", str_sub("2016", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2014", "_", str_sub("2015", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2013", "_", str_sub("2014", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2012", "_", str_sub("2013", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2011", "_", str_sub("2012", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2010", "_", str_sub("2011", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2009", "_", str_sub("2010", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2008", "_", str_sub("2009", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2007", "_", str_sub("2008", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Nesting Summary All Years_19062025.xlsx"),
sheet = paste0("MP", " ", "2006", "_", str_sub("2007", 3, 4)),
col_types = "text", na = "n/a"))) %>%
filter(!is.na(Season)) %>%
rename(first_found = 13,
last_alive = 33,
last_checked = 39,
Fate = `Hatch?`,
season = Season,
# site = Site,
site = `Suggested update to site name`,
nest_ID = `Nest ID_2`,
nest_hab = `Nest habitat`,
management_status = `Nest managed?`,
management_type = `Nest management type`,
nest_lat = `Nest latitude`,
nest_lon = `Nest longitude`,
state_1st_ob = `Age stage first observed`) %>%
rename(
chick_1st_ob = !!names(.)[which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))][1],
clutch_comp = !!names(.)[which(names(.) %in% c("Known clutch complete date", "Known laying date"))][1]
) %>%
# chick_1st_ob = which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))[1],
# clutch_comp = which(names(.) %in% c("Known clutch complete date", "Known laying date"))) %>%
dplyr::select(season, site, nest_ID, first_found, last_alive, last_checked, Fate, nest_hab,
management_status, management_type, nest_lat, nest_lon, site,
chick_1st_ob, clutch_comp, state_1st_ob) %>%
mutate(Fate = ifelse(Fate == "?", "Unk", Fate)) %>%
mutate(Fate = toupper(Fate)) %>%
filter(
is.na(first_found) |
grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", first_found)
) %>%
mutate(last_checked = ifelse(!is.na(last_checked) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_checked),
NA, last_checked)) %>%
mutate(last_alive = ifelse(!is.na(last_alive) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_alive),
NA, last_alive)) %>%
mutate(last_alive = ifelse(str_detect(last_alive, "Unk."), NA, last_alive),
Fate = ifelse(Fate == "Y", "0",
ifelse(Fate == "N", "1",
ifelse(Fate == "UNK", NA, "XXX")))) %>%
# remove nests without Fate information
filter(Fate %in% c("0", "1")) %>%
mutate(chick_1st_ob = case_when(
# Excel serial dates stored as numbers
str_detect(chick_1st_ob, "^[0-9]+$") ~ as.Date(as.numeric(chick_1st_ob), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
),
clutch_comp = case_when(
# Excel serial dates stored as numbers
str_detect(clutch_comp, "^[0-9]+$") ~ as.Date(as.numeric(clutch_comp), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
)) %>%
# if the clutch completion date is NA, then estimate it as 35 days before estimated hatch date #28 days incubation length
# using the date of first chick observation and the size of the chicks when seen
mutate(clutch_comp = ifelse(!is.na(clutch_comp), clutch_comp,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3 - 28,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10 - 28,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19 - 28,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30 - 28,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35 - 28,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob - 28, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob), NA, NA))) %>% as.Date()) %>%
mutate(last_alive2 = ifelse(Fate == "0" & !is.na(clutch_comp) & is.na(last_alive), clutch_comp + 28,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob) & is.na(last_alive),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob) & is.na(last_alive), NA, NA))) %>% as.Date()) %>%
# mutate(last_alive2 = ifelse(Fate == "0" & !is.na(chick_1st_ob) & last_alive2 > chick_1st_ob, chick_1st_ob, last_alive2) %>% as.Date()) %>%
mutate(last_alive = ifelse(!is.na(last_alive2),
paste0(str_sub(as.character(last_alive2), 9, 10),
str_sub(as.character(last_alive2), 6, 7),
str_sub(as.character(last_alive2), 1, 4)), last_alive)) %>%
mutate(
last_checked = ifelse(!is.na(last_alive) & is.na(last_checked),
last_alive,
ifelse(is.na(last_alive) & is.na(last_checked),
first_found, last_checked))) %>%
mutate(
last_alive = ifelse(is.na(last_alive) & Fate == "0" & !is.na(last_checked),
last_checked,
ifelse(is.na(last_alive) & Fate == "1" & !is.na(last_checked),
first_found,
last_alive))) %>%
filter(
(is.na(last_alive) | nchar(last_alive) == 8) &
(is.na(first_found) | nchar(first_found) == 8) &
(is.na(last_checked) | nchar(last_checked) == 8)
) %>%
mutate(first_found2 = as.Date(paste(str_sub(first_found, 5, 8),
str_sub(first_found, 3, 4),
str_sub(first_found, 1, 2), sep = "-")),
last_alive2 = as.Date(paste(str_sub(last_alive, 5, 8),
str_sub(last_alive, 3, 4),
str_sub(last_alive, 1, 2), sep = "-")),
last_checked2 = as.Date(paste(str_sub(last_checked, 5, 8),
str_sub(last_checked, 3, 4),
str_sub(last_checked, 1, 2), sep = "-"))) %>%
mutate(last_checked2 = ifelse(!is.na(last_alive2) & !is.na(last_checked2) & last_checked2 < last_alive2,
last_alive2, last_checked2) %>% as.Date()) %>%
mutate(first_found2 = ifelse(!is.na(clutch_comp) & ((last_alive2 - first_found2) > 40 | (last_alive2 - first_found2) < 0)
& ((last_alive2 - clutch_comp) < 40 | (last_alive2 - clutch_comp) > 0),
clutch_comp, first_found2) %>% as.Date()) %>%
mutate(FirstFound = as.numeric(format(first_found2 + 180, "%j")),
LastPresent = as.numeric(format(last_alive2 + 180, "%j")),
LastChecked = as.numeric(format(last_checked2 + 180, "%j"))) %>%
# mutate(FirstFound = ((as.integer(format(first_found2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(first_found2 + 180, "%j")),
# LastPresent = ((as.integer(format(last_alive2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(last_alive2 + 180, "%j")),
# LastChecked = ((as.integer(format(last_checked2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365)) %>% #as.numeric(format(last_checked2 + 180, "%j"))) %>%
mutate(management_type = tolower(management_type)) %>%
mutate(nest_hab = tolower(nest_hab)) %>%
mutate(management_type = str_replace(management_type, "acess", "access")) %>%
mutate(management_type = str_replace(management_type, "and", ",")) %>%
mutate(management_type = str_replace(management_type, "temporary", "")) %>%
mutate(management_type = str_replace_all(management_type, " ", "")) %>%
mutate(management_type = str_replace_all(management_type, "shelters", "")) %>%
mutate(management_type = str_replace_all(management_type, "banners", "")) %>%
mutate(management_type = str_replace_all(management_type, ",,", ",")) %>%
mutate(sign_access = ifelse(str_detect(management_type, "signaccess"), 1, 0)) %>%
mutate(sign_nest = ifelse(str_detect(management_type, "signnest"), 1, 0)) %>%
mutate(rope_fence = ifelse(str_detect(management_type, "ropefence"), 1, 0)) %>%
mutate(perm_fence = ifelse(str_detect(management_type, "permanentfence"), 1, 0)) %>% # & nest_hab == "dune", 1, 0)) %>%
mutate(wardens = ifelse(str_detect(management_type, "wardens"), 1, 0)) %>%
mutate(none = ifelse(str_detect(management_type, "none"), 1,
ifelse(is.na(management_type) & management_status == "N", 1, 0))) %>%
mutate(other = ifelse(str_detect(management_type, "other"), 1, 0)) %>%
mutate(management_level = ifelse((sign_access == 1 | perm_fence == 1) & sign_nest == 1 & rope_fence == 1 & wardens == 1, 4,
ifelse(rope_fence == 1, 3,
ifelse(sign_nest == 1, 2,
ifelse(sign_access == 1 | perm_fence == 1, 1,
ifelse(none == 1, 0, NA)))))) %>%
mutate(sign_nest_no_sign_access = ifelse(sign_access == 0 & sign_nest == 1, 1, 0)) %>%
mutate(fence_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & rope_fence == 1, 1, 0)) %>%
mutate(wardens_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & wardens == 1, 1, 0)) %>%
mutate(wardens_no_fence = ifelse(rope_fence == 1 & wardens == 1, 1, 0)) %>%
mutate(just_wardens = ifelse(rope_fence == 0 & sign_access == 0 & sign_nest == 0 & wardens == 1, 1, 0)) %>%
dplyr::select(-other, -sign_nest_no_sign_access, -fence_no_sign,
-wardens_no_sign, -wardens_no_fence, -just_wardens) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
mutate(region = "MP") %>%
mutate(site = as.factor(site)) %>%
mutate(issue1 = ifelse(nchar(first_found) != 8, "found date is not 8 characters; ", NA)) %>%
mutate(issue2 = ifelse(nchar(last_alive) != 8, "last seen alive date is not 8 characters; ", NA)) %>%
mutate(issue3 = ifelse(nchar(last_checked) != 8, "last checked date is not 8 characters; ", NA)) %>%
mutate(issue4 = ifelse(is.na(first_found), "found date missing; ", NA)) %>%
mutate(issue5 = ifelse(is.na(last_alive), "last seen alive date missing; ", NA)) %>%
mutate(issue6 = ifelse(is.na(last_checked), "last checked date missing; ", NA)) %>%
mutate(issue7 = ifelse(management_status %!in% c("Y", "N"), "Nest managed? is not Y or N; ", NA)) %>%
mutate(issue8 = ifelse(nest_hab %!in% c("beach", "dune", "foredune/face", "estuary/spit"),
"Nest habitat is not beach, dune, foredune/face, or estuary/spit; ", NA)) %>%
mutate(issue9 = ifelse(is.na(management_level), "Management type is not sufficient for making levels; ", NA)) %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
mutate(issue10 = ifelse(found_and_alive_diff > 40 , "Double check dates because incubation time greater than 40 days; ", NA)) %>%
mutate(issue11 = ifelse(first_found2 > last_alive2, "Found date is after Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issue12 = ifelse(first_found2 > last_checked2, "Found date is after Last Checked date (should be greater or equal); ", NA)) %>%
mutate(issue13 = ifelse(last_checked2 < last_alive2, "Last Checked date is before Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issues = ifelse(is.na(issue1) & is.na(issue2) & is.na(issue3) &
is.na(issue4) & is.na(issue5) & is.na(issue6) &
is.na(issue7) & is.na(issue8) & is.na(issue9) &
is.na(issue10) & is.na(issue11) & is.na(issue12) & is.na(issue13), NA,
paste0(issue1, issue2, issue3,
issue4, issue5, issue6,
issue7, issue8, issue9,
issue10, issue11, issue12, issue13))) %>%
mutate(issues = str_remove_all(issues, "NA")) %>%
mutate(issues = ifelse(is.na(issues), "usable", issues)) %>%
dplyr::select(-issue1, -issue2, -issue3,
-issue4, -issue5, -issue6,
-issue7, -issue8, -issue9,
-issue10, -issue11, -issue12, -issue13) %>%
filter(issues != "usable") %>%
arrange(issues)
#
# MP_nest_data_issues %>%
# filter(str_detect(issues, "date")) %>%
# select(nest_ID, Fate, clutch_comp, chick_1st_ob, first_found2, last_alive2, last_checked2, first_found, last_alive, last_checked, found_and_alive_diff, issues) %>% View()all issues (probably most of these can’t be resolved)
Code
# Create a new workbook to store issues as two sheets
MP_nest_issues <- createWorkbook()
# Add all_issues sheet
addWorksheet(MP_nest_issues, "all_issues")
# view issues interactively in RStudio Viewer
MP_nest_data_issues %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write MP_nest_data_check
writeData(MP_nest_issues, sheet = "all_issues", MP_nest_data_issues)date-specific issues (probably most of these CAN be resolved)
Code
# Add date_issues
addWorksheet(MP_nest_issues, "date_issues")
# view issues interactively in RStudio Viewer
MP_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not Beach, Dune, Foredune/face, Estuary/spit, or Rocks; ")) %>%
dplyr::select(season, nest_ID, nest_hab, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write MP_nest_data_check
MP_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not Beach, Dune, Foredune/face, Estuary/spit, or Rocks; ")) %>%
dplyr::select(season, nest_ID, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
writeData(MP_nest_issues, sheet = "date_issues", .)
# Save the workbook
saveWorkbook(MP_nest_issues,
here("data/nest_issues_commented/MP_nest_data_issues_011025.xlsx"), overwrite = TRUE)Import data
Code
nest_data_MP <-
bind_rows(
nest_import(year_1 = "2023", year_2 = "2024",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 13,
last_alive_date_col = 33,
last_checked_col = 39, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2022", year_2 = "2023",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 13,
last_alive_date_col = 33,
last_checked_col = 39, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2021", year_2 = "2022",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 13,
last_alive_date_col = 33,
last_checked_col = 39, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2020", year_2 = "2021",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2019", year_2 = "2020",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2018", year_2 = "2019",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2017", year_2 = "2018",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2016", year_2 = "2017",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2015", year_2 = "2016",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2014", year_2 = "2015",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2013", year_2 = "2014",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2012", year_2 = "2013",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2011", year_2 = "2012",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2010", year_2 = "2011",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2009", year_2 = "2010",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2008", year_2 = "2009",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2007", year_2 = "2008",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID"),
nest_import(year_1 = "2006", year_2 = "2007",
file_name = "MP Nesting Summary All Years_19062025.xlsx", site = "MP",
first_found_date_col = 12,
last_alive_date_col = 31,
last_checked_col = 36, Nest_ID_col_name = "Nest ID")) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
filter(!is.na(FirstFound) & !is.na(LastPresent) & !is.na(LastChecked)) %>%
filter(management_status %in% c("Y", "N")) %>%
filter(nest_hab %in% c("beach", "dune", "foredune/face", "estuary/spit")) %>%
filter(!is.na(management_level)) %>% # drops 5 nests that have permanentfence in beach or foredune habitat OR have only a nondogssign
mutate(region = "MP") %>%
mutate(site = as.factor(site)) %>%
group_by(season, site) %>%
arrange(first_found2, .by_group = TRUE) %>%
mutate(rank = row_number()) %>%
ungroup() %>%
mutate(nest_ID_new = paste(season, site, rank, sep = "_")) %>%
ungroup()Visual inspections of data
Code
nest_data_MP_check <-
nest_data_MP %>%
ungroup() %>%
mutate(first_found2_md = paste(format(first_found2 + 180, format = "%m"),
format(first_found2 + 180, format = "%d"),
sep = "-"),
last_alive2_md = paste(format(last_alive2 + 180, format = "%m"),
format(last_alive2 + 180, format = "%d"),
sep = "-"),
last_checked2_md = paste(format(last_checked2 + 180, format = "%m"),
format(last_checked2 + 180, format = "%d"),
sep = "-")) %>%
mutate(first_found2_trans = as.Date(paste("2020", first_found2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_alive2_trans = as.Date(paste("2020", last_alive2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_checked2_trans = as.Date(paste("2020", last_checked2_md, sep = "-"), format = "%Y-%m-%d") - 179) %>%
mutate(season_label = paste0(str_sub(season, 1, 4), " to ", str_sub(season, 5, 6)),
Fate = as.factor(Fate))Spatial distribution of data
Note that this map only shows data that are in a decimal degrees format (e.g., -38.31), NOT degree minute seconds (e.g., 38 27.59). The map is interactive, so click on an outlier to see its metadata. Note that all nests in the 200607 and 200708 seasons are missing coordinates
Code
nest_data_MP_sf <-
nest_data_MP %>%
as.data.frame() %>%
mutate(nest_lon = as.numeric(nest_lon) %>% suppressWarnings(),
nest_lat = as.numeric(nest_lat) %>% suppressWarnings()) %>%
filter(!is.na(nest_lon) & !is.na(nest_lat)) %>%
st_as_sf(coords = c("nest_lon", "nest_lat"),
crs = 4326)
mapview(nest_data_MP_sf,
zcol = "season",
popup = popupTable(nest_data_MP_sf,
zcol = c("season",
"site",
"nest_ID")))Year-specific Found Date Distributions
Code
ggplot(nest_data_MP_check, aes(first_found2_trans, fill = Fate)) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
# expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_MP_check$first_found2_trans, na.rm = TRUE),
max(nest_data_MP_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Found date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Alive Date Distributions
Code
ggplot(nest_data_MP_check, aes(last_alive2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_MP_check$first_found2_trans, na.rm = TRUE),
max(nest_data_MP_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last alive date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Checked Date Distributions
Code
ggplot(nest_data_MP_check, aes(last_checked2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_MP_check$first_found2_trans, na.rm = TRUE),
max(nest_data_MP_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last checked date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Tally number of nests per year in the dataset
Code
nest_data_MP_check %>%
group_by(season) %>%
summarise(n())# A tibble: 18 × 2
season `n()`
<fct> <int>
1 200607 18
2 200708 31
3 200809 18
4 200910 23
5 201011 45
6 201112 43
7 201213 48
8 201314 46
9 201415 61
10 201516 76
11 201617 93
12 201718 65
13 201819 64
14 201920 61
15 202021 78
16 202122 88
17 202223 81
18 202324 92Code
# Total number of nests acorss all years
nest_data_MP_check %>%
summarise(n())# A tibble: 1 × 1
`n()`
<int>
1 1031Incubation length distribution
Code
# assess if there are nests with unusually long incubation periods
nest_data_MP_check %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
arrange(desc(found_and_alive_diff)) %>%
filter(first_found2 < last_alive2 & first_found2 < last_checked2 & found_and_alive_diff < 100) %>%
ggplot() +
geom_histogram(aes(found_and_alive_diff)) +
luke_theme +
xlab("Time between found date and last alive date (days)") +
ylab("Frquency of nests")
# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, ….
Double check dates because incubation time greater than 41 days;
2 # A tibble: 2 × 10
found_and_alive_diff season nest_ID first_found2 last_alive2 last_checked2
<drtn> <fct> <chr> <date> <date> <date>
1 59 days 201213 201213_Moa… 2012-12-18 2013-02-15 2013-02-21
2 52 days 201112 201112_Moa… 2011-12-27 2012-02-17 2012-02-22
# ℹ 4 more variables: Fate <chr>, clutch_comp <date>, chick_1st_ob <date>,
# state_1st_ob <chr>Fleurieu Peninsula
Flag potential issues in data
As above, first we import the data and run a few checks to assess if there are any rows with the issues listed above
Code
FP_nest_data_issues <-
suppressMessages(
bind_rows(
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2023", "_", str_sub("2024", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2022", "_", str_sub("2023", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2021", "_", str_sub("2022", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2020", "_", str_sub("2021", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2019", "_", str_sub("2020", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2018", "_", str_sub("2019", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2017", "_", str_sub("2018", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2016", "_", str_sub("2017", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2015", "_", str_sub("2016", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2014", "_", str_sub("2015", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2013", "_", str_sub("2014", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2012", "_", str_sub("2013", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2011", "_", str_sub("2012", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2010", "_", str_sub("2011", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "FP Nesting summary_All years_30062025.xlsx"),
sheet = paste0("FP", " ", "2009", "_", str_sub("2010", 3, 4)),
col_types = "text", na = "n/a"))) %>%
filter(!is.na(Season)) %>%
rename(first_found = 13,
last_alive = 33,
last_checked = 39,
Fate = `Hatch?`,
season = Season,
# site = Site,
site = `Suggested update to site name`,
nest_ID = `Nest ID_2`,
nest_hab = `Nest habitat`,
management_status = `Nest managed?`,
management_type = `Nest management type`,
nest_lat = `Nest latitude`,
nest_lon = `Nest longitude`,
state_1st_ob = `Age stage first observed`) %>%
rename(
chick_1st_ob = !!names(.)[which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))][1],
clutch_comp = !!names(.)[which(names(.) %in% c("Known clutch complete date", "Known laying date"))][1]
) %>%
# chick_1st_ob = which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))[1],
# clutch_comp = which(names(.) %in% c("Known clutch complete date", "Known laying date"))) %>%
dplyr::select(season, site, nest_ID, first_found, last_alive, last_checked, Fate, nest_hab,
management_status, management_type, nest_lat, nest_lon, site,
chick_1st_ob, clutch_comp, state_1st_ob) %>%
mutate(Fate = ifelse(Fate == "?", "Unk", Fate)) %>%
mutate(Fate = toupper(Fate)) %>%
filter(
is.na(first_found) |
grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", first_found)
) %>%
mutate(last_checked = ifelse(!is.na(last_checked) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_checked),
NA, last_checked)) %>%
mutate(last_alive = ifelse(!is.na(last_alive) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_alive),
NA, last_alive)) %>%
mutate(last_alive = ifelse(str_detect(last_alive, "Unk."), NA, last_alive),
Fate = ifelse(Fate == "Y", "0",
ifelse(Fate == "N", "1",
ifelse(Fate == "UNK", NA, "XXX")))) %>%
# remove nests without Fate information
filter(Fate %in% c("0", "1")) %>%
mutate(chick_1st_ob = case_when(
# Excel serial dates stored as numbers
str_detect(chick_1st_ob, "^[0-9]+$") ~ as.Date(as.numeric(chick_1st_ob), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
),
clutch_comp = case_when(
# Excel serial dates stored as numbers
str_detect(clutch_comp, "^[0-9]+$") ~ as.Date(as.numeric(clutch_comp), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
)) %>%
# if the clutch completion date is NA, then estimate it as 28 days before estimated hatch date
# using the date of first chick observation and the size of the chicks when seen
mutate(clutch_comp = ifelse(!is.na(clutch_comp), clutch_comp,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3 - 28,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10 - 28,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19 - 28,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30 - 28,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35 - 28,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob - 28, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob), NA, NA))) %>% as.Date()) %>%
mutate(last_alive2 = ifelse(Fate == "0" & !is.na(clutch_comp) & is.na(last_alive), clutch_comp + 28,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob) & is.na(last_alive),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob) & is.na(last_alive), NA, NA))) %>% as.Date()) %>%
# mutate(last_alive2 = ifelse(Fate == "0" & !is.na(chick_1st_ob) & last_alive2 > chick_1st_ob, chick_1st_ob, last_alive2) %>% as.Date()) %>%
mutate(last_alive = ifelse(!is.na(last_alive2),
paste0(str_sub(as.character(last_alive2), 9, 10),
str_sub(as.character(last_alive2), 6, 7),
str_sub(as.character(last_alive2), 1, 4)), last_alive)) %>%
mutate(
last_checked = ifelse(!is.na(last_alive) & is.na(last_checked),
last_alive,
ifelse(is.na(last_alive) & is.na(last_checked),
first_found, last_checked))) %>%
mutate(
last_alive = ifelse(is.na(last_alive) & Fate == "0" & !is.na(last_checked),
last_checked,
ifelse(is.na(last_alive) & Fate == "1" & !is.na(last_checked),
first_found,
last_alive))) %>%
filter(
(is.na(last_alive) | nchar(last_alive) == 8) &
(is.na(first_found) | nchar(first_found) == 8) &
(is.na(last_checked) | nchar(last_checked) == 8)
) %>%
mutate(first_found2 = as.Date(paste(str_sub(first_found, 5, 8),
str_sub(first_found, 3, 4),
str_sub(first_found, 1, 2), sep = "-")),
last_alive2 = as.Date(paste(str_sub(last_alive, 5, 8),
str_sub(last_alive, 3, 4),
str_sub(last_alive, 1, 2), sep = "-")),
last_checked2 = as.Date(paste(str_sub(last_checked, 5, 8),
str_sub(last_checked, 3, 4),
str_sub(last_checked, 1, 2), sep = "-"))) %>%
mutate(last_checked2 = ifelse(!is.na(last_alive2) & !is.na(last_checked2) & last_checked2 < last_alive2,
last_alive2, last_checked2) %>% as.Date()) %>%
mutate(first_found2 = ifelse(!is.na(clutch_comp) & ((last_alive2 - first_found2) > 40 | (last_alive2 - first_found2) < 0)
& ((last_alive2 - clutch_comp) < 40 | (last_alive2 - clutch_comp) > 0),
clutch_comp, first_found2) %>% as.Date()) %>%
mutate(FirstFound = as.numeric(format(first_found2 + 180, "%j")),
LastPresent = as.numeric(format(last_alive2 + 180, "%j")),
LastChecked = as.numeric(format(last_checked2 + 180, "%j"))) %>%
# mutate(FirstFound = ((as.integer(format(first_found2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(first_found2 + 180, "%j")),
# LastPresent = ((as.integer(format(last_alive2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(last_alive2 + 180, "%j")),
# LastChecked = ((as.integer(format(last_checked2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365)) %>% #as.numeric(format(last_checked2 + 180, "%j"))) %>%
mutate(management_type = tolower(management_type)) %>%
mutate(nest_hab = tolower(nest_hab)) %>%
mutate(management_type = str_replace(management_type, "acess", "access")) %>%
mutate(management_type = str_replace(management_type, "and", ",")) %>%
mutate(management_type = str_replace(management_type, "temporary", "")) %>%
mutate(management_type = str_replace_all(management_type, " ", "")) %>%
mutate(management_type = str_replace_all(management_type, "shelters", "")) %>%
mutate(management_type = str_replace_all(management_type, "banners", "")) %>%
mutate(management_type = str_replace_all(management_type, ",,", ",")) %>%
mutate(sign_access = ifelse(str_detect(management_type, "signaccess"), 1, 0)) %>%
mutate(sign_nest = ifelse(str_detect(management_type, "signnest"), 1, 0)) %>%
mutate(rope_fence = ifelse(str_detect(management_type, "ropefence"), 1, 0)) %>%
mutate(perm_fence = ifelse(str_detect(management_type, "permanentfence"), 1, 0)) %>% # & nest_hab == "dune", 1, 0)) %>%
mutate(wardens = ifelse(str_detect(management_type, "wardens"), 1, 0)) %>%
mutate(none = ifelse(str_detect(management_type, "none"), 1,
ifelse(is.na(management_type) & management_status == "N", 1, 0))) %>%
mutate(other = ifelse(str_detect(management_type, "other"), 1, 0)) %>%
mutate(management_level = ifelse((sign_access == 1 | perm_fence == 1) & sign_nest == 1 & rope_fence == 1 & wardens == 1, 4,
ifelse(rope_fence == 1, 3,
ifelse(sign_nest == 1, 2,
ifelse(sign_access == 1 | perm_fence == 1, 1,
ifelse(none == 1, 0, NA)))))) %>%
mutate(sign_nest_no_sign_access = ifelse(sign_access == 0 & sign_nest == 1, 1, 0)) %>%
mutate(fence_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & rope_fence == 1, 1, 0)) %>%
mutate(wardens_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & wardens == 1, 1, 0)) %>%
mutate(wardens_no_fence = ifelse(rope_fence == 1 & wardens == 1, 1, 0)) %>%
mutate(just_wardens = ifelse(rope_fence == 0 & sign_access == 0 & sign_nest == 0 & wardens == 1, 1, 0)) %>%
dplyr::select(-other, -sign_nest_no_sign_access, -fence_no_sign,
-wardens_no_sign, -wardens_no_fence, -just_wardens) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
mutate(region = "FP") %>%
mutate(site = as.factor(site)) %>%
mutate(issue1 = ifelse(nchar(first_found) != 8, "found date is not 8 characters; ", NA)) %>%
mutate(issue2 = ifelse(nchar(last_alive) != 8, "last seen alive date is not 8 characters; ", NA)) %>%
mutate(issue3 = ifelse(nchar(last_checked) != 8, "last checked date is not 8 characters; ", NA)) %>%
mutate(issue4 = ifelse(is.na(first_found), "found date missing; ", NA)) %>%
mutate(issue5 = ifelse(is.na(last_alive), "last seen alive date missing; ", NA)) %>%
mutate(issue6 = ifelse(is.na(last_checked), "last checked date missing; ", NA)) %>%
mutate(issue7 = ifelse(management_status %!in% c("Y", "N"), "Nest managed? is not Y or N; ", NA)) %>%
mutate(issue8 = ifelse(nest_hab %!in% c("beach", "dune", "foredune/face", "estuary/spit"),
"Nest habitat is not beach, dune, foredune/face, or estuary/spit; ", NA)) %>%
mutate(issue9 = ifelse(is.na(management_level), "Management type is not sufficient for making levels; ", NA)) %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
mutate(issue10 = ifelse(found_and_alive_diff > 40 , "Double check dates because incubation time greater than 40 days; ", NA)) %>%
mutate(issue11 = ifelse(first_found2 > last_alive2, "Found date is after Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issue12 = ifelse(first_found2 > last_checked2, "Found date is after Last Checked date (should be greater or equal); ", NA)) %>%
mutate(issue13 = ifelse(last_checked2 < last_alive2, "Last Checked date is before Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issues = ifelse(is.na(issue1) & is.na(issue2) & is.na(issue3) &
is.na(issue4) & is.na(issue5) & is.na(issue6) &
is.na(issue7) & is.na(issue8) & is.na(issue9) &
is.na(issue10) & is.na(issue11) & is.na(issue12) & is.na(issue13), NA,
paste0(issue1, issue2, issue3,
issue4, issue5, issue6,
issue7, issue8, issue9,
issue10, issue11, issue12, issue13))) %>%
mutate(issues = str_remove_all(issues, "NA")) %>%
mutate(issues = ifelse(is.na(issues), "usable", issues)) %>%
dplyr::select(-issue1, -issue2, -issue3,
-issue4, -issue5, -issue6,
-issue7, -issue8, -issue9,
-issue10, -issue11, -issue12, -issue13) %>%
filter(issues != "usable") %>%
arrange(issues)all issues (probably most of these can’t be resolved)
Code
# Create a new workbook to store issues as two sheets
FP_nest_issues <- createWorkbook()
# Add all_issues sheet
addWorksheet(FP_nest_issues, "all_issues")
# view issues interactively in RStudio Viewer
FP_nest_data_issues %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write FP_nest_data_check
writeData(FP_nest_issues, sheet = "all_issues", FP_nest_data_issues)date-specific issues (probably most of these CAN be resolved)
Code
# Add date_issues
addWorksheet(FP_nest_issues, "date_issues")
# there are some blank (but non-NA) observations of management_type
FP_nest_data_issues %>% filter(is.na(management_type))# A tibble: 0 × 32
# Groups: season [0]
# ℹ 32 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>,
# LastChecked <dbl>, sign_access <dbl>, sign_nest <dbl>, rope_fence <dbl>, …Code
# view issues interactively in RStudio Viewer
FP_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not beach, dune, foredune/face, or estuary/spit; ")) %>%
dplyr::select(season, nest_ID, nest_hab, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write FP_nest_data_check
FP_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not Beach, Dune, Foredune/face, or Estuary/spit; ")) %>%
dplyr::select(season, nest_ID, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
writeData(FP_nest_issues, sheet = "date_issues", .)
# Save the workbook
saveWorkbook(FP_nest_issues, here("data/nest_issues_commented/FP_nest_data_issues_011025.xlsx"), overwrite = TRUE)Import data
Code
nest_data_FP <-
bind_rows(
nest_import(year_1 = "2023", year_2 = "2024",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 13,
last_alive_date_col = 33,
last_checked_col = 39),
nest_import(year_1 = "2022", year_2 = "2023",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 13,
last_alive_date_col = 33,
last_checked_col = 39),
nest_import(year_1 = "2021", year_2 = "2022",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 13,
last_alive_date_col = 33,
last_checked_col = 39),
nest_import(year_1 = "2020", year_2 = "2021",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2019", year_2 = "2020",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2018", year_2 = "2019",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2017", year_2 = "2018",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2016", year_2 = "2017",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2015", year_2 = "2016",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2014", year_2 = "2015",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2013", year_2 = "2014",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2012", year_2 = "2013",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2011", year_2 = "2012",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2010", year_2 = "2011",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2009", year_2 = "2010",
file_name = "FP Nesting summary_All years_30062025.xlsx", site = "FP",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40)) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
filter(!is.na(FirstFound) & !is.na(LastPresent) & !is.na(LastChecked)) %>%
filter(management_status %in% c("Y", "N")) %>%
filter(nest_hab %in% c("beach", "dune", "foredune/face", "estuary/spit")) %>%
filter(!is.na(management_level)) %>% # drops 20 nests
mutate(region = "FP") %>%
mutate(site = as.factor(site)) %>%
group_by(season, site) %>%
arrange(first_found2, .by_group = TRUE) %>%
mutate(rank = row_number()) %>%
ungroup() %>%
mutate(nest_ID_new = paste(season, site, rank, sep = "_")) %>%
ungroup()Visual inspections of data
Code
nest_data_FP_check <-
nest_data_FP %>%
ungroup() %>%
mutate(first_found2_md = paste(format(first_found2 + 180, format = "%m"),
format(first_found2 + 180, format = "%d"),
sep = "-"),
last_alive2_md = paste(format(last_alive2 + 180, format = "%m"),
format(last_alive2 + 180, format = "%d"),
sep = "-"),
last_checked2_md = paste(format(last_checked2 + 180, format = "%m"),
format(last_checked2 + 180, format = "%d"),
sep = "-")) %>%
mutate(first_found2_trans = as.Date(paste("2020", first_found2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_alive2_trans = as.Date(paste("2020", last_alive2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_checked2_trans = as.Date(paste("2020", last_checked2_md, sep = "-"), format = "%Y-%m-%d") - 179) %>%
mutate(season_label = paste0("season ", str_sub(season, 1, 4), " to ", str_sub(season, 5, 6)))Spatial distribution of data
Note that this map only shows data that are in a decimal degrees format (e.g., -38.31), NOT degree minute seconds (e.g., 38 27.59). The map is interactive, so click on an outlier to see its metadata
Code
nest_data_FP_sf <-
nest_data_FP %>%
as.data.frame() %>%
mutate(nest_lon = as.numeric(nest_lon),
nest_lat = as.numeric(nest_lat)) %>%
filter(!is.na(nest_lon) & !is.na(nest_lat)) %>%
st_as_sf(coords = c("nest_lon", "nest_lat"),
crs = 4326) %>% suppressWarnings()
mapview(nest_data_FP_sf,
zcol = "season",
popup = popupTable(nest_data_FP_sf,
zcol = c("season",
"site",
"nest_ID")))Year-specific Found Date Distributions
Code
ggplot(nest_data_FP_check, aes(first_found2_trans, fill = Fate)) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
# expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_FP_check$first_found2_trans, na.rm = TRUE),
max(nest_data_FP_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10)) +
luke_theme +
xlab("Found date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Alive Date Distributions
Code
ggplot(nest_data_FP_check, aes(last_alive2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_FP_check$first_found2_trans, na.rm = TRUE),
max(nest_data_FP_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last alive date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Checked Date Distributions
Code
ggplot(nest_data_FP_check, aes(last_checked2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_FP_check$first_found2_trans, na.rm = TRUE),
max(nest_data_FP_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last checked date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Tally number of nests per year in the dataset
Code
nest_data_FP_check %>%
group_by(season) %>%
summarise(n())# A tibble: 15 × 2
season `n()`
<fct> <int>
1 200910 8
2 201011 22
3 201112 14
4 201213 21
5 201314 25
6 201415 29
7 201516 35
8 201617 50
9 201718 49
10 201819 70
11 201920 73
12 202021 94
13 202122 90
14 202223 100
15 202324 113Code
nest_data_FP_check %>%
summarise(n())# A tibble: 1 × 1
`n()`
<int>
1 793Incubation length distribution
Code
# assess if there are nests with unusually long incubation periods
nest_data_FP_check %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
arrange(desc(found_and_alive_diff)) %>%
filter(first_found2 < last_alive2 & first_found2 < last_checked2 & found_and_alive_diff < 100) %>%
ggplot() +
geom_histogram(aes(found_and_alive_diff)) +
luke_theme +
xlab("Time between found date and last alive date (days)") +
ylab("Frquency of nests")
# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, ….
Double check dates because incubation time greater than 42 days;
1 # A tibble: 1 × 4
issues season nest_ID found_and_alive_diff
<chr> <fct> <chr> <drtn>
1 "Double check dates because incubation ti… 201516 201516… 51 days Bellarine / Surf Coast
Flag potential issues in data
As above, first we import the data and run a few checks to assess if there are any rows with the issues listed above
Code
BSC_nest_data_issues <-
suppressMessages(
bind_rows(
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2024", "_", str_sub("2025", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2023", "_", str_sub("2024", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2022", "_", str_sub("2023", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2021", "_", str_sub("2022", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2020", "_", str_sub("2021", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2019", "_", str_sub("2020", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2018", "_", str_sub("2019", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2017", "_", str_sub("2018", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2016", "_", str_sub("2017", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2015", "_", str_sub("2016", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2014", "_", str_sub("2015", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2013", "_", str_sub("2014", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2012", "_", str_sub("2013", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2011", "_", str_sub("2012", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2010", "_", str_sub("2011", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2009", "_", str_sub("2010", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2008", "_", str_sub("2009", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2007", "_", str_sub("2008", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "BSC Nesting Summaries_All years_17062025.xlsx"),
sheet = paste0("BSC", " ", "2006", "_", str_sub("2007", 3, 4)),
col_types = "text", na = "n/a"))) %>%
filter(!is.na(Season)) %>%
rename(first_found = 13,
last_alive = 33,
last_checked = 39,
Fate = `Hatch?`,
season = Season,
# site = Site,
site = `Suggested update to site name`,
nest_ID = `Nest ID_2`,
nest_hab = `Nest habitat`,
management_status = `Nest managed?`,
management_type = `Nest management type`,
nest_lat = `Nest latitude`,
nest_lon = `Nest longitude`,
state_1st_ob = `Age stage first observed`) %>%
rename(
chick_1st_ob = !!names(.)[which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))][1],
clutch_comp = !!names(.)[which(names(.) %in% c("Known clutch complete date", "Known laying date"))][1]
) %>%
# chick_1st_ob = which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))[1],
# clutch_comp = which(names(.) %in% c("Known clutch complete date", "Known laying date"))) %>%
dplyr::select(season, site, nest_ID, first_found, last_alive, last_checked, Fate, nest_hab,
management_status, management_type, nest_lat, nest_lon, site,
chick_1st_ob, clutch_comp, state_1st_ob) %>%
mutate(Fate = ifelse(Fate == "?", "Unk", Fate)) %>%
mutate(Fate = toupper(Fate)) %>%
filter(
is.na(first_found) |
grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", first_found)
) %>%
mutate(last_checked = ifelse(!is.na(last_checked) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_checked),
NA, last_checked)) %>%
mutate(last_alive = ifelse(!is.na(last_alive) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_alive),
NA, last_alive)) %>%
mutate(last_alive = ifelse(str_detect(last_alive, "Unk."), NA, last_alive),
Fate = ifelse(Fate == "Y", "0",
ifelse(Fate == "N", "1",
ifelse(Fate == "UNK", NA, "XXX")))) %>%
# remove nests without Fate information
filter(Fate %in% c("0", "1")) %>%
mutate(chick_1st_ob = case_when(
# Excel serial dates stored as numbers
str_detect(chick_1st_ob, "^[0-9]+$") ~ as.Date(as.numeric(chick_1st_ob), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
),
clutch_comp = case_when(
# Excel serial dates stored as numbers
str_detect(clutch_comp, "^[0-9]+$") ~ as.Date(as.numeric(clutch_comp), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
)) %>%
# if the clutch completion date is NA, then estimate it as 28 days before estimated hatch date
# using the date of first chick observation and the size of the chicks when seen
mutate(clutch_comp = ifelse(!is.na(clutch_comp), clutch_comp,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3 - 28,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10 - 28,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19 - 28,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30 - 28,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35 - 28,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob - 28, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob), NA, NA))) %>% as.Date()) %>%
mutate(last_alive2 = ifelse(Fate == "0" & !is.na(clutch_comp) & is.na(last_alive), clutch_comp + 28,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob) & is.na(last_alive),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob) & is.na(last_alive), NA, NA))) %>% as.Date()) %>%
# mutate(last_alive2 = ifelse(Fate == "0" & !is.na(chick_1st_ob) & last_alive2 > chick_1st_ob, chick_1st_ob, last_alive2) %>% as.Date()) %>%
mutate(last_alive = ifelse(!is.na(last_alive2),
paste0(str_sub(as.character(last_alive2), 9, 10),
str_sub(as.character(last_alive2), 6, 7),
str_sub(as.character(last_alive2), 1, 4)), last_alive)) %>%
mutate(
last_checked = ifelse(!is.na(last_alive) & is.na(last_checked),
last_alive,
ifelse(is.na(last_alive) & is.na(last_checked),
first_found, last_checked))) %>%
mutate(
last_alive = ifelse(is.na(last_alive) & Fate == "0" & !is.na(last_checked),
last_checked,
ifelse(is.na(last_alive) & Fate == "1" & !is.na(last_checked),
first_found,
last_alive))) %>%
filter(
(is.na(last_alive) | nchar(last_alive) == 8) &
(is.na(first_found) | nchar(first_found) == 8) &
(is.na(last_checked) | nchar(last_checked) == 8)
) %>%
mutate(first_found2 = as.Date(paste(str_sub(first_found, 5, 8),
str_sub(first_found, 3, 4),
str_sub(first_found, 1, 2), sep = "-")),
last_alive2 = as.Date(paste(str_sub(last_alive, 5, 8),
str_sub(last_alive, 3, 4),
str_sub(last_alive, 1, 2), sep = "-")),
last_checked2 = as.Date(paste(str_sub(last_checked, 5, 8),
str_sub(last_checked, 3, 4),
str_sub(last_checked, 1, 2), sep = "-"))) %>%
mutate(last_checked2 = ifelse(!is.na(last_alive2) & !is.na(last_checked2) & last_checked2 < last_alive2,
last_alive2, last_checked2) %>% as.Date()) %>%
mutate(first_found2 = ifelse(!is.na(clutch_comp) & ((last_alive2 - first_found2) > 40 | (last_alive2 - first_found2) < 0)
& ((last_alive2 - clutch_comp) < 40 | (last_alive2 - clutch_comp) > 0),
clutch_comp, first_found2) %>% as.Date()) %>%
mutate(FirstFound = as.numeric(format(first_found2 + 180, "%j")),
LastPresent = as.numeric(format(last_alive2 + 180, "%j")),
LastChecked = as.numeric(format(last_checked2 + 180, "%j"))) %>%
# mutate(FirstFound = ((as.integer(format(first_found2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(first_found2 + 180, "%j")),
# LastPresent = ((as.integer(format(last_alive2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(last_alive2 + 180, "%j")),
# LastChecked = ((as.integer(format(last_checked2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365)) %>% #as.numeric(format(last_checked2 + 180, "%j"))) %>%
mutate(management_type = tolower(management_type)) %>%
mutate(nest_hab = tolower(nest_hab)) %>%
mutate(management_type = str_replace(management_type, "acess", "access")) %>%
mutate(management_type = str_replace(management_type, "and", ",")) %>%
mutate(management_type = str_replace(management_type, "temporary", "")) %>%
mutate(management_type = str_replace_all(management_type, " ", "")) %>%
mutate(management_type = str_replace_all(management_type, "shelters", "")) %>%
mutate(management_type = str_replace_all(management_type, "banners", "")) %>%
mutate(management_type = str_replace_all(management_type, ",,", ",")) %>%
mutate(sign_access = ifelse(str_detect(management_type, "signaccess"), 1, 0)) %>%
mutate(sign_nest = ifelse(str_detect(management_type, "signnest"), 1, 0)) %>%
mutate(rope_fence = ifelse(str_detect(management_type, "ropefence"), 1, 0)) %>%
mutate(perm_fence = ifelse(str_detect(management_type, "permanentfence"), 1, 0)) %>% # & nest_hab == "dune", 1, 0)) %>%
mutate(wardens = ifelse(str_detect(management_type, "wardens"), 1, 0)) %>%
mutate(none = ifelse(str_detect(management_type, "none"), 1,
ifelse(is.na(management_type) & management_status == "N", 1, 0))) %>%
mutate(other = ifelse(str_detect(management_type, "other"), 1, 0)) %>%
mutate(management_level = ifelse((sign_access == 1 | perm_fence == 1) & sign_nest == 1 & rope_fence == 1 & wardens == 1, 4,
ifelse(rope_fence == 1, 3,
ifelse(sign_nest == 1, 2,
ifelse(sign_access == 1 | perm_fence == 1, 1,
ifelse(none == 1, 0, NA)))))) %>%
mutate(sign_nest_no_sign_access = ifelse(sign_access == 0 & sign_nest == 1, 1, 0)) %>%
mutate(fence_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & rope_fence == 1, 1, 0)) %>%
mutate(wardens_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & wardens == 1, 1, 0)) %>%
mutate(wardens_no_fence = ifelse(rope_fence == 1 & wardens == 1, 1, 0)) %>%
mutate(just_wardens = ifelse(rope_fence == 0 & sign_access == 0 & sign_nest == 0 & wardens == 1, 1, 0)) %>%
dplyr::select(-other, -sign_nest_no_sign_access, -fence_no_sign,
-wardens_no_sign, -wardens_no_fence, -just_wardens) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
mutate(region = "MP") %>%
mutate(site = as.factor(site)) %>%
mutate(issue1 = ifelse(nchar(first_found) != 8, "found date is not 8 characters; ", NA)) %>%
mutate(issue2 = ifelse(nchar(last_alive) != 8, "last seen alive date is not 8 characters; ", NA)) %>%
mutate(issue3 = ifelse(nchar(last_checked) != 8, "last checked date is not 8 characters; ", NA)) %>%
mutate(issue4 = ifelse(is.na(first_found), "found date missing; ", NA)) %>%
mutate(issue5 = ifelse(is.na(last_alive), "last seen alive date missing; ", NA)) %>%
mutate(issue6 = ifelse(is.na(last_checked), "last checked date missing; ", NA)) %>%
mutate(issue7 = ifelse(management_status %!in% c("Y", "N"), "Nest managed? is not Y or N; ", NA)) %>%
mutate(issue8 = ifelse(nest_hab %!in% c("beach", "dune", "foredune/face", "estuary/spit"),
"Nest habitat is not beach, dune, foredune/face, or estuary/spit; ", NA)) %>%
mutate(issue9 = ifelse(is.na(management_level), "Management type is not sufficient for making levels; ", NA)) %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
mutate(issue10 = ifelse(found_and_alive_diff > 40 , "Double check dates because incubation time greater than 40 days; ", NA)) %>%
mutate(issue11 = ifelse(first_found2 > last_alive2, "Found date is after Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issue12 = ifelse(first_found2 > last_checked2, "Found date is after Last Checked date (should be greater or equal); ", NA)) %>%
mutate(issue13 = ifelse(last_checked2 < last_alive2, "Last Checked date is before Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issues = ifelse(is.na(issue1) & is.na(issue2) & is.na(issue3) &
is.na(issue4) & is.na(issue5) & is.na(issue6) &
is.na(issue7) & is.na(issue8) & is.na(issue9) &
is.na(issue10) & is.na(issue11) & is.na(issue12) & is.na(issue13), NA,
paste0(issue1, issue2, issue3,
issue4, issue5, issue6,
issue7, issue8, issue9,
issue10, issue11, issue12, issue13))) %>%
mutate(issues = str_remove_all(issues, "NA")) %>%
mutate(issues = ifelse(is.na(issues), "usable", issues)) %>%
dplyr::select(-issue1, -issue2, -issue3,
-issue4, -issue5, -issue6,
-issue7, -issue8, -issue9,
-issue10, -issue11, -issue12, -issue13) %>%
filter(issues != "usable") %>%
arrange(issues)all issues (probably most of these can’t be resolved)
Code
# Create a new workbook to store issues as two sheets
BSC_nest_issues <- createWorkbook()
# Add all_issues sheet
addWorksheet(BSC_nest_issues, "all_issues")
# view issues interactively in RStudio Viewer
BSC_nest_data_issues %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write BSC_nest_data_check
writeData(BSC_nest_issues, sheet = "all_issues", BSC_nest_data_issues)date-specific issues (probably most of these CAN be resolved)
Code
# Add date_issues
addWorksheet(BSC_nest_issues, "date_issues")
# there are some blank (but non-NA) observations of management_type
BSC_nest_data_issues %>% filter(is.na(management_type))# A tibble: 0 × 32
# Groups: season [0]
# ℹ 32 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>,
# LastChecked <dbl>, sign_access <dbl>, sign_nest <dbl>, rope_fence <dbl>, …Code
# view issues interactively in RStudio Viewer
BSC_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not beach, dune, foredune/face, estuary/spit, or rocks; ")) %>%
dplyr::select(season, nest_ID, nest_hab, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write BSC_nest_data_check
BSC_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not Beach, Dune, Foredune/face, Estuary/spit, or Rocks; ")) %>%
dplyr::select(season, nest_ID, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
writeData(BSC_nest_issues, sheet = "date_issues", .)
# Save the workbook
saveWorkbook(BSC_nest_issues, here("data/nest_issues_commented/BSC_nest_data_issues_011025.xlsx"), overwrite = TRUE)Import data
Code
nest_data_BSC <-
bind_rows(
nest_import(year_1 = "2024", year_2 = "2025",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2023", year_2 = "2024",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2022", year_2 = "2023",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 13,
last_alive_date_col = 32,
last_checked_col = 38),
nest_import(year_1 = "2021", year_2 = "2022",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2020", year_2 = "2021",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2019", year_2 = "2020",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2018", year_2 = "2019",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2017", year_2 = "2018",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2016", year_2 = "2017",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2015", year_2 = "2016",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2014", year_2 = "2015",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2013", year_2 = "2014",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2012", year_2 = "2013",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2011", year_2 = "2012",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2010", year_2 = "2011",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2009", year_2 = "2010",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2008", year_2 = "2009",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2007", year_2 = "2008",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40),
nest_import(year_1 = "2006", year_2 = "2007",
file_name = "BSC Nesting Summaries_All years_17062025.xlsx", site = "BSC",
first_found_date_col = 12,
last_alive_date_col = 33,
last_checked_col = 40)) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
filter(!is.na(FirstFound) & !is.na(LastPresent) & !is.na(LastChecked)) %>%
filter(management_status %in% c("Y", "N")) %>%
filter(nest_hab %in% c("beach", "dune", "foredune/face", "estuary/spit")) %>%
filter(!is.na(management_level)) %>% # drops 5 nests that have permanentfence in beach or foredune habitat OR have only a nondogssign
mutate(region = "BSC") %>%
mutate(site = as.factor(site)) %>%
group_by(season, site) %>%
arrange(first_found2, .by_group = TRUE) %>%
mutate(rank = row_number()) %>%
ungroup() %>%
mutate(nest_ID_new = paste(season, site, rank, sep = "_")) %>%
ungroup()Visual inspections of data
Code
nest_data_BSC_check <-
nest_data_BSC %>%
ungroup() %>%
mutate(first_found2_md = paste(format(first_found2 + 180, format = "%m"),
format(first_found2 + 180, format = "%d"),
sep = "-"),
last_alive2_md = paste(format(last_alive2 + 180, format = "%m"),
format(last_alive2 + 180, format = "%d"),
sep = "-"),
last_checked2_md = paste(format(last_checked2 + 180, format = "%m"),
format(last_checked2 + 180, format = "%d"),
sep = "-")) %>%
mutate(first_found2_trans = as.Date(paste("2020", first_found2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_alive2_trans = as.Date(paste("2020", last_alive2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_checked2_trans = as.Date(paste("2020", last_checked2_md, sep = "-"), format = "%Y-%m-%d") - 179) %>%
mutate(season_label = paste0("season ", str_sub(season, 1, 4), " to ", str_sub(season, 5, 6)))Spatial distribution of data
Note that this map only shows data that are in a decimal degrees format (e.g., -38.31), NOT degree minute seconds (e.g., 38 27.59). The map is interactive, so click on an outlier to see its metadata
Code
nest_data_BSC_sf <-
nest_data_BSC %>%
as.data.frame() %>%
mutate(nest_lon = as.numeric(nest_lon),
nest_lat = as.numeric(nest_lat)) %>%
filter(!is.na(nest_lon) & !is.na(nest_lat)) %>%
st_as_sf(coords = c("nest_lon", "nest_lat"),
crs = 4326) %>% suppressWarnings()
mapview(nest_data_BSC_sf,
zcol = "season",
popup = popupTable(nest_data_BSC_sf,
zcol = c("season",
"site",
"nest_ID")))Year-specific Found Date Distributions
Code
ggplot(nest_data_BSC_check, aes(first_found2_trans, fill = Fate)) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
# expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_BSC_check$first_found2_trans, na.rm = TRUE),
max(nest_data_BSC_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10)) +
luke_theme +
xlab("Found date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Alive Date Distributions
Code
ggplot(nest_data_BSC_check, aes(last_alive2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_BSC_check$first_found2_trans, na.rm = TRUE),
max(nest_data_BSC_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last alive date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Checked Date Distributions
Code
ggplot(nest_data_BSC_check, aes(last_checked2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_BSC_check$first_found2_trans, na.rm = TRUE),
max(nest_data_BSC_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last checked date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Tally number of nests per year in the dataset
Code
nest_data_BSC_check %>%
group_by(season) %>%
summarise(n())# A tibble: 19 × 2
season `n()`
<fct> <int>
1 200607 13
2 200708 15
3 200809 13
4 200910 17
5 201011 21
6 201112 30
7 201213 26
8 201314 22
9 201415 32
10 201516 34
11 201617 45
12 201718 40
13 201819 35
14 201920 42
15 202021 53
16 202122 47
17 202223 44
18 202324 49
19 202425 46Code
nest_data_BSC_check %>%
summarise(n())# A tibble: 1 × 1
`n()`
<int>
1 624Incubation length distribution
Code
# assess if there are nests with unusually long incubation periods
nest_data_BSC_check %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
arrange(desc(found_and_alive_diff)) %>%
filter(first_found2 < last_alive2 & first_found2 < last_checked2 & found_and_alive_diff < 100) %>%
ggplot() +
geom_histogram(aes(found_and_alive_diff)) +
luke_theme +
xlab("Time between found date and last alive date (days)") +
ylab("Frquency of nests")
# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, ….
Double check dates because incubation time greater than 41 days;
4 # A tibble: 4 × 3
issues season site
<chr> <fct> <fct>
1 "Double check dates because incubation time greater than 41 days… 200708 Poin…
2 "Double check dates because incubation time greater than 41 days… 201213 8W O…
3 "Double check dates because incubation time greater than 41 days… 201617 16W-…
4 "Double check dates because incubation time greater than 41 days… 202425 Ersk…Bass Coast
Flag potential issues in data
As above, first we import the data and run a few checks to assess if there are any rows with the issues listed above
Code
BC_nest_data_issues <-
suppressMessages(
bind_rows(
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2024", "_", str_sub("2025", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2023", "_", str_sub("2024", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2022", "_", str_sub("2023", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2021", "_", str_sub("2022", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2020", "_", str_sub("2021", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2019", "_", str_sub("2020", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2018", "_", str_sub("2019", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2017", "_", str_sub("2018", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2016", "_", str_sub("2017", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2015", "_", str_sub("2016", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2014", "_", str_sub("2015", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2013", "_", str_sub("2014", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2012", "_", str_sub("2013", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2011", "_", str_sub("2012", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2010", "_", str_sub("2011", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2009", "_", str_sub("2010", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2008", "_", str_sub("2009", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2007", "_", str_sub("2008", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "Bass Coast_nesting summaries.xlsx"),
sheet = paste0("BC", " ", "2006", "_", str_sub("2007", 3, 4)),
col_types = "text", na = "n/a"))) %>%
filter(!is.na(Season)) %>%
rename(first_found = 13,
last_alive = 33,
last_checked = 39,
Fate = `Hatch?`,
season = Season,
# site = Site,
site = `Suggested update to site name`,
nest_ID = `Nest ID_2`,
nest_hab = `Nest habitat`,
management_status = `Nest managed?`,
management_type = `Nest management type`,
nest_lat = `Nest latitude`,
nest_lon = `Nest longitude`,
state_1st_ob = `Age stage first observed`) %>%
rename(
chick_1st_ob = !!names(.)[which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))][1],
clutch_comp = !!names(.)[which(names(.) %in% c("Known clutch complete date", "Known laying date"))][1]
) %>%
# chick_1st_ob = which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))[1],
# clutch_comp = which(names(.) %in% c("Known clutch complete date", "Known laying date"))) %>%
dplyr::select(season, site, nest_ID, first_found, last_alive, last_checked, Fate, nest_hab,
management_status, management_type, nest_lat, nest_lon, site,
chick_1st_ob, clutch_comp, state_1st_ob) %>%
mutate(Fate = ifelse(Fate == "?", "Unk", Fate)) %>%
mutate(Fate = toupper(Fate)) %>%
filter(
is.na(first_found) |
grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", first_found)
) %>%
mutate(last_checked = ifelse(!is.na(last_checked) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_checked),
NA, last_checked)) %>%
mutate(last_alive = ifelse(!is.na(last_alive) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_alive),
NA, last_alive)) %>%
mutate(last_alive = ifelse(str_detect(last_alive, "Unk."), NA, last_alive),
Fate = ifelse(Fate == "Y", "0",
ifelse(Fate == "N", "1",
ifelse(Fate == "UNK", NA, "XXX")))) %>%
# remove nests without Fate information
filter(Fate %in% c("0", "1")) %>%
mutate(chick_1st_ob = case_when(
# Excel serial dates stored as numbers
str_detect(chick_1st_ob, "^[0-9]+$") ~ as.Date(as.numeric(chick_1st_ob), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
),
clutch_comp = case_when(
# Excel serial dates stored as numbers
str_detect(clutch_comp, "^[0-9]+$") ~ as.Date(as.numeric(clutch_comp), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
)) %>%
# if the clutch completion date is NA, then estimate it as 28 days before estimated hatch date
# using the date of first chick observation and the size of the chicks when seen
mutate(clutch_comp = ifelse(!is.na(clutch_comp), clutch_comp,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3 - 28,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10 - 28,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19 - 28,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30 - 28,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35 - 28,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob - 28, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob), NA, NA))) %>% as.Date()) %>%
mutate(last_alive2 = ifelse(Fate == "0" & !is.na(clutch_comp) & is.na(last_alive), clutch_comp + 28,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob) & is.na(last_alive),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob) & is.na(last_alive), NA, NA))) %>% as.Date()) %>%
# mutate(last_alive2 = ifelse(Fate == "0" & !is.na(chick_1st_ob) & last_alive2 > chick_1st_ob, chick_1st_ob, last_alive2) %>% as.Date()) %>%
mutate(last_alive = ifelse(!is.na(last_alive2),
paste0(str_sub(as.character(last_alive2), 9, 10),
str_sub(as.character(last_alive2), 6, 7),
str_sub(as.character(last_alive2), 1, 4)), last_alive)) %>%
mutate(
last_checked = ifelse(!is.na(last_alive) & is.na(last_checked),
last_alive,
ifelse(is.na(last_alive) & is.na(last_checked),
first_found, last_checked))) %>%
mutate(
last_alive = ifelse(is.na(last_alive) & Fate == "0" & !is.na(last_checked),
last_checked,
ifelse(is.na(last_alive) & Fate == "1" & !is.na(last_checked),
first_found,
last_alive))) %>%
filter(
(is.na(last_alive) | nchar(last_alive) == 8) &
(is.na(first_found) | nchar(first_found) == 8) &
(is.na(last_checked) | nchar(last_checked) == 8)
) %>%
mutate(first_found2 = as.Date(paste(str_sub(first_found, 5, 8),
str_sub(first_found, 3, 4),
str_sub(first_found, 1, 2), sep = "-")),
last_alive2 = as.Date(paste(str_sub(last_alive, 5, 8),
str_sub(last_alive, 3, 4),
str_sub(last_alive, 1, 2), sep = "-")),
last_checked2 = as.Date(paste(str_sub(last_checked, 5, 8),
str_sub(last_checked, 3, 4),
str_sub(last_checked, 1, 2), sep = "-"))) %>%
mutate(last_checked2 = ifelse(!is.na(last_alive2) & !is.na(last_checked2) & last_checked2 < last_alive2,
last_alive2, last_checked2) %>% as.Date()) %>%
mutate(first_found2 = ifelse(!is.na(clutch_comp) & ((last_alive2 - first_found2) > 40 | (last_alive2 - first_found2) < 0)
& ((last_alive2 - clutch_comp) < 40 | (last_alive2 - clutch_comp) > 0),
clutch_comp, first_found2) %>% as.Date()) %>%
mutate(FirstFound = as.numeric(format(first_found2 + 180, "%j")),
LastPresent = as.numeric(format(last_alive2 + 180, "%j")),
LastChecked = as.numeric(format(last_checked2 + 180, "%j"))) %>%
# mutate(FirstFound = ((as.integer(format(first_found2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(first_found2 + 180, "%j")),
# LastPresent = ((as.integer(format(last_alive2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(last_alive2 + 180, "%j")),
# LastChecked = ((as.integer(format(last_checked2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365)) %>% #as.numeric(format(last_checked2 + 180, "%j"))) %>%
mutate(management_type = tolower(management_type)) %>%
mutate(nest_hab = tolower(nest_hab)) %>%
mutate(management_type = str_replace(management_type, "acess", "access")) %>%
mutate(management_type = str_replace(management_type, "and", ",")) %>%
mutate(management_type = str_replace(management_type, "temporary", "")) %>%
mutate(management_type = str_replace_all(management_type, " ", "")) %>%
mutate(management_type = str_replace_all(management_type, "shelters", "")) %>%
mutate(management_type = str_replace_all(management_type, "banners", "")) %>%
mutate(management_type = str_replace_all(management_type, ",,", ",")) %>%
mutate(sign_access = ifelse(str_detect(management_type, "signaccess"), 1, 0)) %>%
mutate(sign_nest = ifelse(str_detect(management_type, "signnest"), 1, 0)) %>%
mutate(rope_fence = ifelse(str_detect(management_type, "ropefence"), 1, 0)) %>%
mutate(perm_fence = ifelse(str_detect(management_type, "permanentfence"), 1, 0)) %>% # & nest_hab == "dune", 1, 0)) %>%
mutate(wardens = ifelse(str_detect(management_type, "wardens"), 1, 0)) %>%
mutate(none = ifelse(str_detect(management_type, "none"), 1,
ifelse(is.na(management_type) & management_status == "N", 1, 0))) %>%
mutate(other = ifelse(str_detect(management_type, "other"), 1, 0)) %>%
mutate(management_level = ifelse((sign_access == 1 | perm_fence == 1) & sign_nest == 1 & rope_fence == 1 & wardens == 1, 4,
ifelse(rope_fence == 1, 3,
ifelse(sign_nest == 1, 2,
ifelse(sign_access == 1 | perm_fence == 1, 1,
ifelse(none == 1, 0, NA)))))) %>%
mutate(sign_nest_no_sign_access = ifelse(sign_access == 0 & sign_nest == 1, 1, 0)) %>%
mutate(fence_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & rope_fence == 1, 1, 0)) %>%
mutate(wardens_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & wardens == 1, 1, 0)) %>%
mutate(wardens_no_fence = ifelse(rope_fence == 1 & wardens == 1, 1, 0)) %>%
mutate(just_wardens = ifelse(rope_fence == 0 & sign_access == 0 & sign_nest == 0 & wardens == 1, 1, 0)) %>%
dplyr::select(-other, -sign_nest_no_sign_access, -fence_no_sign,
-wardens_no_sign, -wardens_no_fence, -just_wardens) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
mutate(region = "MP") %>%
mutate(site = as.factor(site)) %>%
mutate(issue1 = ifelse(nchar(first_found) != 8, "found date is not 8 characters; ", NA)) %>%
mutate(issue2 = ifelse(nchar(last_alive) != 8, "last seen alive date is not 8 characters; ", NA)) %>%
mutate(issue3 = ifelse(nchar(last_checked) != 8, "last checked date is not 8 characters; ", NA)) %>%
mutate(issue4 = ifelse(is.na(first_found), "found date missing; ", NA)) %>%
mutate(issue5 = ifelse(is.na(last_alive), "last seen alive date missing; ", NA)) %>%
mutate(issue6 = ifelse(is.na(last_checked), "last checked date missing; ", NA)) %>%
mutate(issue7 = ifelse(management_status %!in% c("Y", "N"), "Nest managed? is not Y or N; ", NA)) %>%
mutate(issue8 = ifelse(nest_hab %!in% c("beach", "dune", "foredune/face", "estuary/spit"),
"Nest habitat is not beach, dune, foredune/face, or estuary/spit; ", NA)) %>%
mutate(issue9 = ifelse(is.na(management_level), "Management type is not sufficient for making levels; ", NA)) %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
mutate(issue10 = ifelse(found_and_alive_diff > 40 , "Double check dates because incubation time greater than 40 days; ", NA)) %>%
mutate(issue11 = ifelse(first_found2 > last_alive2, "Found date is after Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issue12 = ifelse(first_found2 > last_checked2, "Found date is after Last Checked date (should be greater or equal); ", NA)) %>%
mutate(issue13 = ifelse(last_checked2 < last_alive2, "Last Checked date is before Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issues = ifelse(is.na(issue1) & is.na(issue2) & is.na(issue3) &
is.na(issue4) & is.na(issue5) & is.na(issue6) &
is.na(issue7) & is.na(issue8) & is.na(issue9) &
is.na(issue10) & is.na(issue11) & is.na(issue12) & is.na(issue13), NA,
paste0(issue1, issue2, issue3,
issue4, issue5, issue6,
issue7, issue8, issue9,
issue10, issue11, issue12, issue13))) %>%
mutate(issues = str_remove_all(issues, "NA")) %>%
mutate(issues = ifelse(is.na(issues), "usable", issues)) %>%
dplyr::select(-issue1, -issue2, -issue3,
-issue4, -issue5, -issue6,
-issue7, -issue8, -issue9,
-issue10, -issue11, -issue12, -issue13) %>%
filter(issues != "usable") %>%
arrange(issues)all issues (probably most of these can’t be resolved)
Code
# Create a new workbook to store issues as two sheets
BC_nest_issues <- createWorkbook()
# Add all_issues sheet
addWorksheet(BC_nest_issues, "all_issues")
# view issues interactively in RStudio Viewer
BC_nest_data_issues %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write BC_nest_data_check
writeData(BC_nest_issues, sheet = "all_issues", BC_nest_data_issues)date-specific issues (probably most of these CAN be resolved)
Code
# Add date_issues
addWorksheet(BC_nest_issues, "date_issues")
# there are some blank (but non-NA) observations of management_type
BC_nest_data_issues %>% filter(is.na(management_type))# A tibble: 0 × 32
# Groups: season [0]
# ℹ 32 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>,
# LastChecked <dbl>, sign_access <dbl>, sign_nest <dbl>, rope_fence <dbl>, …Code
# view issues interactively in RStudio Viewer
BC_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not beach, dune, foredune/face, estuary/spit, or rocks; ")) %>%
dplyr::select(season, nest_ID, nest_hab, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write BC_nest_data_check
BC_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not Beach, Dune, Foredune/face, or Estuary/spit; ")) %>%
dplyr::select(season, nest_ID, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
writeData(BC_nest_issues, sheet = "date_issues", .)
# Save the workbook
saveWorkbook(BC_nest_issues, here("data/nest_issues_commented/BC_nest_data_issues_011025.xlsx"), overwrite = TRUE)Import data
Code
## I can't get the 2009/10 season to import properly so have removed it for now. There is only 1 nest in this season. Are you able to fix this for me
## LEH (1/10/25): fixed it. The issue was that the nest_import() function was specifying the "Fate" column as a numeric for the 2009/10 season, while Fate for the other years was specified as character. All variables need to have consistent classifications across sheets in order for it to merge.
nest_data_BC <-
bind_rows(
nest_import(year_1 = "2024", year_2 = "2025",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2023", year_2 = "2024",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2022", year_2 = "2023",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2021", year_2 = "2022",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2020", year_2 = "2021",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2019", year_2 = "2020",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2018", year_2 = "2019",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2017", year_2 = "2018",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2016", year_2 = "2017",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2015", year_2 = "2016",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2014", year_2 = "2015",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2013", year_2 = "2014",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2012", year_2 = "2013",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2011", year_2 = "2012",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2010", year_2 = "2011",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 13,
last_alive_date_col = 31,
last_checked_col = 37),
nest_import(year_1 = "2009", year_2 = "2010",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 12,
last_alive_date_col = 30,
last_checked_col = 36),
nest_import(year_1 = "2008", year_2 = "2009",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 12,
last_alive_date_col = 30,
last_checked_col = 36),
nest_import(year_1 = "2007", year_2 = "2008",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2006", year_2 = "2007",
file_name = "Bass Coast_nesting summaries.xlsx", site = "BC",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35)) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
filter(!is.na(FirstFound) & !is.na(LastPresent) & !is.na(LastChecked)) %>%
filter(management_status %in% c("Y", "N")) %>%
filter(nest_hab %in% c("beach", "dune", "foredune/face", "estuary/spit")) %>%
filter(!is.na(management_level)) %>% #
mutate(region = "BC") %>%
mutate(site = as.factor(site)) %>%
group_by(season, site) %>%
arrange(first_found2, .by_group = TRUE) %>%
mutate(rank = row_number()) %>%
ungroup() %>%
mutate(nest_ID_new = paste(season, site, rank, sep = "_")) %>%
ungroup()Visual inspections of data
Code
nest_data_BC_check <-
nest_data_BC %>%
ungroup() %>%
mutate(first_found2_md = paste(format(first_found2 + 180, format = "%m"),
format(first_found2 + 180, format = "%d"),
sep = "-"),
last_alive2_md = paste(format(last_alive2 + 180, format = "%m"),
format(last_alive2 + 180, format = "%d"),
sep = "-"),
last_checked2_md = paste(format(last_checked2 + 180, format = "%m"),
format(last_checked2 + 180, format = "%d"),
sep = "-")) %>%
mutate(first_found2_trans = as.Date(paste("2020", first_found2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_alive2_trans = as.Date(paste("2020", last_alive2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_checked2_trans = as.Date(paste("2020", last_checked2_md, sep = "-"), format = "%Y-%m-%d") - 179) %>%
mutate(season_label = paste0("season ", str_sub(season, 1, 4), " to ", str_sub(season, 5, 6)))Spatial distribution of data
Note that this map only shows data that are in a decimal degrees format (e.g., -38.31), NOT degree minute seconds (e.g., 38 27.59). The map is interactive, so click on an outlier to see its metadata
Code
nest_data_BC_sf <-
nest_data_BC %>%
as.data.frame() %>%
mutate(nest_lon = as.numeric(nest_lon),
nest_lat = as.numeric(nest_lat)) %>%
filter(!is.na(nest_lon) & !is.na(nest_lat)) %>%
st_as_sf(coords = c("nest_lon", "nest_lat"),
crs = 4326) %>% suppressWarnings()
mapview(nest_data_BC_sf,
zcol = "season",
popup = popupTable(nest_data_BC_sf,
zcol = c("season",
"site",
"nest_ID")))Year-specific Found Date Distributions
Code
ggplot(nest_data_BC_check, aes(first_found2_trans, fill = Fate)) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
# expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_BC_check$first_found2_trans, na.rm = TRUE),
max(nest_data_BC_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10)) +
luke_theme +
xlab("Found date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Alive Date Distributions
Code
ggplot(nest_data_BC_check, aes(last_alive2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_BC_check$first_found2_trans, na.rm = TRUE),
max(nest_data_BC_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last alive date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Checked Date Distributions
Code
ggplot(nest_data_BC_check, aes(last_checked2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_BC_check$first_found2_trans, na.rm = TRUE),
max(nest_data_BC_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last checked date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Tally number of nests per year in the dataset
Code
nest_data_BC_check %>%
group_by(season) %>%
summarise(n()) %>%
print(n = Inf)# A tibble: 19 × 2
season `n()`
<fct> <int>
1 200607 13
2 200708 11
3 200809 11
4 200910 1
5 201011 9
6 201112 30
7 201213 22
8 201314 37
9 201415 45
10 201516 62
11 201617 94
12 201718 74
13 201819 59
14 201920 50
15 202021 69
16 202122 52
17 202223 66
18 202324 65
19 202425 67Code
nest_data_BC_check %>%
summarise(n())# A tibble: 1 × 1
`n()`
<int>
1 837Incubation length distribution
Code
# assess if there are nests with unusually long incubation periods
nest_data_BC_check %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
arrange(desc(found_and_alive_diff)) %>%
filter(first_found2 < last_alive2 & first_found2 < last_checked2 & found_and_alive_diff < 100) %>%
ggplot() +
geom_histogram(aes(found_and_alive_diff)) +
luke_theme +
xlab("Time between found date and last alive date (days)") +
ylab("Frquency of nests")
# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, ….
Double check dates because incubation time greater than 41 days;
4 # A tibble: 4 × 3
issues season site
<chr> <fct> <fct>
1 "Double check dates because incubation time greater than 41 days… 201213 Powl…
2 "Double check dates because incubation time greater than 41 days… 201718 Coal…
3 "Double check dates because incubation time greater than 41 days… 201718 Inve…
4 "Double check dates because incubation time greater than 41 days… 202324 Inve…Phillip Island
Flag potential issues in data
As above, first we import the data and run a few checks to assess if there are any rows with the issues listed above
Code
PI_nest_data_issues <-
suppressMessages(
bind_rows(
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2024", "_", str_sub("2025", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2023", "_", str_sub("2024", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2022", "_", str_sub("2023", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2021", "_", str_sub("2022", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2020", "_", str_sub("2021", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2019", "_", str_sub("2020", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2018", "_", str_sub("2019", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2017", "_", str_sub("2018", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2016", "_", str_sub("2017", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2015", "_", str_sub("2016", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2014", "_", str_sub("2015", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2013", "_", str_sub("2014", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "PI Nesting summaries.xlsx"),
sheet = paste0("PI", " ", "2012", "_", str_sub("2013", 3, 4)),
col_types = "text", na = "n/a")))%>%
filter(!is.na(Season)) %>%
rename(first_found = 11,
last_alive = 29,
last_checked = 35,
Fate = `Hatch?`,
season = Season,
#site = Site,
site = `Suggested update to site name`,
nest_ID = `Nest ID_2`,
nest_hab = `Nest habitat`,
management_status = `Nest managed?`,
management_type = `Nest management type`,
nest_lat = `Nest latitude`,
nest_lon = `Nest longitude`,
state_1st_ob = `Age stage first observed`) %>%
rename(
chick_1st_ob = !!names(.)[which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))][1],
clutch_comp = !!names(.)[which(names(.) %in% c("Known clutch complete date", "Known laying date"))][1]
) %>%
# chick_1st_ob = which(names(.) %in% c("Date chicks sighted", "Chicks first obsv date"))[1],
# clutch_comp = which(names(.) %in% c("Known clutch complete date", "Known laying date"))) %>%
dplyr::select(season, site, nest_ID, first_found, last_alive, last_checked, Fate, nest_hab,
management_status, management_type, nest_lat, nest_lon, site,
chick_1st_ob, clutch_comp, state_1st_ob) %>%
mutate(Fate = ifelse(Fate == "?", "Unk", Fate)) %>%
mutate(Fate = toupper(Fate)) %>%
filter(
is.na(first_found) |
grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", first_found)
) %>%
mutate(last_checked = ifelse(!is.na(last_checked) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_checked),
NA, last_checked)) %>%
mutate(last_alive = ifelse(!is.na(last_alive) & !grepl("^\\s*-?\\d+(\\.\\d+)?\\s*$", last_alive),
NA, last_alive)) %>%
mutate(last_alive = ifelse(str_detect(last_alive, "Unk."), NA, last_alive),
Fate = ifelse(Fate == "Y", "0",
ifelse(Fate == "N", "1",
ifelse(Fate == "UNK", NA, "XXX")))) %>%
# remove nests without Fate information
filter(Fate %in% c("0", "1")) %>%
mutate(chick_1st_ob = case_when(
# Excel serial dates stored as numbers
str_detect(chick_1st_ob, "^[0-9]+$") ~ as.Date(as.numeric(chick_1st_ob), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(chick_1st_ob, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
),
clutch_comp = case_when(
# Excel serial dates stored as numbers
str_detect(clutch_comp, "^[0-9]+$") ~ as.Date(as.numeric(clutch_comp), origin = "1899-12-30"),
# Dates with extra text in parentheses — extract only the date part
str_detect(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}") ~ suppressWarnings(
dmy(str_extract(clutch_comp, "^\\d{1,2}/\\d{1,2}/\\d{2,4}"))
),
# Everything else (including "Unk", "Failed during laying") → NA
TRUE ~ as.Date(NA)
)) %>%
# if the clutch completion date is NA, then estimate it as 28 days before estimated hatch date
# using the date of first chick observation and the size of the chicks when seen
mutate(clutch_comp = ifelse(!is.na(clutch_comp), clutch_comp,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3 - 28,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10 - 28,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19 - 28,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30 - 28,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35 - 28,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob - 28, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob), NA, NA))) %>% as.Date()) %>%
mutate(last_alive2 = ifelse(Fate == "0" & !is.na(clutch_comp) & is.na(last_alive), clutch_comp + 28,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob) & is.na(last_alive),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10,
ifelse(state_1st_ob == "2/3 adult size, mottled grey", chick_1st_ob - 19,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob) & is.na(last_alive), NA, NA))) %>% as.Date()) %>%
# mutate(last_alive2 = ifelse(Fate == "0" & !is.na(chick_1st_ob) & last_alive2 > chick_1st_ob, chick_1st_ob, last_alive2) %>% as.Date()) %>%
mutate(last_alive = ifelse(!is.na(last_alive2),
paste0(str_sub(as.character(last_alive2), 9, 10),
str_sub(as.character(last_alive2), 6, 7),
str_sub(as.character(last_alive2), 1, 4)), last_alive)) %>%
mutate(
last_checked = ifelse(!is.na(last_alive) & is.na(last_checked),
last_alive,
ifelse(is.na(last_alive) & is.na(last_checked),
first_found, last_checked))) %>%
mutate(
last_alive = ifelse(is.na(last_alive) & Fate == "0" & !is.na(last_checked),
last_checked,
ifelse(is.na(last_alive) & Fate == "1" & !is.na(last_checked),
first_found,
last_alive))) %>%
filter(
(is.na(last_alive) | nchar(last_alive) == 8) &
(is.na(first_found) | nchar(first_found) == 8) &
(is.na(last_checked) | nchar(last_checked) == 8)
) %>%
mutate(first_found2 = as.Date(paste(str_sub(first_found, 5, 8),
str_sub(first_found, 3, 4),
str_sub(first_found, 1, 2), sep = "-")),
last_alive2 = as.Date(paste(str_sub(last_alive, 5, 8),
str_sub(last_alive, 3, 4),
str_sub(last_alive, 1, 2), sep = "-")),
last_checked2 = as.Date(paste(str_sub(last_checked, 5, 8),
str_sub(last_checked, 3, 4),
str_sub(last_checked, 1, 2), sep = "-"))) %>%
mutate(last_checked2 = ifelse(!is.na(last_alive2) & !is.na(last_checked2) & last_checked2 < last_alive2,
last_alive2, last_checked2) %>% as.Date()) %>%
mutate(first_found2 = ifelse(!is.na(clutch_comp) & ((last_alive2 - first_found2) > 40 | (last_alive2 - first_found2) < 0)
& ((last_alive2 - clutch_comp) < 40 | (last_alive2 - clutch_comp) > 0),
clutch_comp, first_found2) %>% as.Date()) %>%
mutate(FirstFound = as.numeric(format(first_found2 + 180, "%j")),
LastPresent = as.numeric(format(last_alive2 + 180, "%j")),
LastChecked = as.numeric(format(last_checked2 + 180, "%j"))) %>%
# mutate(FirstFound = ((as.integer(format(first_found2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(first_found2 + 180, "%j")),
# LastPresent = ((as.integer(format(last_alive2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365),#as.numeric(format(last_alive2 + 180, "%j")),
# LastChecked = ((as.integer(format(last_checked2, "%j")) -
# as.integer(format(as.Date("2001-06-01"), "%j")) + 1) %% 365) %>%
# replace(. == 0, 365)) %>% #as.numeric(format(last_checked2 + 180, "%j"))) %>%
mutate(management_type = tolower(management_type)) %>%
mutate(nest_hab = tolower(nest_hab)) %>%
mutate(management_type = str_replace(management_type, "acess", "access")) %>%
mutate(management_type = str_replace(management_type, "and", ",")) %>%
mutate(management_type = str_replace(management_type, "temporary", "")) %>%
mutate(management_type = str_replace_all(management_type, " ", "")) %>%
mutate(management_type = str_replace_all(management_type, "shelters", "")) %>%
mutate(management_type = str_replace_all(management_type, "banners", "")) %>%
mutate(management_type = str_replace_all(management_type, ",,", ",")) %>%
mutate(sign_access = ifelse(str_detect(management_type, "signaccess"), 1, 0)) %>%
mutate(sign_nest = ifelse(str_detect(management_type, "signnest"), 1, 0)) %>%
mutate(rope_fence = ifelse(str_detect(management_type, "ropefence"), 1, 0)) %>%
mutate(perm_fence = ifelse(str_detect(management_type, "permanentfence"), 1, 0)) %>% # & nest_hab == "dune", 1, 0)) %>%
mutate(wardens = ifelse(str_detect(management_type, "wardens"), 1, 0)) %>%
mutate(none = ifelse(str_detect(management_type, "none"), 1,
ifelse(is.na(management_type) & management_status == "N", 1, 0))) %>%
mutate(other = ifelse(str_detect(management_type, "other"), 1, 0)) %>%
mutate(management_level = ifelse((sign_access == 1 | perm_fence == 1) & sign_nest == 1 & rope_fence == 1 & wardens == 1, 4,
ifelse(rope_fence == 1, 3,
ifelse(sign_nest == 1, 2,
ifelse(sign_access == 1 | perm_fence == 1, 1,
ifelse(none == 1, 0, NA)))))) %>%
mutate(sign_nest_no_sign_access = ifelse(sign_access == 0 & sign_nest == 1, 1, 0)) %>%
mutate(fence_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & rope_fence == 1, 1, 0)) %>%
mutate(wardens_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & wardens == 1, 1, 0)) %>%
mutate(wardens_no_fence = ifelse(rope_fence == 1 & wardens == 1, 1, 0)) %>%
mutate(just_wardens = ifelse(rope_fence == 0 & sign_access == 0 & sign_nest == 0 & wardens == 1, 1, 0)) %>%
dplyr::select(-other, -sign_nest_no_sign_access, -fence_no_sign,
-wardens_no_sign, -wardens_no_fence, -just_wardens) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
mutate(region = "MP") %>%
mutate(site = as.factor(site)) %>%
mutate(issue1 = ifelse(nchar(first_found) != 8, "found date is not 8 characters; ", NA)) %>%
mutate(issue2 = ifelse(nchar(last_alive) != 8, "last seen alive date is not 8 characters; ", NA)) %>%
mutate(issue3 = ifelse(nchar(last_checked) != 8, "last checked date is not 8 characters; ", NA)) %>%
mutate(issue4 = ifelse(is.na(first_found), "found date missing; ", NA)) %>%
mutate(issue5 = ifelse(is.na(last_alive), "last seen alive date missing; ", NA)) %>%
mutate(issue6 = ifelse(is.na(last_checked), "last checked date missing; ", NA)) %>%
mutate(issue7 = ifelse(management_status %!in% c("Y", "N"), "Nest managed? is not Y or N; ", NA)) %>%
mutate(issue8 = ifelse(nest_hab %!in% c("beach", "dune", "foredune/face", "estuary/spit"),
"Nest habitat is not beach, dune, foredune/face, or estuary/spit; ", NA)) %>%
mutate(issue9 = ifelse(is.na(management_level), "Management type is not sufficient for making levels; ", NA)) %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
mutate(issue10 = ifelse(found_and_alive_diff > 40 , "Double check dates because incubation time greater than 40 days; ", NA)) %>%
mutate(issue11 = ifelse(first_found2 > last_alive2, "Found date is after Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issue12 = ifelse(first_found2 > last_checked2, "Found date is after Last Checked date (should be greater or equal); ", NA)) %>%
mutate(issue13 = ifelse(last_checked2 < last_alive2, "Last Checked date is before Last Alive date (should be greater or equal); ", NA)) %>%
mutate(issues = ifelse(is.na(issue1) & is.na(issue2) & is.na(issue3) &
is.na(issue4) & is.na(issue5) & is.na(issue6) &
is.na(issue7) & is.na(issue8) & is.na(issue9) &
is.na(issue10) & is.na(issue11) & is.na(issue12) & is.na(issue13), NA,
paste0(issue1, issue2, issue3,
issue4, issue5, issue6,
issue7, issue8, issue9,
issue10, issue11, issue12, issue13))) %>%
mutate(issues = str_remove_all(issues, "NA")) %>%
mutate(issues = ifelse(is.na(issues), "usable", issues)) %>%
dplyr::select(-issue1, -issue2, -issue3,
-issue4, -issue5, -issue6,
-issue7, -issue8, -issue9,
-issue10, -issue11, -issue12, -issue13) %>%
filter(issues != "usable") %>%
arrange(issues)all issues (probably most of these can’t be resolved)
Code
# Create a new workbook to store issues as two sheets
PI_nest_issues <- createWorkbook()
# Add all_issues sheet
addWorksheet(PI_nest_issues, "all_issues")
# view issues interactively in RStudio Viewer
PI_nest_data_issues %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write PI_nest_data_check
writeData(PI_nest_issues, sheet = "all_issues", PI_nest_data_issues)date-specific issues (probably most of these CAN be resolved)
Code
# Add date_issues
addWorksheet(PI_nest_issues, "date_issues")
# there are some blank (but non-NA) observations of management_type
PI_nest_data_issues %>% filter(is.na(management_type))# A tibble: 0 × 32
# Groups: season [0]
# ℹ 32 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>,
# LastChecked <dbl>, sign_access <dbl>, sign_nest <dbl>, rope_fence <dbl>, …Code
# view issues interactively in RStudio Viewer
PI_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not beach, dune, foredune/face, estuary/spit, or rocks; ")) %>%
dplyr::select(season, nest_ID, nest_hab, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
# write PI_nest_data_check
PI_nest_data_issues %>%
filter(first_found != "Not found" & last_alive != "Not seen" & last_checked != "Not seen" & last_checked != "Not revisited") %>%
filter(str_detect(issues, "date")) %>%
mutate(issues = str_remove_all(issues, "Management type is not sufficient for making levels; ")) %>%
mutate(issues = str_remove_all(issues, "Nest habitat is not Beach, Dune, Foredune/face, or Estuary/spit; ")) %>%
dplyr::select(season, nest_ID, first_found, first_found2, last_alive, last_alive2, last_checked, last_checked2,
Fate, found_and_alive_diff, issues) %>%
writeData(PI_nest_issues, sheet = "date_issues", .)
# Save the workbook
saveWorkbook(PI_nest_issues, here("data/nest_issues_commented/PI_nest_data_issues_011025.xlsx"), overwrite = TRUE)Import data
Code
nest_data_PI <-
bind_rows(
nest_import(year_1 = "2024", year_2 = "2025",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2023", year_2 = "2024",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2022", year_2 = "2023",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2021", year_2 = "2022",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2020", year_2 = "2021",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2019", year_2 = "2020",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2018", year_2 = "2019",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2017", year_2 = "2018",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2016", year_2 = "2017",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2015", year_2 = "2016",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2014", year_2 = "2015",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2013", year_2 = "2014",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35),
nest_import(year_1 = "2012", year_2 = "2013",
file_name = "PI Nesting summaries.xlsx", site = "PI",
first_found_date_col = 11,
last_alive_date_col = 29,
last_checked_col = 35)) %>%
group_by(season) %>%
mutate(nocc = max(max(LastChecked, na.rm = TRUE), max(LastPresent, na.rm = TRUE)),
season = as.factor(season),
nest_hab = as.factor(nest_hab),
management_status = as.factor(management_status)) %>%
filter(!is.na(FirstFound) & !is.na(LastPresent) & !is.na(LastChecked)) %>%
filter(management_status %in% c("Y", "N")) %>%
filter(nest_hab %in% c("beach", "dune", "foredune/face", "estuary/spit")) %>%
filter(!is.na(management_level)) %>% #
mutate(region = "PI") %>%
mutate(site = as.factor(site)) %>%
group_by(season, site) %>%
arrange(first_found2, .by_group = TRUE) %>%
mutate(rank = row_number()) %>%
ungroup() %>%
mutate(nest_ID_new = paste(season, site, rank, sep = "_")) %>%
ungroup()Visual inspections of data
Code
nest_data_PI_check <-
nest_data_PI %>%
ungroup() %>%
mutate(first_found2_md = paste(format(first_found2 + 180, format = "%m"),
format(first_found2 + 180, format = "%d"),
sep = "-"),
last_alive2_md = paste(format(last_alive2 + 180, format = "%m"),
format(last_alive2 + 180, format = "%d"),
sep = "-"),
last_checked2_md = paste(format(last_checked2 + 180, format = "%m"),
format(last_checked2 + 180, format = "%d"),
sep = "-")) %>%
mutate(first_found2_trans = as.Date(paste("2020", first_found2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_alive2_trans = as.Date(paste("2020", last_alive2_md, sep = "-"), format = "%Y-%m-%d") - 179,
last_checked2_trans = as.Date(paste("2020", last_checked2_md, sep = "-"), format = "%Y-%m-%d") - 179) %>%
mutate(season_label = paste0("season ", str_sub(season, 1, 4), " to ", str_sub(season, 5, 6)))Spatial distribution of data
Note that this map only shows data that are in a decimal degrees format (e.g., -38.31), NOT degree minute seconds (e.g., 38 27.59). The map is interactive, so click on an outlier to see its metadata
Code
nest_data_PI_sf <-
nest_data_PI %>%
as.data.frame() %>%
mutate(nest_lon = as.numeric(nest_lon),
nest_lat = as.numeric(nest_lat)) %>%
filter(!is.na(nest_lon) & !is.na(nest_lat)) %>%
st_as_sf(coords = c("nest_lon", "nest_lat"),
crs = 4326) %>% suppressWarnings()
mapview(nest_data_PI_sf,
zcol = "season",
popup = popupTable(nest_data_PI_sf,
zcol = c("season",
"site",
"nest_ID")))Year-specific Found Date Distributions
Code
ggplot(nest_data_PI_check, aes(first_found2_trans, fill = Fate)) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
# expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_PI_check$first_found2_trans, na.rm = TRUE),
max(nest_data_PI_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10)) +
luke_theme +
xlab("Found date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Alive Date Distributions
Code
ggplot(nest_data_PI_check, aes(last_alive2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_PI_check$first_found2_trans, na.rm = TRUE),
max(nest_data_PI_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last alive date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Year-specific Last Checked Date Distributions
Code
ggplot(nest_data_PI_check, aes(last_checked2_trans, fill = as.factor(Fate))) +
geom_histogram(bins = 30,
alpha = 0.8, color = "white", linewidth = 0.2) +
scale_fill_manual(values = c("1" = brewer.pal(8, "Set1")[c(1)], "0" = brewer.pal(8, "Set1")[c(2)]),
name = "Nest Fate",
labels = c("Hatched", "Failed")) +
ylab("weekly number of nests") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_data_PI_check$first_found2_trans, na.rm = TRUE),
max(nest_data_PI_check$last_checked2_trans, na.rm = TRUE))) +
facet_wrap("season_label") +
scale_y_continuous(limits = c(0, 12), breaks = c(2, 4, 6, 8, 10, 12)) +
luke_theme +
xlab("Last checked date") +
theme(legend.position = "top",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Tally number of nests per year in the dataset
Code
nest_data_PI_check %>%
group_by(season) %>%
summarise(n()) %>%
print(n = Inf)# A tibble: 13 × 2
season `n()`
<fct> <int>
1 201213 34
2 201314 15
3 201415 27
4 201516 35
5 201617 32
6 201718 40
7 201819 43
8 201920 38
9 202021 45
10 202122 27
11 202223 29
12 202324 19
13 202425 31Code
nest_data_PI_check %>%
summarise(n())# A tibble: 1 × 1
`n()`
<int>
1 415Incubation length distribution
Code
# assess if there are nests with unusually long incubation periods
nest_data_PI_check %>%
mutate(found_and_alive_diff = last_alive2 - first_found2) %>%
arrange(desc(found_and_alive_diff)) %>%
filter(first_found2 < last_alive2 & first_found2 < last_checked2 & found_and_alive_diff < 100) %>%
ggplot() +
geom_histogram(aes(found_and_alive_diff)) +
luke_theme +
xlab("Time between found date and last alive date (days)") +
ylab("Frquency of nests")
# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …# A tibble: 0 × 35
# ℹ 35 variables: season <fct>, site <fct>, nest_ID <chr>, first_found <chr>,
# last_alive <chr>, last_checked <chr>, Fate <chr>, nest_hab <fct>,
# management_status <fct>, management_type <chr>, nest_lat <chr>,
# nest_lon <chr>, chick_1st_ob <date>, clutch_comp <date>,
# state_1st_ob <chr>, chick_management_status <chr>,
# chick_management_type <chr>, last_alive2 <date>, first_found2 <date>,
# last_checked2 <date>, FirstFound <dbl>, LastPresent <dbl>, …< table of extent 0 ># A tibble: 0 × 3
# ℹ 3 variables: issues <chr>, season <fct>, site <fct>Importing and checking threat data
Code
MP_threat_data <-
read_excel("data/Threat data all years 25062025.xlsx",
sheet = "MP threats (all years)",
col_types = "text") %>%
mutate(season = str_remove(Season, "/")) %>%
rename(obs_lon = `Observation Longitude`,
obs_lat = `Observation Latitude`,
obs_date = `Observation Date`) %>%
mutate(obs_date = as.Date(as.numeric(obs_date),
origin = "1899-12-30")) %>%
mutate(obs_date2 = as.numeric(format(obs_date + 180, "%j"))) %>%
mutate(region = "MP")
FP_threat_data <-
read_excel("data/Threat data all years 25062025.xlsx",
sheet = "FP threat (all years)",
col_types = "text") %>%
mutate(season = str_remove(Season, "/")) %>%
rename(obs_lon = `Observation Longitude`,
obs_lat = `Observation Latitude`,
obs_date = `Observation Date`) %>%
mutate(obs_date = as.Date(as.numeric(obs_date),
origin = "1899-12-30")) %>%
mutate(obs_date2 = as.numeric(format(obs_date + 180, "%j"))) %>%
mutate(region = "FP")
BSC_threat_data <-
read_excel("data/Threat data all years 25062025.xlsx",
sheet = "BSC threats (all years)",
col_types = "text") %>%
mutate(season = str_remove(Season, "/")) %>%
rename(obs_lon = `Observation Longitude`,
obs_lat = `Observation Latitude`,
obs_date = `Observation date`) %>%
mutate(obs_date = as.Date(as.numeric(obs_date),
origin = "1899-12-30")) %>%
mutate(obs_date2 = as.numeric(format(obs_date + 180, "%j"))) %>%
mutate(region = "BSC")
BC_threat_data <-
read_excel("data/Threat data all years 25062025.xlsx",
sheet = "BC threat (all years)",
col_types = "text") %>%
mutate(season = str_remove(Season, "/")) %>%
rename(obs_lon = `Observation Longitude`,
obs_lat = `Observation Latitude`,
obs_date = `Observation date`) %>%
mutate(obs_date = as.Date(as.numeric(obs_date),
origin = "1899-12-30")) %>%
mutate(obs_date2 = as.numeric(format(obs_date + 180, "%j"))) %>%
mutate(region = "BC")
PI_threat_data <-
read_excel("data/Threat data all years 25062025.xlsx",
sheet = "PI threat (all years)",
col_types = "text") %>%
mutate(season = str_remove(Season, "/")) %>%
rename(obs_lon = `Observation Longitude`,
obs_lat = `Observation Latitude`,
obs_date = `Observation date`) %>%
mutate(obs_date = as.Date(as.numeric(obs_date),
origin = "1899-12-30")) %>%
mutate(obs_date2 = as.numeric(format(obs_date + 180, "%j"))) %>%
mutate(region = "PI")
threat_data <-
bind_rows(FP_threat_data,
MP_threat_data,
BC_threat_data,
BSC_threat_data,
PI_threat_data)###Import threat data
Code
threat_data_ <-
threat_data %>%
rename(site = `Site name`) %>%
# first convert all the count columns to numeric
mutate_at(vars(
`Walkers/Joggers (wet sand)`,`Walkers/Joggers (dry sand)`,
`Walkers/Joggers (signs/fence)`,`Walkers/Joggers (Dune)`,`People sunbaking/sitting (wet sand)`,
`People sunbaking/sitting (dry sand)`,`People sunbaking/sitting (signs/fence)`,
`People sunbaking/sitting (Dune)`,`Surfers/Swimmers (wet sand)`,
`Surfers/Swimmers (dry sand)`,`Surfers/Swimmers (signs/fence)`,
`Surfers/Swimmers (Dune)`,`People Fishing (wet sand)`,
`People Fishing (dry sand)`,`People Fishing (signs/fence)`,
`People Fishing (Dune)`,`People Playing Games (wet sand)`,
`People Playing Games (dry sand)`,`People Playing Games (signs/fence)`,
`People Playing Games (Dune)`,`Dog Walkers (wet sand)`,
`Dog Walkers (dry sand)`,`Dog Walkers (signs/fence)`,
`Dog Walkers (Dune)`,`Dog On Leash (# dogs) (wet sand)`,
`Dog On Leash (# dogs) (dry sand)`,`Dog On Leash (# dogs) (signs/fence)`,
`Dog On Leash (# dogs) (Dune)`,`Dog Off Leash (# dogs) (wet sand)`,
`Dog Off Leash (# dogs) (dry sand)`,`Dog Off Leash (# dogs) (signs/fence)`,
`Dog Off Leash (# dogs) (Dune)`,`Horses (wet sand)`,
`Horses (dry sand)`,`Horses (signs/fence)`,
`Horses (Dune)`,`Permitted vehicle (wet sand)`,
`Permitted vehicle (dry sand)`,`Permitted vehicle (signs/fence)`,
`Permitted vehicle (Dune)`,`Illegal vehicle (wet sand)`,
`Illegal vehicle (dry sand)`,`Illegal vehicle (signs/fence)`,
`Illegal vehicle (Dune)`,`Ravens (wet sand)`,
`Ravens (dry sand)`,`Ravens (signs/fence)`,
`Ravens (Dune)`,`Magpies (wet sand)`,
`Magpies (dry sand)`,`Magpies (signs/fence)`,
`Magpies (Dune)`,`Silver Gulls (wet sand)`,
`Silver Gulls (dry sand)`,`Silver Gulls (signs/fence)`,
`Silver Gulls (Dune)`,`Pacific/Kelp Gulls (wet sand)`,
`Pacific/Kelp Gulls (dry sand)`,`Pacific/Kelp Gulls (signs/fence)`,
`Pacific/Kelp Gulls (Dune)`,`Nankeen Kestrels (wet sand)`,
`Nankeen Kestrels (dry sand)`,`Nankeen Kestrels (signs/fence)`,
`Nankeen Kestrels (Dune)`,`Other bird of prey (wet sand)`,
`Other bird of prey (dry sand)`,`Other bird of prey (signs/fence)`,
`Other bird of prey (Dune)`,
`Stock (cattle/sheep) (wet sand)`,
`Stock (cattle/sheep) (dry sand)`,`Stock (cattle/sheep) (signs/fence)`,
`Stock (cattle/sheep) (Dune)`), as.numeric) %>%
ungroup() %>%
# take the total sum of counts for each threat type (e.g., humans includes
# Dog Walkers, People Playing Games, People Fishing, Surfers/Swimmers,
# People sunbaking/sitting, and Walkers/Joggers)
mutate(humans = rowSums(dplyr::select(.,`Walkers/Joggers (wet sand)`,
`Walkers/Joggers (dry sand)`,
`Walkers/Joggers (signs/fence)`,
`Walkers/Joggers (Dune)`,
`People sunbaking/sitting (wet sand)`,
`People sunbaking/sitting (dry sand)`,
`People sunbaking/sitting (signs/fence)`,
`People sunbaking/sitting (Dune)`,
`Surfers/Swimmers (wet sand)`,
`Surfers/Swimmers (dry sand)`,
`Surfers/Swimmers (signs/fence)`,
`Surfers/Swimmers (Dune)`,
`People Fishing (wet sand)`,
`People Fishing (dry sand)`,
`People Fishing (signs/fence)`,
`People Fishing (Dune)`,
`People Playing Games (wet sand)`,
`People Playing Games (dry sand)`,
`People Playing Games (signs/fence)`,
`People Playing Games (Dune)`,
`Dog Walkers (wet sand)`,
`Dog Walkers (dry sand)`,
`Dog Walkers (signs/fence)`,
`Dog Walkers (Dune)`), na.rm = TRUE),
# do a micro-habitat specific sum for humans
humans_wet = rowSums(dplyr::select(.,`Walkers/Joggers (wet sand)`,
`People sunbaking/sitting (wet sand)`,
`Surfers/Swimmers (wet sand)`,
`People Fishing (wet sand)`,
`People Playing Games (wet sand)`,
`Dog Walkers (wet sand)`), na.rm = TRUE),
humans_dry = rowSums(dplyr::select(.,`Walkers/Joggers (dry sand)`,
`People sunbaking/sitting (dry sand)`,
`Surfers/Swimmers (dry sand)`,
`People Fishing (dry sand)`,
`People Playing Games (dry sand)`,
`Dog Walkers (dry sand)`), na.rm = TRUE),
humans_dune = rowSums(dplyr::select(.,`Walkers/Joggers (Dune)`,
`People sunbaking/sitting (Dune)`,
`Surfers/Swimmers (Dune)`,
`People Fishing (Dune)`,
`People Playing Games (Dune)`,
`Dog Walkers (Dune)`), na.rm = TRUE),
humans_SF = rowSums(dplyr::select(.,`Walkers/Joggers (signs/fence)`,
`People sunbaking/sitting (signs/fence)`,
`Surfers/Swimmers (signs/fence)`,
`People Fishing (signs/fence)`,
`People Playing Games (signs/fence)`,
`Dog Walkers (signs/fence)`), na.rm = TRUE),
dogs = rowSums(dplyr::select(., `Dog On Leash (# dogs) (wet sand)`,
`Dog On Leash (# dogs) (dry sand)`,
`Dog On Leash (# dogs) (signs/fence)`,
`Dog On Leash (# dogs) (Dune)`,
`Dog Off Leash (# dogs) (wet sand)`,
`Dog Off Leash (# dogs) (dry sand)`,
`Dog Off Leash (# dogs) (signs/fence)`,
`Dog Off Leash (# dogs) (Dune)`), na.rm = TRUE),
# specify a dog on leash and a dog of leash summary
dogs_on = rowSums(dplyr::select(., `Dog On Leash (# dogs) (wet sand)`,
`Dog On Leash (# dogs) (dry sand)`,
`Dog On Leash (# dogs) (signs/fence)`,
`Dog On Leash (# dogs) (Dune)`), na.rm = TRUE),
dogs_off = rowSums(dplyr::select(., `Dog Off Leash (# dogs) (wet sand)`,
`Dog Off Leash (# dogs) (dry sand)`,
`Dog Off Leash (# dogs) (signs/fence)`,
`Dog Off Leash (# dogs) (Dune)`), na.rm = TRUE),
pred_birds = rowSums(dplyr::select(., `Ravens (wet sand)`,
`Ravens (dry sand)`,
`Ravens (signs/fence)`,
`Ravens (Dune)`,
`Magpies (wet sand)`,
`Magpies (dry sand)`,
`Magpies (signs/fence)`,
`Magpies (Dune)`#,
# `Silver Gulls (wet sand)`,
# `Silver Gulls (dry sand)`,
# `Silver Gulls (signs/fence)`,
# `Silver Gulls (Dune)`,
# `Pacific/Kelp Gulls (wet sand)`,
# `Pacific/Kelp Gulls (dry sand)`,
# `Pacific/Kelp Gulls (signs/fence)`,
# `Pacific/Kelp Gulls (Dune)`,
# `Nankeen Kestrels (wet sand)`,
# `Nankeen Kestrels (dry sand)`,
# `Nankeen Kestrels (signs/fence)`,
# `Nankeen Kestrels (Dune)`,
# `Other bird of prey (wet sand)`,
# `Other bird of prey (dry sand)`,
# `Other bird of prey (signs/fence)`,
# `Other bird of prey (Dune)`
), na.rm = TRUE),
gulls = rowSums(dplyr::select(., #`Ravens (wet sand)`,
# `Ravens (dry sand)`,
# `Ravens (signs/fence)`,
# `Ravens (Dune)`,
# `Magpies (wet sand)`,
# `Magpies (dry sand)`,
# `Magpies (signs/fence)`,
# `Magpies (Dune)`
`Silver Gulls (wet sand)`,
`Silver Gulls (dry sand)`,
`Silver Gulls (signs/fence)`,
`Silver Gulls (Dune)`,
`Pacific/Kelp Gulls (wet sand)`,
`Pacific/Kelp Gulls (dry sand)`,
`Pacific/Kelp Gulls (signs/fence)`,
`Pacific/Kelp Gulls (Dune)`
# `Nankeen Kestrels (wet sand)`,
# `Nankeen Kestrels (dry sand)`,
# `Nankeen Kestrels (signs/fence)`,
# `Nankeen Kestrels (Dune)`,
# `Other bird of prey (wet sand)`,
# `Other bird of prey (dry sand)`,
# `Other bird of prey (signs/fence)`,
# `Other bird of prey (Dune)`
), na.rm = TRUE),
vehicles = rowSums(dplyr::select(., `Permitted vehicle (wet sand)`,
`Permitted vehicle (dry sand)`,
`Permitted vehicle (signs/fence)`,
`Permitted vehicle (Dune)`,
`Illegal vehicle (wet sand)`,
`Illegal vehicle (dry sand)`,
`Illegal vehicle (signs/fence)`,
`Illegal vehicle (Dune)`), na.rm = TRUE),
hoofed_animals = rowSums(dplyr::select(.,`Horses (wet sand)`,
`Horses (dry sand)`,
`Horses (signs/fence)`,
`Horses (Dune)`,
`Stock (cattle/sheep) (wet sand)`,
`Stock (cattle/sheep) (dry sand)`,
`Stock (cattle/sheep) (signs/fence)`,
`Stock (cattle/sheep) (Dune)`), na.rm = TRUE)) %>%
# consolidate columns names
rename(hum_pri_wet = `Human Prints (wet sand)`,
hum_pri_dry = `Human Prints (dry sand)`,
hum_pri_dune = `Human Prints (Dune)`,
hum_pri_SF = `Human Prints (signs/fence)`,
fox_pri_wet = `Fox Prints (wet sand)`,
fox_pri_dry = `Fox Prints (dry sand)`,
fox_pri_dune = `Fox Prints (Dune)`,
fox_pri_SF = `Fox Prints (signs/fence)`,
dog_pri_wet = `Dog Prints (wet sand)`,
dog_pri_dry = `Dog Prints (dry sand)`,
dog_pri_dune = `Dog Prints (Dune)`,
dog_pri_SF = `Dog Prints (signs/fence)`,
vehicle_pri_wet = `Vehicle Tracks (wet sand)`,
vehicle_pri_dry = `Vehicle Tracks (dry sand)`,
vehicle_pri_dune = `Vehicle Tracks (Dune)`,
vehicle_pri_SF = `Vehicle Tracks (signs/fence)`,
trailbike_pri_wet = `Trail bike tracks (wet sand)`,
trailbike_pri_dry = `Trail bike tracks (dry sand)`,
trailbike_pri_dune = `Trail bike tracks (Dune)`,
trailbike_pri_SF = `Trail bike tracks (signs/fence)`,
stock_pri_wet = `Stock (wet sand)`,
stock_pri_dry = `Stock (dry sand)`,
stock_pri_dune = `Stock (Dune)`,
stock_pri_SF = `Stock (signs/fence)`,
horse_pri_wet = `Horses Prints (wet sand)`,
horse_pri_dry = `Horses Prints (dry sand)`,
horse_pri_dune = `Horses Prints (Dune)`,
horse_pri_SF = `Horses Prints (signs/fence)`) %>%
# specify coordinates as numeric
mutate(obs_lon = as.numeric(obs_lon),
obs_lat = as.numeric(obs_lat)) %>%
# clean up factor levels (e.g., sometime "Light", sometimes just "L")
mutate(hum_pri_wet = ifelse(hum_pri_wet %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(hum_pri_wet, 1, 1),
ifelse(hum_pri_wet == "0", "N",
ifelse(hum_pri_wet == "1", "L",
ifelse(hum_pri_wet == "2", "M",
ifelse(hum_pri_wet == "3", "H",
ifelse(hum_pri_wet == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
hum_pri_dry = ifelse(hum_pri_dry %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(hum_pri_dry, 1, 1),
ifelse(hum_pri_dry == "0", "N",
ifelse(hum_pri_dry == "1", "L",
ifelse(hum_pri_dry == "2", "M",
ifelse(hum_pri_dry == "3", "H",
ifelse(hum_pri_dry == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
hum_pri_SF = ifelse(hum_pri_SF %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(hum_pri_SF, 1, 1),
ifelse(hum_pri_SF == "0", "N",
ifelse(hum_pri_SF == "1", "L",
ifelse(hum_pri_SF == "2", "M",
ifelse(hum_pri_SF == "3", "H",
ifelse(hum_pri_SF == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
hum_pri_dune = ifelse(hum_pri_dune %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(hum_pri_dune, 1, 1),
ifelse(hum_pri_dune == "0", "N",
ifelse(hum_pri_dune == "1", "L",
ifelse(hum_pri_dune == "2", "M",
ifelse(hum_pri_dune == "3", "H",
ifelse(hum_pri_dune == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
fox_pri_wet = ifelse(fox_pri_wet %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(fox_pri_wet, 1, 1),
ifelse(fox_pri_wet == "0", "N",
ifelse(fox_pri_wet == "1", "L",
ifelse(fox_pri_wet == "2", "M",
ifelse(fox_pri_wet == "3", "H",
ifelse(fox_pri_wet == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
fox_pri_dry = ifelse(fox_pri_dry %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(fox_pri_dry, 1, 1),
ifelse(fox_pri_dry == "0", "N",
ifelse(fox_pri_dry == "1", "L",
ifelse(fox_pri_dry == "2", "M",
ifelse(fox_pri_dry == "3", "H",
ifelse(fox_pri_dry == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
fox_pri_SF = ifelse(fox_pri_SF %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(fox_pri_SF, 1, 1),
ifelse(fox_pri_SF == "0", "N",
ifelse(fox_pri_SF == "1", "L",
ifelse(fox_pri_SF == "2", "M",
ifelse(fox_pri_SF == "3", "H",
ifelse(fox_pri_SF == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
fox_pri_dune = ifelse(fox_pri_dune %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(fox_pri_dune, 1, 1),
ifelse(fox_pri_dune == "0", "N",
ifelse(fox_pri_dune == "1", "L",
ifelse(fox_pri_dune == "2", "M",
ifelse(fox_pri_dune == "3", "H",
ifelse(fox_pri_dune == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
dog_pri_wet = ifelse(dog_pri_wet %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(dog_pri_wet, 1, 1),
ifelse(dog_pri_wet == "0", "N",
ifelse(dog_pri_wet == "1", "L",
ifelse(dog_pri_wet == "2", "M",
ifelse(dog_pri_wet == "3", "H",
ifelse(dog_pri_wet == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
dog_pri_dry = ifelse(dog_pri_dry %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(dog_pri_dry, 1, 1),
ifelse(dog_pri_dry == "0", "N",
ifelse(dog_pri_dry == "1", "L",
ifelse(dog_pri_dry == "2", "M",
ifelse(dog_pri_dry == "3", "H",
ifelse(dog_pri_dry == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
dog_pri_SF = ifelse(dog_pri_SF %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(dog_pri_SF, 1, 1),
ifelse(dog_pri_SF == "0", "N",
ifelse(dog_pri_SF == "1", "L",
ifelse(dog_pri_SF == "2", "M",
ifelse(dog_pri_SF == "3", "H",
ifelse(dog_pri_SF == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
dog_pri_dune = ifelse(dog_pri_dune %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(dog_pri_dune, 1, 1),
ifelse(dog_pri_dune == "0", "N",
ifelse(dog_pri_dune == "1", "L",
ifelse(dog_pri_dune == "2", "M",
ifelse(dog_pri_dune == "3", "H",
ifelse(dog_pri_dune == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
vehicle_pri_wet = ifelse(vehicle_pri_wet %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(vehicle_pri_wet, 1, 1),
ifelse(vehicle_pri_wet == "0", "N",
ifelse(vehicle_pri_wet == "1", "L",
ifelse(vehicle_pri_wet == "2", "M",
ifelse(vehicle_pri_wet == "3", "H",
ifelse(vehicle_pri_wet == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
vehicle_pri_dry = ifelse(vehicle_pri_dry %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(vehicle_pri_dry, 1, 1),
ifelse(vehicle_pri_dry == "0", "N",
ifelse(vehicle_pri_dry == "1", "L",
ifelse(vehicle_pri_dry == "2", "M",
ifelse(vehicle_pri_dry == "3", "H",
ifelse(vehicle_pri_dry == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
vehicle_pri_SF = ifelse(vehicle_pri_SF %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(vehicle_pri_SF, 1, 1),
ifelse(vehicle_pri_SF == "0", "N",
ifelse(vehicle_pri_SF == "1", "L",
ifelse(vehicle_pri_SF == "2", "M",
ifelse(vehicle_pri_SF == "3", "H",
ifelse(vehicle_pri_SF == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
vehicle_pri_dune = ifelse(vehicle_pri_dune %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(vehicle_pri_dune, 1, 1),
ifelse(vehicle_pri_dune == "0", "N",
ifelse(vehicle_pri_dune == "1", "L",
ifelse(vehicle_pri_dune == "2", "M",
ifelse(vehicle_pri_dune == "3", "H",
ifelse(vehicle_pri_dune == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
trailbike_pri_wet = ifelse(trailbike_pri_wet %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(trailbike_pri_wet, 1, 1),
ifelse(trailbike_pri_wet == "0", "N",
ifelse(trailbike_pri_wet == "1", "L",
ifelse(trailbike_pri_wet == "2", "M",
ifelse(trailbike_pri_wet == "3", "H",
ifelse(trailbike_pri_wet == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
trailbike_pri_dry = ifelse(trailbike_pri_dry %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(trailbike_pri_dry, 1, 1),
ifelse(trailbike_pri_dry == "0", "N",
ifelse(trailbike_pri_dry == "1", "L",
ifelse(trailbike_pri_dry == "2", "M",
ifelse(trailbike_pri_dry == "3", "H",
ifelse(trailbike_pri_dry == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
trailbike_pri_SF = ifelse(trailbike_pri_SF %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(trailbike_pri_SF, 1, 1),
ifelse(trailbike_pri_SF == "0", "N",
ifelse(trailbike_pri_SF == "1", "L",
ifelse(trailbike_pri_SF == "2", "M",
ifelse(trailbike_pri_SF == "3", "H",
ifelse(trailbike_pri_SF == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
trailbike_pri_dune = ifelse(trailbike_pri_dune %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(trailbike_pri_dune, 1, 1),
ifelse(trailbike_pri_dune == "0", "N",
ifelse(trailbike_pri_dune == "1", "L",
ifelse(trailbike_pri_dune == "2", "M",
ifelse(trailbike_pri_dune == "3", "H",
ifelse(trailbike_pri_dune == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
horse_pri_wet = ifelse(horse_pri_wet %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(horse_pri_wet, 1, 1),
ifelse(horse_pri_wet == "0", "N",
ifelse(horse_pri_wet == "1", "L",
ifelse(horse_pri_wet == "2", "M",
ifelse(horse_pri_wet == "3", "H",
ifelse(horse_pri_wet == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
horse_pri_dry = ifelse(horse_pri_dry %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(horse_pri_dry, 1, 1),
ifelse(horse_pri_dry == "0", "N",
ifelse(horse_pri_dry == "1", "L",
ifelse(horse_pri_dry == "2", "M",
ifelse(horse_pri_dry == "3", "H",
ifelse(horse_pri_dry == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
horse_pri_SF = ifelse(horse_pri_SF %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(horse_pri_SF, 1, 1),
ifelse(horse_pri_SF == "0", "N",
ifelse(horse_pri_SF == "1", "L",
ifelse(horse_pri_SF == "2", "M",
ifelse(horse_pri_SF == "3", "H",
ifelse(horse_pri_SF == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
horse_pri_dune = ifelse(horse_pri_dune %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(horse_pri_dune, 1, 1),
ifelse(horse_pri_dune == "0", "N",
ifelse(horse_pri_dune == "1", "L",
ifelse(horse_pri_dune == "2", "M",
ifelse(horse_pri_dune == "3", "H",
ifelse(horse_pri_dune == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
stock_pri_wet = ifelse(stock_pri_wet %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(stock_pri_wet, 1, 1),
ifelse(stock_pri_wet == "0", "N",
ifelse(stock_pri_wet == "1", "L",
ifelse(stock_pri_wet == "2", "M",
ifelse(stock_pri_wet == "3", "H",
ifelse(stock_pri_wet == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
stock_pri_dry = ifelse(stock_pri_dry %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(stock_pri_dry, 1, 1),
ifelse(stock_pri_dry == "0", "N",
ifelse(stock_pri_dry == "1", "L",
ifelse(stock_pri_dry == "2", "M",
ifelse(stock_pri_dry == "3", "H",
ifelse(stock_pri_dry == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
stock_pri_SF = ifelse(stock_pri_SF %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(stock_pri_SF, 1, 1),
ifelse(stock_pri_SF == "0", "N",
ifelse(stock_pri_SF == "1", "L",
ifelse(stock_pri_SF == "2", "M",
ifelse(stock_pri_SF == "3", "H",
ifelse(stock_pri_SF == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H")),
stock_pri_dune = ifelse(stock_pri_dune %in% c("Light", "Moderate", "Heavy", "L", "M", "H"),
substr(stock_pri_dune, 1, 1),
ifelse(stock_pri_dune == "0", "N",
ifelse(stock_pri_dune == "1", "L",
ifelse(stock_pri_dune == "2", "M",
ifelse(stock_pri_dune == "3", "H",
ifelse(stock_pri_dune == "high", "H", NA)))))) %>% as.factor(.) %>% factor(., levels = c("L", "M", "H"))) %>%
# to control for multiple threat surveys per date, summarise by date
group_by(region, site, season, obs_date, obs_date2) %>%
summarise(obs_lon = mean(obs_lon, na.rm = TRUE),
obs_lat = mean(obs_lat, na.rm = TRUE),
# in the case of multiple surveys on a single date at a specific
# site, take the max humans counted, etc., and the highest level of
# prints, etc.
humans = max(humans, na.rm = TRUE),
humans_wet = max(humans_wet, na.rm = TRUE),
humans_dry = max(humans_dry, na.rm = TRUE),
humans_dune = max(humans_dune, na.rm = TRUE),
humans_SF = max(humans_SF, na.rm = TRUE),
hoofed_animals = max(hoofed_animals, na.rm = TRUE),
vehicles = max(vehicles, na.rm = TRUE),
pred_birds = max(pred_birds, na.rm = TRUE),
gulls = max(gulls, na.rm = TRUE),
dogs_off = max(dogs_off, na.rm = TRUE),
dogs_on = max(dogs_on, na.rm = TRUE),
dogs = max(dogs, na.rm = TRUE),
hum_pri_wet = ifelse(all(is.na(hum_pri_wet)), NA,
levels(hum_pri_wet)[max(as.integer(hum_pri_wet), na.rm = TRUE)]),
hum_pri_dry = ifelse(all(is.na(hum_pri_dry)), NA,
levels(hum_pri_dry)[max(as.integer(hum_pri_dry), na.rm = TRUE)]),
hum_pri_dune = ifelse(all(is.na(hum_pri_dune)), NA,
levels(hum_pri_dune)[max(as.integer(hum_pri_dune), na.rm = TRUE)]),
hum_pri_SF = ifelse(all(is.na(hum_pri_SF)), NA,
levels(hum_pri_SF)[max(as.integer(hum_pri_SF), na.rm = TRUE)]),
fox_pri_wet = ifelse(all(is.na(fox_pri_wet)), NA,
levels(fox_pri_wet)[max(as.integer(fox_pri_wet), na.rm = TRUE)]),
fox_pri_dry = ifelse(all(is.na(fox_pri_dry)), NA,
levels(fox_pri_dry)[max(as.integer(fox_pri_dry), na.rm = TRUE)]),
fox_pri_dune = ifelse(all(is.na(fox_pri_dune)), NA,
levels(fox_pri_dune)[max(as.integer(fox_pri_dune), na.rm = TRUE)]),
fox_pri_SF = ifelse(all(is.na(fox_pri_SF)), NA,
levels(fox_pri_SF)[max(as.integer(fox_pri_SF), na.rm = TRUE)]),
dog_pri_wet = ifelse(all(is.na(dog_pri_wet)), NA,
levels(dog_pri_wet)[max(as.integer(dog_pri_wet), na.rm = TRUE)]),
dog_pri_dry = ifelse(all(is.na(dog_pri_dry)), NA,
levels(dog_pri_dry)[max(as.integer(dog_pri_dry), na.rm = TRUE)]),
dog_pri_dune = ifelse(all(is.na(dog_pri_dune)), NA,
levels(dog_pri_dune)[max(as.integer(dog_pri_dune), na.rm = TRUE)]),
dog_pri_SF = ifelse(all(is.na(dog_pri_SF)), NA,
levels(dog_pri_SF)[max(as.integer(dog_pri_SF), na.rm = TRUE)]),
vehicle_pri_wet = ifelse(all(is.na(vehicle_pri_wet)), NA,
levels(vehicle_pri_wet)[max(as.integer(vehicle_pri_wet), na.rm = TRUE)]),
vehicle_pri_dry = ifelse(all(is.na(vehicle_pri_dry)), NA,
levels(vehicle_pri_dry)[max(as.integer(vehicle_pri_dry), na.rm = TRUE)]),
vehicle_pri_dune = ifelse(all(is.na(vehicle_pri_dune)), NA,
levels(vehicle_pri_dune)[max(as.integer(vehicle_pri_dune), na.rm = TRUE)]),
vehicle_pri_SF = ifelse(all(is.na(vehicle_pri_SF)), NA,
levels(vehicle_pri_SF)[max(as.integer(vehicle_pri_SF), na.rm = TRUE)]),
trailbike_pri_wet = ifelse(all(is.na(trailbike_pri_wet)), NA,
levels(trailbike_pri_wet)[max(as.integer(trailbike_pri_wet), na.rm = TRUE)]),
trailbike_pri_dry = ifelse(all(is.na(trailbike_pri_dry)), NA,
levels(trailbike_pri_dry)[max(as.integer(trailbike_pri_dry), na.rm = TRUE)]),
trailbike_pri_dune = ifelse(all(is.na(trailbike_pri_dune)), NA,
levels(trailbike_pri_dune)[max(as.integer(trailbike_pri_dune), na.rm = TRUE)]),
trailbike_pri_SF = ifelse(all(is.na(trailbike_pri_SF)), NA,
levels(trailbike_pri_SF)[max(as.integer(trailbike_pri_SF), na.rm = TRUE)]),
horse_pri_wet = ifelse(all(is.na(horse_pri_wet)), NA,
levels(horse_pri_wet)[max(as.integer(horse_pri_wet), na.rm = TRUE)]),
horse_pri_dry = ifelse(all(is.na(horse_pri_dry)), NA,
levels(horse_pri_dry)[max(as.integer(horse_pri_dry), na.rm = TRUE)]),
horse_pri_dune = ifelse(all(is.na(horse_pri_dune)), NA,
levels(horse_pri_dune)[max(as.integer(horse_pri_dune), na.rm = TRUE)]),
horse_pri_SF = ifelse(all(is.na(horse_pri_SF)), NA,
levels(horse_pri_SF)[max(as.integer(horse_pri_SF), na.rm = TRUE)]),
stock_pri_wet = ifelse(all(is.na(stock_pri_wet)), NA,
levels(stock_pri_wet)[max(as.integer(stock_pri_wet), na.rm = TRUE)]),
stock_pri_dry = ifelse(all(is.na(stock_pri_dry)), NA,
levels(stock_pri_dry)[max(as.integer(stock_pri_dry), na.rm = TRUE)]),
stock_pri_dune = ifelse(all(is.na(stock_pri_dune)), NA,
levels(stock_pri_dune)[max(as.integer(stock_pri_dune), na.rm = TRUE)]),
stock_pri_SF = ifelse(all(is.na(stock_pri_SF)), NA,
levels(stock_pri_SF)[max(as.integer(stock_pri_SF), na.rm = TRUE)])) %>%
# make the print variables a factor
mutate_at(vars(hum_pri_wet, hum_pri_dry, hum_pri_dune, hum_pri_SF,
dog_pri_wet, dog_pri_dry, dog_pri_dune, dog_pri_SF,
fox_pri_wet, fox_pri_dry, fox_pri_dune, fox_pri_SF,
vehicle_pri_wet, vehicle_pri_dry, vehicle_pri_dune, vehicle_pri_SF,
trailbike_pri_wet, trailbike_pri_dry, trailbike_pri_dune, trailbike_pri_SF,
horse_pri_wet, horse_pri_dry, horse_pri_dune, horse_pri_SF,
stock_pri_wet, stock_pri_dry, stock_pri_dune, stock_pri_SF),
~ as.factor(.)) %>%
# specify the level order of the print variables
mutate_at(vars(hum_pri_wet, hum_pri_dry, hum_pri_dune, hum_pri_SF,
dog_pri_wet, dog_pri_dry, dog_pri_dune, dog_pri_SF,
fox_pri_wet, fox_pri_dry, fox_pri_dune, fox_pri_SF,
vehicle_pri_wet, vehicle_pri_dry, vehicle_pri_dune, vehicle_pri_SF,
trailbike_pri_wet, trailbike_pri_dry, trailbike_pri_dune, trailbike_pri_SF,
horse_pri_wet, horse_pri_dry, horse_pri_dune, horse_pri_SF,
stock_pri_wet, stock_pri_dry, stock_pri_dune, stock_pri_SF),
~ factor(., levels = c("L", "M", "H"))) %>%
# summarize the print variables across the wet, dry, dune, and sign/fence micro habitats
mutate(hum_pri = ifelse(all(is.na(hum_pri_wet)) && all(is.na(hum_pri_dry)) &&
all(is.na(hum_pri_dune)) && all(is.na(hum_pri_SF)), NA,
pmax(as.integer(hum_pri_wet), as.integer(hum_pri_dry),
as.integer(hum_pri_dune), as.integer(hum_pri_SF), na.rm = TRUE)),
fox_pri = ifelse(all(is.na(fox_pri_wet)) && all(is.na(fox_pri_dry)) &&
all(is.na(fox_pri_dune)) && all(is.na(fox_pri_SF)), NA,
pmax(as.integer(fox_pri_wet), as.integer(fox_pri_dry),
as.integer(fox_pri_dune), as.integer(fox_pri_SF), na.rm = TRUE)),
dog_pri = ifelse(all(is.na(dog_pri_wet)) && all(is.na(dog_pri_dry)) &&
all(is.na(dog_pri_dune)) && all(is.na(dog_pri_SF)), NA,
pmax(as.integer(dog_pri_wet), as.integer(dog_pri_dry),
as.integer(dog_pri_dune), as.integer(dog_pri_SF), na.rm = TRUE)),
vehicle_pri = ifelse(all(is.na(vehicle_pri_wet)) && all(is.na(vehicle_pri_dry)) &&
all(is.na(vehicle_pri_dune)) && all(is.na(vehicle_pri_SF)) &&
all(is.na(trailbike_pri_wet)) && all(is.na(trailbike_pri_dry)) &&
all(is.na(trailbike_pri_dune)) && all(is.na(trailbike_pri_SF)), NA,
pmax(as.integer(vehicle_pri_wet), as.integer(vehicle_pri_dry),
as.integer(vehicle_pri_dune), as.integer(vehicle_pri_SF),
as.integer(trailbike_pri_wet), as.integer(trailbike_pri_dry),
as.integer(trailbike_pri_dune), as.integer(trailbike_pri_SF), na.rm = TRUE)),
hoofed_pri = ifelse(all(is.na(horse_pri_wet)) && all(is.na(horse_pri_dry)) &&
all(is.na(horse_pri_dune)) && all(is.na(horse_pri_SF)) &&
all(is.na(stock_pri_wet)) && all(is.na(stock_pri_dry)) &&
all(is.na(stock_pri_dune)) && all(is.na(stock_pri_SF)), NA,
pmax(as.integer(horse_pri_wet), as.integer(horse_pri_dry),
as.integer(horse_pri_dune), as.integer(horse_pri_SF),
as.integer(stock_pri_wet), as.integer(stock_pri_dry),
as.integer(stock_pri_dune), as.integer(stock_pri_SF), na.rm = TRUE))) %>%
# consolidate the threat data into a clean dataframe
dplyr::select(region, site, season, obs_date, obs_date2, obs_lon, obs_lat,
humans, vehicles, dogs, dogs_on, dogs_off, hoofed_animals, pred_birds,
gulls, hum_pri, fox_pri, dog_pri, vehicle_pri, hoofed_pri) %>%
ungroup()
saveRDS(threat_data_, file = "output/threat_data_(all_years).rds")Visual inspection of threat data
determine the 99% quantile limit for each threat (i.e., to remove outlier data) - shown as the red vertical line here
Code
# determine the 99% quantile limit for each threat (i.e., to remove outlier data)
threat_data_99_ql <-
threat_data_ %>%
summarise_at(c("humans", "vehicles", "dogs", "dogs_on", "dogs_off", "hoofed_animals", "pred_birds", "gulls"),
~ quantile(.x, probs = c(0.99)))
threat_data_99_ql# A tibble: 1 × 8
humans vehicles dogs dogs_on dogs_off hoofed_animals pred_birds gulls
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 62 4 15 8 10 0 10 151Code
# check histograms of threat data while inspecting the 99% cut-off
threat_data_ %>%
ggplot() +
geom_histogram(aes(log(humans + 1))) +
geom_vline(xintercept = log(as.numeric(threat_data_99_ql$humans[1])), color = "red") +
luke_theme
Code
threat_data_ %>%
ggplot() +
geom_histogram(aes(log(dogs + 1))) +
geom_vline(xintercept = log(as.numeric(threat_data_99_ql$dogs[1])), color = "red") +
luke_theme
Code
threat_data_ %>%
ggplot() +
geom_histogram(aes(log(pred_birds + 1))) +
geom_vline(xintercept = log(as.numeric(threat_data_99_ql$pred_birds[1])), color = "red") +
luke_theme
Code
threat_data_ %>%
ggplot() +
geom_histogram(aes(log(gulls + 1))) +
geom_vline(xintercept = log(as.numeric(threat_data_99_ql$pred_birds[1])), color = "red") +
luke_theme
Code
threat_data_ %>%
ggplot() +
geom_histogram(aes(log(vehicles))) +
geom_vline(xintercept = log(as.numeric(threat_data_99_ql$vehicles[1])), color = "red") +
luke_theme
Code
threat_data_ %>%
ggplot() +
geom_histogram(aes(log(dogs_off + 1))) +
geom_vline(xintercept = log(as.numeric(threat_data_99_ql$dogs_off[1])), color = "red") +
luke_theme
Code
threat_data_ %>%
ggplot() +
geom_histogram(aes(log(dogs_on + 1))) +
geom_vline(xintercept = log(as.numeric(threat_data_99_ql$dogs_on[1])), color = "red") +
luke_theme
Code
threat_data_ %>%
ggplot() +
geom_histogram(aes(hoofed_animals)) +
geom_vline(xintercept = as.numeric(threat_data_99_ql$hoofed_animals[1]), color = "red") +
luke_theme
Extract holidays (an index of human disturbance)
Code
# extract public holidays and merge them to the threat data
#### FP ----
FP_holidays <-
bind_rows(
holiday_aus(2009, state = "SA"),
holiday_aus(2010, state = "SA"),
holiday_aus(2011, state = "SA"),
holiday_aus(2012, state = "SA"),
holiday_aus(2013, state = "SA"),
holiday_aus(2014, state = "SA"),
holiday_aus(2015, state = "SA"),
holiday_aus(2016, state = "SA"),
holiday_aus(2017, state = "SA"),
holiday_aus(2018, state = "SA"),
holiday_aus(2019, state = "SA"),
holiday_aus(2020, state = "SA"),
holiday_aus(2021, state = "SA"),
holiday_aus(2022, state = "SA"),
holiday_aus(2023, state = "SA"),
holiday_aus(2024, state = "SA")) %>%
mutate(event = holiday) %>%
mutate(region = "FP",
end_date = date) %>%
rename(start_date = date) %>%
mutate(year = year(start_date)) %>%
mutate(season = ifelse(month(start_date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(start_date) >= 6,
paste0(year, substr(year + 1, 3, 4)),
paste0(season, substr(year, 3, 4))))
SA_start_end_holidays <-
read_excel("data/School holiday dates.xlsx",
sheet = "SA Sch. Hol Dates",
col_types = "text") %>%
separate(`Autumn school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(autumn_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
autumn_end = as.Date(paste(Year, new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(new_col1:new_col5)) %>%
separate(`Winter school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(winter_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
winter_end = as.Date(paste(Year, new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(new_col1:new_col5)) %>%
separate(`Spring school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(spring_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
spring_end = as.Date(paste(Year, new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(new_col1:new_col5)) %>%
separate(`Summer school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(summer_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
summer_end = as.Date(paste(as.character(as.numeric(Year)+1), new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(Year:Source))
FP_start_school_holidays <-
SA_start_end_holidays %>%
select(autumn_start, winter_start, spring_start, summer_start) %>%
pivot_longer(cols = everything(), names_to = "event") %>%
mutate(event = str_remove(event, "_start")) %>%
mutate(region = "FP") %>%
rename(start_date = value) %>%
mutate(year = year(start_date)) %>%
mutate(season = ifelse(month(start_date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(start_date) >= 6,
paste0(year, substr(year+1, 3, 4)),
paste0(season, substr(year, 3, 4))))
FP_school_holidays <-
SA_start_end_holidays %>%
select(autumn_end, winter_end, spring_end, summer_end) %>%
pivot_longer(cols = everything(), names_to = "event") %>%
mutate(event = str_remove(event, "_end")) %>%
rename(end_date = value) %>%
mutate(year = year(end_date)) %>%
mutate(season = ifelse(month(end_date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(end_date) >= 6,
paste0(year, substr(year+1, 3, 4)),
paste0(season, substr(year, 3, 4)))) %>%
left_join(FP_start_school_holidays, ., by = c("season", "event")) %>%
mutate(event = paste(event, "school", sep = "_")) %>%
select(-c(year.x, year.y))
FP_holidays <-
bind_rows(FP_school_holidays, FP_holidays) %>%
select(season, region, event, start_date, end_date) %>%
arrange(start_date)
#### MP ----
MP_holidays <-
bind_rows(
holiday_aus(2006, state = "VIC"),
holiday_aus(2007, state = "VIC"),
holiday_aus(2008, state = "VIC"),
holiday_aus(2009, state = "VIC"),
holiday_aus(2010, state = "VIC"),
holiday_aus(2011, state = "VIC"),
holiday_aus(2012, state = "VIC"),
holiday_aus(2013, state = "VIC"),
holiday_aus(2014, state = "VIC"),
holiday_aus(2015, state = "VIC"),
holiday_aus(2016, state = "VIC"),
holiday_aus(2017, state = "VIC"),
holiday_aus(2018, state = "VIC"),
holiday_aus(2019, state = "VIC"),
holiday_aus(2020, state = "VIC"),
holiday_aus(2021, state = "VIC"),
holiday_aus(2022, state = "VIC"),
holiday_aus(2023, state = "VIC"),
holiday_aus(2024, state = "VIC")) %>%
mutate(event = holiday) %>%
mutate(region = "MP",
end_date = date) %>%
rename(start_date = date) %>%
mutate(year = year(start_date)) %>%
mutate(season = ifelse(month(start_date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(start_date) >= 6,
paste0(year, substr(year + 1, 3, 4)),
paste0(season, substr(year, 3, 4))))
VIC_start_end_holidays <-
read_excel("data/School holiday dates.xlsx",
sheet = "VIC Sch. Hol. Dates",
col_types = "text") %>%
separate(`Autumn school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(autumn_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
autumn_end = as.Date(paste(Year, new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(new_col1:new_col5)) %>%
separate(`Winter school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(winter_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
winter_end = as.Date(paste(Year, new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(new_col1:new_col5)) %>%
separate(`Spring school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(spring_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
spring_end = as.Date(paste(Year, new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(new_col1:new_col5)) %>%
separate(`Summer school holiday dates`,
into = paste0("new_col", 1:5), sep = " ") %>%
mutate(summer_start = as.Date(paste(Year, new_col2, new_col1, sep = "-"),
format = "%Y-%b-%d"),
summer_end = as.Date(paste(as.character(as.numeric(Year)+1), new_col5, new_col4, sep = "-"),
format = "%Y-%b-%d")) %>%
dplyr::select(-c(Year:Source))
MP_start_school_holidays <-
VIC_start_end_holidays %>%
select(autumn_start, winter_start, spring_start, summer_start) %>%
pivot_longer(cols = everything(), names_to = "event") %>%
mutate(event = str_remove(event, "_start")) %>%
mutate(region = "MP") %>%
rename(start_date = value) %>%
mutate(year = year(start_date)) %>%
mutate(season = ifelse(month(start_date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(start_date) >= 6,
paste0(year, substr(year+1, 3, 4)),
paste0(season, substr(year, 3, 4))))
MP_school_holidays <-
VIC_start_end_holidays %>%
select(autumn_end, winter_end, spring_end, summer_end) %>%
pivot_longer(cols = everything(), names_to = "event") %>%
mutate(event = str_remove(event, "_end")) %>%
rename(end_date = value) %>%
mutate(year = year(end_date)) %>%
mutate(season = ifelse(month(end_date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(end_date) >= 6,
paste0(year, substr(year+1, 3, 4)),
paste0(season, substr(year, 3, 4)))) %>%
left_join(MP_start_school_holidays, ., by = c("season", "event")) %>%
mutate(event = paste(event, "school", sep = "_")) %>%
select(-c(year.x, year.y)) %>% arrange(start_date)
MP_holidays <-
bind_rows(MP_school_holidays, MP_holidays) %>%
select(season, region, event, start_date, end_date) %>%
arrange(start_date)
BSC_holidays <-
bind_rows(MP_school_holidays, MP_holidays) %>%
select(season, region, event, start_date, end_date) %>%
arrange(start_date) %>%
mutate(region = "BSC")
PI_holidays <-
bind_rows(MP_school_holidays, MP_holidays) %>%
select(season, region, event, start_date, end_date) %>%
arrange(start_date) %>%
mutate(region = "PI")
BC_holidays <-
bind_rows(MP_school_holidays, MP_holidays) %>%
select(season, region, event, start_date, end_date) %>%
arrange(start_date) %>%
mutate(region = "BC")
holidays <-
bind_rows(FP_holidays, BC_holidays, MP_holidays, BSC_holidays, PI_holidays)# %>%
# pivot_longer(-c(season:holiday), names_to = "start_end", values_to = "date")Extract Weekdays (another index of human disturbance)
Code
threat_data__ <-
threat_data_ %>%
mutate(season_site = paste(season, site, sep = "_"),
weekday = factor(as.factor(weekdays(obs_date)),
levels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday"))) %>%
rename(date = obs_date) %>%
left_join(., holidays, by = c("region", "season"), relationship = "many-to-many") %>%
mutate(holiday = ifelse(date >= start_date & date <= end_date, 1, 0)) %>%
# dplyr::select(region, season, site, date, start_date, end_date, event, holiday) %>% distinct() %>%
group_by(region, season, site, date) %>%
mutate(holiday = max(holiday, na.rm = TRUE)) %>%
dplyr::select(-c(event, start_date, end_date)) %>%
distinct() %>%
ungroup() %>%
mutate(day_type = ifelse(holiday == 1 | weekday %in%
c("Saturday", "Sunday"), "funday", "workday")) %>%
mutate(funday = ifelse(day_type == "funday", 1, 0)) %>%
mutate(humans_ = ifelse(humans > as.numeric(threat_data_99_ql$humans[1]), NA, humans),
vehicles_ = ifelse(vehicles > as.numeric(threat_data_99_ql$vehicles[1]), NA, vehicles),
dogs_ = ifelse(dogs > as.numeric(threat_data_99_ql$dogs[1]), NA, dogs),
dogs_off_ = ifelse(dogs_off > as.numeric(threat_data_99_ql$dogs_off[1]), NA, dogs_off),
dogs_on_ = ifelse(dogs_on > as.numeric(threat_data_99_ql$dogs_on[1]), NA, dogs_on),
hoofed_animals_ = ifelse(hoofed_animals > as.numeric(threat_data_99_ql$hoofed_animals[1]), NA, hoofed_animals),
pred_birds_ = ifelse(pred_birds > as.numeric(threat_data_99_ql$pred_birds[1]), NA, pred_birds),
gulls_ = ifelse(gulls > as.numeric(threat_data_99_ql$gulls[1]), NA, pred_birds)) %>%
mutate(weekdayN = as.numeric(weekday) - 1) %>%
mutate(weekdayC = circular::circular(weekdayN, type = "angles", units = "radians")) %>%
filter(!is.na(weekday))
threat_data__ %>%
ggplot() +
geom_histogram(aes(funday)) +
# geom_vline(xintercept = log(10), color = "red") +
luke_theme
test if weekends and holidays have more threat counts than other days using zero-inflated models. For all threats, there are more counted on weekends and holidays than during the week, except for vehicles (which occur randomly across the week)
Code
#### test if weekends and holidays have more threat counts than other days
# use a zero-inflated model (https://stats.oarc.ucla.edu/r/dae/zip/)
# for all threats, there are more counted on weekends and holidays than during the week,
# except for vehicles (which occur randomly across the week)
mod_hum_zi <- pscl::zeroinfl(humans_ ~ day_type, data = threat_data__, dist = "poisson")
summary(mod_hum_zi)
Call:
pscl::zeroinfl(formula = humans_ ~ day_type, data = threat_data__, dist = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-1.2061 -0.8818 -0.6253 0.3427 18.3750
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.252603 0.002228 1011 <2e-16 ***
day_typeworkday -0.549608 0.003764 -146 <2e-16 ***
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.64079 0.01169 -54.79 <2e-16 ***
day_typeworkday 0.57244 0.01549 36.96 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 9
Log-likelihood: -2.649e+05 on 4 DfCode
mod_dogs_zi <- pscl::zeroinfl(dogs_ ~ day_type, data = threat_data__, dist = "poisson")
summary(mod_dogs_zi)
Call:
pscl::zeroinfl(formula = dogs_ ~ day_type, data = threat_data__, dist = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-0.64229 -0.64229 -0.54623 0.07525 8.77584
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.276497 0.005065 252.01 <2e-16 ***
day_typeworkday -0.238079 0.007812 -30.47 <2e-16 ***
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.51706 0.01177 43.94 <2e-16 ***
day_typeworkday 0.27768 0.01646 16.87 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 12
Log-likelihood: -9.928e+04 on 4 DfCode
mod_dogs_on_zi <- pscl::zeroinfl(dogs_on_ ~ day_type, data = threat_data__, dist = "poisson")
summary(mod_dogs_on_zi)
Call:
pscl::zeroinfl(formula = dogs_on_ ~ day_type, data = threat_data__, dist = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-0.4587 -0.4587 -0.3609 -0.3609 9.5352
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.729730 0.009064 80.51 <2e-16 ***
day_typeworkday -0.303207 0.015292 -19.83 <2e-16 ***
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.05820 0.01434 73.80 <2e-16 ***
day_typeworkday 0.38903 0.02166 17.96 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 9
Log-likelihood: -5.444e+04 on 4 DfCode
mod_dogs_off_zi <- pscl::zeroinfl(dogs_off_ ~ day_type, data = threat_data__, dist = "poisson")
summary(mod_dogs_off_zi)
Call:
pscl::zeroinfl(formula = dogs_off_ ~ day_type, data = threat_data__,
dist = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-0.5126 -0.5126 -0.4528 -0.4528 7.5219
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.98225 0.00709 138.55 <2e-16 ***
day_typeworkday -0.13328 0.01050 -12.69 <2e-16 ***
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.91466 0.01305 70.10 <2e-16 ***
day_typeworkday 0.22205 0.01831 12.13 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 15
Log-likelihood: -7.246e+04 on 4 DfCode
mod_pred_birds_zi <- pscl::zeroinfl(pred_birds_ ~ day_type, data = threat_data__, dist = "poisson")
summary(mod_pred_birds_zi)
Call:
pscl::zeroinfl(formula = pred_birds_ ~ day_type, data = threat_data__,
dist = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-0.3752 -0.3752 -0.3457 -0.3457 8.4648
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.984160 0.009857 99.849 < 2e-16 ***
day_typeworkday 0.045493 0.012806 3.552 0.000382 ***
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.76109 0.01645 107.036 < 2e-16 ***
day_typeworkday -0.15735 0.02173 -7.241 4.46e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 10
Log-likelihood: -5.053e+04 on 4 DfCode
mod_gulls_zi <- pscl::zeroinfl(gulls_ ~ day_type, data = threat_data__, dist = "poisson")
summary(mod_gulls_zi)
Call:
pscl::zeroinfl(formula = gulls_ ~ day_type, data = threat_data__, dist = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-0.3928 -0.3928 -0.3628 -0.3628 181.8698
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.392991 0.007522 185.195 < 2e-16 ***
day_typeworkday -0.040040 0.009980 -4.012 6.02e-05 ***
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.77261 0.01590 111.507 <2e-16 ***
day_typeworkday -0.17449 0.02101 -8.305 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 16
Log-likelihood: -7.234e+04 on 4 DfCode
mod_vehicles_zi <- pscl::zeroinfl(vehicles_ ~ day_type, data = threat_data__, dist = "poisson")
summary(mod_vehicles_zi)
Call:
pscl::zeroinfl(formula = vehicles_ ~ day_type, data = threat_data__,
dist = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-0.1179 -0.1179 -0.1048 -0.1048 15.0266
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.35633 0.04101 8.689 <2e-16 ***
day_typeworkday 0.08162 0.05717 1.427 0.153
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.73507 0.04632 80.640 < 2e-16 ***
day_typeworkday 0.27076 0.06551 4.133 3.57e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 19
Log-likelihood: -7256 on 4 DfFit circular GAM to weekly count data to assess trends over the week.
how do human counts vary over the week?
Code
#### Fit circular GAM to weekly count data ----
mod_hum <-
mgcv::gam(humans_ ~ s(weekdayN, bs = "cc", k = 7), data = threat_data__)
summary(mod_hum)
Family: gaussian
Link function: identity
Formula:
humans_ ~ s(weekdayN, bs = "cc", k = 7)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.37295 0.02932 149.2 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(weekdayN) 4.968 5 194.4 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.0134 Deviance explained = 1.34%
GCV = 61.49 Scale est. = 61.485 n = 71535Code
# estimate model predictions
newdata_weekdays <-
data.frame(weekdayN = seq(0, 6))
# plot the weekly variation in the human counts
mod_hum_fits <-
predict(mod_hum,
newdata = newdata_weekdays,
type = 'response', se = TRUE)
mod_hum_predicts <-
data.frame(newdata_weekdays, mod_hum_fits) %>%
mutate(lower = fit - 1.96 * se.fit,
upper = fit + 1.96 * se.fit) %>%
left_join(., threat_data__ %>% dplyr::select(weekdayN, weekday) %>% distinct(), by = "weekdayN")
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = log(humans_ + 1)),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1, color = "grey70"
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = log(humans_ + 1)),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA, color = "grey70"
) +
# geom_smooth(aes(x = as.numeric(weekday), y = humans_),
# method = lm,
# formula = y ~ splines::bs(x, 5)) +
geom_ribbon(data = mod_hum_predicts,
aes(x = as.numeric(weekday), ymin = log(lower + 1), ymax = log(upper + 1))) +
geom_line(data = mod_hum_predicts, aes(x = as.numeric(weekday), y = log(fit + 1)), color = "white") +
luke_theme +
xlab("Day of the week") +
ylab("Number of humans counted (log)")
how do dog counts vary over the week?
Code
mod_dogs <-
mgcv::gam(dogs_ ~ s(weekdayN, bs = "cc", k = 7), data = threat_data__)
summary(mod_dogs)
Family: gaussian
Link function: identity
Formula:
dogs_ ~ s(weekdayN, bs = "cc", k = 7)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.087289 0.008379 129.8 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(weekdayN) 4.766 5 57.85 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.00402 Deviance explained = 0.408%
GCV = 5.0247 Scale est. = 5.0243 n = 71567Code
# plot the weekly variation in the dog counts
mod_dogs_fits <-
predict(mod_dogs,
newdata = newdata_weekdays,
type = 'response', se = TRUE)
mod_dogs_predicts <-
data.frame(newdata_weekdays, mod_dogs_fits) %>%
mutate(lower = fit - 1.96 * se.fit,
upper = fit + 1.96 * se.fit) %>%
left_join(., threat_data__ %>% dplyr::select(weekdayN, weekday) %>% distinct(), by = "weekdayN")
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = log(dogs_ + 1)),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = log(dogs_ + 1)),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_ribbon(data = mod_dogs_predicts,
aes(x = as.numeric(weekday), ymin = log(lower + 1), ymax = log(upper + 1))) +
geom_line(data = mod_dogs_predicts, aes(x = as.numeric(weekday), y = log(fit + 1)), color = "white") +
# geom_smooth(aes(x = as.numeric(weekday), y = dogs_), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Number of dogs counted (log)")
how do dogs on leash counts vary over the week?
Code
mod_dogs_on <-
mgcv::gam(dogs_on_ ~ s(weekdayN, bs = "cc", k = 7), data = threat_data__)
summary(mod_dogs_on)
Family: gaussian
Link function: identity
Formula:
dogs_on_ ~ s(weekdayN, bs = "cc", k = 7)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.401783 0.004032 99.66 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(weekdayN) 4.768 5 46.31 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.0032 Deviance explained = 0.327%
GCV = 1.1633 Scale est. = 1.1632 n = 71566Code
# plot the weekly variation in the dog on leash counts
mod_dogs_on_fits <-
predict(mod_dogs_on,
newdata = newdata_weekdays,
type = 'response', se = TRUE)
mod_dogs_on_predicts <-
data.frame(newdata_weekdays, mod_dogs_on_fits) %>%
mutate(lower = fit - 1.96 * se.fit,
upper = fit + 1.96 * se.fit) %>%
left_join(., threat_data__ %>% dplyr::select(weekdayN, weekday) %>% distinct(), by = "weekdayN")
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = log(dogs_on_ + 1)),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = log(dogs_on_ + 1)),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_ribbon(data = mod_dogs_on_predicts,
aes(x = as.numeric(weekday), ymin = log(lower + 1), ymax = log(upper + 1))) +
geom_line(data = mod_dogs_on_predicts, aes(x = as.numeric(weekday), y = log(fit + 1)), color = "white") +
# geom_smooth(aes(x = as.numeric(weekday), y = dogs_on_), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Number of dogs on leashes counted (log)")
how do dogs off leash counts vary over the week?
Code
mod_dogs_off <-
mgcv::gam(dogs_off_ ~ s(weekdayN, bs = "cc", k = 7), data = threat_data__)
summary(mod_dogs_off)
Family: gaussian
Link function: identity
Formula:
dogs_off_ ~ s(weekdayN, bs = "cc", k = 7)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.656742 0.005721 114.8 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(weekdayN) 4.615 5 40.29 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.0028 Deviance explained = 0.286%
GCV = 2.345 Scale est. = 2.3448 n = 71637Code
# plot the weekly variation in the dog off leash counts
mod_dogs_off_fits <-
predict(mod_dogs_off,
newdata = newdata_weekdays,
type = 'response', se = TRUE)
mod_dogs_off_predicts <-
data.frame(newdata_weekdays, mod_dogs_off_fits) %>%
mutate(lower = fit - 1.96 * se.fit,
upper = fit + 1.96 * se.fit) %>%
left_join(., threat_data__ %>% dplyr::select(weekdayN, weekday) %>% distinct(), by = "weekdayN")
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = log(dogs_off_ + 1)),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = log(dogs_off_ + 1)),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_ribbon(data = mod_dogs_off_predicts,
aes(x = as.numeric(weekday), ymin = log(lower + 1), ymax = log(upper + 1))) +
geom_line(data = mod_dogs_off_predicts, aes(x = as.numeric(weekday), y = log(fit + 1)), color = "white") +
# geom_smooth(aes(x = as.numeric(weekday), y = dogs_off_), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Number of dogs off leashes counted (log)")
how do corvid counts vary over the week?
Code
mod_pred_birds <-
mgcv::gam(pred_birds_ ~ s(weekdayN, bs = "cc", k = 7), data = threat_data__)
summary(mod_pred_birds)
Family: gaussian
Link function: identity
Formula:
pred_birds_ ~ s(weekdayN, bs = "cc", k = 7)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.43397 0.00485 89.48 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(weekdayN) 4.193 5 6.911 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.000466 Deviance explained = 0.0524%
GCV = 1.6861 Scale est. = 1.6859 n = 71676Code
# plot the weekly variation in the predatory bird counts
mod_pred_birds_fits <-
predict(mod_pred_birds,
newdata = newdata_weekdays,
type = 'response', se = TRUE)
mod_pred_birds_predicts <-
data.frame(newdata_weekdays, mod_pred_birds_fits) %>%
mutate(lower = fit - 1.96 * se.fit,
upper = fit + 1.96 * se.fit) %>%
left_join(., threat_data__ %>% dplyr::select(weekdayN, weekday) %>% distinct(), by = "weekdayN")
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = log(pred_birds_ + 1)),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = log(pred_birds_ + 1)),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_ribbon(data = mod_pred_birds_predicts,
aes(x = as.numeric(weekday), ymin = log(lower + 1), ymax = log(upper + 1))) +
geom_line(data = mod_pred_birds_predicts, aes(x = as.numeric(weekday), y = log(fit + 1)), color = "white") +
# geom_smooth(aes(x = as.numeric(weekday), y = pred_birds_), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Number of corvids counted (log)")
how do gulls counts vary over the week?
Code
mod_pred_birds <-
mgcv::gam(gulls_ ~ s(weekdayN, bs = "cc", k = 7), data = threat_data__)
summary(mod_pred_birds)
Family: gaussian
Link function: identity
Formula:
gulls_ ~ s(weekdayN, bs = "cc", k = 7)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.62076 0.01318 47.11 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(weekdayN) 2.396 5 1.948 0.00548 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.000132 Deviance explained = 0.0165%
GCV = 12.422 Scale est. = 12.421 n = 71533Code
# plot the weekly variation in the predatory bird counts
mod_gulls_fits <-
predict(mod_pred_birds,
newdata = newdata_weekdays,
type = 'response', se = TRUE)
mod_gulls_predicts <-
data.frame(newdata_weekdays, mod_gulls_fits) %>%
mutate(lower = fit - 1.96 * se.fit,
upper = fit + 1.96 * se.fit) %>%
left_join(., threat_data__ %>% dplyr::select(weekdayN, weekday) %>% distinct(), by = "weekdayN")
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = log(gulls_ + 1)),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = log(gulls_ + 1)),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_ribbon(data = mod_gulls_predicts,
aes(x = as.numeric(weekday), ymin = log(lower + 1), ymax = log(upper + 1))) +
geom_line(data = mod_gulls_predicts, aes(x = as.numeric(weekday), y = log(fit + 1)), color = "white") +
# geom_smooth(aes(x = as.numeric(weekday), y = gulls_), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Number of gulls counted (log)")
how do vehicle counts vary over the week?
Code
mod_vehicles <-
mgcv::gam(vehicles_ ~ s(weekdayN, bs = "cc", k = 7), data = threat_data__)
summary(mod_vehicles)
Family: gaussian
Link function: identity
Formula:
vehicles_ ~ s(weekdayN, bs = "cc", k = 7)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.030248 0.001024 29.53 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(weekdayN) 3.52 5 2.006 0.0184 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R-sq.(adj) = 0.000121 Deviance explained = 0.017%
GCV = 0.075064 Scale est. = 0.075059 n = 71543Code
# plot the weekly variation in the vehicle counts
mod_vehicles_fits <-
predict(mod_vehicles,
newdata = newdata_weekdays,
type = 'response', se = TRUE)
mod_vehicles_predicts <-
data.frame(newdata_weekdays, mod_vehicles_fits) %>%
mutate(lower = fit - 1.96 * se.fit,
upper = fit + 1.96 * se.fit) %>%
left_join(., threat_data__ %>% dplyr::select(weekdayN, weekday) %>% distinct(), by = "weekdayN")
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = vehicles_),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = vehicles_),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_ribbon(data = mod_vehicles_predicts,
aes(x = as.numeric(weekday), ymin = lower, ymax = upper)) +
geom_line(data = mod_vehicles_predicts, aes(x = as.numeric(weekday), y = fit), color = "white") +
# geom_smooth(aes(x = as.numeric(weekday), y = vehicles_), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Number of vehicles counted")
Inspect the weekly variation in human prints
Code
# plot the weekly variation in the human print detections
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = hum_pri),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = hum_pri),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_smooth(aes(x = as.numeric(weekday), y = hum_pri), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Level of human prints recorded")
Inspect the weekly variation in dog prints
Code
# plot the weekly variation in the dog print detections
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = dog_pri),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = dog_pri),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_smooth(aes(x = as.numeric(weekday), y = dog_pri), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Level of dogs prints recorded")
Inspect the weekly variation in vehicle prints
Code
# plot the weekly variation in the vehicle print detections
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = vehicle_pri),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = vehicle_pri),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_smooth(aes(x = as.numeric(weekday), y = vehicle_pri), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Level of vehicle prints recorded")
Inspect the weekly variation in fox prints
Code
# plot the weekly variation in the fox print detections
threat_data__ %>%
ggplot() +
gghalves::geom_half_point(aes(x = weekday, y = fox_pri),
size = 1,
width = 0.5,
side = "l",
range_scale = .4,
alpha = 0.1
) +
gghalves::geom_half_boxplot(aes(x = weekday, y = fox_pri),
size = 0.5,
width = 0.5,
side = "r",
alpha = 0.1, outlier.color = NA
) +
geom_smooth(aes(x = as.numeric(weekday), y = fox_pri), method = lm, formula = y ~ splines::bs(x, 5)) +
luke_theme +
xlab("Day of the week") +
ylab("Level of fox prints recorded")
check correlation between counts of the various threats
Code
# check correlation between humans counts
threat_data__ %>%
dplyr::select(humans_, vehicles_, dogs_, pred_birds_) %>%
na.omit() %>%
cor() %>%
corrplot(type = "upper", method = "number", tl.srt = 45)
Code
# relationship between human counts and dog counts
threat_data__ %>%
ggplot() +
geom_jitter(aes(x = humans_, y = dogs_), alpha = 0.1) +
geom_smooth(aes(x = humans_, y = dogs_)) +#, method = lm, formula = y ~ splines::bs(x, 2)) +
luke_theme +
xlab("Number of humans counted") +
ylab("Number of dogs counted")
Code
# relationship between human counts and corvid counts
threat_data__ %>%
ggplot() +
geom_jitter(aes(x = humans_, y = pred_birds_), alpha = 0.1) +
geom_smooth(aes(x = humans_, y = pred_birds_)) +#, method = lm) +
luke_theme +
xlab("Number of humans counted") +
ylab("Number of corvids counted")
Code
# relationship between human counts and gull counts
threat_data__ %>%
ggplot() +
geom_jitter(aes(x = humans_, y = gulls_), alpha = 0.1) +
geom_smooth(aes(x = humans_, y = gulls_)) +#, method = lm) +
luke_theme +
xlab("Number of humans counted") +
ylab("Number of gulls counted")
Code
# relationship between human counts and vehicle counts
threat_data__ %>%
ggplot() +
geom_jitter(aes(x = humans_, y = vehicles_), alpha = 0.1) +
geom_smooth(aes(x = humans_, y = vehicles_)) + #, method = lm, formula = y ~ splines::bs(x, 2)) +
luke_theme +
xlab("Number of humans counted") +
ylab("Number of vehicles counted")
Code
# determine which territories are in the threat data and the nest data
sites_intersect_FP <-
inner_join(nest_data_FP, threat_data__ %>% filter(region == "FP"), by = c("season", "site"), relationship = "many-to-many") %>%
dplyr::select(season, site) %>% distinct() %>%
mutate(season_site = paste(season, site, sep = "_"))
sites_intersect_MP <-
inner_join(nest_data_MP, threat_data__ %>% filter(region == "MP"), by = c("season", "site"), relationship = "many-to-many") %>%
dplyr::select(season, site) %>% distinct() %>%
mutate(season_site = paste(season, site, sep = "_"))
sites_intersect_BSC <-
inner_join(nest_data_BSC, threat_data__ %>% filter(region == "BSC"), by = c("season", "site"), relationship = "many-to-many") %>%
dplyr::select(season, site) %>% distinct() %>%
mutate(season_site = paste(season, site, sep = "_"))
sites_intersect_PI <-
inner_join(nest_data_PI, threat_data__ %>% filter(region == "PI"), by = c("season", "site"), relationship = "many-to-many") %>%
dplyr::select(season, site) %>% distinct() %>%
mutate(season_site = paste(season, site, sep = "_"))
sites_intersect_BC <-
inner_join(nest_data_BC, threat_data__ %>% filter(region == "BC"), by = c("season", "site"), relationship = "many-to-many") %>%
dplyr::select(season, site) %>% distinct() %>%
mutate(season_site = paste(season, site, sep = "_"))determine which site names are not mutually recognized between the nest and the threat data
Code
# FP
# Combine and classify unique season_site values
sites_diff_FP <- full_join(nest_data_FP %>% ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_FP"),
threat_data__ %>% ungroup() %>% filter(region == "FP") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_FP"),
by = "season_site")
# Find unique values in each dataframe
only_in_nest_data_FP <-
sites_diff_FP %>%
filter(is.na(source.y)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in nest survival data")
only_in_threat_data_FP <-
sites_diff_FP %>%
filter(is.na(source.x)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in threat data")
# fuzzy matching and data cleaning
clean_text <- function(x) {
x %>%
str_replace_all("[^a-zA-Z0-9 _]", "") %>% # Remove special characters but keep letters, numbers, and spaces
str_replace_all("\\s+", "_") %>% # Replace spaces with underscores
tolower() # Convert to lowercase
}
# Perform fuzzy join
fuzzy_result_FP <-
stringdist_full_join(
nest_data_FP %>%
ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_FP") %>%
mutate(season_site_clean = clean_text(season_site)),
threat_data__ %>%
ungroup() %>%
filter(region == "FP") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_FP") %>%
mutate(season_site_clean = clean_text(season_site)),
by = "season_site_clean",
method = "jw", # Jaro-Winkler distance
max_dist = 0.2, # Adjust for optimal matching
distance_col = "dist") %>%
left_join(., only_in_nest_data_FP, by = c("season_site.x" = "season_site")) %>%
left_join(., only_in_threat_data_FP, by = c("season_site.y" = "season_site")) %>%
mutate(nest_season = str_extract(season_site.x, "^\\d{6}"),
threat_season = str_extract(season_site.y, "^\\d{6}")) %>%
filter(nest_season == threat_season) %>%
group_by(season_site.x) %>%
slice_min(order_by = dist) %>% # Keep only the best fuzzy match per season
ungroup() %>%
mutate(site_nest_data = str_remove(season_site.x, "^\\d{6}_?"),
site_threat_data = str_remove(season_site.y, "^\\d{6}_?")) %>%
filter(issue.x == "only in nest survival data") %>%
select(nest_season, site_nest_data, site_threat_data, dist) %>%
arrange(desc(dist)) %>%
rename(season = nest_season)
# Create a new workbook
sites_diff_FP_xl <- createWorkbook()
# Add each dataframe as a sheet
addWorksheet(sites_diff_FP_xl, "only_in_nest_data_FP")
writeData(sites_diff_FP_xl, "only_in_nest_data_FP", only_in_nest_data_FP)
addWorksheet(sites_diff_FP_xl, "only_in_threat_data_FP")
writeData(sites_diff_FP_xl, "only_in_threat_data_FP", only_in_threat_data_FP)
addWorksheet(sites_diff_FP_xl, "fuzzy_matches_to_consider")
writeData(sites_diff_FP_xl, "fuzzy_matches_to_consider", fuzzy_result_FP)
# Save the workbook
saveWorkbook(sites_diff_FP_xl, here("data/nest_issues_commented/site_names_issues_FP.xlsx"), overwrite = TRUE)
# MP
# Combine and classify unique season_site values
sites_diff_MP <- full_join(nest_data_MP %>% ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_MP"),
threat_data__ %>% ungroup() %>% filter(region == "MP") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_MP"),
by = "season_site")
# Find unique values in each dataframe
only_in_nest_data_MP <-
sites_diff_MP %>%
filter(is.na(source.y)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in nest survival data")
only_in_threat_data_MP <-
sites_diff_MP %>%
filter(is.na(source.x)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in threat data")
# Perform fuzzy join
fuzzy_result_MP <-
stringdist_full_join(
nest_data_MP %>%
ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_MP") %>%
mutate(season_site_clean = clean_text(season_site)),
threat_data__ %>%
ungroup() %>%
filter(region == "MP") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_MP") %>%
mutate(season_site_clean = clean_text(season_site)),
by = "season_site_clean",
method = "jw", # Jaro-Winkler distance
max_dist = 0.2, # Adjust for optimal matching
distance_col = "dist") %>%
left_join(., only_in_nest_data_MP, by = c("season_site.x" = "season_site")) %>%
left_join(., only_in_threat_data_MP, by = c("season_site.y" = "season_site")) %>%
mutate(nest_season = str_extract(season_site.x, "^\\d{6}"),
threat_season = str_extract(season_site.y, "^\\d{6}")) %>%
filter(nest_season == threat_season) %>%
group_by(season_site.x) %>%
slice_min(order_by = dist) %>% # Keep only the best fuzzy match per season
ungroup() %>%
mutate(site_nest_data = str_remove(season_site.x, "^\\d{6}_?"),
site_threat_data = str_remove(season_site.y, "^\\d{6}_?")) %>%
filter(issue.x == "only in nest survival data") %>%
select(nest_season, site_nest_data, site_threat_data, dist) %>%
arrange(desc(dist)) %>%
rename(season = nest_season)
# Create a new workbook
sites_diff_MP_xl <- createWorkbook()
# Add each dataframe as a sheet
addWorksheet(sites_diff_MP_xl, "only_in_nest_data_MP")
writeData(sites_diff_MP_xl, "only_in_nest_data_MP", only_in_nest_data_MP)
addWorksheet(sites_diff_MP_xl, "only_in_threat_data_MP")
writeData(sites_diff_MP_xl, "only_in_threat_data_MP", only_in_threat_data_MP)
addWorksheet(sites_diff_MP_xl, "fuzzy_matches_to_consider")
writeData(sites_diff_MP_xl, "fuzzy_matches_to_consider", fuzzy_result_MP)
# Save the workbook
saveWorkbook(sites_diff_MP_xl, here("data/nest_issues_commented/site_names_issues_MP.xlsx"), overwrite = TRUE)
# BSC
# Combine and classify unique season_site values
sites_diff_BSC <- full_join(nest_data_BSC %>% ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_BSC"),
threat_data__ %>% ungroup() %>% filter(region == "BSC") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_BSC"),
by = "season_site")
# Find unique values in each dataframe
only_in_nest_data_BSC <-
sites_diff_BSC %>%
filter(is.na(source.y)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in nest survival data")
only_in_threat_data_BSC <-
sites_diff_BSC %>%
filter(is.na(source.x)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in threat data")
# Perform fuzzy join
fuzzy_result_BSC <-
stringdist_full_join(
nest_data_BSC %>%
ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_BSC") %>%
mutate(season_site_clean = clean_text(season_site)),
threat_data__ %>%
ungroup() %>%
filter(region == "BSC") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_BSC") %>%
mutate(season_site_clean = clean_text(season_site)),
by = "season_site_clean",
method = "jw", # Jaro-Winkler distance
max_dist = 0.2, # Adjust for optimal matching
distance_col = "dist") %>%
left_join(., only_in_nest_data_BSC, by = c("season_site.x" = "season_site")) %>%
left_join(., only_in_threat_data_BSC, by = c("season_site.y" = "season_site")) %>%
mutate(nest_season = str_extract(season_site.x, "^\\d{6}"),
threat_season = str_extract(season_site.y, "^\\d{6}")) %>%
filter(nest_season == threat_season) %>%
group_by(season_site.x) %>%
slice_min(order_by = dist) %>% # Keep only the best fuzzy match per season
ungroup() %>%
mutate(site_nest_data = str_remove(season_site.x, "^\\d{6}_?"),
site_threat_data = str_remove(season_site.y, "^\\d{6}_?")) %>%
filter(issue.x == "only in nest survival data") %>%
select(nest_season, site_nest_data, site_threat_data, dist) %>%
arrange(desc(dist)) %>%
rename(season = nest_season)
# Create a new workbook
sites_diff_BSC_xl <- createWorkbook()
# Add each dataframe as a sheet
addWorksheet(sites_diff_BSC_xl, "only_in_nest_data_BSC")
writeData(sites_diff_BSC_xl, "only_in_nest_data_BSC", only_in_nest_data_BSC)
addWorksheet(sites_diff_BSC_xl, "only_in_threat_data_BSC")
writeData(sites_diff_BSC_xl, "only_in_threat_data_BSC", only_in_threat_data_BSC)
addWorksheet(sites_diff_BSC_xl, "fuzzy_matches_to_consider")
writeData(sites_diff_BSC_xl, "fuzzy_matches_to_consider", fuzzy_result_BSC)
# Save the workbook
saveWorkbook(sites_diff_BSC_xl, here("data/nest_issues_commented/site_names_issues_BSC.xlsx"), overwrite = TRUE)
# BC
# Combine and classify unique season_site values
sites_diff_BC <- full_join(nest_data_BC %>% ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_BC"),
threat_data__ %>% ungroup() %>% filter(region == "BC") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_BC"),
by = "season_site")
# Find unique values in each dataframe
only_in_nest_data_BC <-
sites_diff_BC %>%
filter(is.na(source.y)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in nest survival data")
only_in_threat_data_BC <-
sites_diff_BC %>%
filter(is.na(source.x)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in threat data")
# Perform fuzzy join
fuzzy_result_BC <-
stringdist_full_join(
nest_data_BC %>%
ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_BC") %>%
mutate(season_site_clean = clean_text(season_site)),
threat_data__ %>%
ungroup() %>%
filter(region == "BC") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_BC") %>%
mutate(season_site_clean = clean_text(season_site)),
by = "season_site_clean",
method = "jw", # Jaro-Winkler distance
max_dist = 0.2, # Adjust for optimal matching
distance_col = "dist") %>%
left_join(., only_in_nest_data_BC, by = c("season_site.x" = "season_site")) %>%
left_join(., only_in_threat_data_BC, by = c("season_site.y" = "season_site")) %>%
mutate(nest_season = str_extract(season_site.x, "^\\d{6}"),
threat_season = str_extract(season_site.y, "^\\d{6}")) %>%
filter(nest_season == threat_season) %>%
group_by(season_site.x) %>%
slice_min(order_by = dist) %>% # Keep only the best fuzzy match per season
ungroup() %>%
mutate(site_nest_data = str_remove(season_site.x, "^\\d{6}_?"),
site_threat_data = str_remove(season_site.y, "^\\d{6}_?")) %>%
filter(issue.x == "only in nest survival data") %>%
select(nest_season, site_nest_data, site_threat_data, dist) %>%
arrange(desc(dist)) %>%
rename(season = nest_season)
# Create a new workbook
sites_diff_BC_xl <- createWorkbook()
# Add each dataframe as a sheet
addWorksheet(sites_diff_BC_xl, "only_in_nest_data_BC")
writeData(sites_diff_BC_xl, "only_in_nest_data_BC", only_in_nest_data_BC)
addWorksheet(sites_diff_BC_xl, "only_in_threat_data_BC")
writeData(sites_diff_BC_xl, "only_in_threat_data_BC", only_in_threat_data_BC)
addWorksheet(sites_diff_BC_xl, "fuzzy_matches_to_consider")
writeData(sites_diff_BC_xl, "fuzzy_matches_to_consider", fuzzy_result_BC)
# Save the workbook
saveWorkbook(sites_diff_BC_xl, here("data/nest_issues_commented/site_names_issues_BC.xlsx"), overwrite = TRUE)
# PI
# Combine and classify unique season_site values
sites_diff_PI <- full_join(nest_data_PI %>% ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_PI"),
threat_data__ %>% ungroup() %>% filter(region == "PI") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_PI"),
by = "season_site")
# Find unique values in each dataframe
only_in_nest_data_PI <-
sites_diff_PI %>%
filter(is.na(source.y)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in nest survival data")
only_in_threat_data_PI <-
sites_diff_PI %>%
filter(is.na(source.x)) %>%
select(season_site) %>%
# separate(season_site, into = c("season", "site"), sep = "_", extra = "merge") %>%
mutate(issue = "only in threat data")
# Perform fuzzy join
fuzzy_result_PI <-
stringdist_full_join(
nest_data_PI %>%
ungroup() %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "nest_data_PI") %>%
mutate(season_site_clean = clean_text(season_site)),
threat_data__ %>%
ungroup() %>%
filter(region == "PI") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
select(season_site) %>% distinct() %>% mutate(source = "threat_data_PI") %>%
mutate(season_site_clean = clean_text(season_site)),
by = "season_site_clean",
method = "jw", # Jaro-Winkler distance
max_dist = 0.2, # Adjust for optimal matching
distance_col = "dist") %>%
left_join(., only_in_nest_data_PI, by = c("season_site.x" = "season_site")) %>%
left_join(., only_in_threat_data_PI, by = c("season_site.y" = "season_site")) %>%
mutate(nest_season = str_extract(season_site.x, "^\\d{6}"),
threat_season = str_extract(season_site.y, "^\\d{6}")) %>%
filter(nest_season == threat_season) %>%
group_by(season_site.x) %>%
slice_min(order_by = dist) %>% # Keep only the best fuzzy match per season
ungroup() %>%
mutate(site_nest_data = str_remove(season_site.x, "^\\d{6}_?"),
site_threat_data = str_remove(season_site.y, "^\\d{6}_?")) %>%
filter(issue.x == "only in nest survival data") %>%
select(nest_season, site_nest_data, site_threat_data, dist) %>%
arrange(desc(dist)) %>%
rename(season = nest_season)
# Create a new workbook
sites_diff_PI_xl <- createWorkbook()
# Add each dataframe as a sheet
addWorksheet(sites_diff_PI_xl, "only_in_nest_data_PI")
writeData(sites_diff_PI_xl, "only_in_nest_data_PI", only_in_nest_data_PI)
addWorksheet(sites_diff_PI_xl, "only_in_threat_data_PI")
writeData(sites_diff_PI_xl, "only_in_threat_data_PI", only_in_threat_data_PI)
addWorksheet(sites_diff_PI_xl, "fuzzy_matches_to_consider")
writeData(sites_diff_PI_xl, "fuzzy_matches_to_consider", fuzzy_result_PI)
# Save the workbook
saveWorkbook(sites_diff_PI_xl, here("data/nest_issues_commented/site_names_issues_PI.xlsx"), overwrite = TRUE)
nrow(fuzzy_result_BC) + nrow(fuzzy_result_FP) + nrow(fuzzy_result_MP)+ nrow(fuzzy_result_BSC)+ nrow(fuzzy_result_PI)[1] 24Summarise Threat data and Merge to Nest Data
Fleurieu Peninsula: summarise threat data
Code
nest_data_FP_with_threat_data <-
nest_data_FP %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_FP$season_site) %>%
dplyr::select(season, site, region, nest_ID,
FirstFound, LastPresent, LastChecked,
first_found2, last_alive2, last_checked2,
management_status, management_level,
nest_hab, Fate) %>%
rename(status = management_status,
level = management_level) %>%
mutate(level = paste0("L", level)) %>%
mutate(level = factor(level,
levels = c("L0", "L1",
"L2", "L3",
"L4"))) %>%
ungroup() %>%
mutate(
hum_a = NA,
veh_a = NA,
dog_a = NA,
don_a = NA,
dof_a = NA,
hof_a = NA,
pbd_a = NA,
gul_a = NA,
hum_m = NA,
veh_m = NA,
dog_m = NA,
don_m = NA,
dof_m = NA,
hof_m = NA,
pbd_m = NA,
gul_m = NA,
hum_b = NA,
veh_b = NA,
dog_b = NA,
don_b = NA,
dof_b = NA,
pbd_b = NA,
gul_b = NA,
hof_b = NA,
hum_p = NA,
veh_p = NA,
dog_p = NA,
hof_p = NA,
fox_p = NA,
n_surveys = NA,
days_active = NA,
fundays = NA,
uncertain_days = NA,
halfway = NA) %>%
filter(FirstFound <= LastPresent & FirstFound <= LastChecked & LastPresent <= LastChecked) %>%
filter(first_found2 <= last_alive2 & first_found2 <= last_checked2 & last_alive2 <= last_checked2)
FP_threat_data_subset <-
threat_data__ %>%
filter(region == "FP") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_FP$season_site) %>%
ungroup()
for(i in 1:nrow(nest_data_FP_with_threat_data)){
FirstFound <- nest_data_FP_with_threat_data$FirstFound[i]
LastPresent <- nest_data_FP_with_threat_data$LastPresent[i]
LastChecked <- nest_data_FP_with_threat_data$LastChecked[i]
FirstFound2 <- nest_data_FP_with_threat_data$first_found2[i]
LastPresent2 <- nest_data_FP_with_threat_data$last_alive2[i]
LastChecked2 <- nest_data_FP_with_threat_data$last_checked2[i]
halfway <- (LastChecked - LastPresent)/2
days_active <- (LastPresent + halfway) - FirstFound
uncertain_days <- LastChecked - LastPresent
site_ <- as.character(nest_data_FP_with_threat_data$site[i])
season_ <- as.character(nest_data_FP_with_threat_data$season[i])
fundays_df <-
data.frame(date = seq(from = LastPresent2, to = LastChecked2, 1)) %>%
# mutate(weekday = weekdays(dates)) %>%
mutate(weekday = factor(as.factor(weekdays(date)),
levels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday")),
region = "FP") %>%
mutate(year = year(date)) %>%
mutate(season = ifelse(month(date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(date) >= 6,
paste0(year, substr(year + 1, 3, 4)),
paste0(season, substr(year, 3, 4)))) %>%
left_join(., holidays, by = c("region", "season"), relationship = "many-to-many") %>%
mutate(holiday = ifelse(date >= start_date & date <= end_date, 1, 0)) %>%
group_by(region, season, date) %>%
mutate(holiday = max(holiday, na.rm = TRUE)) %>%
dplyr::select(-c(event, start_date, end_date)) %>%
distinct() %>%
ungroup() %>%
mutate(day_type = ifelse(holiday == 1 | weekday %in%
c("Saturday", "Sunday"), "funday", "workday")) %>%
mutate(funday = ifelse(day_type == "funday", 1, 0))
FP_threat_data_subset_ <-
FP_threat_data_subset %>%
ungroup() %>%
# data.frame() %>%
# dplyr::filter(as.numeric(obs_date2) >= FirstFound & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
# dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + (LastChecked - LastPresent)) & season == season_) %>%
dplyr::filter(site == site_)
if(nrow(FP_threat_data_subset_) > 0){
avgeraged_threats <-
FP_threat_data_subset_ %>%
mutate(hum_bi = ifelse(humans > 0 | (!is.na(hum_pri)), 1, 0),
vehicle_bi = ifelse(vehicles > 0 | (!is.na(vehicle_pri)), 1, 0),
dogs_bi = ifelse(dogs > 0 | (!is.na(dog_pri)), 1, 0),
dogs_off_bi = ifelse(dogs_off > 0, 1, 0),
dogs_on_bi = ifelse(dogs_on > 0, 1, 0),
p_birds_bi = ifelse(pred_birds > 0, 1, 0),
gulls_bi = ifelse(gulls > 0, 1, 0),
hoof_bi = ifelse(hoofed_animals > 0 | (!is.na(hoofed_pri)), 1, 0)) %>%
dplyr::summarise(
# fundays = sum(funday),
hum_avg = mean(humans, na.rm = TRUE),
vehicles_avg = mean(vehicles, na.rm = TRUE),
dogs_avg = mean(dogs, na.rm = TRUE),
dogs_on_avg = mean(dogs_on, na.rm = TRUE),
dogs_off_avg = mean(dogs_off, na.rm = TRUE),
hoof_avg = mean(hoofed_animals, na.rm = TRUE),
p_birds_avg = mean(pred_birds, na.rm = TRUE),
gulls_avg = mean(gulls, na.rm = TRUE),
hum_max = max(humans, na.rm = TRUE),
vehicles_max = max(vehicles, na.rm = TRUE),
dogs_max = max(dogs, na.rm = TRUE),
dogs_on_max = max(dogs_on, na.rm = TRUE),
dogs_off_max = max(dogs_off, na.rm = TRUE),
hoof_max = max(hoofed_animals, na.rm = TRUE),
p_birds_max = max(pred_birds, na.rm = TRUE),
gulls_max = max(gulls, na.rm = TRUE),
hum_bi = max(hum_bi, na.rm = TRUE),
vehicle_bi = max(vehicle_bi, na.rm = TRUE),
dogs_bi = max(dogs_bi, na.rm = TRUE),
dogs_on_bi = max(dogs_on_bi, na.rm = TRUE),
dogs_off_bi = max(dogs_off_bi, na.rm = TRUE),
p_birds_bi = max(p_birds_bi, na.rm = TRUE),
gulls_bi = max(gulls_bi, na.rm = TRUE),
hoof_bi = max(hoof_bi, na.rm = TRUE),
hum_pr = max(hum_pri, na.rm = TRUE),
vehicle_pr = max(vehicle_pri, na.rm = TRUE),
dog_pr = max(dog_pri, na.rm = TRUE),
hoof_pr = max(hoofed_pri, na.rm = TRUE),
fox_pr = max(fox_pri, na.rm = TRUE),
n_surveys = n(),
days_active = days_active,
halfway = halfway,
uncertain_days = uncertain_days)
nest_data_FP_with_threat_data$fundays[i] <- sum(fundays_df$funday)
nest_data_FP_with_threat_data$hum_a[i] <- avgeraged_threats$hum_avg
nest_data_FP_with_threat_data$veh_a[i] <- avgeraged_threats$vehicles_avg
nest_data_FP_with_threat_data$dog_a[i] <- avgeraged_threats$dogs_avg
nest_data_FP_with_threat_data$don_a[i] <- avgeraged_threats$dogs_on_avg
nest_data_FP_with_threat_data$dof_a[i] <- avgeraged_threats$dogs_off_avg
nest_data_FP_with_threat_data$hof_a[i] <- avgeraged_threats$hoof_avg
nest_data_FP_with_threat_data$pbd_a[i] <- avgeraged_threats$p_birds_avg
nest_data_FP_with_threat_data$gul_a[i] <- avgeraged_threats$gulls_avg
nest_data_FP_with_threat_data$hum_m[i] <- avgeraged_threats$hum_max
nest_data_FP_with_threat_data$veh_m[i] <- avgeraged_threats$vehicles_max
nest_data_FP_with_threat_data$dog_m[i] <- avgeraged_threats$dogs_max
nest_data_FP_with_threat_data$don_m[i] <- avgeraged_threats$dogs_on_max
nest_data_FP_with_threat_data$dof_m[i] <- avgeraged_threats$dogs_off_max
nest_data_FP_with_threat_data$hof_m[i] <- avgeraged_threats$hoof_max
nest_data_FP_with_threat_data$pbd_m[i] <- avgeraged_threats$p_birds_max
nest_data_FP_with_threat_data$gul_m[i] <- avgeraged_threats$gulls_max
nest_data_FP_with_threat_data$hum_b[i] <- avgeraged_threats$hum_bi
nest_data_FP_with_threat_data$veh_b[i] <- avgeraged_threats$vehicle_bi
nest_data_FP_with_threat_data$dog_b[i] <- avgeraged_threats$dogs_bi
nest_data_FP_with_threat_data$don_b[i] <- avgeraged_threats$dogs_on_bi
nest_data_FP_with_threat_data$dof_b[i] <- avgeraged_threats$dogs_off_bi
nest_data_FP_with_threat_data$pbd_b[i] <- avgeraged_threats$p_birds_bi
nest_data_FP_with_threat_data$hof_b[i] <- avgeraged_threats$hoof_bi
nest_data_FP_with_threat_data$gul_b[i] <- avgeraged_threats$gulls_bi
nest_data_FP_with_threat_data$hum_p[i] <- avgeraged_threats$hum_pr
nest_data_FP_with_threat_data$veh_p[i] <- avgeraged_threats$vehicle_pr
nest_data_FP_with_threat_data$dog_p[i] <- avgeraged_threats$dog_pr
nest_data_FP_with_threat_data$hof_p[i] <- avgeraged_threats$hoof_pr
nest_data_FP_with_threat_data$fox_p[i] <- avgeraged_threats$fox_pr
nest_data_FP_with_threat_data$n_surveys[i] <- avgeraged_threats$n_surveys
nest_data_FP_with_threat_data$days_active[i] <- avgeraged_threats$days_active
nest_data_FP_with_threat_data$halfway[i] <- avgeraged_threats$halfway
nest_data_FP_with_threat_data$uncertain_days[i] <- avgeraged_threats$uncertain_days
}else{
nest_data_FP_with_threat_data$hum_a[i] <- NA
nest_data_FP_with_threat_data$veh_a[i] <- NA
nest_data_FP_with_threat_data$dog_a[i] <- NA
nest_data_FP_with_threat_data$don_a[i] <- NA
nest_data_FP_with_threat_data$dof_a[i] <- NA
nest_data_FP_with_threat_data$hof_a[i] <- NA
nest_data_FP_with_threat_data$pbd_a[i] <- NA
nest_data_FP_with_threat_data$gul_a[i] <- NA
nest_data_FP_with_threat_data$hum_m[i] <- NA
nest_data_FP_with_threat_data$veh_m[i] <- NA
nest_data_FP_with_threat_data$dog_m[i] <- NA
nest_data_FP_with_threat_data$don_m[i] <- NA
nest_data_FP_with_threat_data$dof_m[i] <- NA
nest_data_FP_with_threat_data$hof_m[i] <- NA
nest_data_FP_with_threat_data$pbd_m[i] <- NA
nest_data_FP_with_threat_data$gul_m[i] <- NA
nest_data_FP_with_threat_data$hum_b[i] <- NA
nest_data_FP_with_threat_data$veh_b[i] <- NA
nest_data_FP_with_threat_data$dog_b[i] <- NA
nest_data_FP_with_threat_data$don_b[i] <- NA
nest_data_FP_with_threat_data$dof_b[i] <- NA
nest_data_FP_with_threat_data$pbd_b[i] <- NA
nest_data_FP_with_threat_data$gul_b[i] <- NA
nest_data_FP_with_threat_data$hof_b[i] <- NA
nest_data_FP_with_threat_data$hum_p[i] <- NA
nest_data_FP_with_threat_data$veh_p[i] <- NA
nest_data_FP_with_threat_data$dog_p[i] <- NA
nest_data_FP_with_threat_data$hof_p[i] <- NA
nest_data_FP_with_threat_data$fox_p[i] <- NA
nest_data_FP_with_threat_data$n_surveys[i] <- 0
nest_data_FP_with_threat_data$days_active[i] <- days_active
nest_data_FP_with_threat_data$halfway[i] <- halfway
nest_data_FP_with_threat_data$uncertain_days[i] <- uncertain_days
nest_data_FP_with_threat_data$fundays[i] <- NA
}
}
saveRDS(nest_data_FP_with_threat_data, file = "output/nest_data_FP_with_threat_data.rds")Mornington Peninsula: summarise threat data
Code
nest_data_MP_with_threat_data <-
nest_data_MP %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_MP$season_site) %>%
dplyr::select(season, site, region, nest_ID,
FirstFound, LastPresent, LastChecked,
first_found2, last_alive2, last_checked2,
management_status, management_level,
nest_hab, Fate) %>%
rename(status = management_status,
level = management_level) %>%
mutate(level = paste0("L", level)) %>%
mutate(level = factor(level,
levels = c("L0", "L1",
"L2", "L3",
"L4"))) %>%
ungroup() %>%
mutate(
hum_a = NA,
veh_a = NA,
dog_a = NA,
don_a = NA,
dof_a = NA,
hof_a = NA,
pbd_a = NA,
gul_a = NA,
hum_m = NA,
veh_m = NA,
dog_m = NA,
don_m = NA,
dof_m = NA,
hof_m = NA,
pbd_m = NA,
gul_m = NA,
hum_b = NA,
veh_b = NA,
dog_b = NA,
don_b = NA,
dof_b = NA,
pbd_b = NA,
gul_b = NA,
hof_b = NA,
hum_p = NA,
veh_p = NA,
dog_p = NA,
hof_p = NA,
fox_p = NA,
n_surveys = NA,
days_active = NA,
fundays = NA,
uncertain_days = NA,
halfway = NA) %>%
filter(FirstFound <= LastPresent & FirstFound <= LastChecked & LastPresent <= LastChecked) %>%
filter(first_found2 <= last_alive2 & first_found2 <= last_checked2 & last_alive2 <= last_checked2)
MP_threat_data_subset <-
threat_data__ %>%
filter(region == "MP") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_MP$season_site) %>%
ungroup()
for(i in 1:nrow(nest_data_MP_with_threat_data)){
FirstFound <- nest_data_MP_with_threat_data$FirstFound[i]
LastPresent <- nest_data_MP_with_threat_data$LastPresent[i]
LastChecked <- nest_data_MP_with_threat_data$LastChecked[i]
FirstFound2 <- nest_data_MP_with_threat_data$first_found2[i]
LastPresent2 <- nest_data_MP_with_threat_data$last_alive2[i]
LastChecked2 <- nest_data_MP_with_threat_data$last_checked2[i]
halfway <- (LastChecked - LastPresent)/2
days_active <- (LastPresent + halfway) - FirstFound
uncertain_days <- LastChecked - LastPresent
site_ <- as.character(nest_data_MP_with_threat_data$site[i])
season_ <- as.character(nest_data_MP_with_threat_data$season[i])
fundays_df <-
data.frame(date = seq(from = LastPresent2, to = LastChecked2, 1)) %>%
# mutate(weekday = weekdays(dates)) %>%
mutate(weekday = factor(as.factor(weekdays(date)),
levels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday")),
region = "MP") %>%
mutate(year = year(date)) %>%
mutate(season = ifelse(month(date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(date) >= 6,
paste0(year, substr(year + 1, 3, 4)),
paste0(season, substr(year, 3, 4)))) %>%
left_join(., holidays, by = c("region", "season"), relationship = "many-to-many") %>%
mutate(holiday = ifelse(date >= start_date & date <= end_date, 1, 0)) %>%
group_by(region, season, date) %>%
mutate(holiday = max(holiday, na.rm = TRUE)) %>%
dplyr::select(-c(event, start_date, end_date)) %>%
distinct() %>%
ungroup() %>%
mutate(day_type = ifelse(holiday == 1 | weekday %in%
c("Saturday", "Sunday"), "funday", "workday")) %>%
mutate(funday = ifelse(day_type == "funday", 1, 0))
MP_threat_data_subset_ <-
MP_threat_data_subset %>%
ungroup() %>%
# data.frame() %>%
# dplyr::filter(as.numeric(obs_date2) >= FirstFound & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
# dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + (LastChecked - LastPresent)) & season == season_) %>%
dplyr::filter(site == site_)
if(nrow(MP_threat_data_subset_) > 0){
avgeraged_threats <-
MP_threat_data_subset_ %>%
mutate(hum_bi = ifelse(humans > 0 | (!is.na(hum_pri)), 1, 0),
vehicle_bi = ifelse(vehicles > 0 | (!is.na(vehicle_pri)), 1, 0),
dogs_bi = ifelse(dogs > 0 | (!is.na(dog_pri)), 1, 0),
dogs_off_bi = ifelse(dogs_off > 0, 1, 0),
dogs_on_bi = ifelse(dogs_on > 0, 1, 0),
p_birds_bi = ifelse(pred_birds > 0, 1, 0),
gulls_bi = ifelse(gulls > 0, 1, 0),
hoof_bi = ifelse(hoofed_animals > 0 | (!is.na(hoofed_pri)), 1, 0)) %>%
dplyr::summarise(
# fundays = sum(funday),
hum_avg = mean(humans, na.rm = TRUE),
vehicles_avg = mean(vehicles, na.rm = TRUE),
dogs_avg = mean(dogs, na.rm = TRUE),
dogs_on_avg = mean(dogs_on, na.rm = TRUE),
dogs_off_avg = mean(dogs_off, na.rm = TRUE),
hoof_avg = mean(hoofed_animals, na.rm = TRUE),
p_birds_avg = mean(pred_birds, na.rm = TRUE),
gulls_avg = mean(gulls, na.rm = TRUE),
hum_max = max(humans, na.rm = TRUE),
vehicles_max = max(vehicles, na.rm = TRUE),
dogs_max = max(dogs, na.rm = TRUE),
dogs_on_max = max(dogs_on, na.rm = TRUE),
dogs_off_max = max(dogs_off, na.rm = TRUE),
hoof_max = max(hoofed_animals, na.rm = TRUE),
p_birds_max = max(pred_birds, na.rm = TRUE),
gulls_max = max(gulls, na.rm = TRUE),
hum_bi = max(hum_bi, na.rm = TRUE),
vehicle_bi = max(vehicle_bi, na.rm = TRUE),
dogs_bi = max(dogs_bi, na.rm = TRUE),
dogs_on_bi = max(dogs_on_bi, na.rm = TRUE),
dogs_off_bi = max(dogs_off_bi, na.rm = TRUE),
p_birds_bi = max(p_birds_bi, na.rm = TRUE),
gulls_bi = max(gulls_bi, na.rm = TRUE),
hoof_bi = max(hoof_bi, na.rm = TRUE),
hum_pr = max(hum_pri, na.rm = TRUE),
vehicle_pr = max(vehicle_pri, na.rm = TRUE),
dog_pr = max(dog_pri, na.rm = TRUE),
hoof_pr = max(hoofed_pri, na.rm = TRUE),
fox_pr = max(fox_pri, na.rm = TRUE),
n_surveys = n(),
days_active = days_active,
halfway = halfway,
uncertain_days = uncertain_days)
nest_data_MP_with_threat_data$fundays[i] <- sum(fundays_df$funday)
nest_data_MP_with_threat_data$hum_a[i] <- avgeraged_threats$hum_avg
nest_data_MP_with_threat_data$veh_a[i] <- avgeraged_threats$vehicles_avg
nest_data_MP_with_threat_data$dog_a[i] <- avgeraged_threats$dogs_avg
nest_data_MP_with_threat_data$don_a[i] <- avgeraged_threats$dogs_on_avg
nest_data_MP_with_threat_data$dof_a[i] <- avgeraged_threats$dogs_off_avg
nest_data_MP_with_threat_data$hof_a[i] <- avgeraged_threats$hoof_avg
nest_data_MP_with_threat_data$pbd_a[i] <- avgeraged_threats$p_birds_avg
nest_data_MP_with_threat_data$gul_a[i] <- avgeraged_threats$gulls_avg
nest_data_MP_with_threat_data$hum_m[i] <- avgeraged_threats$hum_max
nest_data_MP_with_threat_data$veh_m[i] <- avgeraged_threats$vehicles_max
nest_data_MP_with_threat_data$dog_m[i] <- avgeraged_threats$dogs_max
nest_data_MP_with_threat_data$don_m[i] <- avgeraged_threats$dogs_on_max
nest_data_MP_with_threat_data$dof_m[i] <- avgeraged_threats$dogs_off_max
nest_data_MP_with_threat_data$hof_m[i] <- avgeraged_threats$hoof_max
nest_data_MP_with_threat_data$pbd_m[i] <- avgeraged_threats$p_birds_max
nest_data_MP_with_threat_data$gul_m[i] <- avgeraged_threats$gulls_max
nest_data_MP_with_threat_data$hum_b[i] <- avgeraged_threats$hum_bi
nest_data_MP_with_threat_data$veh_b[i] <- avgeraged_threats$vehicle_bi
nest_data_MP_with_threat_data$dog_b[i] <- avgeraged_threats$dogs_bi
nest_data_MP_with_threat_data$don_b[i] <- avgeraged_threats$dogs_on_bi
nest_data_MP_with_threat_data$dof_b[i] <- avgeraged_threats$dogs_off_bi
nest_data_MP_with_threat_data$pbd_b[i] <- avgeraged_threats$p_birds_bi
nest_data_MP_with_threat_data$hof_b[i] <- avgeraged_threats$hoof_bi
nest_data_MP_with_threat_data$gul_b[i] <- avgeraged_threats$gulls_bi
nest_data_MP_with_threat_data$hum_p[i] <- avgeraged_threats$hum_pr
nest_data_MP_with_threat_data$veh_p[i] <- avgeraged_threats$vehicle_pr
nest_data_MP_with_threat_data$dog_p[i] <- avgeraged_threats$dog_pr
nest_data_MP_with_threat_data$hof_p[i] <- avgeraged_threats$hoof_pr
nest_data_MP_with_threat_data$fox_p[i] <- avgeraged_threats$fox_pr
nest_data_MP_with_threat_data$n_surveys[i] <- avgeraged_threats$n_surveys
nest_data_MP_with_threat_data$days_active[i] <- avgeraged_threats$days_active
nest_data_MP_with_threat_data$halfway[i] <- avgeraged_threats$halfway
nest_data_MP_with_threat_data$uncertain_days[i] <- avgeraged_threats$uncertain_days
}else{
nest_data_MP_with_threat_data$hum_a[i] <- NA
nest_data_MP_with_threat_data$veh_a[i] <- NA
nest_data_MP_with_threat_data$dog_a[i] <- NA
nest_data_MP_with_threat_data$don_a[i] <- NA
nest_data_MP_with_threat_data$dof_a[i] <- NA
nest_data_MP_with_threat_data$hof_a[i] <- NA
nest_data_MP_with_threat_data$pbd_a[i] <- NA
nest_data_MP_with_threat_data$gul_a[i] <- NA
nest_data_MP_with_threat_data$hum_m[i] <- NA
nest_data_MP_with_threat_data$veh_m[i] <- NA
nest_data_MP_with_threat_data$dog_m[i] <- NA
nest_data_MP_with_threat_data$don_m[i] <- NA
nest_data_MP_with_threat_data$dof_m[i] <- NA
nest_data_MP_with_threat_data$hof_m[i] <- NA
nest_data_MP_with_threat_data$pbd_m[i] <- NA
nest_data_MP_with_threat_data$gul_m[i] <- NA
nest_data_MP_with_threat_data$hum_b[i] <- NA
nest_data_MP_with_threat_data$veh_b[i] <- NA
nest_data_MP_with_threat_data$dog_b[i] <- NA
nest_data_MP_with_threat_data$don_b[i] <- NA
nest_data_MP_with_threat_data$dof_b[i] <- NA
nest_data_MP_with_threat_data$pbd_b[i] <- NA
nest_data_MP_with_threat_data$gul_b[i] <- NA
nest_data_MP_with_threat_data$hof_b[i] <- NA
nest_data_MP_with_threat_data$hum_p[i] <- NA
nest_data_MP_with_threat_data$veh_p[i] <- NA
nest_data_MP_with_threat_data$dog_p[i] <- NA
nest_data_MP_with_threat_data$hof_p[i] <- NA
nest_data_MP_with_threat_data$fox_p[i] <- NA
nest_data_MP_with_threat_data$n_surveys[i] <- 0
nest_data_MP_with_threat_data$days_active[i] <- days_active
nest_data_MP_with_threat_data$halfway[i] <- halfway
nest_data_MP_with_threat_data$uncertain_days[i] <- uncertain_days
nest_data_MP_with_threat_data$fundays[i] <- NA
}
}
saveRDS(nest_data_MP_with_threat_data, file = "output/nest_data_MP_with_threat_data.rds")Bellarine / Surf Coast: summarise threat data
Code
nest_data_BSC_with_threat_data <-
nest_data_BSC %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_BSC$season_site) %>%
dplyr::select(season, site, region, nest_ID,
FirstFound, LastPresent, LastChecked,
first_found2, last_alive2, last_checked2,
management_status, management_level,
nest_hab, Fate) %>%
rename(status = management_status,
level = management_level) %>%
mutate(level = paste0("L", level)) %>%
mutate(level = factor(level,
levels = c("L0", "L1",
"L2", "L3",
"L4"))) %>%
ungroup() %>%
mutate(
hum_a = NA,
veh_a = NA,
dog_a = NA,
don_a = NA,
dof_a = NA,
hof_a = NA,
pbd_a = NA,
gul_a = NA,
hum_m = NA,
veh_m = NA,
dog_m = NA,
don_m = NA,
dof_m = NA,
hof_m = NA,
pbd_m = NA,
gul_m = NA,
hum_b = NA,
veh_b = NA,
dog_b = NA,
don_b = NA,
dof_b = NA,
pbd_b = NA,
gul_b = NA,
hof_b = NA,
hum_p = NA,
veh_p = NA,
dog_p = NA,
hof_p = NA,
fox_p = NA,
n_surveys = NA,
days_active = NA,
fundays = NA,
uncertain_days = NA,
halfway = NA) %>%
filter(FirstFound <= LastPresent & FirstFound <= LastChecked & LastPresent <= LastChecked) %>%
filter(first_found2 <= last_alive2 & first_found2 <= last_checked2 & last_alive2 <= last_checked2)
BSC_threat_data_subset <-
threat_data__ %>%
filter(region == "BSC") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_BSC$season_site) %>%
ungroup()
for(i in 1:nrow(nest_data_BSC_with_threat_data)){
FirstFound <- nest_data_BSC_with_threat_data$FirstFound[i]
LastPresent <- nest_data_BSC_with_threat_data$LastPresent[i]
LastChecked <- nest_data_BSC_with_threat_data$LastChecked[i]
FirstFound2 <- nest_data_BSC_with_threat_data$first_found2[i]
LastPresent2 <- nest_data_BSC_with_threat_data$last_alive2[i]
LastChecked2 <- nest_data_BSC_with_threat_data$last_checked2[i]
halfway <- (LastChecked - LastPresent)/2
days_active <- (LastPresent + halfway) - FirstFound
uncertain_days <- LastChecked - LastPresent
site_ <- as.character(nest_data_BSC_with_threat_data$site[i])
season_ <- as.character(nest_data_BSC_with_threat_data$season[i])
fundays_df <-
data.frame(date = seq(from = LastPresent2, to = LastChecked2, 1)) %>%
# mutate(weekday = weekdays(dates)) %>%
mutate(weekday = factor(as.factor(weekdays(date)),
levels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday")),
region = "BSC") %>%
mutate(year = year(date)) %>%
mutate(season = ifelse(month(date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(date) >= 6,
paste0(year, substr(year + 1, 3, 4)),
paste0(season, substr(year, 3, 4)))) %>%
left_join(., holidays, by = c("region", "season"), relationship = "many-to-many") %>%
mutate(holiday = ifelse(date >= start_date & date <= end_date, 1, 0)) %>%
group_by(region, season, date) %>%
mutate(holiday = max(holiday, na.rm = TRUE)) %>%
dplyr::select(-c(event, start_date, end_date)) %>%
distinct() %>%
ungroup() %>%
mutate(day_type = ifelse(holiday == 1 | weekday %in%
c("Saturday", "Sunday"), "funday", "workday")) %>%
mutate(funday = ifelse(day_type == "funday", 1, 0))
BSC_threat_data_subset_ <-
BSC_threat_data_subset %>%
ungroup() %>%
# data.frame() %>%
# dplyr::filter(as.numeric(obs_date2) >= FirstFound & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
# dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + (LastChecked - LastPresent)) & season == season_) %>%
dplyr::filter(site == site_)
if(nrow(BSC_threat_data_subset_) > 0){
avgeraged_threats <-
BSC_threat_data_subset_ %>%
mutate(hum_bi = ifelse(humans > 0 | (!is.na(hum_pri)), 1, 0),
vehicle_bi = ifelse(vehicles > 0 | (!is.na(vehicle_pri)), 1, 0),
dogs_bi = ifelse(dogs > 0 | (!is.na(dog_pri)), 1, 0),
dogs_off_bi = ifelse(dogs_off > 0, 1, 0),
dogs_on_bi = ifelse(dogs_on > 0, 1, 0),
p_birds_bi = ifelse(pred_birds > 0, 1, 0),
gulls_bi = ifelse(gulls > 0, 1, 0),
hoof_bi = ifelse(hoofed_animals > 0 | (!is.na(hoofed_pri)), 1, 0)) %>%
dplyr::summarise(
# fundays = sum(funday),
hum_avg = mean(humans, na.rm = TRUE),
vehicles_avg = mean(vehicles, na.rm = TRUE),
dogs_avg = mean(dogs, na.rm = TRUE),
dogs_on_avg = mean(dogs_on, na.rm = TRUE),
dogs_off_avg = mean(dogs_off, na.rm = TRUE),
hoof_avg = mean(hoofed_animals, na.rm = TRUE),
p_birds_avg = mean(pred_birds, na.rm = TRUE),
gulls_avg = mean(gulls, na.rm = TRUE),
hum_max = max(humans, na.rm = TRUE),
vehicles_max = max(vehicles, na.rm = TRUE),
dogs_max = max(dogs, na.rm = TRUE),
dogs_on_max = max(dogs_on, na.rm = TRUE),
dogs_off_max = max(dogs_off, na.rm = TRUE),
hoof_max = max(hoofed_animals, na.rm = TRUE),
p_birds_max = max(pred_birds, na.rm = TRUE),
gulls_max = max(gulls, na.rm = TRUE),
hum_bi = max(hum_bi, na.rm = TRUE),
vehicle_bi = max(vehicle_bi, na.rm = TRUE),
dogs_bi = max(dogs_bi, na.rm = TRUE),
dogs_on_bi = max(dogs_on_bi, na.rm = TRUE),
dogs_off_bi = max(dogs_off_bi, na.rm = TRUE),
p_birds_bi = max(p_birds_bi, na.rm = TRUE),
gulls_bi = max(gulls_bi, na.rm = TRUE),
hoof_bi = max(hoof_bi, na.rm = TRUE),
hum_pr = max(hum_pri, na.rm = TRUE),
vehicle_pr = max(vehicle_pri, na.rm = TRUE),
dog_pr = max(dog_pri, na.rm = TRUE),
hoof_pr = max(hoofed_pri, na.rm = TRUE),
fox_pr = max(fox_pri, na.rm = TRUE),
n_surveys = n(),
days_active = days_active,
halfway = halfway,
uncertain_days = uncertain_days)
nest_data_BSC_with_threat_data$fundays[i] <- sum(fundays_df$funday)
nest_data_BSC_with_threat_data$hum_a[i] <- avgeraged_threats$hum_avg
nest_data_BSC_with_threat_data$veh_a[i] <- avgeraged_threats$vehicles_avg
nest_data_BSC_with_threat_data$dog_a[i] <- avgeraged_threats$dogs_avg
nest_data_BSC_with_threat_data$don_a[i] <- avgeraged_threats$dogs_on_avg
nest_data_BSC_with_threat_data$dof_a[i] <- avgeraged_threats$dogs_off_avg
nest_data_BSC_with_threat_data$hof_a[i] <- avgeraged_threats$hoof_avg
nest_data_BSC_with_threat_data$pbd_a[i] <- avgeraged_threats$p_birds_avg
nest_data_BSC_with_threat_data$gul_a[i] <- avgeraged_threats$gulls_avg
nest_data_BSC_with_threat_data$hum_m[i] <- avgeraged_threats$hum_max
nest_data_BSC_with_threat_data$veh_m[i] <- avgeraged_threats$vehicles_max
nest_data_BSC_with_threat_data$dog_m[i] <- avgeraged_threats$dogs_max
nest_data_BSC_with_threat_data$don_m[i] <- avgeraged_threats$dogs_on_max
nest_data_BSC_with_threat_data$dof_m[i] <- avgeraged_threats$dogs_off_max
nest_data_BSC_with_threat_data$hof_m[i] <- avgeraged_threats$hoof_max
nest_data_BSC_with_threat_data$pbd_m[i] <- avgeraged_threats$p_birds_max
nest_data_BSC_with_threat_data$gul_m[i] <- avgeraged_threats$gulls_max
nest_data_BSC_with_threat_data$hum_b[i] <- avgeraged_threats$hum_bi
nest_data_BSC_with_threat_data$veh_b[i] <- avgeraged_threats$vehicle_bi
nest_data_BSC_with_threat_data$dog_b[i] <- avgeraged_threats$dogs_bi
nest_data_BSC_with_threat_data$don_b[i] <- avgeraged_threats$dogs_on_bi
nest_data_BSC_with_threat_data$dof_b[i] <- avgeraged_threats$dogs_off_bi
nest_data_BSC_with_threat_data$pbd_b[i] <- avgeraged_threats$p_birds_bi
nest_data_BSC_with_threat_data$hof_b[i] <- avgeraged_threats$hoof_bi
nest_data_BSC_with_threat_data$gul_b[i] <- avgeraged_threats$gulls_bi
nest_data_BSC_with_threat_data$hum_p[i] <- avgeraged_threats$hum_pr
nest_data_BSC_with_threat_data$veh_p[i] <- avgeraged_threats$vehicle_pr
nest_data_BSC_with_threat_data$dog_p[i] <- avgeraged_threats$dog_pr
nest_data_BSC_with_threat_data$hof_p[i] <- avgeraged_threats$hoof_pr
nest_data_BSC_with_threat_data$fox_p[i] <- avgeraged_threats$fox_pr
nest_data_BSC_with_threat_data$n_surveys[i] <- avgeraged_threats$n_surveys
nest_data_BSC_with_threat_data$days_active[i] <- avgeraged_threats$days_active
nest_data_BSC_with_threat_data$halfway[i] <- avgeraged_threats$halfway
nest_data_BSC_with_threat_data$uncertain_days[i] <- avgeraged_threats$uncertain_days
}else{
nest_data_BSC_with_threat_data$hum_a[i] <- NA
nest_data_BSC_with_threat_data$veh_a[i] <- NA
nest_data_BSC_with_threat_data$dog_a[i] <- NA
nest_data_BSC_with_threat_data$don_a[i] <- NA
nest_data_BSC_with_threat_data$dof_a[i] <- NA
nest_data_BSC_with_threat_data$hof_a[i] <- NA
nest_data_BSC_with_threat_data$pbd_a[i] <- NA
nest_data_BSC_with_threat_data$gul_a[i] <- NA
nest_data_BSC_with_threat_data$hum_m[i] <- NA
nest_data_BSC_with_threat_data$veh_m[i] <- NA
nest_data_BSC_with_threat_data$dog_m[i] <- NA
nest_data_BSC_with_threat_data$don_m[i] <- NA
nest_data_BSC_with_threat_data$dof_m[i] <- NA
nest_data_BSC_with_threat_data$hof_m[i] <- NA
nest_data_BSC_with_threat_data$pbd_m[i] <- NA
nest_data_BSC_with_threat_data$gul_m[i] <- NA
nest_data_BSC_with_threat_data$hum_b[i] <- NA
nest_data_BSC_with_threat_data$veh_b[i] <- NA
nest_data_BSC_with_threat_data$dog_b[i] <- NA
nest_data_BSC_with_threat_data$don_b[i] <- NA
nest_data_BSC_with_threat_data$dof_b[i] <- NA
nest_data_BSC_with_threat_data$pbd_b[i] <- NA
nest_data_BSC_with_threat_data$gul_b[i] <- NA
nest_data_BSC_with_threat_data$hof_b[i] <- NA
nest_data_BSC_with_threat_data$hum_p[i] <- NA
nest_data_BSC_with_threat_data$veh_p[i] <- NA
nest_data_BSC_with_threat_data$dog_p[i] <- NA
nest_data_BSC_with_threat_data$hof_p[i] <- NA
nest_data_BSC_with_threat_data$fox_p[i] <- NA
nest_data_BSC_with_threat_data$n_surveys[i] <- 0
nest_data_BSC_with_threat_data$days_active[i] <- days_active
nest_data_BSC_with_threat_data$halfway[i] <- halfway
nest_data_BSC_with_threat_data$uncertain_days[i] <- uncertain_days
nest_data_BSC_with_threat_data$fundays[i] <- NA
}
}
saveRDS(nest_data_BSC_with_threat_data, file = "output/nest_data_BSC_with_threat_data.rds")Bass Coast: summarise threat data
Code
nest_data_BC_with_threat_data <-
nest_data_BC %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
####
####
filter(season_site %in% sites_intersect_BC$season_site) %>%
dplyr::select(season, site, region, nest_ID,
FirstFound, LastPresent, LastChecked,
first_found2, last_alive2, last_checked2,
management_status, management_level,
nest_hab, Fate) %>%
rename(status = management_status,
level = management_level) %>%
mutate(level = paste0("L", level)) %>%
mutate(level = factor(level,
levels = c("L0", "L1",
"L2", "L3",
"L4"))) %>%
ungroup() %>%
mutate(
hum_a = NA,
veh_a = NA,
dog_a = NA,
don_a = NA,
dof_a = NA,
hof_a = NA,
pbd_a = NA,
gul_a = NA,
hum_m = NA,
veh_m = NA,
dog_m = NA,
don_m = NA,
dof_m = NA,
hof_m = NA,
pbd_m = NA,
gul_m = NA,
hum_b = NA,
veh_b = NA,
dog_b = NA,
don_b = NA,
dof_b = NA,
pbd_b = NA,
gul_b = NA,
hof_b = NA,
hum_p = NA,
veh_p = NA,
dog_p = NA,
hof_p = NA,
fox_p = NA,
n_surveys = NA,
days_active = NA,
fundays = NA,
uncertain_days = NA,
halfway = NA) %>%
filter(FirstFound <= LastPresent & FirstFound <= LastChecked & LastPresent <= LastChecked) %>%
filter(first_found2 <= last_alive2 & first_found2 <= last_checked2 & last_alive2 <= last_checked2)
BC_threat_data_subset <-
threat_data__ %>%
filter(region == "BC") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_BC$season_site) %>%
ungroup()
for(i in 1:nrow(nest_data_BC_with_threat_data)){
FirstFound <- nest_data_BC_with_threat_data$FirstFound[i]
LastPresent <- nest_data_BC_with_threat_data$LastPresent[i]
LastChecked <- nest_data_BC_with_threat_data$LastChecked[i]
FirstFound2 <- nest_data_BC_with_threat_data$first_found2[i]
LastPresent2 <- nest_data_BC_with_threat_data$last_alive2[i]
LastChecked2 <- nest_data_BC_with_threat_data$last_checked2[i]
halfway <- (LastChecked - LastPresent)/2
days_active <- (LastPresent + halfway) - FirstFound
uncertain_days <- LastChecked - LastPresent
site_ <- as.character(nest_data_BC_with_threat_data$site[i])
season_ <- as.character(nest_data_BC_with_threat_data$season[i])
fundays_df <-
data.frame(date = seq(from = LastPresent2, to = LastChecked2, 1)) %>%
# mutate(weekday = weekdays(dates)) %>%
mutate(weekday = factor(as.factor(weekdays(date)),
levels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday")),
region = "BC") %>%
mutate(year = year(date)) %>%
mutate(season = ifelse(month(date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(date) >= 6,
paste0(year, substr(year + 1, 3, 4)),
paste0(season, substr(year, 3, 4)))) %>%
left_join(., holidays, by = c("region", "season"), relationship = "many-to-many") %>%
mutate(holiday = ifelse(date >= start_date & date <= end_date, 1, 0)) %>%
group_by(region, season, date) %>%
mutate(holiday = max(holiday, na.rm = TRUE)) %>%
dplyr::select(-c(event, start_date, end_date)) %>%
distinct() %>%
ungroup() %>%
mutate(day_type = ifelse(holiday == 1 | weekday %in%
c("Saturday", "Sunday"), "funday", "workday")) %>%
mutate(funday = ifelse(day_type == "funday", 1, 0))
BC_threat_data_subset_ <-
BC_threat_data_subset %>%
ungroup() %>%
# data.frame() %>%
# dplyr::filter(as.numeric(obs_date2) >= FirstFound & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
# dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + (LastChecked - LastPresent)) & season == season_) %>%
dplyr::filter(site == site_)
if(nrow(BC_threat_data_subset_) > 0){
avgeraged_threats <-
BC_threat_data_subset_ %>%
mutate(hum_bi = ifelse(humans > 0 | (!is.na(hum_pri)), 1, 0),
vehicle_bi = ifelse(vehicles > 0 | (!is.na(vehicle_pri)), 1, 0),
dogs_bi = ifelse(dogs > 0 | (!is.na(dog_pri)), 1, 0),
dogs_off_bi = ifelse(dogs_off > 0, 1, 0),
dogs_on_bi = ifelse(dogs_on > 0, 1, 0),
p_birds_bi = ifelse(pred_birds > 0, 1, 0),
gulls_bi = ifelse(gulls > 0, 1, 0),
hoof_bi = ifelse(hoofed_animals > 0 | (!is.na(hoofed_pri)), 1, 0)) %>%
dplyr::summarise(
# fundays = sum(funday),
hum_avg = mean(humans, na.rm = TRUE),
vehicles_avg = mean(vehicles, na.rm = TRUE),
dogs_avg = mean(dogs, na.rm = TRUE),
dogs_on_avg = mean(dogs_on, na.rm = TRUE),
dogs_off_avg = mean(dogs_off, na.rm = TRUE),
hoof_avg = mean(hoofed_animals, na.rm = TRUE),
p_birds_avg = mean(pred_birds, na.rm = TRUE),
gulls_avg = mean(gulls, na.rm = TRUE),
hum_max = max(humans, na.rm = TRUE),
vehicles_max = max(vehicles, na.rm = TRUE),
dogs_max = max(dogs, na.rm = TRUE),
dogs_on_max = max(dogs_on, na.rm = TRUE),
dogs_off_max = max(dogs_off, na.rm = TRUE),
hoof_max = max(hoofed_animals, na.rm = TRUE),
p_birds_max = max(pred_birds, na.rm = TRUE),
gulls_max = max(gulls, na.rm = TRUE),
hum_bi = max(hum_bi, na.rm = TRUE),
vehicle_bi = max(vehicle_bi, na.rm = TRUE),
dogs_bi = max(dogs_bi, na.rm = TRUE),
dogs_on_bi = max(dogs_on_bi, na.rm = TRUE),
dogs_off_bi = max(dogs_off_bi, na.rm = TRUE),
p_birds_bi = max(p_birds_bi, na.rm = TRUE),
gulls_bi = max(gulls_bi, na.rm = TRUE),
hoof_bi = max(hoof_bi, na.rm = TRUE),
hum_pr = max(hum_pri, na.rm = TRUE),
vehicle_pr = max(vehicle_pri, na.rm = TRUE),
dog_pr = max(dog_pri, na.rm = TRUE),
hoof_pr = max(hoofed_pri, na.rm = TRUE),
fox_pr = max(fox_pri, na.rm = TRUE),
n_surveys = n(),
days_active = days_active,
halfway = halfway,
uncertain_days = uncertain_days)
nest_data_BC_with_threat_data$fundays[i] <- sum(fundays_df$funday)
nest_data_BC_with_threat_data$hum_a[i] <- avgeraged_threats$hum_avg
nest_data_BC_with_threat_data$veh_a[i] <- avgeraged_threats$vehicles_avg
nest_data_BC_with_threat_data$dog_a[i] <- avgeraged_threats$dogs_avg
nest_data_BC_with_threat_data$don_a[i] <- avgeraged_threats$dogs_on_avg
nest_data_BC_with_threat_data$dof_a[i] <- avgeraged_threats$dogs_off_avg
nest_data_BC_with_threat_data$hof_a[i] <- avgeraged_threats$hoof_avg
nest_data_BC_with_threat_data$pbd_a[i] <- avgeraged_threats$p_birds_avg
nest_data_BC_with_threat_data$gul_a[i] <- avgeraged_threats$gulls_avg
nest_data_BC_with_threat_data$hum_m[i] <- avgeraged_threats$hum_max
nest_data_BC_with_threat_data$veh_m[i] <- avgeraged_threats$vehicles_max
nest_data_BC_with_threat_data$dog_m[i] <- avgeraged_threats$dogs_max
nest_data_BC_with_threat_data$don_m[i] <- avgeraged_threats$dogs_on_max
nest_data_BC_with_threat_data$dof_m[i] <- avgeraged_threats$dogs_off_max
nest_data_BC_with_threat_data$hof_m[i] <- avgeraged_threats$hoof_max
nest_data_BC_with_threat_data$pbd_m[i] <- avgeraged_threats$p_birds_max
nest_data_BC_with_threat_data$gul_m[i] <- avgeraged_threats$gulls_max
nest_data_BC_with_threat_data$hum_b[i] <- avgeraged_threats$hum_bi
nest_data_BC_with_threat_data$veh_b[i] <- avgeraged_threats$vehicle_bi
nest_data_BC_with_threat_data$dog_b[i] <- avgeraged_threats$dogs_bi
nest_data_BC_with_threat_data$don_b[i] <- avgeraged_threats$dogs_on_bi
nest_data_BC_with_threat_data$dof_b[i] <- avgeraged_threats$dogs_off_bi
nest_data_BC_with_threat_data$pbd_b[i] <- avgeraged_threats$p_birds_bi
nest_data_BC_with_threat_data$hof_b[i] <- avgeraged_threats$hoof_bi
nest_data_BC_with_threat_data$gul_b[i] <- avgeraged_threats$gulls_bi
nest_data_BC_with_threat_data$hum_p[i] <- avgeraged_threats$hum_pr
nest_data_BC_with_threat_data$veh_p[i] <- avgeraged_threats$vehicle_pr
nest_data_BC_with_threat_data$dog_p[i] <- avgeraged_threats$dog_pr
nest_data_BC_with_threat_data$hof_p[i] <- avgeraged_threats$hoof_pr
nest_data_BC_with_threat_data$fox_p[i] <- avgeraged_threats$fox_pr
nest_data_BC_with_threat_data$n_surveys[i] <- avgeraged_threats$n_surveys
nest_data_BC_with_threat_data$days_active[i] <- avgeraged_threats$days_active
nest_data_BC_with_threat_data$halfway[i] <- avgeraged_threats$halfway
nest_data_BC_with_threat_data$uncertain_days[i] <- avgeraged_threats$uncertain_days
}else{
nest_data_BC_with_threat_data$hum_a[i] <- NA
nest_data_BC_with_threat_data$veh_a[i] <- NA
nest_data_BC_with_threat_data$dog_a[i] <- NA
nest_data_BC_with_threat_data$don_a[i] <- NA
nest_data_BC_with_threat_data$dof_a[i] <- NA
nest_data_BC_with_threat_data$hof_a[i] <- NA
nest_data_BC_with_threat_data$pbd_a[i] <- NA
nest_data_BC_with_threat_data$gul_a[i] <- NA
nest_data_BC_with_threat_data$hum_m[i] <- NA
nest_data_BC_with_threat_data$veh_m[i] <- NA
nest_data_BC_with_threat_data$dog_m[i] <- NA
nest_data_BC_with_threat_data$don_m[i] <- NA
nest_data_BC_with_threat_data$dof_m[i] <- NA
nest_data_BC_with_threat_data$hof_m[i] <- NA
nest_data_BC_with_threat_data$pbd_m[i] <- NA
nest_data_BC_with_threat_data$gul_m[i] <- NA
nest_data_BC_with_threat_data$hum_b[i] <- NA
nest_data_BC_with_threat_data$veh_b[i] <- NA
nest_data_BC_with_threat_data$dog_b[i] <- NA
nest_data_BC_with_threat_data$don_b[i] <- NA
nest_data_BC_with_threat_data$dof_b[i] <- NA
nest_data_BC_with_threat_data$pbd_b[i] <- NA
nest_data_BC_with_threat_data$gul_b[i] <- NA
nest_data_BC_with_threat_data$hof_b[i] <- NA
nest_data_BC_with_threat_data$hum_p[i] <- NA
nest_data_BC_with_threat_data$veh_p[i] <- NA
nest_data_BC_with_threat_data$dog_p[i] <- NA
nest_data_BC_with_threat_data$hof_p[i] <- NA
nest_data_BC_with_threat_data$fox_p[i] <- NA
nest_data_BC_with_threat_data$n_surveys[i] <- 0
nest_data_BC_with_threat_data$days_active[i] <- days_active
nest_data_BC_with_threat_data$halfway[i] <- halfway
nest_data_BC_with_threat_data$uncertain_days[i] <- uncertain_days
nest_data_BC_with_threat_data$fundays[i] <- NA
}
}
saveRDS(nest_data_BC_with_threat_data, file = "output/nest_data_BC_with_threat_data.rds")Phillip Island: summarise threat data
Code
nest_data_PI_with_threat_data <-
nest_data_PI %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
####
####
filter(season_site %in% sites_intersect_PI$season_site) %>%
dplyr::select(season, site, region, nest_ID,
FirstFound, LastPresent, LastChecked,
first_found2, last_alive2, last_checked2,
management_status, management_level,
nest_hab, Fate) %>%
rename(status = management_status,
level = management_level) %>%
mutate(level = paste0("L", level)) %>%
mutate(level = factor(level,
levels = c("L0", "L1",
"L2", "L3",
"L4"))) %>%
ungroup() %>%
mutate(
hum_a = NA,
veh_a = NA,
dog_a = NA,
don_a = NA,
dof_a = NA,
hof_a = NA,
pbd_a = NA,
gul_a = NA,
hum_m = NA,
veh_m = NA,
dog_m = NA,
don_m = NA,
dof_m = NA,
hof_m = NA,
pbd_m = NA,
gul_m = NA,
hum_b = NA,
veh_b = NA,
dog_b = NA,
don_b = NA,
dof_b = NA,
pbd_b = NA,
gul_b = NA,
hof_b = NA,
hum_p = NA,
veh_p = NA,
dog_p = NA,
hof_p = NA,
fox_p = NA,
n_surveys = NA,
days_active = NA,
fundays = NA,
uncertain_days = NA,
halfway = NA) %>%
filter(FirstFound <= LastPresent & FirstFound <= LastChecked & LastPresent <= LastChecked) %>%
filter(first_found2 <= last_alive2 & first_found2 <= last_checked2 & last_alive2 <= last_checked2)
PI_threat_data_subset <-
threat_data__ %>%
filter(region == "PI") %>%
mutate(season_site = paste(season, site, sep = "_")) %>%
filter(season_site %in% sites_intersect_PI$season_site) %>%
ungroup()
for(i in 1:nrow(nest_data_PI_with_threat_data)){
FirstFound <- nest_data_PI_with_threat_data$FirstFound[i]
LastPresent <- nest_data_PI_with_threat_data$LastPresent[i]
LastChecked <- nest_data_PI_with_threat_data$LastChecked[i]
FirstFound2 <- nest_data_PI_with_threat_data$first_found2[i]
LastPresent2 <- nest_data_PI_with_threat_data$last_alive2[i]
LastChecked2 <- nest_data_PI_with_threat_data$last_checked2[i]
halfway <- (LastChecked - LastPresent)/2
days_active <- (LastPresent + halfway) - FirstFound
uncertain_days <- LastChecked - LastPresent
site_ <- as.character(nest_data_PI_with_threat_data$site[i])
season_ <- as.character(nest_data_PI_with_threat_data$season[i])
fundays_df <-
data.frame(date = seq(from = LastPresent2, to = LastChecked2, 1)) %>%
# mutate(weekday = weekdays(dates)) %>%
mutate(weekday = factor(as.factor(weekdays(date)),
levels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday")),
region = "PI") %>%
mutate(year = year(date)) %>%
mutate(season = ifelse(month(date) < 6, year - 1, year)) %>%
mutate(season = ifelse(month(date) >= 6,
paste0(year, substr(year + 1, 3, 4)),
paste0(season, substr(year, 3, 4)))) %>%
left_join(., holidays, by = c("region", "season"), relationship = "many-to-many") %>%
mutate(holiday = ifelse(date >= start_date & date <= end_date, 1, 0)) %>%
group_by(region, season, date) %>%
mutate(holiday = max(holiday, na.rm = TRUE)) %>%
dplyr::select(-c(event, start_date, end_date)) %>%
distinct() %>%
ungroup() %>%
mutate(day_type = ifelse(holiday == 1 | weekday %in%
c("Saturday", "Sunday"), "funday", "workday")) %>%
mutate(funday = ifelse(day_type == "funday", 1, 0))
PI_threat_data_subset_ <-
PI_threat_data_subset %>%
ungroup() %>%
# data.frame() %>%
# dplyr::filter(as.numeric(obs_date2) >= FirstFound & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
# dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + halfway) & season == season_) %>%
dplyr::filter(as.numeric(obs_date2) >= LastPresent & as.numeric(obs_date2) <= (LastPresent + (LastChecked - LastPresent)) & season == season_) %>%
dplyr::filter(site == site_)
if(nrow(PI_threat_data_subset_) > 0){
avgeraged_threats <-
PI_threat_data_subset_ %>%
mutate(hum_bi = ifelse(humans > 0 | (!is.na(hum_pri)), 1, 0),
vehicle_bi = ifelse(vehicles > 0 | (!is.na(vehicle_pri)), 1, 0),
dogs_bi = ifelse(dogs > 0 | (!is.na(dog_pri)), 1, 0),
dogs_off_bi = ifelse(dogs_off > 0, 1, 0),
dogs_on_bi = ifelse(dogs_on > 0, 1, 0),
p_birds_bi = ifelse(pred_birds > 0, 1, 0),
gulls_bi = ifelse(gulls > 0, 1, 0),
hoof_bi = ifelse(hoofed_animals > 0 | (!is.na(hoofed_pri)), 1, 0)) %>%
dplyr::summarise(
# fundays = sum(funday),
hum_avg = mean(humans, na.rm = TRUE),
vehicles_avg = mean(vehicles, na.rm = TRUE),
dogs_avg = mean(dogs, na.rm = TRUE),
dogs_on_avg = mean(dogs_on, na.rm = TRUE),
dogs_off_avg = mean(dogs_off, na.rm = TRUE),
hoof_avg = mean(hoofed_animals, na.rm = TRUE),
p_birds_avg = mean(pred_birds, na.rm = TRUE),
gulls_avg = mean(gulls, na.rm = TRUE),
hum_max = max(humans, na.rm = TRUE),
vehicles_max = max(vehicles, na.rm = TRUE),
dogs_max = max(dogs, na.rm = TRUE),
dogs_on_max = max(dogs_on, na.rm = TRUE),
dogs_off_max = max(dogs_off, na.rm = TRUE),
hoof_max = max(hoofed_animals, na.rm = TRUE),
p_birds_max = max(pred_birds, na.rm = TRUE),
gulls_max = max(gulls, na.rm = TRUE),
hum_bi = max(hum_bi, na.rm = TRUE),
vehicle_bi = max(vehicle_bi, na.rm = TRUE),
dogs_bi = max(dogs_bi, na.rm = TRUE),
dogs_on_bi = max(dogs_on_bi, na.rm = TRUE),
dogs_off_bi = max(dogs_off_bi, na.rm = TRUE),
p_birds_bi = max(p_birds_bi, na.rm = TRUE),
gulls_bi = max(gulls_bi, na.rm = TRUE),
hoof_bi = max(hoof_bi, na.rm = TRUE),
hum_pr = max(hum_pri, na.rm = TRUE),
vehicle_pr = max(vehicle_pri, na.rm = TRUE),
dog_pr = max(dog_pri, na.rm = TRUE),
hoof_pr = max(hoofed_pri, na.rm = TRUE),
fox_pr = max(fox_pri, na.rm = TRUE),
n_surveys = n(),
days_active = days_active,
halfway = halfway,
uncertain_days = uncertain_days)
nest_data_PI_with_threat_data$fundays[i] <- sum(fundays_df$funday)
nest_data_PI_with_threat_data$hum_a[i] <- avgeraged_threats$hum_avg
nest_data_PI_with_threat_data$veh_a[i] <- avgeraged_threats$vehicles_avg
nest_data_PI_with_threat_data$dog_a[i] <- avgeraged_threats$dogs_avg
nest_data_PI_with_threat_data$don_a[i] <- avgeraged_threats$dogs_on_avg
nest_data_PI_with_threat_data$dof_a[i] <- avgeraged_threats$dogs_off_avg
nest_data_PI_with_threat_data$hof_a[i] <- avgeraged_threats$hoof_avg
nest_data_PI_with_threat_data$pbd_a[i] <- avgeraged_threats$p_birds_avg
nest_data_PI_with_threat_data$gul_a[i] <- avgeraged_threats$gulls_avg
nest_data_PI_with_threat_data$hum_m[i] <- avgeraged_threats$hum_max
nest_data_PI_with_threat_data$veh_m[i] <- avgeraged_threats$vehicles_max
nest_data_PI_with_threat_data$dog_m[i] <- avgeraged_threats$dogs_max
nest_data_PI_with_threat_data$don_m[i] <- avgeraged_threats$dogs_on_max
nest_data_PI_with_threat_data$dof_m[i] <- avgeraged_threats$dogs_off_max
nest_data_PI_with_threat_data$hof_m[i] <- avgeraged_threats$hoof_max
nest_data_PI_with_threat_data$pbd_m[i] <- avgeraged_threats$p_birds_max
nest_data_PI_with_threat_data$gul_m[i] <- avgeraged_threats$gulls_max
nest_data_PI_with_threat_data$hum_b[i] <- avgeraged_threats$hum_bi
nest_data_PI_with_threat_data$veh_b[i] <- avgeraged_threats$vehicle_bi
nest_data_PI_with_threat_data$dog_b[i] <- avgeraged_threats$dogs_bi
nest_data_PI_with_threat_data$don_b[i] <- avgeraged_threats$dogs_on_bi
nest_data_PI_with_threat_data$dof_b[i] <- avgeraged_threats$dogs_off_bi
nest_data_PI_with_threat_data$pbd_b[i] <- avgeraged_threats$p_birds_bi
nest_data_PI_with_threat_data$hof_b[i] <- avgeraged_threats$hoof_bi
nest_data_PI_with_threat_data$gul_b[i] <- avgeraged_threats$gulls_bi
nest_data_PI_with_threat_data$hum_p[i] <- avgeraged_threats$hum_pr
nest_data_PI_with_threat_data$veh_p[i] <- avgeraged_threats$vehicle_pr
nest_data_PI_with_threat_data$dog_p[i] <- avgeraged_threats$dog_pr
nest_data_PI_with_threat_data$hof_p[i] <- avgeraged_threats$hoof_pr
nest_data_PI_with_threat_data$fox_p[i] <- avgeraged_threats$fox_pr
nest_data_PI_with_threat_data$n_surveys[i] <- avgeraged_threats$n_surveys
nest_data_PI_with_threat_data$days_active[i] <- avgeraged_threats$days_active
nest_data_PI_with_threat_data$halfway[i] <- avgeraged_threats$halfway
nest_data_PI_with_threat_data$uncertain_days[i] <- avgeraged_threats$uncertain_days
}else{
nest_data_PI_with_threat_data$hum_a[i] <- NA
nest_data_PI_with_threat_data$veh_a[i] <- NA
nest_data_PI_with_threat_data$dog_a[i] <- NA
nest_data_PI_with_threat_data$don_a[i] <- NA
nest_data_PI_with_threat_data$dof_a[i] <- NA
nest_data_PI_with_threat_data$hof_a[i] <- NA
nest_data_PI_with_threat_data$pbd_a[i] <- NA
nest_data_PI_with_threat_data$gul_a[i] <- NA
nest_data_PI_with_threat_data$hum_m[i] <- NA
nest_data_PI_with_threat_data$veh_m[i] <- NA
nest_data_PI_with_threat_data$dog_m[i] <- NA
nest_data_PI_with_threat_data$don_m[i] <- NA
nest_data_PI_with_threat_data$dof_m[i] <- NA
nest_data_PI_with_threat_data$hof_m[i] <- NA
nest_data_PI_with_threat_data$pbd_m[i] <- NA
nest_data_PI_with_threat_data$gul_m[i] <- NA
nest_data_PI_with_threat_data$hum_b[i] <- NA
nest_data_PI_with_threat_data$veh_b[i] <- NA
nest_data_PI_with_threat_data$dog_b[i] <- NA
nest_data_PI_with_threat_data$don_b[i] <- NA
nest_data_PI_with_threat_data$dof_b[i] <- NA
nest_data_PI_with_threat_data$pbd_b[i] <- NA
nest_data_PI_with_threat_data$gul_b[i] <- NA
nest_data_PI_with_threat_data$hof_b[i] <- NA
nest_data_PI_with_threat_data$hum_p[i] <- NA
nest_data_PI_with_threat_data$veh_p[i] <- NA
nest_data_PI_with_threat_data$dog_p[i] <- NA
nest_data_PI_with_threat_data$hof_p[i] <- NA
nest_data_PI_with_threat_data$fox_p[i] <- NA
nest_data_PI_with_threat_data$n_surveys[i] <- 0
nest_data_PI_with_threat_data$days_active[i] <- days_active
nest_data_PI_with_threat_data$halfway[i] <- halfway
nest_data_PI_with_threat_data$uncertain_days[i] <- uncertain_days
nest_data_PI_with_threat_data$fundays[i] <- NA
}
}
saveRDS(nest_data_PI_with_threat_data, file = "output/nest_data_PI_with_threat_data.rds")remove outlier data and those with very infrequent threat surveys (cut off: visited at least once per week)
Code
##### remove outlier data and those with very infrequent threat surveys
nest_data_with_threat_data <-
bind_rows(nest_data_FP_with_threat_data,
nest_data_MP_with_threat_data,
nest_data_BSC_with_threat_data,
nest_data_PI_with_threat_data,
nest_data_BC_with_threat_data) %>%
filter(n_surveys > 0) %>%
# mutate(dof_b = ifelse(dof_b == 1, "Y", "N")) %>%
# dplyr::select(-fox_p) %>%
mutate_at(vars(hum_b, veh_b, dog_b, don_b, dof_b, pbd_b, gul_b, hof_b),
~ as.factor(.)) %>%
mutate_at(vars(hum_p, veh_p, dog_p, hof_p, fox_p),
~ ifelse(is.na(.), 0, .))
# nest_data_with_threat_data %>% filter(fundays > 10) %>% dplyr::select(fundays) %>% arrange(desc(fundays))
# nest_data_with_threat_data %>%
# filter()
# nest_data_with_threat_data %>%
# # filter(fundays <= 25) %>%
# ggplot() +
# geom_histogram(aes(fundays)) +
# # geom_vline(xintercept = log(10), color = "red") +
# luke_theme
nest_data_with_threat_data %>%
ggplot() +
geom_histogram(aes(halfway/n_surveys), binwidth = 1) +
geom_vline(xintercept = 7, color = "red") +
luke_theme
Code
nest_data_with_threat_data %>%
ggplot() +
geom_histogram(aes(fundays)) +
geom_vline(xintercept = 7, color = "red") +
luke_theme
Code
nest_data_with_threat_data_7d <-
nest_data_with_threat_data %>%
filter(halfway/n_surveys <= 7)
# nest_data_with_threat_data
#### check variable distributions and collinearity ----
# determine the 99% quantile limit for each threat (i.e., to remove outlier data)
threat_data_99_ql_ <-
nest_data_with_threat_data_7d %>%
summarise_at(c("hum_a", "pbd_a", "gul_a", "veh_a", "dog_a", "don_a", "dof_a",
"hum_m", "pbd_m", "gul_m", "veh_m", "dog_m", "don_m", "dof_m",
"hum_p", "veh_p", "dog_p", "hof_p", "fox_p"),
~ quantile(.x, probs = c(0.99)))
nest_data_with_threat_data_7d %>%
filter(hum_a <= ceiling(as.numeric(threat_data_99_ql_$hum_a[1]))) %>%
ggplot() +
geom_histogram(aes(hum_a), binwidth = 5) +
luke_theme +
xlab("average number of humans counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(veh_a <= ceiling(as.numeric(threat_data_99_ql_$veh_a[1]))) %>%
ggplot() +
geom_histogram(aes(veh_a), binwidth = 1) +
luke_theme +
xlab("average number of vehicles counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dog_a <= ceiling(as.numeric(threat_data_99_ql_$dog_a[1]))) %>%
ggplot() +
geom_histogram(aes(dog_a), binwidth = 1) +
luke_theme +
xlab("average number of dogs counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(don_a <= ceiling(as.numeric(threat_data_99_ql_$don_a[1]))) %>%
ggplot() +
geom_histogram(aes(don_a), binwidth = 1) +
luke_theme +
xlab("average number of dogs on leashes counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dof_a <= ceiling(as.numeric(threat_data_99_ql_$dof_a[1]))) %>%
ggplot() +
geom_histogram(aes(dof_a), binwidth = 1) +
luke_theme +
xlab("average number of dogs off leashes counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dof_a <= ceiling(as.numeric(threat_data_99_ql_$pbd_a[1]))) %>%
ggplot() +
geom_histogram(aes(pbd_a), binwidth = 1) +
luke_theme +
xlab("average number of corvids counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dof_a <= ceiling(as.numeric(threat_data_99_ql_$gul_a[1]))) %>%
ggplot() +
geom_histogram(aes(gul_a), binwidth = 1) +
luke_theme +
xlab("average number of gulls counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(hum_m <= ceiling(as.numeric(threat_data_99_ql_$hum_m[1]))) %>%
ggplot() +
geom_histogram(aes(hum_m), binwidth = 1) +
luke_theme +
xlab("maximum number of humans counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(veh_m <= ceiling(as.numeric(threat_data_99_ql_$veh_m[1]))) %>%
ggplot() +
geom_histogram(aes(veh_m), binwidth = 1) +
luke_theme +
xlab("maximum number of vehicles counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dog_m <= ceiling(as.numeric(threat_data_99_ql_$dog_m[1]))) %>%
ggplot() +
geom_histogram(aes(dog_m), binwidth = 1) +
luke_theme +
xlab("maximum number of dogs counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(don_m <= ceiling(as.numeric(threat_data_99_ql_$don_m[1]))) %>%
ggplot() +
geom_histogram(aes(don_m), binwidth = 1) +
luke_theme +
xlab("maximum number of dogs on leashes counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dof_m <= ceiling(as.numeric(threat_data_99_ql_$dof_m[1]))) %>%
ggplot() +
geom_histogram(aes(dof_m), binwidth = 1) +
luke_theme +
xlab("maximum number of dogs off leashes counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dof_a <= ceiling(as.numeric(threat_data_99_ql_$pbd_m[1]))) %>%
ggplot() +
geom_histogram(aes(pbd_m), binwidth = 1) +
luke_theme +
xlab("maximum number of corvids counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dof_a <= ceiling(as.numeric(threat_data_99_ql_$gul_m[1]))) %>%
ggplot() +
geom_histogram(aes(gul_m), binwidth = 1) +
luke_theme +
xlab("maximum number of gulls counted in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(hum_p <= ceiling(as.numeric(threat_data_99_ql_$hum_p[1]))) %>%
ggplot() +
geom_histogram(aes(hum_p), binwidth = 1) +
luke_theme +
xlab("maximum human print level detected in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(veh_p < ceiling(as.numeric(threat_data_99_ql_$veh_p[1]))) %>%
ggplot() +
geom_histogram(aes(veh_p), binwidth = 1) +
luke_theme +
xlab("maximum vehicle print level detected in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(dog_p <= ceiling(as.numeric(threat_data_99_ql_$dog_p[1]))) %>%
ggplot() +
geom_histogram(aes(dog_p), binwidth = 1) +
luke_theme +
xlab("maximum dog print level detected in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(hof_p <= ceiling(as.numeric(threat_data_99_ql_$hof_p[1]))) %>%
ggplot() +
geom_histogram(aes(hof_p), binwidth = 1) +
luke_theme +
xlab("maximum hooved print level detected in territory during active nest")
Code
nest_data_with_threat_data_7d %>%
filter(fox_p <= ceiling(as.numeric(threat_data_99_ql_$fox_p[1]))) %>%
ggplot() +
geom_histogram(aes(fox_p), binwidth = 1) +
luke_theme +
xlab("maximum fox print level detected in territory during active nest")
Code
nest_data_with_threat_data_7d_ <-
nest_data_with_threat_data_7d %>%
filter(fox_p <= ceiling(as.numeric(threat_data_99_ql_$fox_p[1]))) %>%
filter(hum_a <= ceiling(as.numeric(threat_data_99_ql_$hum_a[1]))) %>%
filter(veh_a <= ceiling(as.numeric(threat_data_99_ql_$veh_a[1]))) %>%
filter(dog_a <= ceiling(as.numeric(threat_data_99_ql_$dog_a[1]))) %>%
filter(don_a <= ceiling(as.numeric(threat_data_99_ql_$don_a[1]))) %>%
filter(dof_a <= ceiling(as.numeric(threat_data_99_ql_$dof_a[1]))) %>%
filter(pbd_a <= ceiling(as.numeric(threat_data_99_ql_$pbd_a[1]))) %>%
filter(gul_a <= ceiling(as.numeric(threat_data_99_ql_$gul_a[1]))) %>%
filter(hum_m <= ceiling(as.numeric(threat_data_99_ql_$hum_m[1]))) %>%
filter(veh_m <= ceiling(as.numeric(threat_data_99_ql_$veh_m[1]))) %>%
filter(dog_m <= ceiling(as.numeric(threat_data_99_ql_$dog_m[1]))) %>%
filter(don_m <= ceiling(as.numeric(threat_data_99_ql_$don_m[1]))) %>%
filter(dof_m <= ceiling(as.numeric(threat_data_99_ql_$dof_m[1]))) %>%
filter(pbd_m <= ceiling(as.numeric(threat_data_99_ql_$pbd_m[1]))) %>%
filter(gul_m <= ceiling(as.numeric(threat_data_99_ql_$gul_m[1]))) %>%
filter(hum_p <= ceiling(as.numeric(threat_data_99_ql_$hum_p[1]))) %>%
filter(veh_p <= ceiling(as.numeric(threat_data_99_ql_$veh_p[1]))) %>%
filter(dog_p <= ceiling(as.numeric(threat_data_99_ql_$dog_p[1]))) %>%
filter(hof_p <= ceiling(as.numeric(threat_data_99_ql_$hof_p[1]))) %>%
mutate(fox_p = ifelse(is.infinite(fox_p), 0, fox_p) %>% as.factor())Code
write.csv(nest_data_with_threat_data_7d_, "data/nest_data_with_threat_data_7d_01102025.csv")Sample size summary
Number of nests
Code
nest_data_with_threat_data_7d_ %>%
summarise(n_obs = n())# A tibble: 1 × 1
n_obs
<int>
1 2956Number of nests at each region
Code
nest_data_with_threat_data_7d_ %>%
group_by(region) %>%
summarise(n_obs = n())# A tibble: 5 × 2
region n_obs
<chr> <int>
1 BC 604
2 BSC 453
3 FP 666
4 MP 917
5 PI 316Number of nests at each region for each year
Code
nest_data_with_threat_data_7d_ %>%
group_by(region, season) %>%
summarise(n_obs = n()) %>%
pivot_wider(names_from = region, values_from = n_obs) %>%
mutate(season = as.numeric(as.character(season))) %>%
arrange(season)# A tibble: 19 × 6
season BC BSC FP MP PI
<dbl> <int> <int> <int> <int> <int>
1 200607 11 9 NA 14 NA
2 200708 9 9 NA 19 NA
3 200809 5 7 NA 9 NA
4 200910 1 7 6 15 NA
5 201011 3 13 21 22 NA
6 201112 8 27 10 40 NA
7 201213 5 17 12 45 14
8 201314 14 10 19 45 6
9 201415 29 15 23 59 17
10 201516 51 22 27 72 21
11 201617 49 43 42 87 29
12 201718 60 38 45 64 32
13 201819 43 33 60 59 33
14 201920 38 42 68 55 34
15 202021 57 48 75 75 38
16 202122 42 29 72 85 24
17 202223 60 27 82 76 28
18 202324 57 31 104 76 15
19 202425 62 26 NA NA 25Number of nests at each region in each management level
Code
nest_data_with_threat_data_7d_ %>%
group_by(region, level) %>%
summarise(n_obs = n()) %>%
pivot_wider(names_from = region, values_from = n_obs) # A tibble: 5 × 6
level BC BSC FP MP PI
<fct> <int> <int> <int> <int> <int>
1 L0 162 24 136 169 46
2 L1 37 38 50 70 14
3 L2 19 73 22 84 8
4 L3 386 314 444 591 246
5 L4 NA 4 14 3 2Number of nests at each region in each management status
Code
nest_data_with_threat_data_7d_ %>%
group_by(region, status) %>%
summarise(n_obs = n()) %>%
pivot_wider(names_from = region, values_from = n_obs) # A tibble: 2 × 6
status BC BSC FP MP PI
<fct> <int> <int> <int> <int> <int>
1 N 162 60 155 230 46
2 Y 442 393 511 687 270Nest survival modeling
Prepare data for RMark
Code
nest_data_with_threat_data_7d_ <-
read.csv("data/nest_data_with_threat_data_7d_01102025.csv") %>%
mutate(season = as.factor(season),
nest_hab = as.factor(nest_hab),
status = as.factor(status),
level = as.factor(level))Code
occ_FP <-
nest_data_with_threat_data_7d_ %>%
filter(region == "FP") %>%
pull(LastChecked) %>%
max(., na.rm = TRUE)
occ_MP <-
nest_data_with_threat_data_7d_ %>%
filter(region == "MP") %>%
pull(LastChecked) %>%
max(., na.rm = TRUE)
occ_BSC <-
nest_data_with_threat_data_7d_ %>%
filter(region == "BSC") %>%
pull(LastChecked) %>%
max(., na.rm = TRUE)
occ_BC <-
nest_data_with_threat_data_7d_ %>%
filter(region == "BC") %>%
pull(LastChecked) %>%
max(., na.rm = TRUE)
occ_PI <-
nest_data_with_threat_data_7d_ %>%
filter(region == "PI") %>%
pull(LastChecked) %>%
max(., na.rm = TRUE)
# create processed RMARK data format as NestSurvival with Year as group
nest_data.processed_FP_5d <-
RMark::process.data(nest_data_with_threat_data_7d_ %>%
filter(region == "FP"),
model = "Nest",
nocc = occ_FP, groups = c("season",
"nest_hab",
"status",
# "site"
# "fox_p",
"level"))
nest_data.processed_MP_5d <-
RMark::process.data(nest_data_with_threat_data_7d_ %>%
filter(region == "MP"),
model = "Nest",
nocc = occ_MP, groups = c("season",
"nest_hab",
"status",
# "site",
# "fox_p",
"level"))
nest_data.processed_BC_5d <-
RMark::process.data(nest_data_with_threat_data_7d_ %>%
filter(region == "BC"),
model = "Nest",
nocc = occ_BC, groups = c("season",
"nest_hab",
"status",
# "site",
# "fox_p",
"level"))
nest_data.processed_BSC_5d <-
RMark::process.data(nest_data_with_threat_data_7d_ %>%
filter(region == "BSC"),
model = "Nest",
nocc = occ_BSC, groups = c("season",
"nest_hab",
"status",
# "site",
# "fox_p",
"level"))
nest_data.processed_PI_5d <-
RMark::process.data(nest_data_with_threat_data_7d_ %>%
filter(region == "PI"),
model = "Nest",
nocc = occ_PI, groups = c("season",
"nest_hab",
"status",
# "site",
# "fox_p",
"level"))
# create the design data
nest_fate.ddl_FP_5d <- RMark::make.design.data(nest_data.processed_FP_5d)
nest_fate.ddl_MP_5d <- RMark::make.design.data(nest_data.processed_MP_5d)
nest_fate.ddl_BSC_5d <- RMark::make.design.data(nest_data.processed_BSC_5d)
nest_fate.ddl_BC_5d <- RMark::make.design.data(nest_data.processed_BC_5d)
nest_fate.ddl_PI_5d <- RMark::make.design.data(nest_data.processed_PI_5d)
# add a new variable to the design data that is the quadratic transformation of
# time
time <- c(0:(occ_FP-1))
Cubic <- time^3
Quadratic <- time^2
quad_time <- data.frame(time, Quadratic, Cubic)
quad_time$time <- c(1:occ_FP)
nest_fate.ddl_FP_5d$S <-
RMark::merge_design.covariates(nest_fate.ddl_FP_5d$S, quad_time,
bygroup = FALSE, bytime = TRUE)
time <- c(0:(occ_MP-1))
Cubic <- time^3
Quadratic <- time^2
quad_time <- data.frame(time, Quadratic, Cubic)
quad_time$time <- c(1:occ_MP)
nest_fate.ddl_MP_5d$S <-
RMark::merge_design.covariates(nest_fate.ddl_MP_5d$S, quad_time,
bygroup = FALSE, bytime = TRUE)
time <- c(0:(occ_BC-1))
Cubic <- time^3
Quadratic <- time^2
quad_time <- data.frame(time, Quadratic, Cubic)
quad_time$time <- c(1:occ_BC)
nest_fate.ddl_BC_5d$S <-
RMark::merge_design.covariates(nest_fate.ddl_BC_5d$S, quad_time,
bygroup = FALSE, bytime = TRUE)
time <- c(0:(occ_BSC-1))
Cubic <- time^3
Quadratic <- time^2
quad_time <- data.frame(time, Quadratic, Cubic)
quad_time$time <- c(1:occ_BSC)
nest_fate.ddl_BSC_5d$S <-
RMark::merge_design.covariates(nest_fate.ddl_BSC_5d$S, quad_time,
bygroup = FALSE, bytime = TRUE)
time <- c(0:(occ_PI-1))
Cubic <- time^3
Quadratic <- time^2
quad_time <- data.frame(time, Quadratic, Cubic)
quad_time$time <- c(1:occ_PI)
nest_fate.ddl_PI_5d$S <-
RMark::merge_design.covariates(nest_fate.ddl_PI_5d$S, quad_time,
bygroup = FALSE, bytime = TRUE)
# nest_fate.ddl$S <-
# RMark::merge_design.covariates(nest_fate.ddl$S, data.frame(management_level = c(0, 1, 2, 3, 4)),
# bygroup = FALSE, bytime = FALSE)
# nest_fate.ddl$S <-
# inner_join(nest_fate.ddl$S, int_threat_data, by = c("site", "time"))
RMark_data_FP <-
list(nest_data.processed = nest_data.processed_FP_5d,
nest_fate.ddl = nest_fate.ddl_FP_5d)
RMark_data_MP <-
list(nest_data.processed = nest_data.processed_MP_5d,
nest_fate.ddl = nest_fate.ddl_MP_5d)
RMark_data_BC <-
list(nest_data.processed = nest_data.processed_BC_5d,
nest_fate.ddl = nest_fate.ddl_BC_5d)
RMark_data_BSC <-
list(nest_data.processed = nest_data.processed_BSC_5d,
nest_fate.ddl = nest_fate.ddl_BSC_5d)
RMark_data_PI <-
list(nest_data.processed = nest_data.processed_PI_5d,
nest_fate.ddl = nest_fate.ddl_PI_5d)
# RMark_data_FP$nest_data.processed$data %>% summary()
# RMark_data_MP$nest_data.processed$data %>% summary()
# RMark_data_BC$nest_data.processed$data %>% summary()
# RMark_data_BSC$nest_data.processed$data %>% summary()
# RMark_data_PI$nest_data.processed$data %>% summary()Fleurieu Peninsula model selection
Code
nest_survival_FP <- function()
{
# Specify models to test
# constant daily survival rate (DSR)
S.dot <-
list(formula = ~1)
# fox print status
# S.fox_p <-
# list(formula = ~fox_p)
#### maximum counts of threats
# max humans detected
S.hum_m <-
list(formula = ~hum_m)
# max vehicles detected
S.veh_m <-
list(formula = ~veh_m)
# max dogs detected
S.dog_m <-
list(formula = ~dog_m)
# max dogs off leash detected
S.dof_m <-
list(formula = ~dof_m)
# max corvids detected
S.pbd_m <-
list(formula = ~pbd_m)
# max gulls detected
S.gul_m <-
list(formula = ~gul_m)
#### interaction of max threat counts and management status
# max humans detected
S.hum_m_x_status <-
list(formula = ~hum_m * status)
# max vehicles detected and management status
S.veh_m_x_status <-
list(formula = ~veh_m * status)
# max dogs detected and management status
S.dog_m_x_status <-
list(formula = ~dog_m * status)
# max dogs off leash detected and management status
S.dof_m_x_status <-
list(formula = ~dof_m * status)
# max corvids detected and management status
S.pbd_m_x_status <-
list(formula = ~pbd_m * status)
# max gulls detected and management status
S.gul_m_x_status <-
list(formula = ~gul_m * status)
# specify to run as a nest survival model in program MARK
cml <- RMark::create.model.list("Nest")
# run model list in MARK. Supress generation of MARK files.
model.list <- RMark::mark.wrapper(cml,
data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
threads = 4,
brief = TRUE,
delete = TRUE)
# store completed model list
return(model.list)
}
nest_survival_run_FP <- nest_survival_FP()
nest_survival_run_FP
nest_survival_FP <-
list(RMark_data = RMark_data_FP,
model_selection = nest_survival_run_FP)
saveRDS(nest_survival_FP, file = "output/nest_survival_FP_threats&status_011025.rds")Mornington Peninsula model selection
Code
nest_survival_MP <- function()
{
# Specify models to test
# constant daily survival rate (DSR)
S.dot <-
list(formula = ~1)
# fox print status
# S.fox_p <-
# list(formula = ~fox_p)
#### maximum counts of threats
# max humans detected
S.hum_m <-
list(formula = ~hum_m)
# max vehicles detected
# S.veh_m <-
# list(formula = ~veh_m)
# max dogs detected
S.dog_m <-
list(formula = ~dog_m)
# max dogs off leash detected
S.dof_m <-
list(formula = ~dof_m)
# max corvids detected
S.pbd_m <-
list(formula = ~pbd_m)
# max gulls detected
S.gul_m <-
list(formula = ~gul_m)
#### interaction of max threat counts and management status
# max humans detected
S.hum_m_x_status <-
list(formula = ~hum_m * status)
# max vehicles detected and management status
# S.veh_m_x_status <-
# list(formula = ~veh_m * status)
# max dogs detected and management status
S.dog_m_x_status <-
list(formula = ~dog_m * status)
# max dogs off leash detected and management status
S.dof_m_x_status <-
list(formula = ~dof_m * status)
# max corvids detected and management status
S.pbd_m_x_status <-
list(formula = ~pbd_m * status)
# max gulls detected and management status
S.gul_m_x_status <-
list(formula = ~gul_m * status)
# specify to run as a nest survival model in program MARK
cml <- RMark::create.model.list("Nest")
# run model list in MARK. Supress generation of MARK files.
model.list <- RMark::mark.wrapper(cml,
data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
threads = 4,
brief = TRUE,
delete = TRUE)
# store completed model list
return(model.list)
}
nest_survival_run_MP <- nest_survival_MP()
nest_survival_run_MP
nest_survival_MP <-
list(RMark_data = RMark_data_MP,
model_selection = nest_survival_run_MP)
saveRDS(nest_survival_MP, file = "output/nest_survival_MP_threats&status_011025.rds")Bellarine / Surf Coast model selection
Code
nest_survival_BSC <- function()
{
# Specify models to test
# constant daily survival rate (DSR)
S.dot <-
list(formula = ~1)
# fox print status
# S.fox_p <-
# list(formula = ~fox_p)
#### maximum counts of threats
# max humans detected
S.hum_m <-
list(formula = ~hum_m)
# max vehicles detected
# S.veh_m <-
# list(formula = ~veh_m)
# max dogs detected
S.dog_m <-
list(formula = ~dog_m)
# max dogs off leash detected
S.dof_m <-
list(formula = ~dof_m)
# max corvids detected
S.pbd_m <-
list(formula = ~pbd_m)
# max gulls detected
S.gul_m <-
list(formula = ~gul_m)
#### interaction of max threat counts and management status
# max humans detected
S.hum_m_x_status <-
list(formula = ~hum_m * status)
# max vehicles detected and management status
# S.veh_m_x_status <-
# list(formula = ~veh_m * status)
# max dogs detected and management status
S.dog_m_x_status <-
list(formula = ~dog_m * status)
# max dogs off leash detected and management status
S.dof_m_x_status <-
list(formula = ~dof_m * status)
# max corvids detected and management status
S.pbd_m_x_status <-
list(formula = ~pbd_m * status)
# max gulls detected and management status
S.gul_m_x_status <-
list(formula = ~gul_m * status)
# specify to run as a nest survival model in program MARK
cml <- RMark::create.model.list("Nest")
# run model list in MARK. Supress generation of MARK files.
model.list <- RMark::mark.wrapper(cml,
data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
threads = 4,
brief = TRUE,
delete = TRUE)
# store completed model list
return(model.list)
}
nest_survival_run_BSC <- nest_survival_BSC()
nest_survival_run_BSC
nest_survival_BSC <-
list(RMark_data = RMark_data_BSC,
model_selection = nest_survival_run_BSC)
saveRDS(nest_survival_BSC, file = "output/nest_survival_BSC_threats&status_011025.rds")Bass Coast model selection
Code
nest_survival_BC <- function()
{
# Specify models to test
# constant daily survival rate (DSR)
S.dot <-
list(formula = ~1)
# fox print status
# S.fox_p <-
# list(formula = ~fox_p)
#### maximum counts of threats
# max humans detected
S.hum_m <-
list(formula = ~hum_m)
# max vehicles detected
# S.veh_m <-
# list(formula = ~veh_m)
# max dogs detected
S.dog_m <-
list(formula = ~dog_m)
# max dogs off leash detected
S.dof_m <-
list(formula = ~dof_m)
# max corvids detected
S.pbd_m <-
list(formula = ~pbd_m)
# max gulls detected
S.gul_m <-
list(formula = ~gul_m)
#### interaction of max threat counts and management status
# max humans detected
S.hum_m_x_status <-
list(formula = ~hum_m * status)
# max vehicles detected and management status
# S.veh_m_x_status <-
# list(formula = ~veh_m * status)
# max dogs detected and management status
S.dog_m_x_status <-
list(formula = ~dog_m * status)
# max dogs off leash detected and management status
S.dof_m_x_status <-
list(formula = ~dof_m * status)
# max corvids detected and management status
S.pbd_m_x_status <-
list(formula = ~pbd_m * status)
# max gulls detected and management status
S.gul_m_x_status <-
list(formula = ~gul_m * status)
# specify to run as a nest survival model in program MARK
cml <- RMark::create.model.list("Nest")
# run model list in MARK. Supress generation of MARK files.
model.list <- RMark::mark.wrapper(cml,
data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
threads = 4,
brief = TRUE,
delete = TRUE)
# store completed model list
return(model.list)
}
nest_survival_run_BC <- nest_survival_BC()
nest_survival_run_BC
nest_survival_BC <-
list(RMark_data = RMark_data_BC,
model_selection = nest_survival_run_BC)
saveRDS(nest_survival_BC, file = "output/nest_survival_BC_threats&status_011025.rds")Phillip Island model selection
Code
nest_survival_PI <- function()
{
# Specify models to test
# constant daily survival rate (DSR)
S.dot <-
list(formula = ~1)
# fox print status
# S.fox_p <-
# list(formula = ~fox_p)
#### maximum counts of threats
# max humans detected
S.hum_m <-
list(formula = ~hum_m)
# max vehicles detected
# S.veh_m <-
# list(formula = ~veh_m)
# max dogs detected
S.dog_m <-
list(formula = ~dog_m)
# max dogs off leash detected
S.dof_m <-
list(formula = ~dof_m)
# max corvids detected
S.pbd_m <-
list(formula = ~pbd_m)
# max gulls detected
S.gul_m <-
list(formula = ~gul_m)
#### interaction of max threat counts and management status
# max humans detected
S.hum_m_x_status <-
list(formula = ~hum_m * status)
# max vehicles detected and management status
# S.veh_m_x_status <-
# list(formula = ~veh_m * status)
# max dogs detected and management status
S.dog_m_x_status <-
list(formula = ~dog_m * status)
# max dogs off leash detected and management status
S.dof_m_x_status <-
list(formula = ~dof_m * status)
# max corvids detected and management status
S.pbd_m_x_status <-
list(formula = ~pbd_m * status)
# max gulls detected and management status
S.gul_m_x_status <-
list(formula = ~gul_m * status)
# specify to run as a nest survival model in program MARK
cml <- RMark::create.model.list("Nest")
# run model list in MARK. Supress generation of MARK files.
model.list <- RMark::mark.wrapper(cml,
data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
threads = 4,
brief = TRUE,
delete = TRUE)
# store completed model list
return(model.list)
}
nest_survival_run_PI <- nest_survival_PI()
nest_survival_run_PI
nest_survival_PI <-
list(RMark_data = RMark_data_PI,
model_selection = nest_survival_run_PI)
saveRDS(nest_survival_PI, file = "output/nest_survival_PI_threats&status_011025.rds")Results
model selection results
Code
nest_survival_FP <- readRDS(file = "output/nest_survival_FP_threats&status_011025.rds")
nest_survival_run_FP <- nest_survival_FP$model_selection
nest_survival_run_FP$model.table %>%
dplyr::select(model, npar, DeltaAICc, weight) %>%
gt() %>%
cols_label(model = html("<i>Fleurieu Peninsula</i>"),
npar = "K",
DeltaAICc = "DeltaAICc",
weight = "weight") %>%
fmt_number(columns = DeltaAICc,
rows = 1:10,
decimals = 2,
use_seps = FALSE) %>%
fmt_number(columns = weight,
rows = 1:10,
decimals = 3,
use_seps = FALSE) %>%
cols_align(align = "left",
columns = vars(model)) %>%
tab_options(row_group.font.weight = "bold",
row_group.background.color = brewer.pal(9,"Greys")[3],
table.font.size = 12,
data_row.padding = 3,
row_group.padding = 4,
summary_row.padding = 2,
column_labels.font.size = 14,
row_group.font.size = 12,
table.width = pct(80))| Fleurieu Peninsula | K | DeltaAICc | weight |
|---|---|---|---|
| S(~pbd_m * status) | 4 | 0.00 | 0.823 |
| S(~gul_m * status) | 4 | 3.11 | 0.174 |
| S(~dof_m * status) | 4 | 12.00 | 0.002 |
| S(~dog_m * status) | 4 | 13.16 | 0.001 |
| S(~veh_m * status) | 4 | 17.65 | 0.000 |
| S(~hum_m * status) | 4 | 17.81 | 0.000 |
| S(~pbd_m) | 2 | 86.78 | 0.000 |
| S(~gul_m) | 2 | 116.36 | 0.000 |
| S(~1) | 1 | 116.96 | 0.000 |
| S(~dog_m) | 2 | 117.50 | 0.000 |
| S(~dof_m) | 2 | 118.3299 | 0 |
| S(~veh_m) | 2 | 118.7142 | 0 |
| S(~hum_m) | 2 | 118.8322 | 0 |
Code
nest_survival_MP <- readRDS(file = "output/nest_survival_MP_threats&status_011025.rds")
nest_survival_run_MP <- nest_survival_MP$model_selection
nest_survival_run_MP$model.table %>%
dplyr::select(model, npar, DeltaAICc, weight) %>%
gt() %>%
cols_label(model = html("<i>Mornington Peninsula</i>"),
npar = "K",
DeltaAICc = "DeltaAICc",
weight = "weight") %>%
fmt_number(columns = DeltaAICc,
rows = 1:10,
decimals = 2,
use_seps = FALSE) %>%
fmt_number(columns = weight,
rows = 1:10,
decimals = 3,
use_seps = FALSE) %>%
cols_align(align = "left",
columns = vars(model)) %>%
tab_options(row_group.font.weight = "bold",
row_group.background.color = brewer.pal(9,"Greys")[3],
table.font.size = 12,
data_row.padding = 3,
row_group.padding = 4,
summary_row.padding = 2,
column_labels.font.size = 14,
row_group.font.size = 12,
table.width = pct(80))| Mornington Peninsula | K | DeltaAICc | weight |
|---|---|---|---|
| S(~gul_m * status) | 4 | 0.00 | 0.967 |
| S(~pbd_m * status) | 4 | 9.01 | 0.011 |
| S(~hum_m * status) | 4 | 9.06 | 0.010 |
| S(~dof_m * status) | 4 | 9.80 | 0.007 |
| S(~dog_m * status) | 4 | 10.76 | 0.004 |
| S(~gul_m) | 2 | 21.55 | 0.000 |
| S(~pbd_m) | 2 | 29.14 | 0.000 |
| S(~hum_m) | 2 | 30.75 | 0.000 |
| S(~1) | 1 | 31.15 | 0.000 |
| S(~dof_m) | 2 | 31.52 | 0.000 |
| S(~dog_m) | 2 | 31.6885 | 1.271944e-07 |
Code
nest_survival_BSC <- readRDS(file = "output/nest_survival_BSC_threats&status_011025.rds")
nest_survival_run_BSC <- nest_survival_BSC$model_selection
nest_survival_run_BSC$model.table %>%
dplyr::select(model, npar, DeltaAICc, weight) %>%
gt() %>%
cols_label(model = html("<i>Bellarine / Surf Coast</i>"),
npar = "K",
DeltaAICc = "DeltaAICc",
weight = "weight") %>%
fmt_number(columns = DeltaAICc,
rows = 1:10,
decimals = 2,
use_seps = FALSE) %>%
fmt_number(columns = weight,
rows = 1:10,
decimals = 3,
use_seps = FALSE) %>%
cols_align(align = "left",
columns = vars(model)) %>%
tab_options(row_group.font.weight = "bold",
row_group.background.color = brewer.pal(9,"Greys")[3],
table.font.size = 12,
data_row.padding = 3,
row_group.padding = 4,
summary_row.padding = 2,
column_labels.font.size = 14,
row_group.font.size = 12,
table.width = pct(80))| Bellarine / Surf Coast | K | DeltaAICc | weight |
|---|---|---|---|
| S(~dof_m * status) | 4 | 0.00 | 0.553 |
| S(~pbd_m * status) | 4 | 2.64 | 0.147 |
| S(~gul_m * status) | 4 | 3.15 | 0.115 |
| S(~dog_m * status) | 4 | 3.36 | 0.103 |
| S(~hum_m * status) | 4 | 3.83 | 0.081 |
| S(~1) | 1 | 40.49 | 0.000 |
| S(~dof_m) | 2 | 40.71 | 0.000 |
| S(~pbd_m) | 2 | 40.96 | 0.000 |
| S(~gul_m) | 2 | 41.21 | 0.000 |
| S(~dog_m) | 2 | 42.05 | 0.000 |
| S(~hum_m) | 2 | 42.47748 | 3.304073e-10 |
Code
nest_survival_BC <- readRDS(file = "output/nest_survival_BC_threats&status_011025.rds")
nest_survival_run_BC <- nest_survival_BC$model_selection
nest_survival_run_BC$model.table %>%
dplyr::select(model, npar, DeltaAICc, weight) %>%
gt() %>%
cols_label(model = html("<i>Bellarine / Surf Coast</i>"),
npar = "K",
DeltaAICc = "DeltaAICc",
weight = "weight") %>%
fmt_number(columns = DeltaAICc,
rows = 1:10,
decimals = 2,
use_seps = FALSE) %>%
fmt_number(columns = weight,
rows = 1:10,
decimals = 3,
use_seps = FALSE) %>%
cols_align(align = "left",
columns = vars(model)) %>%
tab_options(row_group.font.weight = "bold",
row_group.background.color = brewer.pal(9,"Greys")[3],
table.font.size = 12,
data_row.padding = 3,
row_group.padding = 4,
summary_row.padding = 2,
column_labels.font.size = 14,
row_group.font.size = 12,
table.width = pct(80))| Bellarine / Surf Coast | K | DeltaAICc | weight |
|---|---|---|---|
| S(~pbd_m * status) | 4 | 0.00 | 0.574 |
| S(~dog_m * status) | 4 | 1.59 | 0.259 |
| S(~dof_m * status) | 4 | 4.36 | 0.065 |
| S(~gul_m * status) | 4 | 4.87 | 0.050 |
| S(~hum_m * status) | 4 | 5.07 | 0.045 |
| S(~pbd_m) | 2 | 9.70 | 0.004 |
| S(~1) | 1 | 13.98 | 0.001 |
| S(~dog_m) | 2 | 14.56 | 0.000 |
| S(~gul_m) | 2 | 15.61 | 0.000 |
| S(~dof_m) | 2 | 15.80 | 0.000 |
| S(~hum_m) | 2 | 15.90069 | 0.0002023696 |
Code
nest_survival_PI <- readRDS(file = "output/nest_survival_PI_threats&status_011025.rds")
nest_survival_run_PI <- nest_survival_PI$model_selection
nest_survival_run_PI$model.table %>%
dplyr::select(model, npar, DeltaAICc, weight) %>%
gt() %>%
cols_label(model = html("<i>Phillip Island</i>"),
npar = "K",
DeltaAICc = "DeltaAICc",
weight = "weight") %>%
fmt_number(columns = DeltaAICc,
rows = 1:10,
decimals = 2,
use_seps = FALSE) %>%
fmt_number(columns = weight,
rows = 1:10,
decimals = 3,
use_seps = FALSE) %>%
cols_align(align = "left",
columns = vars(model)) %>%
tab_options(row_group.font.weight = "bold",
row_group.background.color = brewer.pal(9,"Greys")[3],
table.font.size = 12,
data_row.padding = 3,
row_group.padding = 4,
summary_row.padding = 2,
column_labels.font.size = 14,
row_group.font.size = 12,
table.width = pct(80))| Phillip Island | K | DeltaAICc | weight |
|---|---|---|---|
| S(~pbd_m * status) | 4 | 0.00 | 0.965 |
| S(~dog_m * status) | 4 | 6.98 | 0.029 |
| S(~dof_m * status) | 4 | 10.35 | 0.005 |
| S(~gul_m * status) | 4 | 16.53 | 0.000 |
| S(~hum_m * status) | 4 | 18.04 | 0.000 |
| S(~pbd_m) | 2 | 21.58 | 0.000 |
| S(~gul_m) | 2 | 43.55 | 0.000 |
| S(~1) | 1 | 43.71 | 0.000 |
| S(~dog_m) | 2 | 44.71 | 0.000 |
| S(~dof_m) | 2 | 44.82 | 0.000 |
| S(~hum_m) | 2 | 45.59224 | 1.213902e-10 |
Effect of weekends and holidays on nest survival
Code
S.fundays <-
mark(data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays) npar= 2 lnl = 2615.6245 AICc = 2619.6258Code
min.fundays = min(RMark_data_FP$nest_data.processed$data$fundays)
max.fundays = max(RMark_data_FP$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays, to = max.fundays, length = 100)
pred.fundays <-
covariate.predictions(model = S.fundays,
data = data.frame(fundays = fundays.values),
indices = 1)
pred.fundays_FP <-
pred.fundays$estimates %>%
mutate(region = "FP")
S.fundays <-
mark(data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays) npar= 2 lnl = 3120.103 AICc = 3124.104Code
min.fundays = min(RMark_data_MP$nest_data.processed$data$fundays)
max.fundays = max(RMark_data_MP$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays, to = max.fundays, length = 100)
pred.fundays <-
covariate.predictions(model = S.fundays,
data = data.frame(fundays = fundays.values),
indices = 1)
pred.fundays_MP <-
pred.fundays$estimates %>%
mutate(region = "MP")
S.fundays <-
mark(data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays) npar= 2 lnl = 1721.3286 AICc = 1725.3308Code
min.fundays = min(RMark_data_BC$nest_data.processed$data$fundays)
max.fundays = max(RMark_data_BC$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays, to = max.fundays, length = 100)
pred.fundays <-
covariate.predictions(model = S.fundays,
data = data.frame(fundays = fundays.values),
indices = 1)
pred.fundays_BC <-
pred.fundays$estimates %>%
mutate(region = "BC")
S.fundays <-
mark(data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays) npar= 2 lnl = 1614.6491 AICc = 1618.6514Code
min.fundays = min(RMark_data_BSC$nest_data.processed$data$fundays)
max.fundays = max(RMark_data_BSC$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays, to = max.fundays, length = 100)
pred.fundays <-
covariate.predictions(model = S.fundays,
data = data.frame(fundays = fundays.values),
indices = 1)
pred.fundays_BSC <-
pred.fundays$estimates %>%
mutate(region = "BSC")
S.fundays <-
mark(data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays) npar= 2 lnl = 1006.1515 AICc = 1010.1553Code
min.fundays = min(RMark_data_PI$nest_data.processed$data$fundays)
max.fundays = max(RMark_data_PI$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays, to = max.fundays, length = 100)
pred.fundays <-
covariate.predictions(model = S.fundays,
data = data.frame(fundays = fundays.values),
indices = 1)
pred.fundays_PI <-
pred.fundays$estimates %>%
mutate(region = "PI")
pred.fundays <-
bind_rows(pred.fundays_FP, pred.fundays_MP,
pred.fundays_BC, pred.fundays_BSC, pred.fundays_PI)
ggplot(pred.fundays,
aes(x = covdata, y = estimate, color = region, fill = region)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2") +
scale_fill_brewer(palette = "Dark2") +
scale_x_continuous(breaks = c(0, 5, 10, 15, 20, 25)) +
luke_theme +
theme(legend.position = "none",
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("number of weekend days and holidays exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Effect of management on mitigating the impact of weekends and holidays on nest survival
Code
S.fundays_status <-
mark(data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays + status)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays + status) npar= 3 lnl = 2546.4738 AICc = 2552.4764Code
min.fundays_status = min(RMark_data_FP$nest_data.processed$data$fundays)
max.fundays_status = max(RMark_data_FP$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays_status, to = max.fundays_status, length = 100)
pred.fundays_status <-
covariate.predictions(model = S.fundays_status,
data = data.frame(fundays = fundays.values),
indices = c(as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.fundays_status_FP <-
pred.fundays_status$estimates %>%
mutate(region = "FP",
status = ifelse(par.index == 1, "N", "Y"))
S.fundays_status <-
mark(data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays + status)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays + status) npar= 3 lnl = 3114.8422 AICc = 3120.8442Code
min.fundays_status = min(RMark_data_MP$nest_data.processed$data$fundays)
max.fundays_status = max(RMark_data_MP$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays_status, to = max.fundays_status, length = 100)
pred.fundays_status <-
covariate.predictions(model = S.fundays_status,
data = data.frame(fundays = fundays.values),
indices = c(as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.fundays_status_MP <-
pred.fundays_status$estimates %>%
mutate(region = "MP",
status = ifelse(par.index == 1, "N", "Y"))
S.fundays_status <-
mark(data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays + status)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays + status) npar= 3 lnl = 1714.4468 AICc = 1720.451Code
min.fundays_status = min(RMark_data_BC$nest_data.processed$data$fundays)
max.fundays_status = max(RMark_data_BC$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays_status, to = max.fundays_status, length = 100)
pred.fundays_status <-
covariate.predictions(model = S.fundays_status,
data = data.frame(fundays = fundays.values),
indices = c(as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.fundays_status_BC <-
pred.fundays_status$estimates %>%
mutate(region = "BC",
status = ifelse(par.index == 1, "N", "Y"))
S.fundays_status <-
mark(data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays + status)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays + status) npar= 3 lnl = 1573.2605 AICc = 1579.265Code
min.fundays_status = min(RMark_data_BSC$nest_data.processed$data$fundays)
max.fundays_status = max(RMark_data_BSC$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays_status, to = max.fundays_status, length = 100)
pred.fundays_status <-
covariate.predictions(model = S.fundays_status,
data = data.frame(fundays = fundays.values),
indices = c(as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.fundays_status_BSC <-
pred.fundays_status$estimates %>%
mutate(region = "BSC",
status = ifelse(par.index == 1, "N", "Y"))
S.fundays_status <-
mark(data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ fundays + status)),
brief = TRUE,
delete = TRUE)
Model: S(~fundays + status) npar= 3 lnl = 985.4834 AICc = 991.49111Code
min.fundays_status = min(RMark_data_PI$nest_data.processed$data$fundays)
max.fundays_status = max(RMark_data_PI$nest_data.processed$data$fundays)
fundays.values = seq(from = min.fundays_status, to = max.fundays_status, length = 100)
pred.fundays_status <-
covariate.predictions(model = S.fundays_status,
data = data.frame(fundays = fundays.values),
indices = c(as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "Y")[1,]))))
pred.fundays_status_PI <-
pred.fundays_status$estimates %>%
mutate(region = "PI",
status = ifelse(par.index == 1, "N", "Y"))
pred.fundays_status <-
bind_rows(pred.fundays_status_FP, pred.fundays_status_MP, pred.fundays_status_BC, pred.fundays_status_BSC, pred.fundays_status_PI)
ggplot(pred.fundays_status,
aes(x = covdata, y = estimate, color = status, fill = status)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_fill_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_x_continuous(breaks = c(0, 5, 10, 15, 20, 25)) +
luke_theme +
theme(legend.position = c(0.25, 0.1),
legend.title = element_blank(),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("number of weekend days and holidays exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Effect of management Levels on nest survival
Code
S.levels <-
mark(data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ level)),
brief = TRUE,
delete = TRUE)
Model: S(~level) npar= 5 lnl = 2583.9027 AICc = 2593.9092Code
nest_survival_reals_FP_levels <-
S.levels$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_FP_levels), " ", n = 4))
nest_survival_reals_FP_levels <- cbind(Groups, nest_survival_reals_FP_levels)
nest_survival_reals_FP_levels$rows <- rownames(nest_survival_reals_FP_levels)
nest_survival_reals_FP_levels$management_level <-
as.factor(str_sub(nest_survival_reals_FP_levels$rows,
nchar(nest_survival_reals_FP_levels$rows) - 7, nchar(nest_survival_reals_FP_levels$rows) - 6))
nest_survival_reals_FP_levels <-
nest_survival_reals_FP_levels %>%
dplyr::select(management_level, estimate, se, lcl, ucl) %>%
mutate(region = "FP")
row.names(nest_survival_reals_FP_levels) <- NULL
S.levels <-
mark(data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ level)),
brief = TRUE,
delete = TRUE)
Model: S(~level) npar= 5 lnl = 3227.2874 AICc = 3237.2923Code
nest_survival_reals_MP_levels <-
S.levels$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_MP_levels), " ", n = 4))
nest_survival_reals_MP_levels <- cbind(Groups, nest_survival_reals_MP_levels)
nest_survival_reals_MP_levels$rows <- rownames(nest_survival_reals_MP_levels)
nest_survival_reals_MP_levels$management_level <-
as.factor(str_sub(nest_survival_reals_MP_levels$rows,
nchar(nest_survival_reals_MP_levels$rows) - 7, nchar(nest_survival_reals_MP_levels$rows) - 6))
nest_survival_reals_MP_levels <-
nest_survival_reals_MP_levels %>%
dplyr::select(management_level, estimate, se, lcl, ucl) %>%
mutate(region = "MP")
row.names(nest_survival_reals_MP_levels) <- NULL
S.levels <-
mark(data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ level)),
brief = TRUE,
delete = TRUE)
Model: S(~level) npar= 4 lnl = 1733.8583 AICc = 1741.8655Code
nest_survival_reals_BC_levels <-
S.levels$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BC_levels), " ", n = 4))
nest_survival_reals_BC_levels <- cbind(Groups, nest_survival_reals_BC_levels)
nest_survival_reals_BC_levels$rows <- rownames(nest_survival_reals_BC_levels)
nest_survival_reals_BC_levels$management_level <-
as.factor(str_sub(nest_survival_reals_BC_levels$rows,
nchar(nest_survival_reals_BC_levels$rows) - 7, nchar(nest_survival_reals_BC_levels$rows) - 6))
nest_survival_reals_BC_levels <-
nest_survival_reals_BC_levels %>%
dplyr::select(management_level, estimate, se, lcl, ucl) %>%
mutate(region = "BC")
row.names(nest_survival_reals_BC_levels) <- NULL
S.levels <-
mark(data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ level)),
brief = TRUE,
delete = TRUE)
Model: S(~level) npar= 5 lnl = 1588.2042 AICc = 1598.2156Code
nest_survival_reals_BSC_levels <-
S.levels$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BSC_levels), " ", n = 4))
nest_survival_reals_BSC_levels <- cbind(Groups, nest_survival_reals_BSC_levels)
nest_survival_reals_BSC_levels$rows <- rownames(nest_survival_reals_BSC_levels)
nest_survival_reals_BSC_levels$management_level <-
as.factor(str_sub(nest_survival_reals_BSC_levels$rows,
nchar(nest_survival_reals_BSC_levels$rows) - 7, nchar(nest_survival_reals_BSC_levels$rows) - 6))
nest_survival_reals_BSC_levels <-
nest_survival_reals_BSC_levels %>%
dplyr::select(management_level, estimate, se, lcl, ucl) %>%
mutate(region = "BSC")
row.names(nest_survival_reals_BSC_levels) <- NULL
S.levels <-
mark(data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ level)),
brief = TRUE,
delete = TRUE)
Model: S(~level) npar= 5 lnl = 1007.7148 AICc = 1017.7341Code
nest_survival_reals_PI_levels <-
S.levels$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_PI_levels), " ", n = 4))
nest_survival_reals_PI_levels <- cbind(Groups, nest_survival_reals_PI_levels)
nest_survival_reals_PI_levels$rows <- rownames(nest_survival_reals_PI_levels)
nest_survival_reals_PI_levels$management_level <-
as.factor(str_sub(nest_survival_reals_PI_levels$rows,
nchar(nest_survival_reals_PI_levels$rows) - 7, nchar(nest_survival_reals_PI_levels$rows) - 6))
nest_survival_reals_PI_levels <-
nest_survival_reals_PI_levels %>%
dplyr::select(management_level, estimate, se, lcl, ucl) %>%
mutate(region = "PI")
row.names(nest_survival_reals_PI_levels) <- NULL
nest_survival_reals_levels <-
bind_rows(nest_survival_reals_FP_levels,
nest_survival_reals_MP_levels,
nest_survival_reals_BC_levels,
nest_survival_reals_BSC_levels,
nest_survival_reals_PI_levels)
ggplot() +
geom_line(data = nest_survival_reals_levels,
aes(x = management_level, y = estimate, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
geom_errorbar(data = nest_survival_reals_levels,
aes(ymin = lcl, ymax = ucl,
x = management_level,
y = estimate, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = nest_survival_reals_levels,
aes(x = management_level, y = estimate, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
scale_colour_brewer(palette = "Set1") +
luke_theme +
theme(legend.position = c(0.8, 0.2),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("management level") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0.4, 1)) +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island"))
Effect of management status on nest survival
Code
S.status <-
mark(data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ status)),
brief = TRUE,
delete = TRUE)
Model: S(~status) npar= 2 lnl = 2600.6034 AICc = 2604.6047Code
nest_survival_reals_FP_status <-
S.status$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_FP_status), " ", n = 4))
nest_survival_reals_FP_status <- cbind(Groups, nest_survival_reals_FP_status)
nest_survival_reals_FP_status$rows <- rownames(nest_survival_reals_FP_status)
nest_survival_reals_FP_status$status <-
as.factor(str_sub(nest_survival_reals_FP_status$rows,
nchar(nest_survival_reals_FP_status$rows) - 8, nchar(nest_survival_reals_FP_status$rows) - 8))
nest_survival_reals_FP_status <-
nest_survival_reals_FP_status %>%
dplyr::select(status, estimate, se, lcl, ucl) %>%
mutate(region = "FP")
row.names(nest_survival_reals_FP_status) <- NULL
S.status <-
mark(data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ status)),
brief = TRUE,
delete = TRUE)
Model: S(~status) npar= 2 lnl = 3231.7168 AICc = 3235.7177Code
nest_survival_reals_MP_status <-
S.status$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_MP_status), " ", n = 4))
nest_survival_reals_MP_status <- cbind(Groups, nest_survival_reals_MP_status)
nest_survival_reals_MP_status$rows <- rownames(nest_survival_reals_MP_status)
nest_survival_reals_MP_status$status <-
as.factor(str_sub(nest_survival_reals_MP_status$rows,
nchar(nest_survival_reals_MP_status$rows) - 8, nchar(nest_survival_reals_MP_status$rows) - 8))
nest_survival_reals_MP_status <-
nest_survival_reals_MP_status %>%
dplyr::select(status, estimate, se, lcl, ucl) %>%
mutate(region = "MP")
row.names(nest_survival_reals_MP_status) <- NULL
S.status <-
mark(data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ status)),
brief = TRUE,
delete = TRUE)
Model: S(~status) npar= 2 lnl = 1752.8385 AICc = 1756.8407Code
nest_survival_reals_BC_status <-
S.status$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BC_status), " ", n = 4))
nest_survival_reals_BC_status <- cbind(Groups, nest_survival_reals_BC_status)
nest_survival_reals_BC_status$rows <- rownames(nest_survival_reals_BC_status)
nest_survival_reals_BC_status$status <-
as.factor(str_sub(nest_survival_reals_BC_status$rows,
nchar(nest_survival_reals_BC_status$rows) - 8, nchar(nest_survival_reals_BC_status$rows) - 8))
nest_survival_reals_BC_status <-
nest_survival_reals_BC_status %>%
dplyr::select(status, estimate, se, lcl, ucl) %>%
mutate(region = "BC")
row.names(nest_survival_reals_BC_status) <- NULL
S.status <-
mark(data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ status)),
brief = TRUE,
delete = TRUE)
Model: S(~status) npar= 2 lnl = 1013.4942 AICc = 1017.4981Code
nest_survival_reals_PI_status <-
S.status$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_PI_status), " ", n = 4))
nest_survival_reals_PI_status <- cbind(Groups, nest_survival_reals_PI_status)
nest_survival_reals_PI_status$rows <- rownames(nest_survival_reals_PI_status)
nest_survival_reals_PI_status$status <-
as.factor(str_sub(nest_survival_reals_PI_status$rows,
nchar(nest_survival_reals_PI_status$rows) - 8, nchar(nest_survival_reals_PI_status$rows) - 8))
nest_survival_reals_PI_status <-
nest_survival_reals_PI_status %>%
dplyr::select(status, estimate, se, lcl, ucl) %>%
mutate(region = "PI")
row.names(nest_survival_reals_PI_status) <- NULL
S.status <-
mark(data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ status)),
brief = TRUE,
delete = TRUE)
Model: S(~status) npar= 2 lnl = 1604.0904 AICc = 1608.0927Code
nest_survival_reals_BSC_status <-
S.status$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BSC_status), " ", n = 4))
nest_survival_reals_BSC_status <- cbind(Groups, nest_survival_reals_BSC_status)
nest_survival_reals_BSC_status$rows <- rownames(nest_survival_reals_BSC_status)
nest_survival_reals_BSC_status$status <-
as.factor(str_sub(nest_survival_reals_BSC_status$rows,
nchar(nest_survival_reals_BSC_status$rows) - 8, nchar(nest_survival_reals_BSC_status$rows) - 8))
nest_survival_reals_BSC_status <-
nest_survival_reals_BSC_status %>%
dplyr::select(status, estimate, se, lcl, ucl) %>%
mutate(region = "BSC")
row.names(nest_survival_reals_BSC_status) <- NULL
nest_survival_reals_status <-
bind_rows(nest_survival_reals_FP_status,
nest_survival_reals_MP_status,
nest_survival_reals_BC_status,
nest_survival_reals_BSC_status,
nest_survival_reals_PI_status)
ggplot() +
geom_line(data = nest_survival_reals_status,
aes(x = status, y = estimate, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
geom_errorbar(data = nest_survival_reals_status,
aes(ymin = lcl, ymax = ucl,
x = status,
y = estimate, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = nest_survival_reals_status,
aes(x = status, y = estimate, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
# scale_colour_brewer(palette = "Set1") +
luke_theme +
theme(legend.position = c(0.8, 0.2),
legend.justification = c(1, 0),
strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 10)) +
xlab("management level") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0.4, 1)) +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_x_discrete(labels = c("No management", "management"))
Effect of Habitat on nest survival
Code
S.habitat <-
mark(data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ nest_hab)),
brief = TRUE,
delete = TRUE)
Model: S(~nest_hab) npar= 4 lnl = 2702.2083 AICc = 2710.2126Code
nest_survival_reals_FP_habitat <-
S.habitat$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_FP_habitat), " ", n = 4))
nest_survival_reals_FP_habitat <- cbind(Groups, nest_survival_reals_FP_habitat)
nest_survival_reals_FP_habitat$habitat <- str_extract(nest_survival_reals_FP_habitat$X2, "beach|dune|foredune/face|estuary/spit")
nest_survival_reals_FP_habitat <-
nest_survival_reals_FP_habitat %>%
dplyr::select(habitat, estimate, se, lcl, ucl) %>%
mutate(region = "FP")
row.names(nest_survival_reals_FP_habitat) <- NULL
S.habitat <-
mark(data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ nest_hab)),
brief = TRUE,
delete = TRUE)
Model: S(~nest_hab) npar= 3 lnl = 3245.5948 AICc = 3251.5968Code
nest_survival_reals_MP_habitat <-
S.habitat$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_MP_habitat), " ", n = 4))
nest_survival_reals_MP_habitat <- cbind(Groups, nest_survival_reals_MP_habitat)
nest_survival_reals_MP_habitat$habitat <- str_extract(nest_survival_reals_MP_habitat$X2, "beach|dune|foredune/face|estuary/spit")
nest_survival_reals_MP_habitat <-
nest_survival_reals_MP_habitat %>%
dplyr::select(habitat, estimate, se, lcl, ucl) %>%
mutate(region = "MP")
row.names(nest_survival_reals_MP_habitat) <- NULL
S.habitat <-
mark(data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ nest_hab)),
brief = TRUE,
delete = TRUE)
Model: S(~nest_hab) npar= 4 lnl = 1766.7888 AICc = 1774.796Code
nest_survival_reals_BC_habitat <-
S.habitat$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BC_habitat), " ", n = 4))
nest_survival_reals_BC_habitat <- cbind(Groups, nest_survival_reals_BC_habitat)
nest_survival_reals_BC_habitat$habitat <- str_extract(nest_survival_reals_BC_habitat$X2, "beach|dune|foredune/face|estuary/spit")
nest_survival_reals_BC_habitat <-
nest_survival_reals_BC_habitat %>%
dplyr::select(habitat, estimate, se, lcl, ucl) %>%
mutate(region = "BC")
row.names(nest_survival_reals_BC_habitat) <- NULL
S.habitat <-
mark(data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ nest_hab)),
brief = TRUE,
delete = TRUE)
Model: S(~nest_hab) npar= 4 lnl = 1643.1384 AICc = 1651.146Code
nest_survival_reals_BSC_habitat <-
S.habitat$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BSC_habitat), " ", n = 4))
nest_survival_reals_BSC_habitat <- cbind(Groups, nest_survival_reals_BSC_habitat)
nest_survival_reals_BSC_habitat$habitat <- str_extract(nest_survival_reals_BSC_habitat$X2, "beach|dune|foredune/face|estuary/spit")
nest_survival_reals_BSC_habitat <-
nest_survival_reals_BSC_habitat %>%
dplyr::select(habitat, estimate, se, lcl, ucl) %>%
mutate(region = "BSC")
row.names(nest_survival_reals_BSC_habitat) <- NULL
S.habitat <-
mark(data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ nest_hab)),
brief = TRUE,
delete = TRUE)
Model: S(~nest_hab) npar= 4 lnl = 1123.8333 AICc = 1125.8346Code
nest_survival_reals_PI_habitat <-
S.habitat$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_PI_habitat), " ", n = 4))
nest_survival_reals_PI_habitat <- cbind(Groups, nest_survival_reals_PI_habitat)
nest_survival_reals_PI_habitat$habitat <- str_extract(nest_survival_reals_PI_habitat$X2, "beach|dune|foredune/face|estuary/spit")
nest_survival_reals_PI_habitat <-
nest_survival_reals_PI_habitat %>%
dplyr::select(habitat, estimate, se, lcl, ucl) %>%
mutate(region = "PI")
row.names(nest_survival_reals_PI_habitat) <- NULL
nest_survival_reals_habitat <-
bind_rows(nest_survival_reals_FP_habitat,
nest_survival_reals_MP_habitat,
nest_survival_reals_BSC_habitat,
nest_survival_reals_BC_habitat,
nest_survival_reals_PI_habitat)
ggplot() +
geom_errorbar(data = nest_survival_reals_habitat,
aes(ymin = lcl, ymax = ucl,
x = habitat,
y = estimate, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = nest_survival_reals_habitat,
aes(x = habitat, y = estimate, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
# scale_colour_brewer(palette = "Set1") +
luke_theme +
theme(legend.position = c(0.5, 0.2),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("habitat") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0.75, 1)) +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island"))
Effect of dogs on nest survival (max counted)
Code
S.dog_m <- nest_survival_run_FP[[4]]
min.dog_m = min(RMark_data_FP$nest_data.processed$data$dog_m)
max.dog_m = max(RMark_data_FP$nest_data.processed$data$dog_m)
dog_m.values = seq(from = min.dog_m, to = max.dog_m, length = 100)
pred.dog_m_status <-
covariate.predictions(model = S.dog_m,
data = data.frame(dog_m = dog_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dog_m_status_FP <-
pred.dog_m_status$estimates %>%
mutate(region = "FP",
status = ifelse(par.index == 1, "N", "Y"))
S.dog_m <- nest_survival_run_MP[[4]]
min.dog_m = min(RMark_data_MP$nest_data.processed$data$dog_m)
max.dog_m = max(RMark_data_MP$nest_data.processed$data$dog_m)
dog_m.values = seq(from = min.dog_m, to = max.dog_m, length = 100)
pred.dog_m_status <-
covariate.predictions(model = S.dog_m,
data = data.frame(dog_m = dog_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dog_m_status_MP <-
pred.dog_m_status$estimates %>%
mutate(region = "MP",
status = ifelse(par.index == 1, "N", "Y"))
S.dog_m <- nest_survival_run_BC[[4]]
min.dog_m = min(RMark_data_BC$nest_data.processed$data$dog_m)
max.dog_m = max(RMark_data_BC$nest_data.processed$data$dog_m)
dog_m.values = seq(from = min.dog_m, to = max.dog_m, length = 100)
pred.dog_m_status <-
covariate.predictions(model = S.dog_m,
data = data.frame(dog_m = dog_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dog_m_status_BC <-
pred.dog_m_status$estimates %>%
mutate(region = "BC",
status = ifelse(par.index == 1, "N", "Y"))
S.dog_m <- nest_survival_run_PI[[4]]
min.dog_m = min(RMark_data_PI$nest_data.processed$data$dog_m)
max.dog_m = max(RMark_data_PI$nest_data.processed$data$dog_m)
dog_m.values = seq(from = min.dog_m, to = max.dog_m, length = 100)
pred.dog_m_status <-
covariate.predictions(model = S.dog_m,
data = data.frame(dog_m = dog_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dog_m_status_PI <-
pred.dog_m_status$estimates %>%
mutate(region = "PI",
status = ifelse(par.index == 1, "N", "Y"))
S.dog_m <- nest_survival_run_BSC[[4]]
min.dog_m = min(RMark_data_BSC$nest_data.processed$data$dog_m)
max.dog_m = max(RMark_data_BSC$nest_data.processed$data$dog_m)
dog_m.values = seq(from = min.dog_m, to = max.dog_m, length = 100)
pred.dog_m_status <-
covariate.predictions(model = S.dog_m,
data = data.frame(dog_m = dog_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dog_m_status_BSC <-
pred.dog_m_status$estimates %>%
mutate(region = "BSC",
status = ifelse(par.index == 1, "N", "Y"))
pred.dog_m <-
bind_rows(pred.dog_m_status_FP,
pred.dog_m_status_MP,
pred.dog_m_status_BC,
pred.dog_m_status_BSC,
pred.dog_m_status_PI)
ggplot(pred.dog_m,
aes(x = covdata, y = estimate, color = status, fill = status)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_fill_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_x_continuous(breaks = c(0, 5, 10, 15, 20, 25)) +
luke_theme +
theme(legend.position = c(0.25, 0.1),
legend.title = element_blank(),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("maximum number of dogs exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Effect of Dogs off leash on nest survival (max counted)
Code
S.dof_m <- nest_survival_run_FP[[2]]
min.dof_m = min(RMark_data_FP$nest_data.processed$data$dof_m)
max.dof_m = max(RMark_data_FP$nest_data.processed$data$dof_m)
dof_m.values = seq(from = min.dof_m, to = max.dof_m, length = 100)
pred.dof_m_status <-
covariate.predictions(model = S.dof_m,
data = data.frame(dof_m = dof_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dof_m_status_FP <-
pred.dof_m_status$estimates %>%
mutate(region = "FP",
status = ifelse(par.index == 1, "N", "Y"))
S.dof_m <- nest_survival_run_MP[[2]]
min.dof_m = min(RMark_data_MP$nest_data.processed$data$dof_m)
max.dof_m = max(RMark_data_MP$nest_data.processed$data$dof_m)
dof_m.values = seq(from = min.dof_m, to = max.dof_m, length = 100)
pred.dof_m_status <-
covariate.predictions(model = S.dof_m,
data = data.frame(dof_m = dof_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dof_m_status_MP <-
pred.dof_m_status$estimates %>%
mutate(region = "MP",
status = ifelse(par.index == 1, "N", "Y"))
S.dof_m <- nest_survival_run_BC[[2]]
min.dof_m = min(RMark_data_BC$nest_data.processed$data$dof_m)
max.dof_m = max(RMark_data_BC$nest_data.processed$data$dof_m)
dof_m.values = seq(from = min.dof_m, to = max.dof_m, length = 100)
pred.dof_m_status <-
covariate.predictions(model = S.dof_m,
data = data.frame(dof_m = dof_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dof_m_status_BC <-
pred.dof_m_status$estimates %>%
mutate(region = "BC",
status = ifelse(par.index == 1, "N", "Y"))
S.dof_m <- nest_survival_run_BSC[[2]]
min.dof_m = min(RMark_data_BSC$nest_data.processed$data$dof_m)
max.dof_m = max(RMark_data_BSC$nest_data.processed$data$dof_m)
dof_m.values = seq(from = min.dof_m, to = max.dof_m, length = 100)
pred.dof_m_status <-
covariate.predictions(model = S.dof_m,
data = data.frame(dof_m = dof_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dof_m_status_BSC <-
pred.dof_m_status$estimates %>%
mutate(region = "BSC",
status = ifelse(par.index == 1, "N", "Y"))
S.dof_m <- nest_survival_run_PI[[2]]
min.dof_m = min(RMark_data_PI$nest_data.processed$data$dof_m)
max.dof_m = max(RMark_data_PI$nest_data.processed$data$dof_m)
dof_m.values = seq(from = min.dof_m, to = max.dof_m, length = 100)
pred.dof_m_status <-
covariate.predictions(model = S.dof_m,
data = data.frame(dof_m = dof_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "Y")[1,]))))
pred.dof_m_status_PI <-
pred.dof_m_status$estimates %>%
mutate(region = "PI",
status = ifelse(par.index == 1, "N", "Y"))
pred.dof_m <-
bind_rows(pred.dof_m_status_FP,
pred.dof_m_status_MP,
pred.dof_m_status_BC,
pred.dof_m_status_BSC,
pred.dof_m_status_PI)
ggplot(pred.dof_m,
aes(x = covdata, y = estimate, color = status, fill = status)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_fill_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_x_continuous(breaks = c(0, 5, 10, 15, 20, 25)) +
luke_theme +
theme(legend.position = c(0.25, 0.1),
legend.title = element_blank(),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("maximum number of dogs off leash exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Effect of humans on nest survival (max counted)
Code
S.hum_m <- nest_survival_run_FP[[9]]
min.hum_m = min(RMark_data_FP$nest_data.processed$data$hum_m)
max.hum_m = max(RMark_data_FP$nest_data.processed$data$hum_m)
hum_m.values = seq(from = min.hum_m, to = max.hum_m, length = 100)
pred.hum_m_status <-
covariate.predictions(model = S.hum_m,
data = data.frame(hum_m = hum_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.hum_m_status_FP <-
pred.hum_m_status$estimates %>%
mutate(region = "FP",
status = ifelse(par.index == 1, "N", "Y"))
S.hum_m <- nest_survival_run_MP[[9]]
min.hum_m = min(RMark_data_MP$nest_data.processed$data$hum_m)
max.hum_m = max(RMark_data_MP$nest_data.processed$data$hum_m)
hum_m.values = seq(from = min.hum_m, to = max.hum_m, length = 100)
pred.hum_m_status <-
covariate.predictions(model = S.hum_m,
data = data.frame(hum_m = hum_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.hum_m_status_MP <-
pred.hum_m_status$estimates %>%
mutate(region = "MP",
status = ifelse(par.index == 1, "N", "Y"))
S.hum_m <- nest_survival_run_BC[[9]]
min.hum_m = min(RMark_data_BC$nest_data.processed$data$hum_m)
max.hum_m = max(RMark_data_BC$nest_data.processed$data$hum_m)
hum_m.values = seq(from = min.hum_m, to = max.hum_m, length = 100)
pred.hum_m_status <-
covariate.predictions(model = S.hum_m,
data = data.frame(hum_m = hum_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.hum_m_status_BC <-
pred.hum_m_status$estimates %>%
mutate(region = "BC",
status = ifelse(par.index == 1, "N", "Y"))
S.hum_m <- nest_survival_run_PI[[9]]
min.hum_m = min(RMark_data_PI$nest_data.processed$data$hum_m)
max.hum_m = max(RMark_data_PI$nest_data.processed$data$hum_m)
hum_m.values = seq(from = min.hum_m, to = max.hum_m, length = 100)
pred.hum_m_status <-
covariate.predictions(model = S.hum_m,
data = data.frame(hum_m = hum_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "Y")[1,]))))
pred.hum_m_status_PI <-
pred.hum_m_status$estimates %>%
mutate(region = "PI",
status = ifelse(par.index == 1, "N", "Y"))
S.hum_m <- nest_survival_run_BSC[[9]]
min.hum_m = min(RMark_data_BSC$nest_data.processed$data$hum_m)
max.hum_m = max(RMark_data_BSC$nest_data.processed$data$hum_m)
hum_m.values = seq(from = min.hum_m, to = max.hum_m, length = 100)
pred.hum_m_status <-
covariate.predictions(model = S.hum_m,
data = data.frame(hum_m = hum_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.hum_m_status_BSC <-
pred.hum_m_status$estimates %>%
mutate(region = "BSC",
status = ifelse(par.index == 1, "N", "Y"))
pred.hum_m <-
bind_rows(pred.hum_m_status_FP,
pred.hum_m_status_MP,
pred.hum_m_status_BC,
pred.hum_m_status_BSC,
pred.hum_m_status_PI)
ggplot(pred.hum_m,
aes(x = covdata, y = estimate, color = status, fill = status)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_fill_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50)) +
luke_theme +
theme(legend.position = c(0.25, 0.1),
legend.title = element_blank(),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("maximum number of humans exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Effect of corvids on nest survival (max counted)
Code
S.pbd_m <- nest_survival_run_FP[[11]]
min.pbd_m = min(RMark_data_FP$nest_data.processed$data$pbd_m)
max.pbd_m = max(RMark_data_FP$nest_data.processed$data$pbd_m)
pbd_m.values = seq(from = min.pbd_m, to = max.pbd_m, length = 100)
pred.pbd_m_status <-
covariate.predictions(model = S.pbd_m,
data = data.frame(pbd_m = pbd_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.pbd_m_status_FP <-
pred.pbd_m_status$estimates %>%
mutate(region = "FP",
status = ifelse(par.index == 1, "N", "Y"))
S.pbd_m <- nest_survival_run_MP[[11]]
min.pbd_m = min(RMark_data_MP$nest_data.processed$data$pbd_m)
max.pbd_m = max(RMark_data_MP$nest_data.processed$data$pbd_m)
pbd_m.values = seq(from = min.pbd_m, to = max.pbd_m, length = 100)
pred.pbd_m_status <-
covariate.predictions(model = S.pbd_m,
data = data.frame(pbd_m = pbd_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.pbd_m_status_MP <-
pred.pbd_m_status$estimates %>%
mutate(region = "MP",
status = ifelse(par.index == 1, "N", "Y"))
S.pbd_m <- nest_survival_run_BC[[11]]
min.pbd_m = min(RMark_data_BC$nest_data.processed$data$pbd_m)
max.pbd_m = max(RMark_data_BC$nest_data.processed$data$pbd_m)
pbd_m.values = seq(from = min.pbd_m, to = max.pbd_m, length = 100)
pred.pbd_m_status <-
covariate.predictions(model = S.pbd_m,
data = data.frame(pbd_m = pbd_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.pbd_m_status_BC <-
pred.pbd_m_status$estimates %>%
mutate(region = "BC",
status = ifelse(par.index == 1, "N", "Y"))
S.pbd_m <- nest_survival_run_BSC[[11]]
min.pbd_m = min(RMark_data_BSC$nest_data.processed$data$pbd_m)
max.pbd_m = max(RMark_data_BSC$nest_data.processed$data$pbd_m)
pbd_m.values = seq(from = min.pbd_m, to = max.pbd_m, length = 100)
pred.pbd_m_status <-
covariate.predictions(model = S.pbd_m,
data = data.frame(pbd_m = pbd_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.pbd_m_status_BSC <-
pred.pbd_m_status$estimates %>%
mutate(region = "BSC",
status = ifelse(par.index == 1, "N", "Y"))
S.pbd_m <- nest_survival_run_PI[[11]]
min.pbd_m = min(RMark_data_PI$nest_data.processed$data$pbd_m)
max.pbd_m = max(RMark_data_PI$nest_data.processed$data$pbd_m)
pbd_m.values = seq(from = min.pbd_m, to = max.pbd_m, length = 100)
pred.pbd_m_status <-
covariate.predictions(model = S.pbd_m,
data = data.frame(pbd_m = pbd_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "Y")[1,]))))
pred.pbd_m_status_PI <-
pred.pbd_m_status$estimates %>%
mutate(region = "PI",
status = ifelse(par.index == 1, "N", "Y"))
pred.pbd_m <-
bind_rows(pred.pbd_m_status_FP,
pred.pbd_m_status_MP,
pred.pbd_m_status_BC,
pred.pbd_m_status_BSC,
pred.pbd_m_status_PI)
ggplot(pred.pbd_m,
aes(x = covdata, y = estimate, color = status, fill = status)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_fill_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_x_continuous(breaks = c(0, 5, 10, 15, 20, 25)) +
luke_theme +
theme(legend.position = c(0.25, 0.1),
legend.title = element_blank(),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("maximum number of ravens and magpies exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Effect of gulls on nest survival (max counted)
Code
S.gul_m <- nest_survival_run_FP[[7]]
min.gul_m = min(RMark_data_FP$nest_data.processed$data$gul_m)
max.gul_m = max(RMark_data_FP$nest_data.processed$data$gul_m)
gul_m.values = seq(from = min.gul_m, to = max.gul_m, length = 100)
pred.gul_m_status <-
covariate.predictions(model = S.gul_m,
data = data.frame(gul_m = gul_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.gul_m_status_FP <-
pred.gul_m_status$estimates %>%
mutate(region = "FP",
status = ifelse(par.index == 1, "N", "Y"))
S.gul_m <- nest_survival_run_MP[[7]]
min.gul_m = min(RMark_data_MP$nest_data.processed$data$gul_m)
max.gul_m = max(RMark_data_MP$nest_data.processed$data$gul_m)
gul_m.values = seq(from = min.gul_m, to = max.gul_m, length = 100)
pred.gul_m_status <-
covariate.predictions(model = S.gul_m,
data = data.frame(gul_m = gul_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "Y")[1,]))))
pred.gul_m_status_MP <-
pred.gul_m_status$estimates %>%
mutate(region = "MP",
status = ifelse(par.index == 1, "N", "Y"))
S.gul_m <- nest_survival_run_BC[[7]]
min.gul_m = min(RMark_data_BC$nest_data.processed$data$gul_m)
max.gul_m = max(RMark_data_BC$nest_data.processed$data$gul_m)
gul_m.values = seq(from = min.gul_m, to = max.gul_m, length = 100)
pred.gul_m_status <-
covariate.predictions(model = S.gul_m,
data = data.frame(gul_m = gul_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.gul_m_status_BC <-
pred.gul_m_status$estimates %>%
mutate(region = "BC",
status = ifelse(par.index == 1, "N", "Y"))
S.gul_m <- nest_survival_run_PI[[7]]
min.gul_m = min(RMark_data_PI$nest_data.processed$data$gul_m)
max.gul_m = max(RMark_data_PI$nest_data.processed$data$gul_m)
gul_m.values = seq(from = min.gul_m, to = max.gul_m, length = 100)
pred.gul_m_status <-
covariate.predictions(model = S.gul_m,
data = data.frame(gul_m = gul_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_PI$nest_fate.ddl$S, status == "Y")[1,]))))
pred.gul_m_status_PI <-
pred.gul_m_status$estimates %>%
mutate(region = "PI",
status = ifelse(par.index == 1, "N", "Y"))
S.gul_m <- nest_survival_run_BSC[[7]]
min.gul_m = min(RMark_data_BSC$nest_data.processed$data$gul_m)
max.gul_m = max(RMark_data_BSC$nest_data.processed$data$gul_m)
gul_m.values = seq(from = min.gul_m, to = max.gul_m, length = 100)
pred.gul_m_status <-
covariate.predictions(model = S.gul_m,
data = data.frame(gul_m = gul_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "N")[1,])),
as.numeric(row.names(filter(RMark_data_BSC$nest_fate.ddl$S, status == "Y")[1,]))))
pred.gul_m_status_BSC <-
pred.gul_m_status$estimates %>%
mutate(region = "BSC",
status = ifelse(par.index == 1, "N", "Y"))
pred.gul_m <-
bind_rows(pred.gul_m_status_FP,
pred.gul_m_status_MP,
pred.gul_m_status_BC,
pred.gul_m_status_BSC,
pred.gul_m_status_PI)
ggplot(pred.gul_m,
aes(x = covdata, y = estimate, color = status, fill = status)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_fill_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
# scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60, 70)) +
luke_theme +
theme(legend.position = c(0.25, 0.1),
legend.title = element_blank(),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("maximum number of gulls exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Effect of Fox activity on nest survival (max print level) (NOT RUN IN MODELS)
Code
# S.fox_prints <-
# mark(data = RMark_data_FP$nest_data.processed,
# ddl = RMark_data_FP$nest_fate.ddl,
# model = "Nest",
# model.parameters = list("S" = list(formula = ~ fox_p)),
# brief = TRUE,
# delete = TRUE)
# nest_survival_reals_FP_fox_p <-
# S.fox_prints$results$real
# Groups <- data.frame(
# str_split_fixed(rownames(nest_survival_reals_FP_fox_p), " ", n = 4))
# nest_survival_reals_FP_fox_p <- cbind(Groups, nest_survival_reals_FP_fox_p)
# nest_survival_reals_FP_fox_p$rows <- rownames(nest_survival_reals_FP_fox_p)
# nest_survival_reals_FP_fox_p$fox_p <-
# str_sub(nest_survival_reals_FP_fox_p$rows,
# nchar(nest_survival_reals_FP_fox_p$rows) - 6,
# nchar(nest_survival_reals_FP_fox_p$rows) - 6)
# nest_survival_reals_FP_fox_p$fox_p <-
# as.factor(nest_survival_reals_FP_fox_p$fox_p)
# nest_survival_reals_FP_fox_p <-
# nest_survival_reals_FP_fox_p %>%
# dplyr::select(fox_p, estimate, se, lcl, ucl) %>%
# mutate(region = "FP") %>%
# slice(-1)
# row.names(nest_survival_reals_FP_fox_p) <- NULL
#
# S.fox_prints <-
# mark(data = RMark_data_MP$nest_data.processed,
# ddl = RMark_data_MP$nest_fate.ddl,
# model = "Nest",
# model.parameters = list("S" = list(formula = ~ fox_p)),
# brief = TRUE,
# delete = TRUE)
# nest_survival_reals_MP_fox_p <-
# S.fox_prints$results$real
# Groups <- data.frame(
# str_split_fixed(rownames(nest_survival_reals_MP_fox_p), " ", n = 4))
# nest_survival_reals_MP_fox_p <- cbind(Groups, nest_survival_reals_MP_fox_p)
# nest_survival_reals_MP_fox_p$rows <- rownames(nest_survival_reals_MP_fox_p)
# nest_survival_reals_MP_fox_p$fox_p <-
# str_sub(nest_survival_reals_MP_fox_p$rows,
# nchar(nest_survival_reals_MP_fox_p$rows) - 6,
# nchar(nest_survival_reals_MP_fox_p$rows) - 6)
# nest_survival_reals_MP_fox_p$fox_p <-
# as.factor(nest_survival_reals_MP_fox_p$fox_p)
# nest_survival_reals_MP_fox_p <-
# nest_survival_reals_MP_fox_p %>%
# dplyr::select(fox_p, estimate, se, lcl, ucl) %>%
# mutate(region = "MP") %>%
# slice(-1)
# row.names(nest_survival_reals_MP_fox_p) <- NULL
#
# S.fox_prints <-
# mark(data = RMark_data_BC$nest_data.processed,
# ddl = RMark_data_BC$nest_fate.ddl,
# model = "Nest",
# model.parameters = list("S" = list(formula = ~ fox_p)),
# brief = TRUE,
# delete = TRUE)
# nest_survival_reals_BC_fox_p <-
# S.fox_prints$results$real
# Groups <- data.frame(
# str_split_fixed(rownames(nest_survival_reals_BC_fox_p), " ", n = 4))
# nest_survival_reals_BC_fox_p <- cbind(Groups, nest_survival_reals_BC_fox_p)
# nest_survival_reals_BC_fox_p$rows <- rownames(nest_survival_reals_BC_fox_p)
# nest_survival_reals_BC_fox_p$fox_p <-
# str_sub(nest_survival_reals_BC_fox_p$rows,
# nchar(nest_survival_reals_BC_fox_p$rows) - 6,
# nchar(nest_survival_reals_BC_fox_p$rows) - 6) %>% as.factor()
# nest_survival_reals_BC_fox_p <-
# nest_survival_reals_BC_fox_p %>%
# dplyr::select(fox_p, estimate, se, lcl, ucl) %>%
# mutate(region = "BC") %>%
# slice(-1)
# row.names(nest_survival_reals_BC_fox_p) <- NULL
#
# nest_survival_reals_levels <-
# bind_rows(nest_survival_reals_FP_fox_p,
# nest_survival_reals_MP_fox_p,
# nest_survival_reals_BC_fox_p)
#
# ggplot() +
# geom_line(data = nest_survival_reals_levels,
# aes(x = fox_p, y = estimate, color = region, group = region),
# position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
# geom_errorbar(data = nest_survival_reals_levels,
# aes(ymin = lcl, ymax = ucl,
# x = fox_p,
# y = estimate, group = region), position = position_dodge(width = 0.5),
# alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
# geom_point(data = nest_survival_reals_levels,
# aes(x = fox_p, y = estimate, fill = region),
# shape = 21, size = 4, position = position_dodge(width = 0.5)) +
# luke_theme +
# theme(legend.position = c(0.9, 0.1),
# legend.justification = c(1, 0),
# strip.background = element_blank()) +
# xlab("fox prints") +
# ylab("estimated daily survival rate (± 95% CI)") +
# ylim(c(0.55, 1)) +
# scale_colour_brewer(palette = "Dark2",
# name = "Region",
# labels = #c("Bellarine/Surf Coast",
# # "Fleurieu Peninsula",
# "Mornington Peninsula") +#) +
# scale_fill_brewer(palette = "Dark2",
# name = "Region",
# labels = #c("Bellarine/Surf Coast",
# # "Fleurieu Peninsula",
# "Mornington Peninsula")Effect of vehicles on nest survival (max)
Code
S.veh_m <- nest_survival_run_FP[[12]]
min.veh_m = min(RMark_data_FP$nest_data.processed$data$veh_m)
max.veh_m = max(RMark_data_FP$nest_data.processed$data$veh_m)
veh_m.values = seq(from = min.veh_m, to = max.veh_m, length = 100)
pred.veh_m_status <-
covariate.predictions(model = S.veh_m,
data = data.frame(veh_m = veh_m.values),
indices = c(as.numeric(row.names(filter(RMark_data_FP$nest_fate.ddl$S, status == "N")[1,]))))
pred.veh_m_status_FP <-
pred.veh_m_status$estimates %>%
mutate(region = "FP")
# S.veh_m <- nest_survival_run_MP[[13]]
# min.veh_m = min(RMark_data_MP$nest_data.processed$data$veh_m)
# max.veh_m = max(RMark_data_MP$nest_data.processed$data$veh_m)
# veh_m.values = seq(from = min.veh_m, to = max.veh_m, length = 100)
# pred.veh_m_status <-
# covariate.predictions(model = S.veh_m,
# data = data.frame(veh_m = veh_m.values),
# indices = c(as.numeric(row.names(filter(RMark_data_MP$nest_fate.ddl$S, status == "N")[1,]))))
# pred.veh_m_status_MP <-
# pred.veh_m_status$estimates %>%
# mutate(region = "MP")
#
# S.veh_m <- nest_survival_run_BC[[13]]
# min.veh_m = min(RMark_data_BC$nest_data.processed$data$veh_m)
# max.veh_m = max(RMark_data_BC$nest_data.processed$data$veh_m)
# veh_m.values = seq(from = min.veh_m, to = max.veh_m, length = 100)
# pred.veh_m_status <-
# covariate.predictions(model = S.veh_m,
# data = data.frame(veh_m = veh_m.values),
# indices = c(as.numeric(row.names(filter(RMark_data_BC$nest_fate.ddl$S, status == "N")[1,]))))
# pred.veh_m_status_BC <-
# pred.veh_m_status$estimates %>%
# mutate(region = "BC")
#
# pred.veh_m <-
# bind_rows(pred.veh_m_status_FP, pred.veh_m_status_MP, pred.veh_m_status_BC)
ggplot(pred.veh_m_status_FP,
aes(x = covdata, y = estimate)) +
geom_line(size = 1.5) +
geom_ribbon(aes(ymin = lcl, ymax = ucl), alpha = 0.2) +
scale_colour_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_fill_brewer(palette = "Dark2",
labels = c("No management", "Management")) +
scale_x_continuous(breaks = c(0, 1, 2, 3, 4, 5)) +
luke_theme +
theme(legend.position = c(0.25, 0.1),
legend.title = element_blank(),
legend.justification = c(1, 0),
strip.background = element_blank()) +
xlab("maximum number of vehicles exposed to") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0, 1)) +
facet_grid(. ~ region, labeller = as_labeller(region_names))
Annual variation in nest survival
Code
S.year <-
mark(data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ season)),
brief = TRUE,
delete = TRUE)
Model: S(~season) npar= 16 lnl = 2690.1106 AICc = 2722.16922700722Code
nest_survival_reals_FP_year <-
S.year$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_FP_year), " ", n = 4))
nest_survival_reals_FP_year <- cbind(Groups, nest_survival_reals_FP_year)
nest_survival_reals_FP_year$season <-
as.numeric(str_sub(nest_survival_reals_FP_year$X2, 2, 5))
nest_survival_reals_FP_year$season <- paste(nest_survival_reals_FP_year$season, nest_survival_reals_FP_year$season + 1, sep = " - ")
nest_survival_reals_FP_year <-
nest_survival_reals_FP_year %>%
dplyr::select(season, estimate, se, lcl, ucl) %>%
mutate(region = "FP")
row.names(nest_survival_reals_FP_year) <- NULL
S.year <-
mark(data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ season)),
brief = TRUE,
delete = TRUE)
Model: S(~season) npar= 18 lnl = 3240.9 AICc = 3276.9563Code
nest_survival_reals_MP_year <-
S.year$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_MP_year), " ", n = 4))
nest_survival_reals_MP_year <- cbind(Groups, nest_survival_reals_MP_year)
nest_survival_reals_MP_year$season <-
as.numeric(str_sub(nest_survival_reals_MP_year$X2, 2, 5))
nest_survival_reals_MP_year$season <- paste(nest_survival_reals_MP_year$season, nest_survival_reals_MP_year$season + 1, sep = " - ")
nest_survival_reals_MP_year <-
nest_survival_reals_MP_year %>%
dplyr::select(season, estimate, se, lcl, ucl) %>%
mutate(region = "MP")
row.names(nest_survival_reals_MP_year) <- NULL
S.year <-
mark(data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ season)),
brief = TRUE,
delete = TRUE)
Model: S(~season) npar= 19 lnl = 1742.8149 AICc = 1780.95112512995Code
nest_survival_reals_BC_year <-
S.year$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BC_year), " ", n = 4))
nest_survival_reals_BC_year <- cbind(Groups, nest_survival_reals_BC_year)
nest_survival_reals_BC_year$season <-
as.numeric(str_sub(nest_survival_reals_BC_year$X2, 2, 5))
nest_survival_reals_BC_year$season <- paste(nest_survival_reals_BC_year$season, nest_survival_reals_BC_year$season + 1, sep = " - ")
nest_survival_reals_BC_year <-
nest_survival_reals_BC_year %>%
dplyr::select(season, estimate, se, lcl, ucl) %>%
mutate(region = "BC")
row.names(nest_survival_reals_BC_year) <- NULL
S.year <-
mark(data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ season)),
brief = TRUE,
delete = TRUE)
Model: S(~season) npar= 19 lnl = 1617.1688 AICc = 1655.31353433632Code
nest_survival_reals_BSC_year <-
S.year$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BSC_year), " ", n = 4))
nest_survival_reals_BSC_year <- cbind(Groups, nest_survival_reals_BSC_year)
nest_survival_reals_BSC_year$season <-
as.numeric(str_sub(nest_survival_reals_BSC_year$X2, 2, 5))
nest_survival_reals_BSC_year$season <- paste(nest_survival_reals_BSC_year$season, nest_survival_reals_BSC_year$season + 1, sep = " - ")
nest_survival_reals_BSC_year <-
nest_survival_reals_BSC_year %>%
dplyr::select(season, estimate, se, lcl, ucl) %>%
mutate(region = "BSC")
row.names(nest_survival_reals_BSC_year) <- NULL
S.year <-
mark(data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ season)),
brief = TRUE,
delete = TRUE)
Model: S(~season) npar= 14 lnl = 1031.0835 AICc = 1059.21898387097Code
nest_survival_reals_PI_year <-
S.year$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_PI_year), " ", n = 4))
nest_survival_reals_PI_year <- cbind(Groups, nest_survival_reals_PI_year)
nest_survival_reals_PI_year$season <-
as.numeric(str_sub(nest_survival_reals_PI_year$X2, 2, 5))
nest_survival_reals_PI_year$season <- paste(nest_survival_reals_PI_year$season, nest_survival_reals_PI_year$season + 1, sep = " - ")
nest_survival_reals_PI_year <-
nest_survival_reals_PI_year %>%
dplyr::select(season, estimate, se, lcl, ucl) %>%
mutate(region = "PI")
row.names(nest_survival_reals_PI_year) <- NULL
nest_survival_reals_year <-
bind_rows(nest_survival_reals_FP_year,
nest_survival_reals_MP_year,
nest_survival_reals_BC_year,
nest_survival_reals_BSC_year,
nest_survival_reals_PI_year)
ggplot() +
geom_line(data = nest_survival_reals_year,
aes(x = season, y = estimate, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2) +
geom_errorbar(data = nest_survival_reals_year,
aes(ymin = lcl, ymax = ucl,
x = season,
y = estimate, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = nest_survival_reals_year,
aes(x = season, y = estimate, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
scale_colour_brewer(palette = "Set1") +
luke_theme +
theme(legend.position = "none",
legend.justification = c(1, 0),
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
xlab("season") +
ylab("estimated daily survival rate (± 95% CI)") +
ylim(c(0.7, 1)) +
scale_colour_brewer(palette = "Dark2") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(region ~ ., labeller = as_labeller(region_names))
Variation within years
Code
# Extract estimates of survival from Cubic model with management
# (non-linear season variation and management effect)
S.season <-
mark(data = RMark_data_MP$nest_data.processed,
ddl = RMark_data_MP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ Time + Quadratic + Cubic)),
brief = TRUE,
delete = TRUE)
Model: S(~Time + Quadratic + Cubic) npar= 4 lnl = 3237.8876 AICc = 3245.8909Code
nest_survival_reals_MP_season <-
S.season$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_MP_season), " ", n = 4))
nest_survival_reals_MP_season <- cbind(Groups, nest_survival_reals_MP_season)
nest_survival_reals_MP_season$day_of_season <-
unlist(str_extract_all(nest_survival_reals_MP_season$X4, "\\d+")) %>% unique() %>% as.numeric()
nest_survival_reals_MP_season$region = "MP"
S.season <-
mark(data = RMark_data_BC$nest_data.processed,
ddl = RMark_data_BC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ Time + Quadratic + Cubic)),
brief = TRUE,
delete = TRUE)
Model: S(~Time + Quadratic + Cubic) npar= 4 lnl = 1744.7265 AICc = 1752.7336Code
nest_survival_reals_BC_season <-
S.season$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BC_season), " ", n = 4))
nest_survival_reals_BC_season <- cbind(Groups, nest_survival_reals_BC_season)
nest_survival_reals_BC_season$day_of_season <-
unlist(str_extract_all(nest_survival_reals_BC_season$X4, "\\d+")) %>% unique() %>% as.numeric()
nest_survival_reals_BC_season$region = "BC"
S.season <-
mark(data = RMark_data_FP$nest_data.processed,
ddl = RMark_data_FP$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ Time + Quadratic + Cubic)),
brief = TRUE,
delete = TRUE)
Model: S(~Time + Quadratic + Cubic) npar= 4 lnl = 2700.9422 AICc = 2708.9466Code
nest_survival_reals_FP_season <-
S.season$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_FP_season), " ", n = 4))
nest_survival_reals_FP_season <- cbind(Groups, nest_survival_reals_FP_season)
nest_survival_reals_FP_season$day_of_season <-
unlist(str_extract_all(nest_survival_reals_FP_season$X4, "\\d+")) %>% unique() %>% as.numeric()
nest_survival_reals_FP_season$region = "FP"
S.season <-
mark(data = RMark_data_PI$nest_data.processed,
ddl = RMark_data_PI$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ Time + Quadratic + Cubic)),
brief = TRUE,
delete = TRUE)
Model: S(~Time + Quadratic + Cubic) npar= 4 lnl = 1032.968 AICc = 1040.9808Code
nest_survival_reals_PI_season <-
S.season$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_PI_season), " ", n = 4))
nest_survival_reals_PI_season <- cbind(Groups, nest_survival_reals_PI_season)
nest_survival_reals_PI_season$day_of_season <-
unlist(str_extract_all(nest_survival_reals_PI_season$X4, "\\d+")) %>% unique() %>% as.numeric()
nest_survival_reals_PI_season$region = "PI"
S.season <-
mark(data = RMark_data_BSC$nest_data.processed,
ddl = RMark_data_BSC$nest_fate.ddl,
model = "Nest",
model.parameters = list("S" = list(formula = ~ Time + Quadratic + Cubic)),
brief = TRUE,
delete = TRUE)
Model: S(~Time + Quadratic + Cubic) npar= 4 lnl = 1618.3474 AICc = 1626.355Code
nest_survival_reals_BSC_season <-
S.season$results$real
Groups <- data.frame(
str_split_fixed(rownames(nest_survival_reals_BSC_season), " ", n = 4))
nest_survival_reals_BSC_season <- cbind(Groups, nest_survival_reals_BSC_season)
nest_survival_reals_BSC_season$day_of_season <-
unlist(str_extract_all(nest_survival_reals_BSC_season$X4, "\\d+")) %>% unique() %>% as.numeric()
nest_survival_reals_BSC_season$region = "BSC"
nest_survival_reals_season <-
bind_rows(
nest_survival_reals_MP_season,
nest_survival_reals_BC_season,
nest_survival_reals_FP_season,
nest_survival_reals_BSC_season,
nest_survival_reals_PI_season) %>%
dplyr::select(region, day_of_season, estimate, lcl, ucl)
row.names(nest_survival_reals_season) <- NULL
# FP
dates_for_plot_FP <-
data.frame(date = as.Date(min(as.numeric(RMark_data_FP$nest_data.processed$data$FirstFound)):
max(max(as.numeric(RMark_data_FP$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_FP$nest_data.processed$data$LastPresent))),
origin = "2023-01-01") - 180,
day_of_season = c(0:(max(max(as.numeric(RMark_data_FP$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_FP$nest_data.processed$data$LastPresent))) -
min(as.numeric(RMark_data_FP$nest_data.processed$data$FirstFound)))))
nest_survival_reals_dates_FP <-
left_join(nest_survival_reals_FP_season, dates_for_plot_FP, by = "day_of_season")
nest_survival_season_plot_FP <-
ggplot(data = nest_survival_reals_dates_FP) +
geom_ribbon(aes(x = date, ymin = lcl, ymax = ucl),
fill = brewer.pal(8, "Dark2")[c(1)],
alpha = 0.3) +
geom_line(aes(x = date, y = estimate),
color = brewer.pal(8, "Dark2")[c(1)],
size = 1) +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months") +
ylab("daily nest survival ± 95% CI") +
scale_y_continuous(limits = c(0, 1)) +
luke_theme +
theme(legend.position = "bottom",
legend.title = element_blank(),
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
hjust = 1,
vjust = 1))
nest_discovery_season_plot_FP <-
ggplot(RMark_data_FP$nest_data.processed$data,
aes(as.Date(FirstFound, origin = "2023-01-01") - 180,
fill = Fate)) +
geom_histogram(bins = 30,
# fill = brewer.pal(8, "Set1")[c(2)],
alpha = 0.5) +
scale_fill_manual(values = brewer.pal(8, "Set1")[c(2, 1)],
labels = c("Failed", "Hatched")) +
ylab("nests found\nweekly") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_survival_reals_dates_FP$date, na.rm = TRUE),
max(nest_survival_reals_dates_FP$date, na.rm = TRUE))) +
scale_y_continuous(breaks = c(10, 20, 30, 40)) +
luke_theme +
theme(legend.position = "none",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# merge plots together
hooded_plover_nest_plot_FP <-
nest_discovery_season_plot_FP /
nest_survival_season_plot_FP +
plot_layout(widths = c(5),
heights = unit(c(1, 3), c('in', 'in'))) +
plot_annotation(tag_levels = 'A', title = "Fleurieu Peninsula")
### MP ----
dates_for_plot_MP <-
data.frame(date = as.Date(min(as.numeric(RMark_data_MP$nest_data.processed$data$FirstFound)):
max(max(as.numeric(RMark_data_MP$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_MP$nest_data.processed$data$LastPresent))),
origin = "2023-01-01") - 180,
day_of_season = c(0:(max(max(as.numeric(RMark_data_MP$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_MP$nest_data.processed$data$LastPresent))) -
min(as.numeric(RMark_data_MP$nest_data.processed$data$FirstFound)))))
nest_survival_reals_dates_MP <-
left_join(nest_survival_reals_MP_season, dates_for_plot_MP, by = "day_of_season")
nest_survival_season_plot_MP <-
ggplot(data = nest_survival_reals_dates_MP) +
geom_ribbon(aes(x = date, ymin = lcl, ymax = ucl),
fill = brewer.pal(8, "Dark2")[c(1)],
alpha = 0.3) +
geom_line(aes(x = date, y = estimate),
color = brewer.pal(8, "Dark2")[c(1)],
size = 1) +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months") +
ylab("daily nest survival ± 95% CI") +
scale_y_continuous(limits = c(0, 1)) +
luke_theme +
theme(legend.position = "bottom",
legend.title = element_blank(),
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
hjust = 1,
vjust = 1))
nest_discovery_season_plot_MP <-
ggplot(RMark_data_MP$nest_data.processed$data,
aes(as.Date(FirstFound, origin = "2023-01-01") - 180,
fill = Fate)) +
geom_histogram(bins = 30,
# fill = brewer.pal(8, "Set1")[c(2)],
alpha = 0.5) +
scale_fill_manual(values = brewer.pal(8, "Set1")[c(2, 1)],
labels = c("Failed", "Hatched")) +
ylab("nests found\nweekly") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_survival_reals_dates_MP$date, na.rm = TRUE),
max(nest_survival_reals_dates_MP$date, na.rm = TRUE))) +
scale_y_continuous(breaks = c(10, 20, 30, 40)) +
luke_theme +
theme(legend.position = "none",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# merge plots together
hooded_plover_nest_plot_MP <-
nest_discovery_season_plot_MP /
nest_survival_season_plot_MP +
plot_layout(widths = c(5),
heights = unit(c(1, 3), c('in', 'in'))) +
plot_annotation(tag_levels = 'A', title = "Mornington Peninsula")
### BC ----
dates_for_plot_BC <-
data.frame(date = as.Date(min(as.numeric(RMark_data_BC$nest_data.processed$data$FirstFound)):
max(max(as.numeric(RMark_data_BC$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_BC$nest_data.processed$data$LastPresent))),
origin = "2023-01-01") - 180,
day_of_season = c(0:(max(max(as.numeric(RMark_data_BC$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_BC$nest_data.processed$data$LastPresent))) -
min(as.numeric(RMark_data_BC$nest_data.processed$data$FirstFound)))))
nest_survival_reals_dates_BC <-
left_join(nest_survival_reals_BC_season, dates_for_plot_BC, by = "day_of_season")
nest_survival_season_plot_BC <-
ggplot(data = nest_survival_reals_dates_BC) +
geom_ribbon(aes(x = date, ymin = lcl, ymax = ucl),
fill = brewer.pal(8, "Dark2")[c(1)],
alpha = 0.3) +
geom_line(aes(x = date, y = estimate),
color = brewer.pal(8, "Dark2")[c(1)],
size = 1) +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_survival_reals_dates_BC$date, na.rm = TRUE),
max(nest_survival_reals_dates_BC$date, na.rm = TRUE))) +
ylab("daily nest survival ± 95% CI") +
scale_y_continuous(limits = c(0, 1)) +
luke_theme +
theme(legend.position = "bottom",
legend.title = element_blank(),
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
hjust = 1,
vjust = 1))
nest_discovery_season_plot_BC <-
ggplot(RMark_data_BC$nest_data.processed$data,
aes(as.Date(FirstFound, origin = "2023-01-01") - 180,
fill = Fate)) +
geom_histogram(bins = 30,
# fill = brewer.pal(8, "Set1")[c(2)],
alpha = 0.5) +
scale_fill_manual(values = brewer.pal(8, "Set1")[c(2, 1)],
labels = c("Failed", "Hatched")) +
ylab("nests found\nweekly") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_survival_reals_dates_BC$date, na.rm = TRUE),
max(nest_survival_reals_dates_BC$date, na.rm = TRUE))) +
scale_y_continuous(breaks = c(10, 20, 30, 40)) +
luke_theme +
theme(legend.position = "none",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# merge plots together
hooded_plover_nest_plot_BC <-
nest_discovery_season_plot_BC /
nest_survival_season_plot_BC +
plot_layout(widths = c(5),
heights = unit(c(1, 3), c('in', 'in'))) +
plot_annotation(tag_levels = 'A', title = "Bellarine / Surf Coast")
### BSC ---
dates_for_plot_BSC <-
data.frame(date = as.Date(min(as.numeric(RMark_data_BSC$nest_data.processed$data$FirstFound)):
max(max(as.numeric(RMark_data_BSC$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_BSC$nest_data.processed$data$LastPresent))),
origin = "2023-01-01") - 180,
day_of_season = c(0:(max(max(as.numeric(RMark_data_BSC$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_BSC$nest_data.processed$data$LastPresent))) -
min(as.numeric(RMark_data_BSC$nest_data.processed$data$FirstFound)))))
nest_survival_reals_dates_BSC <-
left_join(nest_survival_reals_BSC_season, dates_for_plot_BSC, by = "day_of_season")
nest_survival_season_plot_BSC <-
ggplot(data = nest_survival_reals_dates_BSC) +
geom_ribbon(aes(x = date, ymin = lcl, ymax = ucl),
fill = brewer.pal(8, "Dark2")[c(1)],
alpha = 0.3) +
geom_line(aes(x = date, y = estimate),
color = brewer.pal(8, "Dark2")[c(1)],
size = 1) +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months") +
ylab("daily nest survival ± 95% CI") +
scale_y_continuous(limits = c(0, 1)) +
luke_theme +
theme(legend.position = "bottom",
legend.title = element_blank(),
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
hjust = 1,
vjust = 1))
nest_discovery_season_plot_BSC <-
ggplot(RMark_data_BSC$nest_data.processed$data,
aes(as.Date(FirstFound, origin = "2023-01-01") - 180,
fill = Fate)) +
geom_histogram(bins = 30,
# fill = brewer.pal(8, "Set1")[c(2)],
alpha = 0.5) +
scale_fill_manual(values = brewer.pal(8, "Set1")[c(2, 1)],
labels = c("Failed", "Hatched")) +
ylab("nests found\nweekly") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_survival_reals_dates_BSC$date, na.rm = TRUE),
max(nest_survival_reals_dates_BSC$date, na.rm = TRUE))) +
scale_y_continuous(breaks = c(10, 20, 30, 40)) +
luke_theme +
theme(legend.position = "none",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# merge plots together
hooded_plover_nest_plot_BSC <-
nest_discovery_season_plot_BSC /
nest_survival_season_plot_BSC +
plot_layout(widths = c(5),
heights = unit(c(1, 3), c('in', 'in'))) +
plot_annotation(tag_levels = 'A', title = "Bellarine / Surf Coast")
### PI ---
dates_for_plot_PI <-
data.frame(date = as.Date(min(as.numeric(RMark_data_PI$nest_data.processed$data$FirstFound)):
max(max(as.numeric(RMark_data_PI$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_PI$nest_data.processed$data$LastPresent))),
origin = "2023-01-01") - 180,
day_of_season = c(0:(max(max(as.numeric(RMark_data_PI$nest_data.processed$data$LastChecked)),
max(as.numeric(RMark_data_PI$nest_data.processed$data$LastPresent))) -
min(as.numeric(RMark_data_PI$nest_data.processed$data$FirstFound)))))
nest_survival_reals_dates_PI <-
left_join(nest_survival_reals_PI_season, dates_for_plot_PI, by = "day_of_season")
nest_survival_season_plot_PI <-
ggplot(data = nest_survival_reals_dates_PI) +
geom_ribbon(aes(x = date, ymin = lcl, ymax = ucl),
fill = brewer.pal(8, "Dark2")[c(1)],
alpha = 0.3) +
geom_line(aes(x = date, y = estimate),
color = brewer.pal(8, "Dark2")[c(1)],
size = 1) +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months") +
ylab("daily nest survival ± 95% CI") +
scale_y_continuous(limits = c(0, 1)) +
luke_theme +
theme(legend.position = "bottom",
legend.title = element_blank(),
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
hjust = 1,
vjust = 1))
nest_discovery_season_plot_PI <-
ggplot(RMark_data_PI$nest_data.processed$data,
aes(as.Date(FirstFound, origin = "2023-01-01") - 180,
fill = Fate)) +
geom_histogram(bins = 30,
# fill = brewer.pal(8, "Set1")[c(2)],
alpha = 0.5) +
scale_fill_manual(values = brewer.pal(8, "Set1")[c(2, 1)],
labels = c("Failed", "Hatched")) +
ylab("nests found\nweekly") +
scale_x_date(date_labels = "%B",
expand = c(0.01, 0.01),
date_breaks = "1 months", limits = c(min(nest_survival_reals_dates_PI$date, na.rm = TRUE),
max(nest_survival_reals_dates_PI$date, na.rm = TRUE))) +
scale_y_continuous(breaks = c(10, 20, 30, 40)) +
luke_theme +
theme(legend.position = "none",
panel.grid.major = element_line(colour = "grey70",
size = 0.15),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# merge plots together
hooded_plover_nest_plot_PI <-
nest_discovery_season_plot_PI /
nest_survival_season_plot_PI +
plot_layout(widths = c(5),
heights = unit(c(1, 3), c('in', 'in'))) +
plot_annotation(tag_levels = 'A', title = "Phillip Island")
hooded_plover_nest_plot_FP
Code
hooded_plover_nest_plot_MP
Code
hooded_plover_nest_plot_BC
Code
hooded_plover_nest_plot_BSC
Code
hooded_plover_nest_plot_PI
Chick Survival
Code
hatch_dates_BSC_MP_FP_BC_PI <-
bind_rows(nest_data_BSC, nest_data_MP, nest_data_FP, nest_data_BC, nest_data_PI) %>%
filter(Fate == "0") %>%
mutate(hatch_date = ifelse(Fate == "0" & !is.na(clutch_comp), clutch_comp + 28,
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & !is.na(state_1st_ob),
ifelse(state_1st_ob == "1/3 adult size, fluffy", chick_1st_ob - 3,
ifelse(state_1st_ob == "1/2 adult size, fluffy", chick_1st_ob - 10,
ifelse(state_1st_ob == "2/3 adult size, fluffy", chick_1st_ob - 19,
ifelse(state_1st_ob == "Adult size, mottled grey", chick_1st_ob - 30,
ifelse(state_1st_ob == "Fledged", chick_1st_ob - 35,
ifelse(state_1st_ob == "wet in nest", chick_1st_ob, NA)))))),
ifelse(Fate == "0" & is.na(clutch_comp) & !is.na(chick_1st_ob) & is.na(state_1st_ob), NA, NA))) %>% as.Date())check errors on import of chick data and wrangle
Code
MP_chick_data <-
suppressMessages(
bind_rows(
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2023", "_", str_sub("2024", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2022", "_", str_sub("2023", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2021", "_", str_sub("2022", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2020", "_", str_sub("2021", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2019", "_", str_sub("2020", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2018", "_", str_sub("2019", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2017", "_", str_sub("2018", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2016", "_", str_sub("2017", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2015", "_", str_sub("2016", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2014", "_", str_sub("2015", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2013", "_", str_sub("2014", 3, 4)),
col_types = "text", na = "n/a"),
read_excel(paste0("data/", "MP Chicks sightings 2006_07 to 2023_24.xlsx"),
sheet = paste0("MP", " ", "2012", "_", str_sub("2013", 3, 4)),
col_types = "text", na = "n/a"))) %>%
rename(region = Region,
site = Site,
nest_ID = Nest_ID,
date = `Observation Date_code`,
n_adults_seen = `# adult birds`,
n_juvs_seen = `# juvenile or sub-adults`,
n_chicks_seen = `# chicks_updated`,
habitat = Habitat,
chick_age = `Chick description`,
management_type = Management,
lat = `Observation Latitude`,
lon = `Observation Longitude`) %>%
mutate(season = str_sub(nest_ID, 1, 6)) %>%
dplyr::select(region, season, site, nest_ID, date, n_adults_seen, n_juvs_seen,
n_chicks_seen, habitat, chick_age, lat, lon) %>%
left_join(., hatch_dates_BSC_MP_FP_BC_PI %>%
select(nest_ID, hatch_date, chick_management_status,
chick_management_type), by = "nest_ID") %>%
rename(management_type = chick_management_type,
management_status = chick_management_status)
# age key for descriptions of chick developmental stage (email from Jas)
age_offsets <- c(
"1/3 adult size, fluffy" = -3,
"1/2 adult size, fluffy" = -10,
"2/3 adult size, fluffy" = -19,
"Adult size, mottled grey" = -30,
"Fledged" = -35,
"wet in nest" = 0
)
# calculate hatch dates from the chick data (first observation and developmental stage)
hatch_dates_from_chick_data <- MP_chick_data %>%
mutate(date = dmy(date)) %>% # convert from "DDMMYYYY" to Date
group_by(nest_ID) %>%
slice_min(order_by = date, n = 1, with_ties = FALSE) %>%
mutate(hatch_date2 = date + age_offsets[chick_age]) %>%
select(nest_ID, hatch_date2) %>%
ungroup()
# merge data
MP_chick_data_clean_hatch_dates <-
MP_chick_data %>%
left_join(., hatch_dates_from_chick_data, by = "nest_ID") %>%
filter(hatch_date == hatch_date2 |
(is.na(hatch_date) & !is.na(hatch_date2)) |
(!is.na(hatch_date) & is.na(hatch_date2)))
#### assess errors in hatch dates ----
# check for nest_IDs with differences in the two hatch_dates, these need to be checked
MP_chick_data_clean_hatch_dates <-
MP_chick_data %>%
left_join(., hatch_dates_from_chick_data, by = "nest_ID") %>%
select(nest_ID, hatch_date, hatch_date2, chick_age, date) %>%
distinct() %>%
mutate(date = dmy(date)) %>% # convert from "DDMMYYYY" to Date%>%
group_by(nest_ID) %>%
slice_min(order_by = date, n = 1, with_ties = FALSE) %>%
mutate(hatch_date_diff = hatch_date - hatch_date2) %>%
mutate(hatch_date_diff = ifelse(is.na(hatch_date_diff), 0, hatch_date_diff)) %>%
arrange(desc(abs(hatch_date_diff))) %>%
filter(hatch_date_diff == 0 &
(!is.na(hatch_date) & !is.na(hatch_date2)) |
(is.na(hatch_date) & !is.na(hatch_date2)))
#### wrangle management data ----
MP_chick_data_issues <-
MP_chick_data %>%
filter(nest_ID %in% MP_chick_data_clean_hatch_dates$nest_ID) %>%
mutate(management_type = tolower(management_type)) %>%
mutate(habitat = tolower(habitat)) %>%
mutate(management_type = str_replace(management_type, "acess", "access")) %>%
mutate(management_type = str_replace(management_type, "and", ",")) %>%
mutate(management_type = str_replace(management_type, "temporary", "")) %>%
mutate(management_type = str_replace_all(management_type, " ", "")) %>%
mutate(management_type = str_replace_all(management_type, "shelters", "")) %>%
mutate(management_type = str_replace_all(management_type, "banners", "")) %>%
mutate(management_type = str_replace_all(management_type, ",,", ",")) %>%
mutate(sign_access = ifelse(str_detect(management_type, "signaccess"), 1, 0)) %>%
mutate(sign_nest = ifelse(str_detect(management_type, "signnest"), 1, 0)) %>%
mutate(rope_fence = ifelse(str_detect(management_type, "ropefence"), 1, 0)) %>%
mutate(perm_fence = ifelse(str_detect(management_type, "permanentfence"), 1, 0)) %>%
mutate(wardens = ifelse(str_detect(management_type, "wardens"), 1, 0)) %>%
mutate(none = ifelse(str_detect(management_type, "none"), 1,
ifelse(is.na(management_type) | management_type == "", 1, 0))) %>%
mutate(other = ifelse(str_detect(management_type, "other"), 1, 0)) %>%
mutate(management_level = ifelse((sign_access == 1 | perm_fence == 1) & sign_nest == 1 & rope_fence == 1 & wardens == 1, 4,
ifelse(rope_fence == 1, 3,
ifelse(sign_nest == 1, 2,
ifelse(sign_access == 1 | perm_fence == 1, 1,
ifelse(none == 1, 0, NA)))))) %>%
mutate(sign_nest_no_sign_access = ifelse(sign_access == 0 & sign_nest == 1, 1, 0)) %>%
mutate(fence_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & rope_fence == 1, 1, 0)) %>%
mutate(wardens_no_sign = ifelse((sign_access == 0 & sign_nest == 0) & wardens == 1, 1, 0)) %>%
mutate(wardens_no_fence = ifelse(rope_fence == 1 & wardens == 1, 1, 0)) %>%
mutate(just_wardens = ifelse(rope_fence == 0 & sign_access == 0 & sign_nest == 0 & wardens == 1, 1, 0)) %>%
dplyr::select(-other, -sign_nest_no_sign_access, -fence_no_sign,
-wardens_no_sign, -wardens_no_fence, -just_wardens) %>%
mutate(management_status = ifelse(management_level == 0 & !is.na(management_level), "N",
ifelse(management_level > 0 & !is.na(management_level), "Y",
NA))) %>%
group_by(season) %>%
mutate(season = as.factor(season),
habitat = as.factor(habitat),
management_status = as.factor(management_status)) %>%
mutate(region = ifelse(str_detect(region, "Mornington"), "MP",
ifelse(str_detect(region, "Fleurieu"), "FP",
ifelse(str_detect(region, "Bellarine"), "BSC", NA)))) %>%
mutate(site = as.factor(site)) %>%
mutate(issue1 = ifelse(nchar(date) != 8, "observation date is not 8 characters; ", NA)) %>%
mutate(issue2 = ifelse(is.na(date), "observation date missing; ", NA)) %>%
mutate(issue3 = ifelse(management_status %!in% c("Y", "N"), "Nest managed? is not Y or N; ", NA)) %>%
mutate(issue4 = ifelse(is.na(management_level), "Management type is not sufficient for making levels; ", NA)) %>%
left_join(., hatch_dates_from_chick_data, by = "nest_ID") %>%
mutate(hatch_date_diff = ifelse(is.na(hatch_date) & !is.na(hatch_date2), 0,
hatch_date - hatch_date2)) %>%
mutate(issue5 = ifelse(hatch_date_diff != 0 , "Double check dates because hatch dates from chick data do not match hatch dates from nest data; ", NA)) %>%
mutate(obs_date_diff = ifelse(hatch_date_diff == 0, 0,
ifelse(!is.na(hatch_date) & is.na(hatch_date2), hatch_date - dmy(date), NA))) %>%
mutate(issue6 = ifelse(obs_date_diff > 0, "Double check dates because hatch date from nest data is after first chick observation; ", NA)) %>%
mutate(issue7 = ifelse(is.na(hatch_date) & is.na(hatch_date2), "Hatch dates derived from chick data and nest data are missing ; ", NA)) %>%
mutate(issues = ifelse(is.na(issue1) & is.na(issue2) & is.na(issue3) &
is.na(issue4) & is.na(issue5) & is.na(issue6) & is.na(issue7), NA,
paste0(issue1, issue2, issue3,
issue4, issue5, issue6, issue7))) %>%
mutate(issues = str_remove_all(issues, "NA")) %>%
mutate(issues = ifelse(is.na(issues), "usable", issues)) %>%
dplyr::select(-issue1, -issue2, -issue3,
-issue4, -issue5, -issue6, -issue7) %>%
arrange(issues)
# view issues interactively in RStudio Viewer
MP_chick_data_issues %>%
filter(issues != "usable") %>%
dplyr::select(season, nest_ID, date, management_status, management_level,
hatch_date, hatch_date2, hatch_date_diff, issues) %>%
datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')Code
MP_chick_data_issues %>% select(-c(date, management_status, management_level)) %>%
distinct() %>%
pull(issues) %>%
table().
Nest managed? is not Y or N; Management type is not sufficient for making levels;
147
usable
2161 Code
MP_chick_data_clean <-
MP_chick_data_issues %>%
filter(issues == "usable") %>%
# mutate(hatch_date = ifelse(!is.na(hatch_date) & is.na(hatch_date2), hatch_date, hatch_date2) %>% as.Date()) %>%
mutate(date = dmy(date)) %>%
select(region, season, site, nest_ID, hatch_date, date,
n_adults_seen, n_juvs_seen, n_chicks_seen, lat, lon,
management_level, management_status) %>%
group_by(nest_ID) %>%
arrange(date) %>%
ungroup() %>%
mutate(n_adults_seen = ifelse(is.na(n_adults_seen), 0, n_adults_seen),
n_juvs_seen = ifelse(is.na(n_juvs_seen), 0, n_juvs_seen),
n_chicks_seen = ifelse(is.na(n_chicks_seen), 0, n_chicks_seen)) %>%
mutate(n_offspring_seen = as.numeric(n_juvs_seen) + as.numeric(n_chicks_seen)) %>%
mutate(offspring_seen = ifelse(n_offspring_seen > 0, 1, 0)) %>%
mutate(brood_age = (date - hatch_date) %>% as.numeric()) %>%
group_by(nest_ID) %>%
mutate(management_level_max = max(management_level),
management_status_max = max(as.character(management_status)) %>% as.factor())Code
# assess how often management levels and status changed over the chick obs
MP_chick_data_clean %>%
group_by(nest_ID) %>%
summarise(n_management_level = n_distinct(management_level),
n_management_status = n_distinct(management_status)) %>%
arrange(desc(n_management_level))# A tibble: 196 × 3
nest_ID n_management_level n_management_status
<chr> <int> <int>
1 201213_Gunnamatta Pair 4 (BJ & LS)_1 1 1
2 201213_Gunnamatta Pair 5 (DU & ??)_4 1 1
3 201213_Moana crt access (east edge)_1 1 1
4 201213_Point King Rd, Portsea_1 1 1
5 201213_Portsea Farnsworth/SLSC west_2 1 1
6 201213_Rye Big Rock_2 1 1
7 201213_Rye car park east_1 1 1
8 201314_Heyfield Pair 2 (east side) Ry… 1 1
9 201314_Koonya East_3 1 1
10 201314_Koonya West_4 1 1
# ℹ 186 more rowsCode
MP_chick_data_clean %>%
pull(brood_age) %>%
as.numeric() %>%
hist(main = "Distribution of brood age",
xlab = "Days difference")
Code
# which nest_IDs have issues with dates
MP_chick_data_clean %>%
filter(brood_age < 0) %>%
pull(nest_ID) %>% unique()character(0)Code
MP_chick_data_clean_ <-
MP_chick_data_clean %>%
filter(brood_age >= 0) %>%
bind_rows(., data.frame(nest_ID = "junk", brood_age = c(1:35), offspring_seen = 1)) %>%
filter(brood_age <= 35)
MP_chick_data_clean_ %>%
select(nest_ID, brood_age, offspring_seen) %>%
as.data.frame() %>%
arrange(nest_ID, brood_age) nest_ID
1 201213_Gunnamatta Pair 4 (BJ & LS)_1
2 201213_Gunnamatta Pair 4 (BJ & LS)_1
3 201213_Gunnamatta Pair 4 (BJ & LS)_1
4 201213_Gunnamatta Pair 5 (DU & ??)_4
5 201213_Gunnamatta Pair 5 (DU & ??)_4
6 201213_Moana crt access (east edge)_1
7 201213_Moana crt access (east edge)_1
8 201213_Moana crt access (east edge)_1
9 201213_Moana crt access (east edge)_1
10 201213_Moana crt access (east edge)_1
11 201213_Moana crt access (east edge)_1
12 201213_Moana crt access (east edge)_1
13 201213_Moana crt access (east edge)_1
14 201213_Moana crt access (east edge)_1
15 201213_Moana crt access (east edge)_1
16 201213_Moana crt access (east edge)_1
17 201213_Moana crt access (east edge)_1
18 201213_Moana crt access (east edge)_1
19 201213_Moana crt access (east edge)_1
20 201213_Moana crt access (east edge)_1
21 201213_Moana crt access (east edge)_1
22 201213_Moana crt access (east edge)_1
23 201213_Moana crt access (east edge)_1
24 201213_Moana crt access (east edge)_1
25 201213_Point King Rd, Portsea_1
26 201213_Point King Rd, Portsea_1
27 201213_Point King Rd, Portsea_1
28 201213_Point King Rd, Portsea_1
29 201213_Point King Rd, Portsea_1
30 201213_Point King Rd, Portsea_1
31 201213_Point King Rd, Portsea_1
32 201213_Point King Rd, Portsea_1
33 201213_Point King Rd, Portsea_1
34 201213_Point King Rd, Portsea_1
35 201213_Point King Rd, Portsea_1
36 201213_Point King Rd, Portsea_1
37 201213_Portsea Farnsworth/SLSC west_2
38 201213_Portsea Farnsworth/SLSC west_2
39 201213_Portsea Farnsworth/SLSC west_2
40 201213_Portsea Farnsworth/SLSC west_2
41 201213_Rye Big Rock_2
42 201213_Rye Big Rock_2
43 201213_Rye Big Rock_2
44 201213_Rye Big Rock_2
45 201213_Rye car park east_1
46 201213_Rye car park east_1
47 201213_Rye car park east_1
48 201213_Rye car park east_1
49 201213_Rye car park east_1
50 201213_Rye car park east_1
51 201213_Rye car park east_1
52 201213_Rye car park east_1
53 201213_Rye car park east_1
54 201213_Rye car park east_1
55 201314_Heyfield Pair 2 (east side) Rye_1
56 201314_Heyfield Pair 2 (east side) Rye_1
57 201314_Koonya West_4
58 201314_Koonya West_4
59 201314_Koonya West_4
60 201314_Koonya West_4
61 201314_Moana crt access west_2
62 201314_Moana crt access west_2
63 201314_Moana crt access west_2
64 201314_Moana crt access west_2
65 201314_Moana crt access west_2
66 201314_Moana crt access west_2
67 201314_Moana crt access west_2
68 201314_Moana crt access west_2
69 201314_Moana crt access west_2
70 201314_Moana crt access west_2
71 201314_Moana crt access west_2
72 201314_Moana crt access west_2
73 201314_Moana crt access west_2
74 201314_Moana crt east (St Andrews)_1
75 201314_Moana crt east (St Andrews)_1
76 201314_Moana crt east (St Andrews)_1
77 201314_Moana crt east (St Andrews)_1
78 201314_Moana crt east (St Andrews)_1
79 201314_Moana crt east (St Andrews)_1
80 201314_Moana crt east (St Andrews)_1
81 201314_Point King Rd, Portsea_1
82 201314_Point King Rd, Portsea_1
83 201314_Point King Rd, Portsea_1
84 201314_Point King Rd, Portsea_1
85 201314_Point King Rd, Portsea_2
86 201314_Point King Rd, Portsea_2
87 201314_Point King Rd, Portsea_2
88 201314_Point King Rd, Portsea_2
89 201314_Point King Rd, Portsea_2
90 201314_Point King Rd, Portsea_2
91 201314_Point King Rd, Portsea_2
92 201314_Point King Rd, Portsea_2
93 201314_Point King Rd, Portsea_2
94 201314_Point King Rd, Portsea_2
95 201314_Point King Rd, Portsea_2
96 201314_Point King Rd, Portsea_2
97 201314_Portsea Franklin rd access (west edge)_1
98 201314_Portsea Franklin rd access (west edge)_1
99 201314_Rye car park west_1
100 201314_Rye car park west_1
101 201314_St Andrews Boags Rocks_5
102 201314_St Andrews Boags Rocks_5
103 201314_St Andrews Boags Rocks_5
104 201314_St Andrews Boags Rocks_5
105 201314_St Andrews Boags Rocks_5
106 201314_St Andrews Boags Rocks_5
107 201314_St Andrews Boags Rocks_5
108 201314_St Andrews Boags Rocks_5
109 201314_St Andrews Boags Rocks_5
110 201314_St Andrews Boags Rocks_5
111 201314_St Andrews Boags Rocks_5
112 201314_St Andrews Boags Rocks_5
113 201314_St Andrews Boags Rocks_5
114 201314_St Andrews Boags Rocks_5
115 201314_St Andrews Boags Rocks_5
116 201314_St Andrews Boags Rocks_5
117 201314_St. Andrews car park east_1
118 201314_St. Andrews car park east_1
119 201314_St. Andrews car park east_1
120 201314_St. Andrews car park east_1
121 201415_Gunida Crt Rye_1
122 201415_Gunida Crt Rye_1
123 201415_Gunida Crt Rye_1
124 201415_Gunida Crt Rye_1
125 201415_Gunnamatta Pair 4 (BJ & LS)_1
126 201415_Gunnamatta Pair 4 (BJ & LS)_1
127 201415_Gunnamatta Pair 4 (BJ & LS)_1
128 201415_Gunnamatta Pair 4 (BJ & LS)_1
129 201415_Heyfield Pair 2 (east side) Rye_1
130 201415_Heyfield Pair 2 (east side) Rye_1
131 201415_Heyfield Pair 2 (east side) Rye_1
132 201415_Heyfield Pair 2 (east side) Rye_1
133 201415_Heyfield Pair 2 (east side) Rye_1
134 201415_Heyfield Pair 2 (east side) Rye_1
135 201415_Heyfield Pair 2 (east side) Rye_1
136 201415_Heyfield Pair 2 (east side) Rye_1
137 201415_Heyfield Pair 2 (east side) Rye_1
138 201415_Heyfield Pair 2 (east side) Rye_1
139 201415_Koonya East_1
140 201415_Koonya East_1
141 201415_Koonya East_1
142 201415_Koonya East_1
143 201415_Koonya East_1
144 201415_Koonya East_1
145 201415_Koonya East_1
146 201415_Koonya East_1
147 201415_Koonya East_1
148 201415_Koonya East_1
149 201415_Koonya East_1
150 201415_Koonya East_1
151 201415_Koonya East_1
152 201415_Koonya East_1
153 201415_Koonya East_1
154 201415_Koonya East_1
155 201415_Koonya East_1
156 201415_Koonya East_1
157 201415_Koonya East_1
158 201415_Koonya East_1
159 201415_Koonya East_1
160 201415_Koonya East_1
161 201415_Moana crt access (east edge)_3
162 201415_Moana crt access (east edge)_3
163 201415_Moana crt access (east edge)_3
164 201415_Moana crt access (east edge)_3
165 201415_Moana crt access (east edge)_3
166 201415_Moana crt access (east edge)_3
167 201415_Moana crt access (east edge)_3
168 201415_Moana crt access (east edge)_3
169 201415_Moana crt access (east edge)_3
170 201415_Moana crt access (east edge)_3
171 201415_Moana crt access (east edge)_3
172 201415_Moana crt access (east edge)_3
173 201415_Moana crt access (east edge)_3
174 201415_Moana crt access (east edge)_3
175 201415_Moana crt access (east edge)_3
176 201415_Portsea Sphinx rocks_1
177 201415_Portsea Sphinx rocks_1
178 201415_Portsea Sphinx rocks_1
179 201415_Rye car park east_1
180 201415_Rye car park east_1
181 201415_Rye car park east_1
182 201415_Rye car park east_1
183 201415_St. Andrews car park east_2
184 201415_St. Andrews car park east_2
185 201415_St. Andrews car park east_2
186 201415_St. Andrews car park east_2
187 201516_Coppins Lookout_1
188 201516_Coppins Lookout_1
189 201516_Coppins Lookout_2
190 201516_Coppins Lookout_2
191 201516_Coppins Lookout_2
192 201516_Coppins Lookout_2
193 201516_Coppins Lookout_2
194 201516_Fowlers_4
195 201516_Fowlers_4
196 201516_Fowlers_4
197 201516_Fowlers_4
198 201516_Fowlers_4
199 201516_Fowlers_4
200 201516_Fowlers_4
201 201516_Fowlers_4
202 201516_Fowlers_4
203 201516_Fowlers_4
204 201516_Fowlers_4
205 201516_Fowlers_4
206 201516_Fowlers_4
207 201516_Fowlers_4
208 201516_Fowlers_4
209 201516_Fowlers_4
210 201516_Fowlers_4
211 201516_Fowlers_4
212 201516_Fowlers_4
213 201516_Fowlers_4
214 201516_Fowlers_4
215 201516_Fowlers_4
216 201516_Fowlers_4
217 201516_Fowlers_4
218 201516_Fowlers_4
219 201516_Fowlers_4
220 201516_Fowlers_4
221 201516_Fowlers_4
222 201516_Fowlers_4
223 201516_Fowlers_4
224 201516_Fowlers_4
225 201516_Fowlers_4
226 201516_Fowlers_4
227 201516_Fowlers_4
228 201516_Fowlers_4
229 201516_Fowlers_4
230 201516_Fowlers_4
231 201516_Fowlers_4
232 201516_Fowlers_4
233 201516_Fowlers_4
234 201516_Fowlers_4
235 201516_Fowlers_4
236 201516_Fowlers_4
237 201516_Fowlers_4
238 201516_Fowlers_4
239 201516_Fowlers_4
240 201516_Fowlers_4
241 201516_Fowlers_4
242 201516_Fowlers_4
243 201516_Fowlers_4
244 201516_Gunnamatta Pair 1_2
245 201516_Gunnamatta Pair 1_2
246 201516_Gunnamatta Pair 1_2
247 201516_Gunnamatta Pair 1_2
248 201516_Gunnamatta Pair 1_2
249 201516_Gunnamatta Pair 1_2
250 201516_Gunnamatta Pair 1_2
251 201516_Gunnamatta Pair 1_2
252 201516_Gunnamatta Pair 1_2
253 201516_Gunnamatta Pair 1_3
254 201516_Gunnamatta Pair 1_3
255 201516_Gunnamatta Pair 1_3
256 201516_Gunnamatta Pair 1_3
257 201516_Gunnamatta Pair 1_3
258 201516_Gunnamatta Pair 1_3
259 201516_Gunnamatta Pair 1_3
260 201516_Gunnamatta Pair 3_2
261 201516_Gunnamatta Pair 3_2
262 201516_Gunnamatta Pair 3_2
263 201516_Gunnamatta Pair 3_2
264 201516_Gunnamatta Pair 3_2
265 201516_Gunnamatta Pair 3_2
266 201516_Gunnamatta Pair 3_2
267 201516_Gunnamatta Pair 3_2
268 201516_Gunnamatta Pair 5 (DU)_1
269 201516_Gunnamatta Pair 5 (DU)_1
270 201516_Gunnamatta Pair 6_3
271 201516_Gunnamatta Pair 6_3
272 201516_Gunnamatta Pair 6_3
273 201516_Gunnamatta Pair 6_3
274 201516_Gunnamatta Pair 6_3
275 201516_Gunnamatta Pair 6_3
276 201516_Gunnamatta Pair 6_3
277 201516_Heyfield Pair 2 (east side) Rye_1
278 201516_Heyfield Pair 2 (east side) Rye_1
279 201516_Heyfield Pair 2 (east side) Rye_1
280 201516_Heyfield Pair 2 (east side) Rye_1
281 201516_Heyfield Pair 2 (east side) Rye_1
282 201516_Miami drive east access_2
283 201516_Miami drive east access_2
284 201516_Miami drive east access_2
285 201516_Miami drive east access_2
286 201516_Miami drive east access_2
287 201516_Miami drive east access_2
288 201516_Miami drive east access_2
289 201516_Miami drive east access_3
290 201516_Miami drive east access_3
291 201516_Miami drive east access_3
292 201516_Miami drive east access_3
293 201516_Miami drive east access_3
294 201516_Miami drive east access_3
295 201516_Miami drive east access_3
296 201516_Miami drive east access_3
297 201516_Miami drive east access_3
298 201516_Miami drive east access_3
299 201516_Miami drive east access_3
300 201516_Miami drive east access_3
301 201516_Miami drive east access_3
302 201516_Miami drive east access_3
303 201516_Miami drive east access_3
304 201516_Miami drive east access_3
305 201516_Miami drive east access_3
306 201516_Miami drive east access_3
307 201516_Miami drive east access_3
308 201516_Miami drive east access_3
309 201516_Miami drive east access_3
310 201516_Miami drive east access_3
311 201516_Miami drive east access_3
312 201516_Miami drive east access_3
313 201516_Miami drive east access_3
314 201516_Miami drive east access_3
315 201516_Miami drive east access_3
316 201516_Miami drive east access_3
317 201516_Miami drive east access_3
318 201516_Miami drive east access_3
319 201516_Miami drive east access_3
320 201516_Miami drive east access_3
321 201516_Miami drive east access_3
322 201516_Miami drive east access_3
323 201516_Miami drive east access_3
324 201516_Miami drive east access_3
325 201516_Miami drive east access_3
326 201516_Miami drive east access_3
327 201516_Miami drive east access_3
328 201516_Miami drive east access_3
329 201516_Miami drive east access_3
330 201516_Miami drive east access_3
331 201516_Miami drive east access_3
332 201516_Miami drive east access_3
333 201516_Miami drive east access_3
334 201516_Miami drive east access_3
335 201516_Miami drive east access_3
336 201516_Miami drive east access_3
337 201516_Miami drive east access_3
338 201516_Moana crt access (east edge)_1
339 201516_Moana crt access (east edge)_1
340 201516_Moana crt access (east edge)_1
341 201516_Moana crt access (east edge)_1
342 201516_Moana crt access (east edge)_1
343 201516_Moana crt access (east edge)_1
344 201516_Moana crt access (east edge)_1
345 201516_Moana crt access (east edge)_1
346 201516_Moana crt access (east edge)_1
347 201516_Moana crt access (east edge)_1
348 201516_Moana crt access (east edge)_2
349 201516_Moana crt access (east edge)_2
350 201516_Moana crt access (east edge)_2
351 201516_Moana crt access (east edge)_2
352 201516_Moana crt access (east edge)_2
353 201516_Moana crt access (east edge)_2
354 201516_Moana crt access (east edge)_2
355 201516_Moana crt access (east edge)_2
356 201516_Moana crt access (east edge)_2
357 201516_Moana crt access (east edge)_2
358 201516_Moana crt access (east edge)_2
359 201516_Moana crt access (east edge)_2
360 201516_Moana crt access (east edge)_2
361 201516_Moana crt access (east edge)_2
362 201516_Moana crt access (east edge)_2
363 201516_Moana crt access (east edge)_2
364 201516_Moana crt access (east edge)_2
365 201516_Moana crt access (east edge)_2
366 201516_Moana crt access (east edge)_2
367 201516_Moana crt access (east edge)_2
368 201516_Moana crt access (east edge)_2
369 201516_Moana crt access (east edge)_2
370 201516_Moana crt access (east edge)_2
371 201516_Moana crt access (east edge)_2
372 201516_Moana crt access (east edge)_2
373 201516_Moana crt access (east edge)_2
374 201516_Moana crt access (east edge)_2
375 201516_Moana crt access (east edge)_2
376 201516_Moana crt access (east edge)_2
377 201516_Moana crt access (east edge)_2
378 201516_Moana crt access (east edge)_2
379 201516_Moana crt access (east edge)_2
380 201516_Moana crt access (east edge)_2
381 201516_Moana crt access (east edge)_2
382 201516_Moana crt access (east edge)_2
383 201516_Moana crt access (east edge)_2
384 201516_Moana crt access (east edge)_2
385 201516_Moana crt access (east edge)_2
386 201516_Moana crt access (east edge)_2
387 201516_Moana crt access (east edge)_2
388 201516_Moana crt access (east edge)_2
389 201516_Moana crt access (east edge)_2
390 201516_Moana crt access (east edge)_2
391 201516_Moana crt access (east edge)_2
392 201516_Moana crt access (east edge)_2
393 201516_Moana crt access (east edge)_2
394 201516_Moana crt access (east edge)_2
395 201516_Moana crt access (east edge)_2
396 201516_Moana crt access (east edge)_2
397 201516_Moana crt access (east edge)_2
398 201516_Moana crt access (east edge)_2
399 201516_Moana crt access (east edge)_2
400 201516_Moana crt access (east edge)_2
401 201516_Moana crt access (east edge)_2
402 201516_Moana crt access (east edge)_2
403 201516_Moana crt access (east edge)_2
404 201516_Moana crt access (east edge)_2
405 201516_Moana crt access (east edge)_2
406 201516_Moana crt access (east edge)_2
407 201516_Moana crt access (east edge)_2
408 201516_Moana crt access (east edge)_2
409 201516_Moana crt access (east edge)_2
410 201516_Moana crt access (east edge)_2
411 201516_Moana crt access (east edge)_2
412 201516_Moana crt access (east edge)_2
413 201516_Moana crt access (east edge)_2
414 201516_Moana crt access (east edge)_2
415 201516_Moana crt access (east edge)_2
416 201516_Moana crt access (east edge)_2
417 201516_Moana crt access (east edge)_2
418 201516_Moana crt access west_2
419 201516_Moana crt access west_2
420 201516_Moana crt access west_2
421 201516_Moana crt access west_2
422 201516_Moana crt access west_2
423 201516_Moana crt access west_2
424 201516_Moana crt access west_2
425 201516_Moana crt access west_2
426 201516_Moana crt access west_2
427 201516_Moana crt access west_2
428 201516_Moana crt access west_2
429 201516_Moana crt access west_2
430 201516_Moana crt access west_2
431 201516_Moana crt access west_2
432 201516_Moana crt access west_2
433 201516_Moana crt access west_2
434 201516_Moana crt access west_2
435 201516_Moana crt access west_2
436 201516_Moana crt access west_3
437 201516_Moana crt access west_3
438 201516_Moana crt access west_3
439 201516_Moana crt access west_3
440 201516_Moana crt access west_3
441 201516_Moana crt access west_3
442 201516_Moana crt access west_3
443 201516_Moana crt access west_3
444 201516_Moana crt access west_3
445 201516_Moana crt access west_3
446 201516_Moana crt access west_3
447 201516_Moana crt access west_3
448 201516_Moana crt access west_3
449 201516_Moana crt access west_3
450 201516_Moana crt access west_3
451 201516_Moana crt access west_3
452 201516_Moana crt access west_3
453 201516_Moana crt access west_3
454 201516_Moana crt access west_3
455 201516_Moana crt access west_3
456 201516_Moana crt access west_3
457 201516_Moana crt access west_3
458 201516_Moana crt access west_3
459 201516_Moana crt access west_3
460 201516_Moana crt access west_3
461 201516_Moana crt access west_3
462 201516_Moana crt access west_3
463 201516_Moana crt access west_3
464 201516_Moana crt access west_3
465 201516_Moana crt access west_3
466 201516_Moana crt access west_3
467 201516_Moana crt access west_3
468 201516_Moana crt access west_3
469 201516_Moana crt access west_3
470 201516_Moana crt access west_3
471 201516_Moana crt access west_3
472 201516_Moana crt access west_3
473 201516_Moana crt access west_3
474 201516_Moana crt access west_3
475 201516_Moana crt access west_3
476 201516_Moana crt access west_3
477 201516_Moana crt access west_3
478 201516_Moana crt access west_3
479 201516_Moana crt access west_3
480 201516_Moana crt access west_3
481 201516_Moana crt access west_3
482 201516_Moana crt access west_3
483 201516_Moana crt access west_3
484 201516_Moana crt access west_3
485 201516_Moana crt access west_3
486 201516_Moana crt access west_3
487 201516_Moana crt access west_3
488 201516_Moana crt access west_3
489 201516_Moana crt access west_3
490 201516_Moana crt access west_3
491 201516_Moana crt access west_3
492 201516_Moana crt access west_3
493 201516_Montforts_1
494 201516_Montforts_1
495 201516_Montforts_1
496 201516_Montforts_1
497 201516_Montforts_1
498 201516_Montforts_1
499 201516_Montforts_1
500 201516_Montforts_1
501 201516_Montforts_1
502 201516_Montforts_1
503 201516_Montforts_1
504 201516_Montforts_1
505 201516_Montforts_1
506 201516_Montforts_1
507 201516_Montforts_1
508 201516_Montforts_1
509 201516_Portsea Farnsworth/SLSC west_3
510 201516_Portsea Farnsworth/SLSC west_3
511 201516_Portsea Farnsworth/SLSC west_3
512 201516_Portsea Farnsworth/SLSC west_3
513 201516_Portsea Farnsworth/SLSC west_3
514 201516_Portsea Farnsworth/SLSC west_3
515 201516_Portsea Farnsworth/SLSC west_3
516 201516_Portsea Farnsworth/SLSC west_3
517 201516_Portsea Farnsworth/SLSC west_3
518 201516_Portsea Farnsworth/SLSC west_3
519 201516_Portsea Farnsworth/SLSC west_3
520 201516_Portsea Farnsworth/SLSC west_3
521 201516_Portsea Farnsworth/SLSC west_3
522 201516_Portsea Farnsworth/SLSC west_3
523 201516_Portsea Farnsworth/SLSC west_3
524 201516_Portsea Farnsworth/SLSC west_3
525 201516_Portsea Farnsworth/SLSC west_3
526 201516_Portsea Franklin rd access_1
527 201516_Portsea Franklin rd access_1
528 201516_Portsea Franklin rd access_1
529 201516_Portsea Franklin rd access_1
530 201516_Portsea Franklin rd access_1
531 201516_Portsea Franklin rd access_1
532 201516_Portsea Franklin rd access_1
533 201516_Portsea Franklin rd access_1
534 201516_Portsea Franklin rd access_1
535 201516_Portsea Franklin rd access_1
536 201516_Portsea Franklin rd access_3
537 201516_Portsea Franklin rd access_3
538 201516_Portsea Franklin rd access_3
539 201516_Portsea Franklin rd access_3
540 201516_Portsea Franklin rd access_3
541 201516_Portsea Franklin rd access_3
542 201516_Portsea Franklin rd access_3
543 201516_Portsea Franklin rd access_3
544 201516_Portsea Franklin rd access_3
545 201516_Portsea Franklin rd access_3
546 201516_Portsea Franklin rd access_3
547 201516_Portsea Franklin rd access_3
548 201516_Portsea Franklin rd access_3
549 201516_Portsea Franklin rd access_3
550 201516_Portsea Franklin rd access_3
551 201516_Portsea Franklin rd access_3
552 201516_Portsea Franklin rd access_3
553 201516_Portsea Franklin rd access_3
554 201516_Portsea Franklin rd access_3
555 201516_Portsea Franklin rd access_3
556 201516_Portsea Franklin rd access_3
557 201516_Portsea Franklin rd access_3
558 201516_Portsea Franklin rd access_3
559 201516_Portsea Franklin rd access_3
560 201516_Portsea Franklin rd access_3
561 201516_Portsea Franklin rd access_3
562 201516_Rye Big Rock_4
563 201516_Rye Big Rock_4
564 201516_Rye Big Rock_4
565 201516_Rye Big Rock_4
566 201516_Rye Big Rock_4
567 201516_Rye Big Rock_4
568 201516_Rye Big Rock_4
569 201516_Rye Big Rock_4
570 201516_Rye Big Rock_4
571 201516_Rye Big Rock_4
572 201516_Rye Big Rock_4
573 201516_Rye Big Rock_4
574 201516_Rye Big Rock_4
575 201516_Rye Big Rock_4
576 201516_Rye car park west_1
577 201516_Rye car park west_1
578 201516_Rye car park west_1
579 201516_Rye car park west_1
580 201516_Rye car park west_1
581 201516_Rye car park west_3
582 201516_Rye car park west_3
583 201516_Rye car park west_3
584 201516_Rye car park west_3
585 201516_Rye car park west_3
586 201516_Rye car park west_3
587 201516_Rye car park west_3
588 201516_St Andrews carpark west_1
589 201516_St Andrews carpark west_1
590 201516_St Andrews carpark west_1
591 201516_St Andrews carpark west_1
592 201516_St Andrews carpark west_2
593 201516_St Andrews carpark west_2
594 201516_St Andrews carpark west_2
595 201516_St Andrews carpark west_2
596 201516_St Andrews carpark west_2
597 201516_St Andrews carpark west_2
598 201516_St Andrews carpark west_2
599 201516_St Andrews carpark west_2
600 201516_St Andrews carpark west_2
601 201516_St Andrews carpark west_2
602 201516_St Andrews carpark west_2
603 201516_St Andrews carpark west_2
604 201516_St Andrews carpark west_2
605 201516_St Andrews carpark west_2
606 201516_St Andrews carpark west_2
607 201516_St Andrews carpark west_2
608 201516_St Andrews carpark west_2
609 201516_St Andrews carpark west_2
610 201516_St Andrews carpark west_2
611 201516_St Andrews carpark west_2
612 201516_St Andrews carpark west_2
613 201516_St Andrews carpark west_2
614 201516_St Andrews carpark west_2
615 201516_St Andrews carpark west_2
616 201516_St Andrews carpark west_2
617 201516_St Andrews carpark west_2
618 201516_St Andrews carpark west_2
619 201516_St Andrews carpark west_2
620 201516_St Andrews carpark west_2
621 201516_St Andrews carpark west_2
622 201516_St Andrews carpark west_2
623 201516_St Andrews carpark west_2
624 201516_St Andrews carpark west_2
625 201516_St Andrews carpark west_2
626 201516_St Andrews carpark west_2
627 201516_St Andrews carpark west_2
628 201516_St Andrews carpark west_2
629 201516_St Andrews carpark west_2
630 201516_St Andrews carpark west_2
631 201516_St Andrews carpark west_2
632 201516_St Andrews carpark west_2
633 201516_St Andrews carpark west_2
634 201516_St Andrews carpark west_2
635 201516_St Andrews carpark west_2
636 201516_St Andrews carpark west_2
637 201516_St Andrews carpark west_2
638 201516_St Andrews carpark west_2
639 201516_St Andrews carpark west_2
640 201516_St. Andrews car park east_1
641 201516_St. Andrews car park east_1
642 201516_St. Andrews car park east_1
643 201516_St. Andrews car park east_1
644 201516_St. Andrews car park east_1
645 201516_St. Andrews car park east_1
646 201516_St. Andrews car park east_1
647 201516_St. Andrews car park east_1
648 201516_St. Andrews car park east_1
649 201516_St. Andrews car park east_1
650 201516_St. Andrews car park east_2
651 201516_St. Andrews car park east_2
652 201516_St. Andrews car park east_2
653 201516_St. Andrews car park east_2
654 201516_St. Andrews car park east_2
655 201516_St. Andrews car park east_2
656 201516_St. Andrews car park east_2
657 201516_St. Andrews car park east_2
658 201516_St. Andrews car park east_2
659 201516_St. Andrews car park east_2
660 201516_St. Andrews car park east_2
661 201516_St. Andrews car park east_2
662 201516_St. Andrews car park east_2
663 201516_St. Andrews car park east_2
664 201516_St. Andrews car park east_2
665 201516_St. Andrews car park east_2
666 201516_St. Andrews car park east_2
667 201516_St. Andrews car park east_2
668 201516_St. Andrews car park east_2
669 201516_St. Andrews car park east_2
670 201516_St. Andrews car park east_2
671 201516_St. Andrews car park east_2
672 201516_St. Andrews car park east_2
673 201516_St. Andrews car park east_2
674 201516_St. Andrews car park east_2
675 201516_St. Andrews car park east_2
676 201516_St. Andrews car park east_2
677 201516_St. Andrews car park east_2
678 201516_St. Andrews car park east_2
679 201516_St. Andrews car park east_2
680 201516_St. Andrews car park east_2
681 201516_St. Andrews car park east_2
682 201516_St. Andrews car park east_2
683 201516_St. Andrews car park east_2
684 201516_St. Andrews car park east_2
685 201516_St. Andrews car park east_2
686 201516_St. Andrews car park east_2
687 201516_St. Andrews car park east_2
688 201516_St. Andrews car park east_2
689 201516_St. Andrews car park east_2
690 201516_St. Andrews car park east_2
691 201516_St. Andrews car park east_2
692 201516_St. Andrews car park east_2
693 201516_St. Andrews car park east_2
694 201516_St. Andrews car park east_2
695 201516_St. Andrews car park east_2
696 201516_St. Andrews car park east_2
697 201617_Coppins Lookout_4
698 201617_Coppins Lookout_4
699 201617_Coppins Lookout_4
700 201617_Coppins Lookout_4
701 201617_Coppins Lookout_4
702 201617_Coppins Lookout_4
703 201617_Coppins Lookout_4
704 201617_Coppins Lookout_4
705 201617_Coppins Lookout_4
706 201617_Coppins Lookout_4
707 201617_Fowlers_3
708 201617_Fowlers_3
709 201617_Fowlers_3
710 201617_Fowlers_3
711 201617_Fowlers_3
712 201617_Fowlers_3
713 201617_Fowlers_3
714 201617_Fowlers_3
715 201617_Fowlers_4
716 201617_Fowlers_4
717 201617_Fowlers_4
718 201617_Fowlers_4
719 201617_Fowlers_4
720 201617_Fowlers_4
721 201617_Fowlers_4
722 201617_Fowlers_4
723 201617_Fowlers_4
724 201617_Fowlers_4
725 201617_Fowlers_4
726 201617_Fowlers_4
727 201617_Fowlers_4
728 201617_Fowlers_4
729 201617_Fowlers_4
730 201617_Fowlers_4
731 201617_Fowlers_4
732 201617_Fowlers_4
733 201617_Fowlers_4
734 201617_Fowlers_4
735 201617_Fowlers_4
736 201617_Fowlers_4
737 201617_Fowlers_4
738 201617_Fowlers_4
739 201617_Fowlers_4
740 201617_Fowlers_4
741 201617_Fowlers_4
742 201617_Fowlers_4
743 201617_Fowlers_4
744 201617_Fowlers_4
745 201617_Fowlers_4
746 201617_Fowlers_4
747 201617_Fowlers_4
748 201617_Fowlers_4
749 201617_Fowlers_4
750 201617_Fowlers_4
751 201617_Fowlers_4
752 201617_Fowlers_4
753 201617_Fowlers_4
754 201617_Fowlers_4
755 201617_Fowlers_4
756 201617_Fowlers_4
757 201617_Fowlers_4
758 201617_Fowlers_4
759 201617_Fowlers_4
760 201617_Fowlers_4
761 201617_Fowlers_4
762 201617_Fowlers_4
763 201617_Fowlers_4
764 201617_Fowlers_4
765 201617_Fowlers_4
766 201617_Fowlers_4
767 201617_Fowlers_4
768 201617_Fowlers_4
769 201617_Gunida Crt Rye_1
770 201617_Gunida Crt Rye_1
771 201617_Gunida Crt Rye_1
772 201617_Gunida Crt Rye_1
773 201617_Gunida Crt Rye_4
774 201617_Gunida Crt Rye_4
775 201617_Gunida Crt Rye_4
776 201617_Gunida Crt Rye_4
777 201617_Gunida Crt Rye_4
778 201617_Gunida Crt Rye_4
779 201617_Gunida Crt Rye_4
780 201617_Gunida Crt Rye_4
781 201617_Gunida Crt Rye_4
782 201617_Gunida Crt Rye_4
783 201617_Gunida Crt Rye_4
784 201617_Gunida Crt Rye_4
785 201617_Gunida Crt Rye_4
786 201617_Gunida Crt Rye_4
787 201617_Gunida Crt Rye_4
788 201617_Gunida Crt Rye_4
789 201617_Gunnamatta Fingal Tk (P4)_3
790 201617_Gunnamatta Fingal Tk (P4)_3
791 201617_Gunnamatta Fingal Tk (P4)_3
792 201617_Gunnamatta Fingal Tk (P4)_3
793 201617_Gunnamatta Fingal Tk (P4)_3
794 201617_Gunnamatta Fingal Tk (P4)_3
795 201617_Gunnamatta Fingal Tk (P4)_3
796 201617_Gunnamatta Fingal Tk (P4)_3
797 201617_Gunnamatta Fingal Tk (P4)_3
798 201617_Gunnamatta Fingal Tk (P4)_3
799 201617_Gunnamatta Fingal Tk (P4)_3
800 201617_Gunnamatta Fingal Tk (P4)_3
801 201617_Gunnamatta Fingal Tk (P4)_3
802 201617_Gunnamatta Fingal Tk (P4)_3
803 201617_Gunnamatta Fingal Tk (P4)_3
804 201617_Gunnamatta Fingal Tk (P4)_3
805 201617_Gunnamatta Fingal Tk East (P5)_4
806 201617_Gunnamatta Fingal Tk East (P5)_4
807 201617_Gunnamatta Fingal Tk West_2
808 201617_Gunnamatta Fingal Tk West_2
809 201617_Gunnamatta Fingal Tk West_2
810 201617_Gunnamatta Fingal Tk West_2
811 201617_Gunnamatta Fingal Tk West_2
812 201617_Gunnamatta Fingal Tk West_2
813 201617_Gunnamatta Fingal Tk West_2
814 201617_Gunnamatta Fingal Tk West_2
815 201617_Gunnamatta Fingal Tk West_2
816 201617_Gunnamatta Fingal Tk West_2
817 201617_Gunnamatta Fingal Tk West_2
818 201617_Gunnamatta Fingal Tk West_2
819 201617_Gunnamatta Fingal Tk West_2
820 201617_Gunnamatta Fingal Tk West_2
821 201617_Gunnamatta Fingal Tk West_2
822 201617_Gunnamatta Fingal Tk West_2
823 201617_Gunnamatta Fingal Tk West_2
824 201617_Gunnamatta Fingal Tk West_2
825 201617_Gunnamatta Fingal Tk West_2
826 201617_Gunnamatta Fingal Tk West_2
827 201617_Gunnamatta Fingal Tk West_2
828 201617_Gunnamatta Fingal Tk West_2
829 201617_Gunnamatta Fingal Tk West_2
830 201617_Gunnamatta Fingal Tk West_2
831 201617_Gunnamatta Fingal Tk West_2
832 201617_Gunnamatta Fingal Tk West_2
833 201617_Gunnamatta Fingal Tk West_2
834 201617_Gunnamatta Fingal Tk West_2
835 201617_Gunnamatta Fingal Tk West_2
836 201617_Gunnamatta Fingal Tk West_2
837 201617_Gunnamatta Fingal Tk West_2
838 201617_Gunnamatta Fingal Tk West_2
839 201617_Gunnamatta Fingal Tk West_2
840 201617_Gunnamatta Fingal Tk West_2
841 201617_Gunnamatta Fingal Tk West_2
842 201617_Gunnamatta Fingal Tk West_2
843 201617_Gunnamatta Fingal Tk West_2
844 201617_Gunnamatta Fingal Tk West_2
845 201617_Gunnamatta Fingal Tk West_2
846 201617_Gunnamatta Fingal Tk West_2
847 201617_Gunnamatta Fingal Tk West_2
848 201617_Gunnamatta Fingal Tk West_2
849 201617_Gunnamatta Fingal Tk West_2
850 201617_Gunnamatta Fingal Tk West_2
851 201617_Gunnamatta No Dogs East (P1)_2
852 201617_Gunnamatta No Dogs East (P1)_2
853 201617_Gunnamatta No Dogs East (P1)_2
854 201617_Gunnamatta No Dogs East (P1)_2
855 201617_Gunnamatta No Dogs East (P1)_2
856 201617_Gunnamatta No Dogs East (P1)_2
857 201617_Gunnamatta No Dogs East (P1)_2
858 201617_Gunnamatta No Dogs East (P1)_2
859 201617_Gunnamatta No Dogs East (P1)_2
860 201617_Gunnamatta No Dogs East (P1)_2
861 201617_Gunnamatta No Dogs East (P1)_2
862 201617_Gunnamatta No Dogs East (P1)_2
863 201617_Gunnamatta No Dogs East (P1)_2
864 201617_Gunnamatta No Dogs East (P1)_2
865 201617_Gunnamatta No Dogs East (P1)_2
866 201617_Gunnamatta No Dogs East (P1)_2
867 201617_Gunnamatta No Dogs East (P1)_2
868 201617_Gunnamatta No Dogs East (P1)_2
869 201617_Gunnamatta No Dogs East (P1)_2
870 201617_Gunnamatta No Dogs East (P1)_2
871 201617_Gunnamatta No Dogs East (P1)_2
872 201617_Gunnamatta No Dogs East (P1)_2
873 201617_Gunnamatta No Dogs East (P1)_2
874 201617_Gunnamatta No Dogs East (P1)_2
875 201617_Gunnamatta No Dogs East (P1)_2
876 201617_Gunnamatta No Dogs East (P1)_2
877 201617_Gunnamatta No Dogs East (P1)_2
878 201617_Gunnamatta No Dogs East (P1)_2
879 201617_Gunnamatta No Dogs East (P1)_2
880 201617_Gunnamatta No Dogs East (P1)_2
881 201617_Gunnamatta No Dogs East (P1)_2
882 201617_Gunnamatta No Dogs East (P1)_2
883 201617_Gunnamatta No Dogs East (P1)_2
884 201617_Gunnamatta No Dogs East (P1)_2
885 201617_Gunnamatta No Dogs East (P1)_2
886 201617_Gunnamatta No Dogs East (P1)_2
887 201617_Gunnamatta No Dogs East (P1)_2
888 201617_Gunnamatta No Dogs East (P1)_2
889 201617_Gunnamatta No Dogs East (P1)_2
890 201617_Gunnamatta No Dogs East (P1)_2
891 201617_Gunnamatta No Dogs East (P1)_2
892 201617_Gunnamatta No Dogs East (P1)_2
893 201617_Gunnamatta No Dogs East (P1)_2
894 201617_Heyfield Pair 2 (east side) Rye_1
895 201617_Heyfield Pair 2 (east side) Rye_1
896 201617_Heyfield Pair 2 (east side) Rye_1
897 201617_Heyfield Pair 2 (east side) Rye_1
898 201617_Heyfield Pair 2 (east side) Rye_1
899 201617_Heyfield Pair 2 (east side) Rye_1
900 201617_Heyfield Pair 2 (east side) Rye_3
901 201617_Heyfield Pair 2 (east side) Rye_3
902 201617_Heyfield Pair 2 (east side) Rye_3
903 201617_Heyfield Pair 2 (east side) Rye_3
904 201617_Heyfield Pair 2 (east side) Rye_3
905 201617_Heyfield Pair 2 (east side) Rye_3
906 201617_Heyfield Pair 2 (east side) Rye_3
907 201617_Heyfield Pair 2 (east side) Rye_3
908 201617_Heyfield Pair 2 (east side) Rye_3
909 201617_Heyfield Pair 2 (east side) Rye_3
910 201617_Heyfield Pair 2 (east side) Rye_3
911 201617_Heyfield Pair 2 (east side) Rye_3
912 201617_Heyfield Pair 2 (east side) Rye_3
913 201617_Heyfield Pair 2 (east side) Rye_3
914 201617_Heyfield Pair 2 (east side) Rye_4
915 201617_Heyfield Pair 2 (east side) Rye_4
916 201617_Heyfield Pair 2 (east side) Rye_4
917 201617_Heyfield Pair 2 (east side) Rye_4
918 201617_Heyfield Pair 2 (east side) Rye_4
919 201617_Heyfield Pair 2 (east side) Rye_4
920 201617_Heyfield Pair 2 (east side) Rye_4
921 201617_Heyfield Pair 2 (east side) Rye_4
922 201617_Heyfield Pair 2 (east side) Rye_4
923 201617_Heyfield Pair 2 (east side) Rye_4
924 201617_Heyfield Pair 2 (east side) Rye_4
925 201617_Heyfield Pair 2 (east side) Rye_4
926 201617_Heyfield Pair 2 (east side) Rye_4
927 201617_Moana crt access (east edge)_2
928 201617_Moana crt access (east edge)_2
929 201617_Moana crt access (east edge)_2
930 201617_Moana crt access (east edge)_2
931 201617_Moana crt access (east edge)_2
932 201617_Moana crt access (east edge)_2
933 201617_Moana crt access (east edge)_2
934 201617_Moana crt access (east edge)_2
935 201617_Moana crt access (east edge)_2
936 201617_Moana crt access (east edge)_2
937 201617_Moana crt access (east edge)_2
938 201617_Moana crt access (east edge)_2
939 201617_Moana crt access (east edge)_2
940 201617_Moana crt access (east edge)_2
941 201617_Moana crt access (east edge)_2
942 201617_Moana crt access (east edge)_2
943 201617_Moana crt access (east edge)_2
944 201617_Moana crt access (east edge)_2
945 201617_Moana crt access (east edge)_2
946 201617_Moana crt access (east edge)_2
947 201617_Moana crt access (east edge)_2
948 201617_Moana crt access (east edge)_2
949 201617_Moana crt access (east edge)_2
950 201617_Moana crt access (east edge)_2
951 201617_Moana crt access (east edge)_2
952 201617_Moana crt access (east edge)_2
953 201617_Moana crt access (east edge)_2
954 201617_Moana crt access (east edge)_2
955 201617_Moana crt access (east edge)_2
956 201617_Moana crt access (east edge)_2
957 201617_Moana crt access (east edge)_2
958 201617_Moana crt access (east edge)_2
959 201617_Moana crt access (east edge)_2
960 201617_Moana crt access (east edge)_2
961 201617_Moana crt access (east edge)_2
962 201617_Moana crt access (east edge)_2
963 201617_Moana crt access (east edge)_2
964 201617_Moana crt access (east edge)_2
965 201617_Moana crt access (east edge)_2
966 201617_Moana crt access (east edge)_2
967 201617_Moana crt access (east edge)_2
968 201617_Moana crt access (east edge)_2
969 201617_Moana crt access (east edge)_2
970 201617_Moana crt access (east edge)_2
971 201617_Moana crt access (east edge)_2
972 201617_Moana crt access (east edge)_2
973 201617_Moana crt access (east edge)_2
974 201617_Moana crt access west_3
975 201617_Moana crt access west_3
976 201617_Moana crt access west_3
977 201617_Moana crt access west_3
978 201617_Moana crt access west_3
979 201617_Moana crt access west_3
980 201617_Moana crt access west_3
981 201617_Moana crt access west_3
982 201617_Moana crt access west_3
983 201617_Moana crt access west_3
984 201617_Moana crt access west_3
985 201617_Moana crt access west_3
986 201617_Moana crt access west_3
987 201617_Moana crt access west_3
988 201617_Moana crt access west_3
989 201617_Moana crt access west_3
990 201617_Moana crt access west_3
991 201617_Moana crt access west_3
992 201617_Moana crt access west_3
993 201617_Moana crt access west_3
994 201617_Moana crt access west_3
995 201617_Moana crt access west_3
996 201617_Moana crt access west_3
997 201617_Moana crt access west_3
998 201617_Moana crt access west_3
999 201617_Moana crt access west_3
1000 201617_Moana crt access west_3
1001 201617_Moana crt access west_3
1002 201617_Montforts_1
1003 201617_Montforts_1
1004 201617_Montforts_1
1005 201617_Montforts_1
1006 201617_Montforts_1
1007 201617_Montforts_1
1008 201617_Montforts_1
1009 201617_Montforts_1
1010 201617_Montforts_1
1011 201617_Montforts_1
1012 201617_Montforts_1
1013 201617_Montforts_1
1014 201617_Montforts_1
1015 201617_Montforts_1
1016 201617_Montforts_1
1017 201617_Montforts_1
1018 201617_Montforts_1
1019 201617_Montforts_1
1020 201617_Montforts_1
1021 201617_Montforts_1
1022 201617_Montforts_1
1023 201617_Montforts_1
1024 201617_Montforts_1
1025 201617_Montforts_1
1026 201617_Montforts_1
1027 201617_Montforts_1
1028 201617_Montforts_1
1029 201617_Montforts_1
1030 201617_Montforts_1
1031 201617_Montforts_1
1032 201617_Montforts_2
1033 201617_Montforts_2
1034 201617_Montforts_2
1035 201617_Montforts_2
1036 201617_Montforts_2
1037 201617_Montforts_2
1038 201617_Montforts_2
1039 201617_Montforts_2
1040 201617_Montforts_2
1041 201617_Montforts_2
1042 201617_Montforts_2
1043 201617_Montforts_2
1044 201617_Portsea Farnsworth/SLSC west_1
1045 201617_Portsea Farnsworth/SLSC west_1
1046 201617_Portsea Farnsworth/SLSC west_1
1047 201617_Portsea Farnsworth/SLSC west_1
1048 201617_Portsea Farnsworth/SLSC west_1
1049 201617_Portsea Farnsworth/SLSC west_1
1050 201617_Portsea Farnsworth/SLSC west_1
1051 201617_Portsea Farnsworth/SLSC west_1
1052 201617_Portsea Farnsworth/SLSC west_1
1053 201617_Portsea Farnsworth/SLSC west_1
1054 201617_Portsea Farnsworth/SLSC west_1
1055 201617_Portsea Farnsworth/SLSC west_1
1056 201617_Portsea Farnsworth/SLSC west_1
1057 201617_Portsea Farnsworth/SLSC west_1
1058 201617_Portsea Farnsworth/SLSC west_1
1059 201617_Portsea Farnsworth/SLSC west_1
1060 201617_Portsea Farnsworth/SLSC west_1
1061 201617_Portsea Farnsworth/SLSC west_1
1062 201617_Portsea Farnsworth/SLSC west_1
1063 201617_Portsea Farnsworth/SLSC west_1
1064 201617_Portsea Farnsworth/SLSC west_1
1065 201617_Portsea Farnsworth/SLSC west_1
1066 201617_Portsea Farnsworth/SLSC west_1
1067 201617_Portsea Farnsworth/SLSC west_1
1068 201617_Portsea Farnsworth/SLSC west_1
1069 201617_Portsea Farnsworth/SLSC west_1
1070 201617_Portsea Farnsworth/SLSC west_1
1071 201617_Portsea Farnsworth/SLSC west_1
1072 201617_Portsea Farnsworth/SLSC west_1
1073 201617_Portsea Farnsworth/SLSC west_1
1074 201617_Pt Nepean (authorised personnel only) - Borgs Rocks_1
1075 201617_Pt Nepean (authorised personnel only) - Borgs Rocks_1
1076 201617_Pt Nepean (authorised personnel only) - Sierra Nevada Rocks_2
1077 201617_Pt Nepean (authorised personnel only) - Sierra Nevada Rocks_2
1078 201617_Pt Nepean (authorised personnel only) - Sierra Nevada Rocks_2
1079 201617_Pt Nepean (authorised personnel only) - Sierra Nevada Rocks_2
1080 201617_Pt Nepean (authorised personnel only) - Spooks_3
1081 201617_Pt Nepean (authorised personnel only) - Spooks_3
1082 201617_Pt Nepean (authorised personnel only) - Spooks_3
1083 201617_Pt Nepean (authorised personnel only) - Spooks_3
1084 201617_Pt Nepean (authorised personnel only) - Spooks_3
1085 201617_Pt Nepean (authorised personnel only) - Spooks_3
1086 201617_Pt Nepean (authorised personnel only) - Spooks_3
1087 201617_Pt Nepean (authorised personnel only) - Spooks_3
1088 201617_Rye Big Rock_2
1089 201617_Rye Big Rock_2
1090 201617_Rye Big Rock_2
1091 201617_Rye Big Rock_2
1092 201617_Rye Big Rock_2
1093 201617_Rye Big Rock_2
1094 201617_Rye Big Rock_2
1095 201617_Rye Big Rock_2
1096 201617_Rye Big Rock_2
1097 201617_Rye Big Rock_2
1098 201617_Rye Big Rock_2
1099 201617_Rye Big Rock_2
1100 201617_Rye Big Rock_2
1101 201617_Rye Big Rock_2
1102 201617_Rye Big Rock_2
1103 201617_Rye Big Rock_2
1104 201617_Rye Big Rock_2
1105 201617_Rye Big Rock_2
1106 201617_Rye Big Rock_2
1107 201617_Rye Big Rock_2
1108 201617_Rye Big Rock_3
1109 201617_Rye Big Rock_3
1110 201617_Rye car park west_2
1111 201617_Rye car park west_2
1112 201617_Rye car park west_2
1113 201617_Rye car park west_2
1114 201617_Rye car park west_2
1115 201617_Rye car park west_2
1116 201617_Rye car park west_2
1117 201617_Rye car park west_2
1118 201617_Rye car park west_2
1119 201617_Somers, Sandy Point_1
1120 201617_Somers, Sandy Point_1
1121 201617_Somers, Sandy Point_1
1122 201617_Somers, Sandy Point_1
1123 201617_Somers, Sandy Point_1
1124 201617_St Andrews carpark west_5
1125 201617_St Andrews carpark west_5
1126 201617_St Andrews carpark west_5
1127 201617_St Andrews carpark west_5
1128 201617_St Andrews carpark west_5
1129 201617_St Andrews carpark west_5
1130 201617_St Andrews carpark west_5
1131 201617_St Andrews carpark west_5
1132 201617_St Andrews carpark west_5
1133 201617_St Andrews carpark west_5
1134 201617_St Andrews carpark west_5
1135 201617_St Andrews carpark west_5
1136 201617_St Andrews carpark west_5
1137 201617_St Andrews carpark west_5
1138 201617_St Andrews carpark west_5
1139 201617_St Andrews carpark west_5
1140 201617_St Andrews carpark west_5
1141 201617_St Andrews carpark west_5
1142 201617_St Andrews carpark west_5
1143 201617_St Andrews carpark west_5
1144 201617_St Andrews carpark west_5
1145 201617_St Andrews carpark west_5
1146 201617_St Andrews carpark west_5
1147 201617_St Andrews carpark west_5
1148 201617_St Andrews carpark west_5
1149 201718_Alison Ave next bay west Rye_1
1150 201718_Alison Ave next bay west Rye_1
1151 201718_Alison Ave next bay west Rye_1
1152 201718_Alison Ave next bay west Rye_1
1153 201718_Alison Ave next bay west Rye_1
1154 201718_Alison Ave next bay west Rye_1
1155 201718_Alison Ave next bay west Rye_1
1156 201718_Alison Ave next bay west Rye_1
1157 201718_Alison Ave next bay west Rye_1
1158 201718_Alison Ave next bay west Rye_1
1159 201718_Alison Ave next bay west Rye_1
1160 201718_Alison Ave next bay west Rye_1
1161 201718_Alison Ave next bay west Rye_1
1162 201718_Alison Ave next bay west Rye_1
1163 201718_Alison Ave next bay west Rye_1
1164 201718_Alison Ave next bay west Rye_1
1165 201718_Alison Ave next bay west Rye_1
1166 201718_Alison Ave next bay west Rye_1
1167 201718_Alison Ave next bay west Rye_1
1168 201718_Alison Ave next bay west Rye_1
1169 201718_Alison Ave next bay west Rye_1
1170 201718_Alison Ave next bay west Rye_1
1171 201718_Alison Ave next bay west Rye_1
1172 201718_Alison Ave next bay west Rye_1
1173 201718_Alison Ave next bay west Rye_1
1174 201718_Alison Ave next bay west Rye_1
1175 201718_Alison Ave next bay west Rye_1
1176 201718_Alison Ave next bay west Rye_1
1177 201718_Alison Ave next bay west Rye_1
1178 201718_Alison Ave next bay west Rye_1
1179 201718_Alison Ave next bay west Rye_1
1180 201718_Alison Ave next bay west Rye_1
1181 201718_Alison Ave next bay west Rye_1
1182 201718_Alison Ave next bay west Rye_1
1183 201718_Alison Ave next bay west Rye_1
1184 201718_Alison Ave next bay west Rye_1
1185 201718_Alison Ave next bay west Rye_1
1186 201718_Alison Ave next bay west Rye_1
1187 201718_Alison ave west Rye_1
1188 201718_Alison ave west Rye_1
1189 201718_Alison ave west Rye_1
1190 201718_Alison ave west Rye_1
1191 201718_Alison ave west Rye_1
1192 201718_Alison ave west Rye_1
1193 201718_Alison ave west Rye_1
1194 201718_Alison ave west Rye_1
1195 201718_Alison ave west Rye_1
1196 201718_Alison ave west Rye_1
1197 201718_Alison ave west Rye_1
1198 201718_Alison ave west Rye_1
1199 201718_Alison ave west Rye_1
1200 201718_Alison ave west Rye_1
1201 201718_Alison ave west Rye_1
1202 201718_Alison ave west Rye_1
1203 201718_Alison ave west Rye_1
1204 201718_Alison ave west Rye_1
1205 201718_Alison ave west Rye_1
1206 201718_Alison ave west Rye_1
1207 201718_Alison ave west Rye_1
1208 201718_Alison ave west Rye_1
1209 201718_Alison ave west Rye_1
1210 201718_Coppins Lookout_3
1211 201718_Coppins Lookout_3
1212 201718_Coppins Lookout_3
1213 201718_Coppins Lookout_3
1214 201718_Coppins Lookout_3
1215 201718_Coppins Lookout_3
1216 201718_Coppins Lookout_3
1217 201718_Coppins Lookout_3
1218 201718_Coppins Lookout_3
1219 201718_Coppins Lookout_3
1220 201718_Coppins Lookout_3
1221 201718_Coppins Lookout_3
1222 201718_Coppins Lookout_3
1223 201718_Coppins Lookout_3
1224 201718_Coppins Lookout_3
1225 201718_Coppins Lookout_3
1226 201718_Coppins Lookout_3
1227 201718_Coppins Lookout_3
1228 201718_Coppins Lookout_3
1229 201718_Coppins Lookout_3
1230 201718_Coppins Lookout_3
1231 201718_Coppins Lookout_3
1232 201718_Coppins Lookout_3
1233 201718_Coppins Lookout_3
1234 201718_Coppins Lookout_3
1235 201718_Coppins Lookout_3
1236 201718_Coppins Lookout_3
1237 201718_Coppins Lookout_3
1238 201718_Coppins Lookout_3
1239 201718_Coppins Lookout_3
1240 201718_Coppins Lookout_3
1241 201718_Coppins Lookout_3
1242 201718_Coppins Lookout_3
1243 201718_Coppins Lookout_3
1244 201718_Coppins Lookout_3
1245 201718_Coppins Lookout_3
1246 201718_Coppins Lookout_3
1247 201718_Coppins Lookout_3
1248 201718_Coppins Lookout_3
1249 201718_Coppins Lookout_3
1250 201718_Coppins Lookout_3
1251 201718_Coppins Lookout_3
1252 201718_Coppins Lookout_3
1253 201718_Coppins Lookout_3
1254 201718_Coppins Lookout_3
1255 201718_Coppins Lookout_3
1256 201718_Coppins Lookout_3
1257 201718_Coppins Lookout_3
1258 201718_Coppins Lookout_3
1259 201718_Coppins Lookout_3
1260 201718_Coppins Lookout_3
1261 201718_Coppins Lookout_3
1262 201718_Coppins Lookout_3
1263 201718_Coppins Lookout_3
1264 201718_Coppins Lookout_3
1265 201718_Heyfield Pair 2 (east side) Rye_1
1266 201718_Heyfield Pair 2 (east side) Rye_1
1267 201718_Heyfield Pair 2 (east side) Rye_1
1268 201718_Heyfield Pair 2 (east side) Rye_1
1269 201718_Heyfield Pair 2 (east side) Rye_1
1270 201718_Heyfield Pair 2 (east side) Rye_1
1271 201718_Heyfield Pair 2 (east side) Rye_1
1272 201718_Heyfield Pair 2 (east side) Rye_2
1273 201718_Heyfield Pair 2 (east side) Rye_2
1274 201718_Heyfield Pair 2 (east side) Rye_2
1275 201718_Heyfield Pair 2 (east side) Rye_2
1276 201718_Heyfield Pair 2 (east side) Rye_2
1277 201718_Heyfield Pair 2 (east side) Rye_2
1278 201718_Heyfield Pair 2 (east side) Rye_2
1279 201718_Heyfield Pair 2 (east side) Rye_2
1280 201718_Heyfield Pair 2 (east side) Rye_2
1281 201718_Heyfield Pair 2 (east side) Rye_2
1282 201718_Heyfield Pair 2 (east side) Rye_2
1283 201718_Heyfield Pair 2 (east side) Rye_2
1284 201718_Heyfield Pair 2 (east side) Rye_2
1285 201718_Heyfield Pair 2 (east side) Rye_2
1286 201718_Heyfield Pair 2 (east side) Rye_2
1287 201718_Heyfield Pair 2 (east side) Rye_2
1288 201718_Heyfield Pair 2 (east side) Rye_2
1289 201718_Heyfield Pair 2 (east side) Rye_2
1290 201718_Heyfield Pair 2 (east side) Rye_2
1291 201718_Heyfield Pair 2 (east side) Rye_2
1292 201718_Heyfield Pair 2 (east side) Rye_2
1293 201718_Heyfield Pair 2 (east side) Rye_2
1294 201718_Heyfield Pair 2 (east side) Rye_2
1295 201718_Heyfield Pair 2 (east side) Rye_2
1296 201718_Heyfield Pair 2 (east side) Rye_2
1297 201718_Heyfield Pair 2 (east side) Rye_2
1298 201718_Heyfield Pair 2 (east side) Rye_2
1299 201718_Heyfield Pair 2 (east side) Rye_2
1300 201718_Heyfield Pair 2 (east side) Rye_2
1301 201718_Heyfield Pair 2 (east side) Rye_2
1302 201718_Heyfield Pair 2 (east side) Rye_2
1303 201718_Heyfield Pair 2 (east side) Rye_2
1304 201718_Heyfield Pair 2 (east side) Rye_2
1305 201718_Heyfield Pair 2 (east side) Rye_2
1306 201718_Heyfield Pair 2 (east side) Rye_2
1307 201718_Heyfield Pair 2 (east side) Rye_2
1308 201718_Heyfield Pair 2 (east side) Rye_2
1309 201718_Heyfield Pair 2 (east side) Rye_2
1310 201718_Heyfield Pair 2 (east side) Rye_2
1311 201718_Heyfield Pair 2 (east side) Rye_2
1312 201718_Heyfield Pair 2 (east side) Rye_2
1313 201718_Koonya East_2
1314 201718_Koonya East_2
1315 201718_Koonya East_2
1316 201718_Koonya East_2
1317 201718_Koonya East_2
1318 201718_Koonya East_2
1319 201718_Koonya East_2
1320 201718_Koonya East_2
1321 201718_Koonya East_2
1322 201718_Koonya East_2
1323 201718_Koonya East_2
1324 201718_Koonya East_2
1325 201718_Koonya East_2
1326 201718_Koonya East_2
1327 201718_Koonya East_2
1328 201718_Koonya East_2
1329 201718_Koonya East_2
1330 201718_Koonya East_2
1331 201718_Koonya East_2
1332 201718_Koonya East_2
1333 201718_Koonya East_2
1334 201718_Koonya East_2
1335 201718_Koonya East_2
1336 201718_Koonya East_2
1337 201718_Koonya East_2
1338 201718_Koonya East_2
1339 201718_Koonya East_2
1340 201718_Koonya East_2
1341 201718_Koonya East_2
1342 201718_Koonya East_2
1343 201718_Koonya East_2
1344 201718_Koonya East_2
1345 201718_Koonya East_2
1346 201718_Koonya East_2
1347 201718_Koonya East_2
1348 201718_Koonya East_2
1349 201718_Koonya East_2
1350 201718_Koonya East_2
1351 201718_Koonya East_2
1352 201718_Koonya East_2
1353 201718_Koonya East_2
1354 201718_Koonya East_2
1355 201718_Koonya East_2
1356 201718_Koonya East_2
1357 201718_Koonya East_2
1358 201718_Koonya East_2
1359 201718_Koonya East_2
1360 201718_Koonya East_2
1361 201718_Koonya East_2
1362 201718_Koonya East_2
1363 201718_Koonya East_2
1364 201718_Koonya East_2
1365 201718_Koonya East_2
1366 201718_Koonya East_2
1367 201718_Koonya East_2
1368 201718_Koonya East_2
1369 201718_Koonya West_4
1370 201718_Koonya West_4
1371 201718_Koonya West_4
1372 201718_Koonya West_4
1373 201718_Koonya West_4
1374 201718_Koonya West_4
1375 201718_Koonya West_4
1376 201718_Koonya West_4
1377 201718_Koonya West_4
1378 201718_Koonya West_4
1379 201718_Miami drive east access_1
1380 201718_Miami drive east access_1
1381 201718_Miami drive east access_1
1382 201718_Miami drive east access_1
1383 201718_Miami drive east access_1
1384 201718_Miami drive east access_1
1385 201718_Miami drive east access_1
1386 201718_Miami drive east access_1
1387 201718_Miami drive east access_1
1388 201718_Miami drive east access_1
1389 201718_Miami drive east access_1
1390 201718_Miami drive east access_1
1391 201718_Miami drive east access_1
1392 201718_Miami drive east access_1
1393 201718_Miami drive east access_1
1394 201718_Montforts_2
1395 201718_Montforts_2
1396 201718_Montforts_2
1397 201718_Montforts_2
1398 201718_Montforts_2
1399 201718_Montforts_2
1400 201718_Montforts_2
1401 201718_Montforts_2
1402 201718_Montforts_2
1403 201718_Montforts_2
1404 201718_Montforts_2
1405 201718_Montforts_2
1406 201718_Montforts_2
1407 201718_Montforts_2
1408 201718_Montforts_2
1409 201718_Montforts_2
1410 201718_Montforts_2
1411 201718_Montforts_2
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1414 201718_Montforts_2
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1416 201718_Montforts_2
1417 201718_Montforts_2
1418 201718_Montforts_2
1419 201718_Montforts_2
1420 201718_Montforts_2
1421 201718_Montforts_2
1422 201718_Montforts_2
1423 201718_Montforts_2
1424 201718_Montforts_2
1425 201718_Montforts_2
1426 201718_Portsea Farnsworth/SLSC west_3
1427 201718_Portsea Farnsworth/SLSC west_3
1428 201718_Portsea Farnsworth/SLSC west_3
1429 201718_Portsea Farnsworth/SLSC west_3
1430 201718_Portsea Farnsworth/SLSC west_3
1431 201718_Portsea Farnsworth/SLSC west_3
1432 201718_Portsea Farnsworth/SLSC west_3
1433 201718_Portsea Farnsworth/SLSC west_3
1434 201718_Portsea Farnsworth/SLSC west_3
1435 201718_Portsea Farnsworth/SLSC west_3
1436 201718_Portsea Farnsworth/SLSC west_3
1437 201718_Portsea Farnsworth/SLSC west_3
1438 201718_Portsea Farnsworth/SLSC west_3
1439 201718_Portsea Farnsworth/SLSC west_3
1440 201718_Portsea Farnsworth/SLSC west_3
1441 201718_Portsea Farnsworth/SLSC west_3
1442 201718_Portsea Farnsworth/SLSC west_3
1443 201718_Portsea Farnsworth/SLSC west_3
1444 201718_Portsea Farnsworth/SLSC west_3
1445 201718_Portsea Farnsworth/SLSC west_3
1446 201718_Portsea Farnsworth/SLSC west_3
1447 201718_Portsea Farnsworth/SLSC west_3
1448 201718_Portsea Farnsworth/SLSC west_3
1449 201718_Portsea Farnsworth/SLSC west_3
1450 201718_Portsea Farnsworth/SLSC west_3
1451 201718_Portsea Farnsworth/SLSC west_3
1452 201718_Portsea Farnsworth/SLSC west_3
1453 201718_Portsea Farnsworth/SLSC west_3
1454 201718_Portsea Farnsworth/SLSC west_3
1455 201718_Portsea Farnsworth/SLSC west_3
1456 201718_Portsea Farnsworth/SLSC west_3
1457 201718_Portsea Farnsworth/SLSC west_3
1458 201718_Portsea Farnsworth/SLSC west_3
1459 201718_Portsea Farnsworth/SLSC west_3
1460 201718_Portsea Farnsworth/SLSC west_3
1461 201718_Portsea Farnsworth/SLSC west_3
1462 201718_Portsea Farnsworth/SLSC west_3
1463 201718_Portsea Farnsworth/SLSC west_3
1464 201718_Portsea Farnsworth/SLSC west_3
1465 201718_Portsea Farnsworth/SLSC west_3
1466 201718_Portsea Farnsworth/SLSC west_3
1467 201718_Portsea Farnsworth/SLSC west_3
1468 201718_Portsea Franklin rd west_1
1469 201718_Portsea Franklin rd west_1
1470 201718_Portsea Franklin rd west_1
1471 201718_Portsea Franklin rd west_1
1472 201718_Portsea Franklin rd west_1
1473 201718_Portsea Franklin rd west_1
1474 201718_Portsea Franklin rd west_1
1475 201718_Portsea Franklin rd west_1
1476 201718_Portsea Franklin rd west_1
1477 201718_Portsea Franklin rd west_1
1478 201718_Portsea Franklin rd west_1
1479 201718_Portsea Franklin rd west_1
1480 201718_Portsea Franklin rd west_1
1481 201718_Portsea Franklin rd west_1
1482 201718_Portsea Franklin rd west_1
1483 201718_Portsea Franklin rd west_1
1484 201718_Portsea Franklin rd west_1
1485 201718_Portsea Franklin rd west_1
1486 201718_Portsea Franklin rd west_1
1487 201718_Portsea Franklin rd west_1
1488 201718_Portsea Franklin rd west_1
1489 201718_Portsea Franklin rd west_1
1490 201718_Portsea Franklin rd west_1
1491 201718_Portsea Franklin rd west_5
1492 201718_Portsea Franklin rd west_5
1493 201718_Portsea Franklin rd west_5
1494 201718_Portsea Franklin rd west_5
1495 201718_Portsea Franklin rd west_5
1496 201718_Portsea Franklin rd west_5
1497 201718_Portsea Franklin rd west_5
1498 201718_Portsea Franklin rd west_5
1499 201718_Portsea Franklin rd west_5
1500 201718_Portsea Franklin rd west_5
1501 201718_Portsea Franklin rd west_5
1502 201718_Portsea Franklin rd west_5
1503 201718_Portsea Franklin rd west_5
1504 201718_Portsea Franklin rd west_5
1505 201718_Portsea Franklin rd west_5
1506 201718_Portsea Franklin rd west_5
1507 201718_Portsea Franklin rd west_5
1508 201718_Portsea Franklin rd west_5
1509 201718_Portsea Franklin rd west_5
1510 201718_Portsea Franklin rd west_5
1511 201718_Portsea Franklin rd west_5
1512 201718_Portsea Franklin rd west_5
1513 201718_Portsea Franklin rd west_5
1514 201718_Portsea Franklin rd west_5
1515 201718_Portsea Franklin rd west_5
1516 201718_Portsea Franklin rd west_5
1517 201718_Portsea Franklin rd west_5
1518 201718_Portsea Sphinx rocks_1
1519 201718_Portsea Sphinx rocks_1
1520 201718_Portsea Sphinx rocks_1
1521 201718_Portsea Sphinx rocks_1
1522 201718_Portsea Sphinx rocks_1
1523 201718_Portsea Sphinx rocks_1
1524 201718_Portsea Sphinx rocks_1
1525 201718_Portsea Sphinx rocks_1
1526 201718_Portsea Sphinx rocks_1
1527 201718_Portsea Sphinx rocks_1
1528 201718_Portsea Sphinx rocks_1
1529 201718_Portsea Sphinx rocks_1
1530 201718_Portsea Sphinx rocks_1
1531 201718_Portsea Sphinx rocks_1
1532 201718_Portsea Sphinx rocks_1
1533 201718_Portsea Sphinx rocks_1
1534 201718_Portsea Sphinx rocks_1
1535 201718_Portsea Sphinx rocks_1
1536 201718_Portsea Sphinx rocks_1
1537 201718_Portsea Sphinx rocks_1
1538 201718_Portsea Sphinx rocks_1
1539 201718_Portsea Sphinx rocks_1
1540 201718_Portsea Sphinx rocks_1
1541 201718_Portsea Sphinx rocks_1
1542 201718_Portsea Sphinx rocks_1
1543 201718_Portsea Sphinx rocks_1
1544 201718_Portsea Sphinx rocks_1
1545 201718_Portsea Sphinx rocks_1
1546 201718_Portsea Sphinx rocks_1
1547 201718_Portsea Sphinx rocks_1
1548 201718_Portsea Sphinx rocks_1
1549 201718_Portsea Sphinx rocks_1
1550 201718_St Andrews carpark west_3
1551 201718_St Andrews carpark west_3
1552 201718_St Andrews carpark west_3
1553 201718_St Andrews carpark west_3
1554 201718_St Andrews carpark west_3
1555 201718_St Andrews carpark west_3
1556 201718_St Andrews carpark west_3
1557 201718_St Andrews carpark west_3
1558 201718_St. Andrews car park east_1
1559 201718_St. Andrews car park east_1
1560 201718_St. Andrews car park east_1
1561 201718_St. Andrews car park east_1
1562 201718_St. Andrews car park east_1
1563 201718_St. Andrews car park east_1
1564 201718_St. Andrews car park east_1
1565 201718_St. Andrews car park east_1
1566 201718_St. Andrews car park east_1
1567 201819_Alison Ave West Rye_2
1568 201819_Alison Ave West Rye_2
1569 201819_Alison Ave West Rye_2
1570 201819_Alison Ave West Rye_3
1571 201819_Alison Ave West Rye_3
1572 201819_Alison Ave West Rye_3
1573 201819_Alison Ave West Rye_3
1574 201819_Alison Ave West Rye_3
1575 201819_Alison Ave West Rye_3
1576 201819_Alison Ave West Rye_3
1577 201819_Alison Ave West Rye_3
1578 201819_Fowlers_1
1579 201819_Fowlers_1
1580 201819_Fowlers_1
1581 201819_Fowlers_1
1582 201819_Fowlers_1
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1601 201819_Fowlers_2
1602 201819_Fowlers_2
1603 201819_Fowlers_2
1604 201819_Fowlers_2
1605 201819_Fowlers_2
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1607 201819_Fowlers_2
1608 201819_Fowlers_2
1609 201819_Fowlers_2
1610 201819_Fowlers_2
1611 201819_Fowlers_2
1612 201819_Fowlers_2
1613 201819_Fowlers_2
1614 201819_Fowlers_2
1615 201819_Fowlers_2
1616 201819_Gunida Crt Rye_1
1617 201819_Gunida Crt Rye_1
1618 201819_Gunida Crt Rye_1
1619 201819_Gunida Crt Rye_1
1620 201819_Gunida Crt Rye_1
1621 201819_Gunida Crt Rye_1
1622 201819_Gunida Crt Rye_1
1623 201819_Gunida Crt Rye_1
1624 201819_Gunida Crt Rye_2
1625 201819_Gunida Crt Rye_2
1626 201819_Gunida Crt Rye_2
1627 201819_Gunida Crt Rye_2
1628 201819_Gunida Crt Rye_2
1629 201819_Gunida Crt Rye_2
1630 201819_Gunida Crt Rye_2
1631 201819_Gunida Crt Rye_2
1632 201819_Gunida Crt Rye_2
1633 201819_Gunida Crt Rye_2
1634 201819_Gunida Crt Rye_2
1635 201819_Gunida Crt Rye_2
1636 201819_Gunida Crt Rye_2
1637 201819_Gunida Crt Rye_2
1638 201819_Gunida Crt Rye_2
1639 201819_Gunida Crt Rye_2
1640 201819_Gunida Crt Rye_2
1641 201819_Gunida Crt Rye_2
1642 201819_Gunida Crt Rye_2
1643 201819_Gunida Crt Rye_2
1644 201819_Gunida Crt Rye_2
1645 201819_Gunida Crt Rye_2
1646 201819_Gunida Crt Rye_2
1647 201819_Gunida Crt Rye_2
1648 201819_Gunida Crt Rye_2
1649 201819_Gunida Crt Rye_2
1650 201819_Gunida Crt Rye_2
1651 201819_Gunida Crt Rye_2
1652 201819_Gunida Crt Rye_2
1653 201819_Gunida Crt Rye_2
1654 201819_Gunida Crt Rye_2
1655 201819_Gunida Crt Rye_2
1656 201819_Gunida Crt Rye_2
1657 201819_Gunida Crt Rye_2
1658 201819_Gunida Crt Rye_2
1659 201819_Gunida Crt Rye_2
1660 201819_Gunida Crt Rye_2
1661 201819_Gunnamatta SLSC Far East (P1)_2
1662 201819_Gunnamatta SLSC Far East (P1)_2
1663 201819_Gunnamatta SLSC Far East (P1)_2
1664 201819_Gunnamatta SLSC Far East (P1)_2
1665 201819_Gunnamatta SLSC Far East (P1)_2
1666 201819_Gunnamatta SLSC Far East (P1)_2
1667 201819_Gunnamatta SLSC Far East (P1)_2
1668 201819_Gunnamatta SLSC Far East (P1)_2
1669 201819_Koonya West_1
1670 201819_Koonya West_1
1671 201819_Koonya West_1
1672 201819_Koonya West_1
1673 201819_Koonya West_1
1674 201819_Koonya West_1
1675 201819_Koonya West_1
1676 201819_Koonya West_1
1677 201819_Koonya West_1
1678 201819_Koonya West_1
1679 201819_Koonya West_1
1680 201819_Koonya West_1
1681 201819_Koonya West_1
1682 201819_Koonya West_1
1683 201819_Koonya West_1
1684 201819_Koonya West_1
1685 201819_Koonya West_1
1686 201819_Koonya West_1
1687 201819_Koonya West_1
1688 201819_Koonya West_1
1689 201819_Koonya West_1
1690 201819_Koonya West_1
1691 201819_Koonya West_1
1692 201819_Koonya West_1
1693 201819_Koonya West_1
1694 201819_Koonya West_1
1695 201819_Koonya West_1
1696 201819_Koonya West_1
1697 201819_Koonya West_1
1698 201819_Koonya West_1
1699 201819_Koonya West_1
1700 201819_Koonya West_1
1701 201819_Koonya West_1
1702 201819_Koonya West_1
1703 201819_Koonya West_1
1704 201819_Koonya West_1
1705 201819_Koonya West_1
1706 201819_Koonya West_1
1707 201819_Koonya West_1
1708 201819_Koonya West_1
1709 201819_Koonya West_1
1710 201819_Koonya West_1
1711 201819_Koonya West_1
1712 201819_Koonya West_1
1713 201819_Koonya West_1
1714 201819_Koonya West_1
1715 201819_Koonya West_1
1716 201819_Koonya West_1
1717 201819_Koonya West_1
1718 201819_Koonya West_1
1719 201819_Koonya West_1
1720 201819_Koonya West_1
1721 201819_Koonya West_1
1722 201819_Koonya West_1
1723 201819_Koonya West_1
1724 201819_Koonya West_1
1725 201819_Koonya West_1
1726 201819_Koonya West_1
1727 201819_Koonya West_1
1728 201819_Koonya West_1
1729 201819_Koonya West_1
1730 201819_Koonya West_1
1731 201819_Koonya West_1
1732 201819_Koonya West_1
1733 201819_Miami drive east access_2
1734 201819_Miami drive east access_2
1735 201819_Miami drive east access_2
1736 201819_Miami drive east access_2
1737 201819_Miami drive east access_2
1738 201819_Miami drive east access_2
1739 201819_Miami drive east access_2
1740 201819_Miami drive east access_2
1741 201819_Miami drive east access_2
1742 201819_Miami drive east access_2
1743 201819_Miami drive east access_2
1744 201819_Miami drive east access_2
1745 201819_Miami drive east access_2
1746 201819_Miami drive east access_2
1747 201819_Miami drive east access_2
1748 201819_Miami drive east access_2
1749 201819_Miami drive east access_2
1750 201819_Miami drive east access_2
1751 201819_Miami drive east access_2
1752 201819_Miami drive east access_2
1753 201819_Miami drive east access_2
1754 201819_Miami drive east access_2
1755 201819_Miami drive east access_2
1756 201819_Miami drive east access_2
1757 201819_Miami drive east access_2
1758 201819_Miami drive east access_2
1759 201819_Miami drive east access_2
1760 201819_Miami drive east access_2
1761 201819_Miami drive east access_2
1762 201819_Miami drive east access_2
1763 201819_Moana crt access (east edge)_2
1764 201819_Moana crt access (east edge)_2
1765 201819_Moana crt access (east edge)_2
1766 201819_Pt Nepean (authorised personnel only) - Sierra Nevada Rocks_1
1767 201819_Pt Nepean (authorised personnel only) - Sierra Nevada Rocks_1
1768 201819_Pt Nepean (authorised personnel only) - Spooks_1
1769 201819_Pt Nepean (authorised personnel only) - Spooks_1
1770 201819_Rye car park west_1
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1776 201819_Rye car park west_1
1777 201819_Rye car park west_1
1778 201819_St Andrews Boags Rocks_5
1779 201819_St Andrews Boags Rocks_5
1780 201819_St Andrews Boags Rocks_5
1781 201819_St Andrews Boags Rocks_5
1782 201819_St Andrews Boags Rocks_5
1783 201819_St. Andrews car park east_1
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1798 201819_St. Andrews car park east_1
1799 201819_St. Andrews car park east_1
1800 201819_St. Andrews car park east_1
1801 201819_St. Andrews car park east_1
1802 201819_St. Andrews car park east_1
1803 201819_St. Andrews car park east_1
1804 201920_Alison ave east Rye_1
1805 201920_Alison ave east Rye_1
1806 201920_Alison ave east Rye_1
1807 201920_Alison ave east Rye_1
1808 201920_Alison ave east Rye_1
1809 201920_Alison ave east Rye_1
1810 201920_Alison ave east Rye_1
1811 201920_Alison ave east Rye_1
1812 201920_Alison ave east Rye_1
1813 201920_Alison ave east Rye_1
1814 201920_Alison ave east Rye_1
1815 201920_Alison ave east Rye_1
1816 201920_Alison ave east Rye_1
1817 201920_Alison ave east Rye_1
1818 201920_Alison ave east Rye_1
1819 201920_Alison ave east Rye_1
1820 201920_Alison ave east Rye_1
1821 201920_Alison ave east Rye_1
1822 201920_Alison ave east Rye_1
1823 201920_Alison ave east Rye_1
1824 201920_Alison ave east Rye_1
1825 201920_Alison ave east Rye_1
1826 201920_Alison ave east Rye_1
1827 201920_Fowlers_2
1828 201920_Fowlers_2
1829 201920_Fowlers_2
1830 201920_Fowlers_2
1831 201920_Fowlers_2
1832 201920_Fowlers_2
1833 201920_Fowlers_2
1834 201920_Fowlers_2
1835 201920_Fowlers_2
1836 201920_Fowlers_2
1837 201920_Fowlers_2
1838 201920_Fowlers_2
1839 201920_Fowlers_2
1840 201920_Fowlers_2
1841 201920_Gunida Crt Rye_1
1842 201920_Gunida Crt Rye_1
1843 201920_Gunida Crt Rye_1
1844 201920_Gunida Crt Rye_1
1845 201920_Gunida Crt Rye_1
1846 201920_Gunida Crt Rye_2
1847 201920_Gunida Crt Rye_2
1848 201920_Gunida Crt Rye_2
1849 201920_Gunida Crt Rye_2
1850 201920_Gunida Crt Rye_2
1851 201920_Gunida Crt Rye_2
1852 201920_Heyfield Pair 1 (west side) Rye_1
1853 201920_Heyfield Pair 1 (west side) Rye_1
1854 201920_Heyfield Pair 1 (west side) Rye_1
1855 201920_Heyfield Pair 1 (west side) Rye_1
1856 201920_Heyfield Pair 1 (west side) Rye_1
1857 201920_Heyfield Pair 1 (west side) Rye_1
1858 201920_Heyfield Pair 1 (west side) Rye_1
1859 201920_Heyfield Pair 1 (west side) Rye_1
1860 201920_Heyfield Pair 1 (west side) Rye_1
1861 201920_Heyfield Pair 1 (west side) Rye_1
1862 201920_Heyfield Pair 1 (west side) Rye_1
1863 201920_Heyfield Pair 1 (west side) Rye_1
1864 201920_Heyfield Pair 1 (west side) Rye_1
1865 201920_Heyfield Pair 1 (west side) Rye_1
1866 201920_Heyfield Pair 1 (west side) Rye_1
1867 201920_Heyfield Pair 1 (west side) Rye_1
1868 201920_Heyfield Pair 1 (west side) Rye_1
1869 201920_Heyfield Pair 1 (west side) Rye_1
1870 201920_Heyfield Pair 1 (west side) Rye_1
1871 201920_Heyfield Pair 1 (west side) Rye_1
1872 201920_Heyfield Pair 2 (east side) Rye_2
1873 201920_Heyfield Pair 2 (east side) Rye_2
1874 201920_Heyfield Pair 2 (east side) Rye_2
1875 201920_Heyfield Pair 2 (east side) Rye_2
1876 201920_Heyfield Pair 2 (east side) Rye_2
1877 201920_Heyfield Pair 2 (east side) Rye_2
1878 201920_Heyfield Pair 2 (east side) Rye_2
1879 201920_Heyfield Pair 2 (east side) Rye_2
1880 201920_Heyfield Pair 2 (east side) Rye_2
1881 201920_Montforts_1
1882 201920_Montforts_1
1883 201920_Montforts_1
1884 201920_Montforts_1
1885 201920_Montforts_1
1886 201920_Montforts_1
1887 201920_Montforts_1
1888 201920_Montforts_1
1889 201920_Montforts_1
1890 201920_Montforts_1
1891 201920_Montforts_1
1892 201920_Montforts_1
1893 201920_Montforts_1
1894 201920_Montforts_1
1895 201920_Montforts_1
1896 201920_Montforts_1
1897 201920_Montforts_1
1898 201920_Montforts_1
1899 201920_Montforts_1
1900 201920_Montforts_1
1901 201920_Montforts_1
1902 201920_Montforts_1
1903 201920_Montforts_1
1904 201920_Montforts_1
1905 201920_Portsea Franklin rd west_4
1906 201920_Portsea Franklin rd west_4
1907 201920_Portsea Franklin rd west_4
1908 201920_Portsea Franklin rd west_4
1909 201920_Portsea Franklin rd west_4
1910 201920_Portsea Franklin rd west_4
1911 201920_Portsea Franklin rd west_4
1912 201920_Portsea Franklin rd west_4
1913 201920_Portsea Franklin rd west_4
1914 201920_Portsea Franklin rd west_4
1915 201920_Portsea Franklin rd west_4
1916 201920_Portsea Franklin rd west_4
1917 201920_Portsea Franklin rd west_4
1918 201920_Portsea Franklin rd west_4
1919 201920_Pt Nepean, Nepean Bay_1
1920 201920_Pt Nepean, Nepean Bay_1
1921 201920_Pt Nepean, Nepean Bay_1
1922 201920_Pt Nepean, Nepean Bay_1
1923 201920_Pt Nepean, Nepean Bay_1
1924 201920_Pt Nepean, Nepean Bay_1
1925 201920_Pt Nepean, Nepean Bay_1
1926 201920_Pt Nepean, Nepean Bay_1
1927 201920_Pt Nepean, Nepean Bay_1
1928 201920_Pt Nepean, Nepean Bay_1
1929 201920_Pt Nepean, Nepean Bay_1
1930 201920_Pt Nepean, Nepean Bay_1
1931 201920_Pt Nepean, Nepean Bay_1
1932 201920_Pt Nepean, Nepean Bay_1
1933 201920_Pt Nepean, Nepean Bay_1
1934 201920_Pt Nepean, Nepean Bay_1
1935 201920_Pt Nepean, Nepean Bay_1
1936 201920_Pt Nepean, Nepean Bay_1
1937 201920_Rye Big Rock_2
1938 201920_Rye Big Rock_2
1939 201920_Rye Big Rock_2
1940 201920_Rye Big Rock_2
1941 201920_Rye Big Rock_2
1942 201920_Rye Big Rock_2
1943 201920_Rye Big Rock_2
1944 201920_Rye Big Rock_2
1945 201920_Rye Big Rock_2
1946 201920_Rye Big Rock_2
1947 201920_Rye Big Rock_2
1948 201920_Rye Big Rock_2
1949 201920_Rye Big Rock_2
1950 201920_Rye Big Rock_2
1951 201920_Rye Big Rock_2
1952 201920_Rye Big Rock_2
1953 201920_Rye Big Rock_2
1954 201920_Rye Big Rock_2
1955 201920_Rye Big Rock_2
1956 201920_Rye Big Rock_2
1957 201920_Rye Big Rock_2
1958 201920_Rye Big Rock_2
1959 201920_Rye Big Rock_2
1960 201920_Rye Big Rock_2
1961 201920_Rye Big Rock_2
1962 201920_Rye Big Rock_2
1963 201920_Rye Big Rock_2
1964 201920_Rye Big Rock_2
1965 201920_Rye Big Rock_2
1966 201920_Rye Big Rock_2
1967 201920_Rye Big Rock_2
1968 201920_Rye Big Rock_2
1969 202021_Balnarring Beach_3
1970 202021_Balnarring Beach_3
1971 202021_Balnarring Beach_3
1972 202021_Balnarring Beach_3
1973 202021_Balnarring Beach_3
1974 202021_Balnarring Beach_3
1975 202021_Balnarring Beach_3
1976 202021_Balnarring Beach_3
1977 202021_Balnarring Beach_3
1978 202021_Balnarring Beach_3
1979 202021_Balnarring Beach_3
1980 202021_Balnarring Beach_3
1981 202021_Balnarring Beach_3
1982 202021_Balnarring Beach_3
1983 202021_Balnarring Beach_3
1984 202021_Balnarring Beach_3
1985 202021_Balnarring Beach_3
1986 202021_Balnarring Beach_3
1987 202021_Balnarring Beach_3
1988 202021_Brewster, Rye_4
1989 202021_Brewster, Rye_4
1990 202021_Brewster, Rye_4
1991 202021_Brewster, Rye_4
1992 202021_Brewster, Rye_4
1993 202021_Brewster, Rye_4
1994 202021_Brewster, Rye_4
1995 202021_Brewster, Rye_4
1996 202021_Brewster, Rye_4
1997 202021_Brewster, Rye_4
1998 202021_Flinders, Mushroom Reef, HP_3
1999 202021_Flinders, Mushroom Reef, HP_3
2000 202021_Flinders, Mushroom Reef, HP_3
2001 202021_Flinders, Mushroom Reef, HP_3
2002 202021_Flinders, Mushroom Reef, HP_3
2003 202021_Flinders, Mushroom Reef, HP_3
2004 202021_Flinders, Mushroom Reef, HP_3
2005 202021_Flinders, Mushroom Reef, HP_3
2006 202021_Flinders, Mushroom Reef, HP_3
2007 202021_Flinders, Mushroom Reef, HP_3
2008 202021_Flinders, Mushroom Reef, HP_3
2009 202021_Flinders, Mushroom Reef, HP_3
2010 202021_Flinders, Mushroom Reef, HP_3
2011 202021_Flinders, Mushroom Reef, HP_3
2012 202021_Flinders, Mushroom Reef, HP_3
2013 202021_Flinders, Mushroom Reef, HP_3
2014 202021_Flinders, Mushroom Reef, HP_3
2015 202021_Flinders, Mushroom Reef, HP_3
2016 202021_Flinders, Mushroom Reef, HP_3
2017 202021_Flinders, Mushroom Reef, HP_3
2018 202021_Flinders, Mushroom Reef, HP_3
2019 202021_Flinders, Mushroom Reef, HP_3
2020 202021_Flinders, Mushroom Reef, HP_3
2021 202021_Flinders, Mushroom Reef, HP_3
2022 202021_Flinders, Mushroom Reef, HP_3
2023 202021_Flinders, Mushroom Reef, HP_3
2024 202021_Flinders, Mushroom Reef, HP_3
2025 202021_Flinders, Mushroom Reef, HP_3
2026 202021_Flinders, Mushroom Reef, HP_3
2027 202021_Flinders, Mushroom Reef, HP_3
2028 202021_Flinders, Mushroom Reef, HP_3
2029 202021_Flinders, Mushroom Reef, HP_3
2030 202021_Flinders, Mushroom Reef, HP_3
2031 202021_Flinders, Mushroom Reef, HP_3
2032 202021_Fowlers_2
2033 202021_Fowlers_2
2034 202021_Fowlers_2
2035 202021_Fowlers_2
2036 202021_Fowlers_2
2037 202021_Fowlers_2
2038 202021_Fowlers_2
2039 202021_Fowlers_2
2040 202021_Fowlers_4
2041 202021_Fowlers_4
2042 202021_Fowlers_4
2043 202021_Fowlers_4
2044 202021_Fowlers_4
2045 202021_Fowlers_4
2046 202021_Fowlers_4
2047 202021_Fowlers_4
2048 202021_Fowlers_4
2049 202021_Fowlers_4
2050 202021_Fowlers_4
2051 202021_Fowlers_4
2052 202021_Fowlers_4
2053 202021_Fowlers_4
2054 202021_Fowlers_4
2055 202021_Fowlers_4
2056 202021_Fowlers_4
2057 202021_Fowlers_4
2058 202021_Fowlers_4
2059 202021_Fowlers_4
2060 202021_Fowlers_4
2061 202021_Fowlers_4
2062 202021_Fowlers_4
2063 202021_Fowlers_4
2064 202021_Fowlers_4
2065 202021_Fowlers_4
2066 202021_Gunnamatta SLSC Far East (P1)_1
2067 202021_Gunnamatta SLSC Far East (P1)_1
2068 202021_Gunnamatta SLSC Far East (P1)_1
2069 202021_Gunnamatta SLSC Far East (P1)_1
2070 202021_Gunnamatta SLSC Far East (P1)_1
2071 202021_Gunnamatta SLSC Far East (P1)_1
2072 202021_Gunnamatta SLSC Far East (P1)_1
2073 202021_Gunnamatta SLSC Far East (P1)_1
2074 202021_Gunnamatta SLSC Far East (P1)_1
2075 202021_Gunnamatta SLSC Far East (P1)_3
2076 202021_Gunnamatta SLSC Far East (P1)_3
2077 202021_Gunnamatta SLSC Far East (P1)_3
2078 202021_Gunnamatta SLSC Far East (P1)_3
2079 202021_Gunnamatta SLSC Far East (P1)_3
2080 202021_Gunnamatta SLSC Far East (P1)_3
2081 202021_Gunnamatta SLSC Far East (P1)_3
2082 202021_Gunnamatta SLSC Far East (P1)_3
2083 202021_Gunnamatta SLSC Far East (P1)_3
2084 202021_Gunnamatta SLSC Far East (P1)_3
2085 202021_Gunnamatta SLSC Far East (P1)_3
2086 202021_Gunnamatta SLSC Far East (P1)_3
2087 202021_Gunnamatta SLSC Far East (P1)_3
2088 202021_Gunnamatta SLSC Far East (P1)_3
2089 202021_Heyfield Pair 1 (west side) Rye_1
2090 202021_Heyfield Pair 1 (west side) Rye_1
2091 202021_Heyfield Pair 1 (west side) Rye_1
2092 202021_Heyfield Pair 1 (west side) Rye_1
2093 202021_Heyfield Pair 1 (west side) Rye_1
2094 202021_Heyfield Pair 1 (west side) Rye_1
2095 202021_Heyfield Pair 1 (west side) Rye_1
2096 202021_Heyfield Pair 1 (west side) Rye_1
2097 202021_Heyfield Pair 1 (west side) Rye_1
2098 202021_Heyfield Pair 1 (west side) Rye_1
2099 202021_Heyfield Pair 1 (west side) Rye_1
2100 202021_Heyfield Pair 1 (west side) Rye_1
2101 202021_Heyfield Pair 1 (west side) Rye_1
2102 202021_Heyfield Pair 1 (west side) Rye_1
2103 202021_Heyfield Pair 1 (west side) Rye_1
2104 202021_Heyfield Pair 1 (west side) Rye_1
2105 202021_Heyfield Pair 1 (west side) Rye_1
2106 202021_Heyfield Pair 1 (west side) Rye_1
2107 202021_Heyfield Pair 1 (west side) Rye_1
2108 202021_Heyfield Pair 1 (west side) Rye_1
2109 202021_Heyfield Pair 1 (west side) Rye_1
2110 202021_Heyfield Pair 1 (west side) Rye_1
2111 202021_Heyfield Pair 1 (west side) Rye_1
2112 202021_Heyfield Pair 1 (west side) Rye_1
2113 202021_Heyfield Pair 1 (west side) Rye_1
2114 202021_Heyfield Pair 1 (west side) Rye_1
2115 202021_Heyfield Pair 1 (west side) Rye_1
2116 202021_Heyfield Pair 1 (west side) Rye_1
2117 202021_Heyfield Pair 1 (west side) Rye_1
2118 202021_Heyfield Pair 1 (west side) Rye_1
2119 202021_Heyfield Pair 1 (west side) Rye_1
2120 202021_Heyfield Pair 1 (west side) Rye_1
2121 202021_Heyfield Pair 1 (west side) Rye_1
2122 202021_Heyfield Pair 1 (west side) Rye_1
2123 202021_Heyfield Pair 1 (west side) Rye_1
2124 202021_Heyfield Pair 1 (west side) Rye_1
2125 202021_Heyfield Pair 1 (west side) Rye_1
2126 202021_Heyfield Pair 1 (west side) Rye_1
2127 202021_Heyfield Pair 1 (west side) Rye_1
2128 202021_Heyfield Pair 1 (west side) Rye_1
2129 202021_Heyfield Pair 1 (west side) Rye_1
2130 202021_Heyfield Pair 1 (west side) Rye_1
2131 202021_Heyfield Pair 1 (west side) Rye_1
2132 202021_Heyfield Pair 2 (east side) Rye_1
2133 202021_Heyfield Pair 2 (east side) Rye_1
2134 202021_Heyfield Pair 2 (east side) Rye_1
2135 202021_Heyfield Pair 2 (east side) Rye_1
2136 202021_Heyfield Pair 2 (east side) Rye_1
2137 202021_Heyfield Pair 2 (east side) Rye_1
2138 202021_Heyfield Pair 2 (east side) Rye_1
2139 202021_Heyfield Pair 2 (east side) Rye_1
2140 202021_Heyfield Pair 2 (east side) Rye_1
2141 202021_Heyfield Pair 2 (east side) Rye_1
2142 202021_Heyfield Pair 2 (east side) Rye_1
2143 202021_Heyfield Pair 2 (east side) Rye_1
2144 202021_Heyfield Pair 2 (east side) Rye_1
2145 202021_Heyfield Pair 2 (east side) Rye_1
2146 202021_Heyfield Pair 2 (east side) Rye_1
2147 202021_Heyfield Pair 2 (east side) Rye_2
2148 202021_Heyfield Pair 2 (east side) Rye_2
2149 202021_Heyfield Pair 2 (east side) Rye_2
2150 202021_Heyfield Pair 2 (east side) Rye_2
2151 202021_Heyfield Pair 2 (east side) Rye_2
2152 202021_Heyfield Pair 2 (east side) Rye_2
2153 202021_Heyfield Pair 2 (east side) Rye_2
2154 202021_Heyfield Pair 2 (east side) Rye_2
2155 202021_Heyfield Pair 2 (east side) Rye_2
2156 202021_Heyfield Pair 2 (east side) Rye_2
2157 202021_Heyfield Pair 2 (east side) Rye_2
2158 202021_Heyfield Pair 2 (east side) Rye_2
2159 202021_Heyfield Pair 2 (east side) Rye_2
2160 202021_Heyfield Pair 2 (east side) Rye_2
2161 202021_Heyfield Pair 2 (east side) Rye_2
2162 202021_Heyfield Pair 2 (east side) Rye_2
2163 202021_Heyfield Pair 2 (east side) Rye_2
2164 202021_Heyfield Pair 2 (east side) Rye_2
2165 202021_Heyfield Pair 2 (east side) Rye_2
2166 202021_Heyfield Pair 2 (east side) Rye_2
2167 202021_Heyfield Pair 2 (east side) Rye_2
2168 202021_Heyfield Pair 2 (east side) Rye_2
2169 202021_Heyfield Pair 2 (east side) Rye_2
2170 202021_Heyfield Pair 2 (east side) Rye_2
2171 202021_Heyfield Pair 2 (east side) Rye_2
2172 202021_Heyfield Pair 2 (east side) Rye_2
2173 202021_Montforts_1
2174 202021_Montforts_1
2175 202021_Montforts_1
2176 202021_Montforts_1
2177 202021_Montforts_1
2178 202021_Montforts_1
2179 202021_Montforts_1
2180 202021_Montforts_1
2181 202021_Montforts_1
2182 202021_Montforts_1
2183 202021_Montforts_1
2184 202021_Montforts_1
2185 202021_Montforts_1
2186 202021_Montforts_1
2187 202021_Montforts_1
2188 202021_Montforts_1
2189 202021_Montforts_2
2190 202021_Montforts_2
2191 202021_Montforts_2
2192 202021_Montforts_2
2193 202021_Montforts_2
2194 202021_Montforts_2
2195 202021_Montforts_2
2196 202021_Montforts_2
2197 202021_Montforts_2
2198 202021_Montforts_2
2199 202021_Montforts_2
2200 202021_Montforts_2
2201 202021_Montforts_2
2202 202021_Montforts_2
2203 202021_Montforts_2
2204 202021_Montforts_2
2205 202021_Montforts_2
2206 202021_Montforts_2
2207 202021_Montforts_2
2208 202021_Montforts_2
2209 202021_Montforts_2
2210 202021_Montforts_2
2211 202021_Portsea Franklin rd access_3
2212 202021_Portsea Franklin rd access_3
2213 202021_Portsea Franklin rd access_3
2214 202021_Portsea Franklin rd access_3
2215 202021_Portsea Franklin rd access_3
2216 202021_Portsea Franklin rd access_3
2217 202021_Portsea Franklin rd access_3
2218 202021_Portsea Franklin rd access_3
2219 202021_Portsea Franklin rd access_3
2220 202021_Portsea Franklin rd access_3
2221 202021_Portsea Franklin rd access_3
2222 202021_Portsea Franklin rd access_3
2223 202021_Portsea Franklin rd access_3
2224 202021_Portsea Franklin rd access_3
2225 202021_Portsea Franklin rd access_3
2226 202021_Portsea Franklin rd access_3
2227 202021_Portsea Franklin rd access_3
2228 202021_Portsea Franklin rd access_3
2229 202021_Portsea Franklin rd access_3
2230 202021_Portsea Franklin rd access_3
2231 202021_Portsea Franklin rd access_3
2232 202021_Portsea Franklin rd access_3
2233 202021_Portsea Franklin rd west_4
2234 202021_Portsea Franklin rd west_4
2235 202021_Portsea Franklin rd west_4
2236 202021_Portsea Franklin rd west_4
2237 202021_Portsea Franklin rd west_4
2238 202021_Portsea Franklin rd west_4
2239 202021_Portsea Franklin rd west_4
2240 202021_Portsea Franklin rd west_4
2241 202021_Portsea Franklin rd west_4
2242 202021_Portsea Franklin rd west_4
2243 202021_Portsea Franklin rd west_4
2244 202021_Portsea Franklin rd west_4
2245 202021_Portsea Franklin rd west_4
2246 202021_Portsea Franklin rd west_4
2247 202021_Portsea Franklin rd west_4
2248 202021_Portsea Franklin rd west_4
2249 202021_Portsea Franklin rd west_4
2250 202021_Portsea Franklin rd west_4
2251 202021_Portsea Sphinx rocks_1
2252 202021_Portsea Sphinx rocks_1
2253 202021_Portsea Sphinx rocks_1
2254 202021_Portsea Sphinx rocks_1
2255 202021_Portsea Sphinx rocks_1
2256 202021_Portsea Sphinx rocks_1
2257 202021_Portsea Sphinx rocks_1
2258 202021_Portsea Sphinx rocks_1
2259 202021_Portsea Sphinx rocks_1
2260 202021_Pt Nepean (authorised personnel only) - Spooks_2
2261 202021_Pt Nepean (authorised personnel only) - Spooks_2
2262 202021_Pt Nepean (authorised personnel only) - Spooks_2
2263 202021_Pt Nepean (authorised personnel only) - Spooks_2
2264 202021_Pt Nepean, Nepean Bay_1
2265 202021_Pt Nepean, Nepean Bay_1
2266 202021_Pt Nepean, Nepean Bay_1
2267 202021_Pt Nepean, Nepean Bay_1
2268 202021_Pt Nepean, Nepean Bay_1
2269 202021_Pt Nepean, Nepean Bay_1
2270 202021_Pt Nepean, Nepean Bay_1
2271 202021_Pt Nepean, Nepean Bay_1
2272 202021_Pt Nepean, Nepean Bay_1
2273 202021_Pt Nepean, Nepean Bay_1
2274 202021_Pt Nepean, Nepean Bay_1
2275 202021_Pt Nepean, Nepean Bay_1
2276 202021_St Andrews Boags Rocks_2
2277 202021_St Andrews Boags Rocks_2
2278 202021_St Andrews Boags Rocks_2
2279 202021_St Andrews Boags Rocks_2
2280 202021_St Andrews Boags Rocks_2
2281 202021_St Andrews Boags Rocks_2
2282 202021_St Andrews Boags Rocks_2
2283 202021_St Andrews Boags Rocks_2
2284 202021_St Andrews Boags Rocks_2
2285 202021_St Andrews Boags Rocks_2
2286 202021_St Andrews Boags Rocks_2
2287 202021_St Andrews Boags Rocks_2
2288 202021_St Andrews Boags Rocks_2
2289 202021_St Andrews Boags Rocks_2
2290 202021_St Andrews Boags Rocks_2
2291 202021_St Andrews Boags Rocks_2
2292 202021_St Andrews Boags Rocks_2
2293 202021_St Andrews Boags Rocks_2
2294 202021_St Andrews Boags Rocks_2
2295 202021_St Andrews Boags Rocks_2
2296 202021_St Andrews Boags Rocks_2
2297 202021_St Andrews Boags Rocks_2
2298 202021_St Andrews Boags Rocks_2
2299 202021_St Andrews Boags Rocks_2
2300 202122_Alison Ave east bay, Rye_2
2301 202122_Alison Ave east bay, Rye_2
2302 202122_Alison Ave east bay, Rye_2
2303 202122_Alison Ave, Rye_1
2304 202122_Alison Ave, Rye_1
2305 202122_Alison Ave, Rye_1
2306 202122_Alison Ave, Rye_1
2307 202122_Alison Ave, Rye_1
2308 202122_Alison Ave, Rye_1
2309 202122_Alison Ave, Rye_1
2310 202122_Alison Ave, Rye_1
2311 202122_Alison Ave, Rye_1
2312 202122_Alison Ave, Rye_1
2313 202122_Alison Ave, Rye_2
2314 202122_Alison Ave, Rye_2
2315 202122_Alison Ave, Rye_2
2316 202122_Alison Ave, Rye_2
2317 202122_Alison Ave, Rye_2
2318 202122_Alison Ave, Rye_2
2319 202122_Alison Ave, Rye_2
2320 202122_Alison Ave, Rye_2
2321 202122_Alison Ave, Rye_2
2322 202122_Fowlers_4
2323 202122_Fowlers_4
2324 202122_Fowlers_4
2325 202122_Fowlers_4
2326 202122_Fowlers_4
2327 202122_Fowlers_4
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2329 202122_Gunnamatta Fingal Tk (P4)_BJ&14_2
2330 202122_Gunnamatta Fingal Tk (P4)_BJ&14_2
2331 202122_Gunnamatta Fingal Tk (P4)_BJ&14_2
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2336 202122_Gunnamatta Fingal Tk East (P5)_2
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2338 202122_Gunnamatta Fingal Tk East (P5)_2
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2342 202122_Heyfield Pair 1 (west side) Rye_2
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2359 202122_Heyfield Pair 1 (west side) Rye_4
2360 202122_Heyfield Pair 1 (west side) Rye_4
2361 202122_Heyfield Pair 1 (west side) Rye_4
2362 202122_Heyfield Pair 2 (east side) Rye_3
2363 202122_Heyfield Pair 2 (east side) Rye_3
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2368 202122_Heyfield Pair 2 (east side) Rye_3
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2370 202122_Heyfield Pair 2 (east side) Rye_3
2371 202122_Heyfield Pair 2 (east side) Rye_3
2372 202122_Heyfield Pair 2 (east side) Rye_3
2373 202122_Heyfield Pair 2 (east side) Rye_3
2374 202122_Heyfield Pair 2 (east side) Rye_3
2375 202122_Heyfield Pair 2 (east side) Rye_3
2376 202122_Heyfield Pair 2 (east side) Rye_3
2377 202122_Heyfield Pair 2 (east side) Rye_3
2378 202122_Heyfield Pair 2 (east side) Rye_3
2379 202122_Koonya East_1
2380 202122_Koonya East_1
2381 202122_Koonya East_1
2382 202122_Koonya East_1
2383 202122_Koonya East_1
2384 202122_Koonya East_1
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2386 202122_Koonya East_1
2387 202122_Koonya East_1
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2390 202122_Koonya East_1
2391 202122_Koonya East_2
2392 202122_Koonya East_2
2393 202122_Koonya East_2
2394 202122_Koonya East_2
2395 202122_Koonya East_2
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2398 202122_Koonya East_2
2399 202122_Koonya West_1
2400 202122_Koonya West_1
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2405 202122_Koonya West_1
2406 202122_Koonya West_4
2407 202122_Koonya West_4
2408 202122_Koonya West_4
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2412 202122_Koonya West_4
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2414 202122_Koonya West_4
2415 202122_Miami drive east access_1
2416 202122_Miami drive east access_1
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2424 202122_Miami drive east access_1
2425 202122_Portsea Farnsworth/SLSC west_1
2426 202122_Portsea Farnsworth/SLSC west_1
2427 202122_Portsea Farnsworth/SLSC west_1
2428 202122_Portsea Farnsworth/SLSC west_1
2429 202122_Portsea Farnsworth/SLSC west_1
2430 202122_Portsea Farnsworth/SLSC west_1
2431 202122_Portsea Farnsworth/SLSC west_1
2432 202122_Rye car park east_1
2433 202122_Rye car park east_1
2434 202122_Rye car park east_1
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2436 202122_Rye car park east_1
2437 202122_Rye car park east_1
2438 202122_Rye car park east_1
2439 202122_Rye car park east_1
2440 202122_Rye car park east_1
2441 202122_Rye car park east_1
2442 202122_Rye car park east_1
2443 202122_Rye car park east_2
2444 202122_Rye car park east_2
2445 202122_Rye car park east_2
2446 202122_Rye car park east_2
2447 202122_Rye car park east_2
2448 202122_Rye car park east_2
2449 202122_Rye car park east_2
2450 202122_Rye car park east_2
2451 202122_Rye car park east_2
2452 202122_Rye car park east_2
2453 202122_Rye car park east_2
2454 202122_Rye car park east_2
2455 202122_Rye car park east_2
2456 202122_Rye car park east_2
2457 202122_Rye car park east_2
2458 202122_Rye car park east_2
2459 202122_Rye car park east_2
2460 202122_Rye car park east_2
2461 202122_Rye car park east_2
2462 202122_Rye car park east_2
2463 202122_Rye car park east_2
2464 202122_Rye car park east_2
2465 202122_St Andrews Boags Rocks_1
2466 202122_St Andrews Boags Rocks_1
2467 202122_St Andrews Boags Rocks_1
2468 202122_St Andrews Boags Rocks_1
2469 202122_St Andrews Boags Rocks_1
2470 202122_St Andrews Boags Rocks_1
2471 202122_St Andrews Boags Rocks_1
2472 202122_St Andrews Boags Rocks_1
2473 202122_St Andrews Boags Rocks_1
2474 202122_St Andrews Boags Rocks_1
2475 202122_St Andrews Boags Rocks_1
2476 202122_St Andrews Boags Rocks_1
2477 202122_St Andrews Boags Rocks_1
2478 202122_St Andrews Boags Rocks_1
2479 202122_St. Andrews car park east_5
2480 202122_St. Andrews car park east_5
2481 202122_St. Andrews car park east_5
2482 202122_St. Andrews car park east_5
2483 202122_St. Andrews car park east_5
2484 202122_St. Andrews car park east_5
2485 202122_St. Andrews car park east_5
2486 202122_St. Andrews car park east_5
2487 202223_Alison Ave east bay, Rye_2
2488 202223_Alison Ave east bay, Rye_2
2489 202223_Alison Ave east bay, Rye_2
2490 202223_Alison Ave east bay, Rye_2
2491 202223_Alison Ave east bay, Rye_2
2492 202223_Alison Ave east bay, Rye_2
2493 202223_Alison Ave east bay, Rye_2
2494 202223_Alison Ave east bay, Rye_2
2495 202223_Alison Ave east bay, Rye_2
2496 202223_Alison Ave east bay, Rye_2
2497 202223_Alison Ave east bay, Rye_2
2498 202223_Alison Ave east bay, Rye_2
2499 202223_Alison Ave east bay, Rye_2
2500 202223_Alison Ave, Rye_1
2501 202223_Alison Ave, Rye_1
2502 202223_Alison Ave, Rye_1
2503 202223_Alison Ave, Rye_1
2504 202223_Alison Ave, Rye_2
2505 202223_Alison Ave, Rye_2
2506 202223_Alison Ave, Rye_2
2507 202223_Alison Ave, Rye_2
2508 202223_Alison Ave, Rye_2
2509 202223_Alison Ave, Rye_2
2510 202223_Alison Ave, Rye_2
2511 202223_Alison Ave, Rye_2
2512 202223_Alison Ave, Rye_2
2513 202223_Alison Ave, Rye_2
2514 202223_Alison Ave, Rye_2
2515 202223_Alison Ave, Rye_2
2516 202223_Alison Ave, Rye_2
2517 202223_Alison Ave, Rye_2
2518 202223_Alison Ave, Rye_3
2519 202223_Alison Ave, Rye_3
2520 202223_Alison Ave, Rye_3
2521 202223_Alison Ave, Rye_3
2522 202223_Alison Ave, Rye_3
2523 202223_Alison Ave, Rye_3
2524 202223_Alison Ave, Rye_3
2525 202223_Alison Ave, Rye_3
2526 202223_Alison Ave, Rye_3
2527 202223_Alison Ave, Rye_3
2528 202223_Alison Ave, Rye_3
2529 202223_Alison Ave, Rye_3
2530 202223_Alison Ave, Rye_3
2531 202223_Alison Ave, Rye_3
2532 202223_Coppins Lookout_1
2533 202223_Coppins Lookout_1
2534 202223_Coppins Lookout_1
2535 202223_Coppins Lookout_1
2536 202223_Coppins Lookout_1
2537 202223_Coppins Lookout_1
2538 202223_Coppins Lookout_1
2539 202223_Coppins Lookout_1
2540 202223_Coppins Lookout_1
2541 202223_Coppins Lookout_1
2542 202223_Coppins Lookout_1
2543 202223_Coppins Lookout_1
2544 202223_Coppins Lookout_1
2545 202223_Coppins Lookout_1
2546 202223_Coppins Lookout_1
2547 202223_Coppins Lookout_1
2548 202223_Coppins Lookout_1
2549 202223_Coppins Lookout_1
2550 202223_Coppins Lookout_1
2551 202223_Gunnamatta Fingal Tk (P4)_3
2552 202223_Gunnamatta Fingal Tk (P4)_3
2553 202223_Gunnamatta Fingal Tk (P4)_3
2554 202223_Gunnamatta Fingal Tk (P4)_3
2555 202223_Gunnamatta Fingal Tk East (P5)_1
2556 202223_Gunnamatta Fingal Tk East (P5)_1
2557 202223_Gunnamatta Fingal Tk East (P5)_1
2558 202223_Gunnamatta Fingal Tk East (P5)_1
2559 202223_Gunnamatta Fingal Tk East (P5)_1
2560 202223_Gunnamatta Fingal Tk East (P5)_1
2561 202223_Gunnamatta Fingal Tk East (P5)_1
2562 202223_Gunnamatta Fingal Tk East (P5)_1
2563 202223_Gunnamatta Fingal Tk East (P5)_2
2564 202223_Gunnamatta Fingal Tk East (P5)_2
2565 202223_Gunnamatta Fingal Tk East (P5)_2
2566 202223_Gunnamatta Fingal Tk East (P5)_2
2567 202223_Gunnamatta Fingal Tk East (P5)_2
2568 202223_Gunnamatta Fingal Tk East (P5)_2
2569 202223_Gunnamatta Fingal Tk East (P5)_2
2570 202223_Gunnamatta Fingal Tk East (P5)_2
2571 202223_Gunnamatta Fingal Tk East (P5)_2
2572 202223_Gunnamatta Fingal Tk East (P5)_2
2573 202223_Gunnamatta Fingal Tk East (P5)_2
2574 202223_Gunnamatta Fingal Tk East (P5)_2
2575 202223_Gunnamatta Fingal Tk West_1
2576 202223_Gunnamatta Fingal Tk West_1
2577 202223_Gunnamatta Fingal Tk West_1
2578 202223_Gunnamatta Fingal Tk West_1
2579 202223_Gunnamatta Fingal Tk West_1
2580 202223_Gunnamatta Fingal Tk West_1
2581 202223_Gunnamatta Fingal Tk West_1
2582 202223_Gunnamatta Fingal Tk West_1
2583 202223_Gunnamatta Fingal Tk West_1
2584 202223_Heyfield Pair 1 (west side) Rye_3
2585 202223_Heyfield Pair 1 (west side) Rye_3
2586 202223_Heyfield Pair 1 (west side) Rye_3
2587 202223_Heyfield Pair 1 (west side) Rye_3
2588 202223_Heyfield Pair 1 (west side) Rye_3
2589 202223_Heyfield Pair 1 (west side) Rye_3
2590 202223_Heyfield Pair 1 (west side) Rye_3
2591 202223_Heyfield Pair 1 (west side) Rye_3
2592 202223_Heyfield Pair 1 (west side) Rye_3
2593 202223_Heyfield Pair 1 (west side) Rye_3
2594 202223_Heyfield Pair 1 (west side) Rye_3
2595 202223_Heyfield Pair 1 (west side) Rye_3
2596 202223_Heyfield Pair 1 (west side) Rye_3
2597 202223_Heyfield Pair 1 (west side) Rye_3
2598 202223_Heyfield Pair 1 (west side) Rye_3
2599 202223_Heyfield Pair 2 (east side) Rye_1
2600 202223_Heyfield Pair 2 (east side) Rye_1
2601 202223_Heyfield Pair 2 (east side) Rye_1
2602 202223_Heyfield Pair 2 (east side) Rye_1
2603 202223_Heyfield Pair 2 (east side) Rye_1
2604 202223_Heyfield Pair 2 (east side) Rye_1
2605 202223_Heyfield Pair 2 (east side) Rye_1
2606 202223_Heyfield Pair 2 (east side) Rye_1
2607 202223_Heyfield Pair 2 (east side) Rye_1
2608 202223_Heyfield Pair 2 (east side) Rye_1
2609 202223_Heyfield Pair 2 (east side) Rye_1
2610 202223_Heyfield Pair 2 (east side) Rye_1
2611 202223_Heyfield Pair 2 (east side) Rye_1
2612 202223_Heyfield Pair 2 (east side) Rye_1
2613 202223_Heyfield Pair 2 (east side) Rye_1
2614 202223_Heyfield Pair 2 (east side) Rye_2
2615 202223_Heyfield Pair 2 (east side) Rye_2
2616 202223_Heyfield Pair 2 (east side) Rye_2
2617 202223_Heyfield Pair 2 (east side) Rye_2
2618 202223_Heyfield Pair 2 (east side) Rye_2
2619 202223_Heyfield Pair 2 (east side) Rye_2
2620 202223_Heyfield Pair 2 (east side) Rye_2
2621 202223_Heyfield Pair 2 (east side) Rye_2
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2623 202223_Heyfield Pair 2 (east side) Rye_2
2624 202223_Heyfield Pair 2 (east side) Rye_2
2625 202223_Koonya East_1
2626 202223_Koonya East_1
2627 202223_Koonya East_1
2628 202223_Koonya East_1
2629 202223_Koonya East_1
2630 202223_Koonya East_1
2631 202223_Koonya East_1
2632 202223_Koonya East_1
2633 202223_Koonya East_2
2634 202223_Koonya East_2
2635 202223_Koonya East_2
2636 202223_Koonya East_2
2637 202223_Koonya East_2
2638 202223_Koonya East_2
2639 202223_Koonya East_2
2640 202223_Koonya West_1
2641 202223_Koonya West_1
2642 202223_Koonya West_1
2643 202223_Koonya West_1
2644 202223_Miami drive east access_4
2645 202223_Miami drive east access_4
2646 202223_Miami drive east access_4
2647 202223_Miami drive east access_4
2648 202223_Miami drive east access_4
2649 202223_Miami drive east access_4
2650 202223_Miami drive east access_4
2651 202223_Miami drive east access_4
2652 202223_Miami drive east access_4
2653 202223_Miami drive east access_4
2654 202223_Miami drive east access_4
2655 202223_Miami drive east access_4
2656 202223_Miami drive east access_4
2657 202223_Miami drive east access_4
2658 202223_Miami drive east access_4
2659 202223_Miami drive east access_4
2660 202223_Miami drive east access_4
2661 202223_Miami drive east access_4
2662 202223_Montforts_2
2663 202223_Montforts_2
2664 202223_Montforts_2
2665 202223_Montforts_2
2666 202223_Montforts_2
2667 202223_Montforts_2
2668 202223_Montforts_2
2669 202223_Montforts_2
2670 202223_Montforts_2
2671 202223_Montforts_2
2672 202223_Montforts_2
2673 202223_Montforts_2
2674 202223_Montforts_2
2675 202223_Montforts_2
2676 202223_Montforts_2
2677 202223_Point Leo_1
2678 202223_Point Leo_1
2679 202223_Point Leo_1
2680 202223_Portsea Franklin rd west_1
2681 202223_Portsea Franklin rd west_1
2682 202223_Portsea Franklin rd west_1
2683 202223_Portsea Franklin rd west_1
2684 202223_Portsea Franklin rd west_1
2685 202223_Portsea London Bridge_1
2686 202223_Portsea London Bridge_1
2687 202223_Portsea London Bridge_1
2688 202223_Portsea London Bridge_1
2689 202223_Rye Big Rock_2
2690 202223_Rye Big Rock_2
2691 202223_Rye Big Rock_2
2692 202223_Rye Big Rock_2
2693 202223_Rye Big Rock_2
2694 202223_Rye Big Rock_2
2695 202223_Rye Big Rock_2
2696 202223_Rye Big Rock_2
2697 202223_Rye Big Rock_2
2698 202223_Rye Big Rock_2
2699 202223_Rye Big Rock_2
2700 202223_Rye Big Rock_2
2701 202223_Rye Big Rock_2
2702 202223_Rye Big Rock_2
2703 202223_Rye Big Rock_2
2704 202223_Rye Big Rock_2
2705 202223_Rye Big Rock_2
2706 202223_Rye Big Rock_2
2707 202223_Rye Big Rock_2
2708 202223_Rye Big Rock_2
2709 202223_Rye Big Rock_2
2710 202223_Rye Big Rock_2
2711 202223_Rye car park east_1
2712 202223_Rye car park east_1
2713 202223_Rye car park east_1
2714 202223_Rye car park east_1
2715 202223_Rye car park east_1
2716 202223_Rye car park east_1
2717 202223_Rye car park east_1
2718 202223_Rye car park east_1
2719 202223_Rye car park east_1
2720 202223_Rye car park east_1
2721 202223_Rye car park east_1
2722 202223_Rye car park east_1
2723 202223_Rye car park east_1
2724 202223_Rye car park east_1
2725 202223_Rye car park east_1
2726 202223_Rye car park east_1
2727 202223_Rye car park east_1
2728 202223_Rye car park east_1
2729 202223_Rye car park east_2
2730 202223_Rye car park east_2
2731 202223_Rye car park east_2
2732 202223_Rye car park east_2
2733 202223_Rye car park east_2
2734 202223_Rye car park east_2
2735 202223_Rye car park east_2
2736 202223_Rye car park east_2
2737 202223_Rye car park east_2
2738 202223_Rye car park east_2
2739 202223_Rye car park east_2
2740 202223_Rye car park east_2
2741 202223_Rye car park east_2
2742 202223_Rye car park east_2
2743 202223_Rye car park east_2
2744 202223_Rye car park east_2
2745 202223_Rye car park east_2
2746 202223_Rye car park east_2
2747 202223_Rye car park east_2
2748 202223_Rye car park east_2
2749 202223_Rye car park east_2
2750 202223_Rye car park east_2
2751 202223_Rye car park east_2
2752 202223_Rye car park east_2
2753 202223_Rye car park east_2
2754 202223_Rye car park east_2
2755 202223_Rye car park east_2
2756 202223_Rye car park east_2
2757 202223_Rye car park east_2
2758 202223_Rye car park east_2
2759 202223_Rye car park east_2
2760 202324_Alison Ave east bay, Rye_2
2761 202324_Alison Ave east bay, Rye_2
2762 202324_Alison Ave east bay, Rye_2
2763 202324_Alison Ave east bay, Rye_2
2764 202324_Alison Ave east bay, Rye_2
2765 202324_Alison Ave east bay, Rye_2
2766 202324_Alison Ave east bay, Rye_2
2767 202324_Alison Ave east bay, Rye_2
2768 202324_Alison Ave east bay, Rye_2
2769 202324_Alison Ave east bay, Rye_2
2770 202324_Alison Ave east bay, Rye_2
2771 202324_Alison Ave, Rye_2
2772 202324_Alison Ave, Rye_2
2773 202324_Alison Ave, Rye_2
2774 202324_Alison Ave, Rye_2
2775 202324_Alison Ave, Rye_2
2776 202324_Alison Ave, Rye_2
2777 202324_Alison Ave, Rye_2
2778 202324_Alison Ave, Rye_2
2779 202324_Alison Ave, Rye_2
2780 202324_Alison Ave, Rye_2
2781 202324_Alison Ave, Rye_2
2782 202324_Alison Ave, Rye_2
2783 202324_Alison Ave, Rye_2
2784 202324_Alison Ave, Rye_2
2785 202324_Alison Ave, Rye_2
2786 202324_Alison Ave, Rye_2
2787 202324_Alison Ave, Rye_2
2788 202324_Alison Ave, Rye_2
2789 202324_Brewster, Rye_3
2790 202324_Brewster, Rye_3
2791 202324_Brewster, Rye_3
2792 202324_Brewster, Rye_3
2793 202324_Brewster, Rye_3
2794 202324_Brewster, Rye_3
2795 202324_Brewster, Rye_3
2796 202324_Brewster, Rye_3
2797 202324_Brewster, Rye_3
2798 202324_Brewster, Rye_3
2799 202324_Brewster, Rye_3
2800 202324_Brewster, Rye_3
2801 202324_Brewster, Rye_3
2802 202324_Fowlers_5
2803 202324_Fowlers_5
2804 202324_Fowlers_5
2805 202324_Fowlers_5
2806 202324_Fowlers_5
2807 202324_Gunnamatta Car Park 1 (P6)_1
2808 202324_Gunnamatta Car Park 1 (P6)_1
2809 202324_Gunnamatta Car Park 1 (P6)_1
2810 202324_Gunnamatta Fingal Tk West_2
2811 202324_Gunnamatta Fingal Tk West_2
2812 202324_Gunnamatta Fingal Tk West_2
2813 202324_Gunnamatta Fingal Tk West_2
2814 202324_Gunnamatta Fingal Tk West_2
2815 202324_Gunnamatta Fingal Tk West_2
2816 202324_Gunnamatta Fingal Tk West_2
2817 202324_Gunnamatta Fingal Tk West_2
2818 202324_Heyfield Pair 1 (west side) Rye_1
2819 202324_Heyfield Pair 1 (west side) Rye_1
2820 202324_Heyfield Pair 1 (west side) Rye_1
2821 202324_Heyfield Pair 1 (west side) Rye_1
2822 202324_Heyfield Pair 1 (west side) Rye_1
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2862 202324_Koonya East_3
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2869 202324_Koonya East_3
2870 202324_Koonya East_3
2871 202324_Koonya East_3
2872 202324_Miami drive east access_4
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2883 202324_Miami drive east access_4
2884 202324_Miami drive east access_4
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2887 202324_Miami drive east access_4
2888 202324_Miami drive east access_4
2889 202324_Miami drive east access_4
2890 202324_Miami drive east access_4
2891 202324_Miami drive east access_4
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2919 202324_Point Leo_1
2920 202324_Point Leo_1
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2928 202324_Portsea Farnsworth/SLSC west_2
2929 202324_Rye car park east_1
2930 202324_Rye car park east_1
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2936 202324_Rye car park east_3
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2958 202324_Rye car park west_1
2959 202324_Rye car park west_1
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2961 202324_St. Andrews car park east_2
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2560 24 1
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2584 3 1
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2599 3 1
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2622 16 0
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2630 9 0
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2640 3 1
2641 4 1
2642 10 0
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2644 3 1
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2674 26 0
2675 27 0
2676 28 0
2677 3 1
2678 6 1
2679 11 0
2680 3 1
2681 5 1
2682 15 1
2683 24 0
2684 28 0
2685 3 1
2686 3 1
2687 3 0
2688 6 0
2689 3 1
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2692 7 1
2693 7 1
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2710 33 1
2711 3 1
2712 3 1
2713 6 0
2714 7 1
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2716 8 1
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2718 11 1
2719 14 1
2720 15 1
2721 16 1
2722 21 0
2723 23 1
2724 24 1
2725 26 1
2726 27 0
2727 28 0
2728 30 0
2729 3 1
2730 3 0
2731 4 1
2732 5 1
2733 6 1
2734 8 1
2735 10 1
2736 10 1
2737 10 0
2738 11 0
2739 11 1
2740 13 1
2741 14 1
2742 15 0
2743 15 1
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2748 21 0
2749 22 1
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2752 26 1
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2754 27 1
2755 28 1
2756 30 1
2757 31 1
2758 34 1
2759 34 1
2760 3 1
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2762 7 1
2763 9 1
2764 9 1
2765 10 1
2766 12 0
2767 15 0
2768 16 0
2769 17 0
2770 19 0
2771 3 1
2772 6 0
2773 7 1
2774 11 0
2775 12 1
2776 13 0
2777 13 1
2778 13 1
2779 17 1
2780 20 1
2781 22 1
2782 24 1
2783 27 1
2784 27 1
2785 29 1
2786 30 1
2787 30 1
2788 32 1
2789 3 1
2790 5 1
2791 7 1
2792 13 0
2793 18 1
2794 19 1
2795 23 1
2796 26 1
2797 29 1
2798 30 1
2799 33 1
2800 33 1
2801 35 0
2802 3 1
2803 5 1
2804 5 1
2805 8 0
2806 9 0
2807 3 1
2808 4 0
2809 6 0
2810 3 1
2811 8 1
2812 12 1
2813 17 1
2814 22 1
2815 26 1
2816 29 1
2817 35 1
2818 3 1
2819 4 1
2820 5 1
2821 8 0
2822 10 1
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2824 14 1
2825 19 1
2826 25 1
2827 25 1
2828 27 1
2829 34 1
2830 3 1
2831 5 1
2832 6 1
2833 6 1
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2857 25 1
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2859 31 0
2860 31 0
2861 32 0
2862 3 1
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2866 4 0
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2871 9 1
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2918 35 1
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2921 3 1
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2926 14 1
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2928 18 0
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2933 13 1
2934 14 0
2935 16 0
2936 3 1
2937 3 1
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2945 14 1
2946 16 1
2947 17 1
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2949 23 1
2950 25 1
2951 26 1
2952 29 1
2953 31 1
2954 32 1
2955 33 1
2956 34 1
2957 34 1
2958 3 1
2959 4 1
2960 10 0
2961 3 1
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2964 7 1
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2969 11 0
2970 12 0
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2972 16 0
2973 17 0
2974 1 1
2975 2 1
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2977 4 1
2978 5 1
2979 6 1
2980 7 1
2981 8 1
2982 9 1
2983 10 1
2984 11 1
2985 12 1
2986 13 1
2987 14 1
2988 15 1
2989 16 1
2990 17 1
2991 18 1
2992 19 1
2993 20 1
2994 21 1
2995 22 1
2996 23 1
2997 24 1
2998 25 1
2999 26 1
3000 27 1
3001 28 1
3002 29 1
3003 30 1
3004 31 1
3005 32 1
3006 33 1
3007 34 1
3008 35 1Code
library(tidyselect)
MP_chick_data_clean_wide <-
MP_chick_data_clean_ %>%
mutate(offspring_seen = as.character(offspring_seen)) %>%
pivot_wider(
id_cols = c(nest_ID, management_level_max, management_status_max, season, region, hatch_date),
names_from = brood_age,
values_from = offspring_seen,
values_fn = list(offspring_seen = max), # in case of duplicates
values_fill = ".", # fill missing with "." (no survey effort)
names_prefix = "age_"
)
# Get the age columns in numeric order
age_cols <- names(MP_chick_data_clean_wide)[-c(1:6)] %>% # drop nest_ID
sub("age_", "", .) %>% # remove prefix
as.numeric() %>% # convert to numeric
sort() %>% # sort numerically
paste0("age_", .) # add prefix back
# Reorder the dataframe
MP_chick_data_clean_wide <- MP_chick_data_clean_wide %>%
select(nest_ID, management_level_max, management_status_max, season, region, hatch_date, all_of(age_cols))
MP_chick_ch <-
MP_chick_data_clean_wide %>%
unite(
col = ch,
starts_with("age_"), # all age_ columns
sep = "", # no separator, just paste values together
remove = TRUE # keep the original columns
) %>%
filter(str_detect(ch, "1")) %>%
mutate(ch = paste0("1", str_sub(ch, start = 2, end = nchar(ch)))) %>%
mutate(hatch_date_j = as.numeric(format(hatch_date + 180, "%j"))) %>%
mutate(management_level_max = as.factor(management_level_max)) %>%
mutate(region = as.factor(region)) %>%
ungroup() %>%
rename(mgmt_l = management_level_max,
mgmt_s = management_status_max,
date_j = hatch_date_j) %>%
filter(nest_ID != "junk") %>%
select(-hatch_date, -nest_ID)mark-recapture modelling
Code
# Create processed RMARK data format as CJS with "Year" as group
MP_chicks.proc <- process.data(MP_chick_ch, model = "CJS",
groups = c("season", "region",
"mgmt_l", "mgmt_s"))
# Create the design data
MP_chicks.ddl <- make.design.data(MP_chicks.proc)
# create a quadratic time variable
time <- c(0:(MP_chicks.proc$nocc[1]-1))
Quadratic <- time^2
Cubic <- time^3
quad_time <- data.frame(time, Quadratic, Cubic)
MP_chicks.ddl$p <- merge_design.covariates(MP_chicks.ddl$Phi,
quad_time, bygroup = FALSE, bytime = TRUE)
MP_chicks.ddl$Phi <- merge_design.covariates(MP_chicks.ddl$Phi,
quad_time, bygroup = FALSE, bytime = TRUE)detection probability modelling
Code
# First assess the best model structure of encounter probability (p) while
# keeping survival (Phi) constant
MP_chicks_p=function()
{
# Constant Phi
Phi.dot <-
list(formula = ~1)
# Constant p
p.dot <-
list(formula = ~1)
# p varies linearly with chick age
p.age <-
list(formula = ~Time)
# p varies quadratically with chick age
p.quadage <-
list(formula = ~Quadratic)
# p varies across year
p.season <-
list(formula = ~season)
# p varies across the hatch date
p.date <-
list(formula = ~date_j)
# interaction between year and age
p.year.x.age <-
list(formula = ~season * Time)
# interaction between year and quadratic age
p.year.x.quadage <-
list(formula = ~season * Quadratic)
# interaction between year and hatch date
p.year.x.date <-
list(formula = ~season * date_j)
# additive effect between year and age
p.year.age <-
list(formula = ~season + Time)
# additive effect between year and quadratic age
p.year.quadage <-
list(formula = ~season + Quadratic)
# additive effect between year and hatch date
p.year.date <-
list(formula = ~season + date_j)
# create model list for all a priori models above that begin with Phi. or p.
cml <- create.model.list("CJS")
# runs model list in program MARK
model.list <- mark.wrapper(model.list = cml, data = MP_chicks.proc,
ddl = MP_chicks.ddl,
threads = 4, brief = TRUE, delete = TRUE)
# store completed model list
return(model.list)
}
# Run model selection to determine best structure of p
MP_chicks_p_run <- MP_chicks_p()
Model: Phi(~1)p(~Time) npar= 3 lnl = 1582.291 AICc = 1588.3032
Model: Phi(~1)p(~date_j) npar= 3 lnl = 1583.0461 AICc = 1589.0583
Model: Phi(~1)p(~1) npar= 2 lnl = 1591.0946 AICc = 1595.1006
Model: Phi(~1)p(~Quadratic) npar= 3 lnl = 1576.1152 AICc = 1582.1273
Model: Phi(~1)p(~season) npar= 13 lnl = 1575.0537 AICc = 1601.23894173028
Model: Phi(~1)p(~season + Time) npar= 14 lnl = 1569.5771 AICc = 1597.79094928717
Model: Phi(~1)p(~season + date_j) npar= 14 lnl = 1568.2765 AICc = 1596.49034928717
Model: Phi(~1)p(~season + Quadratic) npar= 14 lnl = 1563.471 AICc = 1591.68484928717
Model: Phi(~1)p(~season * Time) npar= 25 lnl = 1530.2174 AICc = 1580.88304260113
Model: Phi(~1)p(~season * date_j) npar= 25 lnl = 1548.0958 AICc = 1598.76144260113
Model: Phi(~1)p(~season * Quadratic) npar= 25 lnl = 1527.9514 AICc = 1578.61704260113Code
# inspect p AIC table to find top model
MP_chicks_p_run # best structure: p(~season + Quadratic) model npar AICc DeltaAICc weight Deviance
11 Phi(~1)p(~season * Quadratic) 25 1578.617 0.000000 6.621620e-01 688.7392
9 Phi(~1)p(~season * Time) 25 1580.883 2.266000 2.132596e-01 691.0051
4 Phi(~1)p(~Quadratic) 3 1582.127 3.510309 1.144749e-01 736.9029
1 Phi(~1)p(~Time) 3 1588.303 9.686109 5.219782e-03 743.0788
2 Phi(~1)p(~date_j) 3 1589.058 10.441209 3.578364e-03 1583.0461
8 Phi(~1)p(~season + Quadratic) 14 1591.685 13.067807 9.623346e-04 724.2588
3 Phi(~1)p(~1) 2 1595.101 16.483630 1.744172e-04 751.8823
7 Phi(~1)p(~season + date_j) 14 1596.490 17.873307 8.706128e-05 1568.2765
6 Phi(~1)p(~season + Time) 14 1597.791 19.173907 4.543634e-05 730.3649
10 Phi(~1)p(~season * date_j) 25 1598.761 20.144400 2.796813e-05 1548.0958
5 Phi(~1)p(~season) 13 1601.239 22.621899 8.103661e-06 735.8415Code
saveRDS(MP_chicks_p_run, "output/MP_chicks_p_run.rds")extract results and visualize p
Code
MP_chicks_p_run <- readRDS("output/MP_chicks_p_run.rds")
# Extract estimates of survival from top model
Chick_p_reals <- MP_chicks_p_run[[4]]$results$real
# wrangle dataframe to get annual estimates (grouped by "Year")
Groups <- data.frame(str_split_fixed(rownames(Chick_p_reals), " ", n = 5))
Chick_p_reals <- cbind(Groups, Chick_p_reals)
Chick_p_reals$season <- unlist(substr(Chick_p_reals$X2, 2, 7))
Chick_p_reals$age <- as.numeric(sub("^a", "", Chick_p_reals$X4))
ggplot() +
geom_line(data = Chick_p_reals, aes(x = age, y = estimate, color = as.numeric(season), group = season)) +
geom_ribbon(data = Chick_p_reals, aes(x = age, ymin = lcl, ymax = ucl, fill = as.numeric(season), group = season), alpha = 0.2) +
scale_color_gradient(
low = "black",
high = "red",
name = "season"
) +
scale_fill_gradient(
low = "black",
high = "red",
name = "season"
) +
ylab("daily detection probability of a brood") +
xlab("brood age (days)")
Code
Chick_p_reals <- Chick_p_reals[which(Chick_p_reals$X1 == "p"),]
# clean dataframe
Chick_p_reals <- Chick_p_reals[,-c(1:11)]
Chick_p_reals$Age <- "Chick"
row.names(Chick_p_reals) <- NULLsurvival modelling (Phi)
Code
# Find best structure of Phi while using p(~Year * Day_of_Season)
MP_chicks_Phi <- function()
{
# Constant Phi
Phi.dot <-
list(formula = ~1)
# Phi varies with chick age
Phi.age <-
list(formula = ~Time)
# Phi varies quadratically with chick age
Phi.quadage <-
list(formula = ~Quadratic)
# Phi varies by year
Phi.season <-
list(formula = ~season)
# Phi varies by hatch date
Phi.date <-
list(formula = ~date_j)
# Phi varies by management level
Phi.mgmt_l <-
list(formula = ~mgmt_l)
# Phi varies by management status
Phi.mgmt_s <-
list(formula = ~mgmt_s)
# interaction between hatch date and age
Phi.date.x.age <-
list(formula = ~date_j * Time)
# interaction between hatch date and quadratic age
Phi.date.x.quadage <-
list(formula = ~date_j * Quadratic)
# interaction between year and hatch date
Phi.season.x.date <-
list(formula = ~season * date_j)
# interaction between hatch date and age and management level
Phi.date.x.mgmt_l <-
list(formula = ~date_j * mgmt_l)
# interaction between hatch date and quadratic age and management level
Phi.age.x.mgmt_l <-
list(formula = ~Time * mgmt_l)
# interaction between year and hatch date and management level
Phi.quadage.x.mgmt_l <-
list(formula = ~Quadratic * mgmt_l)
# interaction between hatch date and age and management level
Phi.date.mgmt_l <-
list(formula = ~date_j + mgmt_l)
# interaction between hatch date and quadratic age and management level
Phi.age.mgmt_l <-
list(formula = ~Time + mgmt_l)
# interaction between year and hatch date and management level
Phi.quadage.mgmt_l <-
list(formula = ~Quadratic + mgmt_l)
# interaction between hatch date and age and management status
Phi.date.x.mgmt_s <-
list(formula = ~date_j * mgmt_s)
# interaction between hatch date and quadratic age and management status
Phi.age.x.mgmt_s <-
list(formula = ~Time * mgmt_s)
# interaction between year and hatch date and management status
Phi.quadage.x.mgmt_s <-
list(formula = ~Quadratic * mgmt_s)
# interaction between hatch date and age and management status
Phi.date.mgmt_s <-
list(formula = ~date_j + mgmt_s)
# interaction between hatch date and quadratic age and management status
Phi.age.mgmt_s <-
list(formula = ~Time + mgmt_s)
# interaction between year and hatch date and management status
Phi.quadage.mgmt_s <-
list(formula = ~Quadratic + mgmt_s)
# # additive effect of hach date and age and management level
# Phi.date.age.mgmt_l <-
# list(formula = ~date_j + Time + mgmt_l)
#
# # additive effect of hatch date and quadratic age and management level
# Phi.date.quadage.mgmt_l <-
# list(formula = ~date_j + Quadratic + mgmt_l)
#
# # interaction between hatch date and age and management level
# Phi.date.x.age.mgmt_l <-
# list(formula = ~date_j * Time + mgmt_l)
#
# # interaction between hatch date and quadratic age and management level
# Phi.date.x.quadage.mgmt_l <-
# list(formula = ~date_j * Quadratic + mgmt_l)
#
# # interaction between year and hatch date and management level
# Phi.season.x.date.mgmt_l <-
# list(formula = ~season * date_j + mgmt_l)
#
# # additive between year and hatch date and management level
# Phi.season.date.mgmt_l <-
# list(formula = ~season + date_j + mgmt_l)
#
# # additive effect of hach date and age and management status
# Phi.date.age.mgmt_s <-
# list(formula = ~date_j + Time + mgmt_s)
#
# # additive effect of hatch date and quadratic age and management status
# Phi.date.quadage.mgmt_s <-
# list(formula = ~date_j + Quadratic + mgmt_s)
#
# # interaction between hatch date and age and management status
# Phi.date.x.age.mgmt_s <-
# list(formula = ~date_j * Time + mgmt_s)
#
# # interaction between hatch date and quadratic age and management status
# Phi.date.x.quadage.mgmt_s <-
# list(formula = ~date_j * Quadratic + mgmt_s)
#
# # interaction between year and hatch date and management status
# Phi.season.x.date.mgmt_s <-
# list(formula = ~season * date_j + mgmt_s)
#
# # additive between year and hatch date and management status
# Phi.season.date.mgmt_s <-
# list(formula = ~season + date_j + mgmt_s)
#
# # best structure of p determined from previous analysis
# p.year.x.quadage <-
# list(formula = ~season * Quadratic)
# best structure of p determined from previous analysis
p.quadage <-
list(formula = ~Quadratic)
# create model list for all a priori models above that begin with Phi. or p.
cml <- create.model.list("CJS")
# runs model list in program MARK
model.list <- mark.wrapper(model.list = cml, data = MP_chicks.proc,
ddl = MP_chicks.ddl,
threads = 4, brief = TRUE, delete = TRUE)
# store completed model list
return(model.list)
}
# Run model selection to determine best structure of Phi
MP_chicks_Phi_run <- MP_chicks_Phi()
Model: Phi(~Time)p(~Quadratic) npar= 4 lnl = 1571.8926 AICc = 1579.9128
Model: Phi(~Time + mgmt_l)p(~Quadratic) npar= 8 lnl = 1564.8084 AICc = 1580.8815
Model: Phi(~Time + mgmt_s)p(~Quadratic) npar= 5 lnl = 1568.5068 AICc = 1578.5372
Model: Phi(~Time * mgmt_l)p(~Quadratic) npar= 12 lnl = 1557.9676 AICc = 1582.1263
Model: Phi(~Time * mgmt_s)p(~Quadratic) npar= 6 lnl = 1567.4724 AICc = 1579.515
Model: Phi(~date_j)p(~Quadratic) npar= 4 lnl = 1573.6091 AICc = 1581.6294
Model: Phi(~date_j + mgmt_l)p(~Quadratic) npar= 8 lnl = 1564.6766 AICc = 1580.7497
Model: Phi(~date_j + mgmt_s)p(~Quadratic) npar= 5 lnl = 1598.401 AICc = 1608.4314
Model: Phi(~date_j * Time)p(~Quadratic) npar= 6 lnl = 1568.565 AICc = 1580.6076
Model: Phi(~date_j * mgmt_l)p(~Quadratic) npar= 12 lnl = 1559.6915 AICc = 1583.8502
Model: Phi(~date_j * mgmt_s)p(~Quadratic) npar= 6 lnl = 1564.7165 AICc = 1576.7591
Model: Phi(~date_j * Quadratic)p(~Quadratic) npar= 6 lnl = 1564.2472 AICc = 1576.2898
Model: Phi(~1)p(~Quadratic) npar= 3 lnl = 1576.1152 AICc = 1582.1273
Model: Phi(~mgmt_l)p(~Quadratic) npar= 7 lnl = 1568.286 AICc = 1582.3429
Model: Phi(~mgmt_s)p(~Quadratic) npar= 4 lnl = 1572.555 AICc = 1580.5752
Model: Phi(~Quadratic)p(~Quadratic) npar= 4 lnl = 1568.0157 AICc = 1576.0359
Model: Phi(~Quadratic + mgmt_l)p(~Quadratic) npar= 8 lnl = 1561.0391 AICc = 1577.1122
Model: Phi(~Quadratic + mgmt_s)p(~Quadratic) npar= 5 lnl = 1564.5767 AICc = 1574.6071
Model: Phi(~Quadratic * mgmt_l)p(~Quadratic) npar= 12 lnl = 1553.9182 AICc = 1578.0769
Model: Phi(~Quadratic * mgmt_s)p(~Quadratic) npar= 6 lnl = 1562.7958 AICc = 1574.83839634888
Model: Phi(~season)p(~Quadratic) npar= 14 lnl = 1551.0913 AICc = 1579.3051
Model: Phi(~season * date_j)p(~Quadratic) npar= 26 lnl = 1533.7517 AICc = 1586.471Code
# inspect Phi AIC table to find top model
MP_chicks_Phi_run # best structure: Phi(~Year * Day_of_Season) model npar AICc DeltaAICc weight
17 Phi(~Quadratic + mgmt_s)p(~Quadratic) 5 1574.607 0.0000000 2.322058e-01
20 Phi(~Quadratic * mgmt_s)p(~Quadratic) 6 1574.838 0.2312858 2.068474e-01
18 Phi(~Quadratic)p(~Quadratic) 4 1576.036 1.4288529 1.136584e-01
12 Phi(~date_j * Quadratic)p(~Quadratic) 6 1576.290 1.6826858 1.001112e-01
11 Phi(~date_j * mgmt_s)p(~Quadratic) 6 1576.759 2.1519858 7.917267e-02
16 Phi(~Quadratic + mgmt_l)p(~Quadratic) 8 1577.112 2.5050859 6.635911e-02
19 Phi(~Quadratic * mgmt_l)p(~Quadratic) 12 1578.077 3.4697873 4.096551e-02
2 Phi(~Time + mgmt_s)p(~Quadratic) 5 1578.537 3.9301000 3.254338e-02
21 Phi(~season)p(~Quadratic) 14 1579.305 4.6980387 2.216700e-02
5 Phi(~Time * mgmt_s)p(~Quadratic) 6 1579.515 4.9078858 1.995902e-02
3 Phi(~Time)p(~Quadratic) 4 1579.913 5.3057529 1.635850e-02
15 Phi(~mgmt_s)p(~Quadratic) 4 1580.575 5.9681529 1.174641e-02
9 Phi(~date_j * Time)p(~Quadratic) 6 1580.608 6.0004858 1.155804e-02
6 Phi(~date_j + mgmt_l)p(~Quadratic) 8 1580.750 6.1425859 1.076533e-02
1 Phi(~Time + mgmt_l)p(~Quadratic) 8 1580.881 6.2743859 1.007877e-02
8 Phi(~date_j)p(~Quadratic) 4 1581.629 7.0222529 6.934421e-03
4 Phi(~Time * mgmt_l)p(~Quadratic) 12 1582.126 7.5191873 5.408817e-03
13 Phi(~1)p(~Quadratic) 3 1582.127 7.5202414 5.405967e-03
14 Phi(~mgmt_l)p(~Quadratic) 7 1582.343 7.7357134 4.853826e-03
10 Phi(~date_j * mgmt_l)p(~Quadratic) 12 1583.850 9.2430873 2.284347e-03
22 Phi(~season * date_j)p(~Quadratic) 26 1586.471 11.8638518 6.161272e-04
7 Phi(~date_j + mgmt_s)p(~Quadratic) 5 1608.431 33.8243000 1.049590e-08
Deviance
17 725.3644
20 723.5835
18 728.8034
12 1564.2472
11 1564.7165
16 721.8269
19 714.7060
2 729.2945
21 711.8790
5 728.2602
3 732.6803
15 733.3427
9 1568.5650
6 1564.6766
1 725.5962
8 1573.6091
4 718.7554
13 736.9029
14 729.0738
10 1559.6915
22 1533.7517
7 1598.4010Code
saveRDS(MP_chicks_Phi_run, "output/MP_chicks_Phi_run.rds")extract results and visualize Phi
Code
MP_chicks_Phi_run <- readRDS("output/MP_chicks_Phi_run.rds")
# Extract estimates of survival from top models
Chick_dot_surv <- MP_chicks_Phi_run[[13]]$results$real
Chick_quadage_surv <- MP_chicks_Phi_run[[18]]$results$real
Chick_hatch_date_surv <- MP_chicks_Phi_run[[8]]$results$real
Chick_mgmt_s_surv <- MP_chicks_Phi_run[[15]]$results$real
Chick_mgmt_l_surv <- MP_chicks_Phi_run[[14]]$results$real
Chick_mgmt_s_x_quadage_surv <- MP_chicks_Phi_run[[20]]$results$real
Chick_mgmt_s_quadage_surv <- MP_chicks_Phi_run[[17]]$results$real
Chick_mgmt_l_quadage_surv <- MP_chicks_Phi_run[[16]]$results$real
Groups <- data.frame(str_split_fixed(rownames(Chick_dot_surv), " ", n = 5))
Chick_dot_surv <- cbind(Groups, Chick_dot_surv)
Chick_dot_surv$season <- unlist(substr(Chick_dot_surv$X2, 2, 7))
Chick_dot_surv$age <- as.numeric(sub("^a", "", Chick_dot_surv$X4))
Chick_dot_surv$mgmt_s <- unlist(substr(Chick_dot_surv$X2, 11, 11))
Groups <- data.frame(str_split_fixed(rownames(Chick_quadage_surv), " ", n = 5))
Chick_quadage_surv <- cbind(Groups, Chick_quadage_surv)
Chick_quadage_surv$season <- unlist(substr(Chick_quadage_surv$X2, 2, 7))
Chick_quadage_surv$age <- as.numeric(sub("^a", "", Chick_quadage_surv$X4))
Chick_quadage_surv$mgmt_s <- unlist(substr(Chick_quadage_surv$X2, 11, 11))
Groups <- data.frame(str_split_fixed(rownames(Chick_hatch_date_surv), " ", n = 5))
Chick_hatch_date_surv <- cbind(Groups, Chick_hatch_date_surv)
Chick_hatch_date_surv$season <- unlist(substr(Chick_hatch_date_surv$X2, 2, 7))
Chick_hatch_date_surv$age <- as.numeric(sub("^a", "", Chick_hatch_date_surv$X4))
Chick_hatch_date_surv$mgmt_s <- unlist(substr(Chick_hatch_date_surv$X2, 11, 11))
Groups <- data.frame(str_split_fixed(rownames(Chick_mgmt_s_surv), " ", n = 5))
Chick_mgmt_s_surv <- cbind(Groups, Chick_mgmt_s_surv)
Chick_mgmt_s_surv$season <- unlist(substr(Chick_mgmt_s_surv$X2, 2, 7))
Chick_mgmt_s_surv$age <- as.numeric(sub("^a", "", Chick_mgmt_s_surv$X4))
Chick_mgmt_s_surv$mgmt_s <- unlist(substr(Chick_mgmt_s_surv$X2, 11, 11))
Groups <- data.frame(str_split_fixed(rownames(Chick_mgmt_l_surv), " ", n = 5))
Chick_mgmt_l_surv <- cbind(Groups, Chick_mgmt_l_surv)
Chick_mgmt_l_surv$season <- unlist(substr(Chick_mgmt_l_surv$X2, 2, 7))
Chick_mgmt_l_surv$age <- as.numeric(sub("^a", "", Chick_mgmt_l_surv$X4))
Chick_mgmt_l_surv$mgmt_l <- unlist(substr(Chick_mgmt_l_surv$X2, 10, 10))
Groups <- data.frame(str_split_fixed(rownames(Chick_mgmt_s_quadage_surv), " ", n = 5))
Chick_mgmt_s_quadage_surv <- cbind(Groups, Chick_mgmt_s_quadage_surv)
Chick_mgmt_s_quadage_surv$season <- unlist(substr(Chick_mgmt_s_quadage_surv$X2, 2, 7))
Chick_mgmt_s_quadage_surv$age <- as.numeric(sub("^a", "", Chick_mgmt_s_quadage_surv$X4))
Chick_mgmt_s_quadage_surv$mgmt_s <- unlist(substr(Chick_mgmt_s_quadage_surv$X2, 11, 11))
Groups <- data.frame(str_split_fixed(rownames(Chick_mgmt_s_x_quadage_surv), " ", n = 5))
Chick_mgmt_s_x_quadage_surv <- cbind(Groups, Chick_mgmt_s_x_quadage_surv)
Chick_mgmt_s_x_quadage_surv$season <- unlist(substr(Chick_mgmt_s_x_quadage_surv$X2, 2, 7))
Chick_mgmt_s_x_quadage_surv$age <- as.numeric(sub("^a", "", Chick_mgmt_s_x_quadage_surv$X4))
Chick_mgmt_s_x_quadage_surv$mgmt_s <- unlist(substr(Chick_mgmt_s_x_quadage_surv$X2, 11, 11))
Groups <- data.frame(str_split_fixed(rownames(Chick_mgmt_l_quadage_surv), " ", n = 5))
Chick_mgmt_l_quadage_surv <- cbind(Groups, Chick_mgmt_l_quadage_surv)
Chick_mgmt_l_quadage_surv$season <- unlist(substr(Chick_mgmt_l_quadage_surv$X2, 2, 7))
Chick_mgmt_l_quadage_surv$age <- as.numeric(sub("^a", "", Chick_mgmt_l_quadage_surv$X4))
Chick_mgmt_l_quadage_surv$mgmt_l <- unlist(substr(Chick_mgmt_l_quadage_surv$X2, 10, 10))
# constant survival
ggplot() +
geom_line(data = Chick_dot_surv, aes(x = age, y = estimate, color = as.numeric(season), group = season)) +
geom_ribbon(data = Chick_dot_surv, aes(x = age, ymin = lcl, ymax = ucl, fill = as.numeric(season), group = season), alpha = 0.2) +
scale_color_gradient(
low = "black",
high = "red",
name = "season"
) +
scale_fill_gradient(
low = "black",
high = "red",
name = "season"
) +
ylab("daily brood survival") +
xlab("brood age (days)")
Code
# quadratic age-dependent survival
ggplot() +
geom_line(data = filter(Chick_quadage_surv, X1 == "Phi"),
aes(x = age, y = estimate)) +
geom_ribbon(data = filter(Chick_quadage_surv, X1 == "Phi"),
aes(x = age, ymin = lcl, ymax = ucl), alpha = 0.2) +
ylab("daily brood survival") +
xlab("brood age (days)")
Code
# quadratic age-dependent survival and management status
ggplot() +
geom_line(data = filter(Chick_mgmt_s_quadage_surv, X1 == "Phi"),
aes(x = age, y = estimate, color = mgmt_s)) +
geom_ribbon(data = filter(Chick_mgmt_s_quadage_surv, X1 == "Phi"),
aes(x = age, ymin = lcl, ymax = ucl, fill = mgmt_s), alpha = 0.2) +
ylab("daily brood survival") +
xlab("brood age (days)")
Code
# quadratic age-dependent survival and management level
ggplot() +
geom_line(data = filter(Chick_mgmt_l_quadage_surv, X1 == "Phi"),
aes(x = age, y = estimate, color = mgmt_l)) +
geom_ribbon(data = filter(Chick_mgmt_l_quadage_surv, X1 == "Phi"),
aes(x = age, ymin = lcl, ymax = ucl, fill = mgmt_l), alpha = 0.2) +
ylab("daily brood survival") +
xlab("brood age (days)")
Code
# quadratic age-dependent survival and management status
ggplot() +
geom_line(data = filter(Chick_mgmt_s_x_quadage_surv, X1 == "Phi"),
aes(x = age, y = estimate, color = mgmt_s)) +
geom_ribbon(data = filter(Chick_mgmt_s_x_quadage_surv, X1 == "Phi"),
aes(x = age, ymin = lcl, ymax = ucl, fill = mgmt_s), alpha = 0.2) +
ylab("daily brood survival") +
xlab("brood age (days)")
Code
# management status-dependent survival
ggplot() +
geom_point(data = filter(Chick_mgmt_s_surv, X1 == "Phi"),
aes(x = mgmt_s, y = estimate, color = mgmt_s)) +
geom_errorbar(data = filter(Chick_mgmt_s_surv, X1 == "Phi"),
aes(x = mgmt_s, ymin = lcl, ymax = ucl, color = mgmt_s)) +
ylab("daily brood survival") +
xlab("managed?") +
scale_y_continuous(limits = c(0, 1))
Code
# management level-dependent survival
ggplot() +
geom_point(data = filter(Chick_mgmt_l_surv, X1 == "Phi"),
aes(x = mgmt_l, y = estimate, color = mgmt_l)) +
geom_errorbar(data = filter(Chick_mgmt_l_surv, X1 == "Phi"),
aes(x = mgmt_l, ymin = lcl, ymax = ucl, color = mgmt_l)) +
ylab("daily brood survival") +
xlab("managed?") +
scale_y_continuous(limits = c(0, 1))
Code
filter(Chick_mgmt_s_surv, X1 == "Phi") %>%
mutate(fledgling_success = estimate^35) X1 X2 X3 X4 X5 estimate se lcl
Phi g201213MP0N c1 a0 t1 Phi g201213MP0N c1 a0 t1 0.9151093 0.0349744 0.8168670
Phi g201213MP3Y c1 a0 t1 Phi g201213MP3Y c1 a0 t1 0.9673442 0.0033289 0.9601507
ucl fixed note season age mgmt_s
Phi g201213MP0N c1 a0 t1 0.9630341 201213 0 N
Phi g201213MP3Y c1 a0 t1 0.9732754 201213 0 Y
fledgling_success
Phi g201213MP0N c1 a0 t1 0.04482847
Phi g201213MP3Y c1 a0 t1 0.31284997Code
# seasonal dependent survival (hatch date)
# ggplot() +
# geom_line(data = Chick_p_reals, aes(x = age, y = estimate, color = as.numeric(season), group = season)) +
# geom_ribbon(data = Chick_p_reals, aes(x = age, ymin = lcl, ymax = ucl, fill = as.numeric(season), group = season), alpha = 0.2) +
# scale_color_gradient(
# low = "black",
# high = "red",
# name = "season"
# ) +
# scale_fill_gradient(
# low = "black",
# high = "red",
# name = "season"
# ) +
# ylab("detection probability of a brood") +
# xlab("brood age (days)")Explore correlations between “Fun days” and the threats
We can see in our model selection of nest survival all three sites that our variable “fun days” (a measure of the number of weekends or holidays a nest is exposed to) was the best at explaining variation in daily nest survival. To interpret this effect, it essentially means that if a nest is initiated on, for example, a Friday right before a long period of consecutive holidays, this nest is expected to have extremely low survival compared to a nest initiated on, for example, a Monday at the early or late part of the season when there are no holidays.
Despite “fun days” having such a strong relationship to daily nest survival, our threat variables (e.g., maximum number of dogs off leash counted, maximum number of humans counted, etc.) all showed little relationship with daily nest survival, which we interpret as a methodological effect: the surveys conducted to quantify specific threats were too infrequent to capture a meaningful measure of the pressure that these threats have on nest survival. To link “Fun days” to the threats, we here do an exploratory relationship between fundays and the threats.
Code
library(lme4)
library(gtsummary)
library(ggeffects)
library(effects)
threat_data__scaled_FP <-
threat_data__ %>%
mutate(no_fun = ifelse(funday == 0, 1, 0),
fun = ifelse(funday == 1, 1, 0)) %>%
filter(region == "FP") %>%
mutate(humans_s = scale(humans_),
dogs_s = scale(dogs_),
pred_birds_s = scale(pred_birds_),
gull_s = scale(gulls_),
dogs_off_s = scale(dogs_off_),
obs_date2_s = scale(obs_date2))
threat_data__scaled_MP <-
threat_data__ %>%
mutate(no_fun = ifelse(funday == 0, 1, 0),
fun = ifelse(funday == 1, 1, 0)) %>%
filter(region == "MP") %>%
mutate(humans_s = scale(humans_),
dogs_s = scale(dogs_),
pred_birds_s = scale(pred_birds_),
gull_s = scale(gulls_),
dogs_off_s = scale(dogs_off_),
obs_date2_s = scale(obs_date2))
threat_data__scaled_BC <-
threat_data__ %>%
mutate(no_fun = ifelse(funday == 0, 1, 0),
fun = ifelse(funday == 1, 1, 0)) %>%
filter(region == "BC") %>%
mutate(humans_s = scale(humans_),
dogs_s = scale(dogs_),
pred_birds_s = scale(pred_birds_),
gull_s = scale(gulls_),
dogs_off_s = scale(dogs_off_),
obs_date2_s = scale(obs_date2))
threat_data__scaled_PI <-
threat_data__ %>%
mutate(no_fun = ifelse(funday == 0, 1, 0),
fun = ifelse(funday == 1, 1, 0)) %>%
filter(region == "PI") %>%
mutate(humans_s = scale(humans_),
dogs_s = scale(dogs_),
pred_birds_s = scale(pred_birds_),
gull_s = scale(gulls_),
dogs_off_s = scale(dogs_off_),
obs_date2_s = scale(obs_date2))
threat_data__scaled_BSC <-
threat_data__ %>%
mutate(no_fun = ifelse(funday == 0, 1, 0),
fun = ifelse(funday == 1, 1, 0)) %>%
filter(region == "BSC") %>%
mutate(humans_s = scale(humans_),
dogs_s = scale(dogs_),
pred_birds_s = scale(pred_birds_),
gull_s = scale(gulls_),
dogs_off_s = scale(dogs_off_),
obs_date2_s = scale(obs_date2))Humans and fun days relationships at the three regions
strong relationship between the number of humans detected and the occurrence of a weekend or a holiday at all 3 regions
Code
humans_fun_day_FP_ <-
glmer(humans_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_FP,
family = "poisson")
humans_fun_day_MP_ <-
glmer(humans_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_MP,
family = "poisson")
humans_fun_day_BC_ <-
glmer(humans_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BC,
family = "poisson")
humans_fun_day_BSC_ <-
glmer(humans_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BSC,
family = "poisson")
humans_fun_day_PI_ <-
glmer(humans_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_PI,
family = "poisson")Fleurieu Peninsula
Code
tbl_regression(humans_fun_day_FP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | 0.86 | 0.54, 1.2 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 12 | 11, 13 | <0.001 |
| poly(obs_date2, 3)2 | -16 | -17, -15 | <0.001 |
| poly(obs_date2, 3)3 | -15 | -16, -14 | <0.001 |
| Weekends & Holidays | 0.57 | 0.56, 0.58 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Mornington Peninsula
Code
tbl_regression(humans_fun_day_MP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.04 | -0.41, 0.33 | 0.8 |
| Date | |||
| poly(obs_date2, 3)1 | 12 | 11, 14 | <0.001 |
| poly(obs_date2, 3)2 | -15 | -16, -13 | <0.001 |
| poly(obs_date2, 3)3 | 0.78 | -0.62, 2.2 | 0.3 |
| Weekends & Holidays | 0.93 | 0.92, 0.95 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bellarine / Surf Coast
Code
tbl_regression(humans_fun_day_BSC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | 0.70 | 0.39, 1.0 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 16 | 15, 18 | <0.001 |
| poly(obs_date2, 3)2 | -6.7 | -8.0, -5.4 | <0.001 |
| poly(obs_date2, 3)3 | -12 | -14, -11 | <0.001 |
| Weekends & Holidays | 0.73 | 0.71, 0.76 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bass Coast
Code
tbl_regression(humans_fun_day_BC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.07 | -0.41, 0.28 | 0.7 |
| Date | |||
| poly(obs_date2, 3)1 | 9.7 | 8.5, 11 | <0.001 |
| poly(obs_date2, 3)2 | -7.5 | -8.7, -6.3 | <0.001 |
| poly(obs_date2, 3)3 | 0.95 | -0.29, 2.2 | 0.13 |
| Weekends & Holidays | 0.85 | 0.82, 0.87 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Phillip Island
Code
tbl_regression(humans_fun_day_PI_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | 0.55 | 0.27, 0.83 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 16 | 14, 17 | <0.001 |
| poly(obs_date2, 3)2 | -13 | -14, -12 | <0.001 |
| poly(obs_date2, 3)3 | -11 | -12, -9.4 | <0.001 |
| Weekends & Holidays | 0.88 | 0.86, 0.91 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Code
humans_fun_day_FP__fits <-
as.data.frame(effect(term = "funday", mod = humans_fun_day_FP_,
xlevels = list(funday = seq(min(threat_data__scaled_FP[, "funday"], na.rm = TRUE), max(threat_data__scaled_FP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "FP")
humans_fun_day_MP__fits <-
as.data.frame(effect(term = "funday", mod = humans_fun_day_MP_,
xlevels = list(funday = seq(min(threat_data__scaled_MP[, "funday"], na.rm = TRUE), max(threat_data__scaled_MP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "MP")
humans_fun_day_BC__fits <-
as.data.frame(effect(term = "funday", mod = humans_fun_day_BC_,
xlevels = list(funday = seq(min(threat_data__scaled_BC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BC")
humans_fun_day_PI__fits <-
as.data.frame(effect(term = "funday", mod = humans_fun_day_PI_,
xlevels = list(funday = seq(min(threat_data__scaled_PI[, "funday"], na.rm = TRUE), max(threat_data__scaled_PI[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "PI")
humans_fun_day_BSC__fits <-
as.data.frame(effect(term = "funday", mod = humans_fun_day_BSC_,
xlevels = list(funday = seq(min(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BSC")
humans_fun_day__fits <-
bind_rows(humans_fun_day_FP__fits,
humans_fun_day_MP__fits,
humans_fun_day_BSC__fits,
humans_fun_day_BC__fits,
humans_fun_day_PI__fits) %>%
mutate(funday = ifelse(funday == 0, "no", "yes"))
row.names(humans_fun_day__fits) <- NULL
ggplot() +
geom_line(data = humans_fun_day__fits,
aes(x = funday, y = fit, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
geom_errorbar(data = humans_fun_day__fits,
aes(ymin = lower, ymax = upper,
x = funday,
y = fit, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = humans_fun_day__fits,
aes(x = funday, y = fit, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
luke_theme +
theme(legend.position = "top",
legend.justification = c(1, 0),
strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 10)) +
ylab("number of humans detected (± 95% CI)") +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_x_discrete(labels = c("weekday", "weekend/holiday"))
Dogs and fun days relationships at the three regions
strong relationship between the number of dogs detected and the occurrence of a weekend or a holiday at all 3 regions
Code
dogs_fun_day_FP_ <-
glmer(dogs_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_FP,
family = "poisson")
dogs_fun_day_MP_ <-
glmer(dogs_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_MP,
family = "poisson")
dogs_fun_day_BC_ <-
glmer(dogs_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BC,
family = "poisson")
dogs_fun_day_BSC_ <-
glmer(dogs_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BSC,
family = "poisson")
dogs_fun_day_PI_ <-
glmer(dogs_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_PI,
family = "poisson")Fleurieu Peninsula
Code
tbl_regression(dogs_fun_day_FP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.50 | -0.95, -0.06 | 0.025 |
| Date | |||
| poly(obs_date2, 3)1 | 5.9 | 4.4, 7.4 | <0.001 |
| poly(obs_date2, 3)2 | -10 | -12, -8.6 | <0.001 |
| poly(obs_date2, 3)3 | -10 | -12, -8.6 | <0.001 |
| Weekends & Holidays | 0.30 | 0.28, 0.32 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Mornington Peninsula
Code
tbl_regression(dogs_fun_day_MP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -3.1 | -3.7, -2.5 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | -10 | -16, -4.1 | <0.001 |
| poly(obs_date2, 3)2 | -0.45 | -6.5, 5.6 | 0.9 |
| poly(obs_date2, 3)3 | 1.8 | -4.1, 7.7 | 0.6 |
| Weekends & Holidays | 0.70 | 0.62, 0.77 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bellarine / Surf Coast
Code
tbl_regression(dogs_fun_day_BSC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.14 | -0.45, 0.16 | 0.4 |
| Date | |||
| poly(obs_date2, 3)1 | 2.8 | 0.71, 4.9 | 0.009 |
| poly(obs_date2, 3)2 | 2.0 | -0.13, 4.2 | 0.065 |
| poly(obs_date2, 3)3 | -4.3 | -6.5, -2.2 | <0.001 |
| Weekends & Holidays | 0.53 | 0.49, 0.57 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bass Coast
Code
tbl_regression(dogs_fun_day_BC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.0 | -1.4, -0.71 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 2.1 | -0.08, 4.3 | 0.059 |
| poly(obs_date2, 3)2 | 2.0 | -0.08, 4.1 | 0.059 |
| poly(obs_date2, 3)3 | 1.2 | -0.82, 3.2 | 0.2 |
| Weekends & Holidays | 0.43 | 0.38, 0.48 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Phillip Island
Code
tbl_regression(dogs_fun_day_PI_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -2.0 | -2.6, -1.4 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 10 | 7.8, 13 | <0.001 |
| poly(obs_date2, 3)2 | -3.9 | -6.7, -1.2 | 0.005 |
| poly(obs_date2, 3)3 | -8.6 | -11, -5.8 | <0.001 |
| Weekends & Holidays | 0.62 | 0.57, 0.67 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Code
dogs_fun_day_FP__fits <-
as.data.frame(effect(term = "funday", mod = dogs_fun_day_FP_,
xlevels = list(funday = seq(min(threat_data__scaled_FP[, "funday"], na.rm = TRUE), max(threat_data__scaled_FP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "FP")
dogs_fun_day_MP__fits <-
as.data.frame(effect(term = "funday", mod = dogs_fun_day_MP_,
xlevels = list(funday = seq(min(threat_data__scaled_MP[, "funday"], na.rm = TRUE), max(threat_data__scaled_MP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "MP")
dogs_fun_day_BC__fits <-
as.data.frame(effect(term = "funday", mod = dogs_fun_day_BC_,
xlevels = list(funday = seq(min(threat_data__scaled_BC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BC")
dogs_fun_day_PI__fits <-
as.data.frame(effect(term = "funday", mod = dogs_fun_day_PI_,
xlevels = list(funday = seq(min(threat_data__scaled_PI[, "funday"], na.rm = TRUE), max(threat_data__scaled_PI[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "PI")
dogs_fun_day_BSC__fits <-
as.data.frame(effect(term = "funday", mod = dogs_fun_day_BSC_,
xlevels = list(funday = seq(min(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BSC")
dogs_fun_day__fits <-
bind_rows(dogs_fun_day_FP__fits,
dogs_fun_day_MP__fits,
dogs_fun_day_BSC__fits,
dogs_fun_day_BC__fits,
dogs_fun_day_PI__fits) %>%
mutate(funday = ifelse(funday == 0, "no", "yes"))
row.names(dogs_fun_day__fits) <- NULL
ggplot() +
geom_line(data = dogs_fun_day__fits,
aes(x = funday, y = fit, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
geom_errorbar(data = dogs_fun_day__fits,
aes(ymin = lower, ymax = upper,
x = funday,
y = fit, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = dogs_fun_day__fits,
aes(x = funday, y = fit, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
luke_theme +
theme(legend.position = "top",
legend.justification = c(1, 0),
strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 10)) +
ylab("number of dogs detected (± 95% CI)") +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_x_discrete(labels = c("weekday", "weekend/holiday"))
Dogs off leashes and fun days relationships at the three regions
strong relationship between the number of dogs off leash detected and the occurrence of a weekend or a holiday at all 3 regions
Code
dogs_off_fun_day_FP_ <-
glmer(dogs_off_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_FP,
family = "poisson")
dogs_off_fun_day_MP_ <-
glmer(dogs_off_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_MP,
family = "poisson")
dogs_off_fun_day_BC_ <-
glmer(dogs_off_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BC,
family = "poisson")
dogs_off_fun_day_BSC_ <-
glmer(dogs_off_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BSC,
family = "poisson")
dogs_off_fun_day_PI_ <-
glmer(dogs_off_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_PI,
family = "poisson")Fleurieu Peninsula
Code
tbl_regression(dogs_off_fun_day_FP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.89 | -1.3, -0.46 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 5.6 | 3.8, 7.4 | <0.001 |
| poly(obs_date2, 3)2 | 9.4 | 7.6, 11 | <0.001 |
| poly(obs_date2, 3)3 | -10 | -12, -8.3 | <0.001 |
| Weekends & Holidays | 0.23 | 0.21, 0.25 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Mornington Peninsula
Code
tbl_regression(dogs_off_fun_day_MP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -3.4 | -4.1, -2.8 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | -9.6 | -17, -2.6 | 0.008 |
| poly(obs_date2, 3)2 | 6.2 | -0.90, 13 | 0.087 |
| poly(obs_date2, 3)3 | 7.5 | 0.50, 14 | 0.036 |
| Weekends & Holidays | 0.57 | 0.48, 0.67 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bellarine / Surf Coast
Code
tbl_regression(dogs_off_fun_day_BSC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.48 | -0.79, -0.18 | 0.002 |
| Date | |||
| poly(obs_date2, 3)1 | -3.4 | -5.9, -0.94 | 0.007 |
| poly(obs_date2, 3)2 | 5.5 | 3.0, 8.1 | <0.001 |
| poly(obs_date2, 3)3 | 0.06 | -2.4, 2.5 | >0.9 |
| Weekends & Holidays | 0.40 | 0.35, 0.46 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bass Coast
Code
tbl_regression(dogs_off_fun_day_BC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.3 | -1.6, -0.99 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 0.23 | -2.4, 2.9 | 0.9 |
| poly(obs_date2, 3)2 | 3.9 | 1.5, 6.4 | 0.002 |
| poly(obs_date2, 3)3 | 2.5 | 0.25, 4.8 | 0.030 |
| Weekends & Holidays | 0.35 | 0.29, 0.41 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Phillip Island
Code
tbl_regression(dogs_off_fun_day_PI_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -2.5 | -3.0, -1.9 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 1.3 | -2.0, 4.6 | 0.5 |
| poly(obs_date2, 3)2 | 6.4 | 3.0, 9.8 | <0.001 |
| poly(obs_date2, 3)3 | -4.0 | -7.4, -0.57 | 0.022 |
| Weekends & Holidays | 0.58 | 0.51, 0.65 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Code
dogs_off_fun_day_FP__fits <-
as.data.frame(effect(term = "funday", mod = dogs_off_fun_day_FP_,
xlevels = list(funday = seq(min(threat_data__scaled_FP[, "funday"], na.rm = TRUE), max(threat_data__scaled_FP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "FP")
dogs_off_fun_day_MP__fits <-
as.data.frame(effect(term = "funday", mod = dogs_off_fun_day_MP_,
xlevels = list(funday = seq(min(threat_data__scaled_MP[, "funday"], na.rm = TRUE), max(threat_data__scaled_MP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "MP")
dogs_off_fun_day_BC__fits <-
as.data.frame(effect(term = "funday", mod = dogs_off_fun_day_BC_,
xlevels = list(funday = seq(min(threat_data__scaled_BC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BC")
dogs_off_fun_day_PI__fits <-
as.data.frame(effect(term = "funday", mod = dogs_off_fun_day_PI_,
xlevels = list(funday = seq(min(threat_data__scaled_PI[, "funday"], na.rm = TRUE), max(threat_data__scaled_PI[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "PI")
dogs_off_fun_day_BSC__fits <-
as.data.frame(effect(term = "funday", mod = dogs_off_fun_day_BSC_,
xlevels = list(funday = seq(min(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BSC")
dogs_off_fun_day__fits <-
bind_rows(dogs_off_fun_day_FP__fits,
dogs_off_fun_day_MP__fits,
dogs_off_fun_day_BSC__fits,
dogs_off_fun_day_BC__fits,
dogs_off_fun_day_PI__fits) %>%
mutate(funday = ifelse(funday == 0, "no", "yes"))
row.names(dogs_off_fun_day__fits) <- NULL
ggplot() +
geom_line(data = dogs_off_fun_day__fits,
aes(x = funday, y = fit, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
geom_errorbar(data = dogs_off_fun_day__fits,
aes(ymin = lower, ymax = upper,
x = funday,
y = fit, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = dogs_off_fun_day__fits,
aes(x = funday, y = fit, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
luke_theme +
theme(legend.position = "top",
legend.justification = c(1, 0),
strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 10)) +
ylab("number of dogs off leash detected (± 95% CI)") +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_x_discrete(labels = c("weekday", "weekend/holiday"))
Ravens and Magpies and fun days relationships at the three regions
weak negative relationship at Fleurieu Peninsula and Mornington Peninsula between the number of corvids detected and the occurrence of a weekend or a holiday
Code
pred_birds_fun_day_FP_ <-
glmer(pred_birds_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_FP,
family = "poisson")
pred_birds_fun_day_MP_ <-
glmer(pred_birds_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_MP,
family = "poisson")
pred_birds_fun_day_BC_ <-
glmer(pred_birds_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BC,
family = "poisson")
pred_birds_fun_day_BSC_ <-
glmer(pred_birds_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BSC,
family = "poisson")
pred_birds_fun_day_PI_ <-
glmer(pred_birds_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_PI,
family = "poisson")Fleurieu Peninsula
Code
tbl_regression(pred_birds_fun_day_FP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.2 | -1.5, -0.88 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | -7.6 | -11, -4.3 | <0.001 |
| poly(obs_date2, 3)2 | -31 | -34, -27 | <0.001 |
| poly(obs_date2, 3)3 | 8.1 | 4.8, 11 | <0.001 |
| Weekends & Holidays | -0.12 | -0.16, -0.08 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Mornington Peninsula
Code
tbl_regression(pred_birds_fun_day_MP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.7 | -2.0, -1.3 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 28 | 23, 34 | <0.001 |
| poly(obs_date2, 3)2 | -61 | -67, -56 | <0.001 |
| poly(obs_date2, 3)3 | 13 | 6.2, 19 | <0.001 |
| Weekends & Holidays | -0.44 | -0.49, -0.39 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bellarine / Surf Coast
Code
tbl_regression(pred_birds_fun_day_BSC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.5 | -1.8, -1.1 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 0.04 | -4.3, 4.4 | >0.9 |
| poly(obs_date2, 3)2 | -40 | -45, -36 | <0.001 |
| poly(obs_date2, 3)3 | 8.3 | 3.1, 14 | 0.002 |
| Weekends & Holidays | -0.03 | -0.09, 0.04 | 0.4 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bass Coast
Code
tbl_regression(pred_birds_fun_day_BC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.2 | -1.7, -0.63 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | -10 | -14, -6.3 | <0.001 |
| poly(obs_date2, 3)2 | -34 | -38, -29 | <0.001 |
| poly(obs_date2, 3)3 | 5.9 | 1.3, 10 | 0.013 |
| Weekends & Holidays | -0.17 | -0.24, -0.10 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Phillip Island
Code
tbl_regression(pred_birds_fun_day_PI_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.58 | -0.82, -0.34 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | -15 | -18, -12 | <0.001 |
| poly(obs_date2, 3)2 | -25 | -28, -22 | <0.001 |
| poly(obs_date2, 3)3 | 21 | 18, 24 | <0.001 |
| Weekends & Holidays | -0.41 | -0.47, -0.35 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Code
pred_birds_fun_day_FP__fits <-
as.data.frame(effect(term = "funday", mod = pred_birds_fun_day_FP_,
xlevels = list(funday = seq(min(threat_data__scaled_FP[, "funday"], na.rm = TRUE), max(threat_data__scaled_FP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "FP")
pred_birds_fun_day_MP__fits <-
as.data.frame(effect(term = "funday", mod = pred_birds_fun_day_MP_,
xlevels = list(funday = seq(min(threat_data__scaled_MP[, "funday"], na.rm = TRUE), max(threat_data__scaled_MP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "MP")
pred_birds_fun_day_BC__fits <-
as.data.frame(effect(term = "funday", mod = pred_birds_fun_day_BC_,
xlevels = list(funday = seq(min(threat_data__scaled_BC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BC")
pred_birds_fun_day_PI__fits <-
as.data.frame(effect(term = "funday", mod = pred_birds_fun_day_PI_,
xlevels = list(funday = seq(min(threat_data__scaled_PI[, "funday"], na.rm = TRUE), max(threat_data__scaled_PI[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "PI")
pred_birds_fun_day_BSC__fits <-
as.data.frame(effect(term = "funday", mod = pred_birds_fun_day_BSC_,
xlevels = list(funday = seq(min(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BSC")
pred_birds_fun_day__fits <-
bind_rows(pred_birds_fun_day_FP__fits,
pred_birds_fun_day_MP__fits,
pred_birds_fun_day_BSC__fits,
pred_birds_fun_day_PI__fits,
pred_birds_fun_day_BC__fits) %>%
mutate(funday = ifelse(funday == 0, "no", "yes"))
row.names(pred_birds_fun_day__fits) <- NULL
ggplot() +
geom_line(data = pred_birds_fun_day__fits,
aes(x = funday, y = fit, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
geom_errorbar(data = pred_birds_fun_day__fits,
aes(ymin = lower, ymax = upper,
x = funday,
y = fit, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = pred_birds_fun_day__fits,
aes(x = funday, y = fit, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
luke_theme +
theme(legend.position = "top",
legend.justification = c(1, 0),
strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 10)) +
ylab("number of magpies and ravens detected (± 95% CI)") +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_x_discrete(labels = c("weekday", "weekend/holiday"))
Gulls and fun days relationships at the three regions
negative relationship (albeit only significant at Mornington Peninsula) between the number of corvids detected and the occurrence of a weekend or a holiday
Code
gulls_fun_day_FP_ <-
glmer(gulls_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_FP,
family = "poisson")
gulls_fun_day_MP_ <-
glmer(gulls_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_MP,
family = "poisson")
gulls_fun_day_BC_ <-
glmer(gulls_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BC,
family = "poisson")
gulls_fun_day_PI_ <-
glmer(gulls_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_PI,
family = "poisson")
gulls_fun_day_BSC_ <-
glmer(gulls_ ~
poly(obs_date2, 3) +
funday + (1 | season) + (1 | site),
data = threat_data__scaled_BSC,
family = "poisson")Fleurieu Peninsula
Code
tbl_regression(gulls_fun_day_FP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.1 | -1.4, -0.74 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 0.06 | -3.1, 3.2 | >0.9 |
| poly(obs_date2, 3)2 | -36 | -40, -33 | <0.001 |
| poly(obs_date2, 3)3 | 6.9 | 3.7, 10 | <0.001 |
| Weekends & Holidays | -0.06 | -0.09, -0.03 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Mornington Peninsula
Code
tbl_regression(gulls_fun_day_MP_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -1.6 | -2.0, -1.2 | <0.001 |
| Date | |||
| poly(obs_date2, 3)1 | 34 | 29, 39 | <0.001 |
| poly(obs_date2, 3)2 | -68 | -73, -63 | <0.001 |
| poly(obs_date2, 3)3 | 13 | 7.1, 18 | <0.001 |
| Weekends & Holidays | -0.42 | -0.46, -0.37 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Bellarine / Surf Coast
Code
tbl_regression(gulls_fun_day_BC_, intercept = TRUE,
label = list(obs_date2 ~ "Date", funday ~ "Weekends & Holidays"))| Characteristic | log(IRR) | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -0.90 | -1.6, -0.18 | 0.015 |
| Date | |||
| poly(obs_date2, 3)1 | -8.9 | -13, -5.3 | <0.001 |
| poly(obs_date2, 3)2 | -69 | -74, -65 | <0.001 |
| poly(obs_date2, 3)3 | -36 | -41, -31 | <0.001 |
| Weekends & Holidays | -0.25 | -0.30, -0.21 | <0.001 |
| Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio | |||
Code
gulls_fun_day_FP__fits <-
as.data.frame(effect(term = "funday", mod = gulls_fun_day_FP_,
xlevels = list(funday = seq(min(threat_data__scaled_FP[, "funday"], na.rm = TRUE), max(threat_data__scaled_FP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "FP")
gulls_fun_day_MP__fits <-
as.data.frame(effect(term = "funday", mod = gulls_fun_day_MP_,
xlevels = list(funday = seq(min(threat_data__scaled_MP[, "funday"], na.rm = TRUE), max(threat_data__scaled_MP[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "MP")
gulls_fun_day_BC__fits <-
as.data.frame(effect(term = "funday", mod = gulls_fun_day_BC_,
xlevels = list(funday = seq(min(threat_data__scaled_BC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BC")
gulls_fun_day_BSC__fits <-
as.data.frame(effect(term = "funday", mod = gulls_fun_day_BSC_,
xlevels = list(funday = seq(min(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), max(threat_data__scaled_BSC[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "BSC")
gulls_fun_day_PI__fits <-
as.data.frame(effect(term = "funday", mod = gulls_fun_day_PI_,
xlevels = list(funday = seq(min(threat_data__scaled_PI[, "funday"], na.rm = TRUE), max(threat_data__scaled_PI[, "funday"], na.rm = TRUE), 1)))) %>%
mutate(region = "PI")
gulls_fun_day__fits <-
bind_rows(gulls_fun_day_FP__fits,
gulls_fun_day_MP__fits,
gulls_fun_day_BSC__fits,
gulls_fun_day_BC__fits,
gulls_fun_day_PI__fits) %>%
mutate(funday = ifelse(funday == 0, "no", "yes"))
row.names(gulls_fun_day__fits) <- NULL
ggplot() +
geom_line(data = gulls_fun_day__fits,
aes(x = funday, y = fit, color = region, group = region),
position = position_dodge(width = 0.5), alpha = 0.2, size = 2) +
geom_errorbar(data = gulls_fun_day__fits,
aes(ymin = lower, ymax = upper,
x = funday,
y = fit, group = region), position = position_dodge(width = 0.5),
alpha = 0.5, color = "black", width = 0.3, lwd = 0.5) +
geom_point(data = gulls_fun_day__fits,
aes(x = funday, y = fit, fill = region),
shape = 21, size = 4, position = position_dodge(width = 0.5)) +
luke_theme +
theme(legend.position = "top",
legend.justification = c(1, 0),
strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 10)) +
ylab("number of gulls detected (± 95% CI)") +
scale_colour_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_fill_brewer(palette = "Dark2",
name = "Region",
labels = c("BSC" = "Bellarine/Surf Coast",
"FP" = "Fleurieu Peninsula",
"MP" = "Mornington Peninsula",
"BC" = "Bass Coast",
"PI" = "Phillip Island")) +
scale_x_discrete(labels = c("weekday", "weekend/holiday"))