Setup
Read in key data:
- Site information
- Presence/absence at sites
- Detection history
- Spatial covariates of the sites
Show code
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
cache = TRUE,
message = FALSE,
warning = FALSE,
cache = FALSE
)
mapview::mapviewOptions(fgb = FALSE)
library(deervic)
library(galah)
library(dbplyr)
library(dplyr)
library(sf)
library(DBI)
library(VicmapR)
library(lme4)
library(dismo)
library(raster)
library(terra)
library(Hmisc)
library(kableExtra)
library(data.table)
library(ubms)
library(terra)
library(dismo)
library(galah)
library(SDMtune)
library(tibble)
library(tidyterra)
library(ROCR)
library(brms)
library(ggtext)
galah_config(email = "justin.cally@delwp.vic.gov.au")
options(mc.cores=4)
source("/Users/justincally/Documents/Work/R Repositories/blog/R/themes.R")
source("/Users/justincally/Documents/Work/R Repositories/blog/R/interaction_function.R") # some helpful functions for ubms
delwp_cols <- c(Teal = "#00B2A9",
Navy = "#201547",
Environment = "#CEDC00",
`Climate Change` = "#FDDA24",
Energy = "#0072CE",
Water = "#71C5E8",
Planning = "#642667",
Infrastructure = "#AF272F",
FFR = "#E57200",
Corporate = "#201547")
delwp_cols2 <- c(delwp_cols, "#737373", "#00B2A9")
# delwp_cols_seq <- lapply(delwp_cols, function(x) {
# tinter::tinter(x, direction = "tints", steps = 10)
# })
# Restrict the area of interest in this study to Victoria by pulling down Victoria BBOX and neighbouring LGAs
vic_bbox <- VicmapR::vicmap_query("datavic:VMLITE_LGA") %>%
collect() %>%
sf::st_bbox()
vic_bound <- vicmap_query("datavic:VMLITE_VICTORIA_POLYGON_SU5") %>%
filter(STATE == "VIC") %>% # Philip Island and French Island
collect() %>%
st_transform(3111)
# Select deer species of interest
deer_species <- c("Dama dama", "Rusa unicolor", "Cervus elaphus")
# Connect to database
con <- RPostgreSQL::dbConnect(RPostgres::Postgres(),
host = '10.110.7.201',
dbname = 'ari-dev-weda-psql-01',
user = "psql_admin",
password = keyring::key_get(service = "ari-dev-weda-psql-01", username = "psql_admin"),
port = 5432,
service = NULL,
list(sslmode = "require"))
# Pull down presence absence database vies and filter for species of interest
curated_presence_absence_species_data <- tbl(con, dbplyr::in_schema(schema = "deervic",
table = "curated_presence_absence_species_data")) %>%
filter(Species %in% deer_species) %>%
collect() %>%
rename(SiteID = Site) %>%
mutate(SiteID = as.numeric(SiteID)) %>%
filter(SiteID != 11084)
# Pull down site variable information
curated_site_data <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "curated_site_data")) %>%
collect() %>%
st_as_sf(coords = c("Longitude", "Latitude"), crs = 4283) %>%
filter(SiteID != 11084)
# Pull down grids of sites selected but only select the watercourse distance as that was precisely measured
# All data apart from distance to water is obtained from the raster grid (50m resolution). However for the distance to water we use a more precise Euclidian distance from sf layers. Noting that any distance below 10m was rounded to 10m (in some cases this may be applicable to swampy areas), however for projection reasons they should be discounted (otherwise projections would include water bodies)
spatial_data_wc <- sf::st_read(dsn = con,
layer = Id(schema = "deervic", table = "curated_combined_spatial_data")) %>%
dplyr::select(SiteID, DistancetoWater = WatercourseDistanceClipped) %>%
sf::st_drop_geometry()
# read in spatial data rasters
processed_tifs <- list.files("/Volumes/DeerVic\ Photos/Processed_Rasters", full.names = TRUE)[stringr::str_detect(list.files("/Volumes/DeerVic\ Photos/Processed_Rasters"), ".tif$")]
processed_stack <- terra::rast(processed_tifs)
combined_spatial_data <- bind_cols(curated_site_data %>%
dplyr::select(SiteID),
terra::extract(x = processed_stack, terra::vect(curated_site_data %>%
sf::st_transform(3111))) %>%
dplyr::select(-ID)) %>%
# left_join(combined_spatial_data %>% dplyr::select(SiteID, WatercourseDistance) %>% st_drop_geometry()) %>%
mutate(DeerHunt = as.factor(DeerHunt),
SambarHunt = as.factor(DeerHunt))
# dplyr::select(-DistancetoWater)
# dplyr::left_join(spatial_data_wc, by = "SiteID")
combined_spatial_data <- combined_spatial_data %>%
mutate(DistancetoWater = case_when(DistancetoWater == 0 ~ 10,
TRUE ~ DistancetoWater))
combined_spatial_data[combined_spatial_data$SiteID == 44911 | is.na(combined_spatial_data$LANDMANAGER),]$LANDMANAGER <- "OTHER"
detection_history <- deervic::presence_absence_daily(con, species = c("Rusa unicolor", "Dama dama", "Cervus elaphus"))
curated_site_daily_temp <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "curated_site_daily_temp")) %>%
collect() %>%
as.data.frame() %>%
`rownames<-`(.$SiteID) %>%
dplyr::select(-SiteID)
curated_site_daily_precip <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "curated_site_daily_precip")) %>%
collect() %>%
as.data.frame() %>%
`rownames<-`(.$SiteID) %>%
dplyr::select(-SiteID)
curated_site_daily_day <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "curated_site_daily_day")) %>%
collect() %>%
as.data.frame() %>%
`rownames<-`(.$SiteID) %>%
dplyr::select(-SiteID)
curated_site_daily_understory <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "curated_site_daily_understory")) %>%
collect() %>%
as.data.frame() %>%
`rownames<-`(.$SiteID) %>%
dplyr::select(-SiteID)
curated_site_daily_woody_understory <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "curated_site_daily_woody_understory")) %>%
collect() %>%
as.data.frame() %>%
`rownames<-`(.$SiteID) %>%
dplyr::select(-SiteID)
curated_site_daily_herbaceous_understory <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "curated_site_daily_herbaceous_understory")) %>%
collect() %>%
as.data.frame() %>%
`rownames<-`(.$SiteID) %>%
dplyr::select(-SiteID)
combined_model_data <- curated_presence_absence_species_data %>%
left_join(curated_site_data %>% sf::st_drop_geometry(), by = "SiteID") %>%
left_join(combined_spatial_data, by = "SiteID")
combined_model_data_wide <- curated_presence_absence_species_data %>%
data.table::as.data.table() %>%
data.table::dcast(SiteID ~ Species, value.var = "Presence") %>%
left_join(curated_site_data %>% sf::st_drop_geometry(), by = "SiteID") %>%
left_join(combined_spatial_data, by = "SiteID") %>%
dplyr::mutate(UnderstoryCover = NWUCover + NNWHUCover + EWUCover + ENWHUCover) %>%
filter(SiteID != 11084) # Murray Sunset not picked up cam
regions <- vicmap_query("datavic:VMADMIN_DELWP_REGION") %>%
collect()
options(vicmap.chunk_limit = 15000)
statewide_water <- vicmap_query("datavic:VMLITE_HY_WATERCOURSE") %>%
collect() %>%
sf::st_transform(3111)
options(vicmap.chunk_limit = 1500)
regions_3111 <- regions %>%
sf::st_transform(3111) %>%
sf::st_simplify(500, preserveTopology = TRUE)
statewide_water_simp <- statewide_water %>%
filter(FEATURE_TYPE_CODE == "watercourse_river") %>%
sf::st_simplify(500, preserveTopology = TRUE) %>%
sf::st_intersection(regions_3111)
# One site was placed next to state forest (but technically out of SF)
# Kernel density function
st_kde <- function(points,cellsize, bandwith, extent = NULL){
if(is.null(extent)){
extent_vec <- sf::st_bbox(points)[c(1,3,2,4)]
} else{
extent_vec <- sf::st_bbox(extent)[c(1,3,2,4)]
}
n_y <- ceiling((extent_vec[4]-extent_vec[3])/cellsize)
n_x <- ceiling((extent_vec[2]-extent_vec[1])/cellsize)
extent_vec[2] <- extent_vec[1]+(n_x*cellsize)-cellsize
extent_vec[4] <- extent_vec[3]+(n_y*cellsize)-cellsize
coords <- sf::st_coordinates(points)
matrix <- MASS::kde2d(coords[,1],coords[,2],h = bandwith,n = c(n_x,n_y),lims = extent_vec)
raster::raster(matrix)
}
nrd_new <- function (x)
{
r <- quantile(x, c(0.25, 0.75))
h <- (r[2L] - r[1L])/1.34
4 * 1.06 * min(sqrt(var(x)), h, na.rm = T) * length(x)^(-1/5)
}
Smoothed Kernel Density of Deer Species
For each species we develop a smoothed kernel estimate of their distribution based on presence-only reecords from the past 30 years. This kernel density estimate uses a bandwith determined via the normal reference distribution and is configured so that the species distribution contains 99.5% of presence records (Gormley et al. 2011).
Show code
area <- vic_bound %>%
sf::st_union() %>%
sf::st_buffer(25000) %>%
sf::st_simplify(dTolerance = 10000) %>%
sf::st_transform(4283) %>%
sf::st_as_sf()
if(!file.exists("data/historic_deer_ala_records.rds")) {
historic_deer_ala_records <- galah_call() %>%
galah_identify(c("Cervus unicolor", "Dama dama", "Cervus elaphas")) %>%
galah::galah_geolocate(area) %>%
galah_filter(year >= 1992) %>%
atlas_occurrences() %>%
filter(!is.na(decimalLongitude) & !is.na(decimalLatitude)) %>%
sf::st_as_sf(coords = c("decimalLongitude", "decimalLatitude"), crs = 4283) %>%
mutate(scientificName = case_when(scientificName == "Dama dama dama" ~ "Dama dama",
scientificName == "Cervus unicolor" ~ "Rusa unicolor",
TRUE ~ scientificName))
saveRDS(historic_deer_ala_records, "data/historic_deer_ala_records.rds")
} else {
historic_deer_ala_records <- readRDS("data/historic_deer_ala_records.rds")
}
smoothed_kernel_species <- function(sr, all_area = vic_bound) {
hist_3111 <- sr %>%
sf::st_transform(3111)
points_kde <- st_kde(hist_3111,
cellsize = 1000,
bandwith = nrd_new(hist_3111 %>% sf::st_coordinates()),
extent = terra::ext(all_area))
pred_vals <- raster::extract(points_kde, hist_3111)
q995 <- quantile(pred_vals, 0.001, na.rm = T)
points_kde_cut <- points_kde
terra::values(points_kde_cut)[terra::values(points_kde_cut) >= q995] <- as.factor("1")
terra::values(points_kde_cut)[terra::values(points_kde_cut) < q995] <- NA
points_kde_cut <- terra::rast(points_kde_cut)
points_kde_cut <- terra::crop(points_kde_cut, terra::vect(vic_bound), mask = TRUE)
points_kde_cut
}
split_deer_records <- split(historic_deer_ala_records, historic_deer_ala_records$scientificName)
deer_kde <- lapply(split_deer_records, smoothed_kernel_species) %>%
terra::rast()
names(deer_kde) <- paste(names(deer_kde), "distribution")
Data Summary
Visualisation of detections
At each site a camera was deployed and 3 transects were walked in order to detect deer signs (pellets, rubbings, footprints and wallows). Below we show sites that had at least one sign of deer and whether they were detected using the various methods. Generally, cameras appear to perform the best but do not appear to have 100 % detection probability.
Show code
transect_detection <- curated_presence_absence_species_data %>%
left_join(curated_site_data) %>%
group_by(SiteID) %>%
summarise(`Camera` = sum(Presence),
Pellets = sum(Pellets),
Wallows = sum(Wallows),
Rubbings = sum(Rubbings),
Footprints = if(any(Footprints == "Observed")) 1 else 0) %>%
mutate(across(c(`Camera`, Pellets, Wallows, Rubbings, Footprints),
~ case_when(. > 0 ~ "Detected",
. == 0 ~ "Not Detected")))
# transect_detection[transect_detection$SiteID %in% c(4905, 4257, 30755), "Pellets"] <- "Not Detected"
transect_detection_long <- transect_detection %>%
tidyr::gather(key="Method", value="Presence", -SiteID) %>%
mutate(SiteID = factor(SiteID)) %>%
arrange(Method, desc(Presence)) %>%
group_by(SiteID) %>%
filter(any(Presence == "Detected"))
transect_plot <- ggplot(transect_detection_long, aes(x=Method, y=SiteID, fill=Presence))+
geom_tile(colour="white", size=0.2) +
scale_fill_manual(na.value = "transparent", values = c(Detected = unname(delwp_cols["Navy"]), `Not Detected` = unname(delwp_cols["Environment"]))) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90))
transect_plot
Assigning Species to Deer Sign
Because we did not assign species to the deer sign records in the field or with genetic analysis we have to attempt to do it logically. Below we show the code required to assign deer sign to one of the given species based on the following logic:
For a given detection (1), we assign that observation to a given species based on the following conditions:
- species seen on camera = 1
- species not seen on camera and another species seen on camera = 0
- species not seen on camera and no others seen, but within KDE distribution = 1
- species not seen on camera, and not within KDE distribution = 0
Show code
# Format other deer signs as a transect based detection method:
## Pellets
## Footprints
## Rubbings
## Wallows
raw_transects <- tbl(con, dbplyr::in_schema(schema = "deervic", table = "raw_site_transects")) %>%
collect() %>%
na.omit()
day1vars <- data.frame(SiteID = as.numeric(rownames(curated_site_daily_herbaceous_understory)),
HerbaceousUnderstoryCover = curated_site_daily_herbaceous_understory[,1],
DaysSinceDeploy = 1L,
MaxTemp = curated_site_daily_temp[,1],
Precipitation = curated_site_daily_precip[,1]) %>%
na.omit()
transects_long <- raw_transects %>%
group_by(Data_File_Id, I0_TransectNo) %>%
summarise(Pellet = if(sum(I0_Pellets) > 0) 1 else 0,
Footprint = if(any(I0_Footprints == "Observed")) 1 else 0,
Rubbing = if(sum(I0_Rubbings) > 0) 1 else 0,
Wallow = if(sum(I0_Wallows) > 0) 1 else 0) %>%
ungroup() %>%
left_join(curated_site_data[, c("SiteID", "Data_File_Id")] %>%
sf::st_drop_geometry(), by = "Data_File_Id") %>%
dplyr::select(-Data_File_Id) %>%
dplyr::select(SiteID, everything()) %>%
full_join(day1vars, by = "SiteID") %>%
left_join(curated_presence_absence_species_data %>%
as.data.table() %>%
dcast(SiteID ~ Species), by = "SiteID") %>%
as.data.table() %>%
filter(!is.na(SiteID))
# Assign detections to species based on:
# species seen on camera = 1
# species not seen on camera and another species seen = 0
# species not seen on camera and no others seen, but within KDE distribution = 1
# species not seen on camera, and not within KDE distribution = 0
hif_hany <- function(..., values) {
mtx <- do.call(cbind, list(...))
mtx <- array(mtx %in% values, dim = dim(mtx))
rowSums(mtx) > 0
}
detection_assign <- function(sp, species, othersp) {
sp.d <- paste(species, "distribution")
sf_m <- dplyr::mutate(sp, across(c(Pellet, Footprint, Rubbing, Wallow),
~ dplyr::case_when(.x == 0 ~ 0,
.x == 1 & !! sym(species) == 1 ~ 1,
.x == 1 & !! sym(species) != 1 & hif_hany(!!! syms(othersp), values = 1) ~ 0,
.x == 1 & !! sym(species) != 1 & !hif_hany(!!! syms(othersp), values = 1) & !! sym(sp.d) == 1 ~ 1,
.x == 1 & !! sym(species) != 1 & !! sym(sp.d) != 1 ~ 0)))
transects_detection <- sf_m %>%
as.data.table() %>%
dcast(SiteID ~ I0_TransectNo, value.var = c("Pellet", "Footprint", "Rubbing", "Wallow")) %>%
column_to_rownames(var = "SiteID")
return(transects_detection)
}
species_overlap <- bind_cols(curated_site_data[, "SiteID"], terra::extract(deer_kde, vect(curated_site_data %>% sf::st_transform(3111)))) %>%
sf::st_drop_geometry() %>%
dplyr::select(-ID)
species_overlap[is.na(species_overlap)] <- 0
transects_sp <- left_join(transects_long, species_overlap, by = "SiteID")
Damadama_Detection <- detection_assign(transects_sp, "Dama dama", othersp = c("Rusa unicolor", "Cervus elaphus"))
Rusaunicolor_Detection <- detection_assign(transects_sp, "Rusa unicolor", othersp = c("Dama dama", "Cervus elaphus"))
Cervuselaphus_Detection <- detection_assign(transects_sp, "Cervus elaphus", othersp = c("Dama dama", "Rusa unicolor"))
transects_method_long <- transects_long
for(m in c("Pellet", "Footprint", "Rubbing", "Wallow")) {
transects_method_long[[m]] <- m
}
transects_method <- dcast(transects_method_long, SiteID ~ I0_TransectNo,
value.var = c("Pellet", "Footprint", "Rubbing", "Wallow")) %>%
column_to_rownames(var = "SiteID")
transects_HerbaceousUnderstoryCover <- transects_long %>%
mutate(across(c("Pellet", "Footprint", "Rubbing", "Wallow"), ~ HerbaceousUnderstoryCover)) %>%
dcast(SiteID ~ I0_TransectNo, value.var = c("Pellet", "Footprint", "Rubbing", "Wallow")) %>%
column_to_rownames(var = "SiteID")
transects_DaysSinceDeploy <- transects_long %>%
mutate(across(c("Pellet", "Footprint", "Rubbing", "Wallow"), ~ DaysSinceDeploy)) %>%
dcast(SiteID ~ I0_TransectNo, value.var = c("Pellet", "Footprint", "Rubbing", "Wallow")) %>%
column_to_rownames(var = "SiteID")
transects_MaxTemp <- transects_long %>%
mutate(across(c("Pellet", "Footprint", "Rubbing", "Wallow"), ~ MaxTemp)) %>%
dcast(SiteID ~ I0_TransectNo, value.var = c("Pellet", "Footprint", "Rubbing", "Wallow")) %>%
column_to_rownames(var = "SiteID")
transects_Precipitation <- transects_long %>%
mutate(across(c("Pellet", "Footprint", "Rubbing", "Wallow"), ~ Precipitation)) %>%
dcast(SiteID ~ I0_TransectNo, value.var = c("Pellet", "Footprint", "Rubbing", "Wallow")) %>%
column_to_rownames(var = "SiteID")
curated_site_daily_method <- curated_site_daily_day
curated_site_daily_method[!is.na(curated_site_daily_method)] <- "Camera"
# Combine the detection histories
siteorder <- as.character(combined_model_data_wide$SiteID)
detection_history_joined <- detection_history
detection_history_joined$`Rusa unicolor`$detection_history <- cbind(detection_history_joined$`Rusa unicolor`$detection_history[siteorder,],
Rusaunicolor_Detection[siteorder,])
detection_history_joined$`Dama dama`$detection_history <- cbind(detection_history_joined$`Dama dama`$detection_history[siteorder,],
Damadama_Detection[siteorder,])
detection_history_joined$`Cervus elaphus`$detection_history <- cbind(detection_history_joined$`Cervus elaphus`$detection_history[siteorder,],
Cervuselaphus_Detection[siteorder,])
# Combine the other detection variables
joined_HerbaceousUnderstoryCover <- cbind(curated_site_daily_herbaceous_understory[siteorder,],
transects_HerbaceousUnderstoryCover[siteorder,])
joined_DaysSinceDeploy <- cbind(curated_site_daily_day[siteorder,],
transects_DaysSinceDeploy[siteorder,])
joined_MaxTemp <- cbind(curated_site_daily_temp[siteorder,],
transects_MaxTemp[siteorder,])
joined_Precipitation <- cbind(curated_site_daily_precip[siteorder,],
transects_Precipitation[siteorder,])
joined_Method <- cbind(curated_site_daily_method[siteorder,],
transects_method[siteorder,])
# update combined_model_data
bound_detections <- bind_rows(detection_history_joined)
bound_presence <- data.frame(SiteID = rep(siteorder, times = length(detection_history_joined)),
Species = rep(names(detection_history_joined), each = length(siteorder)),
Presence = rowSums(bound_detections, na.rm = T)) %>%
mutate(Presence = case_when(Presence > 0 ~ 1,
TRUE ~ 0),
SiteID = as.numeric(SiteID))
combined_model_data_allMethod <- left_join(combined_model_data %>%
dplyr::select(-Presence),
bound_presence, by = c("SiteID", "Species"))
Visualisation of Sampled Sites
Below we view the distribution of sites sampled as well as the convex hull we used to influence sampling probability. The convex hull represents areas with a deer record within 50km and within the past 10 years.
Show code
deer_polygon <- readRDS("data/deer_polygon.rds")
crownland_mask <- terra::rast("/Volumes/DeerVic\ Photos/MaxentStack/maxent_stack_masked_cl.grd")[[1]]
crownland <- crownland_mask
crownland_ag <- terra::spatSample(x = crownland, size = 100000, method = "regular", as.raster = TRUE)
values(crownland_ag)[!is.na(values(crownland_ag))] <- 1
site_plot <- ggplot() +
geom_sf(data = regions_3111, fill = "grey", inherit.aes = FALSE, alpha = 0.2, colour = "grey70") +
tidyterra::geom_spatraster(data = crownland_ag, aes(fill = BIO04), alpha = 0.5, na.rm = T, show.legend = FALSE) +
geom_sf(data = deer_polygon, fill = "DarkGreen", inherit.aes = FALSE, alpha = 0.5, colour = "grey70") +
geom_point(aes(x = X, y = Y), size = 2.5, shape = 21, fill = "Orange", inherit.aes = FALSE,
data = combined_spatial_data %>%
st_transform(3111) %>%
st_coordinates() %>%
as.data.frame()) +
scale_fill_continuous(na.value = "transparent") +
ylab("Latitude") +
xlab("Longitude") +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 15),
axis.text.x = element_text(hjust = -1),
legend.title = element_text(size=24),
legend.text = element_text(size=18),
legend.key.size = unit(7, 'cm')) +
theme_bw()
site_plot
Figure 1: Locations of sites sampled across Victoria (2021 – 2022). The Orange points represent the sites visited, with the blue area being the crown land sampling was restricted to. The green area is the convex hull bounding area, where a deer record from the previous 10 years (since 2011) has occurred. This area thus received a five-fold increased inclusion probability in comparison to the area outside of it. Notably, Murray-Sunset National Park, Wyperfeld National Park and Hattah-Kulkyne National Park. Thus, the likelihood of selecting sites outside of the green area was five times less than within the green area (Balanced Acceptance Sampling considerations/methods applied).
Visualisation of Deer Presence
Show code
combined_model_data_allMethod_loc <- cbind(combined_model_data_allMethod,
sf::st_transform(combined_model_data_allMethod %>%
sf::st_as_sf(), 3111) %>% sf::st_coordinates()) %>%
mutate(Presence = case_when(Presence == 1 ~ "Present",
TRUE ~ "Absent"),
Species = case_when(Species == "Rusa unicolor" ~ "Sambar Deer",
Species == "Cervus elaphus" ~ "Red Deer",
Species == "Dama dama" ~ "Fallow Deer")) %>%
arrange(Presence)
state_plot <- ggplot() +
geom_sf(data = regions_3111, fill = "grey", inherit.aes = FALSE, alpha = 0.2, colour = "grey70") +
geom_point(aes(x = X, y = Y, fill = Presence), size = 2, shape = 21, data = combined_model_data_allMethod_loc, inherit.aes = FALSE) +
scale_fill_manual(values = c(Absent = delwp_cols[["Environment"]], Present = delwp_cols[["Teal"]])) +
ylab("Latitude") +
xlab("Longitude") +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 15),
axis.text.x = element_text(hjust = -1),
legend.title = element_text(size=24),
legend.text = element_text(size=18),
legend.key.size = unit(7, 'cm')) +
facet_wrap(~ Species, nrow = 2) +
theme_bw()
shift_legend2(state_plot)
Figure 2: Presence/Absence map of species across Victoria. Presence of deer species was based on camera data or deer signs at the site (footprints, pellets, rubbings and wallows)
Visualisation of Vegetation
We collected a suite of vegetation metrics in the field that may relate to deer impacts and/or presence. Below we briefly visualise the correlation between the measure and the presence of deer.
Show code
anydeer <- transect_detection %>%
tidyr::gather(key="Method", value="Presence", -SiteID) %>%
mutate(SiteID = factor(SiteID)) %>%
arrange(Method, desc(Presence)) %>%
group_by(SiteID) %>%
summarise(DeerSign = if(any(Presence == "Detected")) "Present" else "Absent") %>%
ungroup()
vegetation <- curated_site_data %>%
sf::st_drop_geometry() %>%
dplyr::select(SiteID,
NWUCover,
NNWHUCover,
EWUCover,
ENWHUCover,
BGroundCover,
TopHeight,
CanopyCov,
Saplings,
Seedlings) %>%
mutate(SiteID = as.character(SiteID)) %>%
inner_join(anydeer %>%
mutate(SiteID = as.character(SiteID)),
by = "SiteID") %>%
mutate(Exotics = case_when(EWUCover + ENWHUCover > 0 ~ 1,
TRUE ~ 0),
ExoticSum = EWUCover + ENWHUCover)
vegetation_long <- vegetation %>%
as.data.table() %>%
melt(id.vars = c("SiteID", "DeerSign"))
# Vegetation Plot
veg1 <- vegetation_long %>%
filter(!(variable %in% c("EWUCover", "ENWHUCover", "Saplings", "Seedlings", "Exotics", "ExoticSum"))) %>%
ggplot(aes(x = DeerSign, y = value)) +
geom_boxplot() +
facet_wrap(~variable, scales = "free", nrow = 5) +
theme_bw()
veg2 <- vegetation_long %>%
filter((variable %in% c("Saplings", "Seedlings", "ExoticSum"))) %>%
ggplot(aes(x = DeerSign, y = value)) +
geom_boxplot() +
scale_y_sqrt(limits = c(0,100)) +
facet_wrap(~variable, scales = "free", nrow = 3) +
theme_bw()
# veg3 <- ggcorrplot::ggcorrplot(cor(vegetation %>%
# mutate(DeerSign = case_when(DeerSign == "Present" ~ 1L,
# TRUE ~ 0L)) %>%
# dplyr::select(DeerSign, Exotics)),
# hc.order = TRUE,
# type = "lower",
# lab = TRUE)
cowplot::plot_grid(veg1, veg2, labels = "", ncol = 2)
Figure 3: Relation between deer presence and various vegetation metrics
Show code
vegetation_long %>%
filter(!(variable %in% c("Exotics", "EWUCover", "ENWHUCover"))) %>%
mutate(variable = as.character(variable)) %>%
mutate(variable = case_when(variable == "NWUCover" ~ "Native Woody Understorey (%)",
variable == "NNWHUCover" ~ "Native Herbaceous Understorey (%)",
variable == "BGroundCover" ~ "Bare Ground (%)",
variable == "TopHeight" ~ "Top Height (m)",
variable == "CanopyCov" ~ "Canopy Cover (%)",
variable == "ExoticSum" ~ "Exotics (%)",
TRUE ~ variable)) %>%
arrange(variable) %>%
mutate(value = case_when(value > 250 ~ 250,
TRUE ~ value)) %>%
ggplot(aes(y = value, x = DeerSign, fill = DeerSign)) +
ggbeeswarm::geom_quasirandom(shape = 21) +
geom_violin(draw_quantiles = c(0.5), size = 1, alpha = 0.5) +
facet_wrap(~variable, ncol = 4, scales = "free") +
scale_fill_manual(values = c(`Absent` = delwp_cols[["Environment"]],
`Present` = delwp_cols[["Navy"]])) +
theme_classic()
Based on the plots above, there only appear to be two of-interest relationships for this analysis (at the given time and in regards to occupancy). The first being canopy cover may be higher when deer are present; which may reflect Sambar deer preference for forested environments. The second is native non-woody herbaceous understorey; which may promote increased browsing around the camera or detectibility distance.
Data checks
We undertake several data checks before modelling to assure certain assumptions (e.g. colinearity and normality) are not violated.
Covariate Correlation
Covariates that are strongly correlated may proove to be an issue for fitting models. We assess the covariation in predictor variables at the sites includided in this analysis (not including factors):
Show code
panel.cor <- function(x, y, digits=2, prefix="", cex.cor, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y, use="pairwise.complete.obs"))
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste0(prefix, txt)
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor * r)
}
pairs(combined_model_data_wide %>%
dplyr::select(BIO01,
BIO04,
BIO06,
BIO12,
BIO15,
NPP,
TreeDensity,
BareSoil,
PhotosyntheticVeg,
NonPhotosyntheticVeg,
Elevation,
TWIND,
DistancetoWater,
CrownGrazingDistance,
DeerControl,
DeerHunt,
SambarHunt,
MRVBF,
Nitrogen,
PastureDistance,
SLOPE), lower.panel=panel.smooth, upper.panel=panel.cor)
Due to the correlation of predictors we remove:
+ Elevation
+ BIO06
+ PhotosyntheticVeg
+ NPP
+ Nitrogen
+ Only use DeerHunt or SambarHunt
Check normality of predictors
Show code
combined_model_data_wide %>%
dplyr::select(BIO01,
BIO04,
BIO06,
BIO12,
BIO15,
NPP,
TreeDensity,
BareSoil,
PhotosyntheticVeg,
NonPhotosyntheticVeg,
Elevation,
TWIND,
DistancetoWater,
CrownGrazingDistance,
DeerControl,
MRVBF,
Nitrogen,
PastureDistance,
SLOPE) %>%
hist.data.frame(nclass = 10)
There is some evidence that several variables should be transformed (log). Ideally the following predictors can be transformed:
- Tree density - log transformation
- Bare soil - square root transformation (zero-inflated)
- Elevation - log transformation
- Distance to Water - log transformation
- Pasture distance - log transformation
- Crown grazing distance - square root transformation (zero-inflated)
- MRVBF - square root transformation (zero-inflated)
- Slope - square root transformation
Doing so would result in the following:
Show code
combined_model_data_wide %>%
dplyr::select(BIO01,
BIO04,
BIO06,
BIO12,
BIO15,
NPP,
TreeDensity,
BareSoil,
PhotosyntheticVeg,
NonPhotosyntheticVeg,
Elevation,
TWIND,
DistancetoWater,
CrownGrazingDistance,
DeerControl,
MRVBF,
Nitrogen,
PastureDistance,
SLOPE) %>%
dplyr::mutate(`log(TreeDensity)` = log(TreeDensity),
`sqrt(BareSoil)` = sqrt(BareSoil),
`log(Elevation)` = log(Elevation),
`log(DistancetoWater)` = log(DistancetoWater),
`log(PastureDistance)` = log(PastureDistance),
`sqrt(CrownGrazingDistance)` = sqrt(CrownGrazingDistance),
`sqrt(MRVBF)` = sqrt(MRVBF),
`scrt(SLOPE)` = sqrt(SLOPE)) %>%
dplyr::select(-TreeDensity, -BareSoil, -Elevation, -DistancetoWater, -PastureDistance, -CrownGrazingDistance, -MRVBF, -SLOPE) %>%
hist.data.frame(nclass = 10)
Apart from tree density and MRVBF (which is zero-inflated) these transformations have the desired effect
Boosted Regression Trees
Boosted regression trees (BRTs) can be useful in understanding the role of a wide variety of model covariates. Here we develop BRTs for Sambar Deer. The plots for these BRTs are shown below, keeping in mind that these plots are not necessarily linear and may be inclusive of interactions. However, using these responses will help inform which covariates should be included in a global imperfect detection model.
Sambar Deer BRT (Rusa Unicolor)
Show code
brt_data <- combined_model_data_allMethod %>%
dplyr::select(SiteID,
Species,
Presence,
BIO01,
BIO04,
# BIO06,
BIO12,
BIO15,
# NPP,
TreeDensity,
BareSoil,
# PhotosyntheticVeg,
NonPhotosyntheticVeg,
# Elevation,
TWIND,
DistancetoWater,
CrownGrazingDistance,
DeerControl,
MRVBF,
# Nitrogen,
PastureDistance,
SLOPE,
TSLF_bin,
DeerControl,
SambarHunt,
DeerHunt) %>%
dplyr::mutate(TreeDensity = log(TreeDensity),
BareSoil = sqrt(BareSoil),
# Elevation = log(Elevation),
DistancetoWater = log(DistancetoWater),
PastureDistance = log(PastureDistance),
CrownGrazingDistance = sqrt(CrownGrazingDistance),
MRVBF = sqrt(MRVBF),
SLOPE = sqrt(SLOPE))
brt_sambar <- brt_data %>%
filter(Species == "Rusa unicolor") %>%
as.data.frame()
brt <- gbm.step(data=brt_sambar,
gbm.x = 4:19, gbm.y = 3, family = "bernoulli", tree.complexity = 5,
learning.rate = 0.001, bag.fraction = 0.75, plot.main = FALSE, plot.folds = FALSE)
sambar.int <- gbm.interactions(brt)
b <- summary(brt)
Bayesian Imperfect Detect Models
We use Bayesian imperfect detection models via the implementation of functions and methods from the ubms R package (Kellner 2021).
Given the variable surveying effort and potential variation in detection probabilities due to camera setup features (e.g. amount of surrounding vegetation blocking camera and immediate surrounding grazing area) as well as daily features (temperature, precipitation, days since deployment) we assume that there is variable/imperfect detection across the deployment and possibly between sites. Thus we provide five key detection-level variables for inclusion in a global model. Their use and reasoning are listed below:
- MaxTemp: Max daily temperature (Oceanic and Labratory) 2022b), which may impact the activity of deer throughout the day and night.
- Precipiation: Daily precipitation (Oceanic and Labratory) 2022a), which may impact the activity of deer as well as detectibility of deer moving infront of the camera.
- DaysSinceDeploy: Days since the camera was deployed; ideally this would not impact detectibility but the presence of distance-marker caps may have some impact on drawing in or repelling species, over time this effect may increase or decrease.
- HerbaceousUnderstoryCover: Herbaceous understory fractional cover (native and exotic) measured at a site level (area within a 10m radius from camera). Detection may vary based on herbacous understory as this can often be grass cover around the camera. Higher grass cover around a camera may lead to deer at the site specinding greater proportions of time grazing in that area.
- WoodyUnderstoryCover: Woody understory fractional cover (native and exotic) measured at a site level (area within a 10m radius from camera). High woody understory may block detection of deer at further distances as well as restrict deer movement in the area.
Site-level covariates for the global model have been chosen based on the apparent contributions and effect of BRT models. This includes the potential role of interactions between predictor variables.
Global Sambar Model
We generate a global model based on the apparent responses of the BRT model. Specifically including the variables explaining a large amount of the response. We also include several interaction effects based on the BRT results. The model is regularised strongly with a lasso prior (scale = 1) and contains an RSR component with a threshold of 100km. Some variables were not included in the global model as their contribution increased model complexity and BRT results suggested nearly no impact (e.g. hunting and time since last fire). in the detection submodel precipitation and herbaceous understorey were chosen as the two continuous variables of interest for the ‘camera’ method; ‘method’ being a five-class categorical variable.
Show code
# siteorder <- as.character(combined_model_data_wide$SiteID)
model_data_spatial <- bind_cols(combined_model_data_wide, combined_model_data_wide %>% sf::st_as_sf() %>% sf::st_transform(3111) %>% sf::st_coordinates()) %>%
mutate(Null = 1)
SambarStack <- unmarked::unmarkedFrameOccu(y = detection_history_joined$`Rusa unicolor`$detection_history[siteorder, ],
obsCovs =list(MaxTemp = joined_MaxTemp[siteorder, ],
Precipitation = joined_Precipitation[siteorder, ],
DaysSinceDeploy = joined_DaysSinceDeploy[siteorder, ],
HerbaceousUnderstoryCover = joined_HerbaceousUnderstoryCover[siteorder, ],
Method = joined_Method[siteorder, ]),
siteCovs = model_data_spatial)
if(!file.exists("models/SambarGlobal.rds")) {
SambarGlobal <- ubms::stan_occu(~ Method*scale(sqrt(Precipitation))
+ Method*scale(sqrt(HerbaceousUnderstoryCover))
~ scale(BIO12)
+ scale(BIO04)
+ scale(BIO01)
+ scale(BIO12)
+ scale(sqrt(BareSoil))
+ scale(log(DistancetoWater))
+ scale(BIO12*BIO04)
+ scale(BIO01*BIO12)
+ scale(sqrt(BareSoil)*BIO12)
+ scale(BIO04*log(DistancetoWater))
+ scale(TreeDensity)
+ scale(sqrt(SLOPE)*BIO04)
+ scale(NonPhotosyntheticVeg)
+ scale(log(PastureDistance))
+ scale(sqrt(CrownGrazingDistance))
+ scale(TWIND)
+ scale(BIO15)
+ RSR(X,Y, threshold = 100000),
prior_coef_state = laplace(scale = 1),
prior_coef_det = laplace(scale = 1),
data = SambarStack,
chains=4, iter=4000)
saveRDS(SambarGlobal, "models/SambarGlobal.rds", compress = "xz")
} else {
SambarGlobal <- readRDS("models/SambarGlobal.rds")
}
# SambarGlobal
# (fit_top_gof <- gof(SambarGlobal, draws=100, quiet=TRUE))
Sambar Deer model selection
Our global model shows variable impacts of predictor variables on Sambar deer detectability and occupancy. Here, we utilise leave-one-out cross-validation (LOO) (Vehtari, Gelman, and Gabry 2017). Model selection using LOO allows for the comparison of several models based on their predictive performance. Specifically the expected predictive accuracy (elpd) is calculated for each model, with higher elpds suggesting better predictive performance. the ‘stacking’ weight is recomended for posterior model averaging and not the pseudo BMA+ weight (Yao et al. 2018). A set of candidate models were chosen based on the influence of their covariates from BRT.
Show code
if(!file.exists("models/SambarCandidateModels.rds")) {
SambarNull <- ubms::stan_occu(~Null
~Null,
data = SambarStack,
chains=4, iter=4000)
SambarNullDet <- ubms::stan_occu(~ Null
~ scale(BIO12*BIO04)
+ scale(BIO01*BIO12)
+ scale(sqrt(BareSoil)*BIO12)
+ scale(BIO04*log(DistancetoWater))
+ scale(TreeDensity)
+ scale(sqrt(SLOPE)*BIO04)
+ scale(NonPhotosyntheticVeg)
+ scale(log(PastureDistance))
+ scale(sqrt(CrownGrazingDistance))
+ scale(TWIND)
+ scale(BIO15)
+ RSR(X,Y, threshold = 100000),
data = SambarStack,
chains=4, iter=4000)
SambarNullOcc <- ubms::stan_occu(~ Method*scale(sqrt(Precipitation))
+ Method*scale(sqrt(HerbaceousUnderstoryCover))
~ Null,
data = SambarStack,
chains=4, iter=4000)
SambarRE <- ubms::stan_occu(~ Method*scale(MaxTemp)
+ Method*scale(sqrt(Precipitation))
+ Method*scale(DaysSinceDeploy)
+ Method*scale(sqrt(HerbaceousUnderstoryCover))
~ scale(BIO12*BIO04)
+ scale(BIO01*BIO12)
+ scale(sqrt(BareSoil)*BIO12)
+ scale(BIO04*log(DistancetoWater))
+ scale(TreeDensity)
+ scale(sqrt(SLOPE)*BIO04)
+ scale(NonPhotosyntheticVeg)
+ scale(log(PastureDistance))
+ scale(sqrt(CrownGrazingDistance))
+ scale(TWIND)
+ scale(BIO15)
+ TSLF_bin
+ (1|BIOREGION)
+ (1|EVC),
data = SambarStack,
chains=4, iter=4000)
SambarNoRE <- ubms::stan_occu(~ Method*scale(sqrt(Precipitation))
+ Method*scale(sqrt(HerbaceousUnderstoryCover))
~ scale(BIO12*BIO04)
+ scale(BIO01*BIO12)
+ scale(sqrt(BareSoil)*BIO12)
+ scale(BIO04*log(DistancetoWater))
+ scale(TreeDensity)
+ scale(sqrt(SLOPE)*BIO04)
+ scale(NonPhotosyntheticVeg)
+ scale(log(PastureDistance))
+ scale(sqrt(CrownGrazingDistance))
+ scale(TWIND)
+ scale(BIO15),
data = SambarStack,
chains=4, iter=4000)
SambarKeyVars <- ubms::stan_occu(~ Method*scale(sqrt(Precipitation))
+ Method*scale(sqrt(HerbaceousUnderstoryCover))
~ scale(BIO12*BIO04)
+ scale(BIO01*BIO12)
+ scale(sqrt(BareSoil)*BIO12)
+ scale(sqrt(SLOPE)*BIO04)
+ scale(NonPhotosyntheticVeg)
+ scale(log(PastureDistance))
+ scale(BIO15)
+ RSR(X,Y, threshold = 100000),
data = SambarStack,
chains=4, iter=4000)
SambarCandidateModels <- fitList(SambarGlobal,
SambarNull,
SambarNullDet,
SambarNullOcc,
SambarRE,
SambarNoRE,
SambarKeyVars)
saveRDS(SambarCandidateModels, "models/SambarCandidateModels.rds", compress = "xz")
} else {
SambarCandidateModels <- readRDS("models/SambarCandidateModels.rds")
}
# loolist <- lapply(SambarCandidateModels@models, function(x) x@loo)
# loo::loo_model_weights(loolist, method = "pseudobma")
SambarModSel <- ubms::modSel(SambarCandidateModels)
SambarModSel %>%
tibble::rownames_to_column(var = "model") %>%
kbl("html", caption = "Comparison Sambar Deer Models", digits = 3) %>%
kable_styling()
| model | elpd | nparam | elpd_diff | se_diff | weight |
|---|---|---|---|---|---|
| SambarKeyVars | -867.703 | 42.533 | 0.000 | 0.000 | 0.661 |
| SambarNoRE | -871.173 | 46.688 | -3.470 | 2.380 | 0.000 |
| SambarGlobal | -871.389 | 50.323 | -3.686 | 2.033 | 0.000 |
| SambarRE | -877.017 | 61.243 | -9.314 | 5.528 | 0.142 |
| SambarNullOcc | -896.905 | 35.038 | -29.202 | 7.574 | 0.000 |
| SambarNullDet | -933.880 | 23.441 | -66.177 | 20.935 | 0.126 |
| SambarNull | -959.889 | 9.542 | -92.185 | 22.808 | 0.071 |
Sambar Top Model
Based on the above model selection table, all assessed models apart from the null models have relatively similar predictive accuracy. This outcome provides us with two options in undertaking further analyses. Firstly, we could integrate over the posterior distributions for the non-zero weighted models in the above table. Essentially this would average 7 models and provide us with results that account for model uncertainty. Secondly, we could use the global model with regularised priors; which essentially down-weights parameters without strong informative influence on the presence or detection of Sambar Deer. The latter is a more straightforward task. Given this, SambarGlobal will be henceforth called SambarTopModel in the analysis. Specifically SambarGlobal has an elpd_diff within 2 x the se_diff of the top model.
The regularised top model has several key features:
- laplace/double exponential priors.
- Restricted Spatial Regression (RSR) random effect (Johnson et al. 2013) with a threshold of 20km.
- Detection submodel with interactions between method and (i) Precipitation and (ii) Herbaceous Understory.
Occupancy submodel summary
Below we present the model summary (marginal coefficients on the logit-scale) of the occupancy submodel for Sambar deer.
Show code
# SambarTopModel <- SambarCandidateModels@models[[rownames(SambarModSel[1,])]]
SambarTopModel <- SambarCandidateModels@models[["SambarGlobal"]]
Show code
sambar_top_summary <- summary(SambarTopModel, submodel = "state")
sambar_top_summary_sig <- which((sambar_top_summary$`2.5%` * sambar_top_summary$`97.5%`) > 0)
sambar_top_summary%>%
dplyr::select(-`25%`, -`50%`, -`75%`) %>%
kbl("html", caption = "Model estimates showing the effects of various covariates on the probability of occupancy for Sambar Deer. Estimates with a 95% CI not overlapping zero are shown in bold", digits = 3) %>%
kable_styling(full_width = FALSE) %>%
row_spec(sambar_top_summary_sig, bold = TRUE)
| mean | se_mean | sd | 2.5% | 97.5% | n_eff | Rhat | |
|---|---|---|---|---|---|---|---|
| (Intercept) | -1.311 | 0.015 | 0.484 | -2.368 | -0.482 | 1043.379 | 1.004 |
| scale(BIO12) | -0.103 | 0.015 | 0.845 | -1.946 | 1.573 | 3302.707 | 1.000 |
| scale(BIO04) | 0.363 | 0.014 | 0.685 | -0.937 | 1.835 | 2536.609 | 1.001 |
| scale(BIO01) | -0.630 | 0.012 | 0.644 | -2.052 | 0.453 | 2860.975 | 1.002 |
| scale(sqrt(BareSoil)) | -0.912 | 0.026 | 1.167 | -3.727 | 0.917 | 2053.415 | 1.001 |
| scale(log(DistancetoWater)) | 0.358 | 0.013 | 0.644 | -0.835 | 1.816 | 2480.713 | 1.000 |
| scale(BIO12 * BIO04) | 0.199 | 0.015 | 0.838 | -1.486 | 1.963 | 3233.612 | 1.000 |
| scale(BIO01 * BIO12) | 0.206 | 0.015 | 0.659 | -1.036 | 1.648 | 2013.326 | 1.003 |
| scale(sqrt(BareSoil) * BIO12) | 1.218 | 0.016 | 0.736 | 0.012 | 2.910 | 2120.000 | 1.002 |
| scale(BIO04 * log(DistancetoWater)) | -0.213 | 0.015 | 0.775 | -1.924 | 1.214 | 2581.848 | 1.001 |
| scale(TreeDensity) | 0.198 | 0.013 | 0.593 | -0.971 | 1.453 | 2112.457 | 1.004 |
| scale(sqrt(SLOPE) * BIO04) | 1.388 | 0.021 | 0.754 | 0.049 | 3.027 | 1328.784 | 1.003 |
| scale(NonPhotosyntheticVeg) | -0.175 | 0.014 | 0.547 | -1.369 | 0.843 | 1458.317 | 1.001 |
| scale(log(PastureDistance)) | 0.339 | 0.007 | 0.373 | -0.325 | 1.104 | 3109.023 | 1.001 |
| scale(sqrt(CrownGrazingDistance)) | -0.246 | 0.007 | 0.357 | -0.992 | 0.402 | 2470.382 | 1.001 |
| scale(TWIND) | -0.149 | 0.008 | 0.471 | -1.136 | 0.770 | 3170.897 | 1.001 |
| scale(BIO15) | -0.724 | 0.009 | 0.487 | -1.751 | 0.120 | 2703.923 | 1.000 |
| RSR [tau] | 63.166 | 18.696 | 108.698 | 0.002 | 386.572 | 33.802 | 1.070 |
Detection submodel summary
Below we present the model summary (marginal coefficients on the logit-scale) of the detection submodel for Sambar deer. Interactions are not of interest as the reference level (camera) is the key variable that the continuous variables change over (precipitation and herbaceous understory).
Show code
sambar_top_summary_det <- summary(SambarTopModel, submodel = "det")
sambar_top_summary_sig_det <- which((sambar_top_summary_det$`2.5%` * sambar_top_summary_det$`97.5%`) > 0)
sambar_top_summary_det %>%
dplyr::select(-`25%`, -`50%`, -`75%`) %>%
kbl("html", caption = "Model estimates showing the effects of various covariates on the probability of detection for Sambar Deer. Estimates with a 95% CI not overlapping zero are shown in bold", digits = 3) %>%
kable_styling(full_width = FALSE) %>%
row_spec(sambar_top_summary_sig_det, bold = TRUE)
| mean | se_mean | sd | 2.5% | 97.5% | n_eff | Rhat | |
|---|---|---|---|---|---|---|---|
| (Intercept) | -3.155 | 0.003 | 0.135 | -3.422 | -2.907 | 1575.793 | 1.001 |
| MethodFootprint | 1.681 | 0.007 | 0.290 | 1.109 | 2.226 | 1836.700 | 1.000 |
| MethodPellet | 1.654 | 0.006 | 0.302 | 1.022 | 2.224 | 2505.193 | 1.000 |
| MethodRubbing | 0.481 | 0.008 | 0.382 | -0.286 | 1.203 | 2388.648 | 1.001 |
| MethodWallow | -1.994 | 0.034 | 1.097 | -4.637 | -0.291 | 1069.413 | 1.010 |
| scale(sqrt(Precipitation)) | -0.194 | 0.002 | 0.085 | -0.365 | -0.036 | 2103.555 | 1.002 |
| scale(sqrt(HerbaceousUnderstoryCover)) | 0.727 | 0.003 | 0.123 | 0.497 | 0.972 | 2165.896 | 1.001 |
| MethodFootprint:scale(sqrt(Precipitation)) | 0.279 | 0.003 | 0.203 | -0.122 | 0.678 | 4063.211 | 1.003 |
| MethodPellet:scale(sqrt(Precipitation)) | 0.807 | 0.003 | 0.192 | 0.437 | 1.188 | 3133.304 | 1.003 |
| MethodRubbing:scale(sqrt(Precipitation)) | 0.111 | 0.004 | 0.291 | -0.490 | 0.662 | 4618.527 | 1.000 |
| MethodWallow:scale(sqrt(Precipitation)) | -0.583 | 0.024 | 1.007 | -2.871 | 0.987 | 1818.775 | 1.004 |
| MethodFootprint:scale(sqrt(HerbaceousUnderstoryCover)) | -0.639 | 0.007 | 0.307 | -1.234 | -0.030 | 1906.878 | 1.001 |
| MethodPellet:scale(sqrt(HerbaceousUnderstoryCover)) | -0.166 | 0.005 | 0.299 | -0.748 | 0.417 | 3173.286 | 1.001 |
| MethodRubbing:scale(sqrt(HerbaceousUnderstoryCover)) | -0.454 | 0.009 | 0.406 | -1.257 | 0.358 | 2205.935 | 1.000 |
| MethodWallow:scale(sqrt(HerbaceousUnderstoryCover)) | -0.615 | 0.019 | 0.953 | -2.398 | 1.349 | 2493.971 | 1.001 |
Models Checks and Posterior Predictive Checks
We undertake several additional checks of model convergence and posterior predictive capability.
Bayes R2
Experimental Bayesian R2 for occupancy models has been implemented here. With 100 posterior draws of the R2 for both detection and occupancy submodels.
Show code
TopModelR2 <- bayes_R2_res_ubms(SambarTopModel, re.form = NULL, draws = 1000) %>%
as.data.table() %>%
melt()
R2sum <- TopModelR2 %>%
group_by(variable) %>%
summarise(R2 = quantile(value, 0.5),
LCI = quantile(value, 0.05),
UCI = quantile(value, 0.95)) %>%
as.data.table()
TopModelR2 %>%
# filter(variable %in% c("det", "state")) %>%
ggplot(aes(x = value, fill = variable)) +
geom_density(alpha = 0.5) +
xlim(0,1) +
ggtitle("Bayes R2 of detection and occupancy models") +
scale_fill_brewer(type = "qual", palette = 2) +
labs(fill = "submodel") +
theme_bw()
Figure 4: Bayes R2 values for detection and occupancy submodels
The average Bayes R2 for the ‘state’ submodel was 0.34 [90 % CI: 0.13, 0.49]. The average Bayes R2 for the ‘detection’ submodel was 0.22 [90 % CI: 0.11, 0.39]. Together, this generates an R2 for both models as being 0.57 [90 % CI: 0.52, 0.63]
Posterior Predictive Tests
Using simulated posterior values we compare simulated presences (and absences against the mean true value of presence) for (A) observation-level detections and (B) site-level detections. If the red line falls approximately in the middle of the distribution it is evidence that the model has posterior predictive power.
Show code
n_draws <- 500
nrows <- nrow(SambarStack@siteCovs)
# species <- unique(umf_lbp@siteCovs)
sim_y <- ubms::posterior_predict(SambarTopModel, "y", draws=n_draws, re.form = NULL)
prop0 <- apply(sim_y, 1, function(x) mean(x==0, na.rm=TRUE)) %>%
data.table::as.data.table() %>%
melt()
prop1 <- apply(sim_y, 1, function(x) mean(x==1, na.rm=TRUE)) %>%
data.table::as.data.table() %>%
melt()
actual_prop0 <- mean(getY(SambarTopModel) == 0, na.rm=TRUE)
actual_prop1 <- mean(getY(SambarTopModel) == 1, na.rm=TRUE)
# For each model get the prop quantile
propObvsQ <- prop1 %>%
group_by(model = variable) %>%
summarise(percentile = ecdf(value)(actual_prop1))
#Model posterior predictive comparisons
broad_props <- prop1 %>%
ggplot(aes(x = value)) +
geom_density(fill = "grey90") +
geom_vline(xintercept = actual_prop1, colour = "red") +
xlim(0.01, 0.05) +
xlab("Proportion of observations that \nrecorded presence")+
theme_bw()
obs_it <- dim(SambarStack@obsToY)[1]
vec <- integer()
for(n in 1:nrow(SambarStack@siteCovs)) {
vec <- c(vec, ((n*obs_it)-(obs_it-1)):(n*obs_it))
}
prop0 <- numeric()
prop1 <- numeric()
for(i in 1:n_draws) {
mat <- apply(matrix(sim_y[i,vec], ncol = obs_it, byrow = TRUE), 1, function(x) max(x, na.rm = TRUE))
mat[which(!is.finite(mat))] <- NA
prop1[i] <- mean(mat, na.rm = TRUE)
prop0[i] <- 1 - prop1[i]
}
p1 <- mean(apply(getY(SambarTopModel), 1, function(x) {max(x,na.rm = T)}))
sim_y_props <- data.frame(model = "Sambar Top Model",
prop0 = prop0,
prop1 = prop1,
actual_prop0 = 1-p1,
actual_prop1 = p1)
bound_sim <- bind_rows(sim_y_props)
station_props <- bound_sim %>%
ggplot(aes(x = prop1)) +
geom_density(fill = "grey90") +
geom_vline(aes(xintercept = actual_prop1), colour = "red") +
xlim(0.25, 0.45) +
xlab("Proportion of stations\n where species were recorded")+
theme_bw()
cowplot::plot_grid(broad_props, station_props, labels = "AUTO")
Figure 5: Comparison of posterior predictions from the Bayesian models to true mean values of presences. The results indicate that predictive power is high for being able to predict the broad detection patterns across all sites (A) and the proportion of sites that Sambar Deer were recorded at (B).
Model Results
Below we generate plots of the model covariates and their response on either the probability of occupancy or the probability of detection.
Factors that effect detectability
Show code
newdata_means <- get_mean_df(SambarTopModel["det"], lev = 1)
nd_params <- list()
for(i in 1:ncol(newdata_means)) {
cols_but <- setdiff(1:ncol(newdata_means), i)
nd_params[[colnames(newdata_means)[i]]] <- newdata_means[,cols_but] %>%
left_join(data.frame(d = if(is.factor(SambarTopModel["det"]@data[[colnames(newdata_means)[i]]])) {
levels(SambarTopModel["det"]@data[[colnames(newdata_means)[i]]])
} else {
seq.int(from = min(SambarTopModel["det"]@data[[colnames(newdata_means)[i]]], na.rm = TRUE),
to = max(SambarTopModel["det"]@data[[colnames(newdata_means)[i]]], na.rm = TRUE), length.out = 50) %>% round(., digits = 4)
}) %>%
`colnames<-`(colnames(newdata_means)[i]), by = character())
}
# wt_used <- weights_ordered %>%
# filter(include) %>%
# pull(weight)
ppd <- list()
for(i in 1:length(nd_params)) {
ppd[[i]] <- ubms::posterior_linpred(SambarTopModel,
newdata = nd_params[[i]],
transform = TRUE,
draws = 1000, submodel = "det")
df <- ppd[[i]] %>%
`colnames<-`(nd_params[[i]][,names(nd_params)[i]]) %>%
as.data.table() %>%
melt(variable.name = names(nd_params)[i])
if(!is.character(nd_params[[i]][,ncol(nd_params[[i]])])) {
df[[names(nd_params)[i]]] <- as.numeric(as.character(df[[names(nd_params)[i]]])) %>% round(., digits = 4)
}
ppd[[i]] <- nd_params[[i]] %>%
full_join(df)
}
names(ppd) <- names(nd_params)
ppd_av <- purrr::map(ppd, ~ .x %>%
group_by_at(colnames(.x %>% dplyr::select(-value))) %>%
summarise(median = median(value, na.rm = TRUE),
LCI_95 = quantile(value, 0.025),
LCI_50 = quantile(value, 0.25),
UCI_50 = quantile(value, 0.75),
UCI_95 = quantile(value, 0.975)))
plot_numeric <- function(data_list, number) {
data_list[[number]] %>%
ggplot(aes_string(x = names(data_list)[number], y = "median")) +
geom_ribbon(aes(ymin = LCI_95, ymax = UCI_95), alpha = 0.3, fill = delwp_cols2[number]) +
geom_ribbon(aes(ymin = LCI_50, ymax = UCI_50), alpha = 0.3, fill = delwp_cols2[number]) +
geom_line(colour = delwp_cols2[number], size = 1.5) +
ylab("Detection Probability (0-1)") +
ylim(c(0, 1)) +
theme_bw()
}
plot_cat <- function(data_list, number) {
data_list[[number]] %>%
ggplot(aes_string(x = names(data_list)[number], y = "value")) +
# ggbeeswarm::geom_quasirandom(shape = 21, fill = delwp_cols2[number]) +
geom_violin(draw_quantiles = c(0.025,0.5,0.975), scale = "width", size = 1, alpha = 0.5, colour = "black", fill = delwp_cols2[number]) +
ylab("Detection Probability (0-1)") +
ylim(c(0, 1)) +
theme_bw()
}
ppd_mod <- ppd
ppd_mod[["Method"]] <- ppd_mod[["Method"]] %>%
mutate(value = case_when(Method == "Camera" ~ 1-((1-value)^42),
TRUE ~ 1-((1-value)^3))) %>%
as.data.frame()
meth <- plot_cat(ppd_mod, 1) +
ylab("Detection Probability (0-1)")
meth_table <- ppd_mod[["Method"]] %>%
group_by(Method) %>%
summarise(Median = median(value) %>% round(2),
`2.5%` = quantile(value, 0.025) %>% round(2),
`97.5%` = quantile(value, 0.975) %>% round(2))
md_sum <- unname(round(1-apply(1-meth_table[,2:4], 2, prod), 2))
# tmax <- plot_numeric(ppd_av, 1) +
# xlab("Maximum Daily Temperature") +
# ylim(c(0,0.25))
precip <- plot_numeric(ppd_av, 2) +
xlab("Daily precipitation (mm)") +
ylim(c(0,0.16))
herbund <- plot_numeric(ppd_av, 3) +
xlab("Herbaceous understory (%)") +
ylim(c(0,0.16))
cowplot::plot_grid(meth, precip, herbund, labels = "AUTO", ncol = 2)
Figure 6: Model predictions for the variables that impact detection probability. Predictions have been generated for each variable independently, with other covariates fixed at their mean value. For the ‘method’ covariate (A), we transform the the predictions so that camera detection is for a 6 week period (42 days), and the other deer signs are based on walking three transects. For continuous plots (B and C), 50 % and 95 % CIs are shown with respective shading around the mean line.
Using the estimated detection probabilities we can see that cameras, followed by pellet detection, footprints, rubbings and wallows are methods that can detect deer. When combined the likelihood of detecting Sambar deer at a site is: 0.97 (95% CI: 0.92, 0.99).
Factors that effect occupancy
Show code
newdata_means <- get_mean_df(SambarTopModel["state"], lev = 1)
nd_params <- list()
for(i in 1:ncol(newdata_means)) {
cols_but <- setdiff(1:ncol(newdata_means), i)
nd_params[[colnames(newdata_means)[i]]] <- newdata_means[,cols_but] %>%
left_join(data.frame(d = if(is.factor(SambarTopModel["state"]@data[[colnames(newdata_means)[i]]])) {
levels(SambarTopModel["state"]@data[[colnames(newdata_means)[i]]])
} else {
seq.int(from = min(SambarTopModel["state"]@data[[colnames(newdata_means)[i]]], na.rm = TRUE),
to = max(SambarTopModel["state"]@data[[colnames(newdata_means)[i]]], na.rm = TRUE), length.out = 50) %>% round(., digits = 4)
}) %>%
`colnames<-`(colnames(newdata_means)[i]), by = character())
}
# wt_used <- weights_ordered %>%
# filter(include) %>%
# pull(weight)
ppd <- list()
for(i in 1:length(nd_params)) {
ppd[[i]] <- ubms::posterior_linpred(SambarTopModel,
newdata = nd_params[[i]],
transform = TRUE, re.form = NA,
draws = 1000, submodel = "state")
df <- ppd[[i]] %>%
`colnames<-`(nd_params[[i]][,names(nd_params)[i]]) %>%
as.data.table() %>%
melt(variable.name = names(nd_params)[i])
if(!is.character(nd_params[[i]][,ncol(nd_params[[i]])])) {
df[[names(nd_params)[i]]] <- as.numeric(as.character(df[[names(nd_params)[i]]])) %>% round(., digits = 4)
}
ppd[[i]] <- nd_params[[i]] %>%
full_join(df)
}
names(ppd) <- names(nd_params)
# ppd[["TSLF_bin"]] <- ppd[["TSLF_bin"]] %>%
# mutate(TSLF_bin = factor(TSLF_bin, levels = c("2 - 5", "5 - 20", "> 20", "Unburnt")))
ppd_av <- purrr::map(ppd, ~ .x %>%
group_by_at(colnames(.x %>% dplyr::select(-value))) %>%
summarise(median = median(value, na.rm = TRUE),
LCI_95 = quantile(value, 0.025),
LCI_50 = quantile(value, 0.25),
UCI_50 = quantile(value, 0.75),
UCI_95 = quantile(value, 0.975)))
plot_numeric <- function(data_list, number) {
data_list[[number]] %>%
ggplot(aes_string(x = names(data_list)[number], y = "median")) +
geom_ribbon(aes(ymin = LCI_95, ymax = UCI_95), alpha = 0.3, fill = delwp_cols2[number]) +
geom_ribbon(aes(ymin = LCI_50, ymax = UCI_50), alpha = 0.3, fill = delwp_cols2[number]) +
geom_line(colour = delwp_cols2[number], size = 1.5) +
ylab("Occupancy Probability (0-1)") +
ylim(c(0, 1)) +
theme_bw()
}
plot_cat <- function(data_list, number) {
data_list[[number]] %>%
ggplot(aes_string(x = names(data_list)[number], y = "value")) +
ggbeeswarm::geom_quasirandom(shape = 21, fill = delwp_cols2[number]) +
geom_violin(draw_quantiles = c(0.5), size = 1, alpha = 0.5) +
ylab("Occupancy Probability (0-1)") +
ylim(c(0, 1)) +
theme_bw()
}
pasturedist <- plot_numeric(ppd_av, 9) +
xlab("Distance to pasture (m)") +
scale_x_log10() +
ylim(c(0,1))
treedensity <- plot_numeric(ppd_av, 6) +
xlab("Tree density (%)") +
scale_x_sqrt() +
ylim(c(0,1))
npv <- plot_numeric(ppd_av, 8) +
xlab("Non-photosynthetic vegetation (%)") +
scale_x_sqrt() +
ylim(c(0,1))
slope <- plot_numeric(ppd_av, 10) +
xlab("Crown grazing distance (%)") +
scale_x_sqrt() +
ylim(c(0,1))
twind <- plot_numeric(ppd_av, 11) +
xlab("Topographic wetness index (TWIND)") +
scale_x_sqrt() +
ylim(c(0,1))
bio15 <- plot_numeric(ppd_av, 12) +
xlab('BIO15: Precipitation Seasonality (kg m^(2))') +
ylim(c(0,1)) +
theme(axis.title.x = element_markdown())
# sambarhunt <- plot_cat(ppd, 12) +
# xlab("Sambar Hunting Area") +
# ylim(c(0,1))
#### Interctions on a continous scale ####
# BIO01
# BIO12
# BIO04
# BareSoil
# Water
# Slope
nd_params_int <- list()
nd_params_int[["BIO12_BIO04"]] <- nd_params[["BIO12"]] %>%
dplyr::select(-BIO04) %>%
left_join(nd_params[["BIO04"]] %>%
dplyr::select(BIO04), by = character())
nd_params_int[["BIO04_DistancetoWater"]] <- nd_params[["BIO04"]] %>%
dplyr::select(-DistancetoWater) %>%
left_join(nd_params[["DistancetoWater"]] %>%
dplyr::select(DistancetoWater), by = character())
nd_params_int[["BareSoil_BIO12"]] <- nd_params[["BareSoil"]] %>%
dplyr::select(-BIO12) %>%
left_join(nd_params[["BIO12"]] %>%
dplyr::select(BIO12), by = character())
nd_params_int[["BIO01_BIO12"]] <- nd_params[["BIO01"]] %>%
dplyr::select(-BIO12) %>%
left_join(nd_params[["BIO12"]] %>%
dplyr::select(BIO12), by = character())
nd_params_int[["SLOPE_BIO04"]] <- nd_params[["SLOPE"]] %>%
dplyr::select(-BIO04) %>%
left_join(nd_params[["BIO04"]] %>%
dplyr::select(BIO04), by = character())
ppd_int <- list()
for(i in 1:length(nd_params_int)) {
ppd_int[[i]] <- ubms::posterior_linpred(SambarTopModel,
newdata = nd_params_int[[i]],
transform = TRUE, re.form = NA,
draws = 100, submodel = "state")
two_names <- as.character(stringr::str_split(names(nd_params_int)[i], "_", simplify = TRUE))
df <- ppd_int[[i]] %>%
`colnames<-`(apply(nd_params_int[[i]][,two_names], 1, paste, collapse = "_")) %>%
as.data.table() %>%
melt(variable.name = names(nd_params_int)[i]) %>%
tidyr::separate(col = !!names(nd_params_int)[i], into = two_names, sep = "_")
if(!is.character(nd_params_int[[i]][,ncol(nd_params_int[[i]])])) {
df[,c(two_names) := lapply(.SD, as.numeric), .SDcols = two_names]
}
ppd_int[[i]] <- nd_params_int[[i]] %>%
full_join(df)
}
names(ppd_int) <- names(nd_params_int)
ppd_int_av <- purrr::map(ppd_int, ~ .x %>%
group_by_at(colnames(.x %>% dplyr::select(-value))) %>%
summarise(median = median(value, na.rm = TRUE),
LCI_95 = quantile(value, 0.025),
LCI_50 = quantile(value, 0.25),
UCI_50 = quantile(value, 0.75),
UCI_95 = quantile(value, 0.975)))
plot_numeric_int <- function(data_list, number) {
name_split <- as.character(stringr::str_split(names(data_list)[number], "_", simplify = TRUE))
data_list[[number]] %>%
ggplot(aes_string(x = name_split[1], y = name_split[2])) +
# geom_ribbon(aes(ymin = LCI_95, ymax = UCI_95), alpha = 0.3, fill = delwp_cols2[number]) +
# geom_ribbon(aes(ymin = LCI_50, ymax = UCI_50), alpha = 0.3, fill = delwp_cols2[number]) +
geom_contour_filled(aes_string(z = "median"), bins = 11, breaks = seq(0, 1, 0.2), alpha = 0.8) +
geom_point(data = SambarTopModel@data@siteCovs %>%
mutate(Presence = as.character(`Rusa unicolor`)),
aes(colour = `Presence`), shape = 21, lwd = 2.5, stroke = 2.5, fill = "White") +
scale_colour_manual(values = c(`1` = delwp_cols[["Navy"]], `0` = delwp_cols[["Environment"]])) +
labs(fill = "Occupancy\nProbability") +
lims(fill = c(0,1)) +
theme_bw()
}
bio12_bio4 <- plot_numeric_int(ppd_int_av, 1) +
scale_fill_viridis_d(option = "D", direction = -1, guide = guide_legend(reverse = T)) +
xlab("BIO12: Annual precipitation (kg m^(2))") +
ylab("BIO04: Temperature seasonality (°C)") +
theme(axis.title.x = element_markdown())
bio4_water <- plot_numeric_int(ppd_int_av, 2) +
scale_fill_viridis_d(option = "D", direction = -1, guide = guide_legend(reverse = T)) +
xlab("BIO04: Temperature seasonality (°C)") +
ylab("Distance to water (m)") +
scale_y_log10()
baresoil_bio12 <- plot_numeric_int(ppd_int_av, 3) +
scale_fill_viridis_d(option = "D", direction = -1, guide = guide_legend(reverse = T)) +
xlab("Bare soil (%)") +
ylab("BIO12: Annual precipitation (kg m^(2))") +
scale_x_sqrt() +
theme(axis.title.y = element_markdown())
bio01_bio12 <- plot_numeric_int(ppd_int_av, 4) +
scale_fill_viridis_d(option = "D", direction = -1, guide = guide_legend(reverse = T)) +
xlab("BIO01: Annual mean temperature (°C)") +
ylab("BIO12: Annual precipitation (kg m^(2))") +
theme(axis.title.y = element_markdown())
slope_bio4 <- plot_numeric_int(ppd_int_av, 5) +
scale_fill_viridis_d(option = "D", direction = -1, guide = guide_legend(reverse = T)) +
xlab("Slope") +
ylab("BIO04: Temperature seasonality (°C)") +
scale_x_sqrt()
cowplot::plot_grid(cowplot::plot_grid(pasturedist, treedensity, npv, slope, twind, bio15, labels = "AUTO", ncol = 3),
cowplot::plot_grid(bio12_bio4, bio4_water, baresoil_bio12, bio01_bio12, slope_bio4, labels = c("G", "H", "I", "J", "K"), ncol = 2), ncol = 1, rel_heights = c(0.4,0.6))
Figure 7: Model predictions for the variables that impact occupancy probability. Predictions have been generated for each variable independently, with other covariates fixed at their mean value. For continuous plots (A-F), 50 % and 95 % CIs are shown with respective shading around the mean line. For interaction plots (G-K) the median prediction is shown (no confidence intervals are displayed). However, presences and absences are plotted based on their respective covariate values to highlight the range of covariates from the sampled sites.
Predict to Victoria
We make predictions to Victoria. This prediction generates median predictions as well as 95 % confidence intervals. The resolution of these predictions is set at 1km by 1km grid cells.
Show code
# Read in crown land mask
crownland_mask <- terra::rast("/Volumes/DeerVic\ Photos/MaxentStack/maxent_stack_masked_cl.grd")[[1]]
top_model_vars <- SambarTopModel@call[["formula"]][[3]] %>% all.vars()
if(!file.exists("/Volumes/DeerVic\ Photos/MaxentStack/vic_rast_ubms_mask.tif")) {
vic_rast_ubms_mask <- terra::mask(processed_stack, crownland_mask)
terra::writeRaster(vic_rast_ubms_mask,
filename = "/Volumes/DeerVic\ Photos/MaxentStack/vic_rast_ubms_mask.tif",
overwrite = TRUE)
vic_bound_terra_proj <- rast(raster(vic_bound, res = 1000))
vic_model_data_resampled <- terra::resample(x = vic_rast_ubms_mask,
y = vic_bound_terra_proj,
method = "bilinear")
# vic_model_data_resampled <- terra::spatSample(x = vic_rast_ubms_mask,
# 1000000,
# "regular",
# as.raster = TRUE)
terra::writeRaster(vic_model_data_resampled,
filename = "/Volumes/DeerVic\ Photos/MaxentStack/vic_model_data_resampled.tif",
overwrite = TRUE)
# vic_rast_data <- vic_rast_ubms_mask[[top_model_vars]] %>%
# terra::as.data.frame(., xy = TRUE)
# saveRDS(vic_rast_data, "/Volumes/DeerVic\ Photos/MaxentStack/vic_rast_data.rds")
} else {
vic_rast_ubms_mask <- rast("/Volumes/DeerVic\ Photos/MaxentStack/vic_rast_ubms_mask.tif")
vic_model_data_resampled <- rast("/Volumes/DeerVic\ Photos/MaxentStack/vic_model_data_resampled.tif")
}
vic_rast_data <- terra::as.data.frame(vic_model_data_resampled, xy = TRUE) %>%
mutate(SambarHunt = factor(SambarHunt))
vic_rast_data$BIOREGION <- factor(round(vic_rast_data$BIOREGION))
levels(vic_rast_data$BIOREGION) <- levels(processed_stack$BIOREGION)[[1]][1:28]
if(!file.exists("models/vic_preds.rds")) {
vic_preds <- ubms::predict(object = SambarTopModel,
submodel = "state",
newdata = vic_rast_data,
transform = TRUE, re.form = NA)
saveRDS(vic_preds, "models/vic_preds.rds")
} else {
vic_preds <- readRDS("models/vic_preds.rds")
}
vic_preds_bound <- cbind(vic_rast_data, vic_preds)
vic_preds_terra <- terra::rast(vic_preds_bound[,c("x","y", "Predicted")])
vic_preds_terra_LCI <- terra::rast(vic_preds_bound[,c("x","y", "2.5%")])
vic_preds_terra_UCI <- terra::rast(vic_preds_bound[,c("x","y", "97.5%")])
vic_preds_terra_SD <- terra::rast(vic_preds_bound[,c("x","y", "SD")])
plotstatePA <- function(x) {
rasterVis::gplot(x, maxpixels = 1000000) +
geom_sf(data = regions_3111, fill = "grey", inherit.aes = FALSE, alpha = 0.2, colour = "grey70") +
geom_tile(aes(fill = value), na.rm = TRUE) +
scale_fill_gradient2(na.value = "transparent", low = delwp_cols[["Environment"]], mid = delwp_cols[["Teal"]], high = delwp_cols[["Navy"]], midpoint = 0.5, limits = c(0,1)) +
ylab("Latitude") +
xlab("Longitude") +
labs(fill = "Occupancy \nProbability") +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 15),
legend.title = element_text(size=20),
legend.text = element_text(size=15),
legend.key.size = unit(3, 'cm')) +
theme_bw()
}
sw_bayes_mean <- plotstatePA(vic_preds_terra) + ggtitle("Median")
sw_bayes_lci <- plotstatePA(vic_preds_terra_LCI) + ggtitle("2.5% CI") + theme(legend.position = "none")
sw_bayes_uci <- plotstatePA(vic_preds_terra_UCI)+ ggtitle("97.5% CI") + theme(legend.position = "none")
bottom_row <- cowplot::plot_grid(sw_bayes_lci, sw_bayes_uci, labels = c('B', 'C'), ncol = 2)
bayes_vic_grid <- cowplot::plot_grid(sw_bayes_mean, bottom_row, labels = c('A', ''), ncol = 1, rel_heights = c(1.5,1))
ggsave(plot = bayes_vic_grid,
filename = "plots/bayes_vic_grid.pdf",
width = 16, height = 16, device='pdf', dpi=700)
bayes_vic_grid
Figure 8: Predictions of Smabar Deer occupancy across Victorian crown land from a presence-absence imperfect detection occupancy model. (A) shows the median derived occupancy from the posterior distribution with (B) and (C) showing the respective 95 % confidence intervals.
Thresholded Predictions
Based on the top Sambar model we determine the logistic threshold to classify area as either present or absent. From this threshold we can estimate the range size of Sambar Deer of crown land.
Show code
predSum <- data.frame(SiteID = names(apply(SambarTopModel@response@y, 1, max, na.rm = T)),
Presence = apply(SambarTopModel@response@y, 1, max, na.rm = T)) %>%
bind_cols(predict(SambarTopModel, "state"))
pred <- prediction(predSum$Predicted, predSum$Presence)
perf <- performance(pred, measure="fpr")
pdata <- tibble(Measure = "False positives",
`Logistic threshold` = perf@x.values[[1]],
`Fractional value` = perf@y.values[[1]])
perf2 <- performance(pred, measure="fnr")
pdata2 <- tibble(Measure = "False negatives",
`Logistic threshold` = perf2@x.values[[1]],
`Fractional value` = perf2@y.values[[1]])
perfcombined <- bind_rows(pdata, pdata2) %>%
group_by(`Logistic threshold`) %>%
mutate(CombinedVal = sum(`Fractional value`, na.rm = T),
DiffVal = abs(diff(`Fractional value`))) %>%
ungroup()
lt <- perfcombined[which.min(perfcombined$DiffVal),][["Logistic threshold"]]
rv <- perfcombined[which.min(perfcombined$DiffVal),][["Fractional value"]]
th_conv <- function(x, lte = lt) {
vic_preds_terra_th <- x
values(vic_preds_terra_th)[values(vic_preds_terra_th) >= lte] <- 1
values(vic_preds_terra_th)[values(vic_preds_terra_th) < lte] <- 0
values(vic_preds_terra_th) <- factor(values(vic_preds_terra_th))
levels(vic_preds_terra_th) <- c("Absent", "Present")
names(vic_preds_terra_th) <- "Predicted"
return(vic_preds_terra_th)
}
vic_preds_terra_th <- th_conv(vic_preds_terra)
vic_preds_terra_th_LCI <- th_conv(vic_preds_terra_LCI)
vic_preds_terra_th_UCI <- th_conv(vic_preds_terra_UCI)
area_dist <- vic_preds_bound %>%
filter(Predicted >= lt) %>%
nrow()
area_dist_lci <- vic_preds_bound %>%
filter(`2.5%` >= lt) %>%
nrow()
area_dist_uci <- vic_preds_bound %>%
filter(`97.5%` >= lt) %>%
nrow()
area_dist_form <- paste0(format(units::set_units(area_dist, "km2"), big.mark = ","), " [95% CI:", format(units::set_units(area_dist_lci, "km2"), big.mark = ","), ", ", format(units::set_units(area_dist_uci, "km2"), big.mark = ","), "]")
perfcombined %>%
ggplot(aes(x = `Logistic threshold`, y = `Fractional value`, colour = Measure)) +
geom_line() +
geom_vline(xintercept = lt,
linetype = "dotted",
colour = "red") +
geom_hline(yintercept = rv,
linetype = "dotted",
colour = "red") +
ylim(0,1) +
xlim(0,1) +
scale_colour_manual(values = c(`False positives` = "Dark Green",
`False negatives` = "Dark blue")) +
theme_classic()
Based on the assessment of false positive and false negative errors the logistic threshold to minimize false positive and false negative error rates is 0.53. If we use this logistic threshold to restrict the Sambar Deer predictions we obtain the following map/distribution. The total crown land covered by this threshold is 31,789 [km^2] [95% CI:17,710 [km^2], 41,521 [km^2]].
Show code
plotstatePATH <- function(x) {
name_x <- names(x)
ggplot() +
geom_sf(data = regions_3111, fill = "grey", inherit.aes = FALSE, alpha = 0.2, colour = "grey70") +
tidyterra::geom_spatraster(aes_string(fill = name_x), na.rm = TRUE, data = x) +
scale_fill_manual(na.value = "transparent",
values = c(`Absent` = delwp_cols[["Environment"]],
`Present` = delwp_cols[["Navy"]])) +
geom_point(aes(x = X, y = Y),
size = 1.5,
shape = 21,
alpha = 1,
fill = delwp_cols2[["Water"]],
data = combined_model_data_allMethod_loc %>%
filter(Species == "Rusa unicolor" & Presence == "Absent"),
inherit.aes = FALSE) +
geom_point(aes(x = X, y = Y),
size = 2.5,
shape = 21,
alpha = 1,
fill = delwp_cols2[["FFR"]],
data = combined_model_data_allMethod_loc %>%
filter(Species == "Rusa unicolor" & Presence == "Present"),
inherit.aes = FALSE) +
ylab("Latitude") +
xlab("Longitude") +
labs(fill = "Occupancy \nprobability") +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 15),
legend.title = element_text(size=20),
legend.text = element_text(size=15),
legend.key.size = unit(3, 'cm')) +
theme_bw()
}
# plotstatePATH(vic_preds_terra_th_LCI) +
# ggtitle("Threshold Sambar Distribution")
#
# plotstatePATH(vic_preds_terra_th_UCI) +
# ggtitle("Threshold Sambar Distribution")
thmean <- plotstatePATH(vic_preds_terra_th) +
ggtitle("Median")
th_lci <- plotstatePATH(vic_preds_terra_th_LCI) +
ggtitle("2.5% CI") +
theme(legend.position = "none")
th_uci <- plotstatePATH(vic_preds_terra_th_UCI) +
ggtitle("97.5% CI") +
theme(legend.position = "none")
th_bot <- cowplot::plot_grid(th_lci, th_uci, ncol = 2,labels = c('B', 'C'))
cowplot::plot_grid(thmean, th_bot, nrow = 2, rel_heights = c(1.5,1), labels = c('A', ''))
![Sambar Deer Distribution using a threshold that minimises false negatives and false postives results in a distribution of 31,439 [km^2] over crown land area. Orange dots represent the site locations where Sambar Deer were detected, while blue dots are absences. Plots B and C represent the lower and upper 95 % confidence intervals respectively.](data:image/png;base64,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)
Figure 9: Sambar Deer Distribution using a threshold that minimises false negatives and false postives results in a distribution of 31,439 [km^2] over crown land area. Orange dots represent the site locations where Sambar Deer were detected, while blue dots are absences. Plots B and C represent the lower and upper 95 % confidence intervals respectively.
Show code
# To Compare predictions vs Gormley read in Gormley data
gormley_dist_rat <- terra::rast("data/CurrentDist.asc")
gormleySambarOcc2 <- terra::rast("data/SambarOcc2.asc")
terra::crs(gormleySambarOcc2) <- terra::crs(processed_stack)
terra::crs(vic_preds_terra_th) <- terra::crs(processed_stack)
terra::crs(gormley_dist_rat) <- terra::crs(processed_stack)
values(gormley_dist_rat)[values(gormley_dist_rat) == 0] <- NA
values(gormleySambarOcc2) <- values(gormleySambarOcc2)/100
# Use the same logistic threshold to crop gormley
gormley_th <- th_conv(gormleySambarOcc2, lte = 0.4)
gormley_th_proj <- terra::project(gormley_th, vic_preds_terra_th)
gormley_th_mask <- terra::mask(gormley_th_proj, vic_preds_terra_th)
gormley_th_anti_mask <- terra::mask(gormley_th_proj, vic_preds_terra_th, inverse = T)
gormley_dist_rat_proj <- terra::project(gormley_dist_rat, vic_preds_terra_th)
gormley_th_mask_kde <- terra::mask(gormley_th_mask, gormley_dist_rat_proj)
gormley_th_mask_df <- as.data.frame(gormley_th_mask)
gormley_th_anti_mask_df <- as.data.frame(gormley_th_anti_mask)
gormley_th_mask_df_kde <- as.data.frame(gormley_th_mask_kde, xy = TRUE, na.rm = FALSE)
gormley_dist <- gormley_th_mask_df %>%
filter(Predicted == "Present") %>%
nrow()
gormley_dist_anti <- gormley_th_anti_mask_df %>%
filter(Predicted == "Present") %>%
nrow()
gormley_dist_kde <- gormley_th_mask_df_kde %>%
filter(Predicted == "Present") %>%
nrow()
expert_elicitation <- sf::read_sf("data/Sambar_Deer_Distributions_Final_20201203")
expert_elicitation_rast <- terra::rasterize(vect(expert_elicitation %>%
mutate(Predicted = 1L) %>%
sf::st_transform(3111)), vic_preds_terra_th, field = "Predicted", background = NA)
expert_elicitation_rast_cl <- terra::mask(expert_elicitation_rast, vic_preds_terra_th)
expert_elicitation_rast_cl_df <- as.data.frame(expert_elicitation_rast_cl)
ee_dist <- expert_elicitation_rast_cl_df %>%
filter(Predicted == 1) %>%
nrow()
gormley_dist_form <- format(units::set_units(gormley_dist, "km2"), big.mark = ",")
gormley_dist_form_kde <- format(units::set_units(gormley_dist_kde, "km2"), big.mark = ",")
# Create table comparing estimates
all_preds_list <- list()
all_preds_list[["EE_2015"]] <- as.data.frame(expert_elicitation_rast_cl, xy = TRUE, na.rm = FALSE) %>%
rename(`Expert Elicitation` = Predicted)
all_preds_list[["Gormley_2011"]] <- gormley_th_mask_df_kde %>%
mutate(Predicted = case_when(Predicted == "Present" ~ 1L,
TRUE ~ 0L)) %>%
rename(`KDE + P/A + PO` = Predicted)
all_preds_list[["Median"]] <- as.data.frame(vic_preds_terra_th, xy = TRUE, na.rm = FALSE) %>%
mutate(Predicted = case_when(Predicted == "Present" ~ 1L,
TRUE ~ 0L)) %>%
rename(`Median Estimate` = Predicted)
df_distribution <- all_preds_list %>% purrr::reduce(full_join, by = c("x", "y"))
df_distribution[is.na(df_distribution)] <- 0
df_distribution_summary <- df_distribution %>%
mutate(Overlap = paste0(`Expert Elicitation`, `KDE + P/A + PO`, `Median Estimate`)) %>%
group_by(Overlap) %>%
summarise(Area = n()) %>%
filter(Overlap != "000") %>%
mutate(Name = case_when(Overlap == "001" ~ "This Study ONLY",
Overlap == "010" ~ "Gormley 2011 ONLY",
Overlap == "011" ~ "This Study and Gormley 2011",
Overlap == "100" ~ "Forsyth 2015 ONLY",
Overlap == "101" ~ "This Study and Forsyth 2015",
Overlap == "110" ~ "Gormley 2011 and Forsyth 2015",
Overlap == "111" ~ "All",
TRUE ~ "No Prediction"),
Area = units::set_units(Area, "km2")) %>%
arrange(desc(Area)) %>%
dplyr::select(Name, Area)
df_distribution_summary %>%
kbl("html", caption = "Comparison of deer range between three studies on Sambar deer", digits = 0) %>%
kable_styling(full_width = FALSE)
| Name | Area |
|---|---|
| All | 28105 [km^2] |
| Forsyth 2015 ONLY | 7035 [km^2] |
| Gormley 2011 and Forsyth 2015 | 4482 [km^2] |
| This Study and Forsyth 2015 | 3479 [km^2] |
| This Study ONLY | 168 [km^2] |
| This Study and Gormley 2011 | 37 [km^2] |
| Gormley 2011 ONLY | 20 [km^2] |
Show code
all_distrib <- df_distribution %>%
mutate(Overlap = paste0(`Expert Elicitation`, `KDE + P/A + PO`, `Median Estimate`)) %>%
filter(Overlap != "000") %>%
mutate(Name = case_when(Overlap == "001" ~ "This Study ONLY",
Overlap == "010" ~ "Gormley 2011 ONLY",
Overlap == "011" ~ "This Study and Gormley 2011",
Overlap == "100" ~ "Forsyth 2015 ONLY",
Overlap == "101" ~ "This Study and Forsyth 2015",
Overlap == "110" ~ "Gormley 2011 and Forsyth 2015",
Overlap == "111" ~ "All",
TRUE ~ "No Prediction"))
ggplot() +
geom_sf(data = regions_3111, fill = "grey", inherit.aes = FALSE, alpha = 0.2, colour = "grey70") +
geom_tile(aes(x=x, y=y, fill=Name), data=vic_preds_bound, fill = "grey70", inherit.aes = FALSE, na.rm = TRUE) +
geom_tile(aes(x=x, y=y, fill=Name), data=all_distrib) +
geom_point(data = historic_deer_ala_records %>%
filter(scientificName == "Rusa unicolor") %>%
st_transform(3111) %>%
st_intersection(regions_3111) %>%
st_coordinates() %>%
as.data.frame(),
aes(x = X, y = Y),
shape = 21, fill = "Orange", size = 1) +
viridis::scale_fill_viridis(discrete=TRUE, direction=1, option="D", na.value = "transparent") +
ylab("Latitude") +
xlab("Longitude") +
labs(fill = "Predicted distribution") +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 15),
legend.title = element_text(size=20),
legend.text = element_text(size=15),
legend.key.size = unit(3, 'cm')) +
theme_bw()
Figure 10: Comparison of threshold from this study to two previous studies estimating Sambar Deer Distribution. Atlas of Living Australia records from the past 30 years are plotted as orange points.
Relationship of Sambar Occupancy to Seedlings and Saplings
Deer density is anticipated to impact on vegetation measures such as seedling and sapling count. Here we investigate whether the predicted Sambar occupancy relates to seedlings or saplings via a multivariate response model.
Show code
sambarseeddata <- data.frame(SiteID = names(apply(SambarTopModel@response@y, 1, max, na.rm = T)),
Presence = apply(SambarTopModel@response@y, 1, max, na.rm = T)) %>%
bind_cols(predict(SambarTopModel, "state")) %>%
left_join(vegetation %>%
dplyr::select(SiteID, Saplings, Seedlings),
by = "SiteID") %>%
left_join(combined_spatial_data %>%
dplyr::select(SiteID, BIOREGION, EVC) %>%
mutate(SiteID = as.character(SiteID)),
by = "SiteID")
sambarseeddata %>%
group_by(Presence) %>%
summarise(Seed = median(Seedlings),
Saplings = median(Saplings))
#> # A tibble: 2 × 3
#> Presence Seed Saplings
#> <dbl> <dbl> <dbl>
#> 1 0 0 1
#> 2 1 5 8Show code
bform1 <- bf(mvbind(Saplings, Seedlings) ~ Predicted)
bform2 <- bf(mvbind(Saplings, Seedlings) ~ Predicted + (BIOREGION|EVC))
if(!file.exists("models/vegfit1.rds")) {
vegfit1 <- brm(bform1, family = zero_inflated_poisson(), data = sambarseeddata, chains = 3, iter = 1000)
saveRDS(vegfit1, "models/vegfit1.rds")
} else {
vegfit1 <- readRDS("models/vegfit1.rds")
}
summary(vegfit1)
#> Family: MV(zero_inflated_poisson, zero_inflated_poisson)
#> Links: mu = log; zi = identity
#> mu = log; zi = identity
#> Formula: Saplings ~ Predicted
#> Seedlings ~ Predicted
#> Data: sambarseeddata (Number of observations: 130)
#> Draws: 3 chains, each with iter = 1000; warmup = 500; thin = 1;
#> total post-warmup draws = 1500
#>
#> Population-Level Effects:
#> Estimate Est.Error l-95% CI u-95% CI Rhat
#> Saplings_Intercept 3.62 0.03 3.56 3.68 1.01
#> Seedlings_Intercept 4.93 0.02 4.89 4.97 1.00
#> Saplings_Predicted 0.49 0.05 0.40 0.58 1.00
#> Seedlings_Predicted -0.39 0.03 -0.45 -0.32 1.00
#> Bulk_ESS Tail_ESS
#> Saplings_Intercept 1548 908
#> Seedlings_Intercept 1309 1051
#> Saplings_Predicted 1691 1242
#> Seedlings_Predicted 1604 1163
#>
#> Family Specific Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
#> zi_Saplings 0.41 0.04 0.33 0.49 1.00 1564
#> zi_Seedlings 0.49 0.04 0.41 0.58 1.00 2020
#> Tail_ESS
#> zi_Saplings 871
#> zi_Seedlings 1046
#>
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).Show code
# pp_check(vegfit1, resp = "Seedlings")
Show code
plot(conditional_effects(vegfit1), ask = FALSE, theme = theme_classic())
Figure 11: Relationship between Predicted Sambar Deer occupancy and Seedlings/Saplings count
Figure 12: Relationship between Predicted Sambar Deer occupancy and Seedlings/Saplings count