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15_additional_figures.R
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#Figures
library(tidyverse)
library(patchwork)
library(sf)
species_sort <- readRDS("data/species_sort.rds")
# Figure_1 ----------------------------------------------------------------
# demonstration of neighbours for BASP
# load saved neighbours data from neighbours_define_voronoi(..., save_plot_data = TRUE)
load("Baird's_Sparrow_route_maps_data.RData")
crs_use <- readRDS("functions/custom_crs_for_maps.rds")
strata_map2 <- bbsBayes2::load_map("bbs_usgs")%>%
st_transform(.,crs_use)
state_prov <- bbsBayes2::load_map("prov_state") %>%
st_transform(.,crs_use)
box <- st_as_sfc(st_bbox(strata_map))
xb <- range(st_coordinates(box)[,"X"])
yb <- range(st_coordinates(box)[,"Y"])
ggp1 <- ggplot()+
geom_sf(data = strata_map2,alpha = 0,colour = grey(0.9))+
geom_sf(data = strata_map,alpha = 0,colour = grey(0.8))+
#geom_sf(data = cov_hull_clip,alpha = 0,colour = grey(0.8))+
#geom_sf(data = v,alpha = 0,colour = grey(0.9))+
geom_sf(data = full_clip,alpha = 0,colour = grey(0.1))+
geom_sf(data = vintj,alpha = 0,colour = grey(0.5))+
geom_sf(data = centres,alpha = 1,size = 0.6)+
theme_minimal() +
xlab("")+
ylab("")+
coord_sf(xlim = xb,ylim = yb)+
theme(legend.position = "none",
text = element_text(family = "serif",
size = 11),
panel.grid = element_line(colour = grey(0.95)),
plot.caption = element_text(hjust = 0))
ggp1
ggp2 <- ggplot()+
geom_sf(data = strata_map2,alpha = 0,colour = grey(0.9))+
geom_sf(data = strata_map,alpha = 0,colour = grey(0.8))+
geom_segment(data=DA,aes(x = long, y = lat,xend=long_to,yend=lat_to),
inherit.aes = FALSE,linewidth=0.3,colour = grey(0.6)) +
geom_sf(data = centres,alpha = 1,size = 1.3)+
theme_minimal() +
xlab("")+
ylab("")+
coord_sf(xlim = xb,ylim = yb)+
theme(legend.position = "none",
text = element_text(family = "serif",
size = 11),
panel.grid = element_line(colour = grey(0.95)),
plot.caption = element_text(hjust = 0))
ggp2
ggp_inset <- ggplot()+
geom_sf(data = strata_map2,alpha = 1,fill = "white",colour = grey(0.8))+
geom_sf(data = state_prov,alpha = 0)+
geom_sf(data = box, alpha = 0, linewidth = 0.6)+
#geom_sf(data = strata_map,alpha = 0,colour = grey(0.2))+
theme_void() +
xlab("")+
ylab("")+
#coord_sf(label_axes = list(top = "E", left = "N"))+
#coord_sf(xlim = xb,ylim = yb)+
theme(legend.position = "none",
text = element_text(family = "serif",
size = ),
#panel.grid = element_line(colour = grey(0.95)),
plot.caption = element_text(hjust = 0))
ggp_inset
#
# + inset_element(ggp_inset,left = 0.3,bottom = 0.3,
# right = 1.1, top = 1.1)
des <- "
33311111
33311111
#222222#
#222222#
"
des <- layout <- c(
area(t = 3, l = 1, b = 10, r = 8),
area(t = 3, l = 9, b = 10, r = 16),
area(t = 1, l = 4, b = 6, r = 10)
)
ggp1i <- free(ggp1) + free(ggp2) + free(ggp_inset) + plot_layout(design = des)
ggp1i
pdf("Figures/Figure_1.pdf",
width = 7,
height = 5)
print(ggp1i )
dev.off()
# Alternate GP posterior plot ---------------------------------------------
output_dir <- "output"
species <- "Baird's Sparrow"
firstYear <- 2006
lastYear <- 2021
species_f <- gsub(gsub(species,pattern = " ",replacement = "_",fixed = T),pattern = "'",replacement = "",fixed = T)
out_base_temp <- paste0(species_f,"_GP_",firstYear,"_",lastYear)
sp_data_file <- paste0("Data/",species_f,"_",firstYear,"_",lastYear,"_stan_data.RData")
load(sp_data_file)
spp <- paste0("_iCAR_")
out_base <- paste0(species_f,spp,firstYear,"_",lastYear)
summ_CAR <- readRDS(paste0(output_dir,"/",out_base,"_summ_fit.rds")) %>%
filter(variable %in% c("sdalpha","sdbeta_space"))
summ_CAR
spp <- paste0("_GP_")
out_base <- paste0(species_f,spp,firstYear,"_",lastYear)
summ <- readRDS(paste0(output_dir,"/",out_base,"_summ_fit.rds")) %>%
filter(variable %in% c("gp_sq_rho_beta",
"gp_sq_rho_alpha",
"gp_sq_alpha_beta",
"gp_sq_alpha_alpha"))
summ
stanfit <- readRDS(paste0(output_dir,"/",out_base,"_stanfit.rds"))
dist_sim <- expand_grid(dist = seq(0,3,by = 0.02),
.draw = 1:4000)
gp_sim_function <- function(alpha2,rho2,dist){
cv <- alpha2 * exp(-1*rho2 * dist^2)
return(cv)
}
gp_params <- stanfit$draws(variables = c("gp_sq_rho_beta",
"gp_sq_rho_alpha",
"gp_sq_alpha_beta",
"gp_sq_alpha_alpha"),
format = "draws_df") %>%
sample_n(.,50) %>%
left_join(.,dist_sim,by = ".draw") %>%
mutate(pred_cov_beta = gp_sim_function(gp_sq_alpha_beta,gp_sq_rho_beta,dist),
pred_cov_alpha = gp_sim_function(gp_sq_alpha_alpha,gp_sq_rho_alpha,dist),
km = dist*1000)
col_1 <- RColorBrewer::brewer.pal(4,"Paired")
species_latin <- bbsBayes2::search_species(species)
species_latin <- paste(species_latin[1,"genus"],species_latin[1,"species"])
capt_tmp <- paste("Figure S1. Sample of posterior draws for the distance-based decay of covariance of
abundance and trend among BBS routes for Baird's Sparrow (",species_latin,") estimated using an
isotropic spatial Gaussian Process model. The modeled estimates show that the
covariance among routes in relative abundance of the species decreases very quickly
with increasing distance (green lines), while the covariance among routes in trends
decreases very slowly with distance (blue lines).")
spag <- ggplot(data = gp_params)+
geom_line(aes(x = km,y = pred_cov_beta,
group = .draw),
alpha = 0.9,
colour = col_1[2])+
geom_line(aes(x = km,y = pred_cov_alpha,
group = .draw),
alpha = 0.9,
colour = col_1[3])+
theme_bw()+
labs(caption = capt_tmp)+
theme(plot.caption = element_text(hjust = 0),
text = element_text(family = "serif",
size = 11))+
ylab("Covariance between pairs of routes")+
xlab("Distance between routes (km)")
pdf("Figures/Figure_S1.pdf",
width = 8.5,
height = 11)
print(spag)
dev.off()
# Figure 5 and 6 - 4 model comparison -------------------------------------------
output_dir <- "d:/iCAR_route_2021/output"
base_strata_map <- bbsBayes2::load_map("bbs_usgs")%>%
st_transform(.,crs_use)
firstYear <- 2006
lastYear <- 2021
ppy <- function(x){
p <- exp(x)-1
return(p*100)
}
species <- "Baird's Sparrow"
species_f <- gsub(gsub(species,pattern = " ",replacement = "_",fixed = T),pattern = "'",replacement = "",fixed = T)
out_base_temp <- paste0(species_f,"_GP_",firstYear,"_",lastYear)
sp_data_file <- paste0("Data/",species_f,"_",firstYear,"_",lastYear,"_stan_data.RData")
load(sp_data_file)
outboth <- NULL
for(spp1 in c("BYM","iCAR","nonspatial","GP")){
spp <- paste0("_",spp1,"_")
out_base <- paste0(species_f,spp,firstYear,"_",lastYear)
#stanfit <- readRDS(paste0(output_dir,"/",out_base,"_stanfit.rds"))
summ <- readRDS(paste0(output_dir,"/",out_base,"_summ_fit.rds"))
abundance <- summ %>%
filter(grepl(pattern = "alpha[",
variable,
fixed = TRUE),
!grepl(pattern = "gp",
variable,
fixed = TRUE)) %>%
mutate(routeF = row_number(),
across(mean:q95,exp)) %>%
rename_with(.,
~ paste0("abundance_",.x),
.cols = mean:ess_tail)
slope <- summ %>%
filter(grepl(pattern = "beta[",
variable,
fixed = TRUE),
!grepl(pattern = "gp",
variable,
fixed = TRUE)) %>%
mutate(routeF = row_number(),
across(mean:q95, ppy )) %>%
rename_with(.,
~ paste0("trend_",.x),
.cols = mean:ess_tail) %>%
select(-variable)
both <- inner_join(slope,abundance,
by = "routeF") %>%
mutate(model = spp1)
outboth <- bind_rows(outboth,both) %>%
mutate(species = species)
both_wide <- both %>%
select(routeF,trend_mean,trend_sd,abundance_mean,abundance_sd) %>%
rename_with(.cols = -routeF,
.fn = ~paste(spp1,.x,sep = "_"))
if(spp1 == "BYM"){
both_wide_out <- both_wide
}else{
both_wide_out <- inner_join(both_wide_out,both_wide,
by = "routeF")
}
} #end models loop
strata_bounds <- st_union(route_map) #union to provide a simple border of the realised strata
bb = st_bbox(strata_bounds)
xdif <- as.numeric(bb$xmax - bb$xmin)*1.1
ydif <- as.numeric(bb$ymax - bb$ymin)*1.1
xlms = as.numeric(c(ifelse(bb$xmin > 0,bb$xmin*0.9,bb$xmin*1.1),
bb$xmin + xdif))
ylms = as.numeric(c(ifelse(bb$ymin > 0,bb$ymin*0.9,bb$ymin*1.1),
bb$ymin + ydif))
plot_map <- route_map %>%
left_join(.,outboth,
by = "routeF") %>%
mutate(model = ifelse(model == "nonspatial","Non-spatial",model),
model = factor(model,
levels = c("iCAR","GP","BYM","Non-spatial"),
ordered = TRUE),
abundance_cv = abundance_sd/abundance_mean)
breaks <- c(-7, -4, -2, -1, -0.5, 0.5, 1, 2, 4, 7)
lgnd_head <- "Mean Trend\n"
trend_title <- "Mean Trend"
labls = c(paste0("< ",breaks[1]),paste0(breaks[-c(length(breaks))],":", breaks[-c(1)]),paste0("> ",breaks[length(breaks)]))
labls = paste0(labls, " %/year")
plot_map$Tplot <- cut(plot_map$trend_mean,breaks = c(-Inf, breaks, Inf),labels = labls)
map_palette <- c("#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf",
"#e0f3f8", "#abd9e9", "#74add1", "#4575b4", "#313695")
names(map_palette) <- labls
map <- ggplot()+
geom_sf(data = base_strata_map,
fill = NA,
colour = grey(0.75))+
geom_sf(data = state_prov,
fill = NA,
colour = grey(0.5))+
geom_sf(data = plot_map,
aes(colour = Tplot,
size = abundance_mean))+
scale_size_continuous(range = c(0.05,2),
name = "Mean Count")+
scale_colour_manual(values = map_palette, aesthetics = c("colour"),
guide = guide_legend(reverse=TRUE),
name = paste0(lgnd_head,firstYear,"-",lastYear))+
coord_sf(xlim = xlms,ylim = ylms)+
theme_bw()+
theme(text = element_text(family = "serif",
size = 11),
panel.grid = element_line(colour = grey(0.95)),
plot.caption = element_text(hjust = 0))+
#labs(title = paste(species, "trends"))+
facet_wrap(vars(model))
pdf(paste0("Figures/Figure_5.pdf"),
height = 7,
width = 7.5)
print(map)
dev.off()
# map_se <- ggplot()+
# geom_sf(data = base_strata_map,
# fill = NA,
# colour = grey(0.75))+
# geom_sf(data = plot_map,
# aes(colour = trend_sd,
# size = abundance_sd))+
# scale_size_continuous(range = c(0.05,2),
# name = "SE of Mean Count",
# trans = "reverse")+
# scale_colour_viridis_c(aesthetics = c("colour"),
# guide = guide_legend(reverse=TRUE),
# name = paste0("SE of Trend",firstYear,"-",lastYear))+
# coord_sf(xlim = xlms,ylim = ylms)+
# theme_bw()+
# #labs(title = paste(species, "Standard error"))+
# facet_wrap(vars(model))
#
# # version with teh SE of hte maps
# pdf(paste0("Figures/Figure_S2.pdf"),
# height = 10.5,
# width = 7.5)
# print(map / map_se)
# dev.off()
#
lgnd_head <- "Mean Abundance\n"
map_abund <- ggplot()+
geom_sf(data = base_strata_map,
fill = NA,
colour = grey(0.75))+
geom_sf(data = state_prov,
fill = NA,
colour = grey(0.5))+
geom_sf(data = plot_map,
aes(colour = abundance_mean,
size = abundance_sd/abundance_mean),
alpha = 0.7)+
scale_size_continuous(range = c(0.05,2),
name = "CV Abundance",
trans = "reverse")+
scale_colour_viridis_c(guide = guide_colourbar(),
name = paste0(lgnd_head,firstYear,"-",lastYear))+
coord_sf(xlim = xlms,ylim = ylms)+
theme_bw()+
theme(text = element_text(family = "serif",
size = 11),
panel.grid = element_line(colour = grey(0.95)),
plot.caption = element_text(hjust = 0))+
#labs(title = paste(species, "abundance"))+
facet_wrap(vars(model))
pdf(paste0("Figures/Figure_6.pdf"),
height = 7,
width = 7.5)
print(map_abund)
dev.off()
#
# ab_iCAR <- ggplot(data = both_wide_out)+
# geom_point(aes(x = nonspatial_abundance_mean,
# y = iCAR_abundance_mean))+
# geom_abline(slope = 1,intercept = 0)+
# scale_y_continuous(limits = c(0.05,20),
# trans = "log10")+
# scale_x_continuous(trans = "log10")+
# theme_bw()
#
# ab_BYM <- ggplot(data = both_wide_out)+
# geom_point(aes(x = nonspatial_abundance_mean,
# y = BYM_abundance_mean))+
# geom_abline(slope = 1,intercept = 0)+
# scale_y_continuous(limits = c(0.05,20),
# trans = "log10")+
# scale_x_continuous(trans = "log10")+
# theme_bw()
#
# ab_GP <- ggplot(data = both_wide_out)+
# geom_point(aes(x = nonspatial_abundance_mean,
# y = GP_abundance_mean))+
# geom_abline(slope = 1,intercept = 0)+
# scale_y_continuous(limits = c(0.05,20),
# trans = "log10")+
# scale_x_continuous(trans = "log10")+
# theme_bw()
#
# ab_all <- ab_iCAR + ab_BYM + ab_GP
#
# ab_all
## showing that sdobs is lower with the increased smoothing of the iCAR
# conv_sum <- readRDS("data/convergence_summary_May31.rds")
# sdobs_BASP <- conv_sum %>% filter(species == "Baird's Sparrow", variable == "sdobs")
# sdobs_BASP %>% select(variable,mean,sd,rhat,model)
# # A tibble: 4 × 5
# variable mean sd rhat model
# <chr> <dbl> <dbl> <dbl> <chr>
# 1 sdobs 1.09 0.130 1.01 BYM
# 2 sdobs 1.08 0.125 1.00 iCAR
# 3 sdobs 1.30 0.142 1.00 nonspatial
# 4 sdobs 1.22 0.135 1.01 GP
# Figure 7 ----------------------------------------------------------------
## CV summary for all 4 models
diffs_plot <- readRDS("data_cv_summary_4models_plotting_data.rds")
BASP <- diffs_plot %>%
filter(species == "Baird's Sparrow")
### specific values of CV summary included in text
### describing the CV results for Baird's sparrow
BASP
CATO <- diffs_plot %>%
filter(species == "Canyon Towhee")
### specific values of CV summary included in text
### describing the CV results for Canyon Towhee
CATO
WEBL <- diffs_plot %>%
filter(species == "Western Bluebird")
### specific values of CV summary included in text
### describing the CV results for Western Bluebird
WEBL
cv_comparisons <- diffs_plot %>%
select(model_comparison) %>%
distinct() %>%
mutate(model_comp_plot = str_replace(model_comparison,
"_"," - "),
model_comp_plot = str_replace(model_comp_plot,
"nonspatial","Non-spatial"),
model_comp_plot = factor(model_comp_plot,
levels = rev(c("iCAR - Non-spatial",
"GP - Non-spatial",
"BYM - Non-spatial",
"iCAR - GP",
"iCAR - BYM")),
ordered = TRUE))
diffs_plot_sel <- diffs_plot %>%
filter(grepl("nonspatial",model_comparison)) %>%
inner_join(.,cv_comparisons,
by = "model_comparison") %>%
mutate(species = factor(species,
levels = species_sort$english,
ordered = TRUE))
# Figure S4 ---------------------------------------------------------------
capt_tmp <- paste("Figure S4. Leave Future Out (LFO) cross-validation results for 71 small-range species
from the BBS database, comparing three explicitly spatial models (iCAR, BYM, and GP)
to an otherwise identical non-spatial model. The values represent z-score summaries
of the difference between each of the spatial models and the non-spatial model. For all
of the comparisons, positive values indicate that the spatial model had higher out-of-
sample predictive accuracy (higher log point-wise predictive density, lppd) than the
non-spatial model.")
z_diffs_all <- ggplot(data = diffs_plot_sel,
aes(y = z,x = species,
colour = model_comp_plot))+
geom_point()+
geom_hline(yintercept = 0)+
scale_colour_viridis_d(direction = -1,
end = 0.8,
guide = guide_legend(title = "Model comparison"))+
ylab("Z-score difference in pointwise lpd")+
xlab("")+
geom_hline(yintercept = c(-2,2),
alpha = 0.5)+
theme_bw()+
labs(caption = capt_tmp)+
theme(plot.caption = element_text(hjust = 0),
text = element_text(family = "serif",
size = 11))+
coord_flip()#ylim = c(-0.5,0.5))
### species-level detail from Figure 4 comparing 3 spatial models with nonspatial
pdf("Figures/Figure_S4.pdf",
height = 11,
width = 8.5)
z_diffs_all
dev.off()
## raincloud plot of z-score differences from nonspatial
diffs_plot_sel <- diffs_plot %>%
# filter(grepl("nonspatial",model_comparison)| model_comparison == "iCAR_GP") %>%
inner_join(.,cv_comparisons,
by = "model_comparison")
y_diffs_hist <- ggplot(data = diffs_plot_sel,
aes(y = z,
x = model_comp_plot,
#colour = model_comp_plot,
group = model_comp_plot))+
# ggdist::stat_halfeye(.width = 0,
# #adjust = 0.3,
# justification = 0,
# point_color = NA,
# alpha = 0.3)+
# geom_boxplot(width = 0.12,
# alpha = 0.5)+
ggdist::stat_dots(side = "right",
justification = 0,
binwidth = 0.2)+
theme_bw()+
ylab("Z-score difference in pointwise lppd")+
xlab("")+
scale_x_discrete(guide = guide_axis(position = "right"))+
# theme(axis.text.y = element_blank(),
# axis.ticks.y = element_blank())+
geom_hline(yintercept = c(-2,2), alpha = 0.4)+
geom_hline(yintercept = 0, alpha = 1)+
# guides(colour = guide_legend(title = "Model comparison",
# reverse = TRUE),
# fill = guide_legend(title = "Model comparison",
# reverse = TRUE))+
# scale_colour_viridis_d(begin = 0.2,end = 0.9,
# aesthetics = c("fill","colour"))+
coord_flip()
pdf("Figures/Figure_4.pdf",
height = 4,
width = 7.5)
y_diffs_hist
dev.off()
# Figure 8 ----------------------------------------------------------------
#read in the saved cross validation summary from file "CV_summary_2_models.R"
cv_sum <- readRDS("output/saved_cross_validation_summary_2_models.rds")
n_obs <- readRDS("output/saved_nobs_cross_validation_summary_2_models.rds")
cv_sum$Year <- factor(cv_sum$r_year)
diffs <- cv_sum %>%
select(species,count,Year,r_year,route,observer,model,log_lik_mean) %>%
pivot_wider(.,names_from = model,
values_from = c(log_lik_mean)) %>%
mutate(iCAR_nonspatial = iCAR-nonspatial) %>%
left_join(.,n_obs,by = c("route","species")) %>%
mutate(dist_cat = ifelse(min_distance > 0.1,"Isolated","Close"),
dist_cat_mean = ifelse(mean_distance > 0.1,"Isolated","Close"))
cv_sum <- cv_sum %>%
left_join(.,n_obs,by = c("route","species")) %>%
mutate(model = factor(model,levels = c("nonspatial","BYM","iCAR","GP"),
ordered = FALSE),
I = paste(species,E_pred_i,sep = "-"))
lpos = function(x){
p = length(which(x > 0))/length(x)
}
mndiffs_sp = diffs %>%
group_by(species) %>%
summarise(n_routes = length(unique(route)),
m_iCAR_nonspatial = mean(iCAR_nonspatial),
se_iCAR_nonspatial = sd(iCAR_nonspatial)/sqrt(n()),
z_iCAR_nonspatial = mean(iCAR_nonspatial)/se_iCAR_nonspatial,
lci_iCAR_nonspatial = m_iCAR_nonspatial - se_iCAR_nonspatial*1.96,
uci_iCAR_nonspatial = m_iCAR_nonspatial + se_iCAR_nonspatial*1.96,
nbet_iCAR_nonspatial = lpos(iCAR_nonspatial),
mean_dist = mean(mean_distance),
p_isolated = sum(ifelse(dist_cat == "Isolated",TRUE,FALSE))/n(),
p_isolated_mean = sum(ifelse(dist_cat_mean == "Isolated",TRUE,FALSE))/n()) %>%
mutate(species = fct_reorder(species,z_iCAR_nonspatial))
# diffs_plot_all <- mndiffs_sp %>%
# # filter(grepl("nonspatial",model_comparison)| model_comparison == "iCAR_GP") %>%
# inner_join(.,cv_comparisons,
# by = "model_comparison")
#
#
difs_sp <- ggplot(data = mndiffs_sp,
aes(y = z_iCAR_nonspatial))+
ggdist::stat_dots(side = "right",
justification = 0,
binwidth = 0.3)+
theme_bw()+
ylab("Z-score difference in pointwise lppd iCAR - Non-spatial")+
xlab("")+
scale_x_continuous(limits = c(0,1), expand = expansion(add = 0))+
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank())+
geom_hline(yintercept = c(-2,2), alpha = 0.4)+
geom_hline(yintercept = 0, alpha = 1)+
coord_flip()
pdf("Figures/Figure_5.pdf",
height = 3,
width = 7.5)
difs_sp
dev.off()
# Figure 9 ----------------------------------------------------------------
### iCAR vs nonspatial comparison maps for 4 species (2x4 maps)
species_sel <- c("American Robin","Hairy Woodpecker",
"Common Yellowthroat","Bald Eagle")
output_dir <- "d:/iCAR_route_2021/output"
base_strata_map <- bbsBayes2::load_map("bbs_usgs")%>%
st_transform(.,crs_use)
firstYear <- 2006
lastYear <- 2021
ppy <- function(x){
p <- exp(x)-1
return(p*100)
}
plot_map_out <- NULL
for(species in species_sel){
species_f <- gsub(gsub(species,pattern = " ",replacement = "_",fixed = T),pattern = "'",replacement = "",fixed = T)
out_base_temp <- paste0(species_f,"_nonspatial_",firstYear,"_",lastYear)
if(!file.exists(paste0(output_dir,"/",out_base_temp,"_summ_fit.rds"))){next}
sp_data_file <- paste0("Data/",species_f,"_",firstYear,"_",lastYear,"_stan_data.RData")
load(sp_data_file)
outboth <- NULL
for(spp1 in c("iCAR","nonspatial")){
spp <- paste0("_",spp1,"_")
out_base <- paste0(species_f,spp,firstYear,"_",lastYear)
#stanfit <- readRDS(paste0(output_dir,"/",out_base,"_stanfit.rds"))
summ <- readRDS(paste0(output_dir,"/",out_base,"_summ_fit.rds"))
abundance <- summ %>%
filter(grepl(pattern = "alpha[",
variable,
fixed = TRUE),
!grepl(pattern = "gp",
variable,
fixed = TRUE)) %>%
mutate(routeF = row_number(),
across(mean:q95,exp)) %>%
rename_with(.,
~ paste0("abundance_",.x),
.cols = mean:ess_tail)
slope <- summ %>%
filter(grepl(pattern = "beta[",
variable,
fixed = TRUE),
!grepl(pattern = "gp",
variable,
fixed = TRUE)) %>%
mutate(routeF = row_number(),
across(mean:q95, ppy )) %>%
rename_with(.,
~ paste0("trend_",.x),
.cols = mean:ess_tail) %>%
select(-variable)
both <- inner_join(slope,abundance,
by = "routeF") %>%
mutate(model = spp1)
outboth <- bind_rows(outboth,both)
} #end models loop
plot_map <- route_map %>%
left_join(.,outboth,
by = "routeF") %>%
mutate(model = ifelse(model == "nonspatial","Non-spatial",model),
model = factor(model,
levels = c("iCAR","GP","BYM","Non-spatial"),
ordered = TRUE),
sp = species) %>%
select(-c(routeF,variable))
plot_map_out <- bind_rows(plot_map_out,plot_map)
}#end species loop
breaks <- c(-7, -4, -2, -1, -0.5, 0.5, 1, 2, 4, 7)
lgnd_head <- "Mean Trend\n"
trend_title <- "Mean Trend"
labls = c(paste0("< ",breaks[1]),paste0(breaks[-c(length(breaks))],":", breaks[-c(1)]),paste0("> ",breaks[length(breaks)]))
labls = paste0(labls, " %/year")
plot_map_out$Tplot <- cut(plot_map_out$trend_mean,breaks = c(-Inf, breaks, Inf),labels = labls)
map_palette <- c("#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf",
"#e0f3f8", "#abd9e9", "#74add1", "#4575b4", "#313695")
names(map_palette) <- labls
map <- ggplot()+
geom_sf(data = base_strata_map,
fill = NA,
colour = grey(0.75))+
geom_sf(data = state_prov,
fill = NA,
colour = grey(0.5))+
geom_sf(data = plot_map_out,
aes(colour = Tplot),
size = 0.01,
shape = 20)+
# scale_size_continuous(range = c(0.05,2),
# name = "Mean Count")+
scale_colour_manual(values = map_palette, aesthetics = c("colour"),
guide = guide_legend(reverse=TRUE,
override.aes = list(size = 3)),
name = paste0(lgnd_head,firstYear,"-",lastYear))+
guides(size = "none")+
theme_bw()+
theme(panel.grid = element_line(colour = grey(0.95)))+
facet_grid(cols = vars(model),
rows = vars(sp))
pdf(paste0("Figures/Figure_9.pdf"),
height = 10,
width = 7.5)
print(map)
dev.off()
# Figure S5 ---------------------------------------------------------------
capt_tmp <- paste0("Figure S5. Examples of the spatial patterns in standard error of route-level trend
estimates for four broad-ranging species from an iCAR spatial model and an otherwise identical
non-spatial model. Each point represents the starting location of a Breeding Bird Survey route.")
map_se <- ggplot()+
geom_sf(data = base_strata_map,
fill = NA,
colour = grey(0.75))+
geom_sf(data = state_prov,
fill = NA,
colour = grey(0.5))+
geom_sf(data = plot_map_out,
aes(colour = trend_sd,
size = abundance_sd))+
scale_size_continuous(range = c(0.05,1),
name = "SE of Mean Count")+
scale_colour_viridis_c(aesthetics = c("colour"),
guide = guide_colourbar(reverse=TRUE),
name = paste0("SE of Trend \n",firstYear,"-",lastYear))+
guides(size = "none")+
theme_bw()+
labs(caption = capt_tmp)+
theme(plot.caption = element_text(hjust = 0),
text = element_text(family = "serif",
size = 11),
panel.grid = element_line(colour = grey(0.95)))+
facet_grid(cols = vars(model),
rows = vars(sp))
#
pdf(paste0("Figures/Figure_S5.pdf"),
height = 10,
width = 7.5)
print(map_se)
dev.off()
# Figure 10 ----------------------------------------------------------------
### add a GP vs iCAR map comparison for two extreme cv difference species
### Canyon Towhee - GP favoured
### Western Bluebird - iCAR favoured
species_sel <- c("Canyon Towhee","Western Bluebird")
output_dir <- "d:/iCAR_route_2021/output"
base_strata_map <- bbsBayes2::load_map("bbs_usgs")%>%
st_transform(.,crs_use)
species_latin1 <- bbsBayes2::search_species(species_sel[1])
species_latin1 <- paste(species_latin1[1,"genus"],species_latin1[1,"species"])
species_latin2 <- bbsBayes2::search_species(species_sel[2])
species_latin2 <- paste(species_latin2[1,"genus"],species_latin2[1,"species"])
firstYear <- 2006
lastYear <- 2021
ppy <- function(x){
p <- exp(x)-1
return(p*100)
}
plot_map_out <- NULL
for(species in species_sel){
species_f <- gsub(gsub(species,pattern = " ",replacement = "_",fixed = T),pattern = "'",replacement = "",fixed = T)
out_base_temp <- paste0(species_f,"_nonspatial_",firstYear,"_",lastYear)
if(!file.exists(paste0(output_dir,"/",out_base_temp,"_summ_fit.rds"))){next}
sp_data_file <- paste0("Data/",species_f,"_",firstYear,"_",lastYear,"_stan_data.RData")
load(sp_data_file)
outboth <- NULL
for(spp1 in c("iCAR","GP")){
spp <- paste0("_",spp1,"_")
out_base <- paste0(species_f,spp,firstYear,"_",lastYear)
#stanfit <- readRDS(paste0(output_dir,"/",out_base,"_stanfit.rds"))
summ <- readRDS(paste0(output_dir,"/",out_base,"_summ_fit.rds"))
abundance <- summ %>%
filter(grepl(pattern = "alpha[",
variable,
fixed = TRUE),
!grepl(pattern = "gp",
variable,
fixed = TRUE)) %>%
mutate(routeF = row_number(),
across(mean:q95,exp)) %>%
rename_with(.,
~ paste0("abundance_",.x),
.cols = mean:ess_tail)
slope <- summ %>%
filter(grepl(pattern = "beta[",
variable,
fixed = TRUE),
!grepl(pattern = "gp",
variable,
fixed = TRUE)) %>%
mutate(routeF = row_number(),
across(mean:q95, ppy )) %>%
rename_with(.,
~ paste0("trend_",.x),
.cols = mean:ess_tail) %>%
select(-variable)
both <- inner_join(slope,abundance,
by = "routeF") %>%
mutate(model = spp1)
outboth <- bind_rows(outboth,both)
} #end models loop
plot_map <- route_map %>%
left_join(.,outboth,