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fig_4_biodiversity_by_hab_and_mpa.Rmd
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fig_4_biodiversity_by_hab_and_mpa.Rmd
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---
title: 'habitats and biodiversity risk'
author: "*Compiled on `r date()` by `r Sys.info()['user']`*"
output:
html_document:
code_folding: hide
toc: true
toc_depth: 3
toc_float: yes
number_sections: true
theme: cerulean
highlight: haddock
includes:
in_header: '~/github/src/templates/ohara_hdr.html'
pdf_document:
toc: true
---
``` {r setup, echo = TRUE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.path = 'figs/',
echo = TRUE, message = FALSE, warning = FALSE)
library(raster)
source('https://raw.githubusercontent.com/oharac/src/master/R/common.R')
### includes library(tidyverse); library(stringr);
### dir_M points to ohi directory on Mazu; dir_O points to home dir on Mazu
dir_git <- here()
source(file.path(dir_git, '_setup/common_fxns.R'))
```
# Summary
Compare biodiversity risk within various marine habitats
# Data sources
* IUCN species API: IUCN. (2019). The IUCN Red List of Threatened Species. Version 2019-2.
* IUCN species shapefiles: IUCN. (2019). The IUCN Red List of Threatened Species. Version 2019-2. Retrieved August 2019, from http://www.iucnredlist.org
* BirdLife International shapefiles: BirdLife International and Handbook of the Birds of the World. (2018). Bird species distribution maps of the world. Version 7.0. Available at http://datazone.birdlife.org/species/requestdis
* World Database on Protected Areas: IUCN, & UNEP-WCMC. (2018, June). The World Database on Protected Areas (WDPA). Retrieved June 9, 2018, from Cambridge, UK: UNEP-WCMC website: www.protectedplanet.net
* Marine Ecoregions of the World: Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. A., Finlayson, M. A. X., … others. (2007). Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. BioScience, 57(7), 573–583.
# Methods
## Compare biodiversity risk to habitats
Using CHI habs, compare biodiversity risk by habitat type.
``` {r set up hab raster to 1 km g-p}
habs_files <- list.files(file.path(dir_M, "git-annex/impact_acceleration/habitats"),
pattern = '.tif$', full.names = TRUE)
dir_habs_reproj <- file.path(dir_o_anx, 'habs')
hab_stack <- stack(habs_files)
# plot(hab_stack)
ocean_500m_file <- file.path(dir_o_anx, 'spatial', 'ocean_500m.tif')
ocean_500m_rast <- raster(ocean_500m_file)
tmp <- parallel::mclapply(habs_files, mc.cores = 12, FUN = function(x) {
### x <- habs_files[1]
reproj_file <- file.path(dir_habs_reproj, basename(x)) %>%
str_replace('.tif$', '_500m.tif')
if(!file.exists(reproj_file)) {
cat_msg('Processing ', reproj_file)
y <- raster(x)
z <- projectRaster(y, ocean_500m_rast, method = 'ngb',
# progress = 'text',
filename = reproj_file)
} else {
cat_msg(reproj_file, ' exists; skipping it')
}
})
```
10 km x 10 km habitat raster cells will contain values approximating the number of km^2^ (out of 100) occupied by that habitat.
``` {r aggregate to 10 km g-p}
habs_500m_files <- list.files(dir_habs_reproj,
pattern = '_500m.tif$',
full.names = TRUE)
habs_10km_files <- str_replace(habs_500m_files, '_500m.tif$', '_10km.tif') %>%
setNames(habs_500m_files)
rast_base <- raster(file.path(dir_spatial, 'cell_id_rast.tif'))
for(hab_500m in habs_500m_files) {
### hab_500m <- habs_500m_files[1]
out_file <- habs_10km_files[hab_500m]
if(!file.exists(out_file)) {
cat_msg('Processing ', basename(hab_500m), ' to: ', out_file)
rast <- raster(hab_500m)
rast_10km <- raster::aggregate(rast, fact = 20, fun = sum, na.rm = TRUE)
rast_10km <- rast_10km / 4
writeRaster(rast_10km, out_file)
}
}
# z <- stack(habs_10km_files)
# plot(z)
```
``` {r set up habs and values dataframe}
cell_id_rast <- raster(file.path(dir_spatial, 'cell_id_rast.tif'))
mean_rast <- raster(file.path(dir_output,
'mean_risk_raster_comp.tif'))
mean_rr_rast <- raster(file.path(dir_output,
'mean_rr_risk_raster_comp.tif'))
pct_th_rast <- raster(file.path(dir_output,
'pct_threat_raster_comp.tif'))
pct_th_rr_rast <- raster(file.path(dir_output,
'sr_rr_pct_threat_raster_comp.tif'))
ocean_area_rast <- raster(file.path(dir_spatial, 'ocean_area_rast.tif'))
habs_10km_files <- list.files(file.path(dir_o_anx, 'habs'),
pattern = '_10km.tif$',
full.names = TRUE)
habs_rasts <- stack(habs_10km_files)
### initialize df with cell ID rast, then add columns for habs
habs_df <- data.frame(cell_id = values(cell_id_rast))
for(i in 1:nlayers(habs_rasts)) {
habs_df[ , i + 1] <- values(habs_rasts[[i]])
} ### note: data.frame with cols cell_id and V1-V22
habs_df <- habs_df %>%
setNames(c('cell_id', basename(habs_10km_files)))
mpa_df <- read_csv(file.path(dir_spatial, 'wdpa_mpa_area.csv'),
col_types = 'dddd') %>%
filter(wdpa_category <= 2) %>%
select(-wdpa_category) %>%
group_by(cell_id) %>%
summarize(mpa = sum(prot_area_km2, na.rm = TRUE))
risk_df <- habs_df %>%
mutate(risk = values(mean_rast),
rr_risk = values(mean_rr_rast),
pct_th = values(pct_th_rast),
rr_pct_th = values(pct_th_rr_rast),
ocean_area = values(ocean_area_rast)) %>%
gather(hab, cells, -cell_id, -(risk:ocean_area)) %>%
filter(!is.na(cells) & cells != 0) %>%
filter(!is.na(risk)) %>%
filter(!is.na(ocean_area))
risk_mpa_df <- risk_df %>%
left_join(mpa_df, by = 'cell_id') %>%
mutate(mpa = ifelse(is.na(mpa), 0, mpa),
mpa = ifelse(mpa > ocean_area, ocean_area, mpa),
non_mpa = ocean_area - mpa) %>%
gather(mpa_status, area, mpa, non_mpa) %>%
mutate(mpa_status = (mpa_status == 'mpa'))
# x <- risk_df %>% select(hab) %>% distinct()
# write_csv(x, file.path(dir_setup, 'raw/hab_names_raw.csv'))
habs_lookup <- read_csv(file.path(dir_setup, 'raw',
'hab_names_lookup.csv'))
risk_mpa_df <- risk_mpa_df %>%
left_join(habs_lookup, by = 'hab') %>%
filter(!is.na(order)) %>%
arrange(order) %>%
mutate(hab_desc = fct_inorder(hab_desc))
risk_mpa_df <- risk_mpa_df %>%
arrange(hab_desc) %>%
select(cell_id, risk, rr_risk, mpa_status, area, hab_desc) %>%
group_by(hab_desc, mpa_status) %>%
mutate(prop_area = area / sum(area)) %>%
ungroup()
prot_pct <- risk_mpa_df %>%
group_by(hab_desc) %>%
summarize(pct_prot = sum(area * mpa_status) / sum(area) * 100,
tot_area = sum(area))
### this is independent of range-rarity weighting
no_take_means <- risk_mpa_df %>%
group_by(mpa_status, hab_desc) %>%
summarize(mu = sum(risk * prop_area),
mu_rr = sum(rr_risk * prop_area)) %>%
ungroup()
write_csv(no_take_means, file.path(dir_setup, 'int/mpa_mean_risks_by_hab.csv'))
rm('habs_df', 'risk_df')
```
## Facet plot
``` {r plot_function}
plot_dist <- function(df, means_df, x_lims = c(0, 0.6)) {
x <- ggplot(df, aes(x = risk)) +
ggtheme_plot(base_size = 7) +
theme(strip.text.y = element_text(angle = 0, hjust = 0),
axis.text.y = element_blank(),
axis.title = element_blank(),
panel.grid.major.y = element_blank(),
plot.margin = unit(c(.1, .25, .1, .35), 'cm')) +
geom_density(aes(weight = area, x = risk, ..scaled..,
fill = mpa_status, color = mpa_status),
alpha = .5, size = .25) +
geom_vline(data = means_df,
aes(xintercept = mu, linetype = mpa_status),
color = 'grey20', alpha = .8) +
scale_x_continuous(expand = c(0, 0),
limits = x_lims,
labels = c('LC', 'NT', 'VU', 'EN', 'CR', 'EX'),
breaks = c( 0.0, 0.2, 0.4, 0.6, 0.8, 1.0)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_manual(values = c('red4', 'cadetblue2')) +
scale_color_manual(values = c('red3', 'cadetblue4')) +
facet_grid( hab_desc ~ ., scales = 'free_y') +
labs(fill = 'Marine reserve', color = 'Marine reserve', linetype = 'Marine reserve')
return(x)
}
```
``` {r generate figure}
prot_pct_lbls <- prot_pct %>%
mutate(area_lbl = sprintf('%.1f x 10⁶ km²', tot_area/1e6),
prot_lbl = sprintf('%.1f%% no take', pct_prot))
hab_plot_unweighted <- plot_dist(risk_mpa_df, no_take_means) +
theme(strip.text.y = element_blank())
hab_plot_rrweighted <- plot_dist(risk_mpa_df %>% select(-risk, risk = rr_risk),
no_take_means %>%
select(-mu, mu = mu_rr)) +
geom_text(data = prot_pct_lbls, aes(x = .59, y = .95, label = area_lbl),
hjust = 1, size = 2.1, vjust = 1, color = 'grey20') +
geom_text(data = prot_pct_lbls, aes(x = .59, y = .45, label = prot_lbl),
hjust = 1, size = 2.1, vjust = 1, color = 'grey20')
plot_combined <- cowplot::plot_grid(hab_plot_unweighted +
theme(strip.text.y = element_blank(),
legend.justification = c(1, 1),
legend.position = c(1, 1)),
hab_plot_rrweighted +
theme(legend.position = 'none'),
labels = c('A', 'B'), hjust = 0,
label_size = 9,
rel_widths = c(2, 3))
# ggsave(file.path(dir_git, 'ms_figures/fig4_bd_risk_vs_habitat.tiff'),
# height = 2.8, width = 4.75, dpi = 300, units = 'in')
ggsave(file.path(dir_git, 'ms_figures/fig4_bd_risk_vs_habitat.png'),
height = 2.8, width = 4.75, dpi = 300, units = 'in')
```
## Wherefore the big difference between oceanic waters and high seas in terms of protected coverage?
Examine where MPAs in open ocean and MPAs in EEZs overlap.
``` {r, eval = FALSE}
cell_id_rast <- raster(file.path(dir_spatial, 'cell_id_rast.tif'))
mean_rast <- raster(file.path(dir_output,
'mean_risk_raster_comp.tif'))
surf_10km_file <- list.files(file.path(dir_o_anx, 'habs'),
pattern = 'surface_waters_10km.tif$',
full.names = TRUE)
surf_rast <- raster(surf_10km_file)
eez_rast <- raster(file.path(dir_spatial, 'eez_rast.tif'))
mpa_df <- read_csv(file.path(dir_spatial, 'wdpa_mpa_area.csv'),
col_types = 'dddd') %>%
filter(wdpa_category <= 2) %>%
select(-wdpa_category) %>%
group_by(cell_id) %>%
summarize(mpa = sum(prot_area_km2, na.rm = TRUE))
df <- data.frame(cell_id = values(cell_id_rast),
risk = values(mean_rast),
ocean = values(surf_rast),
eez = values(eez_rast)) %>%
filter(!is.na(risk)) %>%
left_join(mpa_df, by = 'cell_id')
ocean_prot_sum <- df %>%
filter(!is.na(ocean)) %>%
group_by(eez) %>%
summarize(prot = sum(mpa, na.rm = TRUE),
total = n()) %>%
filter(prot > 0)
ocean_prot <- df %>%
filter(!is.na(ocean)) %>%
filter(!is.na(mpa)) %>%
mutate(abnj = ifelse((eez == 213 | eez > 255), 1, -1))
ocean_prot_rast <- subs(cell_id_rast, ocean_prot, by = 'cell_id', which = 'abnj')
### print map
library(sf)
land_poly <- read_sf(file.path(dir_spatial, 'ne_10m_land/ne_10m_land.shp')) %>%
st_transform(gp_proj4)
map_df <- rasterToPoints(ocean_prot_rast) %>%
as.data.frame() %>%
setNames(c('long', 'lat', 'abnj'))
map1 <- ggplot(map_df) +
geom_raster(aes(long, lat, fill = abnj), show.legend = TRUE) +
geom_sf(data = land_poly, aes(geometry = geometry),
fill = 'grey96', color = 'grey40', size = .10) +
ggtheme_map(base_size = 7) +
theme(plot.margin = unit(c(.2, 0, .1, .5), units = 'cm')) +
coord_sf(datum = NA) + ### ditch graticules
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
print(map1)
```