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fig_S1_S4_maps.Rmd
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fig_S1_S4_maps.Rmd
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---
title: 'Biodiversity n and var two-panel figures'
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)
library(data.table)
library(sf)
source('https://raw.githubusercontent.com/oharac/src/master/R/common.R') ###
### includes library(tidyverse); library(stringr); dir_M points to ohi directory
dir_git <- here()
source(file.path(dir_git, '_setup/common_fxns.R'))
```
# Summary
Create a set of maps of the distribution of biodiversity intactness - all species assessed and mapped by IUCN. These maps are generated at 10 km^2^ resolution in a Gall-Peters projection. These maps will be generated using all comprehensively-assessed species:
* S1: Number of species for mean/var calculations
* S3: Variance of risk
# 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
* 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
## Spatial distribution of N spp: comprehensively assessed spp
From the 1a and 1b biodiversity risk map scripts, gather the rasters for the various maps:
* N_spp risk (unweighted and range-rarity-weighted)
* Var risk (unweighted and range-rarity-weighted)
``` {r get_rasts}
nspp_un_rast <- raster(file.path(dir_output, 'n_spp_risk_raster_comp.tif'))
nspp_rr_rast <- raster(file.path(dir_output, 'sr_rr_risk_raster_comp.tif'))
var_un_rast <- raster(file.path(dir_output, 'var_risk_raster_comp.tif'))
var_rr_rast <- raster(file.path(dir_output, 'var_rr_risk_raster_comp.tif'))
```
### And now, the maps
The four-panel map chunks create two maps, with two density plots as scales; first map is for uniform-weighting, second is for range-rarity weighting.
### N spp maps
``` {r fig S1 a b n_spp maps}
### Log-transform nspp rasters for more interesting visual scaling
nspp_un_log <- log10(nspp_un_rast) # range 0, 3.146748
nspp_rr_log <- log10(nspp_rr_rast) # -8.23878, -0.9510185
### aggregate rasters for faster testing:
# nspp_un_rast <- nspp_un_rast %>% aggregate(10)
# nspp_rr_rast <- nspp_rr_rast %>% aggregate(10)
nspp_map_df <- data.frame(val_1 = values(nspp_un_log),
val_2 = values(nspp_rr_log)) %>%
cbind(coordinates(nspp_un_log)) %>%
filter(!is.na(val_1))
nspp_cols <- c(viridis::viridis(4, direction = -1))
### set up a dataframe of values to craft a color bar using geom_segment
### separate for each map due to incompatibility of scales
colorbar_df1 <- data.frame(x = seq(0, max(nspp_map_df$val_1),
length.out = 1000),
y = -1)
lbls1 <- c(1, 10, 100, 1000)
brks1 <- c(0, 1, 2, 3)
lims1 <- c(0, max(nspp_map_df$val_1))
colorbar_df2 <- data.frame(x = seq(min(nspp_map_df$val_2),
max(nspp_map_df$val_2),
length.out = 1000),
y = -1)
lbls2 <- c('1e-8', '1e-6', '1e-4', '1e-2', '1')
brks2 <- c(-8, -6, -4, -2, 0)
lims2 <- c(min(nspp_map_df$val_2), max(nspp_map_df$val_2))
land_poly <- sf::read_sf(file.path(dir_spatial, 'ne_10m_land',
'ne_10m_land_no_casp.shp')) %>%
st_transform(gp_proj4)
map1 <- ggplot(nspp_map_df) +
geom_raster(aes(x, y, fill = val_1), show.legend = FALSE) +
geom_sf(data = land_poly, aes(geometry = geometry),
fill = 'grey96', color = 'grey40', size = .10) +
ggtheme_map() +
theme(plot.margin = unit(c(.2, 0, .1, .5), units = 'cm')) +
coord_sf(datum = NA) + ### ditch graticules
scale_fill_gradientn(colors = nspp_cols, limits = lims1,
labels = lbls1, breaks = brks1) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
map2 <- ggplot(nspp_map_df) +
geom_raster(aes(x, y, fill = val_2), show.legend = FALSE) +
geom_sf(data = land_poly, aes(geometry = geometry),
fill = 'grey96', color = 'grey40', size = .10) +
ggtheme_map() +
theme(plot.margin = unit(c(.1, 0, .2, .5), units = 'cm')) +
coord_sf(datum = NA) + ### ditch graticules
scale_fill_gradientn(colors = nspp_cols, limits = lims2,
labels = lbls2, breaks = brks2) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
###################
dens1 <- ggplot(nspp_map_df) +
ggtheme_plot() +
geom_vline(xintercept = mean(nspp_map_df$val_1, na.rm = TRUE)) +
geom_segment(data = colorbar_df1,
aes(x = x, xend = x, color = x),
y = 0, yend = -10, size = 1,
show.legend = FALSE) +
geom_density(aes(x = val_1, ..scaled..), adjust = 5,
alpha = .3, size = .25, fill = 'grey30') +
scale_color_gradientn(colors = nspp_cols, limits = lims1,
labels = lbls1, breaks = brks1) +
theme(axis.text.x = element_blank(),
axis.title = element_blank(),
panel.grid.major.x = element_blank(),
plot.margin = unit(c(1, 0, 1, 0), units = 'cm')) +
scale_x_continuous(labels = lbls1, breaks = brks1,
expand = c(0, 0)) +
scale_y_continuous(limits = c(-.4, 1)) +
coord_flip()
dens2 <- ggplot(nspp_map_df) +
ggtheme_plot() +
geom_vline(xintercept = mean(nspp_map_df$val_2, na.rm = TRUE)) +
geom_segment(data = colorbar_df2,
aes(x = x, xend = x, color = x),
y = 0, yend = -10, size = 1,
show.legend = FALSE) +
geom_density(aes(x = val_2, ..scaled..),
adjust = 5,
alpha = .3, size = .25, fill = 'grey30') +
scale_color_gradientn(colors = nspp_cols, limits = lims2,
labels = lbls2, breaks = brks2) +
theme(axis.text.x = element_blank(),
axis.title = element_blank(),
panel.grid.major.x = element_blank(),
plot.margin = unit(c(1, 0, 1, 0), units = 'cm')) +
scale_x_continuous(labels = lbls2, breaks = brks2,
expand = c(0, 0)) +
scale_y_continuous(limits = c(-.4, 1)) +
coord_flip()
panel_left <- cowplot::plot_grid(map1, dens1,
axis = 'b',
rel_widths = c(5, 1))
panel_right <- cowplot::plot_grid(map2, dens2,
axis = 'b',
rel_widths = c(5, 1))
four_panel <- cowplot::plot_grid(panel_left, panel_right,
labels = c('A', 'B'),
nrow = 2, ncol = 1, align = 'v')
figS1_path <- file.path(dir_git, 'ms_figures/fig_S1_nspp_maps.png')
ggsave(plot = four_panel,
filename = figS1_path,
width = 5, height = 5, units = 'in', dpi = 300)
```
### Variance maps
``` {r fig S4 a b var maps}
var_cols <- c(viridis::viridis(6, direction = -1))
### set up a dataframe of values to craft a color bar using geom_segment
colorbar_df <- data.frame(x = seq(0, .5, length.out = 1000), y = -1)
lbls = c(0, .1, .2, .3, .4, .5)
brks = c(0, .1, .2, .3, .4, .5)
lims = c(0, .5)
land_poly <- sf::read_sf(file.path(dir_spatial, 'ne_10m_land',
'ne_10m_land_no_casp.shp')) %>%
st_transform(gp_proj4)
var_map_df <- data.frame(val_1 = values(var_un_rast),
val_2 = values(var_rr_rast)) %>%
cbind(coordinates(var_un_rast)) %>%
filter(!is.na(val_1))
### unweighted max = .18
### rr weighted max = .499
map1 <- ggplot(var_map_df) +
geom_raster(aes(x, y, fill = val_1), show.legend = FALSE) +
geom_sf(data = land_poly, aes(geometry = geometry),
fill = 'grey96', color = 'grey40', size = .10) +
ggtheme_map() +
theme(plot.margin = unit(c(.2, 0, .1, .5), units = 'cm')) +
coord_sf(datum = NA) + ### ditch graticules
scale_fill_gradientn(colors = var_cols, labels = lbls,
breaks = brks, limits = lims) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
map2 <- ggplot(var_map_df) +
geom_raster(aes(x, y, fill = val_2), show.legend = FALSE) +
geom_sf(data = land_poly, aes(geometry = geometry),
fill = 'grey96', color = 'grey40', size = .10) +
ggtheme_map() +
theme(plot.margin = unit(c(.1, 0, .2, .5), units = 'cm')) +
coord_sf(datum = NA) + ### ditch graticules
scale_fill_gradientn(colors = var_cols, labels = lbls,
breaks = brks, limits = lims) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
###################
dens1 <- ggplot(var_map_df) +
ggtheme_plot() +
geom_vline(xintercept = mean(var_map_df$val_1, na.rm = TRUE)) +
geom_segment(data = colorbar_df,
aes(x = x, xend = x, color = x),
y = 0, yend = -10, size = 1,
show.legend = FALSE) +
geom_density(aes(x = val_1, ..scaled..),
adjust = 5,
alpha = .3, size = .25, fill = 'grey30') +
scale_color_gradientn(colors = var_cols, labels = lbls,
breaks = brks, limits = lims) +
theme(axis.text.x = element_blank(),
axis.title = element_blank(),
panel.grid.major.x = element_blank(),
plot.margin = unit(c(1, 0, 1, 0), units = 'cm')) +
scale_x_continuous(labels = lbls, breaks = brks,
expand = c(0, 0)) +
scale_y_continuous(limits = c(-.4, 1)) +
coord_flip()
dens2 <- ggplot(var_map_df) +
ggtheme_plot() +
geom_vline(xintercept = mean(var_map_df$val_2, na.rm = TRUE)) +
geom_segment(data = colorbar_df,
aes(x = x, xend = x, color = x),
y = 0, yend = -10, size = 1,
show.legend = FALSE) +
geom_density(aes(x = val_2, ..scaled..),
adjust = 5,
alpha = .3, size = .25, fill = 'grey30') +
scale_color_gradientn(colors = var_cols, labels = lbls,
breaks = brks, limits = lims) +
theme(axis.text.x = element_blank(),
axis.title = element_blank(),
panel.grid.major.x = element_blank(),
plot.margin = unit(c(1, 0, 1, 0), units = 'cm')) +
scale_x_continuous(labels = lbls, breaks = brks,
expand = c(0, 0)) +
scale_y_continuous(limits = c(-.4, 1)) +
coord_flip()
panel_left <- cowplot::plot_grid(map1, dens1,
axis = 'b',
rel_widths = c(5, 1))
panel_right <- cowplot::plot_grid(map2, dens2,
axis = 'b',
rel_widths = c(5, 1))
four_panel <- cowplot::plot_grid(panel_left, panel_right,
labels = c('A', 'B'),
nrow = 2, ncol = 1, align = 'v')
figS3_path <- file.path(dir_git, 'ms_figures/fig_S4_var_maps.png')
ggsave(plot = four_panel,
filename = figS3_path,
width = 5, height = 5, units = 'in', dpi = 300)
```
