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5_Analysis_Realm_Mamm.R
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#===================================================#
# Analyze species richness, phylogenetic diversity, #
# functional richness, and mean functional beta #
# diversity turnover for mammals #
# #
# Using realm as categorical variable #
#===================================================#
library(tidyverse)
library(sf)
library(colorspace)
library(rnaturalearth)
library(rnaturalearthdata)
library(spdep)
sf_use_s2(FALSE) # Avoid some issues of invalid geometries
# Load outlines of landmasses for mapping
world <- ne_coastline(scale = "medium", returnclass = "sf") %>%
st_crop(c(xmin=-180, xmax=180, ymin=-60, ymax=85))
# ----- LOAD CELLS WITH COVARIATES ----------
cells <- st_read("Data/global_cells.shp") %>%
# Landmass area
left_join(read.csv("Data/Raw/landmass_area_cells.csv"), "cell_id") %>%
# Elevation (mean and range)
left_join(read.csv("Data/elev_cells.csv"), "cell_id") %>%
dplyr::select(-X) %>%
# Present climate
left_join(read.csv("Data/clim_Present_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# ----- SPECIES RICHNESS ----------
# ---------- Run models, save R2 ----------
# Load data
data <- read.csv("Data/mamm_SR_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model, all covariates
SR_all <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
Realm,
data=data,
na.action = na.fail)
# Run model, environment
SR_env <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2),
data=data,
na.action = na.fail)
# Run model, realm
SR_realm <- lm(SR ~ Realm,
data=data,
na.action = na.fail)
# Save R2 of each model
R2_all <- summary(SR_all)$adj.r.squared
R2_env <- summary(SR_env)$adj.r.squared
R2_realm <- summary(SR_realm)$adj.r.squared
# Realm only
R2_realm_only <- R2_all - R2_env
# Environment only
R2_env_only <- R2_all - R2_realm
# Shared
R2_shared <- R2_all - R2_realm_only - R2_env_only
# ---------- Map differences in residuals (Supplementary Fig. 2e) ----------
# Difference with/without realm
res_df <- data.frame(
res = abs(resid(SR_env)) - abs(resid(SR_all)),
cell_id = as.numeric(data$cell_id))
res_map <- cells %>% right_join(res_df, by="cell_id")
# Save figure
svg("Results/diffres_mamm_SR_realm.svg",
width = 2.5, height = 1.5)
ggplot() +
geom_sf(data = world, colour="grey85", lwd=.1, linewidth=.1) +
geom_sf(data = res_map, aes(color = res, fill = res),
lwd=.1, linewidth=.1) +
scale_colour_continuous_diverging(palette="Blue-Red") +
scale_fill_continuous_diverging(palette="Blue-Red") +
theme_void() +
theme(legend.title = element_blank(),
legend.text = element_text(size=7),
legend.position = "bottom") +
theme(legend.key.height = unit(.17, "cm"),
legend.key.width = unit(1.1,"cm"))
dev.off()
# ----- PHYLOGENETIC DIVERSITY ----------
# ---------- Run models, save R2 ----------
# Load data
data <- read.csv("Data/mamm_PD_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model, all covariates
PD_all <- lm(PD ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
Realm,
data=data,
na.action = na.fail)
# Run model, environment
PD_env <- lm(PD ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2),
data=data,
na.action = na.fail)
# Run model, realm
PD_realm <- lm(PD ~ Realm,
data=data,
na.action = na.fail)
# Save R2 of each model
R2_all <- summary(PD_all)$adj.r.squared
R2_env <- summary(PD_env)$adj.r.squared
R2_realm <- summary(PD_realm)$adj.r.squared
# Realm only
R2_realm_only <- R2_all - R2_env
# Environment only
R2_env_only <- R2_all - R2_realm
# Shared
R2_shared <- R2_all - R2_realm_only - R2_env_only
# ---------- Map differences in residuals (Supplementary Fig. 2f) ----------
# Difference with/without realm
res_df <- data.frame(
res = abs(resid(PD_env)) - abs(resid(PD_all)),
cell_id = as.numeric(data$cell_id))
res_map <- cells %>% right_join(res_df, by="cell_id")
# Save figure
svg("Results/diffres_mamm_PD_realm.svg",
width = 2.5, height = 1.5)
ggplot() +
geom_sf(data = world, colour="grey85", lwd=.1, linewidth=.1) +
geom_sf(data = res_map, aes(color = res, fill = res),
lwd=.1, linewidth=.1) +
scale_colour_continuous_diverging(palette="Blue-Red") +
scale_fill_continuous_diverging(palette="Blue-Red") +
theme_void() +
theme(legend.title = element_blank(),
legend.text = element_text(size=7),
legend.position = "bottom") +
theme(legend.key.height = unit(.17, "cm"),
legend.key.width = unit(1.1,"cm"))
dev.off()
# ----- FUNCTIONAL RICHNESS ----------
# ---------- Run models, save R2 ----------
# Load data
data <- read.csv("Data/mamm_FR_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model, all covariates
FR_all <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
Realm,
data=data,
na.action = na.fail)
# Run model, environment
FR_env <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2),
data=data,
na.action = na.fail)
# Run model, realm
FR_realm <- lm(FR ~ Realm,
data=data,
na.action = na.fail)
# Save R2 of each model
R2_all <- summary(FR_all)$adj.r.squared
R2_env <- summary(FR_env)$adj.r.squared
R2_realm <- summary(FR_realm)$adj.r.squared
# Realm only
R2_realm_only <- R2_all - R2_env
# Environment only
R2_env_only <- R2_all - R2_realm
# Shared
R2_shared <- R2_all - R2_realm_only - R2_env_only
# ---------- Map differences in residuals (Supplementary Fig. 2g) ----------
# Difference with/without realm
res_df <- data.frame(
res = abs(resid(FR_env)) - abs(resid(FR_all)),
cell_id = as.numeric(data$cell_id))
res_map <- cells %>% right_join(res_df, by="cell_id")
# Save figure
svg("Results/diffres_mamm_FR_realm.svg",
width = 2.5, height = 1.5)
ggplot() +
geom_sf(data = world, colour="grey85", lwd=.1, linewidth=.1) +
geom_sf(data = res_map, aes(color = res, fill = res),
lwd=.1, linewidth=.1) +
scale_colour_continuous_diverging(palette="Blue-Red") +
scale_fill_continuous_diverging(palette="Blue-Red") +
theme_void() +
theme(legend.title = element_blank(),
legend.text = element_text(size=7),
legend.position = "bottom") +
theme(legend.key.height = unit(.17, "cm"),
legend.key.width = unit(1.1,"cm"))
dev.off()
# ----- MEAN FUNC BETA TURNOVER ----------
# ---------- Run models, save R2 ----------
# Load data
data <- read.csv("Data/mamm_mean_fb.csv") %>%
left_join(cells, by="cell_id")
# Run model, all covariates
FB_all <- lm(mean_fb ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
Realm,
data=data,
na.action = na.fail)
# Run model, environment
FB_env <- lm(mean_fb ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2),
data=data,
na.action = na.fail)
# Run model, realm
FB_realm <- lm(mean_fb ~ Realm,
data=data,
na.action = na.fail)
# Save R2 of each model
R2_all <- summary(FB_all)$adj.r.squared
R2_env <- summary(FB_env)$adj.r.squared
R2_realm <- summary(FB_realm)$adj.r.squared
# Realm only
R2_realm_only <- R2_all - R2_env
# Environment only
R2_env_only <- R2_all - R2_realm
# Shared
R2_shared <- R2_all - R2_realm_only - R2_env_only
# ---------- Map differences in residuals (Supplementary Fig. 2h) ----------
# Difference with/without realm
res_df <- data.frame(
res = abs(resid(FB_env)) - abs(resid(FB_all)),
cell_id = as.numeric(data$cell_id))
res_map <- cells %>% right_join(res_df, by="cell_id")
# Save figure
svg("Results/diffres_mamm_FB_realm.svg",
width = 2.5, height = 1.5)
ggplot() +
geom_sf(data = world, colour="grey85", lwd=.1, linewidth=.1) +
geom_sf(data = res_map, aes(color = res, fill = res),
lwd=.1, linewidth=.1) +
scale_colour_continuous_diverging(palette="Blue-Red") +
scale_fill_continuous_diverging(palette="Blue-Red") +
theme_void() +
theme(legend.title = element_blank(),
legend.text = element_text(size=7),
legend.position = "bottom") +
theme(legend.key.height = unit(.17, "cm"),
legend.key.width = unit(1.1,"cm"))
dev.off()