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5_PresentVsPastClimate.R
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5_PresentVsPastClimate.R
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#===================================================================#
# Compare R2 values from models using climate data from present day #
# (Present), last glacial maximum (LGM), & mid-Holocene (MidHol) #
# (Supplementary Table 4) #
#===================================================================#
library(tidyverse)
library(sf)
sf_use_s2(FALSE) # Avoid some issues of invalid geometries
# ----- BIRDS ----------
# 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) %>%
# Phylobetadiversity NMDS axes
left_join(read.csv("Data/bird_pb_NMDS.csv"), "cell_id") %>%
dplyr::select(-X)
# Present climate
cells_Present <- cells %>%
left_join(read.csv("Data/clim_Present_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# LGM climate
cells_LGM <- cells %>%
left_join(read.csv("Data/clim_LGM_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# MidHol climate
cells_MidHol <- cells %>%
left_join(read.csv("Data/clim_MidHol_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# ---------- Species richness ----------
# Load SR data for each climate
data_Present <- read.csv("Data/bird_SR_cells.csv") %>%
left_join(cells_Present, by="cell_id")
data_LGM <- read.csv("Data/bird_SR_cells.csv") %>%
left_join(cells_LGM, by="cell_id")
data_MidHol <- read.csv("Data/bird_SR_cells.csv") %>%
left_join(cells_MidHol, by="cell_id")
# Run model for each climate, environment variables only
SR_env_Present <- 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_Present,
na.action = na.fail)
SR_env_LGM <- 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_LGM,
na.action = na.fail)
SR_env_MidHol <- 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_MidHol,
na.action = na.fail)
summary(SR_env_Present)$adj.r.squared
summary(SR_env_LGM)$adj.r.squared
summary(SR_env_MidHol)$adj.r.squared
# Run model for each climate, environment and phylobetadiversity
SR_all_Present <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_Present,
na.action = na.fail)
SR_all_LGM <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_LGM,
na.action = na.fail)
SR_all_MidHol <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_MidHol,
na.action = na.fail)
summary(SR_all_Present)$adj.r.squared
summary(SR_all_LGM)$adj.r.squared
summary(SR_all_MidHol)$adj.r.squared
# ---------- Functional richness ----------
# Load FR data for each climate
data_Present <- read.csv("Data/bird_FR_cells.csv") %>%
left_join(cells_Present, by="cell_id")
data_LGM <- read.csv("Data/bird_FR_cells.csv") %>%
left_join(cells_LGM, by="cell_id")
data_MidHol <- read.csv("Data/bird_FR_cells.csv") %>%
left_join(cells_MidHol, by="cell_id")
# Run model for each climate, environment variables only
FR_env_Present <- 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_Present,
na.action = na.fail)
FR_env_LGM <- 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_LGM,
na.action = na.fail)
FR_env_MidHol <- 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_MidHol,
na.action = na.fail)
summary(FR_env_Present)$adj.r.squared
summary(FR_env_LGM)$adj.r.squared
summary(FR_env_MidHol)$adj.r.squared
# Run model for each climate, environment and phylobetadiversity
FR_all_Present <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_Present,
na.action = na.fail)
FR_all_LGM <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_LGM,
na.action = na.fail)
FR_all_MidHol <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_MidHol,
na.action = na.fail)
summary(FR_all_Present)$adj.r.squared
summary(FR_all_LGM)$adj.r.squared
summary(FR_all_MidHol)$adj.r.squared
# ----- MAMMALS ----------
# 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) %>%
# Phylobetadiversity NMDS axes
left_join(read.csv("Data/mamm_pb_NMDS.csv"), "cell_id") %>%
dplyr::select(-X)
# Present climate
cells_Present <- cells %>%
left_join(read.csv("Data/clim_Present_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# LGM climate
cells_LGM <- cells %>%
left_join(read.csv("Data/clim_LGM_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# MidHol climate
cells_MidHol <- cells %>%
left_join(read.csv("Data/clim_MidHol_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# ---------- Species richness ----------
# Load SR data for each climate
data_Present <- read.csv("Data/mamm_SR_cells.csv") %>%
left_join(cells_Present, by="cell_id")
data_LGM <- read.csv("Data/mamm_SR_cells.csv") %>%
left_join(cells_LGM, by="cell_id")
data_MidHol <- read.csv("Data/mamm_SR_cells.csv") %>%
left_join(cells_MidHol, by="cell_id")
# Run model for each climate, environment variables only
SR_env_Present <- 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_Present,
na.action = na.fail)
SR_env_LGM <- 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_LGM,
na.action = na.fail)
SR_env_MidHol <- 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_MidHol,
na.action = na.fail)
summary(SR_env_Present)$adj.r.squared
summary(SR_env_LGM)$adj.r.squared
summary(SR_env_MidHol)$adj.r.squared
# Run model for each climate, environment and phylobetadiversity
SR_all_Present <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_Present,
na.action = na.fail)
SR_all_LGM <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_LGM,
na.action = na.fail)
SR_all_MidHol <- lm(SR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_MidHol,
na.action = na.fail)
summary(SR_all_Present)$adj.r.squared
summary(SR_all_LGM)$adj.r.squared
summary(SR_all_MidHol)$adj.r.squared
# ---------- Functional richness ----------
# Load FR data for each climate
data_Present <- read.csv("Data/mamm_FR_cells.csv") %>%
left_join(cells_Present, by="cell_id")
data_LGM <- read.csv("Data/mamm_FR_cells.csv") %>%
left_join(cells_LGM, by="cell_id")
data_MidHol <- read.csv("Data/mamm_FR_cells.csv") %>%
left_join(cells_MidHol, by="cell_id")
# Run model for each climate, environment variables only
FR_env_Present <- 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_Present,
na.action = na.fail)
FR_env_LGM <- 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_LGM,
na.action = na.fail)
FR_env_MidHol <- 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_MidHol,
na.action = na.fail)
summary(FR_env_Present)$adj.r.squared
summary(FR_env_LGM)$adj.r.squared
summary(FR_env_MidHol)$adj.r.squared
# Run model for each climate, environment and phylobetadiversity
FR_all_Present <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_Present,
na.action = na.fail)
FR_all_LGM <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_LGM,
na.action = na.fail)
FR_all_MidHol <- lm(FR ~ log(Landmass_area) +
elev_mean + I(elev_mean^2) +
elev_range +
clim_pca_1 + I(clim_pca_1^2) +
clim_pca_2 + I(clim_pca_2^2) +
clim_pca_3 + I(clim_pca_3^2) +
clim_pca_4 + I(clim_pca_4^2) +
MDS1 + I(MDS1^2) + MDS2 +
I(MDS2^2) + MDS3 + I(MDS3^2) +
MDS1:MDS2 + MDS1:MDS3 + MDS2:MDS3,
data=data_MidHol,
na.action = na.fail)
summary(FR_all_Present)$adj.r.squared
summary(FR_all_LGM)$adj.r.squared
summary(FR_all_MidHol)$adj.r.squared