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5_AdditionalVariables.R
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5_AdditionalVariables.R
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#============================================================#
# Compare R2 values from models using additional environment #
# variables besides climate #
# (Supplementary Table 2) #
#============================================================#
library(tidyverse)
library(sf)
sf_use_s2(FALSE) # Avoid some issues of invalid geometries
# ----- LOAD CELLS WITH ALL 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) %>%
# Climate distance compared to present
left_join(read.csv("Data/clim_diff_cells.csv"), "cell_id") %>%
dplyr::select(-X) %>%
# Past ice cover
left_join(read.csv("Data/ice_cells.csv"), "cell_id") %>%
dplyr::select(-X) %>%
# Years since significant land conversion
left_join(read.csv("Data/KK10_cells.csv"), "cell_id") %>%
dplyr::select(-X) %>%
# Human Impact Index
left_join(read.csv("Data/HII_cells.csv"), "cell_id") %>%
dplyr::select(-X)
# ----- BIRDS ----------
# ---------- Species richness ----------
# Load data
data <- read.csv("Data/bird_SR_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model for climate-only, then each variable plus climate
# Calculate additional variance explained
SR_clim <- lm(SR ~ 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)
# Landmass area
SR_Landmass_area <- lm(SR ~ log(Landmass_area) +
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)
summary(SR_Landmass_area)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Mean elevation
SR_elev_mean <- lm(SR ~ elev_mean + I(elev_mean^2) +
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)
summary(SR_elev_mean)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Elevation range
SR_elev_range <- lm(SR ~ 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)
summary(SR_elev_range)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Climate distance, present vs. LGM
SR_clim_diff_LGM <- lm(SR ~ clim_diff_LGM +
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)
summary(SR_clim_diff_LGM)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Climate distance, present vs. MidHolo
SR_clim_diff_MidHol <- lm(SR ~ clim_diff_MidHol +
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)
summary(SR_clim_diff_MidHol)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Past ice cover
SR_ice_cover <- lm(SR ~ ice_cover +
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)
summary(SR_ice_cover)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Years since significant land conversion
SR_LandConvYears <- lm(SR ~ LandConvYears +
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)
summary(SR_LandConvYears)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Human Impact Index
SR_HII <- lm(SR ~ HII +
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)
summary(SR_HII)$adj.r.squared - summary(SR_clim)$adj.r.squared
# ---------- Functional richness ----------
# Load data
data <- read.csv("Data/bird_FR_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model for climate-only, then each variable plus climate
# Calculate additional variance explained
FR_clim <- lm(FR ~ 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)
# Landmass area
FR_Landmass_area <- lm(FR ~ log(Landmass_area) +
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)
summary(FR_Landmass_area)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Mean elevation
FR_elev_mean <- lm(FR ~ elev_mean + I(elev_mean^2) +
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)
summary(FR_elev_mean)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Elevation range
FR_elev_range <- lm(FR ~ 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)
summary(FR_elev_range)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Climate distance, present vs. LGM
FR_clim_diff_LGM <- lm(FR ~ clim_diff_LGM +
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)
summary(FR_clim_diff_LGM)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Climate distance, present vs. MidHolo
FR_clim_diff_MidHol <- lm(FR ~ clim_diff_MidHol +
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)
summary(FR_clim_diff_MidHol)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Past ice cover
FR_ice_cover <- lm(FR ~ ice_cover +
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)
summary(FR_ice_cover)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Years since significant land conversion
FR_LandConvYears <- lm(FR ~ LandConvYears +
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)
summary(FR_LandConvYears)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Human Impact Index
FR_HII <- lm(FR ~ HII +
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)
summary(FR_HII)$adj.r.squared - summary(FR_clim)$adj.r.squared
# ----- MAMMALS ----------
# ---------- Species richness ----------
# Load data
data <- read.csv("Data/mamm_SR_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model for climate-only, then each variable plus climate
# Calculate additional variance explained
SR_clim <- lm(SR ~ 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)
# Landmass area
SR_Landmass_area <- lm(SR ~ log(Landmass_area) +
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)
summary(SR_Landmass_area)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Mean elevation
SR_elev_mean <- lm(SR ~ elev_mean + I(elev_mean^2) +
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)
summary(SR_elev_mean)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Elevation range
SR_elev_range <- lm(SR ~ 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)
summary(SR_elev_range)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Climate distance, present vs. LGM
SR_clim_diff_LGM <- lm(SR ~ clim_diff_LGM +
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)
summary(SR_clim_diff_LGM)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Climate distance, present vs. MidHolo
SR_clim_diff_MidHol <- lm(SR ~ clim_diff_MidHol +
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)
summary(SR_clim_diff_MidHol)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Past ice cover
SR_ice_cover <- lm(SR ~ ice_cover +
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)
summary(SR_ice_cover)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Years since significant land conversion
SR_LandConvYears <- lm(SR ~ LandConvYears +
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)
summary(SR_LandConvYears)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Human Impact Index
SR_HII <- lm(SR ~ HII +
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)
summary(SR_HII)$adj.r.squared - summary(SR_clim)$adj.r.squared
# ---------- Functional richness ----------
# Load data
data <- read.csv("Data/mamm_FR_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model for climate-only, then each variable plus climate
# Calculate additional variance explained
FR_clim <- lm(FR ~ 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)
# Landmass area
FR_Landmass_area <- lm(FR ~ log(Landmass_area) +
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)
summary(FR_Landmass_area)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Mean elevation
FR_elev_mean <- lm(FR ~ elev_mean + I(elev_mean^2) +
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)
summary(FR_elev_mean)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Elevation range
FR_elev_range <- lm(FR ~ 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)
summary(FR_elev_range)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Climate distance, present vs. LGM
FR_clim_diff_LGM <- lm(FR ~ clim_diff_LGM +
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)
summary(FR_clim_diff_LGM)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Climate distance, present vs. MidHolo
FR_clim_diff_MidHol <- lm(FR ~ clim_diff_MidHol +
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)
summary(FR_clim_diff_MidHol)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Past ice cover
FR_ice_cover <- lm(FR ~ ice_cover +
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)
summary(FR_ice_cover)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Years since significant land conversion
FR_LandConvYears <- lm(FR ~ LandConvYears +
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)
summary(FR_LandConvYears)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Human Impact Index
FR_HII <- lm(FR ~ HII +
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)
summary(FR_HII)$adj.r.squared - summary(FR_clim)$adj.r.squared
# ----- BATS ----------
# ---------- Species richness ----------
# Load data
data <- read.csv("Data/bat_SR_cells.csv") %>%
left_join(cells, by="cell_id")
# Run model for climate-only, then each variable plus climate
# Calculate additional variance explained
SR_clim <- lm(SR ~ 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)
# Landmass area
SR_Landmass_area <- lm(SR ~ log(Landmass_area) +
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)
summary(SR_Landmass_area)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Mean elevation
SR_elev_mean <- lm(SR ~ elev_mean + I(elev_mean^2) +
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)
summary(SR_elev_mean)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Elevation range
SR_elev_range <- lm(SR ~ 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)
summary(SR_elev_range)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Climate distance, present vs. LGM
SR_clim_diff_LGM <- lm(SR ~ clim_diff_LGM +
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)
summary(SR_clim_diff_LGM)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Climate distance, present vs. MidHolo
SR_clim_diff_MidHol <- lm(SR ~ clim_diff_MidHol +
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)
summary(SR_clim_diff_MidHol)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Past ice cover
SR_ice_cover <- lm(SR ~ ice_cover +
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)
summary(SR_ice_cover)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Years since significant land conversion
SR_LandConvYears <- lm(SR ~ LandConvYears +
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)
summary(SR_LandConvYears)$adj.r.squared - summary(SR_clim)$adj.r.squared
# Human Impact Index
SR_HII <- lm(SR ~ HII +
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)
summary(SR_HII)$adj.r.squared - summary(SR_clim)$adj.r.squared
# ---------- Functional richness ----------
# Load data
data <- read.csv("Data/bat_FR_cells.csv") %>%
na.omit() %>%
left_join(cells, by="cell_id")
# Run model for climate-only, then each variable plus climate
# Calculate additional variance explained
FR_clim <- lm(FR ~ 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)
# Landmass area
FR_Landmass_area <- lm(FR ~ log(Landmass_area) +
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)
summary(FR_Landmass_area)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Mean elevation
FR_elev_mean <- lm(FR ~ elev_mean + I(elev_mean^2) +
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)
summary(FR_elev_mean)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Elevation range
FR_elev_range <- lm(FR ~ 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)
summary(FR_elev_range)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Climate distance, present vs. LGM
FR_clim_diff_LGM <- lm(FR ~ clim_diff_LGM +
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)
summary(FR_clim_diff_LGM)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Climate distance, present vs. MidHolo
FR_clim_diff_MidHol <- lm(FR ~ clim_diff_MidHol +
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)
summary(FR_clim_diff_MidHol)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Past ice cover
FR_ice_cover <- lm(FR ~ ice_cover +
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)
summary(FR_ice_cover)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Years since significant land conversion
FR_LandConvYears <- lm(FR ~ LandConvYears +
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)
summary(FR_LandConvYears)$adj.r.squared - summary(FR_clim)$adj.r.squared
# Human Impact Index
FR_HII <- lm(FR ~ HII +
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)
summary(FR_HII)$adj.r.squared - summary(FR_clim)$adj.r.squared