Week 8: Discussion Section

library(tidyverse)
library(sf)
library(terra)
library(dismo)
library(tmap)
library(patchwork)

magpie <- read_csv(here::here("data", "magpie_obvs.csv"))
tule_elk <- read_csv(here::here("data", "tule_elk_obvs.csv"))

bioclim_dir <- here::here("data", "climate", "wc2.1_2.5m")
bioclim <- list.files(bioclim_dir, pattern = glob2rx("*.tif$"), full.names = TRUE)
bioclim_sort <- bioclim[
  # Sort filepaths based on numeric suffix
  order(
  # Extract numeric suffix of filenames and convert to numeric
  as.numeric(gsub(".*_(\\d+)\\.tif$", "\\1", bioclim)))]
bioclim_rast <- rast(bioclim_sort)

Name Raster Layers

variables <- c("annualMeanTemp", "meanDiurnalRange", "isothermality", "tempSeasonality", "maxTempWarmMonth", "maxTempColdMonth", "tempAnnualRange", "meanTempWetQ", "meanTempDryQ", "meanTempWarmQ", "meanTempColdQ", "annualPrecip", "precipWetMonth", "precipDryMonth", "precipSeasonality", "precipWetQ", "precipDryQ", "precipWarmQ", "precipColdQ")
names(bioclim_rast) <- variables

Find Geographic Extent of Species Occurences

magpie_sf <- magpie %>%
  rename(long = longitude,
         lat = latitude) %>%
  drop_na(long, lat) %>%
  st_as_sf(coords = c("long", "lat"), crs = 4326)

# Obtain geographic extent/bounding box of the species occurrences
magpie_bbox <- st_bbox(magpie_sf)

Crop Raster and Extract Points for Species Occurences

# Crop raster to match the geographic extent of the species occurrences
bioclim_crop <- crop(bioclim_rast, magpie_bbox)

# Extract points from raster for all species occurrences
bioClim_pts <- as_tibble(extract(bioclim_crop, magpie_sf))

Crop Raster and Extract Random Points

set.seed(42) # for reproducibility
# Generate random sample points from raster
random_pts <- dismo::randomPoints(mask = raster(bioclim_crop[["annualMeanTemp"]]),
                                  n = nrow(magpie) * 2,
                                  ext = magpie_bbox)

# Extract points from raster for random sample points
bioClim_random_pts <- as_tibble(extract(bioclim_crop, random_pts))

View Map

map_1 <- tm_shape(raster(bioclim_crop[["annualPrecip"]])) +
  tm_raster(palette = "Blues", title = "Annual Precipitation") +
  tm_shape(magpie_sf) +
  tm_dots(col = "#3a5a40", size = 0.15) +
  tm_layout(legend.position = c("left", "bottom"),
            legend.bg.color = "white")

map_2 <- tm_shape(raster(bioclim_crop[["annualMeanTemp"]])) +
  tm_raster(palette = "-RdYlBu", title = "Annual Mean Temp") +
  tm_shape(magpie_sf) +
  tm_dots(col = "#3a5a40", size = 0.15) +
  tm_layout(legend.position = c("left", "bottom"),
            legend.bg.color = "white")

tmap_arrange(map_1, map_2)

View Plot

plot_1 <- ggplot(data = bioClim_pts, aes(x = annualPrecip, y = annualMeanTemp)) +
  geom_point(shape = 16, color = "#3a5a40") +
  labs(x = "Annual Precipitation",
       y = "Annual Mean Temperature", 
       title = "Species Climate Niche") +
  theme_bw()

plot_2 <- ggplot(data = bioClim_random_pts, aes(x = annualPrecip, y = annualMeanTemp)) +
  geom_point(shape = 16) +
  labs(x = "Annual Precipitation",
       y = element_blank(), 
       title = "Background Climate") +
  theme_bw()

plot_1 + plot_2

Create Generalizable Workflow

climate_envelope <- function(clim_rast, clim_var1, clim_var2, occurences, species_name){
  
  species_name <- species_name %>%
    str_to_lower() %>%
    str_replace_all(" ", "_")
  
  occurences_sf <- occurences %>%
  rename(long = longitude,
         lat = latitude) %>%
  drop_na(long) %>%
  st_as_sf(coords = c("long", "lat"), crs = 4326)
  
  occurences_bbox <- st_bbox(occurences_sf)
  
  clim_crop <- crop(clim_rast, occurences_bbox)
  
  clim_pts <- as_tibble(extract(clim_crop, occurences_sf))
  
  random_pts <- randomPoints(mask = raster(clim_rast[[clim_var1]]),
                             n = nrow(occurences) * 2,
                             ext = occurences_bbox)

  clim_random_pts <- as_tibble(extract(clim_crop, random_pts))
  
  map_1 <- tm_shape(raster(clim_crop[[clim_var1]])) +
    tm_raster(palette = "Blues") +
    tm_shape(occurences_sf) +
    tm_dots(col = "#3a5a40", size = 0.15) +
    tm_layout(legend.position = c("left", "bottom"),
              legend.bg.color = "white")
  
  map_2 <- tm_shape(raster(clim_crop[[clim_var2]])) +
    tm_raster(palette = "-RdYlBu") +
    tm_shape(occurences_sf) +
    tm_dots(col = "#3a5a40", size = 0.15) +
    tm_layout(legend.position = c("left", "bottom"),
              legend.bg.color = "white")
  
  plot_1 <- ggplot(data = clim_pts, aes_string(x = clim_var1, y = clim_var2)) +
    geom_point(shape = 16, color = "#3a5a40") +
    labs(title = "Species Climate Niche") +
    theme_bw()
  
  plot_2 <- ggplot(data = clim_random_pts, aes_string(x = clim_var1, y = clim_var2)) +
    geom_point(shape = 16) +
    labs(y = element_blank(), 
         title = "Background Climate") +
    theme_bw()
  
  assign(paste0(species_name, "_map_1"), map_1, envir = .GlobalEnv)
  assign(paste0(species_name, "_map_2"), map_2, envir = .GlobalEnv)
  assign(paste0(species_name, "_plot_1"), plot_1, envir = .GlobalEnv)
  assign(paste0(species_name, "_plot_2"), plot_2, envir = .GlobalEnv)
  
}

climate_envelope(clim_rast = bioclim_rast, clim_var1 = "annualPrecip", clim_var2 = "annualMeanTemp", occurences = tule_elk, species_name = "Tule Elk")

tule_elk_map_1

tule_elk_map_2

tule_elk_plot_1

tule_elk_plot_2