Week 8: Discussion Section

Practice function making with rasters

Author

Alessandra Vidal Meza

Published

November 20, 2024

Photo by Hari Nandakumar on Unsplash

Source Materials

The following materials are modified from Leander Anderegg’s niche modeling exercise.

1. Background

This week, you will practice function making with rasters and create a generalizable workflow to determine the climate envelope (or the climate where a species currently lives) of two endemic species to California:

  • Tule elk (Cervus canadensis nannodes)
  • Yellow-billed magpie (Pica nutalli)

2. Get Started

  • Create an .Rproj as your version controlled project for Week 8
  • Create a Quarto document inside your project
  • Download this data folder from Google Drive and move it inside your project
  • Load all necessary packages and read spatial objects
Warning

If you don’t have dismo installed, make sure to run: 

install.packages("dismo")
library(tidyverse)
library(sf)
library(terra)
library(dismo)
library(tmap)
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)

3. Your Task

Now, to meet this week’s learning objectives, your task:

  1. Update the bioclim_rast layer names to match the Bioclimatic variables using names()
  2. Use st_as_sf to convert and find the geographic extent of species occurrence data magpie using st_bbox()
  3. Crop bioclim_rast to match the extent of species occurrence data magpie
  4. Extract points from the cropped bioclim_rast for each occurrence in magpie
  5. Create “background values” by using dismo::randomPoints() to generate random sample points from the cropped bioclim_rast
  6. Extract points from bioclim_rast for each random sample point generated in Step #5
  7. Plot species climate niche and background climate (temperature vs. precipitation)
  8. Modify steps 2-7 into a generalizable workflow for other species occurrence data
  9. Try your new function with species occurrence data tule_elk