Week 5: Raster Data and Environmental Covariates

Icebreaker

Are there any foods you disliked as a child, but love (or at least tolerate) now?

Introductory business

Fair warning: The icebreaker question next week is going to be "What is a good icebreaker question?"

Paper discussion

Carly will be leading the discussion this week of Van Nuland et al 2025 "Global hotspots of mycorrhizal fungal richness are poorly protected"

Lab Exercise: Raster Data and Environmental Covariates

First, a brief lecture on raster data and environmental covariates
Next, the usual intro update exercise: Go to your JupyterHub and pull the latest version of the class github repository.

Commands to pull the latest version of the class repository

Change directory to your local copy of the course repo
Pull the latest copy of 'upstream' which is my copy of the class website

Push the changes to your own github repo

cd ~/BIO597-SpatialBiodiversity/`
git upstream
git push

Core Questions

How do rasters represent environmental variation?
How do resolution, extent, alignment, and NoData values affect biodiversity analysis?
Which environmental covariates are biologically meaningful for a study system?

Concepts

Raster versus vector data
Cells, pixels, resolution, extent, and NoData
Raster alignment and extraction
Environmental covariates including elevation and climate
Ecological interpretation of predictors

Python Tools

geopandas
rioxarray

Applied Lab

Students extract elevation, temperature, precipitation, and other bioclim values at species occurrence locations, producing an analysis table that combines species, coordinates, and environmental covariates.

Assignment

Submit a raster spatial analysis report with maps, code, and short interpretations for at least three spatial questions.

docs/assignments/Assignment-05-RasterData.ipynb

Paper discussion leader next week: Drew! Please select a paper for the group to discuss before Sunday 10/04 6pm and send it around to the class email list: bio597-fall2026-group@maine.edu