| Week 1 (09/04) |
Spatial thinking in biodiversity science |
NumPy, pandas, GeoPandas, matplotlib |
Map a biodiversity dataset |
| Week 2 (09/11) |
Biodiversity data acquisition |
GeoPandas, pygbif |
Access and download occurrence data using the GBIF API |
| Week 3 (09/18) |
Biodiversity data cleaning |
GeoPandas, pygbif |
Analysing and cleaning GBIF observations |
| Week 4 (09/25) |
Vector spatial analysis |
GeoPandas, Shapely |
Buffers, intersections, spatial joins |
| Week 5 (10/02) |
Raster data and environmental covariates |
rasterio, rioxarray, xarray, NumPy |
Extract climate, elevation, and land cover |
| Week 6 (10/09) |
Species distribution modeling from first principles |
rasterio, xarray, geopandas |
Build a first SDM from scratch by hand |
| Week 7 (10/16) |
Species distribution modeling (Random Forest) |
scikit-learn, rasterio, pandas |
Build a first SDM from scratch w/ ML |
| Week 8 (10/23) |
Exploring SDM software |
Running 'production grade' SDMs |
Build and compare distribution models |
| Week 9 (10/30) |
Gradient Forest: SDMs for genomic data |
pyGF |
Perform an SDM-like analysis with SNP to estimate environmental correlates of genetic turnover |
| Week 10 (11/06) |
Measuring Spatial Biodiversity Patterns |
numpy, pandas, scipy, scikit-learn |
Quantify biodiversity across spatial scales |
| Week 11 (11/13) |
Community composition across space |
pandas, scipy, scikit-learn, GeoPandas |
Analyze beta diversity and distance decay |
| Week 12 (11/20) |
Ordination, gradients, and evolutionary diversity |
scikit-learn, scipy, matplotlib |
Compare geographic, environmental, and diversity spaces |
| Week 13 (12/04) |
tbd |
tbd |
tbd |
| Week 14 (12/11) |
tbd |
tbd |
tbd |