Schedule¶

This 15-week outline is organized around a final project in which each student develops a reproducible spatial biodiversity analysis using Python.

Each week page provides a template for learning goals, readings, lab activities, and deliverables.

Weekly Topic Outline¶

Week 1: Spatial Thinking in Biodiversity Science¶

Students begin with why geography matters in ecology and evolution. They make basic maps, species-specific maps, sampling-density maps, histograms, and a species-by-site matrix from a simple biodiversity dataset.

Week 2: Coordinate Systems and Spatial Geometry¶

The course introduces latitude and longitude, projected coordinate systems, datums, EPSG codes, geometry operations, and the ecological consequences of measuring distances in the wrong coordinate system.

Week 3: Vector Spatial Analysis¶

Students use spatial joins, buffers, intersections, nearest-neighbor operations, point-in-polygon tests, and distance matrices to answer practical spatial ecology questions.

Week 4: Biodiversity Data Acquisition and Cleaning¶

Students download occurrence data, handle taxonomic and coordinate uncertainty, remove duplicates, flag impossible coordinates, and document metadata and cleaning decisions.

Week 5: Raster Data and Environmental Covariates¶

Students learn raster concepts including cells, pixels, resolution, extent, NoData, alignment, elevation, climate, land cover, remote sensing, and microclimate. They extract environmental values at occurrence locations.

Week 6: Spatial Sampling and Sampling Bias¶

Students compare random, stratified, clustered, thinned, and biased samples to see how sampling design affects richness estimates, rare species detection, and downstream models.

Week 7: Measuring Spatial Biodiversity Patterns¶

The course moves from GIS operations to biodiversity metrics: richness, Shannon diversity, Simpson diversity, endemism, rarity, alpha/beta/gamma diversity, moving-window diversity, and spatial aggregation.

Week 8: Spatial Autocorrelation¶

Students calculate global and local spatial autocorrelation and ask when nearby observations are more similar than expected by chance.

Week 9: Species Distribution Modeling¶

Students model species-environment relationships using presence/absence, presence/background, pseudoabsence, logistic regression, and random forests, then compare spatial predictions.

Week 10: Spatial Interpolation and Prediction¶

Students distinguish interpolation from extrapolation, explore nearest-neighbor prediction, inverse distance weighting, kriging concepts, Gaussian processes, and prediction uncertainty.

Week 11: Community Composition Across Space¶

The focus shifts from single-species analysis to multispecies communities using site-by-species matrices, Bray-Curtis and Jaccard dissimilarity, turnover, nestedness, and distance-decay relationships.

Week 12: Ordination, Gradients, and Evolutionary Diversity¶

Students compare geographic space and environmental space using ordination, then connect community patterns to phylogenetic and functional diversity, trait data, missing data, and evolutionary history.

Week 13: Landscape Connectivity and Conservation Prioritization¶

Students build resistance surfaces and graph representations of landscapes, then connect connectivity results to protected areas, complementarity, irreplaceability, and conservation prioritization.

Week 14: Spatial Machine Learning and Model Evaluation¶

Students confront spatial leakage by comparing random train/test splits with spatial train/test splits. Topics include overfitting, spatial cross-validation, transferability, extrapolation, and model uncertainty.

Week 15: Synthesis: Biodiversity Under Environmental Change¶

The final week pulls together biodiversity observations, spatial processing, environmental covariates, statistical and machine-learning models, spatial prediction, uncertainty, ecological interpretation, and final project presentations.