For this module it is recommended that you take GEMSx006.1 and GEMSx006.2, or GEMSx006.6 and GEMSx006.7 beforehand or have some understanding of species distribution models and how and why we need them as well as knowledge of how to access species occurrence data. Knowledge of R or other geospatial and scientific data analysis software or language is required.

The geographic distribution of any species is affected by a number of underlying factors like climatic variables, environmental attributes or socio-economic drivers. Depending on the scale we want to capture the potential or realized distribution of any give species it is important that we choose the appropriate factors or indicators. This workshop will cover selection of the appropriate variable for a given species distribution modelling project as well as cover how to pre-process environmental datasets so that they are ready for species distribution modelling. 

This workshop is part of a 5-module series on species distribution modelling to provide researchers the ability to undertake a spatio-temporal accounting of biotic threats to crops, natural landscapes or the human society. 


  • Types of predictor datasets (climatic, environmental, and socio-economic datasets) for SDMs
  • Sourcing predictors for SDMs
  • Pre-processing predictor datasets
  • Correlation, collinearity, dimension reduction and variable selection
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