Species Distribution and Environmental Niche Modelling

course goals

The course starts with introducing the niche concept and relevant ecological theory that helps in making interpretations of ENMs;
Then the R-package as a modelling environment will be introduced and a number of advanced modelling techniques, such as logistic regression models, boosted regression trees, maximum entropy and expert system models;
Multi-collinearity diagnostics and spatial auto-correlation and how to deal with this will be discussed;
The techniques will be applied to specific thematic application areas such as biodiversity modelling, species distribution probabilities, habitat requirements, Crop Wild relatives (CWR) management and prediction of crop extent under climate change;
How to model the impact of Climate Change on the distribution of species will be explained;
Model calibration, validation, data quality and model comparison will be discussed and exercised;
The above learned theory will be tested in a written test (60% of the mark)
After applying all above mentioned techniques on a model species, students will be assigned another data set (or can bring their own) to work in an individual project to apply all learned techniques to this new data set. This will be assessed based on an individual presentation (25% of the mark). In case of a fail a repair can be made (maximum mark then is a 6)
In a final group project, you will work on understanding the role of ENMs in Essential Biodiversity Variables (EBVs) for sustainable agricultural, semi-natural and protected area landscapes and information needs for policy (SDGs, Aichi targets). This will also be presented per group (15% of the mark, no chance for a repair here)