On the predictive performance of correlative species distribution models

Species Distribution Modelling (SDM) is a widely used tool in ecological studies and wildlife conservation. Despite the vast literature on this topic developed to date, species distribution modelling remains an active area of research because there are so many potential uses for the models but also many challenges to fitting them well. Therefore, it is important to have a clear understanding of how these methods work and how they can be best used in each setting. My thesis focuses on advancing methodological aspects of SDM in particular assessing and improving the predictive performance, including providing tools and guidelines for their use. One key aim of my PhD is to test a broad range of common SDM algorithms across several datasets. As a step towards that goal, in my first research chapter I compared newly developed algorithms and novel implementations of established ones to update a landmark study by Elith and colleagues in 2006, using their data. In summary, I evaluated 13 modelling methods (and several implementations of some) on an independently collected testing dataset. The dataset used includes presence-only records for 226 species across the world with presence-absence data for model evaluation. This dataset is now published, and the manuscript provided as an appendix to my thesis as I contributed to its publication. The result of the first research chapter showed that some models perform generally better than others. An ensemble of five tuned models was the best model in averaged and ranked performance. In contrast, the model implemented by biomod modelling framework with the default parameters was an average performer indicating that ensemble models per se are not the best solution to all modelling problems. Overall non-parametric models with the capability of optimising the bias-variance trade-off performed strongly. This includes boosted regression trees (BRT), MaxEnt and a variant of Random Forest (RF). All the data and code with working examples a