Exploring the potential of neural networks for Species Distribution Modeling (Papers Track)
Robin Zbinden (EPFL); Nina van Tiel (EPFL); Benjamin Kellenberger (Yale University); Lloyd H Hughes (EPFL); Devis Tuia (EPFL)
Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent advances in neural networks may have untapped potential in this field. In this work, we compare the performance of multi-layer perceptron (MLP) neural networks to well-established SDM methods on a benchmark dataset spanning 225 species in six geographical regions. We also compare the performance of MLPs trained separately for each species to an equivalent model trained on a set of species and performing multi-label classification. Our results show that MLP models achieve comparable results to state-of-the-art SDM methods, such as MaxEnt. We also find that multi-species MLPs perform slightly better than single-species MLPs. This study indicates that neural networks, along with all their convenient and valuable characteristics, are worth considering for SDMs.