This paper presents an evaluation of several approaches of plants species distribution modeling based on spatial, environmental and co-occurrences data using machine learning methods. In particular, we re-evaluate the environmental convolutional neural network model that obtained the best performance of the GeoLifeCLEF 2018 challenge but on a revised dataset that fixes some of the issues of the previous one. We also go deeper in the analysis of co-occurrences information by evaluating a new model that jointly takes environmental variables and co-occurrences as inputs of an end-to-end network. Results show that the environmental models are the best performing methods and that there is a significant amount of complementary information between co-occurrences and environment. Indeed, the model learned on both inputs allows a significant performance gain compared to the environmental model alone.
@article{arxiv.1909.08825,
title = {Evaluation of Deep Species Distribution Models using Environment and Co-occurrences},
author = {Benjamin Deneu and Maximilien Servajean and Christophe Botella and Alexis Joly},
journal= {arXiv preprint arXiv:1909.08825},
year = {2019}
}