Résumé
The rapid expansion of citizen science initiatives has led to a significant
growth of biodiversity databases, and particularly presence-only (PO)
observations. PO data are invaluable for understanding species distributions
and their dynamics, but their use in a Species Distribution Model (SDM) is
curtailed by sampling biases and the lack of information on absences. Poisson
point processes are widely used for SDMs, with Maxent being one of the most
popular methods. Maxent maximises the entropy of a probability distribution
across sites as a function of predefined transformations of variables, called
features. In contrast, neural networks and deep learning have emerged as a
promising technique for automatic feature extraction from complex input
variables. Arbitrarily complex transformations of input variables can be
learned from the data efficiently through backpropagation and stochastic
gradient descent (SGD). In this paper, we propose DeepMaxent, which harnesses
neural networks to automatically learn shared features among species, using the
maximum entropy principle. To do so, it employs a normalised Poisson loss where
for each species, presence probabilities across sites are modelled by a neural
network. We evaluate DeepMaxent on a benchmark dataset known for its spatial
sampling biases, using PO data for calibration and presence-absence (PA) data
for validation across six regions with different biological groups and
covariates. Our results indicate that DeepMaxent performs better than Maxent
and other leading SDMs across all regions and taxonomic groups. The method
performs particularly well in regions of uneven sampling, demonstrating
substantial potential to increase SDM performances. In particular, our approach
yields more accurate predictions than traditional single-species models, which
opens up new possibilities for methodological enhancement.