From Presence‐Only to Abundance Species Distribution Models Using Transfer Learning

Abstract

Species Distribution Models based on Convolutional Neural Networks (CNN‐SDMs) have recently emerged, demonstrating greater effectiveness than traditional SDMs in several contexts. A limited number of studies, however, have focused on species abundance patterns, as the datasets available for this purpose are generally too small to effectively learn a deep learning model with millions of parameters. Our study demonstrated that CNN‐SDMs can circumvent the small sample size of species abundance datasets through the combined use of a large presence‐only species dataset and transfer learning to significantly improve the performance of abundance‐based CNN‐SDMs. Applied to Mediterranean coastal fishes, our approach significantly improves the abundance prediction performance of CNN‐SDMs, with average gains of 35% (D‐squared regression score). This allows CNN‐SDMs to perform better than classical SDMs in abundance prediction, with average gains of 10%. These gains are stemming from enhanced abundance predictions for rare species and where widespread species are locally rare.

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