Estimating alpha, beta, and gamma diversity through deep learning

Abstract

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.