Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments. 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 heterogeneous diversity patterns. 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 train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. 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. The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. 8School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia.7Royal Botanic Gardens, Sydney, NSW, Australia.6Royal Botanic Gardens, Kew, Richmond, United Kingdom.5Department of Plant Sciences, University of Oxford, United Kingdom.4Swiss Institute of Bioinformatics, Fribourg, Switzerland.3Department of Biology, University of Fribourg, Fribourg, Switzerland.2Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden.1Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.Tobias Andermann 1,2,3,4 * Alexandre Antonelli 1,2,5,6 Russell L.
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