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 and threat 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 truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including cli

SUBMITTER: Andermann T 

PROVIDER: S-EPMC9062518 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature