Estimating alpha, beta, and gamma diversity through deep learning

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.物种丰富度的可靠制图是识别高保护优先区域的关键步骤，同时需兼顾其他价值考量。目前此类任务通常通过叠加单个物种的分布范围图来完成，这要么需要高密度的物种出现数据，要么需依托生态位模型（environmental niche models）对适宜未采样区域的物种存在性做出假设。本研究提出一种可直接估算物种丰富度的深度学习方法，无需预先估算单个物种的分布范围。我们基于调查样地的物种名录训练神经网络模型，该名录可为监督式机器学习提供实地验证依据。该模型能够基于空间关联变量预测物种丰富度，这些变量包括气候与地理预测因子，以及在线数据库中可用的物种记录数量。我们通过为澳大利亚——一个物种多样性格局差异显著的大陆——绘制高空间分辨率、可独立验证的α、β、γ植物多样性分布图，评估了本方法的实际应用价值。本深度学习框架为生物多样性格局的估算提供了一种高效且灵活的全新方法。