CAMELIA: Enhancing species assemblage predictions by integrating structural and functional indices: A case study on plant communities in the French Alps

作者：Gabrielle Deschamps, Sara Si‐Moussi, Clovis Galiez, Wilfried Thuiller · 发表于：Methods in Ecology and Evolution · 年份：2025 · DOI：10.1111/2041-210x.70158 · 被引用次数：2 · 研究领域：Species Distribution and Climate Change、Ecology and Vegetation Dynamics Studies、Remote Sensing in Agriculture

Abstract Addressing the challenge of predicting biodiversity requires models that fully capture the complex dynamics of species and communities. While species distribution models (SDMs) predict individual species' distributions based on environmental variables, they often fail to reconstruct realistic communities. Macroecological models (MEMs), on the other hand, predict community indices but lack species‐level resolution. To bridge this gap, we introduce CAMELIA (Community Assemblage Modelling macroEcoLogically Informed with AI), a neural network framework integrating species distribution data with community constraints to improve biodiversity predictions. Approach and Methods. CAMELIA uses a multitask neural network to jointly optimize species occurrence probabilities and community indices. It recalibrates species probabilities from stacked SDMs using observed or predicted community indices. We tested CAMELIA on plant communities in the French Alps, using MEM predictions (CAMELIA‐MEM), high‐quality field data (CAMELIA‐OBS) and degraded field data (CAMELIA‐PROXY) for various community indices: species richness, community mean and community standard deviation for plant height, specific leaf area and leaf nitrogen content. CAMELIA substantially improved community predictions over stacked SDMs. With observed indices, it reconstructed communities with functional indices highly correlated with observations (for community mean plant height: R 2 = 0.98 vs. 0.74 with S‐SDMs), reduce...