GNN ‐ SDM : A Graph Neural Network‐Based Framework Integrating Complex Landscape Patterns Into Species Distribution Modelling

ABSTRACT Aim Although species distribution models (SDMs) play a critical role in ecological research and biodiversity conservation, their reliance on limited occurrence point data poses challenges in capturing the complex relationships between landscape structure and biogeographical processes. This limitation is particularly pronounced for many threatened species with insufficient data, making reliable large‐scale ecological assessments difficult to achieve. Innovation Here, we propose a Graph Neural Network‐based Species Distribution Model (GNN‐SDM), a novel framework that leverages graph‐based deep learning to infer habitat suitability. GNN‐SDM uses standardised spatial distribution polygons from the IUCN Red List as model input, placing emphasis on the structural composition of habitats and environmental resources. By aggregating multidimensional environmental layers into landscape patches, this approach allows the model to consider potential ecological functions that emerge from interactions among neighbouring patches. Main Conclusions We evaluated the framework using global virtual species spanning multiple continents and ecological niches. Relative to conventional methods, GNN‐SDM showed generally higher predictive accuracy and consistent performance across species with different niche preferences. By integrating species range polygons with patch‐based environmental features, the approach provides an improved capacity to characterise habitat suitability under complex landscape structures and variable environmental conditions, and offers a practical tool for preliminary ecological assessments and conservation prioritisation of threatened species with limited occurrence data.