Inferring seagrass meadow resilience from self-organized spatial patterns

Abstract Assessing ecosystem resilience at large spatial scales remains a major challenge in ecology and conservation. While resilience is typically inferred from temporal dynamics or perturbation experiments, ecosystems governed by spatial self-organization are thought to encode resilience-related information directly in their spatial structure. Here, we show that the spatial patterns of seagrass meadows can be used to infer ecological deterioration and resilience-related states from a single cartographic snapshot. Using a mechanistic model of Posidonia oceanica self-organization, we generated thousands of synthetic seascapes spanning a mortality-driven gradient from continuous meadows through fragmented and collapsed states and trained deep convolutional neural networks to classify discrete pattern states and estimate continuous levels of deterioration along this gradient. Applied to habitat cartography across the Balearic Islands, the framework revealed ecologically interpretable regional variation in meadow condition, enabling large-scale assessment of seagrass resilience from spatial snapshots alone. Networks trained exclusively on synthetic data generalized effectively to real meadows, showcasing that mechanistic models can substitute for empirical training labels. More broadly, our results establish a transferable strategy for integrating ecological theory and machine learning to monitor the resilience of self-organized ecosystems when direct temporal observations are sparse or unavailable.