Towards ecologically meaningful foundation models

Ecology aims to explain and predict how organisms interact with each other and their environments across space and time. Yet both ecological data and theory are fragmented, leading to models that generalize poorly beyond specific systems or scales. Empirical evidence spans diverse modalities, resolutions and contexts, while theory is distributed across partially overlapping frameworks that are rarely integrated within a single predictive model. We argue that ecological foundation models (ecoFMs), trained on large, multi-modal ecological datasets and informed by ecological theory, offer a route towards unifying data and theory within a common framework. Beyond their scientific value for ecology, ecoFMs present a challenging and consequential testbed for machine learning, demanding advances in multi-modal representation learning, theory-guided modelling and uncertainty-aware inference. By learning shared representations of organisms, environments and interactions, ecoFMs could improve generalization, link pattern to process and enable synthesis across ecological subdisciplines. We outline a roadmap for developing ecoFMs, including requirements for data infrastructure, model architectures, evaluation strategies and governance, and assess where current machine learning and ecological approaches fall short. If developed responsibly and collaboratively with ecological practitioners and other actors, ecoFMs could enable new modes of analysis and strengthen ecological forecasting, while simultaneously driving advances in machine learning for multi-modal integration, theory-guided learning and generalization in complex, data-limited systems. Ecology struggles to make broad predictions, as ecological data and theories are scattered across many different sources, scales and approaches. Gardiner et al. propose to create ecological foundation models, which combine large, multi-modal ecological datasets with ecological theory, as a way to create more generalizable and predictive models.