Towards ecologically meaningful foundation models
Year: 2026
Type: article
Abstract: 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.
Source: Nature Machine Intelligence
Authors: Ross J. Gardiner, Henry Cerbone, Hammed A. Akande, Carly H. Batist, Amber Cowans, Stella M. Felsinger, Premdeep S. Gill, Taniya Kapoor, Eliot T. Miller, Rodrigo Oyanedel, Steven Reece, Yan Ying Tan, Jonas Antony, Carlos Rodriguez-Pardo, Amy Hinsley, Micah Bowles, Sebastian A. Heilpern, Rachel H. Parkinson
Institutions: University of Exeter, University of Oxford, Concordia University, Conservation International, University of St Andrews, British Antarctic Survey, University of Cambridge, Wageningen University & Research, American Bird Conservancy, Bird Conservancy of the Rockies, University of Michigan, RFF-CMCC European Institute on Economics and the Environment, CMCC Foundation - Euro-Mediterranean Center on Climate Change, Politecnico di Milano, Ellison Institute of Technology, Stanford University, Queen Mary University of London
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7133689312