Multi‐Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision‐Making

Year: 2026

Type: review

Abstract: Ecosystem models are increasingly central to the decision-making for environmental policy, conservation planning, and climate-related investments. Yet, the growing reliance on Multi-Model Ensembles (MMEs) of ecosystem models by practitioners and policymakers, sometimes under tight timelines and imperfect information, has frequently outpaced the scientific rigor required to ensure ensemble reliability. Here, MMEs refer to approaches that combine targeted predictions from multiple models with the expectation of improving robustness and quantifying predictive uncertainty. Poorly designed MMEs may create a false sense of confidence and lead to suboptimal policy and market decisions. This perspective argues that robust decision-making-relevant MMEs must be grounded on two pillars: (1) rigorous Model Intercomparison Projects (MIPs), which identify inter-model agreement and disagreement, characterize model uncertainties, and evaluate robustness with observationally based benchmarks-MIPs' diagnostic evaluation is so critical that it must be needed to drive MME's decision in model selection and weighting, especially when only a limited number of models available; and (2) co-design by both stakeholders and scientists to ensure that scenarios, metrics and uncertainty requirements provide decision-relevant information. Building upon the past success and lessons from the existing MIPs-MMEs efforts (e.g., climate/Earth system/crop), we derived the theoretical basis for MMEs, addressed their specific challenges in ecosystem modeling, and highlighted proper consideration of model numbers and diversity, risk of model inter-dependence, effective calibration of model parameters, possible overdue of some ecosystem model development, critical roles of open benchmark data across a wide range of conditions, and suggested use of Artificial Intelligence to support MIPs-MMEs. We highlighted the under-recognized opportunity for MIPs and MMEs to drive scientific progress and innovation through identifying better performing models, systematic benchmarking, feedback loops, and targeted model improvement. By following actionable best practice guidelines, MMEs can evolve from ad hoc aggregation of models into a trusted backbone of environmental policy and decision-making.

Source: Global Change Biology

DOI: 10.1111/gcb.70987

Institutions: University of Illinois Urbana-Champaign, National Center for Supercomputing Applications, Center for Advanced Bioenergy and Bioproducts Innovation, University of Wisconsin–Madison, Aarhus University, Lawrence Berkeley National Laboratory, University of California, Berkeley, North Carolina State University, Technical University of Munich, Cornell University, Foundation for Food and Agriculture Research, University of Minnesota, Oak Ridge National Laboratory, Colorado State University, University of Maryland, College Park, University of Arkansas at Fayetteville

Language: English

Topic: Land Use and Ecosystem Services

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,550

Funders: National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, U.S. Department of Agriculture, Battelle, Foundation for Food and Agriculture Research, National Research Foundation Singapore, Novo Nordisk, Office of Science, Biological and Environmental Research, Oak Ridge National Laboratory

OpenAlex ID: W7203477133