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

Area of Science:

• Ecology
• Environmental Science
• Computational Science

Background:

• Ecosystem models are vital for environmental policy and conservation.
• Reliance on Multi-Model Ensembles (MMEs) is growing, but scientific rigor for reliability is often lacking.
• Poorly designed MMEs can lead to flawed decisions.

Purpose of the Study:

• To outline best practices for developing robust, decision-making-relevant MMEs in ecosystem modeling.
• To emphasize the critical roles of Model Intercomparison Projects (MIPs) and stakeholder co-design.
• To address challenges and highlight opportunities for advancing ecosystem modeling through MMEs.

Main Methods:

• Reviewing existing MIPs-MMEs efforts in related fields (e.g., climate, crop modeling).
• Developing a theoretical basis for MMEs in ecosystem modeling.
• Identifying challenges such as model diversity, parameter calibration, and data needs.

Main Results:

• Robust MMEs require rigorous MIPs for inter-model comparison and benchmarking.
• Co-design involving stakeholders ensures decision-relevant outputs.
• AI can support MIPs-MMEs, driving scientific progress and innovation.

Conclusions:

• MMEs can evolve into a trusted backbone for environmental decision-making with actionable best practices.
• Systematic benchmarking and targeted model improvement are key outcomes of MIPs-MMEs.
• Adopting guidelines will enhance the reliability and utility of ecosystem model ensembles.