Multi-model ensembles for ecosystem prediction

TL;DR

This paper introduces a Bayesian statistical framework to combine multiple ecosystem models, accounting for their biases and uncertainties, to improve predictions of ecosystem outcomes under various scenarios.

Contribution

It presents a novel flexible meta-model that integrates diverse mechanistic ecosystem models using Bayesian methods, enhancing prediction accuracy and uncertainty quantification.

Findings

The approach effectively combines different models for robust ecosystem predictions.

Application to fishing scenarios demonstrates improved decision support.

The framework quantifies uncertainty in ecosystem forecasts.

Abstract

When making predictions about ecosystems, we often have available a number of different ecosystem models that attempt to represent their dynamics in a detailed mechanistic way. Each of these can be used as simulators of large-scale experiments and make forecasts about the fate of ecosystems under different scenarios in order to support the development of appropriate management strategies. However, structural differences, systematic discrepancies and uncertainties lead to different models giving different predictions under these scenarios. This is further complicated by the fact that the models may not be run with the same species or functional groups, spatial structure or time scale. Rather than simply trying to select a 'best' model, or taking some weighted average, it is important to exploit the strengths of each of the available models, while learning from the differences between them. To achieve this, we construct a flexible statistical model of the relationships between a collection or 'ensemble' of mechanistic models and their biases, allowing for structural and parameter uncertainty and for different ways of representing reality. Using this statistical meta-model, we can combine prior beliefs, model estimates and direct observations using Bayesian methods, and make coherent predictions of future outcomes under different scenarios with robust measures of uncertainty. In this paper we present the modelling framework and discuss results obtained using a diverse ensemble of models in scenarios involving future changes in fishing levels. These examples illustrate the value of our approach in predicting outcomes for possible strategies pertaining to climate and fisheries policy aimed at improving food security and maintaining ecosystem integrity.