How to make ecological models useful for environmental management

TLDR

Ecological models are increasingly needed to predict the consequences of management actions, yet they have historically been underused in practical decision making. This synthesis paper outlines how to enhance ecological modeling’s usefulness for environmental management by focusing on methodological improvements. The authors propose six key requirements—mechanistic causality, decision‑aligned inputs/outputs, suitable spatial/temporal resolution, uncertainty quantification, sufficient predictive performance, and transparent communication—to guide model development and use for management decisions.

Abstract

Understanding and predicting the ecological consequences of different management alternatives is becoming increasingly important to support environmental management decisions. Ecological models could contribute to such predictions, but in the past this was often not the case. Ecological models are often developed within research projects but are rarely used for practical applications. In this synthesis paper, we discuss how to strengthen the role of ecological modeling in supporting environmental management decisions with a focus on methodological aspects. We address mainly ecological modellers but also potential users of modeling results. Various modeling approaches can be used to predict the response of ecosystems to anthropogenic interventions, including mechanistic models, statistical models, and machine learning approaches. Regardless of the chosen approach, we outline how to better align the modeling to the decision making process, and identify six requirements that we believe are important to increase the usefulness of ecological models for management support, especially if management decisions need to be justified to the public. These cover: (i) a mechanistic understanding regarding causality, (ii) alignment of model input and output with the management decision, (iii) appropriate spatial and temporal resolutions, (iv) uncertainty quantification, (v) sufficient predictive performance, and (vi) transparent communication. We discuss challenges and synthesize suggestions for addressing these points.