Simplifying the selection of evidence synthesis methods to inform environmental decisions: a guide for decision makers and scientists

Achieving evidence-based environmental management requires that decision-makers have access to evidence that can help identify the most effective interventions for their management context. Evidence synthesis supports evidence-based decision-making because it collates, filters and makes sense of a sometimes large and often conflicting evidence-base, potentially yielding new insights. There are many approaches to evidence synthesis. They each have different strengths and weaknesses, making them suited to different purposes, questions and contexts, given particular constraints. To make sense of the wide array of approaches, we outline the important considerations when selecting the most appropriate method for a particular decision context. These include the purpose for the synthesis, the required outcomes, and the multiple constraints within which decision-makers must operate. We then critically assess a spectrum of approaches to evidence synthesis commonly used within environmental management, detailing the characteristics of each that can be used to determine when it is a suitable method. To guide this selection process we provide a decision tree for those commissioning (e.g., decision-makers or stakeholders) or conducting (e.g., scientists) evidence synthesis, which can be used to identify an appropriate method. The decision tree classifies evidence synthesis methods according to whether their purpose is to test or generate hypotheses, the level of resources they require, the level of certainty in the outputs, and the type and scope of the question being addressed. This tool is a major advance because it helps select an appropriate synthesis method based on the multiple constraints that impact the decision. We conclude that there is an approach to evidence synthesis that will suit all management contexts, but that selecting the right approach requires careful consideration of what is fit for purpose.