BioScience · 2017 · 146 citations · 97 references
Natural ResourcesEnvironmental SciencesEcosystem ManagementEcosystem Services PolicyEnvironmental Decision-makingEcosystem FunctionEcosystem FunctioningEcosystem Service ModelingEcosystem Services AssessmentEcosystem Services ManagementEnvironmental Decision SupportDecision-support ToolsEcological Decision SupportEcological Decision-makingEcosystem Service ProvisionEcosystem ServicesResearch FrontiersScenariosWell-being
Decision‑makers increasingly rely on science to understand how nature supports human well‑being, yet existing empirical ecosystem‑service models are often difficult to adapt and rarely predict multiple services across diverse scenarios. This study identifies three critical research frontiers—spatial‑temporal dynamics of ecosystem services, their link to human well‑being, and the role of technology in substituting or enhancing services—to guide future decision‑support tools. The authors examine each frontier in depth, outlining its significance and describing how current leading knowledge can be integrated into improved ecosystem‑service decision‑support tools.
Many decision-makers are looking to science to clarify how nature supports human well-being. Scientists' responses have typically focused on empirical models of the provision of ecosystem services (ES) and resulting decision-support tools. Although such tools have captured some of the complexities of ES, they can be difficult to adapt to new situations. Globally useful tools that predict the provision of multiple ES under different decision scenarios have proven challenging to develop. Questions from decision-makers and limitations of existing decision-support tools indicate three crucial research frontiers for incorporating cutting-edge ES science into decision-support tools: (1) understanding the complex dynamics of ES in space and time, (2) linking ES provision to human well-being, and (3) determining the potential for technology to substitute for or enhance ES. We explore these frontiers in-depth, explaining why each is important and how existing knowledge at their cutting edges can be incorporated to improve ES decision-making tools.
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