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Considering landscape-level processes in ecosystem service assessments

环境科学 景观评价 环境规划 业务 地理 服务(商务) 景观规划 生态系统评价 生态系统管理 计算机科学
作者
Jean Paul Metzger,Jaramar Villarreal-Rosas,Andrés F. Suárez-Castro,Sofía López-Cubillos,Adrian González-Chaves,Rebecca K. Runting,Camila Hohlenwerger,Jonathan R. Rhodes
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:796: 149028- 被引量:4
标识
DOI:10.1016/j.scitotenv.2021.149028
摘要

The provision of ecosystem services is inherently spatial. Landscape structure affects service provision through multiple landscape-level processes, such as fragmentation, edge and connectivity effects. These processes can affect areas of ecosystem service supply and demand, and the flows linking those areas. Despite the emergence of sophisticated spatial ecosystem service assessments in the last two decades, we show through a literature review that landscape-level processes are still rarely considered in a comprehensive way. Even when they are considered, landscape effects are mostly limited to landscape composition, and configuration effects are underrepresented. Furthermore, most studies infer ecosystem service provision by only evaluating supply, ignoring demand and flows. Here we present a simple conceptual framework that illustrates how to incorporate landscape-level processes in the assessment of the different components of the service provision chain (supply, demand and flows). Using simulations, we evaluated how estimations of ecosystem service provision change when considering different landscape processes and discussed the implications of disregarding landscape effects. However, to fully implement the framework, a series of challenges linked to mapping and quantifying supply and demand, defining adequate scales of analysis, measuring flows, and parameterizing models for different types of services, still need to be overcome. To promote an adequate use and management of ecosystem services, it is essential to better incorporate landscape processes in ecosystem service assessments. This will lead to more quantitatively accurate and spatially precise estimates.
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