A critical assessment of available ecosystem services data according to the Final Ecosystem Goods and Services framework. Issue 3 (18th March 2019)

• Record Type: Journal Article
• Authors: Tashie, Arik; Ringold, Paul
• Is Part Of: Ecosphere. Volume 10:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-03-18
• Subjects: classification system -- ecosystem services -- land use -- landcover; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.2665
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: The last decade has seen a proliferation of studies describing the benefits people accrue from natural processes by translation of spatially explicit land use and landcover data to ecosystem service provision. Yet, critical assessment of systemic bias resulting from reliance on land use and landcover data is limited. Here, we evaluate an extensive collection of ecosystem service‐related data based on land use and landcover according to a broadly applicable ecosystem service framework—Final Ecosystem Goods and Services (FEGS). In this framework, ecosystems are viewed from the perspective of a comprehensive set of beneficiaries and the biophysical features directly relevant to each. In this examination, we create a database identifying over 14, 000 linkages between 255 data layers from EnviroAtlas and FEGS beneficiaries. Through these linkages, we identify major gaps in beneficiary identification and systemic biases resulting from the utilization of translations from land use and landcover data. Importantly, we find that for many beneficiaries there is an absence of data on FEGS at extensive scales in the United States. We provide a roadmap for the integration of extant ecosystem service research efforts using the FEGS classification scheme and critically appraise this scheme, highlighting inconsistent specification among beneficiary categories and environmental classes. We also explore the benefits of crosswalking different ecosystem service data and frameworks for researchers, by reducing the otherwise high buy‐in cost of data exploration, and for data developers, by increasing the exposure of their work.