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Get Free AccessEcosystem degradation represents one of today's major global challenges, threatening human well-being and livelihoods worldwide. To reverse continuing degradation, we need to understand its socio-economic consequences so that these can be incorporated into ecosystem management decisions. This requires links to be made between our understanding of how ecosystems function and change, with socially meaningful representations of those changes. While increasing attempts are being made at such integration, the interface or translation between those two strands remains largely undiscussed. This carries the risk that key aspects of the socio-ecological interactions become 'lost in translation'. In this paper, we document and discuss how models of ecosystem change may be combined with socially meaningful outcomes exposing and discussing the translation process itself (i.e. the 'translation key'). For this, we use an exemplar based on peatland condition. We employ a process-based model, DigiBog, to simulate the effects of land use on blanket peatlands, which we relate to estimates of changes to the public's well-being derived from peatland degradation and restoration, obtained as monetary values from a choice experiment survey in Scotland (UK). By quantifying linkages between environmental conditions and social values, we make the translation between these system components transparent and allow value estimates to be recalculated under different ecological scenarios, or as new evidence emerges. This enhances the replicability of the research and can better inform decision-making. By using peatlands as the exemplar ecosystem, this paper also contributes to a limited body of evidence on the socio-economic impacts of changes to the most space-effective carbon store in the terrestrial biosphere.
Mark S. Reed, Julia Martin‐Ortega, Dylan M. Young, Klaus Glenk, Andy J. Baird, Laurence Jones, E.C. Rowe, Chris Evans, Martin Dallimer (2021). Linking ecosystem changes to their social outcomes: Lost in translation. , 50, DOI: https://doi.org/10.1016/j.ecoser.2021.101327.
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Type
Article
Year
2021
Authors
9
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1016/j.ecoser.2021.101327
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