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Using social-context matching to improve spatial function-transfer performance for cultural ecosystem service models

Abstract

Recreational and aesthetic enjoyment of public lands is increasing across a wide range of activities, highlighting the need to assess and adapt management to accommodate these uses. Despite a growing number of studies on mapping cultural ecosystem services, most are local-scale assessments that rely on costly and time-consuming primary data collection. As a result, the availability of spatial information on non-market values associated with cultural ecosystem services (social values) remains limited. Spatial function transfer, if it could be justified for social-value models, would expedite the development of social-value information and promote its more regular inclusion in ecosystem service assessments. We used survey data from six national forests in Colorado and Wyoming to explore the potential for transferring cultural ecosystem service models between forests and specifically to test the hypothesis that transfer performance increases with social-context similarity between transferring and receiving areas. Results confirm this relationship but fall just short of being able to predict with certainty when transferred models will meet the minimum performance criterion needed for defensible use by managers. Social values are highly variable and can be difficult to predict, but our results suggest that with the right combination of indicators spatial function transfer can become a defensible means of generating social-value information when primary data collection is not feasible.

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90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 37.727280276860036° to 45.00753503123719° latitude; -111.05529785156249° to -104.798583984375° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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BibTeXRIS

Darius J. Semmens, Benson C. Sherrouse, Zachary H. Ancona. 2019. Using social-context matching to improve spatial function-transfer performance for cultural ecosystem service models. https://doi.org/10.1016/j.ecoser.2019.100945

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