Evaluating alternative methods for biophysical and cultural ecosystem services hotspot mapping in natural resource planning View Full Text


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Article Info

DATE

2017-01

AUTHORS

Kenneth J. Bagstad, Darius J. Semmens, Zachary H. Ancona, Benson C. Sherrouse

ABSTRACT

Data for biophysically modeled and Public Participatory GIS (PPGIS)-derived cultural ecosystem services have potential to identify natural resource management synergies and conflicts, but have rarely been combined. Ecosystem service hot/coldspots generated using different methods vary in their spatial extent and connectivity, with important implications. We map biophysically modeled and PPGIS-derived cultural services for six U.S. national forests using six hot/coldspot delineation methods. We evaluate the implications of hotspot methods for management within and outside of designated wilderness areas. We used the ARIES and SolVES modeling tools to quantify four biophysically modeled and 11 largely cultural ecosystem services for six national forests in Colorado and Wyoming, USA. We mapped hot/coldspots using two quantile methods (top and bottom 10 and 33 % of values), two area-based methods (top and bottom 10 and 33 % of area), and two statistical methods (Getis-Ord Gi* at α = 0.05 and 0.10 significance level) and compare results within and outside wilderness areas. Delineation methods vary in their degree of conservatism for hot/coldspot extents and spatial clustering. Hotspots were more common in wilderness areas in national forests near the more densely populated Colorado Front Range, while coldspots were more common in wilderness areas in more urban-distant forests in northwest Wyoming. Statistical hotspot methods of intermediate conservatism (i.e., Getis-Ord Gi*, α = 0.10 significance) may be most useful for ecosystem service hot/coldspot mapping to inform landscape scale planning. We also found spatially explicit evidence in support of past findings about public attitudes toward wilderness areas. More... »

PAGES

77-97

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10980-016-0430-6

DOI

http://dx.doi.org/10.1007/s10980-016-0430-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016502777


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