DEVELOPING A GIS APPLICATION FOR ASSESSING, MAPPING, AND QUANTIFYING THE SOCIAL VALUES OF ECOSYSTEM SERVICES

As human pressures on ecosystems continue to increase, research needs involving the effective incorporation of social values information into the economic context of comprehensive ecosystem services assessments are becoming more critical. Including quantified and spatially explicit measures of social values in such assessments will improve the analysis of tradeoffs among ecosystem services. In response to these needs, the Rocky Mountain Geographic Science Center (RMGSC) has been developing a GIS application, Social Values for Ecosystem Services (SolVES). SolVES can be used to assess, map, and quantify the perceived social values of ecosystem services through the derivation of a quantitative social values metric, the Value Index, from a combination of spatial and non-spatial responses to public attitude and preference surveys. SolVES also generates landscape metrics calculated from spatial data layers describing the underlying physical environment, for example, average elevation and distance to water, at locations along the Value Index gradient. The initial phase of development focused on survey data previously collected by researchers at Colorado State University regarding the Pike and San Isabel National Forests in Colorado. Using kernel density calculations and zonal statistics, SolVES derives and maps the 10-point Value Index and reports landscape metrics associated with each index value for social value types such as aesthetics, biodiversity, and recreation. This can be repeated for various survey subgroups as distinguished by their attitudes and preferences regarding public uses of the forests such as motorized recreation and logging for fuels reduction. The Value Index provides a basis of comparison within and among survey subgroups to consider the effect of social contexts on the valuation of ecosystem services. Additionally, SolVES output facilitates statistical analysis of the relationship between the variation in index values and landscape metrics through correlation and multiple regression. These statistical methods have been used to generate regression coefficients, that when applied to their corresponding landscape data layers, have generated predicted social value maps that compare favorably with SolVES output. Based on these results, a predictive mapping function permitting value transfer to similar areas where survey data are not available has been added to SolVES. A more robust version of SolVES is being developed as a public domain tool, enabling decision makers and researchers to map the social values of ecosystem services and to facilitate discussions among diverse stakeholders involving the tradeoffs between different ecosystem services in a variety of physical and social contexts.