Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services

Highlights

• SolVES creates spatially explicit models of cultural ecosystem service values.

• SolVES has been applied in a variety of ecosystem contexts around the globe.

• SolVES 4.0 has been redeveloped for QGIS.

• The open-source platform will help to expand the user base.

• Future development would enhance users' ability to delineate stakeholder groups.

Abstract

Social Values for Ecosystem Services (SolVES) version 4.0 is a fully open-source, GIS-based tool designed to aid in the creation of quantitative, spatially explicit models of the nonmonetary values attributed to cultural ecosystem services, such as aesthetics and recreation, specifically to facilitate their incorporation into larger ecosystem service assessments. Newly redeveloped for QGIS, SolVES can be applied in a wide variety of biophysical and social contexts including mountain, forest, coastal, riparian, agricultural, and urban settings worldwide. Redeveloping SolVES for an open-source platform was intended to expand its user base by eliminating the cost of proprietary GIS software licenses and to remove the impact of proprietary software changes on SolVES development. Providing additional options would enable users to delineate relevant stakeholder groups to better assess how differing preferences impact the intensity and spatial distribution of perceived social values.

Introduction

Increasing the use of open-source software in geographic information system (GIS) research areas including public participation GIS (PPGIS) and ecosystem services has been recommended to expand the potential for collaborative research and development efforts as well as to increase the number of users who can apply GIS to planning and decision-making processes across the globe (Muenchow et al., 2019). Although open-source software comes with its own challenges and limitations, it offers benefits that we have chosen to leverage by redeveloping Social Values for Ecosystem Services (SolVES) (Sherrouse et al., 2011, 2014) as an open-source tool for the spatially explicit modeling of relationships between human values and environmental characteristics. A recent study by Petway et al. (2020) provides a clear example of how open-source development can assist users in modeling these relationships. The lack of an available license led the authors to forego the ArcGIS version of SolVES and instead replicate its methodology using the R software environment (https://www.r-project.org/) and QGIS (https://www.qgis.org) to model cultural ecosystem service values in Taiwan. Ending the dependency of SolVES on proprietary software provides an opportunity to expand its user base and to define a path of future development to improve value-modeling efforts that is not impacted by proprietary software release schedules or license availability.

Valuation of nature is a process requiring the use of various methods to elicit information from different stakeholders, which leads to results that can be qualitatively different and inconsistently represented (Kronenberg and Andersson, 2019). Kronenberg and Andersson (2019) describe the complexity of relationships between social, monetary, and ecological values that necessitates a social-ecological approach to valuation that is more comprehensive. Such approaches can provide a more explicit accounting for the social value of various aspects of nature—a diversity of conflicting values that are grounded in a wide array of cultural experiences and research disciplines from religion and social psychology to indigenous knowledge and philosophy (Kenter et al., 2019). Consideration of social values, as noted by Katz-Gerro and Orenstein (2015), necessarily broadens our perspective on the value of ecosystem services to humans. This is particularly beneficial when evaluating cultural ecosystem services that, due to their non-materiality (Katz-Gerro and Orenstein, 2015), tend to be a relatively neglected component of ecosystem service assessments and decision-making (Chan et al., 2016; Daniel et al., 2012; Gould et al., 2020). We have previously defined social values as nonmarket values perceived by stakeholders often corresponding to cultural ecosystem services such as aesthetics and recreation (Sherrouse et al., 2014) and continue to use it as the operationalized definition of social values for SolVES analyses. This definition presents value as a magnitude of preference, one of four concepts of value in a values typology described by Tadaki et al. (2017), which also includes the value concepts of contribution to a goal, individual priorities, and relations. Values representing magnitudes of preference or contributions to a goal are often operationalized by valuation tools serving to measure human-environment relations (Tadaki et al., 2017). Information regarding perceived social values representing magnitudes of preference across a value typology, when elicited from the public in a spatially explicit format, can help provide a basis for robust modeling of the relationships between human valuation and the environmental factors with which values are associated—modeling that remains underrepresented to date (Drechsler, 2020) and would assist with addressing the growing demand that stakeholders be more engaged in environmental modeling (Voinov et al., 2016).

The collection of data from the public to inform social-values modeling takes various forms. Photo-based surveys of landscape perception have been the most common method (Dorning et al., 2017). Volunteered geographic information garnered from social media are an increasingly frequent source of data as well (e.g., Clemente et al., 2019; Holtslag, 2017). The interest in PPGIS, which is based on the spatially explicit collection and use of stakeholder data through participatory planning processes, has grown significantly over the past 20 years (Fagerholm et al., 2021). A component of many PPGIS data collections (e.g., Alessa et al., 2008; Brown et al., 2002; Raymond et al., 2009; Wolf et al., 2017) is a value typology originally suggested by Rolston and Coufal (1991) for forest values. Over time, the typology has been modified and adapted and came to be the basis for the social-values typology originally implemented in SolVES (Sherrouse et al., 2011, 2014). Although PPGIS data collection, most commonly via surveys, can be challenging due to resource, institutional, and legal constraints (Brown and Reed, 2009), the resulting data provide great utility for operationalizing the spatial modeling of relationships between human value perception and underlying environmental characteristics, which is recognized as one of the research frontiers of cultural ecosystem services (Gould et al., 2020). Furthermore, the ability to crosswalk between social-value types and ecosystem services, particularly cultural services (Sherrouse et al., 2011, 2014) facilitates the development of SolVES as an effective tool to explicitly account for social values on par with economic and ecological values when assessing ecosystem services. It should be noted, however, that this crosswalk is not a perfect one-to-one relation, and certain elements of commonly used typologies are seemingly more akin to economic or ecological values (e.g., economic, life sustaining) as opposed to social values or cultural services. These do, however, still provide quantitative information regarding publicly perceived values that are not otherwise captured by economic markets or by traditional ecological measures.

SolVES was originally developed as a custom toolbar for ArcGIS® to assess, map, and quantify the social values of ecosystem services (Sherrouse et al., 2011, 2014). Although it was documented upon its initial release (Sherrouse et al., 2011), a full description of the SolVES tool as it has evolved over time has not been published. Certain elements of its design have remained constant whereas others have changed substantially. The relative intensity and spatial distribution of a social value are rendered by SolVES as a 10-point, value-index map derived from modeling the relationship between value and preference data collected from survey respondents and potentially explanatory environmental variables. Individual social-value maps can be generated for survey respondents as a whole or for specific survey subgroups (stakeholder groups) defined by any number of distinguishing characteristics such as demographic groups, preferred recreational activities or means of accessing the area, points of origin, season of use, or attitudes and preferences regarding topics of concern for a study area. Additionally, social-value models developed for a primary study area can be transferred by SolVES to areas of similar biophysical and social context that lack their own survey data. The appropriateness of these benefit function transfers, hereafter referred to as social-value transfers, can be evaluated by consulting user-provided metadata for each model describing the environmental variables and the socio-economic and demographic composition of survey respondents, which facilitates the assessment of site similarities (Sherrouse and Semmens, 2014; Semmens et al., 2019). This, however, is but one factor when considering social-value transfer and should not be considered sufficient on its own. A discussion of various caveats and problems associated with such transfers, such as the compounding of errors in original study data, can be found in Sherrouse et al. (2014). Finally, the processing framework implemented with SolVES allows it to be applied in almost any social or ecological context based on data availability and without the need for site-specific recoding of the software. Each of these existing elements, capabilities, and features of SolVES now exists in a fully open-source environment with the development of SolVES 4.0.

This paper describes the recently developed SolVES 4.0 to distinguish it as an important, open-source GIS tool for social-value modeling. SolVES 4.0 uses the same modeling framework implemented with the previous version of SolVES, version 3.0. However, it offers this framework to an expanded audience of potential users by eliminating reliance on proprietary software. Our objectives are to provide a description of the following:

• the design, capabilities, and data requirements of SolVES 4.0

• the relationship between the SolVES 4.0 user interface, modeling parameters, and underlying methodology

• the geographic and topical range of previous SolVES applications to demonstrate the utility of SolVES 4.0

• important considerations for planning and conducting an analysis with SolVES 4.0

• potential directions for developing future versions and refining analyses.