An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming

Ecological Indicators 36 (2014) 68–79

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming Benson C. Sherrouse a,∗ , Darius J. Semmens b , Jessica M. Clement c a b c

U.S. Geological Survey, 5522 Research Park Drive, Baltimore, MD 21228, USA U.S. Geological Survey, PO Box 25046, DFC, MS-980, Denver, CO 80225, USA University of Wyoming, Ruckelshaus Institute, Bim Kendall House, 804 Fremont Street, Laramie, WY 82072, USA

a r t i c l e

i n f o

Article history: Received 28 February 2013 Received in revised form 4 June 2013 Accepted 11 July 2013 Keywords: Social–ecological systems Cultural ecosystem services Ecosystem-based management National forests Decision support Geographic information system

a b s t r a c t Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders. With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. Published by Elsevier Ltd.

1. Introduction 1.1. The need for a social-value indicator The inclusion of social-value information is critical to the design of effective decision frameworks to support ecosystem services-based approaches to resource management and conservation (Daily et al., 2009). However, unlike ecological and economic values, notably absent from existing ecosystem service valuation efforts is the consistent inclusion of quantitative, social-value

∗ Corresponding author. Tel.: +1 443 498 5606; fax: +1 443 498 5510. E-mail addresses: [email protected] (B.C. Sherrouse), [email protected] (D.J. Semmens), [email protected] (J.M. Clement). 1470-160X/$ – see front matter. Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.ecolind.2013.07.008

information. This lack of consideration for social values, defined here as nonmarket values perceived by ecosystem stakeholders (often corresponding to specific cultural ecosystem services such as aesthetic, recreational, and even spiritual services), has been recognized by researchers from a variety of perspectives (Chan et al., 2012; Kumar and Kumar, 2008; Raymond et al., 2009; Tyrväinen et al., 2007). Completed assessments, however, fall short of addressing the various recommendations for inclusion of socialvalue information in ecosystem assessments (Carpenter et al., 2006; Cowling et al., 2008; de Lange et al., 2010; Millennium Ecosystem Assessment, 2005; Nijkamp et al., 2008). This deficiency is compounded by the general correspondence of social values with cultural ecosystem services, which themselves are not adequately integrated within the ecosystem services framework (Daniel et al., 2012).

B.C. Sherrouse et al. / Ecological Indicators 36 (2014) 68–79 Table 1 Description of the social value types included in the SNF, BTNF, and PSI value and preference surveys. Social value type

Social value description

Aesthetic

I value these forests because I enjoy the scenery, sights, sounds, smells, etc. I value these forests because they provide a variety of fish, wildlife, plant life, etc. I value these forests because they are a place for me to continue to pass down the wisdom and knowledge, traditions, and way of life of my ancestors I value these forests because they provide timber, fisheries, minerals, and/or tourism opportunities such as outfitting and guiding I value these forests because they allow future generations to know and experience the forests as they are now I value these forests because they have places and things of natural and human history that matter to me, others, or the nation I value these forests in and of themselves, whether people are present or not I value these forests because we can learn about the environment through scientific observation or experimentation I value these forests because they help produce, preserve, clean, and renew air, soil, and water I value these forests because they provide a place for my favorite outdoor recreation activities I value these forests because they are a sacred, religious, or spiritually special place to me or because I feel reverence and respect for nature there I value these forests because they provide necessary food and supplies to sustain my life I value these forests because they make me feel better, physically and/or mentally

Biodiversity Cultural

Economic

Future Historic

Intrinsic Learning

Life sustaining Recreation Spiritual

Subsistence Therapeutic

1.2. Defining an effective social-value indicator The social values used in the current study are based on a forest values typology originally proposed by Rolston and Coufal (1991), modified and validated by Brown and Reed (2000), and applied through numerous community-based surveys (e.g., Alessa et al., 2008; Brown, 2005; Brown et al., 2002, 2004; Clement and Cheng, 2011). Variations of these value typologies have been alternatively referred to as ecosystem values (Reed and Brown, 2003), environmental values (Brown et al., 2002, 2004), landscape values (Alessa et al., 2008), and wilderness values (Brown and Alessa, 2005). Social surveys of three national forests conducted by Clement and Cheng (2011), which included such an established socialvalue typology (Table 1) were the basis for our study. Through the elicitation of responses from a random sample of stakeholders, the surveys were designed to examine commonalities and differences among the three forests regarding values, attitudes, and preferences representative of the “silent majority” within the surrounding communities. Using statistical methods commonly applied to social-survey data, including frequency analysis, analysis of variance (ANOVA), discriminant function analysis (Stevens, 2002), and correlation analysis, Clement and Cheng (2011) identified and measured value differences as well as determined value orientations that were predictive of stakeholder attitudes regarding issues and uses relevant within each forest. What also arose from their analyses, however, were complex, and some seemingly contradictory, relationships among stakeholder values, attitudes, and preferences. Although these complex relationships were identified through the traditional analysis methods used by Clement and Cheng (2011), they could not be adequately explained with existing information. Additional information is required to determine the drivers of the complex relationships existing among stakeholder values, attitudes, and preferences. This would assist forest managers

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and stakeholders in assessing the trade-offs involved in resolving related conflicts of values, uses, and management actions. Some of this additional information is potentially contained in results from value allocation and mapping exercises included in Clement and Cheng’s (2011) surveys. These additional quantitative and spatial value data provide us with an opportunity to address two critical questions with our current study: (1) is it possible to derive from these survey data a quantitative, spatially explicit social-value indicator that effectively reproduces information provided by more common survey analysis methods?; and (2) can the addition of an explicit spatial context for analysis more fully account for and communicate trade-offs among the types, locations, and underlying environmental characteristics of intensely held social values? To answer these questions, we need to first consider the nonmarket nature of social values as we have defined them. In their estimation of the total value of the world’s ecosystem services, Costanza et al. (1997) noted that a significant problem with valuation is that many benefits do not pass through economic markets. Within the realm of economics, nonmarket valuation methods such as travel cost (e.g., Hein et al., 2006) and value transfer (e.g., Troy and Wilson, 2006) exist to address this problem. However, the information needed to estimate monetary values may be unavailable, or particular services of interest may not be readily valued with any of the standard economic valuation techniques (Carpenter et al., 2009). In many cases social values, being even more distanced from economic markets, are not readily quantifiable in monetary terms. This is not a critical limitation, however, when considering that disagreements regarding estimated monetary values might distract from the necessary focus on ecosystem management (USDA, 2008) and that certain values might be rendered less meaningful in monetary terms (Daily et al., 2009). Nonmonetary benefit indicators can still improve decision making (Wainger et al., 2010). Given the informative potential of a nonmonetary social-value indicator, we must also consider what else such an indicator can offer by being made spatially explicit. The mapping of social-values information collected through social surveys similar to Clement and Cheng’s has been applied to an array of problems. Examples of these applications include the evaluation of the consistency between management prescriptions for an area and publicly held values for that area through values suitability analysis (Reed and Brown, 2003), identification of hotspots where social and ecological values overlap and indicate areas possibly requiring additional management attention (Alessa et al., 2008), and the assessment of value differences between consumptive and non-consumptive recreationists (van Riper et al., 2012). Mapping of these and other typologies such as community values (Raymond et al., 2009) and landscape services (Fagerholm et al., 2012) are also advanced by the continuing application of public participation geographic information systems (PPGIS) methods that leverage GIS technology to collect spatial information directly from the public (e.g., Brown et al., 2011; Brown and Reed, 2009, 2011; Brown and Weber, 2011, 2012). What results from the mapping of social values is an expression of value, including a spatial component, which allows us to relate social values to the underlying environment. Several studies, to varying degrees, provide examples of how community mapping of social values or ecosystem services through PPGIS or other methods might be used to link perceived values with the underlying environment. For example, Beverly et al. (2008) compared the maximum clustering of mapped value points with mean distance to water and mean road density; Brown and Brabyn (2012) analyzed values data to identify relationships with land-cover data; and Brown et al. (2011) examined spatial associations between land cover and mapped points identifying specific ecosystem services. Finally, most pertinent to the current study’s application of social values is that the mapping of relative social values can be considered similarly to expressions of

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economic value (Brown, 2005). An effective nonmonetary, spatially explicit social-value indicator should leverage these best aspects of previous social-value mapping efforts. 1.3. Evaluating a social-value indicator with SolVES In our attempt to deliver an effective decision-making tool, we operationalized the links between social values and ecological information by developing Social Values for Ecosystem Services (SolVES), a GIS tool to assess, map, and quantify the social values assigned by stakeholders to ecosystem services (Sherrouse et al., 2011). This public-domain tool and supporting documentation are available at http://solves.cr.usgs.gov. Developed as an ESRI ArcGIS toolbar for ArcMap1 , SolVES provides users with three core capabilities: (1) the generation of social-value maps, rendered as a 10-point “value index” (VI) indicator, for various stakeholder groups as derived from a combination of their spatial and nonspatial responses to value and preference surveys; (2) the modeling of relationships between identified value locations and underlying environmental characteristics; and (3) the application of these models through a value-transfer methodology to similar areas where survey data are unavailable. SolVES provides a quantitative, nonmonetary, spatially explicit indicator of social values, which allows value differences within and among stakeholder groups to be consistently expressed and variations in value intensity to be explained in terms of environmental variables. SolVES provides a means to answer the two questions we posed regarding the potential effectiveness of a quantitative, spatially explicit social-value indicator. Guided by these questions, we formulated two study objectives. The first is to evaluate how interpretation of the quantitative VI calculated by SolVES compares to and complements results from other, more common forms of social-survey data analysis. The second is to determine if the spatial results generated by SolVES can provide additional information, beyond that provided by common analysis methods, that is useful for evaluating the relationship among values, attitudes, and preferences, particularly when the interactions among them become more complex. 2. Methods 2.1. Study area The study area consists of three national forests located in Colorado and Wyoming (Fig. 1). The proximity of the Pike and San Isabel (PSI) National Forests to growing, urbanized areas of the Colorado Front Range, encompassing numerous 14,000-ft peaks, wilderness areas, and scenic byways contribute to the area being the third most visited national forest in the nation (USDA, 2012). The Shoshone National Forest (SNF) and Bridger–Teton National Forest (BTNF) are located adjacent to each other along the eastern and southern borders of Yellowstone National Park in Wyoming. The areas surrounding the SNF and BTNF are more rural in character, although growing, and economic activities include recreation and tourism in both areas with livestock grazing and timber in the SNF and natural gas development and agriculture in the BTNF and (Taylor et al., 2008a,b). 2.2. Survey data In support of the efforts of the management of the three national forests to revise their forest plans as required by the National Forest

1 Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Fig. 1. Study area map showing the location and boundaries of the Shoshone (SNF), Bridger–Teton (BTNF), and Pike and San Isabel (PSI) National Forests.

Management Act of 1976 (NFMA), random mail surveys were conducted during the period from 2004 to 2008, with a response rate of approximately 34% for each (Clement and Cheng, 2011). Each survey was developed with input from U.S. Forest Service staff, with the Wyoming forest surveys receiving substantive input from the general public regarding content prior to distribution. Each survey consisted of five sections, including questions and exercises regarding: (1) familiarity with the forests; (2) attitudes regarding uses of the forests as defined by a 5-point Likert scale with values ranging from 1 = Strongly Favor to 5 = Strongly Oppose; (3) policy preferences for revisions to each forest plan; (4) allocation of a hypothetical $100 among values included in a social-values typology along with mapping points on forest maps at locations perceived as representing such values; and (5) demographic information. The spatial and nonspatial responses to survey sections 2 and 4 serve as the primary survey data inputs for SolVES analysis. 2.3. Spatial data The mapped points in each forest collected in section 4 of the surveys were digitized and loaded into the SolVES source geodatabase along with the associated value-allocation data from section 4 and the responses from section 2. Data from the different survey sections were related to each other using a unique identifier for each individual survey. Environmental layers, in the form of 30m resolution rasters were also loaded into each geodatabase. These layers included elevation, slope, distance to roads (DTR) (Watts et al., 2007), distance to water (DTW), National Land Cover Database (NLCD) 2006 land cover, and elevation-derived landform (Table 2). Although these specific environmental layers were selected for inclusion in the current study, SolVES can support the analysis of varying numbers and types of user-supplied, spatially explicit

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Table 2 Environmental data layers included in SolVES analysis. Layer name

Description

Source

Elevation

Digital elevation model (DEM) in meters

Slope Distance to roads (DTR)

Percent slope Horizontal distance to nearest road in meters

Distance to water (DTW)

Horizontal distance to nearest water body in meters

Land cover

16-Class categorical land cover data

Landform

10-Class categorical landform data

USGS National Elevation Dataset (NED), http://seamless.usgs.gov/website/seamless/viewer.htm Derived from elevation layer using ArcGIS Slope tool USGS The Road Indicator Project (TRIP) NORM ED Datasets, http://rmgsc.cr.usgs.gov/trip/data/ Derived from USGS National Hydrography Dataset (NHD), http://viewer.nationalmap.gov/viewer/nhd.html?p=nhd, using ArcGIS Euclidian Distance tool USGS National Land Cover Database (NLCD) 2006 Land Cover, http://www.mrlc.gov/nlcd06 data.php Derived from elevation layer using AML scripts available from USGS Southwest Regional Gap Analysis Project (SWReGAP) at http://earth.gis.usu.edu/swgap/landform.html

environmental variables, so long as they are provided in a raster format. Polygon layers representing national forest boundaries and nearby state and county boundaries were also included in the geodatabase. 2.4. SolVES 2.0 and Maxent We noted at the initial release of SolVES that development would continue in order to produce a more refined and robust tool for ecosystem assessment, valuation, and planning (Sherrouse et al., 2011). To that end, the functionality of SolVES has been improved through the development of SolVES 2.0, which provides a more powerful tool with greater user flexibility. SolVES 2.0 provides several enhancements including the ability of users to define their own social-value typologies and public uses, set their own analysis output scale and kernel-density search radius, vary the type and number of environmental layers to include in their analysis, and have the option to apply a buffer around their study area to include mapped value points falling outside any formal boundary. One of the most significant changes introduced with SolVES 2.0 is the integration with Maxent maximum entropy modeling software (Elith et al., 2010; Phillips et al., 2004, 2006; Phillips and Dudík, 2008). This allows SolVES to generate more complete socialvalue maps and to produce robust statistical models describing the relationship between social-value intensities and explanatory environmental variables. Originally developed to model the geographic distribution of species, the modeling framework of Maxent provides a fitting analogue for application to mapping social values for ecosystems. Maxent relies on point data representing observations of plant and animal species presence. Using these presence-only point data and environmental variables judged by the user to influence the suitability of the environment for a selected species, Maxent applies a machine-learning method to estimate the probability distribution of maximum entropy (i.e., the distribution that is closest to uniform) that satisfies constraints represented by the environmental variables. In other words, the geographic distribution of the selected species is assumed to be uniform across an area except as otherwise indicated by the relationships Maxent models between the presence points and the underlying environmental variables. Most pertinent to SolVES 2.0 is the logistic output Maxent generates. Each cell contains a value ranging from 0 to 1, representing the probability that a location is suitable habitat for a species given the environmental conditions and the known presence of that species. In a social-values mapping context, the logistic output represents the probability that survey respondents would ascribe a social-value type to a location given the underlying environmental characteristics and the survey respondents’ identification of such locations by the mapping of points representing that value type. In either context, the points represent

an expression of value, whether it is related to a species’ particular ecological niche (Elith et al., 2010; Phillips et al., 2004) or to a location a stakeholder perceives as providing a social value they consider important. Studies documenting the application of Maxent beyond the realm of species-distribution mapping to spatial problems of primarily human dimensions (e.g., Bajat et al., 2011; Banks et al., 2011; Braunisch et al., 2011; Heumann et al., 2011; Jenks et al., 2012) also suggest the appropriateness of applying Maxent in a social-values context. Maxent’s logistic output provides more complete maps within a study area where value and preference survey data are available. Maxent also enhances SolVES functionality by generating robust statistical models describing the relationship between mapped points and environmental variables. These models can be used by SolVES to transfer values to similar locations where survey data are unavailable. As described in Sherrouse et al. (2011), SolVES generates results based on user-selected parameters including a specific public use (e.g., motorized recreation) and an attitude or preference (i.e., favor or oppose). This combination of use and attitude defines specific stakeholder groups for analysis. Alternatively, analyses that include all survey data can be generated by not selecting a stakeholder group. Users must also select social-value types for which they wish to generate results. Based on these parameters, SolVES selects the corresponding mapped points along with the related valueallocation data. From the selected data, weighted kernel-density surfaces are generated for every social-value type included in the survey (e.g., if 12 value types were included in the survey then 12 surfaces are generated) with the total amount allocated to each value type serving as the weight applied to mapped points of that same value type. Following from the design of the relevant survey sections, these weights do not vary from point to point but instead from value type to value type because survey respondents are asked to allocate amounts to the value types not the individual points (e.g., all aesthetic points will be weighted by the same total amount allocated to aesthetic value). Nielsen-Pincus (2011) questioned whether the inclusion of weights provides any additional benefit for analyzing value intensities and if it might make completing value-mapping exercises more difficult. The mapping and value-allocation components of the Nielsen-Pincus (2011) survey, however, were combined into a single exercise with weights pre-assigned on stickers for mapping values rather than conducted separately as they were by Clement and Cheng (2011). The kerneldensity weighting methodology is similar to that used by Alessa et al. (2008) to map social–ecological hotspots on the Kenai Peninsula, Alaska, and is based on a quadratic kernel function (Silverman, 1986). Once the weighted kernel-density surfaces have been generated for every social-value type, SolVES identifies the cell containing the single highest density value among all the surfaces. SolVES then

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Fig. 2. Generalized process flow of Social Values for Ecosystem Services (SolVES) 2.0 illustrating how user-supplied survey and environmental data are processed by SolVES and Maxent to generate value index (VI) maps and associated environmental metrics.

scales, or normalizes, the weighted kernel-density surfaces associated with every user-selected value type relative to this single overall maximum density value. The normalization of the weighted kernel-density surfaces can frequently result in highly positively skewed value distributions. This can limit the usefulness of valueindex maps generated for all but a few of the most highly rated social-value types (i.e., value maps for many lower-rated social values quickly diminish to rendering almost no value information). To counter this effect, a square-root transformation is applied to each of the normalized surfaces. The normalized surfaces are composed of dimensionless values ranging from 0 to 1 resulting in a monotonic transformation. The normalized surfaces are then standardized to a 10-point, value-index scale generating intermediate value-index maps for each selected social-value type. Each of these intermediate value-index maps includes a location or locations where the social-value type attained its highest value on the value index. We refer to this maximum value from each social-value map as Max VI, which represents the maximum value attained by each social-value type relative to all other social-value types as calculated for a specified stakeholder group. Max VI is multiplied by its corresponding Maxent logistic surface generated using the same set of user-selected points included in the kernel-density analysis and modeled according to the constraints provided by the environmental layers included in the analysis. Max VI provides the necessary information to scale the logistic output according to the relative values identified through the normalization and standardization of the weighted kernel-density surfaces. The resulting surfaces are again standardized to generate the final value-index maps for each user-selected social-value type. SolVES then uses these final value-index maps to calculate zonal statistics for each environmental layer, which are presented as sets of environmental metrics (mean values for quantitative variables and majority or dominant values for categorical variables) alongside the corresponding maps. A generalized flow diagram summarizes the process from the input of survey and environmental data to the output of social-value maps and environmental metrics (Fig. 2). Average nearest-neighbor statistics for points associated with each social-value type are also calculated to evaluate the relative clustering or dispersion through completely spatial random hypothesis testing, which has been applied previously in similar value-mapping studies (e.g., Brown et al., 2002; Clement, 2006). The statistics include the ratio of the observed distance between points to the expected distance between points, the R value, along with a Z score measuring each R value’s number of standard deviations from the mean. R values of less than 1 with highly negative Z scores indicate statistically significant spatial clustering of points while R values greater than 1 with highly positive Z scores indicate statistically significant dispersion. R values near 1 indicate spatial randomness.

2.5. Data analysis The application of SolVES to the survey data collected for the three forests was guided by results reported by Clement and Cheng (2011). In particular, we focused on three specific sets of their results, which they determined through the use of frequency analysis, ANOVA, discriminant function analysis, and correlation analysis. First, they identified general agreement across all three forests regarding the highest-rated values. Second, they identified economic and recreation value types as consistent indicators of positive and negative attitudes or preferences regarding particular uses of the forests. Third, related to the issue of oil and gas drilling in the BTNF, they observed what they termed a “wicked” problem, which is described by Shindler and Cramer (1999) as a set of complex, interrelated problems with solutions dependent upon one another. A complicated association of multiple values with various resource uses and management options characterize such “hotspot” issues. In particular, recreation value is unexpectedly, negatively correlated with wilderness designation in the BTNF. This presents a seemingly contradictory alignment between values and attitudes or preferences that makes understanding how forest management actions would affect different stakeholder groups more difficult. To compare SolVES results with Clement and Cheng’s (2011) first set of results, SolVES was applied to all survey data from each of the forests (i.e., no stakeholder groups were specified) and the Max VI for each social-value type along with the associated number of points, total allocation amounts, and average nearestneighbor statistics were compiled. To compare with their second set of results, SolVES was used to generate economic and recreation value-type results for the stakeholder groups favoring and opposing uses of particular concern in each of the forests (logging for wood products in the SNF, oil and gas drilling in the BTNF, and motorized recreation in the PSI). Finally, for their third set of results, recreation value was analyzed for stakeholder groups favoring and opposing wilderness in the BTNF. The associated value maps were visually inspected then analyzed further by applying zonal statistics to calculate the mean values of the value index (Mean VI) for the entire forest and within the wilderness areas for both stakeholder groups. Mean VI represents the average value attained on the value index within a defined boundary for a selected social-value type and stakeholder group, which provides a useful indicator for comparing overall value intensities across different geographic areas. Statistics including the mean or dominant value of each environmental layer were also calculated for both the entire forest and the wilderness areas. All spatial results were generated at an output cell size of 450 m. This was determined from the smallest scale among the maps included with each forest survey. In this case, the PSI maps had

2.72 4.77 1.19 13.45 5.01 1.66 1.88 8.93 11.72 3.28 1.68 10.83 3.54 −24.265 −16.544 −3.938 −6.877 −16.115 −5.021 −8.451 −3.018 −15.427 −21.534 −5.047 – −9.729 0.412 0.444 0.757 0.610 0.490 0.736 0.603 0.811 0.477 0.432 0.699 – 0.585 466 242 72 85 273 99 124 70 238 393 77 – 150 9089 7408 2338 2868 7580 3061 4023 2650 9001 9036 2676 – 4226 75 68 32 34 70* 42 43 40** 71** 66 33 – 50* 10 6 2 3 8 3 5 3 7 8 2 – 4 −18.302 −16.182 −8.447 −8.352 −12.800 −5.387 −8.028 −6.268 −12.792 −22.633 −5.045 −7.697 −8.044 0.559 0.543 0.614 0.640 0.537 0.737 0.667 0.640 0.532 0.483 0.762 0.630 0.693 470 342 131 147 209 115 159 83 204 523 123 118 187 5792 6467 1914 3468 4417 2033 2331 1355 5107 7472 1591 2032 2381

*

**

From Clement and Cheng, 2011. Forests with significantly higher mean frequency. Subsistence value was not included in the PSI survey. At p < 0.05, At p < 0.001. a

b

10 8 3 4 6 3 5 2 7 9 2 2 4 412 218 67 90 162 79 76 58 113 386 66 77 125 76 72 28 45** 67* 34 38 33 58 74 28 23 47 Aesthetic Biodiversity Cultural Economic Future Historic Intrinsic Learning Life sustaining Recreation Spiritual Subsistenceb Therapeutic

10 8 3 6 5 4 3 2 4 10 2 2 3

6001 5244 1444 3141 4969 1615 2300 1282 4227 6897 1468 1332 2512

0.623 0.645 0.724 0.900 0.726 0.742 0.875 0.876 0.910 0.577 0.829 0.848 0.717

−14.642 −10.026 −4.321 −1.822 −6.666 −4.390 −2.077 −1.806 −1.833 −15.904 −2.657 −2.552 −6.047

76 75* 37 49** 65 47 41 37 67 77* 30 29** 46

F-valuea

ANOVA

Z score R value N Allocation %a Max VI

PSI

Z score R value N Allocation %a Max VI

BTNF

Z score R value N Allocation %a

The results from Clement and Cheng’s (2011) frequency analysis and ANOVA testing of the surveys’ value-allocation data are the first on which we focused. The purpose of the Clement and Cheng (2011) frequency analysis and ANOVA testing of the surveys’ value-allocation data was to identify commonalities and differences in the rating of values among the three forests. Clement and Cheng (2011) did this by examining the percentage of survey respondents who allocated any amount of a hypothetical $100 to a social-value type. Results of the frequency analysis indicated general agreement across the three forests regarding the most highly rated social-value types: aesthetic, recreation, biodiversity, and future. Life-sustaining value was also indicated among the most highly rated values in the BTNF and the PSI. The comparable SolVES results, as indicated by Max VI, show similar value ratings, including the relatively higher rating of life-sustaining value in the BTNF and the PSI (Table 3). The only instance in which the SolVES results depart from the results of the frequency analysis is with the rating of economic value in the SNF. Here Max VI indicates that the rating for economic value exceeds that for future value. A comparison of the allocation amounts (future = 4969, economic = 3141), number of points (future = 162, economic = 90), and R values (future = 0.726, economic = 0.900) for the two value types would otherwise appear to indicate agreement with Clement and Cheng’s results. The results of their ANOVA testing, however, indicated that the frequency of survey respondent allocations to economic value was significantly higher for the SNF (as well as for the BTNF) than for the PSI. The second set of Clement and Cheng’s (2011) results, obtained through discriminant function and correlation analyses, revealed that the economic and recreation value types are consistent indicators of positive or negative attitudes or preferences across all three forests. In particular, their results indicated significant (p < 0.01, 2-tailed) positive correlations between both of these value types and logging for wood products in the SNF (economic = 0.373, recreation = 0.170), oil and gas drilling in the BTNF (economic = 0.426, recreation = 0.144), and motorized recreation in the PSI (economic = 0.227, recreation = 0.386). The comparable SolVES results demonstrate similar orientations between values and attitudes or preferences. The Max VI indicates a positive orientation with attitudes and preferences when the value is higher for a stakeholder group favoring a use than for one who opposes it and a negative orientation when the value is higher for a stakeholder group opposing a use than for one who favors it. The result is that positive orientations of Max VI correspond to positive correlations while negative orientations of Max VI correspond to negative correlations. In all six instances included here, positive orientations are indicated by the Max VI of the favoring stakeholder group exceeding the Max VI of the opposing stakeholder group (Table 4). Additionally, the difference in Max VI between favoring and opposing groups tends to increase with the correlation coefficient values. The associated social-value maps are also provided to illustrate how SolVES spatially represents such value differences (Fig. 3).

SNF

3.1. Max VI comparison to other statistical analyses

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Max VI

3. Results

Social value type

an approximate scale of 1:450,000 while the SNF and BTNF maps had an approximate scale of 1:360,000. Use of a 450-m output resolution allowed for consistent spatial results for comparison across the forests without overstating the spatial resolution of the PSI data. The kernel-density search radius was also kept constant for all three forests at 4500 m, the default value calculated by SolVES as 10 times the user-entered output cell size. Analysis of the mapped points was limited to only those falling within the formal national forest boundaries as they were presented on the survey maps.

Table 3 Value index maximum (Max VI), percent of respondents who allocated any amount to value type (%) with associated ANOVA F-value, total allocation amount, point count (N), and average nearest neighbor statistics (R value and Z score) for each social value type and forest.

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Fig. 3. Economic (left pairs) and recreation (right pairs) value maps for selected stakeholder groups in the SNF, BTNF, and PSI.

3.2. Spatial variation in Mean VI The third set of results involves the wicked problem identified by Clement and Cheng (2011) surrounding the oil and gas drilling issue in the BTNF. A significant element of this problem is what Clement and Cheng describe as an interesting relationship where they found recreation value to be negatively correlated (−0.255, p < 0.01, two-tailed) with wilderness. There is a similar negative orientation in the SolVES results where the maximum recreation

value calculated for those opposed to the use of BTNF for wilderness (Max VI = 10) is greater than for those favoring wilderness in the BTNF (Max VI = 8). An explanation presented by Clement and Cheng is that the forms of recreation in which those opposed to wilderness engage are more oriented to motorized vehicle access (prohibited in wilderness areas) including off-highway recreation and sport hunting. The environmental metrics calculated by SolVES for each stakeholder group do not indicate any particular differences that might help in sorting out this problem (Fig. 4). For both

B.C. Sherrouse et al. / Ecological Indicators 36 (2014) 68–79

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Fig. 4. Recreation value environmental statistics (mean DTR, DTW, elevation, and slope; dominant landform and land cover) relative to VI for stakeholders favoring wilderness in the BTNF (Max VI = 8) and those opposing wilderness in the BTNF (Max VI = 10).

groups, VI increases in flatter areas closer to roads and water and at lower relative elevations. A visual comparison of each group’s recreation value map does, however, indicate differences in the spatial distribution of recreation value intensity across the entire BTNF and within each wilderness area (Fig. 5). The Mean VI across wilderness areas, but specifically within the Bridger Wilderness and the Teton Wilderness, indicates that recreation value is generally higher within wilderness boundaries for those who favor wilderness (a positive orientation) (Table 5). In the Gros Ventre Wilderness, however, the higher Mean VI for those opposed to wilderness indicates the same negative orientation between recreation and wilderness that exists across the entire BTNF as reflected in both Clement and Cheng’s and our results. Most notable from the environmental layer statistics calculated for these same areas is that the mean DTR within the Gros Ventre Wilderness is more similar to that of the entire BTNF than the other two wilderness areas.

4. Discussion 4.1. Evaluating indicator performance Our results demonstrate the utility of SolVES for the spatial analysis of stakeholder values and preferences and validate the ability of SolVES to effectively reproduce information derived from more common methods of social-survey data analysis. Regarding our first objective of evaluating how well the value index compares with results from more common statistical analyses of social survey data, the general consistency between Clement and Cheng’s (2011) statistical results and the SolVES results indicate that the value index, using Max VI, provides an effective indicator for relative

value assessments. Where Clement and Cheng applied frequency analysis and ANOVA tests to data from the value-allocation exercise of section 4 of their surveys to determine the most highly rated values in each forest, SolVES was able to derive similar results by identifying all but one of the same values. In the one exception (economic value rating higher than future value in the SNF), the Max VI calculated for economic and future values indicated relative value differences that would not necessarily be anticipated from the relative number of points, value allocation amounts, and average nearest neighbor statistics for these two value types. However, Clement and Cheng’s ANOVA results indicated that the frequency at which survey respondents allocated to economic value in the SNF (as well as in the BTNF) was significantly higher than in the PSI. This difference in frequency might have also had an effect on the SolVES results. Because Max VI is ultimately derived from a single maximum value contained in one cell located within a series of value surfaces which are, in turn, a function of the interplay of spatial (both social survey and environmental) and nonspatial data, the SolVES results may reveal the effects of such differences in a manner not accounted for in other forms of analyses. SolVES was also able to generate Max VI results that closely reproduced those that Clement and Cheng achieved through discriminant function and correlation analyses in both the directionality of different stakeholder groups’ value orientations and the relative intensity in which those values are held. These comparable results were achieved with a novel approach that incorporates a spatial dimension into the analysis. We addressed our second objective by using the spatial results produced by SolVES to develop a more complete geographic context for the explanation proposed by Clement and Cheng (2011) of the negative correlation between recreation and wilderness in the BTNF. As a reminder, they suggested that the forms of recreation in which those opposed to wilderness engage (off-highway

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Fig. 5. Recreation value maps for stakeholder groups favoring (left) and opposing (right) wilderness in the BTNF. Designated wilderness areas (Bridger, Gros Ventre, and Teton) within the BTNF are outlined.

recreation and sport hunting) are more oriented to motorized vehicle access. The recreation value maps we generated for the groups favoring and opposing wilderness enabled us to derive an additional metric (Mean VI) from the value index for the entire BTNF and the three wilderness areas therein. By comparing the Mean VI for each of the two groups within each area, along with corresponding environmental statistics, we were able to report the spatial variation of recreation value in terms of differing management areas (as defined by wilderness boundaries) and underlying environmental characteristics. From these results, we were able to identify that the negative correlation between recreation and wilderness in the BTNF was reversed in two of the three wilderness areas while remaining negative within the Gros Ventre Wilderness. We also determined from the environmental statistics that the similarity in Mean DTR between the Gros Ventre Wilderness and the entire BTNF meant that DTR is likely a primary driver of the variation in recreation value across the BTNF and among the three wilderness areas. Close examination reveals that the Gros Ventre Wilderness is a prime destination for hunting outfitters in the region (Wilderness Institute, 2012). Although hunting is also allowed in the other wilderness areas, the particular habitat available to big game in the Gros Ventre Wilderness and proximity to popular areas like Jackson, Wyoming, make it a more attractive area for sport hunting. This proximity is reflected in the lower Mean DTR for the Gros Ventre Wilderness. These factors appear to help offset the lack of motorized access within the wilderness area that, given the lower Mean VI measured in the other two wilderness areas, tends to otherwise diminish the recreation value those opposed to wilderness assigned within its boundaries. The value is enhanced by the availability of a use that is both compatible with the management status of the specific geographic area and attractive to the

stakeholder group that is generally opposed to the wilderness designation. Although this, of course, does not completely untangle the complexities of the problem described by Clement and Cheng, it does illustrate one way in which SolVES can provide the necessary spatial context to begin to better characterize and deconstruct hotspots of complex value trade-offs, which resource managers face regularly as part of the decision-making process (Zendehdel et al., 2009). 4.2. Improving links between social values and ecosystem services Additional work is needed to ensure that SolVES provides useful indicators for land and resource managers as they apply ecosystem service-based approaches to environmental decision making. We will continue to identify opportunities to engage managers who can apply SolVES to practical management problems across a variety of different ecosystems. Employing systematic approaches such as those proposed by Frank et al. (2012) and van Oudenhoven et al. (2012) for identifying environmental metrics, beyond the somewhat rudimentary ones used here, will also be important. The use of SolVES can lead to more effective linkages of ecosystem services to social-value assessments. Links between social values and ecosystem services can also be improved through further investigation of proposed methods for calculating and mapping the actual supply and demand of ecosystem services and spatial mismatches between the two (e.g., Burkhard et al., 2012; Semmens et al., 2011), which can further complicate the relationship between values and management prescriptions. Finally, the integration of SolVES and Maxent necessitates that we continue to investigate the most appropriate settings for the various parameters available through the limited application

Table 4 Economic and recreation value index maximum (Max VI), total allocation amount, point count (N), and average nearest neighbor statistics (R value and Z score) for selected stakeholder groups in the SNF, BTNF, and PSI. Forest and public use

SNF Logging for wood products BTNF Oil and gas drilling

Economic

Recreation

Max VI

Allocation

N

R value

Z score

Max VI

Allocation

N

R value

Z score

Favor or strongly favor Oppose or strongly oppose

8 1

2605 134

73 5

0.875 1.869

−2.039 3.716

10 8

4490 1010

224 70

0.615 0.765

−11.037 −3.762

Favor or strongly favor Oppose or strongly oppose

8 2

1996 855

61 47

0.880 0.786

−1.798 −2.810

10 8

2255 3601

101 305

0.599 0.520

−7.704 −16.035

Favor or strongly favor Oppose or strongly oppose

3 2

1526 999

53 15

0.765 1.348

−3.272 2.579

10 5

5018 3052

201 144

0.572 0.624

−11.612 −8.630

Table 5 Recreation value index mean (Mean VI) with standard deviation (SD) and environmental statistics for the entire BTNF, combined wilderness areas, and individual wilderness areas. Area

Favor or strongly favor wilderness Mean VI (SD)

Oppose or strongly oppose wilderness Mean VI (SD)

Mean DTR (SD)

Mean DTW (SD)

Mean elevation (SD)

Mean slope (SD)

Dominant landform

Dominant land cover

Entire BTNF All wilderness areas Bridger Wilderness Gros Ventre Wilderness Teton Wilderness

2.19 (1.24) 1.84 (1.02) 2.01 (1.15) 1.81 (0.84) 1.74 (0.99)

2.57 (1.81) 1.44 (1.23) 1.83 (1.25) 1.99 (1.05) 0.90 (1.04)

4752.10 (6513.40) 10529.69 (7534.92) 8223.99 (4578.39) 4644.41 (2654.96) 15050.33 (8113.55)

497.53 (392.16) 502.33 (407.26) 395.68 (330.76) 683.12 (490.98) 492.69 (382.75)

2656.29 (342.92) 2867.37 (336.15) 3112.41 (284.66) 2773.80 (285.63) 2734.15 (292.83)

30.10 (21.91) 32.66 (25.18) 34.61 (30.00) 34.59 (22.88) 30.32 (22.00)

Moderately dry slopes Moderately dry slopes Moderately dry slopes Moderately dry slopes Moderately dry slopes

Evergreen forest Evergreen forest Shrub/scrub Evergreen forest Shrub/scrub

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PSI Motorized recreation

Attitude or preference

77

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programming interface of Maxent. This could further improve the mapping and modeling of social values. The evaluation of a recent critique regarding Maxent’s performance in estimating occurrence probabilities (Royle et al., 2012) is also warranted to determine any impacts this might have on its application in a social-value context.

5. Conclusions SolVES provides an effective social-value indicator, Max VI. We achieved our first objective by the favorable comparison of the results obtained from SolVES to those of more common forms of social-survey data analysis including frequency analysis, ANOVA, discriminant function analysis, and correlation analysis. SolVES should not be thought of as a replacement for such analyses but as complementary to them. SolVES conveys similar information, but in a spatially explicit format that allows for social values to be integrated with environmental data, which land and resource managers likely find more familiar. The spatial context provided by SolVES output, along with additional social-value indicators such as Mean VI that can be derived from it, allowed us to achieve our second objective of determining how spatial results generated by SolVES can assist with evaluating more complex relationships among stakeholder values, attitudes, and preferences. SolVES provides additional information where more common social-survey analysis methods are limited. This information can assist managers with evaluating geographic hotspots representing complex interactions. Provided with such information, potential value conflicts and trade-offs along with underlying drivers can be more precisely identified and assessed by managers and more clearly communicated back to various stakeholder groups. We have demonstrated through this study that SolVES can incorporate a nonmonetary, spatially explicit, social-value indicator into the evaluation of social-survey data. Results from the SolVES tool are consistent with and complementary to the information provided by more common forms of social-survey analysis. The incorporation of a spatial dimension linking statistical results to relevant ecological information holds great promise for resolving environmental management issues involving complex relationships between stakeholder values, attitudes, and preferences. Particularly important is the potential to facilitate integrated efforts among social and biophysical scientists and managers who can each identify something familiar to relate to and work with in the information SolVES provides. Additionally, the generalized framework SolVES offers allows it to be applied in a variety of different ecosystems such as those in coastal areas (e.g., van Riper et al., 2012) with only the availability of appropriate social survey and environmental data as limiting factors—factors which we plan to improve upon in the future through the application and validation of value-transfer methodologies along with the use of PPGIS methods to facilitate more frequent and rapid collection of social-values information. Finally, future efforts will emphasize securing opportunities for practical applications of SolVES in order to document performance in analyzing trade-offs and to obtain feedback from managers and stakeholders regarding how SolVES can be improved to better meet their needs.

Acknowledgements This research was supported by the U.S. Geological Survey’s Land Change Science Program. Special thanks are extended to Dianna M. Hogan of the U.S. Geological Survey and the journal’s two anonymous manuscript reviewers for their constructive and insightful review comments. Any use of trade, product, or firm names is for

descriptive purposes only and does not imply endorsement by the U.S. Government.

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