Model specifications for place-specific recreation models developed for the San Pedro case study

From a spatial perspective, we can map sources of recreational value (areas capable of providing the natural setting needed for a particular activity), sinks of recreational value (landscape features that reduce those source values, if applicable), and the users of a particular recreation area for a given activity. A recreation flow model accounts for travel from a person’s home to a particular location suitable for that recreational activity. Travel cost and recreational site choice studies also use basic transportation routing to connect recreationists to recreation sites, but typically do not map the process or facilitate comparisons with other ecosystem services (Clawson and Knetsch 1966, Hunt et al. 2005, Hunt 2008). Users may simultaneously value a particular bundle of recreational attributes (e.g., the quality of an area for hunting or fishing plus the quality of scenic views), built infrastructure meant to enhance the recreational experience (i.e., trails, other facilities), congestion or lack thereof (with potential resulting visitor response), and the management policies that facilitate a particular recreational experience (Lawson and Manning 2002, Arnberger and Haider 2007, Boyd and Banzhaf 2007, Bullock and Lawson 2008). The ARIES recreation models are intended to solely map an ecosystem’s capacity to support a particular recreational activity, as opposed to the other attributes that contribute to the overall quality of a recreational experience, with the understanding that only the ecosystem attributes supporting recreation represent actual natural capital and are thus the ecosystem service (Boyd and Banzhaf 2007). By mapping the ecosystem’s contribution toward different recreational attributes, we can explore tradeoffs between different types of recreational use, tradeoffs between recreation and other ecosystem services, and relative preferences for certain recreational attributes. Please see the ARIES modeling guide for full documentation and references for these models.


Recreation source: Open space capable of providing a particular type of recreational experience (e.g., wildlife for viewing, hunting, fishing; scenic views), exclusive of the area’s infrastucture, congestion, and management policies.

Recreation sink: A feature that depletes the quanitity of a recreational source value for human users (e.g., visual blight that detracts from view quality, landscape features that deplete wildlife populations valued for recreational activities).

Recreation beneficiaries: People who travel to a recreation area to engage in a given recreational activity.

Recreation flow: The movement of people from residences to recreation sites, typically via road networks.

Model structure and assumptions

Case studies 1. San Pedro River Watershed, Arizona

Recreation source models: Birding, hunting, and wildlife viewing. Other ecosystem services researchers have mapped potential recreational value across the landscape by overlaying factors including viewsheds or visibility (Eade and Moran 1996, Chen et al. 2009), proximity or access to roads, population centers, or recreation infrastructure (Eade and Moran 1996, Boyd and Wainger 2003, Chan et al. 2006, Beier et al. 2008), and land ownership and cover characteristics (Boyd and Wainger 2003, Chan et al. 2006). Most of these authors, however, develop a general model of recreation site quality, rather than looking at sites’ suitability for a specific recreational activity. We thus draw selectively from the factors these studies use to model recreation, depending on the particular type of recreation model we are interested in constructing. Potentially valuable birding areas can be identified using spatial data for bird species richness and the presence of rare birds. We map the presence of rare and charismatic birds by noting the number of bird species’ habitats present, based on a list of ten rare or charismatic birds for the San Pedro River Watershed and surrounding mountains (Southwest Wings Festival 2010). Hunting potential can be identified based on habitat maps for the above-listed game species – javelina plus two species each of deer and doves and three species of quail. We map wildlife viewing potential by averaging decile values for diversity of amphibian, bird, mammal, and reptile species. We set the source value for birding, hunting, and wildlife viewing as a function of public access, potential presence of surface water (springs or streams) and riparian habitat quality (where known), along with the appropriate bird richness and rarity, harvestable species habitat, or overall biodiversity value. We set priors for each variable based on reviews of the corresponding spatial data. High diversity of birds and wildlife or habitat for rare or game species are clear prerequisites for supporting related recreational activities, as is public access, particularly in states like Arizona where access on private lands is likely to be controlled. We set these factors as the strongest influences on recreation source values in their respective contingent probability tables. We set the presence of perennial or intermittent surface water, including streams and springs, as an important but slightly lesser influence on source values in the contingent probability tables, since the presence of surface water is highly important for attracting wildlife in arid environments. Where riparian condition is known, we assigned higher values for birding, hunting, and wildlife viewing quality to higher-quality riparian areas.

Recreation sink models. Sinks will be present for some, but not all types of recreation. For most types of recreation where the source value can be assessed in situ, no sink model is necessary. This is true for the birding, hunting, and wildlife viewing models for the San Pedro, where we do not specify the dependence of a particular species on additional habitat outside its currently mapped habitat. However, habitat-based flow models could eventually be developed or an existing ones incorporated to account for spatial dependencies in wildlife habitat (e.g., Semmens et al. 2011). For viewsheds, the sink model identifies areas of visual blight that reduce view quality, similar to the viewshed model described in the aesthetic viewshed module. We assumed that obstructions (e.g., buildings, topography, or vegetation) or undesirable visual features (blight associated with development, energy infrastructure, or roads) reduce view quality (Benson et al. 1998, Bourassa et al. 2004, Gret-Regamey et al. 2008). Views of lost forest cover, including clearcuts, could also reduce view quality (Palmer 2008, Wundscher et al. 2008). We set prior probability distributions using corresponding spatial datasets.

Recreation use models: Birding, hunting, and wildlife viewing. Initial mapping of recreational use relies on population or housing density data. For some activities, it may be possible to estimate the percentage of the population taking part in that recreational activity (i.e., the number of licensed hunters or anglers in a state relative to its total population). Representing users as a uniform percentage of the population engaging in a particular activity makes the admittedly naive assumption that the same percentage of recreational users across all communities engage in a particular activity. It also assumes that different user groups for the same activity have similar preferences, which is not always a realistic assumption. For example, Hunt et al. (2005) found urban and rural hunters to prefer different types of hunting experiences. A more realistic model would account for the fact that different types of communities are likely to prefer different recreational activities and to value attributes of a particular recreational experience differently. Indeed, in some cases individuals will choose their location of residence to provide proximity to an especially valued recreational amenity. Our initial use models for the San Pedro start simply with a population density map and anecdotal information on total visitation and the distance groups typically travel to reach the SPRNCA. We then assign the home locations of visitors to the SPRNCA based on population density for the estimated number of visitors coming from within the watershed, from the Tucson area, and from more distant locations. This is an admittedly simplistic way to map visitors, but in the absence of better data (e.g., surveys where visitors identify their zip code of origin), it at least enables mapping of the spatial dependencies between recreation areas and recreationists. These simplistic assumptions about visitation can be easily replaced with actual data in locations where better survey data are available.

Recreation flow models. With information on how many visitors participate in a given activity at a particular recreation site and how far they travel, we can complete a simple flow model by distributing the visitor population across the landscape based on a population density map and road networks that incorporate data on trailhead locations (if applicable for that activity). This allows the model to estimate travel times for people traveling from their residences to recreational sites. Where data are available, zip code based travel cost, recreational preference surveys, or permit data can show how far people travel to a particular site. For a given recreational area, this will result in a map showing from where its user population is likely derived. Zip code data are often available from state park systems and for the National Park Service through the University of Idaho Park Studies Unit’s Visitor Services Project. For instance, on the San Pedro, birders visit the site from around the nation and world. Hunters and other recreational users (e.g., hikers, mountain bikers, equestrians, viewers of historical sites) are less likely to travel great distances. These visitors are more likely to come from “local” areas such as the San Pedro Valley itself or from Tucson, while recreationists from Phoenix are more likely to choose closer sites for their activities, and more rarely travel to the San Pedro. Key outputs from the flow models include: 1. Recreational user flow: The movement of people toward recreation areas, based on transportation networks, recreational preferences, site quality, and a distance decay function. 2. Recreational use: The amount of recreational use actually seen at a recreation area when accounting for demand and spatial flows of visitors. 3. Actual recreational users: The residential location of users who actually travel to sites via recreation flows to engage in recreational activities at source areas. 4. Transportation restricted recreational use: Recreational areas whose current accessibility via transportation networks makes their use level more limited than their attractiveness alone would dictate.

Spatial data

Models Data theme Source Spatial extent Spatial resolution Year
Source – San Pedro Amphibian, bird, mammal, reptile species richness USGS Southwestern Biological Center Sonoran Desert Research Station AZ, CO, NM, NV, UT Vector polygon data 1999-2001
Habitat for game species Southwest Regional GAP Analysis LULC (SWReGAP) AZ, CO, NM, NV, UT 240 m 1999-2001
Public lands Arizona Geographic Information Council Arizona Vector polygon data 2000
Rare & charismatic bird habitat presence Southwest Regional GAP Analysis LULC (SWReGAP) AZ, CO, NM, NV, UT 240 m 1999-2001
Riparian condition class Stromberg et al. (2006) SPRNCA Vector polygon data 2001-2004
Springs Arizona Geographic Information Council Arizona Rasterized point data n/a
Hydrograpy National Hydrography Dataset Arizona Vector line data n/a
Use – All models Population density U.S. Census Bureau Arizona Vector polygon data 2000-2007
Flow – All models Roads: speed limits/travel capacity TIGER/Line files United States Vector line data 2000
Flow – San Pedro Roads Arizona Geographic Information Council Arizona Vector line data 2009
Trails BLM SPRNCA Vector line data Not available



Arnberger, A. and W. Haider. 2007. Would you displace? It depends! A multivariate visual approach to intended displacement from an urban forest trail. Journal of Leisure Research 39 (2): 345-365.

Beier, C.M., et al. 2008. Ecosystem services and emergent vulnerability in managed ecosystems: A geospatial decision-support tool. Ecosystems 11: 923-938.

Benson E.D., et al. 1998. Pricing residential amenities: the value of a view. Journal of Real Estate Finance and Economics 16: 55-73.

Bourassa, S.C., et al. 2004. What’s in a view? Environment and Planning A 36: 1427-1450.

Boyd, J. and L. Wainger. 2003. Measuring ecosystem service benefits: The use of landscape analysis to evaluate environmental trades and compensation. Discussion Paper 02-63, Resources for the Future: Washington, DC.

Boyd, J., and S. Banzhaf. 2007. What are ecosystem services? The need for standardized environmental accounting units. Ecological Economics 63: 616-626.

Bullock, S.D. and S.R. Lawson. 2008. Managing the ‘commons’ on Cadillac Mountain: A stated choice analysis of Acadia National Park visitors’ preferences. Leisure Sciences 30 (1): 71-86.

Chan, K.M.A., et al. 2006. Conservation planning for ecosystem services. PLOS Biology 4 (11): 2138-2152.

Chen, N., et al. 2009. A GIS-based approach for mapping direct use of ecosystem services at the county scale: Management implications. Ecological Economics 68 (11): 2768-2776.

Chhetri, P. and C. Arrowsmith. 2003. Mapping the potential of scenic views for the Grapmians National Park. In: Proceedings of the 21st International Cartographic Conference, Durban, South Africa, 10-16 August 2003.

Clawson, M. and J. Knetsch 1966. Economics of outdoor recreation. Johns Hopkins University Press: Baltimore, MD.

Eade, J.D.O. and D. Moran. 1996. Spatial economic valuation: Benefits transfer using geographical information systems. Journal of Environmental Management 48: 97-110.

Goonan, K.A., et al. 2007. Using science to manage Northern Forest tourism and recreation. Adirondack Journal of Environmental Studies 14 (2): 6.

Gret-Regamey, A., et al. 2008. Linking GIS-based models to value ecosystem services in an Alpine region. Journal of Environmental Management 89: 197-208.

Hunt, L.M., et al. 2005. Accounting for varying setting preference among moose hunters. Leisure Sciences 27 (4): 297-314.

Hunt, L.M. 2008. Examining state dependence and place attachment within a recreational fishing site choice model. Journal of Leisure Research 40 (1): 110-127.

Hunziker, M. and F. Kienast. 1999. Potential impacts of changing agricultural activities on scenic beauty- a prototypical technique for automated rapid assessment. Landscape Ecology 14: 161-176.

Lawson, S.R. and R.E. Manning. 2002. Tradeoffs among social, resource, and management attributes of the Denali Wilderness Experience: A contextual approach to normative research. Leisure Sciences 24 (3): 297-312.

Manning, R., et al. 2006. Recreation Monitoring at Acadia National Park. The George Wright Forum 23(2): 59-72.

Marcot, B.G., et al. 2006. Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36: 3063-3074.

Palmer, J.F. 2008. Perceived effects of clearcutting in the White Mountains of New Hampshire, USA. Journal of Environmental Management 89 (3): 167-183.

Semmens, D.J., et al. 2011. Accounting for the ecosystem services of migratory species: Quantifying migration support and spatial subsidies. Ecological Economics 70(12):2236-2242.

Southwest Wings Festival. 2010. 19th Annual Southwest Wings Birding and Nature Festival, Program Guide. Southwest Wings Festival: Sierra Vista, AZ.

Stromberg, J.C., et al. 2006. Status of the riparian ecosystem in the Upper San Pedro River: Application of an assessment model. Environmental Monitoring and Assessment 115: 145-173.

U.S. Forest Service (USFS). 1974. The visual management system. In: National Forest Landscape Management, Vol. 2. U.S. Department of Agriculture, Washington, DC.

U.S. Forest Service (USFS). 1995. Landscape aesthetics: A handbook for scenery management. USDA-Forest Service Agriculture Handbook Number 701.

Wundscher, T., et al. 2008. Spatial targeting of payments for environmental services: A tool for boosting conservation benefits. Ecological Economics 65: 822-833.

Zube, E.H., et al (eds). 1975. Landscape Assessment. Community Development Series 11: Halsted Press, United States.

Acknowldegements and additional contributors

An expert review panel including individuals from the U.S. Geological Survey, University of Arizona, Bureau of Land Management, and other organizations provided data and model review for the San Pedro case study.

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