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