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Resistance-Surface-Based Wildlife Conservation Connectivity Modeling: Summary of Efforts in the United States and Guide for Practitioners

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Resistance-Surface-Based Wildlife Conservation Connectivity Modeling: Summary of Efforts in the United States and Guide for Practitioners United States Department of Agriculture Resistance-Surface-Based Wildlife Conservation Connectivity Modeling: Summary of Efforts in the United States and Guide for Practitioners Alisa A. Wade, Kevin S. McKelvey, Michael K. Schwartz Forest Service Rocky Mountain Research Station General Technical Report RMRS-GTR-333 June 2015 Wade, Alisa A.; McKelvey, Kevin S.; Schwartz, Michael K. 2015. Resistance-surface-based wildlife conservation connectivity modeling: Summary of efforts in the United States and guide for practitioners. Gen. Tech. Rep. RMRS-GTR-333. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 93 p. Abstract Resistance-surface-based connectivity modeling has become a widespread tool for conservation planning. The current ease with which connectivity models can be created, however, masks the nu- merous untested assumptions underlying both the rules that produce the resistance surface and the algorithms used to locate low-cost paths across the target landscape. Here we present a process to guide map creation, from conceptualization through validation, that seeks to better consider the complex biological issues inherent to connectivity modeling. Following this organized approach to connectivity modeling will help analysts prevent a plethora of issues common in recently created models, such as the failure to specify the temporal domain, purpose of the mapped connectivity, or the biological ratio- nales for assigned pixel-level resistances. Following these steps will improve both the understanding and biological relevance of constructed connectivity maps. Keywords: connectivity, resistance surface, least-cost path, cost distance modeling, validation, models, conservation. Authors Alisa A. Wade is a private consultant conservation scientist and an affiliate faculty with the University of Montana, Department of Geosciences, Missoula, Montana. Kevin S. McKelvey and Michael K. Schwartz are research ecologists, U.S. Forest Service, Rocky Mountain Research Station, Wildlife and Terrestrial Ecosystems, Missoula, Montana. Cover—Connectivity modeling of greater sage-grouse leks (black dots) in southwest Montana. Resistance of land cover to gene flow is hypothesized and connectivity models are generated by comparing landscape attributes to measures of genetic connectivity. The connectivity map is provided courtesy of Todd Cross ([email protected], 406-209-8633). The sage grouse photo is provided courtesy of John Carlson: Photo Copyright - John Carlson. Table of Contents Chapter 1: Introduction ...........................................................................................1 Chapter 2: Ecological Framework and Key Definitions for Modeling Connectivity .........................................................................................5 Chapter 3: Resistance-Surface-Based Connectivity Modeling: Defining a Process and Reviewing the Literature ...........................................15 Chapter 4: The Importance of Validating Wildlife Connectivity Models ...........43 Chapter 5: Practitioner Efforts ............................................................................. 53 Chapter 6: Guiding Questions for Practitioners .................................................65 Chapter 7: Conclusions and Synthesis ...............................................................69 Literature Cited ......................................................................................................71 Appendices ............................................................................................................ 82 Chapter 1. Introduction “Corridors are a hot topic, perhaps even a fad, in conservation planning these days. ... Planners...are busy drawing...habitat corridors...sometimes with only a vague awareness of the biological issues underlying the corridor strategy” (Noss 1987). Despite originating in a seminal paper dating back a quarter century, Reed Noss’s comment remains relevant today. During that time, the initial controversy surrounding whether wildlife corridors are cost or ecologically effective (the “corridor controversy” reviewed in Anderson and Jenkins 2006) has given way to general acceptance of the importance of landscape connectivity (Hilty and others 2006). Yet, there remains a lack of consensus about exactly what landscape connectivity entails (Crooks and Sanjayan 2006), how it should be modeled and quantified (Beier and others 2008; Kindlmann and Burel 2008; Huber and others 2010; Sawyer and others 2011; Zeller and others 2012), and whether sufficient biological understandings underlie current landscape linkage designs (Bélisle 2005; Chetkiewicz and others 2006; Beier and others 2009; Lowe and Allendorf 2010; Parks and others 2012). Here we primarily focus on connectivity defined as the degree to which a landscape facilitates wildlife movement (sensu Taylor and others 2006). In the face of increasing habitat loss and fragmentation (Wilcove and others 1998), as well as the potential need for species range shifts under novel climates (Heller and Zavaleta 2009; Carroll and others 2010), the debate surrounding the definition, modeling, and implementing of connectivity has not stopped an explosion of research concerning connectivity for wildlife conservation (Crooks and Sanjayan 2006), nor has it slowed efforts amongst land use planners and managers to design connectivity projects. Imple- menting connectivity on-the-ground, however, has high political and economic costs, as well as large opportunity costs: finite resources invested in protecting a putative linkage will not be available for protecting other areas. Thus, it is imperative that linkages be modeled carefully; modeling should be rooted in the principles of both wildlife ecology and model uncertainty. Planning linkage-based conservation should follow a clearly defined process to ensure that the problems are properly framed, landscapes are properly modeled, and the correct methods and metrics have been applied. Early in the process, conservation practitioners must clearly define their conservation goals and consider whether increasing connectivity is the highest priority management approach. Connectivity is not a panacea and is just one of many tools available to land managers (Simberloff and Cox 2005). Other landscape management options (reviewed in Lindenmayer and others 2008) include maintaining large patches of native vegeta- tion, managing for structurally complex and heterogeneous habitats, and creating buffers surrounding sensitive and core habitat areas. Further, certain forms of connectivity may not be ecologically feasible or cost-effective for some species and landscapes (Simber- loff and others 1992; Hannon and Schmiegelow 2002). However, recent studies have provided additional data to support thoughtfully planned connectivity (Collinge 1998; Tewksbury and others 2002; Damschen and others 2006; Worboys and others 2010; Gilbert-Norton and others 2010). A clearly defined conservation goal supporting the decision to increase connectivity will guide practitioners in selecting from a large suite of connectivity modeling meth- ods. Each method has its own unique set of assumptions and best practices, and each produces slightly different types of maps. Connectivity modeling is not a one-size-fits- all endeavor. For example, if the goal is to measure whether a landscape is connected, USDA Forest Service Gen. Tech. Rep. RMRS-GTR-333. 2015 1 construction of landscape graphs between habitat patches may be sufficient (reviewed in Galpern and others 2011). Similarly, for some very coarse scale connectivity mapping, where connectivity is modeled at regional to continental scales, expert opinion may be the only option due to data limitations (e.g., Jongman 1995; process summarized in Beier and others 2011). At the other end of a complexity spectrum are spatially explicit population models (SEPM). These models link population processes and landscape features to assess demographic consequences of connecting populations, and may be particularly helpful in assessing scenarios of land cover or climate change (reviewed in Carroll 2006). If spatially explicit connections are required—e.g., the goal is to design and imple- ment linkages by purchasing or otherwise protecting specific areas—there remain many options. For example, individual Based Models (IBM) that simulate animal movement using species-specific behavioral rules (e.g., movement angles) and mortality risks in response to landscape elements can be applied. There are also IBM-like approaches that use observed animal movement data to assess landscape connectivity (e.g., “morpho- logical image mapping,” Vogt and others 2009). However, the most common type of spatially explicit connectivity modeling uses resistance surfaces. Resistance-surface-based connectivity modeling is a relatively accessible method that does not require excessive data or computational resources. Resistance surfaces represent the degree to which some landscape feature impedes or facilitates some movement process (Adriaensen and others 2003), typically represented as a cell (pixel) value in a grid (raster) within a Geographic Information System (GIS). Linkages are then modeled in areas with lowest resistance to the movement process considered. The models are relatively easy to apply given existing data, and the approach offers the flexibility to develop models ranging from simple to complex, tailored to the specific conservation needs, and able to be refined as better
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