Wyoming Basins Ecoregional Assessment i Version 2.0, March 2006

ASSESSMENT OF THREATS TO SAGEBRUSH HABITATS AND ASSOCIATED SPECIES OF CONCERN IN THE WYOMING BASINS

Version 2.0, March 2006

Produced by USGS Biological Resources Discipline, in collaboration with:

USDI Bureau of Land Management, USDA Forest Service, Pacific Northwest Research Station, and USDA Forest Service National Forest System

in Partial Fulfillment of Interagency Agreement DLI030016 “Ecoregional Analysis of Sagebrush Ecosystems” between the USGS Biological Resources Discipline and the Bureau of Land Management

Suggested citation for this document:

Rowland, M. M., M. Leu, S. Hanser, S. P. Finn, C. A. Aldridge, S. T. Knick, L. H. Suring, J. M. Boyd, M. J. Wisdom, and C. W. Meinke. 2006. Assessment of threats to sagebrush habitats and associated species of concern in the Wyoming Basins. Version 2.0, March 2006, unpublished report on file at USGS Biological Resources Discipline, Snake River Field Station, 970 Lusk St., Boise, ID 83706.

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AUTHORS Mary M. Rowland, USDA Forest Service, Pacific Northwest Research Station, La Grande, OR Matthias Leu, USGS Biological Resources Discipline, Boise, ID Steve Hanser, USGS Biological Resources Discipline, Boise, ID Sean P. Finn, USGS Biological Resources Discipline, Boise, ID Cameron A. Aldridge, USGS Biological Resources Discipline, Ft. Collins, CO Steven T. Knick, USGS Biological Resources Discipline, Boise, ID; [email protected] 208-426-5208 (Principal Investigator) Lowell H. Suring, USDA Forest Service, Terrestrial Wildlife Ecology Unit, Boise, ID Jennifer M. Boyd, USDA Forest Service, Pacific Northwest Research Station, La Grande, OR Michael J. Wisdom, USDA Forest Service, Pacific Northwest Research Station, La Grande, OR (Principal Investigator) Cara W. Meinke, USGS Biological Resources Discipline, Boise, ID

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ACKNOWLEDGMENTS Any large, interagency, cooperative endeavor such as the Wyoming Basins Ecoregional Assessment requires the participation and help of many entities and people. Mark Hilliard, Cal McCluskey, and Tom Rinkes, USDI Bureau of Land Management, were instrumental in promoting the concept of ecoregional assessment in sagebrush ecosystems to complement other studies and assessments at the Field Office-level of planning. Other BLM state wildlife biologists who provided support and assistance included Robin Sell (Colorado), Signe Sather- Blair (Idaho), Roxanne Falise (Montana), and Steve Madsen (Utah). In particular, we thank Tom Rinkes for his support of the project from its inception and for organizing field visits with BLM offices throughout the study area. Many scientists and managers from various BLM offices took time to listen to our explanations of the assessment’s objectives and then accompanied us in the field to show us the key management issues related to sagebrush communities within their respective Field Offices. We consulted many biologists for their expertise in various taxonomic groups or modeling approaches. These included Gary Beauvais, Carol Dawson, Kristi Dubois, Erica Fleishman, Mark Fuller, Bob Gitzen, Ken Henke, Bonnie Heidel, Vicki Herren, Matt Holloran, Douglas Johnson, Todd Katzner, Doug Keinath, Mike Kochert, Bob Lehman, Stuart Markow (deceased), Bob Oakleaf, Janet Rachlow, Carol Spurrier, Karen Steenhof, William Turner, and Eric Yensen. Adam Kozlowski provided information on pygmy rabbit distributions in Utah and Melanie Purcell for the species in Wyoming; Bridgett Naylor and Tom Zarriello helped with GIS support. Steve Campbell and Dave Trochlell with NRCS provided help with soils layers. Joe Bohne, Tom Christiansen, and Carrie Dobey of the Wyoming Game and Fish Department arranged for our use of sage-grouse lek and pronghorn location data from Wyoming as well as seasonal range maps for several species of concern on our lists. Lydia Bailey of Montana Fish, Wildlife, and Parks assisted with range maps for pronghorn in that state, and Wendy Eklund of Idaho Fish and Game provided the same for Idaho. Jay McLeod, Mitchell Hannon, Jennifer Faulkner, Landy Figueroa, Ben Pieper, and Tori Timmerman assisted in the first year of field sampling. The Lander BLM Field Office staff provided office space and other logistical support for the field crews.

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CONTENTS KEY FINDINGS AND MANAGEMENT IMPLICATIONS EXECUTIVE SUMMARY ARRANGEMENT OF MATERIAL BY CHAPTERS CHAPTER 1: Overview of the Wyoming Basins Ecoregional Assessment CHAPTER 2: Ecological and Administrative Setting CHAPTER 3: Sagebrush-Associated Species of Conservation Concern in the Wyoming Basins CHAPTER 4: Changes in the Wyoming Landscape from Oil and Natural Gas Development CHAPTER 5: Evaluating the Human Footprint in the Wyoming Basins Ecoregional Assessment Area CHAPTER 6: Models of Hypothesized Effects of Threats on Example Species CHAPTER 7: Data Gaps and Deficiencies CHAPTER 8: Management Uses and Benefits APPENDIX 1: Methods of Species Selection, Range Mapping, and Assignment of Sensitivity Scores for Species of Concern APPENDIX 2: Methods for Spatial Analysis of Changes in Landcover Resulting from Oil and Gas Development in Wyoming APPENDIX 3: Background and Methods for Analysis of the Human Footprint in the Wyoming Basins APPENDIX 4: Methods and Rationale Used to Develop Predictive Models for Example Vertebrate Species in the Wyoming Basins Assessment Area APPENDIX 5: Miscellaneous Appendix Tables APPENDIX 6: Assumptions and Limitations in the Wyoming Basins Ecoregional Assessment APPENDIX 7: Evaluating Predictions of the Human Footprint and Example Species Models in the Wyoming Basins Ecoregional Assessment Area

APPENDIX 8: Glossary of Terms

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Key Findings and Management Implications

• The Wyoming Basins Ecoregional Assessment (WBEA) area encompasses one of the most expansive regions of sagebrush habitats remaining in the western United States - more than 26 million acres, representing 25% of the sagebrush in the nation. ¾ As such, management of the WBEA area will have a substantial effect on sagebrush habitats and species as a whole. • Concomitant with the amount of sagebrush habitat, the Wyoming Basins area harbors some of the largest extant populations of sagebrush-obligate species, such as greater sage- grouse and pronghorn. ¾ Future persistence of these sagebrush-obligate species therefore is closely linked to effective management of sagebrush habitats in the Wyoming Basins. • Examples of key potential threats identified for the Wyoming Basins included climate change; roads, trails, and two-tracks; oil and gas development; and invasive and noxious plants. ¾ Holistic management of all such threats, which are increasingly pervasive in landscapes of the Wyoming Basins, is required if associated negative effects on the sagebrush ecosystem are to be substantially or fully mitigated. • Forty vertebrates and 65 vascular plants of conservation concern were identified for regional assessment in the Wyoming Basins. Among the vertebrate species were greater sage-grouse, ferruginous hawk, white-tailed prairie dog, and pygmy rabbit. Plants selected for assessment included dwarf mentzelia, Ownbey’s thistle, and starveling milkvetch. ¾ The large number of species of concern, and the diverse taxonomic groups represented, suggest that no single species or environmental characteristic can be used to manage lands effectively for all species of concern in the Wyoming Basins, instead requiring more comprehensive management of many species and conditions. • Sensitivity to disturbance was quantified for the vertebrates in our assessment, based on life history characteristics; among the most sensitive species were the midget faded rattlesnake, Swainson’s hawk, prairie falcon, pronghorn, and spotted bat.

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¾ Effective management and mitigation of human disturbance is an important component of maintaining populations of species that are sensitive to such disturbance. • Species richness of sagebrush-associated vertebrates of concern was greatest in southwestern Wyoming, where as many as 36 of the 40 vertebrate species of concern co- occur. Moreover, some of the areas identified as most affected by anthropogenic disturbance, as estimated by our human footprint model, are also those that have the greatest species richness. ¾ Human activities occurring in southwestern Wyoming are expected to have disproportionately and substantially greater effects on a larger number of species of concern compared to other portions of the WBEA area. • More than 83,000 oil and gas wells have been drilled in the state of Wyoming since 1960; within the Green River Basin alone, well pads and associated roads have eliminated 138,000 acres of shrubland habitats since 1964. Moreover, mean patch size of shrublands in developed fields of the Basin has decreased from 1,280 acres prior to 1964 to 360 acres in 2004. ¾ The spatially pervasive pattern of these oil and gas wells, the substantial loss in habitat resulting from their development, and their effects on adjacent areas indicate that current and future management and mitigation of this land use will have substantial bearing on persistence of species of concern in the Wyoming Basins. • The potential cumulative impacts of anthropogenic features, as estimated in our human footprint analysis, were daunting: nearly 40% of the sagebrush in the study area fell within the moderate or high human footprint classes under 1 scenario. ¾ Holistic management of all anthropogenic features across the entire Wyoming Basins is therefore needed to maintain or improve conditions within sagebrush communities in the study area. • Predictive models were developed for 10 example vertebrate species of concern: Brewer’s sparrow, ferruginous hawk, greater sage-grouse, loggerhead shrike, pronghorn, pygmy rabbit, sage sparrow, sage thrasher, sagebrush lizard, and short-horned lizard. Results of model application in the study area predicted a low probability of occurrence of these

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species across large areas of the Wyoming Basins, although results varied spatially throughout the assessment area and among species. ¾ Extensive field work to evaluate performance of our human footprint and species models is needed to improve model predictions and their use in management. Given the substantial proportion of the Wyoming Basins estimated to have low probabilities of occurrence for these species, based on hypothesized impacts of human disturbance, field validation is critical in understanding the scope and magnitude of habitat problems confronting land managers in the Wyoming Basins assessment area. • We identified data gaps in spatial layers used in our analyses that resulted in under- estimation of the potential effects of some anthropogenic features in our human footprint and species models. Some potential effects, such as those from livestock grazing and recreation, could not be estimated because spatial data were not available in continuous coverage formats. ¾ Future research and management within the Wyoming Basins could benefit from improvements in spatial data used to estimate environmental conditions across the assessment area.

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EXECUTIVE SUMMARY

The Wyoming Basins and surrounding areas contain approximately 26.1 million acres of sagebrush (Artemisia spp.) habitats, which represent nearly 25% of all sagebrush habitats in the United States. The region contains some of the largest expanses of sagebrush remaining throughout the entire biome: over half of the total land base (55%) in the Wyoming Basins Ecoregion is in sagebrush cover. Almost 70% of the sagebrush habitats are publicly owned. The USDI Bureau of Land Management is the primary management agency and has stewardship over 47% of all sagebrush habitats within this region. Consequently, the land use actions authorized by the Bureau of Land Management potentially impact a large proportion of sagebrush habitats and their dependent wildlife. As part of an integrated process to develop management actions, the Bureau of Land Management requested that the USDI Geological Survey and the USDA Forest Service conduct an ecoregional analysis of the Wyoming Basins region. The objectives of the analysis were to (1) conduct an analysis of existing information to identify the primary land uses and their potential impact on sagebrush habitats; (2) delineate the primary distribution of sagebrush habitats and identify wildlife species of conservation concern; (3) develop spatial models of species distributions and identify the areas most influenced by human activities; and (4) develop and implement procedures to sample habitats for distribution and abundance of invasive species. This draft report is the second deliverable to the USDI Bureau of Land Management and USDA Forest Service of the Wyoming Basins Ecoregional Assessment. The area addressed in the assessment includes the Wyoming Basins and the Utah-Wyoming-Rocky Mountains ecoregions and contains 24 Bureau of Land Management Field Offices in 5 states. The report consists of an introduction and background discussion of the primary issues and the need, rationale, and significance of the assessment (Chapter 1); a quantified description of the amount and distribution of landcover types in the assessment area (Chapter 2); a draft list of species of conservation concern, the process for selecting and modeling distributions of plant and animal species, digitized maps of their geographic ranges, and a classification of their sensitivity to human disturbance (Chapter 3); an analysis of the effects of oil and gas development on landscapes in the Wyoming Basins (Chapter 4); presentation of the effects of anthropogenic disturbance or the “human footprint” (Chapter 5); spatially explicit models of 10 species of

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conservation concern and description of the hypothesized effects of dominant threats to these species (Chapter 6); a discussion of data gaps and deficiencies that pose impediments to parts of our assessment (Chapter 7); and identification and discussion of management uses and benefits (Chapter 8). We also have included 8 appendices that document the methods used to derive the results, describe the limitations and accuracy of current information, and identify specific needs for additional information that would improve our results. Almost all of the sagebrush habitats within the Wyoming Basins Ecoregional Assessment area are managed for multiple use and resource extraction; less than 2% of the sagebrush habitats is within national parks or receives permanent legal protection (Status Class 1) in which only natural processes in the absence of human activities are allowed to influence the system (Chapter 2). The effect of the “human footprint” was based on 11 spatial datasets describing the composite anthropogenic influence (Chapter 5). The secondary road network and agriculture were the dominant features that influenced the largest total proportion (13%) of the assessment area. Relatively low levels of human disturbance characterized most of the assessment area as well as lands managed by the Bureau of Land Management. Within the assessment area, the Kemmerer, Kremmling, and Rawlins Field Offices sustained the greatest levels of human disturbance. Oil and gas extraction significantly impacts the landscape and wildlife but is restricted primarily to the Powder River Basin in northeastern Wyoming and southern Montana (primarily outside the boundaries of our assessment), and the Upper Green River Basin in southern and western Wyoming (Chapter 4). Approximately 60,000 well pads have been constructed in the Power River Basin and 17,000 in the Greater Green River Basin. In both regions, oil and gas development removed habitats due to construction of well pads and supporting infrastructure, such as roads, power lines, and pipelines. In addition, landscapes became increasingly fragmented in each region as a consequence of decreased patch size of sagebrush habitats and increased number of habitat edges concurrent with oil and gas development. Although livestock grazing is a dominant land use throughout the Basin, we lacked suitable information to model the overall impact on sagebrush habitats and landscapes. Other potential threats to long-term stability of sagebrush ecosystems include climate change and drought, and the synergistic feedback loop of invasive plant species and wildfire disturbance.

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We identified plant and animal species of concern within the assessment area by reviewing existing literature, state lists, consultation, and expert review (Chapter 3). Of 65 plant species, 59 were found in Wyoming, 40 in Colorado, 43 in Utah, 28 in Montana, and 15 in Idaho. We listed 40 species of vertebrate animals that depend on sagebrush habitats for some or all of their annual life cycle that included 1 amphibian, 4 reptiles, 18 birds, and 17 mammals. We developed range maps and delineated species richness of these 40 species weighted by an overall sensitivity of the group to disturbance. Using this method, the Missoula Field Office ranked lowest in overall diversity and disturbance. In contrast, the Kemmerer and Rock Springs Field Offices had the highest combination of species richness and sensitivity to disturbance. We then selected 10 species based on (1) strength of their association with sagebrush habitats; (2) either required habitats or species populations at moderate to high risk; and (3) designation by the Bureau of Land Management or other entities as species of special status or regulatory concern. We then estimated the distribution of each species based on statistical models describing the relationship between a species and one or more habitat or disturbance variables (Chapter 6). Each of these models was based on literature values or expert opinions describing the relationship between a species and habitat variables. As such, field validation of the models in 2005 and 2006 will be used to assess the accuracy of the relationships and develop new models that better predict the distribution and response of these species to habitats and disturbance. Results from field surveys, model assessment, and updated GIS coverages will be presented in the Final Report (Phase III). Greater sage-grouse (Centrocercus urophasianus) occupied an estimated 40.5 million acres in the assessment area. Habitat strongholds, based on spatial models of potential human effects and landscape cover of sagebrush, were identified for regions in southwestern Montana (Dillon Field Office) and in north-central and southwestern Wyoming, including the Kemmerer, Lander, Pinedale, and Rock Springs Field Offices. Within the assessment area, the White River, Richfield, and Buffalo Field Offices contained a low percentage of habitats predicted to be used by sage-grouse. The model of ferruginous hawk (Buteo regalis) distribution was based on 2 abiotic variables (elevation, slope), and habitat and environmental variables that influence breeding populations either directly through effects on nesting substrate or indirectly by influencing prey availability. Most of the assessment area (43%) was predicted to have low suitability for

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ferruginous hawks. Most moderate and high suitability areas were in the Green River Basin in Utah, Little Snake River drainage in Colorado, Great Divide Basin and north-central portion of Wyoming, and southwestern Montana. The distribution of Brewer’s sparrows (Spizella breweri) was modeled by a single variable describing habitat (% sagebrush cover) modified by the potential effects of roads on breeding birds. Although Brewer’s sparrows were widely distributed across the assessment area, only 13% of the area had a predicted high probability of occurrence. Primary regions for Brewer’s sparrows were the south-central and western portion of Wyoming, in the Green River Basin in Utah, Axial Basin and Platte River drainage in Colorado, and the Big Hole and Red River drainages in the southwestern portion of Montana. Pronghorn (Antilocapra americana) distribution was modeled by 3 habitat variables (landcover type, slope, and distance to water) and disturbance variables that described oil and gas development, fences, roads, and human populations. Most important regions for pronghorn were south-central and southwestern Wyoming, east of the Wasatch Range in northeastern Utah, and portions of northwestern Colorado, including the Platte River drainage. We modeled pygmy rabbit (Brachylagus idahoensis) distributions based on 4 habitat variables (landcover, slope, soil depth, and percent clay) and disturbance variables that facilitated potential predators. The estimated geographic range of pygmy rabbits in the assessment area covered 15.1 million acres, with nearly half that area predicted to have a very low probability of occurrence. The primary regions for pygmy rabbits included parts of Montana (east and south of Grant, southeast of Dillon, and near Red Rocks Lakes) and Wyoming (Rock Springs, Pinedale, and Kemmerer Field Offices). Occurrence of sage sparrows was best predicted and modeled by the proportion of sagebrush habitat present, modified by the disturbance sub-model. Based on the results of the combined habitat and disturbance sub-models, the majority of the WBEA study area was predicted to have a very low occurrence of sage sparrows (32.1 million acres, or 37.7%) with only 13.8 million acres (16.1%) having a high probability of occurrence. The more central regions of the study area (south-central Wyoming), with the greatest abundance of sagebrush, had the highest relative probabilities of occurrence for sage sparrows. The sage thrasher habitat model included 3 variables: (1) proportion of sagebrush habitat present (positively correlated with thrasher counts); (2) sagebrush fragmentation (negatively

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correlated); and (3) NDVI values (negatively correlated). Based on the results of the combined habitat and disturbance sub-models, sage thrasher occurrence was predicted to be low (37.6%) or very low (27.3%) across the majority of the study area. Only 14.3% of the study area was predicted to fall within the high probability occurrence class for sage thrashers. Most high occurrence areas were in south-central Wyoming, where the most contiguous sagebrush habitat exists. The loggerhead shrike habitat sub-model included 3 variables: (1) proportion of sagebrush habitat (positively correlated with shrike counts); (2) sagebrush fragmentation (negatively correlated); and NDVI values (negatively correlated). The combined habitat and disturbance sub-models predicted the majority of habitat (50.0%) as very low occurrence for loggerhead shrikes, with only 11.9% falling within the high probability class. Areas within the central portion of the study area that contained the highest density of contiguous sagebrush were predicted to have highest occurrence. Application of the short-horned lizard model indicated that most areas within the species’ range in the study area were predicted to have a very low probability of occurrence (52.5%). Less than 1% was predicted to have a high probability of occurrence, but 39.9% of the species’ range fell within the moderate probability class. Spatially, higher ranked habitats (predominantly the moderate class) occurred throughout the short-horned lizard range within Wyoming, Colorado, and northeastern Utah. Application of the sagebrush lizard model within the species’ range in the study area indicated that approximately 12% was considered to have a moderate probability of occurrence. About 46% was predicted to have a low probability of sagebrush lizard occurrence, and 42% was predicted to have a high probability of occurrence. The majority of areas predicted as high occurrence were in the central regions of the study area, in Wyoming and along the Colorado- Utah border. Predicted areas of low were mostly near the periphery of the study area. The strength of ecoregional assessments rests on an effective combination of broad-scale depiction of species and habitat distributions, with the potential to identify concurrent influences from natural disturbances, such as wildfires, and human activities across these large extents. For example, areas of high predicted occurrence for greater sage-grouse in northcentral, central, and southwestern Wyoming also were characterized by a high human footprint (Figs. 5.4 and 6.4). In these areas, our results highlight the need for management actions to recognize potential

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment xiii Version 2.0, March 2006 habitat declines as well as the importance of developing appropriate monitoring schemes for habitats and sage-grouse populations. As such, regional assessments develop and expose the larger patterns and processes within which local dynamics of habitats and species are embedded. Thus, the purpose of ecoregional assessments is to provide the contextual foundation for regional and local management actions. Together with the U.S. Bureau of Land Management’s National Sage-grouse Habitat Conservation Strategy and the Management Considerations for Sagebrush (Artemisia), this ecoregional assessment provides an important foundation for conservation planning and actions, the promise of which was a primary factor underlying the recent decision by the U.S. Fish and Wildlife Service not to list the greater sage-grouse under the Endangered Species Act.

ARRANGEMENT OF MATERIAL BY CHAPTERS

Each chapter is self-contained to the degree possible. Chapters 3-6 compose the assessment chapters that contain methods, and results for specific aspects of our assessment, similar to “stand-alone” publications. Materials addressed in chapters before and after these chapters (Chapters 1, 2, 7, and 8) provide context and detail in support of the overall assessment, as do the Appendices and extensive references cited. Formal metadata documentation for this assessment will be posted on the SAGEMAP website (http://sagemap.wr.usgs.gov), as will this report, following peer review. In arranging these chapters, we followed the approximate order of analytical steps described by Wisdom et al. (2005, Habitat Threats in the Sagebrush Ecosystem: Methods of Regional Assessment and Applications in the Great Basin, Alliance Communications Group, Lawrence, KS) for regional assessment of habitats of species of concern in the sagebrush ecosystem. These steps are listed below, followed by the associated chapters for our assessment: (1) Identify the ecoregions and associated spatial extents for regional assessment (Chapters 1, 2); (2) Identify species of conservation concern in the ecoregion (Chapter 3); (3) Delineate species ranges (Chapter 3); (4) Estimate habitat requirements of species (Chapter 6); (5) Identify regional threats and effects of such threats on habitats (Chapters 2, 4, 5);

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(6) Estimate and map the risks of habitat loss or degradation posed by example threats (Chapters 4, 5); (7) Calculate effects of potential threats on individual species of concern (Chapter 6); and (8) List major assumptions, limitations, and guidelines for management (Chapters 7, 8; Appendix 6). Scientific names of the 65 vascular plant and 40 vertebrate species of conservation concern, which are the focus of our regional assessment in the Wyoming Basins, are found in Tables 3.1 and 3.2, respectively, in Chapter 3.

AVAILABILITY OF DATA

Data from our analyses will be available in tables, spreadsheets, databases, AMLs, and GIS layers for use by individual BLM Field Offices within the study area. Because data and GIS coverages often are dynamic, we are continually updating layers as new information becomes available. We intend to make data available when in a final format and at the conclusion of this study. Field offices can request preliminary versions of specific datasets from the authors. Because the species models will be evaluate with field data, these data will be made available in their final, validated forms following 2006 field work and subsequent analysis. Maps in jpg format of any model outputs (e.g., for reports or presentations) are available upon request from the authors. We intend to make all data available on the SAGEMAP website at the conclusion of this study.

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CHAPTER 1: OVERVIEW OF THE WYOMING BASINS ECOREGIONAL ASSESSMENT

BACKGROUND

Concerns within the Sagebrush Ecosystem

Conservation and restoration of the sagebrush (Artemisia spp.) ecosystem are of special concern to federal and state land and wildlife management agencies, in response to extensive habitat loss and degradation within this ecosystem during the last century (Knick 1999, Miller and Eddleman 2000, USDI Bureau of Land Management 2002, Knick et al. 2003, Connelly et al. 2004). Long-term sustainability and stability of populations and habitats of a wide variety of species closely allied with sagebrush communities are at risk (Knick et al. 2003, Dobkin and Sauder 2004, Rich et al. 2005, Wisdom et al. 2005a). Among these are vertebrates of concern, such as ferruginous hawk (Buteo regalis), greater sage-grouse (Centrocercus urophasianus), and pygmy rabbit (Brachylagus idahoensis), and plants, such as Ownbey’s thistle (Cirsium ownbeyi) and Nelson’s milkvetch (Astragalus nelsonianus). Recently Wisdom et al. (2005a) identified more than 350 sagebrush-associated species of conservation concern, a diverse assemblage of vascular plants, invertebrates, and vertebrates. Much of the attention focused on shrubsteppe communities has been directed at greater sage-grouse, a sagebrush-obligate species considered synonymous with the sagebrush ecosystem (Paige and Ritter 1999, Schroeder et al. 1999, Connelly et al. 2004). The U.S. Fish and Wildlife Service (FWS) recently considered multiple petitions to list greater sage-grouse under the U.S. Endangered Species Act (ESA; USDI Fish and Wildlife Service 2005a). Although FWS determined that listing of the species was not warranted, the review team recognized range-wide population declines and expressed uncertainty about “how and if leks can persist in the presence of disturbances” (USDI Fish and Wildlife Service 2005a:2282). The key threats to greater sage- grouse identified during the status review by the FWS were, in decreasing order: invasive species, infrastructure as related to energy development and urbanization, wildfire, agriculture, grazing, energy development, urbanization, strip/coal mining, weather, and pinyon-juniper expansion (USDI Fish and Wildlife Service 2005a). Future listing of greater sage-grouse, a

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species whose current range encompasses an area >165 million acres (Schroeder et al. 2004), would present formidable challenges for land use and management across much of the western United States (Tweit 2000, Connelly et al. 2004). Other sagebrush-associated species also are of concern. The closely related but less abundant Gunnison sage-grouse (Centrocercus minimus), found only in Colorado and Utah, is currently a priority 2 candidate species for federal listing under the ESA (USDI Fish and Wildlife Service 2004). The current range of Gunnison sage-grouse is approximately one-tenth of its historical distribution (Schroeder et al. 2004). This species is currently undergoing a full species status review, and a decision on a proposed rule is anticipated in spring 2006. A petition to list the pygmy rabbit, another sagebrush-obligate species (Green and Flinders 1980), as threatened or endangered across its range was considered by FWS in May 2005; however, the species was found to be “not warranted” for listing (USDI Fish and Wildlife Service 2005b). The Columbia Basin pygmy rabbit, endemic to Washington, was listed as a candidate species in 2001 (USDI Fish and Wildlife Service 2003). Both sage-grouse species and pygmy rabbit are considered globally threatened by the World Conservation Union (Baillie et al. 2004). Although sage-grouse have been proposed as an umbrella species for other sagebrush-associated species and the sagebrush ecosystem (Dobkin 1995, Rich and Altman 2001, Rich et al. 2005), conservation coverage using this approach may be limited for some species due to the lack of commonality in both land-cover affinities and ranges (Rowland et al. 2005, 2006). Due to the preponderance of sagebrush on public lands, the future of this ecosystem will be shaped in large part by public lands management. Despite the myriad potential threats to the sagebrush ecosystem, especially those posed by resource extraction, these threats have not been systematically addressed across all sagebrush-dominated regions. For example, the status review by FWS for greater sage-grouse noted the prospective nature of many threats facing sage-grouse and sagebrush, and the lack of knowledge about how these regional threats will develop and interact with each other (USDI Fish and Wildlife Service 2005a). Unlike forested ecosystems, sagebrush and other shrublands have not benefited from focused, long-term research on effects of land management activities and disturbance processes (Knick et al. 2003), and no national monitoring framework exists for long-term measurements of their ecological health (Weltz et al. 2003). Furthermore, land transformation in arid ecosystems is more difficult to detect due to the relatively low green biomass in such systems and often large

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seasonal changes in vegetation (Knick et al. 1997, Bradley and Mustard 2005). Research on effects of habitat fragmentation on plant and animal populations, the role of corridors, and habitat linkages has historically been focused on forested ecosystems or on grassland and agriculture mosaics, rather than shrublands. Recent studies have begun to fill this gap, however, such as (1) analyses of anthropogenic influences in the sagebrush ecosystem completed as part of the recent conservation assessment for greater sage-grouse (Connelly et al. 2004); and (2) studies of the effects of landscape change on shrubsteppe birds (e.g., McDonald and Reese 1998, Vander Haegen et al. 2000, Knick and Rotenberry 2000). This lack of a well-established body of research on natural and anthropogenic disturbance in shrublands presents special challenges in evaluating and predicting effects of rapid energy development on shrubsteppe habitats and wildlife.

Role of BLM and Forest Service in Addressing Sagebrush-Related Issues

More than two-thirds of the total area covered by sagebrush is publicly owned and managed by state or federal agencies. The USDI Bureau of Land Management (BLM) alone manages 52% of the sagebrush in the United States, and the USDA Forest Service (FS) nearly 9%, primarily at higher elevations (Wisdom et al. 2005b). Only a small portion of the sagebrush ecosystem, however, benefits from permanent legal protection from alteration or conversion (Ricketts et al. 1999, Knick et al. 2003, Connelly et al. 2004). Within this broadly distributed ecosystem, climate change, altered fire regimes, inappropriate livestock grazing, and other threats pose complex challenges to land management and conservation planning (Connelly et al. 2004, Wisdom et al. 2005a; Table 2.7).1 To address concerns about the fate of sagebrush and associated species on public lands, the BLM and FS have taken action in a number of arenas. Key contributions by BLM include:

• Preparation of a sagebrush “management considerations” document (USDI Bureau of Land Management 2002); • Development of a national strategy for sage-grouse habitat conservation (USDI Bureau of Land Management 2004a, b, c); and

1 Potential threats in the sagebrush ecosystem are listed in Table 2.7 (Chapter 2).

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• Development of ecoregional plans within the sagebrush ecosystem.

The document outlining management considerations within the sagebrush ecosystem summarizes current knowledge about responses of various woody sagebrush taxa to management treatments and disturbances (USDI Bureau of Land Management 2002). The report serves as a supplemental reference for BLM managers and staff in planning management activities within sagebrush communities. A wide variety of management issues, such as prescribed fire and livestock grazing and browsing, are discussed. The BLM’s national sage-grouse habitat conservation strategy, issued in 2004, is intended to “guide future actions for conserving sage-grouse and associated sagebrush habitats”; that is, to provide comprehensive management direction for the agency with regard to sage- grouse (USDI Bureau of Land Management 2004a). The strategy presents information on basic life history and habitat requirements of sage-grouse, followed by specific strategies and actions to be undertaken to meet the goals outlined in the document. Other BLM efforts associated with sagebrush and sage-grouse also are listed, such as the involvement with the Great Basin Restoration Initiative (USDI Bureau of Land Management 1999) and the Conservation Planning Framework Team for sage-grouse (USDI Bureau of Land Management 2004a). Finally, specific guidance was written to help BLM planning staff incorporate needs of sagebrush-associated communities and species in land use plans (USDI Bureau of Land Management 2004b) and to manage sagebrush plant communities for sage-grouse (USDI Bureau of Land Management 2004c). The FS is actively involved in sagebrush conservation and management through implementation of the National Forest Management Act, which provides statutory direction for managing the National Forest System to provide for diversity of plant and animal communities. The agency also implements its sensitive species policy to ensure that viability of identified species (several of which are associated with sagebrush communities) is maintained and to preclude trends toward endangerment in these species that would result in listing under the U.S. Endangered Species Act. The FS also is represented on the Conservation Planning Framework Team for sage-grouse (USDI Bureau of Land Management 2004a) and is participating in preparation of the range-wide conservation strategy for sage-grouse. The agency has partnered

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with BLM and USDI Geological Survey (USGS) in conducting ecoregional assessments in the Great Basin (Wisdom et al. 2005a) and in the Wyoming Basins, described below. To further aid in land use planning that may affect sagebrush communities on public lands, a cooperative agreement was entered into between the BLM, USGS, and USDA Forest Service (Pacific Northwest Research Station). The overarching purpose of the agreement was to develop ecoregion-based assessments of environmental conditions within the sagebrush ecosystem on public lands throughout the western United States. The assessments were initiated to identify potential threats and habitat conditions in sagebrush communities in response to the continued loss, degradation, and fragmentation of sagebrush habitats. The initial product of this collaborative agreement was a document outlining the procedural steps for conducting regional assessment of habitats for species of conservation concern in the sagebrush ecosystem (Wisdom et al. 2005b). These procedures were subsequently used to conduct a prototype regional assessment of sagebrush habitats in the Great Basin Ecoregion (Wisdom et al. 2005a). Following completion of the Great Basin work, the BLM determined that, of the remaining sagebrush ecoregions, the area of highest priority for regional assessment was the Wyoming Basins region (Fig. 1.1). This decision was based on the immense energy resources on public lands in this region, coupled with its extensive sagebrush habitats and large intact populations of greater sage-grouse (Fig. 1.2). Building upon the experience and knowledge gained through the Great Basin assessment, the Wyoming Basins Ecoregional Assessment (WBEA) described in the following chapters will inform land management decisions about actions affecting habitats for species of concern within this ecologically diverse area. This collaboration will rely extensively on previous cooperative efforts to acquire spatially explicit data for large-scale analysis of habitats and habitat trends in the sagebrush ecosystem and to make these data available to researchers and managers via the World Wide Web (SAGEMAP http://sagemap.wr.usgs.gov/ [U.S. Geological Survey 2001], and PRAIRIEMAP http://prairiemap.wr.usgs.gov/ ). Several ecoregional assessments have been completed recently within the Wyoming Basins area (Freilich et al. 2001, Noss et al. 2001, Jones et al. 2004). These evaluations, while comprehensive, did not focus on shrubland species, or on conditions existing on federal lands. The Wyoming Basins assessment is designed to provide data tailored for BLM and FS planning

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objectives that can be readily incorporated into ongoing land use planning for sagebrush communities on federal lands.

JUSTIFICATION

Accelerated energy development and the construction of associated infrastructure features such as well pads, compressor stations, roads, power lines, and pipelines, influence a substantial proportion of the sagebrush ecosystem, especially in Wyoming and adjacent states (Braun et al. 2002, Connelly et al. 2004; Chapter 4). A recently compiled inventory of onshore oil and natural gas reserves on federal lands focused on 5 geologic basins that contain the vast majority of these reserves in the 48 contiguous United States (U.S. Departments of the Interior, Agriculture, and Energy 2003). Four of the basins are centered in the Rocky Mountain region and extend across much of Wyoming, as well as parts of Colorado, Montana, and Utah (Fig. 1.3). These 4 basins also encompass 5 of the 7 “focus areas” that were given highest national priority for inventory related to the Energy Policy and Conservation Act, due to the exceptional concentrations of oil and gas reserves found there (U.S. Departments of the Interior, Agriculture, and Energy 2003). Overlying these basins is one of the largest remaining expanses of sagebrush in western North America; the Wyoming Basins Ecoregion alone contains 18.3 million acres of sagebrush (Fig. 1.2), or 17% of all sagebrush in the United States (Knick et al. 2003). The extensive landscapes dominated by sagebrush in this area in turn support some of the largest extant populations of sagebrush obligates, such as greater sage-grouse and pronghorn (Antilocapra americana), in the United States (Clark and Stromberg 1987, Connelly et al. 2004). Climate change scenarios in the sagebrush ecosystem predict that the largest area of sagebrush that will persist in the future is in southern Wyoming, between the northern and central Rocky Mountains (Neilson et al. 2005). Further evidence of the high priority placed by BLM in this region is found in the agency’s current land use planning timelines. As part of the ongoing efforts to revise or develop new land use plans for BLM lands nationwide, the Bureau highlighted 21 plans as “time sensitive”; these plans have the highest priority for expedited completion by BLM. Of the 21

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plans, more than one-third (8) incorporate areas either wholly or partially included in the Wyoming Basins study area.

BENEFITS OF ECOREGIONAL PLANNING

Ecoregional assessments constitute valuable predictive tools for addressing large-scale, range-wide factors likely to affect the well-being of species of concern, and can guide the development of management plans to reduce further loss or degradation of their habitats (Ricketts et al. 1999, Noss et al. 2001, Jones et al. 2004, Wisdom et al. 2005b). For example, by using maps of predicted habitat change as identified in regional assessments, managers can establish habitat protection and restoration plans that promote effective use of available and projected resources. By integrating data on landcover, abiotic features such as soils, and species distributions, ecoregional assessments also can be used to identify potential strongholds for wildlife species of concern, such as sage-grouse or pygmy rabbit, or plant communities of concern, such as mountain big sagebrush/spike fescue (Artemisia tridentata vaseyana / Leucopoa kingii) (Noss et al. 2001). The development of Geographic Information Systems (GIS) has greatly enhanced the feasibility of regional-scale analysis and assessment by enabling the synthesis of complex, interacting spatial features. Because processes operating at larger regional scales can influence local, smaller systems, conservation strategies developed at regional scales are a necessary component of effective conservation and land use planning (Groves et al. 2000, Jennings 2000, USDI Bureau of Land Management 2005). Therefore, regional planning and assessments should be part of a hierarchical planning process, in which coarse-scale data, such as those used in the Wyoming Basins assessment, are used to establish regional context and are complemented by fine-scale data useful for setting local objectives (Hansen et al. 1993, USDI Bureau of Land Management 2005, Wisdom et al. 2005b). Coarse-scale assessments and conservation planning often are more cost-effective and efficient than smaller-scale efforts requiring detailed data on individuals or populations (Corsi et al. 2000). Furthermore, when several levels of biological organization are considered, as in our assessment, management is best approached on a landscape scale, on the order of 250,000 to 25,000,000 acres (May 1994, cited in Corsi et al. 2000). Many sagebrush-associated species,

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such as migratory birds, operate at continental scales, which render regional assessment a necessary component of sound conservation planning for these species (Knick et al. 2003). Multi-species evaluations, such as that described in subsequent chapters of this report, are effective in that management activities may be designed to benefit a suite of species at once, with costs often little more than those associated with managing for single species (Block et al. 1995, Jennings 2000). Multi-species assessments in sagebrush ecosystems are necessary because of the increasing number of sagebrush-associated species demonstrating habitat or population declines (Dobkin and Sauder 2004) and the legal requirements of federal agencies to provide habitat for a wide variety of wildlife species (USDI Bureau of Land Management 2005, Wisdom et al. 2005b). Furthermore, if species of special concern, such as greater sage-grouse or pygmy rabbit, eventually are listed under ESA, ecoregional assessments will become an important regional-scale resource for developing recovery plans. Federal and state land and wildlife management agencies require multi-scale information in order to make effective management decisions affecting species of concern, to prevent further population declines of these species, and to establish a basis for restoring habitats for these species in the most time- and cost- effective manner possible. Regional assessments complement and inform land use plan revisions of BLM by providing data and summaries to meet specific planning requirements outlined in the BLM planning handbook (USDI Bureau of Land Management 2005), such as:

• Identify issues: Through data analysis and synthesis at regional scales, issues and land use problems related to cumulative impacts of habitat alteration and loss may be identified as potential areas of focus for planning or development of Environmental Impact Statements within a Field Office or Resource Management Plan (RMP) boundary. • Collect inventory data: Data on species ranges, distributions, and predicted occurrence resulting from models developed for regional analysis can be used in Field Office-level planning. Such data also may be used in analysis of cumulative effects. • Analyze the management situation: Regional analysis may provide information on pertinent biological and physical characteristics, including environmental conditions, to effectively assess current management options in a single or multiple Field Offices.

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OBJECTIVES OF THE WYOMING BASINS ASSESSMENT

The primary goal identified for the Wyoming Basins assessment in the cooperative agreement and subsequent study plan (Rowland et al. 2004) is as follows: Prepare an ecoregional assessment for species of conservation concern in sagebrush habitats within the Wyoming Basins and the Utah-Wyoming-Rocky Mountains ecoregions and portions of two adjacent ecoregions, the Middle Rockies-Blue Mountains and Southern Rocky Mountains ecoregions. The assessment will rely on the “Procedures for Regional Assessment” (Wisdom et al. 2005b) and other methods, as appropriate. This assessment will be referred to as the “Wyoming Basins Assessment” (Fig. 1.1). The agreement and study plan also described an additional ecoregional assessment, encompassing the Colorado Plateau and the Utah High Plateaus ecoregions and portions of the Southern Rocky Mountains Ecoregion not included in the Wyoming Basins assessment described above. This subsequent assessment is referred to as the “Colorado Plateau Assessment.” If this assessment is initiated, methods previously developed for the Great Basin and Wyoming Basins assessments will provide a template for data collection and analysis. Objectives specified in the agreement for the Wyoming Basins assessment were:

1. Conduct an analysis of existing information to identify the primary land uses, changes in the extent and intensity of land uses, and related potential impacts to sagebrush habitats and associated species of conservation concern. The ecoregional assessment prepared under this study plan will focus on habitats within the sagebrush ecosystem, but also include non- sagebrush plant communities used by species of conservation concern to meet part of their life-cycle requirements. The lack of comprehensive and current information on habitats and status of sagebrush-associated species in the Wyoming Basins Ecoregion, coupled with rapid development of energy resources in this region, necessitate a thorough analysis of environmental conditions in this area. Based on this analysis, the cooperators will (1) develop spatial models of hypothesized effects of threats to the sagebrush ecosystem in this region; (2) obtain thorough peer review of the models and predicted effects on species of conservation concern by species experts; and (3) evaluate the results of these models.

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2. Develop and implement procedures to (1) sample habitats for distribution and abundance of invasive species; and (2) test predictions from spatial models of hypothesized effects. With additional, planned funding, the cooperators will determine the distribution and abundance patterns of selected species of concern. Supporting fieldwork is critical for this aspect because of the extensive habitat modification and attendant biological impacts posed by energy development, the lack of reliable knowledge about the ways in which these activities may impact habitats and populations of species of concern, and the relatively vast expanse of sagebrush habitats in the Wyoming Basins, Colorado Plateau, and adjacent ecoregions. The principal product from Phase II of the Wyoming Basins Assessment is the revised report contained in this and subsequent chapters. Specific products to be delivered as components of this report include: • description of the need, rationale, and significance of the assessment; • quantification and description of the amount and distribution of landcover types in the assessment area; • a draft list of species of conservation concern (including both vascular plants and vertebrates) and reasons for identification, including justification for inclusion or exclusion of species; • digitized maps of geographic ranges of species of concern within the assessment area, for those species deemed suitable for regional assessment; • classification of sensitivity to human disturbance for the species identified as suitable for regional assessment; • a description of the dominant threats to habitats and populations of these species; • hypothesized effects of dominant threats, in the form of predictive models, for 10 example species; • description of methods and preliminary results for field sampling conducted during 2005; and • specific direction and examples for management application of results of this assessment.

This report contains all products and information listed above with the exception of the preliminary results from the field sampling conducted in 2005. We have described our field

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sampling methods (Appendix 7), but results and analyses will be included in the final (Phase III) report. In addition, we (1) describe assumptions and limitations of current information for management use; (2) address the accuracy of spatial data used; (3) identify specific needs for improvements in spatial layers to improve the rigor and credibility of assessment results; (4) list preliminary management implications; and (5) describe future work planned for the final phase of our assessment. Beyond this introductory chapter (Chapter 1), we describe the assessment area (Chapter 2); identify the species of conservation concern, their sensitivity to disturbance, and their geographic ranges in the study area (Chapter 3); analyze effects of oil and gas development on changes in landcover types in a portion of Wyoming (Chapter 4); estimate and map the cumulative influence of all human activities in a human footprint analysis (Chapter 5); predict the probability of occurrence for 10 example species based on the combination of habitat and anthropogenic variables used as predictors (Chapter 6); list assumptions and limitations for management use and the accuracy of spatial data (Chapter 7 and Appendix 6); and describe preliminary management implications and future work for the assessment (Chapter 8).

REFERENCES

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Grouse. Transactions of the North American Wildlife and Natural Resources Conference 67:337-349. Clark, T. W., and M. R. Stromberg. 1987. Mammals in Wyoming. Museum of Natural History, University of Kansas, Lawrence, Kansas, USA. Comer, P., J. Kagan, M. Heiner, C. Tobalske. 2002. Current distribution of sagebrush and associated vegetation in the western United States. Map 1:200,000 scale. USGS Forest and Rangeland Ecosystems Science Center, Boise, Idaho, and The Nature Conservancy, Boulder, Colorado, USA. [http://sagemap.wr.usgs.gov/images/sage1.jpg]. Connelly, J.W., S. T. Knick, M. A. Schroeder, and S. J. Stiver. 2004. Conservation assessment of greater sage-grouse and sagebrush habitats. Unpublished report. Western Association of Fish and Wildlife Agencies, Cheyenne, Wyoming, USA. [http://sagemap.wr.usgs.gov/Docs/Greater_Sage- grouse_Conservation_Assessment_060404.pdf]. Corsi, F., .J de Leeuw, and A. K. Skidmore. 2000. Modeling species distribution with GIS. Chapter 11 in L. Boitani and T. K. Fuller, editors. Research techniques in animal ecology: controversies and consequences. Columbia University Press, New York, New York, USA. Dobkin, D. S. 1995. Management and conservation of sage grouse, denominative species for the ecological health of shrubsteppe ecosystems. USDI Bureau of Land Management, Portland, Oregon, USA. Dobkin, D. S., and J. D. Sauder. 2004. Shrubsteppe landscapes in jeopardy. Distributions, abundances, and the uncertain future of birds and small mammals in the Intermountain West. High Desert Ecological Research Institute, Bend, Oregon, USA. Freilich, J., B. Budd, T. Kohley, and B. Hayden. 2001. The Wyoming Basins Ecoregional plan. The Nature Conservancy, Wyoming Office, Lander, Wyoming, USA. Green, J. S., and J. T. Flinders. 1980. Brachylagus idahoensis. Mammalian Species No. 125: 1- 4. The American Society of Mammalogist. Groves, C., L. Valutis, D. Vosick, B. Neely, K. Wheaton, J. Touval, and B. Runnels. 2000. Designing a geography of hope: a practitioner's handbook for ecoregional conservation planning. The Nature Conservancy, Arlington, Virginia, USA. [http://www.conserveonline.org].

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Hansen, A. J., S. L. Garman, and B. Marks. 1993. An approach for managing vertebrate diversity across multiple-use landscapes. Ecological Applications 3:481-496. Jennings, M. D. 2000. Gap analysis: concepts, methods, and recent results. Landscape Ecology 15:5-20. Jones, A., J. Catlin, T. Lind, J. Frelich, K. Robinson, L. Flaherty, E. Molvar, J. Kessler, and K. Daly. 2004. Heart of the West conservation plan. Wild Utah Project, Salt Lake City, Utah, USA. Knick, S. T. 1999. Forum: requiem for a sagebrush ecosystem? Northwest Science 3:53-57. Knick, S. T., J. T. Rotenberry, and T. J. Zarriello. 1997. Supervised classification of Landsat thematic mapper imagery in a semi-arid rangeland by nonparametric discriminant analysis. Photogrammetric Engineering and Remote Sensing 63:79-86. Knick, S. T., D. S. Dobkin, J. T. Rotenberry, M. A. Schroeder, W. M. Vander Haegen, and C. Van Riper, III. 2003. Teetering on the edge or too late? Conservation and research issues for avifauna of sagebrush habitats. Condor 105:611-634. Knick, S. T., and J. T. Rotenberry. 2000. Ghosts of habitats past: contribution of landscape change to current habitats used by shrubland birds. Ecology 81:220-227. May, R. M. 1994. The effect of spatial scale on ecological questions and answers. Pages 1-17 in P. J. Edwards, R. M. May, and N. R. Webb, editors. Large-scale ecology and conservation biology. Blackwell Scientific, Oxford, United Kingdom. McDonald, M. W., and K. P. Reese. 1998. Landscape changes within the historical distribution of Columbian sharp-tailed grouse in eastern Washington. Northwest Science 72:34-41. Miller, R. F., and L. L. Eddleman. 2000. Spatial and temporal changes of sage-grouse habitat in the sagebrush biome. Oregon State University Agricultural Experiment Station Technical Bulletin 151, Oregon State University, Corvallis, Oregon, USA. Neilson, R. P., J. M. Lenihan, D. Bachelet, and R. J. Drapek. 2005. Climate change implications for sagebrush ecosystems. Transactions of the North American Wildlife and Natural Resources Conference 70: in press. Noss, R., G. Wuerthner, K. Vance-Borland, and C. Carroll. 2001. A biological conservation assessment for the Utah-Wyoming-Rocky Mountains Ecoregion: a report to The Nature Conservancy. Conservation Science, Inc., Corvallis, Oregon, USA.

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Paige, C., and S. A. Ritter. 1999. Birds in a sagebrush sea: managing sagebrush habitats for bird communities. Partners in Flight Western Working Group, Boise, Idaho, USA. Rich, T., and B. Altman. 2001. Under the sage grouse umbrella. Bird Conservation 14:10. Rich, T. D., M. J. Wisdom, and V. A. Saab. 2005. Conservation of priority birds in sagebrush ecosystems. Pages 589-606 in C. J. Ralph and T. D. Rich, editors. Proceedings, Third International Partners in Flight Conference. U.S. Forest Service General Technical Report PSW-GTR-191. Ricketts, T. H., E. Dinerstein, D. M. Olson, C. J. Loucks, W. Eichbaum, D. DellaSala, K. Kavanagh, P. Hedao, P. T. Hurley, K. M. Carney, R. Abell, and S. Walters. 1999. Terrestrial ecoregions of North America: a conservation assessment. Island Press, Washington, DC, USA. Rowland, M., M. Leu, M. J. Wisdom, and S. T. Knick. 2004. Ecoregional analysis of sagebrush ecosystems of the Wyoming Basins. Draft study plan. On file with: USDA Forest Service, Pacific Northwest Research Station, La Grande, Oregon, USA. Rowland, M. M., M. J. Wisdom, C. W. Meinke, and L. H. Suring. 2005. Utility of greater sage- grouse as an umbrella species. Chapter 8 in Part II: Regional assessment of habitats for species of conservation concern in the Great Basin. Pages 232-249 in Wisdom, M. J., M. M. Rowland, and L. H. Suring, editors. Habitat threats in the sagebrush ecosystem: methods of regional assessment and applications in the Great Basin. Alliance Communications Group, Lawrence, Kansas, USA. Rowland, M. M., M. J. Wisdom, L. H. Suring, and C. W. Meinke. 2006. Greater sage-grouse as an umbrella species for sagebrush-associated vertebrates. Biological Conservation 129:323-335. Schroeder, M. A., C. L. Aldridge, A. D. Apa, J. R. Bohne, C. E. Braun, S. D. Bunnell, J. W. Connelly, P. A. Deibert, S. C. Gardner, M. A. Hilliard, G. D. Kobriger, S. M. McAdam, C. W. McCarthy, J. J. McCarthy, D. L. Mitchell, E.V. Rickerson, and S. J. Stiver. 2004. Distribution of sage-grouse in North America. Condor 106:363-376. Schroeder, M. A., J. R. Young, and C. E. Braun. 1999. Sage-grouse (Centrocercus urophasianus). A. Poole and F. Gill, editors. Number 425, The birds of North America, The Academy of Natural Sciences, Philadelphia, Pennsylvania and The American Ornithologists' Union, Washington, DC, USA.

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Tweit, S. J. 2000. The [next] spotted owl. Audubon, November-December 2000:64-71. U.S. Departments of the Interior, Agriculture, and Energy. 2003. Scientific inventory of onshore federal lands’ oil and gas resources and reserves and the extent and nature of restrictions or impediments to their development. BLM/WO/GI-03/002+3100. In compliance with the Energy Policy and Conservation Act Amendments of 2000, P.L. 106-469 §604. [http://www.doi.gov]. USDI Bureau of Land Management. 1999. The Great Basin Restoration Initiative: out of ashes, an opportunity. Bureau of Land Management, National Office of Fire and Aviation, Boise, Idaho, USA. USDI Bureau of Land Management. 2002. Management considerations for sagebrush (Artemisia) in the western United States: a selective summary of current information about the ecology and biology of woody North American sagebrush taxa. USDI Bureau of Land Management, Washington, DC, USA. USDI Bureau of Land Management. 2004a. Bureau of Land Management national sage-grouse habitat conservation strategy. USDI Bureau of Land Management, Washington, DC, USA. USDI Bureau of Land Management. 2004b. Bureau of Land Management national sage-grouse habitat conservation strategy. 1.31. Guidance for addressing sagebrush habitat conservation in BLM land use plans. USDI Bureau of Land Management, Washington, DC, USA. USDI Bureau of Land Management. 2004c. Bureau of Land Management national sage-grouse habitat conservation strategy. 1.41. Guidance for the management of sagebrush plant communities for sage-grouse conservation. USDI Bureau of Land Management, Washington, DC, USA. USDI Bureau of Land Management. 2005. Land use planning handbook. BLM Handbook H- 1601-1. USDI Fish and Wildlife Service. 2003. Endangered and threatened wildlife and plants; final rule to list the Columbia Basin distinct population segment of the pygmy rabbit (Brachylagus idahoensis) as endangered. Federal Register 68:10388-10409. USDI Fish and Wildlife Service. 2004. Endangered and threatened wildlife and plants; review of species that are candidates or proposed for listing as endangered or threatened; annual

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notice of findings on resubmitted petitions; annual description of progress on listing actions; notice of review; proposed rule. Federal Register 69:24875-24904. USDI Fish and Wildlife Service. 2005a. Endangered and threatened wildlife and plants; 12- month finding for petitions to list the greater sage-grouse as threatened or endangered; proposed rule. Federal Register 70:2244-2282. USDI Fish and Wildlife Service. 2005b. Endangered and threatened wildlife and plants; 90-day finding on a petition to list the pygmy rabbit as threatened or endangered; proposed rule. Federal Register 70:29253-29265. U.S. Geological Survey. 2001. SAGEMAP: a GIS database fore Sage Grouse and shrubsteppe management in the Intermountain West. [http://SAGEMAP.wr.usgs.gov] Vander Haegen, W. M., F. C. Dobler, and D. J. Pierce. 2000. Shrubsteppe bird response to habitat and landscape variables in eastern Washington, USA. Conservation Biology 14:1145-1160. Weltz, Mark A., G. Dunn, J. Reeder, and G. Frasier. 2003. Ecological sustainability of rangelands. Arid Land Research and Management 17:369-388. Wisdom, M. J., M. M. Rowland, and L. H. Suring, editors. 2005a. Habitat threats in the sagebrush ecosystem: methods of regional assessment and applications in the Great Basin. Alliance Communications Group, Lawrence, Kansas, USA. Wisdom, M. J., M. M. Rowland, L. H. Suring, L. Schueck, C. W. Meinke, and S. T. Knick. 2005b. Evaluating species of conservation concern at regional scales. Chapter 1 in Wisdom, M. J., M. M. Rowland, and L. H. Suring, editors. Habitat threats in the sagebrush ecosystem: methods of regional assessment and applications in the Great Basin. Alliance Communications Group, Lawrence, Kansas, USA.

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Fig. 1.1. Boundaries of the Wyoming Basins Ecoregional Assessment study area.

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Fig. 1.2. Sagebrush within ecoregions (as defined by The Nature Conservancy) of the western United States; sagebrush was mapped as any of 10 sagebrush communities delineated in the sagebrush landcover map (Comer et al. 2002). See Table 2.1 for details.

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Fig. 1.3. Four of the priority geologic basins in the Wyoming Basins Ecoregional Assessment area inventoried under the auspices of the National Oil and Gas Assessment (NOGA).

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CHAPTER 2: ECOLOGICAL AND ADMINISTRATIVE SETTING

INTRODUCTION

The Wyoming Basins Ecoregional Assessment (WBEA) area encompasses some of the most expansive sagebrush communities remaining in North America (Fig. 2.1). The Wyoming Basins Ecoregion ranks third among all ecoregions in the western United States in amount of sagebrush cover (18.2 million acres), surpassed only by the Columbia Plateau (34.8 million acres) and Great Basin (21.8 million acres) Ecoregions (Knick et al. 2003, Connelly et al. 2004; Fig. 1.2). The Utah-Wyoming-Rocky Mountains Ecoregion contributes another 4.5 million acres of sagebrush within the study area; sagebrush in these 2 ecoregions combined thus comprises 21.3% of the sagebrush biome in the nation (Knick et al. 2003). Moreover, the proportion of the land base covered by sagebrush in the Wyoming Basins is greater than in any ecoregion, with 55% in sagebrush (Knick et al. 2003). This assessment complements other regional assessments in the Wyoming Basins area. Conservation plans have been completed by The Nature Conservancy (TNC) for all 4 ecoregions within the WBEA boundaries: Middle Rockies-Blue Mountains (The Nature Conservancy 2000); Southern Rocky Mountains (Neely et al. 2001); Utah-Wyoming-Middle Rockies (Noss et al. 2001); and Wyoming Basins (Freilich et al. 2001). In addition, a recently completed conservation plan describes a wildlands network that incorporates both the Wyoming Basins and Utah-Wyoming-Rocky Mountains Ecoregions and adjacent lands; the boundaries of this planning effort closely resemble those of our assessment (Jones et al. 2004). Other work complementary to our assessment has been conducted within sagebrush ecosystems across broader scales, such as the SAGEMAP Project [http://sagemap.wr.usgs.gov] led by USGS (U.S. Geological Survey 2001), and the range-wide conservation assessment of greater sage-grouse (Connelly et al. 2004). Although the WBEA includes areas of exceptional biodiversity and national significance, such as Rocky Mountain and Yellowstone National Parks, the focus of our assessment is on sagebrush ecosystems and their management, particularly those on lands managed by the BLM and National Forest System; thus, our report emphasizes evaluation of these shrubland ecosystems in the WBEA area.

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Despite the abundance of sagebrush in the study area, sagebrush communities here face a multitude of potential threats that also beleaguer the sagebrush ecosystem in its entirety.1 Climate change, drought, land-use practices, and human development have altered fire regimes and accelerated the invasion of exotic plants such as cheatgrass (Bromus tectorum) (D’Antonio and Vitousek 1992, Tausch et al. 1993, Knight 1994, Miller and Eddleman 2000, Smith et al. 2000, Neilson et al. 2005). Other threats include encroachment by woody species (e.g., juniper [Juniperus spp.] and Douglas-fir [Pseudotsuga menziesii]) into sagebrush communities (Miller et al. 2000, Tausch and Nowak 2000, Miller and Tausch 2001, Grove et al. 2005) and increases in habitat loss, degradation, and fragmentation associated with roads and development of other infrastructure (e.g., Forman et al. 2003, Gelbard and Belnap 2003, Thomson et al. 2005). Within the Wyoming Basins study area, energy development has accelerated, resulting in increasing rates of habitat fragmentation and disturbance to native wildlife species such as greater sage- grouse and pronghorn (Weller et al. 2002, Lyon and Anderson 2003, Holloran 2005, Thomson et al. 2005; Chapters 4, 5). In this chapter, we (1) explain the rationale for selection of the study area boundary; (2) describe environmental and management conditions within the study area, including vegetation – emphasizing sagebrush communities - wildlife, and land management status; and (3) describe potential threats to sagebrush plant communities and associated species of concern in the Wyoming Basins region.

DEFINING THE ASSESSMENT AREA BOUNDARIES

The delineation by the BLM of the boundary for the WBEA was driven by the juxtaposition of some of the most expansive tracts of intact sagebrush remaining in the western United States with the anticipated acceleration of resource extraction. The boundary was derived ecologically, including 2 complete ecoregions, but then was expanded to include other regions of concern. The area evaluated in the WBEA contains 2 ecoregions in their entirety: Wyoming

1 See “Potential Threats to Sagebrush-associated Species and Habitats in the Wyoming Basins” below for more detail on potential threats to sagebrush habitats in the study area.

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Basins and Utah-Wyoming-Rocky Mountains (Fig. 2.1).2 The remainder of the study area includes (1) a portion of the northern extent of the Southern Rocky Mountains Ecoregion in Colorado and Wyoming; and (2) portions of the Middle Rockies-Blue Mountains Ecoregion in southwestern Montana, primarily the Bitterroot Valley and Beaverhead Mountain sections. The northern reaches of the Southern Rocky Mountains Ecoregion were included to address ongoing and proposed energy development, primarily oil and natural gas, in this area (Fig. 1.3). By contrast, the inclusion of southwestern Montana incorporated sagebrush habitats and associated species that were not specifically addressed in the large-scale assessment of the Interior Columbia Basin (Wisdom et al. 2000). This region (e.g., the Dillon Field Office) supports some of the most extensive stands of sagebrush in southwestern Montana, but populations of greater sage-grouse in this area are of concern due to long-term declines (Connelly and Braun 1997, Dusek et al. 2002, Roscoe 2002, Connelly et al. 2004). The entire area described above is referred to hereafter in this report as the “Wyoming Basins Ecoregional Assessment” (WBEA) or simply “Wyoming Basins.” Assessment boundaries can be ecological, administrative, or a combination of both, depending on the objectives of the assessment; boundary selection in turn influences application of results in land management. Ecologically-based evaluations provide a biologically meaningful spatial framework for resource management agencies and conservation organizations (Groves et al. 2000, McMahon et al. 2001). However, management based solely on ecological boundaries may not effectively consider information gathered at administrative scales, due to the mismatch of spatial extents. To address both ecological and administrative extents within the WBEA, we report results for the entire study area, an ecologically derived boundary, as well as for individual BLM Field Offices (Fig. 2.2). Moreover, in some instances we summarize results separately for lands managed by BLM for each Field Office within their respective states.3 Together, the results from both spatial extents provide a basis for comprehensive land use planning. Ecoregions have been widely adapted in conservation planning, and are used by a variety of organizations and agencies, such as The Nature Conservancy, the U.S. Environmental

2 Ecoregion boundaries are those delineated by TNC, which are in turn a slightly modified version of ecoregions described by Bailey [1995]; see Groves et al. [2000] and http://gis.tnc.org/data/MapbookWebsite/map_page.php?map_id=9. 3 The final report will also present data summaries for individual National Forests within the study area.

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Protection Agency (EPA), the BLM, USDA Forest Service (FS), U.S. Fish and Wildlife Service, and the USDA Natural Resources Conservation Service. Applications include regional conservation planning, biodiversity analysis, and agricultural census (McMahon et al. 2001). By contrast, administrative extents such as BLM Field Offices and FS National Forests remain the standard for land use planning by many federal agencies. New planning guidance issued by the BLM describes the value of ecoregional assessments and spatial datasets in informing more local land use planning, such as revisions of Resource Management Plan (RMPs), which are based on administrative boundaries (USDI Bureau of Land Management 2005b).

DESCRIPTION OF THE STUDY AREA

Overview

The WBEA area encompasses 85.3 million acres, the majority of which (51.0%) is in Wyoming (Table 2.1; Fig. 1.1). The study area also includes parts of southwestern Montana (21.1%), northern Colorado (12.6%), northeastern Utah (10.4%), and a small part of eastern Idaho (4.9%). By ecoregion, 38.7% of the study area is within the Wyoming Basins, 31.7% in the Utah-Wyoming-Rocky Mountains, 16.4% in the Middle Rockies-Blue Mountains, and 13.2% in the Southern Rocky Mountains. Among the ecoregions included in the study area, the Wyoming Basins Ecoregion encompasses 33.1 million acres (Figs. 1.1, 2.1). Beyond the 84% of the ecoregion in Wyoming, smaller portions lie in Utah and Colorado (about 15%), with only a trace in Montana and Idaho (1%; see Freilich et al. [2001] for further details). Climate in this region is arid, with an average annual precipitation of 6-10 inches; the Wyoming Basins Ecoregion includes the most arid parts of the state of Wyoming (Freilich et al. 2001). Extremes of cold winters and hot, dry summers in the region are typical of continental climate patterns. Habitats in the Wyoming Basins Ecoregion are dominated by rolling sagebrush uplands, with Wyoming big sagebrush (A. tridentata wyomingensis) the dominant sagebrush taxon. Black sagebrush (A. nova) reaches its easternmost extension in Wyoming, and large expanses of low sagebrush (A. arbuscula) are present. In the more arid portions of the ecoregion, salt desert shrubs, such as greasewood (Sarcobatus vermiculatus) and saltbush (Atriplex spp.) replace

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sagebrush. Major river systems, including the North Platte, Bighorn, Upper Green, Yampa, and Sweetwater, support riparian corridors vital for maintaining biodiversity in the region. Although some mountain peaks exceed 11,000 ft, most of the ecoregion lies between 5,900 and 7,800 ft. More than a dozen mountain ranges (e.g., Ferris and Pryor Mountains, Wyoming Range) dissect the ecoregion, forming “islands” in the surrounding sagebrush matrix (Freilich et al. 2001). The Utah-Wyoming-Rocky Mountains Ecoregion covers >27 million acres in parts of 5 states: Colorado, Idaho, Montana, Utah, Wyoming (Noss et al. 2001) and, like the Wyoming Basins Ecoregion, is characterized by a continental climate, with cold, wet winters and dry summers (see Noss et al. [2001] for a more complete description of this ecoregion). The ecoregion includes the Greater Yellowstone Ecosystem, along with much of the Beartooth Plateau in Montana, the Bighorn Mountains in eastern Wyoming, the Wasatch Range in Utah, and the Uinta Mountains in Colorado and Utah. Shrub-grass communities dominate lower elevations, whereas the higher elevations, such as those in the Bighorn and Uinta Mountains, are forested. Sagebrush species dominating the lower elevation shrublands include basin big sagebrush (A. tridentata tridentata) and Wyoming big sagebrush, with mountain big sagebrush (A. tridentata vaseyana) occurring at somewhat higher elevations. Saltbush and greasewood shrublands also occur in lower elevations. Douglas- fir is the most abundant tree species in lower-elevation forests, whereas Englemann spruce (Picea engelmanni), lodgepole pine (Pinus contorta), and subalpine fir (Abies lasiocarpa) dominate mid-elevation forests. Alpine tundra occurs at the highest elevations, often >10,000 ft. Human populations in the Utah-Wyoming-Rocky Mountain Ecoregion are largely concentrated along the Wasatch Front in Utah; however, counties in the Greater Yellowstone Ecosystem have also seen rapid growth in recent decades, particularly Teton Counties in Wyoming and in Idaho (Noss et al. 2001). The portion of the Southern Rocky Mountains Ecoregion in the study area includes large intermontane basins, e.g., North Park and Middle Park, which support extensive higher elevation sagebrush communities of primarily mountain big sagebrush, low sagebrush, and silver sagebrush (A. cana) (Neely et al. 2001). Much of the research on greater sage-grouse in Colorado has been conducted in North Park (e.g., Petersen 1980, Remington and Braun 1985, Braun and Beck 1996, Johnson and Braun 1999, Zablan et al. 2003). Sagebrush shrubsteppe vegetation in this ecoregion is estimated to have declined 10% since 1850 (Neely et al. 2001).

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Two sections of the Middle Rockies-Blue Mountains Ecoregion are in the WBEA area, the Beaverhead Mountain and Bitterroot Valley (The Nature Conservancy 2000). The climate here is characterized as cold, dry continental, and elevation in the valleys ranges from 4,000- 7,000 ft. Sagebrush-grasslands are the dominant non-forest vegetation type in this portion of the study area, with most of the sagebrush in the southwestern corner of Montana (Fig. 2.1). Because the WBEA encompasses a vast area ranging from alpine tundra to arid shrublands, a tremendous array of wildlife species persists. The Greater Yellowstone Ecosystem harbors populations of grizzly bears (Ursus arctos horribilis) and gray wolves (Canis lupus), as well as the entire suite of native ungulates of the Rocky Mountain West, including bighorn sheep (Ovis canadensis), moose (Alces alces), white-tailed (Odocoileus virginianus) and mule deer (O. hemionus), Rocky Mountain elk (Cervus elaphus), bison (Bison bison), and pronghorn. Wyoming supports more pronghorn than any other state (Clark and Stromberg 1987); the Sublette herd unit alone has an estimated 48,000 animals, more than most western states (WEST Inc. 2003). Moreover, the WBEA area contains some of the key strongholds for greater sage- grouse populations in the nation (Connelly et al. 2004:13-2). For further details on the flora, fauna, and abiotic environment of the study area as a whole, the reader is referred to the 4 TNC conservation plans that apply to the study area (The Nature Conservancy 2000, Freilich et al. 2001, Neely et al. 2001, Noss et al. 2001), a summary of terrestrial ecoregions of North America (Ricketts et al. 1999), and the synthesis of Wyoming landscapes found in Knight (1994). Additional descriptions of sagebrush-associated vascular plants and vertebrates of concern are provided in Chapter 3.

Land Management Status

Private landowners in the WBEA area manage >28 million acres (33.1%) of the study area, more than any other management entity (Table 2.2; Fig. 2.3). Private lands are well- distributed across the entire study area and form a “checkerboard” pattern where they intermingle with BLM and state lands, especially in a wide swath across southern Wyoming (Fig. 2.3). Two Federal land management agencies, the Forest Service and BLM, are responsible for the majority of the non-private lands; the Forest Service manages 23.9 million acres (28.0%) and the BLM 21.8 million acres (25.6%) within the study area. Most of the remaining land

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management authority rests with states (5.4%), the National Park Service (3.5%), and the Bureau of Indian Affairs (3.4%). The latter is comprised largely of the Wind River Indian Reservation in Wyoming (Fig. 2.3), whereas the majority of the National Park Service lands are in Yellowstone, Teton, and Rocky Mountain National Parks. Portions of 24 BLM Field Offices within 5 states are contained within the WBEA boundaries (Table 2.1; Fig. 2.2).4 Among these, 8 are essentially completely incorporated (>95%) within the study area: Little Snake (Colorado); Dillon (Montana); and Cody, Kemmerer, Lander, Pinedale, Rock Springs, and Worland (Wyoming). By contrast, 4 Field Offices only marginally (<5%) intersect the study area: Royal Gorge (Colorado), Lewistown (Montana), Fillmore (Utah), and Richfield (Utah) (Table 2.1). Wyoming contains the greatest number of Field Offices in the study area (9), whereas Idaho contains only 1 (Idaho Falls).5 Rawlins is the largest Field Office in the WBEA, with >9.6 million acres, followed by Lander (6.5 million acres) and Butte (6.4 million acres) (Table 2.1). Twenty-one National Forests (NFs) either intersect or are completely contained within the study area (Table 2.3, Fig. 2.4). The boundaries of 4 NFs lie essentially completely within the study area: Shoshone (100%), Bridger-Teton (100%), Bighorn (99.9%), and Wasatch-Cache (96.3%). By contrast, 4 NFs scarcely intersect the study area (<1.0% of the total area): Salmon- Challis, Flathead, Manti-La Sal, and Lewis and Clark (Table 2.3). Four NFs encompass relatively large areas (>2 million acres each) within the study area: Bridger-Teton (Wyoming), Beaverhead-Deerlodge (Montana), Medicine Bow-Routt (Colorado-Wyoming), and Shoshone (Wyoming) (Table 2.3, Fig. 2.4). Thirteen of the 21 NFs (62%) have >1 million acres within the study area boundaries (Table 2.3). Land ownership patterns for the portion of the 5 states included in the WBEA differ somewhat from those for the study area as a whole (Table 2.4). For example, although private land is the dominant category across the WBEA, at the state level this is only true for Colorado, Montana, and Utah. Wyoming has the smallest percentage (28%) of private land and the largest percentage (37.3%) of land managed by the BLM among the states in the study area; BLM lands extend across 16.2 million acres of the state. The Forest Service has management responsibility

4 The statistics reported for Field Offices in this document refer to all land ownerships within the Field Office and not only BLM-managed lands, unless described otherwise. 5 For this assessment, the Pocatello and Upper Snake Field Offices were combined and analyzed together as the Idaho Falls District.

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for a relatively large percentage of the land within the study area in Idaho (47%) and Montana (39.6%) (Table 2.4).

Landcover

Landcover mapping.--For all WBEA analyses based on landcover type, including sagebrush, we used a landcover map (“sagestitch”) developed specifically for regional assessment of sagebrush habitats (Comer et al. 2002, Reid et al. 2002). This coverage has been used in a variety of sagebrush- and sage-grouse-related projects, such as (1) the range-wide conservation assessment for greater sage-grouse and sagebrush habitats (Connelly et al. 2004); (2) identification of research issues for avifauna in sagebrush steppe (Knick et al. 2003); (3) an evaluation of sagebrush-associated species and habitats in the Great Basin Ecoregion (Wisdom et al. 2005a); and (4) analyses of habitat connectivity for the draft conservation strategy for greater sage-grouse in Oregon (Hagen 2005). The original sagestitch map was composed of 61 landcover classes, including 10 sagebrush classes (Table A5.1). To increase the accuracy of the landcover map for use in the WBEA, we reclassified the map by combining similar categories into a final 13 landcover types (Table A5.1). We also updated the “burn” class by merging the existing burn landcover class with a coverage of recent (1998-2003) fires in the study area. Of the 61 original landcover types, 5 did not change (agriculture, alpine, burn, juniper, and urban), 1 was omitted (winterfat, as mapped in sagestitch, did not occur in study area), and the remainder (n = 55) were re-classified. All sagebrush landcover types were combined into a “sagebrush” class, and all other shrub landcover types into “other shrubs” (Table A5.1) Furthermore, all forest categories, other than juniper, pinyon pine (Pinus edulis), and western juniper (Juniperus occidentalis) (taxa of interest with regard to potential encroachment into sagebrush communities), were combined as “other forest.” Sagebrush in the WBEA.--Sagebrush is the dominant landcover in the WBEA area (30.6%, or >26 million acres) (Table 2.5; Fig. 2.5). The overwhelming majority (70%) of this sagebrush is in Wyoming (18.4 million acres), but substantial amounts (>2 million acres) also are found in portions of southwestern Montana, northeastern Utah, and northwestern Colorado (Table 2.6; Figs. 2.5, 2.6). Sagebrush taxa that occur in the WBEA include: Wyoming big

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sagebrush, the most abundant taxon in the study area, found in relatively lower elevation, dry upland sites; basin big sagebrush in mesic sites with deep soils; and low and black sagebrush in sites with stony, argillic soils (Knight 1994, Winward 2000). Higher elevation, more mesic sites are dominated by silver sagebrush (A. cana cana, A. c. viscidula), mountain big sagebrush (A. tridentata var. pauciflora), and subalpine big sagebrush (A. t. var. vaseyana). Other high elevation sites support spiked sagebrush (A. spiciformis). The BLM has management authority for nearly 47% of the sagebrush in the study area (12.1 million acres), comparable to the 52% of sagebrush managed by BLM nationwide (Knick et al. 2003; Table 2.6; Fig. 2.7). This pattern varied, however, among states in the WBEA. For example in Wyoming, BLM manages nearly 60% of the sagebrush vegetation (10.2 million acres); by contrast BLM manages only 11% (121,000 acres) of the sagebrush in the Idaho portion of the WBEA (Fig. 2.7). Private landowners manage the second largest percentage (33.6%) of sagebrush in the study area, totaling >8.7 million acres (Table 2.6; Fig. 2.7). Among the 24 BLM Field Offices in our assessment area, Rawlins contained the largest amount (>4.1 million acres) of sagebrush, totaling nearly 16% of all sagebrush in the study area (Table 2.6; Fig. 2.6). Over half (2.2 million acres) of this sagebrush is on BLM-administered lands (Fig. 2.6). The Rock Springs and Lander Field Offices also contained substantial amounts of sagebrush, with >3.5 million acres in each office, and Rock Springs supported more BLM- managed sagebrush (2.7 million acres) than any other Field Office in the study area (Table 2.6; Fig. 2.6). All Field Offices in Wyoming except Buffalo had at least 800,000 acres of sagebrush. In Montana, the Dillon Field Office contained 1.3 million acres of sagebrush, or slightly more than 5% of the sagebrush in the WBEA. The Little Snake and Kremmling Field Offices in Colorado together supported nearly 8% of the total sagebrush. In Utah, Vernal and Salt Lake each had >1.0 million acres of sagebrush, with >500,000 acres BLM-administered sagebrush in these 2 Field Offices combined (Table 2.6; Fig. 2.6). Last, the Idaho Falls District supported 1.1 million acres of sagebrush, although only 11% of this is on BLM lands. Across the study area, about 7% of all sagebrush is managed by the FS (Table 2.6, Fig. 2.7); as was true for sagebrush on BLM-administered lands, this percentage is comparable to the percentage of sagebrush across the United States that is managed by the FS (i.e., 9%; Wisdom et al. 2005b). Nine NFs within the WBEA area contain >100,000 acres of sagebrush (Table 2.3, Fig. 2.4). Within the study area, the Bridger-Teton NF manages the greatest amount (523,000

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 2-10 Version 2.0, March 2006 acres), followed by the Caribou (Idaho; 347,000 acres) and Wasatch-Cache NFs (Utah; 236,000 acres) (Table 2.3). Relatively higher percentages of FS-managed sagebrush were found in Idaho, primarily in the Caribou National Forest, with considerably lower percentages in Colorado and Wyoming (Fig. 2.7). The remainder of sagebrush in the study area is managed by other agencies, including the National Park Service and Bureau of Indian Affairs (Table 2.6). In contrast to the preponderance of Wyoming big sagebrush on BLM lands within the Wyoming Basins, the vast majority (88.9%) of sagebrush on FS lands is mountain big sagebrush, with far less (9.9%) in Wyoming and basin big sagebrush (unpublished data on file). Overall the FS manages 25.4% of the mountain big sagebrush in the WBEA area, but only 1% of the Wyoming and basin big sagebrush. Management considerations for mountain big sagebrush and other sagebrush taxa found at higher elevations differ from those found at lower, warmer sites (USDI Bureau of Land Management 2002). High elevation sagebrush types are often more resistant to fire, tend to be within more diverse plant communities than sagebrush at lower elevations, and are often seasonally important for sagebrush-associated species of concern, for example in providing late brood-rearing habitat for sage-grouse (Connelly et al. 2004). Only a small percentage of the sagebrush ecosystem is permanently protected from alteration or conversion (for example, in National Parks or designated wilderness areas) (Wright et al. 2001, Knick et al. 2003, Connelly et al. 2004). Therefore, we evaluated the relative amount of sagebrush within the WBEA area by the 4 land status classes commonly used by TNC and the Gap Analysis Program (GAP) in assessing degree of protection for conservation targets or land area across a state (Scott et al. 1993, Crist 2000). These categories are: class 1 – areas permanently protected from conversion of natural landcover, with natural disturbance events allowed to proceed; class 2 – permanently protected as above, but where management practices or uses may degrade the natural communities; class 3 – permanently protected from conversion, but subject to resource extraction (e.g., logging, mining) and protection offered to federally listed species; and class 4 – no known mandates, either public or private, to prevent conversion of natural habitat types (Crist 2000). The dominant land status class for sagebrush in the WBEA was class 3 (54.6% of sagebrush), followed by class 4 (42.3%) (Figs. 2.8, 2.9). By contrast, <2% of the sagebrush in the WBEA receives permanent legal protection (i.e., status class 1); sagebrush in this class is located primarily within Yellowstone and Grand Teton National Parks, as well as in designated

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 2-11 Version 2.0, March 2006 wilderness areas managed by the Forest Service (Fig. 2.9). This percentage is similar to all sagebrush in the western United States (Wright et al. 2001). A similarly small fraction of sagebrush in the WBEA (1.4%) is in class 2. Compared to all landcover types within the study area, a disproportionately smaller percentage of sagebrush is protected, i.e., in status categories 1 and 2 (Fig. 2.8). Most of the sagebrush in category 4 is on privately owned lands or the Wind River Indian Reservation in central Wyoming (Fig. 2.9). Therefore, we can expect that landcover conversion and resource extraction will continue to dominate the management policies of sagebrush habitats. Other landcover classes in the WBEA.--The second most dominant landcover class in the study area is “other forest” (29.0%, or 24.7 million acres; Table 2.5; Fig 2.5). This type is chiefly coniferous forest (excluding pinyon and juniper species) in mountainous and high elevation regions (e.g., Yellowstone National Park, FS-managed wilderness areas in northeastern Utah and western Wyoming). Sagebrush and “other forest” were the only landcover types that spanned >15% of the study area. Grasslands cover 11.9% (10.1 million acres) of the study area and are most prevalent in eastern Wyoming and southwestern Montana (Fig. 2.5). The “other shrub” class encompasses 10.4% (8.8 million acres) of the WBEA area, primarily in northcentral Wyoming, northeastern Utah, and northwestern Colorado. These shrubs include saltbush, greasewood, and a variety of other, primarily xeric, upland shrub types. Agricultural lands cover 5.4% (4.5 million acres) in the WBEA area, with large blocks found in northcentral Wyoming, southeastern Idaho, and across southwestern Montana. The juniper class, which included any combination of juniper or pinyon pine types, covers 3.3% (2.8 million acres) of the study area, and is most common in Colorado, northeastern Utah, and northcentral Wyoming (Fig. 2.5). Burned areas (including recent [1998-2003] fires) encompass nearly 2.4 million acres (2.8%) of the WBEA area. These sites largely occurred along the northwest fringes of the study area in Montana, as well as in Yellowstone National Park and in central Wyoming, south of Worland (Fig. 2.5). Only a small fraction (0.3%) of the study area was classified as urban.

POTENTIAL THREATS TO SAGEBRUSH-ASSOCIATED SPECIES AND HABITATS IN THE WYOMING BASINS

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A plethora of potential threats to habitats and species in the sagebrush ecosystem have been identified, ranging from climate change and altered fire regimes to fragmentation by a multitude of anthropogenic disturbances (Knick et al. 2003, Connelly et al. 2004, Wisdom et al. 2005b; Table 2.7). Threats previously identified within the WBEA area include: conversion of sagebrush to non-native perennial grasses, spread of exotic annual grasses, hard-rock mining, oil and gas exploration, inappropriate grazing by domestic livestock, logging, fire suppression, and expansion of recreational and residential developments (Ricketts et al. 1999, Freilich et al. 2001, Neely et al. 2001, Noss et al. 2001, Weller et al. 2002, U.S. Departments of the Interior, Agriculture, and Energy 2003). Although the level of risk posed by each threat varies geographically and temporally across the vast range of sagebrush, all of the threats listed in Table 2.7 have been documented to occur. However, effects of many of these threats, especially anthropogenic disturbance on sagebrush-associated species of concern, have not been well quantified with empirical data (Freilich et al. 2001, WEST Inc. 2003). Furthermore, the synergistic effects of combined threats in the sagebrush ecosystem have not been fully investigated (Wisdom et al. 2005b). The development and evaluation of predictive models to test hypotheses about cumulative effects of key threats in sagebrush ecosystems, as described in Chapters 5 and 6, will allow land managers to better address management actions that may affect a large proportion of the rangelands of the Wyoming Basins. Decisions about which potential threats to address in a particular assessment area may be based on any of several criteria, including:

• Significance of the threat in altering habitat or wildlife population dynamics; • Spatial extent or pervasiveness of the threat across the ecoregion; • Capability to quantify and map the threat; • Agreement among those conducting the assessment about the relative importance of the threat to sagebrush habitats in the ecoregion; • Available resources to address the threat; • Timeframe required to implement effective treatments across the ecoregion; • Costs versus benefits of addressing the threat; and • Potential effects of addressing the threats on non-target species.

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Example Threats in the WBEA

We present below a brief summary of some of the key potential threats to sagebrush- associated species that have been identified in the assessment area. Climate change and drought.--There is increasing recognition of the effect of landcover change and human activities on global climate change (e.g., EPA 1998, Marland et al. 2003, Neilson et al. 2005). In Wyoming, the mean temperature in Laramie has increased 1.5° F during the last 100 years, and precipitation levels have decreased by as much as 20% in parts of the state (EPA 1998). Climate models for Wyoming predict an increasing frequency of extremely hot days in summer, continued increases in temperature during all seasons (e.g., 6° F in winter), and increasing fire frequencies (EPA 1998). Shrublands and arid lands in general in the United States are predicted to decrease in spatial extent under a variety of climate change models and scenarios (e.g., Bachelet et al. 2001, Neilson et al. 2005). However, sagebrush in southwestern Wyoming is predicted to be the least affected by climate-induced losses of all sagebrush in the United States, and thus may represent a future stronghold for this ecosystem (Neilson et al. 2005). Although public lands management may have little effect on climate change in the Wyoming Basins, awareness of the potential synergistic effects of climate change with other ecological processes and land management actions (e.g., invasions by exotic, warm-season annual grasses [Smith et al. 2000], livestock grazing) will lead to more informed decision- making concerning shrublands in this area. Adjustments in livestock management may be an important component of a long-term strategy to minimize the predicted effects of global warming on the ecological health of sagebrush and other arid shrublands in the study area. Roads and trails.--Roads, highways, trails, and off-highway vehicles affect wildlife habitats and biological systems in many ways; these effects have been succinctly described in reviews by Forman and Alexander (1998), Trombulak and Frissell (2000), Gucinski et al. (2001), Forman et al. (2003), and Gaines et al. (2003). Effects of roads and trails range from disturbance of wildlife due to vehicle traffic to the function of roads as conduits for invasive plants (see Table 2.7 for summaries of road effects in sagebrush ecosystems). Although past research focused largely on effects of roads and traffic on native ungulates, more recent research has demonstrated negative effects of roads and vehicles on a variety of taxa, such as sage-grouse

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(Oyler-McCance 1999, Braun et al. 2002, Lyon and Anderson 2003), passerines (Ingelfinger and Anderson 2004), small mammals (Brock and Kelt 2004), and snakes (Munger et al. 2003, Shine et al. 2004). Within the WBEA, the area affected by roads is increasing in part due to development of oil and natural gas fields. For example, in developed wellfields of the Greater Green River Basin in Wyoming, well pads and associated roads have eliminated >50,000 acres of shrublands since 1964 (Table 4.4). Moreover, well pads and roads have removed an estimated 2.3 million acres of native habitats in the Basin as a whole (11.9%), a result of the development of >17,000 wells (Table 4.3). A recent analysis evaluated impacts of the transportation network in the Upper Green River Valley near Pinedale, Wyoming (Thomson et al. 2005). Extensive roading in the study area, essentially encompassing the lower elevation lands for which BLM has surface ownership in the Pinedale RMA, has resulted in highly fragmented habitats for species such as sage-grouse, elk, pronghorn, and mule deer. Within the Jonah Field, a high-density natural gas field in the study area, road densities exceeded 2mi/mi2 across >95% of the area. Within the entire 2.9 million-acre analysis area, no greater sage-grouse lek was >3 miles from a road, and 80% of the crucial winter range for pronghorn had route densities >1 mi/mi2 (Thomson et al. 2005). The impacts of roads and other infrastructure associated with human activities, such as urban and exurban developments, pipelines, power lines, oil and gas wells, and compressor stations, combine to impose an “ecological footprint” on the landscape (Sanderson et al. 2002, Weller et al. 2002, Leu et al. 2003; Chapters 4, 5). Quantification of this footprint at a landscape scale has been greatly advanced due to the advent of spatial analysis conducted in GIS, and will be an important component of future analyses of impacts of anthropogenic disturbance on native ecosystems (Chapter 5; Wisdom et al. 2005b). Oil and gas development.--One threat of special urgency in the WBEA is resource extraction, especially of natural gas and oil (Freilich et al. 2001, Neely et al. 2001, Weller et al. 2002, Thomson et al. 2005; Chapter 4). Infrastructure associated with energy development was ranked second among threats confronting current populations of greater sage-grouse (U.S. Fish and Wildlife Service 2005). The area encompassed by the Wyoming Basins and Utah- Wyoming-Rocky Mountains Ecoregions, and surrounding areas in Colorado, Idaho, Montana, and Utah, has been identified as the center of the largest concentration of onshore oil and gas

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 2-15 Version 2.0, March 2006 reserves in the contiguous 48 United States (U.S. Departments of the Interior, Agriculture, and Energy 2003). Moreover, the Greater Green River Basin, located primarily in southwestern Wyoming and northwestern Colorado (Fig. 4.1), holds the largest volume of oil and natural gas reserves among the key geologic basins recently inventoried for quantification of national oil and gas reserves (U.S. Departments of the Interior, Agriculture, and Energy 2003). The natural gas produced in the Intermountain West constitutes 20% of the nation’s annual supply, and that region in turn holds 41% of the nation’s gas reserves (Limerick et al. 2003). Although oil, coal, and natural gas reserves in the WBEA have been tapped for decades (Weller et al. 2002; Chapter 4), more favorable market conditions and the development of advanced technologies to extract these reserves have led to an unprecedented proliferation of requests for permits to drill (Limerick et al. 2003). The passage of the Energy Policy and Conservation Act Amendments of 2000 mandated not only an accelerated inventory of these reserves on federal lands, but also a thorough examination of leasing restrictions, so that modifications to such restrictions could be made where justified (U.S. Departments of the Interior, Agriculture, and Energy 2003). The Energy Policy Act of 2005 (Public Law 109-58) provides further incentives for energy production in the United States, e.g., removal of the cap on coal lease acreages and subsidies for wind energy and oil production. Of particular concern in the assessment area is production of coalbed natural gas, also known as coalbed methane (CBM) (Braun et al. 2002, Gilbert 2002, Noon 2002, Morton et al. 2002). The development of technologies to profitably extract methane from water in coalbed seams has led to the drilling of thousands of wells in CBM fields, particularly in the Powder River Basin of northeastern Wyoming, just east of our study area boundary (USDI Bureau of Land Management 2003, Braun et al. 2002; Chapter 4). However, potentially profitable CBM reserves have been identified in many other portions of the Rocky Mountain region, including eastern Utah and southwestern Wyoming (U.S. Departments of the Interior, Agriculture, and Energy 2003). The Greater Green River Basin (Fig. 4.1) is projected to contain 8 times the CBM reserves of the Powder River Basin. Among the potential environmental effects from development of oil and gas wells and associated facilities are (1) temporary displacement of wildlife or range abandonment, due to disturbance from vehicle traffic and noise associated with compressor stations and other well- related structures or to increased predation; (2) direct loss of habitat from road and well-pad

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construction; (3) habitat fragmentation from the pipelines, power lines, roads, and other facilities associated with field development; (4) invasion of exotic plant species facilitated by soil disturbance around structures and connecting corridors; (5) depletion of aquifers, from the pumping and discharge of millions of gallons of water during the extraction of methane in CBM fields; (6) changes in local hydrologic regimes as water is discharged into ephemeral streams; and (7) the potential for diseases such as West Nile Virus to infect both humans and wildlife, a result of the creation of hundreds of water storage ponds for discharge from CBM wells.6 Despite nearly a century of energy extraction in the midst of some of the greatest concentrations of native wildlife populations, particularly ungulates, in the western United States, there has been comparatively little research conducted on effects of these activities on native plant and animal communities in the Wyoming Basins (but see Weller et al. 2002, WEST Inc. 2003, Wyoming Game and Fish Department 2004, Thomson et al. 2005). Recently, however, several research projects have been initiated or completed that rigorously examine effects of oil and gas development on wildlife in sagebrush landscapes, especially the Upper Green River Valley. These projects incorporate radiotelemetry and other techniques to evaluate potential impacts on wildlife, and include studies of greater sage-grouse (Lyon 2000, Lyon and Anderson 2003, Holloran and Anderson 2004, Holloran 2005), passerines (King and Holmes 2003), mule deer (Sawyer and Lindzey 2001, Sawyer et al. 2002), elk (Banulis and Lindzey, unpublished data) and pronghorn (Sawyer and Lindzey 2000, Sawyer et al. 2002, Huntington 2004). Investigations such as these will be invaluable in providing data immediately applicable for land use planning by the BLM and other agencies within this portion of the WBEA area. Invasive and noxious plants.--Another potential threat to the sagebrush ecosystem in the Wyoming Basins is the spread of noxious and invasive plants (Hartman and Nelson 2000, TNC 2000). A recent compilation of invasive vascular plants in Wyoming lists 428 taxa, most of which originated outside North America (Hartman and Nelson 2000). The Wyoming Weed and Pest Council (2004) currently lists 24 plant species as noxious weeds. Knapweeds (Centaurea spp.), especially Russian knapweed (C. repens), halogeton (Halogeton glomeratus), slender Russian thistle (Salsola collina), and cheatgrass are of particular concern in Wyoming (Knight 1994, Wyoming Weed and Pest Council 2004).

6 See Wyoming Game and Fish Department (2004) for further details on specific impacts associated with various aspects of oil and gas development.

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Cheatgrass, although not considered a noxious weed by the State of Wyoming, is an emerging problem in the study area as it expands into sites where it was previously thought unable to persist, possibly a result of climate change and the high degree of phenotypic plasticity that the species demonstrates (Knight 1994, Kinter 2003). Increases in atmospheric CO2 predicted by climate change models also will benefit C4 plant species, such as cheatgrass. Effects of invasive plants range from displacement of native vegetation to the creation of dense stands of fine fuels that carry wildfires (Table 2.7). Fragmented and disturbed habitats, which are increasing in the Wyoming Basins (Weller et al. 2002, Thomson et al. 2005; Chapters 4 and 5), are more susceptible to invasion by exotic plants, such as cheatgrass and halogeton (Pavek 1992; Knick and Rotenberry 1997, 2000; Pyke and Knick 2003). Other threats.--A variety of other threats may impact sagebrush ecosystems, such as power lines, fences, recreational use, inappropriate livestock grazing, urbanization and exurban expansion, encroachment of conifers, dams and reservoirs, and wind energy development (Table 2.7). For example, in southwestern Montana conversion of native shrubsteppe to agriculture continues to contribute to habitat loss for sagebrush-associated species (TNC 2000, Dusek et al. 2002). An additional threat in this area is the encroachment of conifers such as Douglas-fir (Pseudotsuga menziesii) into mountain big sagebrush communities, resulting in reductions in sagebrush canopy cover and habitat for sagebrush-associated species (Grove et al. 2005). Although wind farms are not common in the WBEA area, potential for vastly increased wind energy development exists. Within the study area, wind potential (i.e., wind speed and density) is greatest in northcentral Colorado and much of western Wyoming (USDI Bureau of Land Management 2005a). Effects of wind energy development on wildlife include mortalities of bats and birds from collisions with wind turbines (Table 2.7), as well as (1) noise that disrupts reproductive and foraging behaviors; (2) habitat degradation through the introduction and spread of invasive plants; (3) habitat loss and fragmentation; and (4) disturbance from human and vehicle activities at wind energy sites (Leddy et al. 1999; Erickson et al. 2001; Young et al. 2003, USDI Bureau of Land Management 2005a).

Threats Assessment by BLM

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The list of potential threats in sagebrush ecosystems of the WBEA area (Table 2.7) was reviewed by the state-level wildlife biologists employed by BLM from Colorado, Montana, Utah, and Wyoming during development of the study plan in 2004 (Rowland et al. 2004). The biologists were asked to assign a score from 1 (most important) to 4 (least important, or not applicable to the portion of the study area in their state) to each threat, as well as to identify threats not listed. The top-ranking threats resulting from this review were (1) weather, climatic changes, and catastrophes; (2) highways, secondary roads, and trails/two-tracks; (3) improper livestock grazing practices; and (4) oil and natural gas field development (Table A5.2). Obviously, solutions to the plight of sagebrush communities will require complex and innovative approaches.

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Miller, R. F., and J. A. Rose. 1999. Fire history and western juniper encroachment in sagebrush steppe. Journal of Range Management 52:550-559.

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Table 2.1. Land area within the Wyoming Basins Ecoregional Assessment boundary.a

Area %Total State/Field Office Hectares Acres % Field Officeb Colorado Glenwood Springs 423,785 1,047,159 1.2 36.1 Kremmling 1,181,072 2,918,394 3.4 93.9 Little Snake 1,703,419 4,209,096 4.9 99.8 Royal Gorge 534,832 1,321,555 1.5 3.7 White River 519,370 1,283,349 1.5 48.3 Total 4,362,479 10,779,553 12.6 -- Idaho Idaho Fallsc 1,695,700 4,190,024 4.9 23.0 Total 1,695,700 4,190,024 4.9 -- Montana Billings 764,063 1,887,977 2.2 17.5 Butte 2,581,459 6,378,709 7.5 88.6 Dillon 2,319,821 5,732,209 6.7 97.1 Lewistown 142,691 352,586 0.4 3.1 Missoula 1,482,115 3,662,260 4.3 24.1 Total 7,290,150 18,013,741 21.1 -- Utah Fillmore 23,416 57,861 0.1 0.9 Richfield 13,442 33,215 0.0 0.6 Salt Lake 1,915,022 4,731,963 5.5 30.6 Vernal 1,647,346 4,070,542 4.8 74.8 Total 3,599,226 8,893,581 10.4 -- Wyoming Buffalo 470,829 1,163,404 1.4 15.8 Casper 855,961 2,115,054 2.5 24.7 Cody 2,437,980 6,024,176 7.1 100.0 Kemmerer 1,636,216 4,043,041 4.7 100.0 Lander 2,660,896 6,574,995 7.7 100.0 Pinedale 1,904,696 4,706,448 5.5 99.9 Rawlins 3,907,826 9,656,120 11.3 86.9 Rock Springs 2,178,504 5,383,018 6.3 100.0 Worland 1,534,903 3,792,699 4.5 100.0 Total 17,587,811 43,458,955 51.0 --

TOTAL 34,535,366 85,335,854 100.0 -- a Includes all land within each Field Office, regardless of land stewardship, i.e., summaries were not limited to lands managed by the BLM. b Percentage of entire Field Office that occurs within the Wyoming Basins study area boundary. c Area in Idaho is within the Idaho Falls District and includes portions of 2 Field Offices, the Upper Snake and Pocatello.

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Table 2.2. Land management responsibility within the Wyoming Basins Ecoregional Assessment boundary.

Landowner/management Area responsibility Hectares Acres % Private 11,434,557 28,254,447 33.1 Forest Service 9,668,485 23,890,536 28.0 Bureau of Land Management 8,831,988 21,823,578 25.6 State 1,855,123 4,583,953 5.4 National Park Service 1,196,281 2,955,974 3.5 Bureau of Indian Affairs 1,160,098 2,866,568 3.4 Other a 383,430 947,433 1.1 TOTAL 34,529,961 85,322,499 100.0 a Includes lands managed by the Bureau of Reclamation, Department of Defense, The Nature Conservancy, U.S. Fish and Wildlife Service, water, local, miscellaneous federal.

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Table 2.3. National Forests within the Wyoming Basins Ecoregional Assessment (WBEA) study area, listed in decreasing order by amount of sagebrush within study area boundary.

Total area Sagebrush area % Total National Forest Hectares Acres NFa Hectares Acres

Bridger-Teton 1,402,183 3,464,795 100.0 211,941 523,493 Caribou 457,141 1,129,595 93.8 140,328 346,609 Wasatch-Cache 749,906 1,853,018 96.3 95,740 236,479 Beaverhead-Deerlodge 1,359,866 3,360,230 93.6 90,227 222,860 Ashley 487,104 1,203,635 86.1 85,523 211,242 Shoshone 999,218 2,469,067 100.0 60,045 148,312 White River 449,992 1,111,931 44.8 52,277 129,123 Targhee 538,678 1,331,074 71.5 51,787 127,915 Medicine Bow-Routt 1,116,666 2,759,281 60.2 42,816 105,755 Uinta 335,695 829,503 84.6 25,492 62,964 Gallatin 800,569 1,978,206 91.8 20,593 50,866 Custer 244,333 603,748 47.5 8,807 21,754 Bighorn 449,618 1,111,005 99.9 8,704 21,500 Helena 417,454 1,031,530 88.8 8,100 20,007 Arapaho-Roosevelt 645,963 1,596,174 64.4 4,538 11,210 Salmon-Challis 7,136 17,632 0.4 1,095 2,705 Manti-La Sal 1,046 2,584 0.2 248 612 Lolo 289,734 715,934 27.5 132 326 Bitterroot 80,286 198,387 11.9 12 30 Flathead 2,294 5,669 0.2 0 0 Lewis & Clark 515 1,274 0.1 0 0 aPercentage of entire National Forest that occurs within the Wyoming Basins study area boundary.

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Table 2.4. Land ownership/management, by state, within the Wyoming Basins Ecoregional Assessment boundary.

Area State/Agency Hectares Acres % Colorado BIA 2 4 0 BLM 1,010,832 2,497,736 23.2 NPS 168,290 415,841 3.9 Private 1,514,374 3,741,972 34.7 State 220,239 544,204 5.0 U.S. Forest Service 1,436,142 3,548,664 32.9 Other a 11,955 29,540 0.3 Total 4,361,834 10,777,960 100 Idaho BIA 13,064 32,280 0.8 BLM 95,517 236,019 5.6 NPS 14,181 35,040 0.8 Private 645,719 1,595,552 38.1 State 84,127 207,876 5.0 U.S. Forest Service 796,106 1,967,154 47.0 Other a 46,801 115,644 2.8 Total 1,695,515 4,189,566 100 Montana BIA 210,922 521,181 2.9 BLM 624,269 1,542,551 8.6 NPS 78,613 194,250 1.1 Private 2,972,395 7,344,700 40.8 State 434,848 1,074,496 6.0 U.S. Forest Service 2,885,358 7,129,632 39.6 Other a 82,823 204,654 1.1 Total 7,289,228 18,011,464 100 Utah BIA 203,016 501,646 5.6 BLM 547,693 1,353,333 15.2 NPS 52,837 130,559 1.5 Private 1,381,226 3,412,967 38.4 State 217,859 538,324 6.1 U.S. Forest Service 913,530 2,257,306 25.4 Other a 282,517 698,092 7.9 Total 3,598,679 8,892,227 100 Wyoming BIA 733,095 1,811,457 4.2 BLM 6,553,677 16,193,939 37.3 NPS 882,360 2,180,284 5.0 Private 4,920,843 12,159,256 28.0

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Area State/Agency Hectares Acres % State 898,049 2,219,053 5.1 U.S. Forest Service 3,398,560 8,397,740 19.3 Other a 198,122 489,553 1.1 Total 17,584,706 43,451,282 100.0 TOTAL 34,529,962 85,322,499 100.0 aIncludes Bureau of Reclamation, Department of Defense, The Nature Conservancy, U.S. Fish and Wildlife Service, local, and miscellaneous federal lands.

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Table 2.5. Area contained within landcover classes of the Wyoming Basins Ecoregional Assessment area.

Area Landcover class Hectares Acres % Agriculture 1,850,921 4,573,626 5.4 Alpine 339,574 839,087 1.0 Burn 970,911 2,399,120 2.8 Grasslands 4,100,036 10,131,189 11.9 Juniper 1,138,399 2,812,984 3.3 Other forest 10,021,627 24,763,440 29.0 Sagebrush 10,553,577 26,077,888 30.6 Other shrub 3,578,071 8,841,412 10.4 Riparian/wetland 590,808 1,459,885 1.7 Urban 110,561 273,196 0.3 Water 245,578 606,824 0.7 Othera 1,034,593 2,556,479 3.0 Total 34,534,655 85,335,131 100.0 a The class “unknown” was combined with “other;” see Table A5.1 for details on vegetation cover types included in the classes, and “Landcover mapping” in Chapter 2 for details on reclassification of the original landcover map (Comer et al. 2002).

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 2-42 Version 2.0, March 2006 Table 2.6. Area of sagebrush, by landowner/manager and BLM Field Office, within the Wyoming Basins assessment area. Hectares Acres Total sagebrush State/Field office BLM USFS Private Othera BLM USFS Private Other Hectares Acres Colorado Glenwood Springs 10,745 29,311 23,074 995 26,552 72,428 57,016 2,458 64,125 158,454 Kremmling 100,531 15,628 144,876 38,124 248,408 38,617 357,984 94,204 299,159 739,213 Little Snake 174,786 932 270,285 38,265 431,891 2,302 667,866 94,552 484,268 1,196,611 Royal Gorge 155 0 3,656 49 382 0 9,035 122 3,860 9,539 White River 30,059 7,472 33,646 9,452 74,275 18,464 83,138 23,355 80,629 199,232 Total 316,276 53,343 475,537 86,885 781,508 131,811 1,175,039 214,691 932,041 2,303,049 Idaho Idaho Fallsb 49,165 158,449 192,892 37,420 121,486 391,523 476,630 92,463 437,926 1,082,102 Total 49,165 158,449 192,892 37,420 121,486 391,523 476,630 92,463 437,926 1,082,102 Montana Billings 27,917 9,916 40,837 34,996 68,983 24,502 100,907 86,474 113,666 280,866 Butte 19,223 31,330 101,082 16,655 47,499 77,415 249,769 41,155 168,290 415,838 Dillon 167,361 68,059 219,134 87,020 413,543 168,173 541,474 215,024 541,574 1,338,214 Lewistown 0 527 1,111 137 0 1,303 2,746 338 1,775 4,387 Missoula 844 6,333 29,474 4,623 2,086 15,648 72,830 11,422 41,274 101,986 Total 215,345 116,165 391,638 143,431 532,111 287,041 967,726 354,413 866,579 2,141,291 Utah Fillmore 23 781 511 74 56 1,929 1,263 182 1,388 3,430 Richfield 143 134 3,630 265 354 330 8,969 654 4,172 10,307 Salt Lake 60,748 60,285 294,214 39,952 150,105 148,963 726,994 98,721 455,199 1,124,783 Vernal 142,148 47,478 99,106 132,370 351,243 117,317 244,888 327,083 421,102 1,040,531 Total 203,061 108,678 397,461 172,661 501,758 268,539 982,114 426,640 881,861 2,179,051 Wyoming Buffalo 377 176 530 350 933 434 1,309 865 1,433 3,541 Casper 121,955 157 164,386 44,900 301,348 388 406,194 110,946 331,398 818,876 Cody 186,507 33,857 160,634 91,507 460,852 83,660 396,923 226,110 472,505 1,167,545 Kemmerer 419,740 82,774 332,810 47,005 1,037,166 204,532 822,362 116,148 882,329 2,180,208 Lander 778,499 27,192 190,703 433,528 1,923,648 67,190 471,222 1,071,235 1,429,922 3,533,295 Pinedale 308,561 132,144 117,745 54,528 762,446 326,524 290,944 134,738 612,978 1,514,652 Rawlins 873,754 26,808 637,671 126,012 2,159,021 66,241 1,575,666 311,371 1,664,245 4,112,299 Rock Springs 1,087,361 16,080 331,983 50,078 2,686,836 39,733 820,319 123,742 1,485,502 3,670,630 Worland 342,606 7,876 155,462 46,795 846,568 19,460 384,143 115,628 552,739 1,365,799 Total 4,119,360 327,064 2,091,924 894,703 10,178,818 808,162 5,169,082 2,210,783 7,433,051 18,366,845 TOTAL 4,903,207 763,699 3,549,452 1,335,100 12,115,681 1,887,076 8,770,591 3,298,990 10,551,458 26,072,338 a Category includes lands managed by such entities as the Bureau of Indian Affairs, Bureau of Reclamation, National Park Service, states, The Nature Conservancy, and U.S. Fish and Wildlife Service. b Area in Idaho is part of the Idaho Falls District and includes portions of 2 Field Offices, Upper Snake and Pocatello.

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 2-43 Version 2.0, March 2006 Table 2.7. Potential threats and associated effects on habitats and species in the sagebrush ecosystem, with example references (adapted from Wisdom et al. 2005b).

Potential Associated threat effects Examples Example references

Weather, climate Environmental – Gradually increasing temperatures have contributed to Tausch et al. 1993, EPA 1998, Bachelet et al. 1999, change, and habitat loss or drought and more severe and frequent wildfires, escalating Miller and Eddleman 2000, Smith et al. 2000, catastrophes degradation the spread of invasive plants such as cheatgrass in sagebrush Neilson et al. 2005 ecosystems. Drought years in close succession can lead to losses of key forbs used by sagebrush-associated species. Population – Catastrophic events such as floods and severe drought can Burgman et al. 1993, Andelman et al. 2001, Morris stochastic events lead to extirpation of small populations and Doak 2002

Roads and Environmental – Creation of roads and highways and their associated rights- Forman et al. 1997, 2003; Forman 2000; Gucinski et highways habitat loss of-way result in direct loss of habitat al. 2001; Spellerberg 2002 Environmental – Creation of roads and highways and their associated rights- Forman et al. 1997, 2003; Braun 1998; Parendes and habitat of-way fragments sagebrush habitats; roads may accelerate Jones 2000; Gucinski et al. 2001; Neely et al. 2001; fragmentation the spread of invasive plants Havlick 2002; Spellerberg 2002; Gaines et al. 2003; and degradation Gelbard and Belnap 2003; Gelbard and Harrison 2003; Munger et al. 2003 Population – Roads may serve as movement or migration barriers to less Mader 1984, Bennett 1991, Reijnen et al. 1997, barrier to mobile species; animals may avoid traffic or other activities Wisdom et al. 2000, Spellerberg 2002, Forman et al. migration or road associated with roads 2003, Gaines et al. 2003, Brock and Kelt 2004, avoidance Ingelfinger and Anderson 2004 Population – Death or injury from collisions with vehicles, and increased Patterson 1952, Olendorff and Stoddart 1974, direct and mortality from poaching due to improved access Blumton 1989, Wisdom et al. 2000, Todd 2001, indirect mortality Havlick 2002, Forman et al. 2003, Woods et al. 2004

Intensive Environmental – Ecologically inappropriate grazing by domestic stock, Bock et al. 1993, Fleischner 1994, Saab et al. 1995, livestock grazing habitat especially cattle and sheep, leading to loss of native Guthrey 1996, Schroeder et al. 1999, Beck and degradation perennial grasses and forbs in the understory (changes in Mitchell 2000, Miller and Eddleman 2000, Johnson composition and structure), with resulting declines in forage and O’Neil 2001, Freilich et al. 2001, Noss et al. and other habitat components for species of concern and 2001, Holmes et al. 2003, Knick et al. 2003, Dobkin their prey (e.g., invertebrates); trampling may destroy and Sauder 2004, Thines et al. 2004 burrows used by some species such as burrowing owls or pygmy rabbits Population – Mortality from trampling of nests Fleischner 1994, Beck and Mitchell 2000, Holmes et direct mortality al. 2003

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Oil and natural Environmental – Pipelines, roads, well pads, and associated collection Braun 1998, Braun et al. 2002, Noon 2002, Weller et gas field habitat loss and facilities fragment habitats; outright loss of habitat also al. 2002, Connelly et al. 2004, Thomson et al. 2005 development fragmentation occurs from roads and well pads and other facilities constructed for field development Population – Disturbance and potential abandonment of habitat due to Bowles 1995, Warrick and Cypher 1998, Dyer 1999, disturbance vehicular traffic, other noise (e.g., compressor stations), and Braun et al. 2002, Lyon and Anderson 2003, related human activity at well sites Holloran 2005 Environmental – Disturbed sites (e.g., roadsides and well pads) may become Zink et al. 1995, Parendes and Jones 2000, habitat infested with invasive species Trombulak and Frissell 2000, Forman et al. 2003, degradation Gelbard and Belnap 2003

Fences Environmental – Construction of fences in sagebrush ecosystems can Braun 1998, Connelly et al. 2004, O’Gara and habitat fragment habitats and interfere with animal movement (e.g., Yoakum 2004 fragmentation pronghorn) Population – Animals can collide with fences or become entangled, Riddle and Oakley 1973, Fitzner 1975, Call and direct mortality leading to injury or death Maser 1985, Todd 2001, O’Gara and Yoakum 2004

Expansion of Environmental – Changes in climate and fire suppression have led to Blackburn and Tueller 1970; Burkhardt and Tisdale juniper and other habitat loss and expansion of pinyon pine and juniper woodlands or other 1976; Miller and Wigand 1994; Miller and Rose woodland degradation conifers into sites previously occupied by sagebrush, 1995, 1999; Commons et al. 1999; Miller and species in especially in mountain big sagebrush and Wyoming big Eddleman 2000; Miller and Tausch 2001; Grove et sagebrush sagebrush al. 2005 communities

Invasions of Environmental – Altered fire regimes and habitat degradation (e.g., from Yensen 1981, Billings 1994, D'Antonio and exotic plants habitat loss and intensive livestock grazing) have led to increases in exotic Vitousek.1999, Knick 1999, West 1999, D’Antonio degradation plants (e.g., cheatgrass) in sagebrush ecosystems; noxious 2000, Miller and Eddleman 2000, Booth et al. 2003, weeds can also be accidentally introduced during Menakis et al. 2003, Dobkin and Sauder 2004 reclamation of oil and gas well sites

Reservoirs, Environmental – Outright loss of habitat from establishment of reservoirs in Braun 1998, Schroeder et al. 1999, Nachlinger et al. dams, and other habitat loss sagebrush habitat 2001 water Environmental – Altered stream flows and hydrological regimes may degrade Pierson et al. 2001, 2002, 2003 developments habitat or change habitat for aquatic and riparian species degradation Herbicides Environmental – Herbicides used extensively prior to the 1980s for Best 1972, Braun and Beck 1977, Braun 1998, habitat loss and conversion and removal of sagebrush, especially if native Connelly et al. 2000, Miller and Eddleman 2000, fragmentation understory vegetation was in relatively good condition Connelly et al. 2004

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Power lines Environmental – Disturbance of vegetation and soils in corridors can lead to Zink et al. 1995, Braun 1998 habitat increased invasion of exotic species in these areas degradation Population – Poles and towers for power lines may serve as additional Gilmer and Wiehe 1977, Knight and Kawashima increased rates of perches or nest sites for corvids and raptors, increasing the 1993, Steenhof et al. 1993, Braun 1998 predation potential for predation on sagebrush-associated species Population – Birds may collide with power lines, resulting in injury or O’Neil 1988, Harmata et al. 2001 direct mortality death; electrocution of perching raptors and other birds also occurs

Altered fire Environmental – Increases in catastrophic wildfires, often related to invasions Whisenant 1990, Billings 1994, D'Antonio and regimes habitat loss of cheatgrass, have resulted in complete removal of Vitousek 1999, Knick and Rotenberry 1999, Neely et sagebrush cover (i.e., type conversion), especially in al. 2001, Menakis et al. 2003 Wyoming big sagebrush communities Environmental – Fire suppression has led to altered fire cycles in sagebrush Schroeder et al. 1999, Miller and Eddleman 2000, habitat ecosystems, resulting in changes in vegetation composition Connelly et al. 2004 degradation and structure, e.g. encroachment of woodlands into sagebrush

Urban Environmental – Development of urban areas and “ranchettes” surrounding Braun 1998, Connelly et al. 2004 development habitat loss urban sites results in direct loss of sagebrush habitats

Population – Increases in human activities in urban and exurban areas may Berry et al. 1998, Millsap and Bear 2000, Arrowood human negatively affect populations of sagebrush-associated species et al. 2001, Neely et al. 2001, Knick et al. 2003 disturbance by displacement or abandonment. Predation rates on wildlife in sagebrush habitats also may increase from domestic dogs and cats in urban and rural settings, as well as from increased populations of predators such as corvids, due to increased availability of food resources associated with human waste (e.g., garbage dumps, trash in campgrounds).

Herbivory Environmental – Localized, excessive herbivory by native ungulates can lead McArthur et al. 1988, Singer and Renkin 1995, effects from wild habitat to degraded understories in sagebrush ecosystems (e.g., Wambolt and Sherwood 1999, Groves et al. 2000 ungulates degradation changes in species composition and structure) and reductions (Appendix 20) in sagebrush densities and canopy cover

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Disease Population – Disturbance from oil and gas development may lead to Naugle et al. 2004, 2005; Rowland 2004, Walker et transmission direct mortality concentrations of native ungulates on winter ranges, al. 2004, U.S. Government 2005 exacerbating disease transmission during the stressful winter season. In addition, man-made water sources, particularly those whose status has changed from ephemeral to permanent from human activities, may lead to increased transmission of mosquito-borne diseases such as West Nile virus.

Brood parasitism Population – Populations of some avian species (e.g., lark and vesper Friedmann and Kiff 1985, Robinson et al. 1995, by brown- direct mortality sparrows) in the sagebrush ecosystem may be affected by Shaffer et al. 2003 headed cowbirds parasitism from brown-headed cowbirds, a species which may increase in human-altered environments , such as livestock feedlots and overgrazed pastureland

Recreation Environmental – Off-road vehicle use can degrade habitats in the sagebrush Berry 1980, Havlick 2002, Munger et al. 2003 habitat ecosystem, e.g., by increasing presence of exotic annual degradation grasses like cheatgrass Population – Recreational activities, such as off-road vehicle use in Berry 1980, White and Thurow 1985, Braun 1987, human sagebrush habitats, may affect species of concern, e.g., Knight and Gutzwiller 1995, Schroeder et al. 1999, disturbance displacement or nest abandonment. Recreational shooting of Havlick 2002, Munger et al. 2003 small mammals also can directly affect populations.

Conversion of Environmental – Removal of sagebrush cover (e.g., via brush-beating, Vale 1974, Dobler 1994, Fischer et al. 1997, Braun sagebrush to habitat loss chaining, disking, or burning) and planting with crops, such 1998, Knick 1999, Schroeder et al. 1999, West 1999, cropland or tame as alfalfa, or with non-native perennial grasses (e.g., crested Miller and Eddleman 2000, Johnson and O’Neil pasture for wheatgrass) for livestock forage; example affected species: 2001, Knick et al. 2003 livestock greater sage-grouse, swift fox , and ferruginous hawk Environmental – Removal of sagebrush may lead to fragmentation of Knick and Rotenberry 1995, 1997, 2002; Johnson habitat remaining sagebrush habitats, resulting in interference with and O’Neil 2001; Knick et al. 2003; Connelly et al. fragmentation animal movements, dispersal, or population fragmentation 2004 Population – Nest and egg destruction, or directly mortality of animals, Patterson 1952 direct mortality from mechanical or other methods used to remove sagebrush or to cultivate lands adjacent to sagebrush

Mine Environmental – Fragmentation and outright loss of habitat to surface mines Braun 1998, Ricketts et al. 1999, Neely et al. 2001 development habitat loss and and associated mine tailings and roads, especially coal mines fragmentation

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Population – Disturbance and potential abandonment of habitat due to Bednarz 1984, Braun 1998 disturbance traffic, noise, and related human activity at mine site; example affected species: bats, greater sage-grouse

Pesticides Environmental – Decrease in forage base by killing of insects used as prey by Johnson 1987, Holmes et al. 2003 habitat sagebrush-associated species degradation Population – Direct mortality of birds and other vertebrates exposed to Patterson 1952, Blus et al. 1989, Blus 1996 mortality pesticides, and indirect mortality through consumption of contaminated insects

Saline-sodic Environmental – The disposal of millions of barrels of water produced during Groves et al. 2000 (Appendix 20), McBeth et al. water habitat CBM extraction can lead to salinization of surrounding soils 2003 degradation and aquatic systems into which these waters may be dumped. In addition, sodic water discharged from wells can lead to high mortality rates (up to 100%) in vegetation exposed to such discharge.

Wind energy Environmental – Increase of noxious weeds in areas around turbines or along Forman et al. 1997, 2003; Leddy et al. 1999; Gelbard development habitat roads needed to access turbines; loss of habitat from road and Belnap 2003; USDI BLM 2005 degradation construction and turbine installation. In addition, some species may avoid the area near turbines due to the association of such structures with nests or perches of avian predators such as corvids, or because of disturbance from noise associated with turbines. Population – Deaths and injuries of birds and bats from collisions with Erickson et al. 2001, Johnson et al. 2004, mortality wind turbines http://www.nationalwind.org

Collection of Population – loss Collection of rare plants and animals, especially herptiles, Wisdom et al. 2000, Freilich et al. 2001, Woods et al. specimens for of individuals may pose unknown risks to populations of these species; 2004 personal, from the wild example species: midget faded rattlesnake commercial, or scientific uses

Groundwater Environmental – The pumping of water for CBM may lead to excessive Groves et al. 2000 (Appendix 20), Nachlinger et al. depletion habitat groundwater withdrawal in the well sites 2001 degradation

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Grazing by feral Environmental – Loss of native perennial grasses and forbs in the understory USDI BLM et al. 2000, Young and Sparks 2002, horses habitat Beever 2003 degradation

Selenium and Population – Poisoning of animals from uptake of selenium in Lemly 1997 other direct threat of contaminated aquifers, primarily from agricultural runoff environmental mortality contaminants

Military training Environmental – Training exercises in sagebrush habitats may result in loss of Knick and Rotenberry 1997, Holmes and Humple habitat shrubs from both wildfire and destruction from tracked 2000 fragmentation vehicles, and may lead to habitat fragmentation

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Fig. 2.1. Sagebrush plant communities within the Wyoming Basins Ecoregional Assessment study area. (See Table A5.1 for all sagebrush landcover types mapped as sagebrush).

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Fig. 2.2. Field Offices of the Bureau of Land Management encompassed by the Wyoming Basins Ecoregional Assessment boundary.

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Fig. 2.3. Land management status, by management authority, within the Wyoming Basins Ecoregional Assessment area.

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Fig. 2.4. National Forests (NFs) within the Wyoming Basins Ecoregional Assessment (WBEA) boundary. Named and cross-hatched National Forests (9) are those supporting >100,000 acres of sagebrush within the study area; the remaining 12 National Forests are depicted in light green. See Table 2.3 for a complete list of the 21 NFs within the WBEA.

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Fig. 2.5. Landcover classes within the Wyoming Basins Ecoregional Assessment area; cover types were modified from the sagebrush coverage (Comer et al. 2002). See Table A5.1 for details on reclassification of the original map.

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4.5

) 4.0 Other 6

0 BLM

1 3.5 x s

e 3.0 r c a 2.5 h (

us 2.0 br

ge 1.5 a S l 1.0 a

Tot 0.5

0.0 r g s e le ke e lls te s n er llon dy a nal per lin t li ings er land i na o r s ing w m r D C Lak F a m Bu ll pr neda o S lt Ve m Bi Ra S Land m e a C e Pi W l S r ck Ke tt daho K o Li I R Field Office

Fig. 2.6. Amount of sagebrush within BLM Field Offices in the Wyoming Basins Ecoregional Assessment area by management status (BLM vs. other land management authorities). Field Offices containing <1.0% of the total sagebrush in the study area are not displayed (see Table 2.6 for data for all Field Offices).

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BLM Other Private USFS

100%

80% (%) r e n w

o 60% d by Lan 40% ush r Sageb 20%

0% CO ID MT UT WY State

Sagebrush by Landowner (%) in the WBEA

BLM USFS Private Other

Fig. 2.7. Percentage of sagebrush by primary land management authority within states of the Wyoming Basins Ecoregional Assessment (WBEA) boundary and for the study area as a whole.

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60 WBEA Sagebrush 50 40 t n e

c 30 r

Pe 20 10 0 1234 Land Status Class

Fig. 2.8. Comparison of GAP land status class for all lands within the Wyoming Basins Ecoregional Assessment Area (WBEA) versus only the sagebrush landcover type. Land status was derived from standard GAP classifications (Crist 2000) and indicates the relative degree of protection from alteration. Status categories range from 1 (best protected) to 4 (least protected); see text for details.

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Fig. 2.9. Land status categories for sagebrush landcover types in the Wyoming Basins Ecoregional Assessment area; see text for details about land status categories. Status categories range from 1 (best protected) to 4 (least protected); see text for details.

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CHAPTER 3: SAGEBRUSH-ASSOCIATED SPECIES OF CONSERVATION CONCERN IN THE WYOMING BASINS

INTRODUCTION

Mounting problems confront the sagebrush biome and associated species, thus heightening the need for assessments that consider the status of species and habitats within this region (Chapter 1). A necessary first step in conducting environmental assessments, regardless of scale, is to select the particular taxa that will be evaluated. Such lists guide the type of evaluations that are feasible and meaningful, and should be tailored to be most useful to the end user of the assessment. Moreover, lists of species of concern are important for focusing attention on predicted responses of these species to management actions in the assessment area. Regardless of the objectives for compiling a species list for evaluation in regional assessment, no all-inclusive list of species will satisfy all entities with interest in management and conservation in the region. Such lists are merely a starting point for evaluating conditions within the region and should be considered dynamic, changing in composition as information is acquired or as objectives of the assessment evolve. Whether the focus of the assessment should be species or some other level of biological organization, such as plant associations or species guilds, depends in part on the objectives of the assessment. For example, many ecoregional assessments conducted by The Nature Conservancy (TNC) have as a primary objective the identification of a suite of reserves for conservation planning (Groves 2003). The TNC assessments often specify conservation targets that include species and varieties of plants and animals or plant communities, as well as objectives to maximize species richness or biodiversity (Stein et al. 2000, Noss et al. 2001, Groves 2003). The conservation plan for the Utah-Wyoming-Rocky Mountains Ecoregion, for example, identified many plant communities as conservation targets, including 17 Artemisia communities (Noss et al. 2001). In the Great Basin ecoregional assessment, Wisdom et al. (2005a) analyzed conditions for sagebrush-associated species and groups of species, with groupings based on similarities in habitat associations and habitat area in various landcover types. Criteria for selecting species may be based on a variety of factors, including perceived levels of risk to some potential threat, sensitivity to disturbance, status with regard to state or

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federal lists of threatened or sensitive species, current monitoring status, involvement in management decisions, or association with some particular landcover type (e.g., riparian communities, big sagebrush) (Stephenson and Calcarone 1999, Andelman et al. 2004, Morse et al. 2004, Wisdom et al. 2005b). In a recent review of protocols for selecting species at risk, selection methods that included current and future threats were the most useful for determining which species may be negatively affected by proposed management actions (Andelman et al. 2004). Another species selection process recently developed is that of the state-level Comprehensive Wildlife Conservation Strategies1. To receive funds from the State Wildlife Grants program approved by Congress in 2001, each state in the U.S. must prepare a conservation strategy. These strategies include lists of “species in greatest need of conservation;” criteria for selection of species and subsequent monitoring and management vary among states. For example, in Colorado the criteria for inclusion included state ranks of S1 or S2 and a global rank of G4, or species identified as a conservation priority from range-wide status assessment (Colorado Division of Wildlife 2005). By contrast, Utah employed a 3-tier system to rank species, with species in Tier I typically state or federally listed taxa, whereas Tier III species are primarily those requiring more information (Utah Division of Wildlife Resources 2005). For the Wyoming Basins Ecoregional Assessment (WBEA), the primary criteria for selection of species were strong association with sagebrush ecosystems and a status of conservation concern, due to declining habitats, populations, or both. Our intention was to be more inclusive than exclusive, to ensure that all potential species of concern and their habitats in sagebrush ecosystems of the study area were considered. Being more inclusive than exclusive in developing a list of species for evaluation provides more opportunity to evaluate species that may not currently be of concern, but that in future may become so (Wisdom et al. 2005b; Appendix 1).

METHODS

1 See http://www.teaming.com/state_wildlife_strategies.htm for an overview of the strategies and links for each state.

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Selecting Species for Assessment

In addition to sagebrush association, species brought forward for analysis were those identified as being of conservation concern. For this criterion, we used state conservation status ranks as assigned by NatureServe (2005). This ranking system, based on several factors such as number of occurrences of populations within each state, population size, and threats, is widely used in conservation planning throughout the United States and Canada. Additional criteria for selecting species for assessment within the Wyoming Basins study area included: (1) a geographic range large enough to render the species suitable for regional, broad-scale assessment; and (2) a geographic range that overlapped sufficiently with the study area boundaries to warrant inclusion in the assessment (Wisdom et al. 2005b; Fig. 3.1). Specific methods for selection of vascular plants and vertebrates are described below.2, 3 Vascular plants.--Four key sources and methods were used to develop the draft list of vascular plants: (1) the selection procedures developed by Wisdom et al. (2005b) for identifying species of conservation concern within sagebrush ecosystems of North America (Fig. 3.1); (2) a list of regional endemic vascular plants created by TNC (Fertig 1999) for the Wyoming Basins ecoregional plan (Freilich et al. 2001); (3) a list of plants developed for the Wyoming GAP (Gap Analysis Program) project (Merrill et al. 1996), which includes portions of the Utah-Wyoming- Rocky Mountains and Southern Rocky Mountains Ecoregions; and (4) a report on the globally rare plant species of the Dillon BLM Field Office in Montana (Lesica 2003). The draft list of vascular plant taxa of concern resulting from these 4 methods was subsequently reviewed by several botanists for errors of commission or omission, as well as for the validity of the selection process (Table A1.1). Vertebrates.--A draft list of potential species for assessment in the study area was prepared (Wisdom et al. 2005b), as was done for vascular plants (Fig. 3.1). This list was then reviewed by several species experts (Table A1.1), after which a final list was developed for use in phase I of the WBEA. Our list is intended to be inclusive of all species that are associated with sagebrush. The degree of dependency on sagebrush for many species is highly uncertain, and many species are likely to depend on some combination of sagebrush and other habitats.

2 Details of methods of selection for species of concern are in Appendix 1. 3 Our focus was on terrestrial species and habitats within the sagebrush ecosystem; thus, we did not consider aquatic species in our selection process.

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Consequently, we did not refer to our list of species of concern as sagebrush-dependent, but rather sagebrush-associated. Any reduction in amount or quality of sagebrush is likely to affect all sagebrush-associated species on our list, and thus our rationale for being inclusive in identifying species of concern for analysis. Example species for model development.--To meet one of our assessment objectives, developing predictive models for example species of concern (Chapter 1), we selected 10 vertebrates from our draft list of species of concern for modeling during Phases I and II of the WBEA. Selection was based on 1 or more criteria, including: (1) strength of association with sagebrush communities; (2) habitats or populations at moderate to high risk; (3) special status or regulatory concern (e.g., occurrence on state-level BLM sensitive species lists or action pending by FWS); and (4) special concern within the study area (e.g., pronghorn [Thomson et al. 2005]). We also considered the availability of empirical data on habitat relationships and effects of disturbance and recommendations from species experts who reviewed our draft species list (Table A1.1).

Mapping Geographic Ranges

Prior to quantifying environmental conditions for species of concern in the study area, current range maps must be acquired. Such maps are needed because differences in geographic ranges among species may translate into differences in habitat status and response to management. For our assessment, we defined a species’ range as the polygon or polygons that encompass the outer boundaries of a species’ geographic occurrence within the study area; this definition concurs with Gaston (1991) as the “extent of occurrence,” rather than the area of occupancy of a species. These maps often overestimate the true range of species, especially when considered over large spatial extents (Fertig and Reiners 2002, Dobkin and Sauder 2004), but are commonly used in conservation planning and assessment at regional scales (e.g., Knick et al. 2003, Laliberte and Ripple 2004). Many species included in our assessment have geographic ranges that are largely based on incomplete data regarding the internal population structure or distribution within their range. Consequently, we used the more general definition of range being the outer boundaries of each species’ currently estimated occurrence.

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Vertebrates.4-- Range maps for vertebrates of concern in the Wyoming Basins were acquired from several sources (Table A1.4). The majority (78%, n = 31) had been compiled previously as digital range maps for bats (Bat Conservation International), other mammals (Patterson et al. 2003), and birds (Ridgely et al. 2003). The remainder were hand-digitized from published maps in various sources, with the exception of pronghorn and pygmy rabbit (Table A1.4). Because there were no accurate current range maps for these species at the scale of our assessment, we created these maps specifically for the WBEA (see Appendix 1 for more details on mapping geographic ranges of species in our assessment).

Estimating Species Sensitivity to Disturbance

Identifying species’ sensitivity to human disturbance clearly plays an important role in regional assessments that focus on anthropogenic impacts, such as the WBEA. Every species is affected to some degree by both biotic (e.g., vegetation composition and structure) and abiotic habitat characteristics (e.g., geomorphology, soils), as well as other environmental conditions such as disturbance from human-related activities or barriers to dispersal from roads and other anthropogenic alteration of landscapes. Using accurate demographic data on the distribution and status of populations for each identified species of concern across the study area would provide a credible basis for assigning species to risk categories or otherwise evaluating potential threats to population persistence. However, such demographic data are lacking for all but the most thoroughly studied species (e.g., gray wolf and greater sage-grouse), and even then are available for only a part of their ranges. Given that such data are largely unavailable, knowledge about a species’ sensitivity to disturbance, combined with knowledge of life history characteristics, can serve as a useful surrogate for demographic data. Application of this knowledge in regional assessments must be made at appropriate scales and must also include spatial information on the patterns of species’ distributions and the anthropogenic features that may be associated with threats, such as power lines (Hansen et al. 1993). To meet our objective of quantifying sensitivity to disturbance for species of concern in the Wyoming Basins (Chapter 1), we developed an index of sensitivity patterned after Fleishman

4 Geographic range maps were not prepared for vascular plants during this phase of the WBEA; Appendix 1.

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et al. (2001) - the “index of disturbance sensitivity,” or DSI.5 This index is based on life history characteristics for species within a particular taxonomic group (e.g., mammals), and reflects conditions within a specific geographic region. The life history parameters chosen, such as number of young per year, are presumed to relate to the sensitivity of the species to human disturbance, preferably to those activities that can be regulated by management (Fleishman et al. 2001). We developed 3 tables of life history parameters and associated scores, one each for reptiles, birds, and mammals (Tables A1.5-A1.7). Each vertebrate species on our list was then scored, based on extensive review of published literature on life history features. (Supporting data and literature citations for each score are available from the authors.) The mean score across all parameters was then calculated for each species, with potential values ranging from 1.0 (least sensitive) to 3.0 (most sensitive).

Quantifying Species Richness

We overlaid the geographic range maps for 39 of the 40 vertebrate species of concern in our assessment with the study area boundaries to examine spatial patterns of species richness for sagebrush-associated species of concern in the Wyoming Basins.6 For avian species with seasonal ranges in the study area, we restricted our analysis to include only the breeding or year- round ranges for these species (i.e., we omitted any winter or migratory ranges). Species richness was calculated as the total number of species (0-39) that co-occur in each 90-m (2-acre) pixel within the study area. We mapped species richness for the entire study area based on this value and also calculated mean richness for each BLM Field Office. We then calculated a “weighted species richness” by weighting the occurrence of each species by its sensitivity score (DSI), described above. For this calculation, we used each species’ DSI (range 1-3) as the weight and summed these values for each pixel in the study area, only including those species whose ranges occurred within that pixel. We then mapped weighted species richness and summarized the data within each Field Office. The maximum possible

5 See Appendix 1 for more details about the development of the DSI for taxa in our assessment, as well as individual scoring tables for each taxon. 6 We did not include black-footed ferret (Mustela nigripes) in analyses of species richness, because current populations of this species are extremely limited and are the result of reintroductions in extirpated range.

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RESULTS

Vascular Plants of Conservation Concern

We identified 65 taxa of sagebrush-associated vascular plants of conservation concern for assessment in the Wyoming Basins (Table 3.1). Nearly all are found in Wyoming (n = 59; 91%), reflecting its relatively large percentage (51%) of the study area and central location. Only 15 taxa on the list occur in Idaho, which had the smallest area (4.2 million acres) among the 5 states within the assessment boundary. Colorado and Utah had similar representation on the list (n = 40 and 43, respectively). By contrast, Montana composes 21% of the study area, nearly twice the percentage of Colorado (12.6%) and Utah (10.4%), but only 28 taxa on the list are found in Montana. The vast majority (n = 47; 72%) of the vascular plants of concern were forbs; the next most common life form was subshrub/forb (n = 13; 20%), followed by graminoids (n = 3; 5%). Only 2 shrub species, Wyoming threetip sagebrush (Artemisia tripartita ssp. rupicola) and Nuttall’s horsebrush (Tetradymia nuttallii), were included (Table 3.1). Families most commonly represented included Asteraceae (n = 16), Fabaceae (n = 12; primarily Astragalus spp.), and Scrophulariaceae (n = 9). Many of the plants on the list, such as Ownbey’s thistle, were found on several other lists of special status or sensitive species, or were brought forward from >1 of our selection approaches (“Source” column, Table 3.1; Appendix 1). Although no taxa were ranked G1, 1 was ranked G2 - meadow pussytoes (Antennaria arcuata) - and 2 as G2G3 - Evert’s spring-parsley (Cymopterus evertii) and talus spring-parsley (C. lapidosus).7 Only 3 plants were ranked G5 (“demonstrably secure” at a global scale; see footnotes, Table 3.1); most taxa were ranked intermediate to these extremes (i.e., G3 and G4), consistent with our culling of species too rare or ubiquitous for effective assessment at a regional scale. Forty-eight plants (74%) were ranked S1 or S2 in at least one state within the assessment area, indicating imperilment due to rarity, threats, or other factors (Table 3.1). Fifteen

7 Global rank indicators (“G-ranks”) reflect the status of each taxon based on worldwide distributions (Master 1991).

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subspecies or varieties had trinomial ranks of T2 to T4, indicating some level of risk to these taxa at the infraspecific level (Table 3.1). Several plants on the list, such as Nelson’s milkvetch, were also found on BLM lists of special status species (e.g., USDI Bureau of Land Management, Wyoming 2004). Of the 20 plants brought forward from the selection process outlined by Wisdom et al. (2005b) (Appendix 1), 7 were retained on the primary list (taxa retained have a source code of “1” in Table 3.1), 8 were dropped, and 5 were deferred for further review (Table A1.2). Plants dropped from further consideration either were not associated with sagebrush, were too common (e.g., ranked S5 in all states within their range in the study area) to retain as species of concern, or had distributions largely outside the study area (i.e., peripheral). The 5 taxa deferred from the Wisdom et al. (2005b) process were combined with results from the other selection methods (e.g., regional endemics of Fertig 1999) for a total of 19 plant taxa for which there were questions about inclusion in our assessment. These plants were deferred to a second-tier list pending further review (Table A1.2). As was true for the plants retained on our primary list (Table 3.1), nearly all (88%) of the plants on the deferred list (Table A1.2) were forbs.

Vertebrates of Conservation Concern

Forty vertebrates of concern within the sagebrush ecosystem were identified for the WBEA. Among these were 1 amphibian (Great Basin spadefoot [Spea intermontana]), 4 reptiles, 18 birds, and 17 mammals (Table 3.2). The reptiles included 2 snakes and 2 lizards. The majority (56%; n = 10) of the avian taxa were passerines; also included were 5 raptors and 2 gallinaceous species (greater sage-grouse and Columbian sharp-tailed grouse [Tympanuchus phasianellus columbianus]). The 17 mammals included a wide range of taxa, from small mammals (n = 10) to bats (n = 3), carnivores (n = 1), and ungulates (n = 2) (Table 3.2). All 7 vertebrates commonly denoted as sagebrush-obligate species (Paige and Ritter 1999) were identified as species of concern: sagebrush lizard (Sceloporus graciosus), greater sage-grouse, sage thrasher (Oreoscoptes montanus), sage sparrow (Amphispiza belli), Brewer’s sparrow (Spizella breweri), pronghorn, and pygmy rabbit (Table 3.2). Most species occurred in all 5 states of our assessment, and all 40 were found in Wyoming (Table 3.2). Idaho had the lowest representation, with 33 of the 40 (83%) of the

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 3-9 Version 2.0, March 2006 species present in that state. Species not found in all states were those with more limited distributions in the region, such as midget faded rattlesnake (Crotalus viridis concolor) and mountain plover (Charadrius montanus). Several species on our list are endemic to shrubsteppe habitats of the Intermountain West, including Great Basin pocket mouse (Perognathus parvus) and pygmy rabbit (Dobkin and Sauder 2004). Several vertebrate species of concern in the Wyoming Basins are either rare or imperiled, based on their G-ranks (Table 3.2). The rarest species on our list, black-footed ferret, is ranked G1 and listed as endangered by the FWS (NatureServe 2005). This species has been extirpated in the wild, and is now found only in very limited numbers in areas where animals have been successfully re-introduced (NatureServe 2005). Two additional species, mountain plover and Wyoming pocket gopher (Thomomys clusius), are ranked G2, which indicates imperilment at a global scale. The Wyoming pocket gopher is endemic to Wyoming, where it is ranked S2. At the trinomial (infraspecific) level, 2 subspecies were ranked T3 or T4: midget faded rattlesnake (T4) and Columbian sharp-tailed grouse (T3; Table 3.2). The majority of the species on our list, however, are considered secure on a global basis, ranked either G4 (n = 10; 25%) or G5 (n = 24; 60%). At the state level, 9 species (23%) were ranked S1 in 1 or more of the 5 states in the WBEA area. Only 3 species were ranked either S4 or S5, indicating a relatively secure status, in all states in which they occurred in the study area: green-tailed towhee (Pipilo chlorurus), Brewer’s blackbird (Euphagus cyanocephalus), and pronghorn (Table 3.2). In addition to black-footed ferret, several species of concern in the WBEA have been considered for listing by the U.S. Fish and Wildlife Service (FWS) in response to petitions submitted under the Endangered Species Act (ESA). For example, petitions to list the greater sage-grouse as threatened or endangered range-wide were found not warranted by the FWS in December 2004 (U.S. Fish and Wildlife Service 2005a). A petition to list the pygmy rabbit as threatened or endangered across its range was considered by FWS in May 2005; however, the agency found the listing to be “not warranted” (USDI FWS 2005b). The mountain plover was petitioned for listing under the ESA, but withdrawn in 2003 (Dinsmore 2003). The white-tailed prairie dog (Cynomys leucurus) was petitioned for listing in 2002, but listing was denied by the FWS in 2004. A petition to list the Columbian sharp-tailed grouse as threatened or endangered across its historical range was submitted in 2004 (Banerjee 2004).

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Twenty-eight (70%) of the vertebrate species of concern selected for the Wyoming Basins assessment are also found on at least 1 of the recently developed state-level lists compiled as part of the Comprehensive Wildlife Conservation Strategy process (Table 3.2). For example, 27 (68%) species of concern from the WBEA are listed in Wyoming’s “species of greatest conservation need” (Wyoming Game and Fish Department 2005). Similarly, 17 of the Wyoming Basins species (43%) are listed in Utah’s strategy; all are ranked as Tier II or III, with the exception of black-footed ferret (Table 3.2; Utah Division of Wildlife Resources 2005). Example species for modeling in the WBEA.--We selected 10 vertebrate species of concern in the WBEA area as example species for development of models of predicted probability of occurrence based on habitat and anthropogenic variables: Brewer’s sparrow, ferruginous hawk, greater sage-grouse, loggerhead shrike, pronghorn, pygmy rabbit, sage sparrow, sage thrasher, sagebrush lizard, and short-horned lizard (see Chapter 6 for modeling methods and results). Greater sage-grouse was specifically included due to its prominence as a species of concern in sagebrush ecosystems and the commitment by the BLM to managing habitats for this species (USDI Bureau of Land Management 2004a ,b, c). Moreover, 3 of the example species, greater sage-grouse, ferruginous hawk, and pygmy rabbit, are included in the 2004 “red list” of threatened species published by the International Union for the Conservation of Nature (Baillie et al. 2004). All species except 3 - ferruginous hawk, loggerhead shrike, and short-horned lizard - are sagebrush obligates (Paige and Ritter 1999).

Range Maps

Geographic ranges were mapped for the 40 vertebrates on our list, as described above. Digital versions of the grid (i.e., raster-based) maps were used in modeling probability of occurrence for the 10 example species and for calculations of species richness across the WBEA area. An example of a range map used in our assessment is displayed in Fig. 3.2.

Sensitivity to Disturbance

The midget-faded rattlesnake was the only vertebrate species in our assessment that had the maximum possible score for sensitivity to disturbance (DSI = 3.0), with scores of 3 for each

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 3-11 Version 2.0, March 2006 of the 4 life history parameters for reptiles (Table A1.5, Fig. 3.3A). Values for the remaining 3 reptiles ranged from 1.5 for sagebrush lizard to 2.5 for Great Basin gopher snake (Pituophis catenifer deserticola). Among birds, Swainson’s hawk (Buteo swainsoni) had the highest DSI (2.6), followed by prairie falcon (Falco mexicanus) and sage thrasher (Fig. 3.3B). Six passerines had the lowest sensitivity scores, ranging from 1.5 for Brewer’s blackbird to 1.8 for loggerhead shrike (Lanius ludovicianus) (Fig. 3.3B). Among the 17 mammals in our assessment, disturbance sensitivity was highest for pronghorn (2.8) and least for 5 small mammals (DSI = 1.4) (Fig. 3.3C). Other mammals with relatively high indices of disturbance were the 3 bat species and bighorn sheep.

Species Richness

Species richness within each 2-acre cell (pixel) in the WBEA area ranged from a minimum of 11 taxa that co-occurred in the Missoula Field Office to a high of 36 species in Rock Springs (Table 3.3). Species richness was highest in the southern reaches of the Rock Springs Field Office in the southwestern corner of Wyoming (Fig. 3.4), where several species with limited geographic ranges in the study area occur, such as midget faded rattlesnake and Wyoming pocket gopher. Other regions with comparatively high species richness were much of the southwestern quadrant of Wyoming, northeastern Utah, and extreme northwestern Colorado (Fig. 3.4). The Montana portion of the study area had the lowest species richness. Among BLM Field Offices in the study area, mean species richness was highest in Rock Springs, Kemmerer, and Vernal, and lowest in the 4 Field Offices in Montana (Table 3.3; Figs. 3.4, 3.5). Although species richness was also relatively high in Fillmore and Richfield (Table 3.3), these Field Offices together comprise <2.0% of the study area (Table 2.1). The White River and Little Snake Field Offices in Colorado had comparatively moderate richness, whereas Glenwood Springs and Kremmling had relatively low richness (Table 3.3, Fig. 3.5). When species richness was weighted by the disturbance sensitivity index for each species, mean values ranged from a low of 21.3 within the Missoula Field Office to a high of 66.9 in both Kemmerer and Rock Springs (Table 3.3). Means of weighted species richness closely paralleled those of unweighted richness (Fig. 3.5). Rock Springs had the highest mean but Vernal, rather than Kemmerer, was ranked second. Spatial patterns of weighted species

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richness generally were similar to those for species richness, with the highest values in southwestern Wyoming, as well as in portions of northeastern Utah and northwestern Colorado (Figs. 3.6, 3.7). Some differences were apparent, however. For example, species with somewhat lower DSI scores were found in the eastern border of the Vernal Field Office, resulting in more moderate weighted richness values here (i.e., more middle-range, or yellow-shaded; Fig. 3.7B) than seen in the unweighted richness map (Fig. 3.7A).

DISCUSSION

The 65 vascular plants and 40 vertebrates of concern identified for our assessment span a range of taxa and levels of conservation risk. Many of these taxa have been highlighted in other recent assessments of vertebrate species of concern in shrubsteppe communities (e.g., Knick et al. 2003, Connelly et al. 2004, Dobkin and Sauder 2004, Rich et al. 2005, Wisdom et al. 2005a). Moreover, the Wyoming Basins Ecoregion has one of the highest rates of regional endemism for vascular plants in the north-central United States (Fertig 1999). Thus, our results corroborate the selection of taxa important for management consideration in the sagebrush ecosystem of the Wyoming Basins. Our assessment area harbors a large proportion of the sagebrush remaining in the western U.S. (Chapter 2). Corresponding to these expansive tracts of sagebrush is a wide diversity of vertebrates and plants that rely on sagebrush communities for all or part of their life cycles, with “hotspots” of biodiversity evident in several portions of our study area, especially southwestern Wyoming. Many of the species in our evaluation have demonstrated declines in abundance, including greater sage-grouse (Connelly et al. 2004) and a host of shrubsteppe passerine birds (Vander Haegen et al. 2000, Knick et al. 2003, Dobkin and Sauder 2004). Likewise, population declines for several of the mammals on our list, such as white-tailed prairie dog and pygmy rabbit, have been noted in portions of their ranges (Hays 2003, Thines et al. 2004). While broad- scale trends for small mammals are much more difficult to obtain, due to the lack of a systematic monitoring scheme such as the Breeding Bird Survey for avian species, evidence suggests that mammalian distributions may be far more restricted than that portrayed by their geographic ranges (Dobkin and Sauder 2004).

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The relative importance of the Wyoming Basins to birds and small mammals of shrubsteppe landscapes at a larger scale was highlighted in recent analyses (Dobkin and Sauder 2004). (Note that these analyses were not limited to species of concern.) In evaluation of patterns of species richness in the Intermountain West for 21 upland bird species, the Wyoming Basins displayed relatively high richness, especially southwestern Wyoming. Moreover, slightly lower levels of community stability (as measured by Jaccard’s index) were found for upland birds in the southeastern portion of the Wyoming Basins Ecoregion, indicating some fluctuations in community composition over time. Diversity of upland small mammals was moderate in the Wyoming Basins compared to other regions; however, many of the species on our list were not evaluated in their study, due to their inclusion of only those species weighing less than about 2 pounds (Dobkin and Sauder 2004). Furthermore, small-mammal communities in the shrubsteppe of the Wyoming Basins have been little studied (Dobkin and Sauder 2004). Not surprisingly, BLM Field Offices with the highest species richness of sagebrush- associated species of concern were also those with the largest amounts of sagebrush; the correlation of sagebrush area with mean species richness within Field Offices was relatively high (0.74, P = 0.01).8 Areas with the highest species richness in our study area also overlapped areas of high anthropogenic impact, as predicted by our human footprint model (Fig. 5.10; see Chapter 5 for more detail) and by other recent evaluations in the Upper Green River Valley of Wyoming (Weller et al. 2002, Thomson et al. 2005). Species richness of birds is negatively related to landscape variables that measure urban development (Cam et al. 2000). Negative effects of recent development, especially with regard to energy extraction (Chapters 4 and 5) in the Wyoming Basins, may not have translated into measurable effects on diversity. Thus, land management planning in areas of high biodiversity coupled with a large anthropogenic footprint will involve prudent consideration of cumulative effects of present and future actions on sagebrush-associated species in these key areas. Spatial patterns of species richness largely followed those demonstrated for weighted species richness. This result suggests that species’ sensitivity to disturbance, as measured in our study, was consistent across the study area, i.e., concentrations of more sensitive species did not occur disproportionately in areas of low or high species richness. Regardless, the map of weighted species richness highlights areas where a relatively large number of sagebrush-

8 Analysis conducted for the BLM Field Offices (n = 11) that have >80% of their land base within the study area.

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associated species of concern co-occur, as well as concentrations of species with relatively high sensitivity to disturbance. Such maps can inform land-use planning at a regional scale, to ensure that biodiversity is maintained concurrent with management actions.

REFERENCES

Andelman, S. J., C. Groves, and H. M. Regan. 2004. A review of protocols for selecting species at risk in the context of US Forest Service viability assessments. Acta Oecologica 26:75- 83. Baillie, J. E. M., C. Hilton-Taylor, and S. N. Stuart, editors. 2004. 2004 IUCN Red List of threatened species: a global species assessment. IUCN, Gland, Switzerland and Cambridge, UK. [http://www.iucn.org/themes/ssc/red_list_2004/main_EN.htm]. Banerjee, R. 2004. Columbian sharp-tailed grouse (Tympanuchus phasianellus columbianus). Petition to the U.S. Fish and Wildlife Service to list the Columbian sharp-tailed grouse as an endangered or threatened species under the Endangered Species Act, 16 U.S.C. xx 1531 et Seq. (1973 as amended), and to designate critical habitat. Forest Guardians, Sante Fe, New Mexico, USA. Cam, E., J. D. Nichols, J. R. Sauer, J. E. Hines, and C. H. Flather. 2000. Relative species richness and community completeness: birds and urbanization in the mid-Atlantic states. Ecological Applications 10:1196-1210. Colorado Division of Wildlife (CDOW). 2005. Species of concern: threatened and endangered list. In: Colorado’s Comprehensive Wildlife Conservation Strategy. Colorado Division of Wildlife, Denver, Colorado, USA. [http://wildlife.state.co.us/WildlifeSpecies/ComprehensiveWildlifeConservation Strategy/]. Colorado Natural Heritage Program. 2003. Rare plant field guide: Colorado BLM plant list, Craig District. [http://www.cnhp.colostate.edu/rareplants/blmlist.html#Craig]. Colorado Natural Heritage Program. 2005. Element tracking list. Colorado Natural Heritage Program, Fort Collins, Colorado, USA. (updated 2/22/2005).

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Connelly, J. W., S. T. Knick, M. A. Schroeder, and S. J. Stiver. 2004. Conservation assessment of greater sage-grouse and sagebrush. Unpublished report. Western Association of Fish and Wildlife Agencies, Cheyenne, Wyoming, USA. Dinsmore, S. J. 2003. Mountain plover (Charadrius montanus): a technical conservation assessment. [Online.] USDA Forest Service, Rocky Mountain Region. [http://www.fs.fed.us/r2/projects/scp/assessments/mountainplover.pdf]. Dobkin, D. S., and J. D. Sauder. 2004. Shrubsteppe landscapes in jeopardy. Distributions, abundances, and the uncertain future of birds and small mammals in the Intermountain West. High Desert Ecological Research Institute, Bend, Oregon, USA. Fertig, W. 1999. Wyoming Basins Ecoregion target plant species and potential plant conservation sites. Prepared for the Wyoming Nature Conservancy. Wyoming Natural Diversity Database, University of Wyoming, Laramie, Wyoming, USA. Fertig, W., and W. A. Reiners. 2002. Predicting presence/absence of plant species for range mapping: a case study from Wyoming. Pages 483-489 in J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall, and F. B. Samson, editors. Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC, USA. Freilich, J., B. Budd, T. Kohley, B. Hayden. 2001. The Wyoming Basins ecoregional plan. The Nature Conservancy, Lander, Wyoming, USA. Fleishman, E., R. B. Blair, D. D. Murphy. 2001. Empirical validation of a method for umbrella species selection. Ecological Applications 11:1489-1501. Gaston, K. J. 1991. How large is a species’ geographic range? Oikos 61, 434-438. Groves, C. R. 2003. Drafting a conservation blueprint: a practitioner’s guide to planning for biodiversity. Island Press, Washington, DC, USA. Hays, D. W. 2003. Washington pygmy rabbit 2003 recovery plan update: addendum to Washington State Recovery Plan for the pygmy rabbit. Washington Department of Fish and Wildlife, Olympia, Washington, USA. Idaho Department of Fish and Game. 2005. Appendix B: Idaho species of greatest conservation need. In: Idaho Comprehensive Wildlife Conservation Strategy. Idaho Conservation Data Center, Idaho Department of Fish and Game, Boise, Idaho, USA. [http://fishandgame.idaho.gov/cms/tech/CDC/cwcs_table_of_contents.cfm].

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Idaho Rare Plant Society. 2004. The Idaho native plant society rare plant list. Results of the 20th annual Idaho rare plant conference. Idaho Native Plant Society, Boise, Idaho, USA. Keinath, D., B. Heidel, and G. Beauvais. 2003. Wyoming plant and animal species of concern. Wyoming Natural Diversity Database, University of Wyoming, Laramie, Wyoming, USA. Knick, S. T., D. S. Dobkin, J. T. Rotenberry, M. A. Schroeder, W. M. Vander Haegen, and C. Van Riper, III. 2003. Teetering on the edge or too late? Conservation and research issues for avifauna of sagebrush habitats. Condor 105:611-634. Laliberte, A. S., and W. J. Ripple. 2004. Range contractions of North American carnivores and ungulates. BioScience 54:123-138. Lesica, P. 2003. Conserving globally rare plants on lands administered by the Dillon Office of the Bureau of Land Management. Report to the USDI Bureau of Land Management, Dillon Office. Montana Natural Heritage Program, Helena, Montana, USA. Master, L. L. 1991. Assessing threats and setting priorities for conservation. Conservation Biology 5:559-563. Merrill, E. H., T. W. Kohley, M. E. Herdendorf, W. A. Reiners, K. L. Driese, R. W. Mars, and S. H. Anderson. 1996. The Wyoming Gap Analysis Project final report. Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA. Montana Fish, Wildlife, and Parks. 2005. Component III: Species of greatest conservation need (tier I species). In: Montana’s Comprehensive Fish and Wildlife Conservation Strategy, Helena, Montana, USA. [http://fwp.mt.gov/wildthings/cfwcs/strategy.html]. Montana Natural Heritage Program. 2003. Plant species of concern. Montana Natural Heritage Program, Helena, MT. [http://nhp.nris.state.mt.us/plants/reports/2003_PSoC_List.pdf]. Montana Natural Heritage Program. 2004. Montana animal species of concern. Montana Natural Heritage Program, Helena, MT. Morse, L. E., J. M. Randall, N. Benton, R. Hiebert, and S. Lu. 2004. An invasive species assessment protocol: evaluating non-native plants for their impact on biodiversity. Version 1. NatureServe, Arlington, Virginia, USA. NatureServe. 2005. NatureServe Explorer: An online encyclopedia of life [web application]. Version 4.3. NatureServe, Arlington, Virginia, USA.

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[http://www.natureserve.org/explorer]. (Accessed: April 3, 2005). Neely, B., P. Comer, C. Moritz, M. Lammert, R. Rondeau, C. Pague, G. Bell, H. Copeland, J. Humke, S. Spackman, T. Schulz, D. Theobald, and L. Valutis. 2001. Southern Rocky Mountains: an ecoregional assessment and conservation blueprint. The Nature Conservancy, USDA Forest Service, Rocky Mountain Region, Colorado Division of Wildlife, and USDI Bureau of Land Management. Nicholoff, S. H., compiler 2003. Wyoming Bird Conservation Plan. Version 2.0. Wyoming Partners in Flight, Wyoming Game and Fish Department, Lander, Wyoming, USA. Noss, R., G. Wuerthner, K. Vance-Borland, and C. Carroll. 2001. A biological conservation assessment for the Utah-Wyoming Rocky Mountains Ecoregion: report to The Nature Conservancy. Conservation Science, Inc., Corvallis, Oregon, USA. Paige, C., and S. A. Ritter. 1999. Birds in a sagebrush sea: managing sagebrush habitats for bird communities. Partners in Flight Western Working Group, Boise, Idaho, USA. Patterson, B. D., G. Ceballos, W. Sechrest, M. F. Tognelli, T. Brooks, L. Luna, P. Ortega, I. Salazar, and B. E. Young. 2003. Digital Distribution Maps of the Mammals of the Western Hemisphere, version 1.0. NatureServe, Arlington, Virginia, USA. [http://www.natureserve.org/getData/mammalMaps.jsp]. Rich, T. D., M. J. Wisdom, and V. A. Saab. 2005. Conservation of sagebrush steppe birds in the interior Columbia Basin. Pages 589-606 in Ralph, C.J., T. Rich, and L. Long, editors, Proceedings of the Third International Partners in Flight Conference, USDA Forest Service General Technical Report PSW-GTR-191, Albany, California, USA. Ridgely, R. S., T. F. Allnutt, T. Brooks, D. K. McNicol, D. W. Mehlman, B. E. Young, and J. R. Zook. 2003. Digital distribution maps of the birds of the Western Hemisphere, version 1.0. NatureServe, Arlington, Virginia, USA. [http://www.natureserve.org/getData/birdMaps.jsp]. Seeds of Success. 2004. Wyoming Basins. Updated 14 May 2004. [http://www.nps.gov/plants/sos/species/wybasin.htm]. Stein, B. A., L. S. Kutner, and J. S. Adams, editors. 2000. Precious heritage: the status of biodiversity in the United States. Oxford University Press, New York,USA.

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Stephenson, J. R., and G. M. Calcarone. 1999. Southern California mountains and foothills assessment: habitat and species conservation issues. USDA Forest Service General Technical Report GTR-PSW-172, Berkeley, California, USA. The Nature Conservancy. 2000. Middle Rockies – Blue Mountains Planning Team. Middle Rockies – Blue Mountains ecoregional conservation plan. The Nature Conservancy, Arlington, Virginia, USA. Thines, N. J. S., L. A. Shipley, and R. D. Sayler. 2004. Effects of cattle grazing on ecology and habitat of Columbia Basin pygmy rabbits (Brachylagus idahoensis). Biological Conservation 119:525-534. Thomson, J. L., T. S. Schaub, N. W. Culver, and P. C. Aengst. 2005. Wildlife at a crossroads: energy development in western Wyoming. Effects of roads on habitat in the Upper Green River Valley. The Wilderness Society, Washington, DC, USA. USDI Bureau of Land Management. 2004a. Bureau of Land Management national sage-grouse habitat conservation strategy. USDI Bureau of Land Management, Washington, DC, USA. USDI Bureau of Land Management. 2004b. Bureau of Land Management national sage-grouse habitat conservation strategy. 1.31. Guidance for addressing sagebrush habitat conservation in BLM land use plans. USDI Bureau of Land Management, Washington, DC, USA. USDI Bureau of Land Management. 2004c. Bureau of Land Management national sage-grouse habitat conservation strategy. 1.41. Guidance for the management of sagebrush plant communities for sage-grouse conservation. USDI Bureau of Land Management, Washington, DC, USA. USDI Bureau of Land Management, Utah State Office. 2002. Draft - sensitive plant species list for Utah. Unpublished report. USDI Bureau of Land Management, Utah State Office. 2004. Special Status Species. (online). USDI Bureau of Land Management, Wyoming State Office. 2002. BLM Wyoming sensitive species policy and list, September 20, 2002. Unpublished report. USDI Bureau of Land Management, Wyoming State Office. 2004. Wyoming sensitive species by field office. Unpublished report. [http://www.wy.blm.gov/botany/offices.htm].

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USDI Fish and Wildlife Service. 2005a. Endangered and threatened wildlife and plants; 12- month finding for petitions to list the greater sage-grouse as threatened or endangered; proposed rule. Federal Register 70:2244-2282. USDI Fish and Wildlife Service. 2005b. Endangered and threatened wildlife and plants; 90-day finding on a petition to list the pygmy rabbit as threatened or endangered; proposed rule. Federal Register 70:29253-29265. Utah Department of Natural Resources. 2005. Utah sensitive species list. State of Utah, Department of Natural Resources, Division of Wildlife Resources. [http://dwrcdc.nr.utah.gov/ucdc/ViewReports/SSL&Appendices020805.pdf]. Utah Division of Wildlife Resources. 2005. Utah CWCS Tier I, II, and III species list. Table 5.1 in: Utah Comprehensive Wildlife Conservation Strategy (CWCS). Utah Division of Wildlife Resources, Publication Number 05-19, Salt Lake City, Utah, USA. [http://www.wildlife.utah.gov/cwcs/]. Utah Native Plant Society, Inc. 2004. Utah rare plant guide. Last updated 10/06/2004. [http://www.utahrareplants.org/rpg_species.html#All]. Vander Haegen, W. M., F. C. Dobler, and D. J. Pierce. 2000. Shrubsteppe bird response to habitat and landscape variables. Conservation Biology 14:1145-60. Weller, C., J. Thomson, P. Morton, and G. Aplet. 2002. Fragmenting our lands: the ecological footprint from oil and gas development. The Wilderness Society. [http://www.wilderness.org/Library/Documents/FragmentingOurLands.cfm]. Wisdom, M. J., M. M. Rowland, and L. H. Suring, editors. 2005a. Habitat threats in the sagebrush ecosystem: methods of regional assessment and applications in the Great Basin. Alliance Communication Group, Lawrence, Kansas, USA. Wisdom, M. J., M. M. Rowland, L. H. Suring, L. Schueck, C. W. Meinke, and S. T. Knick. 2005b. Evaluating species of conservation concern at regional scales. Chapter 1 in Wisdom, M. J., M. M. Rowland, and L. H. Suring, editors. Habitat threats in the sagebrush ecosystem: methods of regional assessment and applications in the Great Basin. Alliance Communication Group, Lawrence, Kansas, USA. Wyoming Game and Fish Department. 2005. Wyoming species of greatest conservation need: final CWCS species listing. In: A Comprehensive Wildlife Conservation Strategy for

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Wyoming. Wyoming Game and Fish Department, Cheyenne, Wyoming, USA. [http://gf.state.wy.us/wildlife/CompConvStrategy/index.asp].

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Table 3.1. Vascular plants of conservation concern identified for regional assessment of sagebrush ecosystems in the Wyoming Basins Ecoregional Assessment area.

State rank Life Global Species/subspecies/variety Family form ranka CO ID MT UT WY Sourceb

Achnatherum contractum Contracted ricegrassc Poaceae Graminoid G3G4 SU S3 S1 S3S4 8, 9, 12 Achnatherum swallenii Swallen's ricegrass Poaceae Graminoid G5 S5 S2 2, 7, 9 Antennaria arcuata Meadow pussytoes Asteraceae Forb G2 S1 S2 1, 2, 5, 6, 7, 18 Artemisia tripartita var. rupicola Wyoming threetip Asteraceae Shrub G5T3 S3 17 sagebrush Astragalus [sericoleucus var.] aretioides Cushion Fabaceae Forb G4 S1 S2 S1 S3 8, 9, 14 milkvetchd Astragalus detritalis Debris milkvetch Fabaceae Forb G3 S2 S3 1, 5, 7, 12, 14, 15 Astragalus grayi Gray's milkvetch Fabaceae Forb G4? S2 S3 7, 9, 11 Astragalus jejunus var. jejunus Starveling milkvetch Fabaceae Forb G3T3 S1 S2 S1 S3 6, 7, 9, 14, 16, 18 Astragalus nelsonianus Nelson's milkvetch Fabaceae Forb G3 S1 S1 S3 6, 7, 9, 14 Astragalus oreganos Oregon milkvetch Fabaceae Forb G4? S1 S3 7, 9, 11 Astragalus pubentissimus var. pubentissimus Green Fabaceae Forb G4 SNR SNR S2 8, 9 River milkvetche Astragalus scaphoides Bitterroot milkvetch Fabaceae Forb G3 S3 S2 1, 3, 10, 11 Astragalus simplicifolius Little bun milkvetch Fabaceae Forb G3 S3 7, 9 Castilleja pilosa var. longispica Parrot-head Indian- Scrophulariaceae Forb G4G5T4 SNR S3 S2 8, 8 paintbrush Cirsium ownbeyi Ownbey's thistle Asteraceae Forb G3 S2 S1 S2 1, 2, 4, 5, 6, 7, 9, 13, 14, 15, 16 Cryptantha caespitosa Tufted cat’s-eye Boraginaceae Subshrub G4 S2 S1 S1? S3 1, 4, 5, 7, 9, 14 Forb Cryptantha sericea Silky cat's-eye Boraginaceae Subshrub G4 SNR SNR SNR S3 8, 9, 18 Forb Cryptantha stricta Yampa River cat's-eye [Erect cat's- Boraginaceae Subshrub G3 S3 S2S3 S3 7, 9, 14 eye] Forb Cymopterus evertii Evert's spring-parsley Apiaceae Forb G2G3 S1 S2S3 2, 6, 7, 9

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State rank Life Global Species/subspecies/variety Family form ranka CO ID MT UT WY Sourceb

Cymopterus lapidosus Talus spring-parsley Apiaceae Forb G2G3 S1 S2S3 7, 9 Cymopterus longipes Long-stalk spring-parsley Apiaceae Forb G4? SNR SNR S3 8, 9 Descurainia pinnata var. paysonii Western tansy- Brassicaceae Forb G5T3? SNR SNR SNR S2 2, 7, 9, 12 mustard [Payson's tansymustard] Draba juniperina Uinta drabaf Brassicaceae Forb G5 S2 SNR SNR SNR S5 2, 7, 9 Elymus simplex var. simplex Alkali lyme grass [Alkali Poaceae Graminoid G4?Q SNR S1 S2? 7, 9 wildrye]g Erigeron nanus Dwarf fleabane Asteraceae Forb G4 SNR S1 S2 8, 9 Erigeron nematophyllus Needle-leaf fleabane Asteraceae Forb G3 S2? S1S2 S3 8, 9, 14 Eriogonum acaule Single-stem wild buckwheat Polygonaceae Forb G3 S1 S3 7, 9, 14 Eriogonum brevicaule var. canum Parasol wild Polygonaceae Forb G3 S3 S2 1, 6, 7, 9 buckwheat [Rabbit buckwheat]h Eriogonum brevicaule var. micranthum Shortstem Polygonaceae Forb G4T3 S3 8 buchwheat Eriogonum exilifolium Drop-leaf wild buckwheat Polygonaceae Forb G3 S2 S2 2, 7, 9, 14, 19 Hymenopappus filifolius var. luteus Yellowish Asteraceae Subshrub G5T3T5 SNR SU SNR S3S4 8, 11, 12 hymenopappus Forb Hymenopappus filifolius var. nudipes Fine-leaf Asteraceae Subshrub G5T4 SNR S2 8, 9, 12 woollywhite Forb Hymenoxys torreyana Torrey’s four-nerve-daisyi Asteraceae Forb G4 SNR S3 S1 S3 8, 9 Ipomopsis crebrifolia Ballhead skyrocket [Compact Polemoniaceae Subshrub G5T3T4 S1 S2 SNR S3 7, 9, 14 gilia]j Forb Lesquerella condensata Dense bladderpodk Brassicaceae Forb G4Q SU S3 S2 8, 9 Lomatium bicolor var. bicolor Bicolor biscuitroot Apiaceae Forb G4T3T4 S1 SNR S3 SNR S2 8, 14 Lomatium juniperinum Juniper desert-parsley Apiaceae Forb G3G5 SNR SNR SNR S2 8, 9 Lomatium nuttallii Nuttall's desert-parsley Apiaceae Forb G3 S1 S1 S3 8, 9, 11, 14 Mentzelia pumila Dwarf mentzelial Loasaceae Forb G4 SNR S2 S2 S3 8, 9, 11 Mertensia fusiformis Spindle bluebells Boraginaceae Forb G4G5 SNR SNR S2 8, 9 Oenothera pallida var. trichocalyx Pale evening Onagraceae Forb G5T3T5 SNR SNR S3 8 primrose Oxytropis besseyi var. ventosa Bessey's locoweed Fabaceae Forb G5T3? SU S1? S3 8, 9, 11 Oxytropis nana Wyoming locoweed Fabaceae Forb G3 S3 7, 9

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State rank Life Global Species/subspecies/variety Family form ranka CO ID MT UT WY Sourceb

Penstemon angustifolius var. vernalensis Broadbeard Scrophulariaceae Subshrub G5T3 S1 S3 1, 8, 14 beardtongue Forb Penstemon arenicola Red Desert beardtongue Scrophulariaceae Forb G3G4 SNR SNR S3S4 8, 9, 12 Penstemon eriantherus var. cleburnei Fuzzy-tongue Scrophulariaceae Subshrub G4T2T3 SU S1 S2S3 8, 9 beardtongue Forb Penstemon fremontii var. fremontii Fremont's Scrophulariaceae Subshrub G3G4T3T SNR S2S3 S3 8, 9, 12 beardtongue Forb 4 Penstemon laricifolius var. exilifolius Larch-leaf Scrophulariaceae Forb G4T2Q S2 S2 2, 8, 9, 14 beardtongue Penstemon lemhiensis Lemhi beardtongue Scrophulariaceae Forb G3 S3 S2 10 Penstemon pachyphyllus var. mucronatus Thick-leaf Scrophulariaceae Forb G5T4 SNR SNR S2 8, 9 beardtongue Penstemon paysoniorum Payson's beardtongue Scrophulariaceae Subshrub G3 SNR S3 7, 9, 12 Forb Phacelia glandulosa Glandular scorpion-weed Hydrophyllaceae Forb G4 SNR SNR S3 S1 S2? 7, 9 Phlox opalensis Opal phlox Polemoniaceae Forb G3 S1 S3 6, 7, 9, 16 Physaria acutifolia var. purpurea Brassicaceae Forb G5T2 S2 8 Platyschkuhria integrifolia Basin-daisy Asteraceae Subshrub G5 SNR SU S3 S3 8, 9, 11 Forb Sphaeromeria argentea Chicken sage Asteraceae Subshrub G3G4 S1 SNR S3 S3 10, 14 Forb Sphaeromeria capitata Cluster-head chicken-sage Asteraceae Forb G3 S1 S3 S1 S3 8, 9, 11, 14, 16 Stanleya tomentosa var. tomentosa Woolly prince's- Brassicaceae Subshrub G4T3 S2 7, 9 plume Forb Tetradymia nuttallii Nuttall's horsebrush Asteraceae Shrub G3G4 SNR SNR S2S3 8, 9 Tetraneuris torreyana Torrey's four-nerve-daisym Asteraceae Forb G4 SNR S3 S1 S3 8, 9 Thelypodiopsis elegans Westwater tumble-mustard Brassicaceae Forb G3G5 SNR S3 S2S3 8, 9, 12 Townsendia nuttallii Nuttall's Townsend-daisy Asteraceae Forb G3 S3 S1 S3 8, 9, 11 Townsendia spathulata Sword Townsend-daisy Asteraceae Forb G3 S3 S3 8, 9, 11 Townsendia strigosa Hairy Townsend-daisy Asteraceae Forb G4 S1 SNR S3 8, 9, 14 Trifolium andinum Intermountain clover Fabaceae Forb G3 S1 S2 S3 8, 9, 14 a Rankings obtained January 19, 2006 from NatureServe (http://www.natureserve.org/explorer/); G = Global rank indicator, based on worldwide distribution at the species level; T = Global trinomial rank indicator, based on worldwide distribution at the infraspecific level; S = State rank indicator, based on distribution

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Table 3.2. Forty vertebrate species of conservation concern identified for regional assessment within sagebrush ecosystems of the Wyoming Basins Ecoregional Assessment area.

Global State rank Common name Scientific name rank a Colorado Idaho Montana Utah Wyoming Sourceb

Amphibians Great Basin spadefoot Spea intermontana G5 S3 S4 S5 S5 1, 2, 7, 9, 16, 17 Reptiles Short-horned lizard c Phrynosoma hernandesi G5 S5 S3 S4 S4 1, 3, 6, 16, 17

Sagebrush lizard c Sceloporus graciosus G5 S5 S5 S3 S5 S5 1, 3, 17

Midget faded Crotalus viridis concolor G5T4 S3? S1 1, 2, 4, 9, 16, 17 rattlesnake

Great Basin gopher Pituophis catenifer G5T5 S4 S3 2, 17 snake deserticola Birds Ferruginous Hawk c Buteo regalis G4 S3B,S4N S3B S2B S2S3B, S4B,S5N 1, 2, 3, 4, 6, 8, S2N 9, 10, 11, 12, 13, 14, 15, 16, 17, 19(II), 21

Golden Eagle Aquila chrysaetos G5 S3S4B,S4 S4B,S4N S4 S4 S3B 2 N

Swainson's Hawk Buteo swainsoni G5 S5B S4B S3B S3B S4B 1, 3, 6, 10, 13, 17, 21

Prairie Falcon Falco mexicanus G5 S4B,S4N S5B,S3N S4 S4 S4B,S4N 1, 3, 10 (III), 13, 15, 16

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Global State rank Common name Scientific name rank a Colorado Idaho Montana Utah Wyoming Sourceb

Greater Sage-Grouse c Centrocercus urophasianus G4 S4 S4 S3 S2? S4 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19(II), 20, 21

Columbian Sharp-tailed Tympanuchus phasianellus G4T3 S2 S3 S1 S1 1, 2, 3, 4, 5, 6, Grouse columbianus 7, 8, 9, 12, 13, 15, 16, 17, 19(II), 20, 21

Mountain Plover Charadrius montanus G2 S2B S2B S1B S2 2, 3, 6, 10, 11, 14, 16, 17, 19(II), 20

Burrowing Owl Athene cunicularia G4 S4B S3S4 S2B S3B S3 1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 15, 16, 17, 19(II), 20, 21

Gray Flycatcher Empidonax wrightii G5 S5B S2B,S2N S4S5B S4B,S4N 1, 10, 13, 15

Sage Thrasher c Oreoscoptes montanus G5 S5 S5B S3B S4S5B S5 1, 2, 3, 9, 10, 13, 15, 17, 19(II)

Loggerhead Shrike c Lanius ludovicianus G4 S3S4B S3 S3B S4B, S3 1, 2, 3, 9, 10, S3S4N 11, 13, 15

Sage Sparrow c Amphispiza belli G5 S3B S4B S1S3B S3S4 S3 1, 2, 3, 6, 7, 8, 9, 10, 13, 15, 16, 17, 19(III)

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Global State rank Common name Scientific name rank a Colorado Idaho Montana Utah Wyoming Sourceb

Lark Sparrow Chondestes grammacus G5 S4 S5B S5B S5B,S2N S5B,S5N 1, 10, 13, 15

Green-tailed Towhee Pipilo chlorurus G5 S5 S5B S4B S4B S5B,S5N 1, 13, 15

Vesper Sparrow Pooecetes gramineus G5 S5 S4B S5B S5B,S2N S5B,S5N 1, 10, 13, 15

Brewer's Sparrow c Spizella breweri G5 S4B S4B S2B S4S5B S5 1, 2, 3, 6, 9, 10, 13, 15, 17, 19(III), 21

Brewer's Blackbird Euphagus cyanocephalus G5 S5B,S4N S5B,S5N S5B S4S5 S5B,S5N 1, 15

Western Meadowlark Sturnella neglecta G5 S5 S5B,S3N S5B S5 S5B,S5N 1, 13, 15 Mammals Merriam's shrew Sorex merriami G5 S3 S2? S3 SH S3S4 1, 3, 7, 15, 18, 19(III), 21

Spotted bat Euderma maculatum G4 S2 S2 S2 S2S3 S3 1, 2, 3, 12, 15, 16, 17, 18, 19(II), 20, 21

Western small-footed Myotis ciliolabrum G5 S4 S4? S4 S3S4 S3B 1, 2, 17, 18 myotis

Townsend’s big-eared Corynorhinus townsendii G4 S2 S2 S2 S3? S2 1, 2, 3, 4, 12, bat 16, 17, 18, 19(II), 20, 21

Pronghorn c Antilocapra americana G5 S4 S5 S5 S4 S5 1

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Global State rank Common name Scientific name rank a Colorado Idaho Montana Utah Wyoming Sourceb

Bighorn sheep Ovis canadensis G4 S4 S3 S4 S3? S3S4 2, 6, 14, 17, 19(II), 21

Black-footed ferret Mustela nigripes G1 S1 S1 S1 S1 2, 3, 4, 6, 12, 14, 16, 17, 19(I), 20

White-tailed prairie Cynomys leucurus G4 S4 S1 S2? S3 1, 2, 3, 6, 9, 12, dog 16, 17, 19(II), 20

Uinta ground squirrel Spermophilus armatus G5 S4? S3S4 S5 S3S4 1, 2, 3, 8, 17

Wyoming ground Spermophilus elegans G5 S5 S4? S3S4 SH S3S4 1, 2, 3, 8, 17, squirrel 19(III), 21

Wyoming pocket Thomomys clusius G2 S2 2, 9, 17 gopher

Idaho pocket gopher Thomomys idahoensis G4 S4? S2S3 SH S2 1, 2, 3, 8, 9, 17, 19(III), 21

Great Basin pocket Perognathus parvus G5 S1 S5 S2S4 S4 S2 2, 3, 15, 16, 17, mouse 20

Sagebrush vole Lemmiscus curtatus G5 S1 S4 S4 S3S4 S5 1, 6, 15, 16, 17

White-tailed jackrabbit Lepus townsendii G5 S4 S5 S4 S3S4 S4 1

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Global State rank Common name Scientific name rank a Colorado Idaho Montana Utah Wyoming Sourceb

Black-tailed jackrabbit Lepus californicus G5 S5 S5 S2 S5 S5 1, 3

Pygmy rabbit c Brachylagus idahoensis G4 S3 S3 S2 S1 1, 2, 3, 8, 9, 12, 14, 15, 17, 18, 19(II), 20, 21 a Rankings were obtained on January 19, 2006 from NatureServe [http://www.natureserve.org/explorer/] and are as follows: G = Global rank indicator, based on worldwide distribution at the species level; T = Global trinomial rank indicator, based on worldwide distribution at the infraspecific level; S = State rank indicator, based on distribution within the state/province at the lowest taxonomic level; 1 = Critically imperiled, at very high risk of extinction or extirpation due to extreme rarity, very steep declines, or other factors; 2 = Imperiled due to rarity from very restricted range, very few populations, steep declines, or other factors; 3 = Vulnerable due to restricted range, relatively few populations, recent and widespread declines, or other factors; 4 = Apparently secure, uncommon but not rare, some cause for long-term concern due to declines or other factors; 5 = Secure, common, widespread, and abundant; SH = Possibly extirpated; ? = Inexact or uncertain numeric rank; B = Conservation status for breeding populations in the state/province; N = Conservation status for non-breeding populations in the state/province. b Sources included: (1) the selection procedure developed by Wisdom et al. (2005b); (2) Keinath et al. 2003 (includes both Species of Concern and Species of Potential Concern); (3) Montana Natural Heritage Program 2004; (4) Colorado Division of Wildlife 2005; (5) The Nature Conservancy 2000; (6) Neely et al. 2001; (7) Noss et al. 2001; (8) Freilich et al. 2001; 9) USDI BLM, Wyoming 2002, (10) Nicholoff 2003; (11) USDI BLM, Utah 2004; (12) Utah Department of Natural Resources 2005; (13) Rich et al. 2005; (14) Baillie 2004; (15) Dobkin and Sauder 2004; (16) Colorado Natural Heritage Program 2005; (17) Wyoming Game and Fish Department 2005; (18) Idaho’s Special Status Mammals http://fishandgame.idaho.gov/cms/tech/CDC/animals/mammals.cfm (copyright 2006, Idaho Fish and Game); (19) Utah Division of Wildlife Resources 2005; (20) Montana Fish, Wildlife and Parks 2005; and (21) Idaho Department of Fish and Game 2005. c Selected as potential example species for development of prototype models.

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Table 3.3. Mean species richness and weighted species richness of vertebrate species of concern within BLM Field Offices of the Wyoming Basins Ecoregional Assessment areaa.

State/Field Office Species richness Weighted species richness Mean (SD) Range Mean (SD) Range Colorado Glenwood Springs 22.6 (1.9) 19.0 – 26.0 42.1 (3.8) 34.6 - 50.7 Kremmling 21.4 (1.1) 19.0 – 25.0 39.0 (2.4) 34.6 - 46.6 Little Snake 26.6 (3.1) 20.0 – 32.0 48.4 (6.0) 36.8 - 59.5 Royal Gorge 22.4 (1.4) 20.0 – 25.0 41.1 (2.8) 35.4 - 46.3 White River 27.6 (2.3) 22.0 – 31.0 51.3 (4.3) 40.1 - 58.1 Idaho Idaho Falls 25.5 (2.6) 19.0 – 30.0 46.8 (4.7) 35.8 - 54.5 Montana Billings 23.0 (1.1) 21.0 – 25.0 42.3 (2.1) 38.1 - 47.2 Butte 20.0 (3.0) 13.0 – 26.0 36.6 (4.9) 25.3 - 47.3 Dillon 19.2 (2.5) 14.0 – 25.0 36.1 (4.3) 26.7 - 47.5 Lewistown 16.4 (2.5) 13.0 – 24.0 30.0 (4.9) 25.7 - 44.1 Missoula 13.7 (1.0) 11.0 – 18.0 26.4 (1.6) 21.3 - 33.9 Utah Fillmore 28.8 (0.4) 28.0 – 29.0 53.2 (0.6) 51.9 - 53.5 Richfield 28.5 (0.5) 28.0 – 30.0 52.8 (0.9) 51.9 - 55.7 Salt Lake 28.0 (1.4) 23.0 – 32.0 52.6 (2.3) 45.0 - 60.7 Vernal 29.6 (1.6) 24.0 – 33.0 56.0 (2.6) 46.6 - 62.3 Wyoming Buffalo 23.6 (1.3) 20.0 – 27.0 43.6 (2.8) 36.4 - 51.5 Casper 27.8 (1.5) 23.0 – 30.0 51.2 (2.4) 42.4 - 54.0 Cody 22.8 (2.6) 20.0 – 28.0 41.8 (5.6) 35.9 - 53.2 Kemmerer 30.0 (3.0) 23.0 – 35.0 55.5 (6.1) 40.6 - 66.9 Lander 28.3 (2.1) 21.0 – 32.0 52.1 (4.1) 37.6 - 58.4 Pinedale 25.2 (3.0) 20.0 – 31.0 45.6 (5.9) 36.0 - 57.2 Rawlins 27.7 (2.7) 22.0 – 33.0 50.7 (4.4) 39.6 - 59.8 Rock Springs 32.1 (1.8) 25.0 – 36.0 59.4 (3.5) 46.5 - 66.9 Worland 27.2 (1.7) 22.0 – 29.0 51.5 (3.2) 41.1 - 54.6 a Species richness was weighted by the disturbance sensitivity index (DSI); see text for details.

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Consult master list of Step 1 species of concern in the sagebrush ecosystem.

Yes

Is the species ranked S1, S2, S3, or S4 in any Drop species from Step 2 state where the species’ further consideration. range is within the No assessment area?

Yes

Does the species’ range Evaluate species encompass >5%, and through fine-scale local Step 3 >200,000 hectares, of No analysis. the assessment area? Yes

Is the species associated with microhabitat Step 4 Evaluate species features that can be through fine-scale local mapped accurately with No analysis. coarse-scale spatial data? Yes

Compare list to other compilations (e.g., state Species experts review Step 5 sensitive species list, Step 6 list and suggest species TNC conservation to be added or dropped; targets) for the list is finalized. assessment area and add sagebrush-associated species as appropriate (repeat Steps 3 and 4).

Fig. 3.1 Criteria and decision diagram for selecting species of conservation concern for multi-species assessment in an ecoregion (from Wisdom et al. [2005a]).

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Fig. 3.2. Example geographic range map for ferruginous hawk (from Ridgely et al. 2003) as used in the Wyoming Basins Ecoregional Assessment.

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DSI 1.8 1.6 1.4 1.2 1.0 Midget faded rattlesnake Great Basin gopher snake Short-horned lizard Sagebrush lizard Reptiles

3.0 2.8 2.6 2.4 B 2.2 2.0

DSI 1.8 1.6 1.4 1.2 1.0 r k n e e er e e k e w w rk rd w o h v ow wl s w ik o ow lc s o r o u her r rro hee r rr la bi a a rous o tc h a w ar w k ha f r g pl par ng a ha s p o p o c s h n i gr us s t spa la ie w d en eagl lyc ad s ad b on' ir ge- tai o le ld o 's ed rk r s ra a ge s r i y f n he il 's n age t un a ur ta o er a pe me P r s S - G a ugi w t La er ai S e B p rr ger es rn w t Mo r Gr e g re en- V e ew S a B e t r re ha F Lo r B s G es G n W ia mb lu o C Birds

3.0 2.8 2.6 2.4 C

I 2.2 2.0

DS 1.8 1.6 1.4 1.2 1.0 t r n p is et t t e w it g e le el el r or e rr bi bi s e o o rr rr e bat e ba ot e b b r d h gh d h ed y ra ou rabb oph h v qui ui p te s d f kra sh k rie s o on t ar m e y c m 's c i g s sq g r o rn e d t m a t a et ru d t P te o g e m ja k b nd e Sp ho ig- o fo ck a d c u g b o - rri d pr o oun ck f k Py iled j o ile le age Bi s ll- c a p i p S po d’ a la -t in Me -ta ta a gr gro n k e - t ng ng e B c as it te aho n s sm a Id mi mi n n hi Ui o o w r Bl t B Wh te a W To e Wy Wy es Gr W Mammals

Fig. 3.3. Disturbance sensitivity index (DSI) scores for reptiles (A), birds (B), and mammals (C) in the Wyoming Basins Ecoregional Assessment area.

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Fig. 3.4. Species richness (number of species) for vertebrates of conservation concern in the Wyoming Basins Ecoregional Assessment. Maximum number of species co-occurring was 36.

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60.0 60.0 ) ss

n Weighted species richness

50.0 50.0 e

ea Species richness m chn i ( 40.0 40.0 R ss ) s e e n n i

30.0 30.0 a c e e ch i p (m R 20.0 20.0 s e i ed S ht ec 10.0 10.0 g p i e S

0.0 0.0 W

r l r o y s a er e er s d e lls le l s d g tte n la re rn ak p n ve n a g gs in u u ing e nd s a ak ffa n llo o t L wli rl n F eda u illin Co ri B Di s pr me V La l e Ri o S n B p s m Ca Ra it W ho i B mml Sa h tle a P S re Mi ck S Ke t Id od K o W Li o R w n e Gl Field Office

Fig. 3.5. Mean species richness (number of species) and mean weighted species richness for vertebrates of conservation concern within BLM Field Offices in the Wyoming Basins (see text for methods of weighting species richness). Field Offices were ranked left to right in decreasing order of mean species richness; Field Offices with <5.0% landcover in the Wyoming Basins study area are not displayed (see Table 3.3 for data for all Field Offices in the assessment).

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Fig. 3.6. Species richness (number of species) for vertebrates of conservation concern in the Wyoming Basins Ecoregional Assessment, weighted by the sensitivity to disturbance index for each species; see text for details. Maximum weighted species richness was 66.9.

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A B

Fig. 3.7. Comparison of species richness (A) and weighted species richness (B) for vertebrates of concern within a single BLM Field Office (Vernal); see text for details of methods.

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CHAPTER 4: CHANGES IN THE WYOMING LANDSCAPE FROM OIL AND NATURAL GAS DEVELOPMENT

Sean P. Finn and Steven T. Knick1

The placement of wells and the infrastructure needed to support them have influenced native Wyoming habitats since the middle of the 1880’s when commercial production of crude oil began in the Salt Creek area. Changes to the native landscape were confined to small, discrete areas in the early years of energy development because demand and the ability to deliver the product remained low. However, throughout the 20th century, as the American culture and economy became more dependent on petroleum products and as technology to reclaim and deliver oil and gas products efficiently and economically advanced, the number of wells drilled and the amount of land converted from native habitat to an altered state increased dramatically. Before 1960 approximately 15,100 wells were drilled in Wyoming, whereas after 1960, more than 83,000 wells were drilled or are in the planning process (WYOGCC unpublished data). Concern for this alteration, loss, and fragmentation of the native vegetation has been voiced in the scientific literature (e.g., Rotenberry 1998, Braun et al. 2002, Bryner 2003) but, to date, no broad-scale assessment of the impacts of this habitat alteration has been produced. Part of this lack of a broad-scale assessment is due to issues associated with surface land ownership and subsurface mineral rights; because these systems are complex, well development can occur on one parcel with little regard for development on adjacent sections of land. Federal land managers were generally concerned only for their charge and were unlikely to assess development in a region with multiple landowners. A second hindrance to a wide-ranging assessment has been the lack of uniform, homogenous data sets that large-scale assessments might draw upon. This particular roadblock has been minimized within the last decade by the creation of state- and national-scale spatial databases that describe landcover, geography, transportation, and oil and natural gas development. Our objective was to use spatial datasets to assess changes in the Wyoming landscape using a Geographic Information System (GIS) in conjunction with the ecoregional assessment of

1 This chapter is a special contribution to the Wyoming Basins Ecoregional Assessment funded by the U.S. Geological Survey and includes analyses beyond the study area boundary in the Powder River Basin of Wyoming.

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the Wyoming Basins (this volume). We collected spatial data from a variety of sources (Appendix 2) and merged, georectified, and reattributed them to create a uniform and consistent body of data suitable for the assessment. We conducted simultaneous assessments of 2 oil- and gas-producing basins in Wyoming: the Powder River Basin (PRB) and the Greater Green River Basin (GGR; excluding the Yellowstone and Teton regions where oil and gas development is minimal). The changes in these basins are on notably different spatial and temporal trajectories. The PRB is characterized by a mosaic of interspersed habitat types and has recently experienced a rapid increase in development due to an increased production of coal bed natural gas whereas the GGR – traditionally a high producer of conventional oil – is now in the early stages of increased coal bed natural gas development. The GGR landscape is characterized by a more uniform coverage of sagebrush-dominated shrubland. Thus, we assume that wildlife species in the GGR are adapted to large, contiguous patches of shrubland and that species in the PRB are more adapted to an interspersion of shrubland and grassland cover. We describe changes to the Wyoming landscape due to well pad and road construction from 1964 to the present. Other factors, such as surface mining, wildfire, urbanization, pipeline and powerline construction, and ranching activities that also influence these landscapes were not included in this analysis because the data describing these effects are inadequate for a thorough analysis. Direct impacts associated with well pad and road construction include a reduction in vegetation cover and diversity, potential for increased soil erosion and consequent reduction in surface water quality, loss of livestock forage, and loss and fragmentation of wildlife habitat. Indirect affects may include a disruption in ecosystem function (flow of energy and nutrients), the potential invasion of disturbed sites by undesirable plant species, and increased access to fragmented habitat by predatory mammals and birds.

STUDY AREAS AND METHODS

The Powder River Basin, located in northeast Wyoming and southern Montana (Fig. 4.1), is situated in the Northern Great Plains Steppe Ecoregion (The Nature Conservancy 2001) and is characterized by an interspersion of shortgrass prairie (dominant species include blue grama [Bouteloua gracilis] and buffalo grass [Buchloe dactyloides]), mixed-grass prairie (dominant species include needle-and-thread [Hesperostipa comata] and western wheatgrass [Pascopyrum

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smithii]), and sagebrush shrublands with a few riparian habitat patches associated with various- sized watercourses (USDI Bureau of Land Management 2003). Land ownership in the 25,476 mi2 PRB is mostly private (72%) intermingled with federal (19%) and state (8%) lands (University of Wyoming 1996). In contrast, mineral ownership in the PRB consists primarily of federal mineral estates. Primary uses of the basin include livestock grazing, surface coal and scoria mining, subsurface uranium mining, and oil and gas development. The Bureau of Land Management identified pronghorn, mule deer (Odocoileus hemionus), white-tailed deer (O. virginianus), elk (Cervus elaphus), moose (Alces alces), greater sage-grouse, sharp-tailed grouse (Tympanuchus phasianellus), and various raptors as the primary species of wildlife of concern in the PRB (USDI Bureau of Land Management 2003). Approximately 11% of PRB is in the Wyoming Basins Ecoregional Assessment study area. For a more detailed description of the soils, hydrology, and habitats of the region see USDI Bureau of Land Management (2003). The Greater Green River area stretches across 30,108 mi2 of southern and western Wyoming and northern Colorado (Fig. 4.1) and is encompassed by the Wyoming Basins and Utah-Wyoming-Rocky Mountains Ecoregions (The Nature Conservancy 2001). Nearly 100% of the GGR is within the Wyoming Basins Ecoregional Assessment study area. Vegetation in the region is dominated by Wyoming big sagebrush-grasslands with the inclusion of saltbush communities. Forested habitats exist at mid-elevations, and alpine tundra conditions exist above treeline. Linear patches of riparian habitat are found along rivers and streams. Land ownership consists primarily of public parcels managed by federal (58%) and state (5%) agencies intermingled with private lands (35%) (University of Wyoming 1996). Important wildlife species include greater sage-grouse, pronghorn, and mule deer. For a more detailed description of the soils, hydrology, and habitats of the region see USDI Bureau of Land Management (2000, 2005) and Noss et al. (2001). We focused our analyses on the primary oil and natural gas fields (Fig. 4.1) in PRB and GGR (defined as the Field Level) identified by the Wyoming State Geological Survey (De Bruin 2002) because these areas contain the highest concentration of oil and natural gas development. However, we also present comparison data, derived using the same techniques, for each basin (Basin Level) as a whole because development is increasingly occurring outside the fields described by De Bruin (2002) as extraction techniques improve. Our analyses were limited to the Wyoming state boundary because we lacked a uniform landcover map that extended beyond

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the state line. Thus we did not assess the portion of PRB that extends into Montana or the portion of GGR that extends into Colorado (Fig. 4.1). We obtained 26 GIS data layers from federal and state sources and analyzed the data using ArcGIS 9.0 (ESRI 2004) and the Patch Analyst extension (Elkie et al. 1999) in ArcView 3.2a (ESRI 2000; a detailed description of the data and GIS techniques used can be found in Appendix 2). Briefly, we acquired well location data from the Wyoming Oil and Gas Conservation Commission and reclassified the drilling date to identify year. We gathered road location data from multiple sources and merged the source data to provide a uniform coverage for the state of Wyoming. Although we knew a priori that the sum of the road data sets did not completely represent existing roads in Wyoming, we were as thorough as possible when collecting the data and used this summary dataset as our best estimate of existing roads. We modified the Wyoming GAP landcover layer (University of Wyoming 1996) for use as our base habitat map. We reduced the 26 GAP landcover classes to 8 classes (shrubland, grassland, forest, riparian, agriculture, alpine, water, urban) to simplify our analysis (Appendix 2). Each of these layers were then converted to raster-based grids with a cell size of 295 x 295 ft (2 acres) to approximate the average size of a well pad and width of a road. Well pads typically vary in size from about 1.2 acres to over 17 acres (Connelly et al. 2004); however, specific data for each pad was unavailable. Mean roadbed widths were generally <295 ft but the actual effects of roads (e.g., as facilitators of invasive plants and a source of noise and dust; Iverson et al. 1981, Gelbard and Belnap 2003, Lyon and Anderson 2003) extend beyond the actual road surface. We assigned each road segment a “year built” based on the construction date of the closest well. We then merged the well location and road grids and re-classed these cells as a unique cover type: well pad/road. This grid was then parsed by year to reflect temporal changes in the landscape. From this data we created 6 separate grids: a “baseline” map depicting roads not associated with wells and 5 maps representing oil and gas development up to and including the years 1964, 1974, 1984, 1994, and 2004 (the last year for which we had reliable data). These maps are termed “Decade.” Finally, these oil and gas development maps were overlaid with the modified landcover map to produce coverages with the original 8 classes plus well pad/road. On these output coverages, the well pad/road cover class replaced all baseline landcover classes. All subsequent analyses were based on the “Decade” output maps.

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We determined spatial characteristics of the landscape for each of the output maps using Patch Analyst (Elkie et. al 1999). We described the landscape using patch size, patch shape index, patch core area, and edge density metrics (Turner and Gardner 2001). Patch size was the spatial extent of each patch. Patch shape index equaled the sum of the perimeter of each patch divided by the square root of patch area divided by the number of patches (McGarigal and Marks 1995). We defined patch core area as the interior of each patch after applying a 590 ft (2 pixels) buffer at each patch edge. Edge density was determined by dividing total patch edge (in miles) by patch area (in square miles). We graphed the results at the Field and Basin Levels. We compared the results within each Field and Basin by Decade and compared Field data with the Basin containing the Field. Because the changes we describe here are unidirectional (e.g., changing from “shrubland” to “well pad/road” but never the reverse) we do not apply inferential statistics to the output. Instead, we simply present descriptive statistics. Although efforts are made to revegetate well sites that go out of production (USDI Bureau of Land Management 2000, 2003, 2005), few data exist that describe the frequency or success of these efforts.

RESULTS

Powder River Basin

A minimum of 416,360 acres (2.5%) of native habitat in the PRB has been converted to a well pad or road (Table 4.1) following the construction of 62,800 well pads since 1964. In the 953 Fields within PRB (De Bruin 2002), 216,000 of 1,694,167 acres, or 12.7% of the landscape, have been converted to well pads and associated roads, indicating that most of the oil and gas development has occurred in a relatively small portion of the Basin (i.e., the Fields). Before 1964, shrublands covered approximately 41%, grasslands covered 33%, and well pad/road covered 7% of Field in PRB. In 2004, these cover classes represented 35%, 29%, and 20% of Field (Table 4.2). The change occurred at relatively similar rates in grassland and shrubland habitats and was more pronounced there than in the other cover classes (Tables 4.1, 4.2; Fig. 4.2). Mean size of shrubland and grassland patches in Field declined from about 840 acres in 1964 to under 300 acres in 2004. Similarly Field patch core area has declined, and the density of

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 4-6 Version 2.0, March 2006 habitat edges has nearly doubled (Fig. 4.2). Another noticeable change in the PRB was the reduction of total area and patch size of riparian cover types. Although constituting a relatively small proportion of the landscape (< 3% of Basin and only 3.4% of Field), nearly 15% of riparian habitat in Field has been replaced by well pad and road construction, and mean patch size declined by almost 40% since 1964 (Table 4.2, Fig. 4.4).

Greater Green River Basin

Because shrubland habitats covered a large proportion of the GGR landscape (58% of Basin, 82% of Field), changes to other cover types (e.g., grasslands represent 9% and 0.1% of Basin and Field, respectively) were less pronounced (Tables 4.3, 4.4). Therefore, our assessment in GGR focused on changes to shrubland habitats. Since 1964, a minimum of 115,561 acres in the GGR Basin were converted from native habitat to a well pad or road for access to 17,300 wells. The majority of the lost habitat (104,992 acres, or 91%) was originally classed as shrubland (Table 4.3). Within the intensely developed Fields (constituting 4% of the Basin) 160,741 acres (19.9%) of the landscape are currently covered by the well pad/road class; 93% of the replaced habitat was originally shrubland. The proportion of Field area covered by well pad/road increased from 12.1% before 1964 to 19.9% in 2004 (Table 4.4). A consequence of this landscape change was increased fragmentation of shrubland cover (Fig. 4.3). Mean shrubland patch size declined from over 1,200 acres pre-development to 360 acres in 2004 and from 1,907 acres to 1,532 acres in Field and Basin, respectively (Tables 4.3, 4.4). The effects on the GGR landscape of oil and gas development also can be interpreted by measures of edge density, mean core area, and mean patch shape (Fig. 4.3). All of these metrics indicate that shrubland landscapes in the GGR are becoming more fragmented and contain an increased amount of habitat edges. Changes in the mean patch shape index indicated that shrubland patches are approaching a more uniform shape, likely due to straight roads now defining many patch edges.

DISCUSSION

Effects of Development on Landcover Change

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We demonstrated that development of oil and gas resources contributed to fragmentation of the Wyoming landscape. Habitat fragmentation is defined as the loss of a natural habitat and the division of remaining habitats into isolated patches (Wilcove et al. 1986). Potential effects of fragmentation include the elimination of species that occurred in patches that are lost, isolation of remaining habitat patches by the formation of migration barriers, crowding of species into remaining habitat patches, edge effects rendering remaining habitat less suitable, shifts in species composition to those that are more mobile or invasive, and cumulative effects of the above (Wilcove et al. 1986, Lovejoy et al. 1986, Noss and Csuti 1994). Fragmentation may adversely influence the native plant community. The increase of edge influences the microclimate, including light, soil, temperature, moisture, and wind conditions, in a habitat patch which, in turn, may alter plant composition and distribution. Shifts in plant communities may be most detrimental to rare components of the community. For example, the USDI Bureau of Land Management (2005) lists 25 sensitive plant species as occurring within the Jonah Field of GGR. USDI Bureau of Land Management has established management criteria and mitigation measures for these species but acknowledges that “habitat loss (direct and indirect) would occur due to construction, and human presence would further reduce habitat functionality in some of the remaining undisturbed or minimally disturbed areas. This would result in decreased populations of some…species,” (USDI Bureau of Land Management 2005:4-99). Changes in vegetation patch size and distribution may also shift and displace insect pollinators (Steffan-Dewenter and Tscharntke 1999). A disruption of natural pollination mechanisms might further impact rare plant populations. Fragmentation can also alter the frequency and extent of fire, affect the dispersal and regeneration of native plants, and facilitate invasion by non-native plants. Patch size and shape strongly influence the spread of disturbance; smaller patches facilitate increased disturbance from invasions because of the increased amount of perimeter to area compared to larger patches (Turner et al. 2001). In large, relatively undisturbed regions, disturbance is confined to small patches and has little influence on the large patches in the landscape. As the intensity of disturbance increases, the patches that are disturbed become closer together and eventually coalesce, thus increasing the connectivity and the spread of disturbance through corridors (Turner et al. 2001). These effects are likely to be more detrimental in GGR than PRB because of the historically uniform sagebrush dominance in

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GGR and because the assemblage of species found there is less adapted to a fragmented landscape. Habitat loss and fragmentation due to human activities may be the most important factor contributing to the decline and loss of native fauna in shrubland, grassland, and riparian habitats (Noss and Csuti 1994, Bock et al. 1999, Tewksbury et al. 2002, Knick et al. 2003). The shrubland bird species most in need of conservation attention are those most typical of undisturbed shrubsteppe, and include Brewer’s sparrow (Spizella breweri), sage sparrow (Amphispiza belli), black-throated sparrow (A. bilineata), and sage thrasher (Oreoscoptes montanus) (Wiens and Rotenberry 1981, Rotenberry 1998, Inglefinger et al. 2004). These species are sensitive to habitat fragmentation (Knick and Rotenberry 1995, Vander Haegen et. al 2000), although their response to habitat alteration differs by species and by spatial scale (Knick and Rotenberry 2002). Oil and gas development also has direct impacts on greater sage-grouse. The proximity of well pads and roads and increased human presence and activity reduces grouse lek attendance, causes females to place nests further from the lek, and reduces nest initiation rates (Aldridge 2000, Lyon and Anderson 2003). Female grouse breeding in Alberta were more likely to place their nest further from high-density well sites and other anthropogenic disturbances, and brood success was higher in less-fragmented habitats (Aldridge 2005). Rangewide, sage-grouse population declines and the fragmentation of the shrubland habitats that grouse depend on are well documented (Connelly et al. 2004). Our estimate of 416,360 acres in PRB and over 116,000 ha of habitat in GGR converted to well pad/road represents a substantial habitat loss for sage grouse populations. Grassland songbird abundance also is negatively influenced by decreased grassland patch size and presence of habitat edge (Bock et. al 1999, Helzer and Jelinski 1999). Two of the 5 species showing reduced abundance near grassland patch edges (bobolink [Dolichonyx oryzivorus] and grasshopper sparrow [Ammodramus savannarum]) also are listed as “species of concern” by Wyoming Game and Fish Department, which considers their habitat to be “vulnerable but no loss” (WGFD 2005). Similarly, riparian-associated songbirds generally show declining abundance with reduced deciduous riparian habitat and increased altered habitat in the area (Tewksbury et al. 2002). The mechanisms causing the relationship between fragmentation and songbird abundance remain unclear but probably include the cumulative effects of increased

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nest predation and nest parasitism, and increased competition from edge-associated species (Bock et. al 1999, Vander Hagen et al. 2000). Mammalian populations may also be adversely affected by habitat fragmentation (Dobkin and Sauder 2004). For example, lower proportions of prairie vole (Microtus ochrogaster) and deer mice (Peromyscus maniculatus) populations moved between patch fragments, and individuals that did move traveled farther distances, in fragmented than contiguous grassland landscapes (Diffendorfer et al. 1995). Additionally, all individuals that moved between grassland patches moved to a larger patch than the one previously occupied (Diffendorfer et al. 1995). At a larger geographic extent, oil and gas fields in the GGR have been implicated as barriers to pronghorn and mule deer migration (Berger 2004). However, no peer-reviewed studies yet report on this potential impact (Berger 2004). Beyond fragmentation effects, the presence of roads may impact wildlife directly through road kill and behavior modification and indirectly by destroying, fragmenting, and degrading habitat (Table 2.7). Mortality from collisions with vehicles is the greatest directly human-caused source of wildlife mortality in the U.S. (Forman et al. 2003). Prairie voles and cotton rats (Sigmodon hispidus) were reluctant to cross light duty (<20 vehicles/day) 10 ft-wide dirt roads (Swihart and Slade 1984), suggesting that even relatively innocuous roads may serve to isolate small mammal populations. The presence of a road may alter an animal's behavior, such as its movement, dispersal, and migration patterns and reproductive and feeding behavior. Road construction not only replaces native habitat and slices the surrounding habitat into fragments but also alters the physical and chemical environment of adjacent soil and water resources (Trombulak and Frissell 2000). Road networks, along with pipeline and power line corridors, also facilitate the spread of invasive plants (Gelbard and Belnap 2003). Additional questions about how oil and gas development influences ecosystem function, soil erosion, and water quality, remain unanswered. Development of oil and gas introduces dust, air pollution, water pollution, and noise into a relatively undisturbed environment (Bryner 2003). Shifts in ecosystem functioning could also influence landcover patterns and rates of change. Impacts to soils from removal of vegetation include exposure of the soil to wind and water erosion, mixing of soil horizons, loss of topsoil productivity, and soil compaction. Loss of vegetation and exposure of soils could result in a loss of organic matter in the soil. These impacts could, in turn, result in increased runoff, erosion, and sedimentation. These impacts

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 4-10 Version 2.0, March 2006 might lead to a further reduction in native landcover, increasing landscape fragmentation and its effects on ecosystem function. Significant impacts to soils are anticipated under all project alternatives assessed by USDI Bureau of Land Management (2005). Introduction of groundwater to the surface, especially during coal bed natural gas production, may also influence ecosystem function (see Table 2.7). Discharge water is used for crop and livestock production, injected back into the ground, discharged down stream channels, or impounded on the surface in temporary reservoirs (Bryner 2003). Surface water impoundments are relatively small (≤ 2.5 acres; T. Rinkes, personal communication), but there may be several ponds associated with a single coal bed natural gas well. Impacts on the landscape from these ponds outlast the short lifespan of the surface water because residual salts or metals may damage soils after evaporation, and clay soils may become hardpan, resulting in a permanent shift in landcover in and around an impoundment. Not all wells have water impoundments but very few of the ponds created by natural gas extraction are mapped in Wyoming. Therefore, their contribution to the loss and fragmentation of terrestrial habitats is unknown, and could not be measured with our analysis of land cover change in relation to oil and gas development.

Caveats and Missing Data

Our effort to characterize landscape change in areas undergoing development for oil and gas resources in Wyoming was incomplete because many key elements describing these systems were not available. Landcover changes associated with oil and gas infrastructure, including gas compressors and pumping stations, storage tanks, retention ponds, parking areas, pipeline corridors, and power line right-of-ways, are not adequately mapped for inclusion in this assessment. Additionally, road locations were not completely mapped as road construction occurs quickly during field development (Bryner 2003), and cartographers typically do not have the resources to map the construction as quickly (see Fig. 4.4). We did not include other forms of landcover change including surface mines, wildfires, and surface water development, choosing instead to focus our assessment on development associated with oil and natural gas. Conversely, we do not include data on rehabilitation efforts for well pads that failed to produce a resource (i.e., “dry holes”) or that were removed from the surface after completely extracting the

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 4-11 Version 2.0, March 2006 resource. Environmental Impact Statements associated with federal lands require revegetation of defunct oil and gas wells following their removal but data describing the rate and success of these efforts are scant and not in a spatial format suitable for this analysis. Such data sets would improve our assessment and would be a valuable addition to our knowledge of the state of the Wyoming landscape. Nevertheless, our data support the conclusion that the landscape is changing significantly within areas developed for oil and gas resources.

Future Development

This assessment included only 656 wells in the Jonah field and 83 in the Pinedale Anticline where 3,100 and 900 wells, respectively, are expected to be drilled over the next decade (USDI Bureau of Land Management 2000, 2005). The volume of natural gas in the GGR was estimated at 314 trillion cubic feet (Ayers 2002), over 10 times the reserve in PRB. Additionally over 2 billion barrels of oil remain to be recovered in GGR (EPCA 2002). Therefore, we expect even more development and consequent landscape fragmentation in the GGR in the near future. In contrast, the PRB has already experienced a large proportion of all the oil and gas development that will occur there. While more wells will be drilled as new fields are opened to leasing and older fields are “infilled,” coal bed natural gas reserves are expected to be exhausted there in 7–15 years. The legacy of these sites and the success of reclamation efforts remain uncertain. Presidential Executive Order 13212 (2001) expedited the review and approval of oil and gas development proposals in the western United States, indicating a continuing trend of energy development and subsequent conversion and fragmentation of native habitat in Wyoming. Similar developments are occurring in surrounding areas of Utah, Colorado, and Montana, suggesting that the fragmentation we describe is also a potential consideration at a broader spatial extent.

ACKNOWLEDGEMENTS

This work was funded by the USGS Reston, VA office in support of the Wyoming Basins Ecoregional Assessment and the Range-wide Conservation Assessment for Greater Sage-grouse

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 4-12 Version 2.0, March 2006 and Sagebrush Habitats. We thank L. Brady, C. Breckenridge J. Huss, R. Marvel, L. McCarty, R. Nelson, D. Oles, and K. Rogers for providing data. T. Rinkes provided invaluable guidance and initiative. We gratefully acknowledge the technical support of S. Hanser, M. Leu, C. Meinke, M. Rowland, L. Suring, M. Wisdom, and T. Zarriello.

REFERENCES

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Dobkin, D. S., and J. D. Sauder. 2004. Shrubsteppe landscapes in jeopardy. Distributions, abundances, and the uncertain future of birds and small mammals in the Intermountain West. High Desert Ecological Research Institute, Bend, Oregon, USA. Elkie, P. C., R. S. Rempel, and A. P. Carr. 1999. Patch analyst users manual: A tool for quantifying landscape structure. NWST Technical Manual TM-002. Northwest Science and Technology, Ontario, Canada. EPCA. 2002. Scientific inventory of onshore federal lands’ oil and gas resources and reserves and the extent and nature of restrictions or impediments to their development. 2000 Energy Policy and Conservation Act. EPCA Inventory Fact Sheet. ESRI. 2000. ArcView GIS 3.2a. Environmental Systems Research Institute, Redlands, California, USA. ESRI. 2004. ArcMap 9.0. Environmental Systems Research Institute, Redlands, California, USA. Forman, R. T. T., D. Sperling, J. A. Bissonette, A. P. Clevenger, C. D. Cutshall, V. H. Dale, L. Fahrig, R. France, C. R. Goldman, K. Heanue, J. A. Jones, F. J. Swanson, T. Turrentine, and T. C. Winter. 2003. Road ecology: science and solutions. Island Press, Washington, D.C., USA. Gelbard, J. L., and J. Belnap. 2003. Roads as conduits for exotic plant invasions in a semiarid landscape. Conservation Biology 17:420-432. Helzer, C. J., and D. E. Jelinski. 1999. The relative importance of patch area and perimeter-area ratio to grassland breeding birds. Ecological Applications 9:1448-1458. Inglefinger, F., and S. Anderson. 2004. Passerine response to roads associated with natural gas extraction in a sagebrush steppe habitat. Western North American Naturalist 64:385-395. Iverson, R. M., B. S. Hinckley, and R. M. Webb. 1981. Physical effects of vehicular disturbances on arid landscapes. Science 212:915-917. Knick, S. T., and J. T. Rotenberry. 1995. Landscape characteristics of fragmented shrubsteppe habitats and breeding Passerine birds. Conservation Biology 9:1059-1071. Knick, S. T., and J. T. Rotenberry. 2002. Effects of habitat fragmentation on passerine birds breeding in Intermountain shrubsteppe. Studies in Avian Biology 25:131-141.

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Knick, S. T., D. D. Dobkin, J. T. Rotenberry, M. A. Schroeder, W. M. Vander Haegen, and C. van Riper, III. 2003. Teetering on the edge or too late? Conservation and research issues for avifauna of sagebrush habitats. Condor 105:611-634. Lovejoy, T. E, B. O. Bierregaard, Jr., A. B. Rylands, J. R. Malcolm, C. E. Quintela, L. H. Harper, K. S. Brown, Jr., A. H. Powell, G. V. N. Powell, H. O. R. Schubart, and M. B. Hays. 1986. Habitat fragmentation in the temperate zone. Pages 257-285 in M. E. Soulé, editor. Conservation biology: the science of scarcity and diversity. Sinauer Associates, Sunderland, Massachusetts, USA. Lyon, A. G., and S. H. Anderson. 2003. Potential gas development impacts on sage grouse nest initiation and movement. Wildlife Society Bulletin 31:486-491. McGarigal, K., and B. J. Marks. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW- GTR-351, Portland, Oregon, USA. Noss, R. F., and B. Csuti. 1994. Habitat Fragmentation. Pages 237-264 in G. K. Meffe and C. R. Carroll, editors. Principles of conservation biology. Sinauer Associates, Sunderland, Massachusetts, USA. Noss, R., G. Wuerthner, K. Vance-Borland, and C. Carroll. 2001. A biological conservation assessment for the Utah-Wyoming Rocky Mountains ecoregion: report to The Nature Conservancy. Conservation Science, Corvallis, Oregon, USA. Rotenberry, J. T. 1998. Avian conservation research needs in western shrublands: exotic invaders and the alteration of ecosystem processes. Pages 261-272 in J. M. Marzluff and R. Sallabanks, editors. Avian conservation research and management. Island Press, Washington, D.C., USA. Steffan- Dewenter, I., and T. Tscharntke. 1999. Effects of habitat isolation on pollinator communities and seed set. Oecologia 121:432-440 Swihart, R. K., and N. A. Slade. 1984. Road crossing in Sigmodon hispidus and Microtus ochrogaster. Journal of Mammalogy 65:357-360. The Nature Conservancy. 2001. Ecoregions of North America. Western Conservation Science Center, The Nature Conservancy, Boulder, Colorado, USA.

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Turner, M. G., and R. H. Gardner. 1991. Quantitative methods in landscape ecology: the analysis and interpretation of landscape heterogeneity. Springer-Verlag, New York, New York, USA. Turner, M. G., R. H. Gardner, and R. V. O’Neill. 2001. Landscape ecology in theory and practice: pattern and process. Springer-Verlag, New York, New York, USA. Tewksbury, J. J., A. E. Black, N. Nur, V. A. Saab, B. D. Logan, and D. S. Dobkin. 2002. Effects of anthropogenic fragmentation and livestock grazing on western riparian bird communities. Studies in Avian Biology 25:158-202. Trombulak S. C., and C. A. Frissell. 2000. Review of ecological effects of roads on terrestrial and aquatic communities. Conservation Biology 14:18-30. University of Wyoming. 1996. Wyoming Gap Analysis: Land Cover for Wyoming. Spatial Data and Visualization Center, Laramie, Wyoming, USA. [http://www.wygisc.uwyo.edu/clearinghouse/index.html]. USDI Bureau of Land Management. 2000. Final environmental impact statement for the Pinedale Anticline oil and gas exploration and development project, Sublette County, WY. Pinedale Field Office, Pinedale, Wyoming, USA. USDI Bureau of Land Management. 2003. Final environmental impact statement and proposed plan amendment for the Powder River basin oil and gas project. Buffalo Filed Office, Buffalo, Wyoming, USA. USDI Bureau of Land Management. 2005. Draft environmental impact statement, Jonah infield drilling project, Sublette County, WY. Pinedale Field Office, Pinedale, Wyoming, USA. Vander Haegen, W. M., F. C. Dobler, and D. J. Pierce. 2000. Shrubsteppe bird response to habitat and landscape variables in eastern Washington, USA. Conservation Biolgoy 14:1145-1160. WFGD. 2005. Avian species of special concern in Wyoming. Wyoming Game and Fish website. [http://gf.state.wy.us/wildlife/nongame/SpeciesofSpecialConcern/ SSCNSSBirdList1-2005.doc]. Wiens, J. A., and J. T. Rotenberry. 1981. Habitat associations and community structure of birds in shrubsteppe environments. Ecological Monographs 51:21-41.

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Wilcove, D. S., C. H. McLellan, and A. P. Dobson. 1986. Habitat fragmentation in the temperate zone. Pages 237-256 in M. E. Soulé, editor. Conservation biology: the science of scarcity and diversity. Sinauer Associates, Sunderland, Massachusetts, USA.

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Table 4.1. Land cover changes and patch characteristics in relation to well pad and road construction in the Powder River Basin, northeast Wyoming.

% of Mean patch Edge density Mean core Class Yeara Area (ac) area size (ac) (mi/mi2) area (ac) Agriculture Baseline 1,033,076 6.3 563.9 3.32 211.3 1964 1,028,067 6.3 542.9 3.36 201.9 1974 1,024,768 6.3 527.8 3.4 196.4 1984 1,023,043 6.3 515.9 3.41 193.7 1994 1,019,730 6.2 501.9 3.44 186.1 2004 986,801 6.0 417.8 3.76 118.4 Alpine Baseline 124,585 0.8 617.8 0.38 239.2 1964 124,160 0.8 576.2 0.38 235.2 1974 124,049 0.8 567.1 0.39 230.3 1984 123,763 0.8 547.6 0.39 229.6 1994 123,723 0.8 545.6 0.39 228.6 2004 123,634 0.8 541.1 0.39 225.8 Forest Baseline 2,120,573 13.0 1,777.4 4.25 690.4 1964 2,116,925 13.0 1,719.3 4.28 674.6 1974 2,113,506 12.9 1,680.3 4.32 654.3 1984 2,111,724 12.9 1,653.1 4.33 642.2 1994 2,110,345 12.9 1,640.7 4.35 631.1 2004 2,107,227 12.9 1,583.4 4.38 612.8 Grassland Baseline 5,810,542 35.6 1,706.5 12.74 568.1 1964 5,766,585 35.3 1,547.6 13.24 496.7 1974 5,750,089 35.2 1,468.8 13.41 474.4 1984 5,729,043 35.1 1,390.4 13.65 439.1 1994 5,718,519 35.0 1,357.3 13.76 421.6 2004 5,654,772 34.6 1,197.7 14.44 336.8 Riparian Baseline 457,434 2.8 287.4 2.19 71.4 1964 451,429 2.8 263.4 2.24 67.0 1974 449,991 2.8 255.3 2.24 66.5 1984 448,390 2.7 247.1 2.25 65.2 1994 447,118 2.7 241.4 2.26 64.5 2004 438,706 2.7 208.6 2.3 58.1 Shrubland Baseline 4,879,002 29.9 2,068.5 9.19 804.3 1964 4,840,437 29.6 1,796.2 9.56 710.7 1974 4,821,057 29.5 1,637.3 9.75 665.2 1984 4,804,795 29.4 1,557.0 9.91 620.5 1994 4,791,402 29.3 1,496.4 10.07 582.4 2004 4,701,352 28.8 1,220.4 11.03 376.3 Urban Baseline 102,638 0.6 155.7 0.43 174.9 1964 101,533 0.6 147.0 0.43 172.7 1974 101,279 0.6 144.6 0.44 171.0 1984 101,153 0.6 143.6 0.44 165.6 1994 100,755 0.6 140.8 0.44 161.6

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% of Mean patch Edge density Mean core Class Yeara Area (ac) area size (ac) (mi/mi2) area (ac) 2004 98,986 0.6 127.5 0.45 158.6 Water Baseline 9,743 0.1 462.6 0.03 373.9 1964 9,743 0.1 462.6 0.03 373.9 1974 9,743 0.1 462.6 0.03 373.9 1984 9,743 0.1 462.6 0.03 373.9 1994 9,743 0.1 462.6 0.03 373.9 2004 9,721 0.1 444.5 0.03 372.9 Well pad/road Baseline 1,802,730 11.0 196.0 21.37 16.8 1964 1,901,442 11.6 185.1 22.49 15.8 1974 1,945,843 11.9 181.9 22.98 15.3 1984 1,988,671 12.2 166.3 23.48 15.3 1994 2,018,992 12.4 151.0 23.86 15.1 2004 2,219,131 13.6 110.5 26.16 14.3 Total landcover Baseline 16,340,323 100.0 798.4 27.05 511.5 1964 727.7 28.11 464.5 1974 696.6 28.57 444.8 1984 648.9 29.04 421.6 1994 606.9 29.4 405.2 2004 460.6 31.56 311.1 aBaseline is the base landcover data overlaid with roads >656 ft from a well pad and indicates landcover condition prior to energy development. “Year” (e.g., 1964) denotes all cumulative energy development prior to and including that year.

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Table 4.2. Land cover changes and patch characteristics in relation to well pad and road construction in the oil and natural gas fields (De Bruin 2002) in the Powder River Basin, northeast Wyoming.

% of Mean patch Edge density Mean core Class Yeara Area (ac) area size (ac) (mi/mi2) area (ac) Agriculture Baseline 197,705 11.67 670.6 4.7 373.6 1964 195,802 11.56 601.2 4.9 344.5 1974 194,490 11.48 550.8 5.0 336.6 1984 193,860 11.44 519.2 5.0 325.9 1994 192,560 11.37 483.1 5.1 299.2 2004 170,830 10.08 264.9 7.2 50.7 Alpine Baseline 3,677 0.22 174.7 0.2 53.4 1964 3,590 0.21 153.0 0.2 49.4 1974 3,585 0.21 147.5 0.2 49.4 1984 3,492 0.21 130.7 0.2 55.6 1994 3,492 0.21 130.7 0.2 55.6 2004 3,489 0.21 130.5 0.2 55.4 Forest Baseline 50,885 3.00 302.0 1.8 149.5 1964 50,080 2.96 273.5 1.8 132.9 1974 48,540 2.87 242.7 1.9 101.6 1984 47,527 2.81 222.4 2.0 86.2 1994 47,154 2.78 215.7 2.1 78.3 2004 46,185 2.73 189.5 2.1 62.8 Grassland Baseline 563,297 33.25 889.3 12.6 438.6 1964 540,465 31.90 598.5 14.8 273.8 1974 533,714 31.50 511.5 15.4 250.6 1984 522,599 30.85 424.3 16.5 192.0 1994 518,169 30.59 397.1 17.0 171.0 2004 485,154 28.64 296.0 20.4 63.3 Riparian Baseline 58,145 3.43 196.2 3.0 47.4 1964 54,935 3.24 148.8 3.2 35.1 1974 54,330 3.21 139.1 3.2 34.3 1984 53,643 3.17 129.5 3.2 32.6 1994 53,141 3.14 124.8 3.2 30.1 2004 49,455 2.92 90.7 3.4 19.8 Shrubland Baseline 689,594 40.70 824.1 15.8 409.2 1964 668,314 39.45 587.4 17.5 319.3 1974 657,612 38.82 485.1 18.3 285.6 1984 648,684 38.29 437.1 19.1 247.6 1994 641,783 37.88 406.7 19.9 214.2 2004 593,658 35.04 281.4 24.6 63.0 Urban Baseline 6,474 0.38 98.6 0.4 54.6 1964 6,131 0.36 78.8 0.4 54.9 1974 6,108 0.36 77.8 0.4 53.6 1984 6,019 0.36 72.2 0.4 42.7

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% of Mean patch Edge density Mean core Class Yeara Area (ac) area size (ac) (mi/mi2) area (ac) 1994 5,832 0.34 67.2 0.4 33.6 2004 5,026 0.30 43.0 0.4 14.3 Water Baseline 289 0.02 179.1 0.0 4.9 1964 289 0.02 179.1 0.0 4.9 1974 289 0.02 179.1 0.0 4.9 1984 289 0.02 179.1 0.0 4.9 1994 289 0.02 179.1 0.0 4.9 2004 269 0.02 111.2 0.0 4.9 Well pad/road Baseline 124,101 7.33 24.7 16.1 11.6 1964 174,561 10.30 30.9 21.7 10.6 1974 195,501 11.54 34.3 23.9 9.4 1984 218,053 12.87 35.1 26.6 9.6 1994 231,750 13.68 33.6 28.3 9.9 2004 340,103 20.07 31.9 40.8 8.9 Total landcover Baseline 1,694,167 100.00 229.8 33.6 350.9 1964 195.2 38.6 261.4 1974 185.6 40.5 237.5 1984 169.0 42.9 201.6 1994 154.7 44.4 178.7 2004 105.8 55.9 58.1 aBaseline is the base landcover data overlaid with roads >656 ft from a well pad and indicates landcover condition prior to energy development. “Year” (e.g., 1964) denotes all cumulative energy development prior to and including that year.

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Table 4.3. Land cover changes and patch characteristics in relation to well pad and road construction in the Greater Green River Basin, northeast Wyoming.

% of Mean patch Edge density Mean core Class Yeara Area (ac) area size (ac) (mi/mi2) area (ac) Agriculture Baseline 681,015 3.5 673.6 1.4 400.0 1964 679,483 3.5 663.7 1.5 389.7 1974 678,524 3.5 654.6 1.5 384.1 1984 677,570 3.5 641.0 1.5 375.9 1994 677,333 3.5 638.8 1.5 373.7 2004 676,266 3.5 622.2 1.5 366.1 Alpine Baseline 519,775 2.7 819.6 0.9 576.4 1964 519,523 2.7 813.0 0.9 569.5 1974 519,256 2.7 808.3 0.9 567.1 1984 518,863 2.7 797.6 0.9 558.5 1994 518,554 2.7 785.5 0.9 554.0 2004 517,944 2.7 762.1 1.0 550.7 Forest Baseline 2,464,257 12.7 1,793.7 3.4 1,025.2 1964 2,463,221 12.7 1,776.4 3.4 1,014.3 1974 2,462,559 12.7 1,761.3 3.4 1,009.4 1984 2,462,030 12.7 1,742.8 3.4 1,003.1 1994 2,461,598 12.7 1,729.7 3.4 1,001.2 2004 2,461,020 12.7 1,713.6 3.4 994.7 Grassland Baseline 1,811,139 9.3 1,284.4 3.3 544.7 1964 1,808,767 9.3 1,256.0 3.4 530.7 1974 1,808,359 9.3 1,255.0 3.4 529.1 1984 1,808,097 9.3 1,250.6 3.4 527.3 1994 1,808,036 9.3 1,249.8 3.4 527.2 2004 1,807,507 9.3 1,236.2 3.4 524.8 Riparian Baseline 329,056 1.7 262.2 1.2 110.4 1964 327,941 1.7 259.9 1.2 107.0 1974 327,553 1.7 257.5 1.2 106.5 1984 326,960 1.7 254.0 1.2 105.5 1994 326,674 1.7 253.3 1.2 104.8 2004 325,806 1.7 248.1 1.2 102.8 Shrubland Baseline 11,293,723 58.2 1,909.8 16.5 900.5 1964 11,260,952 58.0 1,818.9 16.8 856.9 1974 11,246,574 58.0 1,772.7 16.9 836.6 1984 11,224,169 57.8 1,722.5 17.1 800.8 1994 11,207,557 57.8 1,680.5 17.2 777.3 2004 11,155,960 57.5 1,534.0 17.6 700.2 Urban Baseline 45,340 0.2 77.8 0.2 70.8 1964 45,288 0.2 77.3 0.2 69.8 1974 45,288 0.2 77.3 0.2 69.8 1984 45,286 0.2 77.3 0.2 69.3 1994 45,286 0.2 77.3 0.2 69.3

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% of Mean patch Edge density Mean core Class Yeara Area (ac) area size (ac) (mi/mi2) area (ac) 2004 45,177 0.2 76.6 0.2 68.6 Water Baseline 108,341 0.6 1,114.7 0.2 616.9 1964 108,331 0.6 1,114.4 0.2 616.7 1974 108,331 0.6 1,114.4 0.2 616.7 1984 108,309 0.6 1,114.2 0.2 611.7 1994 108,287 0.6 1,114.2 0.2 616.2 2004 108,267 0.6 1,104.5 0.2 615.7 Well pad/road Baseline 2,150,996 11.1 749.7 20.6 11.4 1964 2,190,134 11.3 723.3 20.9 11.1 1974 2,207,196 11.4 703.7 21.0 11.0 1984 2,232,356 11.5 637.0 21.2 10.9 1994 2,250,320 11.6 577.7 21.4 10.8 2004 2,305,695 11.9 407.7 21.9 10.7 Total landcover Baseline 19,403,644 100.0 1,281.0 24.0 728.7 1964 1,239.5 24.3 701.0 1974 1,216.2 24.4 689.5 1984 1,172.0 24.6 669.6 1994 1,133.0 24.8 657.0 2004 990.1 25.3 614.2 aBaseline is the base landcover data overlaid with roads >656 ft from a well pad and indicates landcover condition prior to energy development. “Year” (e.g., 1964) denotes all cumulative energy development prior to and including that year.

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Table 4.4. Land cover changes and patch characteristics in relation to well pad and road construction in the oil and natural gas fields (De Bruin 2002) in the Greater Green River Basin, northeast Wyoming.

Area % of Mean patch Edge density Mean core Class Yeara (ac) area size (ac) (mi/mi2) area (ac) Agriculture Baseline 15,142 1.87 584.1 0.9 253.5 1964 14,668 1.81 452.7 0.9 188.8 1974 14,146 1.75 371.6 1.0 142.8 1984 13,949 1.72 313.1 1.0 134.4 1994 13,796 1.71 279.2 1.1 119.8 2004 13,410 1.66 220.7 1.1 109.0 Alpine Baseline 16,776 2.07 431.4 1.1 188.0 1964 16,743 2.07 430.7 1.1 182.6 1974 16,575 2.05 417.6 1.1 177.7 1984 16,331 2.02 353.6 1.2 151.0 1994 16,113 1.99 292.6 1.2 136.6 2004 15,963 1.97 252.5 1.2 131.0 Forest Baseline 16,736 2.07 421.8 1.1 180.1 1964 16,380 2.03 361.0 1.1 160.1 1974 16,301 2.02 335.6 1.1 161.9 1984 16,143 2.00 293.1 1.1 157.6 1994 15,834 1.96 241.4 1.2 147.5 2004 15,639 1.93 212.3 1.2 141.6 Grassland Baseline 981 0.12 201.9 0.1 173.7 1964 981 0.12 201.9 0.1 173.7 1974 969 0.12 149.5 0.1 173.7 1984 959 0.12 131.5 0.1 121.1 1994 959 0.12 131.5 0.1 121.1 2004 959 0.12 131.5 0.1 121.1 Riparian Baseline 16,865 2.09 330.4 1.3 104.0 1964 16,632 2.06 320.7 1.4 98.6 1974 16,496 2.04 308.6 1.4 93.2 1984 16,230 2.01 278.2 1.4 89.9 1994 16,027 1.98 257.0 1.5 80.1 2004 15,785 1.95 229.3 1.5 68.4 Shrubland Baseline 659,157 81.50 1,283.4 28.8 566.4 1964 639,544 79.08 925.6 32.1 401.0 1974 632,275 78.18 771.2 33.2 363.5 1984 619,349 76.58 638.8 35.7 272.6 1994 607,424 75.10 536.0 37.8 219.2 2004 585,130 72.35 361.3 41.8 150.7 Urban Baseline 0 0 0.0 0 0 1964 0 0 0.0 0 0 1974 0 0 0.0 0 0 1984 0 0 0.0 0 0

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Area % of Mean patch Edge density Mean core Class Yeara (ac) area size (ac) (mi/mi2) area (ac) 1994 0 0 0.0 0 0 2004 0 0 0.0 0 0 Water Baseline 1,203 0.15 742.5 0.1 232.3 1964 1,203 0.15 742.5 0.1 232.3 1974 1,203 0.15 742.5 0.1 232.3 1984 1,189 0.15 733.9 0.1 218.2 1994 1,171 0.14 722.8 0.1 326.2 2004 1,154 0.14 474.4 0.1 310.1 Well pad/road Baseline 81,916 10.13 37.3 20.4 5.7 1964 102,626 12.69 48.9 24.2 6.9 1974 110,810 13.70 54.4 25.7 6.9 1984 124,630 15.41 59.6 28.6 6.9 1994 137,454 17.00 60.5 31.2 6.7 2004 160,741 19.87 61.5 36.0 7.2 Total landcover Baseline 808,778 100.00 281.2 32.9 475.2 1964 273.3 36.5 340.5 1974 265.9 37.8 308.1 1984 247.1 40.6 238.2 1994 221.9 43.1 194.5 2004 179.6 47.4 137.6 aBaseline is the base landcover data overlaid with roads >656 ft from a well pad and indicates landcover condition prior to energy development. “Year” (e.g., 1964) denotes all cumulative energy development prior to and including that year.

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Fig. 4.1. Analysis area (hatched) of the Powder and Greater Green River Basins, Wyoming, including the oil and gas fields described by De Bruin (2002).

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Mean Patch Size Edge Density

1000 35 900 30 800 25 700 Grassland

s Grassland 20 e

r 600 Shrubland Shrubland

Ac 15 500 Mi/Mi^2 All Patches 400 10 300 5 200 0 Baseline 1964 1974 1984 1994 2004 Baseline 1964 1974 1984 1994 2004 Year Year

Mean Core Area Patch Shape

500 1.8

400 1.7 1.6 300 Grassland

s Grassland e 1.5 Shrubland Shrubland Index Acr 200 All Patches 1.4

100 1.3

0 1.2 Baseline 1964 1974 1984 1994 2004 Baseline 1964 1974 1984 1994 2004 Year Year

Fig. 4.2. Patch size, edge density, core area size, and patch shape index in oil and gas fields (De Bruin 2002) of the Powder River Basin, northeast Wyoming, 1964-2004. Baseline indicates landscape condition prior to energy development. Years indicate all landscape change from energy development prior to and including noted year. Error bars, where present, depict standard error.

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Mean Patch Size Edge Density

1400 35.00 1200 30.00 1000 25.00 800 Grassland 20.00 Shrubland i^2

600 Shrubland i/M 15.00 All Patches Acres M 400 10.00 200 5.00 0 0.00 Baseline 1964 1974 1984 1994 2004 Baseline 1964 1974 1984 1994 2004 Year Year

Mean Core Area Patch Shape Index

600 1.8 500 1.7 400 Grassland 1.6 Grassland 300 Shrubland 1.5 Shrubland Acres 200 Index 1.4 All Patches 100 1.3 0 1.2 Baseline 1964 1974 1984 1994 2004 Baseline 1964 1974 1984 1994 2004 Year Year

Fig. 4.3. Patch size, edge density, core area size, and patch shape index in oil and gas fields (De Bruin 2002) of the Greater Green River Basin, southwest Wyoming. 1964-2004. Baseline indicates landscape condition prior to energy development. Years indicate all landscape change from energy development prior to and including noted year. Error bars, where present, depict standard error.

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Fig. 4.4. An example of oil and natural gas development in the Kitty oil field of the Powder River Basin from 1964-2004. Map centers are at approximately 44°21’42”N, 105°38’06”W.

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CHAPTER 5: EVALUATING THE HUMAN FOOTPRINT IN THE WYOMING BASINS ECOREGIONAL ASSESSMENT AREA

INTRODUCTION

Land managers in the western U.S. face increasing challenges to manage natural resources within landscapes exposed to increasing rates and different types of human land uses. Historically, low densities of humans were present in western U.S. landscapes. In recent decades, however, human populations have increased substantially in this region leading to greater use of wildlands and the resources they provide (Hansen et al. 2002) (see Discussion in Chapters 1, 2, and 4). Managers also are faced with the dilemma that human land use taking place outside of a land manger’s jurisdiction potentially influences ecological processes within their jurisdiction. For example, low-density urbanization adjacent to Yellowstone National Park influenced ecological processes within the park (Hansen et al. 2002). One way to increase a land manager’s ability to manage resources at small scales, yet with a regional scope, is to assess human land use patterns at an ecoregional scale. Few broad-scale assessments of human disturbance patterns have been conducted to date (but see GLOBIO 2002, Sanderson et al. 2002) because of technological limitations and lack of suitable data. Recent models of the human footprint in western U.S. landscapes (Leu et al. 2003) and the conservation assessment of greater-sage grouse and sagebrush habitats (Connelly et al. 2004) have resulted in greatly increased availability of broad-scale spatial data sets of anthropogenic disturbance patterns in the sagebrush biome. Recently, the potential effects of infrastructure, primarily roads and pipelines, on shrubland and other habitats of the Upper Green River Valley in Wyoming (Pinedale Field Office) have been quantified (Weller et al. 2002, Thomson et al. 2005). In response to the dearth of large-scale evaluations of human land uses, and a preponderance of rapid landcover change in many regions of the Western U.S. (Schumacher et al. 2000, Odell and Knight 2001), we modeled the human footprint in the Wyoming Basins Ecoregional Assessment (WBEA). We defined the human footprint as the composite of all anthropogenic disturbances, displayed and evaluated spatially for a large area, such as an ecoregion (Sanderson et al. 2002). Under human footprint analysis, all major land uses, or

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indices to such uses, that may pose threats to species or their habitats are mapped. Typically, the potential effect of each land use is assigned a score or other index, based on a relative scale. For example, 0 may be assigned to the lowest or no effect, and 10 to the highest effect on a scale of 0 to 10 (Sanderson et al. 2002). Scores assigned to land uses or indices of land use are based on available empirical data, in terms of their relative effect, as expressed by their score. The areas potentially influenced by each land use then are estimated by distance from each use, using empirical data or hypothesized relations. All areas influenced by each land use then are assigned the human footprint score associated with the use. The composite of all scores from all land uses for a given area is summarized, such as by averaging the scores, across land uses to portray the cumulative effects. The major goal of this chapter was to present an analysis of the human footprint as a tool for land management planning. For example, managers may use these results to help plan new activities on public lands, such as wind energy development and associated infrastructures, or to evaluate the cumulative effect of existing land use activities. Results from a human footprint analysis will aid in planning of habitat restoration activities, serve as a building block on which improved and new spatial information on human disturbance can be added, and aid in studies to understand sagebrush fragmentation in relation to human activities. Human footprint models provide a spatial representation of human land uses at various scales and highlight areas in which anthropogenic disturbances potentially interact in a positive feedback that potentially causes irreversible loss of habitats (Knick and Rotenberry 1997). For example, synergistic processes occur when invading exotic plants alter fire regimes to such a degree that post-fire plant communities are dominated by exotic plants (d’Antonio and Vitousek 1992, Billings 1994, Brooks and Pyke 2001). Given that secondary roads can act as conduits for exotic plant establishment (Gelbard and Belnap 2003, Gelbard and Harrison 2003) and human- induced fires, the juxtaposition of high versus low traffic volume roads is important to delineate prior to restoring sagebrush communities. Furthermore, the juxtaposition of various linear features, such as railroads, roads, and irrigation channels, to power lines can increase the density of avian predators. For example, common ravens (Corvus corax), a nest predator of greater sage-grouse, may occur in higher densities than would be observed if these anthropogenic features were absent (Knight et al. 1995, Kristan and Boarman 2003).

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The objectives of this chapter were to: (1) apply a human footprint model across the WBEA area, based on existing spatial data sets; (2) assess the area affected by anthropogenic features under 3 different distance (effect zone) scenarios; (3) quantify the overlap of the human footprint model with abiotic factors, such as elevation and soil depth, and biotic factors, such as species richness of shrubsteppe-associated species (Chapter 3); and (4) assess human footprint- induced fragmentation of the sagebrush ecosystems.

METHODS

We used 11 spatial data sets in the human footprint analysis: agricultural land, communication towers, irrigation channels, railroads, oil-gas developments (active and inactive wells), human impact zone, power lines, interstate highways, state and federal highways, and secondary roads (see Appendix 3 for detailed description of methods). Because empirical data on the effective zones influenced by anthropogenic factors are sparse, we created 3 scenarios that varied in the projected extent surrounding each anthropogenic feature: absent (only the physical area occupied by anthropogenic feature), limited (short effective distance), and large (greatest effective distance) (Table 5.1). Distances used to delineate effect zones of anthropogenic features (Table 5.1) were based on a comprehensive literature review (Table A3.2). Overall, we took a conservative approach in estimating effect zones even for the large effect scenario. For example, according to empirical data, the effect zone of interstate highways may extend between 334 yd to 1,313 yd beyond the road surface area (Table 5.1), yet the distance applied to this zone was 935 yd in our large scenario. We evaluated the 3 human footprint model scenarios in the context of habitat use by sagebrush-associated species. We calculated the proportion of cells containing at least 1 anthropogenic feature within 7 home range sizes representative of 38 of the 40 vertebrate species of conservation concern identified for the WBEA (Table A3.3, Fig. A3.1). The moving window analysis was replicated for each of the 3 distance zone scenarios resulting in 7 spatial data sets per scenario. For each effect scenario, we calculated the average proportion of cells with anthropogenic features from the 7 input models (i.e., home range sizes) and classified each layer into 4 human footprint classes: negligible (0), low (0 – 0.33), medium (0.33 – 0.66), or high (0.66 – 1.0).

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We evaluated the area affected by the human footprint at 2 spatial extents: within each BLM Field Office and across the WBEA area. For each Field Office, we also derived a relative score by ranking the percent area within each human footprint class across each Field Office (i.e., area per human footprint class within Field Office/total area within Field Office) and the WBEA area (i.e., area per human footprint class within Field Office/total area within WBEA). First, for the negligible and low human footprint classes, we assigned high scores to those Field Offices containing the highest percent area within these 2 classes. Second, for the medium and high human footprint classes, we assigned high scores to those Field Offices containing the lowest percent area within the 2 classes. Third, we summed the 4 rankings, one for each human footprint class, to derive a relative human footprint score. In the relative human footprint score, low values equate with high human footprint intensity whereas high scores equate with low human footprint intensity. We also evaluated the human footprint relative to elevation (Digital Elevation Models), soil depth (STATSGO data sets), and species richness of shrubland-associated vertebrate species (Chapter 3). Because variable-specific values attributed to neighboring grid cells are not independent of each other - that is, there is a high degree of spatial autocorrelation among neighboring grid cells (Koenig 1999) - we used 95% confidence intervals to visually discern differences among the human footprint classes. We evaluated fragmentation in the sagebrush ecosystem associated with the human footprint by overlaying a sagebrush landcover map with results from the large effect zone scenario. The sagebrush layer represented the combined sagebrush categories from the landcover map (Comer et al. 2002; Table A5.1). We masked out the area impacted by the human footprint from the sagebrush layer, resulting in a spatial data set containing patches of sagebrush. We then determined the maximum and average patch size of sagebrush, as well as patch densities (number of patches/total area of sagebrush), for each BLM Field Office (n = 11) that had >80% of its total area within the WBEA area. We also evaluated sagebrush fragmentation induced by human activities at the scale of the home range of greater sage-grouse (10.8 mi2; Table A3.2).

RESULTS

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Anthropogenic Features

The area covered by each anthropogenic feature (n = 11) varied considerably, ranging from very low (2,559 acres) for communication towers in the absent scenario to a substantial area (18,075,569 acres) for secondary roads in the large scenario (Table 5.2). Regardless of anthropogenic effect zone size, 2 anthropogenic features consistently dominated: secondary roads (range: 6,704,765 acres – 18,075,569 acres for absent to large effect zone) and agriculture (4,597,796 acres – 5,681,137 acres); the area covered by both features was >7-fold higher than the other 9 features used in our analysis (Table 5.2, Fig. 5.1). In contrast, communication towers were the least dominant anthropogenic feature, regardless of effect zone size (2,559 acres – 8,530 acres). Relative ranking of anthropogenic features, according to the percent area affected within the WBEA area, changed across the 3 effect zone scenarios (Fig. 5.1). When comparing the absent with the large scenario, 5 anthropogenic features decreased in their relative ranks (human impact zones, irrigation channels, power lines, railroads, and inactive oil and gas wells), 3 stayed the same (secondary roads, agricultural land, and communication towers), and 3 increased (state/federal highways, interstate highways, and active oil and gas wells). The largest decrease in rank between the absent and large scenarios was observed for irrigation channels, which decreased by 3 ranks. Active oil and gas wells, state and federal highways, and interstate highways had the largest increases, each increasing by 3 ranks.

Human Footprint Spatial Extent and Area of Influence

Spatial extent of human footprint.-- The impact of the human footprint varied spatially. Areas of high human footprint were near major cities (e.g., Missoula, Cody, Laramie) and human impact zones, as well as in agricultural areas (covering the second highest amount of area in the WBEA) (Figs. 5.2-5.4). Low human footprint areas were in Yellowstone National Park and along major mountain ranges, such as the Wind River Range in Wyoming and the Uinta Mountains in Utah. Human footprint influence.-- The area of influence varied considerably among the human footprint classes. Regardless of the size of effective zone, the largest amount of area was within

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the low human footprint class (absent - large: 69,087,332 - 51,434,490 acres), followed by the medium (6,888,390 - 16,847,779 acres) and high classes (4,415,705 - 12,267,882 acres); the least area was within the negligible human footprint class: 4,752,203 - 4,909,168 acres (Table 5.3). With increasing effect zone (i.e., absent to large scenario), the percent area remained the same within the negligible (6%) footprint class, decreased in the low class (81 to 60%), but increased in the medium (8 to 20%) and high classes (5 to 14%). Human footprint influence and land ownership.-- The human footprint influence differed by land ownership (Fig. 5.5). However, the relative ranking, based on percent area within the 4 human footprint classes, did not differ among landowners across the 3 effect zone scenarios. For example, the National Park Service (NPS) consistently had the greatest percentage of land within the negligible human footprint class, regardless of effect zone scenario. Most of the BLM lands were in the low footprint class (92%), followed by the medium class (8%), with the least in the high (1%) and negligible (<1%) classes. Six percent of the WBEA area was in the negligible footprint class. Only 2 landowners, NPS and USFS, had proportionately more area in comparison. Landowners with proportionately less area in the negligible class were the BIA (Bureau of Indian Affairs), Private, BLM, State, USFWS (U.S. Fish and Wildlife Service), DOD (Department of Defense), and BOR (Bureau of Reclamation). The largest proportion of area within the WBEA (81%) was in the low human footprint class. Five landowners, BLM, State, BIA, USFWS, and USFS, had a greater proportion of their area in the low human footprint class compared to the WBEA. Private, BOR, and NPS landowners had a smaller percentage in the low class than the WBEA area. The proportion of the WBEA in the medium human footprint class was 8%. Five landowners, BOR, DOD, Private, USFWS, and State, had a higher proportion compared to the WBEA; BLM BIA, USFS, and NPS each had a lower proportion. Five percent of the WBEA was in the high human footprint class. Two landowners, private and BOR, had a greater proportion compared to the WBEA. USFWS, DOD, State, BLM, NPS, and USFS each had a lower proportion in comparison. Human footprint influence within BLM Field Offices.-- The human footprint varied considerably within all BLM Field Offices having lands within the WBEA analysis area (Table 5.3; Fig. 5.6). However, relative rankings of Field Offices did not change when we applied

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 5-7 Version 2.0, March 2006 different effect zones to features. The Cody Field Office always had the greatest percentage in the negligible human footprint class regardless of the size of the effect zone of influence. We then assessed only 11 Field Offices that had >80% of their total area within the WBEA boundary to avoid any misrepresentation due to disproportionate sampling. Of these, 3 Field Offices (Cody, Pinedale, Butte had proportionately more area and 7 Field Offices (Dillon, Little Snake, Worland, Rock Springs, Rawlins, Kemmerer, Kremmling) had less area in the negligible class than the WBEA average (6%) under all 3 scenarios. Eighty-one percent of the WBEA was in the low footprint class under the absent scenario; 8 Field Offices (Rock Springs, Dillon, Rawlins, Worland, Little Snake, Kemmerer, Kremmling, Lander) had a greater percentage and 3 Field Offices (Butte, Pinedale, Cody) had a lower percentage in comparison. In the medium human footprint class, 7 Field Offices (Kremmling, Rawlins, Kemmerer, Worland, Rock Springs, Butte, Kemmerer, Lander) had a higher percentage of area and 4 Field Offices (Little Snake, Dillon, Pinedale, Cody) had less area proportionately than the WBEA (8%). Five percent of the WBEA was in the high footprint class; 5 Field Offices (Little Snake, Butte, Kremmling, Kemmerer, Pinedale) had a higher percentage of area and 6 Field Offices (Cody, Worland, Lander, Dillon, Rawlins, Rock Springs) had a lower percentage in comparison. Ranking the 11 Field Offices according to their relative human footprint score (Fig. 5.7), the following gradient emerged (ranked from least to most affected by the human footprint): Dillon, Cody, Rock Springs, Lander and Pinedale (tie), Worland, Rawlins, Little Snake, Butte, Kemmerer, Kremmling. Human footprint influence across the WBEA area.- For the absent scenario within the entire WBEA area, the Cody, Pinedale, Butte, and Lander Field Offices had the most overlap (range 2.31% – 0.45%) with the negligible class, whereas the Kremmling, Kemmerer, Rawlins, and Rock Springs Field Offices (range 0.06 – 0%) had the least overlap (Table 5.3). For the low human footprint class, Rawlins, Lander, Butte, and Dillon (range 9.70 – 5.83%) had the highest overlap, where as Kemmerer, Pinedale, Worland, and Kremmling (range 4.11 – 2.79%) had the least overlap. For the medium human footprint class, Rawlins, Lander, Butte, and Rock Springs (range 1.18 – 0.53%) had the greatest overlap, whereas Kremmling, Pinedale, Little Snake, and Cody (range 0.37 – 0.29%) had the least overlap. Last, for the high human footprint class, Butte, Rawlins, Little Snake, and Lander (range 0.48 – 0.35%) had the greatest overlap, and Dillon, Worland, Kremmling, and Rock Springs (range 0.27 – 0.09%) had the least overlap.

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Ranking the 11 Field Offices according to their relative human footprint scores (Fig. 5.8), the following gradient emerged when sorted from least to most affected by the human footprint according to overall score: Cody, Dillon, Pinedale, Little Snake, Lander, Rock Springs and Worland (tie), Butte and Kremmling (tie), Kemmerer, Rawlins.

Human Footprint Class and Sagebrush Extent

Total amount of sagebrush landcover within each of the 4 human footprint classes (Fig. 5.9) differed considerably. Total area dominated by sagebrush was least in the extremes of negligible (range 146,088 - 150,938 acres) and high (90,490 - 3,091,533 million acres) footprint classes; the medium (range 2,656,319 - 7,340,221 acres) and low (15,500,018 - 23,180,165 acres) classes were intermediate. Increasing the size of the effect zone from absent to large considerably affected the amount of sagebrush landcover included. Total sagebrush landcover decreased 33% in the low human footprint class, but increased 176% in the medium and 3,316% in the high human footprint class.

Human Footprint Class and Abiotic and Biotic Factors

The total area influenced by the human footprint differed among abiotic and biotic factors (Fig. 5.10). The class of human footprint increased with decreases in elevation and in regions of deeper soils. With regard to biotic factors, species richness was somewhat higher in the low, medium, and high human footprint classes, compared to the negligible class.

Human Footprint and Sagebrush Fragmentation

The extent of sagebrush fragmentation varied considerably; areas of greatest fragmentation dominated the WBEA (Fig. 5.11). When we measured fragmentation by calculating the proportion of sagebrush within an area the size of a greater sage-grouse home range (10.8 mi2), nearly half (49%) of the WEBA area was in the highest (0-0.20) fragmentation class, 21% in the 0.21-0.40 class, 18% in the 0.41-0.60 class, and 11% in the 0.61-0.80 class.

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The smallest percentage (1%) of the study area was in the most intact sagebrush class (i.e., 0.81- 1.00). Regions with relatively low sagebrush fragmentation were in the Kemmerer, Rock Springs, Lander, and Rawlins Field Offices. Low sagebrush fragmentation was also found in the southern portions of the Pinedale Field Office, central portion of the Little Snake Field Office, and southern portion of the Dillon Field Office. Mean sagebrush patch size (range: 2,339 – 32,192 acres) and patch density (0.57 - 11.63 patches/km2) varied considerably among the Field Offices (Table 5.4). Fragmentation of sagebrush habitats as estimated by these 2 metrics was least in Worland, Rock Springs, Rawlins, Little Snake, and Lander and was highest in the Butte Field Office.

DISCUSSION

Human footprint analyses have 3 major advantages: (1) they provide a comprehensive analysis of threats; (2) they are relatively easy to map and compute results (GLOBIO 2002); and (3) the scoring process provides clear discrimination among areas that are strongly versus weakly affected by human land uses that pose threats to native species and habitats. However, disadvantages include: (1) the precise width of effect zones in relation to the anthropogenic features is difficult to estimate specific to many species or disturbances; (2) the actual effects on species and habitats often are unknown or may be manifested along multiple pathways; and (3) the scoring process often is based on hypothesized rather than empirical relations, and represents a ranking of effects rather than actual effects (i.e., areas shown as having “low” or “negligible” human footprint may still be exposed to pervasive and deleterious effects, but the particular methods used in the analysis may not detect these effects). Nevertheless, human footprint analyses delineate patterns of anthropogenic disturbance at large scales and can serve as a platform for studies that investigate mechanisms of population regulation along a human disturbance gradient.

Analysis of Human Disturbances

Secondary roads and agriculture landcover comprised about 13% of the WBEA area and encompassed more area than any other human disturbance regime. Agriculture directly

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influences sagebrush habitat loss and fragmentation (Knick and Rotenberry 1997), and indirectly influences sagebrush-associated vertebrate species by providing access to avian and mammalian predators that, in the absence of this anthropogenic resource, would be found in limited numbers (Knight et al. 1995, 1998; Vander Haegen et al. 2002; Maestas et al. 2003) The secondary road network is a highly significant factor influencing processes in this landscape and is being developed and expanded rapidly across much of the WBEA (Thomson et al. 2005). Secondary roads are being built as part of the infrastructure to support non-renewable energy extraction (Chapters 2, 4). For example, within the Jonah Field in the Upper Green River Valley, >95% of the area had road densities >2 mi/mi2 (Thomson et al. 2005). Roads can have negative effects on sagebrush-associated vertebrates (Lyon and Anderson 2003, Ingelfinger and Anderson 2004). Roads also may promote exotic plant establishment indirectly via disturbance during road construction and alterations of soil regimes (Tyser and Worley 1992, Forman and Alexander 1998, Parendes and Jones 2000, Safford and Harrison 2001, Gelbard and Belnap 2003), and directly via car seed dispersal (Schmidt 1989). Roads may indirectly promote exotic plant establishment, such as crested wheatgrass (Agropyron cristatum), via seeding along road verges or in disturbed areas near roads as a management strategy to curb the establishment of less desirable exotic grass species (Evans and Young 1978).

Delineation of Areas that are Strongly Versus Weakly Affected by the Human Footprint

Human footprint intensity, as defined by the classes used in our model, varied disproportionately across the WBEA. Low human footprint classes generally were limited to high elevation areas. High elevation ecosystems elsewhere, often found in National Parks, are least affected by humans (Scott et al. 2001). This disproportional effect follows the historic human settlement patterns: areas at low elevation were settled first to accommodate transportation needs for economic enterprises and to support local human populations (Marzluff 2001). The greater concentration of the human influence at low elevations may also have a disproportional effect on sagebrush-associated flora and fauna. Compared to low elevations, high elevation ecosystems tend to have shallower soils, indicative of less productive ecosystems (Hansen and Urban 1992, Scott et al. 2001) and lower biodiversity (Scott et al. 2001, Hansen et

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 5-11 Version 2.0, March 2006 al. 2002). Little is known about how the human footprint influences biodiversity, individual species, and plant and animal communities. However, high avian diversity areas (hotspots) were located at low elevations, and 67% of these hotspots were within 3.7 mi of private land (Hansen et al. 2002). Further, house density was 67% higher within a buffer of 1.4 mi surrounding each hotspot compared to random points. Within those hotspots, population growth rates were below replacement for the yellow warbler (Dendroica petechia) (Hansen et al. 2002).

Summary

Our analysis and summaries on the effects of anthropogenic features can enable land managers to project how the current distribution or the addition of these features to a landscape will influence populations of sagebrush-associated species. Although our knowledge about the specific effect zones is limited, our models incorporated a range of effective zones and illustrated the potential extent of these influences. Despite these limitations, the results from our human footprint analysis can be implemented into management strategies that recognize that anthropogenic features influence ecological processes at different spatial scales and potentially extend well beyond their actual physical location.

REFERENCES

Billings, W. D. 1994. Ecological impacts of cheatgrass and resultant fire on ecosystems in the Great Basin. Pages 22-30 in S. B. Monsen and S. G. Kitchen, compilers. Proceedings— ecology and management of annual grasslands. USDA Forest Service General Technical Report INT-GTR-313, Ogden, Utah, USA. Brooks, M. L., and D. A. Pyke. 2001. Invasive plants and fire in the deserts of North America. Tall Timbers Research Station Miscellaneous Publication No. 11:1-14. Comer, P., J. Kagan, M. Heiner, C. Tobalske. 2002. Current distribution of sagebrush and associated vegetation in the western United States. Map 1:200,000 scale. USGS Forest and Rangeland Ecosystems Science Center, Boise, Idaho, and The Nature Conservancy, Boulder, Colorado, USA. [http://sagemap.wr.usgs.gov/images/sage1.jpg].

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Connelly, J. W., S. T. Knick, M. A. Schroeder, and S. J. Stiver. 2004. Conservation assessment of Greater sage-grouse and sagebrush habitats. Western Association of Fish and Wildlife Agencies, Cheyenne, Wyoming, USA. d’Antonio, C. M., and P. M. Vitousek. 1992. Biological invasions by exotic grasses, the grass/fire cycle, and global change. Annual Review of Ecology and Systematics 23:63- 87. Evans, R. A., and J. A. Young. 1978. Effectiveness of rehabilitation practices following wildfire in a degraded big sagebrush downy brome community. Journal of Range Management 31:185-188. Forman, R. T. T., and L. E. Alexander. 1998. Roads and their major ecological effects. Annual Review in Ecology and Systematics 29:207-231. Gelbard, J. L., and J. Belnap. 2003. Roads as conduits for exotic plant invasions in a semiarid landscape. Conservation Biology 17:420-432. Gelbard, J. L., and S. Harrison. 2003. Roadless habitats as refuges for native grasslands: interactions with soil, aspect and grazing. Ecological Applications 13:404-415. GLOBIO. 2002. The GEO-3 scenarios 2002-2032: variables provided by GLOBIO. [http://www.globio.info/]. Hansen, A. J., R. Rasker, B. Maxwell, J. J. Rotella, J. D. Johnson, A. Wright Parmenter, U. Langner et al. 2002. Ecological causes and consequences of demographic change in the new West. BioScience 52:151-162. Hansen, A. J., and D. L. Urban. 1992. Avian response to landscape pattern: The role of species' life histories. Landscape Ecology 7:163-180. Ingelfinger, F. M., and S. Anderson. 2004. Passerine response to roads associated with natural gas extraction in sagebrush steppe habitat. Western North American Naturalist 64:385- 395. Knick, S. T., and J. T. Rotenberry. 1997. Landscape characteristics of disturbed shrubsteppe habitat in southwestern Idaho (U.S.A.). Landscape Ecology 12:287-297. Knight, R. L., R. J. Camp, and H. A. L. Knight. 1998. Ravens, cowbirds and starlings at springs and stock tanks, Mojave National Preserve, California. Great Basin Naturalist 58:393- 395.

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Knight, R. L., H. A. L. Knight, and R. J. Camp. 1995. Common ravens and number and type of linear rights-of-way. Biological Conservation 74:65-67. Koenig, W. D. 1999. Spatial autocorrelation of ecological phenomena. Trends in Ecology and Evolution 14:22-26. Kristan III, W. B., and W. I. Boarman. 2003. Spatial pattern of risk of common raven predation on desert tortoises. Ecology 84:2432-2443. Leu, M., S. Hanser, and S. T. Knick. 2003. The human footprint in the west- a large-scale analysis of anthropogenic impacts. U.S. Geological Survey Fact Sheet:1-4. Lyon, A. G., and S. H. Anderson. 2003. Potential gas development impacts on sage grouse nest initiation and movement. Wildlife Society Bulletin 31:486-491. Maestas, J. D., R. L. Knight, and W. C. Gilbert. 2003. Biodiversity across a rural land-use gradient. Conservation Biology 17:1425-1434. Marzluff, J. M. 2001. Worldwide urbanization and its effects on birds. Pages 19-47 in J. M. Marzluff, R. Bowman, and R. Donnelly, editors. Avian ecology and conservation in an urbanizing world. Kluwer Academic Publishers, Boston, Massachusetts, USA. Odell, E. A., and R. L. Knight. 2001. Songbird and medium-sized mammal communities associated with exurban development in Pitkin County, Colorado. Conservation Biology 15:1143-1150. Parendes, J. A., and J. A. Jones. 2000. Role of light availability and dispersal of exotic plant invasion along roads and streams in the H. J. Andrews Experimental Forest, Oregon. Conservation Biology 14:64-75. Safford, H. D., and S. Harrison. 2001. Grazing and substrate interact to affect native vs. exotic diversity in roadside grasslands. Ecological Applications 11:1112-1122. Sanderson, E. W., M. Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo, and G. Woolmer. 2002. The human footprint and the last wild. BioScience 52:891-904. Schmidt, W. 1989. Plant dispersal by motor cars. Vegetatio 80:147-152. Schumacher, J. V., R. L. Redmond, M. M. Hart, and M. E. Jensen. 2000. Mapping patterns of human use and potential resource conflicts on public lands. Environmental Monitoring and Assessment 64:127-137.

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Scott, M. J., F. W. Davis, R. G. McGhie, R. G. Wright, C. Groves, and J. Estes. 2001. Nature reserves: do the capture the full range of America's biological diversity? Ecological Applications 11:999-1007. Thomson, J. L., T. S. Schaub, N. Wolff Culver, and P. C. Aengst. 2005. Wildlife at a crossroads: energy development in western Wyoming. The Wilderness Society, Washington, D.C., USA. Tyser, R. W., and C. A. Worley. 1992. Alien flora in grasslands adjacent to road and trail corridors in Glacier Nation Park, Montana (U.S.A.). Conservation Biology 6:253-262. Vander Haegen, W. M., M. A. Schroeder, and R. M. DeGraaf. 2002. Predation on real and artificial nests in shrubsteppe landscapes fragmented by agriculture. Condor 104:496– 506. Weller, C., J. Thompson, P. Morton, and G. Aplet. 2002. Fragmenting our lands: The ecological footprint from oil and gas development. The Wilderness Society, Washington, D.C., USA.

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Table 5.1. Distances used to delineate effect zones surrounding anthropogenic features for human footprint analysis, according to 3 scenarios: absent (physical area occupied by anthropogenic feature, no buffer), limited (short effect distance from anthropogenic feature), and large (long distance from anthropogenic feature).

Effect zone distance (m) Anthropogenic feature Range of empirical distances (m)a Absent Limited Large Agricultural land ≈260 m surrounding pivot fields 0 b 0 135 Communication towers, ≈113 m (10 acres, assuming circular 0 0 90 including associated shape) infrastructure c Human impact zone ≈610 m 0 135 405 Interstate highways 305-1200 m 0 225 855 Irrigation channels No empirical support 0 0 0 Oil/gas wells abandoned c 0.5-1 ha (1.5-2.5acres for well pad) 0 0 90 d 0.7 ha/km (2.9 acres/miles) for roads Oil/gas wells active, 0.5-2 ha (2.7-5 acres for well pad) 0 135 e 225 f including associated 0.7-2.2 ha/km (2.9 – 8.5 acres/miles) infrastructure c for roads 3.2 km: Distance avoided by greater sage-grouse Power lines 300-4000 m 0 0 135 Railroads (active) 0-500 m 0 0 135 Secondary roads 100–600 m 0 0 135 State/federal highways 100–600 m 0 135 405 a See Table A3.2 for detailed information on effect zone delineation. b 0 = grid cell (90 x 90 m) containing anthropogenic feature. c Because we only had point locations for these anthropogenic features, we included surface disturbance associated with infrastructure such as roads, condensation tanks (oil and gas wells only), and power lines. d 90 m: 4 cells surrounding center cell (5-cell pattern), area = 10 acres (4.05 ha). e 135 m: 8 cells surrounding center cell (9-cell pattern), area = 18 acres (7.29 ha). f 225 m: 20 cells surrounding center cell (21-cell pattern), area = 42 acres (17.01 ha).

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Table 5.2. Area (%) occupied by anthropogenic features across the WBEA area according to 3 effect zone scenarios: absent (physical area occupied by anthropogenic feature), limited (short effect distance from anthropogenic feature), and large (long distance from anthropogenic feature).

Area within effect zone (%) a Anthropogenic feature Absent Limited Large Agricultural land 5.39 5.39 6.66 Communication towers, 0.003 0.003 0.01 including associated infrastructure Human impact zone 0.75 1.07 1.92 Interstate highways 0.06 0.25 0.94 Irrigation canals 0.55 0.55 0.55 Oil/gas wells abandoned 0.06 0.06 0.28 Oil/gas wells active 0.04 0.30 0.61 including associated infrastructure Power lines 0.59 0.59 1.74 Railroads (active) 0.14 0.14 0.40 Secondary roads 7.86 7.86 21.19 State/federal highways 0.39 1.16 3.04 aTotal WBEA area: 85,302,355 acres (34,535,367 ha).

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Table 5.3. Summary of area for each BLM Field Office, within each human footprint class (columns) and by effect zone scenarios (rows).

Human Footprint Class Negligible Low Medium High % Field % Study %Field % Study % Field % Study % Field % Study Ha Acres Office area Ha Acres Office area Ha Acres Office area Ha Acres Office area Colorado Glenwood Springs Absent 3,212 7,933 0.76 0.009 384,552 949,844 90.74 1.114 25,848 63,844 6.10 0.075 10,173 25,127 2.40 0.029 Limited 3,212 7,933 0.76 0.009 380,570 940,007 89.80 1.102 27,511 67,952 6.49 0.080 12,493 30,857 2.95 0.036 Large 2,937 7,255 0.69 0.009 308,188 761,224 72.72 0.892 73,496 181,536 17.34 0.213 39,164 96,734 9.24 0.113 Kremmling a Absent 17,109 42,259 1.45 0.050 964,030 2,381,153 81.62 2.791 127,873 315,847 10.83 0.370 72,044 177,948 6.10 0.209 Limited 16,984 41,951 1.44 0.049 950,593 2,347,966 80.49 2.753 134,301 331,724 11.37 0.389 79,194 195,608 6.71 0.229 Large 15,119 37,345 1.28 0.044 721,195 1,781,351 61.06 2.088 227,401 561,681 19.25 0.658 217,357 536,872 18.40 0.629 Little Snake a Absent 32,841 81,118 1.93 0.095 1,426,620 3,523,751 83.78 4.131 120,452 297,516 7.07 0.349 122,883 303,522 7.22 0.356 Limited 32,833 81,096 1.93 0.095 1,413,060 3,490,257 82.95 4.092 131,785 325,510 7.74 0.382 125,741 310,581 7.38 0.364 Large 30,200 74,594 1.77 0.087 1,106,271 2,732,490 64.94 3.203 327,884 809,873 19.25 0.949 239,063 590,487 14.03 0.692 Royal Gorge Absent 14,749 36,431 2.76 0.043 458,782 1,133,190 85.78 1.328 49,703 122,767 9.29 0.144 11,595 28,640 2.17 0.034 Limited 14,686 36,275 2.75 0.043 443,689 10,95,,911 82.96 1.285 58,691 144,967 10.97 0.170 17,767 43,883 3.32 0.051 Large 13,772 34,016 2.57 0.040 330,034 815,183 61.71 0.956 98,274 242,737 18.37 0.285 92,753 229,100 17.34 0.269 White River Absent 27,117 66,979 5.22 0.079 439,359 1,085,216 84.59 1.272 38,755 95,725 7.46 0.112 14,139 34,924 2.72 0.041 Limited 27,117 66,979 5.22 0.079 433,134 1,069,840 83.40 1.254 41,059 101,415 7.91 0.119 18,061 44,610 3.48 0.052 Large 26,019 64,266 5.01 0.075 338,970 837,257 65.27 0.982 99,499 245,762 19.16 0.288 54,882 135,559 10.57 0.159 Idaho b Idaho Falls Absent 29,027 71,697 1.71 0.084 1,268,731 3,133,764 74.82 3.674 149,251 368,649 8.80 0.432 248,692 614,269 14.67 0.720 Limited 28,925 71,445 1.71 0.084 1,257,249 3,105,405 74.14 3.640 154,319 381,167 9.10 0.447 255,208 630,363 15.05 0.739 Large 26,817 66,237 1.58 0.078 993,025 2,452,772 58.56 2.875 275,484 680,446 16.25 0.798 400,374 988,924 23.61 1.159 Montana Billings Absent 105,960 261,722 13.87 0.307 626,076 1,546,407 81.94 1.813 29,963 74,008 3.92 0.087 2,064 5,098 0.27 0.006 Limited 105,872 261,503 13.86 0.307 623,625 1,540,353 81.62 1.806 31,730 78,373 4.15 0.092 2,837 7,006 0.37 0.008 Large 103,406 255,413 13.53 0.299 523,382 1,292,752 68.50 1.515 115,117 284,339 15.07 0.333 22,158 54,731 2.90 0.064 Butte a Absent 182,559 450,922 7.07 0.529 2,015,229 4,977,616 78.07 5.835 215,901 533,275 8.36 0.625 167,770 414,393 6.50 0.486 Limited 182,206 450,049 7.06 0.528 1,978,710 4,887,414 76.65 5.730 235,049 580,571 9.11 0.681 185,494 458,170 7.19 0.537 Large 174,174 430,211 6.75 0.504 1,568,320 3,873,749 60.75 4.541 380,813 940,609 14.75 1.103 458,152 1,131,636 17.75 1.327 Dillon a Absent 53,536 132,234 2.31 0.155 2,014,908 4,976,823 86.86 5.834 160,670 396,855 6.93 0.465 90,707 224,046 3.91 0.263

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Human Footprint Class Negligible Low Medium High % Field % Study %Field % Study % Field % Study % Field % Study Ha Acres Office area Ha Acres Office area Ha Acres Office area Ha Acres Office area Limited 53,492 132,126 2.31 0.155 2,002,875 4,947,101 86.34 5.799 166,461 411,160 7.18 0.482 96,993 239,572 4.18 0.281 Large 49,317 121,813 2.13 0.143 1,647,441 4,069,180 71.02 4.770 373,543 922,651 16.10 1.082 249,521 616,316 10.76 0.723 Lewistown Absent 42,243 104,341 29.60 0.122 96,524 238,415 67.65 0.279 3,897 9,625 2.73 0.011 27 66 0.02 0.000 Limited 42,243 104,341 29.60 0.122 95,966 237,035 67.25 0.278 4,453 11,000 3.12 0.013 29 72 0.02 0.000 Large 41,927 103,560 29.38 0.121 83,268 205,672 58.36 0.241 13,991 34,558 9.81 0.041 3,505 8,657 2.46 0.010 Missoula Absent 31,656 78,189 2.14 0.092 1,247,692 3,081,800 84.18 3.613 144,217 356,217 9.73 0.418 58,549 144,617 3.95 0.170 Limited 31,656 78,189 2.14 0.092 1,228,032 3,033,239 82.86 3.556 149,534 369,349 10.09 0.433 72,893 180,045 4.92 0.211 Large 29,712 73,388 2.00 0.086 935,580 2,310,883 63.12 2.709 284,101 701,730 19.17 0.823 232,722 574,823 15.70 0.674 Utah Fillmore Absent 0 0 0.00 0.000 22,759 56,216 97.19 0.066 657 1,623 2.81 0.002 0 0 0.00 0.000 Limited 0 0 0.00 0.000 22,612 55,852 96.57 0.065 804 1,987 3.43 0.002 0 0 0.00 0.000 Large 0 0 0.00 0.000 19,463 48,073 83.12 0.056 3,170 7,829 13.54 0.009 784 1,937 3.35 0.002 Richfield Absent 0 0 0.00 0.000 9,999 24,699 74.39 0.029 2,206 5,448 16.41 0.006 1,237 3,055 9.20 0.004 Limited 0 0 0.00 0.000 9,987 24,669 74.30 0.029 2,193 5,418 16.32 0.006 1,261 3,115 9.38 0.004 Large 0 0 0.00 0.000 5,867 14,491 43.646 0.017 4,033 9,961 30.003 0.012 3,542 8,749 26.351 0.010 Salt Lake Absent 41,201 101,766 2.15 0.119 1,545,205 3,816,657 80.689 4.474 158,779 392,185 8.291 0.460 169,837 419,497 8.869 0.492 Limited 41,009 101,293 2.14 0.119 1,517,972 3,749,392 79.267 4.395 173,471 428,474 9.058 0.502 182,569 450,946 9.534 0.529 Large 38,879 96,032 2.03 0.113 1,154,429 2,851,440 60.283 3.343 360,159 889,593 18.807 1.043 361,555 893,040 18.880 1.047 Vernal Absent 76,328 188,530 4.63 0.221 1,325,051 3,272,875 80.435 3.837 141,505 349,516 8.590 0.410 104,462 258,022 6.341 0.302 Limited 76,322 188,516 4.63 0.221 1,281,284 3,164,771 77.779 3.710 180,500 445,836 10.957 0.523 109,239 269,820 6.631 0.316 Large 74,163 183,182 4.50 0.215 962,413 2,377,160 58.422 2.787 319,492 789,146 19.394 0.925 291,278 719,456 17.682 0.843 Wyoming Buffalo Absent 17,183 42,441 3.65 0.050 434,797 1,073,948 92.347 1.259 18,272 45,132 3.881 0.053 578 1,426 0.123 0.002 Limited 17,183 42,441 3.65 0.050 431,106 1,064,833 91.563 1.248 21,918 54,137 4.655 0.063 622 1,537 0.132 0.002 Large 16,009 39,542 3.40 0.046 358,761 886,140 76.198 1.039 77,663 191,827 16.495 0.225 18,396 45,438 3.907 0.053 Casper Absent 0 0 0.00 0.000 757,446 1,870,891 88.491 2.193 78,824 194,696 9.209 0.228 19,691 48,637 2.300 0.057 Limited 0 0 0.00 0.000 751,513 1,856,237 87.798 2.176 83,389 205,970 9.742 0.241 21,059 52,016 2.460 0.061 Large 0 0 0.00 0.000 531,439 1,312,655 62.087 1.539 233,040 575,609 27.226 0.675 91,481 225,959 10.688 0.265 Cody a Absent 797,510 1,969,849 32.71 2.309 1,418,465 3,503,608 58.182 4.107 101,819 251,492 4.176 0.295 120,185 296,858 4.930 0.348 Limited 795,062 1,963,803 32.61 2.302 1,394,737 3,444,999 57.209 4.039 120,703 298,136 4.951 0.350 127,479 314,872 5.229 0.369 Large 780,109 1,926,868 32.00 2.259 1,166,751 2,881,874 47.857 3.378 251,744 621,808 10.326 0.729 239,377 591,261 9.819 0.693

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Human Footprint Class Negligible Low Medium High % Field % Study %Field % Study % Field % Study % Field % Study Ha Acres Office area Ha Acres Office area Ha Acres Office area Ha Acres Office area Kemmerer a Absent 22,159 54,733 1.35 0.064 1,366,491 3,375,233 83.515 3.957 152,760 377,316 9.336 0.442 94,806 234,172 5.794 0.275 Limited 22,137 54,679 1.35 0.064 1,339,898 3,309,548 81.890 3.880 173,346 428,164 10.594 0.502 100,835 249,063 6.163 0.292 Large 20,552 50,764 1.26 0.060 935,901 2,311,675 57.199 2.710 393,722 972,492 24.063 1.140 286,042 706,523 17.482 0.828 Lander a Absent 155,432 383,916 5.84 0.450 2,164,517 5,346,357 81.345 6.268 218,810 540,461 8.223 0.634 122,137 301,680 4.590 0.354 Limited 155,432 383,916 5.84 0.450 2,141,054 5,288,402 80.464 6.200 235,461 581,587 8.849 0.682 128,950 318,507 4.846 0.373 Large 151,824 375,005 5.71 0.440 1,554,801 3,840,358 58.431 4.502 606,420 1,497,858 22.790 1.756 347,851 859,193 13.073 1.007 Pinedale a Absent 312,443 771,734 16.40 0.905 1,366,058 3,374,163 71.720 3.956 125,490 309,960 6.588 0.363 100,706 248,743 5.287 0.292 Limited 312,184 771,094 16.39 0.904 1,338,090 3,305,082 70.252 3.875 145,937 360,464 7.662 0.423 108,486 267,960 5.696 0.314 Large 304,916 753,144 16.01 0.883 1,065,331 2,631,367 55.932 3.085 276,274 682,397 14.505 0.800 258,175 637,693 13.555 0.748 Rawlins a Absent 246 608 0.01 0.001 3,348,819 8,271,584 85.696 9.697 407,732 1,007,098 10.434 1.181 150,980 372,920 3.864 0.437 Limited 246 608 0.01 0.001 3,297,033 8,143,671 84.370 9.547 446,538 1,102,948 11.427 1.293 164,009 405,102 4.197 0.475 Large 162 400 0.00 0.000 2,212,084 5,463,848 56.607 6.405 1,089,778 2,691,752 27.887 3.156 605,801 1,496,330 15.502 1.754 Rock Springs a Absent 0 0 0.00 0.000 1,964,766 4,852,972 90.190 5.689 184,038 454,573 8.448 0.533 29,681 73,312 1.362 0.086 Limited 0 0 0.00 0.000 1,932,022 4,772,094 88.69 5.594 211,392 522,139 9.70 0.612 35,090 86,672 1.61 0.102 Large 0 0 0.00 0.000 1,347,914 3,329,347 61.873 3.903 575,111 1,420,523 26.399 1.665 255,480 631,035 11.727 0.740 Worland a Absent 25,006 61,766 1.63 0.072 1,303,705 3,220,151 84.937 3.775 131,401 324,562 8.561 0.380 74,791 184,733 4.873 0.217 Limited 25,006 61,766 1.63 0.072 1,289,107 3,184,094 83.986 3.733 141,333 349,092 9.208 0.409 79,457 196,259 5.177 0.230 Large 23,955 59,169 1.56 0.069 952,854 2,353,549 62.079 2.759 360,754 891,062 23.503 1.045 197,340 487,431 12.857 0.571 TOTAL Absent 1,987,517 4,909,168 5.76 27,970,580 69,087,332 80.99 2,788,822 6,888,390 8.08 1,787,735 4,415,705 5.18 Limited 1,983,807 4,900,004 5.74 27,553,916 68,058,172 79.79 3,071,879 7,587,541 8.90 1,925,764 4,756,636 5.58 Large 1,923,969 4,752,203 5.57 20,823,680 51,434,490 60.30 6,820,963 16,847,779 19.75 4,966,754 12,267,882 14.38 a BLM Field Offices with >80% of their total area within the WBEA boundary. b For this assessment, the Pocatello and Upper Snake Field Offices were combined and analyzed together as the Idaho Falls District.

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Table 5.4. Sagebrush patch metrics for sagebrush fragmentation within 11 BLM Field Offices that had >80% of their total area within WBEA, sorted from lowest to highest patch density.

Maximum patch Mean patch size Patch density BLM Field Office size (ha) (ha) SD (patches/ km2) Worland 13,034 174.1 547.3 0.57 Rock Springs 23,395 163.7 533.3 0.61 Rawlins 23,395 158.8 511.3 0.63 Little Snake 9,710 156.7 454.7 0.64 Lander 8,384 151.9 431.5 0.66 Kremmling 3,056 91.8 251.5 1.09 Cody 11,448 37.6 265.2 2.66 Kemmerer 12,595 34.5 200.0 2.90 Dillon 11,124 21.6 160.0 4.62 Pinedale 3,524 17.6 110.1 5.70 Butte 947 8.6 33.7 11.63

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11 11 Absent 10 Limited 10 9 Large 9 8 8

k 7 7

Ran 6 6 e

tiv 5 5 a 4 4 Rel 3 3 2 2 1 1 0 0 s s s s e e rs e) y v v es i i els e ay a ad ne w o w ctiv rlin o z ro h act a ann e t y g inact d ( w lls ural land ar ch on te e hi i lls c Po e te nd a ral highw ion oads o ricult p ta c g s de s w nicat m r A e a u f Se rigat g Railr te n i r m I e/ Oil/gas w In at ma Oil/ t u S Com H

Anthropogenic Feature

Fig. 5.1. Relative ranking of area (%) across the WBEA area for 11 anthropogenic features according to the 3 effect zone scenarios for the human footprint, arranged from highest to lowest by ranks from the absent scenario (for effect zone delineation see Table 5.1).

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Fig. 5.2. Spatial extent of the human footprint in the WBEA area for the absent effect zone scenario (Table 5.1).

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Fig. 5.3. Spatial extent of the human footprint in the WBEA area for the limited effect zone scenario (Table 5.1).

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Fig. 5.4. Spatial extent of the human footprint in the WBEA area for the large effect zone scenario (Table 5.1).

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Absent 100 90 80 70 60 50 40 30 20 10 0 Limited 100 90 80 70 ) 60 Negligible %

( Low

a 50 Medium e 40 High Ar 30 20 10 0 Large 100 90 80 70 60 50 40 30 20 10 0 S S D M BIA EBA BL NP BOR DO USFS IVATE W STATE USFW PR Ownership

Fig. 5.5. Area (%) within each human footprint class (negligible, low, medium, and high), arranged from most to least area within the negligible human footprint class, for 9 landowners in relation to the 3 effect zone scenarios used in the human footprint analysis.

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Negligible Low Medium High 100

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otprint 60 Fo

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0 r r r s s s e e e d n al e e e r er lls k ke gs tte r w alo el v rg p o a i ody n i and f o dale l oula ff s EBA t a ring r wlin C Bu h s ring Dillon and R o c Vern s illm o F lt La W ne S L Bu i Billin e i Ca l Go mme t Ra wis F Sp i W emmling Ri a P le M Sp t Sa y dah Le Ke ck Kr I Wh Lit Ro Ro ood w Field Office Glen

Fig 5.6. Area (%) within each human footprint class, by BLM Field Office, across 3 effect zone scenarios. Field Offices are arranged from highest to lowest percent area in the negligible human footprint class.

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35 29 28 27 27 24 23 22 20 16 13 10 10 Negligible Low Medium 8 High 8

6 6 Rank e v i 4 4 Relat

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0 0 r r y s d le n lin ling ake a l nde rings n r Cod Butte eda Dillon p La Raw e S Wo emm l Pin r t t ck S Kemmere i K L Ro

BLM Field Office

Fig. 5.7. Relative ranking of BLM Field Offices (Field Offices with ≥80% of their total area within WBEA boundaries) according to percent area within each Field Office (i.e., area per human footprint class within Field Office/total area in Field Office) in relation to the 4 human footprint classes, using the “absent” scenario. High ranks correspond with high percent area in the negligible and low footprint classes but with low percent area for the medium and high classes. Field Offices are ordered from least to most affected by the human footprint. Ranking scores of human footprint intensity are shown above bar graphs (maximum possible score = 44).

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32 28 28 25 24 23 23 22 22 21 16 10 10 Negligible Low Medium 8 8 High ing

nk 6 6 Ra e v i 4 4 lat e R

2 2

0 0 s e t er ins l and er l ring r Cody But mling Dillon p o Lander Raw W em le Snake Pinedale t Kemm Kr Lit Rock S

BLM Field Office

Fig. 5.8. Relative ranking of BLM Field Offices (Field Offices with >80% of their total area within the WBEA boundaries) according to percent area within the WBEA area (i.e., area per human footprint class within Field Office/total area within WBEA) in relation to the 4 human footprint classes, using the “absent” scenario. High ranks correspond with high percent area in the negligible and low footprint classes but with low percent area for the medium and high classes. Field Offices are ordered from least to most affected by the human footprint. Ranking scores of human footprint intensity are shown above bar graphs (maximum possible score = 44).

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100 Absent Limited Large 80 )

60 ush (% gebr 40 Sa ea Ar 20

0 Negligible Low Medium High

Human Footprint Intensity

Fig. 5.9. Sagebrush area (%) within each human footprint intensity class across 3 effect zone scenarios (absent, limited, and large).

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Negligible Low Medium High Negligible Low Medium High Negligible Low Medium High 3000 3000 Absent Limited Large 2500 2500 )

m 2000 2000 on (

i 1500 1500 at

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10 10 Speci 5 5 Absent Limited Large 0 0 Negligible Low Medium High Negligible Low Medium High Negligible Low Medium High Human Footprint Intensity

Fig 5.10. Human footprint intensity according to 3 effect zone scenarios in relation to elevation, soil depth, and vertebrate species richness.

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Fig. 5.11. Proportion of cells containing sagebrush within a greater sage-grouse home range (10.8 mi2) within the WBEA area. Fragmentation of sagebrush was derived by overlaying the “large scenario” human footprint on the landcover map. Sagebrush is absent in areas depicted in white.

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CHAPTER 6: MODELS OF HYPOTHESIZED EFFECTS OF THREATS ON EXAMPLE SPECIES

INTRODUCTION

One objective of the Wyoming Basins Ecoregional Assessment (WBEA) was to develop “spatial models of hypothesized effects of threats to the sagebrush ecosystem in this region” (Rowland et al. 2004; Chapter 1). Predicted effects of anthropogenic disturbance on landscapes of the Wyoming Basins were described in Chapters 4 and 5. In addition to results from these broad-scale analyses, a second objective of the WBEA was to model relative probability of occurrence for selected species of conservation concern under a set of potential threats and “what if” conditions. The basic approach for the 10 example species selected (Brewer’s sparrow, ferruginous hawk, greater sage-grouse, loggerhead shrike, pronghorn, pygmy rabbit, sage sparrow, sage thrasher, sagebrush lizard, and short-horned lizard) was to develop spatially explicit models that predict probability of occurrence for the species within the Wyoming Basins study area.1 The purpose of the species models was to map areas where selected species of conservation concern are most likely to occur, based on habitat and other ecological variables that can be influenced directly as well as indirectly by land management activities. Many other stressors may affect species’ populations and habitats (e.g., global warming, small population size). However, BLM, FS, and other land management agencies need information about potential effects that can be monitored and managed through land use planning and adaptive strategies. Predictive models of species occurrence are important tools to complement empirical data for evaluation of potential effects of land management decisions, especially impacts of anthropogenic disturbance (Manly et al. 1993, Scott et al. 2002). For the Wyoming Basins assessment, our focus was on modeling predicted effects of anthropogenic disturbance on selected species of conservation concern; thus, we modeled habitat (biotic factors) primarily as a coarse-filter screen to narrow the spatial extent over which our disturbance models were applied. This approach was driven in part on the lack of continuous coverages in the Wyoming Basins of

1 See Chapter 3 for details on the selection of example species for modeling in the WBEA.

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landcover and other data at a resolution fine enough to model environmental conditions, such as percent canopy cover of sagebrush or height of herbaceous vegetation (Appendix 6). Our spatial models used GIS layers, such as roads, power lines, livestock grazing allotments, and oil and natural gas wells, combined with maps of the geographic ranges and biotic factors associated with each of our example species to predict effects of threats on these species in the study area. Although the models were developed based on extensive review of published and unpublished literature and consultation with species experts, the relations between model variables, such as distance from power lines, and probability of occurrence for species such as pygmy rabbit or greater sage-grouse, have not been established (see Appendix 6 for assumptions and limitations of models). We currently are evaluating the draft models developed for the WBEA with field data collected within the study area and will present those results in the Final Report. In the following sections, we describe the basic approach used to develop our models. We also provide brief descriptions of the methods used and present the results and interpretation of our model outputs for each of the 10 example species.

METHODS

We developed predictive models of the relative probability of a species’ occurrence, based on a combination of habitat conditions and effects of anthropogenic disturbance for the 10 example species in our assessment. We defined the relative probability of a species’ occurrence as a ranking of the levels of predicted use on a scale from 0.0 to 1.0, estimated across all pixels within our assessment area.2 These rankings were combined into categories of deviation from the mean estimated probability of occurrence, and thus were placed on relative scale that predicted higher versus lower levels of expected use for each species across the study area. We used similar approaches for each species’ model, based on life history characteristics, their season of use in the study area, and available data on habitat relationships and effects of human disturbance. All 10 models incorporated at least 2 “sub-models,” composed of either habitat- or disturbance-based variables (Fig. 6.1). Inclusion of model variables was limited to spatial data layers available as a continuous coverage across the 5 states of our assessment area.

2 See Appendix 4 for more details on modeling approaches and methods for each of the 5 example species.

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Many of the GIS layers used in our models were created for the recently completed range-wide conservation assessment of greater sage-grouse (Connelly et al. 2004) and the assessment of the human footprint across western U.S. landscapes (Leu et al. 2003); these layers and associated metadata are posted on the SAGEMAP web site [http://sagemap.wr.usgs.gov/]. Sub-models for each species were applied within their respective geographic ranges in the Wyoming Basins study area.3 Outputs from the individual habitat and disturbance sub- models for greater sage-grouse are described below as an example, and are available for all species that we modeled. For many species, the final value assigned to each pixel, or cell, was based on a moving window analysis, with the mean value for the window applied to the focal pixel (see Appendix 4 for specific information on species for which the moving window approach was applied). The window size used for each species was approximately equivalent to the home range of the species during the breeding season. The model output, or response variable, was the predicted probability of occurrence of the species for each pixel within the species’ range in the study area. Potential outputs ranged from 0.0 to 1.0 and were classified into 4 categories of relative probability of occurrence, based on the mean and variance of the outputs: very low, low, moderate, and high. For each species’ model, if no pixels had a score of 1.0, the outputs were recalibrated to a scale of 0.0-1.0. The following sections describe the general methods used to create the models. Detailed descriptions of modeling methods for each species are in Appendix 4.

Greater Sage-Grouse

The model developed for greater sage-grouse focused on potential effects of anthropogenic disturbance, with a habitat sub-model designed to select areas most likely to be used by sage-grouse during the breeding or nesting season, based on proportion of sagebrush in the home range. Thus, the primary emphasis of the sage-grouse model was to predict relative probability of occurrence based on human disturbance, across a gradient of habitat conditions as predicted by a coarse-scale habitat sub-model. Many published studies describe habitat use and preference of sage-grouse at a site level (e.g., canopy cover of sagebrush or herbaceous

3 For 5 species whose ranges occupy >80% of the study area (Brewer’s sparrow, ferruginous hawk, loggerhead shrike, sage sparrow, and sage thrasher), and for which only very coarse-scale maps were available, we applied the models across the entire WBEA area.

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understory at nest sites; see Schroeder et al. 1999; Connelly et al. 2000, 2004; and Rowland 2004 for reviews of sage-grouse habitat selection during various seasons). However, landscape-level data describing sage-grouse use of habitats, such as sagebrush patch size or density, are lacking (Dobkin 1995, Rowland and Wisdom 2002, Rowland 2004; but see Connelly et al. 2004). We did not have a vegetation coverage for the entire study area that accurately depicted individual sagebrush taxa (e.g., mountain big sagebrush) or vegetation attributes such as percent sagebrush canopy cover. Thus, we modeled potential habitat for sage-grouse with a single variable, the proportion of sagebrush in a 1.85-mi radius circle around each pixel within the current range of greater sage-grouse (Table A4.1). This variable (i.e., proportion of sagebrush at a landscape scale) has been demonstrated to be a robust discriminator of extirpated versus occupied range of greater sage-grouse (Wisdom et al. in prep.). Accordingly, for the habitat sub- model, scores ranging from 0 to 1.0 were assigned to each 90-m (2-acre) pixel according to the proportion of the surrounding circle (representing the home range of a sage-grouse) composed of sagebrush (Table A4.1). The disturbance sub-model for greater sage-grouse included a suite of anthropogenic variables presumed to influence the probability of occurrence of sage-grouse: burns, campgrounds, communications towers, interstate highways, state/federal highways, secondary roads, irrigation channels, landfills, oil and gas wells, power lines, and human impact zones (Table A4.1). Our general approach was to model the influence of these features on sage-grouse by assigning probabilities of occurrence to various effect zones (i.e., distance from the feature) in a GIS. Effects of human disturbance on sage-grouse have been reported in several studies. For example, of 804 leks in Wyoming, none were within 1.2 miles and only 9 were between 1.2 and 2.5 miles from the interstate (Connelly et al. 2004). In the Upper Green River Valley in Wyoming, maximum counts of males/lek within 2 mi of a drilling rig declined by 32%, compared to a 2% decline in areas >4 mi from a rig (Holloran and Anderson 2004). Therefore, any drilling within 3.4 mi of a sage-grouse lek could have potential indirect or direct negative impacts on sage-grouse.4 The value assigned to each pixel from the disturbance sub-model was the minimum probability for that pixel; for example, if a pixel was assigned a 0.5 because it was within 459 ft of a communication tower, but a 0 because there was a well pad on the site, the pixel received a

4 A more complete review of the literature on human-related disturbance and sage-grouse is found in Appendix 4.

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score of 0. In assigning the minimum value, we assumed that no synergistic effects were present between co-occurring anthropogenic features, but that the lowest value for the cell represented the maximum negative effect. Model output from each sub-model was a probability of occurrence ranging from 0 to 1. These sub-model outputs were then multiplied by each other to obtain a product for each pixel, which then was used to classify the pixel into 1 of 4 categories (very low, low, moderate, or high).

Ferruginous Hawk

The ferruginous hawk model consisted of 3 sub-models: a non-habitat sub-model, and 2 sub-models incorporating variables presumed to influence ferruginous hawk breeding populations either positively (e.g., juniper ecotone, intermediate density of agricultural lands) or negatively (e.g., high densities of roads and oil and gas wells). Our modeling approach reflects this species’ breeding habitat requirements: (1) short prairie and sagebrush habitat (Bechard and Schmutz 1995); (2) opportunistic nesting substrate requirements (natural, i.e., ground nests, and artificial nest platforms [powerline structures; Steenhof et al. 1993]); (3) response to anthropogenic disturbance (Anderson et al. 1990, Richardson and Miller 1997); and (4) response to fluctuating prey populations (Schmutz and Hungle 1989). The non-habitat sub-model was based on 2 abiotic variables, elevation and slope. Any elevation >2,735 yd and slope >25% (Jasikoff 1982) was delineated as non-habitat. The sub- model incorporating variables that positively influence ferruginous hawk breeding populations was based on the juniper ecotone (providing nesting substrate [Bechard and Schmutz 1995]), intermediate density of agricultural lands (used more frequently compared to grassland or low- density agricultural land [Schmutz 1989]), low-density oil and gas well development,5 powerlines (towers as nest substrates [Steenhof et al. 1993]), and prey densities (Schmutz and Hungle 1989). We delineated potentially high-density prey areas by including anthropogenic factors, such as low road densities (Johnson and Collinge 2004), and abiotic factors, such as the mean annual Normalized Difference Vegetation Index (NDVI; Rouse et al. 1974). The NDVI is

5 We assumed that the associated infrastructure, such as powerlines and compressor stations, provides nesting structure for ferruginous hawk; see Appendix 4 for details.

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a suitable surrogate for primary production, because the metric, derived from spectral values in satellite imagery, emphasizes characteristics of green vegetation and reduces the influence of bare ground or soils. Thus, the NDVI correlates highly with “aboveground biomass” (Boelman et al. 2003) or annual aboveground primary production (Paruelo et al. 1997). The sub-model incorporating variables that negatively influence ferruginous hawk breeding populations was based on intermediate to high-densities of oil and gas wells, roads, and agriculture. Also included in this sub-model were anthropogenic features avoided by ferruginous hawks, such as human populated centers, campgrounds, and landfills, and biotic features such as interior juniper and non-juniper forests. We delineated low prey density areas as those with low NDVI values. The sub-models that incorporated factors that either positively or negatively influence ferruginous hawk populations were simplified by selecting either the maximum or minimum grid values, respectively, from the various spatial data sets incorporated in each sub-model. To obtain the final model output, probabilities resulting from these 2 sub-models were multiplied by the non-habitat sub-model to exclude areas of very low predicted occurrence.

Brewer’s Sparrow

We modeled the probability of occurrence for Brewer’s sparrows by combining habitat and disturbance sub-models. We developed the habitat sub-model using (1) Brewer’s sparrow abundance data collected from 1999 to 2003 on USGS Breeding Bird Survey routes (Sauer et al. 2003); (2) sagebrush cover at 3 spatial scales (percent sagebrush within a 3.1-, 11.2-, and 31-mi radius); and (3) fragmentation of sagebrush habitats at 2 spatial scales (percent fragmentation within a 3.1- and 11.2-mi radius). We emphasized landscape attributes of sagebrush in developing our model because Brewer’s sparrows depend almost exclusively on the quantity of sagebrush in the landscape (Knick and Rotenberry 1995, Vander Haegen et al. 2000). Only 1 variable, sagebrush cover within 3.1-mi radius, was significant (p < 0.05) in predicting the presence of Brewer’s sparrows, based on our analyses (Appendix 4). The disturbance sub-model included roads as the only variable because very little is known how Brewer’s sparrows respond to anthropogenic disturbance. In Wyoming, abundance of Brewer’s sparrows increased with increasing distance from secondary roads (Ingelfinger and Anderson

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2004); this trend, however, was not observed in relation to a state highway with higher traffic volumes. Given this discrepancy, we restricted the road-effect zone to the road surface and the immediately adjacent zone of surface disturbance (i.e., area where shrubs have been removed by road construction).Probabilities resulting from the habitat and disturbance sub-models were multiplied by each other to obtain the final model output, which represented the relative probability of occurrence of Brewer’s sparrows in the study area.

Pronghorn

The pronghorn model included habitat and disturbance sub-models. The habitat sub- model was based on 3 variables that have been shown to be useful in predicting occurrence of pronghorn: landcover type, slope, and distance to water (Appendix 4, Tables A4.4-A4.5). The disturbance sub-model was based on active oil and gas well density, fence density (in 1 version; see Appendix 4 for details), road density and distance from roads, and human populated centers.6 For active oil and gas well density, empirical evidence describing the effect of human disturbance on pronghorn suggests that foraging efficiency of pronghorn can be reduced (Berger et al. 1983) and that pronghorn generally use areas with lower noise levels than areas with higher noise levels (Landon et al. 2003). Migration bottlenecks for pronghorn in the Upper Green River Valley may be exacerbated by the increasing development of oil and natural gas in this area (Sawyer et al. 2002, Berger 2003). Fences on pronghorn ranges restrict daily and seasonal movements and may result in injury and mortality (Yoakum and O’Gara 2000). Avoidance of areas near secondary and primitive roads (Ockenfels et al. 1994), suggests that as densities of these types of roads increase, suitability of habitat for pronghorn decreases. Pronghorn have demonstrated an adverse response to the activities of people and their developments (Ockenfels et al. 1994), resulting in avoidance of urban and suburban areas by pronghorn. We first applied the pronghorn model across the entire current range of pronghorn in the study area, excluding the fence variable from the disturbance sub-model. We then applied the model that included a fence variable in its disturbance sub-model to the more limited area (about 88% of the current range of pronghorn in the study area) for which we either had estimated or

6 See Appendix 4 for more detailed literature review of effects of anthropogenic disturbance on pronghorn.

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-8 Version 2.0, March 2006 actual fence density data (Appendix 4). Results from the model without fence density also were reported from this more limited spatial extent, to allow for direct comparison between outputs from the 2 versions of the disturbance sub-model. Probabilities resulting from the habitat and disturbance sub-models were multiplied by each other to obtain the final model output, which represented the relative probability of occurrence of pronghorn in the study area.

Pygmy Rabbit

Several GIS-based models that predict habitat suitability for pygmy rabbits have been developed (Gabler et al. 2001, Rachlow and Svancara 2003, Simons and Laundré 2004; Table A4.6). We considered variables in these models and the spatial data available for the WBEA in developing our model for pygmy rabbits. The model was assumed to apply to year-round habitats because pygmy rabbits are non-migratory, and depend on sagebrush habitats throughout the year (Green and Flinders 1980). Our pygmy rabbit model was composed of 2 sub-models. The habitat sub-model included 4 variables: land cover type, slope, soil depth, and percent clay in the upper soil layers (to 24 in, or 60 cm). The disturbance effects sub-model included a suite of variables that may affect habitats or populations of pygmy rabbits through loss or degradation of habitat or increased predation rates. Many variables included in the disturbance effects sub-model relate to the presumed positive influence of various anthropogenic features on abundance of potential predators, both mammals and raptors, on pygmy rabbits. Predation is considered the primary source of mortality in this species (Green 1978, as cited by Rauscher 1997; Gahr 1993; T. Katzner, personal communication). Empirical data on effects of human disturbance on pygmy rabbits are lacking, with the exception of studies of livestock grazing and pygmy rabbits. Livestock grazing has been associated with decreased burrow density and lower nutritional quality of grasses and forbs important during summer (Thines et al. 2004), larger home range sizes (Gahr 1993), and decreased abundance of pygmy rabbits (Gahr 1993). However, we were unable to model grazing effects consistently across the WBEA area, owing to the lack of spatial data, e.g., livestock stocking rates, across all land ownerships in the study area. Based on literature review of generalized effects of such features as roads and construction of oil and gas

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wells on habitat loss and degradation, we created a disturbance sub-model that included variables such as agricultural land, burns, roads, and transmission lines. Probabilities resulting from the habitat and disturbance sub-models were multiplied by each other to obtain the final model output, which represents the relative probability of occurrence of pygmy rabbit within its geographic range in the study area.7

Sage Sparrow

The sage sparrow model consisted of 2 sub-models, a habitat model and a disturbance model. We used USGS Breeding Bird Survey (BBS) route data for sage sparrows from 1999- 2003 (Sauer et al. 2003) to build the model, including the 110 BBS routes that were completely contained within the Wyoming Basins study area and were sampled in at least 2 of the 5 years surveyed. Sage sparrows respond to the abundance of sagebrush habitat, as well as fragmentation (Weins and Rottenberry 1981, Knick and Rottenberry 1995), but primarily at larger landscape scales (Knick and Rotenberry 1995, Vander Haegen et al. 2000). Thus, we modeled sage sparrow occurrence using the proportion of sagebrush cover at 4 spatial extents (886 ft, 3.1-, 11.2- and 31-mi radius circles), averaged for all cells along each BBS route. We also modeled sagebrush fragmentation along each route (amount of “natural” sagebrush edge habitat) at 2 spatial scales (3.1- and 11.2-mi radius circles). Finally, sage sparrows may select for semi-open habitats for foraging (Misenhelter and Rotenberry 2000). Thus, we generated a vegetation productivity index called Normalized Difference Vegetation Index (NDVI; Sellers 1985), and used the mean of all cells along each route. We used logistic regression (Hosmer and Lemeshow 2000) to contrast habitat characteristics at the sites where sage sparrows were detected (1) with those where they were absent (0). Little research has been conducted on how sage sparrows might respond to anthropogenic disturbances. Sage sparrows tended to avoid habitats <328 feet from secondary roads in Wyoming (Ingelfinger and Anderson 2004); paved roads had no effect on sagebrush in Wyoming or Idaho (Rotenberry and Knick 1995). Thus, we modeled road effects using a similar approach to that used for the Brewer’s sparrow model. We used a road-effect zone limited to the road surface and the adjacent surface disturbance associated with the road. These effect zones

7 See Chapter 3 and Appendix 1 for details on mapping the range of pygmy rabbit in the WBEA.

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were assigned a lower habitat suitability score (0.01) than habitats further away from roads (1.0). We multiplied the 2 sub-models together to come up with a final relative probability of occurrence model and map for sage sparrows across the entire Wyoming Basins study area.

Sage Thrasher

The sage thrasher model consisted of 2 sub-models, a habitat model and a disturbance model. We used USGS Breeding Bird Survey (BBS) route data for sage thrashers from 1999- 2003 (Sauer et al. 2003). Only the 110 BBS routes that were completely contained within the Wyoming Basins study area and were sampled in at least 2 of the 5 years surveyed were used for modeling sage thrasher occurrence. Sage thrashers are associated with shrubsteppe habitats (Weins and Rotenberry 1981) and more so, sagebrush habitats (Petersen and Best 1991, Knick and Rotenberry 1995). Fragmentation has been shown to have a negative (Knick and Rotenberry 1995, 1997), neutral (Weins and Rotenberry 1981), or positive (Vander Haegen et al. 2000) effect on sage thrasher abundance. Thus, we modeled sage thrasher occurrence using the proportion of sagebrush cover at 4 spatial extents (886 ft, 3.1-, 11.2- and 31-mi radius circles), averaged for all cells along each BBS route. We also modeled sagebrush fragmentation (amount of “natural” sagebrush edge habitat) at 2 spatial scales (3.1- and 11.2-mi radius circles) along each BBS route. Finally, sage thrashers may select for more productive habitats (Vander Haegen et al. 2000) or avoid extensively open (bare ground) habitats (Petersen and Best 1991). Thus, we also generated a vegetation productivity index called Normalized Difference Vegetation Index (NDVI; Sellers 1985), and used the mean of all cells along each route. We used logistic regression (Hosmer and Lemeshow 2000) to contrast habitat characteristics at the sites where sage thrashers were detected (1) with those where they were absent (0). While some limited research has been conducted to assess the effects of anthropogenic disturbance on sage thrasher abundance, those results are conflicting. Similar to sage sparrows, the effects of roads on sage thrashers either has been minimal or was not detected (Knick and Rotenberry 1995, Ingelfinger and Anderson 2004). Thus, we took a conservative approach to modeling anthropogenic disturbance, following that used for the Brewer’s and sage sparrow models. We used a road-effect zone limited to the road surface and the adjacent surface disturbance. These effect zones were assigned a lower habitat suitability score (0.01) than

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habitats further away from roads (1.0). We multiplied the 2 sub-models together to come up with a final relative probability of occurrence model and map for sage thrashers across the entire Wyoming Basins study area.

Loggerhead Shrike

The final loggerhead shrike model consisted of 2 sub-models, a habitat model and a disturbance model. We used USGS Breeding Bird Survey (BBS) route data for loggerhead shrikes collected from 1999-2003 (Sauer et al. 2003) to develop the habitat sub-model. Only the 110 BBS routes that were completely contained within the Wyoming Basins study area and were sampled in at least 2 of the 5 years surveyed were used for modeling loggerhead shrike occurrence. Throughout their distribution across grassland and shrubsteppe habitats, loggerhead shrikes select for a heterogeneous mix of habitats that contain grass, forbs, shrubs, trees, and open or bare areas (Smith and Kruse 1992, Bjorge and Prescott 1996, Yosef 1996, Michaels and Cully 1998). Nests are placed in shrubs or trees, and sagebrush is a preferred shrub when available (Woods and Cade 1996). At the landscape scale, shrikes selected for patches of habitat approximately 69 acres in size (circle of 984 ft in radius) in Missouri (Esely and Bollinger 2001). We tested the proportion of sagebrush habitat at 4 spatial scales (886 ft, 3.1-, 11.2- and 31-mi radius circles) in predicting loggerhead shrike occurrence within the Wyoming Basins study area. Values were averaged for all cells along each BBS route. Given the loggerhead shrike’s association with heterogeneous habitats, we also modeled sagebrush fragmentation (amount of “natural” sagebrush edge habitat) at 2 spatial scales (3.1- and 11.2-mi radius circles) along each BBS route. Finally, selection for more open habitats by loggerhead shrikes provides them with easier access to prey resources as they “sit and wait” from available perches (Gawlik and Bildstein 1990, Yosef 1996). Thus, we also generated a vegetation productivity or biomass index called Normalized Difference Vegetation Index (NDVI; Sellers 1985). We estimated the mean NDVI value for all cells along each route. We used logistic regression (Hosmer and Lemeshow 2000) to develop our loggerhead shrike habitat sub-model, contrasting habitat characteristics at the sites where shrikes were detected (1) with those where they were absent (0). Loggerhead shrikes often select for edge habitats associated with linear habitats such as hedgerows, roads, or fences (Robertson 1930, Bjorge and Prescott 1996, Esely and Bollinger

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2001). These habitats provide easier accessibility to prey and an increased number of perching sites (i.e. fences, powerlines, and trees) to forage from, as well as enhanced nesting sites (shrubs and trees; Robertson 1930, Bjorge and Prescott 1996, Yosef 1996). However, birds using these habitats have lower nest success (~50% lower) and lower survival due to increased predator densities and increased mortality from collisions with vehicles (Yosef 1994, 1996; Esely and Bollinger 2001). In contrast, loggerhead shrikes avoided linear hedgerows in Ontario, Canada (Chabot et al. 2001). Given the species’ apparent selection for road/edge habitats, but the “ecological trap” situation it presents, attracting birds to habitats where they will fail to be successful (Delibes et al. 2001), the anthropogenic disturbance model we developed for loggerhead shrikes used the same approach as for the other passerine species modeled here. We used a road-effect zone limited to the road surface and the adjacent surface disturbance. These effect zones were assigned a lower habitat suitability score (0.01) than habitats further away from roads (1.0). We multiplied the habitat and anthropogenic sub-models together to come up with a final relative probability of occurrence model for loggerhead shrikes across the entire Wyoming Basins study area.

Short-horned Lizard

The short-horned lizard model consisted of both habitat and disturbance sub-models. The habitat sub-model was based on 3 different habitat or habitat-derived variables associated with short-horned lizard occurrence (see Appendix 4, Table A4.13). Short horned lizards are found in certain habitats (e.g. sagebrush, other shrub habitats, badland habitats), and are not found in other habitat types (e.g. grass dominated habitats; Pianka and Parker 1975; Reynolds 1979; Montanucci 1981; Werschkul 1982; Powell and Russell 1985, 1998; James 2004). Thus, we modeled probability of occurrence for the habitat sub-model based on these relationships (Table A4.13). Due to the species’ need to move through vegetation and forage in more open habitats, short-horned lizards are found in semi-open, more thinly vegetated habitats, rarely occurring in thick grass-dominated habitats such as crested wheatgrass fields or native grasslands, except when grass patches have been grazed heavily or are interspersed with sagebrush (Reynolds 1979, Werschkul 1982, James 2004). We used NDVI (Sellers 1985, Boelman et al. 2003) to identify

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-13 Version 2.0, March 2006 these semi-open habitats. Moderate NDVI values were assigned higher probability of occurrence than low (limited vegetation) or high (dense vegetation) values (Table A4.13). Finally, given the thermoregulation needs of short-horned lizards, and their behavior of warming themselves in exposed micro-habitats or absorbing heat from exposed objects such as rocks (James 2004), we estimated the amount of solar radiation exposure (Kumar et al. 1997) across the Wyoming Basins study area. Areas with higher solar radiation were modeled as higher probability of occurrence (Table A4.13). All of these probabilities were multiplied to develop the habitat sub-model relative probability, ranging from 0 – 1. No direct research has been conducted to assess the effects of anthropogenic disturbance on short-horned lizard occurrence or survival. However, disturbance to or alteration of otherwise suitable habitat has been shown to makes those habitats unsuitable for the lizards (Powell and Russell 1998, James 2004). Roads can have lethal effects, with lizards being run over by vehicles when basking in these unnatural open habitats (Powell and Russell 1998, James 2004). Similarly, lizards were absent in apparently “suitable” habitats within a 328-foot buffer surrounding cultivated lands (Powell and Russell 1998). Thus, we modeled probability of occurrence for each of these altered habitats, with low probability (0.1) assigned to all pixels (90- m [295-ft] pixels) immediately adjacent to these disturbed habitats [within 45-135 m (148-443 ft; see Table A4.13)]. All disturbance probabilities were multiplied to generate the disturbance sub- model relative probability, ranging from 0 to 1. We multiplied the habitat and disturbance sub- models together to come up with a final relative probability of occurrence model for short- horned lizards across their distribution (Stebbins 2003) within the Wyoming Basins study area.

Sagebrush Lizard

The sagebrush lizard model consisted of habitat and disturbance sub-models. Like the short-horned lizard model, the sagebrush lizard habitat sub-model was based on 3 different habitat or habitat-derived variables associated with sagebrush lizard occurrence (see Appendix 4, Table A4.14). Sagebrush lizards are associated with shrub habitats, primarily occurring in sagebrush, antelope bitterbrush (Purshia tridentata), and juniper/pinyon-pine habitats (Tinkle 1973, Reynolds 1979, Werschkul 1982, M’Closkey et al. 1997, Hammerson 1999, Green et al. 2001, Knox et al. 2001, James and M’Closkey 2002, Stebbins 2003). Both native and non-native

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grass-dominated habitats are avoided (Reynolds 1979, Werschkul 1982, Green et al. 2001). Sagebrush lizards may be found in other shrub habitats as well (e.g. greasewood, shadscale [Atriplex spp.] or hopsage [Grayia spp.]; Hammerson 1999, Morrison and Hall 1999); however they avoid gray rabbitbrush (Chrysothamnus nauseosus; Green et al. 2001). Using these known lizard-habitat relationships, we modeled probability of occurrence for this part of the habitat sub- model (see Table A4.14). Within these shrubby habitats, sagebrush lizards select for a heterogeneous mix of shrubs and open habitat which contains both cover and a high proportion of exposed bare ground (Rose 1976b, Morrison and Hall 1999, Knox et al. 2001, Germaine and Germaine 2003, Stebbins 2003). Sagebrush lizards forage on insect in these open habitats (Rose 1976a, b; Green et al. 2001), as well as bask in the sun to thermoregulate. Shrubs are used as refugia to escape both predators and intense heat (Adolph 1990, Werschkul 1982, Green et al. 2001). We used NDVI (Sellers 1985, Boelman et al. 2003) to identify these patchy mixes of shrubs and semi-open habitats. We assigned high probabilities of occurrence to moderate NDVI values representing patchy vegetation cover, and lower probabilities to habitat with high (dense cover) or low (mostly exposed soils) NDVI values (see Table A4.14). Additionally, we estimated solar radiation exposure (Kumar et al. 1997) across the Wyoming Basins study area, and assigned higher probabilities to areas that received higher solar radiation (see Table A4.14). We multiplied these 3 values (habitat score, NDVI score, and solar radiation score) to generate the habitat sub-model relative probability, ranging from 0 to 1. No research has been conducted on the effects of anthropogenic disturbance on sagebrush lizard occurrence or fitness, with the exception of assessing occurrence within crested wheatgrass (Reynolds 1979) and occurrence related to forest restoration treatments (Knox et al. 2001). However, any direct disturbances resulting in the altering or loss of shrubsteppe habitats would conceivably have negative effects on sagebrush lizards. In addition, permanent human- related structures will restrict movements of lizards (Germaine and Wakeling 2000), and could conceivably have negative survival consequences (e.g. run over by vehicles on roads). Since we had no literature with which to assess larger landscape-scale effects of cumulative disturbances, our disturbance model was conservative, limiting negative effects to single pixels (295-feet) that contained disturbed habitat (e.g. roads, well pads, agriculture, canals, urban areas). These pixels were assigned a zero probability, and all other habitats received a probability of occurrence of 1

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(see Table A4.13). We multiplied the probabilities from habitat and disturbance sub-models together to come up with final relative probability of occurrence estimates for sagebrush lizards across their distribution (Stebbins 2003) within the Wyoming Basins study area.

RESULTS

Greater Sage-Grouse

Greater sage-grouse currently occupy 40.5 million acres within the assessment area; the greatest part (75.1%) of the range is in Wyoming, followed by Colorado (9.1%), Utah (7.3%), Montana (6.8%), and Idaho (1.7%) (Table 6.1, Fig. 6.2). Habitat for sage-grouse, as estimated by our habitat sub-model, was most abundant in central and southwestern Wyoming, with the low habitat values concentrated primarily along the periphery of the species’ range, especially in eastern and northeastern Wyoming (Fig. 6.2). The majority (52%; >21 million acres) of the range of sage-grouse in the study area had a value >0.8 for habitat, indicating sagebrush proportions exceeding 0.55 in the 1.85-mi radius circles surrounding those pixels (Table A4.1). By contrast 15% (6.0 million acres) of the current sage-grouse range in the WBEA had a habitat value of 0, signifying a sagebrush proportion <0.05 in the circle. The mean value from the habitat sub-model across the range of sage-grouse in the study area was 0.63. Results from the disturbance sub-model for greater sage-grouse indicated much more variability across the species’ range in the Wyoming Basins, compared to habitat values (Fig. 6.3). Disturbance was especially prominent along the I-80 corridor and in the portions of the Rawlins, Rock Springs, Kemmerer, and Pinedale Field Offices (Fig. 6.3.). Areas of low disturbance were distributed throughout the study area, but larger patches were found in Montana (Dillon Field Office) and the eastern portion of the Rock Springs Field Office. In contrast to the habitat model, the dominant value from the disturbance sub-model was rather low (0.3); sites with this value covered 50% (20.3 million acres) of the range of sage-grouse in the WBEA. Over 10% (4.2 million acres) of the area modeled had a score of 0, indicating the presence of some anthropogenic feature assumed to affect sage-grouse habitats or populations (Table A4.1). The dominant feature affecting output of the sage-grouse disturbance model was secondary roads, which occupy nearly 8% of the study area (Table 5.2) and are presumed to negatively

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-16 Version 2.0, March 2006 influence an even larger extent. Last, 6.7% of the study area had a score of 1.0, indicating areas beyond the effect zone of any anthropogenic feature included in the sage-grouse disturbance sub- model. The mean output for the disturbance sub-model was 0.41, considerably lower than that for the habitat sub-model. Based on results of the final model (i.e., combined product of the habitat and disturbance sub-models), the vast majority (33.0 million acres) of land within the current range of sage- grouse was predicted to have either very low (38.0%) or low (43.6%) probability of occurrence (Table 6.1, Figs. 6.4, 6.5). Fewer than 3 million acres (6.9%) of the area modeled were classified as moderate, but a slightly larger area (4.7 million acres, 11.5%) as high. The high and moderate classes were widely dispersed throughout the study area, with the largest concentrations in these classes found in north-central and southwestern Wyoming and in Montana (Fig. 6.4). Very low scores were most common along the northern and eastern edges of the species’ range. The mean combined model score for the area in which the sage-grouse model was applied was 0.25. The relative percentages of predicted occurrence by output class across the entire WBEA generally were mirrored in the Field Office level summaries, with some exceptions. Among the 21 Field Offices within the range of sage-grouse, Rock Springs had the greatest percentage (16.0%) in the high class (Table 6.1; Figs. 6.4, 6.5); this Field Office contains 13.3% of the range of sage-grouse in the study area, more than any other Field Office except Rawlins. The Lander Field Office also had a comparatively large area (799,000 acres; 15.3%) in the high class. Among the Field Offices with the greatest percentage of land in the very low class (and, conversely, lowest percentage in the high class) were White River, Richfield, and Buffalo (Table 6.1, Fig. 6.5); these Field Offices, however, contained only a small proportion (<0.02; <1 million acres combined) of current sage-grouse range (Table 6.1). Dillon ranked comparatively high among Field Offices overall, with the greatest percentage in the moderate and high classes combined (Table 6.1, Fig. 6.5).

Ferruginous Hawk

Nearly half of the total area (37.5 million acres, or 43.3%) modeled for ferruginous hawk was predicted to have a very low probability of occurrence (Table 6.2, Fig. 6.4). The second-

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-17 Version 2.0, March 2006 most dominant class was low (33.4%); only 8.9% (7.6 million acres) of the study area had a high probability of occurrence. Substantial landcover in the moderate and high classes was found in the Green River Basin in Utah (Vernal Field Office), Little Snake River drainage in Colorado (Little Snake Field Office), Great Divide Basin (Rock Springs Field Office) and north-central portion of Wyoming, and southwestern Montana (Butte Field Office) (Fig. 6.4). At the Field Office level (Table 6.2, Fig. 6.5), the high probability class was most commonly found in Vernal, Billings, Rock Springs, and Worland (range 22.3% – 17.1%), and lowest in Pinedale, Kremmling, Richfield, and Fillmore (range 0.4% – 0%). Overall, 8 (range 22.3% – 10.1%) and 15 (range 8.6% – 0%) Field Offices had more or less area in the high probability of occurrence class, respectively, compared to the WBEA area (8.9%) as a whole. In contrast, 11 (range 86.5% – 45.8%) and 12 (range 42.8% – 18.7%) Field Offices had more or less area in the very low probability class, respectively, compared to the WBEA area (43.5%).

Brewer’s Sparrow

The dominant occurrence class for Brewer’s sparrow was very low, composing nearly half of the total area (41.6 million acres, or 48.8%) in the Wyoming Basins (Table 6.3, Fig. 6.6). The second-most dominant class was moderate (22.4%); only 12.9% (11.0 million acres) of the study area had a high probability of occurrence. The model predicted high occurrence probabilities in the south-central and western portion of Wyoming, in the Green River Basin in Utah (Vernal Field Office), Axial Basin (Little Snake Field Office) and Platte River drainage (Kremmling Field Office) in Colorado, and the Big Hole and Red River drainages (Dillon Field Office) in the southwestern portion of Montana (Fig. 6.6). At the Field Office level (Table 6.3, Fig. 6.7), the high probability class was most dominant in Rock Springs, Kemmerer, Lander and Rawlins (range 36.6% – 20.1%) and lowest in Richfield, Lewistown, Buffalo, and Fillmore (all 0%). Overall, 7 (range 36.6 – 13.2%) and 17 (range 12.0 – 0%) Field Offices had more or less area in the high probability of occurrence class, respectively, compared to WBEA area (12.9%). In contrast, 14 (range 98.9% – 49.1%) and 10 (range 48.8% – 6.8%) Field Offices had more or less area in the very low probability class, respectively, compared to the WBEA area (48.8%).

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Pronghorn

The current range of pronghorn in the Wyoming Basins covers 38.7 million acres (45%) of the study area (Fig. 6.10). The large majority is in Wyoming, with substantial range also located in southwestern Montana and northern Colorado. The Rawlins Field Office alone contains >20% of the range (Fig. 6.10). The pronghorn model scenario that included the entire suite of variables (i.e., fence density variable added) was applied across 88% of the current range of pronghorn in the study area; results of this model are displayed in Figs. 6.10-6.12 and in Table 6.4. Based on this model, most area within the WBEA area was predicted to have either a very low (32.3%) or low (33.8) probability of occurrence, with a small percentage (6.4%) classified as high (Table 6.4, Figs. 6.10-6.12). The largest concentrations of moderate or high predicted occurrence were scattered throughout southwestern and central Wyoming and northeastern Utah east of the Wasatch Range (Fig. 6.10). Among BLM Field Offices, predicted occurrence was greatest in Salt Lake, Missoula, Kemmerer, and Rock Springs; these Field Offices had the greatest percentage in the moderate and high classes combined (range 50.5% – 78.1%; Fig. 6.11); however, of these 4, only Kemmerer and Rock Springs encompass more than a trace (>1%) percentage of the total area modeled. Other Field Offices with a relatively high percentage of moderate or high predicted occurrence that also contain substantial portions of pronghorn range included Lander, Rawlins, and Pinedale in Wyoming, and Little Snake in Colorado. Of the BLM Field Offices with >5% of the area modeled, Worland had the greatest percentage in the very low class (Figs. 6.10, 6.11), indicating largely unsuitable conditions for pronghorn in this Field Office. The exclusion of the fence variable resulted in marked differences in model output compared to the model without this variable (Figs. 6.10, 6.12). The model scenario that excluded the fence density variable was applied to the complete range of pronghorn in the study area. Within this somewhat larger extent, predicted occurrence was uniformly greater compared to the model scenario with fence effects included, with more area in the moderate (33.2%) and high (19.4%) classes and less in the very low (26.2%) and low (21.1%) classes (Fig. 6.12). Spatial patterns of predicted occurrence resembled those from the model with fences included; however, southwestern Montana (Dillon Field Office) demonstrated considerable improvement

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in estimated occurrence with the exclusion of fence density data, as did substantial portions of the Little Snake, Lander, Rawlins, and Worland Field Offices (Fig. 6.10).

Pygmy Rabbit

The current geographic range of pygmy rabbit in the study area, based on the range map created for the WBEA, was 15.1 million acres (Table 6.5, Fig. 6.13). The majority of this area was in Wyoming (11.5 million acres) and Montana (3.0 million acres), with only small areas in Utah (407,000 ac; Salt Lake Field Office) and Idaho (94,000 ac; Idaho Falls District) (Table 6.5). Only the Dillon, Kemmerer, and Rock Springs Field Offices had a substantial proportion of their land base within the estimated current range of the species in the study area (Figs. 6.13, 6.14). Nearly half of the total area (6.5 million acres, or 43.1%) within the range of pygmy rabbit in the study area was predicted to have a very low probability of occurrence (Table 6.5, Fig. 6.14). The second-most dominant class was low (26.4%); only 11.8% (1.8 million acres) of the estimated range had a high probability of occurrence. Areas with the highest predicted occurrence in Montana were east and south of Grant, southeast of Dillon, and near Red Rocks Lakes (Dillon Field Office) (Fig. 6.13). In Wyoming, much of the range of the species was predicted to have a moderate or high probability of occurrence, especially in the Rock Springs Field Office (Table 6.5, Fig. 6.13). In Utah, 16.5% of the Salt Lake Field Office was predicted to have a moderate probability of occurrence; the remainder of the range in this state was in the very low (36.0%) or low (47.5%) class. The very small area of the species’ range in Idaho was predicted to have mostly very low or low probability of occurrence, with no area in the high class (Table 6.5). The relative percentages of the 4 model output classes across the study area were generally similar for Field Office summaries. Among the 9 Field Offices within the current range of pygmy rabbit in the study area, Rock Springs had the greatest percentage of area in the moderate and high classes, with nearly 50% in these 2 classes combined (Table 6.5, Fig. 6.14). Other Field Offices with relatively high percentages in the moderate and high classes were Lander, Pinedale, and Rawlins (Table 6.5; Figs. 6.13, 6.14). Compared to other Field Offices in the WBEA, Rock Springs contained by far the largest percent area in the species’ range in the

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study area, >5.0 million acres (Table 6.5). Dillon was ranked second, containing nearly 20% (3.0 million acres) of the species’ range in the study area.

Sage Sparrow

The final sage sparrow habitat model included 1 variable, the proportion of sagebrush habitat within a 31-mi radius (Table A4.10). Sage sparrows were positively correlated with sagebrush habitat at this large scale. Based on the combined habitat and disturbance sub-models, the majority of the WBEA study area was predicted to have a very low occurrence (32.1 million acres, or 37.7%) with only 13.8 million acres (16.1%) of the study area having a high probability of occurrence (Table 6.6, Fig. 6.15). Because the only variable that entered into the habitat sub- model was the proportion of sagebrush at 31-mi radius, the more central regions of the study area (south-central Wyoming), with the greatest abundance of sagebrush, had the highest probabilities of occurrence. At the Field Office level (Table 6.6, Fig. 6.16), the predicted high probability class was most dominant in Rock Springs, Kemmerer, Rawlins, and Casper (range 52.7% – 25.9%). However, no habitat in the high probability class was predicted for the Glenwood Springs, Kremmling, Royal Gorge, White River, Billings, Butte, Dillon, Lewiston, Missoula, Fillmore, Richfield, Vernal, Buffalo, Cody, and Worland Field Offices (all 0.0%; Table 6.6). The majority of the very low predicted probabilities, based on habitat, were in the northeastern portion of the study area (Fig. 6.15) and all habitat within Royal Gorge, Butte, Lewiston, and Missoula fell within the very low probability class (Table 6.6, Fig. 6.16).

Sage Thrasher

The final sage thrasher habitat model included 3 variables (Table A4.11). Occurrence of sage thrasher was positively correlated with the proportion of sagebrush habitat at a 3.1-mi scale, negatively correlated with sagebrush fragmentation (amount of “natural” sagebrush edge habitat) at the small 3.1-mi scale, and negatively correlated with high NDVI values. Based on the combined habitat and disturbance sub-models, sage thrasher occurrence was predicted to be low (32.1 million acres, or 37.6%) or very low (23.3 million acres, or 27.3%) across the majority of

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the Wyoming Basins study area. Only 12.1 million acres (14.3%) of the study area was predicted to as high occurrence (Table 6.7, Fig. 6.17). Sage thrashers were more likely to occur in areas with greater abundance of sagebrush habitats that also contained more homogeneous sagebrush (i.e. less natural sagebrush edge; Table A4.11). Thus, most areas of high occurrence were located in south-central Wyoming, commensurate with the most contiguous sagebrush habitat (Figs. 6.17, 6.18). At the Field Office level (Table 6.7, Fig. 6.18), the high occurrence class was more common in Rock Springs, Lander, Kemmerer, Rawlins, and Casper Field Offices (range 45.7% – 23.8%). No high occurrence areas were predicted for Royal Gorge, Lewiston, Missoula, Fillmore, or Richfield (al l 0.0%; Table 6.7). The Royal Gorge, Lewiston, Fillmore and Richfield Field Offices were characterized mostly as very low or low occurrence areas for sage thrashers (Table 6.7, Fig. 6.18). The Buffalo, Vernal, Butte, and Billings Field Offices also had mostly very low or low occurrence areas, with small areas in moderate and high classes (Table 6.7, Fig. 6.18).

Loggerhead Shrike

The final loggerhead shrike habitat sub-model included 3 variables (Table A4.12). Loggerhead shrike occurrence was positively correlated with the proportion of sagebrush habitat at the 886-ft scale and negatively correlated with large-scale (11.2-mi) sagebrush fragmentation (amount of “natural” sagebrush edge habitat). Occurrence also was strongly correlated with lower NDVI values, or less above ground biomass. The majority of habitat (50.0%, or 42.7 million acres) was classified as very low occurrence for loggerhead shrikes, with only 10.1 million acres (11.9%) falling within the high probability class (Table 6.8, Fig 6.19). Areas within the central portion of the study area that contained the highest density of contiguous sagebrush were predicted to be higher occurrence areas (Fig 6.19). At the Field Office level (Table 6.8, Fig 6.20), 4 Wyoming Field Offices (Casper, Lander, Rawlins, and Rock Springs) and the Little Snake Field Office in Colorado, contained the majority of the high probability class (range 19.5% – 29.5%; Table 6.8; Fig 6.20). The Royal Gorge, Idaho Falls, Dillon, Fillmore, Richfield, and Vernal Field Offices each contained virtually no high occurrence areas (all <1.1%; Table 6.8, Fig 6.20). Greater than 60% of habitat within 10

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Field Offices (Kremmling, Royal Gorge, Billings, Butte, Fillmore, Richfield, Salt Lake, Vernal, and Pinedale) were characterized as low probability of occurrence (range 61.0% – 100%; Table 6.8, Fig 6.20).

Short-horned Lizard

We applied the final short-horned lizard model across the species’ geographic range (Stebbins 2003; Table A1.4) within the Wyoming Basins study area. The Kremmling Field Office was outside of the current distribution of the short-horned lizard, and the model was not applied to any habitat with this Field Office. The majority of habitat within the short-horned- lizard distribution in the Wyoming Basins study area was predicted to have a very low probability of occurrence (25.2 million acres; 52.5%; Table 6.9; Figs. 6.21, 6.22). Only 255,839 acres (0.5%) was predicted to have a high probability of occurrence, but 19.1 million acres (39.9%) fell within the moderate probability class (Table 6.9; Figs. 6.21, 6.22). Spatially, higher ranked habitats (predominantly the moderate class) occurred throughout the short-horned lizard range within Wyoming, Colorado, and northeastern Utah (Fig. 6.21). At the Field Office level, a similar pattern existed, with each field office containing a large proportion of very low occurrence (Table 6.9; Figs. 6.21, 6.22). Greater than 90% of habitat within 6 Field Offices (Royal George, Butte, Dillon, Lewiston, Missoula, and Buffalo) was considered very low or low occurrence (Table 6.9; Figs. 6.21, 6.22). Royal Gorge and Salt Lake were the only 2 Field Offices that contained >2% of habitat in the high class, 2.8% and 2.7%, respectively; most field offices contained closer ~0% of high occurrence areas (Table 6.9; Fig. 6.22). Greater than 50% of 5 Field Offices (Little Snake, Kemmerer, Lander, Rock Springs, and Worland) were moderate occurrence (range 53.9% – 62.1%; Table 6.9, Fig. 6.22).

Sagebrush Lizard

We applied the final sagebrush lizard model to the geographic range of sagebrush lizards (Stebbins 2003; Table A1.4) within the Wyoming Basins study area. This resulted in 3 rather than 4 habitat classes, which we refer to as low, moderate, or high (see Appendix 4). Within the Wyoming Basins study area, approximately 12% (5.9 million acres) was considered to have a

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-23 Version 2.0, March 2006 moderate probability of sagebrush lizard occurrence, with about 46% (23.4 million acres) considered low occurrence, and 42% (21.3 million acres) high occurrence (Table 6.10; Figs. 6.23, 6.24). Most areas of high occurrence were in the most central regions of the study area, in Wyoming and along the Colorado-Utah border (Fig. 6.23). Areas of low occurrence often were along the edges of the study area (Fig. 6.23). Five Field Offices (Kremmling, Royal Gorge, Dillion, Lewiston and Missoula) were outside of the range of sagebrush lizards (Stebbins 2003) within the study area, and thus, our model was not applied within these Field Offices. At the Field Office level, only 3 Field Offices (Lander, Rock Springs, and Worland) contained greater than 50% of their habitat within the high occurrence class (range 56.5% – 69.1%, Table 6.10, Fig 6.24). Conversely, >50% of 10 Field Offices (Glenwood Springs, Billings, Butte, Fillmore, Richfield, Salt Lake, Buffalo, Casper, Cody, and Pinedale) were considered a low probability of occurrence for sagebrush lizards (range 52.2% -95.0%; Table 6.10; Fig 6.24). All Field Offices contained a small amount of moderate class habitat (all < 27%; Table 6.10; Fig 6.24).

DISCUSSION

Our modeling approach involved the use of 2 or more sub-models for the 10 species. Outputs from these sub-models, each a probability of occurrence for each cell in the area to which the model was applied, generally were multiplied by each other. Final model outputs were relatively low numbers, with the mean predicted occurrence for most models about 0.3. Thus, it is important to consider model outputs as relative rankings of predicted occurrence, rather than absolutes. Had we taken the geometric mean of the 2 sub-model outputs, for example, the final probabilities would have been much higher. The response variable for our draft models, relative predicted probability of occurrence, was selected in part because this response can be more readily evaluated with empirical data. Other response variables, such as rate of population growth or productivity, require far more extensive, long-term data collection. Simple presence/absence data are relatively easy to collect for model evaluation, and if modeled with appropriate environmental variables, can produce reasonably accurate models of occupancy (Pearce and Ferrier 2001).

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Empirical data are lacking for many of our modeled species about the direct effects of human disturbance, or the results of human alterations of landscapes, such as habitat fragmentation and loss. For example, although we were unable to find data on effects of transmission lines on pygmy rabbits, we assumed that the documented increase in nest and perch sites for raptors afforded by these structures (e.g., Steenhof et al. 1993) could influence predation rates on pygmy rabbits. Thus, the scores as applied in our models must be considered hypotheses that require validation through field sampling and evaluation of existing distribution data for the example species. Empirical data collected as part of 2005 and 2006 field work will be used to evaluate performance the species models and human footprint models (Appendix 7). Results from the field work will be presented in the Final Report. In some cases, additional empirical data from other sources (e.g., Wyoming Observation Database) may be used to evaluate model performance for selected species models, when such data are available. However, we caution that some “opportunistic” data sets, not developed as part of the original design, may not be appropriate in a robust evaluation of model results because of differences in sampling designs or area sampled.

Greater Sage-Grouse

Model results for sage-grouse suggested relatively low probabilities of occurrence across much of the species’ range in the study area. Such results are consistent with the current status of greater sage-grouse, which was petitioned for listing as threatened or endangered under the U.S. Endangered Species Act (USDI Fish and Wildlife Service 2005). Populations declined by about 50% in Wyoming from 1965-2003, with similar declines reported for the other 4 states in the WBEA area (Connelly et al. 2004). Our model outputs were relative, not absolute, rankings of predicted occurrence, and were driven largely by the anthropogenic variables in the disturbance sub-model. Areas predicted to have moderate to high abundance of sage-grouse corresponded closely with current locations of active leks in the study area (unpublished data on file). The primary area of disagreement between lek locations and our model outputs was in the southeastern portion of the Rock Springs Field Office, near the Colorado border. Here, there is relatively little anthropogenic disturbance, as estimated by our model (Fig. 6.3), and a high proportion of

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-25 Version 2.0, March 2006 sagebrush (Fig. 6.2), leading to relatively high predicted occurrence of sage-grouse. However, there are few leks present in this area. The discrepancy appears to be due to errors in landcover mapping in this area. This portion of the Field Office was mapped as sagebrush by the Wyoming GAP project (Merrill et al. 1996), and thus as sagebrush in the landcover map used in our assessment (Comer et al. 2002). In a recently completed vegetation coverage of the Rock Springs Field Office, however, this area was determined to be non-sagebrush (Fig. 7.7). If this more recent vegetation map had been used in our habitat sub-model, this region would have been predicted to have very low, rather than high, occurrence, based on habitat conditions. Landscape-scale requirements for many species, including those we modeled, are often not known. Despite decades of observational study and research on greater sage-grouse, some questions remain unanswered, such as how large a block of intact sagebrush habitat is required for population persistence (Dobkin 1995, Crawford et al. 2004). Furthermore, it is not known whether outright habitat loss or habitat fragmentation is more important in driving population declines, or some combination of these 2 processes (Dobkin 1995). However, the best single variable for discriminating between extirpated versus occupied range of sage-grouse was habitat proportion, while measures of habitat fragmentation were poor discriminators (Wisdom et al., in prep). By contrast, nesting sage-grouse strongly avoided edge-dominated landscapes (Aldridge 2005). Application of fragmentation and other habitat configuration measures on a landscape, without consideration of how species actually respond to these measures, can produce misleading results (Vos et al. 2001). Thus, the model was parameterized with our best estimates of landscape-level requirements of sage-grouse, given the data sets available, but requires evaluation with empirical data on current sage-grouse distribution. Such validation will be completed in 2006 and presented in the Final Report Our model predicted high occurrence of sage-grouse in some areas that also are estimated to have a high human footprint (Figs. 5.2-5.4, 6.4). The discrepancy may be explained in part by the inclusion of different anthropogenic features in the human footprint model (Chapter 5) versus the variables included in the disturbance submodel for sage-grouse. Moreover, the effect zones for the anthropogenic features varied between the 2 models. Last, the sage-grouse model incorporated 2 sub-models of equal weight in the final score – habitat (sagebrush proportion in window) and disturbance. Thus, if sagebrush is a large percentage of the area (e.g., >70%, for

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which the score would be 1.0), the combined model output for this area may be greater than one would expect based on the human footprint alone. An important factor to consider in evaluation of the sage-grouse model predictions is the potential time lag between effects of human disturbance on habitats of sage-grouse (e.g., habitat loss and fragmentation) and population responses of sage-grouse. For example, areas currently supporting relatively high densities of sage-grouse may be depicted as having a low probability of occurrence from our model. Sage-grouse are a long-lived species (Schroeder et al. 1999) that exhibit strong site fidelity, especially the nesting/brood-rearing cohort of hens (Connelly et al. 2004, Rowland 2004). Lags in population responses to disturbance or habitat alteration may be due to long-term, gradual declines in habitat quality or the delayed breeding of yearlings (Crawford et al. 2004). Moreover, areas of high human impact could be serving as “ecological traps” for sage-grouse, where the birds remain but fitness is considerably lower than elsewhere in their range (Aldridge 2005). Although adult survival in sage-grouse is relatively high, survival of juveniles is low, leading to low productivity in many populations (Crawford et al. 2004). Knowledge is lacking about temporal scales of responses of sage-grouse populations to habitat improvement, as well as to habitat loss and degradation (Rowland and Wisdom 2002, Crawford et al. 2004; but see Aldridge 2005). Populations of greater sage-grouse are declining throughout the range of the species, including within the Wyoming Basins (Connelly et al. 2004, Crawford et al 2004). Although our model displays relative spatial patterns of predicted occurrence, we did not include other factors that may be detrimental to sage-grouse populations, such as West Nile virus (Naugle et al.2004, 2005; Walker et al. 2004) or impacts from opportunistic hunting afforded by the proliferation of roads in newly developed oil and gas fields within breeding and wintering ranges of sage-grouse. Moreover, our approach to modeling disturbance effects for greater sage-grouse was conservative (i.e., in using the “worst” score within each pixel for all possible anthropogenic disturbances, rather than multiplying values to simulate a synergistic effect; see text in Appendix 4 for details). Thus, our model may overestimate predicted occurrence, due to the omission of other, potentially important factors that may affect distributions of sage-grouse.

Ferruginous Hawk

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Because ferruginous hawks population fluctuate with prey populations (Schmutz and Hungle 1989), any large-scale ferruginous hawk model most consider how both hawks and their prey respond to human disturbance. We attempted to model differences in prey densities across large scales using NDVI. The NDVI is a suitable surrogate for primary production; it is unknown, however, whether NDVI gradients reflect gradients in prey densities within our study area. The ferruginous hawk model clearly needs to be validated with empirical data (Appendix 6). We assumed that anthropogenic features influence Ferruginous hawk breeding populations both positively and negatively around some threshold; there is little empirical data, however, to support the establishment of these thresholds. Therefore, field data need to be collected to evaluate the hypothetical thresholds for the 4 anthropogenic features that compose substantial proportions of the Wyoming Basins Assessment area: roads (8.3%), agriculture (5.4%; absent effect zone), human populated areas (0.8%), and powerlines (0.6%; Chapter 5). Data are also needed on the relationship between ferruginous hawks and oil and gas development, mainly the associated infrastructure of powerlines and roads, which we hypothesized to have a slightly positive effect on breeding populations when these structures occur at low densities.

Brewer’s Sparrow

The proportion of sagebrush within a 3.1-mi radius was the most significant variable in predicting the presence of Brewer’s sparrows. In the Snake River Plains of Idaho, patch size of sagebrush was an important variable in predicting the presence of Brewer’s sparrows (Knick and Rotenberry 1995). In contrast, shrub cover within 3.1 mi around survey transects in the Columbia Basin in Washington was not a good predictor of relative abundance of Brewer’s sparrows (Vander Haegen et al. 2000). Both of these studies were conducted in heavily fragmented sagebrush landscapes.Surveys of Brewer’s sparrows across the WBEA conducted during the field season in 2005 should help clarify this relationship in the Wyoming Basins. Little knowledge exists about how Brewer’s sparrows respond to anthropogenic disturbance. In Wyoming, abundance of Brewer’s sparrows increased with increasing distance from secondary roads (Ingelfinger and Anderson 2004) with a threshold of 109 yd from the road. This trend, however, was not observed on a state highway with higher traffic volumes.

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Differential responses by songbirds to road effects, mainly to differences in traffic volume, have been documented in Europe (van der Zande et al. 1980, Foppen and Reijnen 1994, Reijnen et al. 1995). In these studies, some songbird species were more abundant near roads, whereas for other species the reverse was true. How sagebrush-associated species respond to the presence of roads and associated effects (e.g., noise, dust) needs further investigation in the Wyoming Basins.

Pronghorn

Our model predicted low to moderate occurrence of pronghorn throughout much of the its range in the Wyoming Basins. Historically, populations of pronghorn were estimated to exceed 35 million animals across the species’ range (Yoakum 2004). Pronghorn were extirpated throughout most of their range by the early 1900s as a result of overexploitation and habitat loss. Implementation of conservation practices such as cessation of over-harvesting, reduction of detrimental grazing by domestic livestock, and reintroductions into previously occupied habitats led to the recovery of a population estimated to be ≥1 million animals by the late twentieth century. However, populations began to decline again in the mid 1990s, especially in the Wyoming Basins (Yoakum et al. 1999). These declines have been attributed to changes in habitat quality and habitat loss due to human activities, including urban expansion and energy development (Sawyer et al. 2002).

Pygmy Rabbit

The pygmy rabbit model developed for the WBEA estimated a relatively small percentage of the species’ range in the study area to have a moderate (19%) or high (12%) probability of occurrence. This result is similar to predictions from other models of habitat suitability for pygmy rabbit. For example, only 17% of the potential habitat for pygmy rabbits in Idaho was highly suitable (priority rank 1) (Rachlow and Svancara 2003). Within the Idaho National Engineering and Environmental Laboratory, only 23% of the 494,000-acre site was suitable for pygmy rabbits (Gabler et al. 2000). Areas mapped as having the highest predicted occurrence by our model closely approximated mapped locations of the species in Montana

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(Rauscher 1997) and in Wyoming (unpublished data on file, WGFD 2003). Pygmy rabbits often occur in disjunct, isolated populations throughout their range (Green and Flinders 1980, Dobler and Dixon 1990); our model predictions concur, depicting a patchy distribution of suitable sites at a regional scale. Pygmy rabbits are habitat specialists that require loose, deep soils for burrows and tall, dense sagebrush for year-round forage and protection from predators, especially during winter (Weiss and Verts 1984, Green and Flinders 1980, Kehne 1991, Katzner and Parker 1997). The model we developed for pygmy rabbit could be improved by using more accurate soils information, such as SSURGO soils, rather than the coarse-scale CONUS soils layer (0.38-mi2 [247 ac] mapping unit; Appendix 4). For example, our habitat sub-model variable of soil depth resulted in virtually the entire range of pygmy rabbit in the study area receiving a score of 1.0 (i.e., depth >124 in [315 cm]; Table A4.7). The percent clay in soil, however, was more variable, with substantial portions of the range in Montana mapped as moderate to low suitability due to relatively high (>12.5%) clay content. In addition, a vegetation layer that accurately mapped taller varieties of sagebrush, such as basin big sagebrush, would improve the predictive ability of the model (Appendix 6). Even using our simplified land cover map, with all sagebrush types combined as one, we discarded one of our original model variables, sagebrush patch size, for pygmy rabbit. With the minimum mapping unit of 247 acres used in the Wyoming portion of the land cover map (see Chapter 7), essentially any sagebrush pixels were classified as being within a sagebrush patch this size (>247 acres) or larger, rendering this variable meaningless. Consequently, we replaced this variable with a simple land cover type variable (Table A4.7). The addition of climate-based variables would also improve our model, especially in Wyoming, where such variables have proven useful in predicting occurrence of pygmy rabbit (D. Keinath, personal communication). However, our coarse-scale model provides a spatially-based estimate of relative occurrence of pygmy rabbits that can be used in guiding survey efforts and current land-use planning.

Sage Sparrow

Using an empirical modeling approach with BBS data for the habitat sub-model, the density of sagebrush at the large extent (31-mi radius) was the only variable correlated with the

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presence of sage sparrows within the Wyoming Basins study area. Sage sparrows are sensitive to habitat integrity as well as the abundance of sagebrush habitat at large spatial scales (Knick and Rotenberry 1995, Vander Haegen et al. 2000). However, little knowledge exists about how sage sparrows respond to anthropogenic disturbance. Sage sparrows may select against edge habitats, particularly those created by road construction (Ingelfinger and Anderson 2004) or other anthropogenic edges (Bolger et al. (1997). While we did not test for a relationship between anthropogenic edge density and sage sparrow occurrence, we did not find a relationship with natural sagebrush ecotone edges. Similarly, sage sparrows were not associated with greater habitat homogeneity in Idaho (Knick and Rotenberry 2000). The BBS routes we used were all sampled along existing roads, which may have biased the occurrence data relative to roads, and prevented us from building anthropogenic variables into our empirical models. Thus, we only assumed negative consequences of roads due to the direct effect-zone associated with the disturbance for our anthropogenic sub-model, which resulted in minimal reductions in habitat quality for our final sage sparrow occurrence model (Fig. 6.15). Questions about anthropogenic influences on many avian species of concern within sage-steppe habitats need to be addressed. The additional biological field data collected within the Wyoming Basins study area in 2005 and 2006 (Appendix 7) will allow us to address the implications of anthropogenic disturbance on sage sparrow occurrence using an empirical modeling approach.

Sage Thrasher

We chose to take an empirical modeling approach using BBS data to develop the sage thrasher habitat sub-model. Sage thrasher occurrence was correlated with greater abundance of sagebrush at the 3.1-mi scale, and more contiguous sagebrush habitat (i.e. less sagebrush habitat edge). Elsewhere, sage thrashers occurrence was positively correlated with abundance of sagebrush at large (3.1-mi radius) landscape scales, extending beyond the home range scale (Knick and Rotenberry 1995, 1997). Similarly, sage thrasher abundance also increases in more homogeneous sagebrush habitats (Knick and Rotenberry 1995). This highlights the importance of large contiguous tracts of sagebrush habitat (Vander Haegen et al. 2000). The negative relationship with NDVI likely highlights the avoidance of sage thrashers of “greener” habitats

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with higher NDVI values, such as more thickly vegetated riparian and forested areas, rather than sage thrashers avoidance of productive areas within sagebrush habitats. Limited data suggests that sage thrasher abundance may (Knick and Rotenberry 1995, Ingelfinger and Anderson 2004), or may not (Rotenberry and Knick 1995), decrease in relation to anthropogenic disturbance. Because the BBS route data was sampled along roads, we chose not to develop the anthropogenic disturbance sub-model based on BBS data, and rather, used a Delphi-type approach. Thus, we limited the road “effect” zone to habitat directly associated with road or disturbance for our anthropogenic sub-model, which may be a conservative estimate of the effects of roads on sage thrasher abundance. When combined with the habitat sub-model, this disturbance model of road-effects had limited impacts on the overall relative probability of occurrence for sage thrashers (Fig. 6.17). This is a limiting feature of this modeling approach. However, the field sampling design and the additional biological field data collected within the Wyoming Basins study area in 2005 and 2006 (Appendix 7) will afford us an opportunity to address the implications of anthropogenic disturbance on sage thrasher occurrence.

Loggerhead Shrike

Loggerhead shrikes select for more heterogeneous habitats (Yosef 1996). In contrast, our empirical modeling approaches using BBS route data predicted that loggerhead shrike occurrence was negatively correlated with the sagebrush fragmentation measure (amount of “natural” sagebrush edge habitat). However, this metric was likely too coarse to measure the dispersion of sagebrush plants, to which shrikes likely respond. Rather, it captures the sagebrush habitat ecotones, which shrikes appear to strongly avoid (negative correlation with large scale fragmentation). Loggerhead shrike occurrence was also correlated with a greater abundance of sagebrush habitat at a fine scale (270 m). This was similar to the 69-acre patch size that best predicted loggerhead shrike occurrence in Missouri (Esely and Bollinger 2001), and may be directly related to the shrike’s nesting and foraging behaviors. Loggerhead shrikes choose place their nests in sagebrush and other shrubs (Woods and Cade 1996, Yosef 1996), yet given that the bird is a “sit and wait” predator, shrub habitats that provide good perching sites to hunt from (Yosef 1996) and a mix of open habitats to hunt in, are often preferred (Michaels and Cully 1998, Gawlik and Bildstein 1990). This selection for sagebrush habitats with interspersed grassy

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-32 Version 2.0, March 2006 and bare habitats areas resulted in the strong negative correlation with higher NDVI values. Birds select for more open habitats with greater prey abundance (primarily insects) and potentially easier access to those prey (Gawlik and Bildstein 1990, Yosef 1996). Linear anthropogenic habitats like hedgerows, fencelines, and roads, and their enhanced structure from trees, fences, and power lines, provide suitable perch sites for loggerhead shrikes to forage from (Robertson 1930, Bjorge and Prescott 1996, Yosef 1996). We could have tested for associations with these features using our empirical modeling approaches, but the BBS sampling design may bias those results, given that all routes are samples along roads. Intuitively, we could have developed a disturbance sub-model with higher scores associated with these linear features. However, even though shrike occurrence may be increased along these linear features, nest success and survival (fitness) of birds along these habitats can be decreased by as much as 50% (Yosef 1994, 1996; Esely and Bollinger 2001). Thus, we only considered the direct effect zone of roads as negative (see Appendix 4 for description). We will address these questions using our stratified field sampling design and the additional field data collected within the Wyoming Basins study area in 2005 and 2006 (Appendix 7). Finally, roadside survey methods such as the BBS methods used for our analyses, can severely underestimate loggerhead shrike abundance (Bjorge and Prescott 1996), even within 219 yd of roads. Thus, our estimates of high occurrence are likely conservative, especially given our low detection rates for loggerhead shrikes (detected on only 19 of 110 BBS routes). As mentioned above, the additional field data we are collecting (Appendix 7) will afford us an opportunity to better address the implications of anthropogenic disturbance on loggerhead shrike occurrence.

Short-horned Lizard

Model results for short-horned lizards suggest that higher occurrence is limited in the study area (Fig. 6.21). This model was based on limited local-scale research on short-horned lizards, the majority of which was conducted in areas outside of the Wyoming Basins. Relatively little is known about short-horned lizard habitat relationships (but see Pianka and Parker 1975, Powell and Russell 1998, James 2004), and even less about the influence of anthropogenic features on the occurrence and abundance of lizards. Our initial habitat modeling

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approach based on existing literature is a preliminary, coarse attempt to outline short-horned lizard environmental relations and resulting occurrence in the study area. This model, and associated effects of individual predictor variables (i.e. with NDVI, solar radiation, anthropogenic influences), needs to be validated with empirical data. In 2005, and continuing in 2006, we are collecting field data on short-horned lizard occurrence across the study area (see Appendix 7). We will use these data to validate this initial model, but more importantly, the field sampling will allow us to inform the current model with empirical data, improving our understanding of short-horned lizard habitat requirements and responses to anthropogenic developments, thus enhancing our ability to predict their occurrence throughout the Wyoming Basins study area.

Sagebrush Lizard

Our model results suggest that within the current range of sagebrush lizards, there appears to be suitable habitat for this species. However, our model was based on limited empirical data. Fine-scale research has been conducted to assess sagebrush lizard-habitat relationships related to occurrence (Rose 1976b, Morrison and Hall 1999, Knox et al. 2001, James and M’Closkey 2002, Germaine and Germaine 2003), foraging (Rose 1976a, b, Green et al. 2001) and some addressing thermoregulation (Adolph 1990, Werschkul 1982). However, limited research has been conducted to assess the effects an anthropogenic disturbances on lizard occurrence [but see Reynolds (1979) and Knox et al. (2001)]. We used previous research to estimate probabilities of occurrence for sagebrush lizards across broad habitat types. We used surrogate metrics (NDVI and solar radiation) to estimate local habitat relationships at landscape scales, particularly heterogeneous shrub habitats and sites with increased thermal capacity. These relationships need to be tested with field data collected across the study area. In addition, our disturbance sub-model was conservative, limiting negative effects to pixels containing the disturbance, owing to lack of data on how sagebrush lizards respond to anthropogenic disturbances. Field data being collected in 2005 and continuing in 2006 on sagebrush lizard occurrence across the Wyoming Basins study area (Appendix 7) will help us to inform our current model with empirical data, enhancing our ability to predict sagebrush lizard occurrence throughout the Wyoming Basins study area.

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Model Evaluation

Data were collected in the study area during spring and summer 2005, and will be collected again in 2006, to evaluate model predictions for our example species (Appendix 7). In addition, for greater sage-grouse and pronghorn, we will also use existing data (e.g., sage-grouse lek data from Wyoming Game and Fish Department) for model validation. Validation of all species models with field data or existing empirical data will occur later in 2006. Results and conclusions will be presented in the Final Report.

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Yosef, R. 1994. The effects of fencelines on the reproductive success of loggerhead shrikes. Conservation Biology 8: 281–285. Yosef, R. 1996. Loggerhead shrike (Lanius ludovicianus). In A. Poole and F. Gill, editors. The Birds of North America, No. 231. The Academy of Natural Sciences, Philadelphia, and The American Ornithologists’ Union, Washington, DC, USA.

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-44 Version 2.0, March 2006 Table 6.1. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the greater sage-grouse model in the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 34,704 85,718 57.9 17,340 42,831 28.9 3,709 9,161 6.2 4,233 10,456 7.1 59,986 148,166 0.4 Kremmling 51,671 127,627 18.1 169,641 419,013 59.4 25,224 62,304 8.8 39,138 96,672 13.7 285,674 705,615 1.7 Little Snake 500,639 1,236,579 50.3 341,835 844,333 34.4 55,440 136,938 5.6 96,915 239,380 9.7 994,830 2,457,230 6.1 White River 106,968 264,210 68.9 40,954 101,157 26.4 4,468 11,036 2.9 2,952 7,291 1.9 155,342 383,694 0.9 Total 693,981 1,714,134 46.4 569,771 1,407,334 38.1 88,842 219,439 5.9 143,238 353,798 9.6 1,495,832 3,694,705 9.1 Idaho Idaho Falls 102,130 252,262 36.4 116,080 286,718 41.4 32,922 81,316 11.7 29,144 71,985 10.4 280,276 692,282 1.7 Total 102,130 252,262 36.4 116,080 286,718 41.4 32,922 81,316 11.7 29,144 71,985 10.4 280,276 692,282 1.7 Montana Billings 47,707 117,837 34.7 56,732 140,129 41.3 21,608 53,371 15.7 11,265 27,824 8.2 137,312 339,161 0.8 Butte 27,126 67,001 52.0 20,231 49,971 38.8 4,688 11,580 9.0 102 252 0.2 52,148 128,805 0.3 Dillon 249,673 616,692 27.0 409,809 1,012,228 44.3 130,422 322,143 14.1 135,694 335,163 14.7 925,598 2,286,226 5.6 Total 324,506 801,530 29.1 486,773 1,202,329 43.7 156,718 387,093 14.1 147,060 363,239 13.2 1,115,057 2,754,192 6.8 Utah Richfield 1,944 4,802 93.8 121 298 5.8 9 22 0.4 0 0 0.0 2,074 5,122 0.0 Salt Lake 236,310 583,686 38.6 293,733 725,520 48.0 32,911 81,290 5.4 49,328 121,841 8.1 612,282 1,512,337 3.7 Vernal 240,034 592,883 41.1 220,606 544,897 37.8 60,724 149,988 10.4 63,002 155,614 10.8 584,366 1,443,383 3.6 Total 478,288 1,181,371 39.9 514,459 1,270,715 42.9 93,644 231,301 7.8 112,330 277,455 9.4 1,198,721 2,960,842 7.3 Wyoming Buffalo 132,276 326,722 98.1 2,279 5,628 1.7 268 662 0.2 28 70 0.0 134,851 333,083 0.8 Casper 326,198 805,710 48.3 261,683 646,358 38.7 35,975 88,857 5.3 51,879 128,141 7.7 675,735 1,669,066 4.1 Cody 453,782 1,120,842 51.6 277,790 686,142 31.6 54,016 133,421 6.1 93,267 230,369 10.6 878,856 2,170,774 5.4 Kemmerer 338,007 834,878 27.9 655,105 1,618,110 54.0 81,530 201,378 6.7 137,622 339,927 11.4 1,212,265 2,994,294 7.4 Lander 656,678 1,621,993 31.1 996,793 2,462,079 47.2 134,272 331,652 6.4 323,532 799,124 15.3 2,111,275 5,214,849 12.9 Pinedale 222,972 550,741 27.8 430,332 1,062,920 53.6 61,447 151,775 7.7 87,815 216,902 10.9 802,566 1,982,338 4.9 Rawlins 1,245,833 3,077,207 42.2 1,264,425 3,123,129 42.8 158,674 391,925 5.4 284,650 703,086 9.6 2,953,582 7,295,346 18.0 Rock Springs 529,804 1,308,616 24.4 1,125,101 2,779,000 51.8 171,734 424,182 7.9 346,907 856,860 16.0 2,173,546 5,368,658 13.3 Worland 720,419 1,779,435 52.8 446,095 1,101,856 32.7 66,029 163,091 4.8 131,654 325,186 9.7 1,364,197 3,369,567 8.3 Total 4,625,969 11,426,144 37.6 5,459,604 13,485,222 44.4 763,945 1,886,944 6.2 1,457,354 3,599,663 11.8 12,306,872 30,397,974 75.1 Total 6,224,875 15,375,442 38.0 7,146,688 17,652,318 43.6 1,136,070 2,806,094 6.9 1,889,126 4,666,141 11.5 16,396,759 40,499,994 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-45 Version 2.0, March 2006 Table 6.2. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the ferruginous hawk model in the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 317,718 784,765 75.0 82,661 204,173 19.5 19,486 48,131 4.6 3,919 9,679 0.9 423,785 1,046,748 1.2 Kremmling 1,000,266 2,470,656 84.7 156,889 387,516 13.3 21,608 53,373 1.8 2,309 5,704 0.2 1,181,072 2,917,249 3.4 Little Snake 608,296 1,502,492 35.7 614,907 1,518,821 36.1 308,072 760,938 18.1 172,143 425,193 10.1 1,703,419 4,207,444 4.9 Royal Gorge 462,487 1,142,344 86.5 50,832 125,556 9.5 12,068 29,808 2.3 9,445 23,328 1.7 534,832 1,321,036 1.6 White River 228,149 563,527 43.9 164,371 405,996 31.6 60,900 150,423 11.8 65,951 162,899 12.7 519,370 1,282,845 1.5 Total 2,616,916 6,463,784 60.0 1,069,661 2,642,062 24.5 422,135 1,042,673 9.7 253,767 626,803 5.8 4,362,479 10,775,322 12.6 Idaho Idaho Falls 777,315 1,919,968 45.8 782,567 1,932,940 46.2 113,053 279,242 6.7 22,765 56,230 1.3 1,695,700 4,188,379 4.9 Total 777,315 1,919,968 45.8 782,567 1,932,940 46.2 113,053 279,242 6.7 22,765 56,230 1.3 1,695,700 4,188,379 4.9 Montana Billings 327,025 807,751 42.8 189,259 467,470 24.8 102,824 253,975 13.5 144,956 358,041 18.9 764,063 1,887,236 2.2 Butte 1,255,962 3,102,225 48.7 824,545 2,036,627 31.9 269,971 666,827 10.5 230,982 570,526 8.9 2,581,459 6,376,205 7.5 Dillon 735,910 1,817,698 31.7 1,094,919 2,704,450 47.2 387,020 955,938 16.7 101,973 251,872 4.4 2,319,821 5,729,959 6.7 Lewistown 57,265 141,445 40.1 64,832 160,136 45.4 11,788 29,116 8.3 8,806 21,750 6.2 142,691 352,447 0.4 Missoula 793,639 1,960,288 53.5 504,659 1,246,508 34.1 136,199 336,412 9.2 47,617 117,615 3.2 1,482,115 3,660,823 4.3 Total 3,169,801 7,829,407 43.5 2,678,215 6,615,191 36.7 907,801 2,242,269 12.5 534,334 1,319,804 7.3 7,290,150 18,006,670 21.1 Utah Fillmore 11,571 28,580 49.4 11,245 27,776 48.0 600 1,483 2.6 0 0 0.0 23,416 57,838 0.1 Richfield 7,123 17,594 53.0 5,748 14,197 42.8 571 1,410 4.2 0 0 0.0 13,442 33,202 0.1 Salt Lake 1,217,017 3,006,032 63.6 593,261 1,465,355 31.0 88,530 218,669 4.6 16,215 40,050 0.8 1,915,022 4,730,105 5.5 Vernal 666,137 1,645,358 40.5 377,618 932,716 22.9 235,679 582,128 14.3 367,912 908,742 22.3 1,647,346 4,068,944 4.8 Total 1,901,848 4,697,564 52.8 987,872 2,440,044 27.4 325,380 803,689 9.0 384,126 948,792 10.7 3,599,226 8,890,088 10.4 Wyoming Buffalo 264,542 653,419 56.2 145,932 360,452 31.0 45,302 111,895 9.6 15,053 37,181 3.2 470,829 1,162,947 1.4 Casper 225,061 555,900 26.3 333,759 824,384 39.0 168,619 416,488 19.7 128,523 317,451 15.0 855,961 2,114,224 2.5 Cody 1,378,862 3,405,790 56.6 583,770 1,441,912 23.9 274,714 678,543 11.3 200,634 495,565 8.2 2,437,980 6,021,811 7.1 Kemmerer 675,925 1,669,534 41.3 662,122 1,635,442 40.5 192,437 475,320 11.8 105,732 261,157 6.4 1,636,216 4,041,454 4.7 Lander 961,732 2,375,479 36.1 796,888 1,968,313 29.9 465,624 1,150,090 17.5 436,652 1,078,531 16.5 2,660,896 6,572,414 7.7 Pinedale 1,117,339 2,759,828 58.7 651,542 1,609,309 34.2 129,065 318,790 6.7 6,751 16,674 0.4 1,904,696 4,704,600 5.5 Rawlins 1,258,517 3,108,538 32.2 1,526,013 3,769,253 39.1 785,539 1,940,281 20.1 337,756 834,258 8.6 3,907,826 9,652,329 11.3 Rock Springs 407,367 1,006,196 18.7 779,987 1,926,568 35.8 609,046 1,504,344 28.0 382,104 943,796 17.5 2,178,504 5,380,905 6.3 Worland 406,744 1,004,658 26.5 542,761 1,340,619 35.4 322,542 796,679 21.0 262,856 649,255 17.1 1,534,903 3,791,210 4.4 Total 6,696,089 16,539,341 38.1 6,022,774 14,876,253 34.2 2,992,887 7,392,430 17.0 1,876,060 4,633,869 10.7 17,587,811 43,441,893 50.9 Total 15,161,969 37,450,063 43.9 11,541,089 28,506,490 33.4 4,761,256 11,760,303 13.8 3,071,052 7,585,498 8.9 34,535,366 85,302,353 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-46 Version 2.0, March 2006 Table 6.3. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the Brewer’s sparrow model in the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 277,163 684,594 65.4 72,270 178,506 17.1 60,520 149,484 14.3 13,832 34,164 3.2 423,785 1,046,748 1.2 Kremmling 669,337 1,653,262 56.6 139,312 344,100 11.8 251,158 620,359 21.3 121,266 299,527 10.3 1,181,072 2,917,249 3.4 Little Snake 906,954 2,240,176 53.2 220,857 545,517 12.9 350,033 864,582 20.5 225,574 557,169 13.2 1,703,419 4,207,444 4.9 Royal Gorge 526,148 1,299,585 98.4 4,800 11,856 0.9 3,425 8,459 0.6 460 1,136 0.1 534,832 1,321,036 1.6 White River 312,459 771,774 60.2 118,074 291,642 22.7 79,169 195,546 15.2 9,669 23,882 1.9 519,370 1,282,845 1.5 Total 2,692,061 6,649,390 61.7 555,313 1,371,622 12.7 744,304 1,838,431 17.1 370,801 915,878 8.5 4,362,479 10,775,322 12.6 Idaho Idaho Falls 613,113 1,514,390 36.2 568,385 1,403,911 33.5 444,315 1,097,458 26.2 69,887 172,620 4.1 1,695,700 4,188,379 4.9 Total 613,113 1,514,390 36.2 568,385 1,403,911 33.5 444,315 1,097,458 26.2 69,887 172,620 4.1 1,695,700 4,188,379 4.9 Montana Billings 486,758 1,202,293 63.7 139,434 344,402 18.2 127,022 313,744 16.6 10,849 26,797 1.5 764,063 1,887,236 2.2 Butte 2,153,286 5,318,617 83.4 359,212 887,252 13.9 68,137 168,299 2.6 825 2,037 0.1 2,581,459 6,376,205 7.5 Dillon 1,138,728 2,812,658 49.1 514,450 1,270,693 22.2 509,498 1,258,460 21.9 157,145 388,148 6.8 2,319,821 5,729,959 6.7 Lewistown 138,118 341,151 96.8 4,200 10,374 2.9 373 922 0.3 0 0 0.0 142,691 352,447 0.4 Missoula 1,401,775 3,462,383 94.6 64,141 158,427 4.3 13,951 34,458 0.9 2,249 5,554 0.2 1,482,115 3,660,823 4.3 Total 5,318,665 13,137,102 73.0 1,081,437 2,671,149 14.8 718,981 1,775,883 9.9 171,067 422,536 2.3 7,290,150 18,006,670 21.1 Utah Fillmore 18,947 46,798 80.9 4,469 11,038 19.1 1 2 0.0 0 0 0.0 23,416 57,838 0.1 Richfield 1,584 3,911 11.8 7,242 17,888 53.9 4,616 11,402 34.3 0 0 0.0 13,442 33,202 0.1 Salt Lake 973,161 2,403,707 50.8 456,872 1,128,473 23.9 364,003 899,089 19.0 120,986 298,837 6.3 1,915,022 4,730,105 5.5 Vernal 796,887 1,968,311 48.4 285,280 704,643 17.3 435,356 1,075,330 26.4 129,822 320,660 7.9 1,647,346 4,068,944 4.8 Total 1,790,578 4,422,727 49.7 753,863 1,862,041 20.9 803,977 1,985,823 22.3 250,808 619,497 7.0 3,599,226 8,890,088 10.4 Wyoming Buffalo 465,988 1,150,991 98.9 3,661 9,043 0.8 1,179 2,913 0.3 0 0 0.0 470,829 1,162,947 1.4 Casper 376,039 928,817 43.9 104,521 258,166 12.2 215,060 531,198 25.1 160,341 396,043 18.8 855,961 2,114,224 2.5 Cody 1,627,148 4,019,056 66.7 286,755 708,284 11.8 313,113 773,389 12.8 210,965 521,082 8.7 2,437,980 6,021,811 7.1 Kemmerer 164,488 406,284 10.1 415,219 1,025,591 25.4 646,873 1,597,777 39.5 409,636 1,011,802 25.0 1,636,216 4,041,454 4.7 Lander 663,776 1,639,528 24.9 327,073 807,871 12.3 924,106 2,282,543 34.7 745,940 1,842,473 28.1 2,660,896 6,572,414 7.7 Pinedale 790,695 1,953,017 41.5 430,509 1,063,358 22.6 455,303 1,124,599 23.9 228,188 563,625 12.0 1,904,696 4,704,600 5.5 Rawlins 1,518,920 3,751,733 38.9 493,331 1,218,528 12.6 1,110,135 2,742,033 28.4 785,439 1,940,035 20.1 3,907,826 9,652,329 11.3 Rock Springs 147,315 363,867 6.7 267,259 660,129 12.3 966,167 2,386,433 44.4 797,763 1,970,475 36.6 2,178,504 5,380,905 6.3 Worland 658,683 1,626,947 42.9 204,761 505,759 13.3 407,040 1,005,390 26.6 264,419 653,115 17.2 1,534,903 3,791,210 4.4 Total 6,413,053 15,840,240 36.5 2,533,089 6,256,729 14.4 5,038,978 12,446,275 28.7 3,602,692 8,898,649 20.5 17,587,811 43,441,893 50.9 Total 16,827,470 41,563,850 48.8 5,492,086 13,565,452 15.9 7,750,555 19,143,870 22.4 4,465,255 11,029,181 12.9 34,535,366 85,302,353 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-47 Version 2.0, March 2006 Table 6.4. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the pronghorn (with fence) model in the Wyoming Basins Ecoregional Assessment (WBEA) area.a

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 2 6 0.3 549 1,356 57.2 408 1,008 42.5 0 0 0.0 960 2,371 0.0 Kremmling 65,011 160,578 24.6 147,516 364,363 55.8 47,116 116,377 17.8 4,680 11,560 1.8 264,323 652,878 1.9 Little Snake 311,519 769,451 42.5 206,884 511,003 28.2 186,631 460,979 25.5 27,746 68,532 3.8 732,779 1,809,965 5.3 White River 77,246 190,797 75.9 19,167 47,343 18.8 4,623 11,418 4.5 754 1,863 0.7 101,789 251,420 0.7 Total 453,778 1,120,832 41.3 374,116 924,065 34.0 238,778 589,782 21.7 33,180 81,955 3.0 1,099,852 2,716,634 8.0 Idaho 0.0 Idaho Falls 27,066 66,853 71.3 10,282 25,397 27.1 607 1,499 1.6 25 62 0.1 37,979 93,809 0.3 Total 27,066 66,853 71.3 10,282 25,397 27.1 607 1,499 1.6 25 62 0.1 37,979 93,809 0.3 Montana 0.0 Billings 3,989 9,853 33.0 5,072 12,528 41.9 2,815 6,952 23.3 222 548 1.8 12,098 29,882 0.1 Butte 61,328 151,479 31.2 97,925 241,875 49.8 35,361 87,341 18.0 2,193 5,416 1.1 196,806 486,110 1.4 Dillon 206,977 511,233 25.2 416,792 1,029,476 50.7 176,396 435,698 21.5 21,156 52,256 2.6 821,321 2,028,664 6.0 Missoula 35 86 1.3 870 2,149 31.9 1,243 3,071 45.6 576 1,422 21.1 2,724 6,728 0.0 Total 272,328 672,651 26.4 520,659 1,286,028 50.4 215,815 533,063 20.9 24,147 59,643 2.3 1,032,949 2,551,385 7.5 Utah 0.0 Salt Lake 3,008 7,431 2.5 23,588 58,262 19.4 64,146 158,439 52.9 30,624 75,640 25.2 121,366 299,773 0.9 Vernal 262,489 648,349 70.9 69,955 172,788 18.9 32,978 81,454 8.9 4,921 12,154 1.3 370,343 914,746 2.7 Total 265,498 655,779 54.0 93,543 231,051 19.0 97,123 239,894 19.8 35,544 87,795 7.2 491,708 1,214,519 3.6 Wyoming 0.0 Buffalo 24,629 60,833 27.4 44,353 109,552 49.4 18,719 46,236 20.9 2,073 5,120 2.3 89,774 221,742 0.7 Casper 221,194 546,349 33.0 235,483 581,644 35.1 191,732 473,578 28.6 21,661 53,503 3.2 670,070 1,655,073 4.9 Cody 322,953 797,695 47.6 250,100 617,748 36.9 88,107 217,624 13.0 16,645 41,112 2.5 677,806 1,674,180 4.9 Kemmerer 212,905 525,876 21.4 268,914 664,216 27.0 347,454 858,212 34.9 167,659 414,117 16.8 996,932 2,462,422 7.2 Lander 345,771 854,055 24.9 478,821 1,182,688 34.5 471,958 1,165,736 34.0 91,980 227,191 6.6 1,388,530 3,429,670 10.1 Pinedale 220,987 545,837 31.8 312,177 771,078 44.9 129,682 320,314 18.7 32,284 79,742 4.6 695,130 1,716,971 5.0 Rawlins 950,928 2,348,792 29.1 1,129,124 2,788,936 34.5 1,030,005 2,544,112 31.5 158,926 392,547 4.9 3,268,983 8,074,387 23.7 Rock Springs 445,227 1,099,711 21.6 577,042 1,425,293 28.0 780,106 1,926,862 37.8 261,217 645,206 12.7 2,063,592 5,097,071 15.0 Worland 696,223 1,719,672 54.2 375,577 927,675 29.3 171,472 423,536 13.4 40,245 99,405 3.1 1,283,517 3,170,287 9.3 Total 3,440,818 8,498,820 30.9 3,671,591 9,068,829 33.0 3,229,235 7,976,211 29.0 792,689 1,957,943 7.1 11,134,333 27,501,802 80.7 Total 4,459,488 11,014,936 32.3 4,670,190 11,535,370 33.8 3,781,558 9,340,448 27.4 885,586 2,187,397 6.4 13,796,822 34,078,151 100.0 a The “with fence” version of the model was developed with the inclusion of a fence-density variable in the disturbance sub-model; see Appendix 4 for details.

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-48 Version 2.0, March 2006 Table 6.5. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the pygmy rabbit model in the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Idaho Idaho Falls 18,407 45,466 48.3 17,167 42,403 45.0 2,544 6,284 6.7 0 0 0.0 38,119 94,153 0.6 Total 18,407 45,466 48.3 17,167 42,403 45.0 2,544 6,284 6.7 0 0 0.0 38,119 94,153 0.6 Montana Butte 18,793 46,418 85.5 2,155 5,324 9.8 693 1,713 3.2 351 866 1.6 21,992 54,321 0.4 Dillon 848,183 2,095,011 70.3 275,743 681,086 22.9 74,686 184,475 6.2 7,842 19,369 0.6 1,206,454 2,979,941 19.8 Total 866,975 2,141,429 70.6 277,899 686,410 22.6 75,379 186,187 6.1 8,192 20,235 0.7 1,228,446 3,034,262 20.1 Utah Salt Lake 59,289 146,443 36.0 78,150 193,030 47.5 27,239 67,282 16.5 0 0 0.0 164,678 406,754 2.7 Total 59,289 146,443 36.0 78,150 193,030 47.5 27,239 67,282 16.5 0 0 0.0 164,678 406,754 2.7 Wyoming Kemmerer 508,277 1,255,445 47.1 333,475 823,684 30.9 155,433 383,920 14.4 82,302 203,287 7.6 1,079,489 2,666,337 17.7 Lander 110,635 273,270 22.6 205,423 507,396 42.0 156,737 387,141 32.0 16,833 41,579 3.4 489,630 1,209,385 8.0 Pinedale 110,863 273,832 35.5 100,212 247,523 32.1 74,328 183,590 23.8 26,817 66,239 8.6 312,220 771,184 5.1 Rawlins 292,446 722,343 39.5 198,126 489,371 26.8 196,114 484,401 26.5 53,411 131,924 7.2 740,097 1,828,040 12.1 Rock Springs 663,459 1,638,743 32.3 402,954 995,296 19.6 452,118 1,116,731 22.0 534,453 1,320,100 26.0 2,052,984 5,070,870 33.6 Total 1,685,681 4,163,633 36.1 1,240,190 3,063,270 26.5 1,034,730 2,555,784 22.1 713,817 1,763,129 15.3 4,674,419 11,545,816 76.6 Total 2,630,353 6,496,971 43.1 1,613,406 3,985,112 26.4 1,139,894 2,815,537 18.7 722,010 1,783,364 11.8 6,105,662 15,080,984 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-49 Version 2.0, March 2006 Table 6.6. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the sage sparrow model in the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 338,781 837,146 79.9 85,004 210,049 20.1 0 0 0.0 0 0 0.0 423,785 1,047,195 1.2 Kremmling 443,997 1,097,140 37.6 737,076 1,821,354 62.4 0 0 0.0 0 0 0.0 1,181,072 2,918,493 3.4 Little Snake 271,821 671,685 16.0 1,048,978 2,592,082 61.6 361,275 892,729 21.2 21,344 52,743 1.3 1,703,419 4,209,239 4.9 Royal Gorge 534,832 1,321,600 100.0 0 0 0.0 0 0 0.0 0 0 0.0 534,832 1,321,600 1.5 White River 139,318 344,263 26.8 380,052 939,129 73.2 0 0 0.0 0 0 0.0 519,370 1,283,392 1.5 Total 1,728,750 4,271,834 40 2,251,110 5,562,614 52 361,275 892,729 8 21,344 52,743 0 4,362,479 10,779,920 12.6 Idaho Idaho Falls 467,417 1,155,013 27.6 1,152,359 2,847,542 68.0 74,629 184,411 4.4 1,295 3,200 0.1 1,695,700 4,190,166 4.9 Total Montana Billings 497,360 1,229,004 65.1 266,703 659,038 34.9 0 0 0.0 0 0 0.0 764,063 1,888,042 2.2 Butte 2,581,459 6,378,925 100.0 0 0 0.0 0 0 0.0 0 0 0.0 2,581,459 6,378,925 7.5 Dillon 858,474 2,121,337 37.0 1,102,480 2,724,287 47.5 358,867 886,780 15.5 0 0 0.0 2,319,821 5,732,404 6.7 Lewistown 142,691 352,598 100.0 0 0 0.0 0 0 0.0 0 0 0.0 142,691 352,598 0.4 Missoula 1,482,115 3,662,385 100.0 0 0 0.0 0 0 0.0 0 0 0.0 1,482,115 3,662,385 4.3 Total 5,562,100 13,744,248 76 1,369,183 3,383,324 19 358,867 886,780 5 0 0 0 7,290,150 18,014,353 21.1 Utah Fillmore 1,060 2,620 4.5 22,356 55,243 95.5 0 0 0.0 0 0 0.0 23,416 57,863 0.1 Richfield 2,084 5,150 15.5 11,358 28,066 84.5 0 0 0.0 0 0 0.0 13,442 33,216 0.0 Salt Lake 493,859 1,220,351 25.8 1,155,249 2,854,682 60.3 180,927 447,081 9.4 84,988 210,009 4.4 1,915,022 4,732,123 5.5 Vernal 131,435 324,782 8.0 1,446,428 3,574,200 87.8 69,483 171,697 4.2 0 0 0.0 1,647,346 4,070,680 4.8 Total 628,438 1,552,903 17 2,635,390 6,512,191 73 250,411 618,778 7 84,988 210,009 2 3,599,226 8,893,882 10.4 Wyoming Buffalo 228,563 564,792 48.5 242,265 598,651 51.5 0 0 0.0 0 0 0.0 470,829 1,163,443 1.4 Casper 142,199 351,381 16.6 336,744 832,112 39.3 155,377 383,946 18.2 221,641 547,687 25.9 855,961 2,115,126 2.5 Cody 1,451,503 3,586,742 59.5 769,140 1,900,587 31.5 217,337 537,051 8.9 0 0 0.0 2,437,980 6,024,380 7.1 Kemmerer 170,060 420,226 10.4 359,636 888,680 22.0 244,445 604,037 14.9 862,076 2,130,236 52.7 1,636,216 4,043,178 4.7 Lander 269,038 664,808 10.1 594,247 1,468,416 22.3 703,061 1,737,303 26.4 1,094,550 2,704,691 41.1 2,660,896 6,575,218 7.7 Pinedale 689,927 1,704,847 36.2 631,794 1,561,198 33.2 283,179 699,751 14.9 299,796 740,811 15.7 1,904,696 4,706,607 5.5 Rawlins 1,162,456 2,872,492 29.7 842,595 2,082,098 21.6 488,294 1,206,601 12.5 1,414,480 3,495,257 36.2 3,907,826 9,656,447 11.3 Rock Springs 226,022 558,512 10.4 34,543 85,358 1.6 343,483 848,765 15.8 1,574,456 3,890,566 72.3 2,178,504 5,383,201 6.3 Worland 297,685 735,595 19.4 795,990 1,966,935 51.9 441,228 1,090,298 28.7 0 0 0.0 1,534,903 3,792,828 4.4 Total 4,637,453 11,459,395 26.4 4,606,955 11,384,034 26.2 2,876,405 7,107,751 16.4 5,466,999 13,509,248 31.1 17,587,811 43,460,429 50.9 Total 13,024,157 32,183,393 37.7 12,014,997 29,689,706 34.8 3,921,586 9,690,450 11.4 5,574,626 13,775,201 16.1 34,535,366 85,338,749 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-50 Version 2.0, March 2006 Table 6.7. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the sage thrasher model in the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 90,348 223,255 21.3 246,118 608,170 58.1 81,832 202,211 19.3 5,487 13,559 1.3 423,785 1,047,195 1.2 Kremmling 635,286 1,569,827 53.8 208,257 514,615 17.6 190,621 471,034 16.1 146,908 363,018 12.4 1,181,072 2,918,493 3.4 Little Snake 331,974 820,325 19.5 583,158 1,441,015 34.2 509,390 1,258,731 29.9 278,897 689,169 16.4 1,703,419 4,209,239 4.9 Royal Gorge 485,827 1,200,506 90.8 47,105 116,398 8.8 1,714 4,235 0.3 186 460 0.0 534,832 1,321,600 1.5 White River 110,657 273,440 21.3 299,877 741,013 57.7 108,563 268,264 20.9 273 675 0.1 519,370 1,283,392 1.5 Total 1,654,093 4,087,353 37.9 1,384,515 3,421,212 31.7 892,119 2,204,475 20.4 431,751 1,066,880 9.9 4,362,479 10,779,920 12.6 Idaho Idaho Falls 285,492 705,466 16.8 1,028,335 2,541,070 60.6 349,457 863,526 20.6 32,417 80,104 1.9 1,695,700 4,190,166 4.9 Total Montana Billings 355,781 879,154 46.6 348,015 859,964 45.5 59,815 147,807 7.8 452 1,117 0.1 764,063 1,888,042 2.2 Butte 1,285,396 3,176,283 49.8 1,173,473 2,899,715 45.5 114,043 281,807 4.4 8,547 21,120 0.3 2,581,459 6,378,925 7.5 Dillon 667,400 1,649,182 28.8 991,790 2,450,766 42.8 612,864 1,514,420 26.4 47,767 118,036 2.1 2,319,821 5,732,404 6.7 Lewistown 77,456 191,399 54.3 61,653 152,348 43.2 3,582 8,851 2.5 0 0 0.0 142,691 352,598 0.4 Missoula 793,354 1,960,420 53.5 628,493 1,553,039 42.4 59,783 147,727 4.0 485 1,199 0.0 1,482,115 3,662,385 4.3 Total 3,179,388 7,856,438 43.6 3,203,423.6 7,915,832 43.9 850,087 2,100,611 11.7 57,252 141,472 0.8 7,290,150 18,014,353 21.1 Utah Fillmore 5,770 14,257 24.6 17,464 43,153 74.6 183 452 0.8 0 0 0.0 23,416 57,863 0.1 Richfield 4,881 12,061 36.3 8,561 21,154 63.7 0 0 0.0 0 0 0.0 13,442 33,216 0.0 Salt Lake 464,113 1,146,848 24.2 1,123,742 2,776,827 58.7 218,762 540,572 11.4 108,406 267,876 5.7 1,915,022 4,732,123 5.5 Vernal 623,637 1,541,040 37.9 850,818 2,102,418 51.6 155,218 383,552 9.4 17,673 43,670 1.1 1,647,346 4,070,680 4.8 Total 1,098,401 2,714,207 30.5 2,000,585 4,943,553 55.6 374,162 924,576 10.4 126,078 311,546 3.5 3,599,226 8,893,882 10.4 Wyoming Buffalo 280,164 692,299 59.5 166,273 410,869 35.3 18,636 46,050 4.0 5,757 14,225 1.2 470,829 1,163,443 1.4 Casper 174,599 431,443 20.4 291,200 719,571 34.0 186,647 461,214 21.8 203,516 502,898 23.8 855,961 2,115,126 2.5 Cody 352,007 869,827 14.4 1,359,412 3,359,181 55.8 549,687 1,358,306 22.5 176,874 437,065 7.3 2,437,980 6,024,380 7.1 Kemmerer 305,553 755,038 18.7 332,673 822,054 20.3 527,073 1,302,427 32.2 470,916 1,163,659 28.8 1,636,216 4,043,178 4.7 Lander 463,588 1,145,551 17.4 601,695 1,486,820 22.6 769,984 1,902,673 28.9 825,629 2,040,174 31.0 2,660,896 6,575,218 7.7 Pinedale 406,734 1,005,062 21.4 777,021 1,920,060 40.8 428,005 1,057,623 22.5 292,937 723,862 15.4 1,904,696 4,706,607 5.5 Rawlins 800,974 1,979,250 20.5 1,063,843 2,628,813 27.2 956,876 2,364,491 24.5 1,086,133 2,683,893 27.8 3,907,826 9,656,447 11.3 Rock Springs 227,513 562,196 10.4 201,794 498,645 9.3 753,578 1,862,131 34.6 995,619 2,460,228 45.7 2,178,504 5,383,201 6.3 Worland 197,903 489,030 12.9 568,748 1,405,407 37.1 544,586 1,345,701 35.5 223,666 552,691 14.6 1,534,903 3,792,828 4.4 Total 3,209,035 7,929,697 18.2 5,362,659 13,251,420 30.5 4,735,071 11,700,616 26.9 4,281,046 10,578,695 24.3 17,587,811 43,460,429 50.9 Total 9,426,407 23,293,160 27.3 12,979,518 32,073,088 37.6 7,200,897 17,793,804 20.9 4,928,544 12,178,697 14.3 34,535,366 85,338,749 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-51 Version 2.0, March 2006 Table 6.8. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the loggerhead shrike model in the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 166,704 411,934 39.3 194,462 480,525 45.9 43,690 107,960 10.3 18,930 46,776 4.5 423,785 423,785 1.2 Kremmling 746,448 1,844,514 63.2 211,042 521,497 17.9 147,545 364,591 12.5 76,037 187,892 6.4 1,181,072 1,181,055 3.4 Little Snake 464,094 1,146,800 27.2 559,538 1,382,649 32.8 346,803 856,969 20.4 332,984 822,821 19.5 1,703,419 1,702,797 4.9 Royal Gorge 442,176 1,092,640 82.7 62,330 154,022 11.7 24,505 60,553 4.6 5,821 14,385 1.1 534,832 534,829 1.5 White River 225,964 558,369 43.5 224,870 555,665 43.3 50,634 125,119 9.7 17,903 44,238 3.4 519,370 519,370 1.5 Total 2,045,385 5,054,257 46.9 1,252,242 3,094,358 28.7 613,176 1,515,192 14.1 451,675 1,116,112 10.4 4,362,479 4,361,836 12.6 Idaho Idaho Falls 1,365,470 3,374,149 80.5 270,993 669,638 16.0 45,758 113,070 2.7 13,480 33,310 0.8 1,695,700 1,695,700 4.9 Total Montana Billings 566,603 1,400,107 74.2 97,045 239,804 12.7 57,494 142,070 7.5 42,921 106,060 5.6 764,063 764,062 2.2 Butte 1,717,795 4,244,763 66.5 641,117 1,584,234 24.8 155,495 384,236 6.0 67,053 165,693 2.6 2,581,459 2,581,459 7.5 Dillon 1,779,221 4,396,551 76.7 455,367 1,125,237 19.6 63,779 157,602 2.7 21,454 53,013 0.9 2,319,821 2,319,821 6.7 Lewistown 62,629 154,760 43.9 48,186 119,070 33.8 25,102 62,028 17.6 6,774 16,739 4.7 142,691 142,691 0.4 Missoula 682,136 1,685,594 46.0 608,926 1,504,688 41.1 137,842 340,614 9.3 53,211 131,488 3.6 1,482,115 1,482,115 4.3 Total 4,808,384 11,881,775 66.0 1,850,641.0 4,573,034 25.4 439,712 1,086,551 6.0 191,414 472,993 2.6 7,290,150 7,290,149 21.1 Utah Fillmore 20,421 50,461 87.2 2,995 7,400 12.8 1 2 0.0 0 0 0.0 23,416 23,416 0.1 Richfield 13,442 33,216 100.0 0 0 0.0 0 0 0.0 0 0 0.0 13,442 13,442 0.0 Salt Lake 1,359,310 3,358,927 71.0 405,171 1,001,199 21.2 93,819 231,832 4.9 56,723 140,165 3.0 1,915,022 1,915,022 5.5 Vernal 1,221,415 3,018,183 74.1 309,611 765,066 18.8 97,562 241,081 5.9 18,757 46,350 1.1 1,647,346 1,647,346 4.8 Total 2,614,588 6,460,787 72.6 717,777 1,773,665 19.9 191,382 472,915 5.3 75,480 186,515 2.1 3,599,226 3,599,226 10.4 Wyoming Buffalo 214,899 531,028 45.6 160,730 397,172 34.1 66,196 163,573 14.1 29,004 71,670 6.2 470,829 470,829 1.4 Casper 274,581 678,505 32.1 258,320 638,323 30.2 133,073 328,831 15.5 189,986 469,466 22.2 855,961 855,961 2.5 Cody 719,972 1,779,089 29.5 911,648 2,252,731 37.4 554,916 1,371,228 22.8 251,444 621,332 10.3 2,437,980 2,437,979 7.1 Kemmerer 913,479 2,257,256 55.8 301,280 744,478 18.4 265,931 657,130 16.3 155,526 384,314 9.5 1,636,216 1,636,216 4.7 Lander 924,975 2,285,662 34.8 658,123 1,626,258 24.7 459,880 1,136,388 17.3 617,918 1,526,909 23.2 2,660,896 2,660,896 7.7 Pinedale 1,162,762 2,873,248 61.0 393,970 973,522 20.7 142,158 351,281 7.5 205,806 508,557 10.8 1,904,696 1,904,696 5.5 Rawlins 1,056,788 2,611,379 27.0 1,202,846 2,972,297 30.8 590,107 1,458,186 15.1 1,058,085 2,614,585 27.1 3,907,826 3,907,778 11.3 Rock Springs 697,121 1,722,623 32.0 496,331 1,226,460 22.8 343,065 847,732 15.7 641,987 1,586,385 29.5 2,178,504 2,178,484 6.3 Worland 467,079 1,154,178 30.4 460,772 1,138,592 30.0 384,227 949,445 25.0 222,825 550,613 14.5 1,534,903 1,534,903 4.4 Total 6,431,656 15,892,968 36.6 4,844,020 11,969,833 27.5 2,939,553 7,263,794 16.7 3,372,582 8,333,833 19.2 17,587,811 17,587,742 50.9 Total 17,265,482 42,663,936 50.0 8,935,672 22,080,527 25.9 4,229,581 10,451,523 12.2 4,104,630 10,142,763 11.9 34,535,366 34,534,654 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-52 Version 2.0, March 2006 Table 6.9. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the short-horned lizard model restricted to their distribution (Stebbins 2003) within the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrence Very Low Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 53,112 131,242 67.2 3,788 9,359 4.8 22,153 54,740 28.0 0 0 0.0 79,052 195,342 0.4 Little Snake 375,951 928,995 36.5 10,427 25,766 1.0 639,331 1,579,820 62.1 3,694 9,129 0.4 1,029,403 2,543,710 5.3 Royal Gorge 69,401 171,493 91.8 1,772 4,379 2.3 2,315 5,720 3.1 2,123 5,246 2.8 75,611 186,839 0.4 White River 242,945 600,330 50.7 8,055 19,905 1.7 225,545 557,333 47.1 2,615 6,463 0.5 479,160 1,184,031 2.5 Total 741,408 1,832,060 44.6 24,042 59,410 1.4 889,343 2,197,614 53.5 8,433 20,838 0.5 1,663,226 4,109,922 8.6 Idaho Idaho Falls 47,264 116,791 54.6 1,866 4,612 2.2 36,910 91,207 42.6 573 1,417 0.7 86,613 214,026 0.4 Total 47,264 116,791 54.6 1,866 4,612 2.2 36,910 91,207 42.6 573 1,417 0.7 86,613 214,026 0.4 Montana Billings 515,757 1,274,463 67.5 80,589 199,141 10.5 158,139 390,769 20.7 9,577 23,666 1.3 764,062 1,888,040 3.9 Butte 1,464,391 3,618,589 76.6 360,838 891,650 18.9 76,354 188,674 4.0 9,719 24,017 0.5 1,911,302 4,722,930 9.9 Dillon 146,877 362,942 62.9 70,634 174,539 30.3 14,996 37,055 6.4 974 2,408 0.4 233,481 576,944 1.2 Lewistown 79,492 196,428 82.1 16,010 39,561 16.5 1,221 3,018 1.3 144 356 0.1 96,867 239,364 0.5 Missoula 80,252 198,308 70.0 28,037 69,280 24.5 5,597 13,831 4.9 704 1,739 0.6 114,590 283,158 0.6 Total 2,286,769 5,650,730 73.3 556,107.1 1,374,171 17.8 256,307 633,348 8.2 21,119 52,187 0.7 3,120,302 7,710,435 16.1 Utah Fillmore 15,881 39,242 67.8 1,582 3,909 6.8 5,932 14,657 25.3 22 54 0.1 23,416 57,863 0.1 Richfield 8,666 21,415 64.5 1,142 2,822 8.5 3,634 8,979 27.0 0 0 0.0 13,442 33,216 0.1 Salt Lake 1,380,695 3,411,772 72.1 106,754 263,795 5.6 420,503 1,039,087 22.0 7,069 17,468 0.4 1,915,021 4,732,121 9.9 Vernal 842,240 2,081,219 51.1 75,471 186,493 4.6 685,772 1,694,579 41.6 43,863 108,388 2.7 1,647,346 4,070,680 8.5 Total 2,247,482 5,553,649 62.4 184,949 457,019 5.1 1,115,841 2,757,302 31.0 50,954 125,910 1.4 3,599,225 8,893,880 18.6 Wyoming Buffalo 160,971 397,769 79.3 41,906 103,552 20.6 220 544 0.1 12 30 0.0 203,110 501,896 1.0 Casper 309,859 765,678 53.8 93,531 231,119 16.3 171,228 423,114 29.8 799 1,974 0.1 575,417 1,421,886 3.0 Cody 575,178 1,421,295 52.7 26,169 64,666 2.4 480,633 1,187,670 44.0 9,665 23,883 0.9 1,091,645 2,697,514 5.6 Kemmerer 483,871 1,195,670 44.2 19,270 47,617 1.8 590,750 1,459,775 53.9 1,614 3,989 0.1 1,095,505 2,707,051 5.7 Lander 838,830 2,072,795 46.1 29,859 73,783 1.6 949,414 2,346,053 52.1 3,110 7,684 0.2 1,821,213 4,500,315 9.4 Pinedale 118,523 292,877 59.7 10,025 24,773 5.1 68,183 168,485 34.4 1,725 4,263 0.9 198,457 490,399 1.0 Rawlins 1,226,694 3,031,227 41.9 338,170 835,637 11.6 1,358,466 3,356,844 46.5 1,170 2,890 0.0 2,924,500 7,226,597 15.1 Rock Springs 789,289 1,950,376 37.5 8,426 20,820 0.4 1,309,219 3,235,151 62.1 399 985 0.0 2,107,332 5,207,332 10.9 Worland 357,516 883,442 40.0 21,309 52,655 2.4 511,015 1,262,746 57.2 3,962 9,790 0.4 893,802 2,208,632 4.6 Total 4,860,731 12,011,129 44.5 588,665 1,454,623 5.4 5,439,130 13,440,383 49.9 22,455 55,487 0.2 10,910,981 26,961,622 56.3 Total 10,183,655 25,164,359 52.5 1,355,630 3,349,834 7.0 7,737,530 19,119,853 39.9 103,534 255,839 0.5 19,380,348 47,889,885 100.0

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-53 Version 2.0, March 2006 Table 6.10. Summary of landcover within each class of probability of occurrence, by BLM Field Office, for the sagebrush lizard model restricted to their distribution (Stebbins 2003) within the Wyoming Basins Ecoregional Assessment (WBEA) area.

Probability of occurrencea Low Moderate High Total BLM Field Office Hectares Acres % Hectares Acres % Hectares Acres % Hectares Acres % Colorado Glenwood Springs 168,592 416,599 53.7 33,983 83,973 10.8 111,102 274,539 35.4 313,677 775,112 1.5 Little Snake 438,156 1,082,706 32.3 365,491 903,147 27.0 551,499 1,362,784 40.7 1,355,145 3,348,637 6.6 White River 216,819 535,772 41.7 71,670 177,099 13.8 230,882 570,521 44.5 519,370 1,283,392 2.5 Total 823,567 2,035,078 37.6 471,143 1,164,220 21.5 893,483 2,207,844 40.8 2,188,192 5,407,141 10.7 Idaho Idaho Falls 945,522 2,336,436 66.6 209,412 517,468 14.8 264,783 654,294 18.7 1,419,717 3,508,197 6.9 Total 945,522 2,336,436 66.6 209,412 517,468 14.8 264,783 654,294 18.7 1,419,717 3,508,197 6.9 Montana Billings 369,344 912,668 66.1 27,725 68,509 5.0 161,722 399,624 28.9 558,791 1,380,802 2.7 Butte 439,301 1,085,537 95.0 7,858 19,417 1.7 15,401 38,058 3.3 462,560 1,143,011 2.3 Total 808,644.9 1,998,205 79.2 35,582 87,926 3.5 177,124 437,682 17.3 1,021,351 2,523,813 5.0 Utah Fillmore 16,887 41,728 72.1 739 1,825 3.2 5,791 14,309 24.7 23,416 57,863 0.1 Richfield 8,401 20,758 62.5 0 0 0.0 5,041 12,458 37.5 13,442 33,216 0.1 Salt Lake 1,378,191 3,405,583 72.0 132,927 328,469 6.9 403,905 998,071 21.1 1,915,022 4,732,123 9.4 Vernal 722,153 1,784,479 43.8 153,274 378,748 9.3 771,919 1,907,453 46.9 1,647,346 4,070,680 8.0 Total 2,125,631 5,252,549 59.1 286,939 709,042 8.0 1,186,656 2,932,290 33.0 3,599,226 8,893,882 17.6 Wyoming Buffalo 24,834 61,366 99.5 131 324 0.5 0 0 0.0 24,965 61,690 0.1 Casper 385,208 951,869 52.9 13,851 34,227 1.9 329,681 814,658 45.2 728,739 1,800,754 3.6 Cody 503,933 1,245,247 58.7 15,026 37,131 1.8 339,618 839,215 39.6 858,578 2,121,593 4.2 Kemmerer 680,904 1,682,550 41.9 316,428 781,911 19.5 627,999 1,551,819 38.6 1,625,331 4,016,279 7.9 Lander 929,190 2,296,078 37.7 142,716 352,658 5.8 1,391,893 3,439,442 56.5 2,463,798 6,088,178 12.0 Pinedale 218,894 540,898 52.2 67,668 167,212 16.1 133,093 328,879 31.7 419,655 1,036,989 2.1 Rawlins 1,281,686 3,167,116 41.8 525,141 1,297,651 17.1 1,260,981 3,115,953 41.1 3,067,809 7,580,720 15.0 Rock Springs 447,137 1,104,900 21.7 287,293 709,917 14.0 1,321,990 3,266,708 64.3 2,056,420 5,081,524 10.0 Worland 287,982 711,618 28.9 19,899 49,172 2.0 687,816 1,699,631 69.1 995,697 2,460,422 4.9 Total 4,759,767 11,761,642 38.9 1,388,154 3,430,203 11.3 6,093,070 15,056,305 49.8 12,240,991 30,248,149 59.8 Total 9,463,132 23,383,909 46.2 2,391,230 5,908,859 11.7 8,615,115 21,288,414 42.1 20,469,478 50,581,182 100.0 aUsing the mean and standard deviation to delineate probability of occurrence classes resulted in only 3 classes, which we refer to as low, moderate, and high.

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 6-54 Version 2.0, March 2006

Habitat Sub-Model Disturbance Sub-Model Example Variables: Example Variables: • Amount of Sagebrush • Distance to Powerlines • Distance to Juniper Ecotone • Distance to Oil and Gas Wells • Percent Slope • Distance to Roads • Elevation • Fence Density • Soil Depth • Density of Agricultural Lands

Habitat Output Anthropogenic Output

Product of Outputs

Predicted Probability of Occurrence

Fig. 6.1. Conceptual diagram of modeling approach for predicting probability of occurrence for example vertebrate species of concern in the Wyoming Basins Ecoregional Assessment.

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Fig. 6.2. Greater sage-grouse probability of occurrence, based on the habitat sub-model, in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Fig. 6.3. Greater sage-grouse probability of occurrence, based on the disturbance sub-model, in the Wyoming Basins Ecoregional Assessment (WBEA) area

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Fig. 6.4. Greater sage-grouse probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Greater Sage-Grouse High Moder ate Low Very Low 100% ass

l 90% C 80% e c 70% en r

r 60% u

c 50% c

O 40% n i 30% h t i 20% 10% ea W r 0% A r n e s e e r o ing er ll e te d o A ngs de ill l er nal ody and lins ngs ak per iv t iel al E ri n m r Fa nak l i s ings u f ff D m C S r ll t L a r B p La m inedal Ve o aw Bi l C p e R ch Bu WB S e m P le W R S it k e tt Sa d Ri c Kr K Idaho o Li o Wh Ro lenw G Field Office

Fig. 6.5. Percent area within each class of probability of occurrence, by BLM Field Office, for greater sage-grouse in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area.

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Fig. 6.6. Ferruginous hawk probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Ferruginous Hawk High Moderate Low Very Low

100%

ass 90% l 80%

ce C 70% n e

r 60%

ccu 50%

O 40% n i

h 30% t i 20%

ea W 10% r

A 0% r r e s s e e te s er n a o e lls e ng d re A nal g er pe v t in n llo al rg li el o r n ing land i nak ody er oul f o Fa ings fi e lli r r as R Bu wl C m tow Di s f r Lak m lm BE V Bi p e S s s G p lt nedal m ch il W S Land C it le Ra i Bu S a e F k Wo tt em Mi al S Pi Ri K oy Idaho Kr oc Wh Li Lew ood R R lenw G Field Office

Fig. 6.7. Percent area within each class of probability of occurrence, by BLM Field Office, for ferruginous hawk in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area.

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Fig. 6.8. Brewer’s sparrow probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Brewer's Sparrow High Moderate Low Very Low 100% ass

l 90% 80% ce C

n 70% e r 60% cu

c 50%

O 40% n i

h 30% t i 20% 10% ea W r 0% A e e y l e ls s a e e d er er nd ing d a on l er g g t n lo re ngs er lins per l n ill a iv r t iel a o EA ri s la nak m r Lak ings in oul o f ff m B m a r Co D t F R ll s Bu h ow ll p Land aw C S nedal m Ve l pr Bi is l G ic ist Bu W S R Wo le Pi e S ite a Fi k em tt Sa h M y R c K Kr Idaho Li ood W Lew Ro Ro lenw G Field Office

Fig. 6.9. Percent area within each class of probability of occurrence, by BLM Field Office, for Brewer’s sparrow in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area.

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Fig. 6.10. Pronghorn probability of occurrence as predicted from models with the fence density variable excluded versus included in the Wyoming Basins Ecoregional Assessment (WBEA) area. The upper map (“without fences”) displays the current range of pronghorn in the study area.

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Pronghorn - With Fence High Moder ate Low Very Low

100%

90% ass l 80% e C c

n 70% e r 60% cu

c 50% O

n 40% i h t i 30% 20% ea W r 10% A 0% e a r s r e e r d n y s g l e r ls s A k l re g e s l k e n lo d lo g in a tt e l g a u e n d lin a a p il o a n l n iv a n E L o ri n d n s la ff li m r u F i B t s m p a w e S a r D C u il e B R r l is L a in C o B B m V e o p W a m S R P le W e it h S S M e k tt r h a d K c i K Id o o L W o R w n le Field Office G

Fig. 6.11. Percent area within each class of probability of occurrence, by BLM Field Office, for pronghorn (fence variable included) in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on combined percent area in the high and moderate classes; the last bar displays results for the entire WBEA area.

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40 Without Fence With Fence

35

30

25

20 Area (%)

15

10

5

0 Very Low Low Moderate High Probability of Occurrence

Fig. 6.12. Comparison of probability of occurrence for pronghorn in the Wyoming Basins Ecoregional Assessment (WBEA) area between a model with fence density excluded and one with fence density included.

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Fig. 6.13. Pygmy rabbit probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Pygmy Rabbit High Moderate Low Very Low 100% 90% ass l 80%

ce C 70% n e

r 60% 50% ccu

O 40% n i 30% h t i 20% 10% ea W r

A 0% e e n e ls er ns er tt o l ings dal er li ill EA r m Bu D Lak Fa p ne aw lt WB S m R Land a k Pi S c Ke Idaho Ro Field Office

Fig. 6.14. Percent area within each class of probability of occurrence, by BLM Field Office, for pygmy rabbit in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area.

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Fig. 6.15. Sage sparrow relative probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Sage Sparrow

High Moderate Low Very Low

100% ass l 90% 80% ce C

n 70% e r 60%

ccu 50%

O 40% in

h 30% t i 20%

ea W 10% r

A 0%

e e e ls g e te a e ld A er er ns l n er lon n o er li per li rg iv ings ut l or ie nal al ody and E ings s Lak nak ings o ll w oul m f r ff l r m and w a Fa r m i B Di to s ll h e C r p L nedal lt S p m G B s s c V WB S Ra C e e l ite R i Fi Bu em Pi Sa tl aho S a Mi Ri Wo k K it Kr y ew c L Id od Wh L Ro o Ro lenw G Field Office

Fig. 6.16. Percent area within each class of probability of occurrence, by BLM Field Office, for the sage sparrow in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area.

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Fig. 6.17. Sage thrasher relative probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Sage Thrasher

High Moderate Low Very Low

100% ass l 90% 80% ce C

n 70% e r 60%

ccu 50%

O 40% in

h 30% t i 20% 10% ea W

r 0% A r e s e e n ls o te r e a re ld er r n ke ing lo l s l t e n e EA ngs e li per and l ody l g fa nal ings iv rg o i i s na rl m C Di Fa in r Bu ll o oul m f B r m w a t Lak uf R s tow ll h p Land C S nedal l pr B Ve Bi e G is s c W S Ra e Pi Wo em a S it l i Fi Ri k em tl S a M K t Kr Idaho y oc Li Wh Lew R ood Ro lenw G Field Office

Fig. 6.18. Percent area within each class of probability of occurrence, by BLM Field Office, for the sage thrasher in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area.

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Fig. 6.19. Loggerhead shrike relative probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area.

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Loggerhead Shrike

High Moderate Low Very Low

ass 100% l 90%

ce C 80% n

e 70% r 60%

ccu 50%

O 40% in

h 30% t i 20% 10% ea W

r 0% A e r r e s e s er e dy e ng lo a e t on ll r d A in per k r li ngs n ve t nal ge ll a o el E ngs l a land o e fa li oul r r fi i w s n r C m m f l ow ings s Ri Lak Bu o Di F lm h pr a nedal t r s lt l WB Land C S m Bu Bi is p i te Ve l G Fi ic S Ra le Wo Pi em S M i a a R k tt Ke Kr S y c Li Lew Wh Idaho Ro ood Ro lenw G Field Office

Fig. 6.20. Percent area within each class of probability of occurrence, by BLM Field Office, for the loggerhead shrike in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area.

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Fig. 6.21. Short-horned lizard relative probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area. Predictions are clipped to the distribution of short- horned lizards within the WBEA (Stebbins 2003).

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Short-horned Lizard

High Moderate Low Very Low

100% s s

a 90% l 80%

ce C 70%

ren 60% u c

c 50% O 40% n i h

t 30% i 20% W 10%

Area 0%

r e y e s a r te d n e r r e s ld A g gs d ll l e t n o k ke e re r lo s r nal in al u v ill a a d pe n a g ie E o r ll d o i la n wn s mo li ings ff n f e Co e Fa s Bu r D L an to me a ll w r i h V Bi in o s lt S L s i p pr c WB l G ite R e i m C F Ra S Bu a P ah Mi Wo Sa tl e S Ri y d t ew K I Wh Li L ck od Ro o o R w n le G Field Office

Fig. 6.22. Percent area within each class of probability of occurrence, by BLM Field Office, for the short-horned lizard in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area. Area summaries are based on model predictions clipped to the distribution of short-horned lizards within the WBEA (Stebbins 2003).

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Fig. 6.23. Sagebrush lizard relative probability of occurrence in the Wyoming Basins Ecoregional Assessment (WBEA) area. Predictions are clipped to the distribution of sagebrush lizards within the WBEA (Stebbins 2003).

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Sagebrush Lizard

High Moderate Low

100%

ass 90% 80% ce Cl

n 70% e r 60% u c

c 50% O 40% n i h

t 30% i 20%

ea W 10%

Ar 0%

s l r r s e y r d e s e e ls e A nd er a e e k d re l s l g r l t lo g d n p v n a o e ie g in o ak ut a E a in n s i li n f da ll m ff rl r a er C m h rin i ll L Fa B u L V aw S ic p ine B t o WB Ca ite R R e Fi al h B Wo Sp R S P S a k ittl Kem d c Wh L Id o oo R w n le G Field Office

Fig. 6.24. Percent area within each class of probability of occurrence, by BLM Field Office, for the sagebrush lizard in the Wyoming Basins Ecoregional Assessment (WBEA) area. Field Offices are displayed left to right based on percent area in the high class; the last bar displays results for the entire WBEA area. Area summaries are based on model predictions clipped to the distribution of sagebrush lizards within the WBEA (Stebbins 2003). Note: Using the mean and standard deviation to delineate probability of occurrence classes resulted in only 3 classes, which we refer to as low, moderate, and high.

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CHAPTER 7: DATA GAPS AND DEFICIENCIES

Although the availability of spatial data for the sagebrush biome has improved markedly since completion of conservation assessment of greater sage-grouse and sagebrush habitats (Connelly et al. 2004), the accuracy of many of these layers has not been fully evaluated. In this chapter we (1) present results of accuracy evaluations of 3 spatial data sets—roads, transmission lines, and sagebrush vegetation cover--that were used either in our human footprint analysis (Chapter 5) or in predictive models of species occurrence (Chapter 6) or both; and (2) discuss spatial data gaps and empirical needs to address these gaps.1 Our analyses and estimates of anthropogenic impacts would benefit from better spatial data than were available for some input variables, as demonstrated below. Nonetheless, the existing spatial layers as used in the Wyoming Basins assessment constitute a comprehensive suite of human-related influences that affect sagebrush-associated species and their habitats in a variety of well-documented ways (Appendix 4).

Accuracy of Spatial Data

We evaluated the accuracy of spatial data used in our assessment by comparing some of the GIS layers used in our analysis - as obtained from data warehouses such as SAGEMAP [http://sagemap.wr.usgs.gov] or the U.S. Census Bureau [http://www.census.gov/geo/www/tiger/tiger2k/tgr2000.html] - with more current and comprehensive spatial data obtained for the same spatial features from various BLM Field Offices.2 Our original intent was to assess spatial accuracy and completeness for certain features, such as roads, across the entire study area. However, current BLM spatial data sets were not consistently available in all BLM Field Offices for many of the anthropogenic features or landcover types used in the example species and human footprint models. Thus, we compared data for 2 infrastructure features, transmission lines and roads, and for sagebrush landcover

1 See Appendix 6 for assumptions of approaches used in the assessment, as well as general limitations of the data available for the assessment. 2 Sean Finn (USGS) collected spatial data sets from BLM Field Offices in the assessment area, primarily during 2004.

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within portions of 9 BLM Field Offices: Buffalo and Vernal (transmission lines); Butte, Dillon, and Pinedale (roads); and Lander, Little Snake, Rawlins, and Rock Springs (sagebrush). The transmission line data used in our area-wide assessment was previously compiled for the Interior Columbia Basin Ecosystem Management Project (ICBEMP). In our comparison of transmission line data (Figs. 7.1, 7.2), the area-wide layer used in our assessment underestimated total length of transmission lines by 80.1% (Vernal) and 89.6% (Buffalo). This data gap was driven largely by the absence of feeder transmission lines in the ICBEMP data set used in our analysis. The roads data set used in the WBEA was a TIGER Roads layer obtained from the U.S. Census Bureau [http://www.census.gov/geo/www/tiger/tiger2k/tgr2000.html]. Although the TIGER layer underestimated roads in the 3 Field Offices used for comparison (Figs. 7.3-7.5), our road information was more concordant than the transmission line data. Total road length was underestimated most in Dillon (39.7%), followed by Butte (37.0%) and Pinedale (15.5%). The landcover map used in the WBEA was compiled primarily from state-level vegetation coverages obtained from all western states that contain sagebrush; the map was intended for use in broad-scale assessment to determine general patterns of sagebrush distribution (Comer et al. 2002). Data used in the “sagestitch” coverage were developed from a variety of sources and years, were based on different mapping unit sizes, and created by different methods. To evaluate the accuracy of the sagestitch coverage in our study area, we compared it to a more recent vegetation classification compiled by the Rock Springs BLM Field Office. This layer was a draft supervised classification from a Landsat scene that covered portions of 4 BLM Field Offices in our study area: Lander, Little Snake, Rawlins, and Rock Springs (Fig. 7.6). Landcover within the Wyoming portion of the sagestitch map was derived from the Wyoming GAP project; the minimum mapping unit for this effort was 247 ac (100 ha), with a classification accuracy of 79% (Merrill et al. 1996). By contrast, the mapping unit for the Rock Springs vegetation map was only 1,075 yd2 (30-m pixel). The entire area covered by the scene and mapped by the Rock Springs Field Office was about 8.2 million acres. Twenty-seven landcover types were identified, including 4 that contain sagebrush: sagebrush-grassland, basin big sagebrush, Wyoming big sagebrush, and mountain big sagebrush. The combined area of these 4 types in the Rock Springs map was approximately 3.51 million acres. By contrast, sagebrush cover within this same spatial extent, as estimated by the

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sagestitch map used in the WBEA, was 5.49 million acres.3 Because the sagebrush-grassland type in the Rock Springs map included some grasslands, but grasslands were mapped as a separate cover type in the sagestitch map, the overestimation of sagebrush in the sagestitch map could be even more pronounced than these numbers suggest. The area mapped in common as sagebrush by the 2 coverages totaled 2.77 million acres. Landcover mapped as sagebrush in sagestitch, but not in the Rock Springs map, totaled 2.72 million acres (“overestimated sagebrush,” red area in Fig. 7.6A). Not all areas mapped as sagebrush in the Rock Springs map were also mapped as sagebrush in the sagestitch map; that is, there was some underestimation of sagebrush by sagestitch (Fig. 7.6B). However, sagebrush not identified and mapped as such in sagestitch (741,000 acres; red area in Fig. 7.6B) was far less than the area mapped as sagebrush in sagestitch, but not in the Rock Springs map (2.72 million acres).

Data Gaps

While compiling data sets for the human footprint analysis and for species modeling in the Wyoming Basins assessment, we identified data gaps or deficiencies that can be summarized in 2 ways. First, there is a need to update existing spatial data sets. As demonstrated in the comparisons above, coarse-scale spatial data sets available from major data warehouses may drastically underestimate the actual extent of certain anthropogenic features, especially in sites undergoing a rapid increase in energy extraction and associated infrastructure and disturbance, such as the Pinedale Field Office (Weller et al. 2002,Thomson et al. 2005). Spatial data sets for the anthropogenic features that are most pervasive throughout the study area, such as secondary roads and areas of human habitation (including rural and exurban development; see Table 5.2 and Fig. 5.1), are most urgently needed. However, regional assessment in the Wyoming Basins would benefit from consistent updating within all BLM Field Offices of the entire suite of anthropogenic features used in our approach. Second, new spatial data sets are needed to better map and estimate effects of other human disturbances not considered in our assessment. Below is list of spatial data sets that we think are most important to credibly and efficiently complete

3 All 10 sagebrush cover types mapped in sagestitch were combined for our assessment as one sagebrush landcover type; see Table A5.1.

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• The spatial extent of livestock grazing needs to be consistently mapped and attributed across all BLM lands to include data on stocking rates, animal unit months, season of grazing, and grazing system used. Inclusion of these spatial data would improve the predictive capability for our human footprint analysis and for the example species models, particularly the pronghorn model. For this species, numerous studies indicate that the presence of livestock in pronghorn habitat is likely to result in a reduction of habitat quality (e.g., reduction in diversity of preferred, succulent, nutritious forage) (Yoakum 2004).

• Better spatial data sets for fences on public lands are urgently needed. Fences on pronghorn ranges restrict daily and seasonal movements and may result in injury and mortality (Spillet et al. 1967, Ryder et al. 1984, Yoakum and O’Gara 2000); however, spatial layers of fences were not available throughout the study area (Fig. 7.7). Fences may also impact greater sage-grouse, by fragmenting habitats, providing perches for raptors which may then prey on sage-grouse, or by causing direct mortalities from collisions of sage-grouse with fences (Connelly et al. 2004). We created a proxy for fence density based on a ratio estimate developed from BLM Field Offices in which both fence and allotment data sets were available (Appendix 4); however, empirical data on fence densities within the study area would improve our ability to model effects of fences on sagebrush- associated wildlife.

• Mapping the spatial extent of off-road vehicle (ORV) use in sagebrush ecosystems would improve our ability to estimate the effects of this increasing recreational use in shrublands of the Wyoming Basins. Off-road vehicle use has drastically increased and may result in ecological damage and disturbance of wildlife (Webb and Wilshire 1983, Joslin and Youmans 1999, Havlick 2002). Roads, including two-tracks, were considered a primary threat to sagebrush habitats and wildlife by BLM state wildlife biologists (Table A5.2). Although the development of maps of all ORV trail systems would be cost-prohibitive, we suggest that spatial data sets delineating areas of high, moderate, and low ORV use would

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be helpful as an input variable for many of our species models, as well as future models predicting occurrence of invasive or noxious plants in the Wyoming Basins.

Availability of Data

We will make data from our analyses available in tables, spreadsheets, databases, AMLs, and GIS layers for use by individual BLM Field Offices within the study area. As updated spatial data sets become available for variables used in the human footprint and species models, as well as improved maps of distributions for species of concern in the Wyoming Basins study area (e.g., more accurate range maps at local scales), the models can be updated and re-run with more current data. Because of the dynamic nature of most datasets, we will attempt to release data only in a “finalized” version or when significant updates are implemented. Specific data sets can be requested at any time. We intend to make all data available on the SAGEMAP website at the conclusion of this study. Outputs from our example species models will be made available following formal peer review of the models, preliminary model validation in 2005 with empirical data, and compilation of metadata. Because the human footprint model will not be explicitly validated with field data, the output of this model will be made available when the metadata are compiled. Maps in jpg format of any model outputs (e.g., for reports or presentations) are available upon request from the authors. All spatial data sets employed in the WBEA to model the human footprint are available on SAGEMAP [http://sagemap.wr.usgs.gov/] under: View and download the data used in the Conservation Assessment of Greater Sage-grouse and Sagebrush Habitats .

REFERENCES

Comer, P., J. Kagan, M. Heiner, C. Tobalske. 2002. Current distribution of sagebrush and associated vegetation in the western United States. Map 1:200,000 scale. USGS Forest and Rangeland Ecosystems Science Center, Boise, Idaho, and The Nature Conservancy, Boulder, Colorado, USA. [http://sagemap.wr.usgs.gov/images/sage1.jpg].

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Connelly, J.W., S. T. Knick, M. A. Schroeder, and S. J. Stiver. 2004. Conservation assessment of greater sage-grouse and sagebrush habitats. Unpublished report. Western Association of Fish and Wildlife Agencies, Cheyenne, Wyoming, USA. [http://sagemap.wr.usgs.gov/Docs/Greater_Sage- grouse_Conservation_Assessment_060404.pdf]. Havlick, D. G. 2002. No place distant: roads and motorized recreation on America’s public lands. Island Press, Washington, D.C., USA. Joslin, G., and H. Youmans, coordinators. 1999. Effects of recreation on Rocky Mountain wildlife: a review for Montana. Committee on Effects of Recreation on Wildlife, Montana Chapter of The Wildlife Society. [http://www.montanatws.org/pages/page4a.html]. Merrill, E. H., T. W. Kohley, M. E. Herdendorf, W. A. Reiners, K. L. Driese, R. W. Mars, and S. H. Anderson. 1996. The Wyoming Gap Analysis Project final report. Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming, USA. Ryder, T. J., L. L. Irwin, and D. S. Moody. 1984. Wyoming’s Red Rim pronghorn controversy: history and current status. Proceedings of the Pronghorn Antelope Workshop 11:195- 206. Spillet, J. J., J. B. Low, and D. Sill. 1967. Livestock fences- how they influence pronghorn antelope movements. Utah State University Agricultural Experimental. Station Bulletin 470. Thomson, J. L., T. S. Schaub, N. Wolff Culver, and P. C. Aengst. 2005. Wildlife at a crossroads: engergy development in western Wyoming. The Wilderness Society, Washington, D.C., USA. Webb, R. H., and H. G. Wilshire, editors. 1983. Environmental effects of off-road vehicles: impacts and management in arid regions. Springer-Verlag, New York, New York, USA. Weller, C., J. Thomson, P. Morton, and G. Aplet. 2002. Fragmenting our lands: the ecological footprint from oil and gas development. The Wilderness Society, Washington, D.C., USA. [http://www.wilderness.org/Library/Documents/FragmentingOurLands.cfm].

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Yoakum, J. D. 2004. Distribution and abundance. Pages 75-105 in B. W. O'Gara and J. D. Yoakum, editors. Pronghorn ecology and management. Wildlife Management Institute and University Press of Colorado, Boulder, Colorado, USA. Yoakum, J. D., and B. W. O’Gara. 2000. Pronghorn. Pages 559-577 in S. Demarais and P. Krausman, editors. Ecology and management of large mammals in North America. Prentice Hall, New Jersey, USA.

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Fig. 7.1. Accuracy of spatial data used to estimate transmission lines, based on updated spatial data obtained from the Buffalo BLM Field Office. The study-wide analysis underestimated the spatial extent of transmission lines by 89.6% in this Field Office.

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Fig. 7.2. Accuracy of spatial data used to estimate transmission lines, based on updated spatial data obtained from the Vernal BLM Field Office. The study-wide analysis underestimated the spatial extent of transmission lines by 80.1% in this Field Office.

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Fig. 7.3. Accuracy of spatial data used to estimate roads, based on updated spatial data obtained from the Butte BLM Field Office. The study-wide analysis underestimated the spatial extent of roads by 37.0% in this Field Office.

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Fig. 7.4. Accuracy of spatial data used to estimate roads, based on updated spatial data obtained from the Dillon Field Office. The study-wide analysis underestimated the spatial extent of roads by 39.7% in this Field Office.

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Fig. 7.5. Accuracy of spatial data used to estimate roads, based on updated spatial data obtained from the Pinedale Field Office. The study-wide analysis underestimated the spatial extent of roads by 15.5% in this Field Office.

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A B

Fig. 7.6. Comparison of sagebrush landcover mapped for the Wyoming Basins Ecoregional Assessment (“WBEA Sagebrush”; A), based on the sagebrush coverage of Comer et al. (2002), versus a more recent vegetation classification completed by the Rock Springs BLM Field Office from remotely-sensed data (“Landsat Sagebrush,” B). In panel A, red areas denote sagebrush mapped by the WBEA, but not in the Landsat scene; this was termed “overestimation” of sagebrush by our assessment. Conversely, red areas in panel B denote areas mapped as sagebrush in the Landsat scene, but not in the WBEA; these areas were considered “underestimated sagebrush” based on the landcover map used in the WBEA (Comer et al. 2002).

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Fig. 7.7. Spatial extent of fence and grazing allotment data sets. Although spatial data on allotments were more available than those for fences, allotment spatial data were underrepresented on BLM-managed lands in Utah, southwestern Wyoming, and Montana. Within the study area, fence-density data were available only within Wyoming, and only in 3 BLM Field Offices.

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CHAPTER 8: MANAGEMENT USES AND BENEFITS

Seven reasons have been identified as justification for conducting regional assessments for sagebrush-associated species of concern (Wisdom et al. 2005): (1) habitats and populations of many sagebrush-associated species are declining across vast areas, and local assessments cannot document these regional trends; (2) the number of sagebrush-associated species of concern is daunting, and many of these species have extensive ranges compatible with ecoregions or other regional extents; (3) major threats to sagebrush habitats are regional in scale, and cannot be effectively mitigated under local, disparate management efforts; (4) consistent, efficient, and credible regional management strategies developed for a comprehensive set of species requires regional knowledge about status and trends of habitats and populations of these species, as can be efficiently gained from regional assessments; (5) results from regional assessments provide essential context for local land use planning, and local planning cannot be done credibly without regional knowledge of species and habitats that are affected by threats operating at regional spatial extents. (6) compliance with the National Environmental Policy Act requires cumulative effects analysis, and cumulative effects can only be assessed accurately at regional scales for species with extensive ranges and whose persistence is affected by regional threats to habitats and populations; (7) results from regional assessments serve as regional hypotheses for testing through research, and as the basis for adaptive management, through which agencies can adjust management direction efficiently and accurately with new knowledge gained from this research- management partnership; local assessments can be used in the same manner at local scales, but cannot replace the role of regional assessments as a basis for adaptive management across large areas that ultimately affect the majority of species of concern.

Regional assessments are essential in establishing regional management strategies for efficient and credible development and implementation of local land use plans (USDI Bureau of Land Management 2005). At the same time, regional management strategies can be refined with

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 8-2 Version 2.0, March 2006 feedback from local planning (Thompson et al. 2000). The interaction of regional strategies with local planning fits the concept of “top-down” and “bottom-up” processes (Peterson and Parker 1998). Top-down processes are those that are pervasive, manifesting over large areas or long time periods. Bottom-up processes occur over small areas or short time periods. Both processes are essential in dealing with land-use issues that are simultaneously regional and local in scale (Groves et al. 2000, 2002). In that context, specific management uses and benefits from regional assessments are many and varied. Effective use of results from a regional assessment, however, depends on a larger process of integration of those results in land use planning and implementation.

Effective Use of a Regional Assessment: Integration with the Management Process

To be effective, a regional assessment like that for Wyoming Basins must be integrated in an explicit management process (e.g., Fig. 8.1). While many variations on this process could be successful, the steps outlined in Fig. 8.1 provide an example framework for effective use of a regional assessment in management. If steps in the framework are not addressed, the regional assessment will be less effective for management. Step 1—The first step is to identify the management issues that deserve attention in a regional assessment. For sagebrush-associated species of concern, the management issues justifying a regional assessment include the 7 reasons identified above. The need to evaluate conditions, trends, and risks associated with habitats and populations of species of concern that have large geographic ranges, which cannot be assessed holistically within local administrative units (e.g., an individual Field Office), is central to the need for a regional assessment. Step 2—Conducting the regional assessment is the obvious, key step in the process, based on the reasons given above and detailed elsewhere (Wisdom et al. 2005). This step, however, often has been viewed as a self-contained, detached process from management. Integration of the regional assessment and its results in a larger process, like that outlined here, substantially increases the likelihood of effective management use. Without an explicit design regarding management use of regional assessment results, subsequent management applications are likely to be piecemeal within and across local administrative units, thus negating many of the assessment’s potential benefits.

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Step 3—A logical and effective use of a regional assessment is to immediately follow its development with a regional management strategy. The strategy’s obvious purpose is to develop management direction, based on assessment results, in a holistic manner for the same region in which the assessment was conducted. The advantages of a regional assessment—to depict broad patterns about conditions and threats associated with habitats and populations of species of concern, when such patterns cannot be depicted clearly or efficiently within individual Field Offices or planning units—also justifies the need for the regional strategy. That is, regional issues cannot be dealt with efficiently and effectively at smaller, isolated spatial extents like that of a local planning unit. Ideally, the regional strategy would: (1) identify species and habitats that deserve management focus, based on assessment results; (2) describe the desired future condition for those species and habitats, defined spatially for all sub-regions (e.g., administrative units or ecological provinces) within the region, resulting in spatial priorities for management of each sub-region and Field Office; and (3) provide explicit, quantitative management direction to achieve the desired future condition and spatial priorities for land use actions or restoration that maintain or recover habitats and populations of targeted species of concern. Step 4—Development of local land use plans links directly with the regional strategy. The regional strategy specifies the spatial priorities and direction for management across the collection of planning units that compose the region. Each planning unit, in turn, specifies the local management direction needed to meet the spatial priorities and direction established by the regional strategy, in combination with local resource considerations. Without quantitative management direction provided by a regional strategy, the local land use plans cannot efficiently use the results of a regional assessment for 3 related reasons: (1) the role of each Field Office or planning unit in addressing regional issues may be unclear and uncertain; (2) each Field Office is likely to interpret and apply results from the regional assessment in a manner inconsistent or incongruent with applications of other planning units within the region, given the lack of formal coordination among units; and (3) the combined contribution of all administrative units in addressing management issues for the region will be difficult to document and monitor. Step 5—Results from the regional assessment, combined with management direction provided by the regional strategy and the local land use plan, provide a powerful set of information encapsulating the multiple scales and effects of proposed management, as required by the National Environmental Policy Act (NEPA). All 3 products—the regional assessment,

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 8-4 Version 2.0, March 2006 the regional strategy, and the local land use plan—are needed to evaluate cumulative effects because all three contain relevant information but focused on different perspectives. The regional strategy contains comprehensive information on potential threats to species and habitats, and thus provides a baseline for evaluating cumulative effects of such threats. The regional strategy provides direction for maintaining or restoring desired conditions, or mitigating undesired conditions or threats, and thus indicates how well any negative cumulative effects, as indicated by the assessment, might be reduced. The local plan then sets direction for implementing the regional strategy in concert with local resource considerations, further indicating how well any negative cumulative effects might be reduced in context with information from the regional assessment and direction from the strategy. This type of cumulative effects analysis is similar to that currently done by federal land management agencies as part of the NEPA process, except that it draws on all available information for the region. Consequently, negative cumulative effects are more likely to be reduced through the integrated process of regional and local assessment and planning. Step 6—Once the cumulative effects analysis is completed and considered in the land use plan, both the local plan and the regional strategy can be refined and integrated to reflect these effects. If further mitigations are desired to offset the magnitude or type of cumulative effects, management direction set forth in both the local plan and the regional strategy can be modified accordingly. This process is iterative, involving a series of changes in proposed management to mitigate undesired cumulative effects, followed by additional analysis of how well the cumulative effects are reduced, followed again by additional changes in management direction to ultimately achieve the desired mitigation. The process can be repeated until undesired cumulative effects are reduced or mitigated, and the regional strategy and local plan are finalized to reflect the associated management direction and mitigations. Steps 7 and 8—The steps outlined above are iterative, flexible, and often require long time periods. Some management issues will be resolved through this process, but controversial issues of high scientific uncertainty are likely to remain. Unresolved management issues of high uncertainty, particularly those with strong economic, ecological, or social implications, can be formally identified for testing and resolution through adaptive management. The process of adaptive management is based on a management-research partnership, where topics of high uncertainty and strong management implications can be evaluated as part of land management

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(Holling 1978, Walters 1986). Managers identify the issues to be addressed and work with scientists to develop testable management hypotheses in relation to the issues. Managers and scientists further collaborate to develop experimental designs to test the hypotheses, and to implement management-based experiments using the experimental designs. Scientists measure responses to the experiments, produce and disseminate the results through scientific publications and other mediums of communication helpful to managers, and work with managers to help interpret the results for land use applications. Managers, in turn, apply the results in management, and the cycle begins again to identify additional knowledge gaps, if any, that are best addressed through adaptive management (Fig. 8.2). If adaptive management is not conducted to address unresolved, controversial issues of high scientific uncertainty, the credibility of the assessment, regional strategy, and land uses plan could be seriously questioned. Consequently, adaptive management is integral to an effective process of conducting regional assessments and applying the results in land management.

Direct Use of Regional Assessments in Local Land Use Plans

What if local managers must use results from a regional assessment without benefit of regional management direction, and without coordination among administrative units within the region? This situation is common, but the process of effectively using results from the regional assessment is unclear. The challenge lies in how well each land manager—charged with developing an individual land use plan or managing an individual administrative unit, and often autonomous from other managers and plans—can efficiently design and implement land use direction that addresses all regional issues in a comprehensive manner, based on results of the regional assessment. A related challenge is how to assess and mitigate any undesired cumulative effects at local scales, when such effects stem from broad, regional processes that manifest across large areas, well beyond the jurisdiction of the local plan. Despite these challenges, results from a regional assessment can be used directly in local land use plans, using a similar process like that shown in Fig. 8.1, but without the steps that involve the regional strategy. As with the regional strategy, results from the regional assessment could be specifically used in the local plan to (1) identify species and habitats that deserve management focus; (2) describe the desired future condition for those species and habitats; (3)

DRAFT – FOR INTERNAL USE ONLY Wyoming Basins Ecoregional Assessment 8-6 Version 2.0, March 2006 provide explicit, quantitative management direction to achieve the desired future condition; and (4) assess cumulative effects of proposed management and adjust management direction to reduce or mitigate any undesired cumulative effects. The assessment of cumulative effects, and subsequent refinements in management direction to reduce undesired cumulative effects, may not be fully resolved unless the set of local plans in the region are coordinated in a manner that demonstrates how all plans work together to minimize or mitigate any undesired cumulative effects for species and habitat management issues that are regional in scale.

Workshop Needs

Two types of workshops—each organized and conducted jointly by scientists and managers—may help facilitate effective management use of regional assessments. The first type of workshop could engage national and regional policy-makers of federal land agencies. The second type could target local managers of federal lands. Scientists involved with regional assessments would participate in both types of workshops. Workshops with national and regional policy-makers could focus on concepts and ideas for effective integration of results from regional assessments with follow-on development of regional management strategies. Workshops with local managers could address methods for effective use of regional assessment results as part of an integrated process like that in Fig. 8.1, versus effective use when results must be taken directly from the assessment, without benefit of a regional strategy. These workshops would substitute for more traditional training of managers and management biologists, as might be conducted by scientists charged with conducting the regional assessments. The workshops could instead be developed and run by leaders from both management and research, and be designed to facilitate an open exchange of ideas about best management uses of regional assessments. The goals of both types of workshop presumably would be to improve the conceptual process identified in Fig. 8.1, and to outline actual case examples of how results of regional assessments could be applied in the Wyoming Basins and other ecoregions—both in the development of a regional strategy and in use in local plans. Managers and scientists could meet periodically to evaluate and refine the process of management use of assessment results that might be agreed upon during the initial workshops.

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REFERENCES

Groves, C., L. Valutis, D. Vosick, B. Neely, K. Wheaton, J. Touval, and B. Runnels. 2000. Designing a geography of hope: a practitioner's handbook for ecoregional conservation planning. The Nature Conservancy, Arlington, Virginia, USA. [http://www.conserveonline.org]. Groves, C. R., D. B. Jensen, L. L. Valutis, K. H. Redford, M. L. Shaffer, J. M. Scott, J. V. Baumgartner, J. V. Higgins, M. W. Beck, and M. G. Anderson. 2002. Planning for biodiversity conservation: putting conservation science into practice. BioScience 52:499- 512. Holling, C. S., editor. 1978. Adaptive environmental assessment and management. John Wiley, New York, New York, USA. Peterson, D. L., and V. T. Parker, editors. 1998. Ecological scale: theory and applications. Complexities in ecological systems series. Cambridge University Press, New York, New York, USA. Thompson, F. R., D. M. Finch, J. R. Probst, G. D. Gaines, and D. S. Dobkin. 2000. Multi- resource and multi-scale approaches for meeting the challenge of managing multiple species. Pages 48-52 in R. Bonney, D. N. Pashley, R. J. Cooper, and L. Niles, editors. Strategies for bird conservation: the Partners in Flight planning process. USDA Forest Service Proceedings RMRS-P-16, Fort Collins, Colorado, USA. [http://birds.cornell.edu/pifcapemay]. USDI Bureau of Land Management. 2005. Land use planning handbook. BLM Handbook H- 1601-1. USDI Bureau of Land Management, Washington, D.C., USA. Walters, C. 1986. Adaptive management of renewable resources. Macmillan Publishing Company, New York, New York, USA. Wisdom, M. J., M. M. Rowland, and L. H. Suring, editors. 2005. Habitat threats in the sagebrush ecosystem: methods of regional assessment and applications in the Great Basin. Alliance Communications Group, Lawrence, KS, USA.

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(1) Identify Regional Management Issues for Species of Concern

(2) Conduct Regional Assessment to Address Regional Issues

(3) Develop Regional Strategy Based on Assessment Results

(4) Develop Local Land Use Plans Based on Regional Strategy

(5) Conduct Cumulative Effects Analysis Based on Regional Assessment, Regional Strategy, and Local Plans

(6) Modify and Finalize Management Direction and Mitigations in Regional Strategy and Local Plans to Reduce Undesired Cumulative Effects

(7) Identify Controversial Issues of High Scientific Uncertainty for Testing through Adaptive Management

(8) Initiate the Adaptive Management Process in Concert with Repeating any of the above Steps as Required

Fig. 8.1. Suggested steps in the process of effective management use of a regional assessment in federal land management of species of conservation concern.

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Identify Critical Knowledge Gaps

for Management

Produce, Disseminate, Develop Testable

and Apply Results Management Hypotheses

Design and Implement

Management Experiments

Fig. 8.2. Conceptual diagram of the process of adaptive management in gaining knowledge to address key knowledge gaps under a management-research partnership (from Wisdom et al. 2005).

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