Vulnerability Assessment of Sagebrush Ecosystems: Four Corners and Upper Rio Grande Regions of the
Southern Rockies Landscape Conservation Cooperative
Prepared by Mary I. Williams and Megan M. Friggens
United States Forest Service Rocky Mountain Research Station
Albuquerque, New Mexico
December 29, 2017
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Cover Photo Credit: Mary Williams, 2010
Mary Williams is an Ecologist with the Nez Perce Tribe, Lapwai, ID
Megan Friggens is a Research Ecologist with the USDA Forest Service, Rocky Mountain Research Station
This analysis was prepared in partial fulfillment of agreements between Rocky Mountain Research Station (RMRS) and the Southern Rockies Landscape Conservation Cooperative (SRLCC: FWS #15-IA-011221632-164 and BOR #15-IA-11221632-163). This is one in a series of four assessments produced for focal landscapes within the SRLCC. Data associated with this assessment can be found at the SRLCC Conservation Planning Atlas web portal: https://srlcc.databasin.org
Acknowledgements: Kristin Peltz, USFS, contributed to early drafts of this report. Stephanie Mueller, Northern Arizona University, processed many of the spatial datasets. We thank the participants of the 2016 and 2017 Adaptation Forum’s held in Durango, CO, and Albuquerque and Taos, NM.
For further questions or comments, contact: [email protected]
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Contents Purpose...... 6 Background ...... 7 Focal Resources and Landscapes: ...... 7 Vulnerability Assessments: ...... 9 Co-producing Vulnerability Assessments for the SRLCC: Adaptation Forums ...... 10 I. Literature and Status Review of Focal Resources: Sagebrush Ecosystems...... 12 Geographic Focus ...... 12 Current status and distribution of resources ...... 12 Issues and Threats for Sagebrush Ecosystems in the Four Corners and Upper Rio Grande . 17 BOX 4. RESISTANCE AND RESILIENCE CONCEPTS ...... 22 Ongoing Activities Regarding Sagebrush Management in the Four Corners and Upper Rio Grande Focal Areas ...... 22 II. Critical Attributes for Measuring Sagebrush Vulnerability ...... 25 Landscape Scale ...... 25 Indicators of desirable condition ...... 26 Critical components of sagebrush ecosystems ...... 27 III. Vulnerability Assessment of Focal Resources: Sagebrush Ecosystems ...... 29 Methodological approach ...... 29 Indicator variables ...... 32 IV. Results ...... 47 V. Discussion and Conclusions ...... 48 Comparison of Upper Rio Grande and Four Corners Focal Areas ...... 49 Core Area Analysis ...... 50 Missing Data and other uncertainties ...... 51 Literature Cited ...... 60 Vulnerability Assessment of Sagebrush Ecosystems: Four Corners and Upper Rio Grande Regions of the Southern Rockies Landscape Conservation Cooperative ...... 73 Appendix A. Landscape Analysis: Sagebrush Ecosystems ...... 73 Methods ...... 73 Sagebrush Core Area Indicator ...... 73
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Abbreviations: AS Adaptation Strategy BLM Bureau of Land Management CNHP Colorado Natural Heritage Program FS Forest Service IAP Intermountain Adaptation Partners LCD Landscape Conservation Design NCCSC North Central Climate Science Center SRLCC Southern Rockies Landscape Conservation Cooperative VA Vulnerability Assessment WWA Western Waters Association
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Purpose
A major challenge of managing rapidly changing landscapes is translating broad concepts into specific, tangible actions. Vulnerability assessments are a key step toward identifying effective management strategies and prioritizing specific management actions to conserve and restore natural resources. As part of the effort to utilize landscape conservation planning and design for the Southern Rockies Landscape Conservation Cooperative (SRLCC), the RMRS developed a vulnerability assessment approach that: 1) covers a diverse set of focal resource targets; 2) incorporates input from science and working group members and other stakeholders; 3) compliments the overarching principles guiding science and management planning within the LCCs (Box 1). Our approach borrows from other successful assessments strategies and used adaptation forums to facilitate stakeholder input. As part of this work, RMRS has produced a series of state of knowledge syntheses and developed spatially explicit vulnerability assessment products including maps, datasets and training materials. It is the objective of this project that these products provide the SRLCC stakeholders with the capacity to identify shared conservation priorities and goals. All data and associated webinar and workshop information is available online to facilitate additional analyses and assessments identified by users. The following report contains the synthesis and analysis component of this project. In the first half of this report, you will find a review of background information and ongoing activities for sagebrush ecosystems within two SRLCC focal areas: The Four Corners and Upper Rio Grande (see https://southernrockieslcc.org/geographical-areas for more information). The second half of this report presents the methods and results of our vulnerability assessment.
BOX 1. COORDINATING WITH THE LCC STRATEGIC PLAN
The vulnerability assessment process and its products contribute to multiple objectives outlined within the LCC Network Strategic Plan. 1. Vulnerability Assessments identify the relative vulnerability of focal targets to climate change thereby providing mechanisms for prioritizing science and management needs. 2. When conducted for multiple time periods or climate scenarios, measures of vulnerability may also identify urgency. 3. Vulnerability Assessments identify how and why resources may be vulnerable to climate change thereby providing starting points for management planning, conservation, and monitoring. 4. Vulnerability Assessments consider measures of uncertainty and identify areas needing additional research.
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Background
In 2016, the Rocky Mountain Research Station began to develop vulnerability assessment products for SRLCC focal resources. The objectives of the vulnerability assessment project were multiple: 1. Summarize current status, potential climate change impacts, and other issues facing SRLCC Focal Resources in the Upper Rio Grande and Four Corners Geographic Focus Areas. 2. Identify climate change impacts and other issues facing natural and cultural resources in the southern tier of SRLCC (includes the Upper Rio Grande and Four Corners Geographic Focus Areas). 3. Develop a Vulnerability Assessment approach to allow integration with other assessments and Landscape Conservation Design (LCD) efforts of BLM, FS, CNHP and other partners. 4. Apply Vulnerability Assessment approach to quantify vulnerability of SRLCC Focal Resources and Geographic Focus Area select targets.
Focal Resources and Landscapes:
Prior to the involvement of RMRS, SRLCC staff and steering committee members identified five focal resources, native fish, stream flow, elk and mule deer, sagebrush ecosystems, and cultural resources, and three focal landscapes, the Green River Basin, the Four Corners, and the Upper Rio Grande (https://southernrockieslcc.org/geographical-areas). The assessments conducted by RMRS regard focal resources in the Four Corners and Upper Rio Grande (Figure 1). After receiving feedback from a series of Adaptation Forums in 2016 (see below), we refined the focal resources targets to include riparian areas and pinyon-juniper habitats. We also dropped cultural resources from the current effort (now addressed through another project: https: //southernrockieslcc.org/issue /cultural-resources).
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Figure 1. Southern Rockies Landscape Conservation Cooperative Boundary and location of Four Corners and Upper Rio Grande focal areas
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Vulnerability Assessments:
Vulnerability assessments are a critical component in adaptive management planning and risk analysis. An assessment of vulnerability can identify relative impacts from disturbance and the source of those impacts, thereby facilitating the identification and prioritization of management strategies. Vulnerability is a key concept for assessing climate impacts to natural resources and has been adopted by the Forest Service and other agencies as a primary mechanism for developing effective adaptation options to manage natural resources under climate change (e.g. USDA Forest Service 2011).
Climate change and related vulnerability assessments (hereafter VA) are initiated during the planning phase of conservation projects to provide information on relative impacts and priority needs within a target system. VA are amendable to multiple data sources and incorporate a variety of analysis methods thereby providing a framework for creating diverse data products that might be useful for planning efforts. Thus, the utility of VA can extend beyond initial efforts to identify climate change impacts. We developed a vulnerability assessment framework based on the core principles common to many climate change vulnerability assessments (Box 2 and see Section III. Methodological Approach).
BOX 2. PRIMARY ELEMENTS OF A VA
Exposure: The magnitude of climatic or ecological changes within target landscape Sensitivity: The response of targets to exposure Adaptive Capacity: The potential of target to cope with exposure
The assessment approach used in this analysis was designed to complement LCC Network Strategic Plan objectives and produce products and outputs with multiple uses (Table 1). Assessment products provide information on the overall vulnerability of the focal resource in each landscape and identify the most sensitive or resilient features within those systems. To implement the assessment process, we identified relevant spatial data that can be used to assess areas of high risk or resilience within the landscapes (see Section III. Methods). These spatial datasets form the core of our assessment scores and can be used to construct maps that classify areas according to the presence of vulnerable components.
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Table 1. Planned inputs and outputs generated as part of the vulnerability assessment (VA) process have a variety of uses. VA element Inputs/Outputs Additional uses Exposure (degree change) Maps depicting % change in temp, Identify areas falling outside of known range of prcp, cover, etc. variability (losers) Projected land use change Identify areas that maintain natural range of Presence or proximity to variability (winners, “Refugia”) disturbance Identify spatial distribution and overlap of Spatial data on distribution of multiple resources cultural and natural resources Analysis of fragmentation, etc. Risk Assessments (likelihood and magnitude of change)
Measures/indicators of Maps showing: Identify changes to biodiversity (turnover) sensitivity or adaptive Biodiversity Biodiversity hotspots current and future capacity Socio-economic metrics Identify monitoring targets Shifts in distribution of species and Identify management targets vegetation Identify resilient landscapes Water quality Identify sensitive landscapes % change in distribution of habitat Identify critical landscapes or landscape or habitat features features Various estimates of magnitude of Risk Assessments (likelihood and magnitude of change change) Identify threshold indicators, likelihood of type conversion
Identification of critical Ranked vulnerability across Incorporate results into agency adaptation vulnerabilities elements/locations plans/strategies Information on magnitude Identify future management and monitoring exposure, key sensitivities, and priorities adaptive capacity Identify actions addressing specific threats and issues Identification of best bet and ROI actions
Co-producing Vulnerability Assessments for the SRLCC: Adaptation Forums
The most effective climate change vulnerability assessments are tailored to the specific objectives of the unit or agency(s) that will use it. Targets and measures used within a vulnerability assessment should align with the strategic goals of stakeholders and output should inform management actions. We used adaptation forums and in-person meetings with SRLCC stakeholders to identify the specific objective and measures for these assessments.
The first Adaptation Forums, facilitated by the Colorado Natural Heritage Program in Durango, CO and Albuquerque, NM, in the spring of 2016 (report available at: https://lccnetwork.org/sites/default/files/2016%20Adaptation%20Forum%20workshops%20FI NALREPORT.pdf ) gathered together stakeholders to identify regionally significant resources
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(targets), ecosystem drivers and limiting factors (threats) which would be used in vulnerability assessments and in the development of conservation strategies for the regional partnership. Feedback from participants allowed RMRS to identify the following final set of targets: native fish, stream flow and riparian habitats, elk and mule deer, sage brush ecosystems, and pinyon juniper woodlands.
The second series of adaptation forums were held in Durango, CO (https:// southernrockieslcc.org/geographical_area/four-corners-region), and Taos, NM (https:// southernrockieslcc.org/geographical_area/upper-rio-grande) in the fall of 2017. These adaptation forums were designed with three objectives in mind: 1) introduce the vulnerability assessment methodology and preliminary results; 2) provide training to improve understanding and use of assessment products and data, and, 3) solicit feedback on assessments before the final dissemination. The following analysis takes into account feedback given by forum participants and presents our final assessment of resource vulnerabilities in the Four Corners and Upper Rio Grande focal areas.
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I. Literature and Status Review of Focal Resources: Sagebrush Ecosystems
Geographic Focus
The Southern Rockies Landscape Conservation Cooperative (SRLCC), encompassing over 127 million acres, covers portions of Arizona, Colorado, New Mexico, Utah, and Wyoming (Figure 1). The focal areas of interest within the SRLCC, Four Corners (FC) which covers parts of Arizona, Colorado, New Mexico, and Utah, and Upper Rio Grande (URG) which covers Colorado and New Mexico are located at the southern end of the SRLCC. Land in the focal areas is primarily owned and managed by Tribes, States, Bureau of Land Management (BLM), Forest Service (FS), and National Park Service (NPS). Tribal governments, including the Navajo Nation and The Hopi Tribe, manage approximately 28% of the land in Arizona (AGFD 2012). Other management agencies include the Bureau of Reclamation (BOR), Department of Defense (DOD), Fish and Wildlife Service (FWS), and Department of Energy (DOE).
Current status and distribution of resources
Sagebrush Ecosystems In the focal areas, sagebrush ecosystems represent the southernmost reach of the greater sagebrush biome that covers much of the Western United States (Figure 2). These ecosystems are composed of varying degrees of cover and composition reflecting climate, topography, soils, and past and current management practices. Ecosystems are primarily dominated by sagebrush species, such as Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis), basin big sagebrush (A.t. ssp. tridentata), mountain big sagebrush (A.t. ssp. vaseyana), bigelow sagebrush (A. bigelovii), black sagebrush (A. nova), and sand sagebrush (A. filifolia), with rabbitbrush (Chrysothamnus sp. and Ericameria sp.), saltbush (Atriplex sp.), greasewood (Sarcobatus vermiculatus), spiny hopsage (Grayia spinosa), succulents, forbs, and grasses, such as Indian ricegrass (Achnatherum hymenoides), needle and thread (Hesperostipa comata), blue grama (Bouteloua gracilis), Idaho fescue (Festuca idahoensis), western wheatgrass (Pascopyrum smithii), James’ galleta (Hilaria jamesii), and muttongrass (Poa fendleriana). Soil crusts are a key element to sagebrush ecosystems in these areas because they protect the soil surfaces and limit erosion and invasion by less desirable plant species (Belnap 1994, Belnap et al. 2001). Current ecosystems tend to be deficient in young and middle age classes and abundant in decadent big sagebrush with cheatgrass (Bromus tectorum) understories. Maintaining and providing a range of self-sustaining sagebrush cover containing a variety of age classes and structures is a common desired condition among surface owners (e.g. BLM 2012a, b). Overgrazing, invasion by non-native annual grasses, energy development, encroachment by pinyon pine (Pinus edulis) and juniper (Juniperus sp.), agriculture, and residential development
12 all cause departure from desired conditions and typically trigger management actions, such as shrub removal or thinning, prescribed fire, and revegetation.
Figure 2. Sagebrush cover across the Southern Rockies Landscape Conservation Cooperative; data from LANDFIRE Existing Vegetation Type (EVT).
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Wildlife Sagebrush ecosystems support a diversity of birds, mammals, reptiles, amphibians, and invertebrates. Some are sagebrush obligate species, those that need sagebrush communities seasonally or year-round, and others are near-obligates, those that occur in sagebrush and grass communities. Sagebrush, grasses, and forbs are important sources of food and cover for wildlife. During winter, sagebrush, an evergreen shrub, may provide the only available live, high-protein forage for sage grouse (Centrocercus sp.), pronghorn (Antilocapra americana), pygmy rabbit (Brachylagus idahoensis), and mule deer (Odocoileus hemionus) (Dealy et al. 1981). Tall sagebrush provides cover for many species and nesting sites for obligates, such as sage grouse, Brewer’s sparrow (Spizella breweri), and sagebrush sparrow (Artemisiospiza nevadensis). Greater sage-grouse (Centrocercus urophasianus, GRSG) and Gunnison sage- grouse (Centrocercus minimus, GUSG), a species listed threatened under the Endangered Species Act (ESA) (FWS 2014), exist within the SRLCC footprint, however greater sage-grouse no longer occurs in Arizona and New Mexico (NatureServe 2017) (Box 3).
BOX 3. SAGE GROUSE IN THE SOUTHERN ROCKIES
Around 1919 sage grouse were extirpated from Arizona and New Mexico.
Reintroduction efforts have failed, likely due to unsuitable habitat conditions (NMDGF 2006). Only a few populations of Gunnison sage-grouse exist in the focal areas in Colorado and Utah (Dove-Creek Monticello, Poncha Pass, and Gunnison Basin populations) (Figure 3) (Stiver et al. 2015). Gunnison sage-grouse were historically present in Colorado in Mesa, Montezuma, Dolores, San Miguel, Ouray, La Plata, Archuleta, Hinsdale, Saguache, Rio Grande, Mineral, Conejos, Costilla, possibly Huerefano, Custer, Fremont, Chaffee Gunnison, Delta, and Montrose counties in Colorado (among others). It is likely that populations existed in nearby areas across the state lines as well. They are now only known in Delta, Dolores, Gunnison, Hinsdale, Mesa, Montrose, Saguache, and San Miguel counties in Colorado (Braun et al. 2014).
Several management plans identify sage grouse as a focal species because they depend on sagebrush for food and cover, interact with habitat at multiple scales, and are sensitive to landscape change (Paige and Ritter 1999, Stiver et al. 2010, Hanser and Knick 2011). As a focal species, sage grouse habitat needs can help pinpoint attributes important to other obligates (Rowland et al. 2006, Wiens et al. 2008, Hanser and Knick 2011, Hayward and Suring 2013), such as sagebrush sparrow, sage thrasher (Oreoscoptes montanus), and pygmy rabbit. One example, the GUSG Rangewide Conservation Plan (GSGRSC 2005) serves as a baseline for grouse management across its range and provides specific structural habitat guidelines and monitoring protocols. The Rangewide Conservation Plan has been adopted in recent federal plans such as the GUSG Rangewide Draft Resource Management Plan Amendment/Draft
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Environmental Impact Statement for BLM lands (BLM 2016) and by BLM offices in Colorado and Utah (BLM 2012b, 2015). Another example is the Gunnison Climate Working Group, an organization operating within the SRLCC footprint that focuses on the Gunnison sage-grouse population within the Gunnison Basin. Their strategy is a “no-regrets” approach focused on “enhancing the resilience of riparian/wetland areas within the sagebrush ecosystem to build adaptive capacity of the imperiled Gunnison Sage-grouse and other wildlife species” (Neeley et al. 2010). In keeping with this management perspective, the next sections review the primary threats to sagebrush ecosystems in general and with respect to sage grouse and other wildlife.
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Figure 3. Critical habitat map for Gunnison sage-grouse (USFWS ECOS 2014). 16
Issues and Threats for Sagebrush Ecosystems in the Four Corners and Upper Rio Grande
Dominant threats to sagebrush ecosystems include overgrazing and browsing, land conversion for energy, agriculture, and residential and invasion by annual grasses and conifers.
Grazing and Browsing Use of sagebrush ecosystems by wild and domestic animals influences the ability of these ecosystems to recover a native plant community following fire. Loss of native forbs and grasses can lead to invasion by exotic annual grasses. Domestic cattle and sheep have grazed on many landscapes since the late 1800s (e.g., Allen et al. 2002). Mule deer (Odocoileus hemionus), which primarily browse on woody vegetation but also consume some herbs and forbs use these areas as well.
The impacts of grazing on sagebrush ecosystems can vary widely depending on local precipitation, plant community composition, and season and intensity of grazing (see Strand et al. 2014 for an excellent review). In general, sagebrush communities with greater dominance of forbs and grasses, and greater overall and summer precipitation, are likely to be more resilient to grazing impacts than more shrub-dominated communities with low total precipitation and winter-dominated precipitation. Sagebrush-dominated areas that evolved with native grazers such as American bison (Bison bison), like many areas in the SRLCC, might be more able to recover from low- to moderate-intensity grazing than areas where they were absent historically (Milchumas et al. 1988). However, ecosystems within the SRLCC are likely to be vulnerable to domestic livestock grazing where prolonged use has reduced the abundance of native, perennial grasses and encouraged an increase in shrub canopy cover and abundance of non- native annual grasses. From the late 1800s through the early 1900s intensive grazing practices were thought to have caused widespread habitat degradation across the sagebrush ecosystem range (Tuhy et al. 2002, AFGD 2012).
In the Southwest, studies have shown mixed effects of grazing on sagebrush plant communities. In New Mexico, 22 years after livestock exclusion, sagebrush and other species’ cover did not change substantially, although many native forbs had been already extirpated by heavy sheep grazing that occurred prior to removal of livestock (Holechek and Stephenson 1983). In western Colorado, Manier and Hobbs (2006) found ungrazed areas inside 40-50 year old enclosures had higher sagebrush and lower forb cover than grazed plots. Ultimately, the results of studies like these demonstrate the importance of local conditions to determine grazing impacts on sagebrush ecosystems.
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Energy Development Sagebrush ecosystems have been significantly impacted by energy development, specifically oil and gas extraction, surface coal mining, and solar and wind development, and associated infrastructure (powerlines, pipelines, and roads) (NMDFG 2006). Demand for both oil, gas, and coal is expected to remain high, and demand for and development of solar and wind power are increasing (NMDFG 2016). Although the footprint of solar and wind development may already only occur on lands previously converted to urban or agriculture and are small relative to other mining efforts, increased demand for solar and wind has the potential to create significant localized impacts to shrub-steppe ecosystems (CPW 2015, NMDFG 2016).
Coal bed methane extraction in the San Juan Basin in New Mexico has caused considerable habitat loss and degradation (BLM 2003). There are approximately 54,000 active wells in northwest and southeast New Mexico (http://octane.nmt.edu/gotech/Petroleum_Data/General.aspx). Although regulations and advanced drilling techniques have reduced the number of open wastewater pits that pose direct threats to wildlife (Custer 1994), they still occur on the landscape. Other impacts of oil and gas development are oil spills, habitat fragmentation from an increased density of roads and well pads, and reduction in large patches of sagebrush.
Threats from energy development for sagebrush wildlife include reduction in habitat, fragmentation and degradation of remaining habitat, direct disturbance and/or mortality of individual birds, and increased predation. Vehicle collisions can lead to direct mortality and roads can block normal behavior patterns of wildlife. As sagebrush patches shrink, wildlife become vulnerable to predation and isolation (NMDFG 2016). Increased human disturbance related to development can also reduce population viability (Walker et al. 2007, CPW 2015).
While the largest oil, gas, and coal reserves in Colorado significantly overlay with GRSG habitat (CGSSC 2008, CPW 2015), much of the energy development has occurred outside the occupied habitat of GUSG. However, the San Miguel Basin and Dove Creek populations located in or near the Four Corners and Upper Rio Grande focal areas, are two areas within the GUSG range that currently have a moderate amount of oil and gas production. There are no active coal operations in GUSG habitat, and recoverable coal is limited. Localized impacts from energy production and infrastructure may occur and reduce habitat or cause disturbance and direct mortality and increase predation; but these may not pose a significant threat to GUSG in Colorado and Utah (CPW 2015).
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Agricultural and Residential Development Human modification of landscapes has resulted in the loss and degradation of sagebrush habitats. Conversion to cropland and use of pesticides and herbicides can directly and indirectly impact sagebrush ecosystems. In Colorado, much of the historic range of sagebrush has been reduced by conversion to agriculture (CPW 2015). Modification of riparian areas because of water withdrawals, habitat fragmentation, and pollution from nearby development and industries threaten sagebrush ecosystems. Withdrawal of water from the San Juan River and Rio Grande in New Mexico for crops and municipalities reduces flows upon which several imperiled fish and invertebrates depend. It also decreases the extent and functionality of riparian habitats, which are critical to several species (NMDFG 2016).
Invasive Plants Noxious and invasive non-native plants are a threat to sagebrush ecosystems and wildlife species. They have the potential to degrade habitat, primarily by changing the fire cycles, decreasing plant diversity, and changing plant and insect community structure (DiTomaso 2000; DiTomaso et al. 2017 and references therein). Invasion of cheatgrass in the understories of sagebrush communities is especially problematic. Cheatgrass germinates earlier than native grasses, and out-competes them for space and resources (Mack and Pyke 1983, James et al. 2011). Cheatgrass invasion is of special concern within sagebrush ecosystems because of its ability to displace native vegetation and retain site dominance following fire in many areas (Whisenant 1990, Baker 2011, Miller et al. 2013, Brooks et al. 2015). However, sagebrush mortality in systems that have already been degraded and have lost native herbaceous grasses or forbs may be especially susceptible to invasion or increased dominance of exotic species such as cheatgrass.
Invasion of sagebrush ecosystems by native conifers is considered a threat to sagebrush ecosystem in many areas, particularly southeastern Oregon and the central Great Basin (Miller and Tausch 2001, Bauer 2006, Johnson and Miller 2006, Weisberg et al. 2007, others). In greater sage grouse habitat areas, even relatively low invasion levels are of critical concern because as little as 4% conifer cover inhibits bird use (Baruch-Mordo et al. 2013). Invasion of conifers may threaten the vegetation community by displacing sagebrush and native forbs and grasses and by allowing severe fires to grow faster due to continuity of tree canopy fuels, leading to the potential for conversion to cheatgrass (Miller and Tausch 2001).
The threat of conifer invasion to sagebrush dominated vegetation communities is likely less in SRLCC focal areas than elsewhere. Though there is evidence of conifer infill and expansion of into sites in Arizona and New Mexico (Miller 1999, Jacobs et al. 2008), at the landscape scale net conifer invasion is less widespread in the southwestern US than the Great Basin (Manier et
19 al. 2005, Romme et al. 2009). This is in part because of drought-related mortality in southwestern piñon-juniper forests. Similarly, conversion to cheatgrass following fire is much less of a concern in the Four Corners and Upper Rio Grande areas than in the Great Basin due to the greater resilience of these landscapes to this invader. The focal areas of the SRLCC have higher precipitation, and colder temperatures (particularly winter minimum temperatures) than areas where cheatgrass has become dominant. The dominance of monsoonal precipitation within this area also likely confers greater resistance to invasive annual grasses (Bradford and Laurenroth 2006).
Climate Change
Climate change is likely to pose the greatest threat to sagebrush indirectly, through its impact on fire and invasive species. Wildland fire and invasive species and are large threats to sagebrush ecosystems (Miller et al. 2011, USFWS 2013, Chambers et al. 2014) though their impacts differ across the range of sagebrush-dominated ecosystems. Factors that influence the degree to which sagebrush is impacted by these threats include site climate, soil type, and existing plant community (Chambers et al. 2014).
Climate change may increase fire frequency due to overall increases in temperatures, length of wildfire seasons, and severity of fire weather (Westerling et al. 2006). Though wildfires naturally occur in sagebrush ecosystems, recent fires have been larger, more severe, and more frequent than in the past in many areas such as the Snake River Plain (e.g., Whisenant et al. 1990). Increased frequency and severity of wildfire may favor invasive species, reduce sagebrush plant community recruitment, and destroy wildlife habitat.
Changes in plant community composition in sagebrush-dominated areas also affect its ability to recover from fire. Loss of perennial grasses and annual forbs in some sagebrush ecosystems, perhaps due to overgrazing, has increased their vulnerability to invasion by exotic species before and after wildfires (Anderson and Inouye 2001, Condon et al. 2011, Whisenant et al. 1990).
Cheatgrass spread is facilitated indirectly by climate-related changes to wildfire regimes and directly through the impact of increased temperatures on cheatgrass viability. Cheatgrass has been the largest problem in the drier, warmer areas of the western U.S., particularly the Columbia Plateau and central and northern Great Basin (Whisenant et al. 1990, Chambers et al. 2014). An increase in water deficit associated with warmer temperatures may favor the expansion of cheatgrass into areas of Colorado and Utah that have so far been more resistant to the species (e.g., Bradley 2009).
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Climate changes will directly affect the plants in sagebrush ecosystem as well. Climates suited to desert shrubland are projected to expand ~25% by 2060, although projections have some uncertainty as to which type of arid land the future climate will suite (i.e. Great Basin, Mojave, or Sonoran, etc.) (Rehfeldt and others 2012). More specific analyses in the Colorado Plateau, which occurs in northern New Mexico and Arizona, and southern Colorado and Utah, show a substantial contraction in the ranges of sagebrush over the next century (NMDFG 2016).
Another analysis shows that under high CO2 emissions scenarios, climates suited to sagebrush are likely to decline, especially at the southern edge of its range, such as in the focal areas, and to increase at higher elevations and at the northern edge of its range (Schlaepfer and others 2012). Still and Richardson (2015) project a 39% loss of suitable habitat for Wyoming big sagebrush with much of this loss occurring in the Great Basin, an area already threatened by wildfire and invasive plants. The velocity of temperature change is an important consideration as it affects how easily a plant or plant community is able to shift in response to shifting conditions. The velocity of temperature change is projected to be high in desert and xeric biomes across the globe (Loarie and others 2009). However, the large amount of land occupied by deserts may mitigate climate velocity, as residence time, the ratio of land size to velocity that acts as an index of time for current climate to cross a particular area, becomes larger (Loarie and others 2009).
Warmer temperatures are already affecting the timing of flowering of rare plants in sagebrush- dominated ecosystems more dramatically than other landscapes in Colorado (Munson and Sher 2015). Earlier blooming was associated with warmer winter temperatures in sagebrush basins. Cascading effects are likely, starting with pollinator communities adapted to consistent plant phenology, followed by crops and other species, which in turn may affect crop production and wildlife species that depend on insects.
Climate-related changes in water availability may also result in increased plant mortality due to water stress, especially when coinciding with other stressors, such as grazing and browsing (e.g., McDowell et al. 2008). The degree to which sagebrush ecosystems are affected by changes in precipitation patterns will depend upon the current state of those systems. For example, the health of the soil microbial community can influence resistance to drought- induced mortality, such that sagebrush plants colonized by arbuscular mycorrhizae are less prone to drought stress than plants without the fungi (Stahl et al. 1998).
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BOX 4. RESISTANCE AND RESILIENCE CONCEPTS
Resistance is “the ability of an area to recover from disturbance, such as
wildfire or drought” and resilience is the “ability of an area of land to remain largely unchanged in the face of stress, disturbance, or invasive species” (p. 8, DOI SO 3336 – The Final Report). In regards to sagebrush ecosystems, those on cool, moist, more productive sites are considered more resistant and resilient to invasive species and fire, respectively, than those at the drier, warmer end of the spectrum (Chambers et al. 2014, 2017; Maestas et al. 2016). According to recent analysis, sagebrush communities in the SRLCC, and particularly in the focal areas of the Four Corners and Upper Rio Grande are likely to have a mix of resistance and resilience than many sagebrush communities, particularly as compared to those further to the west (Chambers et al. 2014). Substantial portions of the focal areas have moderate to high resistance and resilience due to their cool/moist or warm/moist climate conditions. The monsoonal precipitation pattern in the Southwest also promotes perennial forbs and
grasses and therefore resistance to invasive species invasion relative to areas with little summer precipitation further north and west (e.g., Bradford and Laurenroth 2006).
Ongoing Activities Regarding Sagebrush Management in the Four Corners and Upper Rio Grande Focal Areas
To facilitate coordination among SRLCC stakeholders, in this section we review and document ongoing activities related to sagebrush conservation within the focal study areas. Activities are presented in tables and represent either currently active working groups, collaborations, research or other projects and spatial datasets with relevant information.
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Table 2. Spatial data available for analysis of Sagebrush ecosystems. What Website Type of data What is available BLM Landscape datasets https://navigator.blm.go Geospatial data Downloads of BLM spatial v/home data and maps at project, state, and national levels SWReGAP (Southwest http://swregap.nmsu.ed Wall-to-wall vegetation classification, native terrestrial Downloads for all states, Regional Gap Analysis u/ vertebrates, ownership, etc., for states including AZ, NM, UT, and except Wyoming Project) CO. NAIP (National Aerial http://viewer.nationalma Aerial imagery flown biannually; can be used to visualize Imagery for each state is Imagery Program) Imagery p.gov/basic/#productGro vegetation. typically flown every 2 years upSearch and available within 1 year after flight. FIA (Forest Inventory and http://www.fia.fs.fed.us/ Data on tree size, species, and density; non-tree vegetation Plots are available across Analysis) data tools-data/ species and cover; down woody fuels for areas with > 10% whole SRLCC area. canopy cover. One plot is systematically installed in each 6,000 acres of forest across the US, regardless of ownership. LANDFIRE (Landscape Fire www.landfire.gov A variety of vegetation, fuels, and fire data. Vegetation data Full US coverage and Resource management includes existing vegetation type, cover and height. Accuracy is Planning Tools) an issue that needs to be considered.
SSURGO Soils Data http://websoilsurvey.sc.e Varying coverage and quality of soil information. Some but not complete gov.usda.gov/App/WebS coverage in SRLCC area oilSurvey.aspx United States National http://www.natureserve. New in 2016. This database contains type descriptions for natural National wall-to-wall Vegetation Classification org/conservation- vegetation at all levels of the U.S. National Vegetation coverage Database tools/projects/us- Classification. The Ecological Society of America’s Panel on national-vegetation- Vegetation Classification is responsible for managing the review classification and formal adoption of these types into the National Vegetation Classification. See http://usnvc.org/wp- content/uploads/2010/10/USNVCdatabase_23Feb2016.pdf
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Table 3. Ongoing activities related to climate change assessment or study for focal areas. Agency/organization Brief description Geographic focus Duration and Stage Result/ Expected (complete, initiated) Result
Fire and Invasives Assessment Tool BLM and USFS; Great Basin Great Basin Complete Spatial assessment (FIAT) LCC tool BLM Rapid Ecological Colorado Plateaus Complete Report; spatial Assessments data U.S. Forest Service Forest Plan Revision under Rio Grande NF first; Grand Initiated for Rio Grande Revised Forest the 2012 Planning Rule; Mesa, Uncompahgre and Plan requires assessment of Gunnison NF next; San Juan climate change impacts National Forest will be upcoming
San Juan Headwaters Forest Health All are local groups that San Juan Mountains, Ongoing Vulnerability Partnership, Mountain Studies focus on natural resources Colorado assessments, Institute, San Juan Citizens in the footprint of the Focal annual reports Alliance, Public Lands Partnership Areas.
Southern Rockies Landscape Platform to access and Southern Rockies LCC Ongoing Collaborative Conservation Cooperative integrate geospatial data projects/various Conservation Planning Atlas sets, maps, and information management or for use in analysis and project plan conservation planning.
Gunnison Basin Climate Change Partnership among agencies Gunnison Basin Complete Vulnerability Vulnerability Assessment and local groups Assessment
Southwest Colorado Social- Partnership among agencies San Juan and Gunnison Beginning Assess ecological Ecological Climate Resilience and local groups basins and social resilience of area
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II. Critical Attributes for Measuring Sagebrush Vulnerability The following section describes information, where available, on specific attributes that can be used to identify condition, status or health of sagebrush ecosystems. This section describes the literature and other background information used for the data selection process outlined in the Section III. Indicator Variables.
Landscape Scale
At the site scale (~ < 1,000 ac), sagebrush ecosystems are assessed through cover, composition, and plant density by height. These indicators are typically measured using common protocols, such as line-point-intercept with height (Canfield 1941), fixed-area plots, Daubenmire quadrats (Daubenmire 1959), belt transects, visual obstruction (e.g., Robel pole, 1970), and vegetation structure (i.e., cover and height). Cover includes both plant canopy and ground cover type – bare ground, litter, rock, lichen, or moss. Indicators generated from most protocols include percent cover by species, plant species height, plant diversity, plant species frequency, percent cover by ground type, and vegetation structure (i.e., cover and height). Cover of each vegetation species or functional group in Daubenmire frames is estimated in cover classes, often 0-5%, 5-25%, 25-50%, 50-75%, 75-95% and 95-100% (Daubenmire 1959). Other important indicators for assessing sagebrush ecosystems include basal and canopy gap, snag density by height, soil type, soil stability, elevation, slope, aspect, photo points, geographic location, day and year of sampling, and site-specific disturbances (e.g., livestock grazing, erosion, fire, and vehicle tracks). Typical monitoring units are 30-, 50-, or 100-m line transects, or three, line transects arranged in a spoke design in circular plots, or a systematic series of 1-m2 plots along a line transect. The two-volume Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems (Herrick et al. 2009a, b) provides a thorough and excellent guide for assessing sagebrush ecosystems at both the site and landscape level. Ecological site assessments can also be helpful to determine departure from reference conditions and overall ecosystem health (Pellant et al. 2005). There are a variety of protocol modifications that target specific habitat characteristics. As mentioned previously, several federal agencies have adopted monitoring protocols and habitat guidelines specific to Gunnison sage-grouse (e.g. GSGRSC 2005, BLM 2016). Primary measures of Gunnison sage-grouse habitat include sagebrush canopy cover (measured along a 30-m line-point intercept), grass cover and height, forb cover and height and sagebrush height (measured in 20x50 cm Daubenmire quadrats) (see GSGRSC 2005, Appendix H).
Remote sensing techniques can assess sagebrush ecosystems at the landscape scale (> 1000 ac). The term ‘remote sensing’ refers to collection of information from afar—such as satellite or aerial imagery. Both of these data types can allow for general classification of vegetation based
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on the spectral signature of the vegetation. Multi- or hyper-spectral imagery collects information on wavelengths invisible to our eye, such as that in the ultraviolet or infrared portions of the spectrum. This can allow for quite precise categorization of plant cover types. It can also allow for assessment of drought stress and photosynthetic levels of an area. Another remote sensing data type is LiDAR, or Light Detection and Ranging, can allow for estimation of vegetation height and pattern. Some remote sensing data are freely available, and some proprietary high-resolution data sources are available through grants to groups using it for public purposes. Further, the amount and quality of open-access remote sensing data increases every year. Landscape assessment will vary spatially and temporally depending on land use goals and objectives. Important components that can be captured from imaging include land cover type, habitat edges, landscape metrics (e.g., patch size and corridors), management activities (seeding, tilling, and prescribed burns), fire history, elevation, slope, aspect, and proximity to features (structures, roads, water, energy development, and agriculture).
Indicators of desirable condition
Dominance by native shrubs, grasses, and forbs is a major indicator of desirable condition in the sagebrush ecosystems. Sagebrush-obligates like sagebrush sparrow and sage thrasher, rely on large areas of continuous sagebrush habitat (Paige and Ritter 1999). Sagebrush, forbs, and grasses are key foods for many species. Loss of native functional groups, such as native grasses or forbs, is undesirable because it leaves the system more vulnerable to invasion. In communities where biotic soil crusts naturally occur, the presence of intact crusts is an indicator of a desirable condition. When crusts are lost, this can lead to increased invasion by exotics and soil erosion, which can cause systems to depart from desirable conditions.
Many broad-scale habitat indicators for sagebrush-obligate and semi-obligate are lacking, including ideal patterns of habitat attributes, spatial requirements, and threshold values for indicators (Schroeder et al. 1999, Aldridge et al. 2008, Wisdom et al. 2011). Regardless, the amount and condition of sagebrush are key indicators across landscapes. Desired conditions depend on target species (Table 4), vegetation potential, and management goals. In the upper Rio Puerco watershed in New Mexico, which overlaps both focal areas, the desired plant community is a low-seral mosaic of sagebrush and herbaceous understory with 10-30% shrub cover that includes 16-25% big sagebrush cover (BLM 2012b). For many birds, species of sagebrush is less important than its height, density, cover, and patchiness (Paige and Ritter 1999). Merriam’s shrew, a Navajo Nation sensitive species (2008), is a potential indicator of habitat integrity in sagebrush ecosystems (Fitzgerald et al. 1982). For GUSG, sagebrush cover less than 5% is suboptimal, except for lekking habitat (GSGRSC 2005, BLM 2016).
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Critical components of sagebrush ecosystems
Critical components of sagebrush ecosystems include the amount of sagebrush cover, quality of sagebrush, grass, and forb communities, and size and arrangement of sagebrush patches on the landscape. From a wildlife management perspective, effective management for sagebrush- obligate and near-obligate species focuses on maintaining large patches of contiguous sagebrush, primarily big sagebrush species, a diversity of understory perennial native grasses and forbs for food and cover, a complement of well-connected habitats that includes adequate sagebrush cover extending above the snow during winter, and relatively open sites for foraging and breeding routines. The type and amount of important components differ across species. For example, critical components for ferruginous hawks include open, patchy sagebrush, grassland areas, and nearby tall trees, cliffs, and structures for nesting (Bechard and Schmutz 1995, Dechant et al. 2001), while critical components for sagebrush sparrow include tall, dense sagebrush and large, continuous patches of sagebrush across the landscape (Wiens and Rotenberry 1981, Knick and Rotenberry 1995, Williams et al. 2011).
Patch size and pattern on the landscape are very important components to most wildlife species, as well as overall ecosystem function and structure. Sagebrush sparrow, sage thrasher, Brewer’s sparrow, pronghorn, and Gunnison sage-grouse need fairly large expanses of sagebrush while vesper sparrow, ferruginous hawk, and loggerhead shrike occur in open, patchy sagebrush landscapes (Paige and Ritter 1999, CDOW 2003, Boyle and Reeder 2005). Small sagebrush patches (40 to 100 ha) that occur frequently (i.e., high density) and connect large sagebrush patches (> 1000 ha) may provide greater value to sagebrush-obligate species than scattered and isolated patches (Boyle and Reeder 2005). Furthermore, high quality patches of sagebrush and understory grasses and forbs are valuable components. Wildlife populations are sensitive to patch size, such that maximum densities occur in large contiguous patches of sagebrush. Sage thrasher and Brewer’s sparrow may need patches of at least 40 ha, while sagebrush sparrow may need a least a few hundred hectares (Table 4). Merriam’s shrew, in contrast, may be less able to find and use small patches because of their limited mobility, suggesting that they need very large, contiguous patches of sagebrush to persist over time (Boyle and Reeder 2005).
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Table 4. Minimum habitat components for sagebrush-obligate and near-obligate wildlife species that occur in the Four Corners and Upper Rio Grande focal areas (with the exception of Greater sage-grouse). Species Sagebrush Breeding Sagebrush Sagebrush References Patch Size Territory Cover % Height m Size (ha) Brewer's sparrow > 40 ha 0.1 - 2.4 > 10 0.5 - 1.5 Wiens et al. 1985; Petersen and Best 1987; Knick and Rotenberry 1995; Paige and Ritter 1999; Rotenberry et al. 1999; Walker 2004; Wilson et al. 2009 Sagebrush sparrow > 80 ha 0.6 - 6.3 high > 0.5 Wiens et al. 1985; Paige and Ritter 1999; Reynolds 1981; Rich 1980; Wiens and Rotenberry 1985; Lovio 1995, 1996 Sage thrasher > 40 ha 0.4 - 1.9 > 10 > 0.7 Reynolds 1981; Stephens 1985; CO strategy 2005 Loggerhead shrike 2.7 - 25 > 1.0 Collister 1994; Yosef 1994; Woods 1994; Paige and Ritter 1999 Vesper sparrow > 20 ha 0.3 - 8.2 < 15 < 0.5 Reed 1985; Perritt and Best 1989; Jones and Cornely 2002; Wiens 1969; Dechant et al. 2000; Vickery et al. 1994 Greater sage-grouse > 130 ha > 15 Aldridge and Boyce 2007; Connelly et al. 2000; Hagen et al. 2007; Wisdom et al. 2011 Gunnison sage- Large, 5 - 20 .25 - .50 GSGRSC 2005; BLM 2016; grouse - contiguous references therein Summer-Fall Gunnison sage- Large, 10 - 25 .25 - .50 GSGRSC 2005; BLM 2016; grouse - contiguous references therein Breeding Gunnison sage- Large, 30 - 40 .4 - .55 GSGRSC 2005; BLM 2016; grouse - contiguous references therein Winter
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III. Vulnerability Assessment of Focal Resources: Sagebrush Ecosystems
Methodological approach
Background As commonly applied to climate change issues, VA provide a structure for organizing complex information and addressing uncertainty (IPCC 2007). Although there are various definitions, vulnerability is generally thought of as the susceptibility of a target to negative impacts from some disturbance (Füssel 2007, Hinkel 2011). Assessment of climate change vulnerability typically considers three elements: exposure, sensitivity, and adaptive capacity (Glick et al. 2011). Exposure is the magnitude of climate and climate-related phenomena (e.g., fire, floods) whereas sensitivity (i.e., response to exposure) and adaptive capacity (i.e., ability to cope with negative impact) are traits or conditions that predict how a target will respond to that disturbance. These definitions can vary according the goals and the target of an analysis. For instance, sensitivity may represent the innate traits or qualities of a target that increase the likelihood it will experience a negative response. Alternatively, sensitivity may represent the potential cost of a disturbance (e.g. watershed values - Furniss et al. 2013). Adaptive capacity can be identified as the intrinsic and/or externally driven mechanisms that represent the potential for a target or system to withstand a disturbance.
Table 5. Framework for assessing vulnerability of focal resources in the Southern Rockies Landscape Conservation Design Element Definition Examples Indicators Exposure External threat to the • Human Impacts • Urbanization target species, • Natural disturbances • Wildfire potential system, or place • Climate change • Magnitude departure in temperature
Sensitivity Qualities that make • Traits associated with • Narrow physiological the target more increased negative threshold susceptible to response • Current departure from negative impacts • Indicators of potential reference condition from disturbance or cost of disturbance • Presence of T&E species threat • High value watersheds
Adaptive Capacity The ability of the • Traits/conditions • Wide physiological tolerance target to cope with associated with • Diverse prey base disturbance or threat resilience • Capacity to implement • Potential for conservation action (e.g. land successful management ownership profile) intervention • Effective management options available (e.g. thinning can alter wildfire outcome)
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Many species-specific assessments define adaptive capacity through the identification of intrinsic traits, whereas landscape assessments often include externally driven sources of adaptive capacity such as landscape context and potential for management intervention. For the SRLCC assessments we specify definitions and criteria that are inclusive and adaptable to multiple scales of assessment and uses (Table 5).
Structure Vulnerability assessments take a wide range of forms and approaches (Glick et al. 2011). The most effective vulnerability assessments are tailored to address specific objectives of resource managers or others who will use the information for management decisions (Friggens et al. 2013). For the assessments of focal resources with the Southern Rockies Landscape Conservation Cooperative, we identified a Vulnerability Assessment (VA) framework that could be used to identify relevant information and assign it to the appropriate measure of vulnerability. The VA framework contains an inclusive list of potential measure of vulnerability that relates to both species specific and landscape considerations. We use this framework to identify datasets and analyses that could inform our assessment as an indicator of one of the vulnerability elements. Once we compiled relevant and meaningful indicators, we estimate vulnerability as the collective impact of exposure and sensitivity weighted against adaptive capacity (Figure 4). Vulnerability is then visualized by comparing the impact scores with adaptive capacity scores using a matrix (Table 6). This system provides considerable flexibility so that assessments can identify vulnerability across diverse focal resources and be quickly tailored to user needs.
Figure 4. Structure underlying the estimation of vulnerability; exposure and sensitivity collectively represent impact to a resource of interest. Adaptive capacity modulates this impact resulting in more or less vulnerability. In the system used in this assessment, we create scores representing the cumulative impact of disturbances and sensitivities (impact) and adaptive capacity and then compare relative impact and adaptive capacity values to generate vulnerability classes.
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Table 6. Matrix of Impact versus Adaptive Capacity Scores. Indicators are summed to give total scores for Exposure (E), Sensitivity (S), and Adaptive Capacity (AC). Exposure and Sensitivity are added together to represent impact and all values are rescaled to a 1-5 range. Impact increases as values increase along the horizontal and Adaptive Capacity increases as values increase along the vertical. Vulnerability is determined by the relative Impact versus Adaptive Capacity of the Focal Resource according to these potential combinations. Vulnerability Impact (Exposure + Sensitivity) Value 1 2 3 4 5 Adaptive 1 Low Intermediate High Very High Highest Capacity 2 Low Intermediate High Very High Very High Value 3 Very Low Low Intermediate High Very High 4 Very Low Very Low Intermediate High High 5 Lowest Very Low Intermediate Intermediate High Measuring vulnerability The framework identified in Table 6, encompasses the overarching structure to the assessment process. Several steps are involved in the use of this framework to generate vulnerability assessment projects. 1) We identified relevant data that relate to potential threats or issues, state of the focal resource, and traits or conditions that influence how that resource will respond to disturbance. We call these data indicators. For each focal area, we considered a diverse set of data and analysis and selected those that had some capacity to represent the potential disturbance or response of a resource to a disturbance. We collected some information on potential stressors and threats during conversations during Adaptation Forums. Additional threats or stressors were identified from other assessments or primary literature. 2) For each indicator, we determine whether it most appropriately measures exposure, sensitivity or adaptive capacity. Depending on the focal resource, some indicators maybe used to measure more than one vulnerability element. For instance, road density, a common measure of human disturbance and activity might be considered under exposure. Alternatively, roads can also represent a barrier to movement and contribute to a focal resources’ sensitivity to disturbance. The assignment of a particular dataset to a particular element was made based on the relationship of the focal resource to that data and where it was determined the measure would provide the most meaningful output. 3) Once identified and assigned to a vulnerability element, a threshold of effect was determined for each indicator. This threshold was the cutoff value based on the original range of data values that would determine whether an area was considered effected or not. For exposure values that represented a meaningful impact were given a score of 1. Similarly, values of data within datasets contributing to sensitivity were assigned a value
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of 1 where they were considered to represent a condition of increased potential negative response or cost. Adaptive capacity represents resilience and data values that could be inferred to represent greater resilience were assigned a 1. For each element, scores were added to create cumulative indices representing Exposure, Sensitivity, and Adaptive Capacity. Exposure and Sensitivity scores were combined to create an impact score and this was compared to Adaptive Capacity to generate vulnerability scores (Table 6).
Spatial units This assessment is focused on two geographic areas. The Four Corners region consists of the San Juan River Basin and the Little Colorado River Basin in their entirety. The Upper Rio Grande region primarily consists of Rio Grande Basin in Colorado and much of New Mexico. This region also includes portions of streams in the Pecos River, Arkansas River, and Canadian River basins. All spatial data has been processed and summarized at the HUC12 subwatershed scale.
Indicator variables
We selected indicator variables based on results of our literature review, variables used in previous assessments of sagebrush ecosystems, and limiting factors discussed by managers at the 2016 adaptation forum workshops.
We aimed to use spatial datasets covering the full extent of our region. We used presence/absence scoring of exposures, sensitivities, and adaptive capacities, an approach that has been found to perform well in vulnerability assessments. For each HUC12, vulnerability is assessed by creating single indices (Table 7, 8, and 9) for each of the data types: exposure, sensitivity, and adaptive capacity. Exposure considers the proximity or percent area of known sources of disturbance or threats as well as the degree to which a watershed might be affected by a disturbance agent. Measures include wildfire hazard, potential for tree encroachment, presence of energy development, road density, and other disturbance processes. Sensitivity- considers the potential loss or decline of desirable conditions when exposed to various disturbance agents. Those areas with the highest value are considered most sensitive to disturbances that may cause declines. We identify the most valuable HUCs as those with high degree of desirable attributes. Adaptive capacity considers measures that indicate increased coping capacity. We include potential management capacity (conservation potential), resistance and resilience measures for fire and invasive species, and potential for migration to new areas (continuity). Our analysis relied on presence absence data, which is widely available but population trends are a better estimated from abundance data.
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Exposure indicators (see Table 7 and Figure 5) Vegetation Type Change and Soil Sensitivity Source: Peterman and Ferschweiler 2016 Description: Data are vegetation change of any type and soil sensitivity factors for 8 climate models (RCP 4.5 and 8.5) for the time period 2041 to 2050. This dataset combines the sensitive soils datasets for the Southern Rockies Landscape Conservation Cooperative, the projected future vegetation and the simulated historic potential natural vegetation, created using the MC2 dynamic global vegetation model. Justification: Sagebrush ecosystems are particularly vulnerable to changes in site conditions, especially drought, fire, development, and soil disturbance. Loss of vegetation cover and production due to drought and warm temperatures can lead to increased erosion and loss of important soil properties, such as water holding capacity, stability, organic matter, and nutrients (Breshears et al. 2003, Munson et al. 2011). Peterman and Ferschweiler (2016) identify soils sensitive to drought and warming and where projected changes in vegetation could lead to further and prolonged soil disturbance. We used any change in vegetation type to help identify area that might be more vulnerable to soil disturbance (such as a loss in vegetation cover or production). Data Compilation: Subwatersheds, HUC12, were classified with some expected change (1) or no change (0) irrespective of whether they also have sensitivity factors. NOTE: processing steps were largely recorded in the Soils Data Workflow file. Uncertainties: The MC2 global vegetation model simulates the dynamics of lifeforms rather than species, so the data represents vegetation change of any type, not specific to sagebrush species or subspecies. Subwatersheds with any change in vegetation type are coded 1 irrespective of type change. Still, this score indicates those watersheds with a projected shift in climates suitable to current plant assemblages and thus increased likelihood of community disruption and destabilization.
Change in Development, 2040 Source: Land Use/Land Cover, USGS 2014 Description: The scenario-construction process used by USGS incorporated input from an integrated modeling framework to provide top-down proportions of LULC change, with overall socioeconomic assumptions from the IPCC SRES scenarios driving differences between projected scenarios. Within the southwest, agreement was high for trends of change. This assessment used values generated by comparing the difference in the proportion of land classification between year 2005 and 2040 under an A1B emission scenario. Areas are characterized by a high percentage (20% or greater) of constructed material (concrete, asphalt, buildings, etc.).
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Justification: Development of any kind that involves land clearing is a threat to sagebrush ecosystems. Modification of riparian areas because of water withdrawals, habitat fragmentation, and pollution from nearby development and industries threaten sagebrush ecosystems. As urban centers grow over time, they may fragment or eliminate nearby sagebrush habitats. Data Compilation: We converted these values into future estimated exposure to development by classifying watersheds according to trend observed from modeled data under the A1B scenario. Watersheds with an increase in development were given a score of 1 within the exposure element.
Change in Crop Cover, 2040 Source: Land Use/Land Cover, USGS 2014 Description: The scenario-construction process used by USGS incorporated input from an integrated modeling framework to provide top-down proportions of LULC change, with overall socioeconomic assumptions from the IPCC SRES scenarios driving differences between projected scenarios. Within the southwest, agreement was high for trends of change. This assessment used values generated by comparing the difference in the proportion of land classification between year 2005 and 2040 under an A1B emission scenario. Areas dominated by vegetation planted or intensively managed for the production of food, feed, or fiber; or is maintained in developed settings for specific purposes. Includes cultivated crops, row crops, small grains, and fallow fields Justification: Land conversion from sagebrush ecosystems to other types of land cover or development pose a major threat to the ecosystem. Conversion to cropland, use of pesticides and herbicides can directly and indirectly impact sagebrush ecosystems. In Colorado, much of the historic range of sagebrush has been reduced by conversion to agriculture (CPW 2015). Withdrawal of water from the San Juan River and Rio Grande in New Mexico for crops and municipalities reduces flows upon which several imperiled fish and invertebrates depend. It also decreases the extent and functionality of riparian habitats, which are critical to several species (NMDFG 2016). Data Compilation: We converted these values into future estimated exposure to cultivated crop by classifying watersheds according to trend observed from modeled data. Watersheds with an increase in cultivated crop were given a score of 1 within the exposure element.
High to Very High Wildfire Potential, % Source: US Forest Service 2014 Description: Estimate of wildfire fire risk; Wildfire Hazard Potential (WHP) for the conterminous United States (270-m GRID), v2014 classified
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Justification: Wildland fire threatens sagebrush ecosystems (Miller et al. 2011, USFWS 2013, Chambers et al. 2014), but impacts differ across the range of sagebrush-dominated ecosystems. Factors that influence these threats are driven by a site’s climate, soil type, and existing plant community (Chambers et al. 2014). Climate change may increase fire frequency due to overall increases in temperatures, length of wildfire seasons, and severity of fire weather (Westerling et al. 2006). Though wildfires have naturally occurred in sagebrush ecosystems, recent fires have likely been larger, more severe, and more frequent than in the past in many areas such as the Snake River Plain (e.g., Whisenant et al. 1990). Data Compilation: Total area (km2) classified as having High or Very High Wildfire Hazard Potential; Percent area of HUC classified as having High or very High Wildfire Hazard Potential. Subwatersheds with >0% very high to high wildfire hazard potential are coded 1. Uncertainties: Though increases in negative fire behavior is well documented in some areas, it may not be the case in all sagebrush-dominated systems.
Energy Development Source: BLM 2012 and ESRI 2015. We compiled data from two sources: 1. BLM: https://landscape.blm.gov/geoportal/catalog/search/browse/browse.page2 2. Oil and gas well in US from ESRI (2015) (found at : http://www.arcgis.com/home/item.html?id=49102e45079445fabdb9b5c0679d96ee Description: Number of oil and gas development sites, solar sites, mineral disposal sites Justification: Land conversion from sagebrush ecosystems to other types of land cover or development pose a major threat to the ecosystem. Sagebrush ecosystems have been significantly impacted by energy development, specifically oil and gas extraction, surface coal mining, and solar and wind development, and associated infrastructure (powerlines, pipelines, and roads) (NMDFG 2006). Coal bed methane extraction in the San Juan Basin in New Mexico has caused much habitat loss and degradation (BLM 2003). As sagebrush patches shrink, wildlife become vulnerable to predation and isolation (NMDFG 2016). Increased human disturbance related to development can also reduce population viability of some wildlife species, such as sage grouse (Walker et al. 2007, CPW 2015). Data Compilation: Downloaded oil and gas well in US data from ESRI (found at : http://www.arcgis.com/home/item.html?id=49102e45079445fabdb9b5c0679d96ee) Clipped the shapefile to the project boundary; Projected the clipped shapefiles to NAD_1983_UTM_Zone_12N; Selected by attributes for oil and gas wells where “Status” is Active; Includes 'Active' , 'Approved Permit' , 'Domestic Well' , 'Drilling' , 'Gas Storage (liquefied gas)' , 'Injecting' , 'New' , 'New Permit' , 'Permitted Location' , 'Producing Gas Well', 'Producing Oil Well', 'Shut in Oil Well' , 'Water Disposal Well (converted from oil well)' , 'Water Well', or similar; Added a field to the shapefile called “Count”, short integer, that equals 1 (for all rows); Joined the oil and gas well shapefile to the WBDHUC12_LCC shapefile; Joined data from another layer based on spatial location; Summarized the
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attributes by SUM; Output will have a field “Count_” which lists the number of wells per each HUC12; data classified by HUC12 – any wells present = 1, wells not present = 0.
Table 7. Indicators used to evaluate exposures for sagebrush ecosystems
Description Range How Used Source Vegetation Type Change/Soil 0/1 Change = 1 Peterman and Ferschweiler Sensitivity, 2041-2050 2016 Change in Development, 2040 +/- Increase = 1 USGS 2014
Change in Crop, 2040 +/- Increase = 1 USGS 2014 High to Very High Fire Potential, % 0-81.9 > 0 = 1 USFS 2014
Energy (oil & gas wells, solar), count 0/1 Present = 1 ESRI 2015, BLM 2012
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Figure 5. Sagebrush habitat exposure variables for the Four Corners and Upper Rio Grande focal areas
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Sensitivity Indicators (see Table 8 and Figure 6) Terrestrial T and E Critical Habitat, % Source: USFWS 2017 https://ecos.fws.gov/ecp/report/table/critical-habitat.html Description: Percent of threatened and endangered terrestrial critical habitat for plants and animals; Hermit milkvetch (Astragalus eremiticus) (formally Shivwits milk-vetch), Navajo sedge (Carex specicola), Pagosa ipomopsis (Ipomopsis polyantha), Mexican gartersnake (Thamnophis eques), meadow jumping mouse (Zapus hudsonius), narrow-headed gartersnake (Thamnophis rufipunctatus), Parachute beardtongue (Penstemon debilis), Mexican spotted owl (Strix occidentalis lucida) as well as one shapefile for all T&E species (including those not listed above, such as Gunnison Sage-Grouse) Justification: Gunnison sage-grouse, sagebrush obligates, are an ESA-listed threatened species (USFWS 2014). Because sage-grouse depend on sagebrush for food and cover, select habitat at multiple scales, and are sensitive to landscape change, they are a focal species for managing sagebrush ecosystems (Paige and Ritter 1999, Stiver et al. 2010, Hanser and Knick 2011) and can be helpful in identifying attributes important to other obligates (Rowland et al. 2006, Wiens et al. 2008, Hanser and Knick 2011, Hayward and Suring 2013), such as sagebrush sparrow, sage thrasher (Oreoscoptes montanus), and pygmy rabbit. Data Compilation: Downloaded data from ECOS website, calculated area and percent of critical habitat of T and E species; classified HUCs based on presence of any critical habitat, presence = 1.
Wildlife Diversity Source: Gap Analysis Program (GAP;
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(Brachylagus idahoensis), and mule deer (Odocoileus hemionus) (Dealy et al. 1981). Tall sagebrush provides cover for many species and nesting sites for obligates, such as sage grouse, Brewer’s sparrow (Spizella breweri), and sagebrush sparrow (Artemisiospiza nevadensis). Data Compilation: GAP data was downloaded for each species. GAP provides an alphanumerical code, species name followed by season (1 = summer, 2 = winter, 3 = year round) to represent known or probable habitat for species. Any designation was counted as present within a subwatersheds. Individual species’ ranges were compiled into a single dataset and summarized to indicate number of species per subwatershed. HUC12 with 9 or more species were coded 1.
Pinon-Juniper Interface, % Source: LANDFIRE https://www.landfire.gov/vegetation.php Description: Area_km2: area of pinon-juniper interface in each HUC12 (identifies sagebrush land cover cell which are within 120 m of conifer or PJ land cover cells as categorized in the GAP/reGAP land cover raster set, BLM, USGS GAP, 2010); pctSagePJ: % of PJ-sagebrush interface, 0-45% Justification: Conifer encroachment reduces habitat quality for sage brush obligate species, such as the Sage-grouse, by fragmenting sagebrush and grassland communities. Sage- grouse are known to abandon leks where conifer cover is as low as 4% (Baruch-Mordo et al., 2013). Data Compilation: To measure the potential threat from conifer encroachment, we use two measures: classified canopy cover and area of sagebrush-conifer interface within each watershed. Percent cover of trees. We develop percent tree cover by reclassifying LANDFIRE datasets into <10%, 10-30%, and >30% brackets and calculate percent cover for each category within each watershed. We used data produced by BLM that identifies sagebrush conifer interface across the west. We calculate the percent areas within each watershed and classify the resulting data layer as at low, medium and high risk for conversion. Subwatersheds in the top 25th percentile of P-J interface are coded 1.
Medium to High Intensity Development, % Source: National Land Cover Database (NLCD 2011, https://www.mrlc.gov/nlcd11_data.php) Description: Amount of medium to high intensity development, area and percent in HUC12 Justification: Urban development negatively impacts sagebrush ecosystems by causing fragmentation, loss, and disturbance. Modification of riparian areas because of water withdrawals, habitat fragmentation, and pollution from nearby development and industries threaten sagebrush ecosystems.
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Data Compilation: Downloaded NCLD 2011 Land Cover and NCLD 2011 Percent Developed Imperviousness layers for the conterminous United States (found at : https://www.mrlc.gov/nlcd11_data.php)
Road Density, km/km2 Source: 2016 TIGER/Line Shapefiles: Roads (Available at : http://www.census.gov/cgi- bin/geo/shapefiles/index.php?year=2016&layergroup=Roads) Description: Density of primary and secondary roads Justification: Roads can fragment sagebrush ecosystems and can cause these systems and wildlife that depend on them vulnerable; roads can represent a barrier to movement and contribute to a focal resources’ sensitivity to disturbance. Vehicle collisions can lead to direct mortality and roads can block normal behavior patterns of wildlife. As sagebrush patches shrink, wildlife become vulnerable to predation and isolation (NMDFG 2016). Data Compilation: Downloaded road data from 2016 TIGER/Line Shapefiles: Roads (found at: http://www.census.gov/cgi- bin/geo/shapefiles/index.php?year=2016&layergroup=Roads); subwatersheds in the top 25th percentile for road density are coded 1.
Low Soil Resistance and Resilience Source: Data layer of soil taxonomy for SRLCC provided by John Bradford, USGS ([email protected]). Description: Data consists of 10km soil classifications corresponding to several temperature (hyperthermic, thermic, mesic, frigid, cryic) and moisture (aridic extreme, aridic typic, areidic weak, xeric dry, xeric typic, ustic, udic) regimes. Low resistance – ability of an area to recover from disturbance such as wildfire or drought; Low resilience – ability of an area to remain largely unchanged in the face of stress, disturbance, or invasive species. Resilience is based on soil temperature and resilience on soil moisture regimes where low resistance and resilience is typically associated with Warm/Dry (mesic/aridic, bordering xeric) and Cool/Dry (frigid, aridic) soils. Justification: Soil temperature and moisture regimes are a strong indicator of ecological types and of resilience to disturbance and resistance to invasive annual plants. Resilience and resistance predictions coupled with landscape cover of sagebrush can provide critical information for determining focal areas for targeted management actions. Soil temperature and moisture regimes are two of the primary determinants of ecological types and of more detailed ecological site descriptions. See Chambers et al. 2014, 2017; Maestas 2016; Bradford 2017. Typically, sagebrush on cool, moist, and more productive sites have been identified as more resistant and resilient than those at drier, warmer sites.
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Data Compilation: We used a simplified version of schema presented by the above references. We classified each HUC12 as containing a majority of Aridic type (Low resilience), Xeric (Moderate to Moderate-High Resilience), Mesic (Low to Moderate-Low resistance), Frigid (Moderate resistance) or Cryic (High resistance) type soils. HUC with a majority count of low resistance and/or resilience types (Aridic or Mesic) were given a score of 1 indicating increased potential sensitivity to disturbance. Uncertainties: The response of sagebrush ecosystems to fire and invasive species is complex as can be seen by reviewing the discussions presented by Chambers et al. 2014, 2017, Maestas 2016, and Bradford 2017. In this analysis, we have used a very basic classification system to distinguish between areas of potential high versus low sensitivity and adaptive capacity. Users interested in the resilience/resistance framework and its implications may wish to develop schema unique from that used in this analysis.
Table 8. Indicators used to evaluate sensitivity for sagebrush ecosystems
Description Range How Used Source Terrestrial T and E Critical 0-100 Present = 1 USFWS; Habitat, % https://ecos.fws.gov/ecp/report/table/c ritical-habitat.html Wildlife Diversity 0-12 > 9 species = 1 USGS/GAP 2017 (14 terrestrial animal species) Pinon-Juniper Interface, % 0-63 >25th Percentile = 1 LANDFIRE https://www.landfire.gov/vegetation.ph p Development Med-High, % 0-34 > 0 = 1 NLCD 2011 https://www.mrlc.gov/nlcd2011.php
Road Density, km road/km2 0-2.6 >25th Percentile = 1 Tiger 2016 land area
Low Soil Resistance and 0/1 Present = 1 Chambers et al. 2014, 2017; Maestas Resilience Present = 1 2016; Bradford 2017
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Figure 6. Sagebrush habitat sensitivity variables for the Four Corners and Upper Rio Grande focal areas
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Adaptive Capacity Indicators (see Table 9 and Figure 7) Sagebrush Cover, % Source: LANDFIRE https://www.landfire.gov/vegetation.php Description: Area and % cover of sagebrush, range 0-92% Justification: Landscape cover of sagebrush is one of the key determinants of sage-grouse population persistence and, in combination with an understanding of resilience to disturbance and resistance to invasive annuals, provides essential information both for determining priority areas for management and appropriate management actions. From a case example in Rowland et al. 2013, for Greater Sage-grouse, they used the following classes/thresholds:
Sagebrush patches <320ac, >320ac, > 15% sagebrush canopy cover Sagebrush canopy cover 0-5%, 6-15%, 16-25%, and >25% Perennial grass cover 0-15%, >15% for mesic; 0-10%, >10% for arid Perennial grass height < 7.1 in, > 7.1 in for all sites (Aldridge and Boyce 2007, Connelly et al. 2000, Hagen et al. 2007) For Gunnison Sage-grouse, see Table 2 for classes and thresholds, the GSGRC 2005 has much more fine-scale habitat guidelines. Data Compilation: Landscape cover of sagebrush is a measure of large, contiguous patches of sagebrush on the landscape and is calculated from remote sensing databases such as LANDFIRE. Percent cover derived from the LANDFIRE EVT raster data; HUCs with at least 25% cover are coded 1.
Sagebrush Core Areas, % Source: LANDFIRE https://www.landfire.gov/vegetation.php and FRAGSTATS analysis (see Appendix A). Description: Area and % sagebrush cover core areas, amount of HUC12 with core, range 0- 100% Justification: Patch size and pattern on the landscape are very important components to most wildlife species, as well as overall ecosystem function and structure. Sagebrush sparrow, sage thrasher, Brewer’s sparrow, pronghorn, and Gunnison sage-grouse need fairly large expanses of sagebrush while vesper sparrow, ferruginous hawk, and loggerhead shrike occur in open, patchy sagebrush landscapes (Paige and Ritter 1999, CDOW 2003, Boyle and Reeder 2005). Small sagebrush patches (40 to 100 ha) that occur frequently (i.e., high density) and connect large sagebrush patches (> 1000 ha) may provide greater value to sagebrush-obligate species than scattered and isolated patches (Boyle and Reeder 2005). Data Compilation: We used the area-weighted-mean patch size (i.e., core area) output generated from the 1-km moving window analysis as an indicator of adaptive capacity in the vulnerability assessment. The moving window analysis in Fragstats outputs a raster for each
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estimated variable. After converting the raster from floating to integer, we added a class field to assign values to quantile membership – highest 25% = class 4, second 25% = class 3, third 25% = class 2, and lowest 25% = class 1. We ran the tabulate area tool to estimate the amount (m2) of each class within a subwatershed; area was converted to percent of each class within a HUC12. We determined that the highest 25% (class 4) represent large core areas, and we used this class in the adaptive capacity assessment. Subwatersheds with greater than 32% of class 4 core areas are coded 1, and all other subwatersheds are coded 0. See Appendix A for more detail.
Protected Areas, % Source: https://gapanalysis.usgs.gov/padus/ Description: % protected areas Justification: Land management agencies are under directives to protect sagebrush habitat for sensitive and endangered species. We assume that successful management for adaptive capacity is more likely on publicly-owned land. Data Compilation: We downloaded data from the USGS Protected Areas Database of the United States (https://gapanalysis.usgs.gov/padus/data/download/). We selected features to create a shapefile of polygons representing areas owned and managed by government agencies (federal, state, local, and tribal). We used the intersect tool to calculate the area of these lands within catchments. Subwatersheds with >30% protected areas are coded 1.
High Soil Resistance and Resilience Source: Data layer of soil taxonomy for SRLCC provided by John Bradford, USGS ([email protected]). Chambers et al. 2014, 2017; Maestas 2016; Bradford 2017 Description: Data consists of 10km soil classifications corresponding to several temperature (hyperthermic, thermic, mesic, frigid, cryic) and moisture (aridic extreme, aridic typic, areidic weak, xeric dry, xeric typic, ustic, udic) regimes. Low resistance – ability of an area to recover from disturbance such as wildfire or drought; Low resilience – ability of an area to remain largely unchanged in the face of stress, disturbance, or invasive species. Typically, sagebrush on cool, moist, and more productive sites have been identified as more resistant and resilient than those at drier, warmer sites. Justification: Soil temperature and moisture regimes can be used as an indicator of ecological types and of resilience to disturbance and resistance to invasive annual plants. Resilience and resistance predictions coupled with landscape cover of sagebrush can provide critical information for determining focal areas for targeted management actions. The available data for the soil temperature and moisture regimes were recently compiled to predict resilience and resistance. Soil temperature and moisture regimes are two of the primary determinants of ecological types and of more detailed ecological site descriptions. See Chambers et al. 2014, 2017; Maestas 2016; Bradford 2017 for further discussion.
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Data Compilation: We used a simplified version of schema presented by the above references. We classified each HUC12 as containing a majority of Aridic type (Low resilience), Xeric (Moderate to Moderate-High Resilience), Mesic (Low to Moderate-Low resistance), Frigid (Moderate resistance) or Cryic (High resistance) type soils. HUC with a majority count of high resistance and/or resilience types (Xeric plus Frigid/Cryic) were given a score of 1 indicating increased resilience to disturbance. Uncertainties: The response of sagebrush ecosystems to fire and invasive species is complex as can be seen by reviewing the discussions presented by Chambers et al. 2014, 2017, Maestas 2016, and Bradford 2017. In this analysis, we have used a basic classification scheme to distinguish between areas of potential high versus low sensitivity and adaptive capacity. Users interested in the resilience/resistance framework and its implications may wish to develop schema unique from that used in this analysis.
Table 9. Indicators used to determine adaptive capacity
Description Range How Used Source Sagebrush Cover, % 0-92 > 25% = 1 LANDFIRE https://www.landfire.gov/vegetation .php Core Areas, % 0-100 >32% = 1 LANDFIRE https://www.landfire.gov/vegetation .php Protected Areas, % 0-100 >30% = 1 https://gapanalysis.usgs.gov/padus/
High Soil Resistance and 0/1 Present = 1 Chambers et al. 2014, 2017; Maestas Resilience Present = 1 2016; Bradford 2017
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Figure 7. Sagebrush habitat adaptive capacity variables for the Four Corners and Upper Rio Grande focal areas
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IV. Results
Four Corners Exposure The most common threats for sagebrush in the Four Corners include change in vegetation type and very high to high wildland fire potential, energy development and change in cropland cover (Table 10). The majority of subwatersheds had minimal to low exposure scores, and less than 1% scored high (Figure 8). Sensitivity Sensitivity scores are relatively low, with very few subwatersheds scoring above moderate; less than 1% of subwatersheds scored very high (Figure 9). Common sources of high sensitivity include high obligate diversity (increased cost of disturbance), presence of medium to high intensity development, high road density, and subwatersheds with low resistance and resilience soil types. Adaptive Capacity Adaptive capacity scores are generally low across the Four Corners; no subwatersheds have a very high adaptive capacity and less than 1% of subwatersheds received a high score (Figure 11). The most commonly identified sources of adaptive capacity include high percent of sagebrush cover and sagebrush core areas; less than 10% of subwatersheds have high soil resistance and resilience (Table 12). Overall trends High to very high vulnerability scores in subwatersheds in the Four Corners are driven by moderate to high threats, high sensitivity, and minimal to low adaptive capacity. High to very highly vulnerability was most common in areas near Farmington and Gallup (Figure 12). Very low to low vulnerability was associated with intermediate adaptive capacity and low exposure scores.
Upper Rio Grande Exposure The most common sources of exposure in the Upper Rio Grande include change in vegetation type, change in cover crop, and very high to high wildland fire potential (Table 10). Expected impacts from change in development and activities associated with energy development were less frequent within this focal area. The majority of subwatersheds received minimal to low exposure scores, and less than 3% of subwatersheds have high to very high exposure scores. Sensitivity Sensitivity scores are relatively low, with very few subwatersheds scoring above moderate; less than 4% of subwatersheds score as high and very high (Figure 9). The most widespread sources
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of high sensitivity include obligate diversity, presence of medium to high intensity development, and subwatersheds with low resistance and resilience soil types (Table 11). Adaptive Capacity Adaptive capacity scores are generally minimal to low across the Upper Rio Grande; less than 1% of subwatersheds have high to very high adaptive capacity scores (Figure 11). Protected areas contribute the most to high adaptive capacity scores, followed by sagebrush cover, core areas, and the presence of soil types associated with high resistance and resilience (Table 12). Overall trends Subwatersheds in the Upper Rio Grande with high to very high vulnerability scores tend to have high sensitivity, high threats, and low adaptive capacity. The highest vulnerability scores occur for subwatersheds along the Rio Grande corridor (Figure 12). Low vulnerability scores are associated with subwatersheds with low exposure, low sensitivity, and intermediate to very high adaptive capacity scores.
V. Discussion and Conclusions Across the SRLCC focal areas, sagebrush ecosystems, already at the southern extent of their range in the western U.S., are likely to be particularly vulnerable to disturbances such as wildland fire and development. In this assessment, we estimate the cumulative effect of several of these indicators to identify “hotspots” of potential vulnerability. In particular, we note such hotspots near Farmington and Gallup in the Four Corners area and along the Rio Grande corridor in the Upper Rio Grande watershed.
In the Four Corners, subwatersheds with the highest vulnerability occur near Farmington and Gallup (Figure 12). Near Farmington, sagebrush habitats are exposed to high level of energy development and roads and many subwatersheds contain a high proportion of pinon-juniper interface (Figure 5). These areas are also likely to experience some level of increase in agricultural activity (Figure 5) and change vegetation type due to change in climate (Figure 5). At the same time, these watersheds contain protected areas, high wildlife diversity, and high levels of sagebrush cover (Figures 6 and 7). Subwatersheds near Gallup have similar characteristics although wildfire appears to be a greater issue in this area (Figures 5 and 8). Subwatersheds with soil types associated with high resistance and resilience are concentrated in the northernmost region of the Four Corners and, although they also tend to contain high percentages of protected areas, did not necessarily contain large sagebrush cover or sagebrush core areas (Figures 6, 7 and Tables 11, 12).
Subwatersheds with high to very high vulnerability in the Upper Rio Grande Focal area occur along the Rio Grande corridor, especially near urban areas, and the San Luis Valley in southern
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Colorado (Figure 12). These areas contain critical wildlife habitat and high wildlife diversity, and also had several indicators of increased exposure including projected increases in development, and loss of suitable climate conditions (projected decrease in vegetation type) (Figures 5, 6, and7). Existing high road density and less resilient and resistant soil types also contribute to high overall vulnerability scores in these areas (Figures 5, 6, 7, and 12).
In the San Luis Valley, high vulnerability scores result from expected change in vegetation type, increase in development and crop cover, high wildland fire risk (Figures 5 and 9). These disturbances are likely to have a high cost within the San Luis Valley as this area contains critical wildlife habitat, high obligate diversity, and presence of sagebrush high cover and sagebrush core areas (Figures 6, 10, and 11). The San Luis Valley also contained a predominance of soil types associated with low resistance and resilience (Figure 6).
Within the Upper Rio Grande, subwatersheds with soil types indicative of high resistance and resilience occur mostly in mountainous regions and include areas with good sagebrush cover (>0.25 ) and core areas (Figure 7). These subwatersheds, which tend to occur in the northern regions of the Upper Rio Grande, received relatively high adaptive capacity scores (Figure 11) and, ultimately low vulnerability scores (Figure 12).
Comparison of Upper Rio Grande and Four Corners Focal Areas
In general, both focal areas show a similar proportion of subwatersheds affected by change in vegetation type, development, and low soil resistance and resilience (Tables 10-12). Each focal area also contain similar proportions of watersheds supporting terrestrial threatened and endangered species and high obligate diversity (Tables 10-12). Wildland fire, energy development, pinon-juniper interface, road density, are more commonly issues in the Four Corners than in the Upper Rio Grande focal area (Figure 5, Table 10). In addition, Four Corners subwatersheds tend to have high sagebrush cover and sagebrush core areas, which indicates exposure to these disturbances might be particularly costly in terms of potential loss of important sagebrush habitat (Figure 6, Table 11). Increase in cropland activity may be more of an issue in the Upper Rio Grande (Figure 5). Upper Rio Grande subwatersheds generally contain a greater proportion of protected areas and areas with high soil resistance and resilience types (Figures 5 and 6). Though both focal areas contained subwatersheds with soil types associated high resistance and resilience, they tend to occur in mountain regions that have little overlap with sagebrush core and cover. In general, sagebrush ecosystems in the southern portion of the SRLCC do not exist in areas that have soils that support resilience and resistance to wildfire and invasive grass species.
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Table 10. Percentages of HUC12 subwatersheds affected by exposure indicators in the Four Corners (FC) and Upper Rio Grande (URG).
Description How Used % FC % URG Vegetation Type Change Change = 1 57 55 Change in Development, 2040 Increase = 1 3 7 Change in Crop, 2040 Increase = 1 17 29 High to Very High Fire Potential > 0 = 1 35 29 Energy (oil & gas wells, solar) Present = 1 18 7
Table 11. Percentages of HUC12 subwatersheds affected by sensitivity indicators in the Four Corners (FC) and Upper Rio Grande (URG).
Description How Used % FC % URG Terrestrial T and E Present = 1 17 15 Wildlife Diversity, n = 14 > 9 species = 1 54 43 Pinon-juniper Interface >25th percentile = 1 18 5 Development Med-High >0 = 1 38 35 Road Density >25th percentile = 1 32 18 Low Resistance and Resilience Soil Present = 1 30 28 Types
Table 12. Percentages of HUC12 subwatersheds affected by adaptive capacity indicators in the Four Corners (FC) and Upper Rio Grande (URG).
Description How Used % FC % URG Sagebrush Cover > 25% = 1 36 17 Core Areas > 32% = 1 31 14 Protected Areas > 30% = 1 33 52 High Resistance and Resilience Soil Present = 1 6 12 Types
Core Area Analysis
Sagebrush near core areas were associated with low to moderate vulnerability scores indicating several areas with potential conservation opportunities (Figure 11). In this analysis, we favored the presence of large patches as a measure of adaptive capacity, assuming that HUC12 that comprised or contained some portion of core habitat would be more resilient and able to persist in the face of disturbances that HUC12 that contain smaller fragmented patches of
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sagebrush. Conservation efforts focused on these relatively resilient areas may be more successful in the face of changing climate and fire regimes.
Small sagebrush patches (40 to 100 ha) that occur frequently (i.e., high density) and connect large sagebrush patches (> 1000 ha) may provide greater value to sagebrush-obligate species than scattered and isolated patches (Boyle and Reeder 2005). Our criteria for core area considered a 1km (776) moving window and landscape metrics were generated based on a 900- m depth of field and connectance distance index (Appendix A) to reflect home range, territory size, and minimum patch size needs of most species covered in the vulnerability assessment (Section I, Table 2). Metrics of sagebrush cover and edges show that the FC has more sagebrush habitat and larger patches of sagebrush than the URG (Appendix A., Table 1A). However, core area metrics show less core area and smaller mean core areas in the FC versus the URG (Table 2A.). Values computed for mean Core Area Index also indicates the FC has on average less sagebrush core area than the URG (Table 1A). Still, the FC has fewer disjunct core areas (Table 2A) and other metrics (patch cohesion index, aggregation index, Table 3A) are nearly similar between the two focal areas. When core area values were translated to a measure of adaptive capacity, we see that the FC contains a far higher number of subwatersheds with at least 30% core area (Table 11) despite an estimate of lower total core area (Table 2A). One explanation for this contradiction is that sagebrush ecosystems are more concentrated within the URG (Figures 1A and 2A), whereas the FC contains a greater amount but also more dispersed sagebrush. When summarizing the landscape metrics generated in Appendix A at the subwatershed level, we see the effect of this habitat concentration where URG has far fewer positively scored subwatersheds (Table 11). Therefore, when considering these results, it should be recognized that though URG has fewer HUC12 meeting the minimum adaptive capacity criteria, the HUC12 that do meet that criteria likely have larger areas of core sagebrush habitat. From a management standpoint, this may reflect different needs between the FC and URG; increasing or maintaining connectivity between sagebrush patches is likely to be important in the FC, whereas conservation of core areas may be an effective strategy for the URG. These results also point to the potential importance of alternative landscape metrics for analysis of species-specific vulnerability within these areas and a careful consideration of how such metrics are summarized.
Missing Data and other uncertainties
The selection of indicators and data transformations have a large influenced the assessment results. Potential indicators are limited by the quality and extent of available data. For the most part, this assessment relied on data generated at nation-wide or broad regional scales and projections of future conditions centered on the near mid-century time point. The goal of this assessment was to identify conservation priorities and opportunities across broad landscapes
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for which broadly applicable data is probably sufficient. Still, several relevant sources of data are not yet available for inclusion in our assessment of sagebrush ecosystems. We were unable to find spatially explicit data for a number of important indicators including grazing impacts (e.g. seamless coverage for grazing allotment condition), invasive species, projections of soil conditions, wind development, linear features (fences), and better information on the significance, if any, of conifer expansion within the southern portion of the SRLCC. These indicators have been identified in several literature sources as well as by Adaptation Forum participants as important measures of sagebrush ecosystem resilience and condition. Of particular need are data of invasive species presence and risk of spread and grazing impacts. Due to the threat of invasive species to sagebrush ecosystems, an invasive species indicator other than the resistance-resilience indicator would be valuable. Similarly, a measure of rangeland health or condition that reflects grazing impact would be a useful indicator of adaptive capacity. Information on connectivity of sagebrush ecosystems was also lacking though we attempt to provide some insight through our analysis of sagebrush core areas (Appendix A).
Uncertainty in this assessment arises from knowledge gaps, quantifiable errors in data, and uncertain futures (e.g. projections of human behavior). In general, as just discussed, we know this assessment is affected by knowledge gaps because not all relevant measures of sagebrush condition or exposure were available in a spatially explicit format. Quantifiable errors in the data are carried over from the source data or result from the process used to translate the data into a vulnerability score. Data of extrapolated or projected conditions will have inherent sources of uncertainty related to data gaps, measurement error, and model inaccuracies (Vissor 2012). Depending upon the goal of an analysis or assessment, numerous methods may be used to reduce these uncertainties (Lutz et al., 2016, Box 6). In this assessment, we are not creating new information and, therefore, the assessment products are subject to the measures taken to reduce uncertainty by data authors. These methods may or may not be robust to various applications. For the purpose of the landscape scale assessment, we made an effort to use the best available or most appropriate data that would generate meaningful information of resource vulnerability. Still, it must be acknowledged that the method employed in this assessment, which combines various sources of data, may magnify or reduce overall uncertainties in the underlying data (Wilby and Dessai 2010). Second, the way in which the data are linked to an expected outcome also contributes to uncertainty. Ecosystems may respond in linear or non-linear ways to environmental change. A primary challenge to the development of vulnerability assessments is finding sufficient empirical evidence to quantify these relationships. Where possible, we base our thresholds on empirically derived information. Where such information is not available, we used arbitrary cutoff values (e.g. percentiles). In general, this analysis does not distinguish between magnitudes of change. Our
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estimates of future climate conditions or population growth are reduced to simply trends of increasing, decreasing or no change, which in turn are translated to an expected increase or decrease in exposure, sensitivity or adaptive capacity. Though translating relative change to absolute trends can reduce uncertainties related to degrees of change, the trends themselves may vary. Still, we feel that this analysis and associated maps provide a meaningful representations of potential change and a likely outcome of future conditions.
Finally, our selection of data source influences the outcome of our measure of vulnerability. For instance, we compiled data on population growth, cropland conversion, and disturbance change using one of many sources (USGS 2014), each with their advantages and disadvantages (Alexander et al. 2016; Sohl et al., 2016). Given that each vulnerability element and the final vulnerability score is a composite of several indicators, the amount of bias introduced by any one data source may not have overly strong impacts. However, individual users may wish to explore alternative data sources depending on their assessment needs. Many of these alternative data sources have been made available on the SRLCC Conservation Planning Atlas pages associated with this project. For example, this assessment has focused on the projected climate and related conditions around a single end point (~year 2040) and, where applicable, a single RCP. However, as described in Box 6, an effective method for mitigating uncertainties around future projections is to include a suite of potential outcomes upon which a suite of management options can be developed. Therefore, users that apply this assessment framework may want to generate additional maps of vulnerability based on alternative projections (e.g. based on different time points or under different RCP).
However, a tradeoff exists when combining different data projecting future conditions as not all data is produced similarly. We were not able to locate data for all indicators of future conditions that considered the same set of time periods, Global Climate Models (GCM), emission scenarios (SRES) or representative concentration pathways (RCP). It is questionable whether meaningful results can be generated by combining very diverse data. To reduce potential errors associated with the use of independently derived data, we focused this assessment on projections for nearer time points (2030-2040) and comparable scenarios (SRES A1B for CMIP3 generations and RCP4.5 for CMIP5). By focusing on near time periods, we reduce some of the uncertainty associated with variation in GCMs outputs, which begin to show substantial divergence after year 2050 (Sheffeld et al. 2013). SRES A1B and RCP 4.5 both tend to generate intermediate outcomes in ensemble output (Knutti and Sedláček 2012; Sheffeld et al. 2013), and represent neither the most or least extreme future. Importantly, it appears increasingly likely that temperature changes will be greater than those predicted by these middle of the road scenarios (Peters et al. 2013) and so these assessment results may underestimate vulnerability. Ultimately, end users will have to decide whether their planning
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processes will benefit more from assessments that cover a wide range of climate conditions or by including a wider, albeit more diversely produced, range of drivers.
BOX 6. DEALING WITH DATA UNCERTAINTY Patt et al., 2005 note three issues specific to climate change vulnerability assessments: the study systems are complex, there is no way to validate integrative models, and climate change assessments require projecting potential states of a system into the future. Rouse and Norton 2010 demonstrate a process for dealing with these uncertainties that begins with identifying sources of uncertainty and then taking measures to reduce uncertainty where possible. Several methods are used to reduce uncertainties in assessments and risk analysis. We can classify the most common into three approaches: 1. Qualitative description. This may include a standardize system (e.g. IPCC’s guidelines for authors regarding evidence agreement, IPCC 2007). Alternatively, a common approach uses consensus ratings by expert or focus groups 2. Quantify via a range of measures. This is commonly applied to climate change data (e.g. Lutz et al., 2016). Users may explore outputs from a range of climate models or scenarios (e.g. SRES, RCP). 3. Quantify through models. This includes formal uncertainty and sensitivity analyses or decision support tools that use Monte Carlo simulations (e.g. EPA, Park 2008; Green and Weatherhead 2014) Ultimately, these processes may not be able to eliminate all uncertainties and at some point we must acknowledge and manage residual or unavoidable uncertainty (Rouse and Norton 2010). The focal resource assessments presented here as part of a larger SLRCC Landscape Conservation Design (LCD) effort, take the initial step to acknowledge the various sources of uncertainty inherent in underlying data as well as during the assessment process. From a planning level, uncertainty can be dealt with by considering multiple future scenarios, selecting robust and flexible strategies, and using adaptive management (Woodruff and Stults 2016). To this end, additional versions of these assessments can incorporate data with alternative scenarios or a range of future climate measures as desired by end user groups. Finally, we remind potential users that even in absence of perfect knowledge we can identify useful information and strategies for reducing vulnerabilities.
Conclusion: The assessment framework used to generate vulnerability scores for focal resources in the SRLCC is designed to be flexible and easily updated as new information becomes available. This report has been generated using a one of many potential sets of information that can be modified according to user needs. Several alternative data are available in the spatial files associated with this report. This version of the assessment provides an initial look into sagebrush vulnerability in the next 20-30 years within the Four Corners and Upper Rio Grande focal areas. Though assessments such as these will continue to improve as models and research reduce uncertainties in climate projections, we have the ability, with information at hand, to identify many of the primary threats and issues facing sagebrush ecosystems within this region. In particular, this assessment has identified several areas likely to be vulnerable to continuing changes in climate fire and disturbance and areas where conservation opportunities might exist.
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Figure 8. Cumulative exposure map of subwatersheds for sagebrush ecosystems in the Four Corners and Upper Rio Grande region of the Southern Rockies LCC
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Figure 9. Cumulative sensitivity map of subwatersheds for sagebrush ecosystems in the Four Corners and Upper Rio Grande region of the Southern Rockies LCC
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Figure 10. Cumulative impact map of subwatersheds for sagebrush ecosystems in the Four Corners and Upper
Rio Grande region of the Southern Rockies LCC; sensitivity + exposure = impact
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Figure 11. Cumulative adaptive capacity map of subwatersheds for sagebrush ecosystems in the Four Corners and
Upper Rio Grande region of the Southern Rockies LCC
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Figure 12. Vulnerability map of subwatersheds for sagebrush ecosystems in the Four Corners and Upper Rio Grande of the Southern Rockies LCC
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Literature Cited
Aldridge CL, Boyce MS. 2007. Linking occurrence and fitness to persistence: Habitat-based approach for endangered greater sage-grouse. Ecological Applications 17: 508-526.
Aldridge CL, Nielsen SE, Beyer HL [et al.]. 2008. Range-wide patterns of greater sage-grouse persistence. Diversity and Distributions 14: 983-944.
Alexander P, Prestele R, Verburg PH, Arneth A, Baranzelli C, et al. 2017. Assessing uncertainties in land cover projections. Glob Change Biology 23: 767–781. doi:10.1111/gcb.13447
Allen CD, Savage M, Falk DA, Sucking KF, Swetnam TW, Schulke T, Stackey PB, Morgan P, Hoffman M, Kingel JT. 2002. Ecological restoration of southwestern ponderosa pine ecosystems: A broad perspective. Ecological Applications 12: 1418-1433.
Anderson JE, Inouye RS. 2001. Landscape-scale changes in plant species abundance and biodiversity of a sagebrush steppe over 45 years. Ecological monographs 71(4): 531-556.
Arizona Game and Fish Department (AGFD). 2012. Arizona’s state wildlife action plan: 2012- 2022. Phoenix, AZ: Arizona Game and Fish Department.
Baker WH. 2011. Pre-Euro-American and recent fire in sagebrush ecosystems. In Knick ST, Connelly JW, eds. Greater sage-grouse: Ecology and conservation of a landscape species and its habitats. Studies in Avian Biology 38. Berkeley, CA: University of California Press: 185–201.
Baruch-Mordo S, Evans JS, Severson JP, Naugle DE, Maestas JD, Kiesecker JM, Falkowski MJ, Hagen CA, Reese KP. 2013. Saving sage-grouse from the trees: A proactive solution to reducing a key threat to a candidate species. Biological Conservation 167: 233-241.
Bauer J. 2006. Fire history and stand structure of a central Nevada pinyon-juniper woodland. Masters thesis. Reno, NV: University of Nevada, Reno. 143 p.
Bechard MJ, Schmutz MJ. 1995. Ferruginous hawk (Buteo regalis). In Poole A, Gill F, eds. The Birds of North America, No. 172. Philadelphia and Washington, DC: The Academy of Natural Sciences and The American Ornithologists’ Union.
Belnap J. 1994. Potential role of cryptobiotic soil crust in semiarid rangelands. Proceedings of Ecology and Management of Annual Rangelands. Ogden, UT: USDA, Forest Service, Intermountain Research Station. INT-GTR-313.
Belnap J, Kaltenecker JH, Rosentreter R, Williams J, Leonard S, Edlridge D. 2001. Biological soil crusts: Ecology and management. Denver, CO: US Department of the Interior. Technical Reference 1730-2.
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Boyle SA, Reeder DR. 2005. Colorado sagebrush: A conservation assessment and strategy. Grand Junction, CO: Colorado Division of Wildlife.
Bradford JB, Laurenroth WK. 2006. Controls over invasion of Bromus tectorum: The importance of climate, soil, disturbance and seed avaliability. Journal of Vegetation Science 17: 693-704.
Bradley BA. 2009. Regional analysis of the impacts of climate change on cheatgrass invasion shows potential risk and opportunity. Global Change Biology 15(1): 196-208.
Braun CE, Oyler-McCance SJ, Nehring JA, Commons ML, Young JR, Potter KM. 2014. The historical distribution of Gunnison Sage-grouse in Colorado. The Wilson Journal of Ornithology 126: 207-217.
Breshears DD, Whicker JJ, Johansen MP, Pinder, III., JE. 2003. Wind and water erosion and transport in semi-arid shrubland, grassland and forest ecosystems: Quantifying dominance of horizontal and wind driven transport. Earth Surf Proc Landf 28: 1189–1209.
Brooks ML, Matchett JR, Shinneman DJ [et al.]. 2015. Fire patterns in the range of greater sage- grouse, 1984–2013—Implications for conservation and management. Washington, DC: U.S. Department of the Interior, U.S. Geological Survey. Open-File Report 2015-1167. 66 p. http://dx.doi.org/10.3133/ofr20151167
Canfield RH. 1941. Application of the line interception method in sampling range vegetation. Journal of Forestry 39: 388-394.
Carroll C, Lawler JJ, Roberts DR, Hamann A. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10(10): e0140486.
Chambers JC, Miller RF, Board DI, Pyke DA, Roundy BA, Grace JB, Schupp EW, Tausch RJ. 2014. Resilience and resistance of sagebrush ecosystems: Implications for state and transition models and management treatments. Rangeland Ecology and Management 67(5): 440-54.
Chambers JC, Maestas JD, Pyke DA, Boyd CS, Pellant M, Wuenschel A. 2017. Using resilience and resistance concepts to manage persistent threats to sagebrush ecosystems and greater sage-grouse. Rangeland Ecology and Management 70(2): 149-64.
Collister DM. 1994. Breeding ecology and habitat preservation of the loggerhead shrike in southeastern Alberta. Masters thesis. University of Calgary, Calgary.
Colorado Division of Wildlife (CDOW). 2003. Conservation plan for grassland species in Colorado. Denver, CO: Colorado Division of Wildlife. 205 p.
Colorado Parks and Wildlife (CPW). 2015. State wildlife action plan: A strategy for conserving wildlife in Colorado. Denver, CO: Colorado Parks and Wildlife. 865 p.
61
Colorado Greater Sage-Grouse Steering Committee (CGSSC). 2008. Colorado greater sage- grouse conservation plan. Colorado Parks and Wildlife, Denver, CO.
Condon L, Weisberg PJ, Chambers JC. 2011. Abiotic and biotic influences on Bromus tectorum invasion and Artemisia tridentata recovery after fire. International Journal of Wildland Fire 20(4): 597-604.
Connelly JW, Schroeder MA, Sands AR, Braun CE. 2000. Guidelines to manage sage-grouse populations and their habitats. Wildlife Society Bulletin 28: 967–985.
Connelly JW, Reese KP, Schroeder MA. 2003. Monitoring of greater sage-grouse habitats and populations. Moscow, ID: University of Idaho. Station Bulletin 80.
Copeland HE, KE Doherty, DE Naugle, A Pocewicz, JM Kiesecker. 2009. Mapping oil and gas development potential in the US intermountain west and estimating impacts to species. PLoS One, 4, e7400.
Custer TW, Bickham JW, Lyne TB, Lewis T, Ruedas LA, Custer CM, Mekecom MJ. 1994. Flow cytometry for monitoring contaminant exposure in black-crowned night herons. Archive of Environmental Contamination and Toxicology 27: 176-179.
Daubenmire R. 1959. A canopy-coverage method of vegetation analysis. Northwest Science 33: 43-64.
Dealy JE, Leckenby DA, Concannon DM. 1981. Wildlife habitats in managed rangelands – the Great Basin of southeastern Oregon: Plant communities and their importance to wildlife. U.S. Department of Agriculture. General Technical Report PNW-120.
Dechant JA, Sondreal ML, Johnson DH, Igl LD, Goldade CM, Rabie PA, Euliss BR. 2001. Effects of management practices on grassland birds: Ferruginous hawk. Jamestown, ND: Northern Prairie Wildlife Research Center. http://www.npwrc.usgs.gov/resource/literatr/grasbird/ferhawk/ferhawk.htm.
Dechant JA. 2000. A fascination with birds. Prairie Naturalist 31(3): 189-90.
Department of the Interior (DOI). 2015. SO 3336 - The final report: An integrated rangeland fire management strategy. 94 p.
DiTomaso JM. 2000. Invasive weeds in rangelands: Species, impacts, and management. Weed Science 48: 255–265.
62
DiTomaso JM, Monaco TA, James JJ, Firn J. 2017. Invasive plant species and novel rangeland systems. In Briske DD, ed. Rangeland systems: Processes, management and challenges. Springer Series on Environmental Management. Cham, Switzerland: Springer Open. 661 p.
ESRI. 2015. ARC/INFO version 10.4.1. Redlands, CA: Environmental Systems Research Institute, Inc.
Finch DM, Boyce DA, Chambers JC, Colt CJ, Dumroese RK, Kitchen SG, McCarthy C, Meyer SE, Richardson BA, Rowland MM, Rumble MA, Schwartz MK, Tomosy MS, Wisdom MJ. 2016. Conservation and restoration of sagebrush ecosystems and sage-grouse: An assessment of USDA Forest Service Science. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Gen. Tech. Rep. RMRS GTR-348. 54 p.
Fitzgerald JP, Armstrong DM, Halfpenny JC, Freeman J, Bauerle B, Tourtillott C. 1982. Small mammals, furbearers, and small game mammals of northwestern Colorado: A review and synopsis of information. In Crumpacker DW, ed. Wildlife conservation and energy development in northwest Colorado (14 Volume Series). Denver, CO: Northwest Colorado Wildlife Consortium and Ecological Services Section, Colorado Division of Wildlife.
Friggens MM, Bagne K, Finch D, Falk D, Triepke J, Lynch A. 2013. Review and recommendations for climate change vulnerability assessment approaches with examples from the Southwest. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Gen. Tech. Rep. RMRS-GTR-309.
Furniss MJ, Roby KB, Cenderelli D, Chatel J, Clifton CF, Clingenpeel A, Hays PE, Higgins D, Hodges K, Howe C, Jungst L, Louie J, Mai C, Martinez R, Overton K, Staab BP, Steinke R, Weinhold M. 2013. Assessing the vulnerability of watersheds to climate change: Results of national forest watershed vulnerability pilot assessments. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Gen. Tech. Rep. PNW-GTR-884. 32 p.
Füssel H, RJT Klein. 2006. Climate change vulnerability assessments: an evolution of conceptual thinking. Climate Change 75: 301-324.
Füssel HM. 2007. Vulnerability: A generally applicable conceptual framework for climate change research. Global environmental change 17(2): 155-167.
Füssel H. 2009. Review and Quantitative Analysis of Indices of Climate Change Exposure, Adaptive Capacity, Sensitivity and Impacts. World Bank development report 2010: Development and Climate Change
Glick P, Stein BA, Edelson NA. 2011. Scanning the conservation horizon: A guide to climate change vulnerability assessment. Washington, DC: National Wildlife Federation. 168 p.
63
Green M, EK Weatherhead. 2014. Coping with climate change uncertainty for adaptation planning: an improved criterion for decision making under uncertainty using UKCP09. Climate Risk Management 1: 63-75.
Gunnison Sage-grouse Rangewide Steering Committee (GSGRSC). 2005. Gunnison sage-grouse rangewide conservation plan. Denver, CO: Colorado Division of Wildlife. 359 p. + appendices.
Hagen CA, Connelly JW, Schroeder MA. 2007. A meta-analysis of greater sage-grouse Centrocercus urophasianus nesting and brood-rearing habitats. Wildlife Biology 13: 42–50.
Hanser SE, Knick ST. 2011. Greater sage-grouse as an umbrella species for shrubland passerine birds. In Knick ST, Connelly JW, eds. Greater sage-grouse: Ecology and conservation of a landscape species and its habitats. Studies in Avian Biology 38: 475–487.
Hayward GD, Suring LH. 2013. Selection of key habitat attributes for monitoring. Pages 2-1 to 2- 20 in Rowland MM, Vojta CD, tech. eds. A technical guide for monitoring wildlife habitat. Washington, DC: U.S. Department of Agriculture, Forest Service. Gen. Tech. Rep. WO-89. 400 p.
Herrick JE, Van Zee JW, Havstad KM, Burkett LM, Whiteford WG. 2009a. Monitoring manual for grassland, shrubland and savanna ecosystems. Volume 1: quickstart. Las Cruces, NM: US Dept of Agriculture, Agricultural Research Service, Jornada Experimental Range. 36 p.
Herrick JE, Van Zee JW, Havstad KM, Burkett LM, Whiteford WG. 2009b. Monitoring manual for grassland, shrubland and savanna ecosystems. Volume 2: design, supplementary methods and interpretation. Las Cruces, NM: US Dept of Agriculture, Agricultural Research Service, Jornada Experimental Range. 200 p.
Hinkel J. 2011. Indicators of vulnerability and adaptive capacity: Towards a clarification for the science-policy interface. Global Environmental Change 21: 198-208.
Holechek JL, Stephenson T. 1983. Comparison of big sagebrush vegetation in northcentral New Mexico under moderately grazed and grazing excluded conditions. Journal of Range Management 36: 454-455.
IPCC. 2007. Climate change 2007: Impacts, adaptation and vulnerability. In Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE, eds. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. 976pp.
Jacbos BF, Romme WH, Allen CD. 2008. Mapping old versus young piñon-juniper stands with a predictive topo-climatic model in north-central New Mexico, USA. Ecological Applications 18: 1627-1641.
64
James JJ, Drenovsky RA, Monaco TA, [et al.]. 2011. Managing soil nitrogen to restore annual grass-infested plant communities: Effective strategy or incomplete framework? Ecological Applications 21: 490–502.
Johnson DD, Miller RF. 2006. Structure and development of expanding western juniper woodlands as influenced by two topographic variables. Forest Ecology and Management 229: 7- 15.
Jones SL, Cornely JE. 2002. Vesper sparrow (Pooecetes gramineus). In Poole A, Gill F, eds. The birds of North America, No. 624. Philadelphia: The Birds of North America, Inc.
Knick ST, Rotenberry JT. 1995. Landscape characteristics of fragmented shrubsteppe habitats and breeding songbird birds. Conservation Biology 9: 1059–1071.
Knutti R, J Sedláček. 2012. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change. 1716: 369-373.
Lovio JC. 1995. Diegan coastal sage scrub I. Breeding bird census. Journal of Field Ornithology 66: 103.
Lovio JC. 1996. The effects of habitat fragmentation on the breeding-bird assemblage in California coastal sage scrub. Masters thesis. San Diego, CA: San Diego State University.
Lutz AF, ter Maat HW, Biemans H, Shrestha AB, Wester P, WW Immerzeel. 2016. Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach. International Journal of Climatology 36: 3988–4005. doi:10.1002/joc.4608
Mack RN, Pyke DA. Demography of Bromus tectorum: Variation in time and space. Journal of Ecology. 71: 69–93.
Manier DJ, Hobbs NT, Theobald DM, Reich RM, Kalkhan MA, Campbell MR. 2005. Canopy dynamics and human caused disturbance on a semi-arid landscape in the Rocky Mountains, USA. Landscape Ecology 20(1): 1-7.
Manier DJ, Hobbs NT. 2006. Large herbivores influence the composition and diversity of shrub- steppe communities in the Rocky Mountains, USA. Oecologia 146: 641-651.
McDowell N, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, [et al.]. 2008. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought? New phytologist 178(4): 719-739.
McGarigal K, Cushman SA, Ene E. 2015. FRAGSTATS Version 4: Spatial pattern analysis program for categorical and continuous maps. Amherst, MA: University of Massachusetts Landscape Ecology Lab. http://www.umass.edu/landeco/research/fragstats/fragstats.html.
65
Milchunas DG, Sala O, Laurenroth W. 1988. A generalized model of the effects of grazing by large herbivores on grassland community structure. American Naturalist 132: 87-106.
Miller RF, Svejcar TJ, West NE. 1994. Implications of livestock grazing in the intermountain sagebrush region: plant composition. In Proceedings of the 42nd Annual Meeting of the American Institute of Biological Sciences, Society for Range Management.
Miller ME. 1999. Use of historic aerial photography to study vegetation change in the Negrito Creek watershed, southwestern New Mexico. Southwestern Naturalist 44: 121-131.
Miller RF, Tausch RJ. 2001. The role of fire in juniper and pinyon woodlands: A descriptive analysis. Proceedings of the invasive species workshop. Miscellaneous Publication Number 11. Tallahassee, FL: Tall Timbers Research Station. p 15-30.
Miller RF, Chambers JC, Pellant M. 2014. A field guide for selecting the most appropriate treatment in sagebrush and piñon-juniper ecosystems in the Great Basin: Evaluating resilience to disturbance and resistance to invasive annual grasses, and predicting vegetation response. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Gen. Tech. Rep. RMRS-GTR-322-rev. 68 p.
Miller RF, Chambers JC, Pyke DA, Pierson FB, Williams CJ. 2013. A review of fire effects on vegetation and soils in the Great Basin Region: Response and ecological site characteristics. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Gen. Tech. Rep. RMRS-GTR-308. 126 p.
Miller RF, Tausch RJ, McArthur ED, Johnson DJ, Sanderson SC. 2008. Age structure and expansion of piñon-juniper woodlands: A regional perspective in the Intermountain West. Fort Collins, CO: USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP- 69. 15 p.
Miller RF, Knick ST, Pyke DA, Meinke CW, Hanser SE, Wisdom MJ, Hild AL. 2011. Characteristics of sagebrush habitats and limitations to long-term conservation. In Knick, ST, Connelly, JW, eds. Greater sage-grouse - ecology and conservation of a landscape species and its habitats. Studies in Avian Biology 38. Berkeley, CA: University of California Press: 145-185.
Munson SM, Belnap J, Okin G. 2011. Responses of wind erosion to climate induced vegetation changes on the Colorado Plateau. PNAS 108: 3854–3859.
Munson SM, Sher AA. 2015. Long-term shifts in the phenology of rare and endemic Rocky Mountain plants. American journal of botany 102(8): 1268-76.
66
NatureServe. 2017. NatureServe Explorer: An online encyclopedia of life [Web application]. Version 7.1. Arlington, VA: NatureServe. http://www.natureserve.org/explorer. (15 March 2017).
Navajo Natural Heritage Program (NNHP). 2008. Sensitive species list. Navajo Nation Window Rock, AZ: Department of Fish and Wildlife. https://www.nndfw.org/nnhp/trackinglist.htm
Neely B, McCarthy P, Cross M, Enquist C, Garfin G, Gori D, Hayward G, Schulz T. 2010. Climate change adaptation workshop for natural resource managers in the Gunnison Basin: Summary. The Nature Conservancy, Southwest Climate Change Initiative. 73 p.
New Mexico Department of Game and Fish (NMDGF). 2006. Comprehensive Wildlife Conservation Strategy for New Mexico. Santa Fe, NM: New Mexico Department of Game and Fish. 526 p.
New Mexico Department of Game and Fish (NMDGF). 2016. State wildlife action plan for New Mexico. Santa Fe, NM: New Mexico Department of Game and Fish. DRAFT. 401 p.
Paige C, Ritter SA. 1999. Birds in a sagebrush sea: Managing sagebrush habitats for bird communities. Boise, ID: Partners in Flight Western Working Group. 47 p.
Patt A, Richard JT, Klein, A de la Vega-Leinert. 2005. Taking the uncertainty in climate-change vulnerability assessment seriously. Comptes Rendus Geoscience 337: 411-424.
Patt AG, D Schröter, AC De La Vega-Leinert, RJT Klein. 2008. Vulnerability research and assessment to support adaptation and mitigation: common themes from the diversity of approaches. A.G. Patt, D. Schröter, R.J.T. Klein, A.C. De La Vega-Leinert (Eds.), Environmental Vulnerability Assessment, Earthscan, UK (2008).
Pellant M, Shaver P, Pyke DA, Herrick JE. 2005. Interpreting indicators of rangeland health version 4. Denver, CO: US Dept of the Interior, Bureau of Land Management, National Science and Technology Center. Technical Reference 1734-6. 122 p.
Peterman W, Ferschweiler K. 2016. A case study for evaluating potential soil sensitivity in arid land systems. Integrated Environmental Assessment and Management. DOI 10.1002/ieam.1691.
Peters GP, Andrew RM, Boden T, Canadell JG, Ciais P, Le Quéré, Marland G, Raupach MR, C Wilson. 2013. The challenge to keep global warming below 2°C. Nature Climate Change 3: 4-6.
Petersen KL, Best LB. 1985. Brewer's sparrow nest-site characteristics in a sagebrush community. Journal of Field Ornithology 56(1): 23-7.
Reed JM. 1985. A comparison of the" flush" and spot-map methods for estimating the size of Vesper sparrow territories. Journal of Field Ornithology 56(2): 131-137.
67
Rehfeldt GE, NL Crookston, MV Warwell, JS Evans. 2006. Empirical analyses of plant climate relationships for the Western United States. Int. J. Plant Sci. 167(6):1123–1150.
Reynolds TD. 1981. Nesting of the sage thrasher, sage sparrow, and Brewer's sparrow in southeastern Idaho. Condor 1:61-4.
Reynolds TD, Rich TD, Stephens DA. 1999. Sage thrasher (Oreoscoptes montanus). In Poole A, ed. The Birds of North America Online. Ithaca, NY: Cornell Lab of Ornithology.
Rich T. 1980. Nest placement in sage thrashers, sage sparrows and Brewer's sparrows. The Wilson Bulletin 1: 362-368.
Robel RJ, Briggs JN, Dayton AD, Hulbert LC. 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management 23: 295-297.
Romme WH, Allen CD, Bailey JD, Baker WL, Bestelmeyer BT. 2009. Historical and modern disturbance regimes, stand structures, and landscape dynamics in piñon-juniper vegetation of the western United States. Rangeland Ecology and Management 62: 203-222.
Rotenberry JT, Patten MA, Preston KL. 1999. Brewer's sparrow (Spizella breweri). The Birds of North America 390: 24.
Rowland MM, Wisdom MJ, Suring LH, Meinke CW. 2006. Greater sage-grouse as an umbrella species for sagebrush-associated vertebrates in the Great Basin. Biological Conservation 129: 323–335.
Rowland MM, Vojta CD, tech. eds. 2013. A technical guide for monitoring wildlife habitat. Washington, DC: U.S. Department of Agriculture, Forest Service. Gen. Tech. Rep. WO-89. 400 p.
Rouse HL, Norton N. 2010. Managing scientific uncertainty for Resource Management planning in New Zealand. Australasian Journal of Environmental Management. 17. 66-76. 10.1080/14486563.2010.9725252.
Sauer JR, Hines JE, Fallon JE, Pardieck KL, Ziolkowski DJ, Link WA. 2014. The North American breeding bird survey, results and analysis 1966–2013. Laurel, MD: USGS Patuxent Wildlife Research Center. Version 01.30.2015.
Schroeder MA, Young JR, Braun CE. 1999. Sage-grouse (Centrocercus urophasianus). In Poole A, Gill F, eds. The birds of North America, no. 425. Philadelphia, PA and Washington, D.C.: The Academy of Natural Sciences and The American Ornithologists’ Union. 24 p.
Sheffield J, AP Barrett, B Colle, DN Fernando, R Fu, KL Geil, Q Hu, J Kinter, S Kumar, B Langenbrunner, K Lombardo [et al]. 2013. North American climate in CMIP5 experiments. Part i:
68
evaluation of historical simulations of continental and regional climatology. Journal of Climate 26: 9209–9245.
Sohl TL, Wimberly MC, Radeloff VC, Theobald DM, and BM Sleeter. 2016. Divergent projections of future land use in the United States arising from different models and scenarios. Ecological Modelling 337: 281-297.
Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. SSURGO/STATSGO. Available online at https://websoilsurvey.nrcs.usda.gov/. Accessed [2014].
Stahl PD, Frost SM, Williams SE, Schuman GE. 1998. Arbuscular mycorrhizae and water stress tolerance of Wyoming big sagebrush seedlings. Soil Science Society of America Journal 62(5): 1309-1313.
Stephens DA. 1985. Foraging ecology of shrubsteppe birds in central Washington. Masters Thesis. Ellensburg, WA: Central Washington University.
Stiver SJ, Apa AD, Bohne JR, Bunnell SD, Deibert PA, Gardner SC, Hilliard MA, McCarthy CW, Schroeder MA. 2006. Greater sage-grouse comprehensive conservation strategy. Cheyenne, WY: Western Association of Fish and Wildlife Agencies.
Stiver SJ, Rinkes ET, Naugle DE, eds. 2010. Sage-grouse habitat assessment framework. Boise, ID: U.S. Department of the Interior, Bureau of Land Management, Idaho State Office. 47 p. [plus appendices]. 3 vol.
Stiver SJ, RInkes ET, Naugle DE, Makela PD, Nance DA, Karl JW, eds. 2015. Sage-grouse habitat assessment framework: A multiscale assessment tool. Denver, CO: Bureau of Land Management and Western Association of Fish and Wildlife Agencies. Technical Reference 6710- 1.
Strand EK, Launchbaugh KL, Limb R, Torell LA. 2014. Livestock grazing effects on fuel loads for wildland fire in sagebrush dominated ecosystems. Journal of Rangeland Applications 1: 35-57.
Stults M, Woodruff SC. 2017. Looking under the hood of local adaptation plans: shedding light on the actions prioritized to build local resilience to climate change. Mitigation and Adaptation Strategies for Global Change 22: 1249. https://doi.org/10.1007/s11027-016-9725-
Tuhy JS, Comer P, Dorfman D, Lammert M, Humke J, Cholvin B, Bell G, Neely B, Silbert S, Whitham L, Baker B. 2002. A conservation assessment of the Colorado Plateau Ecoregion. Moab, UT: The Nature Conservancy, Moab Project Office. 60 p.
Utah Wildlife Action Plan Joint Team (UDW). 2015. Utah wildlife action plan: A plan for managing native wildlife species and their habitats to help prevent listing under the
69
Endangered Species Act. Salt Lake City, UT: Utah Division of Wildlife Resources. Publication number 15-14.
Utah Governor’s Office (UGO). 2013. Conservation plan for greater sage-grouse in Utah. Salt Lake City, UT: Utah’s Public Lands Policy Coordination Office.
Utah Governor’s Office (UGO). 2015. Executive Order Sage Grouse. http://publiclands.utah.gov/wp-content/uploads/2015/02/EO-Sage-Grouse1.pdf
U.S. Department of the Interior, Bureau of Land Management (BLM). 2003. Farmington resource management plan and final environmental impact statement. BLM-NM-PL-03-014- 1610.
U.S. Department of the Interior, Bureau of Land Management (BLM). 2012a. Taos resource management plan. Taos, NM: USDI-BLM Taos Field Office. BLM/NM/PL-12-09-1610. 209 p.
U.S. Department of the Interior, Bureau of Land Management (BLM). 2012b. Rio Puerco Resource Management Plan/Environmental Impact Statement. Albuquerque, NM: USDI-BLM Rio Puerco Field Office. BLM/NM/PL-12-10-1610, Volumes 1, 2, 3.
U.S. Department of the Interior, Bureau of Land Management (BLM). 2015. Tres Rios Approved Resource Management Plan. Dolores, CO: USDI-BLM Tres Rios Field Office.
U.S. Department of the Interior, Bureau of Land Management (BLM). 2016. Gunnison Sage- grouse Rangewide Draft Resource Management Plan Amendment, Draft Environmental Impact Statement. Lakewood, CO and Salt Lake City, UT: USDI-BLM.
U.S. Department of the Interior, Fish and Wildlife Service (FWS). 2013. Endangered and threatened wildlife and plants: 12-month Finding on a Petition to List the Gunnison’s Prairie Dog as an Endangered or Threatened Species, Proposed Rule. Federal Register 78:68660-68685.
U.S. Department of the Interior, Fish and Wildlife Service (FWS). 2014. Final Rule: Threatened Status for Gunnison Sage-Grouse. Federal Register, Vol. 79, No. 224, 69192-69310, November 20, 2014.
U.S. Department of the Interior, Fish and Wildlife Service (FWS-ECOS). 2014. Critical habitat for Gunnison Sage-Grouse. https://ecos.fws.gov/ecp0/profile/speciesProfile?spcode=B0B0
Vickery PD, Hunter ML Jr, Melvin SM. 1994. Effects of habitat area on the distribution of grassland birds of Maine. Conservation Biology 8: 1087-1097.
Walker B. 2004. Effects of management practices on grassland birds: Brewer's sparrow. Jamestown, ND: Northern Prairie Wildlife Research Center. 32 p.
70
Walker BL, Naugle DE, Doherty KE. 2007. Greater sage-grouse population response to energy development and habitat loss. Journal of Wildlife Management 71: 2644-2654.
Weisberg PJ, Lingua E, Pillai RB. 2007. Spatial patterns of pinyon-juniper expansion in central Nevada. Rangeland Ecology and Management 60: 940-943.
Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW. 2006. Warming and earlier spring increase western US forest wildfire activity. Science 313(5789): 940-943.
Whisenant SG 1990. Changing fire frequencies on Idaho’s Snake River Plains: Ecological and management implications. In McArthur ED, Romney EM, Smith SD, eds. Symposium on cheatgrass invasion, shrub die off, and other aspects of shrub biology and management. Ogden, UT: U.S. Dept of Agriculture, Forest Service, Intermountain Research Station. INT-276: 4-10.
Wiens JA. 1969. An approach to the study of ecological relationships among grassland birds. Ornithological Monographs 1(8): 1-93.
Wiens JA, Crawford CS, Gosz JR. 1985. Boundary dynamics: A conceptual framework for studying landscape ecosystems. Oikos 1: 421-427.
Wiens JA, Rotennberry JT. 1981. Habitat associations and community structure of birds in shrubsteppe environments. Ecological Monographs 51: 21–41.
Wiens JA, Rotenberry JT. 1985. Response of breeding passerine birds to rangeland alteration in a North American shrubsteppe locality. Journal of Applied Ecology 1: 655-668.
Wiens JA, Hayward GD, Holthausen RS, Wisdom MJ. 2008. Using surrogate species and groups for conservation planning and management. BioScience 58: 241–252.
Wilby RL, Dessai S. 2010. Robust adaptation to climate change. Weather 65: 180–185. doi:10.1002/wea.543
Williams MI, Paige GB, Thurow TL, Hild AL, Gerow KG. 2011. Songbird relationships to shrub- steppe ecological site characteristics. Rangeland Ecology and Management 64: 109-118.
Wilson TL, Johnson EJ, Bissonette JA. 2009. Relative importance of habitat area and isolation for bird occurrence patterns in a naturally patchy landscape. Landscape Ecology 24: 351-360.
Wisdom MJ, Meinke CW, Knick ST, Schroeder MA. 2011. Factors associated with extirpation of sage-grouse. In Knick ST, Connelly JW, eds. Greater sage-grouse: Ecology and conservation of a landscape species and its habitats. Studies in Avian Biology 38: 451–472.
Woods CP. 1994. The loggerhead shrike in southwest Idaho. Masters Thesis. Boise, ID: Boise State University 702. http://scholarworks.boisestate.edu/td/702
71
Yosef R. 1994. The effects of fencelines on the reproductive success of loggerhead shrikes. Conservation Biology 8(1): 281-285.
72
Appendix A. Landscape Analysis: Sagebrush Ecosystems
Produced as part of the analysis: Vulnerability Assessment of Sagebrush Ecosystems: Four Corners and Upper Rio Grande Regions of the Southern Rockies Landscape Conservation Cooperative
Prepared by Mary Williams
Methods
We used a 30-m LANDFIRE existing vegetation type (EVT) data layer (LANDFIRE 2014) to identify sagebrush landscape metrics across the entire SRLCC and within each focal area. Due to the memory constraints in FragStats (McGarigal 2015) when estimating landscape metrics on very large and fine-scale data layers we resampled the 30-m grid to 300-m (9 ha), which is still ecologically meaningful to the species of interest and to the goals of the project. Brewer’s sparrow and sagebrush sparrow, for example, are frequently associated with sagebrush patches of at least 40 and 80 ha in size, respectively (Boyce and Reeder 2005, Wilson et al. 2011). As our base land cover map and moving window analyses in ArcMap 10.4.1 (ESRI 2015) and FragStats, we used the 300-m EVT of sagebrush cover. For sagebrush cover (all sagebrush species and subspecies combined- see Section III. Indicator variables for details), we derived landscape metrics in FragStats using the 8-neighborhood rule, including area-weighted mean (AREA_AM) of patches, edge density (EDGE), contagion (CONTAG), and area-weighted mean of correlation length (GYRATE_AM) for each focal area and at three radii (circular moving window at 1, 3, and 5 km (776, 6986, and 19408 acres, respectively)). The search radii reflect home range, territory size, and minimum patch size needs of most species covered in the vulnerability assessment (see Part I and Table 2). For estimate of landscape metrics across each focal area, we used a 900-m depth of field and a 900-m connectance distance index. The metric CONTAG represents contiguity. This is an index ranging from 0 to 1 of patch boundary configuration and shape. Large contiguous patches have a larger contiguity index than smaller patches.
Sagebrush Core Area Indicator
We used the area-weighted-mean patch size (i.e., core area) output generated from the 1-km moving window analysis as an indicator of adaptive capacity in the vulnerability assessment. Other users may use different inputs (i.e., smaller or larger moving window) depending on scope of interest. Core area refers to the interior area of sagebrush patches after omitting a 1- km edge buffer. Core area integrates patch size, shape, and edge effect distance into a single measure. The moving window analysis in Fragstats outputs a raster for each estimated variable. The raster is a range of values where the highest value indicates membership to a large core; values range from -999 (background/no data value) to 261 across the entire SRLCC. After converting the raster from floating to integer, we added a class field to assign values to quantile membership – highest 25% = class 4, second 25% = class 3, third 25% = class 2, and lowest 25% = class 1. We ran the tabulate area tool to estimate the amount (m2) of each class within a
73 subwatershed; area was converted to percent of each class within a HUC12. We determined that the highest 25% (class 4) represent large core areas, and we used this class in the adaptive capacity assessment. Subwatersheds with greater than 32% of class 4 core areas were coded 1, and all other subwatersheds were coded 0.
Table 1A. Area and edge metrics for sagebrush cover in Four Corners and Upper Rio Grande Metrics. Four Corners Upper Rio Grande Total Area (ha) 13,464,932 13,664,300 Sagebrush (ha) 2,919,420 1,524,924 Number of Patches 36,662 21,365 Patch Density (#/100ha) 0.27 0.16 Mean Patch Size (ha) 79.6 71.4 CV Patch Size 3,362 3,062 Area-Weighted Mean Patch Size (ha) 90,098 66,986 Largest Patch Index1 (%) 2.8 2.1 Mean Radius of Gyration2 (m) 247 235 CV Radius of Gyration 191 192 Area-Weighted Radius of Gyration (m) 12,679 10,494 1Largest Patch Index = percentage of the focal area comprised by the largest patch; a simple measure of dominance. 2Radius of Gyration = measure of average distance an organism can move within a patch before encountering the patch boundary from a random starting point.
Table 2A. Core area metrics for sagebrush cover in Four Corners and Upper Rio Grande Landscapes. Metrics Four Corners Upper Rio Grande Total Core Area (ha) 11,682 44,892 Number of Disjunct Core Areas 196 254 Core Density (#/100ha) 0.0015 0.0019 Area-Weighted Mean Core Area Index (%) 0.4 2.9 Mean Core Area (ha) 0.3 2.1 CV Core Area 8,226 11,760 Area-Weighted Mean Core Area (ha) 674 7,003
Table 3A. Aggregation metrics for sagebrush in Four Corners and Upper Rio Grande Metrics Four Corners Upper Rio Grande Connectance Index (%) 0.007 0.010 Patch Cohesion Index 98 97 Aggregation Index (%) 50 53 Normalized Landscape Shape Index 0.50 0.47 Mean Euclidean N-N Distance (m) 727 852 Area-Weighted Mean Euclidean N-N Distance (m) 620 640 CV Euclidean N-N Distance 0.007 0.016
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Table 4A. Landscape aggregation metrics and evenness indices (EI) for Four Corners and Upper Rio Grande Metrics Four Corners Upper Rio Grande Contagion Index (%) 29 55 Shannon's EI 0.8 0.5 Simpson's EI 0.7 0.4 Modified Simpson's EI 0.6 0.3
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Figure 1A. Core areas (ha) of sagebrush cover present in the Four Corners and Upper Rio Grande focal areas; these areas overlap southeastern Utah, northeastern Arizona, northcentral New Mexico, and southwestern Colorado. Core area refers to the interior area of sagebrush patches after a 120-m edge buffer is omitted. Core area integrates patch size, shape, and edge effect distance into a single measure.
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Figure 2A. Spatial connectedness, or contiguity, of sagebrush patches in the Four Corners and Upper
Rio Grande focal areas; these areas overlap southeastern Utah, northeastern Arizona, northcentral
New Mexico, and southwestern Colorado. Large contiguous patches have a larger contiguity index than smaller patches. This is an index of patch boundary configuration and shape.
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