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The sagebrush biome within the western United States has been reshaped by disturbances, management, and changing environmental conditions. As a result, sagebrush cover and configuration have varied over space and time, influencing processes and species that rely on contiguous, connected sagebrush. Previous studies have documented changes in sagebrush cover, but we know little about how the connectivity of sagebrush has changed over time and across the sagebrush biome. We investigated temporal connectivity patterns for sagebrush using a time series (1985–2020) of fractional sagebrush cover and used an omnidirectional circuit algorithm to assess the density of connections among areas with abundant sagebrush. By comparing connectivity patterns over time, we found that most of the biome experienced moderate change; the amount and type of change varied spatially, indicating that areas differ in the trend direction and magnitude of change. Two different types of designated areas of conservation and management interest had relatively high proportions of stable, high-connectivity patterns over time and stable connectivity trends on average. These results provide ecological information on sagebrush connectivity persistence across spatial and temporal scales that can support targeted actions to address changing structural connectivity and to maintain functioning, connected ecosystems.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species' conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., neighborhood-scale) with low genetic diversity, further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity and developed a statistical model for microsatellite data to robustly estimate these values for hierarchically nested populations. We found that 41 of 224 neighborhood-scale clusters had low genetic diversity, 23 of which were coupled with documented local population trend decline. We also found evidence of cross-scale low genetic diversity in the small and isolated Washington population, unlikely to be reversed through typical local management actions alone. The combination of low genetic diversity and a declining population suggests relatively high conservation concern. Our findings could further facilitate conservation action prioritization in combination with population trend assessments and (or) local information, and act as a base-line of genetic diversity for future comparison. Importantly, the approach we used is broadly applicable across taxa. This article is protected by copyright. All rights reserved.

Identifying, protecting, and restoring habitats for declining wildlife populations is foundational to conservation and recovery planning for any species at risk of decline. Resource selection analysis is a key tool to assess habitat and prescribe management actions. Yet, it can be challenging to map suitable resource conditions across a wide range of ecological contexts and use the resulting models to identify effective and universal habitat improvement actions. We developed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses that form the basis of location-specific management prescriptions. To demonstrate this approach, we developed a set of habitat selection models for Gunnison sage-grouse (Centrocercus minimus), a threatened species under the U.S. Endangered Species Act. Conservation, species recovery, and habitat management efforts are needed in six isolated satellite populations (San Miguel, Crawford, Pinon Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) where environmental conditions differ, and the already small number of birds are declining. We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. All models included key habitat variables often altered through management actions to assess their differential influences across models. We found important similarities and differences among satellites, indicating that, although some rules of thumb are generally well-grounded, the consideration of population-specific environmental differences could increase the efficiency of local habitat improvement actions. Sage-grouse also had diverse responses to resource conditions at different scales, indicating that regional spatial (e.g., landscape) and local patch scale can differentially influence expected habitat improvements associated with where such management actions are implemented. Although context variables such as topography cannot be manipulated, sage-grouse associations revealed information that could guide the siting of improvement actions. This approach to balancing management objectives associated with habitat assessment may benefit spatially-structured populations with different environmental contexts and species with complex habitat needs and associations.

Strategic restoration of altered habitat is one method for addressing worldwide biodiversity declines. Within the sagebrush steppe of western North America, habitat degradation has been linked to declines in many species, making restoration a priority for managers; however, limited funding, spatiotemporal variation in restoration success, and the need to manage for diverse wildlife species make decision-making regarding restoration actions challenging. To address the challenge of spatial conservation prioritization, we developed the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET). This decision support tool utilizes the prioritizr package in program R and an integer linear programming algorithm to select parcels representing both high biodiversity value and high probability of restoration success. We tested PReSET on a sagebrush steppe system within southwestern Wyoming using distributional data for six species with diverse life histories and a spatial layer of predicted sagebrush recovery times to identify restoration targets at both broad and local scales. While the broad-scale portion of our tool outputs can inform policy, the local-scale results can be applied directly to on-the-ground restoration. We identified restoration priority areas with greater precision than existing spatial prioritizations and incorporated range differences among species. We noted tradeoffs, including that restoring for habitat connectivity may require restoration actions in areas with lower probability of success. Future applications of PReSET will draw from emerging datasets, including spatially-varying economic costs of restoration, animal movement data, and additional species, to further improve our ability to target effective sagebrush restoration.

Long-term monitoring of natural resources is imperative for increasing the understanding of ecosystem processes, services, and how to manage those ecosystems to maintain or improve function. Challenges with using these data may occur because methods of monitoring changed over time, multiple organizations collect and manage data differently, and monetary resources fluctuate, affecting many aspects of data. Because many species respond to changes in habitat conditions and predator-prey relationships across different spatial scales that span management boundaries, greater efforts for collaborating are essential. We demonstrate the challenges and methods for standardizing greater sage-grouse (Centrocercus urophasianus) long-term monitoring data across the species range in the western United States to inform population modeling needs identified by the Western Association of Fish and Wildlife Agencies. We used automated and repeatable methods of standardizing data via custom open-source software (grsg_lekdb) to improve the scientific integrity of future sage-grouse population assessments within and among states. Data standardization included reconciling uses of different terminology and expunging unusable data, resulting in the removal of 26% of data records due to database insertion errors and modifications to >1 million values to correct formatting and typing errors. Our approaches maximized the inclusion of usable data and identified data that could inform detection probabilities, population trends, and monitoring guidelines. Using sage-grouse databases as an example, we identified the importance of data management and how quality assurance and quality control measures can improve the usefulness of these data for future research needs. Our methods of using informatics and concluding recommendations can support similar endeavors of flora and fauna monitoring programs, whether those efforts are to use existing data or support new monitoring programs.

Prioritizing habitats for conservation is a challenging task, particularly for species with fluctuating populations and seasonally dynamic habitat needs. Although the use of resource selection models to identify and prioritize habitat for conservation is increasingly common, their ability to characterize important long-term habitats for dynamic populations are variable. To examine how habitats might be prioritized differently if resource selection was directly and dynamically linked with population fluctuations and movement limitations among seasonal habitats, we constructed a spatially explicit individual-based model for a dramatically fluctuating population requiring temporally varying resources. Using greater sage-grouse (Centrocercus urophasianus) in Wyoming as a case study, we used resource selection function maps to guide seasonal movement and habitat selection, but emergent population dynamics and simulated movement limitations modified long-term habitat occupancy. We compared priority habitats in RSF maps to long-term simulated habitat use. We examined the circumstances under which the explicit consideration of movement limitations, in combination with population fluctuations and trends, are likely to alter predictions of important habitats. In doing so, we assessed the future occupancy of protected areas under alternative population and habitat conditions. Habitat prioritizations based on resource selection models alone predicted high use in isolated parcels of habitat and in areas with low connectivity among seasonal habitats. In contrast, results based on more biologically-informed simulations emphasized central and connected areas near high-density populations, sometimes predicted to be low selection value. Dynamic models of habitat use can provide additional biological realism that can extend, and in some cases, contradict habitat use predictions generated from short-term or static resource selection analyses. The explicit inclusion of population dynamics and movement propensities via spatial simulation modeling frameworks may provide an informative means of predicting long-term habitat use, particularly for fluctuating populations with complex seasonal habitat needs. Importantly, our results indicate the possible need to consider habitat selection models as a starting point rather than the common end point for refining and prioritizing habitats for protection for cyclic and highly variable populations.

Population monitoring is integral to the conservation and management of wildlife; yet, analyses of population demographic data rarely consider processes occurring across spatial scales, potentially limiting the effectiveness of adaptive management. Therefore, we developed a method to identify hierarchical levels of organization (i.e., populations) to define multiple spatial scales, specifically intended to help guide appropriate conservation and management actions. This approach can support mobile species with high site fidelity where surveys occur on birthing/breeding grounds or migratory stopovers. Our approach used a graph‐based clustering algorithm (Spatial K'luster Analysis by Tree Edge Removal) that explicitly included habitat selection information at multiple scales and further refined with constraint‐based rules. We applied these concepts to greater sage‐grouse leks (breeding grounds), a species of conservation concern, in two different ecological contexts (Nevada and Wyoming, USA). The constraint‐based rules accounted for inter‐lek movement distances based on literature and field studies in Nevada from 2012 to 2016, included methods to support a spatially balanced monitoring design, and identified barriers to movements among leks based on resistance surfaces. We evaluated the performance of our hierarchical clusters in Nevada using independent data from radio‐marked sage‐grouse, and we found the finest‐scaled cluster level captured ~90% of sage‐grouse movements and mid‐level scales captured ~97–99% of movements. We expected comparable performance for Wyoming, where we lacked radio‐marked sage‐grouse for an evaluation, because genetic studies estimate similar dispersal distances to our ~15 km inter‐lek movement distance in Nevada. For sage‐grouse and other mobile species with high site fidelity, our approach to defining these frameworks could prove valuable for conservation and management applications, such as improving estimation of scale‐dependent population trends and guiding the prescription of management actions at spatial scales that align with identified threats. Specific to sage‐grouse, our analysis sets the stage for designing a monitoring framework that relies on comparison of short‐ and long‐term population trends across our defined spatial scales and identifies and disentangles factors driving local (e.g., habitat quality) and regional (e.g., climate) population changes, thereby supporting scale‐dependent management and research needs for adaptive management practices.

Multiple environmental stressors impact wildlife populations, but we often know little about their cumulative and combined influences on population outcomes. We generally know more about past effects than potential future impacts, and direct influences such as changes of habitat footprints than indirect, long-term responses in behavior, distribution, or abundance. Yet, an understanding of all these components is needed to plan for future landscapes that include human activities and wildlife. We developed a case study to assess how spatially explicit individual-based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater Sage-grouse in southwest Wyoming, USA, we projected oil and gas development footprints and climate-induced vegetation changes 50 years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we recalculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term Sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered a useful means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. The inclusion of movement and demographic responses to oil and gas infrastructure resulted in substantive changes in distribution and abundance when cumulated over several decades and throughout the regional population. When combined, additive development and climate-induced vegetation changes reduced abundance by up to half of the original size. In our example, the consideration of only a single population stressor the final possible population size by as much as 50%. Multiple stressors and their cumulative impacts need to be broadly considered through space and time to avoid underestimating the impacts of multiple gradual changes and overestimating the ability of populations to withstand change.

The sagebrush (Artemisia spp.) ecosystem is one of the largest ecosystems in western North America providing habitat for species found nowhere else. Sagebrush habitats have experienced dramatic declines since the 1950s, mostly due to anthropogenic disturbances. The greater sage-grouse (Centrocercus urophasianus) is a sagebrush-obligate species that has experienced population declines over the last several decades, which are attributed to a variety of disturbances including the more recent threat of oil and gas development. We developed a hierarchical, Bayesian state-space model to investigate the impacts of 2 measures of oil and gas development, and environmental and habitat conditions, on sage-grouse populations in Wyoming, USA using male lek counts from 1984 to 2008. Lek attendance of male sage-grouse declined by approximately 2.5%/year and was negatively related to oil and gas well density. We found little support for the influence of sagebrush cover and precipitation on changes in lek counts. Our results support those of other studies reporting negative impacts of oil and gas development on sage-grouse populations and our modeling approach allowed us to make inference to a longer time scale and larger spatial extent than in previous studies. In addition to sage-grouse, development may also negatively affect other sagebrush-obligate species, and active management of sagebrush habitats may be necessary to maintain some species. © 2016 The Wildlife Society.

Using localized studies to understand how ecosystems recover can create uncertainty in recovery predictions across landscapes. Large archives of remote sensing data offer opportunities for quantifying the spatial and temporal factors influencing recovery at broad scales and predicting recovery. For example, energy production is a widespread and expanding land use among many semi-arid ecosystems of the Western United States dominated by sagebrush (Artemisia spp.), a keystone species providing a variety of ecological services. With remotely-sensed (Landsat) estimates of vegetation cover collected every 2–5 years from southwestern Wyoming, USA, over nearly three decades (1985–2015), we modeled changes in sagebrush cover on 375 former oil and gas well pads in response to weather and site-level conditions. We then used modeled relationships to predict recovery time across the landscape as an indicator of resilience for vegetation after well pad disturbances, where faster recovery indicates a greater capacity to recover when similarly disturbed. We found the rate of change in sagebrush cover generally increased with moisture and temperature, particularly at higher elevations. Rate of change in sagebrush cover also increased and decreased with greater percent sand and larger well pads, respectively. We predicted 21% of the landscape would recover to pre-disturbance conditions within 60 years, whereas other areas may require >100 years for recovery. These predictions and maps could inform future restoration efforts as they reflect resilience. This approach also is applicable to other disturbance types (e.g., fires and vegetation removal treatments) across landscapes, which can further improve conservation efforts by characterizing past conditions and monitoring trends in subsequent years.

Human impacts on wildlife populations are widespread and prolific and understanding wildlife responses to human impacts is a fundamental component of wildlife management. The first step to understanding wildlife responses is the documentation of changes in wildlife population parameters, such as population size. Meaningful assessment of population changes in potentially impacted sites requires the establishment of monitoring at similar, nonimpacted, control sites. However, it is often difficult to identify appropriate control sites in wildlife populations. We demonstrated use of Geographic Information System (GIS) data across large spatial scales to select biologically relevant control sites for population monitoring. Greater sage-grouse (Centrocercus urophasianus; hearafter, sage-grouse) are negatively affected by energy development, and monitoring of sage-grouse population within energy development areas is necessary to detect population-level responses. We used population data (1995–2012) from an energy development area in Wyoming, USA, the Atlantic Rim Project Area (ARPA), and GIS data to identify control sites that were not impacted by energy development for population monitoring. Control sites were surrounded by similar habitat and were within similar climate areas to the ARPA. We developed nonlinear trend models for both the ARPA and control sites and compared long-term trends from the 2 areas. We found little difference between the ARPA and control sites trends over time. This research demonstrated an approach for control site selection across large landscapes and can be used as a template for similar impact-monitoring studies. It is important to note that identification of changes in population parameters between control and treatment sites is only the first step in understanding the mechanisms that underlie those changes. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high- and low-quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage-grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low-quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33-km-diameter moving windows were preferred, suggesting small-scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.

Most deposition chambers sold into the optical coating market today are outfitted with an automated control system. We surveyed several of the larger equipment providers, and nine of them responded with information about their hardware architecture, data logging, level of automation, error handling, user interface, and interfacing options. In this paper, we present a summary of the results of the survey and describe commonalities and differences together with some considerations of tradeoffs, such as between capability for high customization and simplicity of operation.

Animal habitat selection is an important and expansive area of research in ecology. In particular, the study of habitat selection is critical in habitat prioritization efforts for species of conservation concern. Landscape planning for species is happening at ever-increasing extents because of the appreciation for the role of landscape-scale patterns in species persistence coupled to improved datasets for species and habitats, and the expanding and intensifying footprint of human land uses on the landscape. We present a large-scale collaborative effort to develop habitat selection models across large landscapes and multiple seasons for prioritizing habitat for a species of conservation concern. Greater sage-grouse (Centrocercus urophasianus, hereafter sage-grouse) occur in western semi- arid landscapes in North America. Range-wide population declines of this species have been documented, and it is currently considered as "warranted but precluded" from listing under the United States Endangered Species Act. Wyoming is predicted to remain a stronghold for sage-grouse populations and contains approximately 37% of remaining birds. We compiled location data from 14 unique radiotelemetry studies (data collected 1994-2010) and habitat data from high-quality, biologically relevant, geographic information system (GIS) layers across Wyoming. We developed habitat selection models for greater sage-grouse across Wyoming for 3 distinct life stages: 1) nesting, 2) summer, and 3) winter. We developed patch and landscape models across 4 extents, producing statewide and regional (southwest, central, northeast) models for Wyoming. Habitat selection varied among regions and seasons, yet preferred habitat attributes generally matched the extensive literature on sage-grouse seasonal habitat requirements. Across seasons and regions, birds preferred areas with greater percentage sagebrush cover and avoided paved roads, agriculture, and forested areas. Birds consistently preferred areas with higher precipitation in the summer and avoided rugged terrain in the winter. Selection for sagebrush cover varied regionally with stronger selection in the Northeast region, likely because of limited availability, whereas avoidance of paved roads was fairly consistent across regions. We chose resource selection function (RSF) thresholds for each model set (seasonalregional combination) that delineated important seasonal habitats for sage-grouse. Each model set showed good validation and discriminatory capabilities within study-site boundaries. We applied the nesting-season models to a novel area not included in model

Although human influence across rural landscapes is often discussed, interactions between the native, natural systems and human activities are challenging to measure explicitly. We assessed the distribution of introduced, invasive species as related to anthropogenic infrastructure and environmental conditions across southwestern Wyoming. to discern direct correlations as well as covariate influences between land use, land cover, and abundance of invasive plants, and assess the supposition that these features affect surrounding rangeland conditions. Our sample units were 1 000 m long and extended outward from target features, which included roads, oil and gas well pads, pipelines, power lines, and featureless background sites. Sample sites were distributed across the region using a stratified, random design with a frame that represented features and land-use intensity. In addition to land-use gradients, we captured a representative, but limited, range of variability in climate, soils, geology, topography, and dominant vegetation. Several of these variables proved significant, in conjunction with distance from anthropogenic features, in regression models of invasive plant abundance. We used general linear models to demonstrate and compare associations between invasive plant frequency and Euclidian distance from features, natural logarithm transformed distances (log-linear), and environmental variables which were presented as potential covariates. We expected a steep curvilinear (log or exponential) decline trending towards an asymptote along the axis representing high abundance near features with rapid decrease beyond approximately 50–100 m. Some of the associations we document exhibit this pattern, but we also found some invasive plant distributions that extended beyond our expectations, suggesting a broader distribution than anticipated. Our results provide details that can inform local efforts for management and control of invasive species, and they provide evidence of the different associations between natural patterns and human land use exhibited by nonnative species in this rural setting, such as the indirect effects of humans beyond impact areas.

Animals can require different habitat types throughout their annual cycles. When considering habitat prioritization, we need to explicitly consider habitat requirements throughout the annual cycle, particularly for species of conservation concern. Understanding annual habitat requirements begins with quantifying how far individuals move across landscapes between key life stages to access required habitats. We quantified individual interseasonal movements for greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) using radio-telemetry spanning the majority of the species distribution in Wyoming. Sage-grouse are currently a candidate for listing under the United States Endangered Species Act and Wyoming is predicted to remain a stronghold for the species. Sage-grouse use distinct seasonal habitats throughout their annual cycle for breeding, brood rearing, and wintering. Average movement distances in Wyoming from nest sites to summer-late brood-rearing locations were 8.1 km (SE ¼ 0.3 km; n ¼ 828 individuals) and the average subsequent distances moved from summer sites to winter locations were 17.3 km (SE ¼ 0.5 km; n ¼ 607 individuals). Average nest-to-winter movements were 14.4 km (SE ¼ 0.6 km; n ¼ 434 individuals). We documented remarkable variation in the extent of movement distances both within and among sites across Wyoming, with some individuals remaining year-round in the same vicinity and others moving over 50 km between life stages. Our results suggest defining any of our populations as migratory or non-migratory is innappropriate as individual strategies vary widely. We compared movement distances of birds marked using Global Positioning System (GPS) and very high frequency (VHF) radio marking techniques and found no evidence that the heavier GPS radios limited movement. Furthermore, we examined the capacity of the sage-grouse core regions concept to capture seasonal locations. As expected, we found the core regions approach, which was developed based on lek data, was generally better at capturing the nesting locations than summer or winter locations. However, across

The U.S. Geological Survey Fort Collins Science Center created statewide roads data for the Bureau of Land Management Wyoming State Office using 2009 aerial photography from the National Agriculture Imagery Program. The updated roads data resolves known concerns of omission, commission, and inconsistent representation of map scale, attribution, and ground reference dates which were present in the original source data. To ensure a systematic and repeatable approach of capturing roads on the landscape using on-screen digitizing from true color National Agriculture Imagery Program imagery, the authors developed a photogrammetry key and quality assurance/quality control protocols. Therefore, the updated statewide roads data will support the Bureau of Land Management’s resource management requirements with a standardized map product representing 2009 ground conditions. The updated Geographic Information System roads data set product, represented at 1:4,000 and +/- 10 meters spatial accuracy, contains 425,275 kilometers within eight attribute classes. The quality control of these products indicated a 97.7 percent accuracy of aspatial information and 98.0 percent accuracy of spatial locations. Approximately 48 percent of the updated roads data was corrected for spatial errors of greater than 1 meter relative to the pre-existing road data. Twenty-six percent of the updated roads involved correcting spatial errors of greater than 5 meters and 17 percent of the updated roads involved correcting spatial errors of greater than 9 meters. The Bureau of Land Management, other land managers, and researchers can use these new statewide roads data set products to support important studies and management decisions regarding land use changes, transportation and planning needs, transportation safety, wildlife applications, and other studies.