Greater sage‐grouse population trends across Wyoming

Edmunds, David R.; Aldridge, Cameron L.; O’Donnell, Michael S.; Monroe, Adrian P.

doi:10.1002/jwmg.21386

Cited by 20 publications

(44 citation statements)

References 51 publications

(81 reference statements)

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“…, Edmunds et al. , b). Further, stressors acting at different scales and extents often affect species like sage‐grouse, influencing patterns of abundance and source–sink dynamics (Aldridge and Boyce , Kirol et al.…”

Section: Discussionmentioning

confidence: 99%

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Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

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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.

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Section: Discussionmentioning

confidence: 99%

“…, Bissonette , Edmunds et al. , b). Thus, a hierarchical monitoring framework that accounts for a continuum of scales for analyzing population changes and trends will better support the continuum between structural and functional habitat relationships.…”

Section: Introductionmentioning

confidence: 98%

Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

et al. 2019

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“…, Edmunds et al. ), but attendance also may be density‐dependent (Blomberg et al. ) and could confound estimates of density dependence in

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.…”

Section: Discussionmentioning

confidence: 99%

The importance of simulation assumptions when evaluating detectability in population models

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Population monitoring is important for investigating a variety of ecological questions, and N‐mixture models are increasingly used to model population size (N) and trends (λ) while estimating detectability (p) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax the closure assumption, but simulations to evaluate models and inform effort (e.g., number of repeated counts) typically assume p is constant or random across sites and years. Thus, it is unknown how these models perform under scenarios where trends in p confound inferences on N and λ, and conclusions regarding effort may be overoptimistic. Here, we used global positioning system data from greater sage‐grouse (Centrocercus urophasianus) to inform simulations of the detection process for lek counts of this species, and we created scenarios with and without linear annual trends in p. We then compared estimates of N and λ from hierarchical population models either fit with single maximum counts or with detectability estimated from repeated counts (dynamic N‐mixture models). We also explored using auxiliary data to correct counts for variation in detectability. Uncorrected count models consistently underestimated N by >50%, whereas N‐mixture models without auxiliary data underestimated N to a lesser degree due to unmodeled heterogeneity in p such as age. Nevertheless, estimates of λ from both types of models were unbiased and similar for scenarios without trends in p. When p declined systematically across years, uncorrected count models underestimated λ, whereas N‐mixture models estimated λ with little bias when all sites were counted repeatedly. Auxiliary data also reduced bias in parameter estimates. Evaluating population models using scenarios with systematic variation in p may better reveal potential biases and inform effort than simulations that assume p is constant or random. Dynamic N‐mixture models can distinguish between trends in p and N, but also require repeated counts within primary periods for accurate estimates. Auxiliary data may be useful when researchers lack repeated counts, wish to monitor more sites less intensively, or require unbiased estimates of N.

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“…Although many Sage-grouse populations are expected to be in decline (Connelly et al 2004, Garton et al 2011, Edmunds et al 2017, this scoping exercise assumed a stable starting population to assess population reductions associated with future development and climate change. We assumed the constancy of habitat protection, habitat selection associations, and indirect Sage-grouse responses to development through space and time.…”

Section: Considerations and Caveatsmentioning

confidence: 99%

Influences of potential oil and gas development and future climate on Sage‐grouse declines and redistribution

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O’Donnell

Aldridge

et al. 2019

Ecological Applications

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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.

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Greater sage‐grouse population trends across Wyoming

Abstract: The scale at which analyses are performed can have an effect on model results and often one scale does not accurately describe the ecological phenomena of interest (e.g., population trends) for wideranging species: yet, most ecological studies are performed at a single, arbitrary scale.

Cited by 20 publications

References 51 publications

Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

The importance of simulation assumptions when evaluating detectability in population models

Influences of potential oil and gas development and future climate on Sage‐grouse declines and redistribution

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