CHAPTER FOUR. Characteristics of Greater Sage-Grouse Habitats: A LANDSCAPE SPECIES AT MICRO- AND MACROSCALES

Connelly, John W.; Rinkes, E. Thomas; Braun, Clait E.

doi:10.1525/9780520948686-008

Cited by 4 publications

(3 citation statements)

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“…Additionally, these predictions emphasize not only the loss of habitat but also the increase in habitat fragmentation, resulting in, as a result, a habitat composed of several small, unconnected patches. The sage-grouse is a very specialist species, having great difficulties in adapting to habitat change [55]. Going out of its habitat and through the inhospitable matrix to move from one patch to another is unlikely, and it would increase its mortality rate [56].…”

Section: External Validation-sage-grouse Habitats In Idahomentioning

confidence: 99%

“…Ultimately, the fragmentation could cause the isolation of some subpopulations of the species, which would likely become extinct due to problems such as inbreeding, diseases or bottleneck effects [58]. Since this species has very important seasonal movements [55], the fragmentation could greatly impact it by impeding its movement from its wintering areas to the breeding ones.…”

Section: External Validation-sage-grouse Habitats In Idahomentioning

confidence: 99%

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Machine Learning for Conservation Planning in a Changing Climate

et al. 2020

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Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.

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Section: External Validation-sage-grouse Habitats In Idahomentioning

confidence: 99%

Section: External Validation-sage-grouse Habitats In Idahomentioning

confidence: 99%

Machine Learning for Conservation Planning in a Changing Climate

et al. 2020

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“…Sage‐grouse rely on sagebrush and shrub cover habitats during all life stages (Connelly et al, 2011). The most abundant shrub species include sagebrush ( Artemisia spp.…”

Section: Methodsmentioning

confidence: 99%

Defining fine‐scaled population structure among continuously distributed populations

et al. 2022

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Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. To facilitate the use of wildlife monitoring data for improved adaptive management, we developed a novel approach to define hierarchical tiers (multiple scales) of population structure. We defined population structure by combining graph theory with biological inference about dispersal capability (based on movement, gene flow, and habitat condition) and functional processes affecting movement (e.g. habitat selection across scales of landscape preferences). First, we developed least‐cost paths between high fidelity sites (habitat patches) using a cost surface, informed from functional processes of habitat characteristics to account for resistance of inter‐patch movements. Second, we combined the paths into a multi‐path graph construct. Third, we used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into hierarchical tiers of connected subpopulations, denoting the degree that dispersal affected population structure. As a case study, we applied our approach across the greater sage‐grouse (Centrocercus urophasianus) range, a species of conservation concern in western United States. We described the relative importance of local populations and where to potentially avoid landscape disturbances that may negatively affect population connectivity using centrality measures supported by graph theory, and we demonstrated close alignment of the resulting population structure with population densities. This method can be adapted for other species with site fidelity and used as a management tool to evaluate population trends and responses to landscape changes across different temporal and spatial scales.

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Seasonal habitat suitability models for a threatened species: the Gunnison sage-grouse

et al. 2021

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ContextThe Gunnison sage-grouse (Centrocercus minimus) has experienced range-wide declines and has been listed as Threatened by the USA Fish and Wildlife Service to receive protections under the USA Endangered Species Act. A draft Recovery Plan was recently completed. No seasonal habitat models have been developed for the small isolated populations. AimsTo develop a habitat suitability model that was collaboratively developed between modellers and conservation practitioners to predict the probability of use by Gunnison sage-grouse during the breeding and summer seasons in designated occupied critical habitat, and extrapolate to adjacent designated unoccupied critical habitat. MethodsWe captured, marked and tracked Gunnison sage-grouse in nine different studies spanning 25 years. We used a suite of biotic, abiotic and vegetation local-level and population-scale covariates in a use-available resource selection function to develop models that predict the probability of use by Gunnison sage-grouse. Key resultsWe used 9140 Gunnison sage-grouse locations from 406 individual birds to develop nine resource selection models for occupied habitat and extrapolated model predictions to adjacent unoccupied critical habitat in five small isolated Gunnison sage-grouse populations. A majority of our models validated well. ConclusionsWe report the first two-season resource use-based habitat suitability models for five of six small isolated Gunnison sage-grouse populations. Because of the unique habitat use by Gunnison sage-grouse in each population, we recommend that resource managers strategically target management actions in individual populations and avoid ‘one-size-fits-all’ habitat management prescriptions. ImplicationsOur models will assist managers in the identification of seasonal habitats within populations to target management actions for Gunnison sage-grouse recovery.

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CHAPTER FOUR. Characteristics of Greater Sage-Grouse Habitats: A LANDSCAPE SPECIES AT MICRO- AND MACROSCALES

Cited by 4 publications

References 0 publications

Machine Learning for Conservation Planning in a Changing Climate

Machine Learning for Conservation Planning in a Changing Climate

Defining fine‐scaled population structure among continuously distributed populations

Seasonal habitat suitability models for a threatened species: the Gunnison sage-grouse

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