Intrinsic, environmental, and anthropogenic factors related to pronghorn summer mortality

Reinking, Adele K.; Smith, Kurt T.; Monteith, Kevin L.; Mong, Tony W.; Read, Mary J.; Beck, Jeffrey L.

doi:10.1002/jwmg.21414

Cited by 21 publications

(29 citation statements)

References 58 publications

(105 reference statements)

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“…During winter, when pronghorn are faced with increased thermoregulatory and locomotive costs (Wesley et al 1973, Barrett 1982, Telfer and Kelsall 1984, Reinking et al 2018, they may be more risk-averse in an effort to avoid the loss of crucial energy reserves. Indeed, we observed avoidance of development during winter at both scales, with animals selecting areas farther from wells and with lower road densities at the home-range scale, and farther from roads and wells during daytime at the patch scale.…”

Section: Discussionmentioning

confidence: 99%

“…These climatic conditions can influence pronghorn mortality (Reinking et al. ), and therefore likely influence resource use. We obtained daily snow depth (cm) and temperature (°C) data using SnowModel (Liston and Elder ; InterWorks Consulting, Loveland, Colorado, USA; 250‐m resolution).…”

Section: Methodsmentioning

confidence: 99%

“…The locomotive difficulty that deeper snow presents, combined with the energy required for self-maintenance in colder temperatures, ultimately increase the expense of winter survival in pronghorn, and in severe winters, die-offs can be extreme (Barrett 1982). These climatic conditions can influence pronghorn mortality (Reinking et al 2018), and therefore likely influence resource use. We obtained daily snow depth (cm) and temperature (°C) data using SnowModel (Liston and Elder 2006a; InterWorks Consulting, Loveland, Colorado, USA; 250-m resolution).…”

Section: Habitat Selection Covariatesmentioning

confidence: 99%

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Across scales, pronghorn select sagebrush, avoid fences, and show negative responses to anthropogenic features in winter

et al. 2019

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Pronghorn (Antilocapra americana) are endemic to western North America where they occupy expanses of grassland and sagebrush (Artemisia spp.) habitats. The Red Desert region in south‐central Wyoming, USA, has historically served as a stronghold for pronghorn populations, but many herds there have experienced declining population trends over the last two decades, concurrent with oil and natural gas development. These demographic changes and the potential for such energy development, its associated infrastructure, and other anthropogenic features including roads and fences to influence pronghorn habitat selection were the impetuses for our study. We sought to evaluate the potential effect of human‐induced disturbance on multi‐scale seasonal resource selection of 142 adult female pronghorn from 2013 to 2016 using 442 unique animal‐season‐year datasets. We utilized a traditional resource selection function to evaluate seasonal home‐range selection and a step‐selection function to assess fine‐scale, patch‐level seasonal selection. We also compared resource selection during daytime and nighttime hours with step‐selection analyses. At the seasonal home‐range scale, pronghorn selected for areas with more sagebrush during both seasons and areas farther from fences during summer. This trend was also apparent at the patch‐scale level, where pronghorn selected sagebrush‐dominant habitats and avoided crossing fences in all seasons during both day and night. Additionally at this scale, pronghorn selected areas farther from fences during daytime in summer. At the broader, home‐range scale, pronghorn selected areas with greater road density during summer, but with lower road densities and farther from wells during winter. Avoidance of anthropogenic features during winter was also observed at the finer, patch‐scale, with pronghorn selecting for increased density of roads and oil and natural gas wells during daytime in summer, but selecting areas farther from these features during daytime in winter. We recommend minimizing fencing and other forms of anthropogenic disturbance in high‐quality seasonal pronghorn habitats with high proportions of sagebrush, particularly during winter when risk‐avoidance responses may be amplified.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Habitat Selection Covariatesmentioning

confidence: 99%

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Across scales, pronghorn select sagebrush, avoid fences, and show negative responses to anthropogenic features in winter

et al. 2019

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“…Compared to remote sensing products, snowpack models offer a more detailed characterization of snow conditions and can also vary in extent, temporal and spatial resolution, and derived snow metrics (Liston and Elder 2006a, Watson et al 2006a, Brennan et al 2013. Although SnowModel has been used extensively for climate and hydrological applications worldwide (Hiemstra et al 2006, Liston and Hiemstra 2011, Semmens et al 2013, Sproles et al 2013, Højlund et al 2016, it has been used in only a small number of wildlife studies to date (Liston et al 2016, Lendrum et al 2017, Reinking et al 2018. SnowModel, in particular, offers users tremendous flexibility as a spatially distributed, multi-layer snowevolution modeling system scalable down to 1-m spatial and 10-min temporal resolutions and can derive over 100 unique snow metrics, including snow depth and snow density (Liston and Elder 2006a, b).…”

Section: Introductionmentioning

confidence: 99%

“…As such, SnowModel could be an ideal tool for investigating the influence of snowpack properties on animal movement and resource selection. Although SnowModel has been used extensively for climate and hydrological applications worldwide (Hiemstra et al 2006, Liston and Hiemstra 2011, Semmens et al 2013, Sproles et al 2013, Højlund et al 2016, it has been used in only a small number of wildlife studies to date (Liston et al 2016, Lendrum et al 2017, Reinking et al 2018. Snowpack models offer many attractive features for the purposes of animal movement and resource selection, though several factors may limit their utility relative to more user-friendly remotesensing products.…”

Section: Introductionmentioning

confidence: 99%

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

Mahoney

Liston

LaPoint

et al. 2018

Ecological Applications

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Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100 kg/m ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300 kg/m ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties.

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Variable effects of wind‐energy development on seasonal habitat selection of pronghorn

et al. 2021

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Intrinsic, environmental, and anthropogenic factors related to pronghorn summer mortality

Cited by 21 publications

References 58 publications

Across scales, pronghorn select sagebrush, avoid fences, and show negative responses to anthropogenic features in winter

Across scales, pronghorn select sagebrush, avoid fences, and show negative responses to anthropogenic features in winter

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

Variable effects of wind‐energy development on seasonal habitat selection of pronghorn

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