Winter Resource Selection by Mule Deer on the Wyoming–Colorado Border Prior to Wind Energy Development

Webb, Stephen L.; Dzialak, Matthew R.; Kosciuch, Karl L.; Winstead, Jeffrey B.

doi:10.2111/rem-d-12-00065.1

Cited by 15 publications

(22 citation statements)

References 47 publications

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“…We view our findings of mule deer avoidance of roads open to motorized vehicle use to be linked to human disturbance, and not the physical features of the road. The strong avoidance of roads open to motorized vehicle use, and associated human disturbance, by ungulates observed in our study and others (Rowland et al 2000, Sawyer et al 2006, Coe et al 2011, Webb et al 2013, Gilbert et al 2017 suggests a substantial portion of what may be suitable habitat may no longer be used (Rogala et al 2011). In a metaanalysis of the effects of flight responses of ungulates to human disturbance, it was found that ungulates, including mule deer, had a strong avoidance and increased movement rates in response to human activity, especially for hunted populations (Stankowich 2008) like our study populations in south-central Oregon.…”

Section: Analysis Area Interceptsupporting

confidence: 74%

“…One of the strongest patterns we observed across all 3 spatio‐temporal analyses was a consistent avoidance of roads open to motorized vehicle use, which has also been documented in Wyoming, Colorado, and southeast Alaska (Sawyer et al , Webb et al , Northrup et al , Gilbert et al ). In contrast, in Idaho, where mule deer shared winter range with elk, mule deer were closer to roads during winter, whereas elk used areas farther from roads (Stewart et al ).…”

Section: Discussionsupporting

confidence: 61%

“…We developed an a priori set of 31 landscape characteristics (i.e., covariates; Table S1, available online in Supporting Information) that may influence winter habitat use of mule deer based on existing literature (Leckenby , Peek et al , D'Eon and Serrouya , Anderson et al , Webb et al ) and expert opinion of wildlife managers within our study areas. Several landscape characteristics (e.g., forest canopy cover) had multiple geographic information system (GIS) data sources available, which increased the number of potential covariates to consider in our analysis of winter habitat use by mule deer to 41.…”

Section: Methodsmentioning

confidence: 99%

“…Although effects of reduced nutritional conditions on summer range may influence population performance of mule deer, it is unknown whether changes to forest canopy cover on winter range is beneficial or detrimental to mule deer. For example, forest canopy cover is an important habitat component for mule deer during winter (Leckenby and Adams , D'Eon and Serrouya , Anderson et al , Webb et al , Smith et al ). In contrast, increased western juniper ( Juniperus occidentalis ) encroachment may reduce availability of forage (Rowland et al , Witt and Patterson , Bombaci and Pejchar ) on winter range and be detrimental to mule deer.…”

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confidence: 99%

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Multiscale models of habitat use by mule deer in winter

et al. 2018

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Mule deer (Odocoileus hemionus hemionus) populations have experienced widespread declines in much of western North America and alteration or loss of habitat could be contributing to these declines. Consequently, understanding habitat features that are important to mule deer is necessary for effective management of the species and their habitat. From 2005–2012 we radio‐marked 452 mule deer with global positioning system collars across 9 distinct winter ranges to evaluate winter habitat use along the east slope of the Cascade Range in south‐central Oregon, USA. Using data from 357 mule deer across 9 analysis areas, we developed regional habitat use models for mule deer on winter range at 3 spatio‐temporal scales: herd range based on 100% minimum convex polygons around deer locations, home range based on 90% kernel density estimates of deer locations, and foraging range based on locations obtained within 2 hours of sunrise or sunset within the boundaries of the home range scale. We assessed habitat use of mule deer using a generalized linear model with a negative‐binomial link function. We validated our models with locations from an independent dataset of 95 deer that wintered within 8 of our analysis areas. Model validation indicated that regional models for all spatio‐temporal scales predicted probability of use moderately to very well. At all spatio‐temporal scales, predicted use by mule deer was greater in areas with forest canopy cover. These findings call into question large‐scale removal of western juniper (Juniperus occidentalis) in Oregon and other portions of western United States to enhance habitat for sagebrush‐dependent wildlife species. Across all spatio‐temporal scales, we documented increased probability of use of areas farther from roads open to motorized vehicle use by mule deer, highlighting the importance of limiting motorized vehicle use and access on mule deer winter range. We also documented increased use of areas with lower snow depth and on moderate slopes by mule deer. Our models can be used in land management planning to spatially predict mule deer distribution and probability of use under alternative management scenarios that affect forest canopy cover or motorized vehicle use on roads while accounting for other physical features of the landscape. Additionally, our models can be used to develop juniper removal projects to mitigate effects on mule deer winter range habitat while benefiting other wildlife. © 2018 The Wildlife Society.

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Section: Analysis Area Interceptsupporting

confidence: 74%

Section: Discussionsupporting

confidence: 61%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Multiscale models of habitat use by mule deer in winter

et al. 2018

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“…Some of these approaches included visual comparison of an RSF map and used points (Mashintonio, Pimm, Harris, van Aarde, & Russell, 2014), generating a contingency table (Mace, Waller, Manley, Ake, & Wittinger, 1999), calculating a correlation between two RSF surfaces built with different data (Brook & McLachlan, 2009), and reporting the percentage of location data in 5 equal sized bins (Webb, Dzialak, Kosciuch, & Winstead, 2013). Multiple studies employed more than one approach to testing predictive accuracy, which is why the methods we describe do not sum to the total number of studies reviewed (101).…”

Section: Resultsmentioning

confidence: 99%

Mapping resource selection functions in wildlife studies: Concerns and recommendations

2016

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Predicting the spatial distribution of animals is an important and widely used tool with applications in wildlife management, conservation, and population health. Wildlife telemetry technology coupled with the availability of spatial data and GIS software have facilitated advancements in species distribution modeling. There are also challenges related to these advancements including the accurate and appropriate implementation of species distribution modeling methodology. Resource Selection Function (RSF) modeling is a commonly used approach for understanding species distributions and habitat usage, and mapping the RSF results can enhance study findings and make them more accessible to researchers and wildlife managers. Currently, there is no consensus in the literature on the most appropriate method for mapping RSF results, methods are frequently not described, and mapping approaches are not always related to accuracy metrics. We conducted a systematic review of the RSF literature to summarize the methods used to map RSF outputs, discuss the relationship between mapping approaches and accuracy metrics, performed a case study on the implications of employing different mapping methods, and provide recommendations as to appropriate mapping techniques for RSF studies. We found extensive variability in methodology for mapping RSF results. Our case study revealed that the most commonly used approaches for mapping RSF results led to notable differences in the visual interpretation of RSF results, and there is a concerning disconnect between accuracy metrics and mapping methods. We make 5 recommendations for researchers mapping the results of RSF studies, which are focused on carefully selecting and describing the method used to map RSF studies, and relating mapping approaches to accuracy metrics.

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Individual heterogeneity in resource selection has implications for mortality risk in white‐tailed deer

et al. 2020

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Individual animals vary in their selection of habitat as a function of sex, age, and experience. Such individual heterogeneity is important when decomposing segments of the population that may or may not respond to habitat or population management, or when targeting management that has greater potential for increasing demographic responses (e.g., survival). We examined individual heterogeneity in habitat selection during the hunting season using multilevel step selection models for 59 adult (≥2.5 yr old) white-tailed deer (Odocoileus virginianus) of both sexes. We used fractal analysis to determine the scale at which animals perceived and responded to the landscape, and modeled selection using covariates for use of agriculture, wetland, edge, forest interior, and distance to road. We tested for differences in individualspecific habitat selection between age classes and modeled survival as a function of individual-specific selection using proportional hazard modeling. At the population level, males selected for agriculture and forest interior and avoided roads, while adult females selected for agriculture. Individual-specific selection of forest interior differed between age classes in males, with mature males selecting interior forest less frequently than immature males. Risk of mortality was related negatively to individual-specific selection for wetland areas in males and forest interior for females. No habitat variables that were selected for or avoided at the population level influenced mortality risk at the individual level, suggesting management efforts based on traditional population-level analysis may be misguided. Managers attempting to influence population dynamics via habitat and harvest management should be aware of the substantial heterogeneity in habitat selection among individual animals before implementing costly practices that may not be effective for their objectives.

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Winter Resource Selection by Mule Deer on the Wyoming–Colorado Border Prior to Wind Energy Development

Cited by 15 publications

References 47 publications

Multiscale models of habitat use by mule deer in winter

Multiscale models of habitat use by mule deer in winter

Mapping resource selection functions in wildlife studies: Concerns and recommendations

Individual heterogeneity in resource selection has implications for mortality risk in white‐tailed deer

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