Winter resource selection by female mule deer <i>Odocoileus hemionus</i>: functional response to spatio‐temporal changes in habitat

Anderson, Eric D.; Long, Ryan A.; Atwood, M. Paul; Kie, John G.; Thomas, Terry R.; Zager, Peter; Bowyer, R. Terry

doi:10.2981/11-048

Cited by 35 publications

(34 citation statements)

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“…Forest cover is an important component of mule deer winter habitat use throughout the inter‐mountain west in Colorado, Oregon, Washington, British Columbia, and Idaho (Leckenby and Adams , Carson and Peek , D'Eon and Serrouya , Anderson et al , Smith et al ). Perhaps the most compelling function of forest cover is that it provides important energetic benefits for mule deer.…”

Section: Discussionmentioning

confidence: 99%

“…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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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: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multiscale models of habitat use by mule deer in winter

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“…We used GIS to map landscape, topographic, and vegetation features known or suspected to influence behavior of mule deer (Kufeld et al 1988; Thomas and Irby 1991;Pierce et al 2004;D'Eon and Serrouya 2005;Sawyer et al 2006;Anderson et al 2012). The DEM was reclassified to 30-m resolution and used to calculate slope (degrees) and terrain roughness (standard deviation of elevation).…”

Section: Covariate Developmentmentioning

confidence: 99%

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

Webb¹,

Dzialak²,

Kosciuch

et al. 2013

Rangeland Ecology & Management

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Discrete choice modeling is a popular technique in marketing research, transportation, and other areas. It is used to help researchers understand people's stated choice of alternative products and services. We discuss designing a choice experiment, preparing the questionnaire, inputting and processing the data, performing the analysis, and interpreting the results. * Most of the discussion is on designing the choice experiment.

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“…Various presence‐only data can be used to inform the models including radio‐telemetry (Walter et al ), sign (e.g., track, scat; Holmes and Laundré , Henderson et al ), and count (e.g., aerial survey; Allen et al ) data. These models have been applied to a range of wildlife conservation and management issues involving disease transmission between sympatric species (Walter et al ), habitat suitability for isolated (Hough and Dieter ) or threatened (Williams et al ) populations, foraging habitat selection (Holmes and Laundré ), and the effects of long‐term habitat change (Anderson et al ) and anthropogenic land use (Seip et al , Sawyer et al , Crane et al ) on populations. Most notably, in regard to spotlight survey goals, are several studies that investigated the relationships between species occurrence and resource use and evaluated methods for relating RSF predictions to population abundance (Boyce and McDonald , Johnson and Seip , Boyce et al ).…”

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Using spotlight observations to predict resource selection and abundance for white‐tailed deer

2019

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Spotlight surveys for white‐tailed deer (Odocoileus virginianus) can yield large presence‐only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat‐based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence‐only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county‐level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat‐based density estimator (extrapolation) and zero‐inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence‐only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society.

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Winter resource selection by female mule deer Odocoileus hemionus: functional response to spatio‐temporal changes in habitat

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 35 publications

References 39 publications

Multiscale models of habitat use by mule deer in winter

Multiscale models of habitat use by mule deer in winter

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

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

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