Elk Foraging Site Selection on Foothill and Mountain Rangeland in Spring

Crane, Kelly; Mosley, Jeffrey C.; Mosley, Tracy K.; Frost, Rachel A.; Smith, Michael A.; Fuller, Wendy L.; Tess, M. W.

doi:10.1016/j.rama.2016.04.001

Cited by 9 publications

(6 citation statements)

References 57 publications

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“…Our understanding of these fine-scale relations between elk and cattle, however, is still quite narrow. This limitation stems largely from past difficulties in continuously observing cryptic ungulates like elk throughout both day and night (e.g., Crane et al, 2016). While developments in GPS tracking technologies certainly have the potential to address this observation problem, to date no comparative GPS tracking studies of sympatric elk and cattle have been conducted at scales fine enough to resolve patchscale behaviors.…”

Section: Introductionmentioning

confidence: 99%

Contrasting Daily and Seasonal Activity and Movement of Sympatric Elk and Cattle

Clark

Johnson

Ganskopp

et al. 2017

Rangeland Ecology & Management

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Elk (Cervus elaphus L.) and cattle (Bos taurus L.) co-occur on rangelands throughout western North America. Literature regarding range relations between elk and cattle, however, is contradictory describing interspecific competition in some cases and complementary or facilitative relations in others. A better understanding of how sympatric elk and cattle behave at fine spatiotemporal scales is needed to properly allocate resources for these species. We used intensively-sampled GPS tracking data (1-sec intervals) to classify elk and cattle behavior and investigate their activity and movement strategies in the Blue Mountains of northeastern Oregon, USA, during summer and fall 2007. An ensemble classification approach was used to identify stationary, foraging, and walking behavior classes within the GPS datasets of mature beef and captive elk cows grazing in forested pastures during two randomized experiments, one in summer and the other fall. During summer, elk traveled further per day, had larger walking budgets, exhibited more and longer walking bouts, and had higher walking velocities than beef cows. Cattle tended to emphasize intensive foraging over extensive movement and thus displayed larger foraging budgets and longer foraging bouts than elk. Site by species interactions, however, were detected for some foraging responses. During fall, when forage quality was limiting, elk exhibited a more foraging-centric mobility strategy while cattle emphasized an energy conservation strategy. These differing movement and energetic strategies tended to support the concept that elk and cattle occupy differing behavioral niches. Extensive foraging by elk and intensive foraging by cattle during summer correspond well with behaviors expected for elk searching out forbs in graminoid-dominated habitats and cattle foraging intensively on graminoids. Behaviors exhibited in the fall were consistent with elk continuing to 3 exercise more selectivity among the available forage than cattle. These differing strategies, consequently, would moderate the potential for direct interspecific competition during summer and fall.

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Section: Introductionmentioning

confidence: 99%

Contrasting Daily and Seasonal Activity and Movement of Sympatric Elk and Cattle

Clark

Johnson

Ganskopp

et al. 2017

Rangeland Ecology & Management

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“…Beta coefficients for all variables stabilized by an available sample size of 100,000; therefore, we selected a random set of 100,000 available points statewide (∼6:1 available to used ratio for each model set as described below) for estimating RSF models (Nielson et al , Buskirk and Millspaugh , Anderson et al ). We considered deer groups as the sampling unit because observations were collected as groups of deer and because deer in groups did not select locations independently of each other (Porter et al , Crane et al ). We pooled deer observations across years assuming statewide habitat availability at the landscape‐scale remained relatively stable across the study period and population‐level habitat selection by deer remained stable because the overall population remained stable during the study (i.e., deer populations did not expand or contract spatially in relation to population size; Ciarniello et al , Walter et al ).…”

Section: Methodsmentioning

confidence: 99%

“…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 ).…”

mentioning

confidence: 99%

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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“…We calculated the absolute distance of each cell from the edge of forest land cover classes in the 2011 National Land Cover Database (Homer et al ). For both spatial extents, we calculated the proportion of forest, grass or shrub, riparian, and elk ( Cervus canadensis ) security cover (≥40% forest canopy cover in patch sizes ≥26 ha) types using 2001, 2008, 2010, and 2012 LANDFIRE vegetation datasets (LANDFIRE , Crane et al ). We calculated the proportion of whitebark pine cover with a map of 2010 whitebark pine distribution (Greater Yellowstone Coordinating Committee ).…”

Section: Methodsmentioning

confidence: 99%

Grizzly bear depredation on grazing allotments in the Yellowstone Ecosystem

et al. 2018

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Grizzly bear (Ursus arctos) conflicts with humans, including livestock depredation on public land grazing allotments, have increased during the last several decades within the Greater Yellowstone Ecosystem (GYE) in the western United States as the grizzly bear population has grown in number and occupied range. Minimizing conflicts and improving conservation efficacy requires information on the relationships between livestock depredations, allotment management, grizzly bear habitat conditions, and their interactions. We used generalized linear mixed models to evaluate spatio‐temporal relationships between grizzly bear depredation of livestock and the characteristics of 316 United States Department of Agriculture Forest Service and National Park Service grazing allotments in the GYE during 1992–2014. We evaluated relationships at 2 spatial extents, representing daily and annual grizzly bear activity areas. During the study period, more grazing allotments became occupied by grizzly bears and most livestock depredations were associated with these areas of population expansion. Number of livestock (β = 1.15 ± 0.19 [SE]) and grizzly bear density index (β = 1.13 ± 0.10) had the greatest effects on the number of livestock depredation events relative to other allotment attributes. Estimated number of depredation events increased by approximately 20% when cow‐calf pairs increased by 100 pairs and grizzly bear density index increased by 1 bear/196 km2 (the average annual home‐range size of a female grizzly bear in the GYE). Additionally, grazing allotment size was positively related to the number of depredation events (β = 0.56 ± 0.16), whereas the presence of bull cattle or horses was associated with an approximately 50% reduction in depredations (β = −0.71 ± 0.37). Livestock depredation events were greater for allotments with lower road density (β = −0.89 ± 0.28), less rugged terrain (β = −0.57 ± 0.25), higher vegetative primary productivity (β = 0.33 ± 0.16), and more whitebark pine coverage (β = 0.30 ± 0.15). Relationships between depredations and grizzly bear habitat conditions varied across spatial extents. As the grizzly bear population continues to expand, natural resource managers and livestock producers could focus efforts on allotments with a higher density of grizzly bears, fewer roads, and quality grizzly bear habitat, including higher vegetative productivity, when developing cooperative management plans and preventative measures to reduce the likelihood of depredation. The perspectives gained from our analysis provide context for long‐term, landscape‐level planning to accommodate livestock production on public lands while meeting conservation goals for grizzly bears. © 2018 The Wildlife Society.

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Elk Foraging Site Selection on Foothill and Mountain Rangeland in Spring

Cited by 9 publications

References 57 publications

Contrasting Daily and Seasonal Activity and Movement of Sympatric Elk and Cattle

Contrasting Daily and Seasonal Activity and Movement of Sympatric Elk and Cattle

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

Grizzly bear depredation on grazing allotments in the Yellowstone Ecosystem

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