Influence of interspecific competition on mule deer birthing and rearing site selection

Ungulates often alter behavior and space use in response to interspecific competition. Despite observable changes in behavior caused by competitive interactions, research describing the effects of competition on survival or growth is lacking. We used spatial modeling to determine if habitat use by female mule deer (Odocoileus hemionus) was affected by other ungulate species prior to, during, and after parturition. We conducted our study in the Book Cliffs region of eastern Utah, USA, during 2019 and 2020. We used resource selection function (RSF) analysis to model space use of 4 ungulate species that potentially competed with mule deer: bison (Bos bison), cattle, elk (Cervus canadensis), and feral horses. We incorporated RSF models for competing species into a random forest analysis to determine if space use by mule deer was influenced by these other ungulate species. We used survival and growth data from neonate mule deer to directly assess potential negative effects of other ungulates. Habitat use by elk was an important variable in predicting use locations of mule deer during birthing and rearing. The relationship was positive, suggesting interference competition was not occurring. Survival of neonate mule deer increased as the probability of use by elk increased (hazard ratio = 0.185 ± 0.497 [SE]). Further, probability of use by elk in rearing habitat had no influence on growth of neonate mule deer from birth to 6 months of age, suggesting that exploitative competition was not occurring.     

A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

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

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.     

Scale for resource selection functions

Resource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information‐theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k‐fold cross validation.     

Accuracy of resource selection functions across spatial scales

Resource selection functions (RSFs) can be used to map suitable habitat of a species based on predicted probability of use. The spatial scale may affect accuracy of such predictions. To provide guidance as to which spatial extent or grain is appropriate and most accurate for animals, we used the concept of hierarchical selection orders to dictate extent and grain. We conducted a meta-analysis from 123 RSF studies of 886 species to identify differences in prediction success that might be expected for five selection orders. Many studies do not constrain spatial extent to the grain of the next broader selection order in the hierarchy, mixing scaling effects. Thus, we also compared accuracy of single- vs. multiple-grain RSFs developed at the unconstrained extent of an entire study area. Results suggested that the geographical range of a species was the easiest to predict of the selection orders. At smaller scales within the geographical range, use of a site was easier to predict when environmental variables were measured at a grain equivalent to the home-range size or a microhabitat feature required for reproduction or resting. Selection of patches within home ranges and locations of populations was often more difficult to predict. Multiple-grain RSFs were more predictive than single-grain RSFs when the entire study area was considered available. Models with variables measured at both small and large (> 100 ha) grains were usually most predictive, even for many species with small home ranges. Multiple-grain models may be particularly important for species with moderate dispersal abilities in habitat fragments surrounded by an unsuitable matrix. We recommend studies should no longer address only one grain to map animal species distributions.     

Evaluation of a black-footed ferret resource utilization function model

Resource utilization function (RUF) models permit evaluation of potential habitat for endangered species; ideally such models should be evaluated before use in management decision-making. We evaluated the predictive capabilities of a previously developed black-footed ferret (Mustela nigripes) RUF. Using the population-level RUF, generated from ferret observations at an adjacent yet distinct colony, we predicted the distribution of ferrets within a black-tailed prairie dog (Cynomys ludovicianus) colony in the Conata Basin, South Dakota, USA. We evaluated model performance, using data collected during post-breeding spotlight surveys (2007–2008) by assessing model agreement via weighted compositional analysis and count-metrics. Compositional analysis of home range use and colony-level availability, and core area use and home range availability, demonstrated ferret selection of the predicted Very high and High occurrence categories in 2007 and 2008. Simple count-metrics corroborated these findings and suggested selection of the Very high category in 2007 and the Very high and High categories in 2008. Collectively, these results suggested that the RUF was useful in predicting occurrence and intensity of space use of ferrets at our study site, the 2 objectives of the RUF. Application of this validated RUF would increase the resolution of habitat evaluations, permitting prediction of the distribution of ferrets within distinct colonies. Additional model evaluation at other sites, on other black-tailed prairie dog colonies of varying resource configuration and size, would increase understanding of influences upon model performance and the general utility of the RUF. © 2011 The Wildlife Society.     

Influence of Well Pad Activity on Winter Habitat Selection Patterns of Mule Deer

ABSTRACT Conversion of native winter range into producing gas fields can affect the habitat selection and distribution patterns of mule deer (Odocoileus hemionus). Understanding how levels of human activity influence mule deer is necessary to evaluate mitigation measures and reduce indirect habitat loss to mule deer on winter ranges with natural gas development. We examined how 3 types of well pads with varying levels of vehicle traffic influenced mule deer habitat selection in western Wyoming during the winters of 2005–2006 and 2006–2007. Well pad types included producing wells without a liquids gathering system (LGS), producing wells with a LGS, and well pads with active directional drilling. We used 36,699 Global Positioning System locations collected from a sample (n = 31) of adult (>1.5-yr-old) female mule deer to model probability of use as a function of traffic level and other habitat covariates. We treated each deer as the experimental unit and developed a population-level resource selection function for each winter by averaging coefficients among models for individual deer. Model coefficients and predictive maps for both winters suggested that mule deer avoided all types of well pads and selected areas further from well pads with high levels of traffic. Accordingly, impacts to mule deer could probably be reduced through technology and planning that minimizes the number of well pads and amount of human activity associated with them. Our results suggested that indirect habitat loss may be reduced by approximately 38–63% when condensate and produced water are collected in LGS pipelines rather than stored at well pads and removed via tanker trucks. The LGS seemed to reduce long-term (i.e., production phase) indirect habitat loss to wintering mule deer, whereas drilling in crucial winter range created a short-term (i.e., drilling phase) increase in deer disturbance and indirect habitat loss. Recognizing how mule deer respond to different types of well pads and traffic regimes may improve the ability of agencies and industry to estimate cumulative effects and quantify indirect habitat losses associated with different development scenarios.     

Effects of Season and Scale on Response of Elk and Mule Deer to Habitat Manipulation

Abstract: Manipulation of forest habitat via mechanical thinning or prescribed fire has become increasingly common across western North America. Nevertheless, empirical research on effects of those activities on wildlife is limited, although prescribed fire in particular often is assumed to benefit large herbivores. We evaluated effects of season and spatial scale on response of Rocky Mountain elk (Cervus elaphus) and mule deer (Odocoileus hemionus) to experimental habitat manipulation at the Starkey Experimental Forest and Range in northeastern Oregon, USA. From 2001 to 2003, 26 densely stocked stands of true fir (Abies spp.) and Douglas-fir (Pseudotsuga menziesii) were thinned and burned whereas 27 similar stands were left untreated to serve as experimental controls. We used location data for elk and mule deer collected during spring (1 Apr-14 Jun) and summer (15 Jun-31 Aug) of 1999–2006 to compare use of treated and untreated stands and to model effects of environmental covariates on use of treated stands. In spring, elk selected burned stands and avoided control stands within the study area (second-order selection; large scale). Within home ranges (third-order selection; small scale), however, elk did not exhibit selection. In addition, selection of treatment stands by elk in spring was not strongly related to environmental covariates. Conversely, in summer elk selected control stands and either avoided or used burned stands proportional to their availability at the large scale; patterns of space use within home ranges were similar to those observed in spring. Use of treatment stands by elk in summer was related to topography, proximity to roads, stand size and shape, and presence of cattle, and a model of stand use explained 50% of variation in selection ratios. Patterns of stand use by mule deer did not change following habitat manipulation, and mule deer avoided or used all stand types proportional to their availability across seasons and scales. In systems similar to Starkey, manipulating forest habitat with prescribed fire might be of greater benefit to elk than mule deer where these species are sympatric, and thus maintaining a mixture of burned and unburned (late successional) habitat might provide better long-term foraging opportunities for both species than would burning a large proportion of a landscape.     

Habitat Selection of Rocky Mountain Elk in a Nonforested Environment

Abstract: Recent expansions by Rocky Mountain elk (Cervus elaphus) into nonforested habitats across the Intermountain West have required managers to reconsider the traditional paradigms of forage and cover as they relate to managing elk and their habitats. We examined seasonal habitat selection patterns of a hunted elk population in a nonforested high-desert region of southwestern Wyoming, USA. We used 35,246 global positioning system locations collected from 33 adult female elk to model probability of use as a function of 6 habitat variables: slope, aspect, elevation, habitat diversity, distance to shrub cover, and distance to road. We developed resource selection probability functions for individual elk, and then we averaged the coefficients to estimate population-level models for summer and winter periods. We used the population-level models to generate predictive maps by assigning pixels across the study area to 1 of 4 use categories (i.e., high, medium-high, medium-low, or low), based on quartiles of the predictions. Model coefficients and predictive maps indicated that elk selected for summer habitats characterized by higher elevations in areas of high vegetative diversity, close to shrub cover, northerly aspects, moderate slopes, and away from roads. Winter habitat selection patterns were similar, except elk shifted to areas with lower elevations and southerly aspects. We validated predictive maps by using 528 locations collected from an independent sample of radiomarked elk (n = 55) and calculating the proportion of locations that occurred in each of the 4 use categories. Together, the high- and medium-high use categories of the summer and winter predictive maps contained 92% and 74% of summer and winter elk locations, respectively. Our population-level models and associated predictive maps were successful in predicting winter and summer habitat use by elk in a nonforested environment. In the absence of forest cover, elk seemed to rely on a combination of shrubs, topography, and low human disturbance to meet their thermal and hiding cover requirements.     

Estimating resource selection with count data

Resource selection functions (RSFs) are typically estimated by comparing covariates at a discrete set of “used” locations to those from an “available” set of locations. This RSF approach treats the response as binary and does not account for intensity of use among habitat units where locations were recorded. Advances in global positioning system (GPS) technology allow animal location data to be collected at fine spatiotemporal scales and have increased the size and correlation of data used in RSF analyses. We suggest that a more contemporary approach to analyzing such data is to model intensity of use, which can be estimated for one or more animals by relating the relative frequency of locations in a set of sampling units to the habitat characteristics of those units with count‐based regression and, in particular, negative binomial (NB) regression. We demonstrate this NB RSF approach with location data collected from 10 GPS‐collared Rocky Mountain elk (Cervus elaphus) in the Starkey Experimental Forest and Range enclosure. We discuss modeling assumptions and show how RSF estimation with NB regression can easily accommodate contemporary research needs, including: analysis of large GPS data sets, computational ease, accounting for among‐animal variation, and interpretation of model covariates. We recommend the NB approach because of its conceptual and computational simplicity, and the fact that estimates of intensity of use are unbiased in the face of temporally correlated animal location data.     

Evaluating habitat selection models to predict critical habitat for mountain goats in northwest British Columbia

Anthropogenic activity imposes increasing pressure on wildlife populations globally; these pressures can affect habitat suitability and function, modify wildlife space use, and influence population viability. Native mountain goat (Oreamnos americanus) populations can be negatively affected by anthropogenic disturbance and modify their space use in response to land development and recreational activity. From 2018 to 2020, we studied space use of mountain goats northeast of Smithers, British Columbia, Canada, an area that is subject to increasing anthropogenic development and yearlong recreational activities. We aimed to generate models that would improve our ability to identify habitat for mountain goats relative to existing survey data and established ungulate winter ranges. Using resource selection function (RSF) analyses generated from global positioning system (GPS) collar data, we identified influential habitat covariates and compared these covariates and RSF values to existing habitat models. Additionally, we compared the extent to which our models were congruent with existing resource selection probability functions, were congruent with aerial survey data, and overlapped existing ungulate winter ranges previously derived from predictive models inside and outside of the study area. Overall, our models noted higher RSF values among GPS data relative to aerial survey data for winter months, while results for summer habitats were comparable. In extending our RSFs outside of the study area and evaluating the overlap with ungulate winter ranges in adjacent areas, values were similar, albeit lower, as is expected given that the models were developed elsewhere. Ultimately, these models, combined with existing methods, improve the accuracy and reliability of identified, important areas of habitat for mountain goats. We recommend that the RSF models generated here be used in conjunction with aerial survey data and existing methods to delineate ungulate winter ranges for mountain goats in similar eco-regions in British Columbia. The models developed here support existing methods that have been used to delineate or validate ungulate winter ranges for mountain goats in British Columbia and help facilitate mitigation measures to support the continued use of important winter habitat and significant landscape features that play a role in ensuring population viability and resilience through time.     

Spatial Partitioning of Predation Risk in a Multiple Predator-Multiple Prey System

ABSTRACT Minimizing risk of predation from multiple predators can be difficult, particularly when the risk effects of one predator species may influence vulnerability to a second predator species. We decomposed spatial risk of predation in a 2-predator, 2-prey system into relative risk of encounter and, given an encounter, conditional relative risk of being killed. Then, we generated spatially explicit functions of total risk of predation for each prey species (elk [Cervus elaphus] and mule deer [Odocoileus hemionus]) by combining risks of encounter and kill. For both mule deer and elk, topographic and vegetation type effects, along with resource selection by their primary predator (cougars [Puma concolor] and wolves [Canis lupus], respectively), strongly influenced risk of encounter. Following an encounter, topographic and vegetation type effects altered the risk of predation for both ungulates. For mule deer, risk of direct predation was largely a function of cougar resource selection. However, for elk, risk of direct predation was not only a function of wolf occurrence, but also of habitat attributes that increased elk vulnerability to predation following an encounter. Our analysis of stage-based (i.e., encounter and kill) predation indicates that the risk effect of elk shifting to structurally complex habitat may ameliorate risk of direct predation by wolves but exacerbate risk of direct predation by cougars. Information on spatiotemporal patterns of predation will be become increasingly important as state agencies in the western United States face pressure to integrate predator and prey management.     

SCALE-DEPENDENT SUMMER RESOURCE SELECTION BY REINTRODUCED ELK IN WISCONSIN,USA

Abstract: Identifying how habitat use is influenced by environmental heterogeneity at different scales is central to understanding ungulate population dynamics on complex landscapes. We used resource selection functions (RSF) to study summer habitat use in a reintroduced and expanding elk (Cervus elaphus nelsoni) population in the Chequamegon National Forest, Wisconsin, USA. Factors were examined that influenced where elk established home ranges and that influenced habitat use within established home ranges. We also determined grain sizes over which elk responded to environmental heterogeneity and the number of categories of habitat selection from low to high that the elk distinguished. At a large spatial extent, elk home-range establishment was largely explained by the spatial distribution of wolf (Canis lupus) territories. Forage abundance was also influential but was relatively more important at a small spatial extent when elk moved within established home ranges. Areas near roads were avoided when establishing a home-range, but areas near roads were selected for use within the established home range. Elk distinguished among 4 different categories of habitat selection when establishing and moving within home ranges. Spatial and temporal cross validation demonstrated that to improve the predictive strength of habitat models in areas of low inter-annual variability in the environment, it is better to follow more individuals across diverse environmental conditions than to follow the same individuals over a longer time period. Last, our results show that the effects of environmental variables on habitat use were scale-dependent and reemphasize the necessity of analyzing habitat use at multiple scales that are fit to address specific research questions.     

Components of Grizzly Bear Habitat Selection: Density,Habitats, Roads,and Mortality Risk

Abstract: We used resource selection functions (RSF) to estimate the relative probability of use for grizzly bears (Ursus arctos) adjacent to the Parsnip River, British Columbia, Canada, 1998-2003. We collected data from 30 radiocollared bears on a rolling plateau where a large portion of the landscape had been modified by human activities, primarily forestry. We also monitored 24 radiocollared bears in mountain areas largely inaccessible to humans. Bears that lived on the plateau existed at less than one-quarter the density of bears in the mountains. Plateau bears ate more high-quality food items, such as meat and berries, leading us to conclude that food limitation was not responsible for the differences in densities. We hypothesized that plateau bears were limited by human-caused mortality associated with roads constructed for forestry activities. Independent estimates of bear population size from DNA-based mark-recapture techniques allowed us to link populations to habitats using RSF models to scale habitat use patterns to population density. To evaluate whether differences in land-cover type, roads, or mortality risk could account for the disparity in density we used the mountain RSF model to predict habitat use and number of bears on the plateau and vice versa. We predicted increases ranging from 34 bears to 96 bears on the plateau when switching model coefficients, excluding land-cover types; when exchanging land-cover coefficients, the model predicted that the plateau population would be 9 bears lower than was observed. Large reductions in the numbers of mountain bears were predicted by habitat-selection models of bears using the plateau landscape. Although RSF models estimated in mountain and plateau landscapes could not predict bear use and abundance in the other areas, contrasts in models between areas provided a useful tool for examining the effects of human activities on grizzly bears.     

Using multilevel models to quantify heterogeneity in resource selection

Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection. © 2011 The Wildlife Society.     

Habitat selection by northern spotted owls in mixed-coniferous forests

Conservation planning for the federally threatened northern spotted owl (Strix occidentalis caurina) requires an ability to predict their responses to existing and future habitat conditions. To inform such planning we modeled habitat selection by northern spotted owls based upon fine-scale (approx. 1.0 ha) characteristics within stands comprised primarily of mixed-aged, mixed coniferous forests of southwestern Oregon and north-central California. We sampled nocturnal (i.e., primarily foraging) habitat use by 71 radio-tagged spotted owls over 5 yr in 3 study areas and sampled vegetative and physical environmental conditions at inventory plots within 95% utilization distributions of each bird. We compared conditions at available forest patches, represented by the inventory plots, with those at patches used by owls using discrete-choice regressions, the coefficients from which were used to construct exponential resource selection functions (RSFs) for each study area and for all 3 areas combined. Cross-validation testing indicated that the combined RSF was reasonably robust to local variation in habitat availability. The relative probability that a fine-scale patch was selected decreased nonlinearly with distances from nests and streams; varied unimodally with increasing average diameter of coniferous trees and also with increasing basal area of Douglas-fir (Pseudotsuga menziesii) trees; increased linearly with increasing basal areas of sugar pine (Pinus lambertiana) and hardwood trees and with increasing density of understory shrubs. Large-diameter trees (>66 cm) appeared important <400 m from nest sites. The RSF can support comparative risk assessments of the short- versus long-term effects of silvicultural alternatives designed to integrate forest ecosystem restoration and habitat improvement for northern spotted owls. Results suggest fine-scale factors may influence population fitness among spotted owls. © 2011 The Wildlife Society.     

Influence of Landscape Characteristics on Hunter Space Use and Success

Sport hunting of ungulates is a predominant recreational pursuit and the primary tool for managing their populations in North America and beyond, given its influence on ungulate distributions, social organization, and population performance. Similarly, land management, such as motorized vehicle access, influences ungulate distributions during and outside hunting seasons. Although research on ungulate responses to hunting and land use is widespread, knowledge gaps persist about space use of hunters and what landscape features discriminate among hunt types and between successful and unsuccessful hunters. We used telemetry location data from hunters (n = 341) to estimate space use from 2008–2013 during 3 types of controlled, 5-day hunts for antlered mule deer (Odocoileus hemionus) and elk (Cervus canadensis) in northeastern Oregon, USA: archery elk, rifle deer, and rifle elk. To evaluate space use, we developed utilization distributions for each hunter, created core areas (50% contours) for groups of hunters, and derived several metrics of space-use overlap between successful and unsuccessful hunters. We also modeled predictors of space use using resource utilization functions with beta regression and stepwise model building. Hunter space use was compressed, with even the largest core area (unsuccessful rifle elk hunters) encompassing <16% (1,178 ha) of the area. We found strong similarities in space use of rifle hunters compared to archers, and core areas of successful hunters were markedly smaller than those of unsuccessful hunters (e.g., = 104 ha vs. 681 ha, respectively, for archers). Percentage cover and distance from open roads were the most consistent covariates in the 6 final models (successful vs. unsuccessful for each of 3 hunts) but with different signs. For example, predicted use of archery and rifle elk hunters increased with cover but decreased for rifle deer hunters. Although the same covariates were in the final models for unsuccessful and successful rifle elk hunters, their negligible spatial overlap suggested they sought those features in different locales, a pattern also documented for rifle deer hunters. Our models performed well (Spearman's rank correlation coefficients = 0.99 for 5 of 6 models), reflecting their utility for managing hunters and landscapes. Our results suggest that strategic management of open roads and forest cover can benefit managers seeking to balance hunter opportunity and satisfaction with harvest objectives, especially for species of special concern such as mule deer, and that differences in space use among hunter groups should be accounted for in hunting season designs. © 2021 The Wildlife Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.     

Approaches to delineate greater sage-grouse winter concentration areas

The usefulness of protected areas as regulatory mechanisms to conserve wildlife populations relies on their ability to contain all seasonal habitats necessary for species persistence. Efficient conservation practices require understanding behavior and habitat needs of individual species and populations rather than simply relying on reserves of approximate size and configuration. Priority Areas of Conservation (PACs) have been delineated as protected areas based on known breeding habitat for greater sage-grouse (Centrocercus urophasianus; sage-grouse) throughout their range. These PACs include Core Areas designated in the Wyoming Sage-grouse Executive Order; however, this order also indicated the need to identify winter concentration areas (WCAs; flocks ≥50 individuals) based on habitat features using validated resource selection functions (RSFs). We used aerial infrared videography to identify locations of wintering sage-grouse in south-central and southwest Wyoming, USA, to evaluate winter sage-grouse habitat selection with individual-based RSFs, RSFs based on WCAs, and relative flock size. We located 4,859 individuals comprising 132 flocks across our study area. Flocks occurred in Core Areas more than expected, but a biologically meaningful number of sage-grouse flocks were located outside of Core Areas. Individual-based RSFs contained useful predictors that were consistent with previous sage-grouse winter habitat selection studies. Flock size and WCA models produced similar predictions to individual-based RSF models. Individual-based and WCA-based RSF model predictions had a high degree of similarity, suggesting that identifying important winter habitats with individual-based RSF modeling is useful for locating potential WCAs when information on flock sizes is not available. Our results and survey technique provide a potential framework for identifying sage-grouse WCAs with implications for improving PAC protection of all seasonal habitats for sage-grouse conservation. © 2019 The Wildlife Society.     

Differential prey use by male and female cougars in Washington

Male and female predators are often assumed to have the same effects on prey. Because of differences in body size and behavior, however, male and female predators may use different species, sexes, and ages of prey, which could have important implications for wildlife conservation and management. We tested for differential prey use by male and female cougars (Puma concolor) from 2003 to 2008 in Washington State. We predicted that male cougars would kill a greater proportion of larger and older prey (i.e., adult elk [Cervus elaphus]), whereas females would kill smaller and younger prey (i.e., elk calves, mule deer [Odocoileus hemionus]). We marked cougars with Global Positioning System (GPS) radio collars and investigated 436 predation sites. We located prey remains at 345 sites from 9 male and 9 female cougars. We detected 184 mule deer, 142 elk, and 17 remains from 4 other species. We used log-linear modeling to detect differences in species and age of prey killed among cougar reproductive classes. Solitary females and females with dependent offspring killed more mule deer than elk (143 vs. 83, P < 0.01), whereas males killed more elk than mule deer (59 vs. 41, P < 0.01). Proportionately, males killed 4 times more adult elk than did females (24% vs. 6% of kills) and females killed 2 times more adult mule deer than did males (26% vs. 15% of kills). Managers should consider the effects of sex of predator in conservation and management of ungulates, particularly when managing for sensitive species. © 2011 The Wildlife Society.