Increased levels of natural gas exploration, development, and production across the Intermountain West have created a variety of concerns for mule deer (Odocoileus hemionus) populations, including direct habitat loss to road and well‐pad construction and indirect habitat losses that may occur if deer use declines near roads or well pads. We examined winter habitat selection patterns of adult female mule deer before and during the first 3 years of development in a natural gas field in western Wyoming. We used global positioning system (GPS) locations collected from a sample of adult female mule deer to model relative frequency or probability of use as a function of habitat variables. Model coefficients and predictive maps suggested mule deer were less likely to occupy areas in close proximity to well pads than those farther away. Changes in habitat selection appeared to be immediate (i.e., year 1 of development), and no evidence of well‐pad acclimation occurred through the course of the study; rather, mule deer selected areas farther from well pads as development progressed. Lower predicted probabilities of use within 2.7 to 3.7 km of well pads suggested indirect habitat losses may be substantially larger than direct habitat losses. Additionally, some areas classified as high probability of use by mule deer before gas field development changed to areas of low use following development, and others originally classified as low probability of use were used more frequently as the field developed. If areas with high probability of use before development were those preferred by the deer, observed shifts in their distribution as development progressed were toward less‐preferred and presumably less‐suitable habitats.

Projections of polar bear (Ursus maritimus) sea ice habitat distribution in the polar basin during the 21st century were developed to understand the consequences of anticipated sea ice reductions on polar bear populations. We used location data from satellite‐collared polar bears and environmental data (e.g., bathymetry, distance to coastlines, and sea ice) collected from 1985 to 1995 to build resource selection functions (RSFs). RSFs described habitats that polar bears preferred in summer, autumn, winter, and spring. When applied to independent data from 1996 to 2006, the RSFs consistently identified habitats most frequently used by polar bears. We applied the RSFs to monthly maps of 21st‐century sea ice concentration projected by 10 general circulation models (GCMs) used in the Intergovernmental Panel of Climate Change Fourth Assessment Report, under the A1B greenhouse gas forcing scenario. Despite variation in their projections, all GCMs indicated habitat losses in the polar basin during the 21st century. Losses in the highest‐valued RSF habitat (optimal habitat) were greatest in the southern seas of the polar basin, especially the Chukchi and Barents seas, and least along the Arctic Ocean shores of Banks Island to northern Greenland. Mean loss of optimal polar bear habitat was greatest during summer; from an observed 1.0 million km2 in 1985–1995 (baseline) to a projected multi‐model mean of 0.32 million km2 in 2090–2099 (−68% change). Projected winter losses of polar bear habitat were less: from 1.7 million km2 in 1985–1995 to 1.4 million km2 in 2090–2099 (−17% change). Habitat losses based on GCM multi‐model means may be conservative; simulated rates of habitat loss during 1985–2006 from many GCMs were less than the actual observed rates of loss. Although a reduction in the total amount of optimal habitat will likely reduce polar bear populations, exact relationships between habitat losses and population demographics remain unknown. Density and energetic effects may become important as polar bears make long‐distance annual migrations from traditional winter ranges to remnant high‐latitude summer sea ice. These impacts will likely affect specific sex and age groups differently and may ultimately preclude bears from seasonally returning to their traditional ranges.

As habitat loss and fragmentation increase across ungulate ranges, identifying and prioritizing migration routes for conservation has taken on new urgency. Here we present a general framework using the Brownian bridge movement model (BBMM) that: (1) provides a probabilistic estimate of the migration routes of a sampled population, (2) distinguishes between route segments that function as stopover sites vs. those used primarily as movement corridors, and (3) prioritizes routes for conservation based upon the proportion of the sampled population that uses them. We applied this approach to a migratory mule deer (Odocoileus hemionus) population in a pristine area of southwest Wyoming, USA, where 2000 gas wells and 1609 km of pipelines and roads have been proposed for development. Our analysis clearly delineated where migration routes occurred relative to proposed development and provided guidance for on-the-ground conservation efforts. Mule deer migration routes were characterized by a series of stopover sites where deer spent most of their time, connected by movement corridors through which deer moved quickly. Our findings suggest management strategies that differentiate between stopover sites and movement corridors may be warranted. Because some migration routes were used by more mule deer than others, proportional level of use may provide a reasonable metric by which routes can be prioritized for conservation. The methods we outline should be applicable to a wide range of species that inhabit regions where migration routes are threatened or poorly understood.

Summary1. Impermeable barriers to migration can greatly constrain the set of possible routes and ranges used by migrating animals. For ungulates, however, many forms of development are semi-permeable, and making informed management decisions about their potential impacts to the persistence of migration routes is difficult because our knowledge of how semi-permeable barriers affect migratory behaviour and function is limited. 2. Here, we propose a general framework to advance the understanding of barrier effects on ungulate migration by emphasizing the need to (i) quantify potential barriers in terms that allow behavioural thresholds to be considered, (ii) identify and measure behavioural responses to semi-permeable barriers and (iii) consider the functional attributes of the migratory landscape (e.g. stopovers) and how the benefits of migration might be reduced by behavioural changes. 3. We used global position system (GPS) data collected from two subpopulations of mule deer Odocoileus hemionus to evaluate how different levels of gas development influenced migratory behaviour, including movement rates and stopover use at the individual level, and intensity of use and width of migration route at the population level. We then characterized the functional landscape of migration routes as either stopover habitat or movement corridors and examined how the observed behavioural changes affected the functionality of the migration route in terms of stopover use. 4. We found migratory behaviour to vary with development intensity. Our results suggest that mule deer can migrate through moderate levels of development without any noticeable effects on migratory behaviour. However, in areas with more intensive development, animals often detoured from established routes, increased their rate of movement and reduced stopover use, while the overall use and width of migration routes decreased. 5. Synthesis and applications. In contrast to impermeable barriers that impede animal movement, semi-permeable barriers allow animals to maintain connectivity between their seasonal ranges. Our results identify the mechanisms (e.g. detouring, increased movement rates, reduced stopover use) by which semi-permeable barriers affect the functionality of ungulate migration routes and emphasize that the management of semi-permeable barriers may play a key role in the conservation of migratory ungulate populations.

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.

Often resource selection functions (RSFs) are developed by comparing resource attributes of used sites to unused or available ones. We present alternative approaches to the analysis of resource selection based on the utilization distribution (UD). Our objectives are to describe the rationale for estimation of RSFs based on UDs, offer advice about computing UDs and RSFs, and illustrate their use in resource selection studies. We discuss the 3 main factors that should be considered when using kernel UD‐based estimates of space use: selection of bandwidth values, sample size versus precision of estimates, and UD shape and complexity. We present 3 case studies that demonstrate use of UDs in resource selection modeling. The first example demonstrates the general case of RSF estimation that uses multiple regression adjusted for spatial autocorrelation to relate UD estimates (i.e., the probability density function) to resource attributes. A second example, involving Poisson regression with an offset term, is presented as an alternative for modeling the relative frequency, or probability of use, within defined habitat units. This procedure uses the relative frequency of locations within a habitat unit as a surrogate of the UD and requires relatively fewer user‐defined options in the modeling of resource selection. Last, we illustrate how the UD can also be used to enhance univariate resource selection analyses, such as compositional analysis, in cases where animals use their range nonrandomly. The UD helps overcome several common shortcomings of some other analytical techniques by treating the animal as the primary sampling unit, summarizing use in a continuous and probabilistic manner, and relying on the pattern of animal space use rather than using individual sampling points. However, several drawbacks are apparent when using the UD in resource selection analyses. Choice of UD estimator is important and sensitive to sample size and user‐defined options, such as bandwidth and software selection. Extensions to these procedures could consider behavioral‐based approaches and alternative techniques to estimate the UD directly.

As the extent and intensity of energy development in North America increases, so do disturbances to wildlife and the habitats they rely upon. Impacts to mule deer are of particular concern because some of the largest gas fields in the USA overlap critical winter ranges. Short-term studies of 2-3 years have shown that mule deer and other ungulates avoid energy infrastructure; however, there remains a common perception that ungulates habituate to energy development, and thus, the potential for a demographic effect is low. We used telemetry data from 187 individual deer across a 17-year period, including 2 years predevelopment and 15 years during development, to determine whether mule deer habituated to natural gas development and if their response to disturbance varied with winter severity. Concurrently, we measured abundance of mule deer to indirectly link behavior with demography.Mule deer consistently avoided energy infrastructure through the 15-year period of development and used habitats that were an average of 913 m further from well pads compared with predevelopment patterns of habitat use. Even during the last 3 years of study, when most wells were in production and reclamation efforts underway, mule deer remained >1 km away from well pads. The magnitude of avoidance behavior, however, was mediated by winter severity, where aversion to well pads decreased as winter severity increased. Mule deer abundance declined by 36% during the development period, despite aggressive onsite mitigation efforts (e.g. directional drilling and liquid gathering systems) and a 45% reduction in deer harvest. Our results indicate behavioral effects of energy development on mule deer are long term and may affect population abundance by displacing animals and thereby functionally reducing the amount of available habitat. K E Y W O R D Savoidance behavior, disturbance, indirect habitat loss, land-use planning, mitigation | INTRODUCTIONHabitat loss and fragmentation are among the most influential factors affecting species distribution and population viability (Fahrig, 2003;Hethcoat & Chalfoun, 2015;Sih, Ferrari, & Harris, 2011). Worldwide, energy development projects are quickly converting native habitats into roads, well pads, pipelines, wind turbines, solar installations and other infrastructure associated with energyThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Discrete‐choice models are a powerful and flexible method for studying habitat selection, in part because they allow resource availability to change at every choice. Here, we consider application of discrete‐choice models to data typically collected in wildlife science because different discrete‐choice data are usually collected in other disciplines. We generalize the classic discrete‐choice model to the situation in which multiple choices are made from 1 or more choice sets, and only 1 random sample from each choice set is available. We discuss analysis using 1) logistic regression, 2) maximum likelihood when choices are made with replacement, 3) maximum likelihood when the temporal order of selection is known, and 4) maximum likelihood when the order of selection is unknown. We show that 1) provides a good approximation to discrete choice models if the expected number of uses is much <1 for all units. We show that 2) and 3) can be fit using stratified Cox proportional hazards software. Analysis 4) must be fit using special purpose maximization routine such as Newton‐Raphson. Finally, we demonstrate 2) on a case study of nightime habitat selection by 28 northern spotted owls (Strix occidentalis caurina), and conclude that these owls selected for locations low on the slope in stands >41 years old with high levels of hardwoods adjacent to stands 6–20 yrs old.