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For gregarious animals the cost-benefit trade-offs that drive habitat selection may vary dynamically with group size, which plays an important role in foraging and predator avoidance strategies. We examined how habitat selection by bison (Bison bison) varied as a function of group size and interpreted these patterns by testing whether habitat selection was more strongly driven by the competing demands of forage intake vs. predator avoidance behavior. We developed an analytical framework that integrated group size into resource selection functions (RSFs). These group-size-dependent RSFs were based on a matched case-control design and were estimated using conditional logistic regression (mixed and population-averaged models). Fitting RSF models to bison revealed that bison groups responded to multiple aspects of landscape heterogeneity and that selection varied seasonally and as a function of group size. For example, roads were selected in summer, but not in winter. Bison groups avoided areas of high snow water equivalent in winter. They selected areas composed of a large proportion of meadow area within a 700-m radius, and within those areas, bison selected meadows. Importantly, the strength of selection for meadows varied as a function of group size, with stronger selection being observed in larger groups. Hence the bison-habitat relationship depended in part on the dynamics of group formation and division. Group formation was most likely in meadows. In contrast, risk of group fission increased when bison moved into the forest and was higher during the time of day when movements are generally longer and more variable among individuals. We also found that stronger selection for meadows by large rather than small bison groups was caused by longer residence time in individual meadows by larger groups and that departure from meadows appears unlikely to result from a depression in food intake rate. These group-size-dependent patterns were consistent with the hypothesis that avoidance of predation risk is the strongest driver of habitat selection.

Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step‐selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat‐ and movement‐related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four‐step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models. New tracking technologies allow users to collect large amount of data and address entirely new questions. The amt (animal movement tools) R package provides tools to manage telemetry data and to fit step‐selection functions and resource‐selection functions.

Popular frameworks for studying habitat selection include resource‐selection functions (RSFs) and step‐selection functions (SSFs), estimated using logistic and conditional logistic regression, respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animals. Conceptually, slopes that vary by individual, that is, random coefficient models, could be used to accommodate inter‐individual heterogeneity with either approach. While fitting such models for RSFs is possible with standard software for generalized linear mixed‐effects models (GLMMs), straightforward and efficient one‐step procedures for fitting SSFs with random coefficients are currently lacking. To close this gap, we take advantage of the fact that the conditional logistic regression model (i.e. the SSF) is likelihood‐equivalent to a Poisson model with stratum‐specific fixed intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference for random‐slope models becomes feasible with standard Bayesian techniques, or with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including models without random slopes and mixed conditional regression models fit using a two‐step algorithm. Using data from mountain goats (Oreamnos americanus) and Eurasian otters (Lutra lutra), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to compare different estimation approaches for SSFs and to demonstrate the importance of including individual‐specific slopes when estimating individual‐ and population‐level habitat‐selection parameters. By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R‐INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual‐specific coefficients are particularly attractive since they can provide insights into movement and habitat‐selection processes at fine‐spatial and temporal scales, but these models had previously been very challenging to fit. The authors provide a coherent framework for fitting resource‐selection functions (RSFs) and step‐selection functions (SSFs) with random effects. To allow fitting of SSFs, the authors reformulate the conditional logistic regression model as a (likelihood‐equivalent) Poisson model, where stratum‐specific intercepts are included as a random effect with a fixed large prior variance.

Habitat‐selection analyses are often used to link environmental covariates, measured within some spatial domain of assumed availability, to animal location data that are assumed to be independent. Step‐selection functions (SSFs) relax this independence assumption, by using a conditional model that explicitly acknowledges the spatiotemporal dynamics of the availability domain and hence the temporal dependence among successive locations. However, it is not clear how to produce an SSF‐based map of the expected utilization distribution. Here, we used SSFs to analyze virtual animal movement data generated at a fine spatiotemporal scale and then rarefied to emulate realistic telemetry data. We then compared two different approaches for generating maps from the estimated regression coefficients. First, we considered a naïve approach that used the coefficients as if they were obtained by fitting an unconditional model. Second, we explored a simulation‐based approach, where maps were generated using stochastic simulations of the parameterized step‐selection process. We found that the simulation‐based approach always outperformed the naïve mapping approach and that the latter overestimated home‐range size and underestimated local space‐use variability. Differences between the approaches were greatest for complex landscapes and high sampling rates, suggesting that the simulation‐based approach, despite its added complexity, is likely to offer significant advantages when applying SSFs to real data.

ABSTRACT From August 2008 to January 2009 we studied habitat selection of red panda (Ailurus fulgens fulgens) in Gaoligongshan Nature Reserve using resource selection functions and resource selection index. Red panda preferred coniferous forest and broad-leaved coniferous forest on eastern and northern slopes located in the mid and upper parts of the hillside in the rainy season, containing larger diameter trees (>30 cm), a low tree density (<6/20 m2), high density bamboo (>70/m2 and 40~70/m2) and few stumps (<3/400 m2). In the dry season, red panda preferred coniferous forest and broad-leaved coniferous forest on eastern and southern slopes in mid and upper parts of the hillside, containing larger diameter trees (15~30 cm), good hiding conditions (visibility <10 m), a small number of stumps (<3/400 m2) and a high bamboo density (>70/m2 and 40~70/m2).

Understanding how animals use their habitat is essential to understand their biology and support conservation efforts. Technological advances in tracking technologies allow us to follow animals at increasingly fine temporal resolutions. Yet, how tracking devices' sampling intervals impact results remains unclear, as well as which method to use. Using simulations and empirical data from wild boars tracked in Germany, we systematically examine how the temporal resolution of movement data in interaction with the spatial autocorrelation of the landscape affects the outcomes of two common techniques for analyzing habitat selection: resource‐selection analysis (RSA) and an autocorrelation‐informed weighted derivative (wRSA) as well as integrated step‐selection analysis (iSSA). Each method differs in the definition of “available” locations (RSA) and the implementation of the movement model during parameter estimation (iSSA). Our simulations suggested that landscape autocorrelation has a much stronger effect on the estimated selection coefficients and their variability than the sampling interval. Higher sampling intervals (i.e., longer time between steps) are required for landscapes with high autocorrelation, enabling the animal to experience enough variability in clumped landscapes. Short sampling intervals generally lead to higher variability and fewer statistically significant estimates (in particular for wRSA). Our results complement recent attempts to outline a coherent framework for habitat‐selection analyses and to explain them to practitioners. We further contribute to these efforts by assessing the sensitivity of two commonly used methods, RSA and iSSA, to the changes in sampling interval of movement data. We expect our findings to further raise awareness of pitfalls underlying the comparison of estimated selection coefficients obtained in different studies and to assist movement ecologists in choosing the appropriate method for habitat‐selection analysis. Advances in tracking technology enable finer temporal resolution in animal movement data, but the impact of sampling intervals on habitat‐selection analysis remains unclear. Using simulations and wild boar data from Germany, researchers found that higher sampling intervals are needed in highly autocorrelated landscapes to capture habitat variability, while shorter intervals result in greater variability and fewer significant estimates. These findings contribute to a broader framework for improving habitat‐selection analyses and guiding practitioners.

1. The problems of analysing used‐available data and presence‐only data are equivalent, and this paper uses this equivalence as a platform for exploring opportunities for advancing analysis methodology. 2. We suggest some potential methodological advances in used‐available analysis, made possible via lessons learnt in the presence‐only literature, for example, using modern methods to improve predictive performance. We also consider the converse – potential advances in presence‐only analysis inspired by used‐available methodology. 3. Notwithstanding these potential advances in methodology, perhaps a greater opportunity is in advancing our thinking about how to apply a given method to a particular data set. 4. It is shown by example that strikingly different results can be achieved for a single data set by applying a given method of analysis in different ways – hence having chosen a method of analysis, the next step of working out how to apply it is critical to performance. 5. We review some key issues to consider in deciding how to apply an analysis method: apply the method in a manner that reflects the study design; consider data properties; and use diagnostic tools to assess how reasonable a given analysis is for the data at hand.

Logistic regression is an important tool for wildlife habitat-selection studies, but the method frequently has been misapplied due to an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To promote better use of this method, we review its application and interpretation under 3 sampling designs: random, case–control, and use–availability. Logistic regression is appropriate for habitat use–nonuse studies employing random sampling and can be used to directly model the conditional probability of use in such cases. Logistic regression also is appropriate for studies employing case–control sampling designs, but careful attention is required to interpret results correctly. Unless bias can be estimated or probability of use is small for all habitats, results of case–control studies should be interpreted as odds ratios, rather than probability of use or relative probability of use. When data are gathered under a use–availability design, logistic regression can be used to estimate approximate odds ratios if probability of use is small, at least on average. More generally, however, logistic regression is inappropriate for modeling habitat selection in use–availability studies. In particular, using logistic regression to fit the exponential model of Manly et al. (2002:100) does not guarantee maximum-likelihood estimates, valid probabilities, or valid likelihoods. We show that the resource selection function (RSF) commonly used for the exponential model is proportional to a logistic discriminant function. Thus, it may be used to rank habitats with respect to probability of use and to identify important habitat characteristics or their surrogates, but it is not guaranteed to be proportional to probability of use. Other problems associated with the exponential model also are discussed. We describe an alternative model based on Lancaster and Imbens (1996) that offers a method for estimating conditional probability of use in use–availability studies. Although promising, this model fails to converge to a unique solution in some important situations. Further work is needed to obtain a robust method that is broadly applicable to use–availability studies.

Global biodiversity is decreasing rapidly. Parks and protected lands, while designed to conserve wildlife, often cannot provide the habitat protection needed for wide‐ranging animals such as the American black bear (Ursus americanus). Conversely, private lands are often working landscapes (e.g., farming) that have high human footprints relative to protected lands. In southwestern Alberta, road densities are highest on private lands and black bears can be hunted year‐round. On protected lands, road densities are lowest, and hunting is prohibited. On public lands under the jurisdiction of the provincial government (Crown lands), seasonal hunting is permitted. Population estimates are needed to calculate sustainable harvest levels and to monitor population trends. In our study area, there has never been a robust estimate of black bear density and spatial drivers of black bear density are poorly understood. We used non‐invasive genetic sampling and indices of habitat productivity and human disturbance to estimate density and abundance for male and female black bears in 2013 and 2014 using two methods: spatially explicit capture–recapture (SECR) and resource‐selection functions (RSF). Land tenure best explained spatial variation in black bear density. Black bear densities for females and males were highest on parkland and lowest on Crown lands. Sex ratios were female‐biased on private lands, likely a result of lower harvests and movement of females out of areas with high male density. Synthesis and application: Both SECR and RSF methods clearly indicate spatial structuring of black bear density, with a strong influence based on how lands are managed. Land tenure influences the distribution of available foods and risk from humans. We emphasize the need for improved harvest reporting, particularly for non‐licensed hunting on private land, to estimate the extent of black bear harvest mortality. American black bears are highly valued species but remain poorly monitored in North America. Using non‐invasive genetic sampling data, we used parallel methods to estimate black bear density and spatial drivers thereof. We found spatial structuring of black bear density and discussed modeling approaches and management implications.

The purpose of our study is to show how ecologists' interpretation of habitat selection by grizzly bears (Ursus arctos) is altered by the scale of observation and also how management questions would be best addressed using predetermined scales of analysis. Using resource selection functions (RSF) we examined how variation in the spatial extent of availability affected our interpretation of habitat selection by grizzly bears inhabiting mountain and plateau landscapes. We estimated separate models for females and males using three spatial extents: within the study area, within the home range, and within predetermined movement buffers. We employed two methods for evaluating the effects of scale on our RSF designs. First, we chose a priori six candidate models, estimated at each scale, and ranked them using Akaike Information Criteria. Using this method, results changed among scales for males but not for females. For female bears, models that included the full suite of covariates predicted habitat use best at each scale. For male bears that resided in the mountains, models based on forest successional stages ranked highest at the study-wide and home range extents, whereas models containing covariates based on terrain features ranked highest at the buffer extent. For male bears on the plateau, each scale estimated a different highest-ranked model. Second, we examined differences among model coefficients across the three scales for one candidate model. We found that both the magnitude and direction of coefficients were dependent upon the scale examined; results varied between landscapes, scales, and sexes. Greenness, reflecting lush green vegetation, was a strong predictor of the presence of female bears in both landscapes and males that resided in the mountains. Male bears on the plateau were the only animals to select areas that exposed them to a high risk of mortality by humans. Our results show that grizzly bear habitat selection is scale dependent. Further, the selection of resources can be dependent upon the availability of a particular vegetation type on the landscape. From a management perspective, decisions should be based on a hierarchical process of habitat selection, recognizing that selection patterns vary across scales.