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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.

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.

Integrated step‐selection analysis (ISSA) is frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSA have been developed, making it possible to quantify variability among animals in their space‐use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused on the former despite the widely acknowledged and important role that movement plays in determining ecological processes from the individual to ecosystem level. One potential explanation for this omission is the absence of readily available software or examples demonstrating methods for estimating movement parameters in ISSA with random effects. We demonstrated methods for characterizing among‐individual variability in both movement and habitat‐selection parameters using a simulated data set and by fitting two models to an acoustic telemetry data set containing locations of 35 red snapper (Lutjanus campechanus). Movement kernels were assumed to depend on either the type of benthic reef habitat in which the fish was located (model 1) or the distance between the fish's current location and the nearest edge habitat (model 2). In both models, we also quantified habitat selection for different benthic habitat classes and distance to edge habitat, and we allowed for individual variability in movement and habitat‐selection parameters using random effects. The simulation example highlights the benefits of a mixed‐effects specification, namely, we can increase precision when estimating individual‐specific movement parameters by borrowing information across like individuals. In our applied example, we found substantial among‐individual variability in both habitat selection and movement parameters. Nonetheless, most red snapper selected for hardbottom habitat and for locations nearer to edge habitat. They also moved less when in hardbottom habitat. Turn angles were frequently near ± π but were more dispersed when fish were far away from edge habitat. We provide code templates and functions for quantifying variability in movement and habitat‐selection parameters when implementing ISSA with random effects. In doing so, we hope to encourage ecologists conducting ISSA to take full advantage of their ability to model among‐individual variability in both habitat‐selection and movement patterns.

1. Fencing is one of the most common methods of mitigating human-wildlife conflicts. At the same time, fencing is considered one of the most pressing threats emerging in conservation globally. Although fences act as barriers and can cause population isolation and fragmentation over time, it is difficult to quantitatively predict the consequences fences have for wildlife. 2. Here, we model how fencing designed to mitigate human-elephant conflict (HEC) on the Borderlands between Kenya and Tanzania will affect functional connectivity and movement corridors for African elephants. Specifically, we (a) model functional landscape connectivity integrating natural and anthropogenic factors; (b) predict seasonal movement corridors used by elephants in non-protected areas; and (c) evaluate whether fencing in one area can potentially intensify human-wildlife conflicts elsewhere. 3. We used GPS movement and remote sensing data to develop monthly step-selection functions to model functional connectivity. For future scenarios, we used an ongoing fencing project designed for mitigation within the study area. We modelled movement corridors using least-cost path and circuit theory methods, evaluated their predictive power and quantified connectivity changes resulting from the planned fencing. 4. Our results suggest that fencing will not cause landscape fragmentation and will not change functional landscape connectivity dramatically. However, fencing will lead to a loss of connectivity locally and will increase the potential for in new areas. We estimate that wetlands, important for movement corridors, will be more intensively used by the elephants, which may also cause problems of overgrazing. Seasonal analysis highlights an increasing usage of non-protected lands in the dry season and equal importance of the pinch point wetlands for preserving overall function connectivity. 5. Synthesis and applications. Fencing is a solution to small-scale human-elephan conflict problems but will not solve the issue at a broader scale. Moreover, our results highlight that it may intensify the conflicts and overuse of habitat patches in other areas, thereby negating conservation benefits. If fencing is employed on a broader scale, then it is imperative that corridors are integrated within protected area networks to ensure local connectivity of affected species.

Hunters can affect the behavior of wildlife by inducing a landscape of fear, selecting individuals with specific traits, or altering resource availability across the landscape. Most research investigating the influence of hunting on wildlife resource selection has focused on target species and less attention has been devoted to nontarget species, such as scavengers that can be both attracted or repelled by hunting activities. We used resource selection functions to identify areas where hunters were most likely to kill moose (Alces alces) in southcentral Sweden during the fall. Then, we used step-selection functions to determine whether female brown bears (Ursus arctos) selected or avoided these areas and specific resources during the moose hunting season. We found that, during both day and nighttime, female brown bears avoided areas where hunters were more likely to kill moose. We found evidence that resource selection by brown bears varied substantially during the fall and that some behavioral changes were consistent with disturbance associated with moose hunters. Brown bears were more likely to select concealed locations in young (i.e., regenerating) and coniferous forests and areas further away from roads during the moose hunting season. Our results suggest that brown bears react to both spatial and temporal variations in apparent risk during the fall: moose hunters create a landscape of fear and trigger an antipredator response in a large carnivore even if bears are not specifically targeted during the moose hunting season. Such antipredator responses might lead to indirect habitat loss and lower foraging efficiency and the resulting consequences should be considered when planning hunting seasons.

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. Roads cause functional habitat loss, alter movement patterns and can become ecological traps for wildlife. Many of the negative effects of roads are likely to be a function of the human use of roads, not the road itself. However, few studies have examined the effect of temporally and spatially varying traffic patterns on large mammals, which could lead to misinterpretations about the impact of roads on wildlife. 2. We developed models of traffic volume for an entire road network in south-western Alberta, Canada, and documented for the first time the response of grizzly bears Ursus arctos L to a wide range of traffic levels. 3. Traffic patterns caused a clear behavioural shift in grizzly bears, with increased use of areas near roads and movement across roads during the night when traffic was low. Bears selected areas near roads travelled by fewer than 20 vehicles per day and were more likely to cross these roads. Bears avoided roads receiving moderate traffic (20—100 vehicles per day) and strongly avoided high-use roads (>100 vehicles per day) at all times. 4. Synthesis and applications. Grizzly bear responses to traffic caused a departure from typical behavioural patterns, with bears in our study being largely nocturnal. In addition, bears selected private agricultural land, which had lower traffic levels, but higher road density, over multi-use public land. These results improve our understanding of bear responses to roads and can be used to refine management practices. Future management plans should employ a multi-pronged approach aimed at limiting both road density and traffic in core habitats. Access management will be critical in such plans and is an important tool for conserving threatened wildlife populations.

Reduced prey abundance and severe weather can lead to a greater risk of mortality for seabirds during the non-breeding winter months. Resource patterns in some regions are shifting and becoming more variable in relation to past conditions, potentially further impacting survival and carryover to the breeding season. As animal tracking technologies and methods to analyze movement data have advanced, it has become increasingly feasible to draw fine-scale inference about how environmental variation affects foraging behavior and habitat use of seabirds during this critical period. Here, we used archival light-sensing tags to evaluate how interannual variation in oceanography affected the winter distribution of Cassin’s Auklets from Southeast Farallon Island, California. Thirty-five out of 93 geolocators deployed from 2015 to 2017 were recovered and successfully recorded light-level data, from which geographic positions were estimated. Step-selection functions were applied to identify environmental covariates that best explained winter movement decisions and habitat use, revealing Cassin’s Auklets dispersed farther from the colony during a winter with warm SST anomalies, but remained more centralized near the breeding colony during two average winters. Movement patterns were driven by avoidance of areas with higher sea surface temperatures and possible limits of dispersal from the breeding colony, and selection for areas with well-defined mesoscale fronts and cooler surface waters. Through multiple years of tagging and the application of step-selection functions, a robust and widely applied approach for analyzing animal movement in terrestrial species, we show how interannual differences in the movement patterns of a small seabird are driven by oceanographic variability across years. Understanding the winter habitat use of seabirds can help inform changes in population structure and measures of reproductive success, aiding managers in determining potential causes of breeding failures.

Step‐selection functions (SSFs), typically fitted using step‐selection analysis (SSA) or integrated step‐selection analysis (iSSA) are widely used to infer habitat selection and movement kernels from high‐frequency telemetry data, but most standard validation tools focus on one‐step‐ahead prediction and do not guarantee that fitted models generate realistic trajectories or emergent space‐use patterns. We propose a multi‐criteria generative validation framework for SSF‐based movement models (typically fitted via SSA/iSSA), built around four pillars that target emergent utilization distributions, mean squared displacement, path sinuosity and barrier crossing. For each pillar, we define an ecologically interpretable trajectory‐level summary (Wasserstein distance between utilization distributions, mean squared displacement, straightness index and barrier‐crossing counts) and embed it in a Monte Carlo rank‐testing scheme that propagates parameter uncertainty. Applied to six synthetic ‘stress tests’ (sedentary home range, hard barrier, corridor follower, multi‐state movement, orbiter and return‐conditioned sinuosity), the framework reveals distinct failure modes that may not be detected by conventional stepwise validation. An empirical application to a GPS‐tracked red deer illustrates partial generative realism at a 6‐h sampling interval: the fitted iSSA reproduces long‐horizon space‐use and path sinuosity but shows a detectable mismatch in displacement dynamics (mean squared displacement). Résumé Les fonctions de sélection de pas (SSF), généralement estimées par analyse de sélection de pas (SSA) ou sa version analyse intégrée (iSSA), sont largement utilisées pour inférer la sélection d’habitat et les noyaux de mouvement à partir de données de télémétrie à haute fréquence. Cependant, les outils de validation standards se concentrent principalement sur la prédiction à un pas et ne garantissent pas que les modèles ajustés génèrent des trajectoires réalistes ou des patrons émergents d’utilisation de l’espace. Nous proposons un cadre de validation générative multi‐critères pour les modèles de mouvement basés sur les SSF (généralement estimés via SSA/iSSA), structuré autour de quatre piliers ciblant les distributions d’utilisation émergentes, le déplacement quadratique moyen, la sinuosité des trajectoires et le franchissement de barrières. Pour chaque pilier, nous définissons un résumé de trajectoire écologiquement interprétable (distance de Wasserstein entre distributions d’utilisation, déplacement quadratique moyen, indice de rectitude et nombre de franchissements de barrières), intégré dans un schéma de tests par rang de Monte Carlo qui propage l’incertitude des paramètres. Appliqué à six scénarios synthétiques de « tests de résistance » (domaine vital sédentaire, barrière imperméable, suivi de corridor, mouvement multi‐états, orbite et sinuosité conditionnée par retour), ce cadre met en évidence des modes d’échec distincts qui peuvent ne pas être détectés par les validations classiques pas à pas. Une application empirique à une trajectoire GPS de cerf élaphe met en évidence un réalisme génératif partiel à une résolution temporelle de 6 heures: le modèle iSSA ajusté reproduit l’utilisation de l’espace à long terme et la sinuosité des trajectoires, mais présente un écart détectable dans la dynamique de déplacement (déplacement quadratique moyen).