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Spatial memory plays a role in the way animals perceive their environments, resulting in memory‐informed movement patterns that are observable to ecologists. Developing mathematical techniques to understand how animals use memory in their environments allows for an increased understanding of animal cognition.Here we describe a model that accounts for the memory of seasonal or ephemeral qualities of an animal's environment. The model captures multiple behaviours at once by allowing for resource selection in the present time as well as long‐distance navigations to previously visited locations within an animal's home range.We performed a set of analyses on simulated data to test our model, determining that it can provide informative results from as little as 1 year of discrete‐time location data. We also show that the accuracy of model selection and parameter estimation increases with more location data.This model has potential to identify a specific mechanism in which animals use memory to optimize their foraging, by revisiting temporally and predictably variable resources at consistent time‐lags. [ABSTRACT FROM AUTHOR]

When sampling animal movement paths, the frequency at which location measurements are attempted is a critical feature for data analysis. Important quantities derived from raw data, e.g. travel distance or sinuosity, can differ largely based on the temporal resolution of the data. Likewise, when movement models are fitted to data, parameter estimates have been demonstrated to vary with sampling rate. Thus, biological statements derived from such analyses can only be made with respect to the resolution of the underlying data, limiting extrapolation of results and comparison between studies. To address this problem, we investigate whether there are models that are robust against changes in temporal resolution. First, we propose a mathematically rigorous framework, in which we formally define robustness as a model property. We then use the framework for a thorough assessment of a range of basic random walk models, in which we also show how robustness relates to other probabilistic concepts. While we found robustness to be a strong condition met by few models only, we suggest a new method to extend models so as to make them robust. Our framework provides a new systematic, mathematically founded approach to the question if, and how, sampling rate of movement paths affects statistical inference. [ABSTRACT FROM AUTHOR]

Home range size estimates are often used to assess the amount of space required for animals to perform the activities essential for their survival and reproduction. However, in moving environments, traditional home range estimates may be ill suited to this task. In particular, traditional home range estimates are inaccurate representations of the space required by polar bears Ursus maritimus. The sea ice is the prime foraging platform of polar bears, and estimating the amount of ice encountered by bears may provide a better approximation of space use. We develop a technique to make these estimates. Our results confirm that polar bears use more space than terrestrial carnivores to find the resources and conditions they require. We also show that the traditional geographic home range can underestimate both the movement of bears and the amount of space encountered. Moreover, area of ice encountered increased with ice drift, indicating that bears living on highly mobile ice might be exposed to higher energetic costs, and potentially larger energetic gains, than bears inhabiting more stable ice. The methods and concepts presented here can serve as a foundation for new approaches to study the space use of the many species living in moving environments. [ABSTRACT FROM AUTHOR]

Complex systems of moving and interacting objects are ubiquitous in the natural and social sciences. Predicting their behaviour often requires models that mimic these systems with sufficient accuracy, while accounting for their inherent stochasticity. Although tools exist to determine which of a set of candidate models is best relative to the others, there is currently no generic goodness-of-fit framework for testing how close the best model is to the real complex stochastic system., We propose such a framework, using a novel application of the Earth mover's distance, also known as the Wasserstein metric. It is applicable to any stochastic process where the probability of the model's state at time t is a function of the state at previous times. It generalizes the concept of a residual, often used to analyse 1D summary statistics, to situations where the complexity of the underlying model's probability distribution makes standard residual analysis too imprecise for practical use., We give a scheme for testing the hypothesis that a model is an accurate description of a data set. We demonstrate the tractability and usefulness of our approach by application to animal movement models in complex, heterogeneous environments. We detail methods for visualizing results and extracting a variety of information on a given model's quality, such as whether there is any inherent bias in the model, or in which situations, it is most accurate. We demonstrate our techniques by application to data on multispecies flocks of insectivore birds in the Amazon rain forest., This work provides a usable toolkit to assess the quality of generic movement models of complex systems, in an absolute rather than a relative sense. [ABSTRACT FROM AUTHOR]

Predicting space use patterns of animals from their interactions with the environment is fundamental for understanding the effect of habitat changes on ecosystem functioning. Recent attempts to address this problem have sought to unify resource selection analysis, where animal space use is derived from available habitat quality, and mechanistic movement models, where detailed movement processes of an animal are used to predict its emergent utilization distribution. Such models bias the animal's movement towards patches that are easily available and resource-rich, and the result is a predicted probability density at a given position being a function of the habitat quality at that position. However, in reality, the probability that an animal will use a patch of the terrain tends to be a function of the resource quality in both that patch and the surrounding habitat., We propose a mechanistic model where this non-local effect of resources naturally emerges from the local movement processes, by taking into account the relative utility of both the habitat where the animal currently resides and that of where it is moving. We give statistical techniques to parametrize the model from location data and demonstrate application of these techniques to GPS location data of caribou ( Rangifer tarandus) in Newfoundland., Steady-state animal probability distributions arising from the model have complex patterns that cannot be expressed simply as a function of the local quality of the habitat. In particular, large areas of good habitat are used more intensively than smaller patches of equal quality habitat, whereas isolated patches are used less frequently. Both of these are real aspects of animal space use missing from previous mechanistic resource selection models., Whilst we focus on habitats in this study, our modelling framework can be readily used with any environmental covariates and therefore represents a unification of mechanistic modelling and step selection approaches to understanding animal space use. [ABSTRACT FROM AUTHOR]

Social carnivores, such as wolves and coyotes, have distinct and well-defined home ranges. During the formation of these home ranges scent marks provide important cues regarding the use of space by familiar and foreign packs. Previous models for territorial pattern formation have required a den site as the organizational center around which the territory is formed. However, well-defined wolf home ranges have been known to form in the absence of a den site, and even in the absence of surrounding packs. To date, the quantitative models have failed to describe a mechanism for such a process. In this paper we propose a mechanism. It involves interaction between scent marking and movement behavior in response to familiar scent marks. We show that the model yields distinct home ranges by this new means, and that the spatial profile of these home ranges is different from those arising from the territorial interactions with den sites. [Copyright &y& Elsevier]