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Uncovering the mechanisms behind animal space use patterns is of vital importance for predictive ecology, thus conservation and management of ecosystems. Movement is a core driver of those patterns so understanding how movement mechanisms give rise to space use patterns has become an increasingly active area of research.This study focuses on a particular strand of research in this area, based around step selection analysis (SSA). SSA is a popular way of inferring drivers of movement decisions, but, perhaps less well appreciated, it also parametrises a model of animal movement. Of key interest is that this model can be propagated forwards in time to predict the space use patterns over broader spatial and temporal scales than those that pertain to the proximate movement decisions of animals.Here, we provide a guide for understanding and using the various existing techniques for scaling up step selection models to predict broad‐scale space use patterns. We give practical guidance on when to use which technique, as well as specific examples together with code in R and Python.By pulling together various disparate techniques into one place, and providing code and instructions in simple examples, we hope to highlight the importance of these techniques and make them accessible to a wider range of ecologists, ultimately helping expand the usefulness of SSA. [ABSTRACT FROM AUTHOR]

The ability to predict animal space use patterns is a fundamental concern in changing environments. Such predictions require a detailed understanding of the movement mechanisms from which spatial distributions emerge. However, these are typically complex, multifaceted, and therefore difficult to uncover.Here, we provide a methodological framework for uncovering the movement mechanisms necessary for building predictive models of animal space use. Our procedure begins by parametrising a movement model of each individual in a population using step selection analysis, from which we build an individual‐based model (IBM) of interacting individuals, derive predicted broad‐scale space use patterns from the IBM and then compare the predicted and empirical patterns. Importantly, discrepancies between these predicted and empirical patterns are used to formulate new hypotheses about the drivers of animal movement decisions and thus iteratively improve the model's predictive power. We demonstrate our method on a population of feral pigs in Mississippi, USA.Our technique incorporates both social interactions between individuals and environmental drivers of movement. At each iteration of model construction, we were able to identify missing features to improve model prediction by analysing the IBM output. These include overuse‐avoidance effects of self‐attractive mechanisms (i.e. attraction to previously visited sites becomes repulsion if there have been multiple visits in quick succession), which were vital for ensuring predicted occurrence distributions do not become vanishingly small.Overall, we have provided a general method for iteratively improving the predictive power of step selection models. This will enable future researchers to maximise the information obtained from step selection analyses and to highlight potentially missing data for uncovering the drivers of movement decisions and emergent space use patterns. Ultimately, this provides a fundamental step towards the general aim of constructing predictive models of animal space use. [ABSTRACT FROM AUTHOR]

The energetic gains from foraging and costs of movement are expected to be key drivers of animal decision‐making, as their balance is a large determinant of body condition and survival. This fundamental perspective is often missing from habitat selection studies, which mainly describe correlations between space use and environmental features, rather than the mechanisms behind these correlations.To address this gap, we present a novel parameterisation of step selection functions (SSFs), that we term the energy selection function (ESF). In this model, the likelihood of an animal selecting a movement step depends directly on the corresponding energetic gains and costs, and we can therefore assess how moving animals choose habitat based on energetic considerations.The ESF retains the mathematical convenience and practicality of other SSFs and can be quickly fitted using standard software. In this article, we outline a workflow, from data gathering to statistical analysis, and use a case study of polar bears Ursus maritimus to demonstrate application of the model.We explain how defining gains and costs at the scale of the movement step allows us to include information about resource distribution, landscape resistance and movement patterns. We further demonstrate this process with a case study of polar bears and show how the parameters can be interpreted in terms of selection for energetic gains and against energetic costs.The ESF is a flexible framework that combines the energetic consequences of both movement and resource selection, thus incorporating a key mechanism into habitat selection analysis. Further, because it is based on familiar habitat selection models, the ESF is widely applicable to any study system where energetic gains and costs can be derived, and has immense potential for methodological extensions. [ABSTRACT FROM AUTHOR]

Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as 'selecting' each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally‐meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning‐points in an animal's movement path. We define a 'step' to be a straight‐line movement between successive turning‐points. We then construct a generalised version of integrated SSA (iSSA), called time‐varying iSSA (tiSSA), which deals with the fact that turning‐points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free‐ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time‐interval.Using (regular) iSSA with constant time‐steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning‐points of animals, we enable step selection to be used on high‐frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning‐points can be viewed as decisions, our method places step selection analysis on a more behaviourally‐meaningful footing compared to previous techniques. [ABSTRACT FROM AUTHOR]

Research Highlight: Bond, M. L., Lee, D. E., Ozgul, A., Farine, D. R., & König, B. (2021). Leaving by staying: Social dispersal in giraffes. Journal of Animal Ecology, https://doi. org/10.1111/1365-2656.13582. Dispersal is a key ecological and evolutionary process, which shows marked variability between and within species. The social and kinship structure of species fundamentally affects the patterns and types of dispersal, but information on how animals with fission-fusion group dynamics disperse is missing. Bond et al. provide novel data on natal dispersal of giraffe calves in relation to their dynamic multilayered social system, showing that individuals from both sexes can disperse socially, by switching association with different social groups, without leaving their natal area. The results highlight that traditional spatial-only measures of dispersal, such as dispersal distance, may be inadequate for social species with overlapping social units. [ABSTRACT FROM AUTHOR]

Mathematical analysis of partial differential equations (PDEs) has led to many insights regarding the effect of organism movements on spatial population dynamics. However, their use has mainly been confined to the community of mathematical biologists, with less attention from statistical and empirical ecologists. We conjecture that this is principally due to the inherent difficulties in fitting PDEs to data.To help remedy this situation, in the context of movement ecology, we show how the popular technique of step selection analysis (SSA) can be used to parametrize a class of PDEs, called diffusion‐taxis models, from an animal's trajectory. We examine the accuracy of our technique on simulated data, then demonstrate the utility of diffusion‐taxis models in two ways. First, for non‐interacting animals, we derive the steady‐state utilization distribution in a closed analytic form. Second, we give a recipe for deriving spatial pattern formation properties that emerge from interacting animals: specifically, do those interactions cause heterogeneous spatial distributions to emerge and if so, do these distributions oscillate at short times or emerge without oscillations? The second question is applied to data on concurrently tracked bank voles Myodes glareolus.Our results show that SSA can accurately parametrize diffusion‐taxis equations from location data, providing the frequency of the data is not too low. We show that the steady‐state distribution of our diffusion‐taxis model, where it is derived, has an identical functional form to the utilization distribution given by resource selection analysis (RSA), thus formally linking (fine scale) SSA with (broad scale) RSA. For the bank vole data, we show how our SSA‐PDE approach can give predictions regarding the spatial aggregation and segregation of different individuals, which are difficult to predict purely by examining results of SSA.Our methods provide a user‐friendly way into the world of PDEs, via a well‐used statistical technique, which should lead to tighter links between the findings of mathematical ecology and observations from empirical ecology. By providing a non‐speculative link between observed movement behaviours and space use patterns on larger spatio‐temporal scales, our findings will also aid integration of movement ecology into understanding spatial species distributions. [ABSTRACT FROM AUTHOR]

Monitoring the movements and behaviour of wildlife using radio telemetry or GPS devices has been critical to the fields of ecology and conservation over several decades. For many field projects however, commercially available devices can be expensive and may not always be ideally suited for collecting desired data.We present a low‐cost solution of customizable tracking devices based on the open‐source Arduino system. These devices can be custom designed for specific studies and easily programmed to collect desired data.Custom‐built collars with GPS and accelerometer units were trialled on 30 free‐ranging domestic dogs in rural Ethiopia. These collars collected high‐resolution data at a frequency of 10 fixes per hour and accelerometer data at 10 Hz over a 10‐day period, at a cost of approximately £100 ($130 USD) per collar.These devices can be placed on any species that can handle a weight of 15.4 g plus battery and housing. The current configuration weighs approximately 240 g which would be suitable for any animal above 8 kg living in terrestrial environments. [ABSTRACT FROM AUTHOR]

The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever‐increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data; however, many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the r software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'.Here, we reviewed and described each tracking package based on a workflow centred around tracking data (i.e. spatio‐temporal locations (x, y, t)), broken down into three stages: pre‐processing, post‐processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioural pattern identification, space use characterization, trajectory simulation and others.Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation and identified 11 packages with good or excellent documentation.Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the r movement‐ecology programming community.Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community. [ABSTRACT FROM AUTHOR]

The paradigm‐changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored.Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF).We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology.We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data.Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high‐frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location‐only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models. [ABSTRACT FROM AUTHOR]

Remote sensing is a valuable tool in movement ecology. However, the way it is combined with animal movement data is far from optimal as most studies overlook differences in the spatial, temporal and thematic resolutions between both data types.rsmove uses a pixel‐based approach to link animal tracking and remote sensing data that bridge the gap between these two disciplines while respecting the limitations of the latest. Additionally, if offers standardize methods to pre‐analyse the connection between animal movement and environmental change.The package guides the choice of study sites, satellite data and environmental predictors though data mining and visualization tools. rsmove offers a simple methodology to analyse animal–environment interactions that helps avoid unnecessary, time‐consuming satellite data processing through an informed decision process.The package offers a basis for a better communication between ecologists and remote sensing experts contributing to the definition of clear remote sensing data requirements. Additionally, it provides future studies on animal movement tools for a more critical approach on the selection of environmental data supporting the reproducibility of animal movement studies. [ABSTRACT FROM AUTHOR]

Telemetry, or the remote monitoring of animals with electronic transmitters and receivers, has vastly enhanced our ability to study aquatic animals. Radio telemetry, acoustic telemetry and passive integrated transponders are three common technologies that generate detection data — time‐stamped, tag‐specific records that are logged by receivers.We review current statistical methods and comment on potential future directions for analysing detection data derived from fixed telemetry receiver arrays.To illustrate how different methods may be used to achieve diverse study objectives, we provide a case study dataset collected by an array of 42 acoustic telemetry receivers on 187 bull trout in the Kinbasket Reservoir of British Columbia. To close, we present a decision tree for guiding the selection of a method based on study objectives and sampling design.This paper provides both experienced and novice telemetry researchers with the knowledge and tools to facilitate more comprehensive analysis of detection data and, in so doing, ask a wide variety of ecological questions that will enhance our understanding of aquatic organisms. [ABSTRACT FROM AUTHOR]

Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of high‐resolution data, often collected many times per second, offers much opportunity for understanding animal movement. However, the sheer size of modern data sets means there is an increasing need for rapid, novel computational techniques to make sense of these data. Most existing methods were designed with smaller data sets in mind and can thus be prohibitively slow. Here, we introduce a method for segmenting high‐resolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and Riotte‐Lambert (2012). Adapting it for use with high‐resolution data. The data's resolution removed the need to interpolate between successive locations, allowing us to increase the algorithm's speed by approximately two orders of magnitude with essentially no drop in accuracy. Furthermore, we incorporate a color scheme for testing the level of confidence in the algorithm's inference (high = green, medium = amber, low = red). We demonstrate the speed and accuracy of our algorithm with application to both simulated and real data (Alpine cattle at 1 Hz resolution). On simulated data, our algorithm correctly identified the sites of interest for 99% of "high confidence" paths. For the cattle data, the algorithm identified the two known sites of interest: a watering hole and a milking station. It also identified several other sites which can be related to hypothesized environmental drivers (e.g., food). Our algorithm gives an efficient method for turning a long, high‐resolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing point‐to‐point analysis techniques such as optimal foraging theory. It is encoded into an R package called SitesInterest, so should serve as a valuable tool for making sense of these increasingly large data streams. We have constructed a new method for identifying sites of interest, tailored specifically for use with high resolution data. This allows for a complicated movement trajectory to be simplified into a "Markov chain‐like" description of behavioural choices, highlighting the animal's broadscale decisions. [ABSTRACT FROM AUTHOR]

Animal‐attached technologies can be powerful means to quantify space use and behavior; however, there are also ethical implications associated with capturing and instrumenting animals. Furthermore, tagging approaches are not necessarily well‐suited for examining the movements of multiple individuals within specific, local areas of interest. Here, we assess a method of quantifying animal space use based on a modified theodolite with an inbuilt laser rangefinder. Using a database of >4200 tracks of migrating birds, we show that detection distance increases with bird body mass (range 5 g to >10 kg). The maximum distance recorded to a bird was 5500 m and measurement error was ≤5 m for targets within this distance range: a level comparable to methods such as GPS tagging. We go on to present a case study where this method was used to assess habitat selection in seabirds operating in dynamic coastal waters close to a tidal turbine. Combining positional data with outputs from a hydrographic model revealed that great cormorants (Phalacrocorax carbo) appeared to be highly selective of current characteristics in space and time, exploiting areas where mean current speeds were <0.8 m·s−1 and diving at times when turbulent energy levels were low. These birds also oriented into tidal currents during dives. Taken together, this suggests that collision risks are low for cormorants at this site, as the 2 conditions avoided by cormorants (high mean current speeds and turbulence levels) are associated with operational tidal turbines. Overall, we suggest that this modified theodolite system is well‐suited to the quantification of movement in small areas associated with particular development strategies, including sustainable energy devices. [ABSTRACT FROM AUTHOR]

Abstract: Recent advances in biologging have resulted in animal location data at unprecedentedly high temporal resolutions, sometimes many times per second. However, many current methods for analysing animal movement (e.g. step selection analysis or state‐space modelling) were developed with lower‐resolution data in mind. To make such methods usable with high‐resolution data, we require techniques to identify features within the trajectory where movement deviates from a straight line. We propose that the intricacies of movement paths, and particularly turns, reflect decisions made by animals so that turn points are particularly relevant to behavioural ecologists. As such, we introduce a fast, accurate algorithm for inferring turning‐points in high‐resolution data. For analysing big data, speed and scalability are vitally important. We test our algorithm on simulated data, where varying amounts of noise were added to paths of straight‐line segments interspersed with turns. We also demonstrate our algorithm on data of free‐ranging oryx Oryx leucoryx. We compare our algorithm to existing statistical techniques for break‐point inference. The algorithm scales linearly and can analyse several hundred‐thousand data points in a few seconds on a mid‐range desktop computer. It identified turnpoints in simulated data with complete accuracy when the noise in the headings had a standard deviation of ±8∘, well within the tolerance of many modern biologgers. It has comparable accuracy to the existing algorithms tested, and is up to three orders of magnitude faster. Our algorithm, freely available in R and Python, serves as an initial step in processing ultra high‐resolution animal movement data, resulting in a rarefied path that can be used as an input into many existing step‐and‐turn methods of analysis. The resulting path consists of points where the animal makes a clear turn, and thereby provides valuable data on decisions underlying movement patterns. As such, it provides an important breakthrough required as a starting point for analysing subsecond resolution data. [ABSTRACT FROM AUTHOR]

A resource selection function is a model of the likelihood that an available spatial unit will be used by an animal, given its resource value. But how do we appropriately define availability? Step selection analysis deals with this problem at the scale of the observed positional data, by matching each 'used step' (connecting two consecutive observed positions of the animal) with a set of 'available steps' randomly sampled from a distribution of observed steps or their characteristics., Here we present a simple extension to this approach, termed integrated step selection analysis ( iSSA), which relaxes the implicit assumption that observed movement attributes (i.e. velocities and their temporal autocorrelations) are independent of resource selection. Instead, iSSA relies on simultaneously estimating movement and resource selection parameters, thus allowing simple likelihood-based inference of resource selection within a mechanistic movement model., We provide theoretical underpinning of iSSA, as well as practical guidelines to its implementation. Using computer simulations, we evaluate the inferential and predictive capacity of iSSA compared to currently used methods., Our work demonstrates the utility of iSSA as a general, flexible and user-friendly approach for both evaluating a variety of ecological hypotheses, and predicting future ecological patterns. [ABSTRACT FROM AUTHOR]

1. Impediments to animal movement are ubiquitous and vary widely in both scale and permeability. It is essential to understand how impediments alter ecological dynamics via their influence on animal behavioural strategies governing space use and, for anthropogenic features such as roads and fences, how to mitigate these effects to effectively manage species and landscapes. 2. Here, we focused primarily on barriers to movement, which we define as features that cannot be circumnavigated but may be crossed. Responses to barriers will be influenced by the movement capabilities of the animal, its proximity to the barriers, and habitat preference. We developed a mechanistic modelling framework for simultaneously quantifying the permeability and proximity effects of barriers on habitat preference and movement. 3. We used simulations based on our model to demonstrate how parameters on movement, habitat preference and barrier permeability can be estimated statistically. We then applied the model to a case study of road effects on wild mountain reindeer summer movements. 4. This framework provided unbiased and precise parameter estimates across a range of strengths of preferences and barrier permeabilities. The quality of permeability estimates, however, was correlated with the number of times the barrier is crossed and the number of locations in proximity to barriers. In the case study we found that reindeer avoided areas near roads and that roads are semi-permeable barriers to movement. There was strong avoidance of roads extending up to c. 1 km for four of five animals, and having to cross roads reduced the probability of movement by 68.6% (range 3.5-99.5%). 5. Human infrastructure has embedded within it the idea of networks: nodes connected by linear features such as roads, rail tracks, pipelines, fences and cables, many of which divide the landscape and limit animal movement. The unintended but potentially profound consequences of infrastructure on animals remain poorly understood. The rigorous framework for simultaneously quantifying movement, habitat preference and barrier permeability developed here begins to address this knowledge gap. [ABSTRACT FROM AUTHOR]

1. Animal space use has been studied by focusing either on geographic (e.g. home ranges, species' distribution) or on environmental (e.g. habitat use and selection) space. However, all patterns of space use emerge from individual movements, which are the primary means by which animals change their environment. 2. Individuals increase their use of a given area by adjusting two key movement components: the duration of their visit and/or the frequency of revisits. Thus, in spatially heterogeneous environments, animals exploit known, high-quality resource areas by increasing their residence time (RT) in and/or decreasing their time to return (TtoR) to these areas. We expected that spatial variation in these two movement properties should lead to observed patterns of space use in both geographic and environmental spaces. We derived a set of nine predictions linking spatial distribution of movement properties to emerging space-use patterns. We predicted that, at a given scale, high variation in RT and TtoR among habitats leads to strong habitat selection and that long RT and short TtoR result in a small home range size. 3. We tested these predictions using moose (Alces alces) GPS tracking data. We first modelled the relationship between landscape characteristics and movement properties. Then, we investigated how the spatial distribution of predicted movement properties (i.e. spatial autocorrelation, mean, and variance of RT and TtoR) influences home range size and hierarchical habitat selection. 4. In landscapes with high spatial autocorrelation of RT and TtoR, a high variation in both RT and TtoR occurred in home ranges. As expected, home range location was highly selective in such landscapes (i.e. second-order habitat selection); RT was higher and TtoR lower within the selected home range than outside, and moose home ranges were small. Within home ranges, a higher variation in both RT and TtoR was associated with higher selectivity among habitat types (i.e. third-order habitat selection). 5. Our findings show how patterns of geographic and environmental space use correspond to the two sides of a coin, linked by movement responses of individuals to environmental heterogeneity. By demonstrating the potential to assess the consequences of altering RT or TtoR (e.g. through human disturbance or climatic changes) on home range size and habitat selection, our work sets the basis for new theoretical and methodological advances in movement ecology. [ABSTRACT FROM AUTHOR]

Mammals use thermoregulatory behavioural strategies to reduce the cost of physiological thermoregulation. Environmental temperatures should, therefore, impact their decisions. We investigated the effect of environmental temperature on the movement decisions of a large mammal with low capacity for physiological thermoregulation: the giant anteater, Myrmecophaga tridactyla. We GPS-tracked 14 giant anteaters in the Brazilian Pantanal wetland over 5 years. We used hidden Markov models to identify two behavioural states (encamping, as a proxy of resting, and moving, as a proxy of being active) across individuals' trajectories. Then, we estimated the effect of environmental temperature on the probability of moving across the hours of the day in open and forested habitats. We also used integrated step selection analysis to understand how environmental temperature drives giant anteater's habitat selection across the day. Giant anteaters showed three important behavioural thermoregulatory strategies in response to environmental temperature changes: they modulated activity duration, completely shifted activity period on a scale of days and selected forests as thermal shelters. With increasing environmental temperature, giant anteaters increased activity duration, nocturnality and diurnal selection for forests, increasing energy intake while avoiding heat gain by solar radiation. With decreasing environmental temperature, they decreased activity duration, increased diurnality and increased nocturnal selection for forests, thus gaining heat from solar radiation when active and taking shelter in milder microclimates when resting. Besides their high short-term behavioural plasticity regarding activity, giant anteaters also used forests to thermoregulate. These results provide insights into how other mammals could respond to climate change. In particular, we highlight the importance of forests as thermal shelters, offering milder temperatures than adjacent open areas during both hot and cold weather spells. Thermal shelters will become more and more indispensable to animal thermoregulation as the frequency and intensity of extreme weather events increase. • Anteaters use activity modulation as a behavioural strategy to thermoregulate. • Anteaters selected forests as thermal shelters. • Anteaters show the importance of forests to mammals' thermoregulation. • Deforestation should intensify climate change effects on mammals' homeostasis. [ABSTRACT FROM AUTHOR]