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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]

Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well‐studied seabirds. Here, we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots Uria aalge breeding on Skomer Island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high‐quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. [ABSTRACT FROM AUTHOR]

Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals M≥30 and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations.We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra).Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than M=30 animals.We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies. [ABSTRACT FROM AUTHOR]

Keywords: accelerometer; animal movement; behaviour; biologging; GPS; modelling; sensor technology; tracking Technological solutions to record the movements, behaviour and physiology of animals, and associated methodological advancements for analysing the data collected, have revolutionized research in animal ecology and beyond (Brisson-Curadeau, Patterson, Whelan, Lazarus, & Elliott, [18]; Kenward, [56]; Ropert-Coudert, Beaulieu, Hanuise, & Kato, [91]; Ropert-Coudert & Wilson, [92]; Weimerskirch, [107]). The general term for this technological approach to study animals is called Biologging - 'the use of miniaturized animal-attached tags for logging and/or relaying data about an animal's movements, behaviour, physiology, and/or environment' (Rutz & Hays, [94]). Thanks to ever smaller loggers which can record an animal's behaviour for ever longer periods of time, biologging is now allowing researchers to study with unprecedented detail how behaviours develop in slow-maturing animals. While an increasing body of research has shown the impact of consistent individual differences in behavioural phenotypes, called animal personalities or behavioural syndromes (Réale et al., [89]; Sih, Bell, Johnson, & Ziemba, [96]), on foraging behaviour, exploratory movements and other spatial behaviours (Bijleveld et al., [12]; Boon, Réale, & Boutin, [15]; Minderman et al., [75]; van Overveld & Matthysen, [103]; Villegas-Ríos, Réale, Freitas, Moland, & Olsen, [105]; Wilson & McLaughlin, [112]), the important relationship between animal personality and foraging site fidelity has not been studied yet. [Extracted from the article]

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]

Land use has large effects on the diversity of ecological assemblages. Differences among land uses in the diversity of local assemblages (alpha diversity) have been quantified at a global scale. Effects on the turnover of species composition between locations (beta diversity) are less clear, with previous studies focusing on particular regions or groups of species. Using a global database on the composition of ecological assemblages in different land uses, we test for differences in the between-site turnover of species composition, within and among land-use types. Overall, we show a strong impact of land use on assemblage composition. While we find that compositional turnover within land uses does not differ strongly among land uses, human land uses and secondary vegetation in an early stage of recovery are poor at retaining the species that characterise primary vegetation. The dissimilarity of assemblages in human-impacted habitats compared with primary vegetation was more pronounced in the tropical than temperate realm. An exploratory analysis suggests that this geographic difference might be caused primarily by differences in climate seasonality and in the numbers of species sampled. Taken together the results suggest that, while small-scale beta diversity within land uses is not strongly impacted by land-use type, compositional turnover between land uses is substantial. Therefore, land-use change will lead to profound changes in the structure of ecological assemblages. [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]

An introduction is presented which discusses various topics within the issue including the effects of individual movements on ecological processes, use of habitat selection to predict species abundances in heterogeneous environments and concept of the movement ecology framework.

1. Home range is the area traversed by an animal in its normal activities. The size of home ranges is thought to be tightly linked to body size, through size effect on metabolic requirements. Due to the structure of Eltonian food pyramids, home range sizes of carnivores are expected to exceed those of herbivorous species. The habitat may also affect home range size, with reduced costs of locomotion or lower food abundance in, for example, aquatic habitats selecting for larger home ranges. Furthermore, home range of males in polygamous species may be large due to sexual selection for increased reproductive output. 2. Comparative studies on home range sizes have rarely been conducted on ectotherms. Because ectotherm metabolic rates are much lower than those of endotherms, energetic considerations of metabolic requirements may be less important in determining the home range sizes of the former, and other factors such as differing habitats and sexual selection may have an increased effect. 3. We collected literature data on turtle home range sizes. We used phylogenetic generalized least squares analyses to determine whether body mass, sex, diet, habitat and social structure affect home range size. 4. Turtle home range size increases with body mass. However, body mass explains relatively little of the variation in home range size. Aquatic turtles have larger home ranges than semiaquatic species. Omnivorous turtles have larger home ranges than herbivores and carnivores, but diet is not a strong predictor. Sex and social structure are unrelated to home range size. 5. We conclude that energetic constraints are not the primary factor that determines home range size in turtles, and energetic costs of locomotion in different habitats probably play a major role. [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]

1. Habitats have substantial influence on the distribution and abundance of animals. Animals' selective movement yields their habitat use. Animals generally are more abundant in habitats that are selected most strongly. 2. Models of habitat selection can be used to distribute animals on the landscape or their distribution can be modelled based on data of habitat use, occupancy, intensity of use or counts of animals. When the population is at carrying capacity or in an ideal-free distribution, habitat selection and related metrics of habitat use can be used to estimate abundance. 3. If the population is not at equilibrium, models have the flexibility to incorporate density into models of habitat selection; but abundance might be influenced by factors influencing fitness that are not directly related to habitat thereby compromising the use of habitat-based models for predicting population size. 4. Scale and domain of the sampling frame, both in time and space, are crucial considerations limiting application of these models. Ultimately, identifying reliable models for predicting abundance from habitat data requires an understanding of the mechanisms underlying population regulation and limitation. [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]