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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]

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In environments that vary unpredictably, many animals are nomadic, moving in an irregular pattern that differs from year to year. Exploring the mechanisms of nomadic movement is needed to understand how animals survive in highly variable environments, and to predict behavioural and population responses to environmental change.We developed a network model to identify plausible mechanisms of nomadic animal movement by comparing the performance of multiple movement rules along a continuum from nomadism to residency. Using simulations and analytical results, we explored how different types of habitat modifications (that augment or decrease resource availability) might affect the abundance and movement rates of animals following each of these rules.Movement rules for which departure from patches depended on resource availability and/or competition performed almost equally well and better than residency or uninformed movement under most conditions, even though animals using each rule moved at substantially different rates. Habitat modifications that stabilized resources, either by resource supplementation or degradation, eroded the benefits of informed nomadic movements, particularly for movements based on resource availability alone.These results suggest that simple movement rules can explain nomadic animal movements and determine species' responses to environmental change. In particular, landscape stabilization and supplementation might be useful strategies for promoting populations of resident animals, but would be less beneficial for managing highly mobile species, many of which are threatened by habitat disruption and changes in climate. [ABSTRACT FROM AUTHOR]

Translocation into a novel environment through common fisheries management practices, such as fish stocking, provides opportunities to study behavioural and fitness impacts of translocations at realistic ecological scales. The process of stocking, as well as the unfamiliarity with novel ecological conditions and the interactions with resident fish may affect translocated individuals, leading to alterations of behaviours and causing fitness impacts.Our objectives were to investigate how aquatic top predators behaviourally establish themselves and compete with resident individuals following introduction in a novel lake environment and to investigate the resulting fitness consequences.Using high‐resolution acoustic telemetry, we conducted whole‐lake experiments and compared the activity, activity‐space size and fate of translocated and resident individuals in two model top predators, the northern pike Esox lucius (n = 160) and European catfish Silurus glanis (n = 33). Additionally, we compared the reproductive success of translocated and resident northern pike. The experiment was conducted with large (adult) individuals of different origins, resilient to predation, but subject to agonistic interactions and competition with resident fish.Over a period of several months, the translocated catfish exhibited consistently larger activity‐space sizes than resident catfish, but did not differ from residents in activity and survival. The pike from one of the two translocated origins we tested also showed elevated space‐use, and both translocated origins revealed higher mortality rates than their resident conspecifics, indicating maladjustment to their novel environment. When non‐resident pike reproduced, they overwhelmingly produced hybrid offspring with resident fish, indicating that introductions fostered gene flow of non‐native genes.Our study indicates that fish introductions result in behavioural and fitness impacts even in large‐bodied top predators that experience low levels of natural predation risk. [ABSTRACT FROM AUTHOR]

Animal movements towards goals or targets are based upon either maximization of resource acquisition or risk avoidance, and the way animals move can reveal information about their motivation.We use hidden Markov models (HMMs) fitted in a Bayesian framework and hourly Global Positioning System fixes to distinguish animal movements into distinct states and analyse the influence of environmental variables on being in, and switching to, a particular state. Specifically, we apply our models to understand elephant movement decisions around agricultural fields, and crop consumption. As it is unclear what the role of habitat features are on this complex process, we analyse whether elephants target agricultural crops for consumption, or simply pass through them in search of water.Our HMMs separate elephant movements into two states: exploratory movements that are fast and directional, and encamped movements that are slow and meandering. For each elephant, we ran 16 models with each possible combination of selected habitat features (river, elephant corridor, agricultural field, trees), and repeated these analyses including interaction effects with both season and time of day. We used cross‐validation to select the best model.In corridors, exploratory movements are dominant. Elephants mainly showed encamped movements at the river during the dry season, when temporary water sources have dried out and elephants relied on this permanent water source. In fields, males most often exhibited exploratory movements to and from the river, while females showed an increase in the frequency of encamped behaviour during the dry season and at night—the times when most crop consumption and movements through fields occur.Adaptation to risk could explain this behaviour, since foraging in fields is likely less risky under the cover of darkness and during the dry season when farmers are absent. This sex segregation in elephant movement decisions highlights the importance of predation risk in shaping movement patterns, which can result in sex segregation in responses to mitigation methods. The increase in encamped movements in the dry season suggests the importance of agricultural timing, and shows the potential for early ploughing and early‐harvest crop types in order to reduce elephant crop consumption. Taking this into account could increase efficiency of elephant crop consumption mitigation. [ABSTRACT FROM AUTHOR]

It is fundamentally important for many animal ecologists to quantify the costs of animal activities, although it is not straightforward to do so. The recording of triaxial acceleration by animal‐attached devices has been proposed as a way forward for this, with the specific suggestion that dynamic body acceleration (DBA) be used as a proxy for movement‐based power.Dynamic body acceleration has now been validated frequently, both in the laboratory and in the field, although the literature still shows that some aspects of DBA theory and practice are misunderstood. Here, we examine the theory behind DBA and employ modelling approaches to assess factors that affect the link between DBA and energy expenditure, from the deployment of the tag, through to the calibration of DBA with energy use in laboratory and field settings.Using data from a range of species and movement modes, we illustrate that vectorial and additive DBA metrics are proportional to each other. Either can be used as a proxy for energy and summed to estimate total energy expended over a given period, or divided by time to give a proxy for movement‐related metabolic power. Nonetheless, we highlight how the ability of DBA to predict metabolic rate declines as the contribution of non‐movement‐related factors, such as heat production, increases.Overall, DBA seems to be a substantive proxy for movement‐based power but consideration of other movement‐related metrics, such as the static body acceleration and the rate of change of body pitch and roll, may enable researchers to refine movement‐based metabolic costs, particularly in animals where movement is not characterized by marked changes in body acceleration. [ABSTRACT FROM AUTHOR]

GPS‐tracking devices have been used in combination with a wide range of additional sensors to study animal behaviour, physiology and interaction with their environment. Tri‐axial accelerometers allow researchers to remotely infer the behaviour of individuals, at all places and times. Collection of accelerometer data is relatively cheap in terms of energy usage, but the amount of raw data collected generally requires much storage space and is particularly demanding in terms of energy needed for data transmission.Here, we propose compressing the raw accelerometer (ACC) data into summary statistics within the tracking device (before transmission) to reduce data size, as a means to overcome limitations in storage and energy capacity.We explored this type of lossy data compression in the accelerometer data of tagged Bewick's swans Cygnus columbianus bewickii collected in spring 2017. Using software settings in which bouts of 2 s of both raw ACC data and summary statistics were collected in parallel but with different bout intervals to keep total data size comparable, we created the opportunity for a direct comparison of time budgets derived by the two data collection methods.We found that the data compression in our case yielded a six times reduction in data size per bout, and concurrent, similar decreases in storage and energy use of the device. We show that with the same accuracy of the behavioural classification, the freed memory and energy of the device can be used to increase the monitoring effort, resulting in a more detailed representation of the individuals' time budget. Rare and/or short behaviours, such as daily roost flights, were picked up significantly more when collecting summary statistics instead of raw ACC data (but note differences in sampling rate). Such level of detail can be of essential importance, for instance to make a reliable estimate of the energy budgets of individuals.In conclusion, we argue that this type of lossy data compression can be a well‐considered choice in study situations where limitations in energy and storage space of the device pose a problem. Ultimately, these developments can allow for long‐term and nearly continuous remote monitoring of the behaviour of free‐ranging animals. [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]

The extent to which prey space use actively minimizes predation risk continues to ignite controversy. Methodological reasons that have hindered consensus include inconsistent measurements of predation risk, biased spatiotemporal scales at which responses are measured and lack of robust null expectations.We addressed all three challenges in a comprehensive analysis of the spatiotemporal responses of adult female elk (Cervus elaphus) to the risk of predation by wolves (Canis lupus) during winter in northern Yellowstone, USA.We quantified spatial overlap between the winter home ranges of GPS‐collared elk and three measures of predation risk: the intensity of wolf space use, the distribution of wolf‐killed elk and vegetation openness. We also assessed whether elk varied their use of areas characterized by more or less predation risk across hours of the day, and estimated encounter rates between simultaneous elk and wolf pack trajectories. We determined whether observed values were significantly lower than expected if elk movements were random with reference to predation risk using a null model approach.Although a small proportion of elk did show a tendency to minimize use of open vegetation at specific times of the day, overall we highlight a notable absence of spatiotemporal response by female elk to the risk of predation posed by wolves in northern Yellowstone.Our results suggest that predator–prey interactions may not always result in strong spatiotemporal patterns of avoidance. [ABSTRACT FROM AUTHOR]

Understanding site fidelity is important in animal ecology, but evidence is lacking that this behaviour is due to an animal choosing a specific location. To discern site selection behaviour, it is necessary to consider the spatial distribution of habitats that animals can occupy within a landscape. Tracking animals and defining clear habitat boundaries, however, is often difficult., We use in situ habitat distribution data and animal movement simulations to investigate behavioural choice in site fidelity patterns. We resolved the difficulty of gathering data by working with intertidal rock pool systems, which are of manageable size and where boundaries are easy to define. Movements of the intertidal starfish Parvulastra exigua were quantified to test the hypotheses that (1) this species displays fidelity to a particular rock pool and that (2) rock pool fidelity is due to site selection behaviour. Observed patterns of individuals ( n = 10 starfish) returning to a previously occupied rock pool ( n = 5 pools per location) were tested against an expected null distribution generated through simulations of random movements within their natural patchy environment., Starfish exhibited site selection behaviour at only one location even though site fidelity was high (av. 7·4 starfish out of 10 found in test pools) in two of the three locations. The random chance of a starfish returning to a pool increased 67% for each metre further a rock pool was from the original pool, and 120% for each square metre increase in surface area of an original pool. The decision of returning to an original rock pool was influenced by food availability. When microalgal cover was >60%, there was a c. 50% chance of animals staying faithful to that pool., Our results show the importance to consider spatial distribution of habitats in understanding patterns of animal movement associated with animal choices and site fidelity. Returning to a particular place does not necessarily mean that an animal is homing; it may be the only place to go. [ABSTRACT FROM AUTHOR]

Many animals undertake movements that are longer scaled and more directed than their typical home ranging behaviour. These movements include seasonal migrations (e.g. between breeding and feeding grounds), natal dispersal, nomadic range shifts and responses to local environmental disruptions. While various heuristic tools exist for identifying range shifts and migrations, none explicitly model the movement of the animals within a statistical framework that facilitates quantitative comparisons., We present the mechanistic range shift analysis (MRSA), a method to estimate a suite of range shift parameters: times of initiation, duration of transitions, centroids and areas of respective ranges. The method can take the autocorrelation and irregular sampling that is characteristic of much movement data into account. The mechanistic parameters suggest an intuitive measure, the range shift index, for the extent of a range shift. The likelihood based estimation further allows for statistical tests of several relevant hypotheses, including a range shift test, a stopover test and a site fidelity test. The analysis tools are provided in an R package ( marcher)., We applied the MRSA to a population of GPS tracked roe deer ( Capreolus capreolus) in the Italian Alps between 2005 and 2008. With respect to seasonal migration, this population is extremely variable and difficult to classify. Using the MRSA, we were able to quantify the behaviours across the population and among individuals across years, identifying extents, durations and locations of seasonal range shifts, including cases that would have been ambiguous to detect using existing tools., The strongest patterns were differences across years: many animals simply did not perform a seasonal migration to wintering grounds during the mild winter of 2006-2007, even though some of these same animals did move extensively in other, harsher winters. For seasonal migrants, however, site fidelity across years was extremely high, even after skipping an entire seasonal migration. These results suggest that for roe deer behavioural plasticity and tactical responses to immediate environmental cues are reflected in the decision of whether rather than where to migrate. The MRSA also revealed a trade-off between the probability of migrating and the size of a home range. [ABSTRACT FROM AUTHOR]

Recent advances in animal ecology have enabled identification of certain mechanisms that lead to the emergence of territories and home ranges from movements considered as unbounded. Among them, memory and familiarity have been identified as key parameters in cognitive maps driving animal navigation, but have been only recently used in empirical analyses of animal movements., At the same time, the influence of landscape features on movements of numerous species and on space division in territorial animals has been highlighted. Despite their potential as exocentric information in cognitive maps and as boundaries for home ranges, few studies have investigated their role in the design of home ranges of non-territorial species., Using step selection analyses, we assessed the relative contribution of habitat characteristics, familiarity preferences and linear landscape features in movement step selection of 60 GPS-collared Mediterranean mouflon Ovis gmelini musimon × Ovis sp. monitored in southern France. Then, we evaluated the influence of these movement-impeding landscape features on the design of home ranges by testing for a non-random distribution of these behavioural barriers within sections of space differentially used by mouflon., We reveal that familiarity and landscape features are key determinants of movements, relegating to a lower level certain habitat constraints (e.g. food/cover trade-off) that we had previously identified as important for this species. Mouflon generally avoid crossing both anthropogenic (i.e. roads, tracks and hiking trails) and natural landscape features (i.e. ridges, talwegs and forest edges) while moving in the opposite direction, preferentially toward familiar areas. These specific behaviours largely depend on the relative position of each movement step regarding distance to the landscape features or level of familiarity in the surroundings. We also revealed cascading consequences on the design of home ranges in which most landscape features were excluded from cores and relegated to the peripheral areas., These results provide crucial information on landscape connectivity in a context of marked habitat fragmentation. They also call for more research on the role of landscape features in the emergence of home ranges in non-territorial species using recent methodological developments bridging the gap between movements and space use patterns. [ABSTRACT FROM AUTHOR]

Understanding the spatial scale of population structure is fundamental to long-standing tenets of population biology, landscape ecology and conservation. Nonetheless, identifying such scales has been challenging because a key factor that influences scaling - movement among patches or local populations - is a multicausal process with substantial phenotypic and temporal variation. We resolve this problem via a novel application of network modularity. When applied to movements, modularity provides a formal description of the functional aggregation of populations and identifies potentially critical scales for ecological and evolutionary dynamics. We first test for modularity using several different types of biologically relevant movements across the entire geographic range of an endangered bird, the snail kite (Rostrhamus sociabilis plumbeus). We then ask whether variation in movement based on (i) age, (ii) sex and (iii) time (annual, seasonal and within-season movements) influences spatial population structure (i.e. modularity) in snail kites. We identified significant modularity in annual dispersal of snail kites (all adults, males only, females only, and juveniles only) and in within-breeding season movements of adults, yet no evidence of modularity in seasonal (non-breeding) movements. For those movements with observed modular structure, we found striking similarities in the spatial configuration of population structure, even though movement properties varied considerably among these different types of movements. Our results suggest that the emergence of modularity in population networks can be robust despite movement heterogeneity and differences in patch-based measures of connectivity. Furthermore, our comparison of the population structure and connectivity across multiple movement phases helps to identify wetland patches most critical to population connectivity at multiple spatiotemporal scales. We argue that understanding modularity in populations may provide a robust complement to existing measures of population structure and connectivity and will help to clarify the limiting roles of movement for populations. Such information is increasingly needed for interpreting population persistence and guiding effective conservation strategies with ongoing environmental change. [ABSTRACT FROM AUTHOR]

Understanding how individual movement scales with body size is of fundamental importance in predicting ecological relationships for diverse species. One-dimensional movement metrics scale consistently with body size yet vary over different temporal scales. Knowing how temporal scale influences the relationship between animal body size and movement would better inform hypotheses about the efficiency of foraging behaviour, the ontogeny of energy budgets, and numerous life-history trade-offs., We investigated how the temporal scaling of allometric patterns in movement varies over the course of a year, specifically during periods of motivated (directional and fast movement) and unmotivated (stationary and tortuous movement) behaviour. We focused on a recently diverged group of species that displays wide variation in movement behaviour - giant Galapagos tortoises ( Chelonoidis spp.) - to test how movement metrics estimated on a monthly basis scaled with body size., We used state-space modelling to estimate seven different movement metrics of Galapagos tortoises. We used log-log regression of the power law to evaluate allometric scaling for these movement metrics and contrasted relationships by species and sex., Allometric scaling of movement was more apparent during motivated periods of movement. During this period, allometry was revealed at multiple temporal intervals (hourly, daily and monthly), with values observed at daily and monthly intervals corresponding most closely to the expected one-fourth scaling coefficient, albeit with wide credible intervals. We further detected differences in the magnitude of scaling among taxa uncoupled from observed differences in the temporal structuring of their movement rates., Our results indicate that the definition of temporal scales is fundamental to the detection of allometry of movement and should be given more attention in movement studies. Our approach not only provides new conceptual insights into temporal attributes in one-dimensional scaling of movement, but also generates valuable insights into the movement ecology of iconic yet poorly understood Galapagos giant tortoises. [ABSTRACT FROM AUTHOR]

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. Decreasing rate of migration in several species as a consequence of climate change and anthropic pressure, together with increasing evidence of space-use strategies intermediate between residency and complete migration, are very strong motivations to evaluate migration occurrence and features in animal populations. 2. The main goal of this paper was to perform a relative comparison between methods for identifying and characterizing migration at the individual and population level on the basis of animal location data. 3. We classified 104 yearly individual trajectories from five populations of three deer species as migratory or non-migratory, by means of three methods: seasonal home range overlap, spatio-temporal separation of seasonal clusters and the Net Squared Displacement (NSD) method. For migratory cases, we also measured timing and distance of migration and residence time on the summer range. Finally, we compared the classification in migration cases across methods and populations. 4. All methods consistently identified migration at the population level, that is, they coherently distinguished between complete or almost complete migratory populations and partially migratory populations. However, in the latter case, methods coherently classified only about 50% of the single cases, that is they classified differently at the individual-animal level. We therefore infer that the comparison of methods may help point to 'less-stereotyped' cases in the residency-to-migration continuum. For cases consistently classified by all methods, no significant differences were found in migration distance, or residence time on summer ranges. Timing of migration estimated by NSD was earlier than by the other two methods, both for spring and autumn migrations. 5. We suggest three steps to identify improper inferences from migration data and to enhance understanding of intermediate space-use strategies. We recommend (i) classifying migration behaviours using more than one method, (ii) performing sensitivity analysis on method parameters to identify the extent of the differences and (iii) investigating inconsistently classified cases as these may often be ecologically interesting (i.e. less-stereotyped migratory behaviours). [ABSTRACT FROM AUTHOR]

1. Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. 2. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. 3. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. 4. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). 5. The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models. [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. The loss, fragmentation and degradation of habitat everywhere on Earth prompts increasing attention to identifying landscape features that support animal movement (corridors) or impedes it (barriers). Most algorithms used to predict corridors assume that animals move through preferred habitat either optimally (e.g. least cost path) or as random walkers (e.g. current models), but neither extreme is realistic. 2. We propose that corridors and barriers are two sides of the same coin and that animals experience landscapes as spatiotemporally dynamic corridor-barrier continua connecting (separating) functional areas where individuals fulfil specific ecological processes. Based on this conceptual framework, we propose a novel methodological approach that uses high-resolution individual-based movement data to predict corridor-barrier continua with increased realism. 3. Our approach consists of two innovations. First, we use step selection functions (SSF) to predict friction maps quantifying corridor-barrier continua for tactical steps between consecutive locations. Secondly, we introduce to movement ecology the randomized shortest path algorithm (RSP) which operates on friction maps to predict the corridor-barrier continuum for strategic movements between functional areas. By modulating the parameter, which controls the trade-off between exploration and optimal exploitation of the environment, RSP bridges the gap between algorithms assuming optimal movements (when Θ approaches infinity, RSP is equivalent to LCP) or random walk (when Θ → 0, RSP → current models). 4. Using this approach, we identify migration corridors for GPS-monitored wild reindeer (Rangifer t. tarandus) in Norway. We demonstrate that reindeer movement is best predicted by an intermediate value of Θ, indicative of a movement trade-off between optimization and exploration. Model calibration allows identification of a corridor-barrier continuum that closely fits empirical data and demonstrates that RSP outperforms models that assume either optimality or random walk. 5. The proposed approach models the multiscale cognitive maps by which animals likely navigate real landscapes and generalizes the most common algorithms for identifying corridors. Because suboptimal, but non-random, movement strategies are likely widespread, our approach has the potential to predict more realistic corridor-barrier continua for a wide range of species. [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]

Behavioural and physiological deficiencies are major reasons why reintroduction programmes suffer from high mortality when captive animals are used. Mitigation of these deficiencies is essential for successful reintroduction programmes., Our study manipulated early developmental diet to better replicate foraging behaviour in the wild. Over 2 years, we hand-reared 1800 pheasants ( Phasianus colchicus), from 1 day old, for 7 weeks under different dietary conditions. In year one, 900 pheasants were divided into three groups and reared with (i) commercial chick crumb, (ii) crumb plus 1% live mealworm or (iii) crumb plus 5% mixed seed and fruit. In year two, a further 900 pheasants were divided into two groups and reared with (i) commercial chick crumb or (ii) crumb plus a combination of 1% mealworm and 5% mixed seed and fruit. In both years, the commercial chick crumb acted as a control treatment, whilst those with live prey and mixed seeds and fruits mimicking a more naturalistic diet. After 7 weeks reared on these diets, pheasants were released into the wild., Postrelease survival was improved with exposure to more naturalistic diets prior to release. We identified four mechanisms to explain this. Pheasants reared with more naturalistic diets (i) foraged for less time and had a higher likelihood of performing vigilance behaviours, (ii) were quicker at handling live prey items, (iii) were less reliant on supplementary feed which could be withdrawn and (iv) developed different gut morphologies., These mechanisms allowed the pheasants to (i) reduce the risk of predation by reducing exposure time whilst foraging and allowing more time to be vigilant; (ii) be better at handling and discriminating natural food items and not be solely reliant on supplementary feed; and (iii) have a better gut system to cope with the natural forage after the cessation of supplementary feeding in the spring., Learning food discrimination, preference and handling skills by the provision of a more naturalistic diet is essential prior to the release of pheasants in a reintroduction programme. Subsequent diet, foraging behaviour, gut morphology and digestive capabilities all work together as one nutritional complex. Simple manipulations during early development can influence these characteristics to better prepare an individual for survival upon release. [ABSTRACT FROM AUTHOR]