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Animal behavior is elicited, in part, in response to external conditions, but understanding how animals perceive the environment and make the decisions that bring about these behavioral responses is challenging. Animal heads often move during specific behaviors and, additionally, typically have sensory systems (notably vision, smell, and hearing) sampling in defined arcs (normally to the front of their heads). As such, head‐mounted electronic sensors consisting of accelerometers and magnetometers, which can be used to determine the movement and directionality of animal heads (where head "movement" is defined here as changes in heading [azimuth] and/or pitch [elevation angle]), can potentially provide information both on behaviors in general and also clarify which parts of the environment the animals might be prioritizing ("environmental framing"). We propose a new approach to visualize the data of such head‐mounted tags that combines the instantaneous outputs of head heading and pitch in a single intuitive spherical plot. This sphere has magnetic heading denoted by "longitude" position and head pitch by "latitude" on this "orientation sphere" (O‐sphere). We construct the O‐sphere for the head rotations of a number of vertebrates with contrasting body shape and ecology (oryx, sheep, tortoises, and turtles), illustrating various behaviors, including foraging, walking, and environmental scanning. We also propose correcting head orientations for body orientations to highlight specific heading‐independent head rotation, and propose the derivation of O‐sphere‐metrics, such as angular speed across the sphere. This should help identify the functions of various head behaviors. Visualizations of the O‐sphere provide an intuitive representation of animal behavior manifest via head orientation and rotation. This has ramifications for quantifying and understanding behaviors ranging from navigation through vigilance to feeding and, when used in tandem with body movement, should provide an important link between perception of the environment and response to it in free‐ranging animals. [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]

Automated movement tracking is essential for high‐throughput quantitative analyses of the behaviour and kinematics of organisms. Automated tracking also improves replicability by avoiding observer bias and allowing reproducible workflows. However, few automated tracking programs exist that are open access, open source and capable of tracking unmarked organisms in noisy environments.Tracktor is an image‐based tracking freeware designed to perform single‐object tracking in noisy environments, or multi‐object tracking in uniform environments while maintaining individual identities. Tracktor is code‐based but requires no coding skills other than the user being able to specify tracking parameters in a designated location, much like in a graphical user interface. The installation and use of the software is fully detailed in a user manual.Through four examples of common tracking problems, we show that Tracktor is able to track a variety of animals in diverse conditions. The main strengths of Tracktor lie in its ability to track single individuals under noisy conditions (e.g. when the object shape is distorted), its robustness to perturbations (e.g. changes in lighting conditions during the experiment), and its capacity to track multiple unmarked individuals while maintaining their identities. Additionally, summary statistics and plots allow measuring and visualising common metrics used in the analysis of animal movement (e.g. cumulative distance, speed, acceleration, activity, time spent in specific areas and distance to conspecific, etc.).Tracktor is a versatile, reliable and easy‐to‐use automated tracking software that is compatible with all operating systems and provides many features not available in other existing freeware. Access Tracktor and the complete user manual here: https://github.com/vivekhsridhar/tracktor [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]

The field of acoustic telemetry has evolved rapidly and now permits the remote sensing of animal behaviour, movement, physiology and survival in environments, and species not previously possible. However, an inability to detect when a telemetered animal is consumed by a predator can complicate accurate interpretation of the telemetry data. In this paper, we describe the efforts taken to test the two generations of a novel prototype acoustic telemetry transmitter designed specifically to detect predation., Testing involved either staged predation events where tagged prey (Rainbow Trout Oncorhynchus mykiss and Yellow Perch Perca flavescens) were fed to captive Largemouth Bass Micropterus salmoides, or false-positive testing where prey fish were tagged and held without the risk of predation. Metrics of interest were (i) the rate of correctly identifying the predation events, (ii) signal lag (i.e. the time required to detect a predation event), (iii) tag retention time in the predator's gut, and (iv) the rate of false-positive triggering in both live and dead prey fishes., Staged predation events were successfully identified in 61/65 and 52/55 trials for generation 1 and 2 tags, respectively. Signal lag time was reduced in generation 1 tags (generally between 1 and 9 h) relative to generation 2 (3-29 h); although signal lag was highly variable. A generalized linear mixed model (GLMM) indicated strong evidence that signal lag and tag retention were both negatively correlated with water temperature, but were not affected by prey species and only slightly by individual predator traits. There was preliminary evidence that prey size may be an important determinant of both signal lag and tag retention. False-positives in live fish were absent after 120 days for generation 1 tags ( n = 31), however, the false-positive rates were significantly higher (10/44) after only 66 days for generation 2 tags. False-positives in dead fish showed that 20% of the generation 2 predation tags would falsely trigger 2-3 days post-mortem., Testing of the novel predation tags was encouraging, however, further testing is recommended. Predation tags will be an important contribution to the field of acoustic telemetry, thus, permitting the improved data interpretation and less-subjective estimates of predation rates in biotelemetry studies. [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]

Home range sizes of territorial animals are often observed to vary periodically in response to seasonal changes in foraging opportunities. Here we develop the first mechanistic model focused on the temporal dynamics of home range expansion and contraction in territorial animals. We demonstrate how simple movement principles can lead to a rich suite of range size dynamics, by balancing foraging activity with defensive requirements and incorporating optimal behavioral rules into mechanistic home range analysis. Our heuristic model predicts three general temporal patterns that have been observed in empirical studies across multiple taxa. First, a positive correlation between age and territory quality promotes shrinking home ranges over an individual’s lifetime, with maximal range size variability shortly before the adult stage. Second, poor sensory information, low population density, and large resource heterogeneity may all independently facilitate range size instability. Finally, aggregation behavior toward forage-rich areas helps produce divergent home range responses between individuals from different age classes. This model has broad applications for addressing important unknowns in animal space use, with potential applications also in conservation and health management strategies. [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. 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]

For diving animals, animal-borne sensors are used to collect time-depth information for studying behaviour, ranging patterns and foraging ecology. Often, this information needs to be compressed for storage or transmission. Widely used devices called conductivity-temperature-depth satellite relay data loggers ( CTD- SRDLs) sample time and depth at high resolution during a dive and then abstract the time-depth trajectory using a broken-stick model ( BSM). This approximation method can summarize efficiently the curvilinear shape of a dive, using a piecewise linear shape with a small, fixed number of vertices, or break points., We present the process of abstracting dives using the BSM and quantify its performance, by measuring the uncertainty associated with the profiles it produces. We develop a method for obtaining a confidence zone and an index for the goodness-of-fit (dive zone index, DZI) for abstracted dive profiles. We validate our results with a case study using dives from elephant seals ( Mirounga spp.). We use generalized additive models ( GAMs) to determine whether the DZI can be used as a proxy for an absolute measure of fit and investigate the relationship between the DZI and the dive shape., We found a strong correlation between the residual sum of squares ( RSS) for the difference between the detailed and abstracted profiles, and the DZI and maximum residual (R4), for dives resulting from CTD- SRDLs (69% deviance explained). On its own, the DZI explained a lower percentage of deviance which was variable for abstracted dives with different numbers of break points. We also found evidence for systematic differences in the DZI for different dive shapes (65% deviance explained)., Although the proportional loss of information in the abstraction of time-depth dive profiles by BSM is high, what remains is sufficient to infer goodness-of-fit of the abstracted profile by reversing the abstraction process. Our results suggest that together the DZI and R4 can be used as a proxy for the RSS, and we present the method for obtaining these metrics for BSM-abstracted profiles. [ABSTRACT FROM AUTHOR]

Animals of many species demonstrate movement behaviour in which decisions are based on a variety of information. Effects of resources have been studied widely, often under the assumption that the environment is constant over the course of the study. Much less understood is the role of dynamic information about continuously changing resource availability and past experiences. Such information can be acquired during movement bouts and used for future decisions via memory., We present a new class of animal movement models, which incorporates a dynamic interplay of movement and information gain processes. Information is contained in a dynamic cognitive map. As an example, we consider time since last visit to locations and how this interacts with environmental information to shape movement patterns. Our models can be fitted to empirical movement trajectories and are therefore amenable to statistical inference (parameter estimation and model selection)., We tested the functionality of our method using simulated data. Parameter estimates were in accordance with true values used in the simulations, and model selection via Bayesian information criterion (BIC) was able to identify true underlying mechanisms of simulated trajectories. Thus, if time since last visit to locations influences movement decisions, our method is able to detect this mechanism., The use of dynamic information such as the one demonstrated in our example models likely requires cognitive abilities such as spatial memory. Therefore, our method can be used to reveal evidence of spatial memory in empirical movement data. Understanding the components of individual movement decisions and their interactions ultimately helps us to predict how population distribution patterns respond to environmental changes, such as landscape fragmentation and changing climate. [ABSTRACT FROM AUTHOR]

Wildlife scientists continue to be interested in studying ways to quantify how the movements of animals are interdependent - dynamic interaction. While a number of applied studies of dynamic interaction exist, little is known about the comparative effectiveness and applicability of available methods used for quantifying interactions between animals., We highlight the formulation, implementation and interpretation of a suite of eight currently available indices of dynamic interaction. Point- and path-based approaches are contrasted to demonstrate differences between methods and underlying assumptions on telemetry data., Correlated and biased correlated random walks were simulated at a range of sampling resolutions to generate scenarios with dynamic interaction present and absent. We evaluate the effectiveness of each index at identifying different types of interactive behaviour at each sampling resolution. Each index is then applied to an empirical telemetry data set of three white-tailed deer ( Odocoileus virginianus) dyads., Results from the simulated data show that three indices of dynamic interaction reliant on statistical testing procedures are susceptible to Type I error, which increases at fine sampling resolutions. In the white-tailed deer examples, a recently developed index for quantifying local-level cohesive movement behaviour (the di index) provides revealing information on the presence of infrequent and varying interactions in space and time., Point-based approaches implemented with finely sampled telemetry data overestimate the presence of interactions (Type I errors). Indices producing only a single global statistic (7 of the 8 indices) are unable to quantify infrequent and varying interactions through time. The quantification of infrequent and variable interactive behaviour has important implications for the spread of disease and the prevalence of social behaviour in wildlife. Guidelines are presented to inform researchers wishing to study dynamic interaction patterns in their own telemetry data sets. Finally, we make our code openly available, in the statistical software R, for computing each index of dynamic interaction presented herein. [ABSTRACT FROM AUTHOR]

Animal migration has long intrigued scientists and wildlife managers alike, yet migratory species face increasing challenges because of habitat fragmentation, climate change and over-exploitation. Central to the understanding migratory species is the objective discrimination between migratory and nonmigratory individuals in a given population, quantifying the timing, duration and distance of migration and the ability to predict migratory movements. Here, we propose a uniform statistical framework to (i) separate migration from other movement behaviours, (ii) quantify migration parameters without the need for arbitrary cut-off criteria and (iii) test predictability across individuals, time and space. We first validated our novel approach by simulating data based on established theoretical movement patterns. We then formulated the expected shapes of squared displacement patterns as nonlinear models for a suite of movement behaviours to test the ability of our method to distinguish between migratory movement and other movement types. We then tested our approached empirically using 108 wild Global Positioning System (GPS)-collared moose Alces alces in Scandinavia as a study system because they exhibit a wide range of movement behaviours, including resident, migrating and dispersing individuals, within the same population. Applying our approach showed that 87% and 67% of our Swedish and Norwegian subpopulations, respectively, can be classified as migratory. Using nonlinear mixed effects models for all migratory individuals we showed that the distance, timing and duration of migration differed between the sexes and between years, with additional individual differences accounting for a large part of the variation in the distance of migration but not in the timing or duration. Overall, the model explained most of the variation (92%) and also had high predictive power for the same individuals over time (69%) as well as between study populations (74%). The high predictive ability of the approach suggests that it can help increase our understanding of the drivers of migration and could provide key quantitative information for understanding and managing a broad range of migratory species. [ABSTRACT FROM AUTHOR]

Home range behaviour is a common pattern of space use, having fundamental consequences for ecological processes. However, a general mechanistic explanation is still lacking. Research is split into three separate areas of inquiry – movement models based on random walks, individual-based models based on optimal foraging theory, and a statistical modelling approach – which have developed without much productive contact. Here we review recent advances in modelling home range behaviour, focusing particularly on the problem of identifying mechanisms that lead to the emergence of stable home ranges from unbounded movement paths. We discuss the issue of spatiotemporal scale, which is rarely considered in modelling studies, as well as highlighting the need to consider more closely the dynamical nature of home ranges. Recent methodological and theoretical advances may soon lead to a unified approach, however, conceptually unifying our understanding of linkages among home range behaviour and ecological or evolutionary processes. [ABSTRACT FROM AUTHOR]

Animal home range use is a central focus of ecological research. However, how and why home range size varies between individuals is not well studied or understood for most species. We develop a hierarchical analytical approach—using generalized linear mixed-effects modeling of time series of home range sizes—that allows variance in home range size to be decomposed into components due to variation in temporal, spatial, and individual-level processes, also facilitating intra- and interspecific comparative analyses. We applied the approach to data from a roe deer population radiotracked in central Italy. Over multiple timescales, temporal variation is explained by photoperiod and climate and spatial variation by the distribution of habitat types and spatial variance in radio-tracking error. Differences between individuals explained a substantial amount of variance in home range size, but only a relatively minor part was explained by the individual attributes of sex and age. We conclude that the choice of temporal scale at which data are collected and the definition of home range can significantly influence biological inference. We suggest that the appropriate choice of scale and definition requires a good understanding of the ecology and life history of the study species. Our findings contrast with several common assumptions about roe deer behavior. [ABSTRACT FROM AUTHOR]