BackgroundResearch on wild animal ecology is increasingly employing GPS telemetry in order to determine animal movement. However, GPS systems record position intermittently, providing no information on latent position or track tortuosity. High frequency GPS have high power requirements, which necessitates large batteries (often effectively precluding their use on small animals) or reduced deployment duration. Dead-reckoning is an alternative approach which has the potential to ‘fill in the gaps’ between less resolute forms of telemetry without incurring the power costs. However, although this method has been used in aquatic environments, no explicit demonstration of terrestrial dead-reckoning has been presented.ResultsWe perform a simple validation experiment to assess the rate of error accumulation in terrestrial dead-reckoning. In addition, examples of successful implementation of dead-reckoning are given using data from the domestic dog Canus lupus, horse Equus ferus, cow Bos taurus and wild badger Meles meles.ConclusionsThis study documents how terrestrial dead-reckoning can be undertaken, describing derivation of heading from tri-axial accelerometer and tri-axial magnetometer data, correction for hard and soft iron distortions on the magnetometer output, and presenting a novel correction procedure to marry dead-reckoned paths to ground-truthed positions. This study is the first explicit demonstration of terrestrial dead-reckoning, which provides a workable method of deriving the paths of animals on a step-by-step scale. The wider implications of this method for the understanding of animal movement ecology are discussed.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0055-4) contains supplementary material, which is available to authorized users.

The tortuosity of the track taken by an animal searching for food profoundly affects search efficiency, which should be optimised to maximise net energy gain. Models examining this generally describe movement as a series of straight steps interspaced by turns, and implicitly assume no turn costs. We used both empirical- and modelling-based approaches to show that the energetic costs for turns in both terrestrial and aerial locomotion are substantial, which calls into question the value of conventional movement models such as correlated random walk or Lévy walk for assessing optimum path types. We show how, because straight-line travel is energetically most efficient, search strategies should favour constrained turn angles, with uninformed foragers continuing in straight lines unless the potential benefits of turning offset the cost.

Researchers hoping to elucidate the behaviour of species that aren’t readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

Satellite telemetry is an increasingly utilized technology in wildlife research, and current devices can track individual animal movements at unprecedented spatial and temporal resolutions. However, as we enter the golden age of satellite telemetry, we need an in-depth understanding of the main technological, species-specific and environmental factors that determine the success and failure of satellite tracking devices across species and habitats. Here, we assess the relative influence of such factors on the ability of satellite telemetry units to provide the expected amount and quality of data by analyzing data from over 3,000 devices deployed on 62 terrestrial species in 167 projects worldwide. We evaluate the success rate in obtaining GPS fixes as well as in transferring these fixes to the user and we evaluate failure rates. Average fix success and data transfer rates were high and were generally better predicted by species and unit characteristics, while environmental characteristics influenced the variability of performance. However, 48% of the unit deployments ended prematurely, half of them due to technical failure. Nonetheless, this study shows that the performance of satellite telemetry applications has shown improvements over time, and based on our findings, we provide further recommendations for both users and manufacturers.

The spatial relationship between predator and prey is often conceptualized as a behavioral response race, in which prey avoid predators while predators track prey. Limiting habitat types can create spatial anchors for prey or predators, influencing the likelihood that the predator or prey response will dominate. Joint spatial anchors emerge when predator and prey occupy similar feeding habitat domains and risk and reward become spatially conflated, confusing predictions of which player will win the space race. These spatial dynamics of risk‐foraging trade‐offs are often obscured by habitat heterogeneity and community complexity in large vertebrate systems, fueling ambiguity regarding the generality of predictions from predator–prey theory. To test how habitat distribution influences the predator–prey space race, we examine correlation in puma and vicuña habitat selection and space use at two sites, one of which generates a distinct risk–foraging trade‐off at a joint spatial anchor. The distribution of vegetation, which serves as both forage for vicuñas and stalking cover for pumas, differs between the sites; the llano contains a single central meadow that acts as a joint spatial anchor, while the canyon is characterized by more heterogeneous vegetation. Puma–vicuña habitat selection correlation was positive in the llano and negative in the canyon, and similarly, utilization distributions were more strongly correlated in the llano than the canyon. Vicuña locations occurred at higher values of puma habitat selection and utilization in the llano than in the canyon. Similarly, puma locations in the llano occurred at higher values of vicuña habitat selection and utilization than in the canyon. Although pumas consistently selected for and utilized vegetative and topographic cover regardless of habitat distribution, vicuñas only selected against vegetation in the heterogeneous canyon site, reducing spatial correlation with pumas. Our work suggests a joint spatial anchor favors pumas in the space race due to the inability for vicuñas to avoid crucial foraging habitat. The outcome of the predator–prey space race appears to be strongly informed by the distribution of habitat, whereby corresponding predictability of predator and prey favors predators in the spatial game.