Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal “movement ecology” (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

Modern, high‐throughput animal tracking increasingly yields ‘big data’ at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis‐estimation of behaviours inferred from movement. ‘Cleaning’ the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce. A pipeline for cleaning massive high‐throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre‐processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time‐consuming, and to boost reproducibility, pre‐processing pipelines must be automated. We provide guidance on building pipelines for pre‐processing high‐throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful ‘residence patches’, for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre‐processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre‐processing pipeline and residence patch method in a fully worked out example. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre‐processing pipeline and atlastools can be used with any high‐throughput animal movement data in which the high data‐volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre‐processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data.

Transparent Cylinder and Barrier tasks are used to purportedly assess inhibitory control in a variety of animals. However, we suspect that performances on these detour tasks are influenced by non-cognitive traits, which may result in inaccurate assays of inhibitory control. We therefore reared pheasants under standardized conditions and presented each bird with two sets of similar tasks commonly used to measure inhibitory control. We recorded the number of times subjects incorrectly attempted to access a reward through transparent barriers, and their latencies to solve each task. Such measures are commonly used to infer the differential expression of inhibitory control. We found little evidence that their performances were consistent across the two different Putative Inhibitory Control Tasks (PICTs). Improvements in performance across trials showed that pheasants learned the affordances of each specific task. Critically, prior experience of transparent tasks, either Barrier or Cylinder, also improved subsequent inhibitory control performance on a novel task, suggesting that they also learned the general properties of transparent obstacles. Individual measures of persistence, assayed in a third task, were positively related to their frequency of incorrect attempts to solve the transparent inhibitory control tasks. Neophobia, Sex and Body Condition had no influence on individual performance. Contrary to previous studies of primates, pheasants with poor performance on PICTs had a wider dietary breadth assayed using a free-choice task. Our results demonstrate that in systems or taxa where prior experience and differences in development cannot be accounted for, individual differences in performance on commonly used detour-dependent PICTS may reveal more about an individual's prior experience of transparent objects, or their motivation to acquire food, than providing a reliable measure of their inhibitory control.

Cognitive abilities probably evolve through natural selection if they provide individuals with fitness benefits. A growing number of studies demonstrate a positive relationship between performance in psychometric tasks and (proxy) measures of fitness. We assayed the performance of 154 common pheasant ( Phasianus colchicus ) chicks on tests of acquisition and reversal learning, using a different set of chicks and different set of cue types (spatial location and colour) in each of two years and then followed their fates after release into the wild. Across all birds, individuals that were slow to reverse previously learned associations were more likely to survive to four months old. For heavy birds, individuals that rapidly acquired an association had improved survival to four months, whereas for light birds, slow acquirers were more likely to be alive. Slow reversers also exhibited less exploratory behaviour in assays when five weeks old. Fast acquirers visited more artificial feeders after release. In contrast to most other studies, we showed that apparently ‘poor’ cognitive performance (slow reversal speed suggesting low behavioural flexibility) correlates with fitness benefits in at least some circumstances. This correlation suggests a novel mechanism by which continued exaggeration of cognitive abilities may be constrained. This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.

Fine‐scale tracking of animal movement is important to understand the proximate mechanisms of animal behaviour. The reverse‐GPS system—ATLAS—uses inexpensive (~€25), lightweight (<1 g) and low‐power (~0.4 mJ/transmission) tags. Six systems are now operational worldwide and have successfully tracked over 50 species in various landscape types. The growing use of ATLAS to track animal movement motivates further refinement of best‐practice application and an assessment of its accuracy. Here, we test the accuracy and precision of the largest ATLAS system, located in the Dutch Wadden Sea, using concurrent GPS measurements as a reference. This large‐scale ATLAS system consists of 26 receivers and covers 1,326 km² of intertidal region, with almost no physical obstacles for radio signals, providing a useful baseline for other systems. We compared ATLAS and GPS location estimates for a route (mobile test) and 16 fixed locations (stationary test) on the Griend mudflat. Precision was estimated using standard deviation during the stationary tests. We also give examples of tracked red knots Calidris canutus islandica to illustrate the use of the system in tracking small shorebirds (~120 g). ATLAS‐derived location estimates differed from GPS by a median of 4.2 m (stationary test) and 5.7 m (mobile test). Signals that were collected by more receiver stations were more accurate, although even three‐receiver localisations were comparable with GPS localisations (~10 m difference). Receivers that detected 90% of the 1 Hz transmissions from our test tag were within 5 km of their furthest detection but height of both receiver and tag seemed to influence detection distance. The test tag (1 Hz) had a fix rate of >90% at 15 of 16 stationary sites. Tags on birds (1/6 Hz) on the Griend mudflat had a mean fix rate of 51%, yielding an average sampling rate of 0.085 Hz. Fix rates were higher in more central parts of the receiver array. ATLAS provides accurate, regional‐scale tracking with which hundreds of relatively small‐bodied species can be tracked simultaneously for long periods of time. Future ATLAS users should consider the height of receivers, their spatial arrangement, density and the movement modes of their study species (e.g. ground‐dwelling or flying).