Phase Transition in a Non-Markovian Animal Exploration Model with Preferential Returns.

We study a non-Markovian and nonstationary model of animal mobility incorporating both exploration and memory in the form of preferential returns. Exact results for the probability of visiting a given number of sites are derived and a practical WKB approximation to treat the nonstationary problem is developed. A mean-field version of this model, first suggested by Song et al., [Modelling the scaling properties of human mobility, Nat. Phys. 6, 818 (2010)NPAHAX1745-247310.1038/nphys1760] was shown to well describe human movement data. We show that our generalized model adequately describes empirical movement data of Egyptian fruit bats (Rousettus aegyptiacus) when accounting for interindividual variation in the population. We also study the probability of visiting any site a given number of times and derive a mean-field equation. Our analysis yields a remarkable phase transition occurring at preferential returns which scale linearly with past visits. Following empirical evidence, we suggest that this phase transition reflects a trade-off between extensive and intensive foraging modes.

Big-data approaches lead to an increased understanding of the ecology of animal movement.

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.

A guide to pre-processing high-throughput animal tracking data.

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.

Cognitive map-based navigation in wild bats revealed by a new high-throughput tracking system.

Seven decades of research on the "cognitive map," the allocentric representation of space, have yielded key neurobiological insights, yet field evidence from free-ranging wild animals is still lacking. Using a system capable of tracking dozens of animals simultaneously at high accuracy and resolution, we assembled a large dataset of 172 foraging Egyptian fruit bats comprising >18 million localizations collected over 3449 bat-nights across 4 years. Detailed track analysis, combined with translocation experiments and exhaustive mapping of fruit trees, revealed that wild bats seldom exhibit random search but instead repeatedly forage in goal-directed, long, and straight flights that include frequent shortcuts. Alternative, non-map-based strategies were ruled out by simulations, time-lag embedding, and other trajectory analyses. Our results are consistent with expectations from cognitive map-like navigation and support previous neurobiological evidence from captive bats.