Environmental factors influencing red knot (Calidris canutus islandica) departure times of relocation flights within the non-breeding period.

Deciding when to depart on long-distance, sometimes global, movements can be especially important for flying species. Adverse weather conditions can affect energetic flight costs and navigational ability. While departure timings and conditions have been well-studied for migratory flights to and from the breeding range, few studies have focussed on flights within the non-breeding season. Yet in some cases, overwintering ranges can be large enough that ecological barriers, and a lack of resting sites en route, may resist movement, especially in unfavorable environmental conditions. Understanding the conditions that will enable or prohibit flights within an overwintering range is particularly relevant in light of climate change, whereby increases in extreme weather events may reduce the connectivity of sites. We tracked 495 (n = 251 in 2019; n = 244 in 2020) overwintering red knots (Calidris canutus islandica) in the Dutch Wadden Sea and investigated how many departed towards the UK (on westward relocation flights), which requires flying over the North Sea. For those that departed, we used a resource selection model to determine the effect of environmental conditions on the timing of relocation flights. Specifically, we investigated the effects of wind, rain, atmospheric pressure, cloud cover, and migratory timing relative to sunset and tidal cycle, which have all been shown to be crucial to migratory departure conditions. Approximately 37% (2019) and 36% (2020) of tagged red knots departed on westward relocation flights, indicating differences between individuals' space use within the overwintering range. Red knots selected for departures between 1 and 2.5 h after sunset, approximately 4 h before high tide, with tailwinds and little cloud cover. However, rainfall and changes in atmospheric pressure appear unimportant. Our study reveals environmental conditions that are important for relocation flights across an ecological barrier, indicating potential consequences of climate change on connectivity.

The Secret Lives of Miniature Batteries.

This article describes the design, implementation, and use of a new system to investigate the behavior of small batteries that power sensor and wireless systems that consume relatively high power during infrequent short activity periods. The system enables simple, low-cost, long-term (days to weeks) monitoring of batteries under such loads. Data collected by this system revealed a major cause of failures in wildlife tracking tags, an effect called concentration polarization, which causes a transient increase in the internal resistance of the battery. The article describes the goals and the design of the system, failures that it revealed, mechanisms to mitigate the limitations of miniature batteries, as well as a methodology to optimize and validate the design of tags powered by miniature batteries.

Robust Time-of-Arrival Location Estimation Algorithms for Wildlife Tracking.

Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife. Outlier measurements can severely corrupt estimated locations. This article describes a new suite of location estimation algorithms for such systems. The new algorithms detect and discard outlier time-of-arrival observations, which can be caused by non-line-of-sight propagation, radio interference, clock glitches, or an overestimation of the signal-to-noise ratio. The new algorithms also detect cases in which two locations are equally consistent with measurements and can usually select the correct one. The new algorithms can also infer approximate altitude information from a digital elevation map to improve location estimates close to one of the sensors. Finally, the new algorithms approximate the covariance matrix of location estimates in a simpler and more reliable way than the baseline algorithm. Extensive testing on real-world data involving mobile transmitters attached to wild animals demonstrates the efficacy of the new algorithms. Performance testing also shows that the new algorithms are fast and that they can easily cope with high-throughput real-time loads.

ViewShedR: a new open-source tool for cumulative, subtractive and elevated line-of-sight analysis.

Many environmental and ecological studies require line of sight (LOS) and/or viewshed analyses. While tools for performing these analyses from digital elevation models (DEMs) are widespread, they are either too restrictive, inaccessible or pricey and difficult to use. This methodological gap is potentially imperative for scholars using solutions like telemetry tracking systems or spatial ecology landscape mapping. Here we present ViewShedR-a free, open-source and intuitive graphical user-interface application for performing LOS calculations, including cumulative, subtractive (areas covered by towers A + B or by A but not by B, respectively), and elevated-target analyses. ViewShedR is implemented in the widely used R environment, thus facilitating usage and further modification by end-users. We provide two working examples for ViewShedR in the context of permanent animal-tracking systems requiring simultaneous tag-detection by multiple towers (receivers): first, the ATLAS system for terrestrial animals in the Harod Valley, Israel; and second, an acoustic telemetry array for marine animals in the Dry Tortugas, Florida. ViewShedR allowed effective tower deployment and finding partially detected tagged animals in the ATLAS system. Similarly, it allowed us to identify reception shadows cast by islands in the marine array. We hope ViewShedR will facilitate deployment of tower arrays for tracking, communication networks and other ecological applications.

Categorizing the geometry of animal diel movement patterns with examples from high-resolution barn owl tracking.

BACKGROUND: Movement is central to understanding the ecology of animals. The most robustly definable segments of an individual's lifetime track are its diel activity routines (DARs). This robustness is due to fixed start and end points set by a 24-h clock that depends on the individual's quotidian schedule. An analysis of day-to-day variation in the DARs of individuals, their comparisons among individuals, and the questions that can be asked, particularly in the context of lunar and annual cycles, depends on the relocation frequency and spatial accuracy of movement data. Here we present methods for categorizing the geometry of DARs for high frequency (seconds to minutes) movement data. METHODS: Our method involves an initial categorization of DARs using data pooled across all individuals. We approached this categorization using a Ward clustering algorithm that employs four scalar "whole-path metrics" of trajectory geometry: 1. net displacement (distance between start and end points), 2. maximum displacement from start point, 3. maximum diameter, and 4. maximum width. We illustrate the general approach using reverse-GPS data obtained from 44 barn owls, Tyto alba, in north-eastern Israel. We conducted a principle components analysis (PCA) to obtain a factor, PC1, that essentially captures the scale of movement. We then used a generalized linear mixed model with PC1 as the dependent variable to assess the effects of age and sex on movement. RESULTS: We clustered 6230 individual DARs into 7 categories representing different shapes and scale of the owls nightly routines. Five categories based on size and elongation were classified as closed (i.e. returning to the same roost), one as partially open (returning to a nearby roost) and one as fully open (leaving for another region). Our PCA revealed that the DAR scale factor, PC1, accounted for 86.5% of the existing variation. It also showed that PC2 captures the openness of the DAR and accounted for another 8.4% of the variation. We also constructed spatio-temporal distributions of DAR types for individuals and groups of individuals aggregated by age, sex, and seasonal quadrimester, as well as identify some idiosyncratic behavior of individuals within family groups in relation to location. Finally, we showed in two ways that DARs were significantly larger in young than adults and in males than females. CONCLUSION: Our study offers a new method for using high-frequency movement data to classify animal diel movement routines. Insights into the types and distributions of the geometric shape and size of DARs in populations may well prove to be more invaluable for predicting the space-use response of individuals and populations to climate and land-use changes than other currently used movement track methods of analysis.

Movement predictability of individual barn owls facilitates estimation of home range size and survival.

BACKGROUND: There is growing attention to individuality in movement, its causes and consequences. Similarly to other well-established personality traits (e.g., boldness or sociability), conspecifics also differ repeatedly in their spatial behaviors, forming behavioral types ("spatial-BTs"). These spatial-BTs are typically described as the difference in the mean-level among individuals, and the intra-individual variation (IIV, i.e., predictability) is only rarely considered. Furthermore, the factors determining predictability or its ecological consequences for broader space-use patterns are largely unknown, in part because predictability was mostly tested in captivity (e.g., with repeated boldness assays). Here we test if (i) individuals differ in their movement and specifically in their predictability. We then investigate (ii) the consequences of this variation for home-range size and survival estimates, and (iii) the factors that affect individual predictability. METHODS: We tracked 92 barn owls (Tyto alba) with an ATLAS system and monitored their survival. From these high-resolution (every few seconds) and extensive trajectories (115.2 ± 112.1 nights; X̅ ± SD) we calculated movement and space-use indices (e.g., max-displacement and home-range size, respectively). We then used double-hierarchical and generalized linear mix-models to assess spatial-BTs, individual predictability in nightly max-displacement, and its consistency across time. Finally, we explored if predictability levels were associated with home-range size and survival, as well as the seasonal, geographical, and demographic factors affecting it (e.g., age, sex, and owls' density). RESULTS: Our dataset (with 74 individuals after filtering) revealed clear patterns of individualism in owls' movement. Individuals differed consistently both in their mean movement (e.g., max-displacement) and their IIV around it (i.e., predictability). More predictable individuals had smaller home-ranges and lower survival rates, on top and beyond the expected effects of their spatial-BT (max-displacement), sex, age and ecological environments. Juveniles were less predictable than adults, but the sexes did not differ in their predictability. CONCLUSION: These results demonstrate that individual predictability may act as an overlooked axis of spatial-BT with potential implications for relevant ecological processes at the population level and individual fitness. Considering how individuals differ in their IIV of movement beyond the mean-effect can facilitate understanding the intraspecific diversity, predicting their responses to changing ecological conditions and their population management.

Spatial memory predicts home range size and predation risk in pheasants.

Most animals confine their activities to a discrete home range, long assumed to reflect the fitness benefits of obtaining spatial knowledge about the landscape. However, few empirical studies have linked spatial memory to home range development or determined how selection operates on spatial memory via the latter's role in mediating space use. We assayed the cognitive ability of juvenile pheasants (Phasianus colchicus) reared under identical conditions before releasing them into the wild. Then, we used high-throughput tracking to record their movements as they developed their home ranges, and determined the location, timing and cause of mortality events. Individuals with greater spatial reference memory developed larger home ranges. Mortality risk from predators was highest at the periphery of an individual's home range in areas where they had less experience and opportunity to obtain spatial information. Predation risk was lower in individuals with greater spatial memory and larger core home ranges, suggesting selection may operate on spatial memory by increasing the ability to learn about predation risk across the landscape. Our results reveal that spatial memory, determined from abstract cognitive assays, shapes home range development and variation, and suggests predation risk selects for spatial memory via experience-dependent spatial variation in mortality.

Insectivorous bats form mobile sensory networks to optimize prey localization: The case of the common noctule bat.

Animals that depend on ephemeral, patchily distributed prey often use public information to locate resource patches. The use of public information can lead to the aggregation of foragers at prey patches, a mechanism known as local enhancement. However, when ephemeral resources are distributed over large areas, foragers may also need to increase search efficiency, and thus apply social strategies when sampling the landscape. While sensory networks of visually oriented animals have already been confirmed, we lack an understanding of how acoustic eavesdropping adds to the formation of sensory networks. Here we radio-tracked a total of 81 aerial-hawking bats at very high spatiotemporal resolution during five sessions over 3 y, recording up to 19 individuals simultaneously. Analyses of interactive flight behavior provide conclusive evidence that bats form temporary mobile sensory networks by adjusting their movements to neighboring conspecifics while probing the airspace for prey. Complementary agent-based simulations confirmed that the observed movement patterns can lead to the formation of mobile sensory networks, and that bats located prey faster when networking than when relying only on local enhancement or searching solitarily. However, the benefit of networking diminished with decreasing group size. The combination of empirical analyses and simulations elucidates how animal groups use acoustic information to efficiently locate unpredictable and ephemeral food patches. Our results highlight that declining local populations of social foragers may thus suffer from Allee effects that increase the risk of collapses under global change scenarios, like insect decline and habitat degradation.

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.

Spatial cognitive ability is associated with transitory movement speed but not straightness during the early stages of exploration.

Memories about the spatial environment, such as the locations of foraging patches, are expected to affect how individuals move around the landscape. However, individuals differ in the ability to remember spatial locations (spatial cognitive ability) and evidence is growing that these inter-individual differences influence a range of fitness proxies. Yet empirical evaluations directly linking inter-individual variation in spatial cognitive ability and the development and structure of movement paths are lacking. We assessed the performance of young pheasants (Phasianus colchicus) on a spatial cognition task before releasing them into a novel, rural landscape and tracking their movements. We quantified changes in the straightness and speed of their transitory paths over one month. Birds with better performances on the task initially made slower transitory paths than poor performers but by the end of the month, there was no difference in speed. In general, birds increased the straightness of their path over time, indicating improved efficiency independent of speed, but this was not related to performance on the cognitive task. We suggest that initial slow movements may facilitate more detailed information gathering by better performers and indicates a potential link between an individual's spatial cognitive ability and their movement behaviour.

Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?

Cognitive biases for encoding spatial information (orientation strategies) in relation to self (egocentric) or landmarks (allocentric) differ between species or populations according to the habitats they occupy. Whether biases in orientation strategy determine early habitat selection or if individuals adapt their biases following experience is unknown. We determined orientation strategies of pheasants, Phasianus colchicus, using a dual-strategy maze with an allocentric probe trial, before releasing them (n = 20) into a novel landscape, where we monitored their movement and habitat selection. In general, pheasants selected for woodland over non-woodland habitat, but allocentric-biased individuals exhibited weaker avoidance of non-woodland habitat, where we expected allocentric navigation to be more effective. Sex did not influence selection but was associated with speed and directional persistence in non-woodland habitat. Our results suggest that an individual's habitat selection is associated with inherent cognitive bias in early life, but it is not yet clear what advantages this may offer.

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.

Movement ecology and sex are linked to barn owl microbial community composition.

The behavioural ecology of host species is likely to affect their microbial communities, because host sex, diet, physiology, and movement behaviour could all potentially influence their microbiota. We studied a wild population of barn owls (Tyto alba) and collected data on their microbiota, movement, diet, size, coloration, and reproduction. The composition of bacterial species differed by the sex of the host and female owls had more diverse bacterial communities than their male counterparts. The abundance of two families of bacteria, Actinomycetaceae and Lactobacillaceae, also varied between the sexes, potentially as a result of sex differences in hormones and immunological function, as has previously been found with Lactobacillaceae in the microbiota of mice. Male and female owls did not differ in the prey they brought to the nest, which suggests that dietary differences are unlikely to underlie the differences in their microbiota. The movement behaviour of the owls was associated with the host microbiota in both males and females because owls that moved further from their nest each day had more diverse bacterial communities than owls that stayed closer to their nests. This novel result suggests that the movement ecology of hosts can impact their microbiota, potentially on the basis of their differential encounters with new bacterial species as the hosts move and forage across the landscape. Overall, we found that many aspects of the microbial community are correlated with the behavioural ecology of the host and that data on the microbiota can aid in generating new hypotheses about host behaviour.

Geometry-aware bases for shape approximation.

We introduce a new class of shape approximation techniques for irregular triangular meshes. Our method approximates the geometry of the mesh using a linear combination of a small number of basis vectors. The basis vectors are functions of the mesh connectivity and of the mesh indices of a number of anchor vertices. There is a fundamental difference between the bases generated by our method and those generated by geometry-oblivious methods, such as Laplacian-based spectral methods. In the latter methods, the basis vectors are functions of the connectivity alone. The basis vectors of our method, in contrast, are geometry-aware since they depend on both the connectivity and on a binary tagging of vertices that are "geometrically important" in the given mesh (e.g., extrema). We show that, by defining the basis vectors to be the solutions of certain least-squares problems, the reconstruction problem reduces to solving a single sparse linear least-squares problem. We also show that this problem can be solved quickly using a state-of-the-art sparse-matrix factorization algorithm. We show how to select the anchor vertices to define a compact effective basis from which an approximated shape can be reconstructed. Furthermore, we develop an incremental update of the factorization of the least-squares system. This allows a progressive scheme where an initial approximation is incrementally refined by a stream of anchor points. We show that the incremental update and solving the factored system are fast enough to allow an online refinement of the mesh geometry.