Behavioral plasticity shapes population aging patterns in a long-lived avian scavenger.

Studying the mechanisms shaping age-related changes in behavior ("behavioral aging") is important for understanding population dynamics in our changing world. Yet, studies that capture within-individual behavioral changes in wild populations of long-lived animals are still scarce. Here, we used a 15-y GPS-tracking dataset of a social obligate scavenger, the griffon vulture (Gyps fulvus), to investigate age-related changes in movement and social behaviors, and disentangle the role of behavioral plasticity and selective disappearance in shaping such patterns. We tracked 142 individuals for up to 12 y and found a nonlinear increase in site fidelity with age: a sharp increase in site fidelity before sexual maturity (<5 y old), stabilization during adulthood (6 to 15 y), and a further increase at old age (>15 y). This pattern resulted from individuals changing behavior throughout their life (behavioral plasticity) and not from selective disappearance. Mature vultures increased the predictability of their movement routines and spent more nights at the most popular roosting sites compared to younger individuals. Thus, adults likely have a competitive advantage over younger conspecifics. These changes in site fidelity and movement routines were mirrored in changes to social behavior. Older individuals interacted less with their associates (decreasing average strength with age), particularly during the breeding season. Our results reveal a variety of behavioral aging patterns in long-lived species and underscore the importance of behavioral plasticity in shaping such patterns. Comprehensive longitudinal studies are imperative for understanding how plasticity and selection shape the persistence of wild animal populations facing human-induced environmental changes.

Resource manipulation reveals interactive phenotype-dependent foraging in free-ranging lizards.

Recent evidence suggests that individuals differ in foraging tactics and this variation is often linked to an individual's behavioural type (BT). Yet, while foraging typically comprises a series of search and handling steps, empirical investigations have rarely considered BT-dependent effects across multiple stages of the foraging process, particularly in natural settings. In our long-term sleepy lizard (Tiliqua rugosa) study system, individuals exhibit behavioural consistency in boldness (measured as an individual's willingness to approach a novel food item in the presence of a threat) and aggressiveness (measured as an individual's response to an 'attack' by a conspecific dummy). These BTs are only weakly correlated and have previously been shown to have interactive effects on lizard space use and movement, suggesting that they could also affect lizard foraging performance, particularly in their search behaviour for food. To investigate how lizards' BTs affect their foraging process in the wild, we supplemented food in 123 patches across a 120-ha study site with three food abundance treatments (high, low and no-food controls). Patches were replenished twice a week over the species' entire spring activity season and feeding behaviours were quantified with camera traps at these patches. We tracked lizards using GPS to determine their home range (HR) size and repeatedly assayed their aggressiveness and boldness in designated assays. We hypothesised that bolder lizards would be more efficient foragers while aggressive ones would be less attentive to the quality of foraging patches. We found an interactive BT effect on overall foraging performance. Individuals that were both bold and aggressive ate the highest number of food items from the foraging array. Further dissection of the foraging process showed that aggressive lizards in general ate the fewest food items in part because they visited foraging patches less regularly, and because they discriminated less between high and low-quality patches when revisiting them. Bolder lizards, in contrast, ate more tomatoes because they visited foraging patches more regularly, and ate a higher proportion of the available tomatoes at patches during visits. Our study demonstrates that BTs can interact to affect different search and handling components of the foraging process, leading to within-population variation in foraging success. Given that individual differences in foraging and movement will influence social and ecological interactions, our results highlight the potential role of BT's in shaping individual fitness strategies and population dynamics.

Beyond simple habituation: Anthropogenic habitats influence the escape behaviour of spur-winged lapwings in response to both human and non-human threats.

Habitat development may affect wildlife behaviour, favouring individuals or behaviours that cope better with perceived threats (predators). Bolder behaviours in human-dominated habitats (HDH; e.g. urban and rural settlements) may represent habituation specifically to humans, or a general reduction in predator-avoidance response. However, such carry-over effects across threat types (i.e. beyond humans) and phases of the escape sequence have not been well studied to date. Here we investigated escape behaviours of a locally common wader species, the spur-winged lapwing Vanellus spinosus. We assayed their flight initiation distance (FID) and subsequent escape behaviours in agricultural areas and in HDH. We found that lapwings in HDH were bolder, and that the difference was manifested in several phases of the predator-avoidance sequence (shorter FIDs, shorter distances fled, and a higher probability of escape by running vs. flying). When re-approached (by an observer) after landing, lapwings in HDH were also more repetitive in their FID than those in other habitats. To determine whether this apparent bolder behaviour in HDH areas is merely a consequence of habituation to humans or represents a broader behavioural change, we introduced an additional threat type-a remotely-operated taxidermic jackal ('Jack-Truck'). Finding bolder responses in the HDH to the human threat alone (and not to the Jack-Truck) could have supported the habituation hypothesis. In contrast, however, we found a bolder response in the HDH to both threat types, as well as a correlation between their FIDs across different sites. These bolder behaviours suggest that HDH impose a broader behavioural change on lapwings, rather than just simple habituation. Overall, our findings demonstrate how FID trials can reveal strong behavioural carry-over effects of HDH following human and non-human threats, including effects on the subsequent phases of escaping the predator. Further, FID assays may reveal consistent behavioural types when assessed under field conditions, and offer a direct way to differentiate among the various poorly understood and non-mutually exclusive mechanisms that lead to behavioural differences among organisms in HDH. The mechanistic perspective is essential for understanding how rapid urbanization impacts wildlife behaviour, populations, and the range of behaviours within them, even in species apparently resilient to such environmental changes.

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.

Personality, spatiotemporal ecological variation and resident/explorer movement syndromes in the sleepy lizard.

Individual variation in movement is profoundly important for fitness and offers key insights into the spatial and temporal dynamics of populations and communities. Nonetheless, individual variation in fine-scale movement behaviours is rarely examined even though animal tracking devices offer the long-term, high-resolution, repeatable data in natural conditions that are ideal for studying this variation. Furthermore, of the few studies that consider individual variation in movement, even fewer also consider the internal traits and environmental factors that drive movement behaviour which are necessary for contextualising individual differences in movement patterns. In this study, we GPS tracked a free-ranging population of sleepy lizards Tiliqua rugosa, each Austral spring over 5 years to examine consistent among-individual variation in movement patterns, as well as how these differences were mediated by key internal and ecological factors. We found that individuals consistently differed in a suite of weekly movement traits, and that these traits strongly covaried among-individuals, forming movement syndromes. Lizards fell on a primary movement continuum, from 'residents' that spent extended periods of time residing within smaller core areas of their home range, to 'explorers' that moved greater distances and explored vaster areas of the environment. Importantly, we also found that these consistent differences in lizard movement were related to two ecologically important animal personality traits (boldness and aggression), their sex, key features of the environment (including food availability, and a key water resource), habitat type and seasonal variation (cool/moist vs. hot/drier) in environmental conditions. Broadly, these movement specialisations likely reflect variation in life-history tactics including foraging and mating tactics that ultimately underlie key differences in space use. Such information can be used to connect phenotypic population structure to key ecological and evolutionary processes, for example social networks and disease-transmission pathways, further highlighting the value of examining individual variation in movement behaviour.

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.

Predicting Habitat Choice after Rapid Environmental Change.

Decisions made while searching for settlement sites (e.g., nesting, oviposition) often have major fitness implications. Despite numerous case studies, we lack theory to explain why some species are thriving while others are making poor habitat choices after environmental change. We develop a model to predict (1) which kinds of environmental change have larger, negative effects on fitness, (2) how evolutionary history affects susceptibility to environmental change, and (3) how much lost fitness can be recovered via readjustment after environmental change. We model the common scenario where animals search an otherwise inhospitable matrix, encountering habitats of varying quality and settling when finding a habitat better than a threshold quality level. We consider decisions and fitness before environmental change, immediately following change (assuming that animals continue to use their previously adaptive decision rules), and after optimal readjustment (e.g., via learning or evolution). We find that decreases in survival per time step searching and declines in habitat quality or availability generally have stronger negative effects than reduced season duration. Animals that were adapted to good conditions remained choosy after conditions declined and thus suffered more from environmental change than those adapted to poor conditions. Readjustment recovered much of the fitness lost through a reduction in average habitat quality but recovered much less following reductions in habitat availability or survival while searching. Our model offers novel predictions for empiricists to test as well as suggestions for prioritizing alternative mitigation steps.

Going through the motions: incorporating movement analyses into disease research.

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

What's your move? Movement as a link between personality and spatial dynamics in animal populations.

Recent studies have established the ecological and evolutionary importance of animal personalities. Individual differences in movement and space-use, fundamental to many personality traits (e.g. activity, boldness and exploratory behaviour) have been documented across many species and contexts, for instance personality-dependent dispersal syndromes. Yet, insights from the concurrently developing movement ecology paradigm are rarely considered and recent evidence for other personality-dependent movements and space-use lack a general unifying framework. We propose a conceptual framework for personality-dependent spatial ecology. We link expectations derived from the movement ecology paradigm with behavioural reaction-norms to offer specific predictions on the interactions between environmental factors, such as resource distribution or landscape structure, and intrinsic behavioural variation. We consider how environmental heterogeneity and individual consistency in movements that carry-over across spatial scales can lead to personality-dependent: (1) foraging search performance; (2) habitat preference; (3) home range utilization patterns; (4) social network structure and (5) emergence of assortative population structure with spatial clusters of personalities. We support our conceptual model with spatially explicit simulations of behavioural variation in space-use, demonstrating the emergence of complex population-level patterns from differences in simple individual-level behaviours. Consideration of consistent individual variation in space-use will facilitate mechanistic understanding of processes that drive social, spatial, ecological and evolutionary dynamics in heterogeneous environments.

Social foraging and individual consistency in following behaviour: testing the information centre hypothesis in free-ranging vultures.

Uncertainties regarding food location and quality are among the greatest challenges faced by foragers and communal roosting may facilitate success through social foraging. The information centre hypothesis (ICH) suggests that uninformed individuals at shared roosts benefit from following informed individuals to previously visited resources. We tested several key prerequisites of the ICH in a social obligate scavenger, the Eurasian griffon vulture (Gyps fulvus), by tracking movements and behaviour of sympatric individuals over extended periods and across relatively large spatial scales, thereby precluding alternative explanations such as local enhancement. In agreement with the ICH, we found that 'informed' individuals returning to previously visited carcasses were followed by 'uninformed' vultures that consequently got access to these resources. When a dyad (two individuals that depart from the same roost within 2 min of each other) included an informed individual, they spent a higher proportion of the flight time close to each other at a shorter distance between them than otherwise. Although all individuals occasionally profited from following others, they differed in their tendencies to be informed or uninformed. This study provides evidence for 'following behaviour' in natural conditions and demonstrates differential roles and information states among foragers within a population. Moreover, demonstrating the possible reliance of vultures on following behaviour emphasizes that individuals in declining populations may suffer from reduced foraging efficiency.

When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries.

Understanding space use remains a major challenge for animal ecology, with implications for species interactions, disease spread, and conservation. Behavioural type (BT) may shape the space use of individuals within animal populations. Bolder or more aggressive individuals tend to be more exploratory and disperse further. Yet, to date we have limited knowledge on how space use other than dispersal depends on BT. To address this question we studied BT-dependent space-use patterns of sleepy lizards (Tiliqua rugosa) in southern Australia. We combined high-resolution global positioning system (GPS) tracking of 72 free-ranging lizards with repeated behavioural assays, and with a survey of the spatial distributions of their food and refuge resources. Bayesian generalized linear mixed models (GLMM) showed that lizards responded to the spatial distribution of resources at the neighbourhood scale and to the intensity of space use by other conspecifics (showing apparent conspecific avoidance). BT (especially aggressiveness) affected space use by lizards and their response to ecological and social factors, in a seasonally dependent manner. Many of these effects and interactions were stronger later in the season when food became scarce and environmental conditions got tougher. For example, refuge and food availability became more important later in the season and unaggressive lizards were more responsive to these predictors. These findings highlight a commonly overlooked source of heterogeneity in animal space use and improve our mechanistic understanding of processes leading to behaviourally driven disease dynamics and social structure.

The statistical building blocks of animal movement simulations.

Animal movement plays a key role in many ecological processes and has a direct influence on an individual's fitness at several scales of analysis (i.e., next-step, subdiel, day-by-day, seasonal). This highlights the need to dissect movement behavior at different spatio-temporal scales and develop hierarchical movement tools for generating realistic tracks to supplement existing single-temporal-scale simulators. In reality, animal movement paths are a concatenation of fundamental movement elements (FuMEs: e.g., a step or wing flap), but these are not generally extractable from a relocation time-series track (e.g., sequential GPS fixes) from which step-length (SL, aka velocity) and turning-angle (TA) time series can be extracted. For short, fixed-length segments of track, we generate their SL and TA statistics (e.g., means, standard deviations, correlations) to obtain segment-specific vectors that can be cluster into different types. We use the centroids of these clusters to obtain a set of statistical movement elements (StaMEs; e.g.,directed fast movement versus random slow movement elements) that we use as a basis for analyzing and simulating movement tracks. Our novel concept is that sequences of StaMEs provide a basis for constructing and fitting step-selection kernels at the scale of fixed-length canonical activity modes: short fixed-length sequences of interpretable activity such as dithering, ambling, directed walking, or running. Beyond this, variable length pure or characteristic mixtures of CAMs can be interpreted as behavioral activity modes (BAMs), such as gathering resources (a sequence of dithering and walking StaMEs) or beelining (a sequence of fast directed-walk StaMEs interspersed with vigilance and navigation stops). Here we formulate a multi-modal, step-selection kernel simulation framework, and construct a 2-mode movement simulator (Numerus ANIMOVER_1), using Numerus RAMP technology. These RAMPs run as stand alone applications: they require no coding but only the input of selected parameter values. They can also be used in R programming environments as virtual R packages. We illustrate our methods for extracting StaMEs from both ANIMOVER_1 simulated data and empirical data from two barn owls (Tyto alba) in the Harod Valley, Israel. Overall, our new bottom-up approach to path segmentation allows us to both dissect real movement tracks and generate realistic synthetic ones, thereby providing a general tool for testing hypothesis in movement ecology and simulating animal movement in diverse contexts such as evaluating an individual's response to landscape changes, release of an individual into a novel environment, or identifying when individuals are sick or unusually stressed.

A wrap-around movement path randomization method to distinguish social and spatial drivers of animal interactions.

Studying the spatial-social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses about the factors determining animal interactions often involves comparing observed interactions with reference or 'null' models. One approach to accounting for spatial drivers of social interactions in reference models is randomizing animal movement paths to decouple spatial and social phenotypes while maintaining environmental effects on movements. Here, we update a reference model that detects social attraction above the effect of spatial constraints. We explore the use of our 'wrap-around' method and compare its performance to the previous approach using agent-based simulations. The wrap-around method provides reference models that are more similar to the original tracking data, while still distinguishing between social and spatial drivers. Furthermore, the wrap-around approach results in fewer false-positives than its predecessor, especially when animals do not return to one place each night but change movement foci, either locally or directionally. Finally, we show that interactions among GPS-tracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from spatial constraints on their movements. We conclude by highlighting the biological situations in which the updated method might be most suitable for testing hypotheses about the underlying causes of social interactions. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Factors influencing foraging search efficiency: why do scarce lappet-faced vultures outperform ubiquitous white-backed vultures?

The search phase is a critical component of foraging behavior, affecting interspecific competition and community dynamics. Nevertheless, factors determining interspecific variation in search efficiency are still poorly understood. We studied differences in search efficiency between the lappet-faced vulture (Torgos tracheliotus; LFV) and the white-backed vulture (Gyps africanus; WBV) foraging on spatiotemporally unpredictable carcasses in Etosha National Park, Namibia. We used experimental food supply and high-resolution GPS tracking of free-ranging vultures to quantify search efficiency and elucidate the factors underlying the observed interspecific differences using a biased correlated random walk simulation model bootstrapped with the GPS tracking data. We found that LFV's search efficiency was higher than WBV's in both first-to-find, first-to-land, and per-individual-finding rate measures. Modifying species-specific traits in the simulation model allows us to assess the relative role of each factor in LFV's higher efficiency. Interspecific differences in morphology (through the effect on perceptual range and motion ability) and searchers' spatial dispersion (due to different roost arrangements) are in correspondence with the empirically observed advantage of LFV over WBV searchers, whereas differences in other aspects of the movement patterns appear to play a minor role. Our results provide mechanistic explanations for interspecific variation in search efficiency for species using similar resources and foraging modes.

Social situations differ in their contribution to population-level social structure in griffon vultures.

Social relationships among animals emerge from interactions in multiple ecological and social situations. However, we seldom ask how each situation contributes to the global structure of a population, and whether different situations contribute different information about social relationships and the position of individuals within the social fabric. Griffon vultures (Gyps fulvus) interact socially in multiple situations, including communal roosting, joint flights, and co-feeding. These social interactions can influence population-level outcomes, such as disease transmission and information sharing that determine survival and response to changes. We examined the unique contribution of each social and ecological situation to the social structure of the population and individuals' positions within the overall social network using high-resolution GPS tracking. We found that the number of individuals each vulture interacted with (degree) was best predicted by diurnal interactions-both during flights and on the ground (such as when feeding). However, the strength of social bonds, that is, the number of interactions an individual had (strength), was best predicted by interactions on the ground-both during the day (e.g., while feeding) and at night (e.g., while roosting) but not by interactions while flying. Thus, social situations differ in their impact on the relationships that individuals form. By incorporating the ecological situations in which social interactions occur we gain a more complete view of how social relationships are formed and which situations are important for different types of interactions.

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.

A telemetry study shows that an endangered nocturnal avian species roosts in extremely dry habitats to avoid predation.

Describing animal space use is essential for understanding their ecological needs and for planning effective conservation schemes. Notably, certain biomes and life histories are understudied due to methodological challenges in tracking animals in their natural habitats. Specifically, both arid environments and nocturnal species are not sufficiently researched compared to diurnal species and to other biomes. This knowledge gap hinders our ability to properly prioritize habitats for species protection in areas undergoing human-related development. Here, we investigate the movement ecology of the Egyptian Nightjar (Caprimulgus aegyptius) in the arid Dead-sea region of Israel, the Palestinian Authority (the West Bank) and Jordan. This nocturnal insectivore is a cryptic desert-dweller and was considered locally extinct until it was rediscovered in 2016. For this work we tracked twelve individuals using GPS tags to determine how this resource-poor environment affects their home range, (predicting large areas), habitat use, and day-roost ecology. We found that the tracked Egyptian Nightjars had a much larger home range area than other Nightjar species, commuting nightly between foraging grounds and day-roosts. We found, as expected, intensive foraging activity at agricultural fields, where artificial irrigation likely supports higher resource (insect) density. Additionally, we found that individuals showed very high roosting site fidelity, often returning to the same specific site, located in extremely dry and exposed habitats, presumably for predator avoidance. This finding highlights the ecological value of these barren habitats that are often considered "lifeless" and therefore of lower priority for conservation. Consequently, our research demonstrates the importance of describing the space-use of nocturnal animals in arid habitats for conservation efforts.

Behavioural ecology at the spatial-social interface.

Spatial and social behaviour are fundamental aspects of an animal's biology, and their social and spatial environments are indelibly linked through mutual causes and shared consequences. We define the 'spatial-social interface' as intersection of social and spatial aspects of individuals' phenotypes and environments. Behavioural variation at the spatial-social interface has implications for ecological and evolutionary processes including pathogen transmission, population dynamics, and the evolution of social systems. We link spatial and social processes through a foundation of shared theory, vocabulary, and methods. We provide examples and future directions for the integration of spatial and social behaviour and environments. We introduce key concepts and approaches that either implicitly or explicitly integrate social and spatial processes, for example, graph theory, density-dependent habitat selection, and niche specialization. Finally, we discuss how movement ecology helps link the spatial-social interface. Our review integrates social and spatial behavioural ecology and identifies testable hypotheses at the spatial-social interface.