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53 results for “Animal movements”
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
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Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
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Data from - Ecological insights from three decades of animal movement tracking across a changing Arctic
<p>We provide here the data used in analysis of 3 test cases, presented in the manuscript "Ecological insights from three decades of animal movement tracking across a changing Arctic". We utilized the new Arctic Animal Movement Archive (AAMA), a growing collection of 201 standardized terrestrial and marine animal tracking studies from 1991–present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With three AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature. </p>
A novel method for detecting extra-home range movements (EHRMs) by animals and recommendations for future EHRM studies
<p>Infrequent, long-distance animal movements outside of typical home range areas provide useful insights into resource acquisition, gene flow, and disease transmission within the fields of conservation and wildlife management, yet understanding of these movements is still limited across taxa. To detect these extra-home range movements (EHRMs) in spatial relocation datasets, most previous studies compare relocation points against fixed spatial and temporal bounds, typified by seasonal home ranges (referred to here as the "Fixed-Period" method). However, utilizing home ranges modelled over fixed time periods to detect EHRMs within those periods likely results in many EHRMs going undocumented, particularly when an animal's space use changes within that period of time. To address this, we propose a novel, "Moving-Window" method of detecting EHRMs through an iterative process, comparing each day's relocation data to the preceding period of space use only. We compared the number and characteristics of EHRM detections by both the Moving-Window and Fixed-Period methods using GPS relocations from 33 white-tailed deer (Odocoileus virginianus) in Alabama, USA. The Moving-Window method detected 1.5 times as many EHRMs as the Fixed-Period method and identified 120 unique movements that were undetected by the Fixed-Period method, including some movements that extended nearly 5 km outside of home range boundaries. Additionally, we utilized our EHRM dataset to highlight and evaluate potential sources of variation in EHRM summary statistics stemming from differences in definition criteria among previous EHRM literature. We found that this spectrum of criteria identified between 15.6% and 100.0% of the EHRMs within our dataset. We conclude that variability in terminology and definition criteria previously used for EHRM detection hinders useful comparisons between studies. The Moving-Window approach to EHRM detection introduced here, along with proposed methodology guidelines for future EHRM studies, should allow researchers to better investigate and understand these behaviors across a variety of taxa.</p>
Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
1. Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. 2. Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time scales, e.g. step lengths obtained from GPS tags every hour, dive depths obtained from time-depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi-stream and multi-scale data is lacking. 3. We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple, connected behavioural processes, each of which operates at the time scale at which the corresponding variables were observed. 4. By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time scales, and in particular help to draw a much more comprehensive picture of an animal's movement patterns, e.g. with regard to long-term vs. short-term movement strategies. 5. The suggested approach is illustrated in two real-data applications, where we jointly model i) coarse-scale horizontal and fine-scale vertical Atlantic cod (Gadus morhua) movements throughout the English Channel, and ii) coarse-scale horizontal movements and corresponding fine-scale accelerations of a horn shark (Heterodontus francisci) tagged off the Californian coast.
Data from: Exact Bayesian inference for animal movement in continuous time
It is natural to regard most animal movement as a continuous-time process, generally observed at discrete times. Most existing statistical methods for movement data ignore this; the remainder mostly use discrete-time approximations, the statistical properties of which have not been widely studied, or are limited to special cases. We aim to facilitate wider use of continuous-time modelling for realistic problems. We develop novel methodology which allows exact Bayesian statistical analysis for a rich class of movement models with behavioural switching in continuous time, without any need for time discretization error. We represent the times of changes in behaviour as forming a thinned Poisson process, allowing exact simulation and Markov chain Monte Carlo inference. The methodology applies to data that are regular or irregular in time, with or without missing values. We apply these methods to GPS data from two animals, a fisher (Pekania [Martes] pennanti) and a wild boar (Sus scrofa), using models with both spatial and temporal heterogeneity. We are able to identify and describe differences in movement behaviour across habitats and over time. Our methods allow exact fitting of realistically complex movement models, incorporating environmental information. They also provide an essential point of reference for evaluating other existing and future approximate methods for continuous-time inference.
Data from: Functional responses in animal movement explain spatial heterogeneity in animal-habitat relationships
1. Understanding why heterogeneity exists in animal-habitat spatial relationships is critical for identifying the drivers of animal distributions. Functional responses in habitat selection – whereby animals adjust their habitat selection depending on habitat availability – are useful for describing animal-habitat spatial heterogeneity. However, they could be yielded by different movement tactics, involving contrasting inter-specific interactions. 2. Identifying functional responses in animal movement, rather than in emergent spatial patterns like habitat selection, could disentangle the effects of different movement behaviours on spatial heterogeneity in animal-habitat relationships. This would clarify how functional responses in habitat selection emerge and provide a general tool for understanding the mechanistic drivers of animal distributions. 3. We tested this approach using data from GPS-collared woodland caribou (Rangifer tarandus), a prey species under top-down control. We tested how caribou selected and moved with respect to a key resource (lichen-conifer stands) as a function of the availability of surrounding refuge land-cover (closed-conifer stands), using step selection functions. 4. Caribou selected resource patches more strongly in areas richer in refuge land-cover – a functional response in habitat selection. However, adjustments in multiple movement behaviours could have generated this pattern: stronger directed movement towards resources patches and/or longer residency within resource patches, in areas richer in refuges. Different contributions of these behaviours would produce contrasting forager spatial dynamics. 5. We identified functional responses in both movement behaviours: caribou were more likely to move towards resource patches in areas richer in refuge land-cover, and to remain in these patches during movement steps. This tactic enables caribou to spend longer foraging in safer areas where they could rapidly seek refuge in dense cover when predators are detected. 6. Our study shows that functional responses in movement can expose the context-dependent movement decisions that generate heterogeneity in animal-habitat spatial relationships. We used these functional responses to characterise anti-predator movement tactics employed by a large herbivore, but they could be applied in many different scenarios. The movement rules from functional responses in movement are well-suited to integration in spatial explicit individual-based models for forecasting animal distributions in landscapes undergoing environmental change.
Data from: Estimating interactions between individuals from concurrent animal movements
1. Animal movements arise from complex interactions of individuals with their environment, including both conspecific and heterospecific individuals. Animals may be attracted to each other for mating, social foraging, or information gain, or may keep at a distance from others to avoid aggressive encounters related to, e.g., interference competition, territoriality, or predation. With modern tracking technology, more data sets are emerging that allow to investigate fine-scale interactions between free-ranging individuals from movement data, however, few methods exist to disentangle fine-scale behavioural responses of interacting individuals when these are highly individual-specific. 2. In a framework of step-selection functions, we related movements decisions of individuals to dynamic occurrence distributions of other individuals obtained through kriging of their movement paths. Using simulated data, we tested the method's ability to identify various combinations of attraction, avoidance, and neutrality between individuals, including asymmetric (i.e. non-mutual) behaviours. Additionally, we analysed radio-telemetry data from concurrently tracked small rodents (bank vole, Myodes glareolus) to test whether our method could detect biologically plausible behaviours. 3. We found that our method was able to successfully detect and distinguish between fine-scale interactions (attraction, avoidance, neutrality), even when these were asymmetric between individuals. The method worked best when confounding factors were taken into account in the step-selection function. However, even when failing to do so (e.g. due to missing information), interactions could be reasonably identified. In bank voles, responses depended strongly on the sexes of the involved individuals and matched expectations. 4. Our approach can be combined with conventional uses of step-selection functions to tease apart the various drivers of movement, e.g. the influence of the physical and the social environment. In addition, the method is particularly useful in studying interactions when responses are highly individual-specific, i.e. vary between and towards different individuals, making our method suitable for both single-species and multi-species analyses (e.g. in the context of predation or competition).
Data from: From animal tracks to fine-scale movement modes: a straightforward approach for identifying multiple, spatial movement patterns
1. Thanks to developments in animal tracking technology, detailed data on the movement tracks of individual animals are now attainable for many species. However, straightforward methods to decompose individual tracks into high-resolution, spatial modes are lacking but are essential to understand what an animal is doing. 2. We developed an analytical approach that combines separately-validated methods into a straightforward tool for converting animal GPS tracks to short-range movement modes. Our three-step analytical process comprises: (1) decomposing data into separate movement segments using behavioural change point analysis; (2) defining candidate movement modes and translating them into non-linear or linear equations between net squared displacement (NSD) and time; and (3) fitting each candidate equation to NSD segments and determining the best-fitting modes using Concordance Criteria, Akaike's Information Criteria and other fine-scale segment characteristics. We illustrate our approach for three sub-adults, male wild boar Sus scrofa tracked at 15 min intervals over 4 months using GPS collars. We defined five candidate movement modes based on previously published studies of short-term movements: encamped, ranging, round trips (complete and partial), and wandering. 3. Our approach successfully classified over 80% of the tracks into these movement modes lasting between 5 and 54 hours and covering between 300 m to 20 km. Repeated analyses of GPS data resampled at different rates indicated that one positional fix every 3-4 h was sufficient for >70% classification success. Classified modes were consistent with published observations of wild boar movement, further validating our method. 4. The proposed approach advances the status quo by permitting classification into multiple movement modes (where these are adequately discernable from spatial fixes) facilitating analyses at high temporal and spatial resolutions, and is straightforward, largely objective, and without restrictive assumptions, necessary parameterizations or visual interpretation. Thus, it should capture the complexity and variability of tracked animal movement mode for a variety of taxa across a wide range of spatial and temporal scales.
Data from: Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival
1. Acoustic telemetry studies have frequently prioritized linear configurations of hydrophone receivers, such as perpendicular from shorelines or across rivers, to detect the presence of tagged aquatic animals. This approach introduces unknown bias when receivers are stationed for convenience at geographic bottlenecks (e.g., at the mouth of an embayment or between islands) as opposed to deployments following a statistical sampling design. 2. We evaluated two-dimensional acoustic receiver arrays (grids: receivers spread uniformly across space) as an alternative approach to provide estimates of survival, movement, and habitat use. Performance of variably-spaced receiver grids (5–25 km spacing) was evaluated by simulating (1) animal tracks as correlated random walks (speed: 0.1–0.9 m/s; turning angle standard deviation: 5–30 degrees); (2) variable tag transmission intervals along each track (nominal delay: 15–300 seconds); and (3) probability of detection of each transmission based on logistic detection range curves (midpoint: 200–1500 m). From simulations, we quantified i) time between successive detections on any receiver (detection time), ii) time between successive detections on different receivers (transit time), and iii) distance between successive detections on different receivers (transit distance). 3. In the most restrictive detection range scenario (200 m), the 95th percentile of transit time was 3.2 days at 5 km grid spacing, 5.7 days at 7 km, and 15.2 days at 25 km; for the 1500 m detection range scenario, it was 0.1 days at 5 km, 0.5 days at 7 km, and 10.8 days at 25 km. These values represented upper bounds on the expected maximum time that an animal could go undetected. Comparison of the simulations with pilot studies on three fishes (walleye Sander vitreus, common carp Cyprinus carpio, and channel catfish Ictalurus punctatus) from two independent large lake ecosystems (lakes Erie and Winnipeg) revealed shorter detection and transit times than what simulations predicted. 4. By spreading effort uniformly across space, grids can improve understanding of fish migration over the commonly employed receiver line approach, but at increased time cost for maintaining grids.
Unmanned aerial vehicles as a useful tool for investigating animal movements - samples
<p><span>Determining animal abundance is crucial for assessing the effectiveness of management measures against pest animals. Meanwhile, investigating animal movements has become important for conducting abundance estimations of unmarked animals since the random encounter model (REM)</span><span> was published. REM is a camera-trapping method </span><span>that derives animal density by using contact ratio between camera-traps and targeted animals that randomly move at a certain speed in a given area. However, it</span><span> requires an independent value, which is animal speed. F</span><span>or investigating animal speed, camera-traps with video recording and GPS tagging are the commonly used tools.</span></p> <p><span>U</span><span>nmanned aerial vehicles (UAVs) are currently used in wildlife monitoring to investigate the abundance of target species. It is evaluated as a tool that is non-invasive and suitable for surveys in inaccessible landscapes. Considering these characteristics, we regarded a distant survey using this technology as suitable for investigating animal movements. </span><span>Therefore, we proposed a method for estimating animal movements using UAVs and conducted a case study that aimed to investigate wild boars' movements.</span></p> <p><span>We collected 11 video samples that successfully followed the movements of wild boars from 26 UAV flights in total, and the average speed of their movements derived from all the samples was 1.54 km/24 h.</span></p> <p><span>We found that issues can be improved, including species identification, video sample length, and animal behaviours or activity patterns. On the other hand, our method showed potential for applying to species with specific characteristics in their body size, shape or activity patterns. With improvements in the issues mentioned above, UAVs would become an alternative tool for investigating animal movements.</span></p>
Caution is warranted when using animal space-use and movement to infer behavioral states
<p><strong>Background:</strong> Identifying the behavioral state for wild animals that can't be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals.</p> <p><strong> Methods:</strong> Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events.</p> <p><strong> Results:</strong> For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual's UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events.</p> <p><strong> Conclusions:</strong> Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.</p>
Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
<p>Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially-varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic dataset, then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.</p>
Nocturnal Animal Movements
<p><span>This repository contains a series of videos recorded through thermal scopes. These videos were not taken during Flight-initiation Distance approaches, rather they were taken prior to approaches when observers were seeking to identify the species of interest. We can readily identify a wide range of animals through thermal scopes largely based on the nature of the movement of individual species. For example, here we demonstrate a bandicoot which has a distinctive “run” or “bound” when compared to a similar sized potoroo which has a more distinctive “hop” or “walk”. In this manner we demonstrate the benefit of the recording capacity of these thermal scopes to be able to identify species without influencing their behaviour and enable approaches on known species</span></p>
Data from: Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths
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Data from: Functional responses in animal movement explain spatial heterogeneity in animal-habitat relationships
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Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
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Unmanned aerial vehicles as a useful tool for investigating animal movements - samples
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Data from: Performance and accuracy of lightweight and low-cost GPS data loggers according to antenna positions, fix intervals, habitats and animal movements
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Data from: Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.