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37 results for “animal behaviour”
Elevated temperature effects on animal personality: hormonal stress response underlying behavioural differences in the American bullfrog
<p>Dataset for research paper submitted to Animal Behaviour</p> <p>Behavioural_data.csv: raw data for how individual bullfrogs performed in six different trials on an 8-arm maze before and after they were submitted to thermal stress. Behaviours analyzed: movements against the wall of the maze, posture changes, total ambulatory distance (m), and time on the centre of the arena (s).</p> <p>Hormone_data.csv: raw hormone (corticosterone and testosterone) data collected from individual bullfrogs in four different time points: baseline, 12 hours after stress, 24 days after stress, and 47 days after stress.</p> <p>Mass_data.csv: raw mass data collected from individual bullfrogs at the beginning and end of the experiment. SVL = snout-vent length. Body index is calculated as the residuals of a linear regression between mass as dependent variable and SVL as independent variable.</p>
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
<p>1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. </p> <p>2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. </p> <p>3. The R package <em><span>aniMotum</span></em> eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. </p> <p>4. We highlight <em>aniMotum</em>'s<em> </em>capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use <em>aniMotum</em> for their own analyses. </p>
Quantifying animal social behaviour with ecological field methods
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Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
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How biological invasions affect animal behaviour: a global, cross-taxonomic analysis
<p>1. In the Anthropocene, species are faced with drastic challenges due to rapid, human-induced changes, such as habitat destruction, pollution and biological invasions. In the case of invasions, native species may change their behaviour to minimise the impacts they sustain from invasive species, and invaders may also adapt to the conditions in their new environment in order to survive and establish self-sustaining populations. 2. We aimed at giving an overview of which changes in behaviour are studied in invasions, and what is known about the types of behaviour that change, the underlying mechanisms and the speed of behavioural changes. 3. Based on a review of the literature, we identified 191 studies and 360 records (some studies reported multiple records) documenting behavioural changes caused by biological invasions in native (236 records from 148 species) or invasive (124 records from 50 species) animal species. This global dataset, which we make openly available, is not restricted to particular taxonomic groups. 4. We found a mild taxonomic bias in the literature towards mammals, birds and insects. In line with the enemy release hypothesis, native species changed their anti-predator behaviour more frequently than invasive species. Rates of behavioural change were evenly distributed across taxa, but not across types of behaviour. 5. Our findings may help to better understand the role of behaviour in biological invasions as well as temporal changes in both population densities and traits of invasive species, and of native species affected by them.</p>
Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers
<p>1: Machine learning-based behaviour classification using acceleration data is a powerful tool in bio-logging research. Deep learning architectures such as convolutional neural networks (CNN), long short-term memory (LSTM), and self-attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration-based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached, and complexity in data due to complex animal-specific behaviours, which may have limited the application of deep learning techniques in this area.</p> <p>2: To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup, and pre-training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state-of-the-art deep learning model architectures.</p> <p>3: Data augmentation improved the overall model performance when one of various techniques (none, scaling, jittering, permutation, time-warping, and rotation) was randomly applied to each data during mini-batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre-training with unlabelled data did not improve model performance. The state-of-the-art deep learning models, including a model consisting of four CNN layers, an LSTM layer, and a multi-head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features.</p> <p>4: Our experiments showed that deep learning techniques are promising for acceleration-based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning, and self-supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time-series sensor data.</p> <p>This abstract is cited from the original article "Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers" in Methods in Ecology and Evolution (Otsuka et al., 2024).<br><br>Please see README for the details of the datasets.</p>
Behavioural ecology meets oncology: quantifying the recovery of animal behaviour to a transient exposure to a cancer risk factor
<p>Wildlife is increasingly exposed to sublethal transient cancer risk factors, including mutagenic substances, which activate their anti-cancer defences, promote tumourigenesis, and may negatively impact populations. Little is known about how exposure to cancer risk factors impacts the behaviour of wildlife. Here, we investigated the effects of a sublethal, short-term exposure to a carcinogen at environmentally relevant concentrations on the activity patterns of wild <em>Girardia tigrina</em> planaria during a two-phase experiment, consisting of a 7-day exposure to cadmium period followed by a 7-day recovery period. To comprehensively explore the effects of the exposure on activity patterns, we employed the double hierarchical generalized linear model framework which explicitly models residual intraindividual variability in addition to the mean and variance of the population. We found that exposed planaria were less active compared to unexposed individuals and were able to recover to pre-exposure activity levels albeit with a reduced variance in activity at the start of the recovery phase. Planaria showing high activity levels were less predictable with larger daily activity variations and higher residual variance. Thus, the shift in behavioural variability induced by an exposure to a cancer risk factor can be quantified using advanced tools from the field of behavioural ecology. This is required to understand how tumourous processes affect the ecology of species.</p>
Predation risk can modify the foraging behaviour of frugivorous carnivores: implications of rewilding apex predators for plant-animal mutualisms
<p>Apex predators play key roles in food webs and their recovery can trigger trophic cascades in some ecosystems. Intra-guild competition can reduce the abundances of smaller predators and perceived predation risk can alter their foraging behaviour thereby limiting seed dispersal by frugivorous carnivores. However, little is known about how plant-frugivore mutualism could be disturbed in the presence of larger predators.</p> <p>We evaluated the top-down effect of the regional superpredator, the Iberian lynx (Lynx pardinus), on the number of visits and fruits consumed by medium-sized frugivorous carnivores, as well as the foraging behaviour of identified individuals, by examining the consumption likelihood and the foraging time.</p> <p>We carried out a field experiment in which we placed Iberian pear (Pyrus bourgaeana) fruits beneath fruiting trees and monitored pear removal by frugivorous carnivores, both inside and outside of lynx ranges. Using camera traps, we recorded the presence of the red fox (Vulpes vulpes), the Eurasian badger (Meles meles) and the stone marten (Martes foina), as well as the number of fruits they consumed and their time spent foraging.</p> <p>Red fox was the most frequent fruit consumer carnivore. We found there were fewer visits and less fruit consumed by foxes inside of lynx ranges, but lynx presence did not seem to affect badgers. We did not observe any stone marten visits inside of lynx territories. The foraging behaviour of red foxes was also altered when inside of lynx ranges whereby foxes were less efficient, consuming less fruit per unit of time and having shorter visits. Local availability of fruit resources, forest coverage and individual personality also were important variables to understand visitation and foraging in a landscape of fear. 5. Our results show a potential trophic cascade from apex predators to primary producers. The presence of lynx can reduce frugivorous carnivore numbers and induce shifts in their feeding behaviour that may modify the seed dispersal patterns with likely consequences for the demography of many fleshy-fruited plant species. We conclude that knowledge of the ecological interactions making up trophic webs is anasset to design effective conservation strategies, particularly in rewilding programs.</p>
Modelling migraine-related features in the nitroglycerin animal model: trigeminal hyperalgesia is associated with affective status and motor behaviour
<p>This dataset comprises the findings obtained in the study aimed at exploring the correlation between trigeminal hyperalgesia and affective status or behavioral components in a migraine-specific animal model based on nitroglycerin administration (see article <em>Modelling migraine-related features in the nitroglycerin animal model: trigeminal hyperalgesia is associated with affective status and motor behaviour).</em></p> <p>In vivo assessments performed in male Sprague-Dawley rats four hours after treatment with nitroglycerin (10mg/kg, i.p.) or its vehicle:</p> <p>- in the open field test were evaluated: time spent (expressed in seconds) in the center of the apparatus as a measure of anxiety; distance (expressed in meters) travelled in the apparatus as a measure of motor behaviour; number of rearings as a measure of exploratory behaviour; time spent in grooming behavior (expressed in seconds) used as nociception index;</p> <p>- evaluation of trigeminal hyperalgesia in the orofacial formalin test: the face rubbing was measured counting the seconds the animal spent grooming the injected area (upper lip, lateral to the nose) with the ipsilateral forepaw or hindpaw 0–3 min (Phase I) or 12–45 min (Phase II) after formalin injection (50 µl, s.c.). The observation time was divided into 15 blocks of 3 min each.</p> <p><strong>RESULTS</strong></p> <p>The data analysis shows an inverse correlation between trigeminal hyperalgesia and motor or exploratory behavior, and a positive association with anxiety-like behavior of spontaneous grooming.</p> <p>These findings further expand on the translational value of the migraine-specific model based on nitroglycerin administration and prompt additional parameters that can be investigated to explore the complexity of the disease.</p>
Linking animal behaviour and tree recruitment: Caching decisions by a scatter hoarder corvid determine seed fate in a Mediterranean agroforestry system
<p><span>1. Seed dispersal by scatter-hoarder corvids is key for the establishment of important tree species from the Holarctic region such as the walnut (<em>Juglans regia</em>). However, the factors that drive animal decisions to cache seeds in specific locations and the consequences of these decisions on seed fate are poorly understood. </span></p> <p><span>2. We experimentally created four distinct, replicated habitat types in a Mediterranean agricultural landscape where the Eurasian magpie (<em>Pica</em> <em>pica</em>) is a common scatter-hoarder: soft bare soil; compacted bare soil; compacted soil with a dense herbaceous cover; and soft linear bare soil made up of the irrigation furrows that separated the rest of the treatments. We also experimentally placed visual landmarks (stones, sticks, and bunches of dry plants) to test if magpies use them to place seed caches. Walnut dispersal from feeders to the habitats was monitored by radio-tracking and camera traps. </span></p> <p><span>3. A sowing experiment simulating natural caches tested the effect of caching type on seed germination and seedling emergence. Seed mass was controlled for the dispersal and sowing experiments.</span></p> <p><span>4. Magpies selected the two habitats with soft soil, and avoided the one with compacted soil, to cache nuts. Seed mass did not affect dispersal distance, germination, or emergence; however, heavier seeds were cached more often under litter and in the habitat with herbaceous cover, whereas lighter seeds were more often buried in the soft bare soil habitat. Seed burial under soil or litter determined seed fate, as there was virtually no emergence from unburied nuts. There was no evidence of any effect of the visual landmarks.</span></p> <p>5. Synthesis. The consequences of seed caching for seedling early establishment are driven by a fine decision-making process of the disperser. Magpies seemed to ponder the characteristics of the habitat and the seed itself to determine where and how to cache each nut. By doing so, magpies reinforced the quality of seed dispersal effectiveness, as they cached walnuts in locations that enhanced both seed survival and seedling emergence.</p>
Supporting material for "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test"
<p>The data were uploaded to support the manuscript "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test" for the submission to Journal of Pharmacological and Toxicological Methods.</p>
Adapting camera-trap placement based on animal behaviour for rapid detection: a focus on the Endangered, white-bellied pangolin (Phataginus tricuspis)
<p>Table containing detection data of species using two camera trap placement strategies (log vs non-log)</p>
Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers
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How biological invasions affect animal behaviour: a global, cross-taxonomic analysis
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Linking animal behaviour and tree recruitment: Caching decisions by a scatter hoarder corvid determine seed fate in a Mediterranean agroforestry system
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Predation risk can modify the foraging behaviour of frugivorous carnivores: implications of rewilding apex predators for plant-animal mutualisms
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Data from: Baboon travel progressions as a ‘social spandrel’ in collective animal behaviour
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Behavioural ecology meets oncology: quantifying the recovery of animal behaviour to a transient exposure to a cancer risk factor
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Seek and learn: automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning
<p>1. Animal-borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer's high sensitivity to movement and fast response times present the unprecedented opportunity to resolve fine-scale behaviour, leveraging this opportunity will require overcoming the challenge of developing general, automated methods to analyse the nonstationary signals generated by nonlinear processes governing erratic, impulsive movement characteristic of fine-scale behaviour. 2. We address this issue by conceptualising fine-scale behaviour in terms of characteristic microevents: impulsive movements producing brief (<1 s) shock signals in accelerometer data. We propose a 'seek-and-learn' approach: a novel microevent detection step first locates where shock signals occur ('seek') by searching for peaks in envelopes of acceleration data. Robust machine learning ('learn') employing meaningful features then separates microevents. We showcase the application of our method on tri-axial accelerometer data collected on ten free-living meerkats (Suricata suricatta) for four fine-scale foraging behaviours – searching for digging sites, one-armed digging, two-armed digging, and head jerks during prey ingestion. Annotated videos served as groundtruth, and performance was benchmarked against that of a variety of classical machine-learning approaches. 3. Microevent identification (μEvId) with eight features in a three-node hierarchical classification scheme employing logistic regression at each node achieved a mean overall accuracy of >85% during leave-one-individual-out cross-validation, and exceeded that of the best classical machine learning approach by 9%. μEvId was found to be robust not only to inter-individual variation but also to large changes in model parameters. 4. Our results show that microevents can be modelled as impulse responses of the animal body-and-sensor system. The microevent detection step retains only informative regions of the signal, which results in the selection of discriminative features that reflect biomechanical differences between microevents. Moving-window-based classical machine learning approaches lack this prefiltering step, and were found to be suboptimal for capturing the nonstationary dynamics of the recorded signals. The general, automated technique of μEvId, together with existing models that can identify broad behavioural categories, provides future studies with a powerful toolkit to exploit the full potential of accelerometers for animal behaviour recognition.</p>
Behavioural correlations across multiple stages of the antipredator response: do animals that escape sooner hide longer?
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.