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196 results for “Free-ranging”
Data from: Local interactions and global properties of wild, free-ranging stickleback shoals
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Data from: The influence of plant defensive chemicals, diet composition, and winter severity on the nutritional condition of a free-ranging, generalist herbivore
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Data from: Nutrient-specific compensation for seasonal cold stress in a free-ranging temperate colobine monkey
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Data from: SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate
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Data from: Foraging and fasting can influence contaminant concentrations in animals: an example with mercury contamination in a free-ranging marine mammal
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Data from: An active-radio-frequency-identification system capable of identifying co-locations and social-structure: validation with a wild free-ranging animal
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Data from: Free-ranging farm cats: home range size and predation on a livestock unit in Northwest Georgia
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Data from: Puma energetics: laboratory oxygen consumption and GPS information from free-ranging individuals
<span><b><u>Abstract</u></b></span> <p class="Default"><b>Background</b></p> <p class="Default">Under current scenarios of climate change and habitat loss, many wild animals, especially large predators, are moving into novel energetically challenging environments. Consequently, changes in terrain associated with such moves may heighten energetic costs and effect the decline of populations in new localities.</p> <p class="Default"><b>Methods</b></p> <p class="Default">To examine locomotor costs of a carnivorous mammal moving in mountainous habitats, the oxygen consumption of captive pumas (<i>Puma concolor</i>) was measured during treadmill locomotion on level and incline (6.8ᵒ) surfaces. These data were used to predict energetic costs of locomotor behaviours of free-ranging pumas equipped with GPS/accelerometer collars in California's Santa Cruz Mountains.</p> <p class="Default"><b>Results</b></p> <p class="Default">Incline walking resulted in a 42.0%±7.2 SEM increase in transport costs compared to level performance. Wild pumas modified their locomotor behaviour in response to terrain steepness by traversing the mean hillside incline of 17.2ᵒ±0.3 SEM and choosing shallower paths of 7.3ᵒ±0.1 SEM. Pumas also walked more slowly up steeper paths, thereby minimizing the energetic impact of vertical terrains. Estimated daily energy expenditure of (DEE) based on GPS-derived speeds of free-ranging pumas was 18.3 MJ day<sup>-1</sup> ±0.2 SEM. Calculations show that a 20ᵒ increase in mean terrain steepness would increase DEE by <1% as pumas spent a small (10%) proportion of their day travelling and avoided elevated costs by utilizing slower speeds and shallower paths.</p> <p class="Default"><b>Conclusions</b></p> <p class="Default">While many factors influence survival in novel habitats, we illustrate the importance of behaviours which reduce locomotor costs when traversing new, energetically challenging environments, and demonstrate that these behaviours are utilised by pumas in the wild.</p> <p> </p> <p> </p>
Data from: Performance assessment of two whole-lake acoustic positional telemetry systems - is reality mining of free-ranging aquatic animals technologically possible?
Acoustic positional telemetry systems (APTs) represent a novel approach to study the behaviour of free ranging aquatic animals in the wild at unprecedented detail. System manufactures promise remarkably high temporal and spatial resolution. However, the performance of APTs has rarely been rigorously tested at the level of entire ecosystems. Moreover, the effect of habitat structure on system performance has only been poorly documented. Two APTs were deployed to cover two small lakes and a series of standardized stationary tests were conducted to assess system performance. Furthermore, a number of tow tests were conducted to simulate moving fish. Based on these data, we quantified system performance in terms of data yield, accuracy and precision as a function of structural complexity in relation to vegetation. Mean data yield of the two systems was 40 % (Lake1) and 60 % (Lake2). Average system accuracy (acc) and precision (prec) were Lake1: acc = 3.1 m, prec = 1.1 m; Lake2: acc = 1.0 m, prec = 0.2 m. System performance was negatively affected by structural complexity, i.e., open water habitats yielded far better performance than structurally complex vegetated habitats. Post-processing greatly improved data quality, and sub-meter accuracy and precision were, on average, regularly achieved in Lake2 but remained the exception in the larger and structurally more complex Lake1. Moving transmitters were tracked well by both systems. Whereas overestimation of moved distance is inevitable for stationary transmitters due to accumulation of small tracking errors, moving transmitters can result in both over- and underestimation of distances depending on circumstances. Both deployed APTs were capable of providing high resolution positional data at the scale of entire lakes and are suitable systems to mine the reality of free ranging fish in their natural environment. This opens important opportunities to advance several fields of study such as movement ecology and animal social networks in the wild. It is recommended that thorough performance tests are conducted in any study utilizing APTs. The APTs tested here appear best suited for studies in structurally simple ecosystems or for studying pelagic species. In such situations, the data quality provided by the APTs is exceptionally high.
Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas)
<p> </p> <p>Data accompanying the paper: Jeantet, L., Vigon, V., Geiger, S., & Chevallier, D. (2021). Fully convolutional neural network: A solution to infer animal behaviours from multi-sensor data. <em>Ecological Modelling</em>, <em>450</em>, 109555.. doi : <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolmodel.2021.109555" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecolmodel.2021.109555</a></p> <p> </p> <p>In this paper we developped a fully convolutional network, the V-Net, to automatically identify the behaviors of green turtle from acceleration, gyroscope, depth sensor data. With minimal preprocessing, we obtained a F1-score of 81.1% and a Global accuracy of 97.2%. </p> <p> </p> <p><strong>Associated Github with the V-Net script : </strong><a href="https://github.com/jeantetlorene/Vnet_seaturtle_behavior">https://github.com/jeantetlorene/Vnet_seaturtle_behavior</a></p> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 13 free-ranging green turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are not provided in this dataset. </p> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, primarily night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. A total of 46 behaviors were observed and are described in detail in Jeantet et al. (2020). The labels for these behaviors are found in the column "beh." The behaviors were grouped into six main categories: Breathing, Feeding, Gliding, Resting, Scratching, and Swimming. Any other observed behavior was categorized as Other. The associated labels for the categories can be found in the column "beh_merge."</p> <p>To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).</p> <p>"In total, the green turtle dataset contained 68.6 hours of labelled sequences from 13 individuals (approximately 5.29 hours per individual, max = 14.67 hours, min = 0.96 hours, standard deviation = 3.39 hours). The predominant behavior observed in the videos was Resting, totaling over 34.3 hours, followed by Swimming and Breathing, with 22.3 hours and 5.7 hours, respectively. The other behaviors were expressed in minority (Gliding: 2.3 hours, Feeding: 1.8 hours, Scratching: 1.2 hours and Other: 1 hour). "</p> <p> </p> <p>The folder contains 16 Python matrices, each with 11 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, Depth, beh, beh_merge, Pressure_corr, Pressur_diff) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-07-XX) and the deployment day (DD-MM-YYYY), with an additional number if the file was split due to unlabeled sequences.</p> <p> </p> <p>The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the "beh" column. Two dictionaries (behInd_to_behName_cat, behName_to_behInd_cat) that specify the behavioral categories associated with each number used as a label in the "beh_merge" column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.</p> <p> </p> <p><strong>Please cite this dataset as :</strong> </p> <p>Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hielard, G., Arque, A., Regis, S., Lecerf, N., Frouin, C., Benhalilou, A., Murgale, C., Maillet, T., Andreani, L., Campistron, G., … Chevallier, D. (2024). Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11643602</p> <p> </p>
Data from: Performance assessment of two whole-lake acoustic positional telemetry systems - is reality mining of free-ranging aquatic animals technologically possible?
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Data from: Puma energetics: laboratory oxygen consumption and GPS information from free-ranging individuals
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Using Domestic and Free-Ranging Arctic Canid Models for Environmental Molecular Toxicology Research
GEO Series GSE71407. Canis lupus familiaris; Vulpes lagopus. 12 samples. Type: Expression profiling by array.
Subspecies and Distribution. B.b.bisonLinnaeus,1758—scatteredinfree-rangingconservationherdsinW&CCanada(BritishColumbia&Saskatchewan),WUSA(Alaska,Montana,Utah&Wyoming),andextremeNMexico(Chihuahua). B. b. athabascae Rhoads, 1898 — scattered in free-ranging conservation herds in W & C Canada. in Bovidae
Subspecies and Distribution. B.b.bisonLinnaeus,1758—scatteredinfree-rangingconservationherdsinW&CCanada(BritishColumbia&Saskatchewan),WUSA(Alaska,Montana,Utah&Wyoming),andextremeNMexico(Chihuahua). B. b. athabascae Rhoads, 1898 — scattered in free-ranging conservation herds in W & C Canada.
Data used for "Effects of environmental change on population growth: monitoring time-varying carrying capacity in free-ranging spotted hyenas"
<p>These data are to be used within the context of the R code provided <a title="link to GitHub" href="https://github.com/hyenaproject/Bailey2024K" target="_blank" rel="noopener">here</a>. See the linked GitHub repository for details.</p>
Impacts of free-ranging yaks on habitat occupancy and population density of a high-mountain endangered pheasant species
<p>This dataset include all raw data for reproducing all of the analyses in this paper.</p>
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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.