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29 results for “GPS tracking data”
Data and Code for "Extracting reproductive parameters from GPS tracking data for a nesting raptor in Europe"
<p>Understanding population dynamics requires estimation of demographic parameters. We build on existing approaches to develop a new tool that uses GPS tracking data to estimate breeding propensity and breeding success, and show that this tool yielded accurate predictions for two red kite populations in Central Europe. The tool is available as an R package at <a href="https://github.com/Vogelwarte/NestTool">https://github.com/Vogelwarte/NestTool</a> and will facilitate the estimation of demographic parameters from tracking data to inform population assessments. The files in this repository contain the data and analytical code to replicate the results of the publication in the Journal of Avian Biology (DOI: 10.1111/jav.03246). The version contained in this repository does not include updates and improvements that occurred after the 29 August 2024.</p>
Analysis of migration patterns of western marsh harriers using GPS tracking data
<p>This repository contains analysis code for Vansteelant et al. (2020, <a href="https://doi.org/10.1007/s10336-020-01785-6">https://doi.org/10.1007/s10336-020-01785-6</a>). See the <code>README.md</code> for more information.</p>
GPS tracking data for: Male mating season range expansion results from an increase in scale of daily movements for a polygynous-promiscuous bird
<p>Males of species with promiscuous mating systems are commonly observed to use larger ranges during the mating season relative to non-mating seasons, which is often attributed to a change in movements related to reproductive activities. However, few studies link seasonal range sizes to variations in daily space use patterns to provide insight into the behavioral mechanisms underlying mating season range expansion. We studied 20 GPS-tagged male wild turkeys (<em>Meleagris gallopavo</em>), a large upland gamebird, during the mating and summer non-mating seasons to test the hypothesis that larger mating season ranges resulted from male wild turkeys expanding the scale of daily movement activities to locate and court females. We delineated mating and non-mating seasons based on the intensity of gobbling, a vocalization tied to courtship behavior, recorded by autonomous recording units distributed across the study area. Mating season ranges were significantly larger than non-mating season ranges. Daily ranges were larger in the mating season, as were distances between roost sites used on consecutive nights. Variance in daily range size was greater in the mating season, but low temporal autocorrelation suggested considerable daily variability in both seasons. We found no evidence that male wild turkeys changed how they distributed daily movements within seasonal ranges, or differences in habitat use, suggesting larger mating season ranges result from male wild turkeys increasing the scale of their daily movements, rather than a systematic shift to a nomadic movement strategy. Likely, the distribution of females is more dynamic and ephemeral compared to other resources, prompting males to traverse larger daily ranges during the mating season to locate and court females. Our work illustrates the utility of using daily movement to understand the behavioral process underlying larger space use patterns.</p>
Data for: Simultaneous GPS-tracking of parents reveals a similar parental investment within pairs, but no immediate co-adjustment on a trip-to-trip basis
<p>This repository contains data for the paper: Kavelaars et al. 2021. Simultaneous GPS-tracking of parents reveals a similar parental investment within pairs, but no immediate co-adjustment on a trip-to-trip basis. <strong>Movement Ecology</strong>. https://doi.org/10.1186/s40462-021-00279-1</p>
GPS and activity data from wild boars tracked in Southern France
<p><span>Many animals living in anthropized landscapes try to avoid encountering people by being active at night. By doing so, however, they risk being disturbed while at rest during the day. To mitigate this risk, diurnally resting species may be highly selective about where they rest. Here, we used GPS and activity sensors to study how wild boars </span><span>(<em>Sus scrofa</em>) might </span><span>adjust their resting site selection and revisitation patterns to the risk of disturbance by people. </span><span>We </span><span>evaluated the probability of daytime relocation to</span><span> assess the efficacy of wild boars' resting strategy in reducing</span><span> the risk of human encounter while at rest. We attempted to identify the cause of some relocations using audio recordings. Generally, we found that wild boars did not specifically avoid resting near villages or roads, i.e. where the risk of encountering people is higher, if they could find sites with suitable vegetation cover. The risk of disturbance by people was low, even near villages. Resting sites located close to villages were visited more repeatedly than those located further away, suggesting that focusing on a few familiar and quiet resting sites was a successful strategy for resting undisturbed in an anthropized landscape.</span></p>
GPS and activity data from wild boars tracked in Southern France
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Argos and GPS data for a polar bear track
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Data for: The spatial consistency and repeatability of migratory flight routes and stationary sites of individual European nightjars based on multiannual GPS tracks
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GPS tracking data for: Male mating season range expansion results from an increase in scale of daily movements for a polygynous-promiscuous bird
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Population census and GPS tracking data for Mexican long-nosed bats (Leptonycteris nivalis) in Texas
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Data from: Flying south: foraging locations of the Hutton's shearwater (Puffinus huttoni) revealed by time-depth recorders and GPS tracking
1. The Hutton's shearwater Puffinus huttoni is an endangered seabird endemic to Kaikōura, New Zealand, but the spatial and temporal aspects of its at-sea foraging behaviour are not well known. 2. To identify foraging areas and estimate trip durations, we deployed Global Positioning Systems (GPS) devices and Time-Depth Recorders (TDR) on 26 adult Hutton's shearwaters during the chick-rearing period in 2017 and 2018. 3. We found Hutton's shearwaters travelled much further from their breeding grounds at Kaikōura than previously considered, with most individuals foraging in coastal and oceanic areas 125–365 km south and near Banks Peninsula. Trip durations varied from 1 to 15 days (mean = 5 days), and total track lengths varied from 264 to 2157 km (mean = 1092.9 km). 4. Although some diving occurred in near-shore waters near the breeding colony, most foraging was concentrated in four regions south of Kaikōura. Dive durations averaged 23.2 seconds (range 8.1 to 71.3 sec) and dive depths averaged 7.1 m (range 1.5 to 30 m). Foraging locations had higher chlorophyll a levels and shallower water depths than non-foraging locations. Birds did not feed at night, but tended to raft in areas with deeper water than foraging locations. 5. Mapping the spatial and temporal distribution of Hutton's shearwaters at-sea will be fundamental to their conservation, as it can reveal potential areas of overlap with fisheries and other industrial users of the marine environment.
Data from: GPS tracking and population genomics suggest itinerant breeding across drastically different habitats in the Phainopepla
Migratory birds generally divide the annual cycle between discrete breeding and non-breeding ranges. Itinerant breeders, however, reproduce twice at different geographic locations, migrating between them. This unusual flexibility in movement ecology and breeding biology suggests that some species can rapidly modulate the conflicting physiological and behavioral traits required for migration and reproduction. The Phainopepla (Phainopepla nitens), a songbird of the southwestern USA, has long been suspected to breed first in desert habitats in spring, then migrate to woodland habitats to breed again in summer. However, direct evaluation of movement and gene flow among individuals breeding in different locations has previously been logistically intractable. We deployed GPS tags on free-flying Phainopeplas in southern California, all of which migrated to hypothesized woodland breeding habitats after desert breeding (an average distance of 232 km). GPS data also revealed previously unknown fall and spring stopover sites. Population genomic analyses revealed no genetic differentiation among desert and woodland breeding populations, indicating significant movement and gene flow across the region. Finally, we used random forest analyses to quantify substantial environmental differences among temporal stages. Our results provide direct evidence that individual Phainopeplas do indeed move between two drastically different breeding habitats in the same year, representing a rare and extreme example of life-history flexibility.
GPS data and plotting codes for Remote Sensing paper titled Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity
<p>Data files (meteorological data, sattelite derived velocity time series, and seismic station GPS data) and plotting codes to reproduce the dataset and plots presented in the paper Gajek et al., Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity</p>
Data from: GPS tracking and population genomics suggest itinerant breeding across drastically different habitats in the Phainopepla
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Data from: Flying south: foraging locations of the Hutton’s shearwater (Puffinus huttoni) revealed by time-depth recorders and GPS tracking
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Figure 1 from: Milotic T, Desmet P, Anselin A, De Bruyn L, De Regge N, Janssens K, Klaassen R, Koks B, Schaub T, Schlaich A, Spanoghe G, T'jollyn F, Vanoverbeke J, Bouten W (2020) GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands. ZooKeys 947: 143-155. https://doi.org/10.3897/zookeys.947.52570
Figure 1 INBO researcher Anny Anselin holding Peter (animal ID L143457), one of the tagged Western marsh harriers in the MH_WATERLAND dataset (tag ID 623).
Figure 2 from: Milotic T, Desmet P, Anselin A, De Bruyn L, De Regge N, Janssens K, Klaassen R, Koks B, Schaub T, Schlaich A, Spanoghe G, T'jollyn F, Vanoverbeke J, Bouten W (2020) GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands. ZooKeys 947: 143-155. https://doi.org/10.3897/zookeys.947.52570
Figure 2 Left: Map giving an overview of the extent of the three datasets including the winter migration tracks; top right: summering data in H_GRONINGEN; middle right: summering data of MH_WATERLAND; and bottom right: summering data in MH_ANTWERPEN.
Figure 2 from: Stienen EWM, Desmet P, Aelterman B, Courtens W, Feys S, Vanermen N, Verstraete H, Van de walle M, Deneudt K, Hernandez F, Houthoofdt R, Vanhoorne B, Bouten W, Buijs RJ, Kavelaars MM, Müller W, Herman D, Matheve H, Sotillo A, Lens L (2016) GPS tracking data of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast. ZooKeys 555: 115-124. https://doi.org/10.3897/zookeys.555.6173
Figure 2 - Left: map of western Europe and northwest Africa, showing the full extent of the gull tracking data, including two migration/wintering seasons. Right: map of the southern North Sea coast, showing mainly breeding season data. Each point represents a recorded occurrence, LBBG are indicated in orange, HG in blue. Overlapping points are brighter in colour. Maps created with CartoDB, basemap based on OpenStreetMap data. https://inbo.cartodb.com/u/lifewatch/viz/da04f120-ea70-11e4-a3f2-0e853d047bba/public_map
Figure 1 from: Stienen EWM, Desmet P, Aelterman B, Courtens W, Feys S, Vanermen N, Verstraete H, Van de walle M, Deneudt K, Hernandez F, Houthoofdt R, Vanhoorne B, Bouten W, Buijs RJ, Kavelaars MM, Müller W, Herman D, Matheve H, Sotillo A, Lens L (2016) GPS tracking data of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast. ZooKeys 555: 115-124. https://doi.org/10.3897/zookeys.555.6173
Figure 1 - One of the tracked Lesser Black-backed Gulls ("Hans", ring code: L906682), photographed near its nest in Zeebrugge on May 29, 2013 shortly after he was equipped with a tracker (device info serial: 861). Photo by Misjel Decleer, VLIZ photo gallery.
Figure 3 from: Stienen EWM, Desmet P, Aelterman B, Courtens W, Feys S, Vanermen N, Verstraete H, Van de walle M, Deneudt K, Hernandez F, Houthoofdt R, Vanhoorne B, Bouten W, Buijs RJ, Kavelaars MM, Müller W, Herman D, Matheve H, Sotillo A, Lens L (2016) GPS tracking data of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast. ZooKeys 555: 115-124. https://doi.org/10.3897/zookeys.555.6173
Figure 3 - Researchers equipping a Herring Gull with a UvA-BiTS GPS tracker on the roof of the Vismijn in Ostend on May 24, 2013. Photo by Misjel Decleer, VLIZ photo gallery.
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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)
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DANDI Archive for NWB datasets
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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.