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308 results for “habitat selection”
Data from: "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal"
<p>Processed datasets used for analysis in "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal" by Hooven et al. published in <em>Mammal Research</em>. R scripts used to process and analyze these data are available from: <a href="https://github.com/nhooven/elk-individual-habitat">https://github.com/nhooven/elk-individual-habitat</a></p> <p>WS_sampled.csv, SU_sampled.csv, UA_sampled.csv, AW_sampled.csv - Processed telemetry datasets (with relocation data removed), resultant files from script "01 - Pre-processing.R".</p> <p>WS_HRs.csv, SU_HRs.csv, UA_HRs.csv, AW_HRs.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by individual. </p> <p>WS_groups.csv, UA_groups.csv, AW_groups.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by groups. </p> <p>Note: Raw telemetry data and home range polygons are not available due to the sensitive nature of providing animal locations publicly. Please direct any questions or concerns to the corresponding author (nathan.d.hooven@gmail.com). </p>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Fig. 3 in Habitat Preference And Prey Selection Of Marsh Harrier (Circus Aeruginosus) In Overwintering Area Of Southeast China
Fig. 3. Abundances of passerines (), pheasant () and marsh harrier (+) of the four years in the four habitats in Shahu Nature Reserve, China, with line transects 2000 m × 200 m (A, autumn; W,
Fig. 2 in Habitat Preference And Prey Selection Of Marsh Harrier (Circus Aeruginosus) In Overwintering Area Of Southeast China
Fig. 2. Wintering marsh harrier's abundance in different habitats in Shahu Nature Reserve, China in autumn and winter of 2001, 2003, 2004 and 2006
Fig. 1 in Habitat Preference And Prey Selection Of Marsh Harrier (Circus Aeruginosus) In Overwintering Area Of Southeast China
Fig. 1. Shahu Nature Reserve (SNR, autumn and winter). The up left shows the location of SNR; HB, Hubei Province; BWH, Beiwu Lake; NWH, Nanwu Lake; DC, Daocao Lake; DJ, Dongji River; YR, Yangtze River
Fig. 3 in Exploratory Analyses Of Foraging Habitat Selection Of The Red-Footed Falcon (Falco Vespertinus)
Fig. 3. Duality diagram of the eigenanalysis of selection ratios of radio-tracked Red-footed Falcons. The top figure shows the habitat loadings () on two factorial axes, while the lower figure shows the habitat preference of individuals (•) in the same factorial space (see also Table 1). The birds can be
Fig. 2 in Exploratory Analyses Of Foraging Habitat Selection Of The Red-Footed Falcon (Falco Vespertinus)
Fig. 2. Global Manly Selection ratios ± Confidence intervals (CI) of the habitat types analysed. The black dots (•) represent the mean selectivity rate of each habitat type considered. A habitat type can be considered as avoided if the global selection ratio is located in the 0–1 interval, while it can be con-
Fig. 4 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 4. Mean (± SE) captures of Cnestus mutilatus, Xylosandrus compactus, X. cra`ssiusculus, and X. germanus in ethanol-baited Baker traps deployed at various distances from the nursery–forest interface at 2 sites in South Carolina in 2011 and 2012.
Fig. 3 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 3. Mean captures of Cnestus mutilatus, Xylosandrus compactus, X. crassiusculus, and X. germanus in ethanol-baited Baker traps at 2 sites in Louisiana and Mississippi in 2013 and 2014.
Fig. 1 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 1. Satellite image of the Mississippi research site (Google, Mountain View, California, USA) with an overlay showing a randomized complete block design of 5 blocks. Representing the 2013 test, each block shown here had a trap placed at −25, 25, 50, 100, and 200 m from the nursery–forest interface.
Fig. 5 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 5. Mean (± SE) captures of Cnestus mutilatus, Xylosandrus compactus, X. crassiusculus, and X. germanus in ethanol-baited Baker traps deployed at various distances from the nursery–forest interface at 2 sites in Louisiana and Mississippi in 2013 and 2014.
Fig. 1 in Habitat selection of the roe deer Capreolus capreolus (Artiodactyla: Cervidae) in an agroforestry system
Fig. 1 - Study area: Vallevecchia (Venice). The walked transects (T1 to T6) are highlighted in red. / Area di studio: Vallevecchia (Venezia). I transetti percorsi (da T1 a T6) sono evidenziati in rosso.
Fig. 1 in Eurasian badger Meles meles habitat and sett site selection in the northern Apennines
Fig. 1 - Geographic position of the study area, with Sett Points, Random Points and water (rivers and streams) layer.
Fig. 2. Selected photographs showing the sargassum habitat and Scyllaea fulva. A in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 2. Selected photographs showing the sargassum habitat and Scyllaea fulva. A, Sargassum bed at Tai She Wan; B, In situ swimming Scyllaea fulva; C, Dorsal view of Scyllaea fulva in a beaker; D, Lateral view of Scyllaea fulva in a beaker. Scale bar = 0.5 cm.
Figure 3 in Habitat selection of Williams' Jerboa (Allactaga williamsi Thomas, 1897) in Ardabil Province, Iran
Figure 3. Percent cover of dominant plant species in burrow plots (white columns) and paired plots (dark columns). Acronyms: Artemisia herba-alba (AH), Trifolium montanum (TM), Festuca ovina (FO), Acantholimon senganense (AS), Bromus scoparius (BS), Acantholimon acerosum (AA), and Acantholimon embergeri (AE).
Figure 1 in Testing the habitat selectivity of bdelloid rotifers in a restricted area
Figure 1. Geographic localisation of the 90 samples in the six habitats. Coordinates are expressed as latitude and longitude in decimal degrees.
Figure 2 in The Red-footed Falcon Falco vespertinus population in the Danube Delta and its habitat selection for breeding
Figure 2. Relationship between the presence of a breeding population of RfF and explanatory variables selected. The graphics show the relationship between RfF nest presence and (a) the mean temperature of the warmest quarter, (b) the precipitation of the warmest quarter, (c) the number of patches of habitat in 3000 m radius from the nest, (d) the percent of open habitats in 3000 m radius from the nest, (e) the type of nest used (colonial rook nest or solitary magpie and hooded crow nest), (f) the Simpson index.
Figure 1 in The Red-footed Falcon Falco vespertinus population in the Danube Delta and its habitat selection for breeding
Figure 1. Distribution of the occupied nests of Falco vespertinus inside the ROSPA0031 Danube Delta and Razim–Sinoe Complex (and the 3000 m buffer area outside its perimeter) during the breeding season of 2020.
Fig. 2 in Home range, habitat use and roost-site selection by lowland female Siamese fireback Lophura diardi in northeastern Thailand
Fig. 2. The variation in home ranges and core areas of the eight female Siamese fireback in 2011 during different reproductive periods, estimated using 95% MCP and CHP Hot Spot methods. Locations shown were the food supplement sites (1 and 2) and nesting sites during the breeding season.
Fig. 3 in Home range, habitat use and roost-site selection by lowland female Siamese fireback Lophura diardi in northeastern Thailand
Fig. 3. The occurrence probability of Siamese fireback in relation to habitat variables. Shown are predicted values and 95% confidence limits for breeding (black solid lines) and non-breeding (gray dashed lines) periods.
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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
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.