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308 results for “habitat selection”
Data and code from Rodkey et al. (2024): "Sod farms drive habitat selection of a migratory grassland shorebird during a critical stopover period"
<p>The data and code in this repository was used to fit the final models for the submitted manuscript "Sod farms drive habitat selection of a migratory grassland shorebird" by Rodkey and coauthors.</p> <p><strong>Abstract:</strong></p> <p>Migratory shorebirds are one of the fastest declining groups of North American avifauna. Yet, relatively little is known about how these species select habitat during migration. We explored the habitat selection of Buff-breasted Sandpipers (<em>Calidris subruficollis</em>) during spring and fall migration through the Texas Coastal Plain, a major stopover region for this species. Using tracking data from 118 birds compiled over 4 years, we found Buff-breasted Sandpipers selected intensively managed crops such as sod and short-stature crop fields, but generally avoided rangeland and areas near trees and shrubs. This work supports prior studies that also indicate the importance of short-stature vegetation for this species. Use of sod and corn varied by season, with birds preferring sod in spring, and avoiding corn when it is tall, but selecting for corn in fall after harvest. This dependence on cropland in the Texas Coastal Plain is contrary to habitat use observed in other parts of their non-breeding range, where rangelands are used extensively. The species' almost complete reliance on a highly specialized crop, sod, at this critical stopover site raises concerns about potential exposure to contaminants as well as questions about whether current management practices are providing suitable conditions for migratory grassland birds.</p> <p> </p> <p><strong>Data and code for model fitting </strong><em>bbsa_sodfarm_selection.zip</em><br>Following files are used to fit final models for the Texas study and the range-wide study.</p> <p><strong>script_tx </strong>is the script used to fit models on Texas dataset. <strong> </strong><em>script_tx.R</em></p> <p><strong>script_rw </strong> is the script used to fit models on range-wide dataset. <em>script_rw.R</em></p> <p><strong>data_tx </strong>is the datafile for containing model input data for the Texas models. data<em>_tx.csv</em></p> <p><strong>data_rw </strong>is the datafile containing model input data for the rangewide models. <em>data_rw.csv</em></p> <p>Columns in the datafiles for both Texas and rangewide models (data_tx and data_rw) are organized as follows:</p> <p>ID - unique identifier for tracked birds</p> <p>year - year in which bird was tracked</p> <p>season - season in which bird was tracked (spring or fall)</p> <p>fromTexas - (rangewide dataset only) whether or not the tracked bird was captured in the study region, the Texas Coastal Plain (T = bird was caught within study region; F = bird was caught outside of study region)</p> <p>night- (Texas dataset only) whether or not the location fell within daytime or nightime hours (y = nightime, n = daytime)</p> <p>droad - distance of location in meters from nearest road</p> <p>dwoody - distance of location in meters from nearest woody cover</p> <p>corn_prop250 - proportion of corn/sorghum landcover type within a 250m buffer around location</p> <p>cotton_prop500 - proportion of cotton/soybean landcover type within a 500m buffer around location</p> <p>herb_prop250 - proportion of herbaceous (i.e., grassy) landcover type within a 250m buffer around location</p> <p>rice_prop1000 - proportion of rice landcover type within a 1000m buffer around location</p> <p>sod_prop250 - proportion of sod landcover type within a 250m buffer around location</p> <p>presence - identifying used versus available locations (1 = used, as in, an actual GPS location of tracked bird; 0 = available, as in, a randomly generated location within the bird's area of use)</p> <p> </p> <p> </p> <p><strong>Tracking data </strong><em>bbsa_txStudy_movebank_20240731.csv</em></p> <p>Tracking data used for Texas study. Lotek PinPoint GPS data were imported and decoded via Movebank and relevant columns were selected. Columns are organized as follows:</p> <p> </p> <p>timestamp - time of GPS location in UTC (format = YYYY-MM-DD HH:MM:SS.SSS)</p> <p>location.long - longitude of GPS location in decimal degrees, projected in the WGS 84 datum</p> <p>location.lat - latitude of GPS location in decimal degrees, projected in the WGS 84 datum</p> <p>lotek.crc.status.text - value of Lotek's cyclical redundancy check (OK = good data, OK(corrected) = message was corrected with Lotek's error correction algorithm, Fail = data has been corrupted on transmission, and therefore cannot be trusted)</p> <p>sensor.type - type of sensor, all rows are "gps"</p> <p>tag.local.identifier - Argos ID of the attached Lotek PinPoint GPS transmitter</p> <p>individual.local.identifier - unique individual identifier, in this case the USGS-issued band number attached to each tracked bird</p> <p>individual.taxon.canonical.name - species taxonomical name, all rows are "Calidris subruficollis"</p> <p>sex - sex of tracked bird (M = male, F = female)</p> <p>age - age of tracke bird (AHY = after-hatch-year, HY = hatch-year)</p> <p> </p> <p> </p> <p><strong>Sod farm layer </strong><em>sodLayer_tgcp.tif</em><br>Raster layer representing sod farms in the Western Gulf Coastal Plain ecoregion of Texas. Resolution of 30m with values from 0 - 1, with 1 representing sod farms. Sod farms were identified via visual inspection, cross-referencing the U.S. Department of Agriculture's Cropland Data Layer with Google Earth high resolution imagery from 2022. This was a modification of preliminary data on sod farm distribution in the region compiled by B. Ortego, S. Gifford and J. Lyons.</p> <p> </p>
Data from: Match and mismatch: integrating consumptive effects of predators, prey traits, and habitat selection in colonizing aquatic insects
<p>Predators are a particularly critical component of habitat quality, as they affect survival, morphology, behavior, population size, and community structure through both consumptive and non-consumptive effects. Non-consumptive effects can often exceed consumptive effects, but their relative importance is undetermined in many systems. Our objective was to determine the consumptive and non-consumptive effects of a predaceous aquatic insect, <em>Notonecta irrorata</em>, on colonizing aquatic beetles. We tested how <em>N. irrorata </em>affected survival and habitat selection of colonizing aquatic beetles, how beetle traits contributed to their vulnerability to predation by <em>N. irrorata,</em> and how combined consumptive and non-consumptive effects affected populations and community structure. Predation vulnerabilities ranged from 0–95% mortality, with size, swimming, and exoskeleton traits generating species-specific vulnerabilities. Habitat selection ranged from predator avoidance to preferentially colonizing predator patches. Attraction of Dytiscidae to <em>N. irrorata</em> may be a natural ecological trap given similar cues produced by these taxa. Hence, species-specific habitat selection by prey can be either predator-avoidance responses that reduce consumptive effects, or responses that magnify predator effects. <em>Notonecta irrorata</em> had both strong consumptive and non-consumptive effects on populations and communities, while combined effects predicted even more distinct communities and populations across patches with or without predators. Our results illustrate that an aquatic invertebrate predator can have functionally-unique consumptive effects on prey, attracting and repelling prey, while prey have functionally-unique responses to predators. Determining species-specific consumptive and non-consumptive effects is important to understand patterns of species diversity across landscapes.</p>
Fig. 3. A in Sub-Montane Habitat Selection By A Lowland Pheasant
Fig. 3. A comparison of slope between nest site locations (n=10) and random areas (n=90).
Data for: Foraging habitat and site selection do not affect feeding rates in European shags
<p>Igor files of depth, temperature and 3-axis acceleration of data-loggers deployed on European shags at Isle of May Scotland in late May-early June 2006. File name is bird ID. Information of all birds is in the Excel file "Shag2006Birds."</p>
Lesser prairie-chicken habitat selection and survival relative to a wind energy facility located in a fragmented landscape
<p>The overlap of renewable wind energy with the range of lesser prairie-chickens (<em>Tympanuchus</em> <em>pallidicinctus</em>) raises concern of population declines and habitat loss. Lesser prairie-chickens are adversely affected by landscape change, however, it is unclear how this species may respond to wind energy development. Therefore, managers and wind energy developers are currently tasked with making management or siting recommendations of future wind energy facilities based on lesser prairie-chicken behavioral responses to other forms of anthropogenic development or responses of other grouse species to wind energy development. The current strategy of siting wind turbines in cultivated cropland within lesser prairie-chicken range has not been evaluated for its effectiveness at minimizing potential adverse impacts. We captured 60 female and 66 male lesser prairie-chicken from leks located along a gradient from wind turbines in southern Kansas, USA, from 2017–2021. Over the study period, we collected lesser prairie-chicken location data and demographic information to evaluate resource selection, movements, and demography relative to environmental predictors and metrics associated with the wind energy facility. Lesser prairie-chickens used habitats in close proximity to wind turbines, provided that turbine density was low; however, avoidance associated with cultivated cropland appeared to be more predictive than the presence of wind turbines. We observed movement between turbines suggesting that wind turbines did not act as a barrier to local movements. We did not detect an influence of wind turbines on nest success or individual survival during breeding or non-breeding periods, a relationship that is consistent among multiple grouse species using habitats near wind energy infrastructure. Additional research is necessary to evaluate impacts associated with wind energy development in intact lesser prairie-chicken habitats, but placing wind turbines in cultivated croplands or other fragmented landscapes appears to be an important siting measure when considering wind energy facility siting across the lesser prairie-chicken range.</p>
Data for: Fear before food: Scale-dependence in elk habitat selection
<ol> <li>Habitat selection is a critical aspect of a species' ecology requiring complex decision-making that is both hierarchical and scale-dependent, since factors that influence selection may be nested or unequal across scales.</li> <li>Elk (<em>Cervus</em> <em>canadensis</em>) ranged widely across diverse habitats in North America prior to European settlement and subsequent eastern extirpation. Most habitat studies have occurred within their contemporary western range, even after eastern elk reintroductions began. As habitat selection can vary by geographic location, available cover, season, and diel period, it is important to understand how a non-migratory, reintroduced population in northern Wisconsin, USA is limited by the lack of variation in topography, elevation, and vegetation.</li> <li>We tested scale-dependent habitat selection on 79 adult elk from 2017–2020. We used resource selection functions across both temporal and spatial scales to understand differences in selection of topographic and environmental features.</li> <li>We found that selection varied both spatially and temporally and elk selected areas with the greatest potential to influence fitness at larger scales (i.e., landscape scale), meaning elk selected areas closer to escape cover and further from "risky" features (e.g., wolf territory centers, county roads and highways). We found stronger avoidance to wolf territory centers during spring, suggesting elk were selecting safer habitats during calving season. We found elk selected habitats with less canopy cover across both spatial scales and all seasons, suggesting that elk selected these areas for better access to forage as forest stands in early seral stages have greater nutritional value and forage biomass than closed-canopy forests and direct solar radiation to provide warmth in the cooler seasons.</li> <li>This study highlights how processes at different spatial and temporal scales influence species' decision-making. It provides insight into the complexity of making informed decisions in which an individual is responding to their immediate environment while simultaneously making decisions in the context of the larger landscape. Scale-dependent behavior is crucial to understand within specific geographic regions as these decisions scale up to influence population dynamics. </li> </ol>
Understanding habitat selection of range-expanding populations of large carnivores: 20 years of grey wolves (Canis lupus) recolonizing Germany
<p><strong>Aim</strong>: The non-stationarity in habitat selection of expanding populations poses a significant challenge for spatial forecasting. Focusing on the grey wolf (<em>Canis lupus</em>) natural recolonization of Germany, we compared the performance of different distribution modelling approaches for predicting habitat suitability in unoccupied areas. Furthermore, we analysed whether grey wolf showed non-stationarity in habitat selection in newly colonized areas, which will impact the predictions for potential habitat.</p> <p><strong>Location</strong>: Germany</p> <p><strong>Methods</strong>: Using telemetry data as presence points, we compared the predictive performance of five modelling approaches based on combinations of distribution modelling algorithms –GLMM, MaxEnt, and ensemble modelling– and two background point selection strategies. We used a homogeneous Poisson point process to draw background points from either the minimum convex polygons derived from telemetry or the whole area known to be occupied by wolves. Models were fit to the data of the first years and validated against independent data representing the expansion of the species. The best-performing approach was then used to further investigate non-stationarity in the species' response in spatiotemporal restricted datasets that represented different colonization steps.</p> <p><strong>Results</strong>: Whilst all approaches performed similarly when evaluated against a subset of the data used to fit the models, the ensemble model based on integrated data performed best when predicting range expansion. Models for subsequent colonization steps differed substantially from the global model, highlighting the non-stationarity of wolf habitat selection towards human disturbance during the colonization process.</p> <p><strong>Main conclusions</strong>: While telemetry-only data overfitted the models, using all available datasets increased the reliability of the range expansion forecasts. The non-stationarity in habitat selection pointed to wolves settling in the best areas first, and filling in nearby lower-quality habitat as the population increases. Our results caution against spatial extrapolation and space-for-time substitutions in habitat models, at least with expanding species.</p>
An experimental test of information use by North American wood ducks (Aix sponsa): external habitat cues, not social visual cues, influence initial nest-site selection
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Variation in habitat selection by male Strix nebulosa (Great Gray Owls) across the diel cycle, data archive
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Data for: Foraging habitat and site selection do not affect feeding rates in European shags
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Sea temperature effects on depth use and habitat selection in a marine fish community
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Ecological causes of fluctuating natural selection on habitat choice in an amphibian
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Data from: Post-release movement and habitat selection of translocated pine martens Martes martes
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Data from: Spatial heterogeneity of habitat selection of large carnivores and their ungulate prey in proximity to roads
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Data from: The burning question: does fire affect habitat selection and forage preference of the black rhinoceros Diceros bicornis in East African savannahs?
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Experimental evidence that social information affects habitat selection in Marbled Murrelets
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Data from: Delineating seasonal shifts in Reindeer habitat and diet selection by integrating GPS telemetry and stable Isotope analysis
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Scripts from: Remotely sensed microhabitat characteristics associated with Haematopus palliatus (American Oystercatcher) nest-site selection can inform beach habitat restoration along the U.S. Atlantic Coast
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Spatially explicit habitat selection: testing contagion and the ideal free distribution with culex mosquitoes
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Landscape features and seasonal habitat predicts lek-site selection and lek size of a <em>Tympanuchus</em> grouse
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Allen Brain Atlas
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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.