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"name": "Webster, Sarah C",
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"name": "Jackson, Pat J",
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{
"name": "Casazza, Michael L",
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"name": "Chenaille, Michael P",
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"titles": [
{
"title": "Data to Support Hierarchical Models and Decision Support Maps to Guide Management of Subsidized Avian Predator Densities"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2023,
"subjects": [
{
"subject": "Anthropogenic subsidies, biota, conservation planning, decision support, density surface model, distance sampling, ecology, human impacts, native species, nest survival, population decline, predation, predator-prey, sagebrush, species distribution, species of concern, wildlife, California, Great Basin, Idaho, Nevada, bi-state, Ecology"
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"description": "We combined approximately 28,000 raven point count surveys with data from more than 900 sage-grouse nests between 2009 and 2019 within the Great Basin, USA. We modeled variation in raven density using a Bayesian hierarchical distance sampling approach with environmental covariates on detection and abundance. Concurrently, we modeled sage-grouse nest survival using a hierarchical frailty model as a function of raven density as well as other environmental covariates that influence risk of failure. Raven density commonly exceeded more than 0.5 ravens per square kilometer and increased at low relative elevations with prevalent anthropogenic development and/or agriculture. Reduced sage-grouse nest survival was strongly associated with elevated raven density (e.g., more than 0.5 ravens per square kilometer) and varied with topographic ruggedness, shrub cover, and burned areas. For conservation application, we developed a spatially explicit planning tool that predicts nest survival under current and reduced raven numbers at local areas within the Great Basin to help direct management actions to locations where sage-grouse nests are at highest risk of failure. Our modeling framework can be generalized to multiple species where spatially registered abundance and demographic data are available.",
"descriptionType": "Abstract"
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"created": "2023-06-16T17:19:07Z",
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