{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "CreativeWork", "additionalType": "Federal Government Series", "name": "Modeling at-sea density of marine birds to support renewable energy planning on the Pacific outer continental shelf of the contiguous United States", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70228321", "url": "https://pubs.usgs.gov/publication/70228321" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70228321 } ], "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "OCS Study" } ], "datePublished": "2021", "dateModified": "2022-02-08", "abstract": "This report describes the at-sea spatial distributions of marine birds in Pacific OCS waters off the contiguous U.S. (Figure 1.1) to inform marine spatial planning in the region. The goal was to estimate long-term average spatial distributions for marine bird species using all available science-quality transect survey data and numerous bathymetric, oceanographic, and atmospheric predictor variables. We developed seasonal habitat-based spatial models of the at-sea distribution for 33 individual species and 13 taxonomic groups of marine birds throughout the study region. A statistical modeling framework was used to estimate numerical relationships between bird sighting data (i.e., standardized counts) and a range of temporal (e.g., Pacific Decadal Oscillation [PDO] index), spatially static (e.g., depth), and spatially dynamic (e.g., sea surface chlorophyll-a concentration) environmental variables. The estimated relationships were then used to predict spatially explicit long-term average density (individuals per km2) throughout the study area for each species/group in each of four seasons. Bird sighting data came from multiple scientific survey programs and consisted of at-sea counts of birds collected between 1980 and 2017 using boat-based and fixed-wing aerial transect survey methods. Spatial environmental variables were derived from remote sensing satellite data and an ocean dynamics model.", "description": "xix, 385 p.", "publisher": { "@type": "Organization", "name": "Bureau of Ocean Energy Management" }, "author": [ { "@type": "Person", "name": "Sydeman, William J.", "givenName": "William J.", "familyName": "Sydeman", "affiliation": [ { "@type": "Organization", "name": "Farallon Institute" } ] }, { "@type": "Person", "name": "Strong, Craig", "givenName": "Craig", "familyName": "Strong", "affiliation": [ { "@type": "Organization", "name": "Crescent Coastal Research, Crescent City, CA, USA" } ] }, { "@type": "Person", "name": "Pearson, Scott", "givenName": "Scott", "familyName": "Pearson", "affiliation": [ { "@type": "Organization", "name": "Washington Department of Fish and Wildlife" } ] }, { "@type": "Person", "name": "Miller, Peter I", "givenName": "Peter I", "familyName": "Miller", "affiliation": [ { "@type": "Organization", "name": "Plymouth Marine Laboratory, PML Applications Ltd, Plymouth, UK" } ] }, { "@type": "Person", "name": "McIver, William", "givenName": "William", "familyName": "McIver", "affiliation": [ { "@type": "Organization", "name": "United States Fish and Wildlife Service, Arcata Fish and Wildlife Office, Arcata, CA, USA" } ] }, { "@type": "Person", "name": "Jahncke, Jaime", "givenName": "Jaime", "familyName": "Jahncke", "affiliation": [ { "@type": "Organization", "name": "Point Blue Conservation Science; GFNMS SAC" } ] }, { "@type": "Person", "name": "Evenson, Joseph R.", "givenName": "Joseph R.", "familyName": "Evenson", "affiliation": [ { "@type": "Organization", "name": "Washington Department of Fish and Wildlife" } ] }, { "@type": "Person", "name": "Croll, Donald", "givenName": "Donald", "familyName": "Croll", "affiliation": [ { "@type": "Organization", "name": "Ecology and Evolutionary Biology Department, University of California Santa Cruz, Santa Cruz, California, United States of America" } ] }, { "@type": 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