This global geologic map of Mars, which records the distribution of geologic units and landforms on the planet's surface through time, is based on unprecedented variety, quality, and quantity of remotely sensed data acquired since the Viking Orbiters. These data have provided morphologic, topographic, spectral, thermophysical, radar sounding, and other observations for integration, analysis, and interpretation in support of geologic mapping. In particular, the precise topographic mapping now available has enabled consistent morphologic portrayal of the surface for global mapping (whereas previously used visual-range image bases were less effective, because they combined morphologic and albedo information and, locally, atmospheric haze). Also, thermal infrared image bases used for this map tended to be less affected by atmospheric haze and thus are reliable for analysis of surface morphology and texture at even higher resolution than the topographic products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3292","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Tanaka, K.L., Skinner, J., Dohm, J.M., Irwin, R.P., Kolb, E.J., Fortezzo, C.M., Platz, T., Michael, G.G., and Hare, T.M., 2014, Geologic map of Mars: U.S. Geological Survey Scientific Investigations Map 3292, Map Sheet: 55.89 x 42.70 inches; Pamphlet: ii, 43 p.; Figure 2; Table D1; Description of Map Units with locality images; Readme; Metadata: TXT; Metadata: XML; Database, https://doi.org/10.3133/sim3292.","productDescription":"Map Sheet: 55.89 x 42.70 inches; Pamphlet: ii, 43 p.; Figure 2; Table D1; Description of Map Units with locality images; Readme; Metadata: TXT; Metadata: XML; Database","numberOfPages":"48","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-042532","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":438753,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PBKSGE","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3292 Geologic Map of Mars"},{"id":289770,"rank":11,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3292.jpg"},{"id":289766,"rank":10,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3292/downloads/sim3292_readme.txt","size":"6 kB","linkFileType":{"id":2,"text":"txt"}},{"id":289761,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3292/pdf/sim3292_map.pdf","text":"Map Sheet","size":"35.3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":289763,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3292/pdf/sim3292_figure2.pdf","text":"Figure 2","size":"370 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":289767,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3292/downloads/sim3292_metadata.txt","text":"Metadata TXT","size":"14 kB","linkFileType":{"id":2,"text":"txt"}},{"id":289768,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3292/downloads/sim3292_metadata.xml","text":"Metadata XML","size":"15 kB"},{"id":289762,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3292/pdf/sim3292_pamphlet.pdf","text":"Pamphlet","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":289769,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3292/downloads/sim3292_database.zip","text":"Database ZIP","size":"790 MB"},{"id":400816,"rank":12,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9PBKSGE","text":"Interactive map","linkHelpText":"- Geologic Map of Mars, 1:20M. 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(2014)"},{"id":289765,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3292/pdf/sim3292_dmu_with_locality_images.pdf","text":"Description of Map Units with locality images","size":"791 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":289764,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3292/pdf/sim3292_tabled1.pdf","text":"Table D1","size":"133 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":289760,"rank":4,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3292/"}],"scale":"20000000","projection":"Robinson Pseudocylindrical Projection","otherGeospatial":"Mars","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53c4edd5e4b0b58d96eeb540","contributors":{"authors":[{"text":"Tanaka, Kenneth L. ktanaka@usgs.gov","contributorId":610,"corporation":false,"usgs":true,"family":"Tanaka","given":"Kenneth","email":"ktanaka@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":491760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skinner, James A. 0000-0002-3644-7010 jskinner@usgs.gov","orcid":"https://orcid.org/0000-0002-3644-7010","contributorId":3187,"corporation":false,"usgs":true,"family":"Skinner","given":"James A.","email":"jskinner@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":491761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dohm, James M.","contributorId":83610,"corporation":false,"usgs":true,"family":"Dohm","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":491767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Rossman P. III","contributorId":59718,"corporation":false,"usgs":true,"family":"Irwin","given":"Rossman","suffix":"III","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":491765,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kolb, Eric J.","contributorId":97823,"corporation":false,"usgs":true,"family":"Kolb","given":"Eric","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":491768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fortezzo, Corey M. 0000-0001-8188-5530 cfortezzo@usgs.gov","orcid":"https://orcid.org/0000-0001-8188-5530","contributorId":25383,"corporation":false,"usgs":true,"family":"Fortezzo","given":"Corey","email":"cfortezzo@usgs.gov","middleInitial":"M.","affiliations":[{"id":130,"text":"Astrogeology Research Center","active":false,"usgs":true}],"preferred":false,"id":491763,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Platz, Thomas","contributorId":64974,"corporation":false,"usgs":true,"family":"Platz","given":"Thomas","affiliations":[],"preferred":false,"id":491766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Michael, Gregory G.","contributorId":36467,"corporation":false,"usgs":true,"family":"Michael","given":"Gregory","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":491764,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":491762,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70103895,"text":"tm11D2 - 2014 - A methodological toolkit for field assessments of artisanally mined alluvial diamond deposits","interactions":[],"lastModifiedDate":"2014-07-11T13:22:04","indexId":"tm11D2","displayToPublicDate":"2014-07-11T12:57:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-D2","title":"A methodological toolkit for field assessments of artisanally mined alluvial diamond deposits","docAbstract":"This toolkit provides a standardized checklist of critical issues relevant to artisanal mining-related field research. An integrated sociophysical geographic approach to collecting data at artisanal mine sites is outlined. The implementation and results of a multistakeholder approach to data collection, carried out in the assessment of Guinea’s artisanally mined diamond deposits, also are summarized. This toolkit, based on recent and successful field campaigns in West Africa, has been developed as a reference document to assist other government agencies or organizations in collecting the data necessary for artisanal diamond mining or similar natural resource assessments.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section D: Field survey methods in Book 11 Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11D2","collaboration":"Prepared under the auspices of the U.S. Department of State. This report is Chapter 2 of Section D: Field survey methods in Book 11 Collection and Delineation of Spatial Data.","usgsCitation":"Chirico, P., and Malpeli, K., 2014, A methodological toolkit for field assessments of artisanally mined alluvial diamond deposits: U.S. Geological Survey Techniques and Methods 11-D2, iv, 29 p., https://doi.org/10.3133/tm11D2.","productDescription":"iv, 29 p.","numberOfPages":"37","onlineOnly":"Y","ipdsId":"IP-049488","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":289794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm11D2.jpg"},{"id":289793,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/d02/pdf/tm11-D2.pdf"},{"id":289792,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/11/d02/"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53c0eb15e4b065ccca5fe218","contributors":{"authors":[{"text":"Chirico, Peter G.","contributorId":27086,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter G.","affiliations":[],"preferred":false,"id":493531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malpeli, Katherine C.","contributorId":55106,"corporation":false,"usgs":true,"family":"Malpeli","given":"Katherine C.","affiliations":[],"preferred":false,"id":493532,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70112475,"text":"sir20145115 - 2014 - Streamflow statistics for unregulated and regulated conditions for selected locations on the Upper Yellowstone and Bighorn Rivers, Montana and Wyoming, 1928-2002","interactions":[],"lastModifiedDate":"2014-07-11T11:18:51","indexId":"sir20145115","displayToPublicDate":"2014-07-11T11:11:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5115","title":"Streamflow statistics for unregulated and regulated conditions for selected locations on the Upper Yellowstone and Bighorn Rivers, Montana and Wyoming, 1928-2002","docAbstract":"Major floods in 1996 and 1997 intensified public debate about the effects of human activities on the Yellowstone River. In 1999, the Yellowstone River Conservation District Council was formed to address conservation issues on the river. The Yellowstone River Conservation District Council partnered with the U.S. Army Corps of Engineers to carry out a cumulative effects study on the main stem of the Yellowstone River. The cumulative effects study is intended to provide a basis for future management decisions within the watershed. Streamflow statistics, such as flow-frequency data calculated for unregulated and regulated streamflow conditions, are a necessary component of the cumulative effects study.
\nThe U.S. Geological Survey, in cooperation with the Yellowstone River Conservation District Council and the U.S. Army Corps of Engineers, calculated low-flow frequency data and general monthly and annual statistics for unregulated and regulated streamflow conditions for the Upper Yellowstone and Bighorn Rivers for the 1928–2002 study period; these data are presented in this report. Unregulated streamflow represents flow conditions during the 1928–2002 study period if there had been no water-resources development in the Yellowstone River Basin. Regulated streamflow represents estimates of flow conditions during the 1928–2002 study period if the level of water-resources development existing in 2002 was in place during the entire study period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145115","collaboration":"Prepared in cooperation with the Yellowstone River Conservation District Council and the U.S. Army Corps of Engineers","usgsCitation":"Chase, K.J., 2014, Streamflow statistics for unregulated and regulated conditions for selected locations on the Upper Yellowstone and Bighorn Rivers, Montana and Wyoming, 1928-2002: U.S. Geological Survey Scientific Investigations Report 2014-5115, Report: xiii, 117 p.; Appendixes 2-1 and 2-2, https://doi.org/10.3133/sir20145115.","productDescription":"Report: xiii, 117 p.; Appendixes 2-1 and 2-2","numberOfPages":"136","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1928-01-01","temporalEnd":"2002-12-31","ipdsId":"IP-052172","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":289790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145115.jpg"},{"id":289787,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5115/pdf/sir2014-5115.pdf"},{"id":289786,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5115/"},{"id":289788,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5115/downloads/sir2014-5155_APP_2.1_loc_and_da.xlsx"},{"id":289789,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5115/downloads/sir2014-5155_APP_2.2_lowflowfreq.xlsx"}],"projection":"Lambert Conformal Conic projection","datum":"North American Datum of 1983","country":"United States","state":"Montana;Wyoming","otherGeospatial":"Bighorn River;Upper Yellowstone River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -112.0,42.0 ], [ -112.0,49.0 ], [ -103.0,49.0 ], [ -103.0,42.0 ], [ -112.0,42.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53c0ed2be4b065ccca5fe552","contributors":{"authors":[{"text":"Chase, Katherine J. 0000-0002-5796-4148 kchase@usgs.gov","orcid":"https://orcid.org/0000-0002-5796-4148","contributorId":454,"corporation":false,"usgs":true,"family":"Chase","given":"Katherine","email":"kchase@usgs.gov","middleInitial":"J.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":494759,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70116453,"text":"70116453 - 2014 - Guild-specific responses of avian species richness to LiDAR-derived habitat heterogeneity","interactions":[],"lastModifiedDate":"2017-08-31T13:55:55","indexId":"70116453","displayToPublicDate":"2014-07-11T09:56:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":639,"text":"Acta Oecologica","active":true,"publicationSubtype":{"id":10}},"title":"Guild-specific responses of avian species richness to LiDAR-derived habitat heterogeneity","docAbstract":"Ecological niche theory implies that more heterogeneous habitats have the potential to support greater biodiversity. Positive heterogeneity-diversity relationships have been found for most studies investigating animal taxa, although negative relationships also occur and the scale dependence of heterogeneity-diversity relationships is little known. We investigated multi-scale, heterogeneity-diversity relationships for bird communities in a semi-arid riparian landscape, using airborne LiDAR data to derive key measures of structural habitat complexity. Habitat heterogeneity-diversity relationships were generally positive, although the overall strength of relationships varied across avian life history guilds (R2 range: 0.03–0.41). Best predicted were the species richness indices of cavity nesters, habitat generalists, woodland specialists, and foliage foragers. Heterogeneity-diversity relationships were also strongly scale-dependent, with strongest associations at the 200-m scale (4 ha) and weakest associations at the 50-m scale (0.25 ha). Our results underscore the value of LiDAR data for fine-grained quantification of habitat structure, as well as the need for biodiversity studies to incorporate variation among life-history guilds and to simultaneously consider multiple guild functional types (e.g. nesting, foraging, habitat). Results suggest that certain life-history guilds (foliage foragers, cavity nesters, woodland specialists) are more susceptible than others (ground foragers, ground nesters, low nesters) to experiencing declines in local species richness if functional elements of habitat heterogeneity are lost. Positive heterogeneity-diversity relationships imply that riparian conservation efforts need to not only provide high-quality riparian habitat locally, but also to provide habitat heterogeneity across multiple scales.","language":"English","publisher":"Elsevier","doi":"10.1016/j.actao.2014.06.002","usgsCitation":"Weisberg, P.J., Dilts, T.E., Becker, M.E., Young, J.S., Wong-Kone, D.C., Newton, W.E., and Ammon, E.M., 2014, Guild-specific responses of avian species richness to LiDAR-derived habitat heterogeneity: Acta Oecologica, v. 59, p. 72-83, https://doi.org/10.1016/j.actao.2014.06.002.","productDescription":"12 p.","startPage":"72","endPage":"83","numberOfPages":"12","ipdsId":"IP-052509","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":289779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289778,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.actao.2014.06.002"}],"country":"United States","state":"California;Nevada","otherGeospatial":"Walker River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.2488,39.3635 ], [ -120.2488,38.1178 ], [ -118.5288,38.1178 ], [ -118.5288,39.3635 ], [ -120.2488,39.3635 ] ] ] } } ] }","volume":"59","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53c0ec3fe4b065ccca5fe3bd","contributors":{"authors":[{"text":"Weisberg, Peter J.","contributorId":33631,"corporation":false,"usgs":true,"family":"Weisberg","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":495793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dilts, Thomas E.","contributorId":36833,"corporation":false,"usgs":true,"family":"Dilts","given":"Thomas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":495794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Becker, Miles E.","contributorId":88277,"corporation":false,"usgs":true,"family":"Becker","given":"Miles","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":495796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Jock S.","contributorId":28154,"corporation":false,"usgs":true,"family":"Young","given":"Jock","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":495792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wong-Kone, Diane C.","contributorId":79790,"corporation":false,"usgs":true,"family":"Wong-Kone","given":"Diane","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":495795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":495791,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ammon, Elisabeth M.","contributorId":106785,"corporation":false,"usgs":true,"family":"Ammon","given":"Elisabeth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":495797,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70116321,"text":"70116321 - 2014 - Re-Os geochronology and Os isotope fingerprinting of petroleum sourced from a Type I lacustrine kerogen: insights from the natural Green River petroleum system in the Uinta Basin and hydrous pyrolysis experiments","interactions":[],"lastModifiedDate":"2014-07-10T15:01:11","indexId":"70116321","displayToPublicDate":"2014-07-10T14:37:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Re-Os geochronology and Os isotope fingerprinting of petroleum sourced from a Type I lacustrine kerogen: insights from the natural Green River petroleum system in the Uinta Basin and hydrous pyrolysis experiments","docAbstract":"Rhenium–osmium (Re–Os) geochronology of marine petroleum systems has allowed the determination of the depositional age of source rocks as well as the timing of petroleum generation. In addition, Os isotopes have been applied as a fingerprinting tool to correlate oil to its source unit. To date, only classic marine petroleum systems have been studied. Here we present Re–Os geochronology and Os isotope fingerprinting of different petroleum phases (oils, tar sands and gilsonite) derived from the lacustrine Green River petroleum system in the Uinta Basin, USA. In addition we use an experimental approach, hydrous pyrolysis experiments, to compare to the Re–Os data of naturally generated petroleum in order to further understand the mechanisms of Re and Os transfer to petroleum.
\nThe Re–Os geochronology of petroleum from the lacustrine Green River petroleum system (19 ± 14 Ma – all petroleum phases) broadly agrees with previous petroleum generation basin models (∼25 Ma) suggesting that Re–Os geochronology of variable petroleum phases derived from lacustrine Type I kerogen has similar systematics to Type II kerogen (e.g., Selby and Creaser, 2005a, Selby and Creaser, 2005b and Finlay et al., 2010). However, the large uncertainties (over 100% in some cases) produced for the petroleum Re–Os geochronology are a result of multiple generation events occurring through a ∼3000-m thick source unit that creates a mixture of initial Os isotope compositions in the produced petroleum phases. The 187Os/188Os values for the petroleum and source rocks at the time of oil generation vary from 1.4 to 1.9, with the mode at ∼1.6. Oil-to-source correlation using Os isotopes is consistent with previous correlation studies in the Green River petroleum system, and illustrates the potential utility of Os isotopes to characterize the spatial variations within a petroleum system.
\nHydrous pyrolysis experiments on the Green River Formation source rocks show that Re and Os transfer are mimicking the natural system. This transfer from source to bitumen to oil does not affect source rock Re–Os systematics or Os isotopic compositions. This confirms that Os isotope compositions are transferred intact from source to petroleum during petroleum generation and can be used as a powerful correlation tool. These experiments further confirm that Re–Os systematics in source rocks are not adversely affected by petroleum maturation. Overall this study illustrates that the Re–Os petroleum geochronometer and Os isotope fingerprinting tools can be used on a wide range of petroleum types sourced from variable kerogen types.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geochimica et Cosmochimica Acta","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2014.04.016","usgsCitation":"Cumming, V.M., Selby, D., Lillis, P.G., and Lewan, M., 2014, Re-Os geochronology and Os isotope fingerprinting of petroleum sourced from a Type I lacustrine kerogen: insights from the natural Green River petroleum system in the Uinta Basin and hydrous pyrolysis experiments: Geochimica et Cosmochimica Acta, v. 138, p. 32-56, https://doi.org/10.1016/j.gca.2014.04.016.","productDescription":"25 p.","startPage":"32","endPage":"56","numberOfPages":"25","ipdsId":"IP-049279","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":472880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2014.04.016","text":"Publisher Index Page"},{"id":289753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289751,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.gca.2014.04.016"}],"country":"United States","state":"Utah","otherGeospatial":"Uinta Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.0,39.0 ], [ -111.0,41.0 ], [ -108.4,41.0 ], [ -108.4,39.0 ], [ -111.0,39.0 ] ] ] } } ] }","volume":"138","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bfa7d5e4b06d97a6487cf2","contributors":{"authors":[{"text":"Cumming, Vivien M.","contributorId":69044,"corporation":false,"usgs":true,"family":"Cumming","given":"Vivien","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":495769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Selby, David","contributorId":58167,"corporation":false,"usgs":true,"family":"Selby","given":"David","affiliations":[],"preferred":false,"id":495768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lillis, Paul G. 0000-0002-7508-1699 plillis@usgs.gov","orcid":"https://orcid.org/0000-0002-7508-1699","contributorId":1817,"corporation":false,"usgs":true,"family":"Lillis","given":"Paul","email":"plillis@usgs.gov","middleInitial":"G.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":495767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewan, Michael D. mlewan@usgs.gov","contributorId":940,"corporation":false,"usgs":true,"family":"Lewan","given":"Michael D.","email":"mlewan@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":495766,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70111868,"text":"sir20145110 - 2014 - Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010","interactions":[],"lastModifiedDate":"2014-07-10T14:29:34","indexId":"sir20145110","displayToPublicDate":"2014-07-10T13:15:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5110","title":"Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010","docAbstract":"Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types for modeling habitat capacities and needs of marsh-reliant wildlife (such as waterfowl and alligator). Detailed information on the extent and distribution of marsh vegetation zones throughout the Texas coast has been historically unavailable. In response, the U.S. Geological Survey, in cooperation and collaboration with the U.S. Fish and Wildlife Service via the Gulf Coast Joint Venture, Texas A&M University-Kingsville, the University of Louisiana-Lafayette, and Ducks Unlimited, Inc., has produced a classification of marsh vegetation types along the middle and upper Texas coast from Corpus Christi Bay to the Sabine River. This study incorporates approximately 1,000 ground reference locations collected via helicopter surveys in coastal marsh areas and about 2,000 supplemental locations from fresh marsh, water, and “other” (that is, nonmarsh) areas. About two-thirds of these data were used for training, and about one-third were used for assessing accuracy. Decision-tree analyses using Rulequest See5 were used to classify emergent marsh vegetation types by using these data, multitemporal satellite-based multispectral imagery from 2009 to 2011, a bare-earth digital elevation model (DEM) based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables believed to be important for delineating the extent and distribution of marsh vegetation communities. Image objects were generated from segmentation of high-resolution airborne imagery acquired in 2010 and were used to refine the classification. The classification is dated 2010 because the year is both the midpoint of the multitemporal satellite-based imagery (2009–11) classified and the date of the high-resolution airborne imagery that was used to develop image objects. Overall accuracy corrected for bias (accuracy estimate incorporates true marginal proportions) was 91 percent (95 percent confidence interval [CI]: 89.2–92.8), with a kappa statistic of 0.79 (95 percent CI: 0.77–0.81). The classification performed best for saline marsh (user’s accuracy 81.5 percent; producer’s accuracy corrected for bias 62.9 percent) but showed a lesser ability to discriminate intermediate marsh (user’s accuracy 47.7 percent; producer’s accuracy corrected for bias 49.5 percent). Because of confusion in intermediate and brackish marsh classes, an alternative classification containing only three marsh types was created in which intermediate and brackish marshes were combined into a single class. Image objects were reattributed by using this alternative three-marsh-type classification. Overall accuracy, corrected for bias, of this more general classification was 92.4 percent (95 percent CI: 90.7–94.2), and the kappa statistic was 0.83 (95 percent CI: 0.81–0.85). Mean user’s accuracy for marshes within the four-marsh-type and three-marsh-type classifications was 65.4 percent and 75.6 percent, respectively, whereas mean producer’s accuracy was 56.7 percent and 65.1 percent, respectively.
\nThis study provides a more objective and repeatable method for classifying marsh types of the middle and upper Texas coast at an extent and greater level of detail than previously available for the study area. The seamless classification produced through this work is now available to help State agencies (such as the Texas Parks and Wildlife Department) and landscape-scale conservation partnerships (such as the Gulf Coast Prairie Landscape Conservation Cooperative and the Gulf Coast Joint Venture) to develop and (or) refine conservation plans targeting priority natural resources. Moreover, these data may improve projections of landscape change and serve as a baseline for monitoring future changes resulting from chronic and episodic stressors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145110","collaboration":"Prepared in cooperation and collaboration with U.S. Fish and Wildlife Service via Gulf Coast Joint Venture, Texas A&M University-Kingsville, University of Louisiana-Lafayette, and Ducks Unlimited, Inc.","usgsCitation":"Enwright, N.M., Hartley, S.B., Brasher, M., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., Couvillion, B., and Wilson, B.C., 2014, Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010: U.S. Geological Survey Scientific Investigations Report 2014-5110, Report: v, 18 p.; Map: 60.00 x 48.00 inches; Downloads Directory, https://doi.org/10.3133/sir20145110.","productDescription":"Report: v, 18 p.; Map: 60.00 x 48.00 inches; Downloads Directory","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2010-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-054024","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":289747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145110.jpg"},{"id":289730,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5110/"},{"id":289744,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5110/pdf/sir2014-5110.pdf"},{"id":289745,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2014/5110/pdf/sir2014-5110_plate.pdf"},{"id":289746,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5110/downloads/"}],"country":"United States","state":"Texas","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.00,27.75 ], [ -98.00,30.50 ], [ -93.75,30.50 ], [ -93.75,27.75 ], [ -98.00,27.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bfa7cfe4b06d97a6487cec","contributors":{"authors":[{"text":"Enwright, Nicholas M. 0000-0002-7887-3261 enwrightn@usgs.gov","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":4880,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","email":"enwrightn@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":494489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":494488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brasher, Michael G.","contributorId":17139,"corporation":false,"usgs":true,"family":"Brasher","given":"Michael G.","affiliations":[],"preferred":false,"id":494491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Visser, Jenneke M.","contributorId":90397,"corporation":false,"usgs":true,"family":"Visser","given":"Jenneke","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":494495,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mitchell, Michael K.","contributorId":24688,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":494492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ballard, Bart M.","contributorId":62932,"corporation":false,"usgs":true,"family":"Ballard","given":"Bart","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":494493,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parr, Mark W.","contributorId":79023,"corporation":false,"usgs":true,"family":"Parr","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":494494,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Couvillion, Brady R. 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":98834,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady R.","affiliations":[],"preferred":false,"id":494496,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, Barry C.","contributorId":12968,"corporation":false,"usgs":true,"family":"Wilson","given":"Barry","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":494490,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70074066,"text":"sir20105090N - 2014 - Porphyry copper assessment of western Central Asia","interactions":[{"subject":{"id":70074066,"text":"sir20105090N - 2014 - Porphyry copper assessment of western Central Asia","indexId":"sir20105090N","publicationYear":"2014","noYear":false,"chapter":"N","title":"Porphyry copper assessment of western Central Asia"},"predicate":"IS_PART_OF","object":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"id":1}],"isPartOf":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"lastModifiedDate":"2022-12-08T17:56:33.874283","indexId":"sir20105090N","displayToPublicDate":"2014-07-10T11:59:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5090","chapter":"N","title":"Porphyry copper assessment of western Central Asia","docAbstract":"The U.S. Geological Survey conducted an assessment of resources associated with porphyry copper deposits in the western Central Asia countries of Kyrgyzstan, Uzbekistan, Kazakhstan, and Tajikistan and the southern Urals of Kazakhstan and Russia as part of a global mineral resource assessment. The purpose of the study was to (1) delineate permissive areas (tracts) for undiscovered porphyry copper deposits; (2) compile a database of known porphyry copper deposits and significant prospects; (3) where data permit, estimate numbers of undiscovered deposits within those permissive tracts; and (4) provide probabilistic estimates the amounts of copper (Cu), molybdenum (Mo), gold (Au), and silver (Ag) that could be contained in those undiscovered deposits.
\nWestern Central Asia, a region diverse in its geologic complexity, is situated north of the Tarim and North China tectonic blocks and sandwiched between the East European and Siberian cratons. The Ural Mountains form the western margin of the region; the southern margin is formed by the high-standing ranges that make up the Tian Shan mountain range in the border regions of Kazakhstan, Kyrgyzstan, and western China, where the effects of collisional tectonics are well displayed. The tectonic collage that makes up the core of western Central Asia is perhaps the geologically least understood part of the region. There is broad agreement that the early Paleozoic is made up of tectonically juxtaposed blocks that vary from Precambrian-cored microcontinents to magmatic arc and related complexes to subduction-related accretionary complexes. The rudiments of an incipient, contiguous single Kazakhstan block were formed by the end of the Silurian. In the middle to late Paleozoic, the block was unconformably superposed by two large, nested magmatic-arc belts, one Devonian, the other Carboniferous. Both magmatic-arc complexes were folded into a horseshoe-shaped, southeast-opening orocline in response to the final collisions of the various surrounding cratonic blocks with the Kazakhstan block. Additional deformation in the upper Cenozoic derived from the collision of India and China significantly redistributed fragments of the various mosaicked blocks, particularly in the central and southern parts of the western Central Asian region. Porphyry copper deposits are associated with many of the magmatic-arc fragments and belts throughout the geologically complex region, and economically important deposits are found in arc sequences of all Paleozoic Periods. The economically most productive arcs are Carboniferous.
\nThe assessment includes a discussion of the tectonic and geologic setting of porphyry copper deposits in western Central Asia (chapter 1), an application of remote sensing data for hydrothermal alteration mapping as a tool for porphyry copper assessment in the region (chapter 2), and a probabilistic assessment of undiscovered porphyry copper resources in four areas that represent Ordovician and Late Paleozoic (Carboniferous-Permian) magmatic arcs (chapter 3). The principal litho-tectonic terrane concept used to delineate permissive tracts was that of a magmatic arc that formed in the subduction boundary zone above a subducting plate. Eight permissive tracts are delineated on the basis of mapped and inferred subsurface distributions of igneous rocks assigned to magmatic arcs of specified age ranges that define areas where the occurrence of porphyry copper deposits within 1 kilometer of the Earth’s surface is possible. These tracts range in area from about 8,000 to 200,000 square kilometers and host 18 known porphyry copper deposits that contain about 54 million metric tons of copper. Available data included geologic maps, the distribution of significant porphyry copper occurrences and potentially related deposit types, the distribution of hydrothermal alteration patterns that are consistent with porphyry copper mineralization, and information on possible subsurface extensions of permissive rocks. On the basis of analyses of these data, the assessment team estimated a mean of 25 undiscovered porphyry copper deposits for the study area. Estimates of numbers of undiscovered deposits were combined with grade and tonnage models in a Monte Carlo simulation to yield a mean estimate of 95 million metric tons of copper in undiscovered porphyry copper deposits; this represents about twice the amount of identified porphyry copper resources (54 million metric tons).
\nDetailed descriptions of each permissive tract, including the rationales for delineation and assessment, are given in appendixes, along with a geographic information system (GIS) that includes permissive tract boundaries, point locations of known porphyry copper deposits and significant occurrences, and hydrothermal alteration data based on analysis of remote sensing data.
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Reimar","contributorId":73450,"corporation":false,"usgs":true,"family":"Seltmann","given":"Reimar","email":"","affiliations":[],"preferred":false,"id":489374,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Herrington, Richard J.","contributorId":70688,"corporation":false,"usgs":true,"family":"Herrington","given":"Richard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":489372,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70116235,"text":"ofr20141144 - 2014 - Behavior and dam passage of juvenile Chinook salmon and juvenile steelhead at Detroit Reservoir and Dam, Oregon, March 2012-February 2013","interactions":[],"lastModifiedDate":"2014-07-10T11:05:14","indexId":"ofr20141144","displayToPublicDate":"2014-07-09T16:32:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1144","title":"Behavior and dam passage of juvenile Chinook salmon and juvenile steelhead at Detroit Reservoir and Dam, Oregon, March 2012-February 2013","docAbstract":"The in-reservoir movements and dam passage of individual juvenile Chinook salmon (Oncorhynchus tshawytscha) and juvenile steelhead (Oncorhynchus mykiss) were studied at Detroit Reservoir and Dam, near Detroit, Oregon, during 2012 and 2013. The goal of the study was to provide data to inform decisions about future downstream passage alternatives and factors affecting downstream passage rates with the existing dam configuration. In 2012, 468 juvenile Chinook salmon and 200 juvenile steelhead were tagged and released during a 3-month period in the spring, and another 514 juvenile Chinook salmon were tagged and released during a 3-month period in the fall. The fish were surgically implanted with a small acoustic transmitter with an expected life of about 3 months and a passive integrated transponder tag with an indefinite life, and were released into the two main tributaries several kilometers upstream of the reservoir. Juvenile Chinook salmon migrated from the release sites to the reservoir in a greater proportion than juvenile steelhead, but once in the reservoir, juvenile steelhead migrated to the forebay faster and had a higher dam passage rate than juvenile Chinook salmon. The routes available for passing water and fish varied throughout the year, with low reservoir elevations in winter and high reservoir elevations in summer in accordance with the flood-control purpose of the dam. Most dam passage was through the spillway during the spring and summer, when the reservoir elevation was high and the spillway and powerhouse were the most common routes in operation, and via the powerhouse during the fall and winter period, when the reservoir elevation was low and the regulating outlet and powerhouse were the most common routes in operation. Few tagged fish passed when the powerhouse was the only route in operation. Dam passage rates during the spring and summer were greatest at night, increased with dam discharge, and were greater when water was passed freely over the spillway compared to when it was controlled by the spillway Tainter gates. Dam passage rates during the fall and winter, when the reservoir elevation usually was too low for spillway operation, were lower than during the spring and summer, negatively related to reservoir elevation, and positively related to dam discharge, though the latter relation diminished as reservoir elevation decreased. Fish locations near the dam from estimates of three-dimensional positions often were near the locations of dam discharge and fish depths were surface oriented relative to the depth of the forebay. Fish passage rates with the existing dam configuration were greatest when the spillway was in operation and were lowest when the powerhouse was the only route in operation; the latter result may be related to the relatively low magnitude or variability in discharge during that condition. The available data suggest that a properly designed surface outlet could be a viable passage route for juvenile Chinook salmon and juvenile steelhead at Detroit Dam. A second year of data collection based on a similar study design was complete at the time of this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141144","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Beeman, J.W., Hansel, H.C., Hansen, A.C., Evans, S.D., Haner, P.V., Hatton, T., Kofoot, E.E., Sprando, J.M., and Smith, C., 2014, Behavior and dam passage of juvenile Chinook salmon and juvenile steelhead at Detroit Reservoir and Dam, Oregon, March 2012-February 2013: U.S. Geological Survey Open-File Report 2014-1144, vi, 62 p., https://doi.org/10.3133/ofr20141144.","productDescription":"vi, 62 p.","numberOfPages":"72","onlineOnly":"Y","temporalStart":"2012-03-01","temporalEnd":"2013-02-28","ipdsId":"IP-049970","costCenters":[{"id":654,"text":"Western Fisheries Research 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and its relationship to temporal urban landscape evapotranspiration factors","interactions":[],"lastModifiedDate":"2014-07-08T15:12:01","indexId":"70115942","displayToPublicDate":"2014-07-08T15:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors","docAbstract":"Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index) for shrubs (r2 = 0.66) and trees (r2 = 0.63). However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p > 0.05) and the lowest one was for turf (r2 = 0.88, p > 0.05). In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI) from MODIS was employed. The results revealed a significant positive relationship between ETMODIS and ETWV2 (r2 = 0.9857, p > 0.05). This indicates that the relationship between NDVI using high resolution WorldView-2 imagery and ground-based validation approaches could provide an effective predictive tool for determining ET rates from unstressed mixed urban landscape plantings.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"MDPI AG","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs6010580","usgsCitation":"Nouri, H., Beecham, S., Anderson, S., and Nagler, P., 2014, High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors: Remote Sensing, v. 6, no. 1, p. 580-602, https://doi.org/10.3390/rs6010580.","productDescription":"23 p.","startPage":"580","endPage":"602","numberOfPages":"23","ipdsId":"IP-049133","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":472885,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6010580","text":"Publisher Index Page"},{"id":289558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289556,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3390/rs6010580"}],"country":"Australia","city":"Adelaide","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 138.593373,-34.942277 ], [ 138.593373,-34.935021 ], [ 138.602208,-34.935021 ], [ 138.602208,-34.942277 ], [ 138.593373,-34.942277 ] ] ] } } ] }","volume":"6","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-01-06","publicationStatus":"PW","scienceBaseUri":"53bd04d9e4b00cbf31f7232b","contributors":{"authors":[{"text":"Nouri, Hamideh 0000-0002-7424-5030","orcid":"https://orcid.org/0000-0002-7424-5030","contributorId":16327,"corporation":false,"usgs":true,"family":"Nouri","given":"Hamideh","email":"","affiliations":[],"preferred":false,"id":495715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beecham, Simon","contributorId":95397,"corporation":false,"usgs":true,"family":"Beecham","given":"Simon","affiliations":[],"preferred":false,"id":495717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Sharolyn","contributorId":22697,"corporation":false,"usgs":true,"family":"Anderson","given":"Sharolyn","affiliations":[],"preferred":false,"id":495716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagler, Pamela 0000-0003-0674-103X","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":8748,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","affiliations":[],"preferred":false,"id":495714,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70093761,"text":"ds827 - 2014 - Vegetation database for land-cover mapping, Clark and Lincoln Counties, Nevada","interactions":[],"lastModifiedDate":"2014-07-08T14:57:53","indexId":"ds827","displayToPublicDate":"2014-07-08T14:52:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"827","title":"Vegetation database for land-cover mapping, Clark and Lincoln Counties, Nevada","docAbstract":"Floristic and other vegetation data were collected at 3,175 sample sites to support land-cover mapping projects in Clark and Lincoln Counties, Nevada, from 2007 to 2013. Data were collected at sample sites that were selected to fulfill mapping priorities by one of two different plot sampling approaches. Samples were described at the stand level and classified into the National Vegetation Classification hierarchy at the alliance level and above. The vegetation database is presented in geospatial and tabular formats.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds827","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Charlet, D.A., Damar, N.A., and Leary, P.J., 2014, Vegetation database for land-cover mapping, Clark and Lincoln Counties, Nevada: U.S. Geological Survey Data Series 827, vi, 18 p., https://doi.org/10.3133/ds827.","productDescription":"vi, 18 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-045394","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":289554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds827.jpg"},{"id":289553,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/827/pdf/ds827.pdf"},{"id":289550,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/827/"}],"projection":"Universal Transverse Mercator projection","datum":"North American Datum of 1983","country":"United States","state":"Nevada","county":"Clark County;Lincoln County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.0,35.0 ], [ -116.0,37.25 ], [ -114.0,37.25 ], [ -114.0,35.0 ], [ -116.0,35.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bd04dbe4b00cbf31f72337","contributors":{"authors":[{"text":"Charlet, David A.","contributorId":14732,"corporation":false,"usgs":true,"family":"Charlet","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":490198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Damar, Nancy A. 0000-0002-7520-7386 nadamar@usgs.gov","orcid":"https://orcid.org/0000-0002-7520-7386","contributorId":4154,"corporation":false,"usgs":true,"family":"Damar","given":"Nancy","email":"nadamar@usgs.gov","middleInitial":"A.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":490196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leary, Patrick J.","contributorId":9575,"corporation":false,"usgs":true,"family":"Leary","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":490197,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70110903,"text":"sir20145076 - 2014 - Land-cover mapping of Red Rock Canyon National Conservation Area and Coyote Springs, Piute-Eldorado Valley, and Mormon Mesa Areas of Critical Environmental Concern, Clark County, Nevada","interactions":[],"lastModifiedDate":"2014-07-08T14:47:11","indexId":"sir20145076","displayToPublicDate":"2014-07-08T14:36:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5076","title":"Land-cover mapping of Red Rock Canyon National Conservation Area and Coyote Springs, Piute-Eldorado Valley, and Mormon Mesa Areas of Critical Environmental Concern, Clark County, Nevada","docAbstract":"DigitalGlobe’s QuickBird satellite high-resolution multispectral imagery was classified by using Visual Learning Systems’ Feature Analyst feature extraction software to produce land-cover data sets for the Red Rock Canyon National Conservation Area and the Coyote Springs, Piute-Eldorado Valley, and Mormon Mesa Areas of Critical Environmental Concern in Clark County, Nevada. Over 1,000 vegetation field samples were collected at the stand level. The field samples were classified to the National Vegetation Classification Standard, Version 2 hierarchy at the alliance level and above. Feature extraction models were developed for vegetation on the basis of the spectral and spatial characteristics of selected field samples by using the Feature Analyst hierarchical learning process. Individual model results were merged to create one data set for the Red Rock Canyon National Conservation Area and one for each of the Areas of Critical Environmental Concern. Field sample points and photographs were used to validate and update the data set after model results were merged. Non-vegetation data layers, such as roads and disturbed areas, were delineated from the imagery and added to the final data sets. The resulting land-cover data sets are significantly more detailed than previously were available, both in resolution and in vegetation classes.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145076","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Smith, J.L., Damar, N.A., Charlet, D.A., and Westenburg, C.L., 2014, Land-cover mapping of Red Rock Canyon National Conservation Area and Coyote Springs, Piute-Eldorado Valley, and Mormon Mesa Areas of Critical Environmental Concern, Clark County, Nevada: U.S. Geological Survey Scientific Investigations Report 2014-5076, viii, 42 p., https://doi.org/10.3133/sir20145076.","productDescription":"viii, 42 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-045395","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":289552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145076.jpg"},{"id":289551,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5076/pdf/sir2014-5076.pdf"},{"id":289549,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5076/"}],"projection":"Universal Transverse Mercator Projection, Zone 11","datum":"North American Datum of 1983","country":"United States","state":"Nevada","county":"Clark County","otherGeospatial":"Coyote Springs Area Of Critical Environmental Concern;Mormon Mesa Area Of Critical Environmental Concern;Piute-eldorado Valley Area Of Critical Environmental Concern;Red Rock Canyon National Conservation Area","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -115.8969,35.0019 ], [ -115.8969,36.8537 ], [ -114.0428,36.8537 ], [ -114.0428,35.0019 ], [ -115.8969,35.0019 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bd04dae4b00cbf31f72331","contributors":{"authors":[{"text":"Smith, J. LaRue jlsmith@usgs.gov","contributorId":1863,"corporation":false,"usgs":true,"family":"Smith","given":"J.","email":"jlsmith@usgs.gov","middleInitial":"LaRue","affiliations":[],"preferred":true,"id":494191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Damar, Nancy A. 0000-0002-7520-7386 nadamar@usgs.gov","orcid":"https://orcid.org/0000-0002-7520-7386","contributorId":4154,"corporation":false,"usgs":true,"family":"Damar","given":"Nancy","email":"nadamar@usgs.gov","middleInitial":"A.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Charlet, David A.","contributorId":14732,"corporation":false,"usgs":true,"family":"Charlet","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":494193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Westenburg, Craig L.","contributorId":63831,"corporation":false,"usgs":true,"family":"Westenburg","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":494194,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70103557,"text":"sir20145084 - 2014 - Maximum known stages and discharges of New York streams and their annual exceedance probabilities through September 2011","interactions":[],"lastModifiedDate":"2018-04-11T10:57:02","indexId":"sir20145084","displayToPublicDate":"2014-07-08T13:40:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5084","title":"Maximum known stages and discharges of New York streams and their annual exceedance probabilities through September 2011","docAbstract":"Maximum known stages and discharges at 1,400 sites on 796 streams within New York are tabulated. Stage data are reported in feet. Discharges are reported as cubic feet per second and in cubic feet per second per square mile. Drainage areas range from 0.03 to 298,800 square miles; excluding the three sites with larger drainage areas on the St. Lawrence and Niagara Rivers, which drain the Great Lakes, the maximum drainage area is 8,288 square miles (Hudson River at Albany). Most data were obtained from U.S. Geological Survey (USGS) compilations and records, but some were provided by State, local, and other Federal agencies and by private organizations.
\nThe stage and discharge information is grouped by major drainage basins and U.S. Geological Survey site number, in downstream order. Site locations and their associated drainage area, period(s) of record, stage and discharge data, and flood-frequency statistics are compiled in a Microsoft Excel spreadsheet. Flood frequencies were derived for 1,238 sites by using methods described in Bulletin 17B (Interagency Advisory Committee on Water Data, 1982), Ries and Crouse (2002), and Lumia and others (2006).
\nCurves that “envelope” maximum discharges within their range of drainage areas were developed for each of six flood-frequency hydrologic regions and for sites on Long Island, as well as for the State of New York; the New York curve was compared with a curve derived from a plot of maximum known discharges throughout the United States. Discharges represented by the national curve range from at least 2.7 to 4.9 times greater than those represented by the New York curve for drainage areas of 1.0 and 1,000 square miles. The relative magnitudes of discharge and runoff in the six hydrologic regions of New York and Long Island suggest the largest known discharges per square mile are in the southern part of western New York and the Catskill Mountain area, and the smallest are on Long Island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145084","collaboration":"Prepared in cooperation with the New York State Department of Transportation","usgsCitation":"Wall, G.R., Murray, P.M., Lumia, R., and Suro, T.P., 2014, Maximum known stages and discharges of New York streams and their annual exceedance probabilities through September 2011: U.S. Geological Survey Scientific Investigations Report 2014-5084, Report: vi, 16 p.; Table 1, https://doi.org/10.3133/sir20145084.","productDescription":"Report: vi, 16 p.; Table 1","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-046176","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":289545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145084.jpg"},{"id":289543,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5084/pdf/sir2014-5084.pdf"},{"id":289544,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5084/table/sir2014-5084_table1.xlsx"},{"id":289542,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5084/"}],"projection":"Universal Transverse Mercator projections, zone 18","datum":"North American Datum of 1983","country":"United States","state":"New 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2011","indexId":"pp1798I","publicationYear":"2014","noYear":false,"chapter":"I","title":"Geomorphic change on the Missouri River during the flood of 2011"},"predicate":"IS_PART_OF","object":{"id":70047427,"text":"pp1798 - 2013 - 2011 floods of the central United States","indexId":"pp1798","publicationYear":"2013","noYear":false,"title":"2011 floods of the central United States"},"id":1}],"isPartOf":{"id":70047427,"text":"pp1798 - 2013 - 2011 floods of the central United States","indexId":"pp1798","publicationYear":"2013","noYear":false,"title":"2011 floods of the central United States"},"lastModifiedDate":"2024-10-18T13:26:27.451742","indexId":"pp1798I","displayToPublicDate":"2014-07-08T13:07:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1798","chapter":"I","title":"Geomorphic change on the Missouri River during the flood of 2011","docAbstract":"The 2011 flood on the Missouri River was one of the largest floods since the river became regulated by a series of high dams in the mid-20th century (greater than 150,000 cubic feet per second during the peak). The flood persisted through most of the summer, eroding river banks, adding sand to sandbars, and moving the thalweg of the channel in many places. The U.S. Geological Survey monitored and assessed the changes in two reaches of the Missouri River: the Garrison Reach in North Dakota, bounded by the Garrison Dam and the Lake Oahe Reservoir, and the Recreational Reach along the boundary of South Dakota and Nebraska bounded upstream by the Gavins Point Dam and extending downstream from Ponca, Nebraska. Historical cross-section data from the Garrison Dam closure until immediately before the flood indicate that the upper reaches of the river near the dam experienced rapid erosion, channel incision, and island/sandbar loss following the dam closure. The erosion, incision, and land loss lessened with time. Conversely, the lower reach near the Lake Oahe Reservoir slackwaters became depositional with channel in-filling and sandbar growth through time as the flow slowed upon reaching the reservoir. Preliminary post-flood results in the Garrison Reach indicate that the main channel has deepened at most cross-sections whereas sandbars and islands have grown vertically. Sandbars and the thalweg migrated within the Recreational Reach, however net scouring and aggradation was minimal. Changes in the two-dimensional area of sandbars and islands are still being assessed using high-resolution satellite imagery. A sediment balance can be constructed for the Garrison Reach using cross-sections, bathymetric data, sand traps for wind-blown material, a quasi-three-dimensional numerical model, and dating of sediment cores. Data collection and analysis for a reach-scale sediment balance and a concurrent analysis of the effects of riparian and island vegetation on sediment deposition currently (2014) is ongoing.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2011 Floods of the Central United States (Professional Paper 1798)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1798I","usgsCitation":"Schenk, E.R., Skalak, K.J., Benthem, A.J., Dietsch, B.J., Woodward, B.K., Wiche, G.J., Galloway, J.M., Nustad, R.A., and Hupp, C.R., 2014, Geomorphic change on the Missouri River during the flood of 2011: U.S. Geological Survey Professional Paper 1798, vi, 25 p., https://doi.org/10.3133/pp1798I.","productDescription":"vi, 25 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-050746","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":289541,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp1798I.jpg"},{"id":289537,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/1798i/"},{"id":289540,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1798i/pdf/pp1798i.pdf"}],"country":"United States","state":"Nebraska, North Dakota, South Dakota","otherGeospatial":"Missouri River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -104.36,42.4 ], [ -104.36,48.0 ], [ -96.0,48.0 ], [ -96.0,42.4 ], [ -104.36,42.4 ] ] ] } } ] }","contact":"","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bd04d9e4b00cbf31f72327","contributors":{"authors":[{"text":"Schenk, Edward R. 0000-0001-6886-5754 eschenk@usgs.gov","orcid":"https://orcid.org/0000-0001-6886-5754","contributorId":2183,"corporation":false,"usgs":true,"family":"Schenk","given":"Edward","email":"eschenk@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - 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However, native plant and animal species within these shrubland systems are increasingly exposed to human-caused wildfires and an expansion of the human–wildland interface. Few data exist to evaluate the effects of fire and anthropogenic pressures on plant and animal communities found in these environments. This is particularly true for carnivore communities. To address this knowledge gap, we collected detection–non-detection data with motion-sensor cameras and track plots to measure carnivore occupancy patterns following a large, human-caused wildfire (1134 km2) in eastern San Diego County, California, USA, in 2003. Our focal species set included coyote (Canis latrans), gray fox (Urocyon cinereoargenteus), bobcat (Lynx rufus) and striped skunk (Mephitis mephitis). We evaluated the influence on species occupancies of the burned environment (burn edge, burn interior and unburned areas), proximity of rural homes, distance to riparian area and elevation. Gray fox occupancies were the highest overall, followed by striped skunk, coyote and bobcat. The three species considered as habitat and foraging generalists (gray fox, coyote, striped skunk) were common in all conditions. Occupancy patterns were consistent through time for all species except coyote, whose occupancies increased through time. In addition, environmental and anthropogenic variables had weak effects on all four species, and these responses were species-specific. Our results helped to describe a carnivore community exposed to frequent fire and rural human residences, and provide baseline data to inform fire management policy and wildlife management strategies in similar fire-prone ecosystems.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Wildland Fire","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"CSIRO Publishing","publisherLocation":"Collingwood, Australia","doi":"10.1071/WF13062","usgsCitation":"Schuette, P., Diffendorfer, J., Deutschman, D., Tremor, S., and Spencer, W., 2014, Carnivore distributions across chaparral habitats exposed to wildfire and rural housing in southern California: International Journal of Wildland Fire, v. 23, no. 4, p. 591-600, https://doi.org/10.1071/WF13062.","productDescription":"10 p.","startPage":"591","endPage":"600","numberOfPages":"10","ipdsId":"IP-053061","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":472887,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarworks.montana.edu/xmlui/handle/1/8925","text":"External Repository"},{"id":289515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289497,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1071/WF13062"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.777778,32.791667 ], [ -116.777778,33.083333 ], [ -116.444444,33.083333 ], [ -116.444444,32.791667 ], [ -116.777778,32.791667 ] ] ] } } ] }","volume":"23","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bd04d7e4b00cbf31f72321","contributors":{"authors":[{"text":"Schuette, P.A.","contributorId":34438,"corporation":false,"usgs":true,"family":"Schuette","given":"P.A.","email":"","affiliations":[],"preferred":false,"id":495674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, J.E.","contributorId":28569,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"J.E.","email":"","affiliations":[],"preferred":false,"id":495673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deutschman, D.H.","contributorId":13183,"corporation":false,"usgs":true,"family":"Deutschman","given":"D.H.","affiliations":[],"preferred":false,"id":495671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tremor, S.","contributorId":93396,"corporation":false,"usgs":true,"family":"Tremor","given":"S.","affiliations":[],"preferred":false,"id":495675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spencer, W.","contributorId":14303,"corporation":false,"usgs":true,"family":"Spencer","given":"W.","email":"","affiliations":[],"preferred":false,"id":495672,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70115120,"text":"ofr20141135 - 2014 - 2010 weather and aeolian sand-transport data from the Colorado River corridor, Grand Canyon, Arizona","interactions":[],"lastModifiedDate":"2014-07-08T08:57:21","indexId":"ofr20141135","displayToPublicDate":"2014-07-08T08:35:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1135","title":"2010 weather and aeolian sand-transport data from the Colorado River corridor, Grand Canyon, Arizona","docAbstract":"Measurements of weather parameters and aeolian sand transport were made in 2010 near selected archeological sites in the Colorado River corridor through Grand Canyon, Arizona. Data collected in 2010 indicate event- and seasonal-scale variations in rainfall, wind, temperature, humidity, and barometric pressure. Differences in weather patterns between 2009 and 2010 included a slightly later spring windy season, greater spring precipitation and annual rainfall totals, and a later onset and length of the reduced diurnal barometric-pressure fluctuations commonly associated with summer monsoon conditions. The increase in spring precipitation was consistent with the 2010 spring El Niño conditions compared to the 2009 spring La Niña conditions, whereas the subsequent transition to an El Niño-Southern Oscillation neutral phase appeared to delay the reduction in diurnal barometric fluctuations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141135","usgsCitation":"Dealy, T.P., East, A., and Fairley, H., 2014, 2010 weather and aeolian sand-transport data from the Colorado River corridor, Grand Canyon, Arizona: U.S. Geological Survey Open-File Report 2014-1135, vi, 90 p., https://doi.org/10.3133/ofr20141135.","productDescription":"vi, 90 p.","numberOfPages":"100","onlineOnly":"Y","ipdsId":"IP-041383","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":289503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141135.jpg"},{"id":289502,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1135/pdf/ofr2014-1135.pdf"},{"id":289499,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1135/"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River;Grand Canyon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -114.5,35.5 ], [ -114.5,37.25 ], [ -110.75,37.25 ], [ -110.75,35.5 ], [ -114.5,35.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bd04d2e4b00cbf31f7231f","contributors":{"authors":[{"text":"Dealy, Timothy P.","contributorId":19263,"corporation":false,"usgs":true,"family":"Dealy","given":"Timothy","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":495564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"East, Amy E.","contributorId":91407,"corporation":false,"usgs":true,"family":"East","given":"Amy E.","affiliations":[],"preferred":false,"id":495565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fairley, Helen C.","contributorId":10506,"corporation":false,"usgs":true,"family":"Fairley","given":"Helen C.","affiliations":[],"preferred":false,"id":495563,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70115626,"text":"70115626 - 2014 - Effects of disturbance associated with seismic exploration for oil and gas reserves in coastal marshes","interactions":[],"lastModifiedDate":"2014-07-07T13:32:37","indexId":"70115626","displayToPublicDate":"2014-07-07T13:17:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of disturbance associated with seismic exploration for oil and gas reserves in coastal marshes","docAbstract":"Anthropogenic disturbances in wetland ecosystems can alter the composition and structure of plant assemblages and affect system functions. Extensive oil and gas extraction has occurred in wetland habitats along the northern Gulf of Mexico coast since the early 1900s. Activities involved with three-dimensional (3D) seismic exploration for these resources cause various disturbances to vegetation and soils. We documented the impact of a 3D seismic survey in coastal marshes in Louisiana, USA, along transects established before exploration began. Two semi-impounded marshes dominated by Spartina patens were in the area surveyed. Vegetation, soil, and water physicochemical data were collected before the survey, about 6 weeks following its completion, and every 3 months thereafter for 2 years. Soil cores for seed bank emergence experiments were also collected. Maximum vegetation height at impact sites was reduced in both marshes 6 weeks following the survey. In one marsh, total vegetation cover was also reduced, and dead vegetation cover increased, at impact sites 6 weeks after the survey. These effects, however, did not persist 3 months later. No effects on soil or water properties were identified. The total number of seeds that germinated during greenhouse studies increased at impact sites 5 months following the survey in both marshes. Although some seed bank effects persisted 1 year, these effects were not reflected in standing vegetation. The marshes studied were therefore resilient to the impacts resulting from 3D seismic exploration because vegetation responses were short term in that they could not be identified a few months following survey completion.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s00267-014-0274-2","usgsCitation":"Howard, R.J., Wells, C.J., Michot, T.C., and Johnson, D., 2014, Effects of disturbance associated with seismic exploration for oil and gas reserves in coastal marshes: Environmental Management, v. 54, no. 1, p. 30-50, https://doi.org/10.1007/s00267-014-0274-2.","productDescription":"21 p.","startPage":"30","endPage":"50","numberOfPages":"21","ipdsId":"IP-046156","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":289476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289475,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s00267-014-0274-2"}],"projection":"Universal Transverse Mercator, zone 15","datum":"North American Datum of 1983","country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.0,29.666667 ], [ -94.0,30.166667 ], [ -93.166667,30.166667 ], [ -93.166667,29.666667 ], [ -94.0,29.666667 ] ] ] } } ] }","volume":"54","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-05-01","publicationStatus":"PW","scienceBaseUri":"53bbb34fe4b084059e8bfeab","contributors":{"authors":[{"text":"Howard, Rebecca J. 0000-0001-7264-4364 howardr@usgs.gov","orcid":"https://orcid.org/0000-0001-7264-4364","contributorId":2429,"corporation":false,"usgs":true,"family":"Howard","given":"Rebecca","email":"howardr@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":495655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wells, Christopher J. wellsc@usgs.gov","contributorId":5607,"corporation":false,"usgs":true,"family":"Wells","given":"Christopher","email":"wellsc@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":495656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michot, Thomas C. 0000-0002-7044-987X","orcid":"https://orcid.org/0000-0002-7044-987X","contributorId":57935,"corporation":false,"usgs":true,"family":"Michot","given":"Thomas","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":495657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Darren J.","contributorId":100291,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren J.","affiliations":[],"preferred":false,"id":495658,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70111390,"text":"sir20145092 - 2014 - Modeled sulfate concentrations in North Dakota streams, 1993-2008, based on spatial basin characteristics","interactions":[],"lastModifiedDate":"2015-05-01T09:36:36","indexId":"sir20145092","displayToPublicDate":"2014-07-07T10:04:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5092","title":"Modeled sulfate concentrations in North Dakota streams, 1993-2008, based on spatial basin characteristics","docAbstract":"Sulfate concentration data collected from North Dakota streams during recent (1993–2008) years indicates generally higher sulfate concentrations across much of the State compared to concentrations during earlier years. The higher sulfate concentrations have been attributed in other studies to wetter climatic conditions, associated increases in contributing drainage areas, and rising water tables. The State’s current (2013) stream classification system, which includes a standard for 30-day average sulfate concentration, is based on earlier data and thus may not reflect natural conditions for more recent years. The U.S. Geological Survey, in cooperation with the North Dakota Department of Health and the North Dakota State Water Commission, completed a study to evaluate the relation of maximum seasonal (30-day moving average) sulfate concentrations during 1993–2008 to characteristics of the contributing basins to model expected naturally-occurring sulfate concentrations in North Dakota streams.
\nSulfate concentration data for 75 stream sampling sites in North Dakota were analyzed for this study. A spatial analysis was conducted with digital data using a Geographic Information System to obtain selected basin characteristics, which were in turn used as explanatory variables in a regression analysis to model the maximum seasonal (30-day moving average) sulfate concentration. Characteristics used in the regression analysis included mean annual precipitation, mean percent soil clay content, and mean percent saturation overland flow.
\nModeled sulfate concentrations generally were highest (greater than 750 milligrams per liter) in basins in western North Dakota and lowest (less than 250 milligrams per liter) in basins in the upper Sheyenne River and upper James River. Area-weighted means for the basin characteristics also were computed for 10-digit and 8-digit hydrologic units for streams in North Dakota and modeled sulfate concentrations were computed from the characteristics. The resulting distribution of modeled sulfate concentrations was similar to the distribution of estimates for the 12-digit hydrologic units, but less variable because the basin characteristics were averaged over larger areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145092","collaboration":"Prepared in cooperation with the North Dakota Department of Health and the North Dakota State Water Commission","usgsCitation":"Galloway, J.M., and Vecchia, A.V., 2014, Modeled sulfate concentrations in North Dakota streams, 1993-2008, based on spatial basin characteristics: U.S. Geological Survey Scientific Investigations Report 2014-5092, iv, 22 p., https://doi.org/10.3133/sir20145092.","productDescription":"iv, 22 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1993-01-01","temporalEnd":"2008-12-31","ipdsId":"IP-054465","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":289454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145092.jpg"},{"id":289447,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5092/"},{"id":289453,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5092/pdf/sir2014-5092.pdf"}],"projection":"Universal Transverse Mercator projection, Zone 14","country":"United States","state":"North Dakota","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -105.47,44.59 ], [ -105.47,49.27 ], [ -94.5,49.27 ], [ -94.5,44.59 ], [ -105.47,44.59 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bbb350e4b084059e8bfead","contributors":{"authors":[{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":41810,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":494334,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70103403,"text":"pp1798J - 2014 - Monitoring of levees, bridges, pipelines, and other critical infrastructure during the 2011 flooding in the Mississippi River Basin","interactions":[{"subject":{"id":70103403,"text":"pp1798J - 2014 - Monitoring of levees, bridges, pipelines, and other critical infrastructure during the 2011 flooding in the Mississippi River Basin","indexId":"pp1798J","publicationYear":"2014","noYear":false,"chapter":"J","title":"Monitoring of levees, bridges, pipelines, and other critical infrastructure during the 2011 flooding in the Mississippi River Basin"},"predicate":"IS_PART_OF","object":{"id":70047427,"text":"pp1798 - 2013 - 2011 floods of the central United States","indexId":"pp1798","publicationYear":"2013","noYear":false,"title":"2011 floods of the central United States"},"id":1}],"isPartOf":{"id":70047427,"text":"pp1798 - 2013 - 2011 floods of the central United States","indexId":"pp1798","publicationYear":"2013","noYear":false,"title":"2011 floods of the central United States"},"lastModifiedDate":"2024-10-18T13:27:45.835749","indexId":"pp1798J","displayToPublicDate":"2014-07-02T16:19:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1798","chapter":"J","title":"Monitoring of levees, bridges, pipelines, and other critical infrastructure during the 2011 flooding in the Mississippi River Basin","docAbstract":"During the 2011 Mississippi River Basin flood, the U.S. Geological Survey evaluated aspects of critical river infrastructure at the request of and in support of local, State, and Federal Agencies. Geotechnical and hydrographic data collected by the U.S. Geological Survey at numerous locations were able to provide needed information about 2011 flood effects to those managing the critical infrastructure. These data were collected and processed in a short time frame to provide managers the ability to make a timely evaluation of the safety of the infrastructure and, when needed, to take action to secure and protect critical infrastructure. Critical infrastructure surveyed by the U.S. Geological Survey included levees, bridges, pipeline crossings, power plant intakes and outlets, and an electrical transmission tower.
Capacitively coupled resistivity data collected along the flood-protection levees surrounding the Omaha Public Power District Nebraska City power plant (Missouri River Levee Unit R573), mapped the near-subsurface electrical properties of the levee and the materials immediately below it. The near-subsurface maps provided a better understanding of the levee construction and the nature of the lithology beneath the levee. Comparison of the capacitively coupled resistivity surveys and soil borings indicated that low-resistivity value material composing the levee generally is associated with lean clay and silt to about 2 to 4 meters below the surface, overlying a more resistive layer associated with sand deposits. In general, the resistivity structure becomes more resistive to the south and the southern survey sections correlate well with the borehole data that indicate thinner clay and silt at the surface and thicker sand sequences at depth in these sections. With the resistivity data Omaha Public Power District could focus monitoring efforts on areas with higher resistivity values (coarser-grained deposits or more loosely compacted section), which typically are more prone to erosion or scour.
Data collected from multibeam echosounder hydrographic surveys at selected bridges aided State agencies in evaluating the structural integrity of the bridges during the flood, by assessing the amount of scour present around piers and abutments. Hydrographic surveys of the riverbed detected scour depths ranging from zero (no scour) to approximately 5.8 meters in some areas adjacent to North Dakota bridge piers, zero to approximately 6 meters near bridge piers in Nebraska, and zero to approximately 10.4 meters near bridge piers in Missouri. Substructural support elements of some bridge piers in North Dakota, Nebraska, and Missouri that usually are buried were exposed to moving water and sediment. At five Missouri bridge piers the depth of scour left less than 1.8 meters of bed material between the bottom of the scour hole and bedrock. State agencies used this information along with bridge design and construction information to determine if reported scour depths would have a substantial effect on the stability of the structure.
Multibeam echosounder hydrographic surveys of the riverbed near pipeline crossings did not detect exposed pipelines. However, analysis of the USGS survey data by pipeline companies aided in their evaluation of pipeline safety and led one company to further investigate the safety of their line and assisted another company in getting one offline pipeline back into operation. Multibeam echosounder hydrographic surveys of the banks, riverbed, and underwater infrastructure at Omaha Public Power District power plants documented the bed and scour conditions. These datasets were used by Omaha Public Power District to evaluate the effects that the flood had on operation, specifically to evaluate if scour during the peak of the flood or sediment deposition during the flood recession would affect the water intake structures. Hydrographic surveys at an Omaha Public Power District electrical transmission tower documented scour so that they could evaluate the structural integrity of the tower as well as have the information needed to make proper repairs after flood waters receded.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2011 floods of the central United States (Professional Paper 1798)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1798J","usgsCitation":"Densmore, B.K., Burton, B., Dietsch, B.J., Cannia, J.C., and Huizinga, R.J., 2014, Monitoring of levees, bridges, pipelines, and other critical infrastructure during the 2011 flooding in the Mississippi River Basin: U.S. Geological Survey Professional Paper 1798, iv, 28 p., https://doi.org/10.3133/pp1798J.","productDescription":"iv, 28 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-045564","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":289409,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/1798j/"},{"id":289410,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1798j/pdf/pp1798j.pdf"},{"id":289411,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp1798j.jpg"}],"scale":"70000","projection":"Universal Transverse Mercator projection","datum":"North American Datum of 1983","country":"United States","otherGeospatial":"Missouri River Basin, Mississippi River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -110.0,37.0 ], [ -110.0,50.0 ], [ -90.0,50.0 ], [ -90.0,37.0 ], [ -110.0,37.0 ] ] ] } } ] }","contact":"","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b1bce4b0388651d91825","contributors":{"authors":[{"text":"Densmore, Brenda K. 0000-0003-2429-638X bdensmore@usgs.gov","orcid":"https://orcid.org/0000-0003-2429-638X","contributorId":4896,"corporation":false,"usgs":true,"family":"Densmore","given":"Brenda","email":"bdensmore@usgs.gov","middleInitial":"K.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burton, Bethany L. 0000-0001-5011-7862 blburton@usgs.gov","orcid":"https://orcid.org/0000-0001-5011-7862","contributorId":1341,"corporation":false,"usgs":true,"family":"Burton","given":"Bethany L.","email":"blburton@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":493331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dietsch, Benjamin J. 0000-0003-1090-409X bdietsch@usgs.gov","orcid":"https://orcid.org/0000-0003-1090-409X","contributorId":1346,"corporation":false,"usgs":true,"family":"Dietsch","given":"Benjamin","email":"bdietsch@usgs.gov","middleInitial":"J.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cannia, James C.","contributorId":94356,"corporation":false,"usgs":true,"family":"Cannia","given":"James","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":493335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huizinga, Richard J. 0000-0002-2940-2324 huizinga@usgs.gov","orcid":"https://orcid.org/0000-0002-2940-2324","contributorId":2089,"corporation":false,"usgs":true,"family":"Huizinga","given":"Richard","email":"huizinga@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493333,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70114431,"text":"ofr20141131 - 2014 - Users' guide to system dynamics model describing Coho salmon survival in Olema Creek, Point Reyes National Seashore, Marin County, California","interactions":[],"lastModifiedDate":"2018-03-21T14:38:50","indexId":"ofr20141131","displayToPublicDate":"2014-07-02T15:28:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1131","title":"Users' guide to system dynamics model describing Coho salmon survival in Olema Creek, Point Reyes National Seashore, Marin County, California","docAbstract":"The system dynamics model described in this report is the result of a collaboration between U.S. Geological Survey (USGS) scientists and National Park Service (NPS) San Francisco Bay Area Network (SFAN) staff, whose goal was to develop a methodology to integrate inventory and monitoring data to better understand ecosystem dynamics and trends using salmon in Olema Creek, Marin County, California, as an example case. The SFAN began monitoring multiple life stages of coho salmon (Oncorhynchus kisutch) in Olema Creek during 2003 (Carlisle and others, 2013), building on previous monitoring of spawning fish and redds. They initiated water-quality and habitat monitoring, and had access to flow and weather data from other sources.
\nThis system dynamics model of the freshwater portion of the coho salmon life cycle in Olema Creek integrated 8 years of existing monitoring data, literature values, and expert opinion to investigate potential factors limiting survival and production, identify data gaps, and improve monitoring and restoration prescriptions. A system dynamics model is particularly effective when (1) data are insufficient in time series length and/or measured parameters for a statistical or mechanistic model, and (2) the model must be easily accessible by users who are not modelers. These characteristics helped us meet the following overarching goals for this model:
\nSummarize and synthesize NPS monitoring data with data and information from other sources to describe factors and processes affecting freshwater survival of coho salmon in Olema Creek.
\nProvide a model that can be easily manipulated to experiment with alternative values of model parameters and novel scenarios of environmental drivers.
\nAlthough the model describes the ecological dynamics of Olema Creek, these dynamics are structurally similar to numerous other coastal streams along the California coast that also contain anadromous fish populations. The model developed for Olema can be used, at least as a starting point, for other watersheds. This report describes each of the model elements with sufficient detail to guide the primary target audience, the NPS resource specialist, to run the model, interpret the results, change the input data to explore hypotheses, and ultimately modify and improve the model. Running the model and interpreting the results does not require modeling expertise on the part of the user. Additional companion publications will highlight other aspects of the model, such as its development, the rationale behind the methodological approach, scenario testing, and discussions of its use.
\nSystem dynamics models consist of three basic elements: stocks, flows, and converters. Stocks are measurable quantities that can change over time, such as animal populations. Flows are any processes or conditions that change the quantity in a stock over time (Ford, 1999), are expressed in the model as a rate of change, and are diagrammed as arrows to or from stocks. Converters are processes or conditions that change the rate of flows. A converter is connected to a flow with an arrow indicating that it alters the rate of change. Anything that influences the rate of change (such as different environmental conditions, other external factors, or feedbacks from other stocks or flows) is modeled as a converter. For example, the number of fish in a population is appropriately modeled as a stock. Mortality is modeled as a flow because it is a rate of change over time used to determine the number of fish in the population. The density-dependent effect on mortality is modeled as a converter because it influences the rate of morality. Together, the flow and converter change the number, or stock, of juvenile coho. The instructions embedded in the stocks, flows, converters, and the sequence in which they are linked are processed by the simulation software with each completed sequence composing a model run. At each modeled time step within the model run, the stock counts will go up, down, or stay the same based on the modeled flows and the influence of converters on those flows.
\nThe model includes a user-friendly interface to change model parameters, which allows park staff and others to conduct sensitivity analyses, incorporate future knowledge, and implement scenarios for various future conditions. The model structure incorporates place holders for relationships that we hypothesize are significant but data are currently lacking. Future climate scenarios project stream temperatures higher than any that have ever been recorded at Olema Creek. Exploring climate change impacts on coho survival is a high priority for park staff, therefore the model provides the user with the option to experiment with hypothesized effects and to incorporate effects based on future observations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141131","issn":"2331-1258","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Woodward, A., Torregrosa, A.A., Madej, M.A., Reichmuth, M., and Fong, D., 2014, Users' guide to system dynamics model describing Coho salmon survival in Olema Creek, Point Reyes National Seashore, Marin County, California: U.S. Geological Survey Open-File Report 2014-1131, Report: iv, 58 p.; Olema Creek system dynamic simulation model; Input file, https://doi.org/10.3133/ofr20141131.","productDescription":"Report: iv, 58 p.; Olema Creek system dynamic simulation model; Input file","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-052935","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":289408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141131.jpg"},{"id":289404,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1131/"},{"id":289406,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1131/downloads/ofr2014-1131_Olema-Stella10.zip"},{"id":289405,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1131/pdf/ofr2014-1131.pdf"},{"id":289407,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2014/1131/downloads/ofr2014-1131_Olema-Stella-Input.xlsx"}],"country":"United States","state":"California","county":"Marin County","otherGeospatial":"Olema Creek;Point Reyes National Seashore","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -123.028633,37.896415 ], [ -123.028633,38.244664 ], [ -122.701214,38.244664 ], [ -122.701214,37.896415 ], [ -123.028633,37.896415 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b27ee4b0388651d91989","contributors":{"authors":[{"text":"Woodward, Andrea 0000-0003-0604-9115 awoodward@usgs.gov","orcid":"https://orcid.org/0000-0003-0604-9115","contributorId":3028,"corporation":false,"usgs":true,"family":"Woodward","given":"Andrea","email":"awoodward@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":495313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":495314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madej, Mary Ann 0000-0003-2831-3773 mary_ann_madej@usgs.gov","orcid":"https://orcid.org/0000-0003-2831-3773","contributorId":40304,"corporation":false,"usgs":true,"family":"Madej","given":"Mary","email":"mary_ann_madej@usgs.gov","middleInitial":"Ann","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":495315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reichmuth, Michael","contributorId":97429,"corporation":false,"usgs":true,"family":"Reichmuth","given":"Michael","email":"","affiliations":[],"preferred":false,"id":495317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fong, Darren","contributorId":17715,"corporation":false,"usgs":true,"family":"Fong","given":"Darren","affiliations":[],"preferred":false,"id":495316,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70111684,"text":"sir20145104 - 2014 - Scaling up watershed model parameters: flow and load simulations of the Edisto River Basin, South Carolina, 2007-09","interactions":[],"lastModifiedDate":"2018-08-06T12:41:18","indexId":"sir20145104","displayToPublicDate":"2014-07-02T13:20:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5104","title":"Scaling up watershed model parameters: flow and load simulations of the Edisto River Basin, South Carolina, 2007-09","docAbstract":"As part of an ongoing effort by the U.S. Geological Survey to expand the understanding of relations among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations within the Edisto River Basin, analyses and simulations of the hydrology of the Edisto River Basin were made using the topography-based hydrological model (TOPMODEL). A primary focus of the investigation was to assess the potential for scaling up a previous application of TOPMODEL for the McTier Creek watershed, which is a small headwater catchment to the Edisto River Basin. Scaling up was done in a step-wise manner, beginning with applying the calibration parameters, meteorological data, and topographic-wetness-index data from the McTier Creek TOPMODEL to the Edisto River TOPMODEL. Additional changes were made for subsequent simulations, culminating in the best simulation, which included meteorological and topographic wetness index data from the Edisto River Basin and updated calibration parameters for some of the TOPMODEL calibration parameters. The scaling-up process resulted in nine simulations being made. Simulation 7 best matched the streamflows at station 02175000, Edisto River near Givhans, SC, which was the downstream limit for the TOPMODEL setup, and was obtained by adjusting the scaling factor, including streamflow routing, and using NEXRAD precipitation data for the Edisto River Basin. The Nash-Sutcliffe coefficient of model-fit efficiency and Pearson’s correlation coefficient for simulation 7 were 0.78 and 0.89, respectively. Comparison of goodness-of-fit statistics between measured and simulated daily mean streamflow for the McTier Creek and Edisto River models showed that with calibration, the Edisto River TOPMODEL produced slightly better results than the McTier Creek model, despite the substantial difference in the drainage-area size at the outlet locations for the two models (30.7 and 2,725 square miles, respectively).
\nAlong with the TOPMODEL hydrologic simulations, a visualization tool (the Edisto River Data Viewer) was developed to help assess trends and influencing variable in the stream ecosystem. Incorporated into the visualization tool were the water-quality load models TOPLOAD, TOPLOAD–H, and LOADEST. Because the focus of this investigation was on scaling up the models from McTier Creek, water-quality concentrations that were previously collected in the McTier Creek Basin were used in the water-quality load models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145104","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Feaster, T., Benedict, S., Clark, J.M., Bradley, P.M., and Conrads, P., 2014, Scaling up watershed model parameters: flow and load simulations of the Edisto River Basin, South Carolina, 2007-09: U.S. Geological Survey Scientific Investigations Report 2014-5104, 34 p., https://doi.org/10.3133/sir20145104.","productDescription":"34 p.","numberOfPages":"46","onlineOnly":"Y","temporalStart":"2007-01-01","temporalEnd":"2009-12-31","ipdsId":"IP-052559","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":289389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145104.jpg"},{"id":289387,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5104/"},{"id":289388,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5104/pdf/sir2014-5104.pdf"}],"projection":"Universal Transverse Mercator projection","datum":"North American Datum of 1983","country":"United States","state":"South Carolina","otherGeospatial":"Edisto River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -82.0,32.25 ], [ -82.0,34.0 ], [ -80.0,34.0 ], [ -80.0,32.25 ], [ -82.0,32.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b20ae4b0388651d918c4","contributors":{"authors":[{"text":"Feaster, Toby D. 0000-0002-5626-5011 tfeaster@usgs.gov","orcid":"https://orcid.org/0000-0002-5626-5011","contributorId":1109,"corporation":false,"usgs":true,"family":"Feaster","given":"Toby D.","email":"tfeaster@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benedict, Stephen T. benedict@usgs.gov","contributorId":3198,"corporation":false,"usgs":true,"family":"Benedict","given":"Stephen T.","email":"benedict@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Jimmy M. 0000-0002-3138-5738 jmclark@usgs.gov","orcid":"https://orcid.org/0000-0002-3138-5738","contributorId":4773,"corporation":false,"usgs":true,"family":"Clark","given":"Jimmy","email":"jmclark@usgs.gov","middleInitial":"M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494420,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494421,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70111685,"text":"ofr20141113 - 2014 - Low-flow frequency and flow duration of selected South Carolina streams in the Catawba-Wateree and Santee River Basins through March 2012","interactions":[],"lastModifiedDate":"2016-12-08T16:48:23","indexId":"ofr20141113","displayToPublicDate":"2014-07-02T12:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1113","title":"Low-flow frequency and flow duration of selected South Carolina streams in the Catawba-Wateree and Santee River Basins through March 2012","docAbstract":"Part of the mission of both the South Carolina Department of Health and Environmental Control and the South Carolina Department of Natural Resources is to protect and preserve South Carolina’s water resources. Doing so requires an ongoing understanding of streamflow characteristics of the rivers and streams in South Carolina. A particular need is information concerning the low-flow characteristics of streams, which is especially important for effectively managing the State’s water resources during critical flow periods, such as during the historic droughts that South Carolina has experienced in the past few decades.
\nIn 2008, the U.S. Geological Survey, in cooperation with the South Carolina Department of Health and Environmental Control, initiated a study to update low-flow statistics at continuous-record streamgaging stations operated by the U.S. Geological Survey in South Carolina. This report presents the low-flow statistics for 11 selected streamgaging stations in the Catawba-Wateree and Santee River Basins in South Carolina and 2 in North Carolina. For five of the streamgaging stations, low-flow statistics include daily mean flow durations or the 5-, 10-, 25-, 50-, 75-, 90-, and 95-percent probability of exceedance and the annual minimum 1-, 3-, 7-, 14-, 30-, 60-, and 90-day mean flows with recurrence intervals of 2, 5, 10, 20, 30, and 50 years, depending on the length of record available at the streamgaging station. For the other eight streamgaging stations, only daily mean flow durations and (or) exceedance percentiles of annual minimum 7-day average flows are provided due to regulation. In either case, the low-flow statistics were computed from records available through March 31, 2012.
\nOf the five streamgaging stations for which recurrence interval computations were made, three streamgaging stations in South Carolina were compared to low-flow statistics that were published in previous U.S. Geological Survey reports. A comparison of the low-flow statistics for the annual minimum 7-day average streamflow with a 10-year recurrence interval (7Q10) from this study with the most recently published values indicated that two of the streamgaging stations had values lower than the previous values and the 7Q10 for the third station remained unchanged at zero. Low-flow statistics are influenced by length of record, hydrologic regime under which the data were collected, analytical techniques used, and other factors, such as urbanization, diversions, and droughts that may have occurred in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141113","issn":"2331-1258","collaboration":"Prepared in cooperation with the South Carolina Department of Health and Environmental Control","usgsCitation":"Feaster, T., and Guimaraes, W.B., 2014, Low-flow frequency and flow duration of selected South Carolina streams in the Catawba-Wateree and Santee River Basins through March 2012: U.S. Geological Survey Open-File Report 2014-1113, vi, 34 p., https://doi.org/10.3133/ofr20141113.","productDescription":"vi, 34 p.","numberOfPages":"44","onlineOnly":"Y","temporalEnd":"2012-03-31","ipdsId":"IP-054453","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":289382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141113.jpg"},{"id":289380,"type":{"id":15,"text":"Index 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Carolina\",\"nation\":\"USA \"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b19ce4b0388651d917f4","contributors":{"authors":[{"text":"Feaster, Toby D. 0000-0002-5626-5011 tfeaster@usgs.gov","orcid":"https://orcid.org/0000-0002-5626-5011","contributorId":1109,"corporation":false,"usgs":true,"family":"Feaster","given":"Toby D.","email":"tfeaster@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":494425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guimaraes, Wladmir B. wbguimar@usgs.gov","contributorId":3818,"corporation":false,"usgs":true,"family":"Guimaraes","given":"Wladmir","email":"wbguimar@usgs.gov","middleInitial":"B.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":494426,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70125292,"text":"70125292 - 2014 - Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation","interactions":[],"lastModifiedDate":"2014-09-16T10:40:26","indexId":"70125292","displayToPublicDate":"2014-07-02T10:39:22","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation","docAbstract":"Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0101205","usgsCitation":"Tracey, J.A., Sheppard, J., Zhu, J., Wei, F., Swaisgood, R.R., and Fisher, R.N., 2014, Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation: PLoS ONE, v. 9, no. 7, HTML Document, https://doi.org/10.1371/journal.pone.0101205.","productDescription":"HTML Document","ipdsId":"IP-055995","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":472891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0101205","text":"Publisher Index Page"},{"id":293916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293874,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/journal.pone.0101205"}],"volume":"9","issue":"7","noUsgsAuthors":false,"publicationDate":"2014-07-02","publicationStatus":"PW","scienceBaseUri":"54195148e4b091c7ffc8e78b","contributors":{"authors":[{"text":"Tracey, Jeff A. 0000-0002-1619-1054 jatracey@usgs.gov","orcid":"https://orcid.org/0000-0002-1619-1054","contributorId":5780,"corporation":false,"usgs":true,"family":"Tracey","given":"Jeff","email":"jatracey@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheppard, James","contributorId":45232,"corporation":false,"usgs":true,"family":"Sheppard","given":"James","affiliations":[],"preferred":false,"id":501156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Jun","contributorId":73485,"corporation":false,"usgs":true,"family":"Zhu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":501158,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wei, Fu-Wen","contributorId":26605,"corporation":false,"usgs":true,"family":"Wei","given":"Fu-Wen","email":"","affiliations":[],"preferred":false,"id":501155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swaisgood, Ronald R.","contributorId":69490,"corporation":false,"usgs":false,"family":"Swaisgood","given":"Ronald","email":"","middleInitial":"R.","affiliations":[{"id":12762,"text":"San Diego Zoo Institure for Conservation Research","active":true,"usgs":false}],"preferred":false,"id":501157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501153,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70115405,"text":"70115405 - 2014 - Status and trends of Caribbean coral reefs: 1970-2012","interactions":[],"lastModifiedDate":"2019-06-04T13:06:40","indexId":"70115405","displayToPublicDate":"2014-07-02T09:33:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Status and trends of Caribbean coral reefs: 1970-2012","docAbstract":"This it the 9th status report since the Global Coral Reef Monitoring Network (GCRMN) was founded in 1995 was the data arm of the International Coral Reef Initiative (ICRI) to document the ecological condition or corral reefs, strengthen monitoring efforts, and link existing organizations and people working on reefs worldwide. The US Government provided the initial funding to help set up a global network of coral reef workers and has continued to provide core support. Since then, the series of reports have aimed to present the current status of coral reefs of the world or particular regions, the major threats to reefs and their consequences, and any initiative undertaken under the auspices of ICRI or other bodies to arrest or reverse the decline of coral reefs.
IUCN assumed responsibility for hosting the global coordination of the GCRMN in 2010 under the scientific direction of Jeremy Jackson with the following objectives:
1. Document quantitatively the global status and trends for corals, macroalgae, sea urchins, and fishes based on available data from individual scientists as well as the peer reviewed scientific literature, monitoring programs, and report.
2. Bring together regional experts in a series of workshops to involve them in data compilation, analysis, and synthesis.
3. Integrate coral reef status and trends with independent environmental, management, and socioeconomic data to better understand the primary factors responsible for coral reef decline, the possible synergies among factors that may further magnify their impacts, and how these stresses may be more effectively alleviated.
Work with GCRMN partners to establish simple and practical standardized protocols for future monitoring and assessment.
Disseminate information and results to help guide member state policy and actions.
The overarching objective is to understand why some reefs are much healthier than others, to identify what kinds of actions have been particularly beneficial or harmful, and to vigorously communicate results in simple and straightforward terms to foster more effective conservation and management.
This and subsequent reports will focus on separate biogeographic regions in a stepwise fashion and combine all of the results for a global synthesis in the coming years. We began in the wide Caribbean region because the historical data are so extensive and to refine methods of analysis before moving on to other regions. This report documents quantitative trends for Caribbean reef corals, macroalgae, sea urchins, and fishes based on data from 90 reef locations over the past 43 tears. This is the first report to combine all these disparate kinds of data in a single place to explore how the different major components of coral reef ecosystems interact on a broadly regional oceanic scale.
We obtained data from more than 35,000 ecological surveys carried out by 78 principal investigators (PIs) and some 200 colleagues working in 34 countries, states, and territories throughout the wide Caribbean region. We conducted two workshops in Panama and Brisbane, Australia to bring together people who provided the data to assist in data quality control, analysis, and synthesis. The first workshop at the Smithsonian Tropical Research Institute (STRI) in the Republic of Panama 29 April to 5 May, 2012 included scientists from 18 countries and territories to verify and expand the database and to conduct exploratory analyses of status and trends. Preliminary results based on the Panama workshop were presented to the DC Marine Community and Smithsonian Institution Senate of Scientists in May 2012 and at the International Coral Reef Symposium (ICRS) and annual ICRI meeting in Cairns, Australia in July 2012. The second workshop in Brisbane, Australia in December 2012 brought together eight coral reef scientists for more detailed data analysis and organization of results for this report and subsequent publications. Subsequent presentations to solicit comments while the report was being finalized were made in 2013-2014 at the ICRI General meeting in Belize, the biennial meeting of the Association of Island Marine Laboratories in Jamaica, the Panamerican Coral Reef Congress in Merida, Mexico, the annual meeting of he Western Society of Naturalists, and numerous universities in Costa Rica, the USA and Europe.
The main body of the report is in two sections. Part I provides an overview of overall status and trends and detailed analyses of the multiple factors responsible for the decline of reef corals throughout the entire wider Caribbean region. The editors are grateful to all the people who have so generously provided data and expertise, but we assume responsibility for the many statements, conclusions and recommendations and final wording of the text. Part II provides a more detailed analysis of the status and trends of coral reef ecosystems in the 32 countries, states, and territories for which we have data. The format includes maps indicating all locations sampled, a detailed table of data sources and sites surveyed, timelines of ecologically important evens, and relevant references. Each of these reports was compiled in consultation with local experts and all those who provided data and advice are listed as authors of each country report.
","language":"English","publisher":"Global Coral Reef Monitoring Network","publisherLocation":"Washington, D.C.","usgsCitation":"2014, Status and trends of Caribbean coral reefs: 1970-2012, 304 p.","productDescription":"304 p.","numberOfPages":"306","ipdsId":"IP-052859","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":289451,"type":{"id":24,"text":"Thumbnail"},"url":"https://www.icriforum.org/icri-documents/icri-publications-reports-and-posters/status-and-trends-caribbean-coral-reefs-1970-20"},{"id":293054,"type":{"id":15,"text":"Index Page"},"url":"https://www.iucn.org/content/status-and-trends-caribbean-coral-reefs-1970-2012"}],"otherGeospatial":"Caribbean","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -85.17,9.99 ], [ -85.17,27.26 ], [ -59.42,27.26 ], [ -59.42,9.99 ], [ -85.17,9.99 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bbc184e4b084059e8bfefc","contributors":{"editors":[{"text":"Jackson, Jeremy","contributorId":10331,"corporation":false,"usgs":true,"family":"Jackson","given":"Jeremy","affiliations":[],"preferred":false,"id":695437,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Donovan, Mary","contributorId":78648,"corporation":false,"usgs":true,"family":"Donovan","given":"Mary","affiliations":[],"preferred":false,"id":695438,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Cramer, Katie","contributorId":41341,"corporation":false,"usgs":true,"family":"Cramer","given":"Katie","email":"","affiliations":[],"preferred":false,"id":695439,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Lam, Vivian","contributorId":44076,"corporation":false,"usgs":true,"family":"Lam","given":"Vivian","email":"","affiliations":[],"preferred":false,"id":695440,"contributorType":{"id":2,"text":"Editors"},"rank":4}]}} ,{"id":70107000,"text":"sir20145099 - 2014 - Assessing potential effects of highway runoff on receiving-water quality at selected sites in Oregon with the Stochastic Empirical Loading and Dilution Model (SELDM)","interactions":[],"lastModifiedDate":"2014-07-01T16:14:17","indexId":"sir20145099","displayToPublicDate":"2014-07-01T16:05:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5099","title":"Assessing potential effects of highway runoff on receiving-water quality at selected sites in Oregon with the Stochastic Empirical Loading and Dilution Model (SELDM)","docAbstract":"In 2012, the U.S. Geological Survey and the Oregon Department of Transportation began a cooperative study to demonstrate use of the Stochastic Empirical Loading and Dilution Model (SELDM) for runoff-quality analyses in Oregon. SELDM can be used to estimate stormflows, constituent concentrations, and loads from the area upstream of a stormflow discharge site, from the site of interest and in the receiving waters downstream of the discharge. SELDM also can be used to assess the potential effectiveness of best management practices (BMP) for mitigating potential effects of runoff in receiving waters. Nominally, SELDM is a highway-runoff model, but it is well suited for analysis of runoff from other land uses as well.
\nThis report provides case studies and examples to demonstrate stochastic-runoff modeling concepts and to demonstrate application of the model. Basin characteristics from six Oregon highway study sites were used to demonstrate various applications of the model. The highway catchment and upstream basin drainage areas of these study sites ranged from 3.85 to 11.83 acres and from 0.16 to 6.56 square miles, respectively. The upstream basins of two sites are urbanized, and the remaining four sites are less than 5 percent impervious.
\nSELDM facilitates analysis by providing precipitation, pre-storm streamflow, and other variables by region or from hydrologically similar sites. In Oregon, there can be large variations in precipitation and streamflow among nearby sites. Therefore, spatially interpolated geographic information system data layers containing storm-event precipitation and pre-storm streamflow statistics specific to Oregon were created for the study using Kriging techniques.
\nConcentrations and loads of cadmium, chloride, chromium, copper, iron, lead, nickel, phosphorus, and zinc were simulated at the six Oregon highway study sites by using statistics from sites in other areas of the country. Water‑quality datasets measured at hydrologically similar basins in the vicinity of the study sites in Oregon were selected and compiled to estimate stormflow-quality statistics for the upstream basins. The quality of highway runoff and some upstream stormflow constituents were simulated by using statistical moments (average, standard deviation, and skew) of the logarithms of data. Some upstream stormflow constituents were simulated by using transport curves, which are relations between stormflow and constituent concentrations.
\nStochastic analyses were done by using SELDM to demonstrate use of the model and to illustrate the types of information that stochastic analyses may provide:
\n1. An analysis was done to demonstrate use of dilution factors as an initial reconnaissance tool for comparing relative risk among sites.
\n2. An analysis of hardness-dependent, water-quality criteria was done to illustrate the effects of variations in hardness and flow on the application and interpretation of such criteria. This analysis shows that hardness-dependent criteria can vary by an order of magnitude among storm events because hardness is diluted by stormflows.
\n3. An analysis of uncertainties in input and output values was done to demonstrate that properly selected robust datasets are needed to represent conditions at a site of interest. This analysis shows that the rate of water-quality exceedances that are measured or simulated may depend on sample size and the luck of the draw.
\n4. An analysis was done to demonstrate that SELDM and other Monte Carlo models may generate extreme values from input statistics, which may or may not be feasible based on physicochemical or hydrological limits.
\n5. An analysis of BMP modeling methods was done to demonstrate use of the model for estimating treatment requirements for meeting water-quality objectives.
\n6. An analysis of the use of grab sampling and nonstochastic upstream modeling methods was done to evaluate the potential effects on modeling outcomes.
Additional analyses using surrogate water-quality datasets for the upstream basin and highway catchment were provided for six Oregon study sites to illustrate the risk-based information that SELDM will produce. These analyses show that the potential effects of highway runoff on receiving-water quality downstream of the outfall depends on the ratio of drainage areas (dilution), the quality of the receiving water upstream of the highway, and the concentration of the criteria of the constituent of interest. These analyses also show that the probability of exceeding a water-quality criterion may depend on the input statistics used, thus careful selection of representative values is important.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145099","collaboration":"Prepared in cooperation with the Oregon Department of Transportation and the U.S. Department of Transportation Federal Highway Administration","usgsCitation":"Risley, J.C., and Granato, G., 2014, Assessing potential effects of highway runoff on receiving-water quality at selected sites in Oregon with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2014-5099, Report: ix, 73 p.; GIS Data Layers; Appendix Tables B1-B3, https://doi.org/10.3133/sir20145099.","productDescription":"Report: ix, 73 p.; GIS Data Layers; Appendix Tables B1-B3","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-049582","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":289354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145099.jpg"},{"id":289349,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5099/pdf/sir2014-5099.pdf"},{"id":289348,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5099/"},{"id":289350,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2014/5099/downloads/GIS_Data_Layers.zip"},{"id":289351,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5099/downloads/sir2014-5099_AppTableB1.xlsx"},{"id":289352,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5099/downloads/sir2014-5099_AppTableB2.xlsx"},{"id":289353,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2014/5099/downloads/sir2014-5099_AppTableB3.xlsx"}],"country":"United States","state":"Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.61,41.99 ], [ -124.61,46.29 ], [ -116.46,46.29 ], [ -116.46,41.99 ], [ -124.61,41.99 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b3ca51e4b07c5f79a7f30f","contributors":{"authors":[{"text":"Risley, John C. 0000-0002-8206-5443 jrisley@usgs.gov","orcid":"https://orcid.org/0000-0002-8206-5443","contributorId":2698,"corporation":false,"usgs":true,"family":"Risley","given":"John","email":"jrisley@usgs.gov","middleInitial":"C.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":1692,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory E.","email":"ggranato@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":493849,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70118976,"text":"70118976 - 2014 - Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty","interactions":[],"lastModifiedDate":"2016-09-21T09:07:04","indexId":"70118976","displayToPublicDate":"2014-07-01T15:57:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2622,"text":"Limnology and Oceanography: Methods","active":true,"publicationSubtype":{"id":10}},"title":"Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty","docAbstract":"Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.","language":"English","publisher":"American Society of Limnology and Oceanography","doi":"10.4319/lom.2014.12.303","usgsCitation":"Rose, K., Winslow, L., Read, J.S., Read, E., Solomon, C.T., Adrian, R., and Hanson, P.C., 2014, Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty: Limnology and Oceanography: Methods, v. 12, no. 5, p. 303-312, https://doi.org/10.4319/lom.2014.12.303.","productDescription":"10 p.","startPage":"303","endPage":"312","numberOfPages":"10","ipdsId":"IP-051184","costCenters":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"links":[{"id":472892,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4319/lom.2014.12.303","text":"Publisher Index Page"},{"id":291540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":291537,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.4319/lom.2014.12.303"}],"volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2014-05-13","publicationStatus":"PW","scienceBaseUri":"53dca9c3e4b0761578637726","contributors":{"authors":[{"text":"Rose, Kevin C.","contributorId":64580,"corporation":false,"usgs":true,"family":"Rose","given":"Kevin C.","affiliations":[],"preferred":false,"id":497545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winslow, Luke A.","contributorId":9947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke A.","affiliations":[],"preferred":false,"id":497541,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":497539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Emily K.","contributorId":56570,"corporation":false,"usgs":true,"family":"Read","given":"Emily K.","affiliations":[],"preferred":false,"id":497544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Solomon, Christopher T.","contributorId":34014,"corporation":false,"usgs":false,"family":"Solomon","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":497542,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adrian, Rita","contributorId":8007,"corporation":false,"usgs":true,"family":"Adrian","given":"Rita","affiliations":[],"preferred":false,"id":497540,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":497543,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}