This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan.
\nFlown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.
\nThe reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.
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\nFlown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.
\nThe reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.
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\nFlown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.
\nThe reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.
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\nFlown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.
\nThe reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131201A","collaboration":"Prepared in cooperation with the U.S. Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"Kokaly, R., King, T., Hoefen, T.M., Livo, K.E., Johnson, M., and Giles, S.A., 2013, Hyperspectral surface materials map of quadrangle 3568, Pul-e Khumri (503) and Charikar (504) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials: U.S. Geological Survey Open-File Report 2013-1201, 38 x 23 inches, https://doi.org/10.3133/ofr20131201A.","productDescription":"38 x 23 inches","onlineOnly":"Y","ipdsId":"IP-050468","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":282353,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131201A.jpg"},{"id":283570,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1201/A/"},{"id":283571,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1201/A/pdf/ofr2013-1201a.pdf"}],"scale":"250000","projection":"Universal Transverse Mercator","datum":"WGS 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 68.0,35.0 ], [ 68.0,36.0 ], [ 70.0,36.0 ], [ 70.0,35.0 ], [ 68.0,35.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61dbe4b0b290850fdca6","contributors":{"authors":[{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":486501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":486500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Livo, Keith E. 0000-0001-7331-8130 elivo@usgs.gov","orcid":"https://orcid.org/0000-0001-7331-8130","contributorId":1750,"corporation":false,"usgs":true,"family":"Livo","given":"Keith","email":"elivo@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486499,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486497,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Giles, Stuart A. 0000-0002-8696-5078 sgiles@usgs.gov","orcid":"https://orcid.org/0000-0002-8696-5078","contributorId":1233,"corporation":false,"usgs":true,"family":"Giles","given":"Stuart","email":"sgiles@usgs.gov","middleInitial":"A.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486498,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70056193,"text":"ofr20131201B - 2013 - Hyperspectral surface materials map of quadrangle 3568, Pul-e Khumri (503) and Charikar (504) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","interactions":[],"lastModifiedDate":"2014-03-10T10:06:41","indexId":"ofr20131201B","displayToPublicDate":"2014-03-10T12:00:00","publicationYear":"2013","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":"2013-1201","chapter":"B","title":"Hyperspectral surface materials map of quadrangle 3568, Pul-e Khumri (503) and Charikar (504) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","docAbstract":"This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan.
\nFlown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.
\nThe reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.
","language":"English","publisher":"U.S. Geological","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131201B","collaboration":"Prepared in cooperation with the U.S. Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"King, T., Hoefen, T.M., Kokaly, R., Livo, K.E., Johnson, M., and Giles, S.A., 2013, Hyperspectral surface materials map of quadrangle 3568, Pul-e Khumri (503) and Charikar (504) quadrangles, Afghanistan, showing iron-bearing minerals and other materials: U.S. Geological Survey Open-File Report 2013-1201, 37 x 23 inches, https://doi.org/10.3133/ofr20131201B.","productDescription":"37 x 23 inches","ipdsId":"IP-050469","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":282354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131201B.jpg"},{"id":283572,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1201/B/"},{"id":283573,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1201/B/pdf/ofr2013-1201b.pdf"}],"scale":"250000","projection":"Universal Transverse Mercator","datum":"WGS 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 68.0,35.0 ], [ 68.0,36.0 ], [ 70.0,36.0 ], [ 70.0,35.0 ], [ 68.0,35.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61dbe4b0b290850fdca8","contributors":{"authors":[{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":486506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":486507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Livo, Keith E. 0000-0001-7331-8130 elivo@usgs.gov","orcid":"https://orcid.org/0000-0001-7331-8130","contributorId":1750,"corporation":false,"usgs":true,"family":"Livo","given":"Keith","email":"elivo@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486505,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486503,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Giles, Stuart A. 0000-0002-8696-5078 sgiles@usgs.gov","orcid":"https://orcid.org/0000-0002-8696-5078","contributorId":1233,"corporation":false,"usgs":true,"family":"Giles","given":"Stuart","email":"sgiles@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":486504,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70093792,"text":"ofr20131186 - 2013 - Development of CE-QUAL-W2 models for the Middle Fork Willamette and South Santiam Rivers, Oregon","interactions":[],"lastModifiedDate":"2014-02-13T08:39:02","indexId":"ofr20131186","displayToPublicDate":"2014-02-13T08:24:00","publicationYear":"2013","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":"2013-1186","title":"Development of CE-QUAL-W2 models for the Middle Fork Willamette and South Santiam Rivers, Oregon","docAbstract":"Hydrodynamic (CE-QUAL-W2) models of Hills Creek Lake (HCL), Lookout Point Lake (LOP), and Dexter Lake (DEX) on the Middle Fork Willamette River (MFWR), and models of Green Peter Lake and Foster Lake on the South Santiam River systems in western Oregon were updated and recalibrated for a wide range of flow and meteorological conditions. These CE-QUAL-W2 models originally were developed by West Consultants, Inc., for the U.S. Army Corps of Engineers. This study by the U.S. Geological Survey included a reassessment of the models’ calibration in more recent years—2002, 2006, 2008, and 2011—categorized respectively as low, normal, high, and extremely high flow calendar years. These years incorporated current dam-operation practices and more available data than the time period used in the original calibration. Modeled water temperatures downstream of both HCL and LOP-DEX on the MFWR were within an average of 0.68 degree Celsius (°C) of measured values; modeled temperatures downstream of Foster Dam on the South Santiam River were within an average of 0.65°C of measured values. A new CE-QUAL-W2 model was developed and calibrated for the riverine MFWR reach between Hills Creek Dam and the head of LOP, allowing an evaluation of the flow and temperature conditions in the entire MFWR system from HCL to Dexter Dam.
\nThe complex bathymetry and long residence time of HCL, combined with the relatively deep location of the power and regulating outlet structures at Hills Creek Dam, led to a HCL model that was highly sensitive to several outlet and geometric parameters related to dam structures (STR TOP, STR BOT, STR WIDTH). Release temperatures from HCL were important and often persisted downstream as they were incorporated in the MFWR model and the LOP-DEX model (downstream of MFWR). The models tended to underpredict the measured temperature of water releases from Dexter Dam during the late-September-through-December drawdown period in 2002, and again (to a lesser extent) in 2011, but simulations were much more accurate in 2006 and 2008. This episodic model bias may have been a result of hot, dry conditions; lower lake elevations; and earlier drawdown at both HCL and LOP in 2002. These dry conditions in 2002 may have contradicted assumptions inherent in the estimation of certain model inputs, such as unmeasured inflows and water temperatures, which may respond differently during dry years than during normal and wet years.
\nThis report documents the development and calibration of new and revised flow and water-temperature models for riverine and reservoir reaches in the Middle Fork Willamette River and South Santiam River systems. Methods and model parameter values were established for the accurate simulation of flows and temperatures in these systems under current conditions. By extension, these models should be able to accurately simulate flows and temperatures under potential future conditions in which dam operations and dam outlet structures may be changed as part of a strategy to improve habitat, fish passage, and temperature conditions for endangered fish.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131186","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers Portland District","usgsCitation":"Buccola, N., Stonewall, A., Sullivan, A.B., Kim, Y., and Rounds, S.A., 2013, Development of CE-QUAL-W2 models for the Middle Fork Willamette and South Santiam Rivers, Oregon: U.S. Geological Survey Open-File Report 2013-1186, viii, 55 p., https://doi.org/10.3133/ofr20131186.","productDescription":"viii, 55 p.","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-048844","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":282339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131186.jpg"},{"id":282336,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1186/"},{"id":282338,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1186/pdf/ofr2013-1186.pdf"}],"projection":"Oregon Lambert Conformal Conic","datum":"NAD 1983, NAVD 1988","country":"United States","state":"Oregon","otherGeospatial":"Dexter Lake;Foster Lake;Green Peter Lake;Hills Creek Lake;Lookout Point Lake;Middle Fork Willamette River;South Santiam River;Willamette River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.3,43.1992 ], [ -124.3,46.2511 ], [ -120.9924,46.2511 ], [ -120.9924,43.1992 ], [ -124.3,43.1992 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd54a3e4b0b290850f5daa","contributors":{"authors":[{"text":"Buccola, Norman L. nbuccola@usgs.gov","contributorId":4295,"corporation":false,"usgs":true,"family":"Buccola","given":"Norman L.","email":"nbuccola@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":490220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stonewall, Adam J.","contributorId":6704,"corporation":false,"usgs":true,"family":"Stonewall","given":"Adam J.","affiliations":[],"preferred":false,"id":490222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":56317,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett","email":"annett@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":490223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, Yoonhee yoonhee@usgs.gov","contributorId":4889,"corporation":false,"usgs":true,"family":"Kim","given":"Yoonhee","email":"yoonhee@usgs.gov","affiliations":[],"preferred":true,"id":490221,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":490219,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70103044,"text":"70103044 - 2013 - A description of the nearshore fish communities in the Huron-Erie Corridor using multiple gear types","interactions":[],"lastModifiedDate":"2015-11-30T11:27:59","indexId":"70103044","displayToPublicDate":"2014-02-01T14:12:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"A description of the nearshore fish communities in the Huron-Erie Corridor using multiple gear types","docAbstract":"Great Lakes coastal wetlands provide a critical habitat for many fish species throughout their life cycles. Once home to one of the largest wetland complexes in the Great Lakes, coastal wetlands in the Huron–Erie Corridor (HEC) have decreased dramatically since the early 1900s. We characterized the nearshore fish communities at three different wetland complexes in the HEC using electrofishing, seines, and fyke nets. Species richness was highest in the Detroit River (63), followed by the St. Clair Delta (56), and Western Lake Erie (47). The nearshore fish communities in the Detroit River and St. Clair Delta consisted primarily of shiners, bluntnose minnow, centrarchids, and brook silverside, while the Western Lake Erie sites consisted of high proportions of non-native taxa including common carp, gizzard shad, goldfish, and white perch. Species richness estimates using individual-based rarefaction curves were higher when using electrofishing data compared to fyke nets or seine hauls at each wetland. Twelve fish species were captured exclusively during electrofishing assessments, while one species was captured exclusively in fyke nets, and none exclusively during seine hauls. Western Lake Erie wetlands were more indicative of degraded systems with lower species richness, lower proportion of turbidity intolerant species, and increased abundance of non-native taxa. This work highlights the importance of coastal wetlands in the HEC by capturing 69 different fish species utilizing these wetlands to fulfill life history requirements and provides insight when selecting gears to sample nearshore littoral areas.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Great Lakes Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2014.01.007","usgsCitation":"Francis, J.T., Chiotti, J.A., Boase, J., Thomas, M.V., Manny, B.A., and Roseman, E., 2013, A description of the nearshore fish communities in the Huron-Erie Corridor using multiple gear types: Journal of Great Lakes Research, v. 40, p. 52-61, https://doi.org/10.1016/j.jglr.2014.01.007.","productDescription":"10 p.","startPage":"52","endPage":"61","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050304","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":295235,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jglr.2014.01.007"},{"id":295236,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Detroit River, Great Lakes, Lake Erie, St. Clair Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.529296875,\n 42.73894375124379\n ],\n [\n -82.232666015625,\n 42.374778361114195\n ],\n [\n -82.3699951171875,\n 42.261049162113856\n ],\n [\n -82.63916015625,\n 42.19189902447192\n ],\n [\n -82.8369140625,\n 42.200038266046754\n ],\n [\n -82.957763671875,\n 42.1104489601222\n ],\n [\n -82.891845703125,\n 42.0615286181226\n ],\n [\n -83.0291748046875,\n 41.64828831259535\n ],\n [\n -83.21044921875,\n 41.529141988723104\n ],\n [\n -83.5565185546875,\n 41.475660200278234\n ],\n [\n -83.6773681640625,\n 41.63597302844412\n ],\n [\n -83.770751953125,\n 41.87774145109676\n ],\n [\n -83.66638183593749,\n 42.02889410108475\n ],\n [\n -83.42468261718749,\n 42.12267315117259\n ],\n [\n -83.2708740234375,\n 42.22851735620852\n ],\n [\n -83.16650390625,\n 42.4112905190282\n ],\n [\n -83.023681640625,\n 42.62587560259137\n ],\n [\n -82.90283203125,\n 42.75104599038353\n ],\n [\n -82.7490234375,\n 42.79136972365016\n ],\n [\n -82.59521484375,\n 42.771211138625894\n ],\n [\n -82.529296875,\n 42.73894375124379\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5438f518e4b0c47db4296bb6","contributors":{"authors":[{"text":"Francis, James T.","contributorId":81826,"corporation":false,"usgs":true,"family":"Francis","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":493133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiotti, Justin A.","contributorId":59371,"corporation":false,"usgs":true,"family":"Chiotti","given":"Justin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":493131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":493130,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomas, Mike V.","contributorId":61363,"corporation":false,"usgs":true,"family":"Thomas","given":"Mike","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":493132,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Manny, Bruce A. 0000-0002-4074-9329 bmanny@usgs.gov","orcid":"https://orcid.org/0000-0002-4074-9329","contributorId":3699,"corporation":false,"usgs":true,"family":"Manny","given":"Bruce","email":"bmanny@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":493129,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roseman, Edward F.","contributorId":103204,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[],"preferred":false,"id":493134,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70095164,"text":"70095164 - 2013 - Identifying when tagged fishes have been consumed by piscivorous predators: application of multivariate mixture models to movement parameters of telemetered fishes","interactions":[],"lastModifiedDate":"2018-09-25T11:29:40","indexId":"70095164","displayToPublicDate":"2014-02-01T07:50:58","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Identifying when tagged fishes have been consumed by piscivorous predators: application of multivariate mixture models to movement parameters of telemetered fishes","docAbstract":"Consumption of telemetered fishes by piscivores is problematic for telemetry studies because tag detections from the piscivore could introduce bias into the analysis of telemetry data. We illustrate the use of multivariate mixture models to estimate group membership (smolt or predator) of telemetered juvenile Chinook salmon (Oncorhynchus tshawytscha), juvenile steelhead trout (O. mykiss), striped bass (Morone saxatilis), smallmouth bass (Micropterus dolomieu) and spotted bass (M. punctulatus) in the Sacramento River, CA, USA. First, we estimated two types of track statistics from spatially explicit two-dimensional movement tracks of telemetered fishes: the Lévy exponent (b) and tortuosity (τ). Second, we hypothesized that the distribution of each track statistic would differ between predators and smolts. To estimate the distribution of track statistics for putative predators and smolts, we fitted a bivariate normal mixture model to the mixed distribution of track statistics. Lastly, we classified each track as a smolt or predator using parameter estimates from the mixture model to estimate the probability that each track was that of a predator or smolt.
\nTracks classified as predators exhibited movement that was tortuous and consistent with prey searching tactics, whereas tracks classified as smolts were characterized by directed, linear downstream movement. The estimated mean tortuosity was 0.565 (SD = 0.07) for predators and 0.944 (SD = 0.001) for smolts. The estimated mean Lévy exponent was 1.84 (SD = 1.23) for predators and -0.304 (SD = 1.46) for smolts. We correctly classified 90% of the Micropterus species and 72% of the striped bass as predators. For tagged smolts, 80% of Chinook salmon and 74% of steelhead trout were not classified as predators.
\nMixture models proved valuable as a means to differentiate between salmonid smolts and predators that consumed salmonid smolts. However, successful application of this method requires that telemetered fishes and their predators exhibit measurable differences in movement behavior. Our approach is flexible, allows inclusion of multiple track statistics and improves upon rule-based manual classification methods.
\nMuch of the native prairie managed by the U.S. Fish and Wildlife Service (FWS) in the Prairie Pothole Region (PPR) of the northern Great Plains is extensively invaded by the introduced cool-season grasses, smooth brome (Bromus inermis) and Kentucky bluegrass (Poa pratensis). Management to suppress these invasive plants has had poor to inconsistent success. The central challenge to managers is selecting appropriate management actions in the face of biological and environmental uncertainties. In partnership with the FWS, the U.S. Geological Survey (USGS) developed an adaptive decision support framework to assist managers in selecting management actions under uncertainty and maximizing learning from management outcomes. This joint partnership is known as the Native Prairie Adaptive Management (NPAM) initiative. The NPAM decision framework is built around practical constraints faced by FWS refuge managers and includes identification of the management objective and strategies, analysis of uncertainty and construction of competing decision models, monitoring, and mechanisms for model feedback and decision selection. Nineteen FWS field stations, spanning four states of the PPR, have participated in the initiative. These FWS cooperators share a common management objective, available management strategies, and biological uncertainties. Though the scope is broad, the initiative interfaces with individual land managers who provide site-specific information and receive updated decision guidance that incorporates understanding gained from the collective experience of all cooperators. We describe the technical components of this approach, how the components integrate and inform each other, how data feedback from individual cooperators serves to reduce uncertainty across the whole region, and how a successful adaptive management project is coordinated and maintained on a large scale.
\nDuring an initial scoping workshop, FWS cooperators developed a consensus management objective: increase the composition of native grasses and forbs on native sod while minimizing cost. Cooperators agreed that decision guidance should be provided annually and should account for local, real-time vegetation conditions observed on the ground. Over the course of development, two prototypes of the decision framework were considered. The final framework recognized four alternative actions that managers could take in any given year: (1) Graze—targeted use of grazing ungulates to achieve defoliation, (2) Burn—application of prescribed fire as the single form of defoliation, (3) Burn/Graze—a combination treatment, and (4) Rest—no action. The study area included northern mixed-grass and tallgrass prairie. Native vegetation in mixed–grass prairie has a strong cool-season component and thus the dominant native species have a phenology similar to that of smooth brome and Kentucky bluegrass, making management of those species challenging. In contrast, tallgrass prairie has a strong warm-season native component, leading to an existence of cool-season windows, periods of time in the fall and spring when cool‐season invasive grass species are actively growing and vulnerable to damage via select management actions, but warm‐season grass species are not active and are thus less susceptible to damage via the same actions. This dichotomy between prairie types necessitated the development of separate but parallel decision support systems for mixed-grass and tallgrass biomes.
\nManagement units are parcels of native prairie that receive a single management treatment at any one time over their entire extent. At any particular time, the vegetation state of each management unit is characterized by the amount of cover of native grasses and forbs and the type of invasive grass that is dominant. In addition, each unit has a defoliation state which reflects the number of years since the last defoliation event and an index to how intensively the unit was managed during the previous 7 years. State-transition models are used to predict the state of a management unit in year t+1 from its state in year t and a prescribed management action that was applied between the two monitoring events. Alternative models are built around key uncertainties that make choice of a management action difficult. Three uncertainties revolve around whether the effect of management actions depends on (1) type of dominant invader, (2) past defoliation history, and (3) level of invasion. Two additional uncertainties are considered when choosing a management action for tallgrass units: (4) the effectiveness of grazing within the cool-season window as a surrogate for burning when smooth brome is the dominant invader, and (5) the differential effect of active management outside the window as compared to rest.
\nBecause data on the probability of transitioning from one state to another under the various models were lacking, expert opinion and elicitation were used to parameterize the models. In addition, cooperators participated in elicitation exercises to extract their beliefs regarding the value of having native prairie compared to the cost of achieving it. Quantifying the subjective expression of utility in this way allowed for mathematical representation of the management objective into an objective function. By maximizing the objective function, cumulative utility is maximized, leading to the identification of a sequence of decisions that will achieve the management objective.
\nThe NPAM system adopted a vegetation monitoring protocol that was rapid, inexpensive, and familiar to many of the cooperators. The monitoring protocol served three purposes: (1) determining current vegetation and defoliation states of each unit, (2) evaluating progress toward the management objective, and (3) assessing predictive performance of the alternative models. The management year runs from September 1 to August 31. Management can be applied anytime during that period and monitoring takes places from late June to mid-August. Cooperators enter vegetation data and management information into a centralized database by August 25 of each year. Given the current state of the system (vegetation and defoliation states) and the current understanding of the system (or the belief state), identifying the current best management decision is a matter of looking up the combination (that is, system state and belief state) in the appropriate (mixed-grass or tallgrass) optimal decision table. Given complete uncertainty at the outset of decision-making, initial assignment of equal belief weights to each model was believed reasonable. The decisions in the optimal decision table that correspond to the current belief state constitute the current optimal decision policy. By August 31 of each year, individual cooperators are provided with a recommended management action for each of their management units for the upcoming management year. Upon receiving the management recommendations for their units, managers consider the recommendation, along with other relevant information, and at some point during the year one of the management alternatives is carried out. This iterative cycle of making and implementing a management decision, predicting the response, monitoring the outcome, comparing predicted and observed outcomes, updating model weights, and recommending a management action for the next cycle is expected to result in an accumulation of weight on a representative model of system dynamics, thereby increasing understanding needed to effectively manage native prairies.
\nThe NPAM system is now entering its second full year of complete operation, and represents one of only a few fully implemented applications of adaptive management within the U.S. Fish and Wildlife Service. NPAM is truly unique in that it originated from the ground up as a result of the leadership and steadfastness of several refuge biologists and managers confronted with a common problem. These biologists recognized that working together across a large landscape presented perhaps the best opportunity for halting and reversing the invasion of native grasslands by non-native cool-season grasses. Importantly, the NPAM system encapsulates the collective thinking and experience of tens if not hundreds of individuals who have battled this vexing problem for much of their careers.
\nThe NPAM initiative is rooted in principles of adaptive management, thereby affording the opportunity for grassland managers to pursue management objectives while acquiring information to reduce uncertainty and improve future management. The project introduced a number of technical innovations that will serve as templates for conservation efforts throughout and beyond the U.S. Fish and Wildlife Service. First, NPAM is an on-the-ground implementation of active adaptive management—possibly the first of its kind in conservation management—in which recommended management actions result from a prospective analysis of future learning (Williams, 1996). Second, by the use of dynamic optimization, NPAM demonstrates how decisions can be made that take into account possible future transitions of the system. Third, NPAM demonstrates how models of partial controllability are an effective means of accommodating unpredictable circumstances that cause a manager to follow a different course than was intended. Finally, the database developed for NPAM is an unparalleled system that enables the rapid integration of data from the field for the generation of ‘just-in-time’ management recommendations. In all, NPAM provides an example of how a science-management partnership can be forged to achieve large-scale conservation objectives.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131279","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Gannon, J., Shaffer, T.L., and Moore, C., 2013, Native Prairie Adaptive Management: a multi region adaptive approach to invasive plant management on Fish and Wildlife Service owned native prairies: U.S. Geological Survey Open-File Report 2013-1279, Report: vii, 184 p.; Downloads Directory, https://doi.org/10.3133/ofr20131279.","productDescription":"Report: vii, 184 p.; Downloads Directory","numberOfPages":"190","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-043840","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":281449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131279.jpg"},{"id":280311,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1279/"},{"id":281447,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1279/pdf/of2013-1279.pdf"},{"id":281448,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1279/Downloads/"}],"country":"United States","state":"Minnesota;Montana;North Dakota;South Dakota","otherGeospatial":"Great Plains;Prairie Pothole Region","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.32,40.75 ], [ -116.32,50.04 ], [ -90.88,50.04 ], [ -90.88,40.75 ], [ -116.32,40.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd68a3e4b0b290851022f6","contributors":{"authors":[{"text":"Gannon, Jill J.","contributorId":12722,"corporation":false,"usgs":true,"family":"Gannon","given":"Jill J.","affiliations":[],"preferred":false,"id":487376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaffer, Terry L. 0000-0001-6950-8951 tshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-6950-8951","contributorId":3192,"corporation":false,"usgs":true,"family":"Shaffer","given":"Terry","email":"tshaffer@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":487374,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Clinton T.","contributorId":9767,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton T.","affiliations":[],"preferred":false,"id":487375,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70072582,"text":"sim3282 - 2013 - Bathymetric map, area/capacity table, and sediment volume estimate for Millwood Lake near Ashdown, Arkansas, 2013","interactions":[],"lastModifiedDate":"2014-01-21T14:48:23","indexId":"sim3282","displayToPublicDate":"2014-01-21T14:39:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3282","title":"Bathymetric map, area/capacity table, and sediment volume estimate for Millwood Lake near Ashdown, Arkansas, 2013","docAbstract":"Millwood Lake, in southwestern Arkansas, was constructed and is operated by the U.S. Army Corps of Engineers (USACE) for flood-risk reduction, water supply, and recreation. The lake was completed in 1966 and it is likely that with time sedimentation has resulted in the reduction of storage capacity of the lake. The loss of storage capacity can cause less water to be available for water supply, and lessens the ability of the lake to mitigate flooding. Excessive sediment accumulation also can cause a reduction in aquatic habitat in some areas of the lake. Although many lakes operated by the USACE have periodic bathymetric and sediment surveys, none have been completed for Millwood Lake. In March 2013, the U.S. Geological Survey (USGS), in cooperation with the USACE, surveyed the bathymetry of Millwood Lake to prepare an updated bathymetric map and area/capacity table. The USGS also collected sediment thickness data in June 2013 to estimate the volume of sediment accumulated in the lake.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3282","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers","usgsCitation":"Richards, J.M., and Green, W.R., 2013, Bathymetric map, area/capacity table, and sediment volume estimate for Millwood Lake near Ashdown, Arkansas, 2013: U.S. Geological Survey Scientific Investigations Map 3282, Map: 36 x 34 inches, https://doi.org/10.3133/sim3282.","productDescription":"Map: 36 x 34 inches","onlineOnly":"Y","ipdsId":"IP-051522","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":281342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3282.jpg"},{"id":281339,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3282/"},{"id":281340,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3282/pdf/sim3282.pdf"}],"projection":"Universal Transverse Mercator projection","datum":"North American Datum of 1983","country":"United States","state":"Arkansas","otherGeospatial":"Millwood Lake","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.209553,33.649381 ], [ -94.209553,33.848867 ], [ -93.899514,33.848867 ], [ -93.899514,33.649381 ], [ -94.209553,33.649381 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd4eefe4b0b290850f264b","contributors":{"authors":[{"text":"Richards, Joseph M. 0000-0002-9822-2706 richards@usgs.gov","orcid":"https://orcid.org/0000-0002-9822-2706","contributorId":2370,"corporation":false,"usgs":true,"family":"Richards","given":"Joseph","email":"richards@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":488502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, W. Reed","contributorId":87886,"corporation":false,"usgs":true,"family":"Green","given":"W.","email":"","middleInitial":"Reed","affiliations":[],"preferred":false,"id":488503,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70055685,"text":"ofr20131262 - 2013 - Technical evaluation of a total maximum daily load model for Upper Klamath and Agency Lakes, Oregon","interactions":[],"lastModifiedDate":"2014-01-21T13:40:33","indexId":"ofr20131262","displayToPublicDate":"2014-01-21T13:30:00","publicationYear":"2013","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":"2013-1262","title":"Technical evaluation of a total maximum daily load model for Upper Klamath and Agency Lakes, Oregon","docAbstract":"We reviewed a mass balance model developed in 2001 that guided establishment of the phosphorus total maximum daily load (TMDL) for Upper Klamath and Agency Lakes, Oregon. The purpose of the review was to evaluate the strengths and weaknesses of the model and to determine whether improvements could be made using information derived from studies since the model was first developed. The new data have contributed to the understanding of processes in the lakes, particularly internal loading of phosphorus from sediment, and include measurements of diffusive fluxes of phosphorus from the bottom sediments, groundwater advection, desorption from iron oxides at high pH in a laboratory setting, and estimates of fluxes of phosphorus bound to iron and aluminum oxides. None of these processes in isolation, however, is large enough to account for the episodically high values of whole-lake internal loading calculated from a mass balance, which can range from 10 to 20 milligrams per square meter per day for short periods.
\nThe possible role of benthic invertebrates in lake sediments in the internal loading of phosphorus in the lake has become apparent since the development of the TMDL model. Benthic invertebrates can increase diffusive fluxes several-fold through bioturbation and biodiffusion, and, if the invertebrates are bottom feeders, they can recycle phosphorus to the water column through metabolic excretion. These organisms have high densities (1,822–62,178 individuals per square meter) in Upper Klamath Lake. Conversion of the mean density of tubificid worms (Oligochaeta) and chironomid midges (Diptera), two of the dominant taxa, to an areal flux rate based on laboratory measurements of metabolic excretion of two abundant species suggested that excretion by benthic invertebrates is at least as important as any of the other identified processes for internal loading to the water column.
\nData from sediment cores collected around Upper Klamath Lake since the development of the TMDL model also contributed to this review. Cores were sequentially extracted to determine the distribution of phosphorus associated with several matrices in the sediment (freely exchangeable, metal-oxides, acid-soluble minerals, and residual). The concentrations of phosphorus in these fractions varied around the lake in patterns that reflect transport processes in the lake and the ultimate deposition of organic and inorganic forms of phosphorus from the water column. Both organic and inorganic phosphorus had higher concentrations in the northern part of the lake, in and just west of Goose Bay. At the time that these cores were collected, prior to restoration of the Williamson River Delta, this area was close to the shoreline of the lake and east of the Williamson River mouth. This contrasts with erosional inputs, which, in addition to being high to the east of the pre-restoration Williamson River mouth, were higher in the middle of the lake than at the northern end. Organic forms of phosphorus had particularly high concentrations in the northern bays. When these cores were used to calculate a new estimate of the whole-lake-averaged concentration of total phosphorus in the top 10 centimeters of the lake sediments, the estimate was about one-third of the best estimate available when the TMDL model was developed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131262","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Wood, T.M., Wherry, S., Carter, J.L., Kuwabara, J.S., Simon, N.S., and Rounds, S.A., 2013, Technical evaluation of a total maximum daily load model for Upper Klamath and Agency Lakes, Oregon: U.S. Geological Survey Open-File Report 2013-1262, vi, 75 p., https://doi.org/10.3133/ofr20131262.","productDescription":"vi, 75 p.","numberOfPages":"84","onlineOnly":"Y","ipdsId":"IP-037641","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":281330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131262.GIF"},{"id":281328,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1262/"},{"id":281329,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1262/pdf/ofr2013-1262.pdf"}],"projection":"Universal Transverse Mercator","datum":"North American Datum of 1927","country":"United States","state":"Oregon","otherGeospatial":"Agency Lake;Goose Bay;Upper Klamath Lake;Williamson River;Williamson River Delta","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.197797,42.082902 ], [ -122.197797,42.650173 ], [ -121.577757,42.650173 ], [ -121.577757,42.082902 ], [ -122.197797,42.082902 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd7657e4b0b2908510ad44","contributors":{"authors":[{"text":"Wood, Tamara M. 0000-0001-6057-8080 tmwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6057-8080","contributorId":1164,"corporation":false,"usgs":true,"family":"Wood","given":"Tamara","email":"tmwood@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan A.","contributorId":79403,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan A.","affiliations":[],"preferred":false,"id":486208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, James L. 0000-0002-0104-9776 jlcarter@usgs.gov","orcid":"https://orcid.org/0000-0002-0104-9776","contributorId":3278,"corporation":false,"usgs":true,"family":"Carter","given":"James","email":"jlcarter@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":486206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kuwabara, James S. 0000-0003-2502-1601 kuwabara@usgs.gov","orcid":"https://orcid.org/0000-0003-2502-1601","contributorId":3374,"corporation":false,"usgs":true,"family":"Kuwabara","given":"James","email":"kuwabara@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":486207,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simon, Nancy S. 0000-0003-2706-7611 nssimon@usgs.gov","orcid":"https://orcid.org/0000-0003-2706-7611","contributorId":838,"corporation":false,"usgs":true,"family":"Simon","given":"Nancy","email":"nssimon@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":486203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486204,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70073508,"text":"70073508 - 2013 - Wildlife mortality investigation and disease research: contributions of the USGS National Wildlife Health Center to endangered species management and recovery","interactions":[],"lastModifiedDate":"2015-05-15T16:43:00","indexId":"70073508","displayToPublicDate":"2014-01-21T10:39:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1443,"text":"EcoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Wildlife mortality investigation and disease research: contributions of the USGS National Wildlife Health Center to endangered species management and recovery","docAbstract":"The U.S. Geological Survey—National Wildlife Health Center (NWHC) provides diagnostic services, technical assistance, applied research, and training to federal, state, territorial, and local government agencies and Native American tribes on wildlife diseases and wildlife health issues throughout the United States and its territories, commonwealth, and freely associated states. Since 1975, >16,000 carcasses and specimens from vertebrate species listed under the Endangered Species Act have been submitted to NWHC for determination of causes of morbidity or mortality or assessment of health/disease status. Results from diagnostic investigations, analyses of the diagnostic database, technical assistance and consultation, field investigation of epizootics, and wildlife disease research by NWHC wildlife disease specialists have contributed importantly to the management and recovery of listed species.
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cbrand@usgs.gov","contributorId":1186,"corporation":false,"usgs":true,"family":"Brand","given":"Christopher","email":"cbrand@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":488861,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70055869,"text":"sir20135211 - 2013 - Real-time piscicide tracking using Rhodamine WT dye for support of application, transport, and deactivation strategies in riverine environments","interactions":[],"lastModifiedDate":"2014-01-24T11:27:16","indexId":"sir20135211","displayToPublicDate":"2014-01-20T08:43:00","publicationYear":"2013","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":"2013-5211","title":"Real-time piscicide tracking using Rhodamine WT dye for support of application, transport, and deactivation strategies in riverine environments","docAbstract":"Piscicide applications in riverine environments are complicated by the advection and dispersion of the piscicide by the flowing water. Proper deactivation of the fish toxin is required outside of the treatment reach to ensure that there is minimal collateral damage to fisheries downstream or in connecting and adjacent water bodies. In urban settings and highly managed waterways, further complications arise from the influence of industrial intakes and outfalls, stormwater outfalls, lock and dam operations, and general unsteady flow conditions. These complications affect the local hydrodynamics and ultimately the transport and fate of the piscicide. This report presents two techniques using Rhodamine WT dye for real-time tracking of a piscicide plume—or any passive contaminant—in rivers and waterways in natural and urban settings. Passive contaminants are those that are present in such low concentration that there is no effect (such as buoyancy) on the fluid dynamics of the receiving water body. These methods, when combined with data logging and archiving, allow for visualization and documentation of the application and deactivation process.\n\nReal-time tracking and documentation of rotenone applications in rivers and urban waterways was accomplished by encasing the rotenone plume in a plume of Rhodamine WT dye and using vessel-mounted submersible fluorometers together with acoustic Doppler current profilers (ADCP) and global positioning system (GPS) receivers to track the dye and map the water currents responsible for advection and dispersion. In this study, two methods were used to track rotenone plumes: (1) simultaneous injection of dye with rotenone and (2) delineation of the upstream and downstream boundaries of the treatment zone with dye. All data were logged and displayed on a shipboard laptop computer, so that survey personnel provided real-time feedback about the extent of the rotenone plume to rotenone application and deactivation personnel. Further, these strategies facilitate adjustment of rotenone application and deactivation strategies in real time if necessary based on the observed advection and dispersion of the rotenone plume.\n\nTwo large-scale and complex applications of rotenone in the Chicago Area Waterway System (CAWS) in 2009 and 2010 to combat invasive Asian carp are documented in this report. The application in Chicago Sanitary and Ship Canal (CSSC) in December 2009 involved more than 1,800 gallons of rotenone injected at multiple stations through a 6.2-mile reach of the canal near Lockport, Illinois. The rotenone plume was encased in Rhodamine WT dye so that two survey boats provided real-time feedback to shore personnel regarding the plume extent as it advected downstream. Real-time tracking of the rotenone was essential in this large-scale application because of the multistage injection strategy and the numerous deactivation points required to minimize collateral damage to fisheries in surrounding and receiving water bodies. All timing of application and deactivation operations relied on dye tracking. A second application of rotenone in May 2010 to the Little Calumet River near O’Brien Lock and Dam (Illinois) provided another opportunity for dye-tracking support operations; however, application and deactivation strategies were designed considering zero-flow conditions within the reach of interest. Therefore, dye was injected at the upstream and downstream boundaries of the rotenone application reach and was used to track movement of water in and out of a treatment reach, allowing proper deactivation to occur and avoiding unnecessary damage to fisheries downstream. The data collected during the real-time tracking operations for both applications allowed full documentation of the rotenone treatment for archival purposes and provided information for future applications. \nThe methods presented in this report for real-time tracking\nand documentation of piscicide applications in riverine environments worked exceptionally well and allowed the multiagency\nAsian Carp Rapid Response Workgroup to carry out large-scale\nrotenone applications in urban waterways in an environmentally\nresponsible manner with minimal collateral damage to fisheries\noutside the treatment reach. Traveltime information extracted\nfrom the boat-mounted and fixed-position fluorometers agrees\nwell with empirical predictions from a preliminary dye study\n(mock rotenone injection) on this system completed in November 2009 on the CSSC and with previously published methods for estimating traveltimes of the peak, leading edge, and trailing\nedge of the plume. Although the rotenone application strategy\ncalled for zero-flow conditions on the Little Calumet River in\n2010, downstream advection of treated water did occur, and dye\ntracing combined with velocity mapping allowed this advection\nto be documented and exposed the unique hydrodynamics and\nmixing within this reach.\nThe large volumes of data collected during the operations\nallow documentation and visualization of the rotenone applications, thus providing feedback to planners and archival of the\ntreatments for future reference. The methods developed in this\nreport are directly transferrable to piscicide applications in water\nbodies in other locations, including rivers, ponds, or lakes, and\ncan be used for real-time tracking of any passive contaminant\nthat may enter a water body.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135211","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Jackson, P.R., and Lageman, J.D., 2013, Real-time piscicide tracking using Rhodamine WT dye for support of application, transport, and deactivation strategies in riverine environments: U.S. Geological Survey Scientific Investigations Report 2013-5211, vii, 43, https://doi.org/10.3133/sir20135211.","productDescription":"vii, 43","numberOfPages":"50","ipdsId":"IP-045568","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":281146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135211.jpg"},{"id":281144,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5211/"},{"id":281145,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5211/pdf/sir2013-5211.pdf"}],"country":"United States","state":"Illinois","city":"Chicago","otherGeospatial":"Chicago Area Waterway System","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.25,41.5 ], [ -89.25,42.25 ], [ -87.5,42.25 ], [ -87.5,41.5 ], [ -89.25,41.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd6f47e4b0b290851064ec","contributors":{"authors":[{"text":"Jackson, Patrick Ryan","contributorId":34043,"corporation":false,"usgs":true,"family":"Jackson","given":"Patrick","email":"","middleInitial":"Ryan","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":486269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lageman, Jonathan D. jlageman@usgs.gov","contributorId":1910,"corporation":false,"usgs":true,"family":"Lageman","given":"Jonathan","email":"jlageman@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":true,"id":486268,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70049064,"text":"ofr20131257 - 2013 - Geologic assessment of undiscovered oil and gas resources: Oligocene Frio and Anahuac Formations, United States Gulf of Mexico coastal plain and State waters","interactions":[],"lastModifiedDate":"2014-01-16T08:34:03","indexId":"ofr20131257","displayToPublicDate":"2014-01-16T08:19:00","publicationYear":"2013","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":"2013-1257","title":"Geologic assessment of undiscovered oil and gas resources: Oligocene Frio and Anahuac Formations, United States Gulf of Mexico coastal plain and State waters","docAbstract":"The Oligocene Frio and Anahuac Formations were assessed as part of the 2007 U.S. Geological Survey (USGS) assessment of Tertiary strata of the U.S. Gulf of Mexico Basin onshore and State waters. The Frio Formation, which consists of sand-rich fluvio-deltaic systems, has been one of the largest hydrocarbon producers from the Paleogene in the Gulf of Mexico. The Anahuac Formation, an extensive transgressive marine shale overlying the Frio Formation, contains deltaic and slope sandstones in Louisiana and Texas and carbonate rocks in the eastern Gulf of Mexico. In downdip areas of the Frio and Anahuac Formations, traps associated with faulted, rollover anticlines are common. Structural traps commonly occur in combination with stratigraphic traps. Faulted salt domes in the Frio and Anahuac Formations are present in the Houston embayment of Texas and in south Louisiana. In the Frio Formation, stratigraphic traps are found in fluvial, deltaic, barrier-bar, shelf, and strandplain systems.
\nThe USGS Tertiary Assessment Team defined a single, Upper Jurassic-Cretaceous-Tertiary Composite Total Petroleum System (TPS) for the Gulf Coast basin, based on previous studies and geochemical analysis of oils in the Gulf Coast basin. The primary source rocks for oil and gas within Cenozoic petroleum systems, including Frio Formation reservoirs, in the northern, onshore Gulf Coastal region consist of coal and shale rich in organic matter within the Wilcox Group (Paleocene–Eocene), with some contributions from the Sparta Sand of the Claiborne Group (Eocene). The Jurassic Smackover Formation and Cretaceous Eagle Ford Formation also may have contributed substantial petroleum to Cenozoic reservoirs. Modeling studies of thermal maturity by the USGS Tertiary Assessment Team indicate that downdip portions of the basal Wilcox Group reached sufficient thermal maturity to generate hydrocarbons by early Eocene; this early maturation is the result of rapid sediment accumulation in the early Tertiary, combined with the reaction kinetic parameters used in the models. A number of studies indicate that the migration of oil and gas in the Cenozoic Gulf of Mexico basin is primarily vertical, occurring along abundant growth faults associated with sediment deposition or along faults associated with salt domes.
\nThe USGS Tertiary assessment team developed a geologic model based on recurring regional-scale structural and depositional features in Paleogene strata to define assessment units (AUs). Three general areas, as described in the model, are found in each of the Paleogene stratigraphic intervals assessed: “Stable Shelf,” “Expanded Fault,” and “Slope and Basin Floor” zones. On the basis of this model, three AUs for the Frio Formation were defined: (1) the Frio Stable Shelf Oil and Gas AU, containing reservoirs with a mean depth of about 4,800 feet in normally pressured intervals; (2) the Frio Expanded Fault Zone Oil and Gas AU, containing reservoirs with a mean depth of about 9,000 feet in primarily overpressured intervals; and (3) the Frio Slope and Basin Floor Gas AU, which currently has no production but has potential for deep gas resources (>15,000 feet). AUs also were defined for the Hackberry trend, which consists of a slope facies stratigraphically in the middle part of the Frio Formation, and the Anahuac Formation. The Frio Basin Margin AU, an assessment unit extending to the outcrop of the Frio (or basal Miocene), was not quantitatively assessed because of its low potential for production. Two proprietary, commercially available databases containing field and well production information were used in the assessment. Estimates of undiscovered resources for the five AUs were based on a total of 1,734 reservoirs and 586,500 wells producing from the Frio and Anahuac Formations. Estimated total mean values of technically recoverable, undiscovered resources are 172 million barrels of oil (MMBO), 9.4 trillion cubic feet of natural gas (TCFG), and 542 million barrels of natural gas liquids for all of the Frio and Anahuac AUs. Of the five units assessed, the Frio Slope and Basin Floor Gas AU has the greatest potential for undiscovered gas resources, having an estimated mean of 5.6 TCFG. The Hackberry Oil and Gas AU shows the second highest potential for gas of the five units assessed, having an estimated mean of 1.8 TCFG. The largest undiscovered, conventional crude oil resource was estimated for the Frio Slope and Basin Floor Gas AU; the estimated mean for oil in this AU is 110 MMBO.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131257","usgsCitation":"Swanson, S.M., Karlsen, A.W., and Valentine, B.J., 2013, Geologic assessment of undiscovered oil and gas resources: Oligocene Frio and Anahuac Formations, United States Gulf of Mexico coastal plain and State waters: U.S. Geological Survey Open-File Report 2013-1257, Report: viii, 66 p.; Appendix 1: 10 p., https://doi.org/10.3133/ofr20131257.","productDescription":"Report: viii, 66 p.; Appendix 1: 10 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051257","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":281142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131257.jpg"},{"id":281139,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1257/"},{"id":281140,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1257/pdf/of2013-1257.pdf"},{"id":281141,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1257/pdf/ofr2013-1257_appendix1_input_data.pdf"}],"scale":"2000000","projection":"Albers Equal-Area Conic projection","country":"United States","state":"Louisiana;Texas","otherGeospatial":"Gulf Of Mexico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -101.0,24.84 ], [ -101.0,33.0 ], [ -88.5,33.0 ], [ -88.5,24.84 ], [ -101.0,24.84 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52d8ff61e4b08fdd528145fd","contributors":{"authors":[{"text":"Swanson, Sharon M. 0000-0002-4235-1736 smswanson@usgs.gov","orcid":"https://orcid.org/0000-0002-4235-1736","contributorId":590,"corporation":false,"usgs":true,"family":"Swanson","given":"Sharon","email":"smswanson@usgs.gov","middleInitial":"M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlsen, Alexander W.","contributorId":105382,"corporation":false,"usgs":true,"family":"Karlsen","given":"Alexander","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":486098,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valentine, Brett J. 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":3846,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","middleInitial":"J.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486097,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70059127,"text":"ofr20131270 - 2013 - Hurricane Isaac: observations and analysis of coastal change","interactions":[],"lastModifiedDate":"2014-01-14T16:17:00","indexId":"ofr20131270","displayToPublicDate":"2014-01-14T16:05:00","publicationYear":"2013","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":"2013-1270","title":"Hurricane Isaac: observations and analysis of coastal change","docAbstract":"Understanding storm-induced coastal change and forecasting these changes require knowledge of the physical processes associated with a storm and the geomorphology of the impacted coastline. The primary physical process of interest is sediment transport that is driven by waves, currents, and storm surge associated with storms. Storm surge, which is the rise in water level due to the wind, barometric pressure, and other factors, allows both waves and currents to impact parts of the coast not normally exposed to these processes.
\nCoastal geomorphology reflects the coastal changes associated with extreme-storm processes. Relevant geomorphic variables that are observable before and after storms include sand dune elevation, beach width, shoreline position, sediment grain size, and foreshore beach slope. These variables, in addition to hydrodynamic processes, can be used to quantify coastal change and are used to predict coastal vulnerability to storms (Stockdon and others, 2007).
\nThe U.S. Geological Survey (USGS) National Assessment of Coastal Change Hazards (NACCH) project (http://coastal.er.usgs.gov/national-assessment/) provides hazard information to those concerned about the Nation’s coastlines, including residents of coastal areas, government agencies responsible for coastal management, and coastal researchers. Extreme-storm research is a component of the NACCH project (http://coastal.er.usgs.gov/hurricanes/) that includes development of predictive understanding, vulnerability assessments using models, and updated observations in response to specific storm events. In particular, observations were made to determine morphological changes associated with Hurricane Isaac, which made landfall in the United States first at Southwest Pass, at the mouth of the Mississippi River, at 0000 August 29, 2012 UTC (Coordinated Universal Time) and again, 8 hours later, west of Port Fourchon, Louisiana (Berg, 2013). Methods of observation included oblique aerial photography, airborne light detection and ranging (lidar) topographic surveys, and ground-based topographic surveys. This report documents data-collection efforts and presents qualitative and quantitative descriptions of hurricane-induced changes to the shoreline, beaches, dunes, and infrastructure in the region that was heavily impacted by Hurricane Isaac.
\nThe report is divided into the following sections:
\nThe geology of Oldonyo Lengai volcano and the southernmost Lake Natron basin, Tanzania, is presented on this geologic map at scale 1:50,000. The map sheet can be downloaded in pdf format for online viewing or ready to print (48 inches by 36 inches).
\nA 65-page explanatory pamphlet describes the geologic history of the area. Its goal is to place the new findings into the framework of previous investigations while highlighting gaps in knowledge. In this way questions are raised and challenges proposed to future workers.
\nThe southernmost Lake Natron basin is located along the East African rift zone in northern Tanzania. Exposed strata provide a history of volcanism, sedimentation, and faulting that spans 2 million years. It is here where Oldonyo Lengai, Tanzania’s most active volcano of the past several thousand years, built its edifice. Six new radiometric ages, by the 40Ar/39Ar method, and 48 new geochemical analyses from Oldonyo Lengai and surrounding volcanic features deepen our understanding of the area.
\nThose who prefer the convenience and access offered by Geographic Information Systems (GIS) may download an electronic database, suitable for most GIS software applications. The GIS database is in a Transverse Mercator projection, zone 36, New (1960) Arc datum. The database includes layers for hypsography (topography), hydrography, and infrastructure such as roads and trails.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131306","usgsCitation":"Sherrod, D.R., Magigita, M.M., and Kwelwa, S., 2013, Geologic map of Oldonyo Lengai (Oldoinyo Lengai) Volcano and surroundings, Arusha Region, United Republic of Tanzania: U.S. Geological Survey Open-File Report 2013-1306, Map: 47.99 x 35.98 inches; Pamphlet: v, 65 p.; GIS files; Metadata; Chemical Analyses, https://doi.org/10.3133/ofr20131306.","productDescription":"Map: 47.99 x 35.98 inches; Pamphlet: v, 65 p.; GIS files; Metadata; Chemical Analyses","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-043114","costCenters":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":280810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131306.jpg"},{"id":280806,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2013/1306/pdf/ofr2013-1306_pamphlet.pdf"},{"id":280807,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1306/downloads/ofr2013-1306_GIS.zip"},{"id":280808,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2013/1306/downloads/ofr2013-1306_Metadata.zip"},{"id":280805,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1306/pdf/ofr2013-1306.pdf"},{"id":280809,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2013/1306/downloads/ChemAnalyses_OldonyoLengai_20101221.xls"},{"id":280802,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1306"}],"scale":"50000","projection":"Transverse Mercator projection","datum":"New (1960) Arc datum","country":"United Republic Of Tanzania","otherGeospatial":"Arusha Region;Lake Natron;Oldonyo Lengai Volcano","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 35.783333,-2.845 ], [ 35.783333,-2.5 ], [ 36.02005,-2.5 ], [ 36.02005,-2.845 ], [ 35.783333,-2.845 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52d11661e4b072eb3e0c4984","contributors":{"authors":[{"text":"Sherrod, David R. 0000-0001-9460-0434 dsherrod@usgs.gov","orcid":"https://orcid.org/0000-0001-9460-0434","contributorId":527,"corporation":false,"usgs":true,"family":"Sherrod","given":"David","email":"dsherrod@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":488023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magigita, Masota M.","contributorId":53286,"corporation":false,"usgs":true,"family":"Magigita","given":"Masota","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":488024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwelwa, Shimba","contributorId":58180,"corporation":false,"usgs":true,"family":"Kwelwa","given":"Shimba","email":"","affiliations":[],"preferred":false,"id":488025,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70047656,"text":"70047656 - 2013 - Correcting length-frequency distributions for imperfect detection","interactions":[],"lastModifiedDate":"2014-01-08T14:04:05","indexId":"70047656","displayToPublicDate":"2014-01-08T13:03:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Correcting length-frequency distributions for imperfect detection","docAbstract":"Sampling gear selects for specific sizes of fish, which may bias length-frequency distributions that are commonly used to assess population size structure, recruitment patterns, growth, and survival. To properly correct for sampling biases caused by gear and other sources, length-frequency distributions need to be corrected for imperfect detection. We describe a method for adjusting length-frequency distributions when capture and recapture probabilities are a function of fish length, temporal variation, and capture history. The method is applied to a study involving the removal of Smallmouth Bass Micropterus dolomieu by boat electrofishing from a 38.6-km reach on the Yampa River, Colorado. Smallmouth Bass longer than 100 mm were marked and released alive from 2005 to 2010 on one or more electrofishing passes and removed on all other passes from the population. Using the Huggins mark–recapture model, we detected a significant effect of fish total length, previous capture history (behavior), year, pass, year×behavior, and year×pass on capture and recapture probabilities. We demonstrate how to partition the Huggins estimate of abundance into length frequencies to correct for these effects. Uncorrected length frequencies of fish removed from Little Yampa Canyon were negatively biased in every year by as much as 88% relative to mark–recapture estimates for the smallest length-class in our analysis (100–110 mm). Bias declined but remained high even for adult length-classes (≥200 mm). The pattern of bias across length-classes was variable across years. The percentage of unadjusted counts that were below the lower 95% confidence interval from our adjusted length-frequency estimates were 95, 89, 84, 78, 81, and 92% from 2005 to 2010, respectively. Length-frequency distributions are widely used in fisheries science and management. Our simple method for correcting length-frequency estimates for imperfect detection could be widely applied when mark–recapture data are available.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"North American Journal of Fisheries Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2013.829141","usgsCitation":"Breton, A., Hawkins, J.A., and Winkelman, D.L., 2013, Correcting length-frequency distributions for imperfect detection: North American Journal of Fisheries Management, v. 33, no. 6, p. 1156-1165, https://doi.org/10.1080/02755947.2013.829141.","productDescription":"10 p.","startPage":"1156","endPage":"1165","numberOfPages":"10","ipdsId":"IP-041180","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":280735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280734,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/02755947.2013.829141"}],"country":"United States","state":"Colorado","otherGeospatial":"Little Yampa Canyon;Yampa River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.0509,40.219 ], [ -109.0509,41.0009 ], [ -107.3119,41.0009 ], [ -107.3119,40.219 ], [ -109.0509,40.219 ] ] ] } } ] }","volume":"33","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-11-15","publicationStatus":"PW","scienceBaseUri":"52ce747ae4b073e0995b2dcb","contributors":{"authors":[{"text":"Breton, André R.","contributorId":47682,"corporation":false,"usgs":false,"family":"Breton","given":"André R.","affiliations":[],"preferred":false,"id":482644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawkins, John A.","contributorId":50076,"corporation":false,"usgs":true,"family":"Hawkins","given":"John","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":482645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":482643,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048353,"text":"70048353 - 2013 - NGA-West 2 Equations for predicting PGA, PGV, and 5%-Damped PSA for shallow crustal earthquakes","interactions":[],"lastModifiedDate":"2014-09-23T12:59:38","indexId":"70048353","displayToPublicDate":"2014-01-07T10:44:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"NGA-West 2 Equations for predicting PGA, PGV, and 5%-Damped PSA for shallow crustal earthquakes","docAbstract":"We provide ground-motion prediction equations for computing medians and standard deviations of average horizontal component intensity measures (IMs) for shallow crustal earthquakes in active tectonic regions. The equations were derived from a global database with M 3.0–7.9 events. We derived equations for the primary M- and distance-dependence of the IMs after fixing the VS30-based nonlinear site term from a parallel NGA-West 2 study. We then evaluated additional effects using mixed effects residuals analysis, which revealed no trends with source depth over the M range of interest, indistinct Class 1 and 2 event IMs, and basin depth effects that increase and decrease long-period IMs for depths larger and smaller, respectively, than means from regional VS30-depth relations. Our aleatory variability model captures decreasing between-event variability with M, as well as within-event variability that increases or decreases with M depending on period, increases with distance, and decreases for soft sites.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Earthquake Spectra","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Earthquake Engineering Research Institute","publisherLocation":"Berkeley, CA","doi":"10.1193/070113EQS184M","usgsCitation":"Boore, D.M., Stewart, J.P., Seyhan, E., and Atkinson, G.M., 2013, NGA-West 2 Equations for predicting PGA, PGV, and 5%-Damped PSA for shallow crustal earthquakes: Earthquake Spectra, v. 30, no. 3, p. 1057-1085, https://doi.org/10.1193/070113EQS184M.","productDescription":"29 p.","startPage":"1057","endPage":"1085","numberOfPages":"29","ipdsId":"IP-049134","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":280644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280643,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1193/070113EQS184M"}],"volume":"30","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-08-01","publicationStatus":"PW","scienceBaseUri":"52cd21fde4b0c3f95143ecfe","contributors":{"authors":[{"text":"Boore, David M. boore@usgs.gov","contributorId":2509,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":484389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, Jon P.","contributorId":78644,"corporation":false,"usgs":true,"family":"Stewart","given":"Jon","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":484392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seyhan, Emel","contributorId":51193,"corporation":false,"usgs":false,"family":"Seyhan","given":"Emel","email":"","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":484390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atkinson, Gail M.","contributorId":60515,"corporation":false,"usgs":false,"family":"Atkinson","given":"Gail","email":"","middleInitial":"M.","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":484391,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048978,"text":"sir20135184 - 2013 - Hydrogeology and water quality in the Snake River alluvial aquifer at Jackson Hole Airport, Jackson, Wyoming, water years 2011 and 2012","interactions":[],"lastModifiedDate":"2014-01-06T13:57:09","indexId":"sir20135184","displayToPublicDate":"2014-01-06T13:41:00","publicationYear":"2013","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":"2013-5184","title":"Hydrogeology and water quality in the Snake River alluvial aquifer at Jackson Hole Airport, Jackson, Wyoming, water years 2011 and 2012","docAbstract":"The hydrogeology and water quality of the Snake River alluvial aquifer at the Jackson Hole Airport in northwest Wyoming was studied by the U.S. Geological Survey, in cooperation with the Jackson Hole Airport Board, during water years 2011 and 2012 as part of a followup to a previous baseline study during September 2008 through June 2009. Hydrogeologic conditions were characterized using data collected from 19 Jackson Hole Airport wells. Groundwater levels are summarized in this report and the direction of groundwater flow, hydraulic gradients, and estimated groundwater velocity rates in the Snake River alluvial aquifer underlying the study area are presented. Analytical results of groundwater samples collected from 10 wells during water years 2011 and 2012 are presented and summarized.
\nThe water table at Jackson Hole Airport was lowest in early spring and reached its peak in July or August, with an increase of 12.5 to 15.5 feet between April and July 2011. Groundwater flow was predominantly horizontal but generally had the hydraulic potential for downward flow. Groundwater flow within the Snake River alluvial aquifer at the airport was from the northeast to the west-southwest, with horizontal velocities estimated to be about 25 to 68 feet per day. This range of velocities slightly is broader than the range determined in the previous study and likely is due to variability in the local climate. The travel time from the farthest upgradient well to the farthest downgradient well was approximately 52 to 142 days. This estimate only describes the average movement of groundwater, and some solutes may move at a different rate than groundwater through the aquifer.
\nThe quality of the water in the alluvial aquifer generally was considered good. Water from the alluvial aquifer was fresh, hard to very hard, and dominated by calcium carbonate. No constituents were detected at concentrations exceeding U.S. Environmental Protection Agency maximum contaminant levels or health advisories; however, reduction and oxidation (redox) measurements indicate oxygen-poor water in many of the wells. Gasoline-range organics, three volatile organic compounds, and triazoles were detected in some groundwater samples. The quality of groundwater in the alluvial aquifer generally was suitable for domestic and other uses; however, dissolved iron and manganese were detected in samples from many of the monitor wells at concentrations exceeding U.S. Environmental Protection Agency secondary maximum contaminant levels. Iron and manganese likely are both natural components of the geologic materials in the area and may have become mobilized in the aquifer because of redox processes. Additionally, measurements of dissolved-oxygen concentrations and analyses of major ions and nutrients indicate reducing conditions exist at 7 of the 10 wells sampled.
\nMeasurements of dissolved-oxygen concentrations (less than 0.1 to 9 milligrams per liter) indicated some variability in the oxygen content of the aquifer. Dissolved-oxygen concentrations in samples from 3 of the 10 wells indicated oxic conditions in the aquifer, whereas low dissolved-oxygen concentrations (less than 1 milligram per liter) in samples from 7 wells indicated anoxic conditions. Nutrients were present in low concentrations in all samples collected. Nitrate plus nitrite was detected in samples from 6 of the 10 monitored wells, whereas dissolved ammonia was detected in small concentrations in 8 of the 10 monitored wells. Dissolved organic carbon concentrations generally were low. At least one dissolved organic carbon concentration was quantified by the laboratory in samples from all 10 wells; one of the concentrations was an order of magnitude higher than other detected dissolved organic carbon concentrations, and slightly exceeded the estimated range for natural groundwater.
\nSamples were collected for analyses of dissolved gases, and field analyses of ferrous iron, hydrogen sulfide, and low-level dissolved oxygen were completed to better understand the redox conditions of the alluvial aquifer. Dissolved gas analyses confirmed low concentrations of dissolved oxygen in samples from wells where reducing conditions exist and indicated the presence of methane gas in samples from several wells. Redox processes in the alluvial aquifer were identified using a model designed to use a multiple-lines-of-evidence approach to distinguish reduction processes. Results of redox analyses indicate iron reduction was the dominant redox process; however, the model indicated manganese reduction and methanogenesis also were taking place in the aquifer.
\nEach set of samples collected during this study included analysis of at least two, but often many anthropogenic compounds. During the previous 2008–09 study at Jackson Hole Airport, diesel-range organics were measured in small (estimated) concentrations in several samples. Samples collected from all 10 wells sampled during the 2011–12 study were analyzed for diesel-range organics, and there were no detections; however, several other anthropogenic compounds were detected in groundwater samples during water years 2011—12 that were not detected during the previous 2008–09 study. Gasoline-range organics, benzene, ethylbenzene, and total xylene were each detected (but reported as estimated concentrations) in at least one groundwater sample. These compounds were not detected during the previous study or consistently during this study. Several possible reasons these compounds were not detected consistently include (1) these compounds are present in the aquifer at concentrations near the analytical method detection limit and are difficult to detect, (2) these compounds were not from a persistent source during this study, and (3) these compounds were detected because of contamination introduced during sampling or analysis. During water years 2011–2012, groundwater samples were analyzed for triazoles, specifically benzotriazole, 4-methyl-1H-benzotriazole, and 5-methyl-1H-benzotriazole. Triazoles are anthropogenic compounds often used as an additive in deicing and anti-icing fluids as a corrosion inhibitor, and can be detected at lower laboratory reporting levels than glycols, which previously had not been detected. Two of the three triazoles measured, 4-methyl-1H-benzotriazole and 5-methyl-1H-benzotriazole, were detected at low concentrations in groundwater at 7 of the 10 wells sampled. The detection of triazole compounds in groundwater downgradient from airport operations makes it unlikely there is a natural cause for the high rates of reduction present in many airport monitor wells. It is more likely that aircraft deicers, anti-icers, or pavement deicers have seeped into the groundwater system and caused the reducing conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135184","collaboration":"Prepared in cooperation with the Jackson Hole Airport Board","usgsCitation":"Wright, P., 2013, Hydrogeology and water quality in the Snake River alluvial aquifer at Jackson Hole Airport, Jackson, Wyoming, water years 2011 and 2012: U.S. Geological Survey Scientific Investigations Report 2013-5184, vii, 56 p., https://doi.org/10.3133/sir20135184.","productDescription":"vii, 56 p.","numberOfPages":"68","temporalStart":"2010-10-01","temporalEnd":"2012-09-30","ipdsId":"IP-042348","costCenters":[{"id":684,"text":"Wyoming Water Science Center","active":false,"usgs":true}],"links":[{"id":280625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135184.jpg"},{"id":280624,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5184/pdf/sir2013-5184.pdf"},{"id":280623,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5184/"}],"projection":"Lambert Conformal Conic projection","datum":"North American Datum of 1983","country":"United States","state":"Wyoming","city":"Jackson","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.047058,43.400059 ], [ -111.047058,43.899871 ], [ -110.398865,43.899871 ], [ -110.398865,43.400059 ], [ -111.047058,43.400059 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52cbd082e4b03116c9ddb9fc","contributors":{"authors":[{"text":"Wright, Peter R. prwright@usgs.gov","contributorId":1828,"corporation":false,"usgs":true,"family":"Wright","given":"Peter R.","email":"prwright@usgs.gov","affiliations":[],"preferred":true,"id":485917,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70067332,"text":"70067332 - 2013 - Fluvial rainbow trout contribute to the colonization of steelhead (Oncorhynchus mykiss) in a small stream","interactions":[],"lastModifiedDate":"2016-04-26T11:00:16","indexId":"70067332","displayToPublicDate":"2014-01-06T13:30:56","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Fluvial rainbow trout contribute to the colonization of steelhead (Oncorhynchus mykiss) in a small stream","docAbstract":"Life history polymorphisms provide ecological and genetic diversity important to the long term persistence of species responding to stochastic environments. Oncorhynchus mykiss have complex and overlapping life history strategies that are also sympatric with hatchery populations. Passive integrated transponder (PIT) tags and parentage analysis were used to identify the life history, origin (hatchery or wild) and reproductive success of migratory rainbow/steelhead for two brood years after barriers were removed from a small stream. The fluvial rainbow trout provided a source of wild genotypes to the colonizing population boosting the number of successful spawners. Significantly more parr offspring were produced by anadromous parents than expected in brood year 2005, whereas significantly more parr offspring were produced by fluvial parents than expected in brood year 2006. Although hatchery steelhead were prevalent in the Methow Basin, they produced only 2 parr and no returning adults in Beaver Creek. On average, individual wild steelhead produced more parr offspring than the fluvial or hatchery groups. Yet, the offspring that returned as adult steelhead were from parents that produced few parr offspring, indicating that high production of parr offspring may not be related to greater returns of adult offspring. These data in combination with other studies of sympatric life histories of O. mykiss indicate that fluvial rainbow trout are important to the conservation and recovery of steelhead and should be included in the management and recovery efforts.
","language":"English","publisher":"Kluwer Academic Publishers","doi":"10.1007/s10641-013-0204-9","usgsCitation":"Weigel, D.E., Connolly, P., and Powell, M.S., 2013, Fluvial rainbow trout contribute to the colonization of steelhead (Oncorhynchus mykiss) in a small stream: Environmental Biology of Fishes, v. 97, no. 10, p. 1149-1159, https://doi.org/10.1007/s10641-013-0204-9.","productDescription":"11 p.","startPage":"1149","endPage":"1159","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-040005","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":280739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Methow River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.21,48.05 ], [ -120.21,48.48 ], [ -119.93,48.48 ], [ -119.93,48.05 ], [ -120.21,48.05 ] ] ] } } ] }","volume":"97","issue":"10","noUsgsAuthors":false,"publicationDate":"2013-11-22","publicationStatus":"PW","scienceBaseUri":"53cd5a0ce4b0b290850f9167","contributors":{"authors":[{"text":"Weigel, Dana E.","contributorId":79389,"corporation":false,"usgs":true,"family":"Weigel","given":"Dana","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":487991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connolly, Patrick J. 0000-0001-7365-7618 pconnolly@usgs.gov","orcid":"https://orcid.org/0000-0001-7365-7618","contributorId":2920,"corporation":false,"usgs":true,"family":"Connolly","given":"Patrick J.","email":"pconnolly@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":487989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, Madison S.","contributorId":33609,"corporation":false,"usgs":true,"family":"Powell","given":"Madison","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":487990,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70100728,"text":"70100728 - 2013 - Powassan virus in mammals, Alaska and New Mexico, USA, and Russia, 2004–2007","interactions":[],"lastModifiedDate":"2018-08-20T18:06:21","indexId":"70100728","displayToPublicDate":"2014-01-05T14:03:16","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1493,"text":"Emerging Infectious Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Powassan virus in mammals, Alaska and New Mexico, USA, and Russia, 2004–2007","docAbstract":"Powassan virus is endemic to the United States, Canada, and the Russian Far East. We report serologic evidence of circulation of this virus in Alaska, New Mexico, and Siberia. These data support further studies of viral ecology in rapidly changing Arctic environments.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Emerging Infectious Diseases","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Centers for Disease Control and Prevention","doi":"10.3201/eid1912.130319","usgsCitation":"Deardorff, E.R., Nofchissey, R.A., Cook, J.A., Hope, A.G., Tsvetkova, A., Talbot, S.L., and Ebel, G.D., 2013, Powassan virus in mammals, Alaska and New Mexico, USA, and Russia, 2004–2007: Emerging Infectious Diseases, v. 19, no. 12, Online Article, https://doi.org/10.3201/eid1912.130319.","productDescription":"Online Article","ipdsId":"IP-049225","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":473365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3201/eid1912.130319","text":"Publisher Index Page"},{"id":285735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285704,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3201/eid1912.130319"},{"id":285705,"type":{"id":15,"text":"Index Page"},"url":"https://wwwnc.cdc.gov/eid/article/19/12/13-0319_article.htm"}],"volume":"19","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"535594f8e4b0120853e8c110","contributors":{"authors":[{"text":"Deardorff, Eleanor R.","contributorId":96194,"corporation":false,"usgs":true,"family":"Deardorff","given":"Eleanor","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":492403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nofchissey, Robert A.","contributorId":7620,"corporation":false,"usgs":true,"family":"Nofchissey","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":492398,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cook, Joseph A.","contributorId":8323,"corporation":false,"usgs":false,"family":"Cook","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":492399,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hope, Andrew G. 0000-0003-3814-2891 ahope@usgs.gov","orcid":"https://orcid.org/0000-0003-3814-2891","contributorId":4309,"corporation":false,"usgs":true,"family":"Hope","given":"Andrew","email":"ahope@usgs.gov","middleInitial":"G.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":492402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tsvetkova, Albina","contributorId":26971,"corporation":false,"usgs":true,"family":"Tsvetkova","given":"Albina","affiliations":[],"preferred":false,"id":492400,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":492397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebel, Gregory D.","contributorId":33220,"corporation":false,"usgs":true,"family":"Ebel","given":"Gregory","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":492401,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70111661,"text":"70111661 - 2013 - Next-generation sequencing reveals cryptic mtDNA diversity of Plasmodium relictum in the Hawaiian Islands","interactions":[],"lastModifiedDate":"2014-06-06T08:37:35","indexId":"70111661","displayToPublicDate":"2014-01-05T08:32:23","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3011,"text":"Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Next-generation sequencing reveals cryptic mtDNA diversity of Plasmodium relictum in the Hawaiian Islands","docAbstract":"Next-generation 454 sequencing techniques were used to re-examine diversity of mitochondrial cytochrome b lineages of avian malaria (Plasmodium relictum) in Hawaii. We document a minimum of 23 variant lineages of the parasite based on single nucleotide transitional changes, in addition to the previously reported single lineage (GRW4). A new, publicly available portal (Integroomer) was developed for initial parsing of 454 datasets. Mean variant prevalence and frequency was higher in low elevation Hawaii Amakihi (Hemignathus virens) with Avipoxvirus-like lesions (P = 0·001), suggesting that the variants may be biologically distinct. By contrast, variant prevalence and frequency did not differ significantly among mid-elevation Apapane (Himatione sanguinea) with or without lesions (P = 0·691). The low frequency and the lack of detection of variants independent of GRW4 suggest that multiple independent introductions of P. relictum to Hawaii are unlikely. Multiple variants may have been introduced in heteroplasmy with GRW4 or exist within the tandem repeat structure of the mitochondrial genome. The discovery of multiple mitochondrial lineages of P. relictum in Hawaii provides a measure of genetic diversity within a geographically isolated population of this parasite and suggests the origins and evolution of parasite diversity may be more complicated than previously recognized.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Parasitology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0031182013000905","usgsCitation":"Jarvi, S., Farias, M., Lapointe, D., Belcaid, M., and Atkinson, C., 2013, Next-generation sequencing reveals cryptic mtDNA diversity of Plasmodium relictum in the Hawaiian Islands: Parasitology, v. 140, no. 14, p. 1741-1750, https://doi.org/10.1017/S0031182013000905.","productDescription":"10 p.","startPage":"1741","endPage":"1750","ipdsId":"IP-025078","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":288131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288129,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1017/S0031182013000905"},{"id":288130,"type":{"id":15,"text":"Index Page"},"url":"https://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9071724"}],"country":"United States","state":"Hawai'i","volume":"140","issue":"14","noUsgsAuthors":false,"publicationDate":"2013-08-19","publicationStatus":"PW","scienceBaseUri":"53ae7786e4b0abf75cf2c170","contributors":{"authors":[{"text":"Jarvi, S.I.","contributorId":60341,"corporation":false,"usgs":true,"family":"Jarvi","given":"S.I.","affiliations":[],"preferred":false,"id":494389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farias, M.E.","contributorId":43675,"corporation":false,"usgs":true,"family":"Farias","given":"M.E.","email":"","affiliations":[],"preferred":false,"id":494388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lapointe, D.A.","contributorId":69691,"corporation":false,"usgs":true,"family":"Lapointe","given":"D.A.","email":"","affiliations":[],"preferred":false,"id":494390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belcaid, M.","contributorId":80193,"corporation":false,"usgs":true,"family":"Belcaid","given":"M.","email":"","affiliations":[],"preferred":false,"id":494391,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atkinson, C. 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The lack of a dedicated, easily accessible, quality-controlled database of sediment and ancillary data has made it difficult to identify sediment-related water-quality impairments and has limited understanding of how human actions affect suspended-sediment concentrations and transport. The purpose of this report is to describe the creation of a quality-controlled U.S. Geological Survey suspended-sediment database, provide guidance for its use, and summarize characteristics of suspended-sediment data through 2010. The database is provided as an online application at http://cida.usgs.gov/sediment to allow users to view, filter, and retrieve available suspended-sediment and ancillary data.
A data recovery, filtration, and quality-control process was performed to expand the availability, representativeness, and utility of existing suspended-sediment data collected by the U.S. Geological Survey in the United States before January 1, 2011. Information on streamflow condition, sediment grain size, and upstream landscape condition were matched to sediment data and sediment-sampling sites to place data in context with factors that may influence sediment transport. Suspended-sediment and selected ancillary data are presented from across the United States with respect to time, streamflow, and landscape condition. Examples of potential uses of this database for identifying sediment-related impairments, assessing trends, and designing new data collection activities are provided. This report and database can support local and national-level decision making, project planning, and data mining activities related to the transport of suspended-sediment and sediment-associated constituents.
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J.","affiliations":[],"preferred":false,"id":485899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glysson, G. Douglas","contributorId":13607,"corporation":false,"usgs":true,"family":"Glysson","given":"G.","email":"","middleInitial":"Douglas","affiliations":[],"preferred":false,"id":485898,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70055665,"text":"sir20125267 - 2013 - Analysis of postfire hydrology, water quality, and sediment transport for selected streams in areas of the 2002 Hayman and Hinman fires, Colorado","interactions":[],"lastModifiedDate":"2014-01-04T13:55:43","indexId":"sir20125267","displayToPublicDate":"2014-01-04T13:42:00","publicationYear":"2013","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":"2012-5267","title":"Analysis of postfire hydrology, water quality, and sediment transport for selected streams in areas of the 2002 Hayman and Hinman fires, Colorado","docAbstract":"The U.S. Geological Survey (USGS) began a 5-year study in 2003 that focused on postfire stream-water quality and postfire sediment load in streams within the Hayman and Hinman fire study areas. This report compares water quality of selected streams receiving runoff from unburned areas and burned areas using concentrations and loads, and trend analysis, from seasonal data (approximately April–November) collected 2003–2007 at the Hayman fire study area, and data collected from 1999–2000 (prefire) and 2003 (postfire) at the Hinman fire study area. The water-quality data collected during this study include onsite measurements of streamflow, specific conductance, and turbidity, laboratory-determined pH, and concentrations of major ions, nutrients, organic carbon, trace elements, and suspended sediment. Postfire floods and effects on water quality of streams, lakes and reservoirs, drinking-water treatment, and the comparison of measured concentrations to applicable water quality standards also are discussed.
\nExceedances of Colorado water-quality standards in streams of both the Hayman and Hinman fire study areas only occurred for concentrations of five trace elements (not all trace-element exceedances occurred in every stream). Selected samples analyzed for total recoverable arsenic (fixed), dissolved copper (acute and chronic), total recoverable iron (chronic), dissolved manganese (acute, chronic, and fixed) and total recoverable mercury (chronic) exceeded Colorado aquatic-life standards.
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