Geographic Information Systems (GIS) layers of current, and likely former, tidal wetlands in two Oregon estuaries were generated by enhancing the 2010 National Wetlands Inventory (NWI) data with expert local field knowledge, Light Detection and Ranging-derived elevations, and 2009 aerial orthophotographs. Data were generated for two purposes: First, to enhance the NWI by recommending revised Cowardin classifications for certain NWI wetlands within the study area; and second, to generate GIS data for the 1999 Yaquina and Alsea River Basins Estuarine Wetland Site Prioritization study. Two sets of GIS products were generated: (1) enhanced NWI shapefiles; and (2) shapefiles of prioritization sites. The enhanced NWI shapefiles contain recommended changes to the Cowardin classification (system, subsystem, class, and/or modifiers) for 286 NWI polygons in the Yaquina estuary (1,133 acres) and 83 NWI polygons in the Alsea estuary (322 acres). These enhanced NWI shapefiles also identify likely former tidal wetlands that are classified as upland in the current NWI (64 NWI polygons totaling 441 acres in the Yaquina estuary; 16 NWI polygons totaling 51 acres in the Alsea estuary). The former tidal wetlands were identified to assist strategic planning for tidal wetland restoration. Cowardin classifications for the former tidal wetlands were not provided, because their current hydrology is complex owing to dikes, tide gates, and drainage ditches. The scope of this project did not include the field evaluation that would be needed to determine whether the former tidal wetlands are currently wetlands, and if so, determine their correct Cowardin classification. The prioritization site shapefiles contain 49 prioritization sites totaling 2,177 acres in the Yaquina estuary, and 39 prioritization sites totaling 1,045 acres in the Alsea estuary. The prioritization sites include current and former (for example, diked) tidal wetlands, and provide landscape units appropriate for basin-scale wetland restoration and conservation action planning. Several new prioritization sites (not included in the 1999 prioritization) were identified in each estuary, consisting of NWI polygons formerly classified as nontidal wetland or upland. The GIS products of this project improve the accuracy and utility of the NWI data, and provide useful tools for estuarine resource management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121038","collaboration":"Prepared in cooperation with Green Point Consulting and the U.S. Environmental Protection Agency","usgsCitation":"Brophy, L.S., Reusser, D.A., and Janousek, C.N., 2013, Tidal wetlands of the Yaquina and Alsea River estuaries, Oregon: Geographic Information Systems layer development and recommendations for National Wetlands Inventory revisions: U.S. Geological Survey Open-File Report 2012-1038, vi, 60 p., https://doi.org/10.3133/ofr20121038.","productDescription":"vi, 60 p.","numberOfPages":"68","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":272177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121038.gif"},{"id":272323,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1038/"},{"id":272176,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1038/pdf/ofr2012-1038.pdf","text":"Report","size":"18.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Oregon","otherGeospatial":"Yaquina And Alsea Estuaries","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.16,44.16 ], [ -124.16,44.5 ], [ -123.5,44.5 ], [ -123.5,44.16 ], [ -124.16,44.16 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518e08f7e4b05ebc8f7cc2de","contributors":{"authors":[{"text":"Brophy, Laura S.","contributorId":47266,"corporation":false,"usgs":false,"family":"Brophy","given":"Laura","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":478548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reusser, Deborah A. dreusser@usgs.gov","contributorId":2423,"corporation":false,"usgs":true,"family":"Reusser","given":"Deborah","email":"dreusser@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":547809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":478549,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045936,"text":"ofr20131084 - 2013 - Digital tabulation of stratigraphic data from oil and gas wells in Cuyama Valley and surrounding areas, central California","interactions":[],"lastModifiedDate":"2013-05-10T15:33:06","indexId":"ofr20131084","displayToPublicDate":"2013-05-10T00:00:00","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":"2013-1084","title":"Digital tabulation of stratigraphic data from oil and gas wells in Cuyama Valley and surrounding areas, central California","docAbstract":"Stratigraphic information from 391 oil and gas exploration wells from Cuyama Valley, California, and surrounding areas are herein compiled in digital form from reports that were released originally in paper form. The Cuyama Basin is located within the southeasternmost part of the Coast Ranges and north of the western Transverse Ranges, west of the San Andreas fault. Knowledge of the location and elevation of stratigraphic tops of formations throughout the basin is a first step toward understanding depositional trends and the structural evolution of the basin through time, and helps in understanding the slip history and partitioning of slip on San Andreas and related faults.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131084","usgsCitation":"Sweetkind, D., Bova, S.C., Langenheim, V., Shumaker, L., and Scheirer, D., 2013, Digital tabulation of stratigraphic data from oil and gas wells in Cuyama Valley and surrounding areas, central California: U.S. Geological Survey Open-File Report 2013-1084, Report: vii, 44 p.; Appendix 1, https://doi.org/10.3133/ofr20131084.","productDescription":"Report: vii, 44 p.; Appendix 1","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":272184,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131084.gif"},{"id":272183,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1084/Appendix1.xlsx"},{"id":272181,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1084/"},{"id":272182,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1084/OF13-1084_508.pdf"}],"country":"United States","state":"California","otherGeospatial":"Cuyama Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.5,34.58 ], [ -120.5,35.5 ], [ -119,35.5 ], [ -119,34.58 ], [ -120.5,34.58 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518e08d1e4b05ebc8f7cc2d2","contributors":{"authors":[{"text":"Sweetkind, Donald S.","contributorId":18732,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald S.","affiliations":[],"preferred":false,"id":478559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bova, Shiera C.","contributorId":45607,"corporation":false,"usgs":true,"family":"Bova","given":"Shiera","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":478560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":1526,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":478557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shumaker, Lauren E.","contributorId":99666,"corporation":false,"usgs":true,"family":"Shumaker","given":"Lauren E.","affiliations":[],"preferred":false,"id":478561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scheirer, Daniel S. dscheirer@usgs.gov","contributorId":2325,"corporation":false,"usgs":true,"family":"Scheirer","given":"Daniel S.","email":"dscheirer@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":478558,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045890,"text":"ofr20121215 - 2013 - Remote sensing survey of Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas","interactions":[],"lastModifiedDate":"2018-01-05T10:27:56","indexId":"ofr20121215","displayToPublicDate":"2013-05-08T00:00:00","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":"2012-1215","title":"Remote sensing survey of Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas","docAbstract":"We applied Hyperion sensor satellite data acquired by the National Aeronautics and Space Administration’s Earth Observing-1 (EO-1) satellite in conjunction with reconnaissance surveys to map the occurrences of the invasive Chinese tallow tree (Triadica sebifera) in the Toledo Bend Reservoir study area of northwestern Louisiana and northeastern Texas. The rationale for application of high spectral resolution EO-1 Hyperion data was based on the successful use of Hyperion data in the mapping of Chinese tallow tree in southwestern Louisiana in 2005. In contrast to the single Hyperion image used in the 2005 project, more than 20 EO-1 Hyperion and Advanced Land Imager (ALI) images of the study area were collected in 2009 and 2010 during the fall senescence when Chinese tallow tree leaves turn red. Atmospherically corrected reflectance spectra of Hyperion imagery collected at ground and aerial observation locations provided the input datasets used in the program for spectral discrimination analysis. Discrimination analysis was used to identify spectral indicator sets to best explain variance contained in the input databases. The expectation was that at least one set of Hyperion-based indicator spectra would uniquely identify occurrences of red-leaf Chinese tallow tree; however, no combination of Hyperion-based reflectance datasets produced a unique identifier.\n\nThe inability to discover a unique spectral indicator resulted primarily from relatively sparse coverage by red-leaf Chinese tallow tree within the study area (percentage of coverage was less than 5 percent per 30- by 30-meter Hyperion pixel). To enhance the performance of the spectral discrimination analysis, leaf and canopy spectra of Chinese tallow tree were added to the input datasets to guide the indicator selection. In addition, input databases were segregated by land class obtained from an ALI-based landcover classification in order to reduce the input variance and to promote spectral discrimination of red-leaf Chinese tallow tree. Although no unique spectral identifier for red-leaf Chinese tallow tree was uncovered with these enhanced methods, in some cases predicted spatial patterns throughout the Hyperion images revealed alignment with vegetation associations within each land class that was often observed to contain Chinese tallow trees. These instances were associated particularly with the addition of helicopter-based spectra to the input databases. It was attempted to extend such predictions of likely occurrences of Chinese tallow tree by mapping six of the nine Hyperion swaths and four of the nine land classes, but this attempt produced uncertain results that could not be fully evaluated for accuracy. Even though the final mapping showed promise in identifying likely Chinese tallow tree occurrences, the low percentage of occurrences hindered mapping performance and validation. Results of the mapping suggested that successful detection of Chinese tallow tree in the study area would require a spectral sensor similar to the Hyperion but with a higher ground-level spatial resolution.\n\nAlthough the Hyperion-based spectral mapping did not provide the desired results, the associated field (ground and aerial) surveys did provide for a qualitative assessment of the overall Chinese tallow tree distribution within the study area. Ground and aerial surveys suggested that Chinese tallow tree occurrences were uncommon and were without an observed pattern in relation to proximity to the Toledo Bend Reservoir. Although uncommon and scattered, Chinese tallow trees and shrubs most commonly existed along forest edges, water edges, and fence lines, probably most in line with seed dispersal by birds. Chinese tallow trees were observed to be more densely dispersed within some scrublands and grasslands than were observed in pine, hardwood, and mixed forests.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121215","collaboration":"Prepared in cooperation with the Toledo Bend Project","usgsCitation":"Ramsey, E., Rangoonwala, A., Bannister, T., and Suzuoki, Y., 2013, Remote sensing survey of Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas: U.S. Geological Survey Open-File Report 2012-1215, xi, 74 p.; Table 14; Database, https://doi.org/10.3133/ofr20121215.","productDescription":"xi, 74 p.; Table 14; Database","numberOfPages":"89","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":272068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121215.gif"},{"id":272066,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2012/1215/Table14_RedTallowMapping.xlsx"},{"id":272064,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1215/"},{"id":272067,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2012/1215/Database/ToledoBend_click"},{"id":272065,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1215/OFR%202012-1215.pdf"}],"country":"United States","state":"Louisiana;Texas","county":"Toledo Bend Reservoir","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.1,31.1 ], [ -94.1,32.0 ], [ -93.5,32.0 ], [ -93.5,31.1 ], [ -94.1,31.1 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518b65e6e4b0037667dbc7eb","contributors":{"authors":[{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":72769,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah W.","suffix":"III","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":478492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":478490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bannister, Terri","contributorId":82836,"corporation":false,"usgs":true,"family":"Bannister","given":"Terri","email":"","affiliations":[],"preferred":false,"id":478493,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suzuoki, Yukihiro","contributorId":25283,"corporation":false,"usgs":true,"family":"Suzuoki","given":"Yukihiro","email":"","affiliations":[],"preferred":false,"id":478491,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045914,"text":"sir20135086 - 2013 - Methods for estimating annual exceedance-probability discharges for streams in Iowa, based on data through water year 2010","interactions":[],"lastModifiedDate":"2013-05-08T20:55:26","indexId":"sir20135086","displayToPublicDate":"2013-05-08T00:00:00","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":"2013-5086","title":"Methods for estimating annual exceedance-probability discharges for streams in Iowa, based on data through water year 2010","docAbstract":"A statewide study was performed to develop regional regression equations for estimating selected annual exceedance-probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedance-probability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage.\n\nRegional regression analysis, using generalized least-squares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions.\n\nAverage standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized least-squares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations.\n\nThese regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135086","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project TR-519)","usgsCitation":"Eash, D.A., Barnes, K., and Veilleux, A.G., 2013, Methods for estimating annual exceedance-probability discharges for streams in Iowa, based on data through water year 2010: U.S. Geological Survey Scientific Investigations Report 2013-5086, viii, 63 p.; Downloads Directory, https://doi.org/10.3133/sir20135086.","productDescription":"viii, 63 p.; Downloads Directory","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalEnd":"2010-10-01","ipdsId":"IP-032892","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":272115,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135086.gif"},{"id":272113,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5086/sir13_5086web.pdf"},{"id":272114,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5086/downloads/"},{"id":272112,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5086/"}],"country":"United States","state":"Iowa","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -96.64,40.38 ], [ -96.64,43.5 ], [ -90.14,43.5 ], [ -90.14,40.38 ], [ -96.64,40.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518b65e6e4b0037667dbc7e7","contributors":{"authors":[{"text":"Eash, David A. 0000-0002-2749-8959 daeash@usgs.gov","orcid":"https://orcid.org/0000-0002-2749-8959","contributorId":1887,"corporation":false,"usgs":true,"family":"Eash","given":"David","email":"daeash@usgs.gov","middleInitial":"A.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Kimberlee K.","contributorId":41476,"corporation":false,"usgs":true,"family":"Barnes","given":"Kimberlee K.","affiliations":[],"preferred":false,"id":478530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Veilleux, Andrea G. aveilleux@usgs.gov","contributorId":4404,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":478529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045873,"text":"ofr20131017 - 2013 - Water-quality, bed-sediment, and biological data (October 2010 through September 2011) and statistical summaries of data for streams in the Clark Fork basin, Montana","interactions":[],"lastModifiedDate":"2013-05-07T15:55:52","indexId":"ofr20131017","displayToPublicDate":"2013-05-07T00:00:00","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":"2013-1017","title":"Water-quality, bed-sediment, and biological data (October 2010 through September 2011) and statistical summaries of data for streams in the Clark Fork basin, Montana","docAbstract":"Water, bed sediment, and biota were sampled in streams from Butte to near Missoula, Montana, as part of a monitoring program in the upper Clark Fork basin of western Montana; additional water samples were collected from near Galen to near Missoula at select sites as part of a supplemental sampling program. The sampling program was conducted by the U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency to characterize aquatic resources in the Clark Fork basin, with emphasis on trace elements associated with historic mining and smelting activities. Sampling sites were located on the Clark Fork and selected tributaries. Water samples were collected periodically at 20 sites from October 2010 through September 2011. Bed-sediment and biota samples were collected once at 14 sites during August 2011. This report presents the analytical results and quality-assurance data for water-quality, bed-sediment, and biota samples collected at sites from October 2010 through September 2011. Water-quality data include concentrations of selected major ions, trace elements, and suspended sediment. Turbidity was analyzed for water samples collected at the four sites where seasonal daily values of turbidity were being determined. Daily values of suspended-sediment concentration and suspended-sediment discharge were determined for four sites. Bed-sediment data include trace-element concentrations in the fine-grained fraction. Biological data include trace-element concentrations in whole-body tissue of aquatic benthic insects. Statistical summaries of water-quality, bed-sediment, and biological data for sites in the upper Clark Fork basin are provided for the period of record since 1985.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131017","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Dodge, K.A., Hornberger, M.I., and Dyke, J., 2013, Water-quality, bed-sediment, and biological data (October 2010 through September 2011) and statistical summaries of data for streams in the Clark Fork basin, Montana: U.S. Geological Survey Open-File Report 2013-1017, vi, 134 p., https://doi.org/10.3133/ofr20131017.","productDescription":"vi, 134 p.","numberOfPages":"142","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":400,"text":"Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":272046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131017.gif"},{"id":272044,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1017/"},{"id":272045,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1017/OF13-1017_508.pdf"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ], [ -0.01638888888888889,0.0011111111111111111 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a1460e4b061e1bd533363","contributors":{"authors":[{"text":"Dodge, Kent A. kdodge@usgs.gov","contributorId":1036,"corporation":false,"usgs":true,"family":"Dodge","given":"Kent","email":"kdodge@usgs.gov","middleInitial":"A.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hornberger, Michelle I. 0000-0002-7787-3446 mhornber@usgs.gov","orcid":"https://orcid.org/0000-0002-7787-3446","contributorId":1037,"corporation":false,"usgs":true,"family":"Hornberger","given":"Michelle","email":"mhornber@usgs.gov","middleInitial":"I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":478476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dyke, Jessica jldyke@usgs.gov","contributorId":1035,"corporation":false,"usgs":true,"family":"Dyke","given":"Jessica","email":"jldyke@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":478474,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045859,"text":"70045859 - 2013 - Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China","interactions":[],"lastModifiedDate":"2013-06-17T09:24:06","indexId":"70045859","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China","docAbstract":"Hydrological models have been increasingly used by hydrologists and water resource managers to understand natural processes and human activities that affect watersheds. In this study, we use the physically based model, Soil and Water Assessment Tool (SWAT), to investigate the hydrological processes in the East River Basin in South China, a coastal area dominated by monsoonal climate. The SWAT model was calibrated using 8-year (1973–1980) record of the daily streamflow at the basin outlet (Boluo station), and then validated using data collected during the subsequent 8 years (1981–1988). Statistical evaluation shows that SWAT can consistently simulate the streamflow of the East River with monthly Nash–Sutcliffe efficiencies of 0.93 for calibration and 0.90 for validation at the Boluo station. We analyzed the model simulations with calibrated parameters, presented the spatiotemporal distribution of the key hydrological components, and quantified their responses to different land uses. Watershed managers can use the results of this study to understand hydrological features and evaluate water resources of the East River in terms of sustainable development and effective management.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s00267-013-0045-5","usgsCitation":"Wu, Y., and Chen, J., 2013, Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China: Environmental Management, v. 51, no. 6, p. 1174-1186, https://doi.org/10.1007/s00267-013-0045-5.","productDescription":"13 p.","startPage":"1174","endPage":"1186","ipdsId":"IP-042191","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":272031,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272012,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s00267-013-0045-5"}],"country":"China","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 73.5,18.2 ], [ 73.5,53.6 ], [ 134.8,53.6 ], [ 134.8,18.2 ], [ 73.5,18.2 ] ] ] } } ] }","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-04-23","publicationStatus":"PW","scienceBaseUri":"518a1451e4b061e1bd533337","contributors":{"authors":[{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":478445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Ji","contributorId":101960,"corporation":false,"usgs":true,"family":"Chen","given":"Ji","email":"","affiliations":[],"preferred":false,"id":478446,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045884,"text":"sir20125242 - 2013 - Simulations of groundwater flow, transport, and age in Albuquerque, New Mexico, for a study of transport of anthropogenic and natural contaminants (TANC) to public-supply wells","interactions":[],"lastModifiedDate":"2013-05-07T21:26:46","indexId":"sir20125242","displayToPublicDate":"2013-05-07T00:00:00","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":"2012-5242","title":"Simulations of groundwater flow, transport, and age in Albuquerque, New Mexico, for a study of transport of anthropogenic and natural contaminants (TANC) to public-supply wells","docAbstract":"Vulnerability to contamination from manmade and natural sources can be characterized by the groundwater-age distribution measured in a supply well and the associated implications for the source depths of the withdrawn water. Coupled groundwater flow and transport models were developed to simulate the transport of the geochemical age-tracers carbon-14, tritium, and three chlorofluorocarbon species to public-supply wells in Albuquerque, New Mexico. A separate, regional-scale simulation of transport of carbon-14 that used the flow-field computed by a previously documented regional groundwater flow model was calibrated and used to specify the initial concentrations of carbon-14 in the local-scale transport model. Observations of the concentrations of each of the five chemical species, in addition to water-level observations and measurements of intra-borehole flow within a public-supply well, were used to calibrate parameters of the local-scale groundwater flow and transport models.\n\nThe calibrated groundwater flow model simulates the mixing of “young” groundwater, which entered the groundwater flow system after 1950 as recharge at the water table, with older resident groundwater that is more likely associated with natural contaminants. Complexity of the aquifer system in the zone of transport between the water table and public-supply well screens was simulated with a geostatistically generated stratigraphic realization based upon observed lithologic transitions at borehole control locations. Because effective porosity was simulated as spatially uniform, the simulated age tracers are more efficiently transported through the portions of the simulated aquifer with relatively higher simulated hydraulic conductivity. Non-pumping groundwater wells with long screens that connect aquifer intervals having different hydraulic heads can provide alternate pathways for contaminant transport that are faster than the advective transport through the aquifer material. Simulation of flow and transport through these wells requires time discretization that adequately represents periods of pumping and non-pumping. The effects of intra-borehole flow are not fully represented in the simulation because it employs seasonal stress periods, which are longer than periods of pumping and non-pumping. Further simulations utilizing daily pumpage data and model stress periods may help quantify the relative effects of intra-borehole versus advective aquifer flow on the transport of contaminants near the public-supply wells. The fraction of young water withdrawn from the studied supply well varies with simulated pumping rates due to changes in the relative contributions to flow from different aquifer intervals.\n\nThe advective transport of dissolved solutes from a known contaminant source to the public-supply wells was simulated by using particle-tracking. Because of the transient groundwater flow field, scenarios with alternative contaminant release times result in different simulated-particle fates, most of which are withdrawn from the aquifer at wells that are between the source and the studied supply well. The relatively small effective porosity required to simulate advective transport from the simulated contaminant source to the studied supply well is representative of a preferential pathway and not the predominant aquifer effective porosity that was estimated by the calibration of the model to observed chemical-tracer concentrations.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125242","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Heywood, C.E., 2013, Simulations of groundwater flow, transport, and age in Albuquerque, New Mexico, for a study of transport of anthropogenic and natural contaminants (TANC) to public-supply wells: U.S. Geological Survey Scientific Investigations Report 2012-5242, ix, 51 p., https://doi.org/10.3133/sir20125242.","productDescription":"ix, 51 p.","numberOfPages":"65","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":272049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20125242.gif"},{"id":272047,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5242/"},{"id":272048,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5242/pdf/sir2012-5242.pdf"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.088,34.95 ], [ -106.088,35.22 ], [ -106.47,35.22 ], [ -106.47,34.95 ], [ -106.088,34.95 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145fe4b061e1bd533357","contributors":{"authors":[{"text":"Heywood, Charles E. cheywood@usgs.gov","contributorId":2043,"corporation":false,"usgs":true,"family":"Heywood","given":"Charles","email":"cheywood@usgs.gov","middleInitial":"E.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478477,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045851,"text":"sir20135071 - 2013 - Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012","interactions":[],"lastModifiedDate":"2013-05-07T13:25:37","indexId":"sir20135071","displayToPublicDate":"2013-05-07T00:00:00","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":"2013-5071","title":"Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012","docAbstract":"Cheney Reservoir, located in south-central Kansas, is the primary water supply for the city of Wichita. The U.S. Geological Survey has operated a continuous real-time water-quality monitoring station since 1998 on the North Fork Ninnescah River, the main source of inflow to Cheney Reservoir. Continuously measured water-quality physical properties include streamflow, specific conductance, pH, water temperature, dissolved oxygen, and turbidity. Discrete water-quality samples were collected during 1999 through 2009 and analyzed for sediment, nutrients, bacteria, and other water-quality constituents. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to compute concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints. Regression models were published in 2006 that were based on data collected during 1997 through 2003. This report updates those models using discrete and continuous data collected during January 1999 through December 2009. Models also were developed for four new constituents, including additional nutrient species and indicator bacteria. In addition, a conversion factor of 0.68 was established to convert the Yellow Springs Instruments (YSI) model 6026 turbidity sensor measurements to the newer YSI model 6136 sensor at the North Ninnescah River upstream from Cheney Reservoir site. Newly developed models and 14 years of hourly continuously measured data were used to calculate selected constituent concentrations and loads during January 1999 through December 2012. The water-quality information in this report is important to the city of Wichita because it allows the concentrations of many potential pollutants of interest to Cheney Reservoir, including nutrients and sediment, to be estimated in real time and characterized over conditions and time scales that would not be possible otherwise. In general, model forms and the amount of variance explained by the models was similar between the original and updated models. The amount of variance explained by the updated models changed by 10 percent or less relative to the original models. Total nitrogen, nitrate, organic nitrogen, E. coli bacteria, and total organic carbon models were newly developed for this report. Additional data collection over a wider range of hydrological conditions facilitated the development of these models. The nitrate model is particularly important because it allows for comparison to Cheney Reservoir Task Force goals. Mean hourly computed total suspended solids concentration during 1999 through 2012 was 54 milligrams per liter (mg/L). The total suspended solids load during 1999 through 2012 was 174,031 tons. On an average annual basis, the Cheney Reservoir Task Force runoff (550 mg/L) and long-term (100 mg/L) total suspended solids goals were never exceeded, but the base flow goal was exceeded every year during 1999 through 2012. Mean hourly computed nitrate concentration was 1.08 mg/L during 1999 through 2012. The total nitrate load during 1999 through 2012 was 1,361 tons. On an annual average basis, the Cheney Reservoir Task Force runoff (6.60 mg/L) nitrate goal was never exceeded, the long-term goal (1.20 mg/L) was exceeded only in 2012, and the base flow goal of 0.25 mg/L was exceeded every year. Mean nitrate concentrations that were higher during base flow, rather than during runoff conditions, suggest that groundwater sources are the main contributors of nitrate to the North Fork Ninnescah River above Cheney Reservoir. Mean hourly computed phosphorus concentration was 0.14 mg/L during 1999 through 2012. The total phosphorus load during 1999 through 2012 was 328 tons. On an average annual basis, the Cheney Reservoir Task Force runoff goal of 0.40 mg/L for total phosphorus was exceeded in 2002, the year with the largest yearly mean turbidity, and the long-term goal (0.10 mg/L) was exceeded in every year except 2011 and 2012, the years with the smallest mean streamflows. The total phosphorus base flow goal of 0.05 mg/L was exceeded every year. Given that base flow goals for total suspended solids, nitrate, and total phosphorus were exceeded every year despite hydrologic conditions, the established base flow goals are either unattainable or substantially more best management practices will need to be implemented to attain them. On an annual average basis, no discernible patterns were evident in total suspended sediment, nitrate, and total phosphorus concentrations or loads over time, in large part because of hydrologic variability. However, more rigorous statistical analyses are required to evaluate temporal trends. A more rigorous analysis of temporal trends will allow evaluation of watershed investments in best management practices.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135071","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Stone, M.L., Graham, J.L., and Gatotho, J.W., 2013, Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012: U.S. Geological Survey Scientific Investigations Report 2013-5071, viii, 46 p., https://doi.org/10.3133/sir20135071.","productDescription":"viii, 46 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":272007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135071.gif"},{"id":272005,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5071/"},{"id":272006,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5071/sir13-5071.pdf"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir;North Fork Ninnescah River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -99.25,37.5 ], [ -99.25,38.16 ], [ -97.75,38.16 ], [ -97.75,37.5 ], [ -99.25,37.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145be4b061e1bd53333b","contributors":{"authors":[{"text":"Stone, Mandy L. 0000-0002-6711-1536 mstone@usgs.gov","orcid":"https://orcid.org/0000-0002-6711-1536","contributorId":4409,"corporation":false,"usgs":true,"family":"Stone","given":"Mandy","email":"mstone@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":478424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gatotho, Jackline W.","contributorId":76616,"corporation":false,"usgs":true,"family":"Gatotho","given":"Jackline","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":478425,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042106,"text":"70042106 - 2013 - Reconciling resource utilization and resource selection functions","interactions":[],"lastModifiedDate":"2013-10-30T10:08:14","indexId":"70042106","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Reconciling resource utilization and resource selection functions","docAbstract":"Summary: 1. Analyses based on utilization distributions (UDs) have been ubiquitous in animal space use studies, largely because they are computationally straightforward and relatively easy to employ. Conventional applications of resource utilization functions (RUFs) suggest that estimates of UDs can be used as response variables in a regression involving spatial covariates of interest. 2. It has been claimed that contemporary implementations of RUFs can yield inference about resource selection, although to our knowledge, an explicit connection has not been described. 3. We explore the relationships between RUFs and resource selection functions from a hueristic and simulation perspective. We investigate several sources of potential bias in the estimation of resource selection coefficients using RUFs (e.g. the spatial covariance modelling that is often used in RUF analyses). 4. Our findings illustrate that RUFs can, in fact, serve as approximations to RSFs and are capable of providing inference about resource selection, but only with some modification and under specific circumstances. 5. Using real telemetry data as an example, we provide guidance on which methods for estimating resource selection may be more appropriate and in which situations. In general, if telemetry data are assumed to arise as a point process, then RSF methods may be preferable to RUFs; however, modified RUFs may provide less biased parameter estimates when the data are subject to location error.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Animal Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/1365-2656.12080","usgsCitation":"Hooten, M., Hanks, E., Johnson, D., and Alldredge, M.W., 2013, Reconciling resource utilization and resource selection functions: Journal of Animal Ecology, v. 52, no. 6, p. 1146-1154, https://doi.org/10.1111/1365-2656.12080.","productDescription":"9 p.","startPage":"1146","endPage":"1154","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-038934","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":473837,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.12080","text":"Publisher Index Page"},{"id":271989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271986,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/1365-2656.12080"}],"volume":"52","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-04-09","publicationStatus":"PW","scienceBaseUri":"518a145ee4b061e1bd533353","contributors":{"authors":[{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":470778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, Ephraim M.","contributorId":104630,"corporation":false,"usgs":true,"family":"Hanks","given":"Ephraim M.","affiliations":[],"preferred":false,"id":470781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":470779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alldredge, Mat W.","contributorId":65361,"corporation":false,"usgs":true,"family":"Alldredge","given":"Mat","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":470780,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042647,"text":"70042647 - 2013 - Practical guidance on characterizing availability in resource selection functions under a use-availability design","interactions":[],"lastModifiedDate":"2013-07-15T09:20:03","indexId":"70042647","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Practical guidance on characterizing availability in resource selection functions under a use-availability design","docAbstract":"Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/12-1688.1","usgsCitation":"Northrup, J.M., Hooten, M., Anderson, C.R., and Wittemyer, G., 2013, Practical guidance on characterizing availability in resource selection functions under a use-availability design: Ecology, v. 94, no. 7, p. 1456-1463, https://doi.org/10.1890/12-1688.1.","productDescription":"8 p.","startPage":"1456","endPage":"1463","ipdsId":"IP-040982","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":473839,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/12-1688.1","text":"Publisher Index Page"},{"id":271952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271946,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/12-1688.1"}],"volume":"94","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145de4b061e1bd53334b","contributors":{"authors":[{"text":"Northrup, Joseph M.","contributorId":101965,"corporation":false,"usgs":true,"family":"Northrup","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":471981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":471978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Charles R. Jr.","contributorId":75042,"corporation":false,"usgs":true,"family":"Anderson","given":"Charles","suffix":"Jr.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":471980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wittemyer, George","contributorId":25058,"corporation":false,"usgs":true,"family":"Wittemyer","given":"George","affiliations":[],"preferred":false,"id":471979,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045819,"text":"ds709T - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-05-06T21:08:57","indexId":"ds709T","displayToPublicDate":"2013-05-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"T","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Herat mineral district, which has barium and limestone deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2007,2008,2009), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 1,000-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Herat) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the Herat area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Herat study area, one subarea was designated for detailed field investigations (that is, the Barium-Limestone subarea); this subarea was extracted from the area's image mosaic and is provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709T","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Maps: 11 x 8.5 inches and 97.39 x 69.63 inches; 18 Image Files; 18 Metadata Files; 1 Shapefile; DS 709, https://doi.org/10.3133/ds709T.","productDescription":"Readme; 2 Maps: 11 x 8.5 inches and 97.39 x 69.63 inches; 18 Image Files; 18 Metadata Files; 1 Shapefile; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":271905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":271896,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/t/"},{"id":271898,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/t/index_maps/Herat_Area-of-Interest_Index_Map.pdf"},{"id":271899,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/t/index_maps/Herat_Image_Index_Map.pdf"},{"id":271897,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/t/1_readme.txt"},{"id":271900,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/t/index_maps/index_maps.html"},{"id":271901,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/t/image_files/image_files.html"},{"id":271902,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/t/metadata/metadata.html"},{"id":271903,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/t/shapefiles/shapefiles.html"},{"id":271904,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"}],"country":"Afghanistan","state":"Herat","otherGeospatial":"Herat Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.9,34.25 ], [ 60.9,35.5 ], [ 63.1,35.5 ], [ 63.1,34.25 ], [ 60.9,34.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5188d465e4b023d2d75b9a38","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":478391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":478393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":478392,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045803,"text":"ofr20131097 - 2013 - Mercury and water-quality data from Rink Creek, Salmon River, and Good River, Glacier Bay National Park and Preserve, Alaska, November 2009-October 2011","interactions":[],"lastModifiedDate":"2013-05-06T12:39:33","indexId":"ofr20131097","displayToPublicDate":"2013-05-06T00:00:00","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":"2013-1097","title":"Mercury and water-quality data from Rink Creek, Salmon River, and Good River, Glacier Bay National Park and Preserve, Alaska, November 2009-October 2011","docAbstract":"Glacier Bay National Park and Preserve (GBNPP), Alaska, like many pristine high latitude areas, is exposed to atmospherically deposited contaminants such as mercury (Hg). Although the harmful effects of Hg are well established, information on this contaminant in southeast Alaska is scarce. Here, we assess the level of this contaminant in several aquatic components (water, sediments, and biological tissue) in three adjacent, small streams in GBNPP that drain contrasting landscapes but receive similar atmospheric inputs: Rink Creek, Salmon River, and Good River.\n\nTwenty water samples were collected from 2009 to 2011 and processed and analyzed for total mercury and methylmercury (filtered and particulate), and dissolved organic carbon quantity and quality. Ancillary stream water parameters (discharge, pH, dissolved oxygen, specific conductance, and temperature) were measured at the time of sampling. Major cations, anions, and nutrients were measured four times. In addition, total mercury was analyzed in streambed sediment in 2010 and in juvenile coho salmon and several taxa of benthic macroinvertebrates in the early summer of 2010 and 2011.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131097","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Nagorski, S.A., Neal, E., and Brabets, T.P., 2013, Mercury and water-quality data from Rink Creek, Salmon River, and Good River, Glacier Bay National Park and Preserve, Alaska, November 2009-October 2011: U.S. Geological Survey Open-File Report 2013-1097, vi, 20 p., https://doi.org/10.3133/ofr20131097.","productDescription":"vi, 20 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2009-11-01","temporalEnd":"2011-10-31","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":271881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131097.jpg"},{"id":271879,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1097/"},{"id":271880,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1097/pdf/ofr20131097.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -138.22,58.43 ], [ -138.22,59.24 ], [ -135.78,59.24 ], [ -135.78,58.43 ], [ -138.22,58.43 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5188d46ce4b023d2d75b9a3c","contributors":{"authors":[{"text":"Nagorski, Sonia A.","contributorId":32940,"corporation":false,"usgs":true,"family":"Nagorski","given":"Sonia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":478374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neal, Edward G.","contributorId":68775,"corporation":false,"usgs":true,"family":"Neal","given":"Edward G.","affiliations":[],"preferred":false,"id":478375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brabets, Timothy P. tbrabets@usgs.gov","contributorId":2087,"corporation":false,"usgs":true,"family":"Brabets","given":"Timothy","email":"tbrabets@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":478373,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173425,"text":"70173425 - 2013 - Microhabitat use of the diamond darter","interactions":[],"lastModifiedDate":"2016-06-16T15:44:18","indexId":"70173425","displayToPublicDate":"2013-05-06T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Microhabitat use of the diamond darter","docAbstract":"The only known extant population of the diamond darter (Crystallaria cincotta) exists in the lower 37 km of Elk River, WV, USA. Our understanding of diamond darter habitat use was previously limited, because few individuals have been observed during sampling with conventional gears. We quantified microhabitat use of diamond darters based on measurements of water depth, water velocity and per cent substrate composition. Using spotlights at night-time, we sampled 16 sites within the lower 133 km of Elk River and observed a total of 82 diamond darters at 10 of 11 sampling sites within the lower 37 km. Glides, located immediately upstream of riffles, were the primary habitats sampled for diamond darters, which included relatively shallow depths (<1 m), moderate-to-low water velocities (often < 0.5 m·s−1) and a smooth water surface. Microhabitat use (mean ± SE) of diamond darters was estimated for depth (0.47 ± 0.02 m), average velocity (0.27 ± 0.01 m·s−1) and bottom velocity (0.15 ± 0.01 m·s−1). Substrate used (mean ± SE) by diamond darters was predominantly sand intermixed with lesser amounts of gravel and cobble: % sand (52.1 ± 1.6), % small gravel (12.2 ± 0.78), % large gravel (14.2 ± 0.83), % cobble (19.8 ± 0.96) and % boulder (1.6 ± 0.36). Based on our microhabitat use data, conservation and management efforts for this species should consider preserving glide habitats within Elk River. Spotlighting, a successful sampling method for diamond darters, should be considered for study designs of population estimation and long-term monitoring.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12062","usgsCitation":"Welsh, S., Smith, D.M., and Taylor, N.D., 2013, Microhabitat use of the diamond darter: Ecology of Freshwater Fish, v. 22, no. 4, p. 587-595, https://doi.org/10.1111/eff.12062.","productDescription":"9 p.","startPage":"587","endPage":"595","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-043471","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":473842,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12062","text":"Publisher Index Page"},{"id":323796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Elk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.62704467773438,\n 38.37019391098433\n ],\n [\n -81.53915405273438,\n 38.430463025162666\n ],\n [\n -81.35856628417967,\n 38.501967316378874\n ],\n [\n -81.19171142578125,\n 38.517549061739984\n ],\n [\n -81.12510681152344,\n 38.484769753492536\n ],\n [\n -81.04133605957031,\n 38.55031345037904\n ],\n [\n -80.91087341308594,\n 38.60560305052739\n ],\n [\n -80.86851596832275,\n 38.60919137718264\n ],\n [\n -80.86379528045654,\n 38.619184088742585\n ],\n [\n -80.86770057678223,\n 38.62635920407884\n ],\n [\n -80.86641311645508,\n 38.627800846245115\n ],\n [\n -80.86246490478516,\n 38.62733147755926\n ],\n [\n -80.85671424865723,\n 38.62357641746297\n ],\n [\n -80.8564567565918,\n 38.614389102209635\n ],\n [\n -80.86461067199707,\n 38.60332252537733\n ],\n [\n -80.8868408203125,\n 38.58520989992523\n ],\n [\n -80.8926773071289,\n 38.5580995115263\n ],\n [\n -80.93353271484375,\n 38.55434082866844\n ],\n [\n -80.96134185791014,\n 38.53876704899609\n ],\n [\n -80.98846435546875,\n 38.53339599812303\n ],\n [\n -81.01043701171875,\n 38.52372709602293\n ],\n [\n -81.02657318115234,\n 38.50384805076651\n ],\n [\n -81.03687286376953,\n 38.48342602101765\n ],\n [\n -81.09661102294922,\n 38.43449710809626\n ],\n [\n -81.14982604980469,\n 38.422663159088394\n ],\n [\n -81.20304107666014,\n 38.42642871684965\n ],\n [\n -81.21917724609375,\n 38.47267525933593\n ],\n [\n -81.2607192993164,\n 38.47294404791815\n ],\n [\n -81.37470245361328,\n 38.4546641418137\n ],\n [\n -81.4529800415039,\n 38.43422817624596\n ],\n [\n -81.58721923828125,\n 38.37692286554253\n ],\n [\n -81.6170883178711,\n 38.3564649034123\n ],\n [\n -81.62704467773438,\n 38.37019391098433\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2013-05-06","publicationStatus":"PW","scienceBaseUri":"5763cdb7e4b07657d19ba783","contributors":{"authors":[{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":637109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Dustin M.","contributorId":171829,"corporation":false,"usgs":false,"family":"Smith","given":"Dustin","email":"","middleInitial":"M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":639404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Nate D.","contributorId":172042,"corporation":false,"usgs":false,"family":"Taylor","given":"Nate","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":639405,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045775,"text":"sir20135037 - 2013 - Plankton communities and summertime declines in algal abundance associated with low dissolved oxygen in the Tualatin River, Oregon","interactions":[],"lastModifiedDate":"2013-05-05T16:03:22","indexId":"sir20135037","displayToPublicDate":"2013-05-05T00:00:00","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":"2013-5037","title":"Plankton communities and summertime declines in algal abundance associated with low dissolved oxygen in the Tualatin River, Oregon","docAbstract":"Phytoplankton populations in the Tualatin River in northwestern Oregon are an important component of the dissolved oxygen (DO) budget of the river and are critical for maintaining DO levels in summer. During the low-flow summer period, sufficient nutrients and a long residence time typically combine with ample sunshine and warm water to fuel blooms of cryptophyte algae, diatoms, green and blue-green algae in the low-gradient, slow-moving reservoir reach of the lower river. Algae in the Tualatin River generally drift with the water rather than attach to the river bottom as a result of moderate water depths, slightly elevated turbidity caused by suspended colloidal material, and dominance of silty substrates. Growth of algae occurs as if on a “conveyor belt” of streamflow, a dynamic system that is continually refreshed with inflowing water. Transit through the system can take as long as 2 weeks during the summer low-flow period. Photosynthetic production of DO during algal blooms is important in offsetting oxygen consumption at the sediment-water interface caused by the decomposition of organic matter from primarily terrestrial sources, and the absence of photosynthesis can lead to low DO concentrations that can harm aquatic life. \n\nThe periods with the lowest DO concentrations in recent years (since 2003) typically occur in August following a decline in algal abundance and activity, when DO concentrations often decrease to less than State standards for extended periods (nearly 80 days). Since 2003, algal populations have tended to be smaller and algal blooms have terminated earlier compared to conditions in the 1990s, leading to more frequent declines in DO to levels that do not meet State standards. This study was developed to document the current abundance and species composition of phytoplankton in the Tualatin River, identify the possible causes of the general decline in algae, and evaluate hypotheses to explain why algal blooms diminish in midsummer. \n\nPlankton and water-quality sample data from 2006 to 2008 were combined with parts of a larger discrete-sample and continuous water-quality monitoring dataset and examined to identify patterns in water-quality and algal conditions since 1991, with a particular emphasis on 2003–08. Longitudinal plankton surveys were conducted in 2006–08 at six sites between river miles (RM) 24.5 and 3.4 at 2- to 3-week intervals, or 5–6 per season, and in-situ bioassay experiments were conducted in 2008 to examine the potential effects of wastewater treatment facility (WWTF) effluent and phosphorus additions on phytoplankton biomass and algal photosynthesis. Phytoplankton and zooplankton community composition, streamflow, and water-quality data were analyzed using multivariate statistical techniques to gain insights into plankton dynamics to determine what factors might be most tied to the abundance and characteristics of the phytoplankton assemblages, and identify possible causes of their declines.\n\nThe connection between low-DO events and algal declines was clearly evident, as bloom crashes were nearly always followed by periods of low DO. Algal blooms occurred each year during 2006–08, producing maximum chlorophyll-a (Chl-a) values in June or July generally in the range of 50–80 micrograms per liter (µg/L). Bloom crashes and absence of sufficient algal photosynthesis in mid- to late-summer contributed to minimum DO concentrations that were less than the State standard of 6.5 milligrams per liter (mg/L) based on the 30-day mean daily concentration, for 62–74 days each year. At times, the absolute minimum State standard (4 mg/L DO) also was not met. To learn more about why low-DO events occurred, specific algal declines during 2003–08 were scrutinized to determine their likely causal factors. From this information, a series of hypotheses were formulated and evaluated in terms of their ability to explain recent declines in algal populations in the river in late summer.\n\nMeteorological, streamflow, turbidity, water temperature, and conductance conditions in the Tualatin River during the 2006–08 summer seasons were not atypical. Natural flow comprised the majority (70–80 percent) of flow each year during spring, but then reduced to 38–40 percent during midsummer when WWTF effluent—which contributed as much as 36 percent—and flow augmentation releases comprised a greater fraction of the flow. Summer 2008 was unusual, however, in the prolonged influence from the Wapato Lake agricultural area near Gaston in the upper part of the basin. The previous winter flooding and levee breach at Wapato Lake caused a much greater area of inundation. As a result, drainage from this area continued into July, much later than normal. A subsequent algal bloom in Wapato Lake then seeded the upper Tualatin River, and this drainage had a profound effect on the downstream plankton community. A large blue-green algae bloom developed—the largest in recent memory—prompting a public health advisory for recreational contact for about two weeks.\n\nAlgal growths and surface blooms are a common feature of the Tualatin River. Most of the dominant algae have growth forms and morphologies that are well suited for planktonic life, employing spines and gas vacuoles to resist settling, forming colonies, and producing mucilage (or toxins) to resist zooplankton grazing. In 2006–08, 143 algal taxa were identified in 117 main-stem samples; diatoms and green algae were more diverse than blue-green, golden, and cryptophyte algae, although these later groups sometimes dominated the overall volumetric abundance (biovolume). The most frequently occurring taxa, occurring in 97–99 percent of samples, were flagellated cryptophytes Cryptomonas erosa and Rhodomonas minuta. Other important algal taxa included centric diatoms Stephanodiscus, Cyclotella, and Melosira species and colonial green algae Scenedesmus and Actinastrum. These taxa comprised the majority of the algal biovolume during much of the growing season. A general seasonal trend in the phytoplankton assemblages was observed, with dominance by filamentous centric diatoms Stephanodiscus and Melosira in spring and early summer, and flagellated cryptophytes and green algae, particularly Chlamydomonas sp., in late-summer; or, in 2008, dominance by blue-green algae Anabaena flos-aquae and Aphanizomenon flos-aquae during the Wapato Lake bloom event.\n\nThere were 99 zooplankton taxa identified from the Tualatin River in 2006–08, composed primarily of cladocerans, copepods, and rotifers. A seasonal increase in zooplankton abundance was observed in early summer just as or shortly after the phytoplankton population began to increase, with populations growing to 15,000−120,000 organisms per cubic meter in the lower river. Zooplankton abundance showed a predictable and distinct longitudinal downstream increase, particularly downstream of Highway 99W (RM 11.6). Although grazing rates were not measured, the data suggest that, at times, zooplankton grazing may affect algal abundance and species composition in the Tualatin River, with diatoms becoming relatively less abundant and flagellated cryptophytes and green algae relatively more abundant during periods when zooplankton densities were highest.\n\nMultivariate statistical analyses identified soluble reactive phosphorus (SRP), natural flow, flow augmentation, and WWTF effluent as important factors influencing Tualatin River phytoplankton populations, with zooplankton density (particularly rotifers and copepods), specific conductance, chloride, and water temperature also having an important influence. Although SRP was highly correlated with the plankton communities, that correlation was likely the result of high or low algal activity (uptake) as SRP concentrations were often reduced to low levels during blooms. While previous studies have already established that phosphorus, among other factors such as flow, places a theoretical cap on the size of the phytoplankton population in the river, sometimes algal declines occur when SRP concentrations are apparently sufficient. To identify alternative causal factors, additional analyses were performed without SRP to focus on other water-quality parameters, zooplankton density, and flow factors. Considering data for all 3 years and including just those samples from the lower Tualatin River not affected by the 2008 Wapato Lake drainage event, three factors (percentage of reservoir flow augmentation, total natural flow, and rotifer density) best explained variations in the phytoplankton assemblages.\n\nAnalyses focusing on the possible causes of algal declines included the above multivariate analyses, scrutiny of 10 specific instances of declines in algal populations during 2003–08 including several bloom–crash sequences, and analyses of historic routine watershed monitoring data from Clean Water Services. Six factors were hypothesized to be important in causing bloom crashes or impeding blooms from rebounding in August: (1) light limitation from cloudy weather, (2) a reduction in the plankton inocula or “seed” entering the lower river from upstream sources, (3) increased summer streamflows, (4) changes in the dominant sources of flow as the percentage of flow augmentation and WWTF discharges have increased, (5) zooplankton grazing, and (6) low concentrations of bioavailable phosphorus (<0.015 milligram per liter). All of these hypotheses are supported in some fashion by the available data and statistical analyses. Zooplankton grazing, short-term declines in photosynthesis from cloudy weather, total flow as it affects residence time, and the dominant source of flow are primary factors responsible for the low-DO events caused by declines in algae in the lower Tualatin River during late summer.\n\nCloudy weather and increased turbidity are known to inhibit algal growth in the Tualatin River, and slight increases in turbidity in recent years may be a problem. Upstream sources of algae are critical in determining the characteristics and size of downstream populations, as illustrated by the Wapato Lake bloom in 2008, but more data are needed from upstream to fully define the importance of this connection. The sources of flow, through their differential contribution of plankton inocula (quality and amount), were, at times, important factors affecting phytoplankton populations. While SRP concentrations were often most highly correlated with phytoplankton species community, the bioavailability of phosphorus is still somewhat unknown and there are several sources to consider. Preliminary bioassay tests suggested that while treated wastewater effluent may stimulate algae at 30 percent concentrations, negative effects (or decreased stimulation) on Chl-a and DO production may occur at concentrations of 50 percent. Targeted data collection and future research will be needed to further understand the importance of these factors on Tualatin River phytoplankton.\n\nWhile the data and analysis completed for this report provide insights into future research and monitoring that would be useful to continue, additional monitoring of turbidity, Chl-a, and plankton abundance and species composition in the upper part of the basin would enhance our understanding of plankton dynamics and factors affecting phytoplankton abundance in the lower river. Assessment of the key upstream sources of algal inocula via surveys of the major flow sources as well as tributaries and wetlands would provide useful information for the management of river water quality. Other studies that could prove useful for developing management strategies include targeted experiments to evaluate the bioavailability of phosphorus from a variety of sources. New research on phytoplankton–zooplankton interactions, and studies of planktivorous fish, might also provide insight about food web dynamics and potential “top-down” effects of fish predation on the plankton communities. In addition, further development of neural-network or other water-quality models would help to evaluate management strategies and provide forecasts of water-quality conditions. Finally, periodic future reassessments of the available data with the multivariate statistical tools used in this study would be helpful to assess whether and how plankton communities are changing, and to continue to shed light on the importance of factors shaping the plankton. Although certain types and sizes of algal blooms are undesirable, minimum phytoplankton populations are an important part of aquatic food webs and are needed to maintain healthy levels of DO in the river. By understanding the sources, characteristics, causal factors, and responses of the plankton communities, management strategies can be developed to improve DO conditions in the lower Tualatin River during the important summer low-flow period.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135037","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Carpenter, K., and Rounds, S.A., 2013, Plankton communities and summertime declines in algal abundance associated with low dissolved oxygen in the Tualatin River, Oregon: U.S. Geological Survey Scientific Investigations Report 2013-5037, x, 78 p.; Appendixes A-C; Table 10, https://doi.org/10.3133/sir20135037.","productDescription":"x, 78 p.; Appendixes A-C; Table 10","additionalOnlineFiles":"Y","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":271825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135037.jpg"},{"id":271821,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_appendixA.xlsx"},{"id":271822,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_appendixB.xlsx"},{"id":271823,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_appendixC.xlsx"},{"id":271824,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5037/appendixes/sir20135037_table10.pdf"},{"id":271819,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5037/"},{"id":271820,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5037/pdf/sir20135037.pdf"}],"country":"United States","state":"Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.6,42.0 ], [ -124.6,46.3 ], [ -116.5,46.3 ], [ -116.5,42.0 ], [ -124.6,42.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5187716ce4b078fc9c244b63","contributors":{"authors":[{"text":"Carpenter, Kurt D. kdcar@usgs.gov","contributorId":1372,"corporation":false,"usgs":true,"family":"Carpenter","given":"Kurt D.","email":"kdcar@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":478342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":478341,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045773,"text":"ds745 - 2013 - Classifications for Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) site-specific projects: 2010","interactions":[],"lastModifiedDate":"2013-05-05T16:05:08","indexId":"ds745","displayToPublicDate":"2013-05-05T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"745","title":"Classifications for Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) site-specific projects: 2010","docAbstract":"The Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) funds over 100 wetland restoration projects across Louisiana. Integral to the success of CWPPRA is its long-term monitoring program, which enables State and Federal agencies to determine the effectiveness of each restoration effort. One component of this monitoring program is the classification of high-resolution, color-infrared aerial photography at the U.S. Geological Survey’s National Wetlands Research Center in Lafayette, Louisiana.\n\nColor-infrared aerial photography (9- by 9-inch) is obtained before project construction and several times after construction. Each frame is scanned on a photogrametric scanner that produces a high-resolution image in Tagged Image File Format (TIFF). By using image-processing software, these TIFF files are then orthorectified and mosaicked to produce a seamless image of a project area and its associated reference area (a control site near the project that has common environmental features, such as marsh type, soil types, and water salinities.) The project and reference areas are then classified according to pixel value into two distinct classes, land and water. After initial land and water ratios have been established by using photography obtained before and after project construction, subsequent comparisons can be made over time to determine land-water change.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds745","collaboration":"Prepared in cooperation with Coastal Protection and Restoration Authority of Louisiana, U.S. Army Corps of Engineers, U.S. Environmental Protection Agency, U.S. Fish and Wildlife Service, Natural Resources Conservation Service, and National Oceanic and Atmospheric Administration","usgsCitation":"Jones, W.R., and Garber, A., 2013, Classifications for Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) site-specific projects: 2010: U.S. Geological Survey Data Series 745, Pamphlet: iv, 8 p.; 2010 CWPPRA Maps: 10 Sheets: 58 x 47 inches; Data and Metadata Files, https://doi.org/10.3133/ds745.","productDescription":"Pamphlet: iv, 8 p.; 2010 CWPPRA Maps: 10 Sheets: 58 x 47 inches; Data and Metadata Files","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2010-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-037884","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":271818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds745.gif"},{"id":271805,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/745/"},{"id":271806,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/745/DS745.pdf"},{"id":271807,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/cs27_2010l_Black%20Bayou%20Hydrologic%20Restoration_letter.pdf"},{"id":271808,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/cs27_2010l_Black%20Bayou%20Hydrologic%20Restoration_poster.pdf"},{"id":271809,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/cs27_2010l_Black%20Bayou%20Hydrologic%20Restoration_mosaic_ltr.pdf"},{"id":271810,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/cs30_2005_2010l_GIWW-Perry%20Ridge%20West%20Bank%20Stabilization_poster.pdf"},{"id":271811,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/cs30_2005_2010l_GIWW-Perry%20Ridge%20West%20Bank%20Stabilization_tabloid.pdf"},{"id":271812,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/cs30_2010_GIWW-Perry%20Ridge%20West%20Bank%20Stabilization_mosaic_letter.pdf"},{"id":271813,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/cs30_2010l_GIWW-Perry%20Ridge%20West%20Bank%20Stabilization_letter.pdf"},{"id":271814,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/tv18_2010_Four%20Mile%20Canal%20Terracing%20and%20Sediment%20Trapping_letter.pdf"},{"id":271815,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/tv18_2010_Four%20Mile%20Canal%20Terracing%20and%20Sediment%20Trapping_poster.pdf"},{"id":271816,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/745/Maps_online%20only/po06_2010lpr_Fritchie%20Marsh%20Restoration_letter.pdf"},{"id":271817,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/745/downloads2010/"}],"country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.0,28.9 ], [ -94.0,33.0 ], [ -88.8,33.0 ], [ -88.8,28.9 ], [ -94.0,28.9 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51877151e4b078fc9c244b4f","contributors":{"authors":[{"text":"Jones, William R. 0000-0002-5493-4138 jonesb@usgs.gov","orcid":"https://orcid.org/0000-0002-5493-4138","contributorId":463,"corporation":false,"usgs":true,"family":"Jones","given":"William","email":"jonesb@usgs.gov","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":478339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garber, Adrienne 0000-0003-1139-8256 garbera@usgs.gov","orcid":"https://orcid.org/0000-0003-1139-8256","contributorId":464,"corporation":false,"usgs":true,"family":"Garber","given":"Adrienne","email":"garbera@usgs.gov","affiliations":[],"preferred":true,"id":478340,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045769,"text":"sir20135084 - 2013 - Groundwater conditions in Georgia, 2010–2011","interactions":[],"lastModifiedDate":"2017-01-17T20:46:02","indexId":"sir20135084","displayToPublicDate":"2013-05-03T00:00:00","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":"2013-5084","title":"Groundwater conditions in Georgia, 2010–2011","docAbstract":"The U.S. Geological Survey collects groundwater data and conducts studies to monitor hydrologic conditions, better define groundwater resources, and address problems related to water supply, water use, and water quality. In Georgia, water levels were monitored continuously at 186 wells during calendar year 2010 and at 181 wells during calendar year 2011. Because of missing data or short periods of record (less than 3 years) for several of these wells, a total of 168 wells are discussed in this report. These wells include 17 in the surficial aquifer system, 19 in the Brunswick aquifer system and equivalent sediments, 70 in the Upper Floridan aquifer, 16 in the Lower Floridan aquifer and underlying units, 10 in the Claiborne aquifer, 1 in the Gordon aquifer, 11 in the Clayton aquifer, 14 in the Cretaceous aquifer system, 2 in Paleozoic-rock aquifers, and 8 in crystalline-rock aquifers. Data from the well network indicate that water levels generally declined during the 2010 through 2011 calendar-year period, with water levels declining in 158 wells and rising in 10. Water levels declined over the period of record at 106 wells, increased at 56 wells, and remained relatively constant at 6 wells. In addition to continuous water-level data, periodic water-level measurements were collected and used to construct potentiometric-surface maps for the Upper Floridan aquifer in Camden, Charlton, and Ware Counties, Georgia, and adjacent counties in Florida during May–June 2010, and in the following areas in Georgia: the Brunswick area during August 2010 and August 2011, in the Albany–Dougherty County area during November 2010 and November 2011, and in the Augusta–Richmond County area during October 2010 and August 2011. In general, water levels in these areas were lower during 2011 than during 2010; however, the configuration of the potentiometric surfaces in each of the areas showed little change. Groundwater quality in the Floridan aquifer system is monitored in the Albany, Savannah, and Brunswick areas of Georgia. In the Albany area, nitrate as nitrogen concentrations in the Upper Floridan aquifer during 2011 generally decreased from 2010; however, concentrations in two wells remained above the U.S. Environmental Protection Agency (USEPA) 10-milligrams-per-liter (mg/L) drinking-water standard. In the Savannah area, specific conductance and chloride concentrations were measured in water samples from discrete depths in two wells completed in the Upper Floridan aquifer. Data from the two wells indicate that chloride concentrations in the Upper Floridan aquifer showed little change during calendar years 2010 through 2011 and remained below the 250 mg/L USEPA secondary drinking-water standard. During calendar years 2010 through 2011, chloride concentrations in the Lower Floridan aquifer increased slightly at Tybee Island and Skidaway Island, remaining above the drinking-water standard. In the Brunswick area, maps showing the chloride concentration of water in the Upper Floridan aquifer constructed using data collected from 32 wells during August 2010 and from 30 wells during August 2011 indicate that chloride concentrations remained above the USEPA secondary drinking-water standard in an approximately 2-square-mile area. During calendar years 2010 through 2011, chloride concentrations generally decreased in over 70 percent of the wells sampled during 2011, with a maximum decrease of 200 mg/L in a well located in the north-central part of the Brunswick area.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135084","usgsCitation":"Peck, M., Gordon, D., and Painter, J.A., 2013, Groundwater conditions in Georgia, 2010–2011: U.S. Geological Survey Scientific Investigations Report 2013-5084, iv, 65 p., https://doi.org/10.3133/sir20135084.","productDescription":"iv, 65 p.","numberOfPages":"71","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":271798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135084.gif"},{"id":271796,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5084/"},{"id":271797,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5084/pdf/sir2013-5084.pdf"}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.640625,\n 43.34116005412307\n ],\n [\n -76.640625,\n 44.85586880735725\n ],\n [\n -72.97119140625,\n 44.85586880735725\n ],\n [\n -72.97119140625,\n 43.34116005412307\n ],\n [\n -76.640625,\n 43.34116005412307\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.562255859375,\n 33.96158628979907\n ],\n [\n -82.430419921875,\n 33.87041555094183\n ],\n [\n -82.210693359375,\n 33.779147331286474\n ],\n [\n -82.1337890625,\n 33.660353121928814\n ],\n [\n -82.210693359375,\n 33.62376800118811\n ],\n [\n -82.056884765625,\n 33.55055114384406\n ],\n [\n -81.925048828125,\n 33.44977658311846\n ],\n [\n -81.93603515625,\n 33.358061612778876\n ],\n [\n -81.84814453125,\n 33.293803558346596\n ],\n [\n -81.7822265625,\n 33.211116472416855\n ],\n [\n -81.705322265625,\n 33.17434155100208\n ],\n [\n -81.6064453125,\n 33.100745405144245\n ],\n [\n -81.54052734375,\n 33.04550781490999\n ],\n [\n -81.463623046875,\n 32.90726224488304\n ],\n [\n -81.441650390625,\n 32.80574473290688\n ],\n [\n -81.4306640625,\n 32.69486597787505\n ],\n [\n -81.34277343749999,\n 32.61161640317033\n ],\n [\n -81.298828125,\n 32.54681317351514\n ],\n [\n -81.1669921875,\n 32.43561304116276\n ],\n [\n -81.903076171875,\n 32.03602003973755\n ],\n [\n -83.1884765625,\n 31.82156451492074\n ],\n [\n -83.265380859375,\n 33.00866349457558\n ],\n [\n -82.96875,\n 33.50475906922609\n ],\n [\n -82.781982421875,\n 34.15272698011818\n ],\n [\n -82.6611328125,\n 34.125447565116126\n ],\n [\n -82.584228515625,\n 34.016241889667015\n ],\n [\n -82.562255859375,\n 33.96158628979907\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5184ce60e4b04d6ec94d6299","contributors":{"authors":[{"text":"Peck, Michael F. mfpeck@usgs.gov","contributorId":1467,"corporation":false,"usgs":true,"family":"Peck","given":"Michael F.","email":"mfpeck@usgs.gov","affiliations":[],"preferred":false,"id":478325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gordon, Debbie W. 0000-0002-5195-6657","orcid":"https://orcid.org/0000-0002-5195-6657","contributorId":79591,"corporation":false,"usgs":true,"family":"Gordon","given":"Debbie W.","affiliations":[],"preferred":false,"id":478326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Painter, Jaime A. 0000-0001-8883-9158 jpainter@usgs.gov","orcid":"https://orcid.org/0000-0001-8883-9158","contributorId":1466,"corporation":false,"usgs":true,"family":"Painter","given":"Jaime","email":"jpainter@usgs.gov","middleInitial":"A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478324,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045771,"text":"ofr20121051 - 2013 - Benthic substrate classification map: Gulf Islands National Seashore","interactions":[],"lastModifiedDate":"2013-05-03T15:17:16","indexId":"ofr20121051","displayToPublicDate":"2013-05-03T00:00:00","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":"2012-1051","title":"Benthic substrate classification map: Gulf Islands National Seashore","docAbstract":"The 2005 hurricane season was devastating for the Mississippi Gulf Coast. Hurricane Katrina caused significant degradation of the barrier islands that compose the Gulf Islands National Seashore (GUIS). Because of the ability of coastal barrier islands to help mitigate hurricane damage to the mainland, restoring these habitats prior to the onset of future storms will help protect the islands themselves and the surrounding habitats. During Hurricane Katrina, coastal barrier islands reduced storm surge by approximately 10 percent and moderated wave heights (Wamsley and others, 2009). Islands protected the mainland by preventing ocean waves from maintaining their size as they approached the mainland. In addition to storm protection, it is advantageous to restore these islands to preserve the cultural heritage present there (for example, Fort Massachusetts) and because of the influence that these islands have on marine ecology. For example, these islands help maintain a salinity regime favorable to oysters in the Mississippi Sound and provide critical habitats for many migratory birds and endangered species such as sea turtles (Chelonia mydas, Caretta caretta, and Dermochelys coriacea), Gulf sturgeon (Acipenser oxyrinchus desotoi), and piping plovers (Charadrius melodus) (U.S. Army Corps of Engineers, 2009a). As land manager for the GUIS, the National Park Service (NPS) has been working with the State of Mississippi and the Mobile District of the U.S. Army Corps of Engineers to provide a set of recommendations to the Mississippi Coastal Improvements Program (MsCIP) that will guide restoration planning. The final set of recommendations includes directly renourishing both West Ship Island (to protect Fort Massachusetts) and East Ship Island (to restore the French Warehouse archaeological site); filling Camille Cut to recreate a continuous Ship Island; and restoring natural regional sediment transport processes by placing sand in the littoral zone just east of Petit Bois Island. Prevailing sediment transport processes will provide natural renourishment of the westward islands in the barrier system (U.S. Army Corps of Engineers, 2009b). One difficulty in developing the final recommendations is that few data are available to incorporate into restoration plans related to bathymetry, sediment type, and biota. For example, the most recent bathymetry available dates to when East and West Ship Islands were a single continuous island (1917). As a result, the MsCIP program has encouraged post-hurricane bathymetric data collection for future reference. Furthermore, managing a complex environment such as this barrier island system for habitat conservation and best resource usage requires significant knowledge about those habitats and resources. To effectively address these issues, a complete and comprehensive understanding of the type, geographic extent, and condition of marine resources included within the GUIS is required. However, the data related to the GUIS marine resources are limited either spatially or temporally. Specifically, there is limited knowledge and information about the distribution of benthic habitats and the characteristics of the offshore region of the GUIS, even though these are the habitats that will be most affected by habitat restoration. The goal of this project is to develop a comprehensive map of the benthic marine habitats within the GUIS to give park managers the ability to develop strategies for coastal and ocean-resource management and to aid decisionmakers in evaluating conservation priorities.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121051","collaboration":"Prepared as part of the U.S. Geological Survey Northern Gulf of Mexico Progam","usgsCitation":"Lavoie, D., Flocks, J., Twichell, D., and Rose, K., 2013, Benthic substrate classification map: Gulf Islands National Seashore: U.S. Geological Survey Open-File Report 2012-1051, vi, 14 p., https://doi.org/10.3133/ofr20121051.","productDescription":"vi, 14 p.","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":271804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121051.gif"},{"id":271802,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1051/"},{"id":271803,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1051/pdf/ofr2012-1051.pdf"}],"country":"United States","state":"Mississippi","otherGeospatial":"Mississippi Gulf Coast","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88.74,28.49 ], [ -88.74,30.4 ], [ -85.8,30.4 ], [ -85.8,28.49 ], [ -88.74,28.49 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5184ce51e4b04d6ec94d6295","contributors":{"authors":[{"text":"Lavoie, Dawn","contributorId":43881,"corporation":false,"usgs":true,"family":"Lavoie","given":"Dawn","affiliations":[],"preferred":false,"id":478333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flocks, James","contributorId":62266,"corporation":false,"usgs":true,"family":"Flocks","given":"James","affiliations":[],"preferred":false,"id":478334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Twichell, Dave","contributorId":23421,"corporation":false,"usgs":true,"family":"Twichell","given":"Dave","affiliations":[],"preferred":false,"id":478332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rose, Kate","contributorId":66154,"corporation":false,"usgs":true,"family":"Rose","given":"Kate","email":"","affiliations":[],"preferred":false,"id":478335,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045740,"text":"70045740 - 2013 - Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska","interactions":[],"lastModifiedDate":"2018-01-12T17:20:50","indexId":"70045740","displayToPublicDate":"2013-05-02T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3032,"text":"Permafrost and Periglacial Processes","active":true,"publicationSubtype":{"id":10}},"title":"Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska","docAbstract":"Machine-learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at-sensor reflectance, thermal, TM-derived spectral indices, digital elevation models and other relevant spatial data to estimate near-surface (0–2.6-m depth) resistivity at 30-m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active-layer thickness (ALT) (< 101 cm) and the probability of near-surface (up to 123-cm depth) permafrost occurrence from field data, modelled near-surface (0–2.6 m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher-elevation loess deposits than on low-lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Permafrost and Periglacial Processes","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/ppp.1775","usgsCitation":"Pastick, N.J., Jorgenson, M., Wylie, B.K., Minsley, B.J., Ji, L., Walvoord, M.A., Smith, B.D., Abraham, J., and Rose, J.R., 2013, Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska: Permafrost and Periglacial Processes, v. 24, no. 3, p. 184-199, https://doi.org/10.1002/ppp.1775.","productDescription":"16 p.","startPage":"184","endPage":"199","ipdsId":"IP-037584","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":271728,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/ppp.1775"},{"id":271729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -149.55,65.47 ], [ -149.55,67.47 ], [ -142.43,67.47 ], [ -142.43,65.47 ], [ -149.55,65.47 ] ] ] } } ] }","volume":"24","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-04-29","publicationStatus":"PW","scienceBaseUri":"51837ce5e4b0a21483941a49","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":478219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M. Torre","contributorId":40486,"corporation":false,"usgs":true,"family":"Jorgenson","given":"M. Torre","affiliations":[],"preferred":false,"id":478220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":478216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478215,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":2832,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":478218,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366 walvoord@usgs.gov","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":147211,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"walvoord@usgs.gov","middleInitial":"Ann","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":478223,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Bruce D. 0000-0002-1643-2997 bsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-1643-2997","contributorId":845,"corporation":false,"usgs":true,"family":"Smith","given":"Bruce","email":"bsmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478217,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Abraham, Jared D.","contributorId":42630,"corporation":false,"usgs":true,"family":"Abraham","given":"Jared D.","affiliations":[],"preferred":false,"id":478221,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rose, Joshua R.","contributorId":90147,"corporation":false,"usgs":true,"family":"Rose","given":"Joshua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":478222,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70045722,"text":"70045722 - 2013 - Spatial capture-recapture models for jointly estimating population density and landscape connectivity","interactions":[],"lastModifiedDate":"2013-05-02T10:21:54","indexId":"70045722","displayToPublicDate":"2013-05-02T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial capture-recapture models for jointly estimating population density and landscape connectivity","docAbstract":"Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on “ecological distance,” i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture–recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture–recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ESA","doi":"10.1890/12-0413.1","usgsCitation":"Royle, J., Chandler, R.B., Gazenski, K.D., and Graves, T.A., 2013, Spatial capture-recapture models for jointly estimating population density and landscape connectivity: Ecology, v. 94, no. 2, p. 287-294, https://doi.org/10.1890/12-0413.1.","productDescription":"8 p.","startPage":"287","endPage":"294","ipdsId":"IP-042013","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473845,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/12-0413.1","text":"Publisher Index Page"},{"id":271735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271734,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/12-0413.1"}],"volume":"94","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51837cebe4b0a21483941a65","contributors":{"authors":[{"text":"Royle, J. Andrew 0000-0003-3135-2167","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":80808,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":478207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandler, Richard B. rchandler@usgs.gov","contributorId":63524,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":478206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gazenski, Kimberly D.","contributorId":55306,"corporation":false,"usgs":true,"family":"Gazenski","given":"Kimberly","email":"","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":478205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Tabitha A. 0000-0001-5145-2400 tgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":5898,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha","email":"tgraves@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":478204,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045720,"text":"70045720 - 2013 - Presence-only modeling using MAXENT: when can we trust the inferences?","interactions":[],"lastModifiedDate":"2013-05-02T10:08:03","indexId":"70045720","displayToPublicDate":"2013-05-02T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Presence-only modeling using MAXENT: when can we trust the inferences?","docAbstract":"1. Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability.\n2. Based on our interactions with researchers using these tools, we hypothesized that many presence-only studies were ignoring important assumptions of presence-only modelling. We tested this hypothesis by reviewing 108 articles published between 2008 and 2012 that used the MAXENT algorithm to analyse empirical (i.e. not simulated) data. We chose to focus on these articles because MAXENT has been the most popular algorithm in recent years for analysing presence-only data.\n3. Many articles (87%) were based on data that were likely to suffer from sample selection bias; however, methods to control for sample selection bias were rarely used. In addition, many analyses (36%) discarded absence information by analysing presence–absence data in a presence-only framework, and few articles (14%) mentioned detection probability. We conclude that there are many misconceptions concerning the use of presence-only models, including the misunderstanding that MAXENT, and other presence-only methods, relieve users from the constraints of survey design.\n4. In the process of our literature review, we became aware of other factors that raised concerns about the validity of study conclusions. In particular, we observed that 83% of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships and that MAXENT's logistic output was frequently (54% of articles) and incorrectly interpreted as occurrence probability.\n5. We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Methods in Ecology and Evolution","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/2041-210x.12004","usgsCitation":"Yackulic, C.B., Chandler, R., Zipkin, E., Royle, J., Nichols, J., Grant, E., and Veran, S., 2013, Presence-only modeling using MAXENT: when can we trust the inferences?: Methods in Ecology and Evolution, v. 4, no. 3, p. 236-243, https://doi.org/10.1111/2041-210x.12004.","productDescription":"8 p.","startPage":"236","endPage":"243","ipdsId":"IP-041882","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12004","text":"Publisher Index Page"},{"id":271731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271730,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/2041-210x.12004"}],"volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-11-21","publicationStatus":"PW","scienceBaseUri":"51837cebe4b0a21483941a61","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":478192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandler, Richard rchandler@usgs.gov","contributorId":2511,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","affiliations":[{"id":13266,"text":"Warnell School of Forestry and Natural Resources, The University of Georgia","active":true,"usgs":false}],"preferred":false,"id":478190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zipkin, Elise F.","contributorId":70528,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise F.","affiliations":[],"preferred":false,"id":478193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royle, J. Andrew 0000-0003-3135-2167","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":80808,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":478195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":478189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grant, Evan H. Campbell ehgrant@usgs.gov","contributorId":3696,"corporation":false,"usgs":true,"family":"Grant","given":"Evan H. Campbell","email":"ehgrant@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":478191,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Veran, Sophie","contributorId":76983,"corporation":false,"usgs":true,"family":"Veran","given":"Sophie","email":"","affiliations":[],"preferred":false,"id":478194,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70045686,"text":"70045686 - 2013 - Spatial consistency of chinook salmon redd distribution within and among years in the Cowlitz River, Washington","interactions":[],"lastModifiedDate":"2013-05-02T10:15:21","indexId":"70045686","displayToPublicDate":"2013-05-02T00:00:00","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":"Spatial consistency of chinook salmon redd distribution within and among years in the Cowlitz River, Washington","docAbstract":"We investigated the spawning patterns of Chinook Salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington, using a unique set of fine- and coarse-scale temporal and spatial data collected during biweekly aerial surveys conducted in 1991–2009 (500 m to 28 km resolution) and 2008–2009 (100–500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held GPS synchronized with in-flight audio recordings. We examined spatial patterns of Chinook Salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook Salmon spawned in the same sections each year with little variation among years. On a coarse scale, 5 years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years. Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations. On a finer temporal scale, we observed that Chinook Salmon spawned in the same sections during the first and last week. Redds were clustered in both 2008 and 2009. Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook Salmon spawning surveys.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"North American Journal of Fisheries Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2013.778924","usgsCitation":"Klett, K.J., Torgersen, C., Henning, J.A., and Murray, C.J., 2013, Spatial consistency of chinook salmon redd distribution within and among years in the Cowlitz River, Washington: North American Journal of Fisheries Management, v. 33, no. 3, p. 508-518, https://doi.org/10.1080/02755947.2013.778924.","productDescription":"11 p.","startPage":"508","endPage":"518","ipdsId":"IP-043269","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":271733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271732,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/02755947.2013.778924"}],"country":"United States","volume":"33","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-04-28","publicationStatus":"PW","scienceBaseUri":"51837cebe4b0a21483941a69","contributors":{"authors":[{"text":"Klett, Katherine J.C.","contributorId":10699,"corporation":false,"usgs":true,"family":"Klett","given":"Katherine","email":"","middleInitial":"J.C.","affiliations":[],"preferred":false,"id":478046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":48143,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian E.","affiliations":[],"preferred":false,"id":478048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henning, Julie A.","contributorId":15579,"corporation":false,"usgs":true,"family":"Henning","given":"Julie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":478047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, Christopher J.","contributorId":58537,"corporation":false,"usgs":true,"family":"Murray","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":478049,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045747,"text":"sir20135026 - 2013 - Hydrogeology and water quality of the Dublin and Midville aquifer systems at Waynesboro, Burke County, Georgia, 2011","interactions":[],"lastModifiedDate":"2017-01-17T20:37:46","indexId":"sir20135026","displayToPublicDate":"2013-05-02T00:00:00","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":"2013-5026","title":"Hydrogeology and water quality of the Dublin and Midville aquifer systems at Waynesboro, Burke County, Georgia, 2011","docAbstract":"The hydrogeology and water quality of the Dublin and Midville aquifer systems were characterized in the City of Waynesboro area in Burke County, Georgia, based on geophysical and drillers’ logs, flowmeter surveys, a 24-houraquifer test, and the collection and chemical analysis of water samples in a newly constructed well. At the test site, the Dublin aquifer system consists of interlayered sands and clays between depths of 396 and 691 feet, and the Midville aquifer system consists of a sandy clay layer overlying a sand and gravel layer between depths of 728 and 936 feet. The new well was constructed with three screened intervals in the Dublin aquifer system and four screened intervals in the Midville aquifer system. Wellbore-flowmeter testing at a pumping rate of 1,000 gallons per minute indicated that 52.2 percent of the total flow was from the shallower Dublin aquifer system with the remaining 47.8 percent from the deeper Midville aquifer system. The lower part of the lower Midville aquifer (900 to 930 feet deep), contributed only 0.1 percent of the total flow.\n\nHydraulic properties of the two aquifer systems were estimated using data from two wellbore-flowmeter surveys and a 24-hour aquifer test. Estimated values of transmissivity for the Dublin and Midville aquifer systems were 2,000 and 1,000 feet squared per day, respectively. The upper and lower Dublin aquifers have a combined thickness of about 150 feet and the horizontal hydraulic conductivity of the Dublin aquifer system averages 10 feet per day. The upper Midville aquifer, lower Midville confining unit, and lower Midville aquifer have a combined thickness of about 210 feet, and the horizontal hydraulic conductivity of the Midville aquifer system averages 6 feet per day. Storage coefficient of the Dublin aquifer system, computed using the Theis method on water-level data from one observation well, was estimated to be 0.0003. With a thickness of about 150 feet, the specific storage of the Dublin aquifer system averages about 2×10-6 per foot.\n\nWater quality of the Dublin and Midville aquifer systems was characterized during the aquifer test on the basis of water samples collected from composite well flow originating from five depths in the completed production well during the aquifer test. Samples were analyzed for total dissolved solids, specific conductance, pH, alkalinity, and major ions. Water-quality results from composite samples, known flow contribution from individual screens, and a mixing equation were used to calculate water-quality values for sample intervals between sample depths or below the bottom sample depth. With the exception of iron and manganese, constituent concentrations of water from each of the sampled intervals and total flow from the well were within U.S. Environmental Protection Agency primary and secondary drinking-water standards. Water from the bottommost sample interval in the lower part of the lower Midville aquifer (900 to 930 feet) contained manganese and iron concentrations of 59.1 and 1,160 micrograms per liter, respectively, which exceeded secondary drinking-water standards. Because this interval contributed only 0.1 percent of the total flow to the well, water quality of this interval had little effect on the composite well water quality. Two other sample intervals from the Midville aquifer system and the total flow from both aquifer systems contained iron concentrations that slightly exceeded the secondary drinking-water standard of 300 micrograms per liter.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135026","collaboration":"Prepared in cooperation with the City of Waynesboro, Georgia","usgsCitation":"Gonthier, G., 2013, Hydrogeology and water quality of the Dublin and Midville aquifer systems at Waynesboro, Burke County, Georgia, 2011: U.S. Geological Survey Scientific Investigations Report 2013-5026, vii, 39 p., https://doi.org/10.3133/sir20135026.","productDescription":"vii, 39 p.","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2011-01-01","temporalEnd":"2011-12-31","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":271738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135026.gif"},{"id":271736,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5026/"},{"id":271737,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5026/pdf/sir2013-5026.pdf"}],"country":"United States","state":"Georgia","county":"Burke County","city":"Waynesboro","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -82.037295,33.072069 ], [ -82.037295,33.117787 ], [ -81.991343,33.117787 ], [ -81.991343,33.072069 ], [ -82.037295,33.072069 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51837ce7e4b0a21483941a55","contributors":{"authors":[{"text":"Gonthier, Gerard 0000-0003-4078-8579 gonthier@usgs.gov","orcid":"https://orcid.org/0000-0003-4078-8579","contributorId":3141,"corporation":false,"usgs":true,"family":"Gonthier","given":"Gerard ","email":"gonthier@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":478240,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045750,"text":"ds764 - 2013 - Petrographic and geochemical data for Cenozoic volcanic rocks of the Bodie Hills, California and Nevada","interactions":[],"lastModifiedDate":"2016-08-24T09:45:47","indexId":"ds764","displayToPublicDate":"2013-05-02T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"764","title":"Petrographic and geochemical data for Cenozoic volcanic rocks of the Bodie Hills, California and Nevada","docAbstract":"Petrographic and geochemical data for Cenozoic volcanic rocks of the Bodie Hills, California and Nevada \n\n\n\n\n\n\n\n\n\n\n\nThis report presents petrographic and geochemical data for samples collected during investigations of Tertiary volcanism in the Bodie Hills of California and Nevada. Igneous rocks in the area are principally 15–6 Ma subduction-related volcanic rocks of the Bodie Hills volcanic field but also include 3.9–0.1 Ma rocks of the bimodal, post-subduction Aurora volcanic field. Limited petrographic results for local basement rocks, including Mesozoic granitoid rocks and their metamorphic host rocks, are also included in the compilation. The petrographic data include visual estimates of phenocryst abundances as well as other diagnostic petrographic criteria. The geochemical data include whole-rock major oxide and trace element data, as well as limited whole-rock isotopic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds764","usgsCitation":"du Bray, E.A., John, D.A., Box, S.E., Vikre, P.G., Fleck, R.J., and Cousens, B.L., 2016, Petrographic and geochemical data for Cenozoic volcanic rocks of the Bodie Hills, California and Nevada (ver. 1.1, August 2016): U.S. Geological Survey Data Series 764, 10 p., https://dx.doi.org/10.3133/ds764.","productDescription":"Report: iii, 10 p.; 3 Appendixes","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":271745,"rank":0,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/764/"},{"id":327107,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/764/versionHist.txt","size":"4 kB","linkFileType":{"id":2,"text":"txt"}},{"id":271748,"rank":0,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/764/Appendix1.xls","text":"Appendix 1","size":"320 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 1"},{"id":271746,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/764/DS764_pamphlet.pdf","text":"Report"},{"id":271749,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/764/Appendix3.xls","text":"Appendix 3","size":"568 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 3"},{"id":271750,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/764/coverthb2.jpg"},{"id":271747,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/764/Appendix2.xls","text":"Appendix 2","size":"488 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 2"}],"country":"United States","state":"California;Nevada","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.41,32.53 ], [ -124.41,42.0 ], [ -114.13,42.0 ], [ -114.13,32.53 ], [ -124.41,32.53 ] ] ] } } ] }","edition":"Version 1.0: Originally posted April 23, 2013; Version 1.1: August 22, 2016","contact":"Director, Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225-0046