Proper understanding of the physical properties of hydrate-bearing sediments is required for interpretation of borehole logs and exploration geophysical data, the analysis of borehole and submarine slope stability, and the formulation of reservoir simulation and production models. Yet current knowledge of geophysical and geotechnical properties of hydrate-bearing sediments is still largely derived from laboratory experiments conducted on disparate soils at different confining pressures, degrees of water saturation, and hydrate concentrations and with hydrates formed by methods unlike those that predominate in nature. We conducted a comprehensive laboratory program using sand, silts, and clay subjected to various confining effective stress levels in standardized geotechnical laboratory devices and containing carefully controlled saturations of tetrahydrofuran (THF) hydrate formed from the dissolved phase. Here, we undertake complete analysis of the trends in the measured geophysical and geotechnical properties (e.g., seismic velocities, strength, electrical conductivity and permittivity, and thermal conductivity) as a function of hydrate saturation, soil characteristics, and effective stress. Results reveal that the electrical properties of hydrate-bearing sediments are not very sensitive to the laboratory method used to form hydrate, which controls the pore-scale arrangement of hydrate and sediment grains, but are sensitive to hydrate saturation. Mechanical properties are strongly influenced by both soil properties and the hydrate formation method. Thermal conductivity depends on the complex interplay of a variety of factors, including formation history, and cannot be easily predicted by volume average formulations but will remain within physical upper and lower bounds. When hydrate forms from dissolved phase guest molecules, the resulting mathematical trends for all physical properties require that the hydrate saturation Sh in pore space, which is a quantity between 0≤ Sh ≤1.0 , be raised to a power greater than 1. This significantly reduces the impact of low-hydrate saturations on the measured physical parameters, an effect that is particularly pronounced at the hydrate saturations characteristic of many natural systems (<0.2 of pore space).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geophysical characterization of gas hydrates","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/1.9781560802197.ch26","usgsCitation":"Santamarina, J., and Ruppel, C.D., 2010, The impact of hydrate saturation on the mechanical, electrical, and thermal properties of hydrate-bearing sand, silts, and clay, chap. 26 of Geophysical characterization of gas hydrates, p. 373-384, https://doi.org/10.1190/1.9781560802197.ch26.","productDescription":"12 p.","startPage":"373","endPage":"384","ipdsId":"IP-005935","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":411963,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2010-03-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Riedel, Michael","contributorId":7518,"corporation":false,"usgs":true,"family":"Riedel","given":"Michael","email":"","affiliations":[],"preferred":false,"id":861708,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Willoughby, Eleanor C.","contributorId":301001,"corporation":false,"usgs":false,"family":"Willoughby","given":"Eleanor","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":861713,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Chopra, Satinder","contributorId":301000,"corporation":false,"usgs":false,"family":"Chopra","given":"Satinder","email":"","affiliations":[],"preferred":false,"id":861714,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Santamarina, J. Carlos","contributorId":300994,"corporation":false,"usgs":false,"family":"Santamarina","given":"J. Carlos","affiliations":[{"id":27815,"text":"Georgia Tech","active":true,"usgs":false}],"preferred":false,"id":861695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":861694,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70047448,"text":"dds49029 - 2010 - Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: 30-year average annual maximum temperature, 1971-2000","interactions":[],"lastModifiedDate":"2013-11-25T16:00:07","indexId":"dds49029","displayToPublicDate":"2010-01-01T11:56:00","publicationYear":"2010","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":"490-29","title":"Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: 30-year average annual maximum temperature, 1971-2000","docAbstract":"This data set represents the 30-year (1971-2000) average annual maximum temperature in Celsius multiplied by 100 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the United States Average Monthly or Annual Minimum Temperature, 1971 - 2000 raster dataset produced by the PRISM Group at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as \"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD and when available building \"walls\" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dds49029","usgsCitation":"Wieczorek, M., and LaMotte, A.E., 2010, Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: 30-year average annual maximum temperature, 1971-2000: U.S. Geological Survey Data Series 490-29, Dataset, https://doi.org/10.3133/dds49029.","productDescription":"Dataset","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":276119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":276118,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/nhd_tmax30yr.xml"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -127.910792,23.243486 ], [ -127.910792,51.657387 ], [ -65.327751,51.657387 ], [ -65.327751,23.243486 ], [ -127.910792,23.243486 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52021ae0e4b0e21cafa49c1d","contributors":{"authors":[{"text":"Wieczorek, Michael mewieczo@usgs.gov","contributorId":2309,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","email":"mewieczo@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":false,"id":482058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaMotte, Andrew E. 0000-0002-1434-6518 alamotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1434-6518","contributorId":2842,"corporation":false,"usgs":true,"family":"LaMotte","given":"Andrew","email":"alamotte@usgs.gov","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":482059,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70118932,"text":"70118932 - 2010 - Vegetation classification and distribution mapping report: Canyon de Chelly National Monument","interactions":[],"lastModifiedDate":"2021-10-27T15:56:39.048541","indexId":"70118932","displayToPublicDate":"2010-01-01T11:53:54","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesNumber":"NPS/SCPN/NRTR—2010/306","title":"Vegetation classification and distribution mapping report: Canyon de Chelly National Monument","docAbstract":"Executive Summary: The classification and distribution mapping of the vegetation of Canyon de Chelly National Monument (CACH) and surrounding environment was accomplished through a multi-agency effort between 2003 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center and Fort Collins Science Center, Navajo Natural Heritage Program, Northern Arizona University, and NatureServe. The project team described 48 plant communities for CACH—35 of which were described from quantitative classification based on field-relevé data collected in 2004. Five additional plant communities were based on field relevés collected in a previous study. The team derived four additional plant communities from field observations during the photointerpretation phase of the project, and field documented them during accuracy assessment. The National Vegetation Classification Standard served as a conceptual framework for assigning these plant communities to the alliance and association level. Ten of the 48 plant communities were designated “park specials”, that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing CACH, with three different map-class schemas: base, group, and management map classes. The base map classes represented the finest level of spatial detail. Photointerpreters delineated initial polygons through manual interpretation of 2003/2004 1:12,000-scale true color aerial photography supplemented by occasional computer screen digitizing on a mosaic of digitized aerial photos. These polygons were labeled with base map classes during photointerpretation. Field visits verified interpretation concepts. The vegetation map database includes • ? 53 base map classes, which consist of associations and park specials classified with the quantitative analysis • ? additional associations noted during photointerpretation • ? non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 4,718 polygons in the project area. A field-based accuracy assessment of the base map classes showed the overall accuracy to be 50.8% The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, Version 2 (Federal Geographic Data Committee 2008). Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as a first approximation of the group level. The project team identified 16 group map classes in this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 79.9%. In consultation with park staff, the team developed management map classes that consisted of park-defined groupings of base map classes and were intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 28 management map classes have an overall accuracy of 77.1%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary geographic information system and digital database products were also produced that can be used independently, or to augment the main products. These products include shapefiles of the location of field-collected data and relational databases of field-collected data.
","language":"English","publisher":"National Park Service, U.S. Department of the Interior","publisherLocation":"Washington, D.C.","usgsCitation":"Thomas, K., McTeague, M., Ogden, L., Schulz, K., Fancher, T.S., Waltermire, R., and Cully, A., 2010, Vegetation classification and distribution mapping report: Canyon de Chelly National Monument, 338 p.","productDescription":"338 p.","numberOfPages":"338","costCenters":[],"links":[{"id":291488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53db584be4b0fba533fa35c3","contributors":{"authors":[{"text":"Thomas, K.A.","contributorId":100934,"corporation":false,"usgs":true,"family":"Thomas","given":"K.A.","email":"","affiliations":[],"preferred":false,"id":497528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McTeague, M.L.","contributorId":22263,"corporation":false,"usgs":true,"family":"McTeague","given":"M.L.","affiliations":[],"preferred":false,"id":497525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ogden, Lindsay","contributorId":54131,"corporation":false,"usgs":true,"family":"Ogden","given":"Lindsay","email":"","affiliations":[],"preferred":false,"id":497526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulz, K.","contributorId":98544,"corporation":false,"usgs":true,"family":"Schulz","given":"K.","affiliations":[],"preferred":false,"id":497527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fancher, Tammy S. 0000-0002-1318-3614 fanchert@usgs.gov","orcid":"https://orcid.org/0000-0002-1318-3614","contributorId":3788,"corporation":false,"usgs":true,"family":"Fancher","given":"Tammy","email":"fanchert@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":497523,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waltermire, Robert","contributorId":18644,"corporation":false,"usgs":true,"family":"Waltermire","given":"Robert","affiliations":[],"preferred":false,"id":497524,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cully, A.","contributorId":101577,"corporation":false,"usgs":true,"family":"Cully","given":"A.","email":"","affiliations":[],"preferred":false,"id":497529,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70074342,"text":"70074342 - 2010 - Use of electrical imaging and distributed temperature sensing methods to characterize surface water–groundwater exchange regulating uranium transport at the Hanford 300 Area, Washington","interactions":[],"lastModifiedDate":"2019-10-23T17:20:09","indexId":"70074342","displayToPublicDate":"2010-01-01T11:50:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Use of electrical imaging and distributed temperature sensing methods to characterize surface water–groundwater exchange regulating uranium transport at the Hanford 300 Area, Washington","docAbstract":"We explored the use of continuous waterborne electrical imaging (CWEI), in conjunction with fiber‐optic distributed temperature sensor (FO‐DTS) monitoring, to improve the conceptual model for uranium transport within the Columbia River corridor at the Hanford 300 Area, Washington. We first inverted resistivity and induced polarization CWEI data sets for distributions of electrical resistivity and polarizability, from which the spatial complexity of the primary hydrogeologic units was reconstructed. Variations in the depth to the interface between the overlying coarse‐grained, high‐permeability Hanford Formation and the underlying finer‐grained, less permeable Ringold Formation, an important contact that limits vertical migration of contaminants, were resolved along ∼3 km of the river corridor centered on the 300 Area. Polarizability images were translated into lithologic images using established relationships between polarizability and surface area normalized to pore volume (Spor). The FO‐DTS data recorded along 1.5 km of cable with a 1 m spatial resolution and 5 min sampling interval revealed subreaches showing (1) temperature anomalies (relatively warm in winter and cool in summer) and (2) a strong correlation between temperature and river stage (negative in winter and positive in summer), both indicative of reaches of enhanced surface water–groundwater exchange. The FO‐DTS data sets confirm the hydrologic significance of the variability identified in the CWEI and reveal a pattern of highly focused exchange, concentrated at springs where the Hanford Formation is thickest. Our findings illustrate how the combination of CWEI and FO‐DTS technologies can characterize surface water–groundwater exchange in a complex, coupled river‐aquifer system.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2010WR009110","usgsCitation":"Slater, L.D., Ntarlagiannis, D., Day-Lewis, F.D., Mwakanyamale, K., Versteeg, R.J., Ward, A., Strickland, C., Johnson, C.D., and Lane, J.W., 2010, Use of electrical imaging and distributed temperature sensing methods to characterize surface water–groundwater exchange regulating uranium transport at the Hanford 300 Area, Washington: Water Resources Research, v. 46, no. 10, W10533; 3 p., https://doi.org/10.1029/2010WR009110.","productDescription":"W10533; 3 p.","onlineOnly":"N","ipdsId":"IP-019421","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":475763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2010wr009110","text":"Publisher Index Page"},{"id":281654,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","city":"Richland","otherGeospatial":"Hanford 300 Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.28319931030273,\n 46.35699885440808\n ],\n [\n -119.26620483398438,\n 46.35699885440808\n ],\n [\n -119.26620483398438,\n 46.37547772047758\n ],\n [\n -119.28319931030273,\n 46.37547772047758\n ],\n [\n -119.28319931030273,\n 46.35699885440808\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"10","noUsgsAuthors":false,"publicationDate":"2010-10-21","publicationStatus":"PW","scienceBaseUri":"53cd7a93e4b0b2908510d92c","contributors":{"authors":[{"text":"Slater, Lee D.","contributorId":95792,"corporation":false,"usgs":true,"family":"Slater","given":"Lee","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":489534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ntarlagiannis, Dimitrios","contributorId":55303,"corporation":false,"usgs":false,"family":"Ntarlagiannis","given":"Dimitrios","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":489531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":489527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mwakanyamale, Kisa","contributorId":75847,"corporation":false,"usgs":true,"family":"Mwakanyamale","given":"Kisa","email":"","affiliations":[],"preferred":false,"id":489533,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Versteeg, Roelof J.","contributorId":73501,"corporation":false,"usgs":true,"family":"Versteeg","given":"Roelof","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":489532,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ward, Andy","contributorId":7184,"corporation":false,"usgs":true,"family":"Ward","given":"Andy","email":"","affiliations":[],"preferred":false,"id":489530,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Strickland, Christopher","contributorId":101991,"corporation":false,"usgs":true,"family":"Strickland","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":489535,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Carole D. 0000-0001-6941-1578 cjohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":1891,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole","email":"cjohnson@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":489529,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lane, John W. Jr. jwlane@usgs.gov","contributorId":1738,"corporation":false,"usgs":true,"family":"Lane","given":"John","suffix":"Jr.","email":"jwlane@usgs.gov","middleInitial":"W.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":false,"id":489528,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70047446,"text":"dds49028 - 2010 - Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: Average Annual Daily Maximum Temperature, 2002","interactions":[],"lastModifiedDate":"2013-11-25T15:59:40","indexId":"dds49028","displayToPublicDate":"2010-01-01T11:38:00","publicationYear":"2010","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":"490-28","title":"Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: Average Annual Daily Maximum Temperature, 2002","docAbstract":"This data set represents the average monthly maximum temperature in Celsius multiplied by 100 for 2002 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the Near-Real-Time High-Resolution Monthly Average Maximum/Minimum Temperature for the Conterminous United States for 2002 raster dataset produced by the Spatial Climate Analysis Service at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as \"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD and when available building \"walls\" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dds49028","usgsCitation":"Wieczorek, M., and LaMotte, A.E., 2010, Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: Average Annual Daily Maximum Temperature, 2002: U.S. Geological Survey Data Series 490-28, Dataset, https://doi.org/10.3133/dds49028.","productDescription":"Dataset","costCenters":[],"links":[{"id":276116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -127.910792,23.243486 ], [ -127.910792,51.657387 ], [ -65.327751,51.657387 ], [ -65.327751,23.243486 ], [ -127.910792,23.243486 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52021ae0e4b0e21cafa49c21","contributors":{"authors":[{"text":"Wieczorek, Michael mewieczo@usgs.gov","contributorId":2309,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","email":"mewieczo@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":false,"id":482055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaMotte, Andrew E. 0000-0002-1434-6518 alamotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1434-6518","contributorId":2842,"corporation":false,"usgs":true,"family":"LaMotte","given":"Andrew","email":"alamotte@usgs.gov","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":482056,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154839,"text":"70154839 - 2010 - Estimation and modeling of electrofishing capture efficiency for fishes in wadeable warmwater streams","interactions":[],"lastModifiedDate":"2015-08-10T10:27:39","indexId":"70154839","displayToPublicDate":"2010-01-01T11:30:00","publicationYear":"2010","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":"Estimation and modeling of electrofishing capture efficiency for fishes in wadeable warmwater streams","docAbstract":"Stream fish managers often use fish sample data to inform management decisions affecting fish populations. Fish sample data, however, can be biased by the same factors affecting fish populations. To minimize the effect of sample biases on decision making, biologists need information on the effectiveness of fish sampling methods. We evaluated single-pass backpack electrofishing and seining combined with electrofishing by following a dual-gear, mark–recapture approach in 61 blocknetted sample units within first- to third-order streams. We also estimated fish movement out of unblocked units during sampling. Capture efficiency and fish abundances were modeled for 50 fish species by use of conditional multinomial capture–recapture models. The best-approximating models indicated that capture efficiencies were generally low and differed among species groups based on family or genus. Efficiencies of single-pass electrofishing and seining combined with electrofishing were greatest for Catostomidae and lowest for Ictaluridae. Fish body length and stream habitat characteristics (mean cross-sectional area, wood density, mean current velocity, and turbidity) also were related to capture efficiency of both methods, but the effects differed among species groups. We estimated that, on average, 23% of fish left the unblocked sample units, but net movement varied among species. Our results suggest that (1) common warmwater stream fish sampling methods have low capture efficiency and (2) failure to adjust for incomplete capture may bias estimates of fish abundance. We suggest that managers minimize bias from incomplete capture by adjusting data for site- and species-specific capture efficiency and by choosing sampling gear that provide estimates with minimal bias and variance. Furthermore, if block nets are not used, we recommend that managers adjust the data based on unconditional capture efficiency.
","language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1577/M09-122.1","usgsCitation":"Price, A., and Peterson, J., 2010, Estimation and modeling of electrofishing capture efficiency for fishes in wadeable warmwater streams: North American Journal of Fisheries Management, v. 30, no. 2, p. 481-498, https://doi.org/10.1577/M09-122.1.","productDescription":"18 p.","startPage":"481","endPage":"498","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-015960","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":306529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2010-04-01","publicationStatus":"PW","scienceBaseUri":"55c9cb33e4b08400b1fdb708","contributors":{"authors":[{"text":"Price, A.","contributorId":78850,"corporation":false,"usgs":true,"family":"Price","given":"A.","email":"","affiliations":[],"preferred":false,"id":567604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":564253,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168546,"text":"70168546 - 2010 - Genetic analysis of individual origins supports isolation of grizzly bears in the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2016-02-19T10:37:01","indexId":"70168546","displayToPublicDate":"2010-01-01T11:30:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3671,"text":"Ursus","active":true,"publicationSubtype":{"id":10}},"title":"Genetic analysis of individual origins supports isolation of grizzly bears in the Greater Yellowstone Ecosystem","docAbstract":"The Greater Yellowstone Ecosystem (GYE) supports the southernmost of the 2 largest remaining grizzly bear (Ursus arctos) populations in the contiguous United States. Since the mid-1980s, this population has increased in numbers and expanded in range. However, concerns for its long-term genetic health remain because of its presumed continued isolation. To test the power of genetic methods for detecting immigrants, we generated 16-locus microsatellite genotypes for 424 individual grizzly bears sampled in the GYE during 1983–2007. Genotyping success was high (90%) and varied by sample type, with poorest success (40%) for hair collected from mortalities found ≥1 day after death. Years of storage did not affect genotyping success. Observed heterozygosity was 0.60, with a mean of 5.2 alleles/marker. We used factorial correspondence analysis (Program GENETIX) and Bayesian clustering (Program STRUCTURE) to compare 424 GYE genotypes with 601 existing genotypes from grizzly bears sampled in the Northern Continental Divide Ecosystem (NCDE) (FST = 0.096 between GYE and NCDE). These methods correctly classified all sampled individuals to their population of origin, providing no evidence of natural movement between the GYE and NCDE. Analysis of 500 simulated first-generation crosses suggested that over 95% of such bears would also be detectable using our 16-locus data set. Our approach provides a practical method for detecting immigration in the GYE grizzly population. We discuss estimates for the proportion of the GYE population sampled and prospects for natural immigration into the GYE.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ursus","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The International Association for Bear Research and Management","publisherLocation":"New York","doi":"10.2192/09GR022.1","usgsCitation":"Haroldson, M.A., Schwartz, C., Kendall, K.C., Gunther, K.A., Moody, D., Frey, K.L., and Paetkau, D., 2010, Genetic analysis of individual origins supports isolation of grizzly bears in the Greater Yellowstone Ecosystem: Ursus, v. 21, no. 1, p. 1-13, https://doi.org/10.2192/09GR022.1.","productDescription":"13 p.","startPage":"1","endPage":"13","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-014839","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":318169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem, Northern Continental Divide Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.983642578125,\n 45.9511496866914\n ],\n [\n -111.566162109375,\n 45.63708709571876\n ],\n [\n -111.14868164062499,\n 45.65244828675087\n ],\n [\n -110.5224609375,\n 45.68315803253308\n ],\n [\n -109.88525390624999,\n 45.590978249451936\n ],\n [\n -109.21508789062499,\n 45.321254361171476\n ],\n [\n -108.73168945312499,\n 44.74673324024678\n ],\n [\n -108.599853515625,\n 44.071800467511565\n ],\n [\n -108.74267578125,\n 43.56447158721811\n ],\n [\n -109.2919921875,\n 43.26120612479979\n ],\n [\n -110.379638671875,\n 43.068887774169625\n ],\n [\n -110.58837890625,\n 42.60970621339408\n ],\n [\n -111.181640625,\n 42.5611728553181\n ],\n [\n -111.4892578125,\n 42.84375132629021\n ],\n [\n 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mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":620842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwartz, Charles","contributorId":149922,"corporation":false,"usgs":false,"family":"Schwartz","given":"Charles","affiliations":[{"id":13248,"text":"University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":620840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendall, Katherine C. 0000-0002-4831-2287 kkendall@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-2287","contributorId":3081,"corporation":false,"usgs":true,"family":"Kendall","given":"Katherine","email":"kkendall@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":620841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gunther, Kerry A.","contributorId":84621,"corporation":false,"usgs":false,"family":"Gunther","given":"Kerry","email":"","middleInitial":"A.","affiliations":[{"id":5118,"text":"Yellowstone National Park, Yellowstone Center for Resources, Bear Management Office, P.O. Box 168, Yellowstone National Park, WY 82190","active":true,"usgs":false}],"preferred":false,"id":620845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moody, David S.","contributorId":167044,"corporation":false,"usgs":false,"family":"Moody","given":"David S.","affiliations":[{"id":16140,"text":"Wyoming Game & Fish Department, Large Carnivore Section, Lander, Wyoming 82520, USA","active":true,"usgs":false}],"preferred":false,"id":620844,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frey, Kevin L.","contributorId":124580,"corporation":false,"usgs":false,"family":"Frey","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":5125,"text":"Montana Fish Wildlife and Parks, Bear Management Office, 1400 South 19th Avenue, Bozeman, MT 59718","active":true,"usgs":false}],"preferred":false,"id":620846,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Paetkau, David","contributorId":97712,"corporation":false,"usgs":false,"family":"Paetkau","given":"David","email":"","affiliations":[],"preferred":false,"id":620843,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70201012,"text":"70201012 - 2010 - Evaluating the meaning of “layer” in the Martian north polar layered deposits and the impact on the climate connection","interactions":[],"lastModifiedDate":"2018-11-20T11:15:27","indexId":"70201012","displayToPublicDate":"2010-01-01T11:15:02","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the meaning of “layer” in the Martian north polar layered deposits and the impact on the climate connection","docAbstract":"Using data from the High Resolution Imaging Science Experiment (HiRISE) aboard the Mars Reconnaissance Orbiter, we reassess the methods by which layers within the north polar layered deposits (NPLD) can be delineated and their thicknesses measured. Apparent brightness and morphology alone are insufficient for this task; high resolution topographic data are necessary. From these analyses, we find that the visible appearance of layers depends to a large degree on the distribution of younger, mantling deposits (which in turn is partially influenced by inherent layer properties) and on the shape and location of the particular outcrop. This younger mantle partially obscures layer morphology and brightness and is likely a cause of the gradational contacts between individual layers at this scale. High resolution images reveal that there are several layers similar in appearance to the well-known marker bed discovered by Malin, M., Edgett, K., 2001. J. Geophys. Res. 106, 23429–23570. The morphology, thicknesses
The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive (“dry”) wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.
","language":"English","publisher":"Springer","doi":"10.1007/s11053-010-9127-8","usgsCitation":"Olea, R., Cook, T.A., and Coleman, J., 2010, A methodology for the assessment of unconventional (continuous) resources with an application to the Greater Natural Buttes gas field, Utah: Natural Resources Research, v. 19, no. 4, p. 237-251, https://doi.org/10.1007/s11053-010-9127-8.","productDescription":"15 p.","startPage":"237","endPage":"251","ipdsId":"IP-017861","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":357661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Greater Natural Buttes Gas Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.08850097656249,\n 39.68182601089365\n ],\n [\n -109.0447998046875,\n 39.68182601089365\n ],\n [\n -109.0447998046875,\n 40.24179856487036\n ],\n [\n -110.08850097656249,\n 40.24179856487036\n ],\n [\n -110.08850097656249,\n 39.68182601089365\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2010-09-25","publicationStatus":"PW","scienceBaseUri":"5c0108d9e4b0815414cc2e0d","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":47873,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, Troy A.","contributorId":52519,"corporation":false,"usgs":true,"family":"Cook","given":"Troy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":746102,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coleman, James L.","contributorId":208106,"corporation":false,"usgs":false,"family":"Coleman","given":"James L.","affiliations":[{"id":37715,"text":"Ex-USGS, now retired","active":true,"usgs":false}],"preferred":false,"id":745926,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70047442,"text":"dds49025 - 2010 - Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: surficial geology","interactions":[],"lastModifiedDate":"2013-11-25T15:59:04","indexId":"dds49025","displayToPublicDate":"2010-01-01T10:52:00","publicationYear":"2010","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":"490-25","title":"Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: surficial geology","docAbstract":"This data set represents the area of surficial geology types in square meters compiled for every catchment of NHDPlus for the conterminous United States. The source data set is the \"Digital data set describing surficial geology in the conterminous US\" (Clawges and Price, 1999). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as \"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD and when available building \"walls\" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dds49025","usgsCitation":"Wieczorek, M., and LaMotte, A.E., 2010, Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: surficial geology: U.S. Geological Survey Data Series 490-25, Dataset, https://doi.org/10.3133/dds49025.","productDescription":"Dataset","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":276108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":276107,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/nhd_sgeol.xml"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -127.910792,23.243486 ], [ -127.910792,51.657387 ], [ -65.327751,51.657387 ], [ -65.327751,23.243486 ], [ -127.910792,23.243486 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52021ae0e4b0e21cafa49c29","contributors":{"authors":[{"text":"Wieczorek, Michael mewieczo@usgs.gov","contributorId":2309,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","email":"mewieczo@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":false,"id":482047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaMotte, Andrew E. 0000-0002-1434-6518 alamotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1434-6518","contributorId":2842,"corporation":false,"usgs":true,"family":"LaMotte","given":"Andrew","email":"alamotte@usgs.gov","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":482048,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70199021,"text":"70199021 - 2010 - Effects of current-use pesticides on amphibians","interactions":[],"lastModifiedDate":"2018-08-29T10:49:25","indexId":"70199021","displayToPublicDate":"2010-01-01T10:46:12","publicationYear":"2010","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"6","title":"Effects of current-use pesticides on amphibians","docAbstract":"For many years, amphibians were understudied in the ecotoxicological literature. In 1989, the Canadian Wildlife Service published a comprehensive review of studies examining the effects of contaminants on amphibians (Power et al. 1989). Just 10 years later, the same organization published an updated review that included twice the number of studies (Pauli et al. 2000), indicating rapid growth in the field of amphibian ecotoxicology. However, Sparling et al. (2000) point out that the number of amphibian ecotoxicological studies remains modest relative to research utilizing other taxa. Relyea and Hoverman (2006) also report that amphibian data appear to be lagging behind other taxa, despite an increasing number of ecotoxicological studies involving freshwater ecosystems in general.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Ecotoxicology of amphibians and reptiles","language":"English","publisher":"Taylor & Francis","usgsCitation":"Lehman, C., and Williams, B.K., 2010, Effects of current-use pesticides on amphibians, chap. 6 of Ecotoxicology of amphibians and reptiles, p. 167-202.","productDescription":"36 p. ","startPage":"167","endPage":"202","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":356912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":356911,"rank":1,"type":{"id":1,"text":"Abstract"},"url":"https://www.taylorfrancis.com/books/e/9781420064179/chapters/10.1201%2FEBK1420064162-13"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98b7dfe4b0702d0e844f63","contributors":{"editors":[{"text":"Sparling, Donald","contributorId":20650,"corporation":false,"usgs":true,"family":"Sparling","given":"Donald","affiliations":[],"preferred":false,"id":743805,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Linder, Greg L. linder2@usgs.gov","contributorId":1766,"corporation":false,"usgs":true,"family":"Linder","given":"Greg","email":"linder2@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":false,"id":743806,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Bishop, Christine A.","contributorId":10749,"corporation":false,"usgs":true,"family":"Bishop","given":"Christine A.","affiliations":[],"preferred":false,"id":743807,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Krest, Sherry K.","contributorId":113670,"corporation":false,"usgs":true,"family":"Krest","given":"Sherry","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":743808,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Lehman, C.","contributorId":75342,"corporation":false,"usgs":true,"family":"Lehman","given":"C.","email":"","affiliations":[],"preferred":false,"id":743803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, B K","contributorId":140651,"corporation":false,"usgs":false,"family":"Williams","given":"B","email":"","middleInitial":"K","affiliations":[{"id":12801,"text":"The Wildlife Society","active":true,"usgs":false}],"preferred":false,"id":743804,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70004074,"text":"70004074 - 2010 - Methylmercury cycling, bioaccumulation, and export from agricultural and non-agricultural wetlands in the Yolo Bypass","interactions":[],"lastModifiedDate":"2019-08-08T11:41:01","indexId":"70004074","displayToPublicDate":"2010-01-01T10:30:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Methylmercury cycling, bioaccumulation, and export from agricultural and non-agricultural wetlands in the Yolo Bypass","docAbstract":"This 18-month field study addresses the seasonal and spatial patterns and processes controlling methylmercury (MeHg) production, bioaccumulation, and export from natural and agricultural wetlands of the Yolo Bypass Wildlife Area (YBWA). The data were collected in conjuntion with a Proposition 40 grant from the State Water Resources Control Board in support of the development of Best Management Practices (BMP's) for reducing MeHg loading from agricultural lands in the wetland-dominated Yolo Bypass to the Sacramento-San Joaquin River Delta. The four managemenr-based questions addressed in this study were:
\n1. Is there a different among agricultural and managfed wetland types in terms of Me Hg dynamic (production, degradation, bioaccumulation, or export)?
\n2. Does water residence time influence MeHg dynamics?
\n3. Does the application of sulfate-based fertilizer impact MeHg production rates?
\n4. Does the presence (or absence) of vegetation influence MeHg production rates?
\nMeasurements of MeHg concentrations in sediment, water, and biota (plants, invertebrates, and fish) were made to assess management-level patterns in five wetland types, which included three type of shallowly-flooded agricultural wetlands (white rice, wild rice, and fallow) and two types of managed wetlands (permanently and seasonally flooded). To strengthen our understanding of the processes underlying the seasonal and spatial patterns of MeHg cycling, additional exploratory factors were measured including ancillary sediment and water quality parameters, stable isotope fractionation (oxygen, sulfur, carbon, and nitrogen), photodemethylation rates, and daily-integrated hydrologic budgets. Samples and field data were collected from May 2007 to July 2008, and nearly all sample analyses were completed by September 2008 as per the Quality Assurance Program Plan (QAPP) requirements.
\nAlthough wetland type was a major factor that drove the study design, within-field hydrology also proved to be an important factor controlling aqueous MeHg and total mercury (THg) concentrations and export. Overall, agricultural wetlands exhibited higher MeHg concentrations in overlying water, sediment, and biota than did managed seasonal and permanent wetlands. This appears to be partly due to higher rates of sediment in microbial production of MeHg on agricultural wetlands during the fall through spring period. Both sulfate- and iron-reducing bacteria have been implicated in the MeHg production process, and both were demonstrably active in all wetlands studied; however, sulfate-reducing bacteria were not stimulated by the addition of sulfate-based fertilizer to agricultural wetlands, suggesting that easily-degraded (labile) organic matter, rather than sulfate, was limiting their activity in these field types. The data suggest that agriculturally-managed soils promoted MeHg production through 1) enhanced microbial activity via higher temperatures and larger pools of labile carbon, and 2) enhanced pools of microbially available inorganic divalent mercury (Hg(II)) resulting from a decrease in reduced-sulfur, solid-phase minerals under oxic or only mildly reducing conditions.
\nMeHg mass balances were assessed by comparing filed-specific MeHg loads for inlets vs. outlet flows. The overall mass balance for MeHg in surface water during the summer irrigation period (June - September 2007) indicated little to no net MeHg export from the six agricultural wetlands taken as a whole. Of the six agricultural wetlands, there was net overall MeHg export from two fields (one fallow and one white rice) during August, and from four of the six fields (one fallow, one white rice, and two wild rice) during September) Over the entire summer irrigation period, two of the fields (one fallow and one wild rive) showed net MeHg export, and the other four fields showed wither net import or no significant change. Rates of measured photomethylation and exchange between sediment and water pools suggest that both processes may be responsible for the lack of MeHg export. Despite significant differences during winter months between fields in surface water concentrations of MeHg, MeHg loads were not calculated in mid-winter because flood waters had overtopped field boundaries and field fidelity could not be established.
\nDuring the summer 2007 irrigation season, surface water out-flows from agricultural wetlands were 9%-36% of inlet flows, and evaporation rates explained most of this water loss, with infiltration likely accounting for the remainder. Unfiltered aqueous MeHg concentrations increased from <1 ng L-1 in source waters to up to 10 ng L-1 in agricultural wetland drains during the summer irrigation period. Increases in solute concentration caused by evapoconcentration were estimated by determining concentration factors (outflow/inflow) for chloride (a conservative dissolved constituent) and by measuring oxygen isotope ratios (18O/16O, expressed as δ18O) in water. Increases in MeHg concentration from inflows-to-outflows exceeded those caused by evapoconcentration on several fields during the summer irrigation season. This was especially true when initial surface water MeHg concentrations were low, as seen in the southern block of fields receiving irrigation water directly from the Toe Drain. The northern block of fields received irrigation water from Greens Lake, which included Toe Drain water plus recirculated drain water from other agricultural fields within the Yolo Bypass and west of the Yolo Bypass; as such, the northern fields showed a smaller percentage increase in MeHg concentration because initial MeHg concentrations in surface water inflows were greater than in inputs to the southern fields.
\nMercury concentrations in fish were greater in agricultural wetlands white rice and wild rice) than in the two permanently flooded wetlands. Additionally, Hg concentrations in biota showed a general increase from inlets to outlets withing agricultural wetlands, but not within permanent wetlands. This was particular evident in white rice fields where caged western mosquitofish at the outlets had Hg concentrations that were more than 4 times higher than in caged fish held at the inlets. Similar spatial patterns in Hg bioaccumulation in agricultural and permanent wetlands were seen for wild populations of western mosquitofish and Mississippi silversides. In contrast to fish, invertebrates, such as water-boatman (Corixidae) and back swimmers (Notonectidae), had greater Hg concentrations in permanent wetlands than in tempoarirly flooded agricultural wetlands, Fish THg concentrations were weakly correlated with water MeHg,a and not correlated with sediment MeHg. In contrast, invertebrate MeHg concentrations were more strongly correlated with sediment MeHg than with water MeHg concentrations. These results illustrate the complexity of MeHg bioaccumulation through food webs and indicate the importance of simultaneously using multiple biosentinels when monitoring MeHg production and bioaccumulation.
\nDespite high sediment production rates and water concentrations in agricultural wetlands, MeHg export was physically limited by hydrologic export for all wetlands studied. We suggest that load reduction is maximized by limiting water throughout, but that on-site biota exposure is maximized by this loner water residence time. While field-specific hydrologic loads could not be fully quantified during flood conditions in February 2008, we suggest that the primary period of MeHg export from Yolo Bypass Wildlife Area is during those winter flooding periods when overall microbial activity and MeHg production in agricultural soils is fueled by the decomposition of rice straw, and when hydrologic flowthrough is maximal.
\nLocal stakeholders participated in two workshops related to this study, demonstrating an interest in understanding factors controlling MeHg production, export, and bioaccumulation. The results of this field study show that permanently flooded, naturally vegetated wetlands are unlikely to a large source of MeHg production within the YBWA, in contrast with agriculturally-managed wetlands. MeHg loading to Toe Drain waters of the Yolo Bypass may be reduced by lowering rated of hydrologic export from agricultural wetlands during the growing season and especially during rice harvest, However, under these water-holding conditions, biota living within agricultural wetlands may thus be exposed to higher MeHg concentrations in surface water, As observed in this study, rapid bioacculumaltion over a 2-month period led to MeHg concentrations in invertebrates and fish more than 6 and 11 times higher, respectively, than proposed TMDL target values to protect wildlife (0.03 ppm ww).
\nThe results of this field study, together with the information from YBWA stakeholders, provide a more definitive understanding of how MeHg cycling and bioaccumulation respond to habitat differences and specific management practices. These results directly address 4 core components of CBDA's Mercury Strategy for the Bay-Delta Ecosystem (Wiener et al., 2003a):
\na) Quantification and evaluation of THg and MeHg sources,
\nb) Quantification of effects of ecosystem restoration on MeHg exposure,
\nc) Assessment of ecological risk, and
\nd) Identification and testing of potential management approaches for reducing MeHg contamination.
\nIn addition, the quantitative results reported here assess the effect of current land use practices in the Yolo Bypass MeHg production, bioaccumulation and export, and provide process-based advice towards achieving current goals of the RWQCB-CVR's Sacramento -- San Joaquin Delta Estuary TMDL for Methyl & Total Mercury (Wood et al., 2010b). Further work is necessary to evaluate biotic exposure in the Yolo Bypass Wildlife Area at higher trophic levels (e.g. birds), to quantify winter hydrologic flux of MeHg to the larger Delta ecosystem, and to evaluate rice straw management options to limit labile carbon supplies to surface sediment during winter months.
\nIn summary, agricultural management of rice fields -- specifically the periodic flooding and production of easily degraded organic matter -- promotes the production of MeHg beyond rates seen in naturally vegetated wetlands, whether seasonally or permanently flooded., The exported load from MeHg from these agricultural wetlands may be controlled by limiting hydrologic export from fields to enhance on-site MeHg removal processes, but the tradeoff is that this impoundement increases Me Hg exposure to resident organisms.
","language":"English","publisher":"San Jose State University Research Foundation","publisherLocation":"San Jose, CA","usgsCitation":"Windham-Myers, L., Marvin-DiPasquale, M., Fleck, J., Alpers, C.N., Ackerman, J., Eagles-Smith, C.A., Stricker, C., Stephenson, M., Feliz, D., Gill, G., Bachand, P., Brice, A., and Kulakow, R., 2010, Methylmercury cycling, bioaccumulation, and export from agricultural and non-agricultural wetlands in the Yolo Bypass, xvii, 116 p.","productDescription":"xvii, 116 p.","numberOfPages":"265","ipdsId":"IP-025308","costCenters":[{"id":148,"text":"Branch of Regional Research-Western Region","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":292018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Yolo","otherGeospatial":"Yolo Bypass","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.821159,38.726961 ], [ -121.821159,38.750153 ], [ -121.796874,38.750153 ], [ -121.796874,38.726961 ], [ -121.821159,38.726961 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53eb2a83e4b0461e44764a81","contributors":{"authors":[{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":350403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marvin-DiPasquale, Mark","contributorId":57423,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","affiliations":[],"preferred":false,"id":350411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleck, Jacob 0000-0002-3217-3972","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":47883,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","affiliations":[],"preferred":false,"id":350408,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":350402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":350406,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":350405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stricker, Craig","contributorId":99483,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","affiliations":[],"preferred":false,"id":350413,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stephenson, Mark","contributorId":56951,"corporation":false,"usgs":false,"family":"Stephenson","given":"Mark","email":"","affiliations":[],"preferred":false,"id":350410,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Feliz, David","contributorId":35664,"corporation":false,"usgs":true,"family":"Feliz","given":"David","email":"","affiliations":[],"preferred":false,"id":350407,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gill, Gary","contributorId":94587,"corporation":false,"usgs":true,"family":"Gill","given":"Gary","affiliations":[],"preferred":false,"id":350412,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bachand, Philip","contributorId":54907,"corporation":false,"usgs":true,"family":"Bachand","given":"Philip","affiliations":[],"preferred":false,"id":350409,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Brice, Ann","contributorId":8395,"corporation":false,"usgs":true,"family":"Brice","given":"Ann","email":"","affiliations":[],"preferred":false,"id":350404,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kulakow, Robin","contributorId":105244,"corporation":false,"usgs":true,"family":"Kulakow","given":"Robin","email":"","affiliations":[],"preferred":false,"id":350414,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70118921,"text":"70118921 - 2010 - Forecasting weed distributions using climate data: a GIS early warning tool","interactions":[],"lastModifiedDate":"2014-07-31T10:36:21","indexId":"70118921","displayToPublicDate":"2010-01-01T10:28:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2100,"text":"Invasive Plant Science and Management","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting weed distributions using climate data: a GIS early warning tool","docAbstract":"The number of invasive exotic plant species establishing in the United States is continuing to rise. When prevention \nof exotic species from entering into a country fails at the national level and the species establishes, reproduces, \nspreads, and becomes invasive, the most successful action at a local level is early detection followed eradication. \nWe have developed a simple geographic information system (GIS) analysis for developing watch lists for early \ndetection of invasive exotic plants that relies upon currently available species distribution data coupled with \nenvironmental data to aid in describing coarse-scale potential distributions. This GIS analysis tool develops \nenvironmental envelopes for species based upon the known distribution of a species thought to be invasive and \nrepresents the first approximation of its potential habitat while the necessary data are collected to perform more in-depth analyses. To validate this method we looked at a time series of species distributions for 66 species in Pacific \nNorthwest, and northern Rocky Mountain counties. The time series analysis presented here did select counties that \nthe invasive exotic weeds invaded in subsequent years, showing that this technique could be useful in developing \nwatch lists for the spread of particular exotic species. We applied this same habitat-matching model based upon \nbioclimaric envelopes to 100 invasive exotics with various levels of known distributions within continental U.S. \ncounties. For species with climatically limited distributions, county watch lists describe county-specific vulnerability \nto invasion. Species with matching habitats in a county would be added to that county's list. These watch lists can \ninfluence management decisions for early warning, control prioritization, and targeted research to determine specific \nlocations within vulnerable counties. This tool provides useful information for rapid assessment of the potential \ndistribution based upon climate envelopes of current distributions for new invasive exotic species.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Invasive Plant Science and Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Weed Science Society of America","publisherLocation":"Lawrence, KS","doi":"10.1614/IPSM-08-073.1","usgsCitation":"Jarnevich, C.S., Holcombe, T.R., Barnett, D., Stohlgren, T.J., and Kartesz, J.T., 2010, Forecasting weed distributions using climate data: a GIS early warning tool: Invasive Plant Science and Management, v. 3, no. 4, p. 365-375, https://doi.org/10.1614/IPSM-08-073.1.","productDescription":"11 p.","startPage":"365","endPage":"375","numberOfPages":"11","costCenters":[],"links":[{"id":475770,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.1614/IPSM-08-073.1","text":"External Repository"},{"id":291475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":291474,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1614/IPSM-08-073.1"}],"volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"53db5843e4b0fba533fa357e","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":497491,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holcombe, Tracy R. holcombet@usgs.gov","contributorId":3694,"corporation":false,"usgs":true,"family":"Holcombe","given":"Tracy","email":"holcombet@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":497492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnett, David T.","contributorId":86234,"corporation":false,"usgs":true,"family":"Barnett","given":"David T.","affiliations":[],"preferred":false,"id":497494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stohlgren, Thomas J. 0000-0001-9696-4450 stohlgrent@usgs.gov","orcid":"https://orcid.org/0000-0001-9696-4450","contributorId":2902,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","email":"stohlgrent@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":497490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kartesz, John T.","contributorId":54128,"corporation":false,"usgs":true,"family":"Kartesz","given":"John","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":497493,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70049350,"text":"70049350 - 2010 - Measurement-derived heat-budget approaches for simulating coastal wetland temperature with a hydrodynamic model","interactions":[],"lastModifiedDate":"2013-11-12T10:26:51","indexId":"70049350","displayToPublicDate":"2010-01-01T10:20:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Measurement-derived heat-budget approaches for simulating coastal wetland temperature with a hydrodynamic model","docAbstract":"Numerical modeling is needed to predict environmental temperatures, which affect a number of biota in southern Florida, U.S.A., such as the West Indian manatee (Trichechus manatus), which uses thermal basins for refuge from lethal winter cold fronts. To numerically simulate heat-transport through a dynamic coastal wetland region, an algorithm was developed for the FTLOADDS coupled hydrodynamic surface-water/ground-water model that uses formulations and coefficients suited to the coastal wetland thermal environment. In this study, two field sites provided atmospheric data to develop coefficients for the heat flux terms representing this particular study area. Several methods were examined to represent the heat-flux components used to compute temperature. A Dalton equation was compared with a Penman formulation for latent heat computations, producing similar daily-average temperatures. Simulation of heat-transport in the southern Everglades indicates that the model represents the daily fluctuation in coastal temperatures better than at inland locations; possibly due to the lack of information on the spatial variations in heat-transport parameters such as soil heat capacity and surface albedo. These simulation results indicate that the new formulation is suitable for defining the existing thermohydrologic system and evaluating the ecological effect of proposed restoration efforts in the southern Everglades of Florida.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Wetlands","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s13157-010-0053-7","usgsCitation":"Swain, E., and Decker, J., 2010, Measurement-derived heat-budget approaches for simulating coastal wetland temperature with a hydrodynamic model: Wetlands, v. 30, no. 3, p. 635-648, https://doi.org/10.1007/s13157-010-0053-7.","productDescription":"14 p.","startPage":"635","endPage":"648","numberOfPages":"14","ipdsId":"IP-004335","costCenters":[{"id":285,"text":"Florida Water Science Center","active":false,"usgs":true}],"links":[{"id":279002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279001,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s13157-010-0053-7"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.5212,24.85 ], [ -81.5212,25.8918 ], [ -80.3887,25.8918 ], [ -80.3887,24.85 ], [ -81.5212,24.85 ] ] ] } } ] }","volume":"30","issue":"3","noUsgsAuthors":false,"publicationDate":"2010-05-04","publicationStatus":"PW","scienceBaseUri":"52835c1ee4b047efbbb4ae02","contributors":{"authors":[{"text":"Swain, Eric 0000-0001-7168-708X","orcid":"https://orcid.org/0000-0001-7168-708X","contributorId":23347,"corporation":false,"usgs":true,"family":"Swain","given":"Eric","affiliations":[],"preferred":false,"id":486104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Decker, Jeremy","contributorId":99662,"corporation":false,"usgs":true,"family":"Decker","given":"Jeremy","affiliations":[],"preferred":false,"id":486105,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70262948,"text":"70262948 - 2010 - Modeling and mapping Golden-winged Warbler abundance to improve regional conservation strategies","interactions":[],"lastModifiedDate":"2025-01-28T15:14:24.702062","indexId":"70262948","displayToPublicDate":"2010-01-01T10:13:41","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling and mapping Golden-winged Warbler abundance to improve regional conservation strategies","docAbstract":"Conservation planning requires identifying pertinent habitat factors and locating geographic locations where land management may improve habitat conditions for high priority species. I derived habitat models and mapped predicted abundance for the Golden-winged Warbler (Vermivora chrysoptera), a species of high conservation concern, using bird counts, environmental variables, and hierarchical models applied at multiple spatial scales. My aim was to understand habitat associations at multiple spatial scales and create a predictive abundance map for purposes of conservation planning for the Golden-winged Warbler. My models indicated a substantial influence of landscape conditions, including strong positive associations with total forest composition within the landscape. However, many of the associations I observed were counter to reported associations at finer spatial extents; for instance, I found Golden-winged Warblers negatively associated with several measures of edge habitat. No single spatial scale dominated, indicating that this species is responding to factors at multiple spatial scales. I found Golden-winged Warbler abundance was negatively related with Blue-winged Warbler (Vermivora cyanoptera) abundance. I also observed a north-south spatial trend suggestive of a regional climate effect that was not previously noted for this species. The map of predicted abundance indicated a large area of concentrated abundance in west-central Wisconsin, with smaller areas of high abundance along the northern periphery of the Prairie Hardwood Transition. This map of predicted abundance compared favorably with independent evaluation data sets and can thus be used to inform regional planning efforts devoted to conserving this species.
","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ACE-00426-050212","usgsCitation":"Thogmartin, W.E., 2010, Modeling and mapping Golden-winged Warbler abundance to improve regional conservation strategies: Avian Conservation and Ecology, v. 5, no. 2, 12, 17 p., https://doi.org/10.5751/ACE-00426-050212.","productDescription":"12, 17 p.","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":489893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-00426-050212","text":"Publisher Index Page"},{"id":481409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":925336,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70256009,"text":"70256009 - 2010 - Establishing a nationwide baseline of historical burn-severity data to support monitoring of trends in wildfire effects and national fire policies","interactions":[],"lastModifiedDate":"2024-07-12T15:07:23.549738","indexId":"70256009","displayToPublicDate":"2010-01-01T10:05:16","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"PNW-GTR-802","title":"Establishing a nationwide baseline of historical burn-severity data to support monitoring of trends in wildfire effects and national fire policies","docAbstract":"There is a need to provide agency leaders, elected officials, and the general public with summary information regarding the effects of large wildfires. Recently, the Wildland Fire Leadership Council (WFLC), which implements and coordinates National Fire Plan (NFP) and Federal Wildland Fire Management Policies adopted a strategy to monitor the effectiveness and effects of the National Fire Plan and the Healthy Forests Restoration Act. One component of this strategy is to assess the environmental impacts of large wildland fires and identify the trends of burn severity on all lands across the United States. To that end, WFLC has sponsored a 6-year project, Monitoring Trends in Burn Severity (MTBS), which requires the U.S. Department of Agriculture, Forest Service (USDA-FS) and the U.S. Geological Survey (USGS) to map and assess the burn severity for all large current and historical fires. Using Landsat data and the differenced Normalized Burn Ratio (dNBR) algorithm, the USGS/EROS Data Center and USDA-FS/ Remote Sensing Applications Center will map burn severity of all fires occurring from 1984 to 2010. Only fires that are greater than 500 ac in the East, and 1,000 ac in the West will be included. We anticipate mapping a total of more than 9,000 historical fires and fires that occur during the course of the study. The MTBS project will generate burn-severity data, maps, and reports, which will be available for use at local, State, and national levels to evaluate trends in burn severity and help develop and assess the effectiveness of land management decisions. Additionally, the information developed will provide a baseline from which to monitor the recovery and health of fire-affected landscapes over time. Spatial and tabular data quantifying burn severity will augment existing information used to estimate risk associated with a range of current and future resource threats. For example, fire severity data along with associated biophysical characteristics provide an analytical basis for assessing risk from invasive species as well as native insects and pathogens. All data and results will be distributed to the public via a Web interface.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Advances in threat assessment and their application to forest and rangeland management","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Department of Agriculture, Forest Service","usgsCitation":"Schwind, B., Quayle, B., and Eidenshink, J.C., 2010, Establishing a nationwide baseline of historical burn-severity data to support monitoring of trends in wildfire effects and national fire policies: General Technical Report PNW-GTR-802, 16 p.","productDescription":"16 p.","startPage":"381","endPage":"396","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":431011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":431010,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://research.fs.usda.gov/treesearch/37081","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schwind, Brian","contributorId":146378,"corporation":false,"usgs":false,"family":"Schwind","given":"Brian","email":"","affiliations":[],"preferred":false,"id":906364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quayle, Brad","contributorId":146381,"corporation":false,"usgs":false,"family":"Quayle","given":"Brad","email":"","affiliations":[],"preferred":false,"id":906365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eidenshink, Jeffery C. eidenshink@usgs.gov","contributorId":1352,"corporation":false,"usgs":true,"family":"Eidenshink","given":"Jeffery","email":"eidenshink@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":true,"id":906366,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70134695,"text":"70134695 - 2010 - Population structure and plumage polymorphism: the intraspecific evolutionary relationships of a polymorphic raptor, Buteo jamaicensis harlani","interactions":[],"lastModifiedDate":"2018-08-20T18:03:34","indexId":"70134695","displayToPublicDate":"2010-01-01T10:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":955,"text":"BMC Evolutionary Biology","active":true,"publicationSubtype":{"id":10}},"title":"Population structure and plumage polymorphism: the intraspecific evolutionary relationships of a polymorphic raptor, Buteo jamaicensis harlani","docAbstract":"Phenotypic and molecular genetic data often provide conflicting patterns of intraspecific relationships confounding phylogenetic inference, particularly among birds where a variety of environmental factors may influence plumage characters. Among diurnal raptors, the taxonomic relationship of Buteo jamaicensis harlani to other B. jamaicensis subspecies has been long debated because of the polytypic nature of the plumage characteristics used in subspecies or species designations.
\nTo address the evolutionary relationships within this group, we used data from 17 nuclear microsatellite loci, 430 base pairs of the mitochondrial control region, and 829 base pairs of the melanocortin 1 receptor (Mc1r) to investigate molecular genetic differentiation among three B. jamaicensis subspecies (B. j. borealis, B. j. calurus, B. j. harlani). Bayesian clustering analyses of nuclear microsatellite loci showed no significant differences between B. j. harlani and B. j. borealis. Differences observed between B. j. harlani and B. j. borealis in mitochondrial and microsatellite data were equivalent to those found between morphologically similar subspecies, B. j. borealis andB. j. calurus, and estimates of migration rates among all three subspecies were high. No consistent differences were observed in Mc1r data between B. j. harlani and other B. jamaicensis subspecies or between light and dark color morphs within B. j. calurus, suggesting that Mc1r does not play a significant role in B. jamaicensis melanism.
\nThese data suggest recent interbreeding and gene flow between B. j. harlani and the other B. jamaicensis subspecies examined, providing no support for the historical designation of B. j. harlani as a distinct species.
","language":"English","publisher":"BioMed Central Ltd.","doi":"10.1186/1471-2148-10-224","usgsCitation":"Hull, J.M., Mindell, D.P., Talbot, S.L., Kay, E.H., Hoekstra, H.E., and Ernest, H.B., 2010, Population structure and plumage polymorphism: the intraspecific evolutionary relationships of a polymorphic raptor, Buteo jamaicensis harlani: BMC Evolutionary Biology, v. 10, no. 224, 12 p., https://doi.org/10.1186/1471-2148-10-224.","productDescription":"12 p.","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-022812","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":475773,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/1471-2148-10-224","text":"Publisher Index Page"},{"id":296431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"224","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"548193c7e4b0aa6d778520f8","contributors":{"authors":[{"text":"Hull, Joshua M.","contributorId":127686,"corporation":false,"usgs":false,"family":"Hull","given":"Joshua","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":526334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mindell, David P.","contributorId":16762,"corporation":false,"usgs":false,"family":"Mindell","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":526335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":526317,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kay, Emily H.","contributorId":127687,"corporation":false,"usgs":false,"family":"Kay","given":"Emily","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":526338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoekstra, Hopi E.","contributorId":127688,"corporation":false,"usgs":false,"family":"Hoekstra","given":"Hopi","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":526339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ernest, Holly B.","contributorId":127689,"corporation":false,"usgs":false,"family":"Ernest","given":"Holly","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":526340,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70047820,"text":"70047820 - 2010 - Effects of model layer simplification using composite hydraulic properties","interactions":[],"lastModifiedDate":"2013-08-26T10:37:39","indexId":"70047820","displayToPublicDate":"2010-01-01T09:38:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Effects of model layer simplification using composite hydraulic properties","docAbstract":"The effects of simplifying hydraulic property layering within an unconfined aquifer and the underlying confining unit were assessed. The hydraulic properties of lithologic units within the unconfined aquifer and confining unit were computed by analyzing the aquifer-test data using radial, axisymmetric two-dimensional (2D) flow. Time-varying recharge to the unconfined aquifer and pumping from the confined Upper Floridan aquifer (USA) were simulated using 3D flow. Conceptual flow models were developed by gradually reducing the number of lithologic units in the unconfined aquifer and confining unit by calculating composite hydraulic properties for the simplified lithologic units. Composite hydraulic properties were calculated using either thickness-weighted averages or inverse modeling using regression-based parameter estimation. No significant residuals were simulated when all lithologic units comprising the unconfined aquifer were simulated as one layer. The largest residuals occurred when the unconfined aquifer and confining unit were aggregated into a single layer (quasi-3D), with residuals over 100% for the leakage rates to the confined aquifer and the heads in the confining unit. Residuals increased with contrasts in vertical hydraulic conductivity between the unconfined aquifer and confining unit. Residuals increased when the constant-head boundary at the bottom of the Upper Floridan aquifer was replaced with a no-flow boundary.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Hydrogeology Journal","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1007/s10040-009-0505-4","usgsCitation":"Sepulveda, N., and Kuniansky, E.L., 2010, Effects of model layer simplification using composite hydraulic properties: Hydrogeology Journal, v. 18, no. 2, p. 405-416, https://doi.org/10.1007/s10040-009-0505-4.","productDescription":"12 p.","startPage":"405","endPage":"416","numberOfPages":"12","ipdsId":"IP-005936","costCenters":[{"id":287,"text":"Florida Water Science Center-Orlando","active":false,"usgs":true}],"links":[{"id":475774,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://openresearchlibrary.org/ext/api/media/ce025b4b-e114-4fb5-9de4-e8e0e692a856/assets/external_content.pdf","text":"External Repository"},{"id":276981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":276979,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10040-009-0505-4"}],"country":"United States","state":"Florida","county":"Lake County;Volusia County","otherGeospatial":"Carrot Barn Sur?cial Aquifer System Well ?eld;Lyonia Preserve Sur?cial Aquifer System Well ?eld","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.783333,28.883333 ], [ -81.783333,28.933333 ], [ -81.216667,28.933333 ], [ -81.216667,28.883333 ], [ -81.783333,28.883333 ] ] ] } } ] }","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2009-09-04","publicationStatus":"PW","scienceBaseUri":"521c78e5e4b01458f784292c","contributors":{"authors":[{"text":"Sepulveda, Nicasio 0000-0002-6333-1865 nsepul@usgs.gov","orcid":"https://orcid.org/0000-0002-6333-1865","contributorId":1454,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Nicasio","email":"nsepul@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":483061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kuniansky, Eve L. 0000-0002-5581-0225 elkunian@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":932,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"elkunian@usgs.gov","middleInitial":"L.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":483060,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70118905,"text":"70118905 - 2010 - A Natural Resource Condition Assessment for Rocky Mountain National Park","interactions":[],"lastModifiedDate":"2018-02-21T16:14:53","indexId":"70118905","displayToPublicDate":"2010-01-01T09:27:12","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesNumber":"NPS/NRPC/WRD/NRR—2010/228","title":"A Natural Resource Condition Assessment for Rocky Mountain National Park","docAbstract":"We conducted a natural resource assessment of Rocky Mountain National Park (ROMO) to provide a synthesis of existing scientific data and knowledge to address the current conditions for a subset of important park natural resources. The intent is for this report to help provide park resource managers with data and information, particularly in the form of spatially-explicit maps and GIS databases, about those natural resources and to place emerging issues within a local, regional, national, or global context. With an advisory team, we identified the following condition indicators that would be useful to assess the condition of the park:
\nAir and Climate: Condition of alpine lakes and atmospheric deposition
\nWater: Extent and connectivity of wetland and riparian areas
\nBiotic Integrity: Extent of exotic terrestrial plant species, extent of fish distributions, and extent of suitable beaver habitat
\nLandscapes: Extent and pattern of major ecological systems and natural landscapes connectivity
\nThese indicators are summarized in the following pages. We also developed two maps of important issues for use by park managers: visitor use (thru accessibility modeling) and proportion of watersheds affected by beetle kill.
\nBased on our analysis, we believe that there is a high degree of concern for the following indicators: condition of alpine lakes; extent and connectivity of riparian/wetland areas; extent of exotic terrestrial plants (especially below 9,500’); extent of fish distributions; extent of suitable beaver habitat; and natural landscapes and connectivity. We found a low degree of concern for: the extent and pattern of major ecological systems.
\nThe indicators and issues were also summarized by the 34 watershed units (HUC12) within the park. Generally, we found six watersheds to be in “pristine” condition: Black Canyon Creek, Comanche Creek, Middle Saint Vrain Creek, South Fork of the Cache la Poudre, Buchanan Creek, and East Inlet. Four watersheds were found to have strong restoration opportunities: Big Thompson River West, Cache la Poudre South, Colorado River North, and Onahu Creek. Ten watersheds were found to have substantial near-term issues: Aspen Brook, Big Thompson River West, Black Canyon Creek, Cabin Creek, Cache la Poudre South, Fall River, Hague Creek, La Poudre Pass Creek, North Fork Big Thompson (East), and Colorado River North.
","language":"English","publisher":"National Park Service","publisherLocation":"Washington, D.C.","usgsCitation":"Theobald, D., Baron, J., Newman, P., Noon, B., Norman, J.B., Leinwand, I., Linn, S., Sherer, R., Williams, K., and Hartman, M., 2010, A Natural Resource Condition Assessment for Rocky Mountain National Park, 179 p.","productDescription":"179 p.","numberOfPages":"179","costCenters":[],"links":[{"id":291451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53db583fe4b0fba533fa355d","contributors":{"authors":[{"text":"Theobald, D.M.","contributorId":15157,"corporation":false,"usgs":true,"family":"Theobald","given":"D.M.","email":"","affiliations":[],"preferred":false,"id":497386,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baron, Jill 0000-0002-5902-6251 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":194124,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":497389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newman, P.","contributorId":94010,"corporation":false,"usgs":true,"family":"Newman","given":"P.","email":"","affiliations":[],"preferred":false,"id":497394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noon, B.","contributorId":22701,"corporation":false,"usgs":true,"family":"Noon","given":"B.","email":"","affiliations":[],"preferred":false,"id":497388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norman, J. B. III","contributorId":31511,"corporation":false,"usgs":true,"family":"Norman","given":"J.","suffix":"III","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":497390,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leinwand, I.","contributorId":70300,"corporation":false,"usgs":true,"family":"Leinwand","given":"I.","affiliations":[],"preferred":false,"id":497392,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Linn, S.E.","contributorId":15122,"corporation":false,"usgs":true,"family":"Linn","given":"S.E.","email":"","affiliations":[],"preferred":false,"id":497385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sherer, R.","contributorId":91414,"corporation":false,"usgs":true,"family":"Sherer","given":"R.","email":"","affiliations":[],"preferred":false,"id":497393,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, K.E.","contributorId":18687,"corporation":false,"usgs":true,"family":"Williams","given":"K.E.","email":"","affiliations":[],"preferred":false,"id":497387,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hartman, M.","contributorId":58195,"corporation":false,"usgs":true,"family":"Hartman","given":"M.","email":"","affiliations":[],"preferred":false,"id":497391,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236663,"text":"70236663 - 2010 - Rainfall-runoff paradox from a natural experimental catchment","interactions":[],"lastModifiedDate":"2022-09-15T14:31:53.464728","indexId":"70236663","displayToPublicDate":"2010-01-01T09:04:33","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3387,"text":"Shuikexue Jinzhan/Advances in Water Science","active":true,"publicationSubtype":{"id":10}},"title":"Rainfall-runoff paradox from a natural experimental catchment","docAbstract":"As a part of the Chuzhou hydrological experimental system,the No.1 experimental catchment,Nandadish,with drainage area of 7 897 m2 sits on the andesite bedrock with Quaternary deposit of 2.46 m in average.Various runoff components,surface runoff and subsurface runoff including interflow from unsaturated zone,groundwater flow from saturated zone are physically measured using special designed troughs.Several combined types of runoff components are identified as the SR type with surface runoff dominated,SSR type with subsurface runoff dominated and other intermediate types.Examples show that surface runoff accounts for 65% of total runoff for SR type,while the subsurface runoff accounts for 90% in SSR type.In July,the main rainy season,in total,the subsurface runoff contributes an amount of 54.5% of total runoff while groundwater flow accounts for 33.0%.Most 18O data of surface runoff is quite different from that of precipitation.Within the rainfall-runoff process with duration of about 1 400 minutes,averaged 18O of precipitation is -1.210%,while that of surface runoff is -1.132% for Hydrohill catchment (512 m2),-1.065% for Nandadish catchment and -0.801% for Morningflower(4573 m2)which is a catchment with thin layer of rock debris on bedrock.It challenges the assumptions involved in current isotopic hydrograph separation,i.e.,the 18O of surface runoff will not always equal to that of event precipitation and,the evaporation fractionation during the pathway of runoff components could not always be ignored.Event rainfall produces runoff but such runoff contains an amount of water not from the event rainfall,such a paradox exists in all of our experimental catchments.The total old water involved in event runoff accounts for 16% for the SR type while 64% for SSR type.
Context. Despite their economic importance and intensive management, many game bird species, including the northern bobwhite Colinus virginianus, are in decline. Declines may be explained, at least in part, by low survival due perhaps to poor habitat quality, high predation or excessive hunting pressure.
Aims. This study sought to estimate and model annual/seasonal survival probabilities, to evaluate factors influencing them and to determine the cause-specific mortality rates for northern bobwhites subject to varying levels of harvest on the Babcock–Webb Wildlife Management Area (BW area), south Florida, USA.
Methods. We applied Cox’s proportional hazard models to data collected from 2066 radio-tagged bobwhites during 2002–2008 to test for intrinsic and extrinsic factors affecting survival and the non-parametric cumulative incidence function estimator to estimate cause-specific mortality rates.
Key results. Mean annual survival (0.091 ± 0.006) in the BW area was lower than most estimates reported for other bobwhite populations. Annual survival differed between adults (0.111 ± 0.008) and juveniles (0.052 ± 0.008), and varied among years. Survival in winter (October–March; 0.295 ± 0.014) was similar to that in summer (April–September; 0.307 ± 0.013). Density of food strips (i.e. long and narrow food plots) did not influence survival, but hunting effort (number of hunters per day per km2) had a substantial negative impact on survival. In the lightly hunted field trial zone, winter (October–March) survival was significantly higher (0.414 ± 0.035) than in the other more heavily hunted management zones (0.319 ± 0.016). Cause-specific mortality analyses revealed that bobwhite mortality during summer (April–September) was mainly due to raptor (39.7%) and mammalian predation (35.6%), whereas hunting was the primary cause of mortality during
winter (47.1%).
Conclusions. Our results highlight the potential role of harvest as an important cause of the northern bobwhite population declines in south Florida. High mortality during winter may reduce recruitment of juveniles to the reproductive segment of the population, and ultimately the population growth.
Implications. Our results suggest that reduction in hunting pressure may be necessary to reverse the declining population trends in heavily hunted game species in public lands, such as the northern bobwhites in the BW area.
A Safe Harbor Agreement (SHA) is a voluntary arrangement between the U.S. Fish and Wildlife Service and non-Federal landowners to promote the protection, conservation, and recovery of listed species without imposing further land use restrictions on the landowners. Kamehameha Schools is considering entering into a SHA for their Keauhou and Kīlauea Forest lands on the island of Hawai′i. Bird surveys were conducted in 2008 to determine the current occurrence and density of listed species for the Keauhou and Kīlauea Forest, a prerequisite for establishing an agreement. Because of different management practices in the proposed SHA area we stratified the survey data into intact and altered forest strata. The listed passerines—′Akiapōlā′au (Hemignathus munroi), Hawai′i Creeper (Oreomystis mana), and Hawai′i ′Ākepa (Loxops coccineus)—occur in both strata but at low densities. The endangered ′Io (Hawaiian Hawk; Buteo solitarius) also occurs within both strata at low densities. This report was prepared for the U.S. Fish and Wildlife Service and Kamehameha Schools to provide information they can use to establish baseline levels for the SHA. In addition, we describe the status and trends of the non-listed native birds.
","language":"English","publisher":"Hawai'i Cooperative Studies Unit","publisherLocation":"Hilo, HI","usgsCitation":"Camp, R.J., Jacobi, J.D., Pratt, T.K., Gorresen, P.M., and Rubenstein, T., 2010, Status and trends of native birds in the Keauhou and Kilauea forest, Hawai`i Island: Technical Report HCSU-016, v, 37 p.","productDescription":"v, 37 p.","numberOfPages":"43","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-013426","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":325615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":325614,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/2697"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.26428222656247,\n 19.464650038331957\n ],\n [\n -155.22994995117188,\n 19.46659223220761\n ],\n [\n -155.22857666015625,\n 19.43616185591159\n ],\n [\n -155.26359558105466,\n 19.433571773164164\n ],\n [\n -155.26428222656247,\n 19.464650038331957\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.9680938720703,\n 19.577905706819973\n ],\n [\n -155.95436096191406,\n 19.57871439015505\n ],\n [\n -155.94938278198242,\n 19.55073158801923\n ],\n [\n -155.9641456604004,\n 19.551702171116087\n ],\n [\n -155.9680938720703,\n 19.577905706819973\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57973831e4b021cadec8ff53","contributors":{"authors":[{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":116175,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":false,"id":514607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobi, James D. 0000-0003-2313-7862 jjacobi@usgs.gov","orcid":"https://orcid.org/0000-0003-2313-7862","contributorId":3705,"corporation":false,"usgs":true,"family":"Jacobi","given":"James","email":"jjacobi@usgs.gov","middleInitial":"D.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":643484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pratt, Thane K. tkpratt@usgs.gov","contributorId":5495,"corporation":false,"usgs":true,"family":"Pratt","given":"Thane","email":"tkpratt@usgs.gov","middleInitial":"K.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":643485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorresen, P. Marcos mgorresen@usgs.gov","contributorId":3975,"corporation":false,"usgs":true,"family":"Gorresen","given":"P.","email":"mgorresen@usgs.gov","middleInitial":"Marcos","affiliations":[],"preferred":false,"id":514610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubenstein, Tanya tanya_rubenstein@contractor.nps.gov","contributorId":116341,"corporation":false,"usgs":true,"family":"Rubenstein","given":"Tanya","email":"tanya_rubenstein@contractor.nps.gov","affiliations":[],"preferred":false,"id":514608,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}