Global climate change poses challenges to areas such as low-lying coastal zones, where sea level rise (SLR) and storm-surge overwash events can have long-term effects on vegetation and on soil and groundwater salinities, posing risks of habitat loss critical to native species. An early warning system is urgently needed to predict and prepare for the consequences of these climate-related impacts on both the short-term dynamics of salinity in the soil and groundwater and the long-term effects on vegetation. For this purpose, the U.S. Geological Survey’s spatially explicit model of vegetation community dynamics along coastal salinity gradients (MANHAM) is integrated into the USGS groundwater model (SUTRA) to create a coupled hydrology–salinity–vegetation model, MANTRA. In MANTRA, the uptake of water by plants is modeled as a fluid mass sink term. Groundwater salinity, water saturation and vegetation biomass determine the water available for plant transpiration. Formulations and assumptions used in the coupled model are presented. MANTRA is calibrated with salinity data and vegetation pattern for a coastal area of Florida Everglades vulnerable to storm surges. A possible regime shift at that site is investigated by simulating the vegetation responses to climate variability and disturbances, including SLR and storm surges based on empirical information.
","language":"English","publisher":"MDPI AG","publisherLocation":"Basel, Germany","doi":"10.3390/jmse3041149","usgsCitation":"Teh, S., Turtora, M., DeAngelis, D.L., Jiang Jiang, Pearlstine, L.G., Smith, T.J., and Koh, H.L., 2015, Application of a coupled vegetation competition and groundwater simulation model to study effects of sea level rise and storm surges on coastal vegetation: Journal of Marine Science and Engineering, v. 3, no. 4, p. 1149-1177, https://doi.org/10.3390/jmse3041149.","productDescription":"29 p.","startPage":"1149","endPage":"1177","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063605","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":471762,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse3041149","text":"Publisher Index Page"},{"id":309044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-25","publicationStatus":"PW","scienceBaseUri":"560ba828e4b058f706e53a41","contributors":{"authors":[{"text":"Teh, Su Yean","contributorId":118102,"corporation":false,"usgs":true,"family":"Teh","given":"Su Yean","affiliations":[],"preferred":false,"id":573736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turtora, Michael mturtora@usgs.gov","contributorId":4260,"corporation":false,"usgs":true,"family":"Turtora","given":"Michael","email":"mturtora@usgs.gov","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"preferred":true,"id":573737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":573735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jiang Jiang","contributorId":148066,"corporation":false,"usgs":false,"family":"Jiang Jiang","affiliations":[{"id":16989,"text":"University of Tennessee, Knoxville, TN","active":true,"usgs":false}],"preferred":false,"id":573738,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearlstine, Leonard G.","contributorId":34751,"corporation":false,"usgs":false,"family":"Pearlstine","given":"Leonard","email":"","middleInitial":"G.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":573739,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Thomas J. tom_j_smith@usgs.gov","contributorId":139562,"corporation":false,"usgs":true,"family":"Smith","given":"Thomas","email":"tom_j_smith@usgs.gov","middleInitial":"J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":573740,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koh, Hock Lye","contributorId":119022,"corporation":false,"usgs":true,"family":"Koh","given":"Hock","email":"","middleInitial":"Lye","affiliations":[],"preferred":false,"id":573741,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70154999,"text":"tm2A13 - 2015 - Environmental DNA sampling protocol - filtering water to capture DNA from aquatic organisms","interactions":[],"lastModifiedDate":"2017-11-22T15:54:39","indexId":"tm2A13","displayToPublicDate":"2015-09-29T17:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2-A13","title":"Environmental DNA sampling protocol - filtering water to capture DNA from aquatic organisms","docAbstract":"Environmental DNA (eDNA) analysis is an effective method of determining the presence of aquatic organisms such as fish, amphibians, and other taxa. This publication is meant to guide researchers and managers in the collection, concentration, and preservation of eDNA samples from lentic and lotic systems. A sampling workflow diagram and three sampling protocols are included as well as a list of suggested supplies. Protocols include filter and pump assembly using: (1) a hand-driven vacuum pump, ideal for sample collection in remote sampling locations where no electricity is available and when equipment weight is a primary concern; (2) a peristaltic pump powered by a rechargeable battery-operated driver/drill, suitable for remote sampling locations when weight consideration is less of a concern; (3) a 120-volt alternating current (AC) powered peristaltic pump suitable for any location where 120-volt AC power is accessible, or for roadside sampling locations. Images and detailed descriptions are provided for each step in the sampling and preservation process.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Biological science in Book 2: Collection of Environmental Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A13","collaboration":"Prepared in cooperation with Washington State University","usgsCitation":"Laramie, M.B., Pilliod, D.S., Goldberg, C.S., and Strickler, K.M., 2015, Environmental DNA sampling protocol—Filtering water to capture DNA from aquatic organisms: U.S. Geological Survey Techniques and Methods, book 2, chap. A13, 15 p., https://dx.doi.org/10.3133/tm2A13.","productDescription":"iv, 15 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062044","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":308858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a13/coverthumb.jpg"},{"id":308824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a13/tm2a13.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 2-A13"}],"publicComments":"This report is Chapter 13 of Section A: Biological science in Book 2 Collection of Environmental Data.","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov
The bedrock geology of the 7.5-minute Worcester South quadrangle, Massachusetts, consists of deformed Neoproterozoic to Paleozoic crystalline metamorphic and intrusive igneous rocks in three fault-bounded terranes (zones), including the Avalon, Nashoba, and Merrimack zones (Zen and others, 1983). This quadrangle spans the easternmost occurrence of Ganderian margin arc-related rocks (Nashoba zone) in the southern New England part of the northern Appalachians, and coincides with the trailing edge of Ganderia (Merrimack and Nashoba zones) where it structurally overlies Avalonia (Hibbard and others, 2006; Pollock and others, 2012; van Staal and others, 2009, 2012).
\nNeoproterozoic intrusive rocks and minor metasedimentary rocks crop out in the Avalon zone and structurally underlie the rocks of the Nashoba zone along the Bluddy Bluff fault. Due to poor exposure, the position of the Bloody Bluff fault is not well-constrained and its location is partly extrapolated from mapping in adjacent areas (Barosh, 2005; Walsh and others, 2011a). Cambrian intrusive rocks and Cambrian to Silurian metasedimentary and metavolcanic rocks crop out in the Nashoba zone, and are overlain by largely Silurian metasedimentary rocks of the Merrimack zone along the Clinton-Newbury fault. Ordovician to Permian(?) plutonic rocks intrude the Merrimack and Nashoba zone rocks. Paleozoic metamorphism in the Merrimack and Nashoba zones peaked during Salinic, Acadian, and Neoacadian orogenesis from the Silurian to Mississippian (Wintsch and others, 2007; Stroud and others, 2009; Walsh and others, 2011a; Hepburn and others, 2014). Metamorphism in the Avalon zone peaked during Alleghanian orogenesis in the Mississippian to Permian (Wintsch and others, 1992, 1993, 2001; Attenoukon, 2008). Evidence for garnet-grade extensional Alleghanian mylonitization showing normal motion along the Clinton-Newbury fault occurred after presumed original terrane juxtaposition by left-lateral Acadian thrusting (Goldstein, 1994). Subsequent post-peak metamorphic deformation produced outcrop-scale open folds and weak cleavage, local faults, veins, shear bands, and pegmatite dikes. Locally, along re-activated ductile faults such as the Bloody Bluff fault and along the Wekepeke fault, late Paleozoic to Mesozoic mainly brittle normal fault motion led to the current configuration of fault-bounded lithotectonic terranes (Goldstein, 1982, 1994, 1998; Goldstein and Hepburn, 1999; Goldsmith, 1991; Attenoukon, 2008; Wintsch and others, 2012). The youngest deformation includes kink bands, brittle faults, and joints.
\nThe bedrock geology was mapped to study the tectonic history of the area and to provide a framework for ongoing hydrogeologic characterization of the fractured bedrock of Massachusetts. This report presents mapping by Gregory J. Walsh and Arthur J. Merschat from 2008 to 2010. The report consists of a map and GIS database, both of which are available for download at http://dx.doi.org/ 10.3133/sim3345. The database includes contacts of bedrock geologic units, faults, outcrop locations, structural information, and photographs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3345","usgsCitation":"Walsh, G.J., and Merschat, A.J., 2015, Bedrock geologic map of the Worcester South quadrangle, Worcester County, Massachusetts: U.S. Geological Survey Scientific Investigations Map 3345, 1 sheet, scale 1:24,000, https://dx.doi.org/10.3133/sim3345.","productDescription":"1 Plate: 62.36 x 37.06 inches; Metadata; Readme; Spatial Data","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062629","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":399009,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103402.htm"},{"id":308451,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3345/downloads/readme.txt","text":"SIM 3345 - Read Me File","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3345"},{"id":308449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3345/coverthb.jpg"},{"id":308457,"rank":8,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3345/downloads/SIM3345.zip","text":"SIM 3345 - With Photographs","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3345"},{"id":308458,"rank":9,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3345/downloads/SIM3345_withoutphotos.zip","text":"SIM 3345 - Without Photographs","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3345"},{"id":308454,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3345/downloads/metadata.html","text":"SIM 3345 - Metadata","linkFileType":{"id":5,"text":"html"},"description":"SIM 3345"},{"id":308455,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3345/downloads/metadata.txt","text":"SIM 3345 - Metadata Txt","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3345"},{"id":308453,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3345/downloads/metadata.faq.html","text":"SIM 3345 - Metadata FAQ","linkFileType":{"id":5,"text":"html"},"description":"SIM 3345"},{"id":308456,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3345/downloads/metadata.xml","text":"SIM 3345 - Metadata (XML)","description":"SIM 3345"},{"id":308450,"rank":2,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3345/sim3345.pdf","text":"SIM 3345 Map","description":"SIM 3345"}],"scale":"24000","country":"United States","state":"Massachusetts","county":"Worcester County","otherGeospatial":"Worcester South quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.875,\n 42.125\n ],\n [\n -71.875,\n 42.25\n ],\n [\n -71.75,\n 42.25\n ],\n [\n -71.75,\n 42.125\n ],\n [\n -71.875,\n 42.125\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geology and Paleoclimate Science Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://geology.er.usgs.gov/egpsc/
Gregory J. Walsh
U.S. Geological Survey
P.O. Box 628
87 State Street, Room 228
Montpelier, VT 05602
Email: gwalsh@usgs.gov
A secretarial order identified climate adaptation as a critical performance objective for future management of U.S. Department of the Interior (DOI) lands and resources in response to global change. Vulnerability assessments can inform climate adaptation planning by providing insight into what natural resources are most at risk and why. Three components of vulnerability—exposure, sensitivity, and adaptive capacity—were defined by the Intergovernmental Panel on Climate Change (IPCC) as necessary for identifying climate adaptation strategies and actions. In 2011, the DOI requested all internal bureaus report ongoing or completed vulnerability assessments about a defined range of assessment targets or climate-related threats. Assessment targets were defined as freshwater resources, landscapes and wildlife habitat, native and cultural resources, and ocean health. Climate-related threats were defined as invasive species, wildfire risk, sea-level rise, and melting ice and permafrost. Four hundred and three projects were reported, but the original DOI survey did not specify that information be provided on exposure, sensitivity, and adaptive capacity collectively as part of the request, and it was unclear which projects adhered to the framework recommended by the IPCC. Therefore, the U.S. Geological Survey National Climate Change and Wildlife Science Center conducted a supplemental survey to determine how frequently each of the three vulnerability components was assessed. Information was categorized for 124 of the 403 reported projects (30.8 percent) based on the three vulnerability components, and it was discovered that exposure was the most common component assessed (87.9 percent), followed by sensitivity (68.5 percent) and adaptive capacity (33.1 percent). The majority of projects did not fully assess vulnerability; projects focused on landscapes/wildlife habitats and sea-level rise were among the minority that simultaneously addressed all three vulnerability components. To maintain consistency with the IPCC definition of vulnerability, DOI may want to focus initial climate adaptation planning only on the outcomes of studies that comprehensively address vulnerability as inclusive of exposure, sensitivity, and adaptive capacity. Although the present study results are preliminary and used an unstructured survey design, they illustrate the importance of a comprehensive and consistent vulnerability definition and of using information on vulnerability components in DOI surveys to ensure relevant data are used to identify adaptation options.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151110","usgsCitation":"Thompson, L.M., Staudinger, M.D., and Carter, S.L., 2015, Summarizing components of U.S. Department of the Interior vulnerability assessments to focus climate adaptation planning: U.S. Geological Survey Open-File Report 2015–1110, 14 p., https://dx.doi.org/10.3133/ofr20151110.","productDescription":"iii, 14 p.","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-053843","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":308292,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1110/ofr20151110.pdf","text":"Report","size":"298 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1110"},{"id":308291,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1110/coverthb.jpg"}],"contact":"National Climate Change and Wildlife Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
https://nccwsc.usgs.gov/
This map and accompanying digital files are the result of the interpretation of aerial photographs from the 1950s as well as more modern imagery. The area, long considered a part of Alaska that was largely not glaciated (see Karlstrom, 1964; Coulter and others, 1965; or Péwé, 1975), actually has a long history reflecting local and more distant glaciations. An unpublished photogeologic map of the Taylor Mountains quadrangle from the 1950s by J.N. Platt Jr. was useful in the construction of this map. Limited new field mapping in the area was conducted as part of a mapping project in the Dillingham quadrangle to the south (Wilson and others, 2003); however, extensive aerial photograph interpretation represents the bulk of the mapping effort. The accompanying digital files show the sources for each line and geologic unit shown on the map.
\nI used the Platt and Muller 1950s-era aerial photographic interpretation map as the starting point for the surficial geology; their unpublished data were produced using a reconnaissance quality topographic base map. In addition to transferring their data to a modern base to use as a guide, all of the photographs were re-examined. As result, in a number of areas, the features have been reinterpreted and the linework revised. A major difference between the maps is the recognition of much more extensive glacially dammed lake deposits and reassignment of some glacial deposits to different glacial events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3334","usgsCitation":"Wilson, F.H., 2017, Reconnaissance surficial geologic map of the Taylor Mountains quadrangle, southwestern Alaska (ver. 1.2, December 2017): U.S. Geological Survey Scientific Investigations Map 3334, pamphlet 12 p., scale 1:250,000, https://doi.org/10.3133/sim3334.","productDescription":"Report: iii, 12 p.; 1 Sheet: 41.01 x 31.37 inches; GIS files and related databases; Metadata; Readme","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061421","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":308630,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_metadata.xml","text":"XML"},{"id":308631,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_metadata.txt","text":"TXT"},{"id":347918,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3334/sim3334versionHist_v1.2.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3334 Version Hystory"},{"id":308228,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_pamphlet_v1.2.pdf","text":"Pamphlet","size":"175 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3334 Pamphlet PDF"},{"id":308229,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_sheet_v1.2.pdf","text":"Sheet","size":"119 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3334 Sheet"},{"id":308629,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_metadata.html","text":"HTML"},{"id":308628,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_database.zip","text":"GIS files and related databases","size":"87.8 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3334 GIS files and databases"},{"id":308632,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_metadata_faq.html","text":"Metadata FAQ"},{"id":308633,"rank":9,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3334/sim3334_readme.pdf","size":"215 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":308227,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3334/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Taylor Mountains quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n \n \n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.895263671875,\n 59.94950917225228\n ],\n [\n -158.895263671875,\n 61.03701223240189\n ],\n [\n -155.73120117187497,\n 61.03701223240189\n ],\n [\n -155.73120117187497,\n 59.94950917225228\n ],\n [\n -158.895263671875,\n 59.94950917225228\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 28, 2015; Version 1.1: October 31, 2017; Version 1.2: December 19, 2017","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
This report presents the results of a cooperative study by the U.S. Geological Survey and the Oklahoma Department of Transportation to estimate the magnitude and frequency of peak streamflows from regional regression equations for ungaged stream sites in and near the Oklahoma Panhandle. These methods relate basin characteristics (physiographic and climatic attributes) to selected peak streamflow frequency statistics with the 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities. These relations were developed based on data from 32 selected streamflow-gaging stations in the Oklahoma Panhandle and in neighboring parts of Colorado, Kansas, New Mexico, and Texas. The basin characteristics for the selected streamflow-gaging stations were determined by using a geographic information system and the Oklahoma StreamStats application. Peak-streamflow frequency statistics were computed from annual peak-streamflow records from the irrigated period of record from water year 1978 through water year 2014.
\nGeneralized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155134","collaboration":"Prepared in cooperation with the Oklahoma Department of Transportation","usgsCitation":"Smith, S.J., Lewis, J.M., and Graves, G.M., 2015, Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle: U.S. Geological Survey Scientific Investigations Report 2015–5134, 35 p., https://dx.doi.org/10.3133/sir20155134.","productDescription":"vi, 35 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066906","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":308637,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5134/sir20155134.pdf","text":"Report","size":"5.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5134"},{"id":308636,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5134/coverthb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","otherGeospatial":"Oklahoma Panhandle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.23828125,\n 35.10193405724606\n ],\n [\n -104.23828125,\n 38.8225909761771\n ],\n [\n -98.3056640625,\n 38.8225909761771\n ],\n [\n -98.3056640625,\n 35.10193405724606\n ],\n [\n -104.23828125,\n 35.10193405724606\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
http://ok.water.usgs.gov/
Baseline survival and mortality data for lesser prairie-chickens (Tympanuchus pallidicinctus) are lacking for shinnery oak (Quercus havardii) prairies. An understanding of the causes and timing of mortalities and breeding season survival in this ecoregion is important because shinnery oak prairies have hotter and drier environmental conditions, as well as different predator communities compared with the northern distribution of the species. The need for this information has become more pressing given the recent listing of the species as threatened under the U.S. Endangered Species Act. We investigated causes of mortality and survival of lesser prairie-chickens during the 6-month breeding season (1 Mar–31 Aug) of 2008–2011 on the Texas Southern High Plains, USA. We recorded 42 deaths of radiotagged individuals, and our results indicated female mortalities were proportionate among avian and mammalian predation and other causes of mortality but survival was constant throughout the 6-month breeding season. Male mortalities were constant across avian and mammalian predation and other causes, but more mortalities occurred in June compared with other months. Male survival also varied by month, and survival probabilities were lower in June–August. We found predation on leks was rare, mortalities from fence collisions were rare, female survival did not decrease during incubation or brood-rearing, and survival was influenced by drought. Our study corroborated recent studies that suggested lesser prairie-chickens are living at the edge of their physiological tolerances to environmental conditions in shinnery oak prairies. As such, lesser prairie-chickens in our study experienced different patterns of mortality and survival that we attributed to hot, dry conditions during the breeding season. Specifically, and converse to other studies on lesser prairie-chicken survival and mortality, drought positively influenced female survival because females did not incubate eggs during drought conditions; the incubation period is when females are most vulnerable to predation. Male mortalities and survival were negatively influenced by drought later in the breeding season, which we attributed to rigorous lekking activities through late May combined with lack of food and cover as the breeding season progressed into summer.
","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/wsb.551","usgsCitation":"Grisham, B.A., and Boal, C.W., 2015, Causes of mortality and temporal patterns in breeding season survival of lesser prairie-chickens in shinnery oak prairies: Wildlife Society Bulletin, v. 39, no. 3, p. 536-542, https://doi.org/10.1002/wsb.551.","productDescription":"7 p.","startPage":"536","endPage":"542","ipdsId":"IP-053468","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":500051,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/e63edd00d8164c8e9c6d9ff649fe69a3","text":"External Repository"},{"id":330682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Cochran County, Hockley County, Terry County, Yoakum County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-103.0469,33.8237],[-102.7603,33.825],[-102.616,33.8257],[-102.0867,33.8237],[-102.0774,33.3894],[-102.0782,32.9611],[-102.2039,32.961],[-102.595,32.9596],[-103.0145,32.9593],[-103.0632,32.9589],[-103.0632,33.0017],[-103.0593,33.209],[-103.0559,33.3903],[-103.0525,33.5738],[-103.0514,33.6402],[-103.0487,33.75],[-103.0469,33.8237]]]},\"properties\":{\"name\":\"Cochran\",\"state\":\"TX\"}}]}","volume":"39","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-06-09","publicationStatus":"PW","scienceBaseUri":"581c4cc4e4b09688d6e90fe0","contributors":{"authors":[{"text":"Grisham, Blake A.","contributorId":75419,"corporation":false,"usgs":true,"family":"Grisham","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":652817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":652816,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156757,"text":"ofr20151161 - 2015 - Status report for the 3D Elevation Program, 2013-2014","interactions":[],"lastModifiedDate":"2017-05-16T16:08:11","indexId":"ofr20151161","displayToPublicDate":"2015-09-25T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1161","title":"Status report for the 3D Elevation Program, 2013-2014","docAbstract":"The 3D Elevation Program (3DEP) goal is to acquire, manage, and distribute enhanced three-dimensional elevation data for the Nation and U.S. territories by 2023. This status report covers implementation activities during 2013–2014 to include meeting funding objectives, developing a management structure, modernizing systems, and collecting and producing initial 3DEP data and products. The Nation will not have complete coverage of 3DEP quality data until 2023 assuming that sufficient funding is available. In spite of the overall condition of government budgets, the 3DEP initiative has gained widespread support and had incremental budget success to include supplemental funding resulting from natural disasters. The 3DEP Executive Forum and a wide range of professional organizations are actively working to maintain support for the program. The systems that have been developed to support increasing acquisition and processing levels are largely in place. The first 3DEP quality datasets were released to the public in late 2014. In addition, light detection and ranging (lidar), interferometric synthetic aperture radar (ifsar), and digital elevation models (DEMs) acquired before 2014 are all supported within the new infrastructure and available for download. Research is ongoing to expand the suite of products and services, and to increase overall throughput and data management efficiency. Emerging technologies may result in lower acquisition costs in the future. Elevation data acquired by 3DEP partnerships will be available through The National Map representing one of the largest and most comprehensive databases publicly available for the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151161","usgsCitation":"Lukas, Vicki, Eldridge, D.F., Jason, A.L., Saghy, D.L., Steigerwald, P.R., Stoker, J.M., Sugarbaker, L.J., and Thunen, D.R., 2015, Status report for the 3D Elevation Program, 2013–2014: U.S. Geological Survey Open-File Report 2015–1161, 17 p., https://dx.doi.org/10.3133/ofr20151161.","productDescription":"iv, 17 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066538","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":333334,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161196","text":"Open-File Report 2016–1196 - ","linkHelpText":"Status Report for the 3D Elevation Program, 2015"},{"id":308532,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1161/coverthb.jpg"},{"id":308533,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1161/ofr20151161.pdf","text":"Report","size":"1.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1161"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive
511 National Center
Reston, VA 20192
Email: 3dep@usgs.gov
http://www.usgs.gov/ngpo/
http://nationalmap.gov/3DEP/
Sampling by spatially replicated counts (point-count) is an increasingly popular method of estimating population size of organisms. Challenges exist when sampling by point-count method, and it is often impractical to sample entire area of interest and impossible to detect every individual present. Ecologists encounter logistical limitations that force them to sample either few large-sample units or many small sample-units, introducing biases to sample counts. We generated a computer environment and simulated sampling scenarios to test the role of number of samples, sample unit area, number of organisms, and distribution of organisms in the estimation of population sizes using N-mixture models. Many sample units of small area provided estimates that were consistently closer to true abundance than sample scenarios with few sample units of large area. However, sample scenarios with few sample units of large area provided more precise abundance estimates than abundance estimates derived from sample scenarios with many sample units of small area. It is important to consider accuracy and precision of abundance estimates during the sample design process with study goals and objectives fully recognized, although and with consequence, consideration of accuracy and precision of abundance estimates is often an afterthought that occurs during the data analysis process.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2015.08.016","usgsCitation":"Kowalewski, L.K., Chizinski, C.J., Powell, L., Pope, K.L., and Pegg, M.A., 2015, Accuracy or precision: Implications of sample design and methodology on abundance estimation: Ecological Modelling, v. 316, p. 185-190, https://doi.org/10.1016/j.ecolmodel.2015.08.016.","productDescription":"6 p.","startPage":"185","endPage":"190","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064775","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":308504,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"316","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560510c7e4b058f706e51299","contributors":{"authors":[{"text":"Kowalewski, Lucas K.","contributorId":147928,"corporation":false,"usgs":false,"family":"Kowalewski","given":"Lucas","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":573275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chizinski, Christopher J.","contributorId":7178,"corporation":false,"usgs":false,"family":"Chizinski","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":573276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, Larkin A.","contributorId":15100,"corporation":false,"usgs":true,"family":"Powell","given":"Larkin A.","affiliations":[],"preferred":false,"id":573277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":573241,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pegg, Mark A.","contributorId":45212,"corporation":false,"usgs":true,"family":"Pegg","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":573278,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156784,"text":"ofr20151166 - 2015 - Whooping crane stopover site use intensity within the Great Plains","interactions":[],"lastModifiedDate":"2018-01-04T12:51:18","indexId":"ofr20151166","displayToPublicDate":"2015-09-23T17:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1166","title":"Whooping crane stopover site use intensity within the Great Plains","docAbstract":"Whooping cranes (Grus americana) of the Aransas-Wood Buffalo population migrate twice each year through the Great Plains in North America. Recovery activities for this endangered species include providing adequate places to stop and rest during migration, which are generally referred to as stopover sites. To assist in recovery efforts, initial estimates of stopover site use intensity are presented, which provide opportunity to identify areas across the migration range used more intensively by whooping cranes. We used location data acquired from 58 unique individuals fitted with platform transmitting terminals that collected global position system locations. Radio-tagged birds provided 2,158 stopover sites over 10 migrations and 5 years (2010–14). Using a grid-based approach, we identified 1,095 20-square-kilometer grid cells that contained stopover sites. We categorized occupied grid cells based on density of stopover sites and the amount of time cranes spent in the area. This assessment resulted in four categories of stopover site use: unoccupied, low intensity, core intensity, and extended-use core intensity. Although provisional, this evaluation of stopover site use intensity offers the U.S. Fish and Wildlife Service and partners a tool to identify landscapes that may be of greater conservation significance to migrating whooping cranes. Initially, the tool will be used by the U.S. Fish and Wildlife Service and other interested parties in evaluating the Great Plains Wind Energy Habitat Conservation Plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151166","collaboration":"Prepared in collaboration with the Canadian Wildlife Service, Crane Trust, Platte River Recovery Implementation Program, and U.S. Fish and Wildlife Service","usgsCitation":"Pearse, A.T., Brandt, D.A., Harrell, W.C., Metzger, K.L., Baasch, D.M., and Hefley, T.J., 2015, Whooping crane stopover site use intensity within the Great Plains: U.S. Geological Survey Open-File Report 2015–1166, 12 p., https://dx.doi.org/10.3133/ofr20151166.","productDescription":"v, 12 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-066665","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":310152,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://www.sciencebase.gov/catalog/item/56253ce5e4b0fb9a11dd3d2b"},{"id":308397,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1166/ofr2015-1166.pdf","text":"Report","size":"2.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2015-1166"},{"id":308396,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1166/coverthb.jpg"}],"country":"Canada, United States","state":"Alberta, Iowa, Kansas, Manitoba, Minnesota, Missouri, Montana, Nebraska, North Dakota, Northwest Territories, Oklahoma, Saskatchewan, South Dakota, Texas","otherGeospatial":"Great Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {\n \"stroke\": \"#555555\",\n \"stroke-width\": 2,\n \"stroke-opacity\": 1,\n \"fill\": \"#555555\",\n \"fill-opacity\": 0.5\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.478515625,\n 61.4597705702975\n ],\n [\n -119.15771484375,\n 61.44927080076419\n ],\n [\n -118.564453125,\n 58.263287052486035\n ],\n [\n -114.521484375,\n 55.19141243527063\n ],\n [\n -109.9951171875,\n 49.023461463214126\n ],\n [\n -104.04052734375,\n 45.9511496866914\n ],\n [\n -98.997802734375,\n 26.49024045886963\n ],\n [\n -95.6689453125,\n 28.57487404744697\n ],\n [\n -94.9658203125,\n 29.248063243796576\n ],\n [\n -94.5703125,\n 33.687781758439364\n ],\n [\n -94.41650390625,\n 36.491973470593685\n ],\n [\n -93.33984375,\n 40.613952441166596\n ],\n [\n -93.42773437499999,\n 43.48481212891603\n ],\n [\n -96.591796875,\n 45.99696161820381\n ],\n [\n -99.31640625,\n 49.023461463214126\n ],\n [\n -101.0302734375,\n 52.24125614966341\n ],\n [\n -105.22705078125,\n 56.30434864830834\n ],\n [\n -110.478515625,\n 61.4597705702975\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, North Dakota 58401
http://www.npwrc.usgs.gov/
A number of different government-funded seismic data centers offer free open-access data (e.g., U.S. Geological Survey, National Earthquake Information Center, the Incorporated Research Institutions for Seismology (IRIS), and Data Management System), which can be freely downloaded and shared among different members of the community (Lay, 2009). To efficiently share data, it is important that different data providers follow a common format. The Standard for the Exchange of Earthquake Data (SEED) provides one such format for storing seismic and other geophysical data. The SEED format is widely used in earthquake seismology; however, SEED and its structure can be difficult for many first-time users (ourselves included). Below is a quick tutorial that outlines the basic structure of SEED format. This write-up is in no way intended to replace the comprehensive SEED manual (Ahern et al., 2009), and instead of going into the details of any specific part of the SEED format we refer the reader to the manual for additional details. The goal of this write-up is to succinctly explain the basic structure of SEED format as well as the associated jargon, as most commonly used now, in a colloquial way so that novice users of SEED can become more familiar with the format and its application quickly. Our goal is to give the reader the necessary background so that when problems or questions about SEED format arise they will have some understanding of where they should look for more details or from where the problem might be stemming. As a secondary goal, we hope to help the reader become familiar with the SEED manual (Ahern et al., 2009), which contains detailed information about all aspects of the SEED format.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220150043","usgsCitation":"Ringler, A.T., and Evans, J.R., 2015, A quick SEED tutorial: Seismological Research Letters, v. 86, no. 6, p. 1717-1725, https://doi.org/10.1785/0220150043.","productDescription":"9 p.","startPage":"1717","endPage":"1725","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067142","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":312605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"86","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-23","publicationStatus":"PW","scienceBaseUri":"567930bce4b0da412f4fb527","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":582717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, John R. jrevans@usgs.gov","contributorId":529,"corporation":false,"usgs":true,"family":"Evans","given":"John","email":"jrevans@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":582718,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70157341,"text":"70157341 - 2015 - Intra-annual patterns in adult band-tailed pigeon survival estimates","interactions":[],"lastModifiedDate":"2015-09-21T13:37:42","indexId":"70157341","displayToPublicDate":"2015-09-21T13:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Intra-annual patterns in adult band-tailed pigeon survival estimates","docAbstract":"Context: The band-tailed pigeon (Patagioenas fasciata) is a migratory species occurring in western North America with low recruitment potential and populations that have declined an average of 2.4% per year since the 1960s. Investigations into band-tailed pigeon demographic rates date back to the early 1900s, and existing annual survival rate estimates were derived in the 1970s using band return data.
\nAims: The primary purpose of the paper was to demonstrate that the apparent paradox between band-tailed pigeon population dynamics (long-term steady decline) and breeding season survival rates (very high) can be explained by changes in survival probability during the remainder of the year.
\nMethods: We trapped Pacific coast band-tailed pigeons during two separate periods: we equipped pigeons with very high frequency (VHF) radio-transmitters in 1999–2000 (1999 = 20; 2000 = 34); and outfitted pigeons with solar powered platform transmitting terminal (PTT) transmitters in 2006–08 (n = 20). We used known fate models to estimate annual survival rates and seasonal survival variation among four periods based on an annual behavioural cycle based on phenological events (nesting, autumn migration, winter and spring migrations). We used model averaged parameter estimates to account for model selection uncertainty.
\nKey results: Neither body condition nor sex were associated with variation in band-tailed pigeon survival rates. Weekly survival during the nesting season did not differ significantly between VHF-marked (0.996; CI = 0.984–0.999) and PTT-marked pigeons (0.998; CI = 0.990–1.00). Model averaged annual survival of PTT-marked pigeons was 0.682 (95% CI = 0.426–0.861) and was similar to annual survival estimated in previous studies using band return data. Survival probability was lowest during both migration periods and highest during the nesting period.
\nConclusions: Our survival estimates are consistent with those of prior studies and suggest that mortality risk is greatest during migration. Weekly survival probability during winter was nearly the same as during the nesting season; however, winter was the longest period and survival throughout winter was lower than other seasons.
\nImplications: We present the first inter-seasonal analysis of survival probability of the Pacific coast race of band-tailed pigeons and illustrate important temporal patterns that may influence future species management including harvest strategies and disease monitoring.
","language":"English","publisher":"CSIRO","publisherLocation":"East Melbourne, Australia","doi":"10.1071/WR14199","collaboration":"CADFW, USFWS, ORFW, WAFW","usgsCitation":"Casazza, M.L., Coates, P.S., Overton, C.T., and Howe, K.H., 2015, Intra-annual patterns in adult band-tailed pigeon survival estimates: Wildlife Research, v. 42, no. 5, p. 454-459, https://doi.org/10.1071/WR14199.","productDescription":"6 p.","startPage":"454","endPage":"459","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062587","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":308315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56011c6fe4b03bc34f5443db","contributors":{"authors":[{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":572752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":572753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":572754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howe, Kristy H. khowe@usgs.gov","contributorId":147803,"corporation":false,"usgs":true,"family":"Howe","given":"Kristy","email":"khowe@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":572755,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70157284,"text":"70157284 - 2015 - Individual heterogeneity in growth and age at sexual maturity: A gamma process analysis of capture–mark–recapture data","interactions":[],"lastModifiedDate":"2017-01-11T16:53:03","indexId":"70157284","displayToPublicDate":"2015-09-21T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2151,"text":"Journal of Agricultural, Biological, and Environmental Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Individual heterogeneity in growth and age at sexual maturity: A gamma process analysis of capture–mark–recapture data","docAbstract":"Knowledge of organisms’ growth rates and ages at sexual maturity is important for conservation efforts and a wide variety of studies in ecology and evolutionary biology. However, these life history parameters may be difficult to obtain from natural populations: individuals encountered may be of unknown age, information on age at sexual maturity may be uncertain and interval-censored, and growth data may include both individual heterogeneity and measurement errors. We analyzed mark–recapture data for Red-backed Salamanders (Plethodon cinereus) to compare sex-specific growth rates and ages at sexual maturity. Aging of individuals was made possible by the use of a von Bertalanffy model of growth, complemented with models for interval-censored and imperfect observations at sexual maturation. Individual heterogeneity in growth was modeled through the use of Gamma processes. Our analysis indicates that female P. cinereus mature earlier and grow more quickly than males, growing to nearly identical asymptotic size distributions as males.
","language":"English","publisher":"American Statistical Association: International Biometric Society","publisherLocation":"Alexandria, VA","doi":"10.1007/s13253-015-0211-8","usgsCitation":"Link, W.A., and Hesed, K.M., 2015, Individual heterogeneity in growth and age at sexual maturity: A gamma process analysis of capture–mark–recapture data: Journal of Agricultural, Biological, and Environmental Statistics, v. 20, no. 3, p. 343-352, https://doi.org/10.1007/s13253-015-0211-8.","productDescription":"10 p.","startPage":"343","endPage":"352","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063230","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":308314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-09","publicationStatus":"PW","scienceBaseUri":"56011c6ae4b03bc34f5443d9","contributors":{"authors":[{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":572599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hesed, Kyle Miller","contributorId":147823,"corporation":false,"usgs":false,"family":"Hesed","given":"Kyle","email":"","middleInitial":"Miller","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":572817,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190286,"text":"70190286 - 2015 - Hydro-bio-geomechanical properties of hydrate-bearing sediments from Nankai Trough","interactions":[],"lastModifiedDate":"2018-03-13T16:11:28","indexId":"70190286","displayToPublicDate":"2015-09-21T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2382,"text":"Journal of Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Hydro-bio-geomechanical properties of hydrate-bearing sediments from Nankai Trough","docAbstract":"Natural hydrate-bearing sediments from the Nankai Trough, offshore Japan, were studied using the Pressure Core Characterization Tools (PCCTs) to obtain geomechanical, hydrological, electrical, and biological properties under in situ pressure, temperature, and restored effective stress conditions. Measurement results, combined with index-property data and analytical physics-based models, provide unique insight into hydrate-bearing sediments in situ. Tested cores contain some silty-sands, but are predominantly sandy- and clayey-silts. Hydrate saturations Sh range from 0.15 to 0.74, with significant concentrations in the silty-sands. Wave velocity and flexible-wall permeameter measurements on never-depressurized pressure-core sediments suggest hydrates in the coarser-grained zones, the silty-sands where Sh exceeds 0.4, contribute to soil-skeletal stability and are load-bearing. In the sandy- and clayey-silts, where Sh < 0.4, the state of effective stress and stress history are significant factors determining sediment stiffness. Controlled depressurization tests show that hydrate dissociation occurs too quickly to maintain thermodynamic equilibrium, and pressure–temperature conditions track the hydrate stability boundary in pure-water, rather than that in seawater, in spite of both the in situ pore water and the water used to maintain specimen pore pressure prior to dissociation being saline. Hydrate dissociation accompanied with fines migration caused up to 2.4% vertical strain contraction. The first-ever direct shear measurements on never-depressurized pressure-core specimens show hydrate-bearing sediments have higher sediment strength and peak friction angle than post-dissociation sediments, but the residual friction angle remains the same in both cases. Permeability measurements made before and after hydrate dissociation demonstrate that water permeability increases after dissociation, but the gain is limited by the transition from hydrate saturation before dissociation to gas saturation after dissociation. In a proof-of-concept study, sediment microbial communities were successfully extracted and stored under high-pressure, anoxic conditions. Depressurized samples of these extractions were incubated in air, where microbes exhibited temperature-dependent growth rates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2015.02.033","usgsCitation":"Santamarina, J., Dai, S., Terzariol, M., Jang, J., Waite, W., Winters, W.J., Nagao, J., Yoneda, J., Konno, Y., Fujii, T., and Suzuki, K., 2015, Hydro-bio-geomechanical properties of hydrate-bearing sediments from Nankai Trough: Journal of Marine and Petroleum Geology, v. 66, no. 2, p. 434-450, https://doi.org/10.1016/j.marpetgeo.2015.02.033.","productDescription":"17 p.","startPage":"434","endPage":"450","ipdsId":"IP-062005","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471780,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpetgeo.2015.02.033","text":"Publisher Index Page"},{"id":345091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"66","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599e944ae4b04935557fe9dd","contributors":{"authors":[{"text":"Santamarina, J.C.","contributorId":50283,"corporation":false,"usgs":true,"family":"Santamarina","given":"J.C.","email":"","affiliations":[],"preferred":false,"id":708293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dai, Shifeng","contributorId":138922,"corporation":false,"usgs":false,"family":"Dai","given":"Shifeng","email":"","affiliations":[{"id":12582,"text":"State Key Laboratory of Coal Resources and Safe Mining, University of Mining and Technology, Beijing, People’s Republic of China","active":true,"usgs":false}],"preferred":false,"id":708294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terzariol, M.","contributorId":195811,"corporation":false,"usgs":false,"family":"Terzariol","given":"M.","email":"","affiliations":[],"preferred":false,"id":708295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jang, Jeonghwan","contributorId":190816,"corporation":false,"usgs":false,"family":"Jang","given":"Jeonghwan","email":"","affiliations":[],"preferred":false,"id":708296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":708292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winters, William J. bwinters@usgs.gov","contributorId":522,"corporation":false,"usgs":true,"family":"Winters","given":"William","email":"bwinters@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":708297,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nagao, J.","contributorId":195812,"corporation":false,"usgs":false,"family":"Nagao","given":"J.","email":"","affiliations":[],"preferred":false,"id":708298,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yoneda, J.","contributorId":195813,"corporation":false,"usgs":false,"family":"Yoneda","given":"J.","email":"","affiliations":[],"preferred":false,"id":708299,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Konno, Y.","contributorId":195814,"corporation":false,"usgs":false,"family":"Konno","given":"Y.","affiliations":[],"preferred":false,"id":708300,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fujii, T.","contributorId":195815,"corporation":false,"usgs":false,"family":"Fujii","given":"T.","email":"","affiliations":[],"preferred":false,"id":708301,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Suzuki, K.","contributorId":178737,"corporation":false,"usgs":false,"family":"Suzuki","given":"K.","email":"","affiliations":[],"preferred":false,"id":708302,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70157348,"text":"70157348 - 2015 - A comparison of estimates of basin-scale soil-moisture evapotranspiration and estimates of riparian groundwater evapotranspiration with implications for water budgets in the Verde Valley, Central Arizona, USA","interactions":[],"lastModifiedDate":"2022-11-03T14:59:40.000811","indexId":"70157348","displayToPublicDate":"2015-09-20T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of estimates of basin-scale soil-moisture evapotranspiration and estimates of riparian groundwater evapotranspiration with implications for water budgets in the Verde Valley, Central Arizona, USA","docAbstract":"Population growth in the Verde Valley in Arizona has led to efforts to better understand water availability in the watershed. Evapotranspiration (ET) is a substantial component of the water budget and a critical factor in estimating groundwater recharge in the area. In this study, four estimates of ET are compared and discussed with applications to the Verde Valley. Higher potential ET (PET) rates from the soil-water balance (SWB) recharge model resulted in an average annual ET volume about 17% greater than for ET from the basin characteristics (BCM) recharge model. Annual BCM PET volume, however, was greater by about a factor of 2 or more than SWB actual ET (AET) estimates, which are used in the SWB model to estimate groundwater recharge. ET also was estimated using a method that combines MODIS-EVI remote sensing data and geospatial information and by the MODFLOW-EVT ET package as part of a regional groundwater-flow model that includes the study area. Annual ET volumes were about same for upper-bound MODIS-EVI ET for perennial streams as for the MODFLOW ET estimates, with the small differences between the two methods having minimal impact on annual or longer groundwater budgets for the study area.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2015.09.005","usgsCitation":"Tillman, F.D., Wiele, S.M., and Pool, D.R., 2015, A comparison of estimates of basin-scale soil-moisture evapotranspiration and estimates of riparian groundwater evapotranspiration with implications for water budgets in the Verde Valley, Central Arizona, USA: Journal of Arid Environments, v. 124, p. 278-291, https://doi.org/10.1016/j.jaridenv.2015.09.005.","productDescription":"14 p.","startPage":"278","endPage":"291","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062528","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":471782,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2015.09.005","text":"Publisher Index Page"},{"id":308335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Verde Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.43291497881991,\n 35.18255355174023\n ],\n [\n -112.43291497881991,\n 34.55130008916785\n ],\n [\n -110.95736033278712,\n 34.55130008916785\n ],\n [\n -110.95736033278712,\n 35.18255355174023\n ],\n [\n -112.43291497881991,\n 35.18255355174023\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"124","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56012a34e4b03bc34f5443ea","chorus":{"doi":"10.1016/j.jaridenv.2015.09.005","url":"http://dx.doi.org/10.1016/j.jaridenv.2015.09.005","publisher":"Elsevier BV","authors":"Tillman F.D, Wiele S.M., Pool D.R.","journalName":"Journal of Arid Environments","publicationDate":"1/2016"},"contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":572773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiele, Stephen M. smwiele@usgs.gov","contributorId":2199,"corporation":false,"usgs":true,"family":"Wiele","given":"Stephen","email":"smwiele@usgs.gov","middleInitial":"M.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":572774,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pool, Donald R. drpool@usgs.gov","contributorId":1121,"corporation":false,"usgs":true,"family":"Pool","given":"Donald","email":"drpool@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":572775,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215738,"text":"70215738 - 2015 - Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River","interactions":[],"lastModifiedDate":"2020-10-28T12:55:07.626365","indexId":"70215738","displayToPublicDate":"2015-09-19T07:49:37","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7182,"text":"Standards in Genomic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River","docAbstract":"Knowledge of the diversity and ecological function of the microbial consortia of James River in Virginia, USA, is essential to developing a more complete understanding of the ecology of this model river system. Metagenomic analysis of James River's planktonic microbial community was performed for the first time using an unamplified genomic library and a 16S rDNA amplicon library prepared and sequenced by Ion PGM and MiSeq, respectively. From the 0.46-Gb WGS library (GenBank:SRR1146621; MG-RAST:4532156.3), 4 × 106 reads revealed >3 × 106 genes, 240 families of prokaryotes, and 155 families of eukaryotes. From the 0.68-Gb 16S library (GenBank:SRR2124995; MG-RAST:4631271.3; EMB:2184), 4 × 106 reads revealed 259 families of eubacteria. Results of the WGS and 16S analyses were highly consistent and indicated that more than half of the bacterial sequences were Proteobacteria, predominantly Comamonadaceae. The most numerous genera in this group were Acidovorax (including iron oxidizers, nitrotolulene degraders, and plant pathogens), which accounted for 10 % of assigned bacterial reads. Polaromonas were another 6 % of all bacterial reads, with many assignments to groups capable of degrading polycyclic aromatic hydrocarbons. Albidiferax (iron reducers) and Variovorax (biodegraders of a variety of natural biogenic compounds as well as anthropogenic contaminants such as polycyclic aromatic hydrocarbons and endocrine disruptors) each accounted for an additional 3 % of bacterial reads. Comparison of these data to other publically-available aquatic metagenomes revealed that this stretch of James River is highly similar to the upper Mississippi River, and that these river systems are more similar to aquaculture and sludge ecosystems than they are to lakes or to a pristine section of the upper Amazon River. Taken together, these analyses exposed previously unknown aspects of microbial biodiversity, documented the ecological responses of microbes to urban effects, and revealed the noteworthy presence of 22 human-pathogenic bacterial genera (e.g., Enterobacteriaceae, pathogenic Pseudomonadaceae, and ‘Vibrionales') and 6 pathogenic eukaryotic genera (e.g., Trypanosomatidae and Vahlkampfiidae). This information about pathogen diversity may be used to promote human epidemiological studies, enhance existing water quality monitoring efforts, and increase awareness of the possible health risks associated with recreational use of James River.
The water resources of Deep Creek Valley were assessed during 2012–13 with an emphasis on better understanding the groundwater flow system and groundwater budget. Surface-water resources are limited in Deep Creek Valley and are generally used for agriculture. Groundwater is the predominant water source for most other uses and to supplement irrigation. Most groundwater withdrawal in Deep Creek Valley occurs from the unconsolidated basin-fill deposits, in which conditions are generally unconfined near the mountain front and confined in the lower-altitude parts of the valley. Productive aquifers are also present in fractured bedrock that occurs along the valley margins and beneath the basin-fill deposits. The consolidated-rock and basin-fill aquifers are hydraulically connected in many areas with much of the recharge occurring in the consolidated-rock mountain blocks and most of the discharge occurring from the lower-altitude basin-fill deposits.
\nAverage annual recharge to the Deep Creek Valley hydrographic area was estimated to be between 19,000 and 29,000 acre-feet. Groundwater recharge occurs mostly from the infiltration of precipitation and snowmelt at high altitudes. Additional, but limited recharge occurs from the infiltration of runoff from precipitation near the mountain front, infiltration along stream channels, and possible subsurface inflow from adjacent hydrographic areas. Groundwater moves from areas of recharge to springs and streams in the mountains, and to evapotranspiration areas, springs, streams, and wells in the basins. Discharge may also occur as subsurface groundwater outflow to adjacent hydrographic areas. Average annual discharge from the Deep Creek Valley hydrographic area was estimated to be between 21,000 and 22,000 acre-feet, with the largest portion of discharge occurring as evapotranspiration.
\nGroundwater samples were collected from 10 sites for geochemical analysis. Dissolved-solids concentrations ranged from 126 to 475 milligrams per liter, and none of the sites sampled during this study had dissolved-solids concentrations that exceeded the Environmental Protection Agency secondary standard for drinking water of 500 milligrams per liter. Tritium concentrations from 1.6 to 10.1 tritium units at 3 of the 10 sample sites indicate the presence of modern (less than 60 years old) groundwater, and apparent tritium/helium-3 ages calculated for these sites ranged from 7 to 29 years. The other seven sample sites had tritium concentrations less than or equal to 0.4 tritium units and are assumed to be pre-modern. Adjusted minimum radiocarbon ages of these seven pre-modern water samples ranged from 1,000 to 8,000 years with the ages of at least four of the samples being more than 3,000 years. Noble-gas recharge temperatures indicate that groundwater sampled along the valley axis recharged at both mountain and valley altitudes, providing evidence for both mountain-block and mountain-front recharge.
\nWater-level altitude contours and groundwater ages indicate the potential for a long flow path from southwest to northeast between northern Spring and Deep Creek Valleys through Tippett Valley. Although information gathered during this study is insufficient to conclude whether or not groundwater travels along this interbasin flow path, dissolved sulfate and chloride data indicate that a small fraction of the lower altitude, northern Deep Creek Valley discharge may be sourced from these areas. Despite the uncertainty due to limited data collection points, a hydraulic connection between northern Spring Valley, Tippett Valley, and Deep Creek Valley appears likely, and potential regional effects resulting from future groundwater withdrawals in northern Spring Valley warrant ongoing monitoring of groundwater levels across this area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155097","collaboration":"Prepared in cooperation with the Bureau of Indian Affairs","usgsCitation":"Gardner, P.M., and Masbruch, M.D., 2015, Hydrogeologic and geochemical characterization of groundwater resources in Deep Creek Valley and adjacent areas, Juab and Tooele Counties, Utah, and Elko and White Pine Counties, Nevada: U.S. Geological Survey Scientific Investigations Report 2015–5097, 53 p., https://dx.doi.org/10.3133/sir20155097.","productDescription":"viii, 54 p.","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-037371","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":308275,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5097/coverthb.jpg"},{"id":308276,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5097/sir20155097.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5097 PDF"}],"country":"United States","state":"Nevada, Utah","county":"Elko County, Juab County, Tooele County, White Pine County","otherGeospatial":"Deep Creek Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.59564208984374,\n 39.35766163717121\n ],\n [\n -114.59564208984374,\n 40.49918094806632\n ],\n [\n -113.72222900390625,\n 40.49918094806632\n ],\n [\n -113.72222900390625,\n 39.35766163717121\n ],\n [\n -114.59564208984374,\n 39.35766163717121\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Utah Water Science Center
U.S. Geological Survey
2329 Orton Circle
Salt Lake City, Utah 84119-2047
http://ut.water.usgs.gov
High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV <15%). Satellite imagery may be an effective monitoring tool in certain areas, but large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.596","usgsCitation":"LaRue, M.A., Stapleton, S.P., Porter, C., Atkinson, S.N., Atwood, T.C., Dyck, M., and Lecomte, N., 2015, Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution: Wildlife Society Bulletin, v. 39, no. 4, p. 772-779, https://doi.org/10.1002/wsb.596.","productDescription":"7 p.","startPage":"772","endPage":"779","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063293","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":500053,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/52601c9e182c489182032129672f748f","text":"External Repository"},{"id":326931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"Rowley Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.44238281249999,\n 69.21525256928653\n ],\n [\n -78.3544921875,\n 69.18404149599671\n ],\n [\n -78.2391357421875,\n 69.2425255645653\n ],\n [\n -78.1842041015625,\n 69.3086176331298\n ],\n [\n -78.1787109375,\n 69.35127582255352\n ],\n [\n -78.2830810546875,\n 69.3899830007604\n ],\n [\n -78.44238281249999,\n 69.39964894620636\n ],\n [\n -78.519287109375,\n 69.40931055535233\n ],\n [\n -78.64562988281249,\n 69.39191653683379\n ],\n [\n -78.7884521484375,\n 69.34740128319982\n ],\n [\n -78.717041015625,\n 69.29114227947211\n ],\n [\n -78.782958984375,\n 69.26976435077849\n ],\n [\n -78.8818359375,\n 69.20940390181205\n ],\n [\n -78.9312744140625,\n 69.15083065920365\n ],\n [\n -78.9422607421875,\n 69.12344255014861\n ],\n [\n -79.0411376953125,\n 69.13518451752405\n ],\n [\n -79.07958984375,\n 69.12148494192485\n ],\n [\n -79.21142578125,\n 69.12148494192485\n ],\n [\n -79.43115234375,\n 68.932736060549\n ],\n [\n -79.4146728515625,\n 68.83180177092166\n ],\n [\n -79.189453125,\n 68.81394200526994\n ],\n [\n -78.9093017578125,\n 68.87341871078524\n ],\n [\n -78.760986328125,\n 68.932736060549\n ],\n [\n -78.6016845703125,\n 69.03321086259241\n ],\n [\n -78.49731445312499,\n 69.08621799303475\n ],\n [\n -78.49731445312499,\n 69.12735724054714\n ],\n [\n -78.4259033203125,\n 69.17037257214531\n ],\n [\n -78.44238281249999,\n 69.21525256928653\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-18","publicationStatus":"PW","scienceBaseUri":"57b82dece4b03fd6b7da3a22","contributors":{"authors":[{"text":"LaRue, Michelle A.","contributorId":20634,"corporation":false,"usgs":true,"family":"LaRue","given":"Michelle","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":646378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stapleton, Seth P. sstapleton@usgs.gov","contributorId":3979,"corporation":false,"usgs":true,"family":"Stapleton","given":"Seth","email":"sstapleton@usgs.gov","middleInitial":"P.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":646379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porter, Claire","contributorId":131120,"corporation":false,"usgs":false,"family":"Porter","given":"Claire","email":"","affiliations":[],"preferred":false,"id":646380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atkinson, Stephen N.","contributorId":12365,"corporation":false,"usgs":false,"family":"Atkinson","given":"Stephen","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":646381,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":646343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dyck, Markus","contributorId":173868,"corporation":false,"usgs":false,"family":"Dyck","given":"Markus","affiliations":[],"preferred":false,"id":646382,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lecomte, Nicolas","contributorId":131119,"corporation":false,"usgs":false,"family":"Lecomte","given":"Nicolas","email":"","affiliations":[],"preferred":false,"id":646383,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70155178,"text":"tm7C12 - 2015 - Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models","interactions":[],"lastModifiedDate":"2017-06-06T11:25:48","indexId":"tm7C12","displayToPublicDate":"2015-09-18T12:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C12","title":"Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models","docAbstract":"The PEST++ Version 1 object-oriented parameter estimation code is here extended to Version 3 to incorporate additional algorithms and tools to further improve support for large and complex environmental modeling problems. PEST++ Version 3 includes the Gauss-Marquardt-Levenberg (GML) algorithm for nonlinear parameter estimation, Tikhonov regularization, integrated linear-based uncertainty quantification, options of integrated TCP/IP based parallel run management or external independent run management by use of a Version 2 update of the GENIE Version 1 software code, and utilities for global sensitivity analyses. The Version 3 code design is consistent with PEST++ Version 1 and continues to be designed to lower the barriers of entry for users as well as developers while providing efficient and optimized algorithms capable of accommodating large, highly parameterized inverse problems. As such, this effort continues the original focus of (1) implementing the most popular and powerful features of the PEST software suite in a fashion that is easy for novice or experienced modelers to use and (2) developing a software framework that is easy to extend.
\nThe PEST++ Version 3 software suite can be compiled for Microsoft Windows®4 and Linux®5 operating systems; the source code is available in a Microsoft Visual Studio®6 2013 solution; Linux Makefiles are also provided. PEST++ Version 3 continues to build a foundation for an open-source framework capable of producing robust and efficient parameter estimation tools for large environmental models.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7 Automated Data Processing and Computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C12","collaboration":"Prepared in cooperation with U.S. Environmental Protection Agency,Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, Wisconsin 53562-3586
http://wi.water.usgs.gov/
A detailed inventory of irrigated crop acreage is not available at the level of resolution needed to accurately estimate water use or to project future water demands in many Florida counties. This report provides a detailed digital map and summary of irrigated areas for 2014 within Jackson, Calhoun, and Gadsden Counties in Florida, and Houston County in Alabama. The irrigated areas were delineated using land-use data and orthoimagery that were then field verified between June and November 2014. Selected attribute data were collected for the irrigated areas, including crop type, primary water source, and type of irrigation system. Results of the 2014 study indicate that an estimated 31,608 acres were irrigated in Jackson County during 2014. This estimate includes 25,733 acres of field crops, 1,534 acres of ornamentals and grasses (including pasture), and 420 acres of orchards. Specific irrigated crops include cotton (11,759 acres), peanuts (9,909 acres), field corn (2,444 acres), and 3,235 acres of various vegetable (row) crops. The vegetable acreage includes 1,714 acres of which 857 acres were planted with both a spring and fall crop on the same field (double cropped). Overall, groundwater was used to irrigate 98.6 percent of the total irrigated acreage in Jackson County during 2014, whereas surface water and wastewater were used to irrigate the remaining 1.4 percent.
\nIrrigated cropland totaled 3,060 acres in Calhoun County; 4,547 acres in Gadsden County; and 10,333 acres in Houston County. In Calhoun County, sod accounted for the largest irrigated acreage (1,145 acres) followed by peanuts (886 acres). In Gadsden County, ornamentals accounted for the largest irrigated acreage (1,104 acres) followed by cotton (977 acres). In Houston County, cotton accounted for the largest irrigated acreage (4,310 acres) followed by peanuts (2,493 acres). Overall, an estimated 49,548 acres of land were irrigated during 2014 in the four counties inventoried. About 45,052 acres were irrigated by a center pivot, permanent or solid overhead fixtures, or a portable or traveling gun. In all, 650 center pivot irrigation systems were identified, and the calculated acreage under these pivots totaled 43,070 acres. There were 405 center pivot irrigation systems counted in Jackson County during the 2014 field verification followed by Houston with 197, Gadsden with 48, and Calhoun with 10. An estimated 35,087 acres of field corn, cotton, peanuts, and sorghum were irrigated by center pivot systems during 2014 in these four counties combined. Vegetable acreage for the four counties combined totaled 6,699 acres, with 54 percent being irrigated by a drip irrigation system and the remaining 46 percent irrigated by a center pivot or traveling gun.
\nThe irrigated acreage estimated for Jackson County in 2014 (31,608) is about 47 percent higher than the 2012 estimated acreage published by the USDA (21,508 acres). The estimates of irrigated acreage field verified during 2014 for Calhoun and Gadsden Counties are also higher than those published by the USDA for 2012 (86 percent and 71 percent, respectively). In Calhoun County the USDA reported 1,647 irrigated acres while the current study estimated 3,060 acres, and in Gadsden County the USDA reported 2,650 acres while the current study estimated 4,547 acres. For Houston County the USDA-reported value of 9,138 acres in 2012 was 13 percent below the 10,333 acres field verified in the current study. Differences between the USDA 2012 values and 2014 field verified estimates in these two datasets may occur because (1) irrigated acreage for some specific crops increased or decreased substantially during the 2-year interval due to commodity prices or economic changes, (2) irrigated acreage calculated for the current study may be estimated high because irrigation was assumed if an irrigation system was present and therefore the acreage was counted as irrigated, when in fact that may not have been the case as some farmers may not have used their irrigation systems during this growing period even if they had a crop in the field, or (3) the amount of irrigated acreages published by the USDA for selected crops may be underestimated in some cases.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151170","collaboration":"Prepared in cooperation with the Florida Department of Agriculture and Consumer Services","usgsCitation":"Marella, R.L., and Dixon, J.F., 2015, Agricultural irrigated land-use inventory for Jackson, Calhoun, and Gadsden Counties in Florida, and Houston County in Alabama, 2014: U.S. Geological Survey Open-File Report 2015–1170, 14 p., https://dx.doi.org/10.3133/ofr20151170.","productDescription":"Report: 14 p.; Appendix; GIS Shape Files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062523","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":308269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1170/coverthb.jpg"},{"id":308270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1170/ofr20151170.pdf","text":"Report","size":"2.78 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Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
http://fl.water.usgs.gov
Historically, the city of Palmdale and vicinity have relied on groundwater as the primary source of water, owing, in large part, to the scarcity of surface water in the region. Despite recent importing of surface water, groundwater withdrawal for municipal, industrial, and agricultural use has resulted in groundwater-level declines near the city of Palmdale in excess of 200 feet since the early 1900s. To meet the growing water demand in the area, the city of Palmdale has proposed the Amargosa Creek Recharge Project (ACRP), which has a footprint of about 150 acres along the Amargosa Creek 2 miles west of Palmdale, California. The objective of this study was to evaluate the long-term feasibility of recharging the Antelope Valley aquifer system by using infiltration of imported surface water from the California State Water Project in percolation basins at the ACRP.
\nThree monitoring sites were constructed, and geophysical surveys (gravity, seismic, and resistivity) were completed to define the thickness of valley-fill deposits, depth to water, and location of faults that could influence groundwater flow. Data collected at the monitoring sites, and results from the geophysical surveys, were used to identify three northwest-southeast trending faults in the vicinity of the proposed recharge facility; these faults are probably related to the nearby San Andreas fault zone. Water levels collected from wells at the monitoring sites showed water-level altitude differences as much as 230 feet between the upgradient and downgradient sides of the faults, indicating that these faults are barriers to groundwater flow. Lithologic and geophysical logs indicated the presence of a coarse gravel and sand unit extending from land surface to about 150 feet below land surface that did not appear to be disrupted by faulting.
\nWater samples collected from the monitoring wells were analyzed for major ions, nutrients, trace elements, dissolved organic carbon, volatile organic compounds, stable isotopes of oxygen (oxygen-18) and hydrogen (hydrogen-2, or deuterium), and the radioactive isotopes of hydrogen (hydrogen-3, or tritium) and carbon (carbon-14, or 14C) to determine the water quality of the aquifer system and to help determine the source and age of the groundwater. Results of the water-quality analysis indicated that the source of natural recharge is Amargosa Creek near the ACRP, but that the creek does not provide modern-day recharge downstream of the ACRP.
\nPotential effects of artificial recharge at the ACRP were evaluated by using a local-scale model of groundwater flow. On the basis of geologic samples collected during drilling, the hydraulic conductivity of the sand and gravel unit in the upper 150 feet was assumed to range from 10 to 100 feet per day. To address the goal of minimizing the potential for liquefaction during an earthquake from water-table rise associated with groundwater recharge at the ACRP, simulated water levels were constrained to remain at least 50 feet below land surface, except beneath the proposed artificial-recharge facility.
\nThe hydraulic conductivities of faults were estimated on the basis of water-level data and an estimate of natural recharge along Amargosa Creek. With assumed horizontal hydraulic conductivities of 10 and 100 feet per day in the upper 150 feet, the simulated maximum artificial recharge rates to the regional flow system at the ACRP were 3,400 and 9,400 acre-feet per year, respectively. These maximum recharge rates were limited primarily by the horizontal hydraulic conductivity in the upper 150 feet and by the liquefaction constraint. Future monitoring of water-level and soil-water content changes during the proposed project would allow improved estimation of aquifer hydraulic properties, the effect of the faults on groundwater movement, and the overall recharge capacity of the ACRP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155054","collaboration":"Prepared in cooperation with the city of Palmdale, California","usgsCitation":"Christensen, A.H., Siade, A.J., Martin, Peter, Langeheim, V.E., Catchings, R.D., and Burgess, M.K., 2015, Feasibility and potential effects of the proposed Amargosa Creek recharge project, Palmdale, California: U.S. Geological Survey Scientific Investigations Report 2015–5054, 48 p., https://dx.doi.org/10.3133/SIR20155054.","productDescription":"viii, 48 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-029364","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":307894,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5054/sir20155054.pdf","text":"Report","size":"24.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5054"},{"id":307893,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5054/coverthb.jpg"}],"country":"United States","state":"California","city":"Palmdale","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.58779907226561,\n 34.41710628141647\n ],\n [\n -118.58779907226561,\n 34.813803317113155\n ],\n [\n -117.73635864257812,\n 34.813803317113155\n ],\n [\n -117.73635864257812,\n 34.41710628141647\n ],\n [\n -118.58779907226561,\n 34.41710628141647\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95829
http://ca.water.usgs.gov
The database in this Open-File Report describes the geology of Joshua Tree National Park and was completed in support of the National Cooperative Geologic Mapping Program of the U.S. Geological Survey (USGS) and in cooperation with the National Park Service (NPS). The geologic observations and interpretations represented in the database are relevant to both the ongoing scientific interests of the USGS in southern California and the management requirements of NPS, specifically of Joshua Tree National Park (JOTR).
Joshua Tree National Park is situated within the eastern part of California’s Transverse Ranges province and straddles the transition between the Mojave and Sonoran deserts. The geologically diverse terrain that underlies JOTR reveals a rich and varied geologic evolution, one that spans nearly two billion years of Earth history. The Park’s landscape is the current expression of this evolution, its varied landforms reflecting the differing origins of underlying rock types and their differing responses to subsequent geologic events. Crystalline basement in the Park consists of Proterozoic plutonic and metamorphic rocks intruded by a composite Mesozoic batholith of Triassic through Late Cretaceous plutons arrayed in northwest-trending lithodemic belts. The basement was exhumed during the Cenozoic and underwent differential deep weathering beneath a low-relief erosion surface, with the deepest weathering profiles forming on quartz-rich, biotite-bearing granitoid rocks. Disruption of the basement terrain by faults of the San Andreas system began ca. 20 Ma and the JOTR sinistral domain, preceded by basalt eruptions, began perhaps as early as ca. 7 Ma, but no later than 5 Ma. Uplift of the mountain blocks during this interval led to erosional stripping of the thick zones of weathered quartz-rich granitoid rocks to form etchplains dotted by bouldery tors—the iconic landscape of the Park. The stripped debris filled basins along the fault zones.
Mountain ranges and basins in the Park exhibit an east-west physiographic grain controlled by left-lateral fault zones that form a sinistral domain within the broad zone of dextral shear along the transform boundary between the North American and Pacific plates. Geologic and geophysical evidence reveal that movement on the sinistral faults zones has resulted in left steps along the zones, resulting in the development of sub-basins beneath Pinto Basin and Shavers and Chuckwalla Valleys. The sinistral fault zones connect the Mojave Desert dextral faults of the Eastern California Shear Zone to the north and east with the Coachella Valley strands of the southern San Andreas Fault Zone to the west.
Quaternary surficial deposits accumulated in alluvial washes and playas and lakes along the valley floors; in alluvial fans, washes, and sheet wash aprons along piedmonts flanking the mountain ranges; and in eolian dunes and sand sheets that span the transition from valley floor to piedmont slope. Sequences of Quaternary pediments are planed into piedmonts flanking valley-floor and upland basins, each pediment in turn overlain by successively younger residual and alluvial surficial deposits.
GMEG staff, Geology, Minerals, Energy, & Geophysics Science Center—Tucson, Arizona
U.S. Geological Survey, c/o University of Arizona
ENRB Bldg, 520 N. Park Ave, Rm 355
Tucson, Arizona 85719-5035
http://geomaps.wr.usgs.gov/
Accurate estimates of species richness are necessary to test predictions of ecological theory and evaluate biodiversity for conservation purposes. However, species richness is difficult to measure in the field because some species will almost always be overlooked due to their cryptic nature or the observer's failure to perceive their cues. Common measures of species richness that assume consistent observability across species are inviting because they may require only single counts of species at survey sites. Single-visit estimation methods ignore spatial and temporal variation in species detection probabilities related to survey or site conditions that may confound estimates of species richness. We used simulated and empirical data to evaluate the bias and precision of raw species counts, the limiting forms of jackknife and Chao estimators, and multi-species occupancy models when estimating species richness to evaluate whether the choice of estimator can affect inferences about the relationships between environmental conditions and community size under variable detection processes. Four simulated scenarios with realistic and variable detection processes were considered. Results of simulations indicated that (1) raw species counts were always biased low, (2) single-visit jackknife and Chao estimators were significantly biased regardless of detection process, (3) multispecies occupancy models were more precise and generally less biased than the jackknife and Chao estimators, and (4) spatial heterogeneity resulting from the effects of a site covariate on species detection probabilities had significant impacts on the inferred relationships between species richness and a spatially explicit environmental condition. For a real dataset of bird observations in northwestern Alaska, the four estimation methods produced different estimates of local species richness, which severely affected inferences about the effects of shrubs on local avian richness. Overall, our results indicate that neglecting the effects of site covariates on species detection probabilities may lead to significant bias in estimation of species richness, as well as the inferred relationships between community size and environmental covariates.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/14-1248.1","collaboration":"Colleen Handel","usgsCitation":"McNew, L.B., and Handel, C.M., 2015, Evaluating species richness: biased ecological inference results from spatial heterogeneity in species detection probabilities: Ecological Applications, v. 25, no. 6, p. 1669-1680, https://doi.org/10.1890/14-1248.1.","productDescription":"12 p.","startPage":"1669","endPage":"1680","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056170","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":438682,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7F18WS3","text":"USGS data release","linkHelpText":"Avian Habitat Data; Seward Peninsula, Alaska, 2012"},{"id":438681,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JS9NG2","text":"USGS data release","linkHelpText":"Avian Point Transect Survey, Seward Peninsula, Alaska, 2012"},{"id":308145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55fa8499e4b05d6c4e501a21","contributors":{"authors":[{"text":"McNew, Lance B. lmcnew@usgs.gov","contributorId":5086,"corporation":false,"usgs":true,"family":"McNew","given":"Lance","email":"lmcnew@usgs.gov","middleInitial":"B.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":572453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Handel, Colleen M. 0000-0002-0267-7408 cmhandel@usgs.gov","orcid":"https://orcid.org/0000-0002-0267-7408","contributorId":3067,"corporation":false,"usgs":true,"family":"Handel","given":"Colleen","email":"cmhandel@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":572454,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156981,"text":"ofr20151169 - 2015 - Sedimentological and radiochemical characteristics of marsh deposits from Assateague Island and the adjacent vicinity, Maryland and Virginia, following Hurricane Sandy","interactions":[],"lastModifiedDate":"2025-05-13T16:53:28.235417","indexId":"ofr20151169","displayToPublicDate":"2015-09-15T16:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1169","title":"Sedimentological and radiochemical characteristics of marsh deposits from Assateague Island and the adjacent vicinity, Maryland and Virginia, following Hurricane Sandy","docAbstract":"The effect of tropical and extratropical cyclones on coastal wetlands and marshes is highly variable and depends on a number of climatic, geologic, and physical variables. The impacts of storms can be either positive or negative with respect to the wetland and marsh ecosystems. Small to moderate amounts of inorganic sediment added to the marsh surface during storms or other events help to abate pressure from sea-level rise. However, if the volume of sediment is large and the resulting deposits are thick, the organic substrate may compact causing submergence and a loss in elevation. Similarly, thick deposits of coarse inorganic sediment may also alter the hydrology of the site and impede vegetative processes. Alternative impacts associated with storms include shoreline erosion at the marsh edge as well as potential emergence. Evaluating the outcome of these various responses and potential long-term implications is possible from a systematic assessment of both historical and recent event deposits. A study was conducted by the U.S. Geological Survey to assess the sedimentological and radiochemical characteristics of marsh deposits from Assateague Island and areas around Chincoteague Bay, Maryland and Virginia, following Hurricane Sandy in 2012. The objectives of this study were to (1) characterize the surficial sediment of the relict to recent washover fans and back-barrier marshes in the study area, and (2) characterize the sediment of six marsh cores from the back-barrier marshes and a single marsh island core near the mainland. These geologic data will be integrated with other remote sensing data collected along Assateague Island in Maryland and Virginia and assimilated into an assessment of coastal wetland response to storms.
\nThis report serves as an archive for sedimentological and radiochemical data derived from the surface sediments and marsh cores collected March 26–April 4, 2014. Select surficial data are available for the additional sampling periods October 21–30, 2014. Downloadable data are available as Excel spreadsheets and as JPEG files. Additional files include: Field documentation, x-radiographs, photographs, detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee metadata (data downloads).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151169","usgsCitation":"Smith, C.G., Marot, M.E., Ellis, A.M., Wheaton, C.J., Bernier, J.C., and Adams, C.S., 2015, Sedimentological and radiochemical characteristics of marsh deposits from Assateague Island and the adjacent vicinity, Maryland and Virginia, following Hurricane Sandy: U.S. Geological Survey Open-File Report 2015–1169, https://dx.doi.org/10.3133/ofr20151169.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-03-26","temporalEnd":"2014-10-30","ipdsId":"IP-065781","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":308090,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1169/index.html","text":"Report (HTML)","description":"OFR 2015-1169"},{"id":308089,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1169/images/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4815673828125,\n 37.82931081282506\n ],\n [\n -75.4815673828125,\n 38.447135775082444\n ],\n [\n -75.02838134765625,\n 38.447135775082444\n ],\n [\n -75.02838134765625,\n 37.82931081282506\n ],\n [\n -75.4815673828125,\n 37.82931081282506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 Fourth Street South
St. Petersburg, FL 33701-4846
(727) 502-8000
http://coastal.er.usgs.gov/