Management decisions for species impacted by emerging infectious diseases are challenging when there are uncertainties in the effectiveness of management actions. Wildlife managers must balance trade‐offs between mitigating the effects of the disease and the associated consequences on other aspects of the managed system. An example of this challenge is exemplified in the response to white‐nose syndrome (WNS), a disease of hibernating bats. The fungal pathogen that causes WNS, Pseudogymnoascus destructans, continues to spread throughout North America. Texas, recently confirmed positive for the fungus, has documented 33 bat species in the state, with nearly half of those species naïve to the pathogen. We explicitly incorporated multiple management objectives, uncertainty, and risk in the Texas Parks and Wildlife Department decision to manage East Texas populations of the tri‐colored bat (Perimyotis subflavus), a species highly susceptible to WNS. Alternatives included individual actions that act against P. destructans or benefit bats, a no active management option, and combinations of actions. Although our main objective was to identify WNS mitigation measures for tri‐colored bats in culverts, we also considered the transferability of the decision for natural caves. In this scenario, the optimal decision differed for culverts and caves, with a “portfolio” combination of actions ranking as the best alternative for culverts and a single vaccine alternative for caves. Because the top management alternatives differed markedly between these two systems, finding treatments that have broad application is likely infeasible, given that each management decision is characterized by different mixtures of competing objectives.
","language":"English","publisher":"Wiley","doi":"10.1111/csp2.106","usgsCitation":"Bernard, R.F., Evans, J., Fuller, N.W., Reichard, J., Coleman, J., Kocer, C.J., and Campbell Grant, E.H., 2020, Different management strategies are optimal for combating disease in East Texas cave versus culvert hibernating bat populations: Conservation Science and Practice, v. 1, no. 10, e106, 14 p., https://doi.org/10.1111/csp2.106.","productDescription":"e106, 14 p.","ipdsId":"IP-106228","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.106","text":"Publisher Index Page"},{"id":377717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"East Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.0966796875,\n 27.293689224852407\n ],\n [\n -93.8671875,\n 27.293689224852407\n ],\n [\n -93.8671875,\n 34.23451236236987\n ],\n [\n -99.0966796875,\n 34.23451236236987\n ],\n [\n -99.0966796875,\n 27.293689224852407\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"10","noUsgsAuthors":false,"publicationDate":"2019-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Bernard, Riley Fehr","contributorId":238969,"corporation":false,"usgs":true,"family":"Bernard","given":"Riley","email":"","middleInitial":"Fehr","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Jonah","contributorId":239062,"corporation":false,"usgs":false,"family":"Evans","given":"Jonah","email":"","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":796997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Nathan W.","contributorId":239065,"corporation":false,"usgs":false,"family":"Fuller","given":"Nathan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":796998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reichard, Jonathan D.","contributorId":138946,"corporation":false,"usgs":false,"family":"Reichard","given":"Jonathan D.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":796999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coleman, Jeremy T. H.","contributorId":138948,"corporation":false,"usgs":false,"family":"Coleman","given":"Jeremy T. H.","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":797000,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kocer, Christina J.","contributorId":239067,"corporation":false,"usgs":false,"family":"Kocer","given":"Christina","email":"","middleInitial":"J.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":797001,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796957,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210831,"text":"70210831 - 2020 - Connectivity in the Crown: Highway 2 wildlife crossings","interactions":[],"lastModifiedDate":"2020-06-30T11:59:57.206245","indexId":"70210831","displayToPublicDate":"2019-07-17T09:58:34","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Connectivity in the Crown: Highway 2 wildlife crossings","docAbstract":"This report summarizes data collected to inform decisions on how to best mitigate the effects on wildlife migration from increasing traffic, development, and recreation along US highway 2. The highway, railway, and river split the Crown of the Continent Ecosystem. This data addresses SO 3362 by providing information on major wildlife trails, observed wildilfe crossings and road kills, and identifying the elk, deer, and other animals that use the areas near 6 potential highway crossing structure locations. \n\nThis effort resulted in 621 wildlife observations of 26 species collected from hundreds of interactions with employees and the public, 31 businesses visited, and 11 events held or attended. We mapped 230 previously unrecorded wildlife trails between West Glacier and Columbia Falls and measured and photographed 390 culverts between East Glacier and Columbia Falls. We installed 12 trail cameras that captured 9248 wildlife images comprised of 12 species.","language":"English","publisher":"NPS","collaboration":"National Park Service (GNP), USFS, Montana DOT, Montana FWP, University of Montana","usgsCitation":"Waller, J.S., Graves, T., Anderson, B., Kittson, B., and Gaulke, S.M., 2020, Connectivity in the Crown: Highway 2 wildlife crossings, 37 p.","productDescription":"37 p.","startPage":"1","endPage":"37","ipdsId":"IP-117730","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":375972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375960,"type":{"id":15,"text":"Index Page"},"url":"https://npshistory.com/publications/glac/hwy-2-wildlife-crossings-2019.pdf"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park, Highway 2","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.0435791015625,\n 48.268569112964336\n ],\n [\n -113.09326171875,\n 48.268569112964336\n ],\n [\n -113.09326171875,\n 48.56388521347092\n ],\n [\n -114.0435791015625,\n 48.56388521347092\n ],\n [\n -114.0435791015625,\n 48.268569112964336\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waller, John S.","contributorId":167055,"corporation":false,"usgs":false,"family":"Waller","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":16272,"text":"National Park Service, Glacier National Park, West Glacier, MT","active":true,"usgs":false}],"preferred":false,"id":791629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":791628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Brad","contributorId":225562,"corporation":false,"usgs":false,"family":"Anderson","given":"Brad","email":"","affiliations":[{"id":41162,"text":"Glacier National Park Conservancy","active":true,"usgs":false}],"preferred":false,"id":791630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kittson, Brandon","contributorId":225563,"corporation":false,"usgs":false,"family":"Kittson","given":"Brandon","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":791631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gaulke, Sarah Mccrimmon 0000-0002-2657-5844","orcid":"https://orcid.org/0000-0002-2657-5844","contributorId":225564,"corporation":false,"usgs":true,"family":"Gaulke","given":"Sarah","email":"","middleInitial":"Mccrimmon","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":791632,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215408,"text":"70215408 - 2020 - Shear velocity structure from ambient noise and teleseismic surface wave tomography in the Cascades around Mount St. Helens","interactions":[],"lastModifiedDate":"2020-10-19T14:02:32.671431","indexId":"70215408","displayToPublicDate":"2019-07-16T08:53:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Shear velocity structure from ambient noise and teleseismic surface wave tomography in the Cascades around Mount St. Helens","docAbstract":"Mount St. Helens (MSH) lies in the forearc of the Cascades where conditions should be too cold for volcanism. To better understand thermal conditions and magma pathways beneath MSH, data from a dense broadband array are used to produce high‐resolution tomographic images of the crust and upper mantle. Rayleigh‐wave phase‐velocity maps and three‐dimensional images of shear velocity (Vs), generated from ambient noise and earthquake surface waves, show that west of MSH the middle‐lower crust is anomalously fast (3.95 ± 0.1 km/s), overlying an anomalously slow uppermost mantle (4.0–4.2 km/s). This combination renders the forearc Moho weak to invisible, with crustal velocity variations being a primary cause; fast crust is necessary to explain the absent Moho. Comparison with predicted rock velocities indicates that the fast crust likely consists of gabbros and basalts of the Siletzia terrane, an accreted oceanic plateau. East of MSH where magmatism is abundant, middle‐lower crust Vs is low (3.45–3.6 km/s), consistent with hot and potentially partly molten crust of more intermediate to felsic composition. This crust overlies mantle with more typical wave speeds, producing a strong Moho. The sharp boundary in crust and mantle Vs within a few kilometers of the MSH edifice correlates with a sharp boundary from low heat flow in the forearc to high arc heat flow and demonstrates that the crustal terrane boundary here couples with thermal structure to focus lateral melt transport from the lower crust westward to arc volcanoes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB017836","usgsCitation":"Crosbie, K., Abers, G.A., Mann, M.E., Janiszewski, H.A., Creager, K.C., Ulberg, C.W., and Moran, S.C., 2020, Shear velocity structure from ambient noise and teleseismic surface wave tomography in the Cascades around Mount St. Helens: Journal of Geophysical Research, v. 124, no. 8, p. 8358-8375, https://doi.org/10.1029/2019JB017836.","productDescription":"18 p.","startPage":"8358","endPage":"8375","ipdsId":"IP-107648","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":458727,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jb017836","text":"Publisher Index Page"},{"id":379510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mt. St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.3876953125,\n 46.02176059146292\n ],\n [\n -121.95098876953125,\n 46.02176059146292\n ],\n [\n -121.95098876953125,\n 46.326068311712596\n ],\n [\n -122.3876953125,\n 46.326068311712596\n ],\n [\n -122.3876953125,\n 46.02176059146292\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"8","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Crosbie, Kayla","contributorId":243333,"corporation":false,"usgs":false,"family":"Crosbie","given":"Kayla","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":802074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abers, Geoff A. 0000-0003-0704-2097","orcid":"https://orcid.org/0000-0003-0704-2097","contributorId":243334,"corporation":false,"usgs":false,"family":"Abers","given":"Geoff","email":"","middleInitial":"A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":802075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mann, Michael Everett 0000-0001-8418-078X","orcid":"https://orcid.org/0000-0001-8418-078X","contributorId":243335,"corporation":false,"usgs":false,"family":"Mann","given":"Michael","email":"","middleInitial":"Everett","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":802076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janiszewski, Helen A. 0000-0003-1425-7969","orcid":"https://orcid.org/0000-0003-1425-7969","contributorId":243336,"corporation":false,"usgs":false,"family":"Janiszewski","given":"Helen","email":"","middleInitial":"A.","affiliations":[{"id":30217,"text":"Carnegie Institution for Science","active":true,"usgs":false}],"preferred":false,"id":802077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Creager, Kenneth C 0000-0003-4501-7415","orcid":"https://orcid.org/0000-0003-4501-7415","contributorId":221910,"corporation":false,"usgs":false,"family":"Creager","given":"Kenneth","email":"","middleInitial":"C","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":802078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ulberg, Carl W 0000-0001-6198-809X","orcid":"https://orcid.org/0000-0001-6198-809X","contributorId":221909,"corporation":false,"usgs":false,"family":"Ulberg","given":"Carl","email":"","middleInitial":"W","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":802079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":224629,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802080,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204069,"text":"70204069 - 2020 - Sedimentary evidence of prehistoric distant-source tsunamis in the Hawaiian Islands","interactions":[],"lastModifiedDate":"2020-05-04T17:28:04.368546","indexId":"70204069","displayToPublicDate":"2019-04-29T12:20:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3369,"text":"Sedimentology","active":true,"publicationSubtype":{"id":10}},"title":"Sedimentary evidence of prehistoric distant-source tsunamis in the Hawaiian Islands","docAbstract":"Over the past 200 years of written records, the Hawaiian Islands have experienced tens of tsunamis generated by earthquakes in the subduction zones of the Pacific \"Ring of Fire\" (e.g., Alaska-Aleutian, Kuril-Kamchatka, Chile, and Japan). Mapping and dating anomalous beds of sand and silt deposited by tsunamis in low-lying areas along Pacific coasts, even those distant from subduction zones, is critical for assessing tsunami hazard throughout the Pacific basin. We searched for evidence of tsunami inundation using stratigraphic and sedimentologic analyses of potential tsunami deposits beneath present and former Hawaiian wetlands, coastal lagoons, and river floodplains. Coastal wetland sites on the islands of Hawai΄i, Maui, O΄ahu, and Kaua΄i were selected based on historical tsunami runup, numerical inundation modeling, proximity to sandy source sediments, degree of historical wetland disturbance, and breadth of prior geologic and archaeologic investigations. We interpret sand beds containing marine calcareous sediment within peaty and/or muddy wetland deposits on the north and northeastern shores of Kaua΄i, O΄ahu, and Hawai΄i as tsunami deposits. At some sites, deposits of the 1946 and 1957 Aleutian tsunamis are analogs for deeper, older probable tsunami deposits. Radiocarbon-based age models date sand beds from three sites to ~700-500 cal yr B.P., which overlaps ages for tsunami deposits in the eastern Aleutian Islands that record a local subduction zone earthquake (Witter et al., 2016; Witter et al., 2018). The overlapping modeled ages for tsunami deposits at our sites support a plausible correlation with an eastern Aleutian earthquake source for a large prehistoric tsunami in the Hawaiian Islands.","language":"English","publisher":"Wiley","doi":"10.1111/sed.12623","usgsCitation":"La Selle, S., Richmond, B.M., Jaffe, B.E., Nelson, A., Griswold, F., Arcos, M.E., Chague, C., Bishop, J., Bellanova, P., Kane, H.H., Lunghino, B., and Gelfenbaum, G.R., 2020, Sedimentary evidence of prehistoric distant-source tsunamis in the Hawaiian Islands: Sedimentology, v. 67, no. 3, p. 1249-1273, https://doi.org/10.1111/sed.12623.","productDescription":"25 p.","startPage":"1249","endPage":"1273","ipdsId":"IP-100398","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science 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Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":765386,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70202572,"text":"ofr20191023A - 2019 - Focus areas for data acquisition for potential domestic sources of critical minerals—Rare earth elements","interactions":[],"lastModifiedDate":"2026-03-25T16:52:23.790127","indexId":"ofr20191023A","displayToPublicDate":"2022-07-14T10:30:00","publicationYear":"2019","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":"2019-1023","chapter":"A","displayTitle":"Focus Areas for Data Acquisition for Potential Domestic Sources of Critical Minerals—Rare Earth Elements","title":"Focus areas for data acquisition for potential domestic sources of critical minerals—Rare earth elements","docAbstract":"Rare earth elements (REEs) are critical mineral commodities for the United States. In response to a need for information on potential domestic sources of REEs in mineral deposits, the U.S. Geological Survey (USGS) identified broad focus areas throughout the conterminous United States and Alaska as a guide for selecting new geoscience research areas. This study was done to support the USGS Earth Mapping Resources Initiative (Earth MRI).
Focus areas are identified in four regions of the United States (Alaska, West, Central, and East) by mineral deposit type. The areas are described in a companion USGS data release that consists of a map in a geographic information system and accompanying tables that document the rationale for each focus area (C.L. Dicken and others, 2019, https://doi.org/10.5066/P95CHIL0). This open-file report describes the methodology that was used to identify focus areas and determine new data acquisition needs. Deposit types that are likely to be of interest for future exploration and development of domestic nonfuel REE resources include deposits associated with carbonatites and peralkaline rocks, iron oxide-apatite deposits, monazite-bearing placers, and REE-enriched phosphorites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023A","usgsCitation":"Hammarstrom, J.H., and Dicken, C.L., 2019, Focus areas for data acquisition for potential domestic sources of critical minerals—Rare earth elements (ver. 1.1, July 2022), chap. A of U.S. Geological Survey, Focus areas for data acquisition for potential domestic sources of critical minerals: U.S. Geological Survey Open-File Report 2019–1023, 11 p, https://doi.org/10.3133/ofr20191023A.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-104700","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":501521,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108444.htm","linkFileType":{"id":5,"text":"html"}},{"id":403730,"rank":9,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023E","text":"Open-File Report 2019-1023-E","linkHelpText":"- Alaska Focus Area Definition for Data Acquisition for Potential Domestic Sources of Critical Minerals in Alaska for Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Magnesium, Manganese, Uranium, Vanadium, and Zirconium"},{"id":403727,"rank":6,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023B","text":"Open-File Report 2019-1023-B","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in the Conterminous United States, Hawaii, and Puerto Rico—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum-Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":403468,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1023/a/versionHist.txt","size":"2.97 KB","linkFileType":{"id":2,"text":"txt"}},{"id":361988,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95CHIL0","text":"USGS data release","description":"USGS data release"},{"id":361987,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20193007","text":"Fact Sheet 2019–3007","linkHelpText":"- The Earth Mapping Resources Initiative (Earth MRI): Mapping the Nation’s Critical Mineral Resources"},{"id":361986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1023/a/ofr20191023a.pdf","text":"Report","size":"1.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1023"},{"id":420419,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1023/a/coverthb2.jpg"},{"id":403729,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023D","text":"Open-File Report 2019-1023-D","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 13 Critical Minerals in the Conterminous United States and Puerto Rico—Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Helium, Magnesium, Manganese, Potash, Uranium, Vanadium, and Zirconium"},{"id":403728,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023C","text":"Open-File Report 2019-1023-C","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in Alaska—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"}],"country":"United 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States\"}}]}","edition":"Version 1.0: March 2019; Version 1.1: July 2022","contact":"Mineral Resources Program
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: Minerals@usgs.gov
Since 1952, wastewater discharged to infiltration ponds (also called percolation ponds) and disposal wells at the Idaho National Laboratory (INL) has affected water quality in the eastern Snake River Plain (ESRP) aquifer and perched groundwater zones underlying the INL. The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy, maintains groundwater-monitoring networks at the INL to determine hydrologic trends and to delineate the movement of radiochemical and chemical wastes in the aquifer and in perched groundwater zones. This report presents an analysis of water-level and water-quality data collected from the ESRP aquifer and perched groundwater wells in the USGS groundwater monitoring networks during 2016–18.
From March–May 2015 to March–May 2018, water levels in wells completed in the ESRP aquifer declined in the northern part of the INL and increased in the southwestern part. Water-level decreases ranged from 0.5 to 3.0 feet (ft) in the northern part of the INL and increases ranged from 0.5 to 3.0 ft in the southwestern part.
Detectable concentrations of radiochemical constituents in water samples from wells in the ESRP aquifer at the INL generally decreased or remained constant during 2016–18. Decreases in concentrations were attributed to radioactive decay, changes in waste-disposal methods, and dilution from recharge and underflow.
In 2018, concentrations of tritium in water samples collected from 46 of 111 aquifer wells were greater than the reporting level of three times the sample standard deviation and ranged from 260±50 to 5,100±190 picocuries per liter (pCi/L). Tritium concentrations in water from 10 wells completed in deep perched groundwater above the ESRP aquifer near the Advanced Test Reactor (ATR) Complex generally were greater than or equal to the reporting level during at least one sampling event during 2016–18, and concentrations ranged from 150 ±50 to 12,900 ±200 pCi/L.
Concentrations of strontium-90 in water from 17 of 60 ESRP aquifer wells sampled during April or October 2018 exceeded the reporting level, ranging from 2.2±0.7 to 363±19 pCi/L. Strontium-90 was not detected in the ESRP aquifer beneath the ATR Complex. During at least one sampling event during 2016–18, concentrations of strontium-90 in water from eight wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex equaled or exceeded the reporting levels, and concentrations ranged from 0.57±0.17 to 34.3±1.2 pCi/L.
During 2016–18, concentrations of cesium-137 were less than the reporting level in all but one ESRP aquifer well, and concentrations of plutonium-238, -239, and -240 (undivided), and americium-241 were less than the reporting level in water samples from all ESRP aquifer wells.
In April 2009, the dissolved chromium concentration in water from one ESRP aquifer well, USGS 65, south of the ATR Complex equaled the maximum contaminant level (MCL) of 100 micrograms per liter (μg/L). In April 2018, the concentration of chromium in water from that well had decreased to 76.0 μg/L, less than the MCL. Concentrations in water samples from 62 other ESRP aquifer wells sampled ranged from less than 0.6 to 21.6 μg/L. During 2016–18, dissolved chromium was detected in water from all wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex, and concentrations ranged from 4.2 to 98.8 μg/L.
In 2018, concentrations of sodium in water from most ESRP aquifer wells in the southern part of the INL were greater than the western tributary background concentration of 8.3 milligrams per liter (mg/L). After the new percolation ponds were put into service in 2002 southwest of the Idaho Nuclear Technology and Engineering Center (INTEC), concentrations of sodium in water samples from the Rifle Range well increased steadily until 2008, when concentrations generally began decreasing. The increases and decreases were attributed to disposal variability in the new percolation ponds. During 2016–18, dissolved sodium concentrations in water from 18 wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex ranged from 6.37 to 143 mg/L.
In 2018, concentrations of chloride in most water samples from ESRP aquifer wells south of the INTEC and at the Central Facilities Area exceeded the background concentrations. Chloride concentrations in water from wells south of the INTEC generally have decreased since 2002 when chloride disposal to the old percolation ponds was discontinued. After the new percolation ponds southwest of the INTEC were put into service in 2002, concentrations of chloride in water samples from one well rose steadily until 2008 then began decreasing. During 2016–18, dissolved chloride concentrations in deep perched groundwater above the ESRP aquifer from 18 wells at the ATR Complex ranged from 3.89 to 176 mg/L.
In 2018, sulfate concentrations in water samples from ESRP aquifer wells in the south-central part of the INL exceeded the background concentration of sulfate and ranged from 22 to 151 mg/L. The greater-than-background concentrations in water from these wells probably resulted from sulfate disposal at the ATR Complex infiltration ponds or the old INTEC percolation ponds. In 2018, sulfate concentrations in water samples from wells near the Radioactive Waste Management Complex (RWMC) mostly were greater than background concentrations and could have resulted from well construction techniques and (or) waste disposal at the RWMC or the ATR complex. The maximum dissolved sulfate concentration in shallow perched groundwater above the ESRP aquifer near the ATR Complex was 215 mg/L in well CWP 3 in April 2016. During 2018, dissolved sulfate concentrations in water from wells completed in deep perched groundwater above the ESRP aquifer near the cold-waste ponds at the ATR Complex ranged from 65.8 to 171 mg/L.
In 2018, concentrations of nitrate in water from most ESRP aquifer wells at and near the INTEC exceeded the western tributary background concentration of 0.655 mg/L. Concentrations of nitrate in wells southwest of the INTEC and farther away from the influence of disposal areas and the Big Lost River show a general decrease in nitrate concentration through time. Two wells south of the INTEC show increasing trends that could be the result of wastewater beneath the INTEC tank farm being mobilized to the aquifer.
During 2016–18, water samples from several ESRP aquifer wells were collected and analyzed for volatile organic compounds (VOCs). Sixteen VOCs were detected. At least 1 and as many as 7 VOCs were detected in water samples from 15 wells. The primary VOCs detected include carbon tetrachloride, trichloromethane, tetrachloroethene, 1,1,1-trichloroethane, and trichloroethene. In 2016–18, concentrations for all VOCs were less than their respective MCLs for drinking water, except carbon tetrachloride in water from two wells and trichloroethene in one well.
During 2016–18, variability and bias were evaluated from 37 replicate and 15 blank quality-assurance samples. Results from replicate analyses were investigated to evaluate sample variability. Constituents with acceptable reproducibility were major ions, trace elements, nutrients, and VOCs. All radiochemical constituents had acceptable reproducibility except for gross alpha- and beta-particle radioactivity. The gross alpha- and beta-particle radioactivity samples that did not meet reproducibility criteria had low concentrations. Bias from sample contamination was evaluated from equipment, field, and source-solution blanks. Cadmium had a concentration slightly greater than its reporting level in a source-solution blank, and chloride and ammonia had concentrations that were slightly greater than their respective reporting levels in field and equipment blanks. Subtracting concentrations of chloride and ammonia in field blanks from the concurrently collected equipment blank indicates that adjusted concentrations for chloride and ammonia in the equipment blanks were less than their respective reporting levels. Therefore, no sample bias was observed for any of the sample periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195149","collaboration":"DOE/ID-22251Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The U.S. Geological Survey National Crustal Model (NCM) is being developed to assist with earthquake hazard and risk assessment by supporting estimates of ground shaking in response to an earthquake. The period-dependent intensity and duration of shaking depend upon the three-dimensional seismic velocity, seismic attenuation, and density distribution of a region, which in turn is governed to a large degree by geology and how that geology behaves under varying temperatures and pressures.
A three-dimensional temperature model is presented here to support the estimation of physical parameters within the U.S. Geological Survey NCM. The crustal model is defined by a geological framework consisting of various lithologies with distinct mineral compositions. A temperature model is needed to calculate mineral density and bulk and shear modulus as a function of position within the crust. These properties control seismic velocity and impedance, which are needed to accurately estimate earthquake travel times and seismic amplitudes in earthquake hazard analyses. The temperature model is constrained by observations of surface temperature, temperature gradient, and conductivity, inferred Moho temperature and depth, and assumed conductivity at the base of the crust. The continental plate is assumed to have heat production that decreases exponentially with depth and thermal conductivity that exponentially changes from a surface value to 3.6 watts per meter-Kelvin at the Moho. The oceanic plate cools as a half-space with a geotherm dependent on plate age. Under these conditions, and the application of observed surface heat production, predicted Moho temperatures match Moho temperatures inferred from seismic P-wave velocities, on average. As has been noted in previous studies, high crustal temperatures are found in the western United States, particularly beneath areas of recent volcanism. In the central and eastern United States, elevated temperatures are found from southeast Texas, into the Mississippi Embayment, and up through Wisconsin. A USGS ScienceBase data release that supports this report is available and consists of grids covering the NCM across the conterminous United States, for example, surface temperature and temperature gradient, that are needed to produce temperature profiles.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191121","usgsCitation":"Boyd, O.S., 2020, Temperature model in support of the U.S. Geological Survey National Crustal Model for seismic hazard studies: U.S. Geological Survey Open-File Report 2019–1121, 15 p., https://doi.org/10.3133/ofr20191121.","productDescription":"Report: iv, 15 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-109788","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437241,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SL2PVR","text":"USGS data release","linkHelpText":"TherMod"},{"id":399419,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109626.htm"},{"id":371516,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935DT1G","text":"USGS data release","linkHelpText":"Grids in support of the U.S. Geological Survey Thermal Model for Seismic Hazard Studies"},{"id":371515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1121/ofr20191121.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1121"},{"id":371514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1121/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n 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Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
This geologic map of the Greater Antilles and the Virgin Islands is a compilation of information from the literature, integrated to provide a seamless geologic map of the region. The geology shown on sheet 1 covers Cuba, the island of Hispaniola, which includes Haiti and the Dominican Republic, Jamaica, the Cayman Islands, Puerto Rico, and the U.S. and British Virgin Islands. A second more detailed sheet shows the geology of Puerto Rico and the Virgin Islands. The map units shown here are integrated across the islands of the Greater Antilles and the Virgin Islands.
The Greater Antilles and the Virgin Islands, although they appear to reflect the character of a magmatic arc, actually represent multiple, distinct geologic features. Only in Cuba are there unquestioned Jurassic-age, and perhaps older, rocks present. On the islands of Hispaniola (Haiti and the Dominican Republic) and Puerto Rico, metamorphic assemblages contain rocks that may be of Jurassic age. Ophiolite assemblages that may include rocks of Jurassic age are present in Cuba, the Dominican Republic, Haiti, and Puerto Rico. Metamorphic rocks of Cretaceous age are more widespread, present in Cuba, Hispaniola, and the U.S. and British Virgin Islands. Cretaceous plutonic rocks are present in Cuba and Puerto Rico, as well as in the Dominican Republic (in the Cordillera Central and in the eastern part of the country). Gabbro and trondhjemite of inferred Early Cretaceous age are present in the U.S. Virgin Islands. Cretaceous volcanic rocks are widespread in Cuba, Hispaniola, Puerto Rico, and the Virgin Islands; they are of variable age and do not appear to reflect a single arc system. Cretaceous volcanic rocks are also found in Jamaica, in inliers on the eastern part of the island. Eocene volcanic rocks are prominent in southern Cuba, Haiti, eastern Jamaica, Puerto Rico, and the Virgin Islands. Volcanic rocks possibly as young as early Miocene are present in the southern Dominican Republic; the youngest volcanic rocks in the region are the Low Layton Lavas of Jamaica of late Miocene age and alkali basalt of Quaternary age on Hispaniola.
Carbonate rocks are an important component of the sedimentary section in the Greater Antilles, which is as old as Jurassic in Cuba and as young as Holocene in many areas. In Cuba, Early Cretaceous sedimentary rocks tend to be dominantly carbonates; volcanic clasts and debris are not present until the Late Cretaceous in Cuba, as well as in Jamaica and Puerto Rico. In contrast, Early Cretaceous volcaniclastic sedimentary rocks are common in the Virgin Islands. Olistostrome deposits are commonly described in latest Cretaceous and Eocene rocks; in the Paleocene and the early Eocene, these deposits are commonly associated with mélange units. Volcanic debris and tuff are common in sedimentary rocks of Paleocene and Eocene age, typically associated with carbonate rocks. Sedimentary rocks that postdate the Eocene either are dominantly carbonates or are mixed clastic and carbonate rocks in which the clastic component reflects erosion of earlier units, including older carbonate rocks. Rocks that contain lignite, which are only present in Cuba and on Hispaniola, generally are of Miocene age.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191036","usgsCitation":"Wilson, F.H., Orris, G., and Gray, F., 2019, Preliminary geologic map of the Greater Antilles and the Virgin Islands: U.S. Geological Survey Open-File Report 2019–1036, pamphlet 50 p., 2 sheets, scales 1:2,500,000 and 1:300,000, https://doi.org/10.3133/ofr20191036.","productDescription":"Pamphlet: iv, 50 p.; 2 Sheets: 51.00 x 34.00 inches and 46.10 x 24.93 inches; 2 Tables; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098596","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":399410,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109592.htm"},{"id":399409,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109591.htm"},{"id":371178,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_spatialdata.zip","text":"OFR 2019-1036 spatial data","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2019-1036 Spatial Data"},{"id":371177,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_table2.xlsx","text":"Table 2","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1036 Table 2 XLSX"},{"id":371176,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_table2.pdf","text":"Table 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036 Table 2 PDF"},{"id":371173,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036"},{"id":371175,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036 Sheet 2"},{"id":371174,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036 Sheet 1"},{"id":371172,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1036/coverthb2.jpg"}],"scale":"2500000","country":"Cuba, Dominican Republic, Great Britain, Haiti, Jamaica, United States","otherGeospatial":"Greater Antilles, Hispaniola, Puerto Rico, Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.517578125,\n 23.725011735951796\n ],\n [\n -85.4296875,\n 22.105998799750566\n ],\n [\n -83.84765625,\n 20.138470312451155\n ],\n [\n -76.2890625,\n 15.623036831528264\n ],\n [\n -69.169921875,\n 15.792253570362446\n ],\n [\n -61.435546875,\n 16.46769474828897\n ],\n [\n -60.64453125000001,\n 17.895114303749143\n ],\n [\n -61.52343749999999,\n 21.04349121680354\n ],\n [\n -72.50976562499999,\n 22.187404991398775\n ],\n [\n -82.265625,\n 24.046463999666567\n ],\n [\n -85.517578125,\n 23.725011735951796\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Repeated sampling and grain-size analysis of surficial sediments along the sandy shorelines of Louisiana is necessary to characterize coastal-zone sediment properties and evaluate sediment transport patterns within the nearshore environments. In 2008, and again in 2015 and 2016, sediment grab samples were collected along the shorelines of the western Chenier plain, the Isles Dernieres (Raccoon, Whiskey, Trinity and East Islands), the Lafourche delta (Timbalier Islands, Caminada Headland, and Grand Isle), the modern delta (Grand Terre Islands from Chaland Headland to Sandy Point), and the Chandeleur Islands (from Curlew Island to Hewes Point). The samples were collected as part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring (BICM) Program in collaboration with the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS–SPCMSC) and the University of New Orleans Pontchartrain Institute for Environmental Studies (UNO–PIES). Physical properties of the samples (sediment grain size and sorting) were measured and provided in data reports to CPRA. Additional samples collected by the USGS from around Breton Island in 2014 and 2015 supplemented the 2015–2016 BICM data to complete the coastwide dataset. This report compares the results of the 2008 and 2015–2016 sedimentologic analyses and documents changes in composition (percent sand) and mean sediment grain size between the two time periods. At most sample sites, differences in mean grain size varied by less than ±0.25 Φ. The largest changes occurred at sites located near tidal inlets or along rapidly eroding shorelines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191132","collaboration":"Prepared in cooperation with the University of New Orleans and Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Bosse, S.T., Flocks, J.G., Bernier, J.C., Georgiou, I.Y., Kulp, M.A., and Brown, M., 2019, Louisiana Coastal Zone sediment characterization; comparison of sediment grain sizes for samples collected in 2008 and 2015–2016 from the western Chenier plain to the Chandeleur Islands, Louisiana—Louisiana Barrier Island Comprehensive Monitoring (BICM) Program: U.S. Geological Survey Open-File Report 2019–1132, 17 p., https://doi.org/10.3133/ofr20191132.","productDescription":"vi, 17 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-107806","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":371101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1132/report-thumb.jpg"},{"id":371102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1132/ofr20191132.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1132"},{"id":399424,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109589.htm"},{"id":399423,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109588.htm"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8333,\n 29.6667\n ],\n [\n -93,\n 29.6667\n ],\n [\n -93,\n 29.7889\n ],\n [\n -93.8333,\n 29.7889\n ],\n [\n -93.8333,\n 29.6667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
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600 4th Street South
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The Amerasia Basin Province encompasses the Canada Basin and the sediment prisms along the Alaska and Canada margins, outboard from basinward margins (hingelines) of the rift shoulders that formed during extensional opening of the Canada Basin. The province includes the Mackenzie River delta and slope, the outer shelves and marine slopes along the Arctic margins of Alaska and Canada, and the deep Canada Basin.
The province is divided into four assessment units (AUs): (1) The Canning-Mackenzie Deformed Margin AU is that part of the rifted margin where the Brooks Range orogenic belt has overridden the rift shoulder and is deforming the rifted-margin prism of sediment outboard of the hingeline. This is the only part of the Amerasia Basin Province that has been explored and—even though more than 3 billion barrels of oil equivalent (BBOE) of oil, gas, and condensate have been discovered— none has been commercially produced. (2) The Alaska Passive Margin AU is the rifted-margin prism of sediment lying beneath the Beaufort Sea outer shelf and slope that has not been deformed by tectonism. (3) The Canada Passive Margin AU is the rifted-margin prism of sediment lying beneath the Arctic outer shelf and slope (also known as the polar margin) of Canada that has not been deformed by tectonism. (4) The Canada Basin AU includes the sedimentary wedge that lies beneath the deep Canada Basin, north of the marine slope developed along the Alaska and Canada margins. Mean estimates of risked, undiscovered, technically recoverable resources include more than 6 billion barrels of oil (BBO), more than 19 trillion cubic feet (TCF) of associated gas, and more than 16 TCF of nonassociated gas in the Canning-Mackenzie Deformed Margin AU; about 1 BBO, about 3 TCF of associated gas, and about 3 TCF of associated gas in the Alaska Passive Margin AU; and more than 2 BBO, about 7 TCF of associated gas, and about 8 TCF of nonassociated gas in the Canada Passive Margin AU. Quantities of natural gas liquids also are assessed in each AU. The Canada Basin AU was not quantitatively assessed because it is judged to hold less than 10 percent probability of containing at least one accumulation of 50 million barrels of oil equivalent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824BB","usgsCitation":"Houseknecht, D.W., Bird, K.J., and Garrity, C.P., 2020, Geology and assessment of undiscovered oil and gas resources of the Amerasia Basin Province, 2008, chap. BB of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 33 p., https://doi.org/10.3133/pp1824BB. [Supersedes USGS Scientific Investigations Report 2012–5146.","productDescription":"Report: viii, 33 p.; 3 Appendixes","onlineOnly":"Y","ipdsId":"IP-114214","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":371072,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb_appendix3.xlsx","text":"Appendix 3 —","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter BB Appendix 3","linkHelpText":"Input data for the Canada Passive Margin Assessment Unit"},{"id":371071,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb_appendix2.xlsx","text":"Appendix 2 —","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter BB Appendix 2","linkHelpText":"Input data for the Alaska Passive Margin Assessment Unit"},{"id":371070,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb_appendix1.xlsx","text":"Appendix 1 —","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter BB Appendix 1","linkHelpText":"Input data for the Canning-Mackenzie Deformed Margin Assessment Unit"},{"id":371069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter BB"},{"id":371068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/bb/coverthb.jpg"},{"id":399505,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109586.htm"}],"country":"United States, Canada","state":"Alaska","otherGeospatial":"Amerasia Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165,\n 69.75\n ],\n [\n -85,\n 69.75\n ],\n [\n -85,\n 80\n ],\n [\n -165,\n 80\n ],\n [\n -165,\n 69.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
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Menlo Park, CA 94025-3591
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During 2014, highly pathogenic (HP) influenza A viruses (IAVs) of the A/Goose/Guangdong/1/1996 lineage (GsGD-HP-H5), originating from Asia, were detected in domestic poultry and wild birds in Canada and the US. These clade 2.3.4.4 GsGD-HP-H5 viruses included reassortants possessing North American lineage gene segments; were detected in wild birds in the Pacific, Central, and Mississippi flyways; and caused the largest HP IAV outbreak in poultry in US history. To determine if an antibody response indicative of previous infection with clade 2.3.4.4 GsGD-HP-H5 IAV could be detected in North American wild waterfowl sampled before, during, and after the 2014–15 outbreak, sera from 2,793 geese and 3,715 ducks were tested by blocking enzyme-linked immunosorbent assay and hemagglutination inhibition (HI) tests using both clade 2.3.4.4 GsGD-HPH5 and North American lineage low pathogenic (LP) H5 IAV antigens. We detected an antibody response meeting a comparative titer-based criteria (HI titer observed with 2.3.4.4 GsGD-HP-H5 antigens exceeded the titer observed for LP H5 antigen by two or more dilutions) for previous infection with clade 2.3.4.4 GsGD-HP-H5 IAV in only five birds, one Blue-winged Teal (Spatula discors) sampled during the outbreak and three Mallards (Anas platyrhynchos) and one Canada Goose (Branta canadensis) sampled during the post-outbreak period. These serologic results are consistent with the spatiotemporal extent of the outbreak in wild birds in North America during 2014 and 2015 and limited exposure of waterfowl to GsGD-HP-H5 IAV, particularly in the central and eastern US.
","language":"English","publisher":"BioOne Complete","doi":"10.7589/2019-01-003","usgsCitation":"Stallknecht, D.E., Kienzle-Dean, C., Davis-Fields, N., Jennelle, C.S., Bowman, A.S., Nolting, J.M., Boyce, W., Crum, J., Santos, J., Brown, J.D., Prosser, D., De La Cruz, S.E., Ackerman, J., Casazza, M.L., Krauss, S., Perez, D., Ramey, A.M., and Poulson, R., 2019, Limited detection of antibodies to clade 2.3.4.4 A/Goose/Guangdong/1/1996 lineage highly pathogenic H5 avian influenza virus in North American waterfowl: Journal of Wildlife Diseases, v. 56, no. 1, p. 47-57, https://doi.org/10.7589/2019-01-003.","productDescription":"11 p.","startPage":"47","endPage":"57","ipdsId":"IP-102272","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research 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Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":776316,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70207534,"text":"ofr20191145 - 2019 - Cross section of the North Carolina coastal plain from Enfield through Cape Hatteras","interactions":[],"lastModifiedDate":"2022-04-21T20:15:36.129511","indexId":"ofr20191145","displayToPublicDate":"2020-01-06T15:40:00","publicationYear":"2019","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":"2019-1145","displayTitle":"Cross Section of the North Carolina Coastal Plain from Enfield through Cape Hatteras","title":"Cross section of the North Carolina coastal plain from Enfield through Cape Hatteras","docAbstract":"The Atlantic Coastal Plain, the southeasternmost physiographic province in the United States, is underlain by strata that regionally dip gently eastward and gradually thicken toward the Atlantic Ocean basin. These strata, ranging in age from Middle Jurassic to Holocene, accumulated along the eastern margin of North America after the break-up of the supercontinent Pangaea during the Early Jurassic. In the east-central United States north of Florida, Cape Hatteras is the point of land that most closely approaches the eastern edge of the Atlantic Continental Shelf of the United States. In 1946, Esso (now part of ExxonMobil) drilled a deep oil exploration well to basement rock near the Cape Hatteras lighthouse. No oil or gas was found there, or in any of the other test wells that were drilled within the onshore North Carolina Coastal Plain. Recent work indicates that the top of the oil window lies at 9,000 feet near the base of the Cape Hatteras Esso #1 test well. Therefore, any mature petroleum source rocks that may be present in the North Carolina Coastal Plain are only likely to be found east of the present coastline.
Although the Cape Hatteras test well did not produce oil or gas, it did produce a wealth of stratigraphic information about the outer portion of the onshore Atlantic Continental Shelf. Advances in global stratigraphic correlation, in tandem with our analyses of calcareous nannofossils and dinoflagellate cysts (dinocysts) from the Cape Hatteras test-well spot samples have produced significant advances beyond earlier interpretations of this well and other deep test wells inshore of Cape Hatteras. These results, when coupled with work done offshore of Cape Hatteras, have allowed us to create a more detailed cross section of the North Carolina Coastal Plain and adjacent continental shelf than previously possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191145","usgsCitation":"Weems, R.E., Self-Trail, J.M., and Edwards L.E., 2019, Cross section of the North Carolina coastal plain from Enfield through Cape Hatteras: U.S. Geological Survey Open-File Report 2019–1145, 2 sheets, https://doi.org/10.3133/ofr20191145.","productDescription":"2 Sheets: 40.00 x 37.00 inches and 35.00 x 37.00 inches","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102465","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":399432,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109585.htm"},{"id":370654,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1145/ofr20191145_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1145"},{"id":371011,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1145/ofr20191145_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1145"},{"id":370630,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1145/coverthb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.6513671875,\n 34.63320791137959\n ],\n [\n -75.5419921875,\n 34.63320791137959\n ],\n [\n -75.5419921875,\n 36.1733569352216\n ],\n [\n -77.6513671875,\n 36.1733569352216\n ],\n [\n -77.6513671875,\n 34.63320791137959\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
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Climate change poses unique challenges in snow-fed river basins across the western United States because the majority of water supply originates as snow (Dettinger, Udall, & Georgakakos, 2015). In the Sierra Nevada, recent observations include changes in snow accumulation and snowmelt, and shifts in peak streamflow timing (Barnhart et al., 2016; Hatchett et al., 2017; Kim & Jain, 2010; McCabe, Wolock, & Valentin, 2018; Mote, Li, Lettenmaier, Xiao, & Engel, 2018). Such changes upstream alter surface water deliveries downstream, as well as groundwater recharge utilized as both primary and supplemental water supply (Godsey et al., 2014; Harpold, 2016; Jasechko et al., 2014).
basin where snowmelt runoff produces substantial water supply to meet diverse agricultural, environmental and urban water demand (Figure 1). The East and West Forks join at the confluence of the Carson River near the north end of the Carson Valley, a rich agricultural region (40,000 acres) that grows primarily alfalfa hay. The majority of irrigators rely on surface water delivered through a network of earthen ditches constructed in the mid-19th and early 20th centuries. Flow through these earthen networks and the practice of flood irrigation contribute significantly to groundwater recharge.
Because no upstream surface water reservoirs exist, snowpack that accumulates through winter and melts slowly through spring has acted as a “natural” reservoir, providing ample supply through the summer irrigati agricultural, environmental and urban water demand (Figure 1). The East and West Forks join at the confluence of the Carson River near the north end of the season. Some irrigators have permitted access to supplemental groundwater that is useful during periods of drought for augmenting shortfalls in surface water delivery. Groundwater is the primary source of municipal and industrial water supply for surrounding communities (e.g., Carson City, Minden, Gardnerville, Dayton).
Across the basin, water use is highly regulated through federal, tribal, state and local water-sharing agreements based on prior appropriation doctrine (Wilds, 2014). Carson River surface water allocations follow the Alpine Decree, initiated by the United States Department of Interior in 1925 and signed into law in 1980, following 55 years of litigation, to adjudicate surface water rights to individual parties (NDWP, 1999). The Alpine Decree acknowledges return flows to lower river segments, and thus each river segment is distributed autonomously. This means that the most junior water right on an upper segment can be fulfilled before considering the most senior water right on a lower segment. Ultimately, the ruling is at the discretion of the Federal Water Master to satisfy the needs of each water right
Downstream of Carson Valley, surface water flows are stored in Lahontan Reservoir, the nation’s first desert reclamation project (est. 1906), where releases are managed to meet the Newland’s Project irrigation water demand and for environmental use on the Stillwater National Wildlife Refuge. Flows from the Carson River are supplemented through diversions from the Truckee River via the Truckee Canal, resulting in a trans-basin water supply system.
In the Truckee-Carson River System, researchers and local water managers are working together to assess climate change impacts to water supply and explore how model simulations can produce useful information to support local climate adaptation. Twelve key water managers represent agricultural, environmental, urban and regulatory water-use communities, and bring to the table diverse input and perspectives on how to adapt to climate change.
Hydrologists use this input to craft scenarios and simulations that meet the information needs of local water managers. Biannual workshops provide an opportunity for information exchange, where researchers and key water managers generate new knowledge of river system function. That is, researchers share results of models that examine the physical potential, and managers validate the on-the-ground potential, further informing the research process.
Coincident to this research program, the region faced a prolonged drought period (2012-2016) with historically low snowpack, followed by a historic wet year (2017) that brought winter and spring flooding as a result of atmospheric river storm events (Sterle et al., 2019). For the Carson River, an important observation made by managers was that peak streamflow that had traditionally coincided with peak irrigation demands, had shifted to earlier in the spring, with summer baseflow also decreasing (Sterle & Singletary, 2017). Managers shared with researchers concerns over potential future impacts that changing snowpack will have on surface water deliveries and reliance on groundwater, as the region’s population and economy continue to grow. During workshops that occurred over this period, local water managers and researchers discussed ways to evaluate water distribution and use that honors the existing legal framework and accounts for changing snowpack regimes (amount, rain versus snow, timing). In response to managers growing interest, researchers introduced the concept of managed aquifer recharge as one potential strategy to adapt and enhance regional water sustainability.
What is managed aquifer recharge? Simply stated, managed aquifer recharge is the intentional recharge of structures to spread water over agricultural lands, allowing water to naturally infiltrate into the groundwater system (Bouwer, 1999; Niswonger et al., 2017). The latter may occur during the irrigation season by applying excess water, or during the nonirrigation season when evapotranspiration losses are low. Figure 2 illustrates managed aquifer recharge in a snow-fed river basin, where streamflow generated from snowmelt runoff is diverted to agricultural lands to recharge the aquifer. Such flood irrigation practices, including water delivery through earthen ditch networks, provide incidental but significant aquifer recharge through seepage and deep drainage beneath fields (Niswonger, Allander, & Jeton, 2014). The effects of managed aquifer recharge can vary depending on the location and intensity of practice.
For example, implementing managed aquifer recharge water into the groundwater system (Dillon, 2009). This differs from the incidental recharge that may occur as part of normal irrigation practices. Managed recharge may occur by injection into the aquifer through existingwells, or by using existing conveyance adjacent to/along the river’s floodplain has the potential to enhance late-season instream flows due to increased return flows, resulting in greater downstream deliveries as well as improving ecological conditions (Niswonger et al., 2017). Implementing managed aquifer recharge away from the river’s floodplain has the potential to enhance groundwater supply which is increasingly relied upon during surface water shortage (Green et al., 2011), by storing water in available aquifer space in the deep aquifer. At the basin scale, managed aquifer recharge may lead to regional groundwater sustainability.
The physical limitations to implementing managed aquifer recharge in the Carson River Basin hinges on three key factors. The first factor relates to the physical connectivity between rivers and streams, and the irrigation delivery network of canals and ditches that divert water to agricultural lands (Niswonger et al., 2017). In the Carson River Basin the mechanisms for getting water to fields is already in place. Thus, intentionally routing high flows that occur in wet years through this system during the nonirrigation season would mimic what occurs naturally during the irrigation season. The second factor relates to the occurrence of atmospheric river storm events that deliver large amounts of precipitation to the region, much greater than average (Dettinger et al., 2015). With increased frequency and intensity projected under a warmer climate, such events have the potential to produce excess water over short periods of time that could be stored through mechanisms such as managed aquifer recharge (Niswonger et al., 2017). The third factor relates to the change in snowpack accumulation and shifts in snowmelt timing observed elsewhere in the Sierra Nevada (e.g., Godsey et al., 2014; Mote et al., 2018). Having a mechanism in place to maximize use of earlier snowmelt and shifts in streamflow timing could be advantageous and enhance regional groundwater sustainability. As part of the Water for the Seasons study, a hypothetical scenario was developed to determine the feasibility of managed aquifer recharge in the Carson River Basin, assuming no legal constraints. During “wet” or above-average water years, irrigators in the Upper Carson Valley would divert high flows and spread water over agricultural lands during the nonirrigation season. Assuming flows are abundant and “early,” diversions would begin prior to the growing season, when water would otherwise flow downstream to the Lahontan Reservoir. During “dry” years or drought periods, when surface water availability is less, irrigators in the Upper Carson Valley could augment surface water shortages with groundwater, allowing available surface water flows to flow downstream. Researchers hypothesize the amount of water has the potential to boost baseflow to support environmental instream flows, for example.
The hypothetical managed aquifer recharge scenario was presented to water managers in a workshop setting. Presentations included an overview of the hydrologic and operations modeling tools used to evaluate managed aquifer recharge by simulating the timing and distribution of water in the upper watershed. Specifically, in the Upper Carson Valley, a hydrologic model (GSFLOW) simulates streamflow driven by snowmelt, and surface and groundwater interactions, while a river basin operations model (MODSIM) allocates water according to the prior appropriation doctrine in the basin (see Figure 1) (Morway, Niswonger, & Triana, 2016; Niswonger et al., 2017). Integrating these two modeling tools advances the evaluation of climate impacts on water availability in agricultural communities and the resulting impacts of alternative management strategies (Morway et al., 2016).
When asked about the viability of managed aquifer recharge, the perspectives of 11 managers varied (Figure 3). Regardless of rating, all managers questioned, “How would thisreally work?” Several managers questioned whether models could simulate the connectivity between surface and groundwater to accurately quantify changes to instream flow. Others raised concerns that managed aquifer recharge violates the Alpine Decree and Nevada Water Law. Still others requested researchers consider alternatives that could work within the confines of current (2019) water law.
Managers posed specific questions that should be considered when evaluating the potential for managed aquifer recharge. For example:
Managers’ perspectives help to validate the on-the-ground potential of particular strategies and further refine alternative management scenarios. For example, understanding that managers are concerned with oversaturated fields helps researchers to define conditions in the model, such as what defines a wet versus “too” wet type of year and where to focus irrigation for managed aquifer recharge. Incorporating these nuances provides more accurate quantification of the potential benefits and consequences for users across the basin. Modeling is underway to simulate managed aquifer recharge scenarios and explore basin-wide implications. Researchers and local water managers will convene to collaboratively review results and further assess whether this or other strategies could work under the confines of existing water law. Subsequent fact sheets will present these findings.
","language":"English","publisher":"University of Nevada, Reno Extension","usgsCitation":"Sterle, K., Kitlasten, W., Morway, E.D., Niswonger, R.G., and Singletary, L., 2019, Managed aquifer recharge in snow-fed river basins: What, why and how?: Fact Sheet 19-10, 8 p.","productDescription":"8 p.","ipdsId":"IP-106943","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":377948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377947,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://extension.unr.edu/publication.aspx?PubID=3416"}],"country":"United States","state":"Nevada","city":"Carson City","otherGeospatial":"Carson River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.74685668945312,\n 38.849333913235476\n ],\n [\n -119.67681884765624,\n 38.976492485539396\n ],\n [\n -119.64248657226562,\n 39.15881700964971\n ],\n [\n -119.06295776367188,\n 39.299236474818194\n ],\n [\n -118.96545410156251,\n 39.454221498848895\n ],\n [\n -118.70590209960938,\n 39.459523110465156\n ],\n [\n -118.61114501953125,\n 39.68288289049806\n ],\n [\n -118.62213134765626,\n 39.79059962227577\n ],\n [\n -118.73886108398438,\n 39.79059962227577\n ],\n [\n -118.8336181640625,\n 39.53899882354987\n ],\n [\n -119.1412353515625,\n 39.527348072681455\n ],\n [\n -119.32662963867188,\n 39.35659979720227\n ],\n [\n -119.53262329101562,\n 39.34598050985849\n ],\n [\n -119.77157592773436,\n 39.196076813671695\n ],\n [\n -119.88418579101561,\n 39.03838632847035\n ],\n [\n -119.86358642578125,\n 38.935911987561624\n ],\n [\n -119.74685668945312,\n 38.849333913235476\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sterle, Kelley","contributorId":195683,"corporation":false,"usgs":false,"family":"Sterle","given":"Kelley","email":"","affiliations":[],"preferred":false,"id":797450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kitlasten, Wesley 0000-0002-2049-9107","orcid":"https://orcid.org/0000-0002-2049-9107","contributorId":217832,"corporation":false,"usgs":true,"family":"Kitlasten","given":"Wesley","email":"","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767634,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":767635,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singletary, Loretta","contributorId":195685,"corporation":false,"usgs":false,"family":"Singletary","given":"Loretta","email":"","affiliations":[],"preferred":false,"id":797451,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207136,"text":"sir20195138 - 2019 - Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon","interactions":[],"lastModifiedDate":"2022-04-25T19:51:40.528534","indexId":"sir20195138","displayToPublicDate":"2019-12-31T11:50:54","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5138","displayTitle":"Hydrogeologic Framework of the Treasure Valley and Surrounding Area, Idaho and Oregon","title":"Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon","docAbstract":"Most of the population of the Treasure Valley and the surrounding area of southwestern Idaho and easternmost Oregon depends on groundwater for domestic supply, either from domestic or municipal-supply wells. As of 2017, 41 percent of Idaho’s population was concentrated in Idaho’s portion of the Treasure Valley, and current and projected rapid population growth in the area has caused concern about the long-term sustainability of the groundwater resource. In 2016, the U.S. Geological Survey, in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources, began a project to construct a numerical groundwater-flow model of the westernmost western Snake River Plain (WSRP) aquifer system. As part of this project, a three-dimensional hydrogeologic framework model (3D HFM) of the aquifer system was generated, primarily from lithologic data compiled from 291 well-driller reports.
Four major hydrogeologic units are shown in the 3D HFM: Coarse-grained fluvial and alluvial deposits, Pliocene-Pleistocene and Miocene basalts, fine-grained lacustrine deposits, and granitic and rhyolitic bedrock. Generally, the 3D HFM is in agreement with the geologic history of the WSRP and hydrogeologic frameworks developed by previous authors. The resolution (voxel size) of the 3D HFM is sufficient for the construction of a regional groundwater-flow model.
The major components of inflow (or recharge) to the WSRP aquifer system are seepage from irrigation canals, direct infiltration from precipitation and excess irrigation water, seepage from the Boise and Payette Rivers and Lake Lowell, and subsurface inflow from adjoining uplands. The major components of outflow (or discharge) from the aquifer system are discharge to surface water (rivers, agricultural drains, and streams), groundwater pumping, and direct evapotranspiration from groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195138","collaboration":"Prepared in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources","usgsCitation":"Bartolino, J.R., 2019, Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon (ver. 1.1, January 2020): U.S. Geological Survey Scientific Investigations Report 2019–5138, 31 p., https://doi.org/10.3133/sir20195138.","productDescription":"Report: v, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-093399","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":371171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5138/coverthb.jpg"},{"id":371344,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5138/sir20195138_v1.1.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019-5138"},{"id":371345,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CAC0F6","linkHelpText":"Hydrogeologic Framework of the Treasure Valley and Surrounding Area, Idaho and Oregon"},{"id":371346,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5138/sir20195138_versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Scientific Investigations Report 2019-5138"},{"id":399614,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109577.htm"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Treasure Valley and surrounding area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.1097,\n 43.1803\n ],\n [\n -115.86,\n 43.1803\n ],\n [\n -115.86,\n 44.0381\n ],\n [\n -117.1097,\n 44.0381\n ],\n [\n -117.1097,\n 43.1803\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: January 2020; Version 1: December 2019","contact":"Director,
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Boise, Idaho 83702-4520
The level to which data are aggregated can impact analytical and predictive modeling results. In this short paper we discuss some of our findings regarding the impacts of data aggregation on estimating change points in the production profiles of horizontal hydraulically fractured Bakken oil wells. Change points occur when production transitions from one flow regime to another. Change point determination is important because it governs calculation of ultimate recovery from these and similar wells drilled in shale plays.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"20th Annual conference of the International Association for Mathematical Geosciences (IAMG2019)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"20th Annual Conference of the International Association for Mathematical Geosciences (IAMG2019)","conferenceDate":"Aug 10-15, 2019","conferenceLocation":"State College, PA","language":"English","usgsCitation":"Coburn, T.C., and Attanasi, E., 2019, Implications of aggregating daily production data on estimates of ultimate recovery from horizontal hydraulically fractured Bakken oil wells, in 20th Annual conference of the International Association for Mathematical Geosciences (IAMG2019), State College, PA, Aug 10-15, 2019, p. 232-236.","productDescription":"5 p.","startPage":"232","endPage":"236","ipdsId":"IP-107885","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Bakken Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.7598876953125,\n 45.96260622242165\n ],\n [\n -102.45849609375,\n 45.96260622242165\n ],\n [\n -102.45849609375,\n 47.71345768748889\n ],\n [\n -105.7598876953125,\n 47.71345768748889\n ],\n [\n -105.7598876953125,\n 45.96260622242165\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coburn, T. C.","contributorId":219832,"corporation":false,"usgs":false,"family":"Coburn","given":"T.","email":"","middleInitial":"C.","affiliations":[{"id":40076,"text":"1 University of Tulsa, School of Energy Economics, Policy and Commerce, USA,","active":true,"usgs":false}],"preferred":false,"id":773271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":773270,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206597,"text":"sir20195133 - 2019 - Iodine-129 in the Eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2017–18","interactions":[],"lastModifiedDate":"2022-04-25T19:40:42.618075","indexId":"sir20195133","displayToPublicDate":"2019-12-31T11:41:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5133","displayTitle":"Iodine-129 in the Eastern Snake River Plain Aquifer at and near the Idaho National Laboratory, Idaho, 2017–18","title":"Iodine-129 in the Eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2017–18","docAbstract":"From 1953 to 1988, approximately 0.941 curies of iodine-129 (129I) were contained in wastewater generated at the Idaho National Laboratory, with almost all of it discharged at or near the Idaho Nuclear Technology and Engineering Center (INTEC). Until 1984, most of the wastewater was discharged directly into the eastern Snake River Plain (ESRP) aquifer through a deep disposal well; however, some wastewater was also discharged into unlined infiltration ponds or leaked from distribution systems below the INTEC.
During 2017–18, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, collected samples for 129I from 30 wells that monitor the ESRP aquifer to track concentrations and changes of the carcinogenic radionuclide that has a 15.7 million-year half-life. Concentrations of 129I in the aquifer ranged from 0.000016 ± 0.000001 to 0.88+/- 0.03 picocuries per liter (pCi/L), and concentrations generally decreased in wells near the INTEC as compared with previously collected samples. The average concentration of 15 wells sampled during 5 different sample periods decreased from 1.15 pCi/L in 1990–91 to 0.168 pCi/L in 2017–18, but average concentrations were similar to 2011–12 within analytical uncertainty. All but four wells within a 3-mile radius of the INTEC showed decreases in concentration, and all samples had concentrations less than the U.S. Environmental Protection Agency’s maximum contaminant level of 1 pCi/L. These decreases are attributed to the discontinuation of disposal of 129I in wastewater and to dilution and dispersion in the aquifer. Some wells southeast of INTEC showed increasing trends; these increases were attributed to variable transmissivity.
Although wells near INTEC sampled in 2017–18 showed decreases in concentrations compared with data collected previously, some wells south of the INL boundary showed small increases. These increases are attributed to historical variable discharge rates of wastewater that eventually moved to these well locations as a pulse of water from a particular disposal period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195133","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Maimer, N.V., and Bartholomay, R.C., 2019, Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2017–18: U.S. Geological Survey Scientific Investigations Report 2019-5133, 20 p., https://doi.org/10.3133/sir20195133.","productDescription":"v, 20 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-096468","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":399612,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109576.htm"},{"id":370908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5133/sir20195133.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":370907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5133/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.9,\n 43.3333\n ],\n [\n -113.1667,\n 43.3333\n ],\n [\n -113.1667,\n 43.5833\n ],\n [\n -112.9,\n 43.5833\n ],\n [\n -112.9,\n 43.3333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
Recent estimates of the world’s swan Cygnus sp. populations indicate that there are currently between 1.5–1.6 million birds in 8 species, including the Coscoroba Swan Coscoroba coscoroba as an honorary swan. Monitoring programmes in Europe and North America indicate that most populations increased following the introduction of national and international legislation to protect the species during the early- to mid-20th century. A switch from feeding primarily on aquatic vegetation to foraging on farmland (especially high-energy arable crops) in winter during the second half of the 20th century, is also considered a contributing factor. Trumpeter Swans Cygnus buccinator famously increased from just 69 individuals known to exist in 1935 (although small numbers were missed) to c. 76,000 at the present time, and most of the northern hemisphere swan populations have continued to show increasing/stable trends over the last 20 years. The exception to this pattern is a decline since 1995 in the Northwest European Bewick’s Swan population, following an increase in its population size during the 1970s–1980s, which is now being addressed through implementation of an International Single Species Action Plan. A proposal to change enforcement regulations of the Migratory Bird Treaty Act in the United States is also of concern, as potentially undermining protection for Trumpeter Swans in North America, illustrating the importance of politics and legislation as well as on-the-ground measures for species conservation. Elsewhere, less is known about the trends and conservation status for swans in central and eastern Asia, though count and research programmes introduced in China, added to those underway in Japan and Korea, have recently greatly enhanced our knowledge of swan populations on the East Asian flyway. Trends for the Black Swan Cygnus atratus in Australia and for the Black-necked Swan Cygnus melancoryphus in South America are also poorly known, because of the large numbers involved for the former and a lack of coordinated counts across difficult terrain for the latter. These southern hemisphere species are considered vulnerable to water resource developments (i.e. where diversion of water is shrinking wetlands), and to droughts associated with El Nino events and climate change. More extensive monitoring is therefore required to determine whether swan populations and species are stable, fluctuating or in decline.
Soil chronosequences on alpine moraine complexes have been used to help unravel the glacial histories of the eastern Sierra Nevada. The moraine sequence along Bishop Creek includes well-preserved moraines that have been previously dated using cosmogenic 36Cl surface exposure ages. The goal of this study was to interpret pedogenesis within a soil geomorphic context on these quantitatively dated moraines. Soil development, surface clast cover, and moraine morphology were studied on seven of the moraines, ranging in age from 15 to 170 ka. Older moraines had gentler slopes, broader crests, and decreased surface rock cover. Soils showed weak development across the chronosequence of moraines. Pedogenesis involved slight increases in clay, the formation of clay lamellae, development of a vesicular horizon in a surface layer of aeolian dust, and weathering of surface and subsurface granitic clasts. Soil reddening and structure development were minimal. Soil formation was likely inhibited by the arid to semi-arid climate and intermittent wind and water erosion during the time span of the chronosequence.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.catena.2019.104222","usgsCitation":"Rossi, A., Graham, R., and Kendrick, K.J., 2019, Pedogenic evolution on the arid Bishop Creek moraines, eastern Sierra Nevada, California: Catena, v. 183, 104222, 14 p., https://doi.org/10.1016/j.catena.2019.104222.","productDescription":"104222, 14 p.","ipdsId":"IP-102151","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":378160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eastern Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.87207031250001,\n 37.69251435532741\n ],\n [\n -118.49304199218749,\n 37.69251435532741\n ],\n [\n -118.49304199218749,\n 37.84015683604136\n ],\n [\n -118.87207031250001,\n 37.84015683604136\n ],\n [\n -118.87207031250001,\n 37.69251435532741\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"183","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Annie 0000-0001-8955-0065","orcid":"https://orcid.org/0000-0001-8955-0065","contributorId":239863,"corporation":false,"usgs":false,"family":"Rossi","given":"Annie","email":"","affiliations":[{"id":48012,"text":"U.S.D.A. - NRCS","active":true,"usgs":false}],"preferred":false,"id":797908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Robert","contributorId":239864,"corporation":false,"usgs":false,"family":"Graham","given":"Robert","affiliations":[{"id":12655,"text":"University of California, Riverside","active":true,"usgs":false}],"preferred":false,"id":797909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendrick, Katherine J. 0000-0002-9839-6861","orcid":"https://orcid.org/0000-0002-9839-6861","contributorId":207907,"corporation":false,"usgs":true,"family":"Kendrick","given":"Katherine","email":"","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797910,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208836,"text":"70208836 - 2019 - Gopherus agassizii (Cooper 1861) – Agassiz’s Desert Tortoise, Mojave Desert Tortoise","interactions":[],"lastModifiedDate":"2021-12-10T15:22:19.1894","indexId":"70208836","displayToPublicDate":"2019-12-31T06:51:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5938,"text":"Chelonian Research Monographs","printIssn":"1088-7105","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Gopherus agassizii (Cooper 1861) – Agassiz’s Desert Tortoise, Mojave Desert Tortoise","title":"Gopherus agassizii (Cooper 1861) – Agassiz’s Desert Tortoise, Mojave Desert Tortoise","docAbstract":"Throughout the western United States, efforts are underway to better understand and preserve migration and movement corridors for mule deer and other big game and to minimize the impacts of development and other land-use change on populations. San Diego County is home to a unique non-migratory subspecies of mule deer, the Southern mule deer (Odocoileus hemionus fuliginatus; herein referred to as “mule deer”). Because it is the only large herbivorous mammal in San Diego, connectivity among mule deer groups is an important indicator of functional connectivity throughout San Diego County urban preserves and has therefore been monitored within central and eastern San Diego County using DNA fingerprinting since 2005. To continue this effort and to assess genetic connectivity in north San Diego County (herein “North County”), we genotyped scat samples from preserves in the area and tissue samples from Marine Corps Base Camp Pendleton (MCBCP). We used non-invasive capture/recapture analyses and pedigree analyses for assessing short-term movement and population clustering analyses to assess gene flow in North County. Additionally, we performed similar analyses on the combined San Diego County dataset, which was composed of the North County dataset collected for this study and a previously collected dataset from central and eastern San Diego County. Using recapture data, we found multiple instances of mule deer crossing roads in urban North County preserves, with several of these events occurring in areas where there are underpasses and culverts known to be used by mule deer. Corroborating previous studies in the region and statewide, pedigree and population structure analyses support the presence of two genetic clusters for mule deer in San Diego County—the “Coastal” and “Inland/Mountain” clusters. Low estimates of effective population size, especially in the Coastal cluster, suggest that to further understand potential vulnerabilities of mule deer in this region, it is important to continue to monitor connectivity, in particular, at the boundary between these two clusters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191138","usgsCitation":"Mitelberg, A., Smith, J.G., and Vandergast, A.G., 2019, DNA Fingerprinting of Southern mule deer (Odocoileus hemionus fuliginatus) in north San Diego County, California (2018–19): U.S. Geological Survey Open-File Report 2019–1138, 25 p., https://doi.org/10.3133/ofr20191138.","productDescription":"vi, 25 p.","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-112707","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437245,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXWXA9","text":"USGS data release","linkHelpText":"Microsatellite Genetic Marker Genotypes from Southern Mule Deer (Odocoileus hemionus fuliginatus) Sampled in San Diego County, California"},{"id":370869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1138/ofr20191138.pdf","text":"Report","size":"31 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":370868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1138/coverthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.31201171875001,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 32.713355353177555\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819