Ptilimnium nodosum (Rose) Mathias (harperella) is an endangered plant species found in Maryland, Virginia, and West Virginia, as well as in other locations throughout the southeastern United States. The narrow range of habitat characteristics for areas in which harperella has been found makes locating potential occurrence sites difficult and attempts at reintroduction of the plant relatively unsuccessful. Sightings of harperella have been made along the banks and in-channel bars of the Potomac River, along the Chesapeake and Ohio Canal National Historic Park, and within the Sideling Hill Wildlife Refuge near Hancock, Md. The large area covered by these sightings presents logistical challenges for repeat studies of harperella growth within the Park and in nearby areas. This study developed a geospatial method for characterizing harperella habitat through remote sensing, geospatial analysis, and field investigation. A geospatial prediction model was developed to model the habitat characteristics discussed in literature and found at harperella field observation sites in order to narrow the potential area for observation of the plant and its habitat. Analysis of historical aerial imagery was conducted within the space of the Potomac River to observe the persistence and flooding conditions of in-channel bars. The products of the geospatial prediction model and the historical aerial image analysis are a geospatial description of where harperella habitat is most likely to be found, as well as a map of in-channel bar locations and their persistence through time. From these two analyses, areas were identified that merited detailed observation. Very high resolution, unmanned aerial systems imagery was collected for 10 sites within this area in the Potomac River in June 2019. Unmanned aerial systems imagery has the potential to greatly improve detailed study of the harperella plant, as it provides the spatial resolution necessary to catalog detailed vegetation conditions (and potentially species identification). More importantly, the timing of imagery collection can be aligned carefully with the plant’s phenological patterns and local weather conditions to maximize cost-effectiveness of repeated imaging for specific areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201088","usgsCitation":"DeWitt, J.D., O’Pry, K.L., Chirico, P.G., and Young, J.A., 2020, Investigation of suitable habitat for the endangered plant Ptilimnium nodosum (Rose) Mathias (harperella) using remote sensing and field analysis—Documentation of methods and results: U.S. Geological Survey Open-File Report 2020–1088, 59 p., https://doi.org/10.3133/ofr20201088.","productDescription":"Report: vii, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113590","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":380054,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1088/ofr20201088.pdf","text":"Report","size":"26.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1088"},{"id":380053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1088/coverthb.jpg"},{"id":380055,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NG1QSQ","text":"USGS data release","linkHelpText":"Data associated with the investigation of suitable habitat for the endangered plant harperella (Ptilimnium nodosum Rose) in the Potomac River near Hancock, Maryland"}],"country":"United States","state":"Maryland, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.27896118164061,\n 39.36190883564925\n ],\n [\n -77.69119262695312,\n 39.36190883564925\n ],\n [\n -77.69119262695312,\n 39.675484393594814\n ],\n [\n -78.27896118164061,\n 39.675484393594814\n ],\n [\n -78.27896118164061,\n 39.36190883564925\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
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The southern sea otter (Enhydra lutris nereis) population at San Nicolas Island, California, has been monitored annually since the translocation of 140 sea otters to the island was completed in 1990. Monitoring efforts have varied in frequency and type across years. In 2017, the U.S. Navy and the U.S. Fish and Wildlife Service initiated a sea otter monitoring and research plan to determine the effects of military readiness activities on the growth or decline of the southern sea otter population at San Nicolas Island. The monitoring program, at its basic level, includes quarterly seasonal surveys of population abundance, distribution, and foraging activity. From 2017 to 2020, we measured a 22-percent per annum increase in population abundance (95-percent confidence interval =11–34 percent) with 114 total individuals as of February 2020. Coinciding with recent population growth, the sea otter distribution, which previously tended to concentrate on the west side, appears to have shifted toward an expansion of use in the north and especially greater seasonal use in the north and south during winter and spring. Foraging data were collected on a total of 2,675 foraging dives in 167 foraging bouts, and the majority of identified prey on successful dives (n=1,335) were sea urchins (940) followed by snails (240) and crabs (78). Small numbers of lobsters (26), octopus (16), and abalone (5) also were identified. Estimates of energy intake rates averaged 17.3 kilocalories per minute (95-percent confidence interval =15.6–19.0 kilocalories per minute) and suggest possible variations across years and seasons, but confidence intervals based on specific years of data were relatively wide. In addition to abundance, trends, distribution, and forage energy intake across seasons and years, these replicated surveys provide information on the precision of data achieved by quarterly survey effort. We used precision estimates and conducted simulation analyses to assess the power of detecting 10-percent or greater decreases in population growth rates and how this power is likely to change with years of observation, survey effort, and the size of decrease. These results can be useful to the planning of future monitoring and research of sea otters at San Nicolas Island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201115","collaboration":"Wildlife ProgramDirector, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The U.S. Geological Survey (USGS) has been monitoring streamflow in South Carolina since the late 1800s. From the beginning, the USGS streamgage network in South Carolina has been dynamic, with streamgages being added or removed depending on their purpose and the availability of funding from Federal, State, and local partners. Streamflow monitoring is important for acquiring real-time data during flood events, but also for collecting long-term data that can be used to compute the magnitude and frequency of floods and to frame flood events in a historical perspective. These data are also critical for being able to develop regional regression equations that can be used to estimate flood characteristics at ungaged locations, which is important for infrastructure planning and design. The historical flooding that occurred in South Carolina in 2015, 2016, and 2018 highlighted the importance of collecting these data. Therefore, the USGS, in cooperation with the South Carolina Department of Transportation, evaluated the USGS streamgage network in South Carolina for the purpose of helping guide decisions concerning future streamgage location selection, both spatially and in terms of the range of drainage basin characteristics that are typically important in flood-frequency analyses. The results of this evaluation are presented in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201104","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Feaster, T.D., and Kolb, K.R., 2020, Evaluation of the U.S. Geological Survey streamgage network in South Carolina, 2017: U.S. Geological Survey Open-File Report 2020–1104, 15 p., https://doi.org/10.3133/ofr20201104.","productDescription":"Report: vii, 15 p.; 1 Plate: 40.00 x 40.00 inches; Appendixes 1-3; Data Release","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116207","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
As part of a cooperative effort between the U.S. Geological Survey and the New York State Department of Environmental Conservation to evaluate the sustainability of Long Island’s sole-source aquifer system, the transmissivities of four aquifers were estimated from specific-capacity tests at 447 production wells in Nassau, Queens, and Kings Counties on Long Island, New York. The specific-capacity test data, which included pumping rate, pumping duration, and drawdown, were obtained from published and unpublished records of driller-reported acceptance tests collected at production wells screened in the upper glacial, Jameco, Magothy, or Lloyd aquifers. Pumping rates from the production wells during the tests generally were greater than 400 gallons per minute and ranged up to 1,800 gallons per minute. Pumping duration generally was 8 hours or more. Transmissivities were estimated from the specific-capacity data by the Cooper-Jacob approximation of the Theis equation. The transmissivity estimates are considered rough approximations because the aquifers do not meet the ideal assumptions of the method, well losses and partial penetration were not accounted for, and aquifer storage coefficients were not known but were only estimated from available data.
The transmissivities estimated from production wells screened in the upper glacial aquifer in the outwash plain south of the moraine generally were greater than those of the aquifer north of the moraine. The transmissivities estimated from the wells screened in the upper glacial aquifer south of the moraine typically ranged (as defined by the 10th and 90th percentiles) from 3,800 to 15,000 feet squared per day (ft2/d), with a median value of 7,300 ft2/d. The transmissivities estimated from the wells screened in the upper glacial aquifer north of the moraine typically ranged from 2,100 to 7,400 ft2/d, with a median value of 4,400 ft2/d. The Jameco aquifer generally had the highest estimated transmissivities of all the aquifers analyzed. The estimated transmissivities for the Jameco aquifer typically ranged from 5,500 to 43,000 ft2/d, with a median value of 16,000 ft2/d. The Magothy and Lloyd aquifers had similar estimated transmissivities. The transmissivities estimated for the Magothy aquifer typically ranged from 2,700 to 13,000 ft2/d, with a median of 7,100 ft2/d. The estimated transmissivities of the Lloyd typically ranged from 3,000 to 14,000 ft2/d, with a median of 7,200 ft2/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201108","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Williams, J.H., Woodley, M., and Finkelstein, J.S., 2020, Aquifer transmissivity in Nassau, Queens, and Kings Counties, New York, estimated from specific-capacity tests at production wells: U.S. Geological Survey Open-File Report 2020–1108, 7 p., https://doi.org/10.3133/ofr20201108.","productDescription":"Report: iv, 7 p.; Dataset; Application Site","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108170","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":379362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1108/coverthb.jpg"},{"id":379363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1108/ofr20201108.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1108"},{"id":379365,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://ny.water.usgs.gov/maps/aq-test/","text":"Aquifer Test Locator","linkFileType":{"id":5,"text":"html"}},{"id":379364,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Nassau County, Queens County, Kings County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0972900390625,\n 40.43858586704331\n ],\n [\n -73.388671875,\n 40.43858586704331\n ],\n [\n -73.388671875,\n 41.000629848685385\n ],\n [\n -74.0972900390625,\n 41.000629848685385\n ],\n [\n -74.0972900390625,\n 40.43858586704331\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The geologic map and cross sections are a redrafted and modified version of the Geologic map and map of mineral resources of the basins of Ghorband, Salang, and Panjsher; located in the Kapisa, Panjsher, Parwan, and Baghlan Provinces, Afghanistan. The original map and cross sections are contained in an unpublished Soviet report no. 1162A (Shadchinev and others, 1975) prepared in cooperation with the Ministry of Mines and Industries of the Royal Government of Afghanistan, in Kabul during 1975, under contract no. 55–184/17500. This redrafted map consists of parts of quadrangle map sheets 503–F, 504–C, 504–D, 504–E, and 504–F shown on an index map that can be found on the original 1:100,000-scale map by Shadchinev and others (1975). The redrafted map and cross sections illustrate the mineral deposits and geologic structure of the Ghorband, Salang, and Panjsher River Basins. Because there were no location coordinates provided on the original Soviet map, the map was registered to drainage patterns identified by contours from the Global Digital Elevation Model (GDEM). The end result can only be considered a best fit for the map extend, and some features may not be positioned in their correct geographic location.
The redrafted geologic map and cross sections reproduce the topology of rock units, contacts, and faults of the original Soviet map and cross sections, and includes minor modifications based on our examination of the originals. Table 1, provided on both map sheets 1 and 2, shows mineral commodity locations also from the original Soviet map. However, because of the poor quality of the original map, some map features could not be identified and some may be misinterpreted. Further, we have attempted to translate the original Russian terminology and rock classifications into modern English geologic usage as literally as possible without changing any genetic or process-oriented implications in the original rock-unit descriptions. We also use the rock-unit age designations from the original maps, however, rock-unit colors and symbols differ from the colors and symbols shown on the original version. Unit colors were selected according to the color and pattern scheme of the Commission for the Geological Map of the World (http://www.ccgm.org). Unit symbols were assigned based on the geologic age and unit descriptions provided on the original Soviet map. Elevations on the cross sections are derived from the original topography and may not match the Global GDEM topography used on the redrafted geologic map of this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201101","collaboration":"Prepared in cooperation with the Afghan Geological Survey under the auspices of the U.S. Agency for International Development","usgsCitation":"Stettner, W.R., Koroleva, N.E., Masonic, L.M., and Shields, D.A., comps., 2020, Geologic and mineral map (modified from the 1975 original map compilation by A.S. Shadchinev and others) and hyperspectral surface materials maps of the Ghorband, Salang, and Panjsher River Basins; Kapisa, Panjsher, Parwan, and Baghlan Provinces, Afghanistan: U.S. Geological Survey Open-File Report 2020–1101, 2 sheets, scale 1:100,000, https://doi.org/10.3133/ofr20201101.","productDescription":"2 Sheets: 41.50 x 30.50 inches and 41.50 x 52.00 inches","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057774","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":379032,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1101/ofr20201101_sheet2.pdf","text":"Sheet 2","size":"203 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":378954,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1101/coverthb.jpg"},{"id":378955,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1101/ofr20201101_sheet1.pdf","text":"Sheet 1","size":"61.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1101"}],"scale":"100000","country":"Afghanistan","state":"Baghlan, Kapisa, Panjsher, Parwan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 67.21435546875,\n 34.32529192442733\n ],\n [\n 71.3671875,\n 34.32529192442733\n ],\n [\n 71.3671875,\n 36.35052700542763\n ],\n [\n 67.21435546875,\n 36.35052700542763\n ],\n [\n 67.21435546875,\n 34.32529192442733\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Sheet 1
Sheet 2
Methods were developed for measuring immune function in Micropterus dolomieu (smallmouth bass). The ultimate objective is to monitor and evaluate changes over time in immune status and disease resistance in conjunction with other characteristics of fish health and environmental stressors. To test these methods for utility in ecotoxicological studies, 192 smallmouth bass, age 2 years and older, were collected from three sites within the Susquehanna River Basin and one site in the Ohio River Basin during spring and fall 2016 and 2017. The anterior kidney was aseptically removed and homogenized for leukocyte isolation. Leukocytes were tested for bactericidal activity against two species of bacteria; respiratory burst activity when stimulated with phorbol 12-myristate 13-acetate; and mitogenesis activity when stimulated with concanavalin A, phytohemagglutinin, and lipopolysaccharide. Tissues were preserved for histopathological analyses.
Two of the sites were part of a monitoring program at which surface-water samples were collected monthly (bimonthly in spring) for chemical contaminants. Significant seasonal and (or) site differences in all three immune function tests were observed. Interpretations of seasonal trends in immune function of wild fish or correlations with environmental variables and other factors are difficult to make owing to the complex nature of the immune response and the environment. Differences in immune function could potentially be related to a variety of confounding factors; therefore, additional endpoints and repeated sampling over an extended period are essential to draw conclusions on the immune status of wild fish.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201077","collaboration":"Prepared in cooperation with Pennsylvania Department of Environmental Protection","usgsCitation":"Smith, C.R., Ottinger, C.A., Walsh, H.L., and Blazer, V.S., 2020, Development of a suite of functional immune assays and initial assessment of their utility in wild smallmouth bass health assessments: U.S. Geological Survey Open-File Report 2020–1077, 23 p., https://doi.org/10.3133/ofr20201077.","productDescription":"vii, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118051","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":379041,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1077/ofr20201077.pdf","text":"Report","size":"13.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1077"},{"id":379040,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1077/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Tionesta Lake, Pine Creek, Upper Juniata River, West Branch Mahantango Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.79919433593749,\n 41.30257109430557\n ],\n [\n -77.38220214843749,\n 41.30257109430557\n ],\n [\n -77.38220214843749,\n 42.00032514831621\n ],\n [\n -79.79919433593749,\n 42.00032514831621\n ],\n [\n -79.79919433593749,\n 41.30257109430557\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
In 2018, the U.S. Geological Survey (USGS) published an Open-File Report (Tracey and others, 2018) presenting a Bayesian habitat selection model for golden eagles (Aquila chrysaetos) in San Diego County, California. The model used telemetry data to examine the effects of urban development, exurban development, and topography (characterized by a topographic position index and a vector ruggedness measure, TPI and VRM respectively) on golden eagle habitat selection probability. Based on figures 3 and 6 of Tracey and others (2018), we received inquiries from cooperators (U.S. Fish and Wildlife Service and California Department of Fish and Wildlife) about how the probability of eagle use declines with decreasing distance to the urban edge. Here, we clarify our results by addressing that question.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201110","collaboration":"Prepared in cooperation with San Diego Association of Governments, U.S. Fish and Wildlife Service, Bureau of Land Management, and California Department of Fish and Wildlife","usgsCitation":"Tracey, J.A., Madden, M.C., Bloom, P.H., and Fisher, R.N., 2020, A clarification on the effects of urbanization on Golden Eagle (Aquila chrysaetos) habitat selection: U.S. Geological Survey Open-File Report 2020–1110, 7 p., https://doi.org/10.3133/ofr20201110.","productDescription":"iv, 7 p.","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-121710","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":379081,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1110/covrthb.jpg"},{"id":379082,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1110/ofr20201110.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":379083,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181067","text":"Open-File Report 2018-1067","linkHelpText":"- Golden eagle (Aquila chrysaetos) habitat selection as a function of land use and terrain, San Diego County, California"}],"contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The long-term persistence of polar bears (Ursus maritimus) is threatened by sea-ice loss due to climate change, which is concurrently providing an opportunity in the Arctic for increased anthropogenic activities including natural resource extraction. Mitigating the risk of those activities, which can adversely affect the population dynamics of the southern Beaufort Sea (SBS) subpopulation, is an emerging challenge as polar bears become more reliant on land and come into more frequent contact with humans. The Marine Mammal Protection Act and Endangered Species Act require the U.S. Fish and Wildlife Service to determine whether industrial activities will have a negligible impact on the SBS subpopulation. Information important to making that determination includes estimates of subpopulation abundance and the number of maternal dens likely to be present in areas where industrial activities occur. We analyzed mark-recapture data collected from SBS polar bears sampled in Alaska during 2001–16 using multistate Cormack-Jolly-Seber models. Estimated survival rates were relatively high during 2001–03, lower during 2004–08, then higher during 2009–15 except for 2012. Estimated abundance in the Alaska part of the SBS was consistent with the estimated survival rates, declining from about 1,300 bears in 2003 to 525 bears in 2006 and then remaining generally stable during 2006–15. The point estimate for the Alaska part of the SBS in 2015, the last year in which abundance could be estimated, was 573 bears (95-percent credible interval = 232, 1,140 bears). To estimate the expected number of terrestrial dens likely to be present in a given region in a given year, we used a Bayesian modeling approach based on calculations derived from SBS demographic and denning data. We estimated that the entire SBS subpopulation produced 123 dens per year (median; 95-percent credible interval = 69, 198 dens), 66 (median; 95-percent credible interval = 35, 110 dens) of which were land-based. Most land-based dens were located between the Colville and Canning Rivers (which includes the Prudhoe Bay-Kuparuk industrial footprint), followed by the 1002 Area of the Arctic National Wildlife Refuge and the National Petroleum Reserve-Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201087","collaboration":"U.S. Geological Survey Wildlife Program","usgsCitation":"Atwood, T.C., Bromaghin, J.F., Patil, V.P., Durner, G.M., Douglas, D.C., and Simac, K.S., 2020, Analyses on subpopulation abundance and annual number of maternal dens for the U.S. Fish and Wildlife Service on polar bears (Ursus maritimus) in the southern Beaufort Sea, Alaska: U.S. Geological Survey Open-File Report 2020-1087, 16 p., https://doi.org/10.3133/ofr20201087.","productDescription":"Report: iv, 16 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-120083","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":378973,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1121","text":"Data Series 1121","description":"DS 1121"},{"id":378971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1087/coverthb.jpg"},{"id":378972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1087/ofr20201087.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1087"},{"id":378974,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A9E5UP","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Multistate capture and search data from the southern Beaufort Sea polar bear subpopulation in Alaska, 2001-2016"}],"country":"United States","state":"Alaska","otherGeospatial":"Southern Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.642578125,\n 69.06856318696033\n ],\n [\n -140.9326171875,\n 69.06856318696033\n ],\n [\n -140.9326171875,\n 72.40899172812024\n ],\n [\n -158.642578125,\n 72.40899172812024\n ],\n [\n -158.642578125,\n 69.06856318696033\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The greater sage-grouse (Centrocercus urophasianus; hereafter GRSG) has been a focus of scientific investigation and management action for the past two decades. The 2015 U.S. Fish and Wildlife Service listing determination of “not warranted” was in part due to a large-scale collaborative effort to develop strategies to conserve GRSG populations and their habitat and to reduce threats to both. New scientific information augments existing knowledge and can help inform updates or modifications to existing plans for managing GRSG and sagebrush ecosystems. However, the sheer number of scientific publications can be a challenge for managers tasked with evaluating and determining the need for potential updates to existing planning documents. To assist in this process, the U.S. Geological Survey (USGS) has reviewed and summarized the scientific literature published since January 1, 2015. The first GRSG literature summary was published early in 2018. Here we provide an update to that document by adding summaries of articles published between January 6, 2018 and October 2, 2019.
To identify articles and reports published about GRSG, we first conducted a structured search of three reference databases (Web of Science, Scopus, and Google Scholar) using the search term “greater sage-grouse.” We refined the initial list of products by (1) removing duplicates, (2) excluding products that were not published as research or scientific review articles in peer-reviewed journals or as formal technical reports, and (3) retaining only those products for which GRSG or their habitat was a research focus.
We summarized the contents of each product by using a consistent structure (background, objectives, methods, location, findings, and implications) and assessed the content of each product relevant to a list of 31 management topics. These topics include GRSG biology and habitat characteristics along with potential management actions, land uses, and environmental factors related to GRSG management and conservation. We also noted which articles/reports created new geospatial data.
Our original search, conducted on January 7, 2018, and the application of our criteria, resulted in the inclusion of 169 published products (2 of these products were published corrections to journal articles). This update adds summaries of 69 products published between then and October 2, 2019. The management topics most commonly addressed were GRSG behavior or demographics and GRSG habitat selection or habitat characteristics at broad or site scales. Few products addressed captive breeding, recreation, wild horses and burros, and range management structures (including fences). The management topics with the largest increase in representation between the 2018 GRSG literature summary and this update were GRSG survival and GRSG population estimates or targets, which were each addressed in 16 percent of products in the original literature summary document, but were addressed in 30 and 33 percent, respectively, of newly summarized products. Topics with the largest declines in representation were conifer expansion, - 17 to 10 percent, and new geospatial data, -31 to 21 percent. We include in this annotated bibliography the full citation, Digital Object Identifier (DOI), product summary, and management topics addressed by each product. The online version of this bibliography (https://apps.usgs.gov/gsgbib/index.php) is searchable by topic and location and includes links to journal landing pages for each original publication.
A substantial body of literature has been compiled on research explicitly related to the conservation, management, monitoring, and assessment of GRSG. These studies may inform planning and management actions that seek to balance conservation, economic, and social objectives and manage diverse resource uses and values across the western United States.
The review process for this product included requesting input on each summary from one or more authors of the original peer-reviewed article or report and a formal review of the entire document by three independent reviewers for the original document and by two independent reviewers for the updated document and, subsequently, the USGS Bureau Approving Official. This process is consistent with USGS Fundamental Science Practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201103","usgsCitation":"Carter, S.K., Arkle, R.S., Bencin, H.L., Harms, B.R., Manier, D.J., Johnston, A.N., Phillips, S.L., Hanser, S.E., and Bowen, Z.H., 2020, Annotated bibliography of scientific research on greater sage-grouse published from 2015 to 2019: U.S. Geological Survey Open-File Report 2020–1103, 264 p., https://doi.org/10.3133/ofr20201103.","productDescription":"v, 264 p.","onlineOnly":"Y","ipdsId":"IP-117994","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":378931,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://apps.usgs.gov/gsgbib/index.php","text":"Interactive, searchable version —","description":"Related Work - Interactive, searchable version","linkHelpText":"Annotated Bibliography of Scientific Research on Greater Sage-Grouse Published since January 2015"},{"id":378930,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1103/ofr20201103.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1103"},{"id":378929,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1103/coverthb.jpg"},{"id":378932,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181017","text":"Open-File Report 2018-1017 —","description":"Related Work - OFR 2018-1017","linkHelpText":"Greater Sage-Grouse Science (2015–17)—Synthesis and Potential Management Implications"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, Oregon, South Dakota, Texas, Utah, Washington, Wyoming","otherGeospatial":"Greater sage-grouse Management Zones","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.71679687499999,\n 32.54681317351514\n ],\n [\n -101.337890625,\n 34.95799531086792\n ],\n [\n -101.07421875,\n 52.482780222078226\n ],\n [\n -126.12304687500001,\n 50.233151832472245\n ],\n [\n -124.71679687499999,\n 32.54681317351514\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Sequoia Scientific’s LISST-ABS is a submersible acoustic instrument that measures the acoustic backscatter sensor (ABS) concentration at a point within a river, stream, or creek. Compared to traditional physical methods for measuring suspended-sediment concentration (SSC), sediment surrogates like the LISST-ABS offer continuous data that can be calibrated with physical SSC samples. Data were collected at 10 U.S. Geological Survey streamflow-gaging stations between January 10, 2016, and February 21, 2018, across the contiguous United States to test the accuracy and effectiveness of using the LISST-ABS as a surrogate for measuring the concentration of suspended sediment in a dynamic fluvial system. Correlation coefficients (Pearson’s r values) relating the ABS concentration and SSC from physical samples ranged from r = 0.718 to r = 0.956 at the 10 stations with the mean percentage of fines (percentage of the sediment less than 62.5 microns in diameter) ranging from 65 to 100 percent (with minimum and maximum values of 18 and 100 percent, respectively). The LISST-ABS instruments used in this field evaluation were factory-calibrated to accurately determine SSC for grains in the diameter range of 75–90 microns. Note that the sensor responds to grains of arbitrary sizes, but the accuracy varies at sizes other than this calibration size. For operational use, regression models could be determined for the ABS concentrations and SSC values or the instrument could be recalibrated to sediments for each fluvial environment. However, such calibrations were beyond the scope of this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201096","collaboration":"Federal Interagency Sedimentation Project and Observing Systems Division","usgsCitation":"Manaster, A.E., Straub, T.D., Wood, M.S., Bell, J.M., Dombroski, D.E., and Curran, C.A., 2020, Field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor: U.S. Geological Survey Open-File Report 2020–1096, 26 p., https://doi.org/10.3133/ofr20201096.","productDescription":"Report: v, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-116096","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":378643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1096/coverthb.jpg"},{"id":378644,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1096/ofr20201096.pdf","text":"Report","size":"3.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1096"},{"id":378645,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LROJE4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Mercury (Hg) has been a contaminant of concern for several decades in South Florida, particularly in the Florida Everglades. The transport and bioavailability of Hg in aquatic systems is intimately linked to dissolved organic carbon (DOC). In aquatic systems, Hg can be converted to methylmercury (MeHg), which is the form of Hg that bioaccumulates in food webs. The bioaccumulation of MeHg poses significant health risks to wildlife and humans. Fish consumption advisories triggered by elevated Hg levels first appeared in the 1980s in South Florida. Multiple structures regulate freshwater distribution to Everglades National Park, including S-12D. This report summarizes seasonal and annual concentration and load data from late September 2013 to April 2017 for the total of (1) filter-passing total mercury (FTHg), (2) filter-passing methylmercury (FMeHg), (3) particulate total mercury (PTHg), (4) particulate methylmercury (PMeHg) and, (5) DOC discharged through control structure S-12D. The loads of Hg fractions and DOC at control structure S-12D were determined by pairing discharge data with constituent concentrations estimated by empirical models based on surrogate in situ water-quality measurements.
Calculated concentrations of DOC ranged from 12.8 milligrams per liter (mg/L) to 27.9 mg/L with a mean of 18.8 mg/L during the study period. Annual loads of DOC ranged from 3,950 tons in 2015 to 10,900 tons in 2016. DOC loads increased linearly with an increase in flow, and the highest monthly DOC load of 1,630 tons was observed in February 2016.
Calculated concentrations of FTHg ranged from 0.35 to 1.55 nanograms per liter (ng/L) with a mean of 0.85 ng/L during the study period. Calculated concentrations of FMeHg ranged from 0.06 ng/L to 0.24 ng/L with a mean of 0.14 ng/L during the study period. Generally, FTHg and FMeHg concentrations were lower during periods of decreased flow and higher during periods of increased flow. Calculated PTHg concentrations ranged from 0.09 ng/L to 4.19 ng/L with a mean of 0.58 ng/L during the study period. Calculated PMeHg concentrations ranged from below the limit of detection <0.01 ng/L to 0.29 ng/L with a mean of 0.03 ng/L during the study period.
Loads of Hg were often zero or lowest from November to May, owing to the lack of flow or low-flow conditions. FTHg and FMeHg loads increased linearly with an increase in flow and typically were highest from June to October. During periods of increasing flow or following changes in gate operations, PTHg and PMeHg constituted a greater percentage of the total Hg load. Annual loads of total Hg (filter-passing and particulate) ranged from 254 grams in 2015 to 658 grams in 2016. FTHg was the predominant contributor to the total Hg load. Information presented herein provides the first assessment of DOC and Hg loads to Everglades National Park through control structure S-12D using continuous in situ measurements of discharge and constituent surrogates and compares the surrogate model approach to loads calculated from monthly sampling. Analysis of calculated and observed loads demonstrates the significance of flow data on calculating constituent loads.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Biological conservation often requires an understanding of how environmental conditions affect species occurrence and detection probabilities. We used a hierarchical framework to evaluate these effects for several Appalachian stream fish species of conservation concern: Chrosomus cumberlandensis (BSD; blackside dace), Etheostoma sagitta (CAD; Cumberland arrow darter), and Etheostoma spilotum (KAD; Kentucky arrow darter). Etheostoma susanae (Cumberland darter) also is present in the study area but was too rare to model in this analysis. In this study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, fish and habitat data were collected from 205 randomly selected stream sites in the upper Cumberland and Kentucky River Basins (120 and 85 sites, respectively) of Kentucky and Tennessee. Sites were sampled with 10 spatial replicates (2 meter x 5 meter electrofishing zones) to enable estimation of detection probabilities and environmental effects. The best models (that is, lowest Akaike information criterion scores) showed the effects of agriculture (negative) on occurrence of BSD and stream conductivity (negative) on occurrence of CAD and KAD. These effects were statistically more important than measures of basin area, elevation, and substrate size. Conductivity and agriculture showed nonlinear effects on species occurrence, and effects of conductivity were more precise above 400 microsiemens per centimeter than below this threshold. Models incorporated detection-level effects of electrofishing time (positive), flow velocity (negative), sand substrate (positive), and gravel/cobble substrate (negative). Models accounting for detection of BSD estimated occupancy rates similar to the observed proportion of occupied sites (0.10), but the best-supported models for CAD and KAD increased expected occupancy by about 4 percent for each species (from 0.17 to 0.21 for CAD and from 0.07 to 0.11 for KAD). Results of this study provide new inferences for modeling stream fish occurrence and detection processes and highlight the importance of continued monitoring and assessment of rare fish species in Appalachian headwater streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201100","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Hitt, N.P., Rogers, K.M., Kessler, K., and Macmillan, H., 2020, Modeling occupancy of rare stream fish species in the upper Cumberland and Kentucky River Basins: U.S. Geological Survey Open-File Report 2020–1100, 22 p., https://doi.org/10.3133/ofr20201100.","productDescription":"vi, 22 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118746","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":378605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1100/ofr20201100.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1100"},{"id":378604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1100/coverthb.jpg"}],"country":"United States","state":"Kentucky, Tennessee, Virginia","otherGeospatial":"Cumberland River basin, Kentucky River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1875,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 38.77121637244273\n ],\n [\n -87.1875,\n 38.77121637244273\n ],\n [\n -87.1875,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, used surface and borehole geophysical methods to delineate the freshwater/saltwater interface in coastal plain aquifers along the southwestern part of Long Island, New York. Over pumping of groundwater in the early 20th century combined with freshwater/saltwater interfaces at the coastline created saltwater intrusion in the upper glacial, Jameco, Magothy, and Lloyd aquifers. This study documents, for the first time, extensive saltwater intrusion of the Lloyd aquifer along the southwestern coast of Long Island, N.Y. Several public-supply wells in the southern parts of Nassau, Queens, and Kings Counties have been adversely affected by saltwater intrusion causing supply wells to be shutdown and abandoned. Due to the ongoing groundwater pumping in southern Nassau County, the freshwater/saltwater interface requires delineation and monitoring for any inland movement.
In 2015–17, the U.S. Geological Survey collected time domain electromagnetic soundings at 12 locations and borehole electromagnetic induction conductivity logs at 9 wells within the study area to delineate several saltwater intrusion wedges. The upper glacial, Jameco, and Magothy aquifers were grouped into one aquifer complex within the study area to simplify interpretations. The coastal plain sediments increase in thickness from west to east and north to south because of their regional dip toward the southeast. Three separate wedges, shallow, intermediate, and deep, of saltwater intrusion were delineated in the upper glacial, Jameco, and Magothy aquifer complex. In addition, analysis of geophysical logs collected in an open borehole of a test well in southern Queens County in 1989 revealed the Lloyd aquifer was nearly completely intruded by saltwater with an estimated chloride concentration of 15,000 milligrams per liter. The geophysical logs from this well provides, for the first time, definitive proof of saltwater intrusion of the Lloyd aquifer on Long Island’s south shore, suggesting the freshwater/saltwater interface was at the coastline and not miles offshore as theorized by previous studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201093","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stumm, F., Como, M.D., and Zuck, M.A., 2020, Use of time domain electromagnetic soundings and borehole electromagnetic induction logs to delineate the freshwater/saltwater interface on southwestern Long Island, New York, 2015–17: U.S. Geological Survey Open-File Report 2020–1093, 27 p., https://doi.org/10.3133/ofr20201093.","productDescription":"Report: vi, 27 p.; Data Release; Database","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118303","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378439,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7X63KT0","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS GeoLog Locator"},{"id":378438,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens County, New York, October–November 2017"},{"id":378437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1093/ofr20201093.pdf","text":"Report","size":"3.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1093"},{"id":378436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1093/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.03961181640625,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.54406959045767\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Power consumption coefficients (PCCs) and dedicated flowmeter records for irrigation wells in three Utah groundwater basins were analyzed to develop a method to better characterize the accuracy of annual groundwater withdrawal estimates. The PCC method has been used by the U.S. Geological Survey in Utah since 1963 as a way to estimate groundwater withdrawal. As a result, most irrigation wells in Utah have historic records consisting of multiple PCCs. Over time, numerous wells have been retrofitted with dedicated flowmeters to more accurately describe groundwater use for irrigation. The combination of historical PCCs and flowmeter data was examined to classify wells as simple, complex, or borderline. The PCCs for each well were statistically analyzed for each period of record to determine the PCC coefficient of variation (CV). Variance, standard deviation, and CV also were calculated for each well, yielding similar results. The CV was selected as the best statistical method for classifying wells. Through field verification and examination of records, CV thresholds were established, allowing wells to be classified as simple, complex, or borderline. This well classification provides information on the uncertainty and best methods for quantifying annual groundwater withdrawals from irrigation wells in a basin.
Annual irrigation groundwater withdrawals in Tooele, Parowan, and Goshen Valleys were calculated by using various combinations of historical PCC records and data from dedicated flowmeters. Differences between annual groundwater withdrawal using the most recent measurements, and historic minimum, maximum, mean, and median PCCs were compared. The smallest percent difference between annual groundwater withdrawal calculated using the most recently measured PCCs, which is the current method for calculating withdrawal in most basins, in Tooele and Parowan Valleys, was 7 and 9 percent respectively, using historical median and mean.
In Goshen Valley, most wells have dedicated flowmeters, and there is a subset of wells that have 2016 power usage data, historical PCC records, and 2016 reported dedicated flowmeter withdrawal. Using this subset of irrigation wells, the smallest percent different between withdrawal from dedicated flowmeters and withdrawal calculated by using other methods was 5 percent (using withdrawal calculated with historical mean PCCs for each well). Annual groundwater withdrawal calculated using the most recently measured PCCs was 9-percent less than dedicated flowmeter reported withdrawal. So, if withdrawal from dedicated flowmeters is as close to reality as possible, then in the case of Goshen Valley, using historical mean PCCs to calculate withdrawal is closer to reality than using the most recently measured PCCs to calculate withdrawal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201106","collaboration":"Water Availability and Use Science ProgramDirector, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
The East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan (HCP/NCCP) Preserve System was designed to protect and enhance ecological diversity and function in eastern Contra Costa County, California. Aquila chrysaetos (golden eagle) is a special-status species expected to benefit from biological goals of the HCP/NCCP. As part of a broader study, we estimated site-occupancy, abundance, and reproduction of golden eagles in the HCP/NCCP inventory area in 2019. We completed 99 surveys and recorded a total of 50 detections of territorial pairs of eagles at 20 (67 percent) of 30 sites (13.9-square-kilometer [km2] plots). Detection probability of territorial pairs was highest in January and February (≥0.75) and lowest in mid-June to late July (<0.50). After correcting for imperfect detection, the expected probability of site-occupancy was 0.69 (standard error [SE] = 0.09), and mean expected abundance was 0.76 pairs per site (SE = 0.16), or 27.4 pairs per 500 km2. We found evidence of successful nesting (≥1 young fledged) for 3 (14 percent) of 22 pairs of eagles monitored in 2019. Our study design and baseline results should be useful for future monitoring and conservation of golden eagles in the HCP/NCCP area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201107","collaboration":"Prepared in cooperation with East Contra Costa County Habitat Conservancy Science and Research Grant Program, East Bay Regional Parks District, Save Mount Diablo’s Mary Bowerman Science and Research Grant Program, and NextEra Energy","usgsCitation":"Wiens, J.D., Kolar, P.S., and Bell, D.A., 2020, Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California: U.S. Geological Survey Open-File Report 2020-1107, 11 p., https://doi.org/10.3133/ofr20201107.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-119617","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":378434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1107/coverthb.jpg"},{"id":378435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1107/ofr20201107.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1107"}],"country":"United States","state":"California","county":"Contra Costa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75048828124999,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 37.37015718405753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Because of its importance for species covered under Federal Fishery Management Plans (FMPs), the San Francisco Bay (SFB) estuary has been designated as Essential Fish Habitat (EFH) under the Magnuson-Stevens Fishery Conservation and Management Act (MSA; 16 United States Code §18559b). Within this estuary, benthic macroinvertebrate communities provide important prey resources for many economically significant fish species that rely on EFH. Periodic maintenance dredging can impact benthic communities; however, there is a lack of scientific information specific to SFB regarding dredging effects on macroinvertebrates in fish foraging areas. In addition, rates of benthic community recolonization and recovery following dredging and subsequent effects on foraging fish are unknown. For this reason, it is difficult for regulatory and resource agencies to determine the impacts of maintenance dredging. Thus, the National Marine Fisheries Service (NMFS) and the consortium of agencies (U.S. Environmental Protection Agency [EPA], U.S. Army Corp of Engineers [USACE], San Francisco Regional Water Quality Control Board [SFRWQCB], and San Francisco Bay Conservation and Development Commission [BCDC]) that make up the San Francisco Bay Long Term Management Strategy for Dredging (LTMS) identified a study of dredging impacts on SFB fish foraging habitat as one of their highest priorities in their 2011 Programmatic EFH Agreement (U.S. Army Corp of Engineers and U.S. Environmental Protection Agency, 2011).
The LTMS agencies identified the region of interest as shallow (<13 feet [<4 meters (m)] mean lower low water [MLLW]), soft-bottom (silt/clay soil texture) areas in the Central Bay of SFB that were periodically dredged (every 1–3 years). Fish species of interest were compiled by NMFS and included those managed by the Pacific Groundfish, Pacific Salmon, and Coastal Pelagic FMPs (pursuant to the MSA) as well as those listed under the California State or Federal Endangered Species Act (ESA; 16 U.S.C. §1531–1544) as threatened or endangered. Target species included leopard shark (Triakis semifasciata), big skate (Raja binoculata), English sole (Parophrys vetulus), starry flounder (Platichthys stellatus), brown rockfish (Sebastes auriculatus), green sturgeon (Acipenser medirostris; threatened species under Federal ESA), northern anchovy (Engraulis mordax), longfin smelt (Spirinchus thaleichthys, threatened under California ESA), and Pacific sardine (Sardinops sagax). In addition, Dungeness crab (Cancer magister), California halibut (Paralichthys californicus), and white sturgeon (Acipenser transmontanus) also were included because they are substantial contributors to the California State fishery.
To address LTMS priorities, U.S. Geological Survey, Western Ecological Research Center, San Francisco Bay Estuary Field Station (hereafter USGS) conducted a multi-phased project including an initial literature review, study design, pilot study, and implementation of a full study. The overarching goal was to assess the effects of periodic dredge operations (every 1–3 years) on benthic habitat for foraging fish in the Central Bay, with emphasis on the foraging requirements of target fish species and analyses of benthic macroinvertebrates in dredged areas compared to adjacent undredged reference areas. The USGS partnered with University of California, Davis, fisheries expert James Hobbs to synthesize existing knowledge of fish foraging ecology and review benthic infauna community composition in SFB with a focus on the Central Bay. The literature review (Phase I; De La Cruz and others, 2016) addressed key questions identified by the LTMS on benthic foraging fish in the study area, including the following: (1) What are target fish eating? (2) What are the seasonal differences in prey items and macroinvertebrate assemblages? (3) What are the annual differences in prey items and macroinvertebrate assemblages? (4) What are the predominant macroinvertebrate functional groups from the perspective of fish foraging? Phase II consisted of creating a framework for a functional assessment of maintenance dredging effects on foraging fish and drafting a full study design (De La Cruz and others, 2017), which was then tested in the Phase III pilot study. The Phase IV full study incorporated lessons learned from the pilot study. Here we focus on the results of the full study and implications for benthic foraging fishes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201086","usgsCitation":"De La Cruz, S.E.W., Woo, I., Hall, L., Flanagan, A., and Mittelstaedt, H., 2020, Impacts of periodic dredging on macroinvertebrate prey availability for benthic foraging fishes in central San Francisco Bay, California: U.S. Geological Survey Open-File Report 2020–1086, 96 p., https://doi.org/10.3133/ofr20201086.","productDescription":"x, 96 p.","onlineOnly":"Y","ipdsId":"IP-112237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":378273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1086/coverthb.jpg"},{"id":378274,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1086/ofr20201086.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1086"}],"country":"United States","state":"California","otherGeospatial":"Central San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65411376953125,\n 37.75334401310656\n ],\n [\n -122.17346191406249,\n 37.75334401310656\n ],\n [\n -122.17346191406249,\n 37.98317483351337\n ],\n [\n -122.65411376953125,\n 37.98317483351337\n ],\n [\n -122.65411376953125,\n 37.75334401310656\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
We assessed the history of flow and riparian ecosystem change along the Wind River using cottonwood tree-ring data, streamgage records, historical temperature and precipitation data, drought indices, and local observations and Traditional Ecological Knowledge from elders of the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming. This assessment identified impacts that have occurred to riparian resources of concern to the Tribes, which will assist in prioritizing drought planning efforts. Impacts included reduced abundance, reduced regeneration, and increased mortality in cottonwoods (Populus deltoides and P. angustifolia); an increase in invasive species, especially Russian olive (Elaeagnus angustifolia), that are gradually replacing cottonwoods and other native woody plants; decreased abundance of native and culturally important plants; reduced abundance of culturally important fish; reduced volume and changes to the timing of flows; and changes in river course. This assessment documented the biophysical and social factors that have contributed to riparian ecosystem change and to reduced water availability and flows, including agricultural diversion, drought, and fire. Cottonwoods along the Wind River are as much as 300 years old. By relating tree-ring width to recorded streamflows, we were able to reconstruct streamflows confidently back to the 1850s and speculatively back to the mid-1700s. Extending the historical record of streamflows allows for a more-complete understanding of hydroclimatic variability and provides a foundation for developing preparedness and response strategies for drought management. Ring width of cottonwood trees at the Boysen Site was more strongly correlated to river flow than to local precipitation or temperature, indicating that growth of trees is controlled more by montane snowmelt than by local weather. Therefore, tree rings are a better indicator of water supply than of the local conditions controlling water demand. The extended flow record from tree rings revealed the occurrence of a major period of low flow from 1870 to 1910 that was not evident in the shorter instrumental records of flow and weather. Information from tree rings, streamflow measurements, drought indices, and elder observations all suggest that the early 2000s drought was the most severe, sustained drought in the last century. Our results illustrate how drought is experienced in different ways across locations and sectors, which underscores the importance of using multiple indicators for drought management. These results will contribute to ongoing assessment, monitoring, and planning efforts at the Wind River Reservation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201072","collaboration":"Prepared in cooperation with the Eastern Shoshone Tribe of the Wind River Reservation, Wyoming, the Northern Arapaho Tribe of the Wind River Reservation, Wyoming, and Colorado State University","usgsCitation":"McNeeley, S.M., Friedman, J.M., Beeton, T.A., and Thaxton, R.D., 2020, Cottonwoods, water, and people—Integrating analysis of tree rings with observations of elders from the Eastern Shoshone and Northern Arapaho Tribes of the Wind River Reservation, Wyoming: U.S. Geological Survey Open-File Report 2020–1072, 33 p., https://doi.org/10.3133/ofr20201072.","productDescription":"Report: iv, 33 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-113563","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":378034,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S1UIAL","text":"USGS data release","linkHelpText":"Tree-Ring Data Collected in 2017 and 2018 From Cottonwood Trees Along the Wind River in Wind River Indian Reservation, Wyoming"},{"id":377898,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1072/coverthb.jpg"},{"id":377899,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1072/ofr20201072.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1072"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.44442749023438,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 42.83569550641452\n ],\n [\n -108.160400390625,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 43.54456658436357\n ],\n [\n -109.44442749023438,\n 42.83569550641452\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Avenue, Bldg. C
Fort Collins, CO 80526
The Gunnison Basin Selenium Management Program implemented a water-quality monitoring network in 2011 to measure concentrations of selenium in the lower Gunnison River Basin in Colorado. Selenium is a trace element that bioaccumulates in aquatic food chains. Selenium is essential for life, but elevated amounts can cause reproductive failure, deformities, and other harmful effects. The primary goal of the Selenium Management Program is to meet the State of Colorado water-quality standard of 4.6 micrograms per liter (µg/L) for dissolved selenium at the U.S. Geological Survey (USGS) streamflow-gaging station number 09152500—Gunnison River near Grand Junction, Colorado—herein referred to as “Whitewater.” The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, has completed a review of dissolved-selenium data collected from the Selenium Management Program network during Water Year (WY) 2017 (October 1, 2016 through September 30, 2017) to further the understanding of the status and trends of selenium in the basin. This report presents the percentile values for selenium because regulatory agencies in Colorado make decisions based on the U.S. Environmental Protection Agency’s Clean Water Act section 303(d), which uses percentile values for concentrations. Also presented are dissolved-selenium loads at 14 sites in the lower Gunnison River Basin for WYs 2011–17. Annual dissolved-selenium loads were calculated for six sites with continuous U.S. Geological Survey streamflow-gaging stations. These six sites are referred to as “core” sites in this report. The remaining sites, which do not have streamflow-gaging stations, are referred to as “ancillary” sites in this report. During WY 2017, the loads calculated at the six core sites ranged from 306 pounds (lb) at Uncompahgre River at Colona to 12,600 lb at Whitewater, respectively.
By using discrete water-quality samples and the associated discharge measurements, instantaneous loads were calculated for 14 sites in WYs 2011–17 where discrete water-quality sampling took place. Median instantaneous loads ranged from 0.52 pounds per day (lb/d) at Uncompahgre River at Colona to 35.7 lb/d at Whitewater. Mean instantaneous loads ranged from 0.63 lb/d at Cummings Gulch at mouth to 35.5 lb/d at Whitewater. Most tributary sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb/d. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado’s water-quality standard for dissolved selenium of 4.6 µg/L was compared to the 85th percentiles for dissolved selenium at selected sites. Annual 85th percentiles for dissolved selenium were calculated by using estimated dissolved-selenium concentrations from linear regression models for the six core sites with U.S. Geological Survey streamflow-gaging stations. The 85th-percentile concentrations for WY 2017 based on this method ranged from 0.68 µg/L at Uncompahgre River at Colona to 140 µg/L at Loutzenhizer Arroyo at North River Road. The 85th percentiles for concentrations of dissolved selenium also were calculated from water-quality samples collected during WY 2017 from sites with sufficient data. The annual 85th-percentile concentrations based on the discrete samples ranged from 0.75 µg/L at Uncompahgre River at Colona to 106 µg/L at Loutzenhizer Arroyo at North River Road.
An analysis was completed for Whitewater to determine if an upward or downward trend exists for dissolved-selenium loads during two time periods. The first time period included all data at Whitewater, whereas the second time period focused on more recent data. The trend analysis indicates a decrease from 22,200 to 12,600 lb, which is a 43.1 percent (9,600 lb) reduction during the time period WY 1986 through WY 2017. The trend analysis for the annual dissolved-selenium load for WY 1995 through WY 2017 indicates a decrease of 6,600 lb per year, or 35.5 percent. An evaluation of laboratory bias was completed for selenium data which was used in the trend analysis. Findings indicated a potential positive bias of approximately 12 percent may exist in the data from October 2005 through August 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201078","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2020, Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17: U.S. Geological Survey Open-File Report 2020–1078, 21 p., https://doi.org/10.3133/ofr20201078","productDescription":"v, 21 p.","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1078/ofr20201078.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1078"},{"id":377860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1078/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Coastal cliffs constitute 80 percent of the world’s coastline, with seacliffs fronting a large proportion of the U.S. West Coast shoreline, particularly in California. Erosion of coastal cliffs can threaten infrastructure and human life, yet the spatial and temporal scope of cliff studies have been limited by cumbersome traditional methods that rely on the manual interpretation of seacliff features—especially seacliff toes and top edges. The Cliff Feature Delineation Tool (CFDT) and the Baseline Builder Tool are designed to increase the efficiency of deriving seacliff features from remote sensing datasets by utilizing an automated, quantitative approach that eliminates traditional interpretive methods and ensures reproducibility. This document functions as a user guide for operating the Cliff Feature Delineation Tool and Baseline Builder Tool and includes a walkthrough of data-visualization and data-review workflows for the tools’ three-dimensional (3D) cliff feature outputs. Also included is a brief overview of cliff feature delineation at the U.S. Geological Survey (USGS) and a detailed description of the tools’ algorithmic logic.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201070","usgsCitation":"Seymour, A.C., Hapke, C.J., and Warrick, J., 2020, Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide: U.S. Geological Survey Open File Report 2020–1070, 54 p.,\nhttps://doi.org/10.3133/ofr20201070.","productDescription":"Report: vi, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112057","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":377578,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1070/ofr20201070.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1070"},{"id":377535,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1070/coverthb2.jpg"},{"id":377532,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UKW7PO","text":"USGS software release","linkHelpText":"Cliff Feature Delineation Tool and Baseline Builder version 1.0"}],"contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Modeling of ecosystems is a part of the U.S. Environmental Protection Agency’s protocol for developing site-specific selenium guidelines for protection of aquatic life. Selenium as an environmental contaminant is known to bioaccumulate and cause reproductive effects in fish and wildlife. Here we apply a modeling methodology—ecosystem-scale selenium modeling—to understand and document the scientific basis for predicting and validating ecological protection for Lake Koocanusa, a transboundary reservoir between Montana and British Columbia. A comprehensive set of site-specific data compiled from public databases (Federal, State, and Provincial) and reports by Teck Coal Ltd., is available in a companion U.S. Geological Survey data release. The tissue guideline used within modeling here to assess protection is the U.S. Environmental Protection Agency’s national selenium guideline for whole-body fish (dry weight); however, other numeric values for a whole-body guideline or other tissue types may be assumed if applicable tissue-to-tissue conversion factors are available.
We consider the report assembled here as a working document that presents a model that can effectively address and structure the needs of (1) scientific understanding in representing the lake’s ecosystem and selenium biodynamics and (2) policy and management development during a decision-making process, but it is open to modification and updating as more ecologically detailed data become available. The approach brings together the main concerns involved in selenium toxicity: likelihood of high exposure, inherent species sensitivity, and close connectivity of ecosystem characteristics and behavioral ecology of predators. Detailed site-specific modeling equations are provided to document the linked factors that determine the responses of ecosystems to selenium. A series of scenarios quantifies the implications of choices of site-specific variables including food-web species, bioavailability of particulate material, and partitioning between the dissolved and particulate phases at the base of food webs. A gradient mapping tool applied to Lake Koocanusa provides a precedent for ecosystem-scale modeling of lakes by recognizing the importance of lake strata and hydrodynamics as components of modeling.
Data requirements for ecosystem modeling, including ecological and hydrological process information fundamental to the dietary biodynamics of selenium in site-specific food webs, were assessed as a precursor to model validation for Lake Koocanusa. Understanding these relationships is necessary to connect modeling outcomes to reproductive effects and establish boundaries, in the case of Lake Koocanusa, for the influences of dam operation, fish-community viability, and its Clean Water Act impaired 303(d)-listing status on ecosystem function.
We find that an assemblage of conditions affects the representation of Lake Koocanusa’s ecosystem within modeling scenarios but that the constructed gradient maps, mechanistic model, and associated bioaccumulation potentials portray and quantify the variables that are determinative to protection of predator species. Ecological and hydrological sorting of compiled individual data points on a site- and species-specific basis helps identify and address model uncertainties. Sources of uncertainty include (1) the scarcity of data for some environmental media compartments across time and locations, (2) the complexity of hydrodynamic conditions that can lead to seasonal ecological disconnects such as in selenium partitioning from water into particulates, and (3) the functional status of Lake Koocanusa’s ecosystem because of cumulative effects of various environmental stresses (for example, fish-community changes, flow regime changes, parasites, gonadal dysfunction, and increasing mining input-selenium concentrations since 1984). To this last point, it is important to determine where Lake Koocanusa is in an impairment-restoration cycle so as not to base protection on survivor bias, the maintenance of a currently degraded ecosystem, or normalized toxicity. In a broader context, one of the overall consequences of revised selenium regulations is that their derivation is now dependent on being able to define and understand the status of the ecosystem on which protection is based.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201098","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Presser, T.S., and Naftz, D.L., 2020, Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada: U.S. Geological Survey Open-File Report 2020–1098, 40 p., https://doi.org/10.3133/ofr20201098.","productDescription":"Report: viii, 40 p.; 3 Tables; Data Releases","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436823,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99LM27E","text":"USGS data release","linkHelpText":"Results of Ecosystem Scale Selenium Modeling in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada, 2020"},{"id":377297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HB5S5F","text":"USGS data release","description":"USGS Data Release","linkHelpText":"USGS measurements of dissolved and suspended particulate material selenium in Lake Koocanusa in the vicinity of Libby Dam (MT), 2015–2017 (update)"},{"id":377296,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VXYSNZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selenium concentrations in food webs of Lake Koocanusa in the vicinity of Libby Dam (MT) and the Elk River (BC) as the basis for applying ecosystem-scale modeling, 2008–2018"},{"id":377295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1098"},{"id":377294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1098/coverthb.jpg"},{"id":377363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098_tables_1_and_3_to_10.xlsx","text":"Tables 1 and 3–10","size":"91.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1098 Tables"}],"country":"United States, Canada","state":"Montana, British Columbia","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.72998046875,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 48.33251726168281\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Mission Area
U.S. Geological Survey
345 Middlefield Rd.
Menlo Park, CA 94025
Kelp forests and rocky reefs are among the most recognized marine ecosystems and provide the primary habitat for several species of fishes, invertebrates, and algal assemblages (Stephens and others, 2006). In addition, kelp forests have been shown to be important carbon dioxide sinks (Wilmers and others, 2012) and are an important source of nearshore marine primary production (Duggins and others, 1989). These highly dynamic ecosystems are extremely variable, and both top-down and bottom-up ecological controls drive this rich trophic environment. Giant kelp (Macrocystis pyrifera) forests and the species that inhabit these ecosystems are influenced by several environmental conditions, such as wave exposure, water temperature, water clarity, bottom depth and composition, species composition, and the density of kelp and other algal assemblages (Schiel and Foster, 2015). However, in addition to “normal” variability, kelp forests can undergo extreme regime shifts from kelp canopy forested areas to barrens characterized by high densities of urchins and encrusting coralline algae (Harrold and Reed, 1985).
San Nicolas Island (SNI), outermost of the California Channel Islands, is home to a diverse group of terrestrial and marine organisms and includes kelp bed and rocky reef habitats (fig. 1). The SNI kelp forests not only provide food and shelter for fishes and invertebrates within the habitat, but also they support higher trophic level consumers such as marine birds and several marine mammal species including the southern sea otter (Enhydra lutris nereis), a major predator on sea urchins and other marine invertebrates.
Owing to concern about the vulnerability of the California population, the U.S. Fish and Wildlife Service (USFWS) translocated 140 southern sea otters from the central California coast to SNI between 1987 and 1990. Although only approximately 14 translocated otters are thought to have remained at SNI (U.S. Fish and Wildlife Service, 2012), their population at the island has increased and is currently greater than 120 individuals (Hatfield and others, 2019). Sea otters are a natural part of the kelp forest ecosystem, but their presence has implications for community dynamics as they repopulate a region from which they were extirpated in the 19th century. At SNI, sea otters have been concentrated mostly around the west end of the island, with some use of the south side and very little, but expanding, use of the northeast side. An ecosystem shift from urchin dominated to kelp dominated, that occurred at a site at the west end of the island in the early 2000s, though initiated by sea urchin disease, was likely facilitated to some degree by sea otter foraging (Kenner and Tinker, 2018).
These ecosystems also are the target of many fisheries, including urchin and lobster. Urchin fisheries, which target the larger red sea urchin, may release the smaller but more mobile purple sea urchin from competitive control (Dayton and others, 1998). Lobster fisheries may release purple sea urchins from predatory control (Lafferty, 2004). Owing to the distance from the mainland, however, SNI kelp forests and reefs have been somewhat protected from the degree of harvest and other anthropogenic impacts experienced by the southern California mainland. Invasive species are another issue, and there are a few invasive subtidal macroalgae of concern in southern California waters. Although the brown alga Sargassum muticum has been established at the island for decades, S. horneri has only recently been seen at SNI and, so far, the invasive kelp Undaria pinnatifida and the green alga Caulerpa taxifolia have not been observed there. Sargassum horneri, in particular, has demonstrated a capability to outcompete native kelps at some of the other Channel Islands but it is unclear what indirect effects it may have on community structure (Marks and others, 2015).
Because the surrounding kelp forests fall within the management boundary of the SNI Integrated Natural Resources Management Plan (INRMP; U.S. Navy, 2015), USGS works with the Navy to provide surveys of this ecologically important ecosystem that inform natural resource managers of trends in the population abundance of particular species. In addition, long-term surveys allow for an understanding of potential changes in species diversity and community composition as a result of trophic or other interactions.
The U.S. Geological Survey (USGS) implemented a kelp forest monitoring program for the U.S. Navy at San Nicolas Island in 2014, building on sites and methods established by USFWS scientists in 1980 (appendix 1). This report focuses on data collected during sampling expeditions to these sites in fall 2018 (October 2–5) and spring 2019 (April 3–6). Together they will be herein referred to as year 5 because, although the trips were made in different calendar years, they were approximately 6 months apart and were conducted under the fifth year of this contract. The previous sampling year (fall 2017 and spring 2018) is referred to as year 4. The year 5 data are compared with data collected during eight trips from fall 2014 through spring 2018. Differences in counts between these expeditions can result from seasonal factors, stochastic variation, or sampling error, but temporal comparison can reveal population trends. Where appropriate, long-term data collected during the 33 years prior to the implementation of these slightly revised protocols will be presented in order to lend some context to the observations reported here.
Genus and species names used in this report are those currently recognized as valid in the Integrated Taxonomic Information System (ITIS.gov). Upon first use, the name recognized as valid by the World Register of Marine Species (WoRMS; marinespecies.org) is shown in brackets if different. The exception is Sargassum horneri which does not show up in any discernable form in ITIS.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201091","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J.A., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, fifth annual report: U.S. Geological Survey Open-File Report 2020–1091, 93 p., https://doi.org/10.3133/ofr20201091.","productDescription":"ix, 93 p.","onlineOnly":"Y","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":377300,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1091/coverthb.jpg"},{"id":377301,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1091/ofr20201091.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1091"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"Naval Facility San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.60197448730467,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.19675310661128\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
As much as 22 inches of rain fell in Oklahoma in May 2019, resulting in historic flooding along the Arkansas River and its tributaries in eastern and northeastern Oklahoma. The flooding along the Arkansas River and its tributaries that began in May continued into June 2019. Peaks of record were measured at nine U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) streamgages on various streams in eastern and northeastern Oklahoma. This report documents the peak streamflows and stages for 38 selected streamgages in eastern and northeastern Oklahoma and is a followup to a previous report by the USGS that documented flood peaks associated with the May 2019 flood event. Most of the flood peaks occurred from May 26 to June 4, 2019. This report includes data from streamgages on tributaries to the Arkansas River and uses modeling methods to extend the period of record for Arkansas River streamgages. The historic flooding caused homes to fall into the river as a result of bank erosion, forced some towns to be evacuated, and resulted in the highest flood depths in Tulsa, Oklahoma, since 1986. Several USGS and USACE streamgages along the Arkansas River and its tributaries recorded new peaks of record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Lewis, J.M., Williams, D.J., Harris, S.J., and Trevisan, A.R., 2020, Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River: U.S. Geological Survey Open-File Report 2020–1090, 18 p., https://doi.org/10.3133/ofr20201090.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-118379","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":377112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3Q6MB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"RiverWare model outputs for flood calculations along the Arkansas River for a flood event in eastern and northeastern Oklahoma during May–June 2019"},{"id":377111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1090/ofr20201090.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1090"},{"id":377110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1090/coverthb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 34.59704151614417\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Hydrodynamic model hindcasts of the surface water and groundwater of the Everglades and the greater Miami, Florida, area were used to simulate hydrology using estimated storm surge height, wind field, and rainfall for the Great Miami Hurricane (GMH), which struck on September 18, 1926. Ranked estimates of losses from hurricanes in inflation-adjusted dollars indicate that the GMH was one of the most damaging tropical cyclones to make landfall in the United States, but little hydrologic data were collected because many types of field stations did not exist at the time. Several techniques were used to estimate previously unknown critical storm variables for model input, demonstrating the value of reanalyzing historical storm events using modern hydrodynamic modeling. This representation of the 1926 GMH was then used to develop a hypothetical simulation of the hydrologic effects of a similar hurricane occurring in contemporary (1996) times. Results indicate that the 18-centimeter sea-level rise between 1926 and 1996 had a greater effect on salinity intrusion than climatic differences or the development of modern canal-based infrastructure. Moreover, the post-1926 canal infrastructure does not seem to substantially mitigate the deleterious effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201010","usgsCitation":"Krohn, M.D., Swain, E.D., Langtimm, C.A., and Obeysekera, J., 2020, Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida: U.S. Geological Survey Open-File Report 2020–1010, 9 p., https://doi.org/10.3133/ofr20201010.","productDescription":"Report: iv, 9 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-073595","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375607,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C681IV","text":"USGS data release","linkHelpText":"FTLOADDS (combined SWIFT2D surface-water model and SEAWAT groundwater model) simulator used to repurpose a hindcast simulation of the 1926 Great Miami Hurricane using the south Florida peninsula for the Biscayne and Southern Everglades Coastal Transport (BISECT) model"},{"id":375605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1010/coverthb.jpg"},{"id":375606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1010/ofr20201010.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1010"}],"country":"United States","state":"Florida","city":"Miami","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.55999755859375,\n 25.209911213827688\n ],\n [\n -80.28533935546875,\n 25.199970890386023\n ],\n [\n -80.04638671875,\n 25.403584973186703\n ],\n [\n -80.04638671875,\n 26.23430203240673\n ],\n [\n -80.52978515625,\n 26.23430203240673\n ],\n [\n -80.55999755859375,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, Florida 33559
Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201053","usgsCitation":"Rigler, E.J., and Claycomb, A.E., 2020, Adjusted geomagnetic data—Theoretical basis and validation: U.S. Geological Survey Open-File Report 2020–1053, 19 p., https://doi.org/10.3133/ofr20201053.","productDescription":"iv, 19 p.","onlineOnly":"Y","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":376988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1053/coverthb.jpg"},{"id":376989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1053/ofr20201053.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1053"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046