Environmental DNA (eDNA) samples that are collected from remote locations depend on rapid stabilization of the DNA. The degradation of eDNA in water samples is minimized when samples are stored at ≤ 4 °C. Developing a preservation technique to maintain eDNA integrity at room temperature would allow a wider range of locations to be sampled. We evaluated an ethanol and sodium acetate solution to maintain the integrity of the DNA samples for the time between collection and lab testing. For this evaluation, replicate water samples taken from a tank housing Asian carp were placed on ice or held at room temperature. At both temperatures, water samples were left untreated or were preserved with an ethanol and sodium acetate solution (EtOH–NaAc). Every day for 6 days following collection, a subset of the samples was removed from each preservation method and DNA was extracted and nuclear and mitochondrial markers were assayed with qPCR. Results showed comparable persistence of DNA between iced samples without the EtOH–NaAc treatment and samples that received EtOH–NaAc treatment that were kept at room temperature. We found that DNA can be amplified from preserved samples using an EtOH–NaAc solution after up to 7 days at room temperature.
","language":"English","publisher":"Springer","doi":"10.1007/s12686-017-0955-2","usgsCitation":"Ladell, B.A., Walleser, L.R., McCalla, S.G., Erickson, R.A., and Amberg, J., 2019, Ethanol and sodium acetate as a preservation method to delay degradation of environmental DNA: Conservation Genetics Resources, v. 11, no. 1, p. 83-88, https://doi.org/10.1007/s12686-017-0955-2.","productDescription":"6 p.","startPage":"83","endPage":"88","ipdsId":"IP-076291","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437635,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FB527Z","text":"USGS data release","linkHelpText":"Ethanol and sodium acetate as a preservation method to delay degradation of environmental DNA: Data"},{"id":353748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-12","publicationStatus":"PW","scienceBaseUri":"5afee6cde4b0da30c1bfbe30","contributors":{"authors":[{"text":"Ladell, Bridget A. 0000-0002-0902-1559","orcid":"https://orcid.org/0000-0002-0902-1559","contributorId":203215,"corporation":false,"usgs":true,"family":"Ladell","given":"Bridget","email":"","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walleser, Liza R.","contributorId":204477,"corporation":false,"usgs":false,"family":"Walleser","given":"Liza","email":"","middleInitial":"R.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":734097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCalla, S. Grace 0000-0003-4292-8694 smccalla@usgs.gov","orcid":"https://orcid.org/0000-0003-4292-8694","contributorId":168436,"corporation":false,"usgs":true,"family":"McCalla","given":"S.","email":"smccalla@usgs.gov","middleInitial":"Grace","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734100,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203825,"text":"70203825 - 2019 - Nitrogen cycling in large temperate floodplain rivers of contrasting nutrient regimes and management","interactions":[],"lastModifiedDate":"2019-06-14T12:15:44","indexId":"70203825","displayToPublicDate":"2018-04-20T12:04:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen cycling in large temperate floodplain rivers of contrasting nutrient regimes and management","docAbstract":"Hydraulic connection between channels and floodplains (“connectivity”) is a fundamental determinant of ecosystem function in large floodplain rivers. Factors controlling material processing in these rivers depend not only on the degree of connectivity but also on the sediment conditions, nutrient loads, and source. Nutrient cycling in the nutrient‐rich upper Mississippi River (MISS) is relatively well studied, whereas that of less eutrophic tributaries is not (e.g., St Croix River; SACN). We examined components of nitrogen cycling in 2 floodplain rivers of contrasting nutrient enrichment and catchment land use to test the hypothesis that N‐cycling rates will be greater in the MISS with elevated nutrient loads and productivity in contrast to the relatively nutrient‐poor SACN. Nitrate (NO3−‐N) concentrations were greatest in flowing habitats in the MISS and often undetectable in isolated backwaters except where groundwater inputs occurred. In the SACN, NO3−‐N concentrations were greatest in the flowing backwater where groundwater inputs were high. Ambient nitrification in the MISS was twice that in the SACN and tended to be lowest in the main channel. Denitrification was 3× greater in the MISS than that in the SACN, N‐limited in both rivers. Community production/respiration was >1 in the MISS and likely provisioned labile C to fuel microbial metabolism and dissimilatory NO3−‐N reduction, whereas the heterotrophic (production/respiration < 1) nature of the SACN likely limited microbial metabolism and NO3−‐N dissimilation. It appears that N‐cycling in the SACN was driven by groundwater, whereas that in the MISS was supported mainly by water column N‐sources.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3267","usgsCitation":"Richardson, W.B., Bartsch, L., Bartsch, M., Kiesling, R.L., and Mroska-LaFrancois, B., 2019, Nitrogen cycling in large temperate floodplain rivers of contrasting nutrient regimes and management: River Research and Applications, v. 35, no. 5, p. 529-539, https://doi.org/10.1002/rra.3267.","productDescription":"11 p.","startPage":"529","endPage":"539","ipdsId":"IP-086420","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468130,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3267","text":"Publisher Index Page"},{"id":364703,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Mississippi River, St. Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.955322265625,\n 45.22848059584359\n ],\n [\n -92.515869140625,\n 45.22848059584359\n ],\n [\n -92.515869140625,\n 45.56021795715051\n ],\n [\n -92.955322265625,\n 45.56021795715051\n ],\n [\n -92.955322265625,\n 45.22848059584359\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.09814453125,\n 44.61393394730626\n ],\n [\n -92.8125,\n 44.61393394730626\n ],\n [\n -92.8125,\n 44.879228141635245\n ],\n [\n -93.09814453125,\n 44.879228141635245\n ],\n [\n -93.09814453125,\n 44.61393394730626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":764289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":764290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartsch, Michelle 0000-0002-9571-5564 mbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-9571-5564","contributorId":216242,"corporation":false,"usgs":true,"family":"Bartsch","given":"Michelle","email":"mbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":764291,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764292,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mroska-LaFrancois, Brenda","contributorId":216243,"corporation":false,"usgs":false,"family":"Mroska-LaFrancois","given":"Brenda","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":764293,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205109,"text":"70205109 - 2019 - A new indicator framework for quantifying the intensity of the terrestrialwater cycle","interactions":[],"lastModifiedDate":"2019-09-03T15:14:53","indexId":"70205109","displayToPublicDate":"2018-04-02T15:10:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A new indicator framework for quantifying the intensity of the terrestrialwater cycle","docAbstract":"A quantitative framework for characterizing the intensity of the water cycle over land is presented, and illustrated using a spatially distributed water-balance model of the conterminous United States (CONUS). We approach water cycle intensity (WCI) from a landscape perspective; WCI is defined as the sum of precipitation (P) and actual evapotranspiration (AET) over a spatially explicit landscape unit of interest, averaged over a specified time period (step) of interest. The time step may be of any length for which data or simulation results are available (e.g., sub-daily to multi-decadal). We define the storage-adjusted runoff (Q0) as the sum of actual runoff (Q) and the rate of change in soil moisture storage (DS/Dt, positive or negative) during the time step of interest. The Q0 indicator is demonstrated to be mathematically complementary to WCI, in a manner that allows graphical interpretation of their relationship. For the purposes of this study, the indicators were demonstrated using long-term, spatially distributed model simulations with an annual time step. WCI was found to increase over most of the CONUS between the 1945 to 1974 and 1985 to 2014 periods, driven primarily by increases in P. In portions of the western and southeastern CONUS, Q0 decreased because of decreases in Q and soil moisture storage. Analysis of WCI and Q0 at temporal scales ranging from sub-daily to multi-decadal could improve understanding of the wide spectrum of hydrologic responses that have been attributed to water cycle intensification, as well as trends in those responses.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.02.048","usgsCitation":"Huntington, T.G., Weiskel, P., Wolock, D.M., and McCabe, G.J., 2019, A new indicator framework for quantifying the intensity of the terrestrialwater cycle: Journal of Hydrology, v. 559, p. 361-372, https://doi.org/10.1016/j.jhydrol.2018.02.048.","productDescription":"12 p.","startPage":"361","endPage":"372","ipdsId":"IP-070433","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science 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dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":770058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":770059,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215594,"text":"70215594 - 2019 - The influence of land-cover changes on the variability of saturated hydraulic conductivity in tropical peatlands","interactions":[],"lastModifiedDate":"2020-10-25T18:10:30.770026","indexId":"70215594","displayToPublicDate":"2018-03-29T13:03:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7177,"text":"Mitigation and Adaption Strategies for Global Change","active":true,"publicationSubtype":{"id":10}},"title":"The influence of land-cover changes on the variability of saturated hydraulic conductivity in tropical peatlands","docAbstract":"Understanding the movement of water through peat is essential for effective conservation and management strategies for peatlands. Saturated hydraulic conductivity, Ks, describes water movement through the peat profile. However, the spatial variability of Ks in tropical peatlands and the effects of land conversion on peat characteristics are poorly understood. Utilizing the slug test method, we estimated hydraulic conductivity in tropical peatlands in West Kalimantan, Indonesia, at three depths (0.75, 3.5, and 5.5 m) across four different land-cover types (undrained forests, recently burned forests, early seral communities, and oil palm (Elaeis guineensis Jacq.) plantations). We found strong spatial autocorrelation among measurements collected at our 19 study sites and evaluated the relationship between hydraulic conductivity and land-cover types, peat properties, and depth of measurement with a hierarchical linear model. Hydraulic conductivity varied greatly (c. 0.001–13.9 m d−1). The best approximating model for estimating Ks contained depth, forest cover, a depth and forest cover interaction, and the von Post degree of decomposition (Ks ~ depth + forest + depth × forest + von Post). Parameter estimates indicated that Ks was greater in forested than non-forested sites and decreased with increasing depth and decomposition stage. There was no evidence that Ks differed among the non-forested sites or was related to other physical and chemical peat properties. Our results suggest that Ks should be measured directly in tropical peatlands rather than estimated as a function of peat properties. Additionally, the strong spatial dependence suggests that similar research designs should examine the sample data for spatial dependence and, if necessary, incorporate hierarchical models.
Larval fishes are sensitive to abiotic conditions and provide a direct measure of spawning success. The St. Clair – Detroit River System, a Laurentian Great Lakes connecting channel with a history of environmental degradation, has undergone improvements in habitat and water quality since the 1970s. We compared 2006–2015 ichthyoplankton community data with those collected prior to remediation efforts (1977–1978) to identify patterns in spatial and temporal variability. Both assemblages exhibited a predictable phenology, with taxa from the subfamily Coregoninae dominant in early spring followed by families Osmeridae, Percidae, and Moronidae (May–June) and Cyprinidae and Clupeidae (June–August). While higher densities of larval fish were found in the Detroit River, greater taxa richness and Shannon diversity were observed in the St. Clair River. System wide, 14 new taxa were observed in the 2000s study period. In addition, relative densities of two nonnative species, alewife (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax), declined since the 1970s. Increased larval fish richness and decreased densities of nonnative taxa in the 2000s are consistent with improvements to environmental conditions.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2017-0511","usgsCitation":"Taaja R. Tucker, Roseman, E.F., DeBruyne, R.L., Jeremy J. Pritt, Bennion, D., Hondorp, D.W., and Boase, J.C., 2019, Long-term assessment of ichthyoplankton in a large North American river system reveals changes in fish community dynamics: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 12, p. 2255-2270, https://doi.org/10.1139/cjfas-2017-0511.","productDescription":"16 p.","startPage":"2255","endPage":"2270","ipdsId":"IP-092433","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":468132,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2017-0511","text":"Publisher Index Page"},{"id":365334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Detroit River, Lake St Claire, St Claire River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.5675048828125,\n 41.89409955811395\n ],\n [\n -82.353515625,\n 41.89409955811395\n ],\n [\n -82.353515625,\n 43.0287452513488\n ],\n [\n -83.5675048828125,\n 43.0287452513488\n ],\n [\n -83.5675048828125,\n 41.89409955811395\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"12","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taaja R. Tucker","contributorId":169481,"corporation":false,"usgs":false,"family":"Taaja R. Tucker","affiliations":[{"id":25527,"text":"CSS-Dynamac","active":true,"usgs":false}],"preferred":false,"id":765614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. 0000-0002-5315-9838 eroseman@usgs.gov","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":168428,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward","email":"eroseman@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeBruyne, Robin L.","contributorId":139769,"corporation":false,"usgs":false,"family":"DeBruyne","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":12902,"text":"MI State UNiversity","active":true,"usgs":false}],"preferred":false,"id":765615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jeremy J. Pritt","contributorId":140823,"corporation":false,"usgs":false,"family":"Jeremy J. Pritt","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":765616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennion, David 0000-0003-4927-4195 dbennion@usgs.gov","orcid":"https://orcid.org/0000-0003-4927-4195","contributorId":149533,"corporation":false,"usgs":true,"family":"Bennion","given":"David","email":"dbennion@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765618,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boase, James C.","contributorId":216809,"corporation":false,"usgs":false,"family":"Boase","given":"James","email":"","middleInitial":"C.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":765619,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203968,"text":"70203968 - 2019 - Non-anthropogenic diet-based oiling of predatory birds","interactions":[],"lastModifiedDate":"2019-06-25T11:33:50","indexId":"70203968","displayToPublicDate":"2018-03-01T11:29:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Non-anthropogenic diet-based oiling of predatory birds","docAbstract":"Oiling of wildlife can have important consequences to individual animals and populations (Kingston 2002). Individual birds that are heavily oiled lose their ability to fly and may become ill or die from hypothermia, starvation, exhaustion, or drowning (Clark 1984, Rocke 1999). For example, large-scale oiling from the Exxon Valdez spill caused local declines in populations of many avian taxa (Irons et al. 2000). Although most oiling reports involve marine wildlife exposed to oil leaked from vessels or oil rigs, oiling also can occur in terrestrial environments, for example, via birds drinking water in puddles on asphalt roadways (Clark and Gorney 1987) or landing in oil field wastewater disposal facilities (Trail 2006, Ramírez 2010).
","language":"English","publisher":"BioOne","doi":"10.3356/JRR-17-23.1","usgsCitation":"Katzner, T., Driscoll, D., Jackman, R.E., Bloom, P., Thomas, S., Cooper, J., Livingstone, S.J., Grubb, T., Doyle, J.M., Bell, D.A., Didonato, J., and DeWoody, J.A., 2019, Non-anthropogenic diet-based oiling of predatory birds: Journal of Raptor Research, v. 52, no. 1, p. 82-88, https://doi.org/10.3356/JRR-17-23.1.","productDescription":"7 p.","startPage":"82","endPage":"88","ipdsId":"IP-076711","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":365011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365005,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.3356/JRR-17-23.1"}],"volume":"52","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":765016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Daniel","contributorId":140137,"corporation":false,"usgs":false,"family":"Driscoll","given":"Daniel","affiliations":[],"preferred":false,"id":765017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackman, Ronald E.","contributorId":190827,"corporation":false,"usgs":false,"family":"Jackman","given":"Ronald","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":765018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bloom, Peter","contributorId":182414,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter","affiliations":[],"preferred":false,"id":765019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thomas, Scott","contributorId":216553,"corporation":false,"usgs":false,"family":"Thomas","given":"Scott","affiliations":[{"id":39474,"text":"Bloom Biological Inc.","active":true,"usgs":false}],"preferred":false,"id":765020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cooper, Jeff","contributorId":199741,"corporation":false,"usgs":false,"family":"Cooper","given":"Jeff","affiliations":[{"id":35592,"text":"Virginia Department of Game and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":765021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Livingstone, Stephen J.","contributorId":179162,"corporation":false,"usgs":false,"family":"Livingstone","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":765022,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grubb, Teryl","contributorId":216554,"corporation":false,"usgs":false,"family":"Grubb","given":"Teryl","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":765023,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Doyle, Jacqueline M.","contributorId":175099,"corporation":false,"usgs":false,"family":"Doyle","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":765024,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bell, Douglas A.","contributorId":199739,"corporation":false,"usgs":false,"family":"Bell","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":24634,"text":"East Bay Regional Park District","active":true,"usgs":false}],"preferred":false,"id":765025,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Didonato, Joseph","contributorId":216555,"corporation":false,"usgs":false,"family":"Didonato","given":"Joseph","email":"","affiliations":[{"id":39475,"text":"Wildlife Consulting & Photography","active":true,"usgs":false}],"preferred":false,"id":765026,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"DeWoody, J. Andrew","contributorId":175103,"corporation":false,"usgs":false,"family":"DeWoody","given":"J.","email":"","middleInitial":"Andrew","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":765027,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70212305,"text":"70212305 - 2019 - Bright carbonate surfaces on Ceres as remnants of salt-rich water fountains","interactions":[],"lastModifiedDate":"2020-08-14T15:10:21.219148","indexId":"70212305","displayToPublicDate":"2018-02-02T10:07:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Bright carbonate surfaces on Ceres as remnants of salt-rich water fountains","docAbstract":"Vinalia and Cerealia Faculae are bright and salt-rich localized areas in Occator crater on Ceres. The predominance of the near-infrared signature of sodium carbonate on these surfaces suggests their original material was a brine. Here we analyze Dawn Framing Camera's images and characterize the surfaces as composed of a central structure, either a possible depression (Vinalia) or a central dome (Cerealia), and a discontinuous mantling. We consider three materials enabling the ascent and formation of the faculae: ice ascent with sublimation and carbonate particle lofting, pure gas emission entraining carbonate particles, and brine extrusion. We find that a mechanism explaining the entire range of morphologies, topographies, as well as the common composition of the deposits is brine fountaining. This process consists of briny liquid extrusion, followed by flash freezing of carbonate and ice particles, particle fallback, and sublimation. Subsequent increase in briny liquid viscosity leads to doming. Dawn observations did not detect currently active water plumes, indicating the frequency of such extrusions is longer than years.","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2018.01.022","usgsCitation":"Ruesch, O., Quick, L., Landis, M.E., Sori, M., Cadek, O., Broz, P., Otto, K., Bland, M.T., Byrne, S., Castillo-Rogez, J., Hiesinger, H., Jaumann, R., Krohn, K., McFadden, L., Nathues, A., Neesemann, A., Preusker, F., Roatsch, T., Schenk, P., Scully, J.E., Sykes, M., Williams, D., Raymond, C., and Russell., C., 2019, Bright carbonate surfaces on Ceres as remnants of salt-rich water fountains: Icarus, v. 320, p. 39-48, https://doi.org/10.1016/j.icarus.2018.01.022.","productDescription":"10 p.","startPage":"39","endPage":"48","ipdsId":"IP-090459","costCenters":[{"id":131,"text":"Astrogeology Science 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C.","affiliations":[],"preferred":false,"id":796275,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Sykes, M.V.","contributorId":238498,"corporation":false,"usgs":false,"family":"Sykes","given":"M.V.","email":"","affiliations":[],"preferred":false,"id":796276,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Williams, D.A.","contributorId":98048,"corporation":false,"usgs":false,"family":"Williams","given":"D.A.","email":"","affiliations":[{"id":7114,"text":"Arizona State Unviersity","active":true,"usgs":false}],"preferred":false,"id":796277,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Raymond, C.A.","contributorId":50301,"corporation":false,"usgs":false,"family":"Raymond","given":"C.A.","email":"","affiliations":[{"id":18954,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA","active":true,"usgs":false}],"preferred":false,"id":796278,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Russell., C.T.","contributorId":238501,"corporation":false,"usgs":false,"family":"Russell.","given":"C.T.","email":"","affiliations":[],"preferred":false,"id":796279,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70194829,"text":"sir20185003 - 2019 - Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","interactions":[],"lastModifiedDate":"2019-02-04T09:40:36","indexId":"sir20185003","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5003","title":"Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","docAbstract":"Beginning in the 1970s, Alameda County Water District began infiltrating imported water through ponds in repurposed gravel quarries at the Quarry Lakes Regional Park, in the Niles Cone groundwater subbasin, to recharge groundwater and to minimize intrusion of saline, San Francisco Bay water into freshwater aquifers. Hydraulic connection between distinct aquifers underlying Quarry Lakes allows water to recharge the upper aquifer system to depths of 400 feet below land surface, and the Deep aquifer to depths of more than 650 feet. Previous studies of the Niles Cone and southern East Bay Plain groundwater subbasins suggested that these two subbasins may be hydraulically connected. Characterization of storage capacities and hydraulic properties of the complex aquifers and the structural and stratigraphic controls on groundwater movement aids in optimal storage and recovery of recharged water and provides information on the ability of aquifers shared by different water management agencies to fulfill competing storage and extraction demands. The movement of recharge water through the Niles Cone groundwater subbasin from Quarry Lakes and the possible hydraulic connection between the Niles Cone and the southern East Bay Plain groundwater subbasins were investigated using interferometric synthetic aperture radar (InSAR), water-chemistry, and isotopic data, including tritium/helium-3, helium-4, and carbon-14 age-dating techniques.
InSAR data collected during refilling of the Quarry Lakes recharge ponds show corresponding ground-surface displacement. Maximum uplift was about 0.8 inches, reasonable for elastic expansion of sedimentary materials experiencing an increase in hydraulic head that resulted from pond refilling. Sodium concentrations increase while calcium and magnesium concentrations in groundwater decrease along groundwater flowpaths from the Niles Cone groundwater subbasin through the Deep aquifer to the northwest toward the southern East Bay Plain groundwater subbasin. Residual effects of pre-1970s intrusion of saline water from San Francisco Bay, including high chloride concentrations in groundwater, are evident in parts of the Niles Cone subbasin. Noble gas recharge temperatures indicate two primary recharge sources (Quarry Lakes and Alameda Creek) in the Niles Cone groundwater subbasin. Although recharge at Quarry Lakes affects hydraulic heads as far as the transition zone between the Niles Cone and East Bay Plain groundwater subbasins (about 5 miles), the effect of recharged water on water quality is only apparent in wells near (less than 2 miles) recharge sources. Groundwater chemistry from upper aquifer system wells near Quarry Lakes showed an evaporated signal (less negative oxygen and hydrogen isotopic values) relative to surrounding groundwater and a tritium concentration (2 tritium units) consistent with recently recharged water from a surface-water impoundment.
Uncorrected carbon-14 activities measured in water sampled from wells in the Niles Cone groundwater subbasin range from 16 to 100 percent modern carbon (pmC). The geochemical reaction modeling software NETPATH was used to interpret carbon-14 ages along a flowpath from Quarry Lakes toward the East Bay Plain groundwater subbasin. Model results indicate that changes in groundwater chemistry are controlled by cation exchange on clay minerals and weathering of primary silicate minerals. Old groundwater (lower carbon-14 activities) is characterized by high dissolved silica and pH. Interpreted carbon-14 ages ranged from 830 to more than 7,000 years before present and are less than helium-4 ages that range from 2,000 to greater than 11,000 years before present. The average horizontal groundwater velocity along the studied flowpath, as calculated using interpreted carbon-14 ages, through the Deep aquifer of the Niles Cone groundwater subbasin is between 3 and 12 feet per year. The groundwater velocity decreases near the boundary of the transition zone to the southern East Bay Plain groundwater subbasin to about 0.5 feet per year. These changes may result from water recharged from different sources converging in flowpaths north of the transition zone, or a boundary to flow between the Niles Cone and southern East Bay Plain groundwater subbasins, likely owing to changes in lithology caused by depositional patterns.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185003","collaboration":"Prepared in cooperation with the East Bay Municipal Utility District, City of Hayward, and Alameda County Water District","usgsCitation":"Teague, Nick, Izbicki, John, Borchers, Jim, Kulongoski, Justin, and Jurgens, Bryant, 2018, Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2018–5003, 62 p., https://doi.org/10.3133/sir20185003.","productDescription":"x, 62 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-043410","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":360934,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5003/versionHist.txt"},{"id":351228,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5003/coverthb.jpg"},{"id":351229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5003/sir20185003_v1.1.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5003"}],"country":"United States","state":"California","county":"Alameda County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.3333,\n 37.5\n ],\n [\n -121.9167,\n 37.5\n ],\n [\n -121.9167,\n 37.8333\n ],\n [\n -122.3333,\n 37.8333\n ],\n [\n -122.3333,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Ver. 1.0: February 2018; Ver. 1.1: February 2019","contact":"Director,
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6000 J Street, Placer Hall
Sacramento, CA 95819
We investigated the direct and indirect influence of tides on net ecosystem exchange (NEE) of carbon dioxide (CO2) in a temperate brackish tidal marsh. NEE displayed a tidally driven pattern with obvious characteristics at the multiday scale, with greater net CO2uptake during spring tides than neap tides. Based on the relative mutual information between NEE and biophysical variables, this was driven by a combination of higher water table depth (WTD), cooler air temperature, and lower vapor pressure deficit (VPD) during spring tides relative to neap tides, as the fortnightly tidal cycle not only influenced water levels but also strongly modulated water and air temperature and VPD. Tides also influenced NEE at shorter timescales, with a reduction in nighttime fluxes during growing season spring tides when the higher of the two semidiurnal tides caused inundation at the site. WTD significantly influenced ecosystem respiration (Reco), with lower Reco during spring tides than neap tides. While WTD did not appear to affect ecosystem photosynthesis (gross ecosystem production, GPP) directly, the impact of tides on temperature and VPD influenced GPP, with higher daily light‐use efficiency and photosynthetic activity during spring tides than neap tides when temperature and VPD were lower. The strong direct and indirect influence of tides on NEE across the diel and multiday timescales has important implications for modeling NEE in tidal wetlands and can help inform the timing and frequency of chamber measurements as annual or seasonal net CO2 uptake may be underestimated if measurements are only taken during nonflooded periods.
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2019 - Isotopic evidence that nitrogen enrichment intensifies nitrogen losses to the atmosphere from subtropical mangroves","interactions":[],"lastModifiedDate":"2019-08-15T11:54:06","indexId":"70202922","displayToPublicDate":"2018-01-08T11:31:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic evidence that nitrogen enrichment intensifies nitrogen losses to the atmosphere from subtropical mangroves","docAbstract":"Nitrogen (N) enrichment can have large effects on mangroves’ capacity to provide critical ecosystem services by affecting fundamental functions such as N cycling and primary productivity. However, our understanding of excess N input effects on N cycling in mangroves remains quite limited. To advance our understanding of how N enrichment via water or air pollution affects mangroves, we evaluated whether increasing N inputs would decrease biological N fixation (BNF), but intensify N dynamics and N losses to the atmosphere in these systems. We measured N concentrations in sediment and vegetation, rates of BNF in sediment and litter, and net sediment ammonification and nitrification rates. We also evaluated long-term integrated N dynamics and N losses to the atmosphere using the natural abundance of N stable isotopes (δ15N) in the sediment–plant system and in estuarine water. We performed these analyses at non-N-enriched and N-enriched (that is, polluted) fringe and basin mangroves in southeastern Brazil. The δ15N in the sediment–plant system was higher at N-enriched than non-N-enriched fringe sites, indicating increased N losses to the atmosphere from N-enriched sites. However, N concentrations in sediment and vegetation were similar or lower at N-enriched relative to non-N-enriched sites. BNF and net ammonification and nitrification rates were also similar between N-enriched and non-N-enriched sites. Excess N inputs intensified N losses to the atmosphere from mangroves, but N pools, BNF, and net ammonification and nitrification rates were not affected by N enrichment, likely because excess N was quickly lost from the system by direct denitrification and volatilization.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-018-0327-0","usgsCitation":"Reis, C.R., Reed, S.C., Oliveira, R.S., and Nardoto, G.B., 2019, Isotopic evidence that nitrogen enrichment intensifies nitrogen losses to the atmosphere from subtropical mangroves: Ecosystems, v. 22, no. 5, p. 1126-1144, https://doi.org/10.1007/s10021-018-0327-0.","productDescription":"19 p.","startPage":"1126","endPage":"1144","ipdsId":"IP-102196","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":362799,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"São Paulo","otherGeospatial":"Estuarine Lagunar Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -47.471923828125,\n -24.705043861733333\n ],\n 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]\n ]\n }\n }\n ]\n}","volume":"22","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Reis, Carla Roberta Goncalves","contributorId":214647,"corporation":false,"usgs":false,"family":"Reis","given":"Carla","email":"","middleInitial":"Roberta Goncalves","affiliations":[{"id":39103,"text":"Programa de Pós-Graduação em Ecologia, Instituto de Ciências Biológicas, Campus Darcy Ribeiro, Universidade de Brasília, 70910-900, Brasília, Distrito Federal, Brazil","active":true,"usgs":false}],"preferred":false,"id":760483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oliveira, Rafael Silva","contributorId":214648,"corporation":false,"usgs":false,"family":"Oliveira","given":"Rafael","email":"","middleInitial":"Silva","affiliations":[{"id":39104,"text":"Departamento de Biologia Vegetal, Rua Monteiro Lobato 255, Cidade Universitária Zeferino Vaz, Universidade Estadual de Campinas, 13083-862, Barão Geraldo, Campinas, São Paulo, Brazil","active":true,"usgs":false}],"preferred":false,"id":760484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nardoto, Gabriela Bielefeld","contributorId":214649,"corporation":false,"usgs":false,"family":"Nardoto","given":"Gabriela","email":"","middleInitial":"Bielefeld","affiliations":[{"id":39103,"text":"Programa de Pós-Graduação em Ecologia, Instituto de Ciências Biológicas, Campus Darcy Ribeiro, Universidade de Brasília, 70910-900, Brasília, Distrito Federal, Brazil","active":true,"usgs":false}],"preferred":false,"id":760485,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204760,"text":"70204760 - 2019 - Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","interactions":[],"lastModifiedDate":"2019-09-03T08:16:10","indexId":"70204760","displayToPublicDate":"2017-12-31T12:46:33","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","docAbstract":"We genotyped 7,588 brook trout representing 406 collections from across the State of North Carolina (Figure 1) at 12 microsatellite loci (King et al. 2012). The vast majority of
collections appeared to represent single populations, based on general conformance to HardyWeinberg equilibrium and limited evidence for linkage-disequilibrium. Allelic diversity was low to moderate relative to Brook Trout Salvelinus fontinalis populations endemic to higher latitudes. Effective population sizes varied widely among populations, but were often very small and indicate that many populations are at risk of losing diversity through genetic drift. Remarkable levels of genetic differentiation exist among populations, which suggests that little, if any, gene flow occurs among most populations. Analysis of molecular variance (AMOVA) revealed that a substantial portion of the observed genetic variation was attributed to differences among patches (44.8%), and there was some variation (11.2%) even among collections within a single patch. These results, taken in conjunction with high levels of genetic differentiation among populations, suggest that the fundamental unit of management for Brook Trout should be the
population. Interestingly, despite extensive stocking across the state, the vast majority of wild populations show limited evidence of introgression by northern origin hatchery strains. These results represent a valuable baseline for management and restoration efforts, and can be used to (a) select suitable donor streams for translocation efforts, (b) identify streams with low effective population sizes that may be vulnerable to extirpation, and (c) target stocking efforts into watersheds where extensive introgression has already occurred. All data associated with this manuscript has been publicly released (Kazyak et al. 2017).
In 2014, the U.S. Geological Survey, in cooperation with the U.S. Department of Defense’s Strategic Environmental Research and Development Program, initiated a project to evaluate the potential impacts of projected climate-change on Department of Defense installations that rely on Guam’s water resources. A major task of that project was to develop a watershed model of southern Guam and a water-balance model for the Fena Valley Reservoir. The southern Guam watershed model provides a physically based tool to estimate surface-water availability in southern Guam. The U.S. Geological Survey’s Precipitation Runoff Modeling System, PRMS-IV, was used to construct the watershed model. The PRMS-IV code simulates different parts of the hydrologic cycle based on a set of user-defined modules. The southern Guam watershed model was constructed by updating a watershed model for the Fena Valley watersheds, and expanding the modeled area to include all of southern Guam. The Fena Valley watershed model was combined with a previously developed, but recently updated and recalibrated Fena Valley Reservoir water-balance model.
Two important surface-water resources for the U.S. Navy and the citizens of Guam were modeled in this study; the extended model now includes the Ugum River watershed and improves upon the previous model of the Fena Valley watersheds. Surface water from the Ugum River watershed is diverted and treated for drinking water, and the Fena Valley watersheds feed the largest surface-water reservoir on Guam. The southern Guam watershed model performed “very good,” according to the criteria of Moriasi and others (2007), in the Ugum River watershed above Talofofo Falls with monthly Nash-Sutcliffe efficiency statistic values of 0.97 for the calibration period and 0.93 for the verification period (a value of 1.0 represents perfect model fit). In the Fena Valley watershed, monthly simulated streamflow volumes from the watershed model compared reasonably well with the measured values for the gaging stations on the Almagosa, Maulap, and Imong Rivers—tributaries to the Fena Valley Reservoir—with Nash-Sutcliffe efficiency values of 0.87 or higher. The southern Guam watershed model simulated the total volume of the critical dry season (January to May) streamflow for the entire simulation period within –0.54 percent at the Almagosa River, within 6.39 percent at the Maulap River, and within 6.06 percent at the Imong River.
The recalibrated water-balance model of the Fena Valley Reservoir generally simulated monthly reservoir storage volume with reasonable accuracy. For the calibration and verification periods, errors in end-of-month reservoir-storage volume ranged from 6.04 percent (284.6 acre-feet or 92.7 million gallons) to –5.70 percent (–240.8 acre-feet or –78.5 million gallons). Monthly simulation bias ranged from –0.48 percent for the calibration period to 0.87 percent for the verification period; relative error ranged from –0.60 to 0.88 percent for the calibration and verification periods, respectively. The small bias indicated that the model did not consistently overestimate or underestimate reservoir storage volume.
In the entirety of southern Guam, the watershed model has a “satisfactory” to “very good” rating when simulating monthly mean streamflow for all but one of the gaged watersheds during the verification period. The southern Guam watershed model uses a more sophisticated climate-distribution scheme than the older model to make use of the sparse climate data, as well as includes updated land-cover parameters and the capability to simulate closed depression areas.
The new Fena Valley Reservoir water-balance model is useful as an updated tool to forecast short-term changes in the surface-water resources of Guam. Furthermore, the now spatially complete southern Guam watershed model can be used to evaluate changes in streamflow and recharge owing to climate or land-cover changes. These are substantial improvements to the previous models of the Fena Valley watershed and Reservoir. Datasets associated with this report are available as a U.S. Geological Survey data release (Rosa and Hay, 2017; DOI:10.5066/F7HH6HV4).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175093","collaboration":"Prepared in cooperation with the U.S. Department of Defense Strategic Environmental Research and Development Program (SERDP)","usgsCitation":"Rosa, S.N., and Hay, L.E., 2019, Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2017–5093, 64 p., https://doi.org/10.3133/sir20175093.","productDescription":"Report: viii, 64 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-081743","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":349631,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5093/coverthb2.jpg"},{"id":349632,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5093/sir20175093.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5093 v1.1"},{"id":361066,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5093/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017-5093 Version History"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.6240234375,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.230587802102518\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 2017; Version 1.1: February 2019","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Salinity has a major effect on water users in the Colorado River Basin, estimated to cause almost $300 million per year in economic damages. The Colorado River Basin Salinity Control Program implements and manages projects to reduce salinity loads, investing millions of dollars per year in irrigation upgrades, canal projects, and other mitigation strategies. To inform and improve mitigation efforts, there is a need to better understand sources of salinity to streams and how salinity has changed over time. This study explores salinity in the baseflow fraction of streamflow, assessing whether groundwater is a significant contributor of dissolved solids to streams in the Upper Colorado River Basin (UCRB). Chemical hydrograph separation was used to estimate baseflow discharge and baseflow dissolved solids loads at stream gages (n = 69) across the UCRB. On average, it is estimated that 89% of dissolved solids loads originate from the baseflow fraction of streamflow, indicating that subsurface transport processes play a dominant role in delivering dissolved solids to streams in the UCRB. A statistical trend analysis using weighted regressions on time, discharge, and season was used to evaluate changes in baseflow dissolved solids loads in streams (n = 27) from 1986 to 2011. Decreasing trends in baseflow dissolved solids loads were observed at 63% of streams. At the three most downstream sites, Green River at Green River, UT, Colorado River at Cisco, UT, and the San Juan River near Bluff, UT, baseflow dissolved solids loads decreased by a combined 823,000 metric tons (mT), which is approximately 69% of projected basin‐scale decreases in total dissolved solids loads as a result of salinity control efforts. Decreasing trends in baseflow dissolved solids loads suggest that salinity mitigation projects, landscape changes, and/or climate are reducing dissolved solids transported to streams through the subsurface. Notably, the pace and extent of decreases in baseflow dissolved solids loads declined during the most recent decade; average decreasing loads during the 2000s (28,200 mT) were only 54% of average decreasing loads in the 1990s (51,700 mT).
A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9101065","usgsCitation":"Xiong, J., Thenkabail, P.S., James C. Tilton, Gumma, M.K., Teluguntla, P.G., Oliphant, A., Congalton, R., Yadav, K., and Gorelick, N., 2019, Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine: Remote Sensing, v. 9, no. 10, Article 1065: 27 p., https://doi.org/10.3390/rs9101065.","productDescription":"Article 1065: 27 p.","ipdsId":"IP-088538","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9101065","text":"Publisher Index Page"},{"id":362333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Africa","volume":"9","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Xiong, Jun 0000-0002-2320-0780 jxiong@usgs.gov","orcid":"https://orcid.org/0000-0002-2320-0780","contributorId":5276,"corporation":false,"usgs":true,"family":"Xiong","given":"Jun","email":"jxiong@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James C. Tilton","contributorId":214483,"corporation":false,"usgs":false,"family":"James C. Tilton","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":760063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gumma, Murali Krishna 0000-0002-3760-3935","orcid":"https://orcid.org/0000-0002-3760-3935","contributorId":192327,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali","email":"","middleInitial":"Krishna","affiliations":[],"preferred":false,"id":760064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teluguntla, Pardhasaradhi G. 0000-0001-8060-9841 pteluguntla@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":5275,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","email":"pteluguntla@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Congalton, Russell G.","contributorId":84646,"corporation":false,"usgs":true,"family":"Congalton","given":"Russell G.","affiliations":[],"preferred":false,"id":760067,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yadav, Kamini","contributorId":214487,"corporation":false,"usgs":false,"family":"Yadav","given":"Kamini","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":760068,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gorelick, Noel ","contributorId":214496,"corporation":false,"usgs":false,"family":"Gorelick","given":"Noel ","affiliations":[],"preferred":false,"id":760069,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203664,"text":"70203664 - 2019 - Avian predation on juvenile Salmonids: Spatial and temporal analysis based on acoustic and passive integrated transponder tags","interactions":[],"lastModifiedDate":"2019-05-30T15:33:21","indexId":"70203664","displayToPublicDate":"2017-06-27T15:21:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Avian predation on juvenile Salmonids: Spatial and temporal analysis based on acoustic and passive integrated transponder tags","docAbstract":"We evaluated the impact of predation on juvenile steelhead Oncorhynchus mykiss and yearling and subyearling Chinook Salmon O. tshawytscha by piscivorous waterbirds from 11 different breeding colonies in the Columbia River basin during 2012 and 2014. Fish were tagged with both acoustic tags and PIT tags and were tracked via a network of hydrophone arrays to estimate total smolt mortality (1 – survival) at various spatial and temporal scales during out‐migration. Recoveries of PIT tags on bird colonies, coupled with the last known detections of live fish passing hydrophone arrays, were used to estimate the impact of avian predation relative to total smolt mortality. Results indicated that avian predation was a substantial source of steelhead mortality, with predation probability (proportion of available fish consumed by birds) ranging from 0.06 to 0.28 for fish traveling through the lower Snake River and the lower and middle Columbia River. Predation probability estimates ranged from 0.03 to 0.09 for available tagged yearling Chinook Salmon and from 0.01 to 0.05 for subyearlings. Smolt predation by gulls Larusspp. was concentrated near hydroelectric dams, while predation by Caspian terns Hydroprogne caspia was concentrated within reservoirs. No concentrated areas of predation were identified for double‐crested cormorants Phalacrocorax auritus or American white pelicans Pelecanus erythrorhynchos. Comparisons of total smolt mortality relative to mortality from colonial waterbirds indicated that avian predation was one of the greatest sources of mortality for steelhead and yearling Chinook Salmon during out‐migration. In contrast, avian predation on subyearling Chinook Salmon was generally low and constituted a minor component of total mortality. Our results demonstrate that acoustic and PIT tag technologies can be combined to quantify where and when smolt mortality occurs and the fraction of mortality that is due to colonial waterbird predation relative to non‐avian mortality sources.
","language":"English","publisher":"Wiley","doi":"10.1080/00028487.2016.1150881","usgsCitation":"Evans, A.F., Payton, Q., Turecek, A., Cramer, B., Collis, K., Roby, D.D., Loschl, P.J., Sullivan, L., Skalski, Weiland, M., and Dotson, C., 2019, Avian predation on juvenile Salmonids: Spatial and temporal analysis based on acoustic and passive integrated transponder tags: Transactions of the American Fisheries Society, https://doi.org/10.1080/00028487.2016.1150881.","ipdsId":"IP-071908","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490058,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/dataset/Avian_Predation_on_Juvenile_Salmonids_Spatial_and_Temporal_Analysis_Based_on_Acoustic_and_Passive_Integrated_Transponder_Tags/3471605","text":"External Repository"},{"id":364261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Allen F.","contributorId":171691,"corporation":false,"usgs":false,"family":"Evans","given":"Allen","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":763477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Payton, Quinn","contributorId":149990,"corporation":false,"usgs":false,"family":"Payton","given":"Quinn","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":763478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turecek, Aaron aturecek@usgs.gov","contributorId":4940,"corporation":false,"usgs":true,"family":"Turecek","given":"Aaron","email":"aturecek@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":763479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cramer, Bradley D.","contributorId":51562,"corporation":false,"usgs":true,"family":"Cramer","given":"Bradley D.","affiliations":[],"preferred":false,"id":763480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collis, Ken","contributorId":149991,"corporation":false,"usgs":false,"family":"Collis","given":"Ken","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":763481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roby, Daniel D. 0000-0001-9844-0992 droby@usgs.gov","orcid":"https://orcid.org/0000-0001-9844-0992","contributorId":3702,"corporation":false,"usgs":true,"family":"Roby","given":"Daniel","email":"droby@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":763482,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loschl, Peter J.","contributorId":7195,"corporation":false,"usgs":true,"family":"Loschl","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":763483,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sullivan, Leah","contributorId":215942,"corporation":false,"usgs":false,"family":"Sullivan","given":"Leah","email":"","affiliations":[],"preferred":false,"id":763484,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Skalski, John","contributorId":120021,"corporation":false,"usgs":true,"family":"Skalski","suffix":"John","affiliations":[],"preferred":false,"id":763485,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weiland, Mark","contributorId":215944,"corporation":false,"usgs":false,"family":"Weiland","given":"Mark","email":"","affiliations":[],"preferred":false,"id":763486,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dotson, Curtis","contributorId":215945,"corporation":false,"usgs":false,"family":"Dotson","given":"Curtis","email":"","affiliations":[],"preferred":false,"id":763487,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70203024,"text":"70203024 - 2019 - Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations","interactions":[],"lastModifiedDate":"2019-04-11T16:06:24","indexId":"70203024","displayToPublicDate":"2017-05-09T15:54:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations","docAbstract":"Water and sediment extracts samples were analyzed for extractable hydrocarbons by gas chromatography/mass spectrometry (GC/MS) using an Agilent (Agilent Technologies, Palo Alto, CA, USA) 7890 series GC and 5975 electron ionization (EI) mass selective detector (MSD) operated in scan mode. Agilent ChemStation software was used for data acquisition and analysis (version E.02.00.493 on GC/MS computer and version F.01.03.2357 on laptop for data workup). A 30 m x 250 m x 0.25 m HP-5MS column (95% dimethyl 5% diphenyl polydimethylsiloxane) was used for GC/MS under the following conditions: 1.0 L splitless injection, constant flow of 0.7 mL/min, solvent delay of 7.5 min, injector temperature of 280C, interface at 300C, temperature program of 50-150C at 7C/min, 150-230C at 6C/min, and 230-300C at 3C/min with mass scanned from 35-500 Da.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.02.016","usgsCitation":"Orem, W.H., Varonka, M.S., Crosby, L.M., Haase, K.B., Loftin, K.A., Hladik, M., Akob, D.M., Tatu, C., Mumford, A.C., Jaeschke, J.B., Bates, A.L., Schell, T., and Cozzarelli, I.M., 2019, Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations: Applied Geochemistry, v. 80, p. 155-167, https://doi.org/10.1016/j.apgeochem.2017.02.016.","productDescription":"13 p.","startPage":"155","endPage":"167","ipdsId":"IP-075085","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":436,"text":"National Research Program - 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Recent studies have quantified fluvial export to the marine environment in many systems, but in-stream losses of DOC are poorly constrained. This study compares DOC yields (kg C/ha) between the area-weighted averages of several tributaries within larger watersheds with the DOC yields of the larger watersheds to gain insight on in-stream losses in larger river systems. Four large watersheds, 22 tributaries to those watersheds, and 5 additional main stem locations in Maine were studied during 1 April to 15 November in 2011 through 2013. There were no significant differences in the area-weighted average DOC yield of the tributaries and the larger watersheds indicating little net in-stream loss in the main stems of the larger rivers. It is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries based on similarity in DOC yield longitudinally along the main stems of two of the rivers. In addition, wetland abundance, which is associated with higher DOC yield in this environment, did not consistently increase from tributaries to the larger watershed or longitudinally along the main stems. This geographic distribution of wetlands therefore also indicates that it is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries. These findings suggest that in-stream losses of DOC in these larger river systems are minimal and that the vast majority of DOC in major rivers in Maine is transported conservatively to the coastal ocean.
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This symposium identified current unknowns and highlighted new electronic tracking technologies and physiological techniques to address these knowledge gaps. A number of presentations, each reviewing ongoing research on the two species, was followed by a round-table discussion, in which each of the participants was asked to share recom-mendations for future research on sturgeon in the watershed. This article presents an in-depth review of the scientific information presented at the sympo-sium with a summary of recommendations for future research.","language":"English","publisher":"eScholarship University of California","doi":"10.15447/sfews.2015v13iss4art1","usgsCitation":"Klimley, A., Chapman, E.D., Cech Jr, J., Cocherell, D.E., Fangue, N.A., Gingras, M., Jackson, Z., Miller, E.A., Mora, E.A., Poletto, J.B., Schreier, A.M., Seesholtz, A., Sulak, K.J., Thomas, M.J., Woodbury, D.J., and Wyman, M.T., 2019, Sturgeon in the Sacramento-San Joaquin watershed: new insights to support conservation and management: San Francisco Estuary and Watershed Science, v. 13, no. 4, p. 1-19, https://doi.org/10.15447/sfews.2015v13iss4art1.","productDescription":"19 p.","startPage":"1","endPage":"19","ipdsId":"IP-071452","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468143,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2015v13iss4art1","text":"Publisher Index Page"},{"id":363476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.9150390625,\n 37.28716518793858\n ],\n [\n -120.81665039062499,\n 37.34395908944491\n ],\n [\n -120.860595703125,\n 38.91668153637508\n ],\n [\n -122.958984375,\n 38.90813299596705\n ],\n [\n -122.9150390625,\n 37.28716518793858\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Klimley, A Peter","contributorId":215246,"corporation":false,"usgs":false,"family":"Klimley","given":"A Peter","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":761924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Eric D","contributorId":215247,"corporation":false,"usgs":false,"family":"Chapman","given":"Eric","email":"","middleInitial":"D","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":761925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cech Jr, J. 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States\"}}]}","edition":"Version 1.1: May 28, 2019","contact":"Chief, EcoScience Synthesis
Director of BISON and ITIS
Core Science Systems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, Mailstop 302
Reston, Virginia 20192
bison@usgs.gov
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A6.3, provides guidance and protocols for the measurement of specific conductance of a water sample, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.3, version 1.2, by D.B. Radtke, J.V. Davis, and F.D. Wilde.
Specific conductance is routinely measured when water samples are collected, is often measured continually at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring specific conductance described in this chapter is applicable to most natural waters.
Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: National field manual for the collection of water-quality data in Book 9: Handbooks for water-resources investigations","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A6.3","usgsCitation":"U.S. Geological Survey, 2019, Chapter A6.3. Specific Conductance: U.S. Geological Survey Techniques and Methods 9-A6.3, vi, 15 p., https://doi.org/10.3133/tm9A6.3.","productDescription":"vi, 15 p.","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":361085,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm9A0","text":"Techniques and Methods 9-A0","linkHelpText":"General Introduction for the “National Field Manual for the Collection of Water-Quality Data\""},{"id":360655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a6.3/tm9-a6_3.pdf","text":"Report","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 9A63"},{"id":360656,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/09/a6.3//versionHist.txt","size":"2.91 KB","linkFileType":{"id":2,"text":"txt"}},{"id":360654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a6.3/coverthb.jpg"}],"contact":"Chief, Office of Quality Assurance
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192
Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Chlorinated volatile organic compounds have affected groundwater beneath a former 9-acre landfill at Operable Unit 1 (OU 1) of Naval Base Kitsap (NBK) Keyport, in Keyport, Washington. The landfill was the primary disposal area for domestic and industrial waste generated by NBK Keyport from the 1930s through 1973. Naval Facilities Engineering Command Northwest, in conjunction with the Environmental Protection Agency, Washington State Department of Ecology, and the Suquamish Tribe, is charged with collecting necessary data to monitor the contamination left in place and to ensure that the site does not pose a risk to human health or the environment.
To support these efforts, refined information was collected on how groundwater levels throughout OU 1 respond to tidal fluctuations at this nearshore site adjacent to Liberty Bay, an inlet of Puget Sound. The information was analyzed to determine the optimal times during the semidiurnal and the neap-spring tidal cycles to sample groundwater for contaminants associated with fresh groundwater originating from OU 1. The optimal times for sampling are presumed to be when fresh groundwater flowing seaward is least impeded by elevated tides, and those times are related to predicted tide levels by tidal lags, the durations between low tides, and corresponding low groundwater levels. Discrete groundwater-specific conductance data also were collected to determine if a seawater/freshwater interface was present at any of the monitoring wells, and to inform decisions on the depth at which groundwater should be sampled in existing wells.
Groundwater and surface-water levels were monitored at 19 monitoring wells and five adjacent surface-water sites. Specific conductance was monitored in each surface-water site. All time-series data parameters were collected every 15 minutes during a 4-week duration to measure how nearshore groundwater responds to tidal forcing. Time-series data were collected from July 12, 2018, to August 8, 2018, a period that included neap and spring tides. Vertical water-quality profiles were measured once in the screened interval of nine selected monitoring wells. The profiles included measurements at the top, middle, and bottom of each saturated screen interval.
Tidal lag times were determined relative to tidal levels in Liberty Bay (rather than in the more nearby Tide Flats) because the predicted tides for the Poulsbo, Washington Station (National Oceanic and Atmospheric Administration [NOAA] Station 9445719) that are used to schedule groundwater sampling represent open-water conditions in the area; a sill that separates Dogfish Bay from the Tide Flats clearly affects the timing and magnitude of low-low tides in the Tide Flats. Calculated tidal lag times were divided into three general groups: (1) wells where groundwater responded to tidal level changes immediately, (2) wells where groundwater responded to tidal level changes within about 2–5 hours, and (3) wells where groundwater had minimal response to tidal level changes. Groundwater levels in the middle group of wells primarily responded in concert with tidal level changes in the Tide Flats rather than tidal level changes in Liberty Bay.
An intended sampling depth refinement based on an assessment of transient seawater intrusion was not completed because of a failure to collect specific-conductance time-series data in select wells. Instead, discrete specific-conductance data from this and prior studies were evaluated to determine that the midpoint of well screens in OU 1 wells can be assumed to be a reasonably representative of undiluted groundwater. When sampling during spring (rather than neap) tides (as has generally been the standard practice at OU 1), the optimal time to sample the monitoring wells influenced by tides would be to add the tidal lags presented in this report to the time of the predicted low-low tide for Liberty Bay as measured at NOAA Station 9445719 at Poulsbo, Washington. Sampling schedules for the six wells where groundwater levels were only minimally influenced by tide changes should not be constrained by tidal conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191098","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Opatz, C.C., and Dinicola, R.S., 2019, Analysis of groundwater response to tidal fluctuations, Operable Unit 1, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Open-File Report 2019-1098, 36 p., https://doi.org/10.3133/ofr20191098.","productDescription":"vi, 36 p.","onlineOnly":"Y","ipdsId":"IP-107656","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":367168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1098/coverthb.jpg"},{"id":367169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1098/ofr20191098.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1098"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62941598892212,\n 47.694699930336995\n ],\n [\n -122.62280702590942,\n 47.694699930336995\n ],\n [\n -122.62280702590942,\n 47.69943693711954\n ],\n [\n -122.62941598892212,\n 47.69943693711954\n ],\n [\n -122.62941598892212,\n 47.694699930336995\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402