A new method of unmanned aerial vehicle (UAV)-based tracer tests using RGB (red, green, blue) images was developed in order to acquire the spatio-temporal concentration distribution of tracer clouds in open channel flows. Tracer tests using Rhodamine WT were conducted to collect the RGB images using a commercial digital camera mounted on a UAV, and the concentration of Rhodamine WT using in-situ fluorometric probes. The correlation analysis showed that the in-situmeasured concentrations of Rhodamine WT were strongly correlated with the digital number (DN) of the RGB images, even though the response of DN to the concentration was spatially heterogeneous. The empirical relationship between the DN values and the Rhodamine WT concentration data was estimated using artificial neural network (ANN) models. The trained ANN models, which consider the effect of water depth and river bed, accurately retrieved the detailed spatio-temporal concentration distributions of all study areas that had an R2 higher than 0.9. The acquired spatio-temporal concentration distributions by the proposed method based on the UAV images gave general as well as detailed views of the tracer cloud moving dynamically in open channel flows that cannot be easily observed using conventional in-situ measurements.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2019.03.007","usgsCitation":"Baek, D., Seo, I.W., Kim, J.S., and Nelson, J.M., 2019, UAV-based measurements of spatio-temporal concentration distributions of fluorescent tracers in open channel flows: Advances in Water Resources, v. 127, p. 76-88, https://doi.org/10.1016/j.advwatres.2019.03.007.","productDescription":"13 p.","startPage":"76","endPage":"88","ipdsId":"IP-102149","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":362275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baek, Donghae","contributorId":214366,"corporation":false,"usgs":false,"family":"Baek","given":"Donghae","email":"","affiliations":[{"id":37780,"text":"Seoul National University","active":true,"usgs":false}],"preferred":false,"id":759728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seo, Il Won","contributorId":214367,"corporation":false,"usgs":false,"family":"Seo","given":"Il","email":"","middleInitial":"Won","affiliations":[{"id":37780,"text":"Seoul National University","active":true,"usgs":false}],"preferred":false,"id":759729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Jun Song","contributorId":214368,"corporation":false,"usgs":false,"family":"Kim","given":"Jun","email":"","middleInitial":"Song","affiliations":[{"id":37780,"text":"Seoul National University","active":true,"usgs":false}],"preferred":false,"id":759730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":759727,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202742,"text":"70202742 - 2019 - Scale‐dependent effects of isolation on seasonal patch colonisation by two Neotropical freshwater fishes","interactions":[],"lastModifiedDate":"2019-03-25T08:48:53","indexId":"70202742","displayToPublicDate":"2019-03-22T10:50:52","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Scale‐dependent effects of isolation on seasonal patch colonisation by two Neotropical freshwater fishes","docAbstract":"The metapopulation paradigm has been central to improve the conservation and management of natural populations. However, despite the large number of studies on metapopulation dynamics, the overall support for the relationships on which the paradigm is based has not been strong. Here, we studied the occupancy dynamics of two Neotropical fishes (i.e., Pimelodella gracilis and Leporinus friderici) to investigate two fundamental premises of the metapopulation paradigm, that is, that isolation and area/habitat quality affect colonisation and extinction probabilities in predictable ways. In order to do this, we used a modification of occupancy models that allows modelling the probability of a site's occupancy as a function of the occupancy of its neighbourhood. We found a weak positive effect of neighbourhood occupancy on P. gracilis colonisation, which is consistent with the propagule rain metapopulation, that is, colonists arriving from outside the studied system. However, we found a strong negative neighbourhood effect on extinction probability, suggesting that declining populations from stream sections are rescued from extinction by neighbouring patches. In contrast, the effect of neighbourhood occupancy on the metapopulation dynamics of L. friderici was in the opposite direction, affecting positively colonisation but not affecting extinction rates, which is consistent with the classical metapopulation model. In addition, the occupancy dynamics of both species were affected by water velocity. To our knowledge, this is the first study to link directly dispersal to local population dynamics in Neotropical fishes, and one of the few studies doing inferences on spatial population dynamics based on direct estimates of neighbourhood occupancy.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12452","usgsCitation":"Penha, J., Hakamada, K.Y., Hines, J.E., and Nichols, J.D., 2019, Scale‐dependent effects of isolation on seasonal patch colonisation by two Neotropical freshwater fishes: Ecology of Freshwater Fish, v. 28, no. 2, p. 274-284, https://doi.org/10.1111/eff.12452.","productDescription":"11 p.","startPage":"274","endPage":"284","ipdsId":"IP-096324","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":362273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Penha, Jerry","contributorId":214384,"corporation":false,"usgs":false,"family":"Penha","given":"Jerry","email":"","affiliations":[{"id":39029,"text":"Instituto de Biociências, Universidade Federal de Mato Grosso, Brazil","active":true,"usgs":false}],"preferred":false,"id":759761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hakamada, Karlo Y. P.","contributorId":214390,"corporation":false,"usgs":false,"family":"Hakamada","given":"Karlo","email":"","middleInitial":"Y. P.","affiliations":[],"preferred":false,"id":759768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759760,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759762,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202730,"text":"70202730 - 2019 - Stream metabolism increases with drainage area and peaks asynchronously across a stream network","interactions":[],"lastModifiedDate":"2019-03-26T08:16:07","indexId":"70202730","displayToPublicDate":"2019-03-21T16:36:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Stream metabolism increases with drainage area and peaks asynchronously across a stream network","docAbstract":"Quantifying the spatial and temporal dynamics of stream metabolism across stream networks is key to understanding carbon cycling and stream food web ecology. To better understand intra-annual temporal patterns of gross primary production (GPP) and ecosystem respiration (ER) and their variability across space, we continuously measured dissolved oxygen and modeled stream metabolism for an entire year at ten sites across a temperate river network in Washington State, USA. We expected GPP and ER to increase with stream size and peak during summer and autumn months due to warmer temperatures and higher light availability. We found that GPP and ER increased with drainage area and that only four sites adhered to our expectations of summer peaks in GPP and autumn peaks in ER while the rest either peaked in winter, spring or remained relatively constant. Our results suggest the spatial arrangement and temporal patterns of discharge, temperature, light and nutrients within watersheds may result in asynchronies in GPP and ER, despite similar regional climatic conditions. These findings shed light on how temporal dynamics of stream metabolism can shift across a river network, which likely influence the dynamics of carbon cycling and stream food webs at larger scales.
","language":"English","publisher":"Springer","doi":"10.1007/s00027-018-0606-z","usgsCitation":"Mejia, F.H., Fremier, A.K., Benjamin, J.R., Bellmore, J., Grimm, A.Z., Watson, G., and Newsom, M., 2019, Stream metabolism increases with drainage area and peaks asynchronously across a stream network: Aquatic Sciences, v. 81, p. 1-17, https://doi.org/10.1007/s00027-018-0606-z.","productDescription":"Article 9, 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-086489","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":362255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Methow River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.38818359375,\n 48.11476663187632\n ],\n [\n -119.79080200195311,\n 48.11476663187632\n ],\n [\n -119.79080200195311,\n 48.539341045937974\n ],\n [\n -120.38818359375,\n 48.539341045937974\n ],\n [\n -120.38818359375,\n 48.11476663187632\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":759692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fremier, Alexander K.","contributorId":214346,"corporation":false,"usgs":false,"family":"Fremier","given":"Alexander","email":"","middleInitial":"K.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":759693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":759694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bellmore, J. Ryan jbellmore@usgs.gov","contributorId":4527,"corporation":false,"usgs":true,"family":"Bellmore","given":"J. Ryan","email":"jbellmore@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":759695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grimm, Adrianne Z.","contributorId":214347,"corporation":false,"usgs":false,"family":"Grimm","given":"Adrianne","email":"","middleInitial":"Z.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":759696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watson, Grace A.","contributorId":214348,"corporation":false,"usgs":false,"family":"Watson","given":"Grace A.","affiliations":[{"id":39012,"text":"Methow Salmon Recovery Foundation","active":true,"usgs":false}],"preferred":false,"id":759697,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Newsom, Michael","contributorId":178562,"corporation":false,"usgs":false,"family":"Newsom","given":"Michael","affiliations":[],"preferred":false,"id":759698,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202720,"text":"70202720 - 2019 - Cryptosporidium incidence and surface water influence of groundwater supplying public water systems in Minnesota, USA","interactions":[],"lastModifiedDate":"2019-06-18T11:08:36","indexId":"70202720","displayToPublicDate":"2019-03-21T13:08:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Cryptosporidium incidence and surface water influence of groundwater supplying public water systems in Minnesota, USA","docAbstract":"Regulations for public water systems (PWS) in the U.S. consider Cryptosporidium a microbial contaminant of surface water supplies. Ground- water is assumed free of Cryptosporidium unless surface water is entering supply wells. We determined the incidence of Cryptosporidium in PWS wells varying in surface water influence. Community and noncommunity PWS wells (n = 145) were sampled (n = 964) and analyzed for Cryptosporidium by qPCR and immunofluorescence assay (IFA). Surface water influence was assessed by stable isotopes and the expert judgment of hydrogeologists using site-specific data. Fifty-eight wells (40%) and 107 samples (11%) were Cryptosporidium- positive by qPCR, and of these samples 67 were positive by IFA. Cryptosporidium concentrations measured by qPCR and IFA were significantly\ncorrelated (p < 0.001). Cryptosporidium incidence was not associated with surface water influence as assessed by stable isotopes or expert judgment. We successfully sequenced 45 of the 107 positive samples to identify species, including C. parvum (41), C. andersoni (2), and C. hominis (2), and the predominant subtype was C. parvum IIa A17G2R1. Assuming USA regulations for surface water-supplied PWS were applicable to the study wells, wells positive for Cryptosporidium by IFA would likely be required to add treatment. Cryptosporidium is not uncommon in groundwater, even when surface water influence is absent.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.8b05446","usgsCitation":"Stokdyk, J.P., Spencer, S., Walsh, J.F., de Lambert, J.R., Fimstahl, A., Anderson, A., Rezania, L.W., and Borchardt, M.A., 2019, Cryptosporidium incidence and surface water influence of groundwater supplying public water systems in Minnesota, USA: Environmental Science & Technology, v. 23, no. 7, p. 3391-3398, https://doi.org/10.1021/acs.est.8b05446.","productDescription":"8 p.","startPage":"3391","endPage":"3398","ipdsId":"IP-102064","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":362245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"23","issue":"7","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spencer, Susan K.","contributorId":39511,"corporation":false,"usgs":true,"family":"Spencer","given":"Susan K.","affiliations":[],"preferred":false,"id":759667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, James F.","contributorId":214333,"corporation":false,"usgs":false,"family":"Walsh","given":"James","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":759668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Lambert, Jane R.","contributorId":214334,"corporation":false,"usgs":false,"family":"de Lambert","given":"Jane","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":759669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fimstahl, Aaron D. 0000-0003-2686-7596","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":214335,"corporation":false,"usgs":false,"family":"Fimstahl","given":"Aaron D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":759670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Anita C.","contributorId":214336,"corporation":false,"usgs":false,"family":"Anderson","given":"Anita C.","affiliations":[],"preferred":false,"id":759671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rezania, Lih-in W.","contributorId":214337,"corporation":false,"usgs":false,"family":"Rezania","given":"Lih-in","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":759672,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":210973,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":759673,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202482,"text":"sir20195013 - 2019 - Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota","interactions":[],"lastModifiedDate":"2019-03-26T08:18:03","indexId":"sir20195013","displayToPublicDate":"2019-03-21T09:45:09","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5013","displayTitle":"Hydraulic Conductivity Estimates from Slug Tests in the Big Sioux Aquifer Near Sioux Falls, South Dakota","title":"Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota","docAbstract":"Hydraulic conductivity estimates were made for 15 observation wells using slug-out (rising-head) tests in the Big Sioux aquifer near Sioux Falls, South Dakota, as part of a cooperative study with the City of Sioux Falls to characterize the hydrogeology and the extent of the Big Sioux aquifer north of the city. Well and aquifer data were collected from field measurements and drillers’ logs. Multiple slug tests were completed at each observation well with a transducer to record the change in water level and a U.S. Geological Survey standard mechanical slug to displace the well’s water column. In total, 110 slug-out test trials were completed among the 15 observation wells. Hydraulic conductivity was estimated by curve fitting with AQTESOLV Pro version 4.50.002. Hydraulic conductivity estimates ranged from 64 to 379 feet per day (ft/d). The mean, standard deviation, and median hydraulic conductivity for the 110 slug-out test trials were 171 ft/d, 73 ft/d, and 157 ft/d, respectively. The mean hydraulic conductivity calculated for each well ranged from 88 to 270 ft/d, the standard deviation ranged from 7 to 66 ft/d, and the median hydraulic conductivity ranged from 86 to 256 ft/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195013","collaboration":"Prepared in cooperation with the City of Sioux Falls","usgsCitation":"Eldridge, W.G., and Medler, C.J., 2019, Hydraulic conductivity estimates from slug tests in the Big Sioux Aquifer near Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5013, 23 p., https://doi.org/10.3133/sir20195013.","productDescription":"Report: v, 24 p., Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-100666","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":362206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5013/coverthb.jpg"},{"id":362207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5013/sir20195013.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5013"},{"id":362208,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release","linkHelpText":"Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","otherGeospatial":"Big Sioux Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.846997999996,\n 43.462111\n ],\n [\n -96.846997999996,\n 43.836203\n ],\n [\n -96.636738000004,\n 43.836203\n ],\n [\n -96.636738000004,\n 43.462111\n ],\n [\n -96.846997999996,\n 43.462111\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Pacific sand lance Ammodytes personatus are a key forage fish in the North Pacific for many species of salmon, groundfish, seabirds, and marine mammals and have historically been important to predators in relatively warm years. However, extreme declines in the nutritional value of sand lance in Prince William Sound, Alaska, USA, during 2012-2016 indicate that energy transfer from lower trophic levels to predators via sand lance may have been disrupted during the North Pacific marine heatwave in 2015 and 2016. Nutritional value (length, energy density, and whole-body energy) was measured in age-0 and age-1 sand lance collected during July in cool (2012-2013) and increasingly warm (2014-2016) years. The value of age-0 fish was relatively stable, with only minor differences among years for length and whole-body energy. By contrast, the value of age-1 fish significantly declined in 2015, and by 2016 they were 38% shorter and 13% lower in energy density compared to cooler years. This contributed to significant declines in whole-body energy of 44% in 2015 and 89% in 2016 compared to cooler years (2012-2014). The 2015 sand lance cohort experienced little growth or lipid accumulation from July 2015 at age-0 to July 2016 at age-1. This effective disruption of energy flow through pelagic food webs probably contributed to population declines and/or breeding failures observed among several predators in the Gulf of Alaska and suggests that tipping points were reached during the heatwave.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps12891","usgsCitation":"von Biela, V.R., Arimitsu, M.L., Piatt, J.F., Heflin, B., Schoen, S.K., Trowbridge, J., and Clawson, C., 2019, Extreme reduction in nutritional value of a key forage fish during the Pacific marine heatwave of 2014–2016: Marine Ecology Progress Series, v. 613, p. 171-182, https://doi.org/10.3354/meps12891.","productDescription":"12 p.","startPage":"171","endPage":"182","ipdsId":"IP-101543","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":467793,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps12891","text":"Publisher Index Page"},{"id":437534,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96N5PVE","text":"USGS data release","linkHelpText":"Pacific Sand Lance Energy Density, Length, and Age, Prince William Sound, Alaska, 2012-2016"},{"id":363578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"613","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":762341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heflin, Brielle 0000-0002-4836-9187 bheflin@usgs.gov","orcid":"https://orcid.org/0000-0002-4836-9187","contributorId":198164,"corporation":false,"usgs":true,"family":"Heflin","given":"Brielle","email":"bheflin@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762344,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schoen, Sarah K. 0000-0002-5685-5185 sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762345,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Trowbridge, Jannelle","contributorId":215435,"corporation":false,"usgs":false,"family":"Trowbridge","given":"Jannelle","affiliations":[{"id":37194,"text":"University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":762346,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clawson, Chelsea","contributorId":215436,"corporation":false,"usgs":false,"family":"Clawson","given":"Chelsea","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":762347,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202707,"text":"70202707 - 2019 - Downstream‐propagating channel responses to decadal‐scale climate variability in a glaciated river basin","interactions":[],"lastModifiedDate":"2019-06-18T11:02:06","indexId":"70202707","displayToPublicDate":"2019-03-20T14:49:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Downstream‐propagating channel responses to decadal‐scale climate variability in a glaciated river basin","docAbstract":"Regional climate is an important control on the rate of coarse sediment mobilization and transport in alpine river systems. Changes in climate are then expected to cause a cascade of geomorphic responses, including adjustments in downstream channel morphology. However, the mechanics and sensitivity of channel response to short‐term climate variability remain poorly documented. In the Nooksack River, which drains a glaciated stratovolcano in Washington State, bed elevation changes were inferred from shifting stage–discharge relations at seven USGS stream gages. Decadal‐scale elevation trends at most sites can be explained as a downstream‐propagating channel response to regional climate variability, where periods of persistent warm, dry [cool, wet] conditions corresponded to periods of aggradation [incision]. The channel elevation response propagated downstream at a rate of one to four kilometers per year; propagation rate scaled closely with channel slope. Historical trends in glacier extent and flood intensity both show some potential to explain climate–sediment linkages, though assessing causation is complicated by the shared climate signal in both records. Results show the influence of the Pacific Decadal Oscillation, with relatively high coarse sediment yields prior to 1950 and since 1980, and notably lower sediment yields from 1950 to 1980. Measured sediment yields from nearby glaciated basins corroborate this history, suggesting a regional coherence to these climate–sediment linkages. These results document consistent relations between climate, sediment supply and downstream channel response at the basin‐scale, with channel responses propagating downstream over periods of decades with little apparent attenuation.
","language":"English","publisher":"AGU","doi":"10.1029/2018JF004734","usgsCitation":"Anderson, S.W., and Konrad, C.P., 2019, Downstream‐propagating channel responses to decadal‐scale climate variability in a glaciated river basin: Journal of Geophysical Research: Earth Surface, v. 124, no. 4, p. 902-919, https://doi.org/10.1029/2018JF004734.","productDescription":"18 p.","startPage":"902","endPage":"919","ipdsId":"IP-097291","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":362211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nooksack River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.64312744140624,\n 48.499317631540286\n ],\n [\n -121.453857421875,\n 48.499317631540286\n ],\n [\n -121.453857421875,\n 48.9991410647952\n ],\n [\n -122.64312744140624,\n 48.9991410647952\n ],\n [\n -122.64312744140624,\n 48.499317631540286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759572,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202709,"text":"70202709 - 2019 - Defining the limits of spectrally based bathymetric mapping on a large river","interactions":[],"lastModifiedDate":"2019-03-20T14:45:46","indexId":"70202709","displayToPublicDate":"2019-03-20T14:45:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Defining the limits of spectrally based bathymetric mapping on a large river","docAbstract":"Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images from a deep, clear-flowing channel to develop techniques for inferring the maximum detectable depth,
The Kankakee River in northern Indiana flows through the area once known as the Grand Marsh. Beginning in the 1860s, anthropogenic changes to the river within Indiana resulted in downstream flooding and additional transport of sediment and nutrients. In 2015, the U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, upgraded the gaging station Kankakee River at Shelby, Indiana, to include the collection of water-quality data. By relating continuously monitored water-quality data to discrete data collected from December 2015 through May 2018, linear regression was used to develop models for estimating concentrations of suspended sediment, total nitrogen, and total phosphorus. Developed regression models indicated a strong correlation between turbidity and specific conductance with suspended-sediment concentration (adjusted coefficient of determination equals 0.92, predicted residual error sum of squares equals 0.151), nitrate plus nitrite and specific conductance with total nitrogen (adjusted coefficient of determination equals 0.95, predicted residual error sum of squares equals 0.0248), and turbidity with total phosphorus (adjusted coefficient of determination equals 0.89, predicted residual error sum of squares equals 0.0103).
Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed as the product of daily mean regression model concentrations and daily mean streamflow. Rloadest models were used to compute daily loads of each constituent during gaps in regression model loads. For 2016 and 2017, the estimated annual suspended-sediment loads were 105,000 and 91,000 tons; estimated total nitrogen loads were 8,690 and 8,890 tons; and estimated total phosphorus loads were 265 and 236 tons, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195005","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Lathrop, T.R., Bunch, A.R., and Downhour, M.S., 2019, Regression models for estimating sediment and nutrient concentrations and loads at the Kankakee River, Shelby, Indiana, December 2015 through May 2018: U.S. Geological Survey Scientific Investigation Report 2019–5005, 13 p., https://doi.org/10.3133/sir20195005.","productDescription":"Report: v, 13 p.; 2 Data Releases","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101520","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":362192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5005/coverthb.jpg"},{"id":362193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5005/sir20195005.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5005"},{"id":362194,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PE9PTD","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and rloadest models for suspended sediment, total nitrogen, and total phosphorus for Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"},{"id":362195,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EKU6X","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and Surrogate Models for Suspended Sediment, Total Nitrogen, and Total Phosphorus for the Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"}],"country":"United States","state":"Indiana","city":"Shelby","otherGeospatial":"Kankakee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.81396484375,\n 41.34897943069752\n ],\n [\n -86.08337402343749,\n 41.34588656996287\n ],\n [\n -86.08337402343749,\n 41.76721469421018\n ],\n [\n -86.81259155273438,\n 41.76823896512856\n ],\n [\n -86.81396484375,\n 41.34897943069752\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily point observations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are included in this manual.
The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically a drainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input and output files with defined formats and naming conventions. A shell application also is available to manage multiple runs of the Draper application.
Other applications in the Draper Suite include (1) a tool to find and interactively remove outliers in the input data, (2) a tool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time-series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a workflow involving physically based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C22","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Donovan, J.M., and Koczot, K.M., 2019, User’s manual for the Draper climate-distribution software suite with data‑evaluation tools: U.S. Geological Survey Techniques and Methods 7-C22, 55 p., https://doi.org/10.3133/tm7C22. ","productDescription":"viii, 55 p","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-086388","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":362190,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c22/coverthb.jpg"},{"id":362191,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c22/tm7c22.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C22"},{"id":365983,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://code.usgs.gov/cawsc/draper","text":"Source code and executables","linkHelpText":"- Users are required to create an account to access the distribution"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Sex determination is the first step toward the establishment of phenotypic sex in most vertebrates. Aquatic poikilotherms such as teleost fishes exhibit a high diversity of sex-determination mechanisms and gonadal phenotypes that are remarkably plastic and responsive to a variety of environmental factors (e.g., water temperature, pH, salinity, photoperiod, population density). This chapter reviews current knowledge of genotypic and environmental sex determination systems in fishes with special reference to Atheriniformes—one of the best-characterized taxa in this field—and offers perspectives to guide and stimulate further research.
","language":"English","publisher":"Elsevier","doi":"10.1016/bs.ctdb.2019.02.003","usgsCitation":"Yamamoto, Y., Hattori, R.S., Patino, R., and Strüssmann, C., 2019, Environmental regulation of sex determination in fishes: Insights from Atheriniformes, p. 49-69, https://doi.org/10.1016/bs.ctdb.2019.02.003.","productDescription":"21 p.","startPage":"49","endPage":"69","ipdsId":"IP-102625","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yamamoto, Y.","contributorId":264653,"corporation":false,"usgs":false,"family":"Yamamoto","given":"Y.","affiliations":[{"id":47624,"text":"Tokyo University of Marine Science and Technology","active":true,"usgs":false}],"preferred":false,"id":821813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hattori, R. S.","contributorId":264656,"corporation":false,"usgs":false,"family":"Hattori","given":"R.","email":"","middleInitial":"S.","affiliations":[{"id":54527,"text":"Sao Paulo Fisheries Institute","active":true,"usgs":false}],"preferred":false,"id":821814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":821815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strüssmann, C. A.","contributorId":264657,"corporation":false,"usgs":false,"family":"Strüssmann","given":"C. A.","affiliations":[{"id":47624,"text":"Tokyo University of Marine Science and Technology","active":true,"usgs":false}],"preferred":false,"id":821816,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215766,"text":"70215766 - 2019 - Growth disparity in sympatric kokanee breeding groups","interactions":[],"lastModifiedDate":"2020-10-30T11:55:18.644759","indexId":"70215766","displayToPublicDate":"2019-03-20T06:48:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2885,"text":"North American Journal of Aquaculture","active":true,"publicationSubtype":{"id":10}},"title":"Growth disparity in sympatric kokanee breeding groups","docAbstract":"Growth is arguably the most important dynamic rate function due to its interaction with survival and recruitment. As such, understanding the mechanisms underlying growth is a primary focus of fisheries research. Kokanee Oncorhynchus nerka in Lake Pend Oreille, Idaho, provide an interesting case study for investigating the factors that influence growth. Early‐run and late‐run kokanee occur in Lake Pend Oreille, but early‐run fish generally grow faster than late‐run fish. The observed growth disparity between early‐ and late‐run fish could be due to genetic differences between the two groups. Conversely, a common hatchery practice of slowing growth by reducing feed has been hypothesized to elicit a compensatory growth response in early‐run fish and to explain the size difference between breeding groups. Using two different experiments, we tested the hypotheses that (1) early‐run kokanee are genetically disposed to grow faster than late‐run kokanee at identical water temperatures; and (2) feed restriction elicits a compensatory growth response in early‐run kokanee that explains the observed size difference between breeding groups. Estimates of mean FL, weight, Fulton's condition factor (K), and specific growth rate (SGR) were not significantly different (P ≥ 0.05) between early‐run and late‐run fish in the first experiment. However, water temperature was positively related to mean FL, weight, K, and SGR for both breeding groups. Fish that were subjected to food deprivation exhibited an increased growth rate and obtained weights similar to those of control fish. Overall, our results suggest that early‐ and late‐run fish have similar growth potential, but certain hatchery practices likely provide early‐run fish with an initial advantage in growth, size, or both.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/naaq.10084","usgsCitation":"Klein, Z.B., Quist, M.C., Dux, A.M., and Corsi, M.P., 2019, Growth disparity in sympatric kokanee breeding groups: North American Journal of Aquaculture, v. 81, no. 2, p. 169-177, https://doi.org/10.1002/naaq.10084.","productDescription":"9 p.","startPage":"169","endPage":"177","ipdsId":"IP-103305","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486797,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2499838","text":"External Repository"},{"id":379957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Pend Oreille","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.70501708984376,\n 47.91450120703987\n ],\n [\n -116.13372802734375,\n 47.91450120703987\n ],\n [\n -116.13372802734375,\n 48.323386716330916\n ],\n [\n -116.70501708984376,\n 48.323386716330916\n ],\n [\n -116.70501708984376,\n 47.91450120703987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Klein, Zachary B.","contributorId":171709,"corporation":false,"usgs":false,"family":"Klein","given":"Zachary","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":803344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":803345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dux, Andrew M.","contributorId":212798,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":803346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corsi, Matthew P.","contributorId":212797,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":803347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216766,"text":"70216766 - 2019 - Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water","interactions":[],"lastModifiedDate":"2020-12-04T22:03:41.306725","indexId":"70216766","displayToPublicDate":"2019-03-19T15:59:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water","docAbstract":"Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free‐ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free‐ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears Ursus maritimus and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free‐ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer‐based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer‐based measures of energy expenditure strongly correlated (r2 = 0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free‐ranging animals.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5053","usgsCitation":"Pagano, A.M., and Williams, T.M., 2019, Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water: Ecology and Evolution, v. 9, no. 7, p. 4210-4219, https://doi.org/10.1002/ece3.5053.","productDescription":"10 p.","startPage":"4210","endPage":"4219","ipdsId":"IP-101615","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":467799,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5053","text":"Publisher Index Page"},{"id":381005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Northwest Territories, Yukon","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.35351562499999,\n 70.19999407534661\n ],\n [\n -125.859375,\n 72.97118902284586\n ],\n [\n -157.58789062499997,\n 73.17589717422607\n ],\n [\n -156.62109374999997,\n 71.30079291637452\n ],\n [\n -148.798828125,\n 70.25945200030638\n ],\n [\n -145.1953125,\n 70.05059634999759\n ],\n [\n -139.5703125,\n 69.47296854140573\n ],\n [\n -135.703125,\n 68.65655498475735\n ],\n [\n -127.35351562499999,\n 70.19999407534661\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"7","noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":806132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Terrie M.","contributorId":191735,"corporation":false,"usgs":false,"family":"Williams","given":"Terrie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":806133,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70204348,"text":"70204348 - 2019 - Discovery of an extensive deep-sea fossil serpulid reef associated with a cold seep, Santa Monica Basin, California","interactions":[],"lastModifiedDate":"2019-07-18T14:04:15","indexId":"70204348","displayToPublicDate":"2019-03-19T13:48:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of an extensive deep-sea fossil serpulid reef associated with a cold seep, Santa Monica Basin, California","docAbstract":"Multi-beam mapping of the Santa Monica Basin in the eastern Pacific has revealed the existence of a number of elevated bathymetric features, or mounds, harboring cold seep communities. During 2013-2014, mounds at ~600 m water depth were observed for the first time and sampled by Monterey Bay Aquarium Research Institute’s ROV Doc Ricketts. Active cold seeps were found, but surprisingly one of these mounds was characterized by massive deposits composed of fossil serpulid worm tubes (Annelida: Serpulidae) exhibiting various states of mineralization by authigenic carbonate. No living serpulids with equivalent tube morphologies were found at the site; hence the mound was termed ‘Fossil Hill’. In the present study, the identity of the fossil serpulids and associated fossil community, the ages of fossils and authigenic carbonates, the formation of the fossil serpulid aggregation, and the geological structure of the mound are explored. Results indicate that the tubes were most likely made by a deep-sea serpulid lineage, with radiocarbon dating suggesting that they have a very recent origin during the Late Pleistocene, specifically to the Last Glacial Maximum ~20,000 years ago. Additional U-Th analyses of authigenic carbonates mostly corroborate the radiocarbon dates, and also indicate that seepage was occurring while the tubes were being formed. We also document similar, older deposits along the approximate trajectory of the San Pedro Basin Fault. We suggest that the serpulid tube facies formed in situ, and that the vast aggregation of these tubes at Fossil Hill is likely due to a combination of optimal physical environmental conditions and chemosynthetic production, which may have been particularly intense as a result of sea-level lowstand during the Last Glacial Maximum.","language":"English","publisher":"Frontiers in Marine Science","doi":"10.3389/fmars.2019.00115","usgsCitation":"Georgieva, M.N., Paull, C.K., Little, C.T., McGann, M., Sahy, D., Condon, D., Lundsten, L., Pewsey, J., Caress, D., and Vrijenhoek, R.C., 2019, Discovery of an extensive deep-sea fossil serpulid reef associated with a cold seep, Santa Monica Basin, California: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2019.00115.","ipdsId":"IP-105040","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460437,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2019.00115","text":"Publisher Index Page"},{"id":365721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365716,"type":{"id":15,"text":"Index Page"},"url":"https://www.frontiersin.org/journals/marine-science"}],"country":"United States","state":"California","otherGeospatial":"Santa Monica basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.59672546386719,\n 33.93253620840842\n ],\n [\n -118.35708618164064,\n 33.93253620840842\n ],\n [\n -118.35708618164064,\n 34.076549928891744\n ],\n [\n -118.59672546386719,\n 34.076549928891744\n ],\n [\n -118.59672546386719,\n 33.93253620840842\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Georgieva, Magdalena N","contributorId":217245,"corporation":false,"usgs":false,"family":"Georgieva","given":"Magdalena","email":"","middleInitial":"N","affiliations":[{"id":39584,"text":"Life Sciences Department, Natural History Museum, London, UK","active":true,"usgs":false}],"preferred":false,"id":766451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":766450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Little, Crispin TS","contributorId":217246,"corporation":false,"usgs":false,"family":"Little","given":"Crispin","email":"","middleInitial":"TS","affiliations":[{"id":39585,"text":"School of Earth and Environment, University of Leeds, Leeds, UK","active":true,"usgs":false}],"preferred":false,"id":766452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":766449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sahy, Diana","contributorId":169649,"corporation":false,"usgs":false,"family":"Sahy","given":"Diana","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":766453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Condon, Daniel","contributorId":217247,"corporation":false,"usgs":false,"family":"Condon","given":"Daniel","email":"","affiliations":[{"id":39586,"text":"NERC Isotope Geosciences Laboratory, British Geological Survey, Keyworth, UK","active":true,"usgs":false}],"preferred":false,"id":766454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundsten, Lonny","contributorId":217248,"corporation":false,"usgs":false,"family":"Lundsten","given":"Lonny","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":766455,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pewsey, Jack","contributorId":217250,"corporation":false,"usgs":false,"family":"Pewsey","given":"Jack","email":"","affiliations":[{"id":39585,"text":"School of Earth and Environment, University of Leeds, Leeds, UK","active":true,"usgs":false}],"preferred":false,"id":766458,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Caress, David W","contributorId":147194,"corporation":false,"usgs":false,"family":"Caress","given":"David W","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":766456,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vrijenhoek, Robert C","contributorId":217249,"corporation":false,"usgs":false,"family":"Vrijenhoek","given":"Robert","email":"","middleInitial":"C","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":766457,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70202458,"text":"ds1108 - 2019 - Quality of surface water in Missouri, water year 2017","interactions":[],"lastModifiedDate":"2019-03-19T16:29:55","indexId":"ds1108","displayToPublicDate":"2019-03-19T11:02:36","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1108","displayTitle":"Quality of Surface Water in Missouri, Water Year 2017","title":"Quality of surface water in Missouri, water year 2017","docAbstract":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2017 (October 1, 2016, through September 30, 2017), data presented in this report were collected at 72 stations: 70 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 72 stations in this report, 4 stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed represented by the station. Some stations have been classified based on the unique hydrology of the waterbodies they monitor. A summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented.
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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Conventional markets can underprovide ecosystem services. Deliberate creation of a market for ecosystem services [e.g., a payments for ecosystem services (PES) scheme] can close the gap. The new ecosystem service market alters behaviors and quantities of ecosystem service provided and reveals prices for the ecosystems service: a market-clearing equilibrium. Assessing the potential for PES programs, which often act as ecological infrastructure investment mechanisms, requires forecasting the market-clearing equilibrium. Forecasting the equilibrium is complicated, especially at relevant social and ecological scales. It requires greater disciplinary integration than valuing ecosystem services or computing the marginal cost of making a land-use change to produce a service. We conduct an ex ante benefit–cost assessment and forecast market-clearing prices and quantities for ecological infrastructure investment contracts in the Panama Canal Watershed. The Panama Canal Authority could offer contracts to private farmers to change land use to increase dry-season water flow and reduce sedimentation. A feasible voluntary contracting system yields a small program of about 1,840 ha of land conversion in a 279,000-ha watershed and generates a 4.9 benefit–cost ratio. Physical and social constraints limit market supply and scalability. Service delays, caused by lags between the time payments must be made and the time services stemming from ecosystem change are realized, hinder program feasibility. Targeting opportunities raise the benefit–cost ratio but reduce the hectares likely to be converted. We compare and contrast our results with prior state-of-the-art assessments on this system.
The Missouri groundwater-level observation well network is a series of wells across the State of Missouri in which groundwater levels are monitored in real time and periodically. The wells monitor the water levels in multiple key aquifers, such as the Ozark aquifer in the Salem and Springfield Plateaus and the Mississippi Alluvial Plain aquifer in the South-eastern Lowlands. As of 2018, 150 real-time sites are operated as a cooperative effort between the Missouri Department of Natural Resources (MoDNR) and the U.S. Geological Survey. This fact sheet describes the network and well data from the network.
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Mercury monitoring results from about 300 Morone saxatilis (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification.
A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at https://doi.org/10.5066/F74X560J.
In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [p] less than (<) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir.
Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being significantly (p<0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ)202Hg and capital delta (Δ)199Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm.
Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll a, dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll a, decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Naftz, D.L., Marvin-DiPasquale, M., Krabbenhoft, D.P., Aiken, G., Boyd, E.S., Conaway, C.H., Ogorek, J., and Anderson, G.M., 2019, Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015: U.S. Geological Survey Open-File Report 2018–1159, 81 p., https://doi.org/10.3133/ofr20181159.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-095917","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1159/coverthb.jpg"},{"id":359577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1159/ofr20181159.pdf","text":"Report","size":"9.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1159"},{"id":359578,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X560J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014–2015"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63551330566406,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 36.75594019674357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Utah Water Science Center
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2329 West Orton Circle West
Valley City, UT 84119
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, is investigating the distribution of saline groundwater in the Genesee River Valley near the former Retsof salt mine (fig. 1). As part of this study, paired time-domain electromagnetic (TEM) soundings and horizontal-to-vertical spectral ratio (HVSR) seismic soundings were made at 39 locations during the fall of 2016 to determine the presence of saline groundwater and depth to the bedrock surface, respectively. All measurement sites were west of Geneseo, New York, on the Genesee River valley floor north and south of the sinkhole area that developed as a result of the roof collapse and flooding of the Retsof mine in 1994 (fig. 1). An integrated analysis of the TEM and HVSR soundings with borehole logs, coupled with groundwater-sample data from previous investigations, allowed the delineation of zones of high electrical conductivity associated with saline water in the lower part of the valley fill and underlying bedrock to depths greater than 1,000 feet (ft). This article describes the TEM sounding method and its application in the ongoing investigation, presents results of the TEM analysis at two of the sounding sites, and identifies proposed sites for additional TEM/HVSR sounding data collection during the fall of 2017. Supporting data for this study are available in a separate data release (Johnson and others, 2017).