Sequoia Scientific’s LISST-ABS is a submersible acoustic instrument that measures the acoustic backscatter sensor (ABS) concentration at a point within a river, stream, or creek. Compared to traditional physical methods for measuring suspended-sediment concentration (SSC), sediment surrogates like the LISST-ABS offer continuous data that can be calibrated with physical SSC samples. Data were collected at 10 U.S. Geological Survey streamflow-gaging stations between January 10, 2016, and February 21, 2018, across the contiguous United States to test the accuracy and effectiveness of using the LISST-ABS as a surrogate for measuring the concentration of suspended sediment in a dynamic fluvial system. Correlation coefficients (Pearson’s r values) relating the ABS concentration and SSC from physical samples ranged from r = 0.718 to r = 0.956 at the 10 stations with the mean percentage of fines (percentage of the sediment less than 62.5 microns in diameter) ranging from 65 to 100 percent (with minimum and maximum values of 18 and 100 percent, respectively). The LISST-ABS instruments used in this field evaluation were factory-calibrated to accurately determine SSC for grains in the diameter range of 75–90 microns. Note that the sensor responds to grains of arbitrary sizes, but the accuracy varies at sizes other than this calibration size. For operational use, regression models could be determined for the ABS concentrations and SSC values or the instrument could be recalibrated to sediments for each fluvial environment. However, such calibrations were beyond the scope of this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201096","collaboration":"Federal Interagency Sedimentation Project and Observing Systems Division","usgsCitation":"Manaster, A.E., Straub, T.D., Wood, M.S., Bell, J.M., Dombroski, D.E., and Curran, C.A., 2020, Field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor: U.S. Geological Survey Open-File Report 2020–1096, 26 p., https://doi.org/10.3133/ofr20201096.","productDescription":"Report: v, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-116096","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":378643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1096/coverthb.jpg"},{"id":378644,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1096/ofr20201096.pdf","text":"Report","size":"3.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1096"},{"id":378645,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LROJE4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The key to Nelson’s Sparrow (Ammospiza nelsoni nelsoni) management is providing dense grasses or emergent vegetation near damp areas or freshwater wetlands. Nelson’s Sparrows have been reported to use habitats with 20–122 centimeters (cm) average vegetation height, 41 cm visual obstruction reading, 40–58 percent grass cover, 24 percent forb cover, 5 percent shrub cover, 13 percent bare ground, and 2–7 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842KK","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Rabie, P.A., and Euliss, B.R., 2020, The effects of management practices on grassland birds—Nelson’s Sparrow (Ammospiza nelsoni nelsoni), chap. KK of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 10 p., https://doi.org/10.3133/pp1842KK.","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-095138","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":378704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/kk/coverthb.jpg"},{"id":378705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/kk/pp1842kk.pdf","text":"Report","size":"2.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–KK"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Migration is a period of high activity and exposure during which risks and energetic demand on individuals may be greater than during nonmigratory periods. Stopover locations can help mitigate these threats by providing supplemental energy en route to the animal’s end destination. Effective conservation of migratory species therefore requires an understanding of use of space that provides resources to migratory animals at stopover sites. We conducted a radio-telemetry study of a short-distance migrant, the American Woodcock (Scolopax minor), at an important stopover site, the Cape May Peninsula, New Jersey. Our objectives were to describe land-cover types used by American Woodcock and evaluate home range habitat selection for individuals that stopover during fall migration and those that choose to overwinter. We radio-marked 271 individuals and collected 1,949 locations from these birds (0–21 points individual–1) over 4 yr (2010 to 2013) to inform resource selection functions of land-cover types and other landscape characteristics by this species. We evaluated these relationships at multiple spatial extents for (1) birds known to have ultimately left the peninsula (presumed migrants), and (2) birds known to have remained on the peninsula into the winter (presumed winter residents). We found that migrants selected deciduous wetland forest, agriculture, mixed shrub, coniferous wetland forest, and coniferous shrub, while wintering residents selected deciduous wetland forest, coniferous shrub, and deciduous shrub. We used these results to develop predictive models of potential habitat: 7.80% of the peninsula was predicted to be potential stopover habitat for American Woodcock (95% classification accuracy) and 4.96% of the peninsula was predicted to be potential wintering habitat (85% classification accuracy). Our study is the first to report habitat relationships for migratory American Woodcock in the coastal U.S. and provides important spatial tools for local and regional managers to support migratory and winter resident woodcock populations into the future.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa046","usgsCitation":"Allen, B.L., McAuley, D., and Blomberg, E.J., 2020, Migratory status determines resource selection by American Woodcock at an important fall stopover, Cape May, New Jersey: The Condor, duaa046, 16 p., https://doi.org/10.1093/condor/duaa046.","productDescription":"duaa046, 16 p.","ipdsId":"IP-092164","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":436781,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R49PZ2","text":"USGS data release","linkHelpText":"Multiscale resource selection by American Woodcock (Scolopax minor) during fall migration at Cape May, New Jersey"},{"id":379018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Cape May","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.6024169921875,\n 38.852542390364235\n ],\n [\n -74.06982421875,\n 38.852542390364235\n ],\n [\n -74.06982421875,\n 39.51675478434244\n ],\n [\n -75.6024169921875,\n 39.51675478434244\n ],\n [\n -75.6024169921875,\n 38.852542390364235\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Brian L.","contributorId":171560,"corporation":false,"usgs":false,"family":"Allen","given":"Brian","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":800447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAuley, Daniel 0000-0003-3674-6392 dmcauley@usgs.gov","orcid":"https://orcid.org/0000-0003-3674-6392","contributorId":215182,"corporation":false,"usgs":true,"family":"McAuley","given":"Daniel","email":"dmcauley@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":800448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blomberg, Erik J.","contributorId":17543,"corporation":false,"usgs":false,"family":"Blomberg","given":"Erik","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":800449,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215097,"text":"70215097 - 2020 - Why did Great Basin Eocene magmatism generate Carlin-type gold deposits when extensive Jurassic to Middle Miocene magmatism did not? Lessons from the Cortez Region, Northern Nevada, USA","interactions":[],"lastModifiedDate":"2020-10-08T14:06:42.685909","indexId":"70215097","displayToPublicDate":"2020-09-24T09:03:04","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Why did Great Basin Eocene magmatism generate Carlin-type gold deposits when extensive Jurassic to Middle Miocene magmatism did not? Lessons from the Cortez Region, Northern Nevada, USA","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Vision for discovery: Geological Society of Nevada symposium proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"8th Symposium of Geological Society of Nevada","conferenceDate":"May 14-24, 2020","conferenceLocation":"Reno/Lake Tahoe, NV","language":"English","publisher":"Geological Society of Nevada","usgsCitation":"Henry, C., John, D.A., Heizler, M.T., Leonardson, R.W., Colgan, J.P., Watts, K., Ressel, M.W., and Cousens, B.L., 2020, Why did Great Basin Eocene magmatism generate Carlin-type gold deposits when extensive Jurassic to Middle Miocene magmatism did not? Lessons from the Cortez Region, Northern Nevada, USA, in Vision for discovery: Geological Society of Nevada symposium proceedings, Reno/Lake Tahoe, NV, May 14-24, 2020.","ipdsId":"IP-115532","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":379229,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Cortez region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.6143798828125,\n 40.11588965267845\n ],\n [\n -115.87829589843751,\n 40.11588965267845\n ],\n [\n -115.87829589843751,\n 40.67647212850004\n ],\n [\n -116.6143798828125,\n 40.67647212850004\n ],\n [\n -116.6143798828125,\n 40.11588965267845\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Henry, Christopher D.","contributorId":175501,"corporation":false,"usgs":false,"family":"Henry","given":"Christopher D.","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":800831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"John, David A. 0000-0001-7977-9106 djohn@usgs.gov","orcid":"https://orcid.org/0000-0001-7977-9106","contributorId":1748,"corporation":false,"usgs":true,"family":"John","given":"David","email":"djohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":800832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heizler, Matt T. 0000-0002-3911-4932","orcid":"https://orcid.org/0000-0002-3911-4932","contributorId":229568,"corporation":false,"usgs":false,"family":"Heizler","given":"Matt","email":"","middleInitial":"T.","affiliations":[{"id":41669,"text":"New Mexico Bureau of Geology and Mineral Resources, New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":800833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leonardson, Robert W.","contributorId":242799,"corporation":false,"usgs":false,"family":"Leonardson","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":800834,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Colgan, Joseph P. 0000-0001-6671-1436 jcolgan@usgs.gov","orcid":"https://orcid.org/0000-0001-6671-1436","contributorId":1649,"corporation":false,"usgs":true,"family":"Colgan","given":"Joseph","email":"jcolgan@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":800835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watts, Kathryn E. 0000-0002-6110-7499","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":204344,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":800836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ressel, Michael W.","contributorId":242800,"corporation":false,"usgs":false,"family":"Ressel","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":800837,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cousens, Brian L. 0000-0002-9704-6974","orcid":"https://orcid.org/0000-0002-9704-6974","contributorId":242801,"corporation":false,"usgs":false,"family":"Cousens","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":800838,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70214091,"text":"sim3461 - 2020 - Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas","interactions":[{"subject":{"id":70214091,"text":"sim3461 - 2020 - Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas","indexId":"sim3461","publicationYear":"2020","noYear":false,"displayTitle":"Geologic Framework and Hydrostratigraphy of the Edwards and Trinity Aquifers Within Northern Medina County, Texas","title":"Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas"},"predicate":"SUPERSEDED_BY","object":{"id":70258397,"text":"sim3526 - 2024 - Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas","indexId":"sim3526","publicationYear":"2024","noYear":false,"title":"Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas"},"id":1}],"supersededBy":{"id":70258397,"text":"sim3526 - 2024 - Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas","indexId":"sim3526","publicationYear":"2024","noYear":false,"title":"Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas"},"lastModifiedDate":"2024-09-20T17:56:40.050301","indexId":"sim3461","displayToPublicDate":"2020-09-24T08:37:02","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3461","displayTitle":"Geologic Framework and Hydrostratigraphy of the Edwards and Trinity Aquifers Within Northern Medina County, Texas","title":"Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas","docAbstract":"The karstic Edwards and Trinity aquifers are classified as major sources of water in south-central Texas by the Texas Water Development Board. During 2018–20 the U.S. Geological Survey, in cooperation with the Edwards Aquifer Authority, mapped and described the geologic framework and hydrostratigraphy of the rocks composing the Edwards and Trinity aquifers in northern Medina County from field observations of the surficial expressions of the rocks. The thicknesses of the mapped lithostratigraphic members and hydrostratigraphic units were also estimated from field observations.
The Cretaceous-age rocks (listed in ascending order) in the study area are part of the Trinity Group (lower and upper members of the Glen Rose Limestone), Edwards Group (Kainer Formation [and its stratigraphic equivalent, the Fort Terrett Formation] and Person Formation), Devils River Limestone, Washita Group (Georgetown Formation, Del Rio Clay, and Buda Limestone), Eagle Ford Group, Austin Group, Taylor Group, and Late Cretaceous igneous intrusive rocks. The groups and formations are composed primarily of relatively thick layers of clays, shales, and limestone. The igneous rocks are coarse-grained ultramafic in composition.
The principal structural feature in northern Medina County is the Balcones fault zone, which is the result of late Oligocene and early Miocene extensional faulting and fracturing resulting from the eastern Edwards Plateau uplift. In the Balcones fault zone, most of the faults in the study area are high-angle to vertical, en echelon, normal faults that are predominately downthrown to the southeast.
Hydrostratigraphically, the rocks exposed in the study area (listed in descending order from land surface as they appear in a stratigraphic column) are igneous, the upper confining unit to the Edwards aquifer, the Edwards aquifer, the upper zone of the Trinity aquifer, and the upper part of the middle zone of the Trinity aquifer. The karstic carbonate Edwards and Trinity aquifers developed as a result of their original depositional history, primary and secondary porosity, diagenesis, fracturing, and faulting. These factors have resulted in development of modified porosity, permeability, and transmissivity within and between the aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3461","collaboration":"Prepared in cooperation with the Edwards Aquifer Authority","usgsCitation":"Clark, A.K., Morris, R.E., and Pedraza, D.E., 2020, Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas: U.S. Geological Survey Scientific Investigations Map 3461, 13 p. pamphlet, 1 pl., scale 1:24,000, https://doi.org/10.3133/sim3461.","productDescription":"Report: vi, 13 p.; Sheet: 48 inches x 36 inches; Data Release","numberOfPages":"23","onlineOnly":"N","ipdsId":"IP-112816","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":378661,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HHMBX8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial dataset of the geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas, at 1:24,000 scale"},{"id":378659,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3461/sim3461_pamphlet.pdf","text":"Pamphlet","size":"1.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3461 Pamphlet"},{"id":378658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3461/coverthb1.jpg"},{"id":378660,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3461/sim3461.pdf","text":"Map sheet","size":"30.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3461"}],"country":"United States","state":"Texas","county":"Medina County","otherGeospatial":"Edwards and Trinity Aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.46609497070312,\n 29.31514119318728\n ],\n [\n -98.79867553710936,\n 29.31514119318728\n ],\n [\n -98.79867553710936,\n 29.6510621496229\n ],\n [\n -99.46609497070312,\n 29.6510621496229\n ],\n [\n -99.46609497070312,\n 29.31514119318728\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Die-offs of seabirds in Alaska have occurred with increased frequency since 2015. In 2018, on St. Lawrence Island, seabirds were reported washing up dead on beaches starting in late May, peaking in June, and continuing until early August. The cause of death was documented to be starvation, leading to the conclusion that a severe food shortage was to blame. We use physiology and colony-based observations to examine whether food shortage is a sufficient explanation for the die-off, or if evidence indicates an alternative cause of starvation such as disease. Specifically, we address what species were most affected, the timing of possible food shortages, and food shortage severity in a historical context. We found that thick-billed murres (Uria lomvia) were most affected by the die-off, making up 61% of all bird carcasses encountered during beach surveys. Thick-billed murre carcasses were proportionately more numerous (26:1) than would be expected based on ratios of thick-billed murres to co-occurring common murres (U. aalge) observed on breeding study plots (7:1). Concentrations of the stress hormone corticosterone, a reliable physiological indicator of nutritional stress, in thick-billed murre feathers grown in the fall indicate that foraging conditions in the northern Bering Sea were poor in the fall of 2017 and comparable in severity to those experienced by murres during the 1976–1977 Bering Sea regime shift. Concentrations of corticosterone in feathers grown during the pre-breeding molt indicate that foraging conditions in late winter 2018 were similar to previous years. The 2018 murre egg harvest in the village of Savoonga (on St. Lawrence Is.) was one-fifth the 1993–2012 average, and residents observed that fewer birds laid eggs in 2018. Exposure of thick-billed murres to nutritional stress in August, however, was no different in 2018 compared to 2016, 2017, and 2019, and was comparable to levels observed on St. George Island in 2003–2017. Prey abundance, measured by the National Oceanic and Atmospheric Administration in bottom-trawl surveys, was also similar in 2018 to 2017 and 2019, supporting the evidence that food was not scarce in the summer of 2018 in the vicinity of St. Lawrence Island. Of two moribund thick-billed murres collected at the end of the mortality event, one tested positive for a novel re-assortment H10 strain of avian influenza with Eurasian components, likely contracted during the non-breeding season. It is not currently known how widely spread infection of murres with the novel virus was, thus insufficient evidence exists to attribute the die-off to an outbreak of avian influenza. We conclude that food shortage alone is not an adequate explanation for the mortality of thick-billed murres in 2018, and highlight the importance of rapid response to mortality events in order to document alternative or confounding causes of mortality.
The Science Data Management Branch (SDM) of the U.S. Geological Survey (USGS) provides data management expertise and leadership and develops guidance and tools to support the USGS in providing the nation with reliable scientific information on the basis of which to describe the Earth. The SDM suite of tools supports the USGS Data Management Lifecycle by facilitating quality assurance, description, curation, and publishing of the Bureau's scientific data. The SDM suite of tools includes the USGS Data Management Website, USGS Science Data Catalog, Digital Object Identifier Tool, ScienceBase, ScienceBase Data Release Tool, Metadata Wizard, and Online Metadata Editor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203041","usgsCitation":"Hutchison, V.B., Liford, A.N., McClees-Funinan, Ricardo, Zolly, Lisa, Ignizio, D.A., Langseth, M.L., Serna, B.S., Sellers, E.A., Hsu, Leslie, Norkin, Tamar, McNiff, Marcia, Donovan, G.C., 2020, USGS enterprise tools for efficient and effective management of science data: U.S. Geological Survey Fact Sheet 2020–3041, 2 p., https://doi.org/10.3133/fs20203041.","productDescription":"4 p.","onlineOnly":"Y","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":378676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3041/coverthb.jpg"},{"id":378677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3041/fs20203041.pdf","text":"Report","size":"2.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3041"}],"contact":"Director, Science Analytics and Synthesis
U.S. Geological Survey
108 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Aquatic waterfowl, particularly those in the order Anseriformes and Charadriiformes, are the ecological reservoir of avian influenza viruses (AIVs). Dabbling ducks play a recognized role in the maintenance and transmission of AIVs. Furthermore, the pathogenesis of highly pathogenic AIV (HPAIV) in dabbling ducks is well characterized. In contrast, the role of diving ducks in HPAIV maintenance and transmission remains unclear. In this study, the pathogenesis of a North American A/Goose/1/Guangdong/96-lineage clade 2.3.4.4 group A H5N2 HPAIV, A/Northern pintail/Washington/40964/2014, in diving sea ducks (surf scoters, Melanitta perspicillata) was characterized.
Intrachoanal inoculation of surf scoters with A/Northern pintail/Washington/40964/2014 (H5N2) HPAIV induced mild transient clinical disease whilst concomitantly shedding high virus titers for up to 10 days post-inoculation (dpi), particularly from the oropharyngeal route. Virus shedding, albeit at low levels, continued to be detected up to 14 dpi. Two aged ducks that succumbed to HPAIV infection had pathological evidence for co-infection with duck enteritis virus, which was confirmed by molecular approaches. Abundant HPAIV antigen was observed in visceral and central nervous system organs and was associated with histopathological lesions.
Collectively, surf scoters, are susceptible to HPAIV infection and excrete high titers of HPAIV from the respiratory and cloacal tracts whilst being asymptomatic. The susceptibility of diving sea ducks to H5 HPAIV highlights the need for additional research and surveillance to further understand the contribution of diving ducks to HPAIV ecology.
","language":"English","publisher":"Springer","doi":"10.1186/s12917-020-02579-x","usgsCitation":"Luczo, J.M., Prosser, D., Pantin-Jackwood, M.J., Berlin, A., and Spackman, E., 2020, The pathogenesis of a North American H5N2 clade 2.3.4.4 group A highly pathogenic avian influenza virus in surf scoters (Melanitta perspicillata): BMC Veterinary Research, v. 16, 351, 10 p., https://doi.org/10.1186/s12917-020-02579-x.","productDescription":"351, 10 p.","ipdsId":"IP-115428","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12917-020-02579-x","text":"Publisher Index Page"},{"id":378746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2020-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Luczo, Jasmine M.","contributorId":241114,"corporation":false,"usgs":false,"family":"Luczo","given":"Jasmine","email":"","middleInitial":"M.","affiliations":[{"id":48207,"text":"USDA SEPRL","active":true,"usgs":false}],"preferred":false,"id":799587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":799588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pantin-Jackwood, Mary J.","contributorId":197094,"corporation":false,"usgs":false,"family":"Pantin-Jackwood","given":"Mary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":799589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berlin, Alicia 0000-0002-5275-3077 aberlin@usgs.gov","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":168416,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","email":"aberlin@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":799590,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spackman, Erica","contributorId":53647,"corporation":false,"usgs":false,"family":"Spackman","given":"Erica","email":"","affiliations":[],"preferred":false,"id":799591,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222610,"text":"70222610 - 2020 - Wait and snap: eastern snapping turtles (Chelydra serpentina) prey on migratory fish at road-stream crossing culverts","interactions":[],"lastModifiedDate":"2021-08-09T13:54:27.396441","indexId":"70222610","displayToPublicDate":"2020-09-23T08:48:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1028,"text":"Biology Letters","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Wait and snap: eastern snapping turtles (Chelydra serpentina) prey on migratory fish at road-stream crossing culverts","title":"Wait and snap: eastern snapping turtles (Chelydra serpentina) prey on migratory fish at road-stream crossing culverts","docAbstract":"There is growing evidence that culverts at road-stream crossings can increase fish density by reducing stream width and fish movement rates, making these passageways ideal predator ambush locations. In this study, we used a combination of videography and δ13C stable isotope analyses to investigate predator–prey interactions at a road-stream crossing culvert. Eastern snapping turtles (Chelydra serpentina) were found to regularly reside within the culvert to ambush migratory river herring (Alosa spp.). Resident fish species displayed avoidance of the snapping turtles, resulting in zero attempted attacks on these fish. In contrast, river herring did not display avoidance and were attacked by a snapping turtle on 79% of approaches with a 15% capture rate. Stable isotope analyses identified an apparent shift in turtle diet to consumption of river herring in turtles from culvert sites that was not observed in individuals from non-culvert sites. These findings suggest that anthropogenic barriers like culverts that are designed to allow passage may create predation opportunities by serving as a bottleneck to resident and migrant fish movement.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rsbl.2020.0218","usgsCitation":"Alcott, D.J., Long, M., and Castro-Santos, T.R., 2020, Wait and snap: eastern snapping turtles (Chelydra serpentina) prey on migratory fish at road-stream crossing culverts: Biology Letters, v. 16, no. 9, 20200218, https://doi.org/10.1098/rsbl.2020.0218.","productDescription":"20200218","ipdsId":"IP-118228","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":455239,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsbl.2020.0218","text":"Publisher Index Page"},{"id":387779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Alcott, Derrick James 0000-0001-7765-1889","orcid":"https://orcid.org/0000-0001-7765-1889","contributorId":261904,"corporation":false,"usgs":true,"family":"Alcott","given":"Derrick","email":"","middleInitial":"James","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Michael 0000-0001-6735-6878","orcid":"https://orcid.org/0000-0001-6735-6878","contributorId":261905,"corporation":false,"usgs":false,"family":"Long","given":"Michael","email":"","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":820740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215716,"text":"70215716 - 2020 - Does the Darcy-Buckingham Law apply to flow through unsaturated porous rock?","interactions":[],"lastModifiedDate":"2020-10-28T13:20:09.284709","indexId":"70215716","displayToPublicDate":"2020-09-23T08:15:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Does the Darcy-Buckingham Law apply to flow through unsaturated porous rock?","docAbstract":"Geomagnetic perturbations (BGEO) related to magnetospheric ultralow frequency (ULF) waves induce electric fields within the conductive Earth—geoelectric fields (EGEO)—that in turn drive geomagnetically induced currents. Though numerous past studies have examined ULF wave BGEO from a space weather perspective, few studies have linked ULF waves with EGEO. Using recently available magnetotelluric impedance and EGEO measurements in the contiguous United States, we explore the relationship between ULF waves and EGEO. We use satellite, ground‐based radar, BGEO, and EGEO measurements in a case study of a plasmaspheric virtual resonance (PVR), demonstrating that the PVR EGEO has significant spatial variation in contrast to a relatively uniform BGEO, consistent with spatially varying Earth conductivity. We further show ULF wave EGEO measurements during two moderate storms of ∼1 V/km. We use both results to highlight the need for more research characterizing ULF wave EGEO.
Soft sediment deformation structures are common in fine-grained pyroclastic deposits and are often taken, along with other characteristics, to indicate that deposits were emplaced in a wet and cohesive state. At Ubehebe Crater (Death Valley, California, USA), deposits were emplaced by multiple explosions, both directly from pyroclastic surges and by rapid remobilization of fresh, fine-ash-rich deposits off steep slopes as local granular flows. With the exception of the soft sediment deformation structures themselves, there is no evidence of wet deposition. We conclude that deformation was a result of destabilization of fresh, fine-grained deposits with elevated pore-gas pressure and dry cohesive forces. Soft sediment deformation alone is not sufficient to determine whether parent pyroclastic surges contained liquid water and caused wet deposition of strata.
During winter 2017–18 coastal areas of New England were impacted by the January 4, and March 2–4, 2018, nor’easters. The U.S. Geological Survey (USGS), under an interagency agreement with the Federal Emergency Management Agency (FEMA), collected total water level data (the combination of tide, storm surge, wave runup and setup, and freshwater input) using the North American Vertical Datum of 1988 (NAVD 88) from high-water marks and continuous water-level sensors, to better understand the areal extent, timing, and impact of coastal flooding from strong storms.
During the January 4, 2018, nor’easter the National Oceanic and Atmospheric Administration (NOAA) Boston, Massachusetts, tide gage recorded the highest total water level on record of 9.66 ft. During the March 2–4, 2018, nor’easter, the Boston tide gage recorded its third highest total water level on record of 9.16 ft.
After the January and March 2018 nor’easter storms, the USGS deployed field teams that identified and flagged high-water marks along the coastlines of eastern Massachusetts in January and from Portland, Maine, south to the Connecticut-New York State border in March. In preparation for the approach of the March 2018 nor’easter, the USGS deployed 35 temporary water-level sensors along the coastline of New England to collect total water level data during the storm. Total water level data were also collected at 28 tide gages and 14 coastal streamgages (affected tidally or by tidal backwater during coastal storms) in New England during both nor’easters.
Total water level elevations at 71 high-water marks collected after the January 2018 nor’easter in coastal areas of eastern Massachusetts ranged from 5.8 to 15.1 feet (ft), with an average elevation of 9.4 ft and a median elevation of 9.6 ft. Total water level elevations at 10 tide gages and 7 coastal streamgages from Portland to Cape Cod Bay ranged from 4.8 to 11.2 ft, with an average of 9.1 ft and a median of 9.6 ft. Following the March 2018 nor’easter, 111 high-water marks were collected along the New England coastline. Of the 111 high-water marks, 100 were along the eastern coastline of New England from Portland to Cape Cod and had elevations that ranged from 5.3 to 15.1 ft, with an average of 8.9 ft and a median of 8.6 ft. The remaining 11 high-water marks along the southern coastline of New England in Connecticut, Rhode Island, and Massachusetts had elevations that ranged from 3.1 to 7.5 ft, with an average of 4.3 ft and a median of 4.9 ft. Total water level elevations for 19 USGS temporary water-level sensors from Portland to Cape Cod Bay ranged from 6.2 to 10.4 ft, with an average of 8.4 ft and a median of 8.7 ft. Total water level elevations at 10 tide gages and 6 coastal streamgages from Portland to Cape Cod Bay ranged from 7.8 to 10.8 ft, with an average of 9.1 ft and a median of 9.2 ft.
There were 10 tide gages and 5 coastal streamgages with data from both nor’easters from Portland to Cape Cod Bay; for the January nor’easter, the average and median elevations were about 0.3 and 0.5 ft higher, respectively, than for the March nor’easter. At the 52 high-water mark locations with data for both nor’easters in Massachusetts, the average and median elevations were 0.1 and 0.4 ft higher, respectively, for the January nor’easter than for the March nor’easter.
At 10 tide gages along the coastline from Portland to Cape Cod Bay, the observed peak total water level elevations for the January nor’easter ranged from 1.6 to 3.7 ft higher than the concurrent predicted elevations, with an average of 2.8 ft and a median of 3.0 ft higher. For the March nor’easter, the observed peak total water level elevations ranged from 1.8 to 4.0 ft higher than the concurrent predicted elevations, with an average of 2.7 ft and a median of 3.0 ft higher. This is approximately the amount of storm surge that was experienced during the highest tides of the two nor’easters along the coastline from Portland to Cape Cod Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205048","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Bent, G.C., and Taylor, N.J., 2020, Total water level data from the January and March 2018 nor’easters for coastal areas of New England: U.S. Geological Survey Scientific Investigations Report 2020–5048, 47 p., https://doi.org/10.3133/sir20205048.","productDescription":"Report: vii, 47 p.; 2 Data Releases","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108335","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":378670,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://stn.wim.usgs.gov/FEV/#NoreasterofMarch2018","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Flood Event Viewer, March 2018 nor’easter"},{"id":378666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5048/coverthb.jpg"},{"id":378669,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://stn.wim.usgs.gov/FEV/#NoreasterJanuary2018","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Flood Event Viewer, January 2018 nor’easter"},{"id":378667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5048/sir20205048.pdf","text":"Report","size":"4.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5048"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.86279296875,\n 44.80132682904856\n ],\n [\n -67.203369140625,\n 45.213003555993964\n ],\n [\n -67.32421875,\n 45.120052841530544\n ],\n [\n -67.412109375,\n 45.282617057517406\n ],\n [\n -67.423095703125,\n 45.606352077118316\n ],\n [\n -67.752685546875,\n 45.72152152227954\n ],\n [\n -67.796630859375,\n 47.092565552235705\n ],\n [\n -68.31298828125,\n 47.368594345213374\n ],\n [\n -68.92822265625,\n 47.21956811231547\n ],\n [\n -69.027099609375,\n 47.42065432071318\n ],\n [\n -69.2138671875,\n 47.45780853075031\n ],\n [\n -70.048828125,\n 46.70973594407157\n ],\n [\n -70.279541015625,\n 46.17983040759436\n ],\n [\n -70.455322265625,\n 45.71385093029221\n ],\n [\n -70.77392578125,\n 45.4524242413431\n ],\n [\n -71.015625,\n 45.298075138707965\n ],\n [\n -71.290283203125,\n 45.298075138707965\n ],\n [\n -71.510009765625,\n 44.99588261816546\n ],\n [\n -71.619873046875,\n 44.55133484083592\n ],\n [\n -72.015380859375,\n 44.33956524809713\n ],\n [\n -72.45483398437499,\n 43.42100882994726\n ],\n [\n -72.53173828125,\n 42.90011265525328\n ],\n [\n -72.4658203125,\n 42.76314586689492\n ],\n [\n -73.27880859375,\n 42.771211138625894\n ],\n [\n -73.509521484375,\n 42.09822241118974\n ],\n [\n -73.54248046875,\n 41.31082388091818\n ],\n [\n -73.740234375,\n 41.07935114946899\n ],\n [\n -73.641357421875,\n 41.00477542222947\n ],\n [\n -72.861328125,\n 41.253032440653186\n ],\n [\n -72.059326171875,\n 41.20345619205131\n ],\n [\n -70.345458984375,\n 41.30257109430557\n ],\n [\n -69.971923828125,\n 41.178653972331674\n ],\n [\n -69.80712890625,\n 41.236511201246216\n ],\n [\n -69.93896484375,\n 42.04113400940807\n ],\n [\n -70.57617187499999,\n 42.73894375124377\n ],\n [\n -69.98291015625,\n 43.6599240747891\n ],\n [\n -66.97265625,\n 44.645208223744035\n ],\n [\n -66.86279296875,\n 44.80132682904856\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Keys to Prairie Falcon (Falco mexicanus) management include maintaining cliffs with suitable recesses for use as nest sites (that is, the substrate that supports the nest or the specific location of the nest on the landscape), protecting nest sites from human disturbance by designating buffer zones, and maintaining open landscapes and habitats that support populations of ground squirrels (Urocitellus species) and small birds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842S","usgsCitation":"DeLong, J.P., and Steenhof, K., 2020, The effects of management practices on grassland birds—Prairie Falcon (Falco mexicanus), chap. S of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 17 p., https://doi.org/10.3133/pp1842S.","productDescription":"iv, 17 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-093908","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":378641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/s/coverthb.jpg"},{"id":378642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/s/pp1842s.pdf","text":"Report","size":"2.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–S"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Mercury (Hg) has been a contaminant of concern for several decades in South Florida, particularly in the Florida Everglades. The transport and bioavailability of Hg in aquatic systems is intimately linked to dissolved organic carbon (DOC). In aquatic systems, Hg can be converted to methylmercury (MeHg), which is the form of Hg that bioaccumulates in food webs. The bioaccumulation of MeHg poses significant health risks to wildlife and humans. Fish consumption advisories triggered by elevated Hg levels first appeared in the 1980s in South Florida. Multiple structures regulate freshwater distribution to Everglades National Park, including S-12D. This report summarizes seasonal and annual concentration and load data from late September 2013 to April 2017 for the total of (1) filter-passing total mercury (FTHg), (2) filter-passing methylmercury (FMeHg), (3) particulate total mercury (PTHg), (4) particulate methylmercury (PMeHg) and, (5) DOC discharged through control structure S-12D. The loads of Hg fractions and DOC at control structure S-12D were determined by pairing discharge data with constituent concentrations estimated by empirical models based on surrogate in situ water-quality measurements.
Calculated concentrations of DOC ranged from 12.8 milligrams per liter (mg/L) to 27.9 mg/L with a mean of 18.8 mg/L during the study period. Annual loads of DOC ranged from 3,950 tons in 2015 to 10,900 tons in 2016. DOC loads increased linearly with an increase in flow, and the highest monthly DOC load of 1,630 tons was observed in February 2016.
Calculated concentrations of FTHg ranged from 0.35 to 1.55 nanograms per liter (ng/L) with a mean of 0.85 ng/L during the study period. Calculated concentrations of FMeHg ranged from 0.06 ng/L to 0.24 ng/L with a mean of 0.14 ng/L during the study period. Generally, FTHg and FMeHg concentrations were lower during periods of decreased flow and higher during periods of increased flow. Calculated PTHg concentrations ranged from 0.09 ng/L to 4.19 ng/L with a mean of 0.58 ng/L during the study period. Calculated PMeHg concentrations ranged from below the limit of detection <0.01 ng/L to 0.29 ng/L with a mean of 0.03 ng/L during the study period.
Loads of Hg were often zero or lowest from November to May, owing to the lack of flow or low-flow conditions. FTHg and FMeHg loads increased linearly with an increase in flow and typically were highest from June to October. During periods of increasing flow or following changes in gate operations, PTHg and PMeHg constituted a greater percentage of the total Hg load. Annual loads of total Hg (filter-passing and particulate) ranged from 254 grams in 2015 to 658 grams in 2016. FTHg was the predominant contributor to the total Hg load. Information presented herein provides the first assessment of DOC and Hg loads to Everglades National Park through control structure S-12D using continuous in situ measurements of discharge and constituent surrogates and compares the surrogate model approach to loads calculated from monthly sampling. Analysis of calculated and observed loads demonstrates the significance of flow data on calculating constituent loads.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Peak ground velocity (PGV) is a commonly used parameter in earthquake ground‐motion models (GMMs) and hazard analyses, because it is closely related to structural damage and felt ground shaking, and is typically measured on broadband seismometers. Here, we demonstrate that strainmeters, which directly measure in situ strain in the bulk rock, can easily be related to ground velocity by a factor of bulk shear‐wave velocity and, thus, can be used to measure strain‐estimated PGV. We demonstrate the parity of velocity to strain utilizing data from borehole strainmeters deployed along the plate boundaries of the west coast of the United States for nine recent
Australapatemon spp. are cosmopolitan trematodes that infect freshwater snails, aquatic leeches, and birds. Despite their broad geographic distribution, relatively little is known about interactions between Australapatemon spp. and their leech hosts, particularly under experimental conditions and in natural settings. We used experimental exposures to determine how Australapatemon burti cercariae dosage (number administered to leech hosts, Erpobdella microstoma) affected infection success (fraction to encyst as metacercariae), infection abundance, host survival, and host size over the 100 days following exposure. Interestingly, infection success was strongly density-dependent, such that there were no differences in metacercariae load even among hosts exposed to a 30-fold difference in cercariae. This relationship suggests that local processes (e.g., resource availability, interference competition, or host defenses) may play a strong role in parasite transmission. Our results also indicated that metacercariae did not become evident until ~4 weeks post exposure, with average load climbing until approximately 13 weeks. There was no evidence of metacercariae death or clearance over the census period. Parasite exposure had no detectable effects on leech size or survival, even with nearly 1,000 cercariae. Complementary surveys of leeches in California revealed that 11 of 14 ponds supported infection by A. burti (based on morphology and molecular sequencing), with an average prevalence of 32% and similar metacercariae intensity as in our experimental exposures. The extended development time and extreme density dependence of A. burti has implications for studying naturally occurring host populations, for which detected infections may represent only a fraction of cercariae to which animals have been exposed. Future investigation of these underlying mechanisms would be benefical in understanding host-parasite relationships.
Slightly brackish groundwater contaminated by polycyclic aromatic hydrocarbons (PAHs) at a Superfund site in the Central Valley of California was pumped from 250 feet below land surface to a water storage tank using solar power and then gravity‐fed into 18, 330‐gallon intermediate bulk containers (totes) as follows:
Five totes contained planting medium with three salt‐tolerant hybrid poplar trees per tote (n = 15);
Seven totes contained planting medium with three salt‐tolerant hybrid poplar trees per tote and inoculated with the naturally occurring, PAH‐degrading endophyte Pseudomonas putida PD1 (n = 21);
Three totes contained planting medium only (n = 0);
One tote contained groundwater with three PD1‐inoculated trees (n = 3) and one tote contained groundwater with three regular trees (n = 3); and
One tote contained groundwater only (n = 0).
All trees grew well during the 7‐month growing season in spite of the area's hot, dry air temperature, little precipitation, tote‐influent chloride concentrations of 290 mg/L, and tote‐influent naphthalene concentrations that ranged from 650 to 5100 mg/L. PD1‐inoculated trees initially had 56% larger tree area (tree height × tree width) than regular trees and up to 69% larger tree area by the end of the growing season, indicating some conferred phytoprotection to the PAH contamination. All trees had similar trunk caliper (diameter) and leaf chlorophyll content by the end of the growing season. Total naphthalene removal ranged from 88% to 100% across all totes. The lowest naphthalene removal of 88% was observed in a tote that contained only planting medium and indicates substantial adsorption of naphthalene onto the high organic content of the planting medium. Contaminant removal due to uptake by the hybrid poplar trees was confirmed by the detection of naphthalene in in vivo passive samplers placed in tree trunks. Benzene, toluene, ethylbenzene, total xylenes, 2‐methylnaphthalene, 1,2,4‐trimethylbenzene, and isopropylbenzene were also detected. These results from the pilot‐scale study indicate that a full‐scale application of using salt‐tolerant hybrid poplar trees at this site could effectively decrease naphthalene concentrations in groundwater pumped from the deep aquifer. These initial results provide hope for similar application at other contaminated sites characterized by groundwater at considerable depths, especially at Superfund sites where costly pump‐and‐treat systems have been used long term to treat low levels of groundwater contamination.
","language":"English","publisher":"Wiley","doi":"10.1002/rem.21664","usgsCitation":"Landmeyer, J.E., Rock, S., Freeman, J., Nagle, G., Samolis, M., Levine, H., Cook, A., and O’Neill, H., 2020, Phytoremediation of slightly brackish, polycyclic aromatic hydrocarbon‐contaminated groundwater from 250 ft below land surface: A pilot‐scale study using salt‐tolerant, endophyte‐enhanced hybrid poplar trees at a Superfund site in the Central Valley of California, April‒November 2019: Remediation Journal, v. 31, no. 1, p. 73-89, https://doi.org/10.1002/rem.21664.","productDescription":"17 p.","startPage":"73","endPage":"89","ipdsId":"IP-118071","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":455249,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8686211","text":"External Repository"},{"id":382092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.904296875,\n 40.413496049701955\n ],\n [\n -121.728515625,\n 40.44694705960048\n ],\n [\n -122.16796875,\n 40.74725696280421\n ],\n [\n -122.958984375,\n 41.07935114946899\n ],\n [\n -123.3984375,\n 40.34654412118006\n ],\n [\n -122.9150390625,\n 38.92522904714054\n ],\n [\n -121.55273437499999,\n 37.19533058280065\n ],\n [\n -120.05859375,\n 35.92464453144099\n ],\n [\n -117.8173828125,\n 34.05265942137599\n ],\n [\n -116.3671875,\n 33.394759218577995\n ],\n [\n -117.59765625,\n 35.71083783530009\n ],\n [\n -120.234375,\n 37.50972584293751\n ],\n [\n -121.904296875,\n 40.413496049701955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Landmeyer, James E. 0000-0002-5640-3816","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":216137,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rock, Steven","contributorId":247586,"corporation":false,"usgs":false,"family":"Rock","given":"Steven","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, John 0000-0003-3403-9360","orcid":"https://orcid.org/0000-0003-3403-9360","contributorId":247587,"corporation":false,"usgs":false,"family":"Freeman","given":"John","email":"","affiliations":[{"id":49585,"text":"Intrinsyx Technologies Corporation","active":true,"usgs":false}],"preferred":false,"id":808015,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagle, Greg","contributorId":247588,"corporation":false,"usgs":false,"family":"Nagle","given":"Greg","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808016,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Samolis, Mark","contributorId":247589,"corporation":false,"usgs":false,"family":"Samolis","given":"Mark","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808017,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Levine, Herb","contributorId":218950,"corporation":false,"usgs":false,"family":"Levine","given":"Herb","email":"","affiliations":[{"id":39943,"text":"U.S. EPA, REGION 9","active":true,"usgs":false}],"preferred":false,"id":808018,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cook, Anna-Marie","contributorId":247590,"corporation":false,"usgs":false,"family":"Cook","given":"Anna-Marie","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808019,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"O’Neill, Harry","contributorId":247591,"corporation":false,"usgs":false,"family":"O’Neill","given":"Harry","email":"","affiliations":[{"id":49586,"text":"Beacon Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":808020,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216924,"text":"70216924 - 2020 - Linking plant and animal functional diversity with an experimental community restoration in a Hawaiian lowland wet forest","interactions":[],"lastModifiedDate":"2020-12-17T12:58:14.053588","indexId":"70216924","displayToPublicDate":"2020-09-22T07:04:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5453,"text":"Food Webs","active":true,"publicationSubtype":{"id":10}},"title":"Linking plant and animal functional diversity with an experimental community restoration in a Hawaiian lowland wet forest","docAbstract":"Testing how plant restoration influences animal taxonomic and functional diversity can shift restoration projects beyond mainly plant community considerations. We incorporated multi-trophic interactions into restoration by describing an ongoing functional trait-based restoration experiment in Hawaiian lowland tropical wet forest (Liko Nā Pilina Experiment), where litter arthropods are examined from a functional perspective thereby linking plants and higher trophic levels. We hypothesized that (1) communities with greater plant functional trait diversity would have cascading effects through food webs, increasing animal diversity and network complexity, and (2) increases in animal species and network complexity would be stronger for restoration efforts in plant communities with more complementary functional traits than those with more redundant traits. We examined experimental treatments of planted communities with the same species richness but with different plant functional trait profiles based on (1) rates of expected carbon turnover (slow or moderate), and (2) the similarity of their functional trait measurements (redundant or complementary), as determined by functional dispersion calculations. Initial data on arthropod communities and leaf litter decomposition rates revealed linkages between plant functional traits and arthropod community diversity. Overall, we argue that a more comprehensive evaluation of restoration accounts for both functional diversity and the multi-trophic nature of animal and plant communities. Developing restoration projects based on plant functional traits that influence both plant and invertebrate species provides a new paradigm, and the incorporation of both native and non-native (but non-invasive) plants shows promise in restoring ecosystem function in disturbed lowland tropical forests.
Effluent-dominated streams are becoming increasingly common in temperate regions and generate complex pharmaceutical mixture exposure conditions that may impact aquatic organisms via drug–drug interactions. Here, we quantified spatiotemporal pharmaceutical exposure concentrations and composition mixture dynamics during baseflow conditions at four sites in a temperate-region effluent-dominated stream (upstream, at, and progressively downstream from effluent discharge). Samples were analyzed monthly for 1 year for 109 pharmaceuticals/degradates using a comprehensive U.S. Geological Survey analytical method and biweekly for 2 years focused on 14 most common pharmaceuticals/degradates. We observed a strong chemical gradient with pharmaceuticals only sporadically detected upstream from the effluent. Seventy-four individual pharmaceuticals/degradates were detected, spanning 5 orders of magnitude from 0.28 to 13 500 ng/L, with 38 compounds detected in >50% of samples. “Biweekly” compounds represented 77 ± 8% of the overall pharmaceutical concentration. The antidiabetic drug metformin consistently had the highest concentration with limited in-stream attenuation. The antihistamine drug fexofenadine inputs were greater during warm- than cool-season conditions but also attenuated faster. Differential attenuation of individual pharmaceuticals (i.e., high = citalopram; low = metformin) contributed to complex mixture evolution along the stream reach. This research demonstrates that variable inputs over multiple years and differential in-stream attenuation of individual compounds generate evolving complex mixture exposure conditions for biota, with implications for interactive effects.
High precision triple oxygen isotope measurements of carbonates can better constrain temperatures and oxygen isotope compositions of seawater through geologic time than 18O/16O measurements alone, but lack of a definitive calibration has hindered progress. In this study, we fluorinated both carbonate and water samples to measure quantitatively the triple oxygen isotope composition of each phase. We compared the oxygen isotope fractionation between carbonate and water for different carbonate materials: calcite synthesized with and without carbonic anhydrase, abiogenic calcite from Devils Hole, and extant biogenic calcite and aragonite of marine origin. We found similar 1000lnα18Occ-wt values for all materials and combined the results with the high temperature experimental data of O'Neil et al. (1969), resulting in the following fractionation equation (T in Kelvins)
The population in the Dallas-Fort Worth metropolitan area in northern Texas is rapidly growing, resulting in a rapid increase in the demand for potable water and an increase in the discharge of wastewater treatment plant effluent. An assessment of compounds of emerging concern (CECs) in samples collected at potable water and wastewater treatment plants in Dallas and downstream from Dallas in the Trinity River was completed by the U.S. Geological Survey in cooperation with the City of Dallas, Dallas Water Utilities. CECs are synthetic or naturally occurring chemicals that are not commonly monitored in the environment but can enter the environment and cause known or suspected adverse ecological or human health effects. CECs can enter the environment through nonpoint sources (for example, runoff) and point sources (for example, concentrated animal feeding operations and treated-effluent discharge from wastewater treatment plants), which can increase concentrations of CECs especially in highly populated areas. CECs include pharmaceuticals (prescription and nonprescription), steroidal hormones, stanols, sterols, detergents and detergent metabolites (hereinafter referred to as “detergents”), personal-use products, pesticides, polycyclic aromatic hydrocarbons (PAHs), flame retardants, plasticizers, and other organic compounds used in everyday domestic, agricultural, and industrial applications. The release of CECs to the environment went largely unrecognized until relatively recently. Increased loading of certain CECs to the environment, combined with advancements in laboratory analysis methods that resulted in appreciably lower detection levels, brought greater attention to the release of CECs. In addition, synthesis of new chemicals or changes in use and disposal of existing chemicals can create new sources of CECs. Some CECs are endocrine disrupting compounds (EDCs), which can elicit adverse effects on development, behavior, and reproduction of wildlife and can cause dysfunction of human and wildlife endocrine (hormone) systems.
Results of studies in the United States and Europe indicate that CECs, their metabolites, and industrial, agricultural, and household wastewater products are present in the aquatic environment, water treatment plants, and septic systems. CECs, especially pharmaceuticals, are of interest because of their persistence, widespread use, and potential to cause adverse effects in humans and nontargeted organisms. There is also concern that some CECs and EDCs resist degradation of water treatment processes at potable water treatment plants (PWTPs) and wastewater treatment plants (WWTPs) and that treated-effluent discharge could contain compounds that negatively affect biota living in receiving waters. Therefore, CECs and EDCs are more likely to be detected in environmental samples collected near areas of high population density where treated effluent from WWTPs can contribute substantially to receiving waters.
The U.S. Geological Survey, in cooperation with the City of Dallas, Dallas Water Utilities, evaluated the occurrence and concentrations of selected CECs in samples collected at PWTPs and WWTPs in Dallas and downstream from the Dallas-Fort Worth metropolitan area in the Trinity River, Texas, from August 2009 to December 2013. Water samples were collected at three PWTP sites, two WWTP sites, and five study sites on the Trinity River; all sites where samples were collected were in or downstream from Dallas. These water samples were analyzed for 120 CECs, including human-health pharmaceuticals (prescription and nonprescription), antibiotics, steroidal hormones, stanols, sterols, detergents, personal-use products (flavors and fragrances), pesticides and repellents, industrial wastewater compounds, disinfection compounds, PAHs, flame retardants, and plasticizers. Additionally, bed-sediment samples were collected at each of the five Trinity River sites. The bed-sediment samples were analyzed for 57 CECs.
In general, the water treatment processes at PWTPs and WWTPs were effective at reducing detections and concentrations of CECs to undetectable levels or transforming the compounds into degradates that were not analyzed. There were 14 and 73 CECs detected in raw water and in untreated-influent water at PWTPs and WWTPs, respectively. Of these, 11 of the 14 CECs detected in raw-water samples and 44 of the 73 CECs detected in untreated-influent samples were not detected in finished water or in treated-effluent water samples, respectively, indicating that these compounds were removed or degraded to compounds that were not analyzed. Some CECs, however, are resistant to degradation and were detected in untreated and treated water at PWTPs and at WWTPs. The three CECs detected at PWTPs in raw-water and finished-water samples were tris(dichloroisopropyl)phosphate, benzophenone, and methyl salicylate. At WWTPs, 29 CECs were detected, including carbamazepine, sulfamethoxazole, 4-androstene-3,17-dione, 3-beta-coprostanol, acetyl-hexamethyl-tetrahydronaphthalene (AHTN), hexahydro-hexamethyl-cyclopenta-benzopyran (HHCB), 1,4-dichlorobenzene, tribromomethane, benzophenone, and tris(dichloroisopropyl)phosphate, in untreated and treated water, indicating that treatment processes likely did not remove or degrade these compounds.
Of the 23 CECs detected in stream-water samples collected at 5 sites on the Trinity River in or near Dallas, 10 CECs (carbamazepine, sulfamethoxazole, caffeine, 3-beta-coprostanol, cholesterol, HHCB, benzophenone, triethyl citrate, tributyl phosphate, and tris(dichloroisopropyl)phosphate) were detected at all 5 sites. The 10 CECs detected in water samples collected at all 5 sites on the Trinity River were also detected in treated-effluent water at WWTPs.
Eleven of the 57 targeted CECs were detected in bed-sediment samples collected at study sites on the Trinity River. Of these 11 CECs, only 2 (beta-sitosterol and cholesterol) were detected in bed-sediment samples at all 5 sites on the Trinity River. Nine of these 11 CECs were not detected in any water-column sample, likely because of the strong hydrophobic characteristics of these compounds.
Results from water treatment plants indicate that the water treatment process is less effective for removing or degrading compounds that are engineered to be resistant to degradation. These results also indicate the presence of CECs and EDCs at locations upstream from PWTPs in Dallas. Results from Trinity River main-stem sites indicate that some compounds are naturally attenuated during transport, but a few are persistent throughout the study reach. Many CECs and EDCs are hydrophobic and were only detected in bed sediment, indicating multiple pathways through which CECs can persist in the environment.
In general, concentrations of CECs in the Dallas-Fort Worth metropolitan area were similar to those found in metropolitan areas nationwide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195019","collaboration":"Prepared in cooperation with the City of Dallas, Dallas Water Utilities","usgsCitation":"Churchill, C.J., Baldys, S., III, Gunn, C.L., Mobley, C.A., and Quigley, D.P., 2020, Compounds of emerging concern detected in water samples from potable water and wastewater treatment plants and detected in water and bed-sediment samples from sites on the Trinity River, Dallas, Texas, 2009–13: U.S. Geological Survey Scientific Investigations Report 2019–5019, 57 p., https://doi.org/10.3133/sir20195019.","productDescription":"Report: vii, 57 p.; Data Release","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-063824","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":378598,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QUPBZK","text":"USGS data release","linkHelpText":"Detections and concentrations of compounds of emerging concern at water treatment plants and in the Trinity River in or near Dallas, Texas, 2009–13"},{"id":378597,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5019/sir20195019.pdf","text":"Report","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5019"},{"id":378596,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5019/coverthb.jpg"}],"country":"United States","state":"Texas","city":"Dallas","otherGeospatial":"Trinity River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.0147705078125,\n 32.22674287041067\n ],\n [\n -96.21826171874999,\n 32.22674287041067\n ],\n [\n -96.21826171874999,\n 32.91648534731439\n ],\n [\n -97.0147705078125,\n 32.91648534731439\n ],\n [\n -97.0147705078125,\n 32.22674287041067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Biological conservation often requires an understanding of how environmental conditions affect species occurrence and detection probabilities. We used a hierarchical framework to evaluate these effects for several Appalachian stream fish species of conservation concern: Chrosomus cumberlandensis (BSD; blackside dace), Etheostoma sagitta (CAD; Cumberland arrow darter), and Etheostoma spilotum (KAD; Kentucky arrow darter). Etheostoma susanae (Cumberland darter) also is present in the study area but was too rare to model in this analysis. In this study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, fish and habitat data were collected from 205 randomly selected stream sites in the upper Cumberland and Kentucky River Basins (120 and 85 sites, respectively) of Kentucky and Tennessee. Sites were sampled with 10 spatial replicates (2 meter x 5 meter electrofishing zones) to enable estimation of detection probabilities and environmental effects. The best models (that is, lowest Akaike information criterion scores) showed the effects of agriculture (negative) on occurrence of BSD and stream conductivity (negative) on occurrence of CAD and KAD. These effects were statistically more important than measures of basin area, elevation, and substrate size. Conductivity and agriculture showed nonlinear effects on species occurrence, and effects of conductivity were more precise above 400 microsiemens per centimeter than below this threshold. Models incorporated detection-level effects of electrofishing time (positive), flow velocity (negative), sand substrate (positive), and gravel/cobble substrate (negative). Models accounting for detection of BSD estimated occupancy rates similar to the observed proportion of occupied sites (0.10), but the best-supported models for CAD and KAD increased expected occupancy by about 4 percent for each species (from 0.17 to 0.21 for CAD and from 0.07 to 0.11 for KAD). Results of this study provide new inferences for modeling stream fish occurrence and detection processes and highlight the importance of continued monitoring and assessment of rare fish species in Appalachian headwater streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201100","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Hitt, N.P., Rogers, K.M., Kessler, K., and Macmillan, H., 2020, Modeling occupancy of rare stream fish species in the upper Cumberland and Kentucky River Basins: U.S. Geological Survey Open-File Report 2020–1100, 22 p., https://doi.org/10.3133/ofr20201100.","productDescription":"vi, 22 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118746","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":378605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1100/ofr20201100.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1100"},{"id":378604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1100/coverthb.jpg"}],"country":"United States","state":"Kentucky, Tennessee, Virginia","otherGeospatial":"Cumberland River basin, Kentucky River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1875,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 35.88905007936091\n ],\n [\n -81.39770507812499,\n 38.77121637244273\n ],\n [\n -87.1875,\n 38.77121637244273\n ],\n [\n -87.1875,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430