Population availability and vessel avoidance effects on hydroacoustic abundance estimates may be scale dependent; therefore, it is important to evaluate these biases across systems. We performed an inter-ship comparison survey to determine the effect of vessel size, day-night period, depth, and environmental gradients on walleye (Sander vitreus) density estimates in Lake Erie, an intermediate-scaled system. Consistent near-bottom depth distributions coupled with horizontal fish movements relative to vessel paths indicated avoidance behavior contributed to higher walleye densities from smaller vessels in shallow water (i.e., <15 m), although the difference decreased with increasing depth. Diel bank migrations in response to seasonally varying onshore-to-offshore environmental gradients likely contributed to day-night differences in densities between sampling locations and seasons. Spatial and unexplained variation accounted for a high proportion of total variation; however, increasing sampling intensity can mitigate effects on precision. Therefore, researchers should minimize systematic avoidance and availability related biases (i.e., vessel and day-night period) to improve population abundance estimates. Quantifying availability and avoidance behavior effects and partitioning sources of variation provides informed flexibility for designing future hydroacoustic surveys in shallow-water nearshore environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2017.11.018","usgsCitation":"DuFour, M.R., Mayer, C.M., Qian, S.S., Vandergoot, C., Kraus, R.T., Kocovsky, P., and Warner, D.M., 2018, Inferred fish behavior its implications for hydroacoustic surveys in nearshore habitats: Fisheries Research, v. 199, p. 63-75, https://doi.org/10.1016/j.fishres.2017.11.018.","productDescription":"13 p.","startPage":"63","endPage":"75","ipdsId":"IP-077301","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":352408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.7435302734375,\n 41.39123380197929\n ],\n [\n -81.6339111328125,\n 41.39123380197929\n ],\n [\n -81.6339111328125,\n 41.76106872528616\n ],\n [\n -82.7435302734375,\n 41.76106872528616\n ],\n [\n -82.7435302734375,\n 41.39123380197929\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"199","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6ffe4b0da30c1bfc044","contributors":{"authors":[{"text":"DuFour, Mark R.","contributorId":203270,"corporation":false,"usgs":false,"family":"DuFour","given":"Mark","email":"","middleInitial":"R.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":730788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayer, Christine M.","contributorId":203271,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":730789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qian, Song S.","contributorId":198934,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":730790,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher 0000-0003-4128-3329 cvandergoot@usgs.gov","orcid":"https://orcid.org/0000-0003-4128-3329","contributorId":178356,"corporation":false,"usgs":true,"family":"Vandergoot","given":"Christopher","email":"cvandergoot@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":730791,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":730787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":730792,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Warner, David M. 0000-0003-4939-5368 dmwarner@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":2986,"corporation":false,"usgs":true,"family":"Warner","given":"David","email":"dmwarner@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":730793,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195979,"text":"70195979 - 2018 - Spatial capture–recapture with partial identity: An application to camera traps","interactions":[],"lastModifiedDate":"2018-03-12T11:06:43","indexId":"70195979","displayToPublicDate":"2018-03-12T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":787,"text":"Annals of Applied Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Spatial capture–recapture with partial identity: An application to camera traps","docAbstract":"Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and, while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM outperforms other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and, in the ocelot example, individual sex is determined from photographs used to further resolve partial identities—one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard two camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.
","language":"English","publisher":"IMS","doi":"10.1214/17-AOAS1091","usgsCitation":"Augustine, B.C., Royle, J., Kelly, M.J., Satter, C.B., Alonso, R.S., Boydston, E.E., and Crooks, K.R., 2018, Spatial capture–recapture with partial identity: An application to camera traps: Annals of Applied Statistics, v. 12, no. 1, p. 67-95, https://doi.org/10.1214/17-AOAS1091.","productDescription":"29 p.","startPage":"67","endPage":"95","ipdsId":"IP-088130","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468923,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1214/17-aoas1091","text":"Publisher Index Page"},{"id":352404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6ffe4b0da30c1bfc048","contributors":{"authors":[{"text":"Augustine, Ben C.","contributorId":203257,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":730769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":138865,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":730768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, Marcella J.","contributorId":179348,"corporation":false,"usgs":false,"family":"Kelly","given":"Marcella","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Satter, Christopher B.","contributorId":203259,"corporation":false,"usgs":false,"family":"Satter","given":"Christopher","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":730771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alonso, Robert S.","contributorId":93739,"corporation":false,"usgs":false,"family":"Alonso","given":"Robert","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":730772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boydston, Erin E. 0000-0002-8452-835X eboydston@usgs.gov","orcid":"https://orcid.org/0000-0002-8452-835X","contributorId":1705,"corporation":false,"usgs":true,"family":"Boydston","given":"Erin","email":"eboydston@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":730773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crooks, Kevin R.","contributorId":51137,"corporation":false,"usgs":false,"family":"Crooks","given":"Kevin","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":730774,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195980,"text":"70195980 - 2018 - Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival","interactions":[],"lastModifiedDate":"2018-06-12T13:29:23","indexId":"70195980","displayToPublicDate":"2018-03-12T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival","docAbstract":"Stream geomorphic characteristics were monitored along a 0.8-mile reach of the Fever River in the Driftless Area of southwestern Wisconsin from 2004 to 2011 where cattle grazed in paddocks along the riverbank at the University of Wisconsin-Platteville’s Pioneer Farm. The study reach encompassed seven paddocks that covered a total of 30 acres on both sides of the river. Monitoring data included channel crosssection surveys, eroding bank measurements and photograph points, erosion-pin measurements, longitudinal profile surveys, measurements of the volume of soft sediment in the channel, and repeated time-lapse photographs. Characteristics were summarized into subreaches by use of a geographic information system. From 2004 to 2007, baseline monitoring was done to identify geomorphic conditions prior to evaluating the effects of management alternatives for riparian grazing. Subsequent to the full-scale baseline monitoring, additional data were collected from 2007 to 2011. Samples of eroding bank and in-channel soft sediment were collected and analyzed for dry bulk density in 2008 for use in a sediment budget. One of the pastures was excluded from cattle grazing in the fall of 2007; in 2009 channel cross sections, longitudinal profiles, erosion-pin measurements, photographs, and a soft sediment survey were again collected along the full 0.8-mile reach for a comparison to baseline monitoring data. Channel cross sections were surveyed a final time in 2011. Lessons learned from bank monitoring with erosion pins were most numerous and included the need for consistent tracking of each pin and whether there was deposition or erosion, timing of measurements and bank conditions during measurements (frozen, postflood), and awareness of pins loosening in place. Repeated freezing and thawing of banks and consequential mass wasting and jointing enhance fluvial erosion. Monitoring equipment in the paddocks was kept flush to the ground or located high on posts to avoid injuring the cattle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161179","collaboration":"Prepared in cooperation with the University of Wisconsin-Platteville Pioneer Farm Program","usgsCitation":"Peppler, M.C., and Fitzpatrick, F.A., 2018, Collection methods, data compilation, and lessons learned from a study of stream geomorphology associated with riparian cattle grazing along the Fever River, University of Wisconsin- Platteville Pioneer Farm, Wisconsin, 2004–11: U.S. Geological Survey Open-File Report 2016–1179, 23 p., https://doi.org/10.3133/ofr20161179.","productDescription":"Report: viii, 21 p.; Appendixes 1-9; Readme","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069094","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":352034,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix1.zip","text":"Appendix 1","size":"39.1 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Channel CrossSection Surveys"},{"id":352035,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix2.zip","text":"Appendix 2","size":"2.10 GB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Eroding Bank Measurements and Photograph Points"},{"id":352036,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix3.zip","text":"Appendix 3","size":"320 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Erosion Pin Measurements"},{"id":352038,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix5.zip","text":"Appendix 5","size":"30 MB ","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- In-Channel Soft Sediment Depth and Volume"},{"id":352037,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix4.zip","text":"Appendix 4","size":"128 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Longitudinal Profile Surveys"},{"id":351479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1179/ofr20161179.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1179"},{"id":352039,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix6.zip","text":"Appendix 6","size":"545 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Geographic Information System Analyses of Reach Characteristics"},{"id":352040,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix7.zip","text":"Appendix 7","size":"28 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Sediment Bulk Density"},{"id":352041,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix8.zip","text":"Appendix 8","size":"73.2 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Time Lapse Photographs"},{"id":351478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1179/coverthb.jpg","text":"Report"},{"id":351870,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/appendixes-readme.pdf","text":"Appendix Readme","size":"22 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":352042,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix9.zip","text":"Appendix 9","size":"611. MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Miscellaneous Photographs and Field Notes"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fever River, University of Wisconsin-Platteville Pioneer Agricultural Stewardship Farm","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.40331840515137,\n 42.71124784262846\n ],\n [\n -90.393168926239,\n 42.71124784262846\n ],\n [\n -90.393168926239,\n 42.71984009899354\n ],\n [\n -90.40331840515137,\n 42.71984009899354\n ],\n [\n -90.40331840515137,\n 42.71124784262846\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Fort Pulaski National Monument is located on Cockspur Island in Chatham County, Georgia, within the Atlantic Coastal Plain province. The island lies near the mouth of the Savannah River, and consists of small mounds (hummocks), salt marshes, and sediment dredged from the river. A 1,017-foot (ft) (310-meter [m])-deep core drilled at Cockspur Island in 2010 by the U.S. Geological Survey revealed several sedimentary units ranging in age from 43 million years old to present. Sand and mud are present at drilling depths from 0 to 182 ft (56 m), limestone is present at depths from 182 ft (56 m) to 965 ft (295 m), and glauconitic sand is present at depths from 965 ft (295 m) to 1,017 ft (310 m). The limestone and the water within the limestone are referred to collectively as the Floridan aquifer system, which is the primary source of drinking water for the City of Savannah and surrounding communities. In addition to details of the subsurface geology, this fact sheet identifies the following geologic materials used in the construction of Fort Pulaski: (1) granite, (2) bricks, (3) sandstone, and (4) lime mud with oyster shells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183011","usgsCitation":"Swezey, C.S., Seefelt, E.L., and Parker, M., 2018, A brief geological history of Cockspur Island at Fort Pulaski National Monument, Chatham County, Georgia: U.S. Geological Survey Fact Sheet 2018‒3011, 4 p., https://doi.org/10.3133/fs20183011.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":" IP-082254","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":352324,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3011/fs20183011.pdf","text":"Report","size":"6.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3011"},{"id":352323,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3011/coverthb.jpg"}],"country":"United States","state":"Georgia","county":"Chatham County","otherGeospatial":"Fort Pulaski National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.92349052429198,\n 32.021321351629176\n ],\n [\n -80.87533950805664,\n 32.021321351629176\n ],\n [\n -80.87533950805664,\n 32.03696591411931\n ],\n [\n -80.92349052429198,\n 32.03696591411931\n ],\n [\n -80.92349052429198,\n 32.021321351629176\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Geology Paleoclimate Science Center
U.S. Geological Survey
Mail Stop 926A
12201 Sunrise Valley Drive
Reston, VA 20192
Excessive rainfall resulted in flooding on numerous rivers throughout the southern Midwestern United States (southern Midwest) in late April and early May of 2017. The heaviest rainfall, between April 28 and 30, resulted in extensive flooding from eastern Oklahoma to southern Indiana including parts of Missouri, Arkansas, and Illinois.
Peak-of-record streamflows were set at 21 U.S. Geological Survey (USGS) streamgages in the southern Midwest during the resulting April–May 2017 flooding and each of the five States included in the study area had at least one streamgage with a peak of record during the flood. The annual exceedance probability (AEP) estimates for the April–May 2017 peak streamflows indicate that peaks at 5 USGS streamgages had AEPs of 0.2 percent or less (500-year recurrence interval or greater), and peak streamflows at 15 USGS streamgages had AEPs in the range from greater than 0.2 to 1 percent (500- to 100-year recurrence intervals).
Examination of the magnitude of the temporal changes in median annual peak streamflows indicated positive increases, in general, throughout the study area for each of the 1930–2017, 1956–2017, 1975–2017, and 1989–2017 analysis periods. The median increase in peak streamflows was greatest in 1975–2017 and 1989–2017 with maximum increases of 8 to 10 percent per year. No stations in the 1975–2017 or 1989–2017 analysis period had median negative changes in peak streamflows.
","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181004","usgsCitation":"Heimann, D.C., Holmes, R.R., Jr., and Harris, T.E., 2018, Flooding in the southern Midwestern United States, April–May 2017: U.S. Geological Survey Open-File Report 2018–1004, 36 p., https://doi.org/10.3133/ofr20181004.","productDescription":"Report: v, 36 p.; 7 Films","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091177","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":352359,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Film1.mp4","text":"Film 1—","size":"15.6 MB","description":"OFR 2018–1004 Film 1","linkHelpText":"April 28, 2017–May 10, 2017, Daily streamflow magnitude in study area compared to long-term median streamflows"},{"id":352364,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Film6_NFK_MO_HWYPP.mp4","text":"Film 6—(film courtesy of Aerial Ozarks)","size":"172 MB","description":"OFR 2018–1004 Film 6","linkHelpText":"James Bridge on MO-PP, North Fork River, Ozark County, Mo."},{"id":352361,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Film3_NFK_SpringCK.mp4","text":"Film 3—(film courtesy of Aerial Ozarks)","size":"159 MB","description":"OFR 2018–1004 Film 3","linkHelpText":"Twin Bridges in Douglas County, Mo., North Fork River and Spring Creek"},{"id":352363,"rank":7,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Film5_BryantCK_Hwy181.mp4","text":"Film 5—(film courtesy of Aerial Ozarks)","size":"137 MB","description":"OFR 2018–1004 Film 5","linkHelpText":"Hodgson Mill on Hwy. 181, Bryant Creek, Ozark County, Mo."},{"id":352341,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1004/coverthb2.jpg"},{"id":352362,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Film4_NFK_MO_HWY_CC(HammondMillBrdge).mp4","text":"Film 4—(film courtesy of Aerial Ozarks)","size":"126 MB","description":"OFR 2018–1004 Film 4","linkHelpText":"Hammond Mill Bridge on MO-CC, North Fork River, Ozark County, Mo."},{"id":352365,"rank":9,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Film7_DryCK_MO_HwyAP.mp4","text":"Film 7—(film courtesy of Aerial Ozarks)","size":"70.0 MB","description":"OFR 2018–1004 Film 7","linkHelpText":"Dry Creek Bridge on MO-AP, Dry Creek, Howell County, Mo."},{"id":352342,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1004/ofr20181004.pdf","text":"Report","size":"8.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1004"},{"id":352394,"rank":10,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Films.zip","text":"Films","size":"746 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2018–1004 Films"},{"id":352360,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2018/1004/downloads/Films/Film2_SpringCK_HwyAP.mp4","text":"Film 2—(film courtesy of Aerial Ozarks)","size":"75.4 MB","description":"OFR 2018–1004 Film 2","linkHelpText":"Spring Creek Bridge on MO-AP"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.20751953125,\n 33.706062655101206\n ],\n [\n -87.36328125,\n 33.706062655101206\n ],\n [\n -87.36328125,\n 39.38526381099774\n ],\n [\n -95.20751953125,\n 39.38526381099774\n ],\n [\n -95.20751953125,\n 33.706062655101206\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
How well seabirds compensate for variability in prey abundance and composition near their breeding colonies influences their distribution and reproductive success. We used tufted puffins (Fratercula cirrhata) as forage fish samplers to study marine food webs from the western Aleutian Islands (53°N, 173°E) to Kodiak Island (57°N, 153°W), Alaska, during August 2012–2014. Around each colony we obtained data on: environmental characteristics (sea surface temperature and salinity, seafloor depth and slope, tidal range, and chlorophyll-a), relative forage fish biomass (hydroacoustic backscatter), and seabird community composition and density at-sea. On colonies, we collected puffin chick-meals to characterize forage communities and determine meal energy density, and measured chicks to obtain a body condition index. There were distinct environmental gradients from west to east, and environmental variables differed by ecoregions: the (1) Western-Central Aleutians, (2) Eastern Aleutians, and, (3) Alaska Peninsula. Forage fish biomass, species richness, and community composition all differed markedly between ecoregions. Forage biomass was strongly correlated with environmental gradients, and environmental gradients and forage biomass accounted for ~ 50% of the variability in at-sea density of tufted puffins and all seabird taxa combined. Despite the local and regional variability in marine environments and forage, the mean biomass of prey delivered to puffin chicks did not differ significantly between ecoregions, nor did chick condition or puffin density at-sea. We conclude that puffins can adjust their foraging behavior to produce healthy chicks across a wide range of environmental conditions. This extraordinary flexibility enables their overall success and wide distribution across the North Pacific Ocean.
","language":"English","publisher":"Springer","doi":"10.1007/s00227-018-3304-4","usgsCitation":"Schoen, S.K., Piatt, J.F., Arimitsu, M.L., Heflin, B., Madison, E.N., Drew, G.S., Renner, M., Rojek, N.A., Douglas, D.C., and DeGange, A.R., 2018, Avian predator buffers against variability in marine habitats with flexible foraging behavior: Marine Biology, v. 165, p. 1-14, https://doi.org/10.1007/s00227-018-3304-4.","productDescription":"Article 47; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-090458","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":437987,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TQ60GV","text":"USGS data release","linkHelpText":"Marine ecology near Tufted Puffin colonies across the Aleutian Archipelago and Alaska Peninsula, 2012-2014"},{"id":352355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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installations is increasing rapidly. We review case studies from around the world of the effects on raptors of wind-energy development. Collision mortality, displacement, and habitat loss have the potential to cause population-level effects, especially for species that are rare or endangered. The impact on raptors has much to do with their behavior, so careful siting of wind-energy developments to avoid areas suited to raptor breeding, foraging, or migration would reduce these effects. At established wind farms that already conflict with raptors, reduction of fatalities may be feasible by curtailment of turbines as raptors approach, and offset through mitigation of other human causes of mortality such as electrocution and poisoning, provided the relative effects can be quantified. Measurement of raptor mortality at wind farms is the subject of intense effort and study, especially where mitigation is required by law, with novel statistical approaches recently made available to improve the notoriously difficult-to-estimate mortality rates of rare and hard-to-detect species. Global standards for wind farm placement, monitoring, and effects mitigation would be a valuable contribution to raptor conservation worldwide.
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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee700e4b0da30c1bfc04e","contributors":{"authors":[{"text":"Watson, Richard T.","contributorId":203218,"corporation":false,"usgs":false,"family":"Watson","given":"Richard","email":"","middleInitial":"T.","affiliations":[{"id":36583,"text":"The Peregrine Fund","active":true,"usgs":false}],"preferred":false,"id":730686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolar, Patrick S. 0000-0002-0076-7565","orcid":"https://orcid.org/0000-0002-0076-7565","contributorId":139543,"corporation":false,"usgs":true,"family":"Kolar","given":"Patrick","email":"","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":730687,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrer, Miguel","contributorId":203219,"corporation":false,"usgs":false,"family":"Ferrer","given":"Miguel","email":"","affiliations":[{"id":36584,"text":"Delegacion del CSIC en Andalucia","active":true,"usgs":false}],"preferred":false,"id":730688,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nygard, Torgeir","contributorId":203220,"corporation":false,"usgs":false,"family":"Nygard","given":"Torgeir","email":"","affiliations":[{"id":36585,"text":"Norwegian Institute for Natural Research","active":true,"usgs":false}],"preferred":false,"id":730689,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnston, Naira","contributorId":203221,"corporation":false,"usgs":false,"family":"Johnston","given":"Naira","email":"","affiliations":[{"id":36586,"text":"Univ. of N British Columbia","active":true,"usgs":false}],"preferred":false,"id":730690,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hunt, W. Grainger","contributorId":139544,"corporation":false,"usgs":false,"family":"Hunt","given":"W.","email":"","middleInitial":"Grainger","affiliations":[{"id":12795,"text":"The Peregrine Fund, Inc.","active":true,"usgs":false}],"preferred":false,"id":730691,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smit-Robinson, Hanneline A.","contributorId":203222,"corporation":false,"usgs":false,"family":"Smit-Robinson","given":"Hanneline","email":"","middleInitial":"A.","affiliations":[{"id":36587,"text":"BirdLife South Africa","active":true,"usgs":false}],"preferred":false,"id":730692,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Farmer, Christopher J","contributorId":203223,"corporation":false,"usgs":false,"family":"Farmer","given":"Christopher","email":"","middleInitial":"J","affiliations":[{"id":36588,"text":"DNV GL – Energy","active":true,"usgs":false}],"preferred":false,"id":730693,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Huso, Manuela M. 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":150012,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":730685,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":730684,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70195620,"text":"ofr20181026 - 2018 - Preliminary stage and streamflow data at selected U.S. Geological Survey streamgages in Maine and New Hampshire for the flood of October 30–31, 2017","interactions":[],"lastModifiedDate":"2018-03-08T12:26:26","indexId":"ofr20181026","displayToPublicDate":"2018-03-08T11:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1026","title":"Preliminary stage and streamflow data at selected U.S. Geological Survey streamgages in Maine and New Hampshire for the flood of October 30–31, 2017","docAbstract":"Rainfall from a storm on October 24–27, 2017, and Tropical Storm Philippe on October 29–30, created conditions that led to flooding across portions of New Hampshire and western Maine. On the basis of streamflow data collected at 30 selected U.S. Geological Survey (USGS) streamgages in the Androscoggin River, Connecticut River, Merrimack River, and Saco River Basins, the storms caused minor to moderate flooding in those basins on October 30–31, 2017. During the storms, the USGS deployed hydrographers to take discrete measurements of streamflow. The measurements were used to confirm the stage-to-streamflow relation (rating curve) at the selected USGS streamgages. Following the storms, hydrographers documented high-water marks in support of indirect measurements of streamflow. Seven streamgages with greater than 50 years of streamflow data recorded preliminary streamflow peaks within the top five for the periods of record. Twelve streamgages recorded preliminary peak streamflows greater than an estimate of the 100-year streamflow based on drainage area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181026","usgsCitation":"Kiah, R.G, and Stasulis, N.W., 2018, Preliminary stage and streamflow data at selected U.S. Geological Survey streamgages in Maine and New Hampshire for the flood of October 30–31, 2017: U.S. Geological Survey Open-File Report 2018–1026, 12 p., https://doi.org/10.3133/ofr20181026.","productDescription":"iv, 12 p.","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092894","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1026/ofr20181026.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1026"},{"id":352269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1026/coverthb.jpg"}],"country":"United States","state":"Maine, New Hampshire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72,\n 42.75\n ],\n [\n -69,\n 42.75\n ],\n [\n -69,\n 46\n ],\n [\n -72,\n 46\n ],\n [\n -72,\n 42.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Earthquake swarms represent a sudden increase in seismicity that may indicate a heterogeneous fault-zone, the involvement of crustal fluids and/or slow fault slip. Swarms sometimes precede major earthquake ruptures. An earthquake swarm occurred in October 2015 near San Ramon, California in an extensional right step-over region between the northern Calaveras Fault and the Concord–Mt. Diablo fault zone, which has hosted ten major swarms since 1970. The 2015 San Ramon swarm is examined here from 11 October through 18 November using template matching analysis. The relocated seismicity catalog contains ∼4000 events with magnitudes between
We analyze the response of Kenyan maize yields to near-term climate change and explore potential mitigation options. We model county level yields as a function of rainfall and temperature during a period of increased regional warming and drying (1989–2008). We then do a counter factual analysis by comparing existing maize yields from 2000 to 2008 to what yields might have been if observed warming and drying trends had not occurred. We also examine maize yields based on projected 2026–2040 climate trends. Without the observed warming and drying trends, Eastern Kenya would have had an 8% increase in maize yields, which in turn would have led to a net production increase of 500,000 metric tons. In Western Kenya, the magnitude of change is higher but the relative changes in predicted values are smaller. If warming and drying trends continue, we expect future maize yields to decline by 11% in Eastern Kenya (vs. 7% in Western Kenya). We also examine whether these future losses might be offset through agricultural development. For that analysis, we use a household panel dataset (2000, 2005) with measurements of individual farm plot yields, inputs, and outputs. We find that under a scenario of aggressive adoption of hybrid seeds and fertilizer usage coupled with warming and drying trends, yields in Western Kenya might increase by 6% while those in Eastern Kenya could increase by 14%. This increase in yields might be larger if there is a corresponding increase in usage of drought-tolerant hybrids. However, wide prediction intervals across models highlight the uncertainty in these outcomes and scenarios.
Non-cultivated plants carry microbial endophytes that may be used to enhance development and disease resistance of crop species where growth-promoting and protective microbes may have been lost. During seedling establishment, seedlings may be infected by several fungal pathogens that are seed or soil borne. Several species of Fusarium, Pythium and other water moulds cause seed rots during germination. Fusariumblights of seedlings are also very common and significantly affect seedling development. In the present study we screened nine endophytic bacteria isolated from the seeds of invasive Phragmites australis by inoculating onto rice, Bermuda grass (Cynodon dactylon), or annual bluegrass (Poa annua) seeds to evaluate plant growth promotion and protection from disease caused by Fusarium oxysporum. We found that three bacteria belonging to genus Pseudomonas spp. (SLB4-P. fluorescens, SLB6-Pseudomonas sp. and SY1-Pseudomonassp.) promoted seedling development, including enhancement of root and shoot growth, and stimulation of root hair formation. These bacteria were also found to increase phosphate solubilization in in vitro experiments. Pseudomonas sp. (SY1) significantly protected grass seedlings from Fusarium infection. In co-culture experiments, strain SY1 strongly inhibited fungal pathogens with 85.71% growth inhibition of F. oxysporum, 86.33% growth inhibition of Curvularia sp. and 82.14% growth inhibition of Alternaria sp. Seedlings previously treated with bacteria were found much less infected by F. oxysporum in comparison to non-treated controls. On microscopic observation we found that bacteria appeared to degrade fungal mycelia actively. Metabolite products of strain SY1 in agar were also found to inhibit fungal growth on nutrient media. Pseudomonas sp. (SY1) was found to produce antifungal volatiles. Polymerase chain reaction (PCR) amplification using specific primers for pyrrolnitirin synthesis and HCN (hydrogen cyanide) production suggested presence of genes for both compounds in the genome of SY1. HCN was detected in cultures of SY1. We conclude that microbes from non-cultivated plants may provide disease protection and promote growth of crop plants.
","language":"English","publisher":"MDPI","doi":"10.3390/microorganisms6010021","usgsCitation":"Verma, S.K., Kingsley, K.L., Bergen, M.S., Kowalski, K., and White, J., 2018, Fungal disease prevention in seedlings of rice (Oryza sativa) and other grasses by growth-promoting seed-associated endophytic bacteria from invasive Phragmites australis: Microorganisms, v. 6, no. 1, p. 1-13, https://doi.org/10.3390/microorganisms6010021.","productDescription":"Article 21; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-094472","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":468929,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/microorganisms6010021","text":"Publisher Index Page"},{"id":352330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-08","publicationStatus":"PW","scienceBaseUri":"5afee701e4b0da30c1bfc05c","contributors":{"authors":[{"text":"Verma, Satish Kumar","contributorId":203175,"corporation":false,"usgs":false,"family":"Verma","given":"Satish","email":"","middleInitial":"Kumar","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":730554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsley, Kathryn L.","contributorId":203176,"corporation":false,"usgs":false,"family":"Kingsley","given":"Kathryn","email":"","middleInitial":"L.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":730555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergen, Marshall S.","contributorId":178394,"corporation":false,"usgs":false,"family":"Bergen","given":"Marshall","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":730556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":730553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, James F.","contributorId":152046,"corporation":false,"usgs":false,"family":"White","given":"James F.","affiliations":[],"preferred":false,"id":730557,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195923,"text":"70195923 - 2018 - Energetic solutions of Rock Sandpipers to harsh winter conditions rely on prey quality","interactions":[],"lastModifiedDate":"2018-03-08T09:44:20","indexId":"70195923","displayToPublicDate":"2018-03-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Energetic solutions of Rock Sandpipers to harsh winter conditions rely on prey quality","docAbstract":"Rock Sandpipers Calidris ptilocnemis have the most northerly non-breeding distribution of any shorebird in the Pacific Basin (upper Cook Inlet, Alaska; 61°N, 151°W). In terms of freezing temperatures, persistent winds and pervasive ice, this site is the harshest used by shorebirds during winter. We integrated physiological, metabolic, behavioural and environmental aspects of the non-breeding ecology of Rock Sandpipers at the northern extent of their range to determine the relative importance of these factors in facilitating their unique non-breeding ecology. Not surprisingly, estimated daily energetic demands were greatest during January, the coldest period of winter. These estimates were greatest for foraging birds, and exceeded basal metabolic rates by a factor of 6.5, a scope of increase that approaches the maximum sustained rate of energetic output by shorebirds during periods of migration, but far exceeds these periods in duration. We assessed the quality of their primary prey, the bivalve Macoma balthica, to determine the daily foraging duration required by Rock Sandpipers to satisfy such energetic demands. Based on size-specific estimates of M. balthica quality, Rock Sandpipers require over 13 h/day of foraging time in upper Cook Inlet in January, even when feeding on the highest quality prey. This range approaches the average daily duration of mudflat availability in this region (c. 18 h), a maximum value that annually decreases due to the accumulation of shore-fast ice. Rock Sandpipers are likely to maximize access to foraging sites by following the exposure of ice-free mudflats across the upper Cook Inlet region and by selecting smaller, higher quality M. balthica to minimize foraging times. Ultimately, this unusual non-breeding ecology relies on the high quality of their prey resources. Compared with other sites across their range, M. balthica from upper Cook Inlet have relatively light shells, potentially the result of the region's depauperate invertebrate predator community. Given the delicate balance between environmental and prey conditions that currently make Cook Inlet a viable wintering area for Rock Sandpipers, small variations in these variables may affect the suitability of the site in the future.
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\"}}]}","contact":"Hydro-Eco Interactions Branch
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025
https://water.usgs.gov
The U.S. Geological Survey (USGS) monitors water quality and suspended-sediment transport in the San Francisco Bay (bay) as part of a multi-agency effort to address management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people, and the bay teems both with resident and with migratory wildlife, plants, and fish. Freshwater mixes with salt water in the bay, which is subject both to riverine influences (floods, droughts, managed reservoir releases and freshwater diversions) and to marine influences (tides, waves, effects of salt water). To understand this environment, the USGS, along with its partners (see “Acknowledgements”), has been monitoring the bay’s waters continuously since 1988. Several water-quality variables are of particular importance to State and Federal resource managers and are monitored at key locations throughout the bay (fig. 1). Salinity, which indicates the relative mixing of fresh and ocean waters in the bay, is derived from specific conductance measurements. Water temperature, along with salinity, affects the density of water, which controls gravity-driven circulation patterns and stratification in the water column. Turbidity, a measure of light scattered from suspended particles in the water, is used to estimate suspended-sediment concentration (SSC). Suspended sediment affects the bay in multiple ways: attenuation of sunlight in the water column, affecting phytoplankton growth; deposition on tidal marsh and intertidal mudflats, which can help sustain these habitats as sea level rises; deposition in ports and shipping channels, which can necessitate dredging; and often, adsorption of contaminants, affecting their distribution and concentrations in the environment. Dissolved oxygen concentration, essential to a healthy ecosystem and a fundamental indicator of water quality, is affected by water temperature, salinity, ecosystem metabolism, tidal currents, and wind. Tidal currents in the bay reverse four times a day, and wind direction and intensity typically vary on a daily cycle. Consequently, salinity, water temperature, SSC, and dissolved-oxygen concentration vary spatially and temporally throughout the bay. Therefore, continuous measurements are needed to observe these changes. The purpose of this fact sheet is to provide information about these variables, as well as internet links to access these continuous water-quality data collected by the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183013","usgsCitation":"Buchanan, P.A., Downing-Kunz, M.A., Schoellhamer, D.H., and Livsey, D.N., 2018, Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2014–15 (ver. 1.1, May 2018): U.S. Geological Survey Fact Sheet 2018–3013, 5 p., https://doi.org/10.3133/fs20183013.","productDescription":"5 p.","ipdsId":"IP-062545","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":354204,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2018/3013/fs20183013_versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"Fact Sheet 2018-3013 Version History"},{"id":352337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3013/coverthb.jpg"},{"id":352338,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3013/fs20183013_v1.1.pdf","text":"Report","size":"650 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3013"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5689697265625,\n 37.399619108675594\n ],\n [\n -121.76834106445311,\n 37.399619108675594\n ],\n [\n -121.76834106445311,\n 38.19825933797085\n ],\n [\n -122.5689697265625,\n 38.19825933797085\n ],\n [\n -122.5689697265625,\n 37.399619108675594\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 8, 2018; Version 1.1: May 15, 2018","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
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Surface motion is an important component of lava lake behavior, but previous studies of lake motion have been focused on short time intervals. In this study, we implement the first continuous, real-time operational routine for tracking lava lake surface motion, applying the technique to the persistent lava lake in Halema‘uma‘u Crater at the summit of Kīlauea Volcano, Hawai‘i. We measure lake motion by using images from a fixed thermal camera positioned on the crater rim, transmitting images to the Hawaiian Volcano Observatory (HVO) in real time. We use an existing optical flow toolbox in Matlab to calculate motion vectors, and we track the position of lava upwelling in the lake, as well as the intensity of spattering on the lake surface. Over the past 2 years, real-time tracking of lava lake surface motion at Halema‘uma‘u has been an important part of monitoring the lake’s activity, serving as another valuable tool in the volcano monitoring suite at HVO.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Methods used in volcano monitoring in Book 13: Volcano monitoring","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm13A3","usgsCitation":"Patrick, M.R., and Orr, T., 2018, Operational tracking of lava lake surface motion at Kīlauea Volcano, Hawai‘i,: U.S. Geological Survey Techniques and Methods 13-A3, 12 p., https://doi.org/10.3133/tm13A3.","productDescription":"iii, 12 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-090901","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":352295,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/13/a3/coverthb.jpg"},{"id":352296,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/13/a3/tm13a3.pdf","text":"Report","size":"6.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods 13–A3"},{"id":399693,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107021.htm"}],"country":"United States","state":"Hawai‘i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.30445098876953,\n 19.391153294849676\n ],\n [\n -155.22994995117188,\n 19.391153294849676\n ],\n [\n -155.22994995117188,\n 19.440694401302856\n ],\n [\n -155.30445098876953,\n 19.440694401302856\n ],\n [\n -155.30445098876953,\n 19.391153294849676\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"This report is Chapter 3 of Section A: Methods used in volcano monitoring in Book 13: Volcano monitoring.","contact":"HVO, Volcano Science Center,
Hawaiian Volcano Observatory
U.S. Geological Survey
P.O. Box 51, 1 Crater Rim Road
Hawaiʻi Volcanoes National Park, HI 96718-0051
A mass balance is formulated to evaluate the mobilization of chlorinated ethene compounds (CE) from the rock matrix of a fractured mudstone aquifer under pre- and postbioremediation conditions. The analysis relies on a sparse number of monitoring locations and is constrained by a detailed description of the groundwater flow regime. Groundwater flow modeling developed under the site characterization identified groundwater fluxes to formulate the CE mass balance in the rock volume exposed to the injected remediation amendments. Differences in the CE fluxes into and out of the rock volume identify the total CE mobilized from diffusion, desorption, and nonaqueous phase liquid dissolution under pre- and postinjection conditions. The initial CE mass in the rock matrix prior to remediation is estimated using analyses of CE in rock core. The CE mass mobilized per year under preinjection conditions is small relative to the total CE mass in the rock, indicating that current pump-and-treat and natural attenuation conditions are likely to require hundreds of years to achieve groundwater concentrations that meet regulatory guidelines. The postinjection CE mobilization rate increased by approximately an order of magnitude over the 5 years of monitoring after the amendment injection. This rate is likely to decrease and additional remediation applications over several decades would still be needed to reduce CE mass in the rock matrix to levels where groundwater concentrations in fractures achieve regulatory standards.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12586","usgsCitation":"Shapiro, A.M., Tiedeman, C.R., Imbrigiotta, T.E., Goode, D.J., Hsieh, P.A., Lacombe, P., DeFlaun, M.F., Drew, S.R., and Curtis, G.P., 2018, Bioremediation in fractured rock: 2. Mobilization of chloroethene compounds from the rock matrix: Groundwater, v. 56, no. 2, p. 317-336, https://doi.org/10.1111/gwat.12586.","productDescription":"20 p.","startPage":"317","endPage":"336","ipdsId":"IP-088890","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":352329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","city":"West Trenton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.81480240821838,\n 40.26764815781309\n ],\n [\n -74.8107898235321,\n 40.26764815781309\n ],\n [\n -74.8107898235321,\n 40.27047242769165\n ],\n [\n -74.81480240821838,\n 40.27047242769165\n ],\n [\n -74.81480240821838,\n 40.26764815781309\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-05","publicationStatus":"PW","scienceBaseUri":"5afee701e4b0da30c1bfc05a","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - 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This complexity can obscure the influence of limiting species interactions, making it difficult to determine if abiotic or biotic drivers are more predominant regulators of richness. Using integrative modeling of freshwater fish richness from 721 lakes along an 11olatitudinal gradient, we find negative interactions to be a relatively minor independent predictor of species richness in lakes despite the widespread presence of predators. Instead, interaction effects, when detectable among major functional groups and 231 species pairs, were strong, often positive, but contextually dependent on environment. These results are consistent with the idea that negative interactions internally structure lake communities but do not consistently ‘scale-up’ to regulate richness independently of the environment. The importance of environment for interaction outcomes and its role in the regulation of species richness highlights the potential sensitivity of fish communities to the environmental changes affecting lakes globally.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-018-03419-1","usgsCitation":"MacDougall, A.S., Harvey, E., McCune, J.L., Nilsson, K.A., Bennett, J., Firn, J., Bartley, T., Grace, J.B., Kelly, J., Tunney, T.D., McMeans, B.C., Matsuzaki, S.S., Kadoya, T., Esch, E., Cazelles, K., Lester, N., and McCann, K.S., 2018, Context-dependent interactions and the regulation of species richness in freshwater fish: Nature Communications, v. 9, p. 1-9, https://doi.org/10.1038/s41467-018-03419-1.","productDescription":"Article number 973; 9 p.","startPage":"1","endPage":"9","ipdsId":"IP-086293","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460987,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-018-03419-1","text":"Publisher Index 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To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175160","usgsCitation":"Terziotti, Silvia, Capel, P.D., Tesoriero, A.J., Hopple, J.A., and Kronholm, S.C., 2017, Estimates of nitrate loads and yields from groundwater to streams in the Chesapeake Bay watershed based on land use and geology: U.S. Geological Survey Scientific Investigations Report 2017–5160, 20 p., https://doi.org/10.3133/sir20175160.","productDescription":"Report: iv, 20 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086227","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":352267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5KP4","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets and metadata for estimates of nitrate loads and yields from groundwater to streams in the 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This report is a user guide for the low-flow analysis methods provided with version 1.0 of the Surface Water Toolbox (SWToolbox) computer program. The software combines functionality from two software programs—U.S. Geological Survey (USGS) SWSTAT and U.S. Environmental Protection Agency (EPA) DFLOW. Both of these programs have been used primarily for computation of critical low-flow statistics. The main analysis methods are the computation of hydrologic frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10), computation of design flows including biologically based flows, and computation of flow-duration curves and duration hydrographs. Other annual, monthly, and seasonal statistics can also be computed. The interface facilitates retrieval of streamflow discharge data from the USGS National Water Information System and outputs text reports for a record of the analysis. Tools for graphing data and screening tests are available to assist the analyst in conducting the analysis.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Statistical analysis in Book 4: Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A11","collaboration":"Prepared in cooperation with the U.S. Environment Protection Agency","usgsCitation":"Kiang, J.E., Flynn, K.M., Zhai, Tong, Hummel, Paul, and Granato, Gregory, 2018, SWToolbox: A surface-water tool-box for statistical analysis of streamflow time series: U.S. Geological Survey Techniques and Methods, book 4, chap. A–11, 33 p., https://doi.org/10.3133/tm4A11.","productDescription":"Report: vii, 34 p.; Software Download and Release Notes","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086817","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":352238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a11/coverthb.jpg"},{"id":352239,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a11/tm4a11.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A11"},{"id":352240,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://water.usgs.gov/osw/swtoolbox/","text":"Software Download and Release Notes","linkHelpText":"- SWToolbox Software Information"}],"publicComments":"This report is Chapter 11 of Section A: Statistical analysis in Book 4: Hydrologic analysis and interpretation.","contact":"Chief, Analysis and Prediction Branch
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 415
Reston, VA 20192
Freshwater mussels are among the most-imperiled organisms worldwide, although they provide a variety of important functions in the streams and rivers they inhabit. Among Atlantic-slope rivers, the Delaware River is known for its freshwater mussel diversity and biomass; however, limited data are available on the freshwater mussel fauna in the lower, non-tidal portion of the river. This section of the Delaware River has experienced decades of water-quality degradation from both industrial and municipal sources, primarily as a function of one of its major tributaries, the Lehigh River. We completed semi-quantitative snorkel surveys in 53.5 of the 121 km of the river to document mussel community composition and the continued impacts from pollution (particularly inputs from the Lehigh River) on mussel fauna. We detected changes in mussel catch per unit effort (CPUE) below the confluence of the Lehigh River, with significant declines in the dominant species Elliptio complanata (Eastern Elliptio) as we moved downstream from its confluence—CPUE dropped from 179 to 21 mussels/h. Patterns in mussel distribution around the Lehigh confluence matched chemical signatures of Lehigh water input. Specifically, Eastern Elliptio CPUE declined more quickly moving downstream on the Pennsylvania bank, where Lehigh River water input was more concentrated compared to the New Jersey bank. A definitive causal link remains to be established between the Lehigh River and the dramatic shifts in mussel community composition, warranting continued investigation as it relates to mussel conservation and restoration in the basin.
","language":"English","publisher":"Eagle Hill Publications","doi":"10.1656/045.025.0106","usgsCitation":"Blakeslee, C.J., Silldorff, E.L., and Galbraith, H.S., 2018, Changes in freshwater mussel communities linked to legacy pollution in the Lower Delaware River: Northeastern Naturalist, v. 25, no. 1, p. 101-116, https://doi.org/10.1656/045.025.0106.","productDescription":"16 p.","startPage":"101","endPage":"116","ipdsId":"IP-073172","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":352300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.25909423828125,\n 40.18097176388719\n ],\n [\n -74.7344970703125,\n 40.18097176388719\n ],\n [\n -74.7344970703125,\n 40.99855696412671\n ],\n [\n -75.25909423828125,\n 40.99855696412671\n ],\n [\n -75.25909423828125,\n 40.18097176388719\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70ce4b0da30c1bfc06f","contributors":{"authors":[{"text":"Blakeslee, Carrie J. 0000-0002-0801-5325 cblakeslee@usgs.gov","orcid":"https://orcid.org/0000-0002-0801-5325","contributorId":5462,"corporation":false,"usgs":true,"family":"Blakeslee","given":"Carrie","email":"cblakeslee@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":730391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silldorff, Erik L.","contributorId":203041,"corporation":false,"usgs":false,"family":"Silldorff","given":"Erik","email":"","middleInitial":"L.","affiliations":[{"id":36569,"text":"Delaware River Basin Commission","active":true,"usgs":false}],"preferred":false,"id":730392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galbraith, Heather S. 0000-0003-3704-3517 hgalbraith@usgs.gov","orcid":"https://orcid.org/0000-0003-3704-3517","contributorId":4519,"corporation":false,"usgs":true,"family":"Galbraith","given":"Heather","email":"hgalbraith@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":730393,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195888,"text":"70195888 - 2018 - Human Bacteroides and total coliforms as indicators of recent combined sewer overflows and rain events in urban creeks","interactions":[],"lastModifiedDate":"2018-03-07T14:59:58","indexId":"70195888","displayToPublicDate":"2018-03-07T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Human Bacteroides and total coliforms as indicators of recent combined sewer overflows and rain events in urban creeks","title":"Human Bacteroides and total coliforms as indicators of recent combined sewer overflows and rain events in urban creeks","docAbstract":"Combined sewer overflows (CSOs) are a known source of human fecal pollution and human pathogens in urban water bodies, which may present a significant public health threat. To monitor human fecal contamination in water, bacterial fecal indicator organisms (FIOs) are traditionally used. However, because FIOs are not specific to human sources and do not correlate with human pathogens, alternative fecal indicators detected using qPCR are becoming of interest to policymakers. For this reason, this study measured correlations between the number and duration of CSOs and mm of rainfall, concentrations of traditional FIOs and alternative indicators, and the presence of human pathogens in two urban creeks. Samples were collected May–July 2016 and analyzed for concentrations of FIOs (total coliforms and E. coli) using membrane filtration as well as for three alternative fecal indicators (human Bacteroides HF183 marker, human polyomavirus (HPoV), pepper mild mottle virus (PMMoV)) and nine human pathogens using qPCR. Four of the nine pathogens analyzed were detected at these sites including adenovirus, Enterohemorrhagic E. coli, norovirus, and Salmonella. Among all indicators studied, human Bacteroides and total coliforms were significantly correlated with recent CSO and rainfall events, while E. coli, PMMoV, and HPoV did not show consistent significant correlations. Further, human Bacteroides were a more specific indicator, while total coliforms were a more sensitive indicator of CSO and rainfall events. Results may have implications for the use and interpretation of these indicators in future policy or monitoring programs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.02.108","usgsCitation":"McGinnis, S., Spencer, S.K., Firnstahl, A.D., Stokdyk, J.P., Borchardt, M.A., McCarthy, D., and Murphy, H., 2018, Human Bacteroides and total coliforms as indicators of recent combined sewer overflows and rain events in urban creeks: Science of the Total Environment, v. 630, p. 967-976, https://doi.org/10.1016/j.scitotenv.2018.02.108.","productDescription":"10 p.","startPage":"967","endPage":"976","ipdsId":"IP-092275","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":352298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","city":"Philadelphia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.38063049316406,\n 39.839649900638\n ],\n [\n -75.04074096679688,\n 39.839649900638\n ],\n [\n -75.04074096679688,\n 40.0759697987031\n ],\n [\n -75.38063049316406,\n 40.0759697987031\n ],\n [\n -75.38063049316406,\n 39.839649900638\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"630","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70ce4b0da30c1bfc06d","contributors":{"authors":[{"text":"McGinnis, Shannon","contributorId":203051,"corporation":false,"usgs":false,"family":"McGinnis","given":"Shannon","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":730401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spencer, Susan K.","contributorId":181738,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":730402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","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":730403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":730400,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":730404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCarthy, David","contributorId":167752,"corporation":false,"usgs":false,"family":"McCarthy","given":"David","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":730405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Murphy, Heather","contributorId":203052,"corporation":false,"usgs":false,"family":"Murphy","given":"Heather","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":730406,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196885,"text":"70196885 - 2018 - Doublethink and scale mismatch polarize policies for an invasive tree","interactions":[],"lastModifiedDate":"2018-05-14T15:27:25","indexId":"70196885","displayToPublicDate":"2018-03-07T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Doublethink and scale mismatch polarize policies for an invasive tree","docAbstract":"Mismatches between invasive species management policies and ecological knowledge can lead to profound societal consequences. For this reason, natural resource agencies have adopted the scientifically-based density-impact invasive species curve to guide invasive species management. We use the density-impact model to evaluate how well management policies for a native invader (Juniperus virginiana) match scientific guidelines. Juniperus virginiana invasion is causing a sub-continental regime shift from grasslands to woodlands in central North America, and its impacts span collapses in endemic diversity, heightened wildfire risk, and crashes in grazing land profitability. We (1) use land cover data to identify the stage of Juniperus virginiana invasion for three ecoregions within Nebraska, USA, (2) determine the range of invasion stages at individual land parcel extents within each ecoregion based on the density-impact model, and (3) determine policy alignment and mismatches relative to the density-impact model in order to assess their potential to meet sustainability targets and avoid societal impacts as Juniperus virginiana abundance increases. We found that nearly all policies evidenced doublethink and policy-ecology mismatches, for instance, promoting spread of Juniperus virginiana regardless of invasion stage while simultaneously managing it as a native invader in the same ecoregion. Like other invasive species, theory and literature for this native invader indicate that the consequences of invasion are unlikely to be prevented if policies fail to prioritize management at incipient invasion stages. Theory suggests a more realistic approach would be to align policy with the stage of invasion at local and ecoregion management scales. There is a need for scientists, policy makers, and ecosystem managers to move past ideologies governing native versus non-native invader classification and toward a framework that accounts for the uniqueness of native species invasions, their anthropogenic drivers, and their impacts on ecosystem services.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0189733","usgsCitation":"Roberts, C.P., Uden, D.R., Allen, C.R., and Twidwell, D., 2018, Doublethink and scale mismatch polarize policies for an invasive tree: PLoS ONE, v. 13, no. 3, p. 1-20, https://doi.org/10.1371/journal.pone.0189733.","productDescription":"e0189733; 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-092986","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0189733","text":"Publisher Index Page"},{"id":354145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","volume":"13","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","scienceBaseUri":"5afee70ce4b0da30c1bfc06b","contributors":{"authors":[{"text":"Roberts, Caleb P. 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":197604,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","middleInitial":"P.","affiliations":[],"preferred":false,"id":735230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uden, Daniel R.","contributorId":74258,"corporation":false,"usgs":true,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":735231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Twidwell, Dirac","contributorId":187431,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","email":"","affiliations":[],"preferred":false,"id":735232,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195854,"text":"70195854 - 2018 - West Nile virus infection in American singer canaries: An experimental model in a highly susceptible avian species","interactions":[],"lastModifiedDate":"2023-06-21T15:17:03.091282","indexId":"70195854","displayToPublicDate":"2018-03-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3687,"text":"Veterinary Pathology","active":true,"publicationSubtype":{"id":10}},"title":"West Nile virus infection in American singer canaries: An experimental model in a highly susceptible avian species","docAbstract":"This study investigated the susceptibility of American singer canaries (Serinus canaria) to West Nile virus (WNV) infection. Adult canaries were inoculated with 105, 102, and 101plaque forming units (PFU) of WNV. All birds became infected and mortality occurred by 5 days postinoculation. The load of viral RNA as determined by RT-qPCR was dose dependent, and was higher at all doses than the level of viral RNA detected in American crows (Corvus brachyrhynchos) challenged with 105 PFU of WNV. In a subset of birds, viremia was detected by virus isolation; canaries inoculated with 101 PFU of WNV developed viremia exceeding 1010 PFU/mL serum, a log higher than American crows inoculated with 105 PFU of virus. In canaries euthanized at 3 days postinoculation, WNV was isolated at >107 PFU of virus/100 mg of lung, liver, heart, spleen, and kidney tissues. Pallor of the liver and splenomegaly were the most common macroscopic observations and histologic lesions were most severe in liver, spleen, and kidney, particularly in canaries challenged with 102 and 101 PFU. Immunoreactivity to WNV was pronounced in the liver and spleen. IgG antibodies to WNV were detected in serum by enzyme immunoassay in 11 of 21 (52%) challenged canaries and, in 4 of 5 (20%) of these sera, neutralization antibodies were detected at a titer ≥ 1:20. American singer canaries provide a useful model as this bird species is highly susceptible to WNV infection.
","language":"English","publisher":"American College of Veterinary Pathologists","doi":"10.1177/0300985818760377","usgsCitation":"Hofmeister, E.K., Lund, M., and Shearn-Bochsler, V.I., 2018, West Nile virus infection in American singer canaries: An experimental model in a highly susceptible avian species: Veterinary Pathology, v. 55, no. 4, p. 531-538, https://doi.org/10.1177/0300985818760377.","productDescription":"8 p.; Data Release","startPage":"531","endPage":"538","ipdsId":"IP-085734","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":460991,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/0300985818760377","text":"Publisher Index Page"},{"id":352250,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418295,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XG9QD9","linkFileType":{"id":5,"text":"html"}}],"volume":"55","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-05","publicationStatus":"PW","scienceBaseUri":"5afee70de4b0da30c1bfc078","contributors":{"authors":[{"text":"Hofmeister, Erik K. 0000-0002-6360-3912 ehofmeister@usgs.gov","orcid":"https://orcid.org/0000-0002-6360-3912","contributorId":3230,"corporation":false,"usgs":true,"family":"Hofmeister","given":"Erik","email":"ehofmeister@usgs.gov","middleInitial":"K.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":730315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lund, Melissa 0000-0003-4577-2015 mlund@usgs.gov","orcid":"https://orcid.org/0000-0003-4577-2015","contributorId":177923,"corporation":false,"usgs":true,"family":"Lund","given":"Melissa","email":"mlund@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":730317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shearn-Bochsler, Valerie I. 0000-0002-5590-6518 vbochsler@usgs.gov","orcid":"https://orcid.org/0000-0002-5590-6518","contributorId":3234,"corporation":false,"usgs":true,"family":"Shearn-Bochsler","given":"Valerie","email":"vbochsler@usgs.gov","middleInitial":"I.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":730316,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195858,"text":"70195858 - 2018 - Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA","interactions":[],"lastModifiedDate":"2018-03-06T11:24:17","indexId":"70195858","displayToPublicDate":"2018-03-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA","docAbstract":"Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO3−) input functions by characterizing unsaturated zone NO3− transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous “vertical flux method” (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO3− source concentration factor (which determines the local NO3− input concentration); unsaturated zone travel time; NO3− concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO3− “extinction depth”, the eventual steady state depth of the NO3−front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R2 values of 0.52 – 0.86 and 0.22 – 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO3− at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO3− extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO3− transport processes in a statistical framework based on readily mappable GIS input variables.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.02.029","usgsCitation":"Nolan, B.T., Green, C.T., Juckem, P.F., Liao, L., and Reddy, J.E., 2018, Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA: Journal of Hydrology, v. 559, p. 428-441, https://doi.org/10.1016/j.jhydrol.2018.02.029.","productDescription":"14 p.","startPage":"428","endPage":"441","ipdsId":"IP-088948","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468931,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2018.02.029","text":"Publisher Index Page"},{"id":437988,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZC81VR","text":"USGS data release","linkHelpText":"Data Release for Metamodeling and Mapping of Nitrate Flux in the Unsaturated Zone and Groundwater, Wisconsin, USA"},{"id":352259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90,\n 43.5\n ],\n [\n -87.38525390624999,\n 43.5\n ],\n [\n -87.38525390624999,\n 46\n ],\n [\n -90,\n 46\n ],\n [\n -90,\n 43.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"559","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70ce4b0da30c1bfc074","contributors":{"authors":[{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":730332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":730331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","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":730333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liao, Lixia 0000-0003-2513-0680 lliao@usgs.gov","orcid":"https://orcid.org/0000-0003-2513-0680","contributorId":5311,"corporation":false,"usgs":true,"family":"Liao","given":"Lixia","email":"lliao@usgs.gov","affiliations":[],"preferred":true,"id":730335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reddy, James E. 0000-0002-6998-7267 jreddy@usgs.gov","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":1080,"corporation":false,"usgs":true,"family":"Reddy","given":"James","email":"jreddy@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730334,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}