{"pageNumber":"326","pageRowStart":"8125","pageSize":"25","recordCount":46619,"records":[{"id":70198018,"text":"fs20183039 - 2018 - A database of biodiversity and habitat quantification tools used in market-based conservation","interactions":[],"lastModifiedDate":"2018-07-16T13:13:36","indexId":"fs20183039","displayToPublicDate":"2018-07-12T14:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3039","title":"A database of biodiversity and habitat quantification tools used in market-based conservation","docAbstract":"<p>Market-based conservation uses economic incentives to leverage market forces in ways that encourage and improve efficiency in the restoration, enhancement, and preservation of species and habitats. Biodiversity and habitat quantification tools are vital to the operation of this conservation strategy, as they are used to measure the quality and functionality of areas of land that have undergone or are proposed for preservation, improvement, or development activities (for example, construction of energy or transportation infrastructure and residential development).</p><p>The U.S. Geological Survey (USGS) Science and Decisions Center in partnership with the U.S. Department of Agriculture Office of Environmental Markets have created a database of the quantification tools available for use in biodiversity and habitat markets in the contiguous United States. This database provides landowners, regulatory agencies, tool developers, and the general public with a central location from which to search for and identify the tools applicable to specific species, habitats, or locations of interest, such as those shown in figures 1 and 2. The database contains summary information about the intended application and features of each tool and will be updated as the need to add new tools warrants.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183039","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Office of Environmental Markets","usgsCitation":"Chiavacci, S.J., and Pindilli, E.J., 2018, A database of biodiversity and habitat quantification tools used in market-based conservation: U.S. Geological Survey Fact Sheet 2018–3039, 4 p., https://doi.org/10.3133/fs20183039.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-095555","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":355521,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5M3X","text":"USGS data release","description":"USGS data release","linkHelpText":"Database of Biodiversity and Habitat Quantification Tools Used for Market-based Conservation in the United States"},{"id":355519,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3039/coverthb2.jpg"},{"id":355520,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3039/fs20183039.pdf","text":"Report","size":"3.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3039"}],"contact":"<p><a href=\"https://www2.usgs.gov/sdc/\" data-mce-href=\"https://www2.usgs.gov/sdc/\">Science and Decisions Center</a><br> U.S. Geological Survey<br> 913 National Center<br> Reston, VA 20192</p>","tableOfContents":"<ul><li>Market-Based Conservation in the United States</li><li>Benefits of the Quantification Tools Database</li><li>What the Database Contains</li><li>How to Access the Database</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2018-07-12","noUsgsAuthors":false,"publicationDate":"2018-07-12","publicationStatus":"PW","scienceBaseUri":"5b6fc417e4b0f5d57878e9d9","contributors":{"authors":[{"text":"Chiavacci, Scott J. 0000-0003-3579-8377","orcid":"https://orcid.org/0000-0003-3579-8377","contributorId":206161,"corporation":false,"usgs":true,"family":"Chiavacci","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":739628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pindilli, Emily 0000-0002-5101-1266 epindilli@usgs.gov","orcid":"https://orcid.org/0000-0002-5101-1266","contributorId":140262,"corporation":false,"usgs":true,"family":"Pindilli","given":"Emily","email":"epindilli@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":739629,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70202307,"text":"70202307 - 2018 - Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry","interactions":[],"lastModifiedDate":"2019-02-21T12:57:44","indexId":"70202307","displayToPublicDate":"2018-07-12T12:57:34","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry","docAbstract":"<p><span>Timely assessment of coastal landforms and structures after storms is important for evaluating storm impacts, aiding emergency response and restoration, and initializing and assessing morphological models. Four-dimensional multiview photogrammetry, also known as structure from motion (4D SfM), provides a method for generating three-dimensional reconstructions of landscapes at two times (before and after events) using only photos and existing information for ground control points. Here, these techniques were applied using National Oceanic and Atmospheric Administration (NOAA)-obtained oblique aerial photos taken before (2015) and immediately after Hurricane Matthew (2016) to assess coastal changes near Matanzas, Florida. This work demonstrated that 3D digital elevation models can be constructed within 48 hours of postevent photo collection without on-site ground control measurements. One advantage of timely SfM elevation-change assessments is that they avoid confusion of storm impacts with changes that occur after the event but before LIDAR surveys can be performed. The accuracy and precision of the 4D SfM maps were assessed&nbsp;</span><i>a posteriori</i><span>&nbsp;using the first-available LIDAR data, which were collected more than a month after the hurricane, and 11 independent ground-truth survey points measured a week after the hurricane. Horizontal coordinates of the 4D SfM reconstruction were biased by an average of 0.79 m (0.83 m root-mean-square difference; RMSD) compared with the ground-truth points, but vertical elevations were more accurate. They were biased from the LIDAR by −0.09 to −0.25 m, with ∼0.20 m RMSD from both the LIDAR data and five ground-truth points with good vertical positioning and 0.25 m RMSD from LIDAR data along a 60-m stretch of pavement. This level of precision was sufficient to quantify geomorphological change that was often in excess of 1 m. The methodology is conducive for rapid assessment of changes along short stretches (tens of kilometers) of coast with modest resources and could be scaled up for larger regions.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/JCOASTRES-D-18-00016.1","usgsCitation":"Sherwood, C.R., Warrick, J.A., Hill, A.D., Ritchie, A.C., Andrews, B.D., and Plant, N.G., 2018, Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry: Journal of Coastal Research, v. 34, no. 6, p. 1303-1316, https://doi.org/10.2112/JCOASTRES-D-18-00016.1.","productDescription":"14 p.","startPage":"1303","endPage":"1316","ipdsId":"IP-094558","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2112/jcoastres-d-18-00016.1","text":"Publisher Index Page"},{"id":361409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.23505592346191,\n              29.655164600486\n            ],\n            [\n              -81.20484352111816,\n              29.655164600486\n            ],\n            [\n              -81.20484352111816,\n              29.70676659773517\n            ],\n            [\n              -81.23505592346191,\n              29.70676659773517\n            ],\n            [\n              -81.23505592346191,\n              29.655164600486\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"34","issue":"6","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hill, Andrew D.","contributorId":213440,"corporation":false,"usgs":false,"family":"Hill","given":"Andrew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":757730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ritchie, Andrew C. 0000-0002-5906-1014 aritchie@usgs.gov","orcid":"https://orcid.org/0000-0002-5906-1014","contributorId":213438,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew","email":"aritchie@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757727,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Brian D. 0000-0003-1024-9400 bandrews@usgs.gov","orcid":"https://orcid.org/0000-0003-1024-9400","contributorId":201662,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian","email":"bandrews@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757728,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757729,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70197297,"text":"sir20185043 - 2018 - Flood-inundation maps for the Pawtuxet River in West Warwick, Warwick, and Cranston, Rhode Island","interactions":[],"lastModifiedDate":"2018-07-13T11:46:00","indexId":"sir20185043","displayToPublicDate":"2018-07-12T08:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5043","title":"Flood-inundation maps for the Pawtuxet River in West Warwick, Warwick, and Cranston, Rhode Island","docAbstract":"<p>A series of 15 digital flood-inundation maps was developed for a 10.2-mile reach of the Pawtuxet River in the municipalities of West Warwick, Warwick, and Cranston, Rhode Island, by the U.S. Geological Survey (USGS), in cooperation with the Rhode Island Emergency Management Agency and the U.S. Army Corps of Engineers. The coverage of the maps extends downstream from Natick Pond dam near State Route 33/Providence Street bridge in West Warwick to the mouth of the river at Pawtuxet Cove (Broad Street bridge) on the border between Cranston and Warwick, R.I. A one-dimensional step-backwater hydraulic model created and calibrated for the Federal Emergency Management Agency Flood Insurance Studies for Kent and Providence Counties in 2015 was updated for this study. The updated hydraulic model reflects the removal of the Pawtuxet Falls dam during 2011 and the raised elevation of a levee surrounding the Warwick Sewer Authority wastewater treatment facility during 2014–17. The hydraulic model was calibrated by using the current (2018) stage-discharge relation at the USGS Pawtuxet River at Cranston, Rhode Island, streamgage (01116500) and documented high-water marks from the March 31, 2010, flood, which had a peak flow greater than the estimated 0.2-percent annual exceedance probability floodflow.</p><p>The hydraulic model was used to compute water-surface profiles for 15 flood stages at 1-foot (ft) intervals referenced to the USGS Pawtuxet River at Cranston, Rhode Island, streamgage (01116500) and ranging from 8.0 ft (15.2 ft, North American Vertical Datum of 1988), which is the National Weather Service Advanced Hydrologic Prediction Service flood category “action stage,” to 22.0 ft (29.2 ft, North American Vertical Datum of 1988), which is the maximum stage of the stage-discharge relation at the streamgage and exceeds the National Weather Service Advanced Hydrologic Prediction Service flood category “major flood stage” of 13.0 ft. The simulated water-surface profiles were combined with a geographic information system digital elevation model derived from light detection and ranging (lidar) data with a 1.0-ft vertical accuracy to create flood-inundation maps. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to 15 selected flood stages at the streamgage. The flood-inundation maps depict only riverine flooding and do not depict any tidal backwater or coastal storm surge that might occur in the lower part of the river reach. The flood-inundation maps can be accessed through the USGS Flood Inundation Mapping Science website at <a href=\"https://water.usgs.gov/osw/flood_inundation\" data-mce-href=\"https://water.usgs.gov/osw/flood_inundation\">https://water.usgs.gov/osw/flood_inundation</a>. Near-real-time stages and discharges at the Pawtuxet River streamgage can be obtained from the USGS National Water Information System at <a href=\"https://waterdata.usgs.gov/\" data-mce-href=\"https://waterdata.usgs.gov/\">https://waterdata.usgs.gov/</a>. The National Weather Service Advanced Hydrologic Prediction Service provides flood forecasts of stage for this site (CRAR1) at <a href=\"https://water.weather.gov/ahps/\" data-mce-href=\"https://water.weather.gov/ahps/\">https:/water.weather.gov/ahps/</a>.</p><p>The availability of flood-inundation maps referenced to current and forecasted water levels at the USGS Pawtuxet River at Cranston, Rhode Island, streamgage (01116500) can provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, and postflood recovery efforts. The flood-inundation maps are nonregulatory but provide Federal, State, and local agencies and the public with estimates of the potential extent of flooding during flood events.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185043","collaboration":"Prepared in cooperation with the Rhode Island Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Bent, G.C., and Lombard, P.J., 2018, Flood-inundation maps for the Pawtuxet River in West Warwick, Warwick, and Cranston, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2018–5043, 16 p., https://doi.org/10.3133/sir20185043.","productDescription":"Report: vii, 16 p.; Application; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090311","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":355600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5043/sir20185043.pdf","text":"Report","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5043"},{"id":355601,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78C9V6B","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation Grids and Shapefiles for the Pawtuxet River in West Warwick, Warwick, and Cranston, Rhode Island"},{"id":355602,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html","linkHelpText":"- Flood Inundation Mapper"},{"id":355599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5043/coverthb.jpg"}],"country":"United States","state":"Rhode Island","city":"Cranston, Warwick, West Warwick","otherGeospatial":"Pawtuxet River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -71.630859375,\n              41.572306568724365\n            ],\n            [\n              -71.27105712890625,\n              41.572306568724365\n            ],\n            [\n              -71.27105712890625,\n              41.912497421968425\n            ],\n            [\n              -71.630859375,\n              41.912497421968425\n            ],\n            [\n              -71.630859375,\n              41.572306568724365\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://newengland.water.usgs.gov\" data-mce-href=\"https://newengland.water.usgs.gov\">New England Water Science Center</a><br> U.S. Geological Survey <br> 10 Bearfoot Road <br> Northborough, MA 01532</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Creation of Flood-Inundation Map Library</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2018-07-12","noUsgsAuthors":false,"publicationDate":"2018-07-12","publicationStatus":"PW","scienceBaseUri":"5b6fc418e4b0f5d57878e9dd","contributors":{"authors":[{"text":"Bent, Gardner C. 0000-0002-5085-3146","orcid":"https://orcid.org/0000-0002-5085-3146","contributorId":205226,"corporation":false,"usgs":true,"family":"Bent","given":"Gardner C.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":205225,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736572,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70216332,"text":"70216332 - 2018 - Limited nitrate retention capacity in the Upper Mississippi River","interactions":[],"lastModifiedDate":"2020-11-12T14:19:13.634131","indexId":"70216332","displayToPublicDate":"2018-07-12T08:13:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Limited nitrate retention capacity in the Upper Mississippi River","docAbstract":"<div class=\"article-text wd-jnl-art-abstract cf\"><p>The Mississippi River and other large rivers have the potential to regulate nitrogen export from terrestrial landscapes, and thus mitigate eutrophication in downstream aquatic ecosystems. In large rivers, human-constructed impoundments and connected backwaters may facilitate nitrogen removal; however, the capacity of these features is poorly quantified and incompletely incorporated into model frameworks. Using a high-resolution and spatially intensive sampling technique, we assessed the contribution of individual navigation pools, as well as impounded open waters and backwater wetlands within them, to overall nitrate retention by mapping the entire length (1370 km) of the Upper Mississippi River (UMR) main channel. Based on this single spatial survey of water chemistry, the river appeared to act primarily as a passive nitrate transporter, retaining only 12.5% of the incoming load, most of which occurred in the upper 150 km of the river, which includes the largest and only naturally impounded reach of the river. Although reservoirs typically are nitrogen sinks, our data indicate that UMR dams do not impede river flows to the extent necessary to promote substantial changes in water residence times and subsequent nitrogen removal. Backwaters routinely had lower nitrate concentrations than the main channel, but their limited hydrologic connectivity to the through-flowing river channel constrained their influence on downstream export. As a whole, the UMR did not remove a substantial proportion of its nitrate load despite optimal N removal conditions, numerous impoundments, and the presence of extensive backwater habitats. These results suggest that efforts to reduce delivery of nitrogen to the Gulf of Mexico should emphasize mitigation strategies that target upland nutrient sources rather than relying on removal within the Mississippi River.</p></div>","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/aacd51","usgsCitation":"Loken, L.C., Crawford, J.T., Dornblaser, M.M., Striegl, R.G., Houser, J.N., Turner, P.A., and Stanley, E.H., 2018, Limited nitrate retention capacity in the Upper Mississippi River: Environmental Research Letters, v. 13, no. 7, 14 p., https://doi.org/10.1088/1748-9326/aacd51.","productDescription":"14 p.","ipdsId":"IP-099033","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":468593,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aacd51","text":"Publisher Index Page"},{"id":380447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -94.0869140625,\n              45.82879925192134\n            ],\n            [\n              -94.2626953125,\n              45.36758436884978\n            ],\n            [\n              -92.8564453125,\n              44.5278427984555\n            ],\n            [\n              -92.4169921875,\n              43.96119063892024\n            ],\n            [\n              -91.58203125,\n              43.100982876188546\n            ],\n            [\n              -91.0546875,\n              42.293564192170095\n            ],\n            [\n              -91.318359375,\n              41.672911819602085\n            ],\n            [\n              -92.10937499999999,\n              40.81380923056958\n            ],\n            [\n              -92.10937499999999,\n              40.17887331434696\n            ],\n            [\n              -91.4501953125,\n              39.13006024213511\n            ],\n            [\n              -90.966796875,\n              38.685509760012\n            ],\n            [\n              -90.966796875,\n              38.30718056188316\n            ],\n            [\n              -90.3076171875,\n              37.64903402157866\n            ],\n            [\n              -89.384765625,\n              37.020098201368114\n            ],\n            [\n              -89.20898437499999,\n              36.80928470205937\n            ],\n            [\n              -89.033203125,\n              37.33522435930639\n            ],\n            [\n              -89.912109375,\n              39.13006024213511\n            ],\n            [\n              -91.14257812499999,\n              39.9434364619742\n            ],\n            [\n              -90.65917968749999,\n              41.07935114946899\n            ],\n            [\n              -89.9560546875,\n              42.4234565179383\n            ],\n            [\n              -90.65917968749999,\n              43.644025847699496\n            ],\n            [\n              -91.8896484375,\n              45.120052841530544\n            ],\n            [\n              -94.0869140625,\n              45.82879925192134\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2018-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Loken, Luke C. 0000-0003-3194-1498 lloken@usgs.gov","orcid":"https://orcid.org/0000-0003-3194-1498","contributorId":195600,"corporation":false,"usgs":true,"family":"Loken","given":"Luke","email":"lloken@usgs.gov","middleInitial":"C.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crawford, John T. 0000-0003-4440-6945 jtcrawford@usgs.gov","orcid":"https://orcid.org/0000-0003-4440-6945","contributorId":4081,"corporation":false,"usgs":true,"family":"Crawford","given":"John","email":"jtcrawford@usgs.gov","middleInitial":"T.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":804722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dornblaser, Mark M. 0000-0002-6298-3757 mmdornbl@usgs.gov","orcid":"https://orcid.org/0000-0002-6298-3757","contributorId":1636,"corporation":false,"usgs":true,"family":"Dornblaser","given":"Mark","email":"mmdornbl@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":804723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":804724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804725,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turner, Peter A 0000-0003-0839-1408","orcid":"https://orcid.org/0000-0003-0839-1408","contributorId":244831,"corporation":false,"usgs":false,"family":"Turner","given":"Peter","email":"","middleInitial":"A","affiliations":[{"id":37643,"text":"University of Minnesota-Twin Cities","active":true,"usgs":false}],"preferred":false,"id":804726,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":804727,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70196300,"text":"ofr20181058 - 2018 - A comparison of synthetic flowpaths derived from light detection and ranging topobathymetric data and National Hydrography Dataset High Resolution Flowlines","interactions":[],"lastModifiedDate":"2018-07-16T13:14:50","indexId":"ofr20181058","displayToPublicDate":"2018-07-12T00:00:00","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":"2018-1058","title":"A comparison of synthetic flowpaths derived from light detection and ranging topobathymetric data and National Hydrography Dataset High Resolution Flowlines","docAbstract":"<p>Bathymetric and topobathymetric light detection and ranging (lidar) digital elevation models created for the Delaware River were provided to the National Geospatial Program and used to evaluate synthetic flowpath extraction from bathymetric/topobathymetric lidar survey data as a data source for improving the density, distribution, and connectivity of the National Hydrography Dataset High Resolution Flowline Network. As the surface-water component of The National Map, the National Hydrography Dataset maintains the Nation’s drainage network flow information and geometries for surface-water features used in hydrologic, hydraulic, and other science and engineering disciplines. The regional lidar survey for the Delaware River between Hancock, New York, and Trenton, New Jersey, was collected for the U.S. Geological Survey using the Experimental Advanced Airborne Research Lidar sensor system and processed by the Coastal National Elevation Database Applications Program.</p><p>Using 1 percent of the maximum flow accumulation value for the surveyed Delaware River corridor as the flow accumulation threshold for grid cells at 1-, 5-, and 10-meter resolution created 223 to 283 kilometers of synthetic flowpaths potentially representing the river channel thalweg, which is the deepest point in a riverbed cross-section. There was potential for improving the High Resolution National Hydrography Dataset (HR NHD) Flowline network in places where the Delaware River channel, depicted as an Artificial Path in the HR NHD, is offset from the extracted synthetic river flowpath which sometimes appeared better positioned than the Artificial Path to represent the river thalweg. For the same area, using 0.05 percent of the maximum flow accumulation at the 1-, 5-, and 10-meter resolutions extracted 744 to 1,317 kilometers of synthetic flowpaths, with extracted synthetic flowpaths representing the main river channel and additional synthetic flowpaths representing tributaries or streams adjacent to the main channel. Overlaying these results with the HR NHDFlowline Network indicates that some of the additional synthetic flowpaths are connected to or extend HR NHD stream/river feature types. Some disconnected or isolated synthetic flowpaths&nbsp;not included in stream/river feature types were validated in orthoimagery and U.S. Topo Maps and provide examples of how extracted synthetic flowpaths could be used to delineate new stream/river features. Other additional extracted synthetic flowpaths depict linear features such as canals, tree lines, roads, or linear topographic depressions.</p><p>For some river reaches where obstructions to flow or where low-relief topographic or bathymetric surfaces alter the flow direction, the software tool used to develop the flow direction grid did not calculate a primary flowpath for the river channel. Based on the results of this analysis, site conditions for the Delaware River corridor did not affect the quality of lidar bathymetric survey data. However, depending on the resolution of the lidar bathymetric digital elevation models (BDEMs), site conditions do have different effects on results for extracted synthetic flowpaths. We found that synthetic flowpaths extracted from 1-meter resolution lidar DEMs had more varied flow directions around in-channel landforms that obstructed flow than synthetic flowpaths extracted from 5- or 10-meter resolution lidar DEMs. As a result the 1-meter resolution DEM created some isolated or discontinuous synthetic flowpath segments where the 5- and 10-meter DEMs developed more continuous flowpaths. In this case the river bed upstream from the in-channel obstruction is shallower than the river bed downstream. Under these conditions the 1-meter resolution DEM provided synthetic flowpaths delineating a potential river thalweg. In this same area, the software solution modified (virtually raised) the river bed in the 5- and 10-meter resolution DEMs and flattened the bathymetric surface to create a continuous downstream flow direction, which caused trellis-patterned synthetic flowpaths to form. Under different site conditions and converse to the above development of synthetic flowpaths at different resolutions, at an abandoned river flood plain (terrace) with low relief that is adjacent to the river channel, the flow direction grid for the 1-meter resolution DEM developed continuous synthetic flowpath corresponding to a HR NHD Flowline network stream/river feature that connected to the main river channel but the larger resolution DEMs created isolated or disconnected synthetic flowpaths.</p><p>A project to continue an evaluation of benefits of or issues caused by extracting synthetic flowpaths to enhance&nbsp;the HR NHD could include a study to assess the potential for merging surface-water flowpaths extracted from lidar topobathymetry and 3D Elevation Program digital elevation models. The merged DEM approach to synthetic flowpath extraction could extend the HR NHDFlowline network and enhance flow accumulations that might develop better flow direction grids in low-relief areas. Because of the confined lateral extent of the Delaware River, the lidar DEMs were not used to create catchments or watersheds; however, the merged DEM approach could also be tested as a resource for enhancing HR NHD catchments and watersheds.</p><p>This lidar DEM synthetic flowpath extraction project supports the National Geospatial Program efforts to collect and produce high-quality lidar data to provide 3-dimensional representations of natural feature and aligns with the National Spatial Data Infrastructure to improve utilization of geospatial data. The results also can be useful for understanding strategies that can help maintain quality data in the HR NHD programs.</p><p>KEYWORDS: bathymetric, digital elevation model, extracted synthetic flowpath, lidar, High Resolution National Hydrography Dataset, topobathymetric</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181058","usgsCitation":"Miller-Corbett, C., 2018, A comparison of synthetic flowpaths derived from light detection and ranging topobathymetric data and National Hydrography Dataset high resolution flowlines: U.S. Geological Survey Open-File Report 2018–1058, 29 p., https://doi.org/10.3133/ofr20181058.","productDescription":"vii, 29 p.","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-079961","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":355596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1058/ofr20181058.pdf","text":"Report","size":"4.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1058"},{"id":355595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1058/coverthb.jpg"}],"country":"United States","state":"New Jersey","city":"Hancock Narrows, Middle River, Trenton","otherGeospatial":"Delaware River","contact":"<p>Director, <a href=\"https://ngtoc.usgs.gov\" data-mce-href=\"https://ngtoc.usgs.gov\">National Geospatial Technical Operations Center</a><br>U.S. Geological Survey<br>1400 Independence Road<br>Rolla, MO 65401</p>","tableOfContents":"<ul><li>Acknowledgments<br></li><li>Abstract<br></li><li>Introduction<br></li><li>Experimental Advanced Airborne Research Lidar Sensor<br></li><li>Delaware River Survey Site Conditions<br></li><li>Lidar Bathymetric and Topobathymetric Data<br></li><li>Method for Developing Synthetic Flowpaths<br></li><li>Comparison of Synthetic Flowpaths and National Hydrography Dataset High Resolution Flowlines<br></li><li>Discussion<br></li><li>Summary<br></li><li>References<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-07-12","noUsgsAuthors":false,"publicationDate":"2018-07-12","publicationStatus":"PW","scienceBaseUri":"5b6fc418e4b0f5d57878e9df","contributors":{"authors":[{"text":"Miller-Corbett, Cynthia 0000-0002-9740-2502 cmcorbet@usgs.gov","orcid":"https://orcid.org/0000-0002-9740-2502","contributorId":203758,"corporation":false,"usgs":true,"family":"Miller-Corbett","given":"Cynthia","email":"cmcorbet@usgs.gov","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":732234,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70197413,"text":"ds1089 - 2018 - Pesticide inputs to the Sacramento–San Joaquin Delta, 2015–16: Results from the Delta Regional Monitoring Program","interactions":[],"lastModifiedDate":"2018-07-16T13:16:57","indexId":"ds1089","displayToPublicDate":"2018-07-11T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1089","title":"Pesticide inputs to the Sacramento–San Joaquin Delta, 2015–16: Results from the Delta Regional Monitoring Program","docAbstract":"<p>Emergent hypotheses about causes of the pelagic organism decline in the Sacramento–San Joaquin Delta (Delta) indicate that a more complete understanding of the quality of water entering the Delta is needed. Less than half of all pesticides used in the Delta watershed are measured in samples collected for routine monitoring, and with new pesticides continually being registered for use, the concentrations of unmonitored pesticides in the Delta ecosystem are unknown. In response, a multi-year, cooperative effort to improve monitoring of mercury, nutrients, pathogens, and pesticides was begun by the Delta Regional Monitoring Program (RMP). In July 2015, the U.S. Geological Survey in cooperation with the Delta RMP began measuring concentrations of 154 pesticide compounds in monthly samples of surface water and suspended sediment collected at five major inputs to the Sacramento–San Joaquin Delta from July 2015 to June 2016. In addition to pesticide concentration measurements, field water-quality indicators (water temperature, specific conductance, dissolved oxygen, pH, and turbidity) were measured at each site and samples were collected for the analysis of dissolved organic carbon, dissolved copper, particulate organic carbon, particulate inorganic carbon, total particulate carbon, and total particulate nitrogen. Pesticide concentrations in particulates were measured in collected suspended-sediment samples by gas chromatography with mass spectrometry, whereas concentrations measured in surface-water samples utilized a combination of gas chromatography with mass spectrometry and liquid chromatography with tandem mass spectrometry. Samples were collected from two sites in the San Joaquin River watershed and at one site for each of the Mokelumne River, Sacramento River, and Ulatis Creek watersheds.</p><p>All water samples contained mixtures of 2–25 pesticides. Pesticides were detected in 100 percent of surface-water samples. A total of 54 pesticide compounds were detected in water samples during the study period (19 fungicides, 18 herbicides, 9 insecticides, 7 breakdown products, and 1 synergist). The most frequently detected pesticide compounds were the herbicides hexazinone (95 percent) and diuron (73 percent) and the fungicides boscalid (93 percent) and azoxystrobin (75 percent). Pesticide concentrations ranged from below the method detection limits to 2,630 nanograms per liter for the herbicide metolachlor.</p><p>A total of 11 pesticide compounds were detected in the suspended sediments filtered from water samples (6 herbicides, 3 insecticides, 1 fungicide, and 1 breakdown product). The most frequently detected compounds were the insecticides permethrin (7 percent) and bifenthrin (5 percent) and the herbicide pendimethalin (5 percent). Pesticide concentrations in the suspended-sediment ranged from below the method detection limit to 265 nanograms per liter for the herbicide pendimethalin.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1089","collaboration":"Prepared in cooperation with the Delta Regional Monitoring Program","usgsCitation":"De Parsia, M., Orlando, J.L., McWayne, M.M., and Hladik, M.L., 2018, Pesticide inputs to the Sacramento–San Joaquin Delta, 2015–16: Results from the Delta Regional Monitoring Program: U.S. Geological Survey Data Series 1089, 49 p., https://doi.org/10.3133/ds1089.","productDescription":"vi, 49 p.","numberOfPages":"59","onlineOnly":"Y","ipdsId":"IP-081632","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":355605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1089/coverthb.jpg"},{"id":355606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1089/ds1089_.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1089"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.310791015625,\n              37.483576550426996\n            ],\n            [\n              -121.14624023437499,\n              37.483576550426996\n            ],\n            [\n              -121.14624023437499,\n              38.44498466889473\n            ],\n            [\n              -122.310791015625,\n              38.44498466889473\n            ],\n            [\n              -122.310791015625,\n              37.483576550426996\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<div><a href=\"mailto:dc_ca@usgs.gov\" target=\"_blank\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,</div><div><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a></div><div><a href=\"https://usgs.gov/\" target=\"_blank\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a></div><div>6000 J Street, Placer Hall</div><div>Sacramento, California 95819</div>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Procedures and Methods<br></li><li>Quality-Control Methods and Results<br></li><li>Results<br></li><li>Summary<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2018-07-11","noUsgsAuthors":false,"publicationDate":"2018-07-11","publicationStatus":"PW","scienceBaseUri":"5b46e53be4b060350a15d04d","contributors":{"authors":[{"text":"De Parsia, Matthew D. 0000-0001-5806-5403","orcid":"https://orcid.org/0000-0001-5806-5403","contributorId":204707,"corporation":false,"usgs":true,"family":"De Parsia","given":"Matthew D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Orlando, James L. 0000-0002-0099-7221 jorlando@usgs.gov","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":1368,"corporation":false,"usgs":true,"family":"Orlando","given":"James","email":"jorlando@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":737079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McWayne, Megan M. 0000-0001-8069-6420","orcid":"https://orcid.org/0000-0001-8069-6420","contributorId":22214,"corporation":false,"usgs":true,"family":"McWayne","given":"Megan M.","affiliations":[],"preferred":false,"id":737080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":189904,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":737081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70198053,"text":"70198053 - 2018 - An update on Toxoplasma gondii infections in northern sea otters (Enhydra lutris kenyoni) from Washington State, USA","interactions":[],"lastModifiedDate":"2018-07-12T22:20:58","indexId":"70198053","displayToPublicDate":"2018-07-11T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3686,"text":"Veterinary Parasitology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An update on Toxoplasma gondii infections in northern sea otters <i>(Enhydra lutris kenyoni) </i> from Washington State, USA","title":"An update on Toxoplasma gondii infections in northern sea otters (Enhydra lutris kenyoni) from Washington State, USA","docAbstract":"<p>Toxoplasmosis in marine mammals is epidemiologically and clinically important. <i>Toxoplasma gondii</i> antibodies (by modified agglutination test, cut-off ≥1:25) were detected in serum of 65 of 70 (92.9%) northern sea otters (<i>Enhydra lutris kenyoni</i>) from Washington State, USA. Brains and/or muscles of 44 sea otters were bioassayed in mice (INF-γ knock-out [KO], Swiss Webster outbred [SW]) and viable <i>T. gondii</i> was isolated from 22 of 44 (50%); <i>T. gondii</i> strains were lethal to KO mice but not SW mice. These <i>T. gondii</i> isolates were further propagated in cell culture. Multi-locus PCR-RFLP genotyping of cell culture-derived tachyzoites revealed four different genotypes among 22 isolates including ToxoDB PCR-RFLP genotype #5 (14 isolates), #1 (three isolates), #3 (four isolates), and #167 (one isolate). PCR-DNA sequencing based genotyping using polymorphic gene GRA6 revealed one of four different alleles. Among the 14 RFLP genotype #5 strains, 10 have GRA6 sequences that match with the Type A, one match with the Type X, two strains did not generate sequence data, and one strain had double peaks at known polymorphic sites indicating a mixed infection. The seven strains belong to genotypes #1 and #3, all have identical sequences to <i>T. gondii</i> Type II reference isolate ME49. Genotype #167 strain has identical sequence to Type I reference strain. In summary, we observed high seroprevalence, and high rate of isolation of <i>T. gondii</i> from northern sea otters and predominant genotype #5 that has been previously reported a dominant and widespread strain among terrestrial wildlife in North America. GRA6 sequence analysis of the genotype #5 isolates indicated the dominance of Type A lineage in sea otters in Washington State.</p>","language":"English","publisher":"Wildlife Disease Association","doi":"10.1016/j.vetpar.2018.05.011","usgsCitation":"Verma, S.K., Knowles, S., Cerqueira-Cezar, C.K., Kwok, O.C., Jiang, T., Su, C., and Dubey, J.P., 2018, An update on Toxoplasma gondii infections in northern sea otters (Enhydra lutris kenyoni) from Washington State, USA: Veterinary Parasitology, v. 258, p. 133-137, https://doi.org/10.1016/j.vetpar.2018.05.011.","productDescription":"5 p.","startPage":"133","endPage":"137","ipdsId":"IP-095836","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":355625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","volume":"258","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e538e4b060350a15d049","contributors":{"authors":[{"text":"Verma, Shiv K.","contributorId":167589,"corporation":false,"usgs":false,"family":"Verma","given":"Shiv","email":"","middleInitial":"K.","affiliations":[{"id":24764,"text":"US Department of Agriculture, Agricultural Research Service, Animal Parasitic Diseases Laboratory, Beltsville, MD, 20705-2350","active":true,"usgs":false}],"preferred":false,"id":739790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":739788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cerqueira-Cezar, Camila K.","contributorId":206207,"corporation":false,"usgs":false,"family":"Cerqueira-Cezar","given":"Camila","email":"","middleInitial":"K.","affiliations":[{"id":37284,"text":"United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Animal Parasitic Diseases Laboratory, Building 1001, Beltsville, MD, 20705-2350, USA","active":true,"usgs":false}],"preferred":false,"id":739791,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kwok, Oliver C.","contributorId":167593,"corporation":false,"usgs":false,"family":"Kwok","given":"Oliver","email":"","middleInitial":"C.","affiliations":[{"id":24764,"text":"US Department of Agriculture, Agricultural Research Service, Animal Parasitic Diseases Laboratory, Beltsville, MD, 20705-2350","active":true,"usgs":false}],"preferred":false,"id":739792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jiang, Tiantian","contributorId":206208,"corporation":false,"usgs":false,"family":"Jiang","given":"Tiantian","email":"","affiliations":[{"id":37286,"text":"16\tDepartment of Microbiology, University of Tennessee, Knoxville, TN 37996-0845,","active":true,"usgs":false}],"preferred":false,"id":739793,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Su, Chunlei","contributorId":167590,"corporation":false,"usgs":false,"family":"Su","given":"Chunlei","email":"","affiliations":[{"id":24765,"text":"University of Tennessee, Department of Microbiology, Knoxville, TN 37996-0845","active":true,"usgs":false}],"preferred":false,"id":739794,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dubey, Jitender P.","contributorId":206206,"corporation":false,"usgs":false,"family":"Dubey","given":"Jitender","email":"","middleInitial":"P.","affiliations":[{"id":37284,"text":"United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Animal Parasitic Diseases Laboratory, Building 1001, Beltsville, MD, 20705-2350, USA","active":true,"usgs":false}],"preferred":false,"id":739789,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70197844,"text":"sir20185083 - 2018 - Temporal and spatial monitoring of cyanobacterial blooms at Willow Creek Reservoir, North-Central Oregon","interactions":[],"lastModifiedDate":"2018-07-13T11:26:04","indexId":"sir20185083","displayToPublicDate":"2018-07-11T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5083","title":"Temporal and spatial monitoring of cyanobacterial blooms at Willow Creek Reservoir, North-Central Oregon","docAbstract":"<p>The U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) investigated the spatial and temporal dynamics of cyanobacterial (blue-green algal) blooms in Willow Creek Reservoir in north-central Oregon in 2015–16. A combination of cameras and water-quality monitoring equipment was used to assess the frequency and duration of blooms and their effects on water quality. A surveillance camera captured color images every 15 minutes during daylight hours of the northwestern corner of Willow Creek Reservoir, where surface blooms tend to accumulate due to the prevailing summer winds. In 2015, a water-quality instrument was deployed in the northwestern corner of the reservoir to continuously measure water temperature, pH, dissolved oxygen, specific conductance, turbidity, total chlorophyll, and the blue-green algae pigment phycocyanin. In 2016, a water-quality instrument was used to collect measurements along transects throughout the reservoir to create spatial maps of water quality. The spatially integrated mapping process was repeated on three different days under varying algal conditions. Also in 2016, a telemetry connection was established allowing resource managers to view the reservoir images in near-real time.</p><p>Results from 2015 indicate that surface accumulations of cyanobacteria can form and dissipate within minutes in the reservoir, and that blooms can cause substantial changes to water quality. A persistent cyanobacterial bloom in August and September 2015 resulted in pH values of 9.5 standard units, 220 percent oxygen saturation, and pronounced increases in turbidity and total chlorophyll. The stationary water-quality instrument collected data during periods with and without blooms, increasing our understanding of the effects of blooms on water quality and revealing potential restoration benchmarks for the freshwater reservoir. The spatially integrated mapping data showed the variation in water quality across the reservoir that occurs during blooms and baseline conditions and indicated regions of the reservoir to focus restoration efforts. Additional spatial data collection can be timed to collect daily extremes.</p><p>The camera deployment in 2016 demonstrated that telemetering images from remote sites is possible and provides valuable and timely information. Monitoring with a surveillance camera is inexpensive and supplies data regarding surface-bloom presence or absence. The use of a camera can help target site visits to periods when blooms are observed, which may increase the accuracy of beginning and ending dates for water body closures.</p><p>Monitoring cyanobacterial blooms in Willow Creek Reservoir with multiple devices provided a more comprehensive dataset than any one monitoring method. The camera images showed when a surface bloom initiated and dissipated while the water-quality instrument revealed the magnitude, or potential severity, of the effects on water quality.&nbsp;&nbsp;</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185083","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Smith, C.D., 2018, Temporal and spatial monitoring of cyanobacterial blooms at Willow Creek Reservoir, north-central Oregon: U.S. Geological Survey Scientific Investigations Report 2018–5083, 26 p., https://doi.org/10.3133/sir20185083.","productDescription":"v, 26 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-096392","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":355555,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5083/coverthb2.jpg"},{"id":355556,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5083/sir20185083.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5083"}],"country":"United States","state":"Oregon","city":"Keppner","otherGeospatial":"Willow Creek Reservoir","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.55923080444335,\n              45.33187500352944\n            ],\n            [\n              -119.51288223266602,\n              45.33187500352944\n            ],\n            [\n              -119.51288223266602,\n              45.35781478828095\n            ],\n            [\n              -119.55923080444335,\n              45.35781478828095\n            ],\n            [\n              -119.55923080444335,\n              45.33187500352944\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/or-water\" target=\"blank\" data-mce-href=\"https://www.usgs.gov/centers/or-water\">Oregon Water Science Center</a><br> U.S. Geological Survey<br> 2130 SW 5th Avenue<br> Portland, Oregon, 97201</p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Data Collection<br></li><li>Water-Quality Analyses and Data Visualization<br></li><li>Temporal and Spatial Monitoring of Cyanobacterial Blooms<br></li><li>Summary and Conclusions<br></li><li>Acknowledgments<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2018-07-11","noUsgsAuthors":false,"publicationDate":"2018-07-11","publicationStatus":"PW","scienceBaseUri":"5b46e53ae4b060350a15d04b","contributors":{"authors":[{"text":"Smith, Cassandra D. 0000-0003-1088-1772 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-1088-1772","contributorId":205220,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":738730,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70194826,"text":"ofr20171161 - 2018 - Long Island South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy","interactions":[],"lastModifiedDate":"2018-07-13T11:12:01","indexId":"ofr20171161","displayToPublicDate":"2018-07-10T11:30:00","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":"2017-1161","title":"Long Island South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy","docAbstract":"<h1>Executive Summary</h1><p>The Long Island South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy (CWRMS) provides an overview of the water-quality and ecological monitoring within the Reserve and presents suggestions from stakeholders for future data collection, data management, and coordination among monitoring programs. The South Shore Estuary Reserve, hereafter referred to as the Reserve, is a 173-square-mile network of bays and tributaries shaped by the south shore of Long Island (New York) and the barrier islands that was formed as a result of the last ice age (roughly 18,000 years ago). This overview and coordination document is based on information assembled from a series of meetings, a workshop, and individual correspondences with the CWRMS Project Advisory Committee, which was formed in 2015 to help guide the creation of the document, which reflects the current (2017) status of the Reserve and the need for additional data to address its water-quality issues and ecological health and to respond to a changing climate. The U.S. Geological Survey (USGS), in cooperation with the New York State Department of State Office of Planning, Development and Community Infrastructure and the South Shore Estuary Reserve Office, compiled information and recommendations to help stakeholders efficiently evaluate waters currently being monitored and address areas where necessary data are lacking. Water-quality monitoring in the Reserve is ongoing on the Federal, State, and local levels, and coordination among the various programs administered by the U.S. Environmental Protection Agency; National Oceanic and Atmospheric Administration; USGS; Shinnecock Tribal Nation; New York State; Nassau and Suffolk Counties; the Towns of Hempstead, Oyster Bay, Babylon, Islip, Brookhaven, and Southampton; and local universities and nonprofit organizations is necessary to ensure cooperation and efficient use of limited resources. Proper collection and archival of data are critical to the usability of data and methods—a sample of available repositories for monitoring data are provided in this report. Equally important are quality assurances of data and proper techniques of archival such that water and ecological data are collected and analyzed in a consistent manner, regardless of their sources, and that differences in methodologies are identified that might result in discrepancies in the compiled data. Details on monitoring programs, data gaps that are perceived by stakeholders and researchers in the area, and Project Advisory Committee recommendations are provided in this report to promote discussion and coordination. In most cases, resources to fill data gaps are needed, and the use of citizen science volunteers has been shown to help extend programs and provide insight into previously unaddressed areas of concern. This document, in conjunction with the CWRMS website and interactive mapper, is intended to inform the latest iteration of the Comprehensive Management Plan for the Reserve. Moreover, resource managers can use the CWRMS and mapper to identify areas of potential overlap and initiate conversations with stakeholders about addressing needs for additional monitoring of water quality and ecological health in the bays and tributaries of the Reserve.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171161","collaboration":"Prepared in cooperation with the New York State Department of State Office of Planning, Development and Community Infrastructure and the South Shore Estuary Reserve Office","usgsCitation":"Fisher, S.C., Welk, R.J., and Finkelstein, J.S., 2018, Long Island South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy: U.S. Geological Survey Open-File Report 2017–1161, 105 p., https://doi.org/10.3133/ofr20171161.","productDescription":"xi, 105 p.","numberOfPages":"122","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":352790,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1161/coverthb.jpg"},{"id":352791,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1161/ofr20171161.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1161"},{"id":355586,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://ny.water.usgs.gov/maps/sser/","linkHelpText":"- South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy mapper"}],"country":"United States","state":"New York","otherGeospatial":"Long Island, South Shore Estuary Reserve","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.3939208984375,\n              40.27533480732468\n            ],\n            [\n              -71.47705078125,\n              40.27533480732468\n            ],\n            [\n              -71.47705078125,\n              41.422134246213616\n            ],\n            [\n              -74.3939208984375,\n              41.422134246213616\n            ],\n            [\n              -74.3939208984375,\n              40.27533480732468\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ny@usgs.gov\" data-mce-href=\"mailto:dc_ny@usgs.gov\">Director</a>, <a href=\"https://ny.water.usgs.gov\" data-mce-href=\"https://ny.water.usgs.gov\">New York Water Science Center</a><br> U.S. Geological Survey<br> 2045 Route 112, Building 4<br> Coram, NY 11727</p>","tableOfContents":"<ul><li>Foreword</li><li>Acknowledgments</li><li>Executive Summary</li><li>Introduction</li><li>Resource Monitoring in the Long Island South Shore Estuary Reserve</li><li>Quality Assurance and Quality Control, Metadata, and Data Archives</li><li>Data Gaps and Specific Recommendations</li><li>General and Coordination Recommendations From the Project Advisory Committee</li><li>Coordinated Water Resources Monitoring Strategy Website</li><li>References Cited</li><li>Appendix 1. Updates to Recommendations Presented in the 2000 Coordinated Water Resources Monitoring Strategy</li><li>Appendix 2. New York State Department of Environmental Conservation 303(d) List of Impaired Waters</li><li>Appendix 3. Expanded List of Management Plans Created or in Progress for Resources Within the Long Island South Shore Estuary Reserve, New York</li><li>Appendix 4. Members of the Project Advisory Committee for the Long Island South Shore Estuary Reserve 2017 Coordinated Water Resources Monitoring Strategy</li><li>Appendix 5. Notes From the South Shore Estuary Reserve Coordinated Water Resources Management Strategy Project Advisory Committee Meetings</li></ul>","publishedDate":"2018-07-10","noUsgsAuthors":false,"publicationDate":"2018-07-10","publicationStatus":"PW","scienceBaseUri":"5b46e53be4b060350a15d04f","contributors":{"authors":[{"text":"Fisher, Shawn C. 0000-0001-6324-1061 scfisher@usgs.gov","orcid":"https://orcid.org/0000-0001-6324-1061","contributorId":4843,"corporation":false,"usgs":true,"family":"Fisher","given":"Shawn","email":"scfisher@usgs.gov","middleInitial":"C.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":725479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welk, Robert J. 0000-0003-0852-5584 rwelk@usgs.gov","orcid":"https://orcid.org/0000-0003-0852-5584","contributorId":194109,"corporation":false,"usgs":true,"family":"Welk","given":"Robert","email":"rwelk@usgs.gov","middleInitial":"J.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":725480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finkelstein, Jason S. 0000-0002-7496-7236 jfinkels@usgs.gov","orcid":"https://orcid.org/0000-0002-7496-7236","contributorId":4949,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Jason","email":"jfinkels@usgs.gov","middleInitial":"S.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":725481,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205251,"text":"70205251 - 2018 - Reestablishing a host–affiliate relationship: Migratory fish reintroduction increases native mussel recruitment","interactions":[],"lastModifiedDate":"2019-09-13T09:58:58","indexId":"70205251","displayToPublicDate":"2018-07-10T11:10:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Reestablishing a host–affiliate relationship: Migratory fish reintroduction increases native mussel recruitment","docAbstract":"<div id=\"pb-page-content\" data-ng-non-bindable=\"\"><div data-pb-dropzone=\"main\" data-pb-dropzone-name=\"Main\"><div class=\"pageBody hub-page-body body-text\" data-widget-def=\"pageBody\" data-widget-id=\"72100436-7a82-49fc-933b-c6c9d8c42914\"><div class=\"page-body pagefulltext\"><div data-pb-dropzone=\"main\"><div class=\"container\"><div class=\"row\"><div class=\" col-md-12\"><div><div class=\"row article-row\"><div id=\"article__content\" class=\"col-sm-12 col-md-8 col-lg-8 article__content article-row-left\"><div class=\"article__body \"><div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Co‐extirpation among host–affiliate species is thought to be a leading cause of biodiversity loss worldwide. Freshwater mussels (Unionida) are at risk globally and face many threats to survival, including limited access to viable host fish required to complete their life history. We examine the relationship between the common eastern elliptio mussel (<i>Elliptio complanata</i>) and its migratory host fish the American eel (<i>Anguilla rostrata</i>), whose distribution in the Chesapeake Bay watershed is limited, in part, by dams. We examined population demographics of <i>E. complanata</i> across locations in the Chesapeake Bay watershed, primarily in the Susquehanna River in the absence of American eels, and conducted experimental restocking of eels to assess potential impacts on mussel recruitment. Compared to surveys completed ~20&nbsp;yr prior, <i>E. complanata</i> could be experiencing declines at several historically abundant sites. These sites also had extremely limited evidence of recruitment. Restoration of host fish improved recruitment, but results were not equivalent between stocking sites, indicating that host reintroduction alone may not be fully effective in reestablishing mussel populations. One site where eels were introduced (Pine Creek, Tioga County, Pennsylvania, USA) experienced an increase from 0 juveniles found during quantitative surveys prior to eel stocking to 151 (21% of individuals collected during quantitative surveys) <i>E. complanata</i> juveniles found four years after stocking. A second site (Buffalo Creek, Union County, Pennsylvania) experienced a more moderate increase from 2 to 7 juveniles found during 2010 and 2014 quantitative surveys, respectively. Continued examination of other potential interacting factors affecting recruitment, including water quality or habitat conditions, is necessary to target favorable sites for successful restoration.</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1775","usgsCitation":"Galbraith, H.S., Devers, J.L., Blakeslee, C.J., Cole, J.C., St. John White, B., Minkkinen, S., and Lellis, W.A., 2018, Reestablishing a host–affiliate relationship: Migratory fish reintroduction increases native mussel recruitment: Ecological Applications, v. 28, no. 7, p. 1841-1852, https://doi.org/10.1002/eap.1775.","productDescription":"12 p.","startPage":"1841","endPage":"1852","ipdsId":"IP-090648","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":367318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania","otherGeospatial":"Buffalo Creek, Pine Creek, Susquehanna 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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":770571,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Jeffrey C. 0000-0002-2477-7231 jccole@usgs.gov","orcid":"https://orcid.org/0000-0002-2477-7231","contributorId":5585,"corporation":false,"usgs":true,"family":"Cole","given":"Jeffrey","email":"jccole@usgs.gov","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":770572,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"St. John White, Barbara 0000-0001-8131-0534 bwhite@usgs.gov","orcid":"https://orcid.org/0000-0001-8131-0534","contributorId":141183,"corporation":false,"usgs":false,"family":"St. John White","given":"Barbara","email":"bwhite@usgs.gov","affiliations":[],"preferred":false,"id":770573,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Minkkinen, Steven","contributorId":16734,"corporation":false,"usgs":true,"family":"Minkkinen","given":"Steven","email":"","affiliations":[],"preferred":false,"id":770574,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lellis, William A. 0000-0001-7806-2904 wlellis@usgs.gov","orcid":"https://orcid.org/0000-0001-7806-2904","contributorId":2369,"corporation":false,"usgs":true,"family":"Lellis","given":"William","email":"wlellis@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":770575,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70196535,"text":"sir20185059 - 2018 - Santa Barbara and Foothill groundwater basins Geohydrology and optimal water resources management—Developed using density dependent solute transport and optimization models","interactions":[],"lastModifiedDate":"2018-08-06T16:46:22","indexId":"sir20185059","displayToPublicDate":"2018-07-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5059","title":"Santa Barbara and Foothill groundwater basins Geohydrology and optimal water resources management—Developed using density dependent solute transport and optimization models","docAbstract":"<p>Groundwater has been a part of the city of Santa Barbara’s water-supply portfolio since the 1800s; however, since the 1960s, the majority of the city’s water has come from local surface water, and the remainder has come from groundwater, State Water Project, recycled water, increased water conservation, and as needed, seawater desalination. Although groundwater from the Santa Barbara and Foothill groundwater basins only accounts for a small percentage of the long-term supply, it is an important source of supplemental water during times of surface-water shortages. During the late 1980s and early 1990s, production wells extracted additional groundwater to compensate for drought related water-delivery shortfalls from other sources; in response, water levels declined substantially in the Santa Barbara and Foothill groundwater basins (below sea level in the Santa Barbara groundwater basin).</p><p>In coastal basins that have groundwater extraction near shore, seawater intrusion is often a problem. Seawater intrusion in the Santa Barbara groundwater basin is thought to be more limited than in other coastal basins because of an offshore fault that acts as a partial barrier to groundwater flow. During the late 1980s and early 1990s, seawater intrusion was observed in the Santa Barbara groundwater basin, as indicated by increased chloride concentrations at several monitoring wells that ranged from 200 ft to 1,300 ft from the ocean and as close as 2,900 ft to the nearest pumping well. This demonstrated that seawater can intrude into the Santa Barbara groundwater basin when groundwater levels fall below sea level near the coast.</p><p>The city of Santa Barbara is interested in developing a better understanding of the sustainability of its groundwater supplies. In 2014, California adopted historic legislation to manage its groundwater: the Sustainable Groundwater Management Act (SGMA). The SGMA requires the development and implementation of “Groundwater Sustainability Plans” in 127 priority groundwater basins; although Santa Barbara was not a designated priority basin, the city is taking steps to achieve sustainability. Sustainability was defined in the SGMA in terms of avoiding undesirable results: significant and unreasonable groundwater-level declines, reduction in groundwater storage, seawater intrusion, water-quality degradation, land subsidence, and surface-water depletion.</p><p>In this project, a cooperative study between the U.S.&nbsp;Geological Survey (USGS) and the city of Santa Barbara, sustainable yield is defined as the volume of groundwater that can be pumped from storage without causing water-level drawdowns and the associated increases in seawater intrusion (as indicated by increases in measured chloride concentrations) at selected wells. In order to estimate the sustainability of Santa Barbara’s groundwater basins, a three-dimensional density-dependent groundwater-flow and solute-transport model (the Santa Barbara Flow and Transport Model, or SBFTM) was developed on the basis of an existing groundwater-flow model. To simulate seawater intrusion to the Santa Barbara Basin under various management strategies, the SBFTM uses the USGS code SEAWAT to simulate salinity transport and variable-density flow. The completed SBFTM was coupled with a management optimization tool, in this case a multi-objective evolutionary algorithm, to determine optimal pumping strategies that maximize the sustainable yield and at the same time satisfy user-defined drawdown and chloride-concentration constraints.</p><p>As part of this study, a three-dimensional hydrogeologic framework model was developed to quantify the extent and hydrogeologic characteristics of the Santa Barbara and Foothill groundwater basins and to help define the discretization and hydraulic properties used in the SBFTM. The development of the hydrogeologic framework model required the collection and reconciliation of geologic and geophysical data from existing maps, reports, and databases, along with geologic and hydrologic data from recently drilled wells. These data were integrated into a three-dimensional hydrogeologic framework model that defines the stratigraphy and geometry of the aquifer zones and the major geologic structures in the basin. The hydrogeologic framework model also quantifies the variation in sediment grain size within each aquifer zone as the percentage of coarse-grained sediment. Previous studies indicated that there are two principal water-producing zones in the Santa Barbara groundwater basin, the upper and lower producing zones; an additional thin, productive zone was identified as part of this study. This “middle producing zone” is not as areally extensive as the upper and lower producing zones and only exists in the coastal part of Storage Unit I. These producing zones are bounded at depth by less productive shallow, middle, and deep zones.</p><p>Two versions of the SBFTM were constructed: an initial-condition model and a modern transient model. The initial-condition model is a long-term transient model that simulates flow and solute-transport conditions during a period with limited anthropogenic influences preceeding the modern transient model. The simulation-transient model simulates flow and transport conditions from 1929 through 2013; however, because of data availability, the focus of the model calibration was 1972–2013. The SBFTM was calibrated to measured groundwater levels and drawdown, as well as measured chloride concentrations and change in concentrations, using a combination of automated and trial-and-error parameter-estimation techniques.<br></p><p>A sensitivity analysis indicated that, in general, the SBFTM was most sensitive to recharge- and pumping-distribution parameters, specifically those controlling the amount of small-catchment recharge and the distribution of water extraction by hydrogeologic layer for production wells. The model was also sensitive to parameters controlling stream-recharge rates, horizontal and vertical hydraulic conductivity, and porosity.</p><p>From 1929 to 1971, most of the water entering the area represented by the SBFTM was from creek and small-catchment recharge, and the majority of water leaving the SBFTM area was from pumping, discharge to creeks, and drains. In addition, about 37 percent of the total pumpage came from a net reduction in groundwater storage. From 1972 to 2013, the amount of water entering and leaving the SBFTM was fairly similar as that from 1929 to 1971, except the reduction in pumpage added about 17,000 acre-ft of water to storage. During this later period, there were also times of storage loss. For example, during July 1990, a month when approximately 705 acre-ft of groundwater was pumped in the study area, the pumpage was much greater than all sources of recharge combined, and about 382 acre-ft of water was removed from groundwater storage.</p><p>Simulated hydraulic heads replicated the observed data to an acceptable matching of the measured water-level, flow direction, and vertical gradients. Simulated hydrographs for selected wells were in good agreement with the measured data, with an average residual of -2.7 ft and a standard deviation of 14.5 ft, indicating that the simulated heads, on average, underestimated the observed water levels. An examination of the model fit indicated that most of the discrepancies were lower simulated heads at wells proximal to production well sites.</p><p>The simulated chloride concentrations reasonably matched the rising limbs of the measured breakthrough curves in terms of timing and magnitude; however, the simulation overestimated the chloride concentrations on the falling limbs. The overestimation of low chloride concentrations was attributed to the model overestimating the advance of the chloride front during periods of heavy pumping and underestimating the retreat of the chloride front during periods of low pumping. These simulation errors would result in a conservative response by local water managers to seawater intrusion.</p><p>The SBFTM was used to develop a collection of predictive simulations optimized to produce pumping schedules that maximize yield, subject to a set of constraints and competing objectives. The simulations were grouped as scenarios that differed in their time horizon, initial conditions for groundwater levels and chloride concentrations, as well as precipitation, which was incorporated into the model through simulated recharge. Overall, five scenarios were developed in a multi-objective framework to obtain optimal pumping rates for all of the wells managed by the city, while minimizing excessive drawdown and seawater intrusion.</p><p>For the current study, complexities in the simulation model and the optimization formulation required additional considerations. Incorporating the solute-transport equations to simulate chloride transport added a highly nonlinear process that is solved iteratively in each time step of the groundwater-flow model. These nonlinearities, coupled with the highly refined grid in the current model, creates challenges for many traditional optimization methods. Therefore, an optimization method was needed that could address nonlinear relationships as well as a very large problem size. Lastly, the optimization problem was reformulated to include multiple objectives without requiring convergence to a single solution. This approach, guided by the city’s objectives, allowed the maximum extraction of information from the complex simulation.</p><p>Borg, a multi-objective evolutionary algorithm, was chosen as the optimization algorithm for this study for several reasons: (1) it is very computationally efficient; (2) it can run in parallel; (3) it requires little user input; and (4) it can solve for multiple competing objectives. The first three points allow the algorithm to proceed toward the optimal solutions at the fastest possible rate. The fourth point is advantageous for large, complex optimization problems because it is difficult to formulate the optimization problem in a way that produces only one optimal solution.</p><p>The problem formulation consisted of four competing objectives and a constraint set in accordance with the main concerns of the city. The objectives were maximizing total pumpage, minimizing seawater intrusion, minimizing total drawdown in production wells, and minimizing the maximum drawdown. The constraints were pump capacity, meeting drinking-water standards for chloride, maintaining a specified minimum flowrate to a groundwater treatment plant, and maintaining minimum water levels in pumping wells. The decision variables either were quarterly pumpage by well or total pumpage by basin.</p><p>Five optimization scenarios were developed that allow the decision makers to evaluate a range of optimal solutions for a variety of water levels and chloride concentrations as well as potential future climatic conditions. Three scenarios (1, 2, and 5) were multi-objective optimization formulations that allowed for variations in management preferences and climatic conditions. The other two scenarios (3 and 4) were designed to examine the optimization results to answer specific questions. Scenario 1 described the best-case sustainable yield assuming a “full” basin (that is, high initial water levels) and typical climate conditions for 10 years. Scenario 2 also started with a “full” basin; however, this was followed by a 10-year drought. Scenario 3 determined if an “empty” basin (that is, low initial water levels) would recover to full conditions (1998 conditions) given climate assumptions and optimal pumping schedules from scenarios 1 and 2. Scenario 4 was designed to produce decision rules that can be used by water managers to help choose an optimal pumping schedule based on measured water-level or chloride data. Scenario 5 identified future pumping schedules based on short-term climate variations during a 2-year management horizon.</p><p>The results from scenarios 1 and 2 described the differences in maximum pumpage in the basin under typical and dry long-term climate projections, respectively. The scenario 1 results indicated the maximum 10-year pumpage of the basin was about 31,300 acre-ft under typical conditions and controlling simulated seawater intrusion and drawdowns. For scenario 2, less recharge over the 10-year dry climate produced a maximum pumpage estimate of 30,000 acre-ft to control seawater intrusion and drawdowns. The larger pumpage for scenario 1 resulted in more seawater intrusion, but less total drawdown, compared to that of scenario 2.</p><p>Results for scenarios 3 and 4 showed the basin’s response to management actions combined with climate projections. Both scenarios used the optimal pumping schedules and the 10-year climates from scenarios 1 and 2. The scenario 3 results showed that under minimal pumping, the basin did not fully recover to 1998 water levels within 10 years under either climate scenario. The relatively larger recharge from the typical climate resulted in less drawdown at coastal monitoring wells after the 10-year recovery period than that from the dry climate. The location of the seawater intrusion front was not appreciably different between the scenarios, however. Scenario 4 used the optimal results from scenarios 1 and 2 to produce decision-rule curves that illustrated the pumpage for each basin, given measured levels of chloride concentration or drawdown. This allowed the use of additional measurements at monitoring wells to assess future management decisions on the basis of the sensitivity of observations of drawdown and seawater intrusion to various pumping rates.</p><p>Scenario 5 allowed managers to investigate the effects of short-term climate variations on optimal pumping schedules. Three specific 2-year simulations were optimized: typical-to-dry (scenario 5A), dry-to-typical (scenario 5B), and dry-to-dry (scenario 5C). The most noteable result from scenario 5 was the overall reduction in optimal pumpage for most schedules in scenario 5C, when the climate is simulated as dry-to-dry. There are also many optimal pumping schedules that produced an overall increase in waterlevels over the two-year simulation period, regardless of climatic condition. Similar to scenario 2, the scenario 5C results represents conservative yield estimates under a minimal-precipitation climatic condition.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185059","collaboration":"Prepared in cooperation with the city of Santa Barbara","usgsCitation":"Nishikawa, T., ed., 2018, Santa Barbara and Foothill groundwater basins Geohydrology and optimal water resources management—Developed using density dependent solute transport and optimization models, U.S. Geological Survey, Scientific Investigations Report 2018-5059, 4 chap. (A–D), variously paged, https://doi.org/10.3133/sir20185059.","productDescription":"xiv, 384 p.","numberOfPages":"402","onlineOnly":"Y","ipdsId":"IP-063921","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":355581,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5059/sir20185059_.pdf","text":"Report","size":"81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5059"},{"id":355580,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5059/coverthb_.jpg"},{"id":356222,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74J0DF5","text":"Data release","description":"USGS Data Release","linkHelpText":"SEAWAT model used to evaluate water management issues in the Santa Barbara and Foothill groundwater basins, California"}],"country":"United States","state":"California","city":"Santa Barbara","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.94299316406249,\n              34.134541681937364\n            ],\n            [\n              -119.10278320312499,\n              34.134541681937364\n            ],\n            [\n              -119.10278320312499,\n              35.10193405724606\n            ],\n            [\n              -120.94299316406249,\n              35.10193405724606\n            ],\n            [\n              -120.94299316406249,\n              34.134541681937364\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<div><a href=\"mailto:dc_ca@usgs.gov\" target=\"_blank\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,</div><div><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a></div><div><a href=\"https://usgs.gov/\" target=\"_blank\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a></div><div>6000 J Street, Placer Hall</div><div>Sacramento, California 95819</div>","tableOfContents":"<ul><li>Abstract<br></li><li>Chapter A: Introduction and Overview of Geology and Hydrogeology<br></li><li>Chapter B: Overview of Hydrogeologic Framework Model<br></li><li>Chapter C: Numerical Model of Groundwater Flow and Solute Transport<br></li><li>Chapter D: Multi-Objective Simulation-Optimization Model<br></li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2018-07-10","noUsgsAuthors":false,"publicationDate":"2018-07-10","publicationStatus":"PW","scienceBaseUri":"5b46e540e4b060350a15d059","contributors":{"editors":[{"text":"Nishikawa, Tracy 0000-0002-7348-3838","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":204242,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733467,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Paulinski, Scott R. 0000-0001-6548-8164","orcid":"https://orcid.org/0000-0001-6548-8164","contributorId":204240,"corporation":false,"usgs":true,"family":"Paulinski","given":"Scott R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nishikawa, Tracy 0000-0002-7348-3838 tnish@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":1515,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","email":"tnish@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cromwell, Geoffrey 0000-0001-8481-405X gcromwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-405X","contributorId":5920,"corporation":false,"usgs":true,"family":"Cromwell","given":"Geoffrey","email":"gcromwell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733464,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanko, Zachary P. 0000-0001-7047-6846","orcid":"https://orcid.org/0000-0001-7047-6846","contributorId":204241,"corporation":false,"usgs":true,"family":"Stanko","given":"Zachary","email":"","middleInitial":"P.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733465,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70196686,"text":"ds1086 - 2018 - Quality of surface water in Missouri, Water Year 2016","interactions":[],"lastModifiedDate":"2018-07-16T14:21:19","indexId":"ds1086","displayToPublicDate":"2018-07-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1086","title":"Quality of surface water in Missouri, Water Year 2016","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2016 (October 1, 2015, through September 30, 2016), data presented in this report were collected at 71 stations: 69 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 71 stations in this report, 4 stations have data presented for additional cooperative efforts with the U.S. Army Corps of Engineers. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, <i>Escherichia coli</i> bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for these 71 stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows is presented.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1086","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., and Bartels, K.A., 2018, Quality of surface water in Missouri, water year 2016: U.S. Geological Survey Data Series 1086, 25 p., https://doi.org/10.3133/ds1086.","productDescription":"v, 25 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-095490","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":355412,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1086/ds1086.pdf","text":"Report","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1086"},{"id":355411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1086/coverthb.jpg"}],"country":"United 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 \"}}]}","contact":"<p><a href=\"mailto: dc_mo@usgs.gov\" data-mce-href=\"mailto: dc_mo@usgs.gov\">Director</a>, <a href=\"https://mo.water.usgs.gov/\" data-mce-href=\"https://mo.water.usgs.gov/\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>1400 Independence Rd <br>Rolla, MO 65401<br></p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>The Ambient Water-Quality Monitoring Network<br></li><li>Laboratory Reporting Conventions<br></li><li>Surface Water-Quality Data Analysis Methods<br></li><li>Station Classification for Data Analysis<br></li><li>Hydrologic Conditions<br></li><li>Distribution, Concentration, and Detection Frequency of Select Constituents<br></li><li>Summary<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-07-10","noUsgsAuthors":false,"publicationDate":"2018-07-10","publicationStatus":"PW","scienceBaseUri":"5b46e540e4b060350a15d057","contributors":{"authors":[{"text":"Barr, Miya N. 0000-0002-9961-9190 mnbarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9961-9190","contributorId":3686,"corporation":false,"usgs":true,"family":"Barr","given":"Miya","email":"mnbarr@usgs.gov","middleInitial":"N.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739327,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartels, Katherine A. 0000-0002-6413-1355 kbartels@usgs.gov","orcid":"https://orcid.org/0000-0002-6413-1355","contributorId":206074,"corporation":false,"usgs":true,"family":"Bartels","given":"Katherine","email":"kbartels@usgs.gov","middleInitial":"A.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739328,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70197577,"text":"ofr20181097 - 2018 - Preliminary evaluation of the hydrogeology and groundwater quality of the Mississippi River Valley alluvial aquifer and Memphis aquifer at the Tennessee Valley Authority Allen Power Plants, Memphis, Shelby County, Tennessee","interactions":[],"lastModifiedDate":"2022-04-19T21:07:45.52464","indexId":"ofr20181097","displayToPublicDate":"2018-07-10T00:00:00","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":"2018-1097","title":"Preliminary evaluation of the hydrogeology and groundwater quality of the Mississippi River Valley alluvial aquifer and Memphis aquifer at the Tennessee Valley Authority Allen Power Plants, Memphis, Shelby County, Tennessee","docAbstract":"<p>The hydrogeology, groundwater quality, and potential for hydraulic connection between the Mississippi River Valley alluvial aquifer and the Memphis aquifer in the area of the Tennessee Valley Authority (TVA) Allen Combined Cycle and Allen Fossil Plants in southwestern Memphis, Tennessee, were evaluated from September through December 2017. The study was designed as a preliminary assessment of the potential for leakage of groundwater from the Mississippi River Valley alluvial aquifer through the underlying upper Claiborne confining unit into the underlying Memphis aquifer at the plants. A short-term aquifer test of four of the five Memphis aquifer production wells installed at the Allen Combined Cycle Plant induced drawdown in water levels in the Mississippi River Valley alluvial aquifer, locally. The largest drawdown occurred in the eastern and southeastern parts of the TVA plants area, and generally was coincident with locations where stratigraphic data show increased thickness of and depth to the base of the alluvium and decreased thickness and inferred offset in the base of the confining unit relative to nearby locations. In contrast, stratigraphic data for most other locations at the site indicate shallower depths to the base of the alluvium and more consistent thickness of and depth to the base of the confining unit, which corresponds with areas where less drawdown was observed during the test. Water-quality results for samples from the production wells and from monitoring wells screened in the Mississippi River Valley alluvial aquifer indicate that groundwater with higher dissolved-solids concentrations and tritium from this shallow aquifer has mixed with water in the upper part of the Memphis aquifer at one of the production wells. Results of the study collectively confirm that the Mississippi River Valley alluvial and Memphis aquifers are hydraulically connected near the TVA plants area.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181097","collaboration":"Prepared for the Tennessee Valley Authority in cooperation with the University of Memphis, Center for Applied Earth Science and Engineering Research","usgsCitation":"Carmichael, J.K., Kingsbury, J.A., Larsen, Daniel, and Schoefernacker, Scott, 2018, Preliminary evaluation of the hydrogeology and groundwater quality of the Mississippi River Valley alluvial aquifer and Memphis aquifer at the Tennessee Valley Authority Allen Power Plants, Memphis, Shelby County, Tennessee: U.S. Geological Survey Open-File Report 2018–1097, 66 p., https://doi.org/10.3133/ofr20181097.","productDescription":"Report: vii, 66 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095383","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":355577,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LSM5YU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-level models used to estimate drawdown in 32 monitoring wells screened in the Mississippi River Valley alluvial aquifer and 4 observation wells screened in the Memphis aquifer during an aquifer test at the Tennessee Valley Authority Allen power plants, Memphis, Shelby County, Tennessee, October 2017"},{"id":399134,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107532.htm"},{"id":355576,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1097/ofr20181097.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1097"},{"id":355575,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1097/coverthb.jpg"}],"country":"United States","state":"Tennessee","county":"Shelby County","city":"Memphis","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -90.2208,\n              35.0428\n            ],\n            [\n              -90.1211,\n              35.0428\n            ],\n            [\n              -90.1211,\n              35.1\n            ],\n            [\n              -90.2208,\n              35.1\n            ],\n            [\n              -90.2208,\n              35.0428\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto: dc_tn@usgs.gov\" data-mce-href=\"mailto: dc_tn@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/lmg-water/\" data-mce-href=\"https://www.usgs.gov/centers/lmg-water/\">Lower Mississippi-Gulf Water Science Center</a>—Tennessee<br>U.S. Geological Survey<br>640 Grassmere Park, Suite 100<br>Nashville, TN 37211<br></p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Geology and Hydrogeology of the Study Area<br></li><li>Methods<br></li><li>Results<br></li><li>Summary and Conclusions<br></li><li>References<br></li><li>Appendix 1. SeriesSEE Water-Level Model Hydrographs—Allen Combined Cycle Plant Monitoring Wells<br></li><li>Appendix 2. SeriesSEE Water-Level Model Hydrographs—Allen Fossil Plant Monitoring Wells<br></li><li>Appendix 3. SeriesSEE Water-Level Model Hydrographs—Memphis Aquifer Observation Wells<br></li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2018-07-10","noUsgsAuthors":false,"publicationDate":"2018-07-10","publicationStatus":"PW","scienceBaseUri":"5b46e53ee4b060350a15d055","contributors":{"authors":[{"text":"Carmichael, John K. 0000-0003-1099-841X jkcarmic@usgs.gov","orcid":"https://orcid.org/0000-0003-1099-841X","contributorId":4554,"corporation":false,"usgs":true,"family":"Carmichael","given":"John","email":"jkcarmic@usgs.gov","middleInitial":"K.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larsen, Daniel","contributorId":199300,"corporation":false,"usgs":false,"family":"Larsen","given":"Daniel","email":"","affiliations":[{"id":17864,"text":"University of Memphis","active":true,"usgs":false}],"preferred":false,"id":737821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schoefernacker, Scott","contributorId":205566,"corporation":false,"usgs":false,"family":"Schoefernacker","given":"Scott","email":"","affiliations":[{"id":17864,"text":"University of Memphis","active":true,"usgs":false}],"preferred":false,"id":737822,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70198050,"text":"70198050 - 2018 - Quantifying variance across spatial scales as part of fire regime classifications","interactions":[],"lastModifiedDate":"2018-07-12T22:54:19","indexId":"70198050","displayToPublicDate":"2018-07-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying variance across spatial scales as part of fire regime classifications","docAbstract":"<p>The emergence of large‐scale fire classifications and products informed by remote sensing data has enabled opportunities to include variability or heterogeneity as part of modern fire regime classifications. Currently, basic fire metrics such as mean fire return intervals are calculated without considering spatial variance in a management context. Fire return intervals are also only applicable at a particular grain size (defined as the spatial unit of interest) even though they are typically applied homogeneously. In this study, we utilized a 29‐yr fire occurrence database to show how spatial variance changes with respect to grain as postulated by Wiens (<span><a class=\"bibLink tab-link\" href=\"https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.2343#ecs22343-bib-0055\" data-tab=\"pane-pcw-references\" data-mce-href=\"https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.2343#ecs22343-bib-0055\">1989</a></span>) when reporting fire patterns within the Great Plains, USA. We utilized data from the Monitoring Trends in Burn Severity database of fire occurrence for the years 1984–2012. We analyzed median numbers of fire along with their variance at four spatial grains ranging from small units (e.g., plots at 3&nbsp;×&nbsp;3&nbsp;km resolution) to large units (e.g., landscapes at 1500&nbsp;×&nbsp;2700&nbsp;km resolution). Median number of fire occurrences was consistently low, irrespective of grain. Despite the consistency in low median numbers of fires across grain, variance in the numbers of fires between units decreased. Variance within units, however, did not change as grain increased indicating fire‐pattern‐scale inconsistencies. Fire pattern interpretations depended entirely on the scale at which it is calculated. Given that the Great Plains region has a large disparity in fire patterns (i.e., some regions burn often, while others may never burn), fire regime classifications will benefit from including scale‐specific variance estimates as a foundation for understanding changes in fire regimes and corresponding social–ecological and policy responses. </p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2343","usgsCitation":"Rheinhardt, S., Fuhlendorf, S.D., Leis, S.A., Picotte, J.J., and Twidwell, D., 2018, Quantifying variance across spatial scales as part of fire regime classifications: Ecosphere, v. 9, no. 7, e02343: 12 p., https://doi.org/10.1002/ecs2.2343.","productDescription":"e02343: 12 p.","ipdsId":"IP-076490","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2343","text":"Publisher Index Page"},{"id":355621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-10","publicationStatus":"PW","scienceBaseUri":"5b46e53ce4b060350a15d051","contributors":{"authors":[{"text":"Rheinhardt, Scholtz 0000-0002-9275-6504","orcid":"https://orcid.org/0000-0002-9275-6504","contributorId":206199,"corporation":false,"usgs":false,"family":"Rheinhardt","given":"Scholtz","email":"","affiliations":[{"id":37281,"text":"Department of Natural Resource Ecology and Management. Oklahoma State University, Stillwater, OK, 74078, USA","active":true,"usgs":false}],"preferred":false,"id":739775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuhlendorf, Samuel D.","contributorId":171488,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":739777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leis, Sherry A.","contributorId":178699,"corporation":false,"usgs":false,"family":"Leis","given":"Sherry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":739776,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":739774,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Twidwell, Dirac","contributorId":187431,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","email":"","affiliations":[],"preferred":false,"id":739778,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70198013,"text":"ofr20181106 - 2018 - Juvenile salmonid monitoring following removal of Condit Dam in the White Salmon River Watershed, Washington, 2017","interactions":[],"lastModifiedDate":"2018-07-10T10:08:29","indexId":"ofr20181106","displayToPublicDate":"2018-07-09T00:00:00","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":"2018-1106","title":"Juvenile salmonid monitoring following removal of Condit Dam in the White Salmon River Watershed, Washington, 2017","docAbstract":"<p class=\"p1\">Condit Dam, at river kilometer 5.3 on the White Salmon River, Washington, was breached in 2011, and removed completely in 2012, providing anadromous salmonids with the opportunity to recolonize habitat blocked for nearly 100 years. Prior to dam removal, a multi-agency workgroup concluded that the preferred salmonid restoration alternative was to allow natural recolonization. Monitoring would assess fish recolonization efficacy, followed by management evaluation 5 years after dam removal. Limited monitoring of salmon and steelhead recolonization has occurred since 2011. The U.S. Geological Survey began juvenile salmonid monitoring in 2016 and did a second year during 2017, with sampling efforts like those of 2016. River conditions differed between the 2 years, both during (that is, high flows in 2017) and prior to (that is, 2015 summer drought conditions and December 2015 White Salmon River flood event) sampling. We operated a rotary screw trap at river kilometer 2.3 (3 kilometers downstream of the former dam site) from early April through early June to assess species diversity, and production of smolt and other migrant life stages. We also used backpack electrofishing during summer to assess juvenile salmonid distribution and abundance. Both sampling methods provided the opportunity to collect genetic samples (analysis of samples was not covered under funding received from the Mid-Columbia Fisheries Enhancement Group for the 2017 monitoring efforts) and to tag fish with passive integrated transponder (PIT) tags, which will provide life-history data through future recaptures and detections.</p><p class=\"p1\">The screw trap captured steelhead (anadromous rainbow trout, <i>Oncorhynchus mykiss</i>), fry, parr, and smolts; coho salmon (<i>O. kisutch</i>) fry, parr, and smolts; and Chinook salmon (<i>O. tshwaytscha</i>) fry, parr, and one smolt. Prolonged high water and some missed trapping periods during 2017 prevented us from generating smolt estimates. Despite difficult trapping conditions, the number of coho salmon fry and parr, and steelhead fry and parr captured in 2017 exceeded those captured during 2016. The number of age-0 Chinook salmon captured in the screw trap during 2017 was much higher (<i>n </i>= 222) than in 2016 (<i>n </i>= 4).</p><p class=\"p1\">Electrofishing in tributaries provided information on distribution and abundance of juvenile coho salmon and <i>O. mykiss</i>. Juvenile coho salmon were again found in Mill and Buck Creeks and, for the first time, in Rattlesnake Creek (all three creeks are upstream of the former dam site). In both Rattlesnake and Buck Creeks, age-0 <i>O. mykiss </i>abundance decreased between 2016 and 2017; however, age-1 and older <i>O. mykiss </i>and age-0 coho salmon abundance increased between years at both sites. Data on <i>O. mykiss </i>abundance at sites in Buck and Rattlesnake Creeks is providing the opportunity to begin to understand trends and variability post-dam removal and to compare to pre-dam removal periods.</p><p class=\"p1\">Mean age-0 <i>O. mykiss </i>abundance (fish per meter [fish/m]) at the Rattlesnake Creek site has been slightly lower during post-dam removal (mean = 3.0, n = 2, range = 2.4–3.6) than pre-dam removal (mean = 3.4, n = 5, range = 1.5–5.1). However, the presence of juvenile coho salmon in Rattlesnake Creek during 2017 (0.5 fish/m) brought total age-0 salmonid abundance in 2017 to 2.9 fish/m. Mean age-1 or older <i>O. mykiss </i>abundance (fish/m) at the Rattlesnake Creek site has been lower post-dam removal (mean = 0.2, n = 2, range = 0.1–0.3) than pre-dam removal (mean = 0.5, n = 2, range = 0.3–0.8). Mean age-0 <i>O. mykiss </i>abundance (fish/m) at the Buck Creek site has been higher post-dam removal (mean = 2.1, n = 2, range = 1.2–3.0) than pre-dam removal (mean = 1.8, n = 2, range = 1.6–1.9). The addition of age-0 coho salmon to Buck Creek brings mean age-0 salmonid abundance post-dam removal to 2.7 fish/m (range = 1.9–3.4). Mean age-1 or older <i>O. mykiss </i>abundance (fish/m) in Buck Creek has been slightly higher post-dam removal (mean = 0.8, n = 2, range = 0.6–1.1) than pre-dam removal (mean = 0.6, n = 2, both years 0.6).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181106","collaboration":"Prepared in cooperation with the Mid-Columbia Fisheries Enhancement Group","usgsCitation":"Jezorek, I.G., and Hardiman, J.M., 2018, Juvenile salmonid monitoring following removal of Condit Dam in the White Salmon River watershed, Washington, 2017: U.S. Geological Survey Open-File Report 2018-1106, 31 p. https://doi.org/10.3133/ofr20181106.","productDescription":"vi, 31 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-094796","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":355554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1106/ofr20181106.pdf","text":"Report","size":"874 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1106"},{"id":355553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1106/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Condit Dam, White Salmon River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.21466064453125,\n              45.64668833372338\n            ],\n            [\n              -121.09680175781249,\n              45.64668833372338\n            ],\n            [\n              -121.09680175781249,\n              46.47191632087041\n            ],\n            [\n              -122.21466064453125,\n              46.47191632087041\n            ],\n            [\n              -122.21466064453125,\n              45.64668833372338\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://wfrc.usgs.gov/\" target=\"blank\" data-mce-href=\"https://wfrc.usgs.gov/\">Western Fisheries Research Center</a><br> U.S. Geological Survey<br> 6505 NE 65th Street<br> Seattle, Washington 98115</p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Description of Study Site<br></li><li>Study Methods<br></li><li>Results<br></li><li>Discussion<br></li><li>Acknowledgments<br></li><li>References Cited<br></li><li>Appendix 1. Length Frequencies<br></li></ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2018-07-09","noUsgsAuthors":false,"publicationDate":"2018-07-09","publicationStatus":"PW","scienceBaseUri":"5b46e541e4b060350a15d065","contributors":{"authors":[{"text":"Jezorek, Ian G. 0000-0002-3842-3485 ijezorek@usgs.gov","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":3572,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","email":"ijezorek@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":739596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hardiman, Jill M. 0000-0002-3661-9695 jhardiman@usgs.gov","orcid":"https://orcid.org/0000-0002-3661-9695","contributorId":2672,"corporation":false,"usgs":true,"family":"Hardiman","given":"Jill","email":"jhardiman@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":739597,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70198028,"text":"70198028 - 2018 - On the reliability of N‐mixture models for count data","interactions":[],"lastModifiedDate":"2018-07-09T21:49:17","indexId":"70198028","displayToPublicDate":"2018-07-09T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1039,"text":"Biometrics","active":true,"publicationSubtype":{"id":10}},"title":"On the reliability of N‐mixture models for count data","docAbstract":"<p>N‐mixture models describe count data replicated in time and across sites in terms of abundance <i>N</i> and detectability <i>p</i>. They are popular because they allow inference about <i>N</i> while controlling for factors that influence <i>p</i> without the need for marking animals. Using a capture–recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of <i>N</i> and <i>p</i> problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in <i>p</i> problematic. By counter example, we show that even if <i>p</i> is constant after adjusting for covariate effects (the “constant <i>p</i>” assumption) scientifically plausible alternative models in which <i>N</i> (or its expectation) is non‐identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N‐mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant <i>p</i> assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for <i>p</i> through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of <i>p</i>.</p>","language":"English","publisher":"Wiley","doi":"10.1111/biom.12734","usgsCitation":"Barker, R.J., Schofield, M., Link, W.A., and Sauer, J.R., 2018, On the reliability of N‐mixture models for count data: Biometrics, v. 74, no. 1, p. 369-377, https://doi.org/10.1111/biom.12734.","productDescription":"9 p.","startPage":"369","endPage":"377","ipdsId":"IP-075479","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":460879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/biom.12734","text":"Publisher Index Page"},{"id":355564,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"74","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-03","publicationStatus":"PW","scienceBaseUri":"5b46e541e4b060350a15d063","contributors":{"authors":[{"text":"Barker, Richard J.","contributorId":206174,"corporation":false,"usgs":false,"family":"Barker","given":"Richard","email":"","middleInitial":"J.","affiliations":[{"id":37272,"text":"University of Otago; Dunedin, New Zealand","active":true,"usgs":false}],"preferred":false,"id":739705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schofield, Matthew J.","contributorId":206175,"corporation":false,"usgs":false,"family":"Schofield","given":"Matthew J.","affiliations":[{"id":37272,"text":"University of Otago; Dunedin, New Zealand","active":true,"usgs":false}],"preferred":false,"id":739706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":739704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":739707,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70198011,"text":"70198011 - 2018 - Using cluster analysis to compartmentalize a large managed wetland based on physical, biological, and climatic geospatial attributes","interactions":[],"lastModifiedDate":"2018-09-10T10:59:41","indexId":"70198011","displayToPublicDate":"2018-07-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Using cluster analysis to compartmentalize a large managed wetland based on physical, biological, and climatic geospatial attributes","docAbstract":"<p><span>Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward’s linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00267-018-1050-5","usgsCitation":"Hahus, I., Migliaccio, K., Douglas-Mankin, K.R., Klarenberg, G., and Muñoz-Carpena, R., 2018, Using cluster analysis to compartmentalize a large managed wetland based on physical, biological, and climatic geospatial attributes: Environmental Management, v. 62, no. 3, p. 571-583, https://doi.org/10.1007/s00267-018-1050-5.","productDescription":"13 p.","startPage":"571","endPage":"583","ipdsId":"IP-094746","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":355526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.446,\n              26.356\n            ],\n            [\n              -80.222,\n              26.356\n            ],\n            [\n              -80.222,\n              26.683\n            ],\n            [\n              -80.446,\n              26.683\n            ],\n            [\n              -80.446,\n              26.356\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"62","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-27","publicationStatus":"PW","scienceBaseUri":"5b46e542e4b060350a15d06b","contributors":{"authors":[{"text":"Hahus, Ian","contributorId":206143,"corporation":false,"usgs":false,"family":"Hahus","given":"Ian","email":"","affiliations":[{"id":37258,"text":"Department of Agricultural and Biological Engineering, University of Florida","active":true,"usgs":false}],"preferred":false,"id":739586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Migliaccio, Kati","contributorId":111526,"corporation":false,"usgs":true,"family":"Migliaccio","given":"Kati","affiliations":[],"preferred":false,"id":739587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":203927,"corporation":false,"usgs":true,"family":"Douglas-Mankin","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klarenberg, Geraldine","contributorId":206145,"corporation":false,"usgs":false,"family":"Klarenberg","given":"Geraldine","email":"","affiliations":[{"id":37258,"text":"Department of Agricultural and Biological Engineering, University of Florida","active":true,"usgs":false}],"preferred":false,"id":739588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muñoz-Carpena, Rafael","contributorId":206146,"corporation":false,"usgs":false,"family":"Muñoz-Carpena","given":"Rafael","affiliations":[{"id":37258,"text":"Department of Agricultural and Biological Engineering, University of Florida","active":true,"usgs":false}],"preferred":false,"id":739589,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70198015,"text":"70198015 - 2018 - An updated method for estimating landslide‐event magnitude","interactions":[],"lastModifiedDate":"2018-07-13T14:28:34","indexId":"70198015","displayToPublicDate":"2018-07-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"An updated method for estimating landslide‐event magnitude","docAbstract":"<p><span>Summary statistics derived from the frequency–area distribution (FAD) of inventories of triggered landslides allows for direct comparison of landslides triggered by one event (e.g. earthquake, rainstorm) with another. Such comparisons are vital to understand links between the landslide‐event and the environmental characteristics of the area affected. This could lead to methods for rapid estimation of landslide‐event magnitude, which in turn could lead to estimates of the total triggered landslide area. Previous studies proposed that the FAD of landslides follows an inverse power‐law, which provides the basis to model the size distribution of landslides and to estimate landslide‐event magnitude (</span><i>mLS</i><span>), which quantifies the severity of the event. In this study, we use a much larger collection of earthquake‐induced landslide (EQIL) inventories (</span><i>n</i><span>=45) than previous studies to show that size distributions are much more variable than previously assumed. We present an updated model and propose a method for estimating<span>&nbsp;</span></span><i>mLS</i><span><span>&nbsp;</span>and its uncertainty that better fits the observations and is more reproducible, robust, and consistent than existing methods. We validate our model by computing<span>&nbsp;</span></span><i>mLS</i><span><span>&nbsp;</span>for all of the inventories in our dataset and comparing that with the total landslide areas of the inventories. We show that our method is able to estimate the total landslide area of the events in this larger inventory dataset more successfully than the existing methods.<span>&nbsp;</span></span></p>","language":"English","publisher":"Wiley","doi":"10.1002/esp.4359","usgsCitation":"Tanyas, H., Allstadt, K.E., and van Weston, C.J., 2018, An updated method for estimating landslide‐event magnitude: Earth Surface Processes and Landforms, v. 43, no. 9, p. 1836-1847, https://doi.org/10.1002/esp.4359.","productDescription":"12 p.","startPage":"1836","endPage":"1847","ipdsId":"IP-090008","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468601,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.4359","text":"Publisher Index Page"},{"id":437830,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79022QD","text":"USGS data release","linkHelpText":"landslides-mLS"},{"id":355523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5b46e541e4b060350a15d067","contributors":{"authors":[{"text":"Tanyas, Hakan","contributorId":198731,"corporation":false,"usgs":false,"family":"Tanyas","given":"Hakan","affiliations":[],"preferred":false,"id":739604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allstadt, Kate E. 0000-0003-4977-5248 kallstadt@usgs.gov","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":167684,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"kallstadt@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":739603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Weston, Cees J.","contributorId":206153,"corporation":false,"usgs":false,"family":"van Weston","given":"Cees","email":"","middleInitial":"J.","affiliations":[{"id":37261,"text":"University of Twente, Netherlands","active":true,"usgs":false}],"preferred":false,"id":739605,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70197579,"text":"sim3410 - 2018 - Map of recently active traces of the Rodgers Creek Fault, Sonoma County, California","interactions":[],"lastModifiedDate":"2018-07-16T13:25:56","indexId":"sim3410","displayToPublicDate":"2018-07-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3410","title":"Map of recently active traces of the Rodgers Creek Fault, Sonoma County, California","docAbstract":"<p>The accompanying map and digital data identify recently active strands of the Rodgers Creek Fault in Sonoma County, California, interpreted primarily from the geomorphic expression of recent faulting on aerial photography and hillshade imagery derived from airborne lidar data. A recently active fault strand is defined here as having evidence consistent with slip during the Holocene epoch (approximately the past 11,700 years). The purpose of the map is to update the fundamental fault dataset for characterizing surface-rupture hazard, siting slip-rate and paleoseismic studies, and studying the geometry and evolution of slip. To serve a range of users, the map is presented in several formats: as an image map, as a digital database for use within GIS, and as a KML file for visualizing the fault using virtual globe software.</p><p>Important outcomes of this mapping revision include the following: (1) a northward 17-km increase in the known length of Holocene-active faulting to include most of the Healdsburg Fault, a structural continuation of the Rodgers Creek Fault northwest of a bend in the fault at Santa Rosa; (2) first-time identification of fault strands across the Santa Rosa Creek floodplain in central Santa Rosa; (3) increases in the known width and complexity of faulting; and (4) identification of fault splays that project toward the Bennett Valley-Maacama Fault system to the east and toward a recently mapped active extension of the Hayward Fault to the south beneath San Pablo Bay.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3410","usgsCitation":"Hecker, S., and Randolph Loar, C.E., 2018, Map of recently active traces of the Rodgers Creek Fault, Sonoma County, California: U.S. Geological Survey Scientific Investigations Map 3410, 7 p., 1 sheet, https://doi.org/10.3133/sim3410.","productDescription":"Sheet: 39.85 x 40.25 inches; Pamphlet: iii, 7 p.; Metadata; Spatial data; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-094680","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":355540,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3410/sim3410_mapsheet.pdf","text":"Map sheet","size":"17.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3410"},{"id":355541,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3410/sim3410_rcf_hfsec.shp.xml","text":"Northern section","size":"40 KB xml","description":"SIM 3410","linkHelpText":" - Healdsburg Fault section of the Rodgers Creek Fault "},{"id":355542,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3410/sim3410_rcf_rcfsec.shp.xml","text":"Southern section","size":"40 KB xml","description":"SIM 3410","linkHelpText":" - Rodgers Creek Fault section of the Rodgers Creek Fault"},{"id":355543,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3410/sim3410_data.zip","text":"Database","size":"1 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3410"},{"id":355544,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3410/sim3410_rodgerscreekfault.kmz","text":"KMZ file","size":"450 KB kmz","description":"SIM 3410"},{"id":355545,"rank":8,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3410/sim3410_readme.txt","text":"Read Me","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3410"},{"id":355538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3410/coverthb.jpg"},{"id":355539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3410/sim3410_pamphlet.pdf","text":"Pamphlet","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3410"}],"country":"United States","state":"California","otherGeospatial":"Rodgers Creek Fault","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.4167,\n              38.16667\n            ],\n            [\n              -122.4833,\n              38.16667\n            ],\n            [\n              -122.9833,\n              38.68333\n            ],\n            [\n              -122.8667,\n              38.68333\n            ],\n            [\n              -122.4167,\n              38.16667\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://earthquake.usgs.gov/contactus/menlo/\" target=\"_blank\" data-mce-href=\"https://earthquake.usgs.gov/contactus/menlo/\">Contact Information</a><br><a href=\"https://earthquake.usgs.gov/\" target=\"_blank\" data-mce-href=\"https://earthquake.usgs.gov/\">Earthquake Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>345 Middlefield Road, MS 977<br>Menlo Park, CA 94025<br></p>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2018-07-06","noUsgsAuthors":false,"publicationDate":"2018-07-06","publicationStatus":"PW","scienceBaseUri":"5b46e543e4b060350a15d071","contributors":{"authors":[{"text":"Hecker, Suzanne 0000-0002-5054-372X","orcid":"https://orcid.org/0000-0002-5054-372X","contributorId":205568,"corporation":false,"usgs":true,"family":"Hecker","given":"Suzanne","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":737818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Randolph Loar, Carolyn E.","contributorId":205569,"corporation":false,"usgs":false,"family":"Randolph Loar","given":"Carolyn","email":"","middleInitial":"E.","affiliations":[{"id":37115,"text":"Stantec Consulting Services Inc","active":true,"usgs":false}],"preferred":false,"id":737819,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70198005,"text":"70198005 - 2018 - Variation in inbreeding rates across the range of Northern Spotted Owls (Strix occidentalis caurina): Insights from over 30 years of monitoring data","interactions":[],"lastModifiedDate":"2018-07-06T13:28:28","indexId":"70198005","displayToPublicDate":"2018-07-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3544,"text":"The Auk","onlineIssn":"1938-4254","printIssn":"0004-8038","active":true,"publicationSubtype":{"id":10}},"title":"Variation in inbreeding rates across the range of Northern Spotted Owls (Strix occidentalis caurina): Insights from over 30 years of monitoring data","docAbstract":"<p><span>Inbreeding has been difficult to quantify in wild populations because of incomplete parentage information. We applied and extended a recently developed framework for addressing this problem to infer inbreeding rates in Northern Spotted Owls (</span><i>Strix occidentalis caurina</i><span>) across the Pacific Northwest, USA. Using pedigrees from 14,187 Northern Spotted Owls, we inferred inbreeding rates for 14 types of matings among relatives that produce pedigree inbreeding coefficients of<span>&nbsp;</span></span><i>F</i><span><span>&nbsp;</span>= 0.25 or<span>&nbsp;</span></span><i>F</i><span><span>&nbsp;</span>= 0.125. Inbreeding was most common in the Washington Cascades, where an estimated 15% of individuals are inbred. Inbreeding was lowest in western Oregon (3.5%) and northern California (2.7%), and intermediate for the Olympic Peninsula of Washington (6.1%). Estimates from the Olympic Peninsula were likely underestimates because of small sample sizes and the presence of few pedigrees capable of resolving inbreeding events. Most inbreeding resulted from matings between full siblings or half siblings, although a high rate of inbreeding from mother–son pairs was identified in the Olympic Peninsula. Geographic variation in inbreeding rates may reflect population declines and bottlenecks that have been detected in prior investigations. We show that there is strong selection against inbred birds. Only 3 of 44 inbred birds were later identified as parents (6.8%), whereas 2,823 of 10,380 birds that represented a comparable cross section of the data were later seen as reproducing parents (27.2%). Habitat loss and competition with Barred Owls (</span><i>S. varia</i><span>) remain primary threats to Northern Spotted Owls. However, given the negative consequences of inbreeding, Spotted Owl populations in Washington with suitable habitat and manageable numbers of Barred Owls may benefit from translocations of individuals from Oregon and California to introduce new genetic variation and reduce future inbreeding events.</span></p>","language":"English","publisher":"American Ornithological Society","doi":"10.1642/AUK-18-1.1","usgsCitation":"Miller, M.P., Haig, S.M., Forsman, E.D., Anthony, R., Diller, L., Dugger, K.M., Franklin, A.B., Fleming, T.L., Gremel, S., Lesmeister, D.B., Higley, M., Herter, D.R., and Sovern, S.G., 2018, Variation in inbreeding rates across the range of Northern Spotted Owls (Strix occidentalis caurina): Insights from over 30 years of monitoring data: The Auk, v. 135, no. 4, p. 821-833, https://doi.org/10.1642/AUK-18-1.1.","productDescription":"13 p.","startPage":"821","endPage":"833","ipdsId":"IP-096546","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1642/auk-18-1.1","text":"Publisher Index Page"},{"id":355529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -125.13427734374999,\n              37.64903402157866\n            ],\n            [\n              -119.5751953125,\n              37.64903402157866\n            ],\n            [\n              -119.5751953125,\n              49.03786794532644\n            ],\n            [\n              -125.13427734374999,\n              49.03786794532644\n            ],\n            [\n              -125.13427734374999,\n              37.64903402157866\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"135","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e543e4b060350a15d06f","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":739563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":739564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forsman, Eric D.","contributorId":96792,"corporation":false,"usgs":false,"family":"Forsman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":739565,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anthony, Robert G.","contributorId":61324,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert G.","affiliations":[],"preferred":false,"id":739566,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diller, Lowell","contributorId":206137,"corporation":false,"usgs":false,"family":"Diller","given":"Lowell","affiliations":[{"id":24606,"text":"Green Diamond Resource Company","active":true,"usgs":false}],"preferred":false,"id":739567,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dugger, Katie M. 0000-0002-4148-246X","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":36037,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":739568,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Franklin, Alan B.","contributorId":101999,"corporation":false,"usgs":false,"family":"Franklin","given":"Alan","email":"","middleInitial":"B.","affiliations":[{"id":12434,"text":"USDA, Wildlife Services, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":739569,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fleming, Tracy L.","contributorId":96199,"corporation":false,"usgs":true,"family":"Fleming","given":"Tracy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":739638,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gremel, Scott","contributorId":206139,"corporation":false,"usgs":false,"family":"Gremel","given":"Scott","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":739570,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lesmeister, Damon B. 0000-0003-1102-0122","orcid":"https://orcid.org/0000-0003-1102-0122","contributorId":205006,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon","email":"","middleInitial":"B.","affiliations":[{"id":37019,"text":"USDA Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":739571,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Higley, Mark","contributorId":206140,"corporation":false,"usgs":false,"family":"Higley","given":"Mark","email":"","affiliations":[{"id":37256,"text":"Hoopa Valley Tribal Forestry","active":true,"usgs":false}],"preferred":false,"id":739572,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Herter, Dale R.","contributorId":206141,"corporation":false,"usgs":false,"family":"Herter","given":"Dale","email":"","middleInitial":"R.","affiliations":[{"id":37257,"text":"Raedeke Associates, Inc","active":true,"usgs":false}],"preferred":false,"id":739573,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sovern, Stan G","contributorId":206142,"corporation":false,"usgs":false,"family":"Sovern","given":"Stan","email":"","middleInitial":"G","affiliations":[{"id":27990,"text":"Deceased","active":true,"usgs":false}],"preferred":false,"id":739574,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70198084,"text":"70198084 - 2018 - Modeling the distributions of tegu lizards in native and potential invasive ranges","interactions":[],"lastModifiedDate":"2018-07-13T10:13:21","indexId":"70198084","displayToPublicDate":"2018-07-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the distributions of tegu lizards in native and potential invasive ranges","docAbstract":"<p>Invasive reptilian predators can have substantial impacts on native species and ecosystems. Tegu lizards are widely distributed in South America east of the Andes, and are popular in the international live animal trade. Two species are established in Florida (U.S.A.) - <i>Salvator merianae</i> (Argentine black and white tegu) and <i>Tupinambis teguixin sensu lato</i> (gold tegu) – and a third has been recorded there—<i> S. rufescens</i> (red tegu). We built species distribution models (SDMs) using 5 approaches (logistic regression, multivariate adaptive regression splines, boosted regression trees, random forest, and maximum entropy) based on data from the native ranges. We then projected these models to North America to develop hypotheses for potential tegu distributions. Our results suggest that much of the southern United States and northern México probably contains suitable habitat for one or more of these tegu species. <i>Salvator rufescens</i> had higher habitat suitability in semi-arid areas, whereas <i>S. merianae</i> and <i>T. teguixin</i> had higher habitat suitability in more mesic areas. We propose that Florida is not the only state where these taxa could become established, and that early detection and rapid response programs targeting tegu lizards in potentially suitable habitat elsewhere in North America could help prevent establishment and abate negative impacts on native ecosystems.</p>","language":"English","publisher":"Springer","doi":"10.1038/s41598-018-28468-w","usgsCitation":"Jarnevich, C.S., Hayes, M., Fitzgerald, L.A., Yackel, A., Falk, B., Collier, M., Bonewell, L., Klug, P., Naretto, S., and Reed, R., 2018, Modeling the distributions of tegu lizards in native and potential invasive ranges: Scientific Reports, v. 8, e10193; 12 p., https://doi.org/10.1038/s41598-018-28468-w.","productDescription":"e10193; 12 p.","ipdsId":"IP-090713","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468602,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-28468-w","text":"Publisher Index Page"},{"id":437831,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JZZE4W","text":"USGS data release","linkHelpText":"Data for modeling tegu lizard distributions in the Americas"},{"id":355667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-05","publicationStatus":"PW","scienceBaseUri":"5b6fc418e4b0f5d57878e9e1","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Mark","contributorId":206268,"corporation":false,"usgs":false,"family":"Hayes","given":"Mark","affiliations":[],"preferred":false,"id":739938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzgerald, Lee A.","contributorId":141035,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Lee","email":"","middleInitial":"A.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":739939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackel, Amy 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":152310,"corporation":false,"usgs":true,"family":"Yackel","given":"Amy","email":"yackela@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falk, Bryan 0000-0002-9690-5626 bfalk@usgs.gov","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":150075,"corporation":false,"usgs":true,"family":"Falk","given":"Bryan","email":"bfalk@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739941,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collier, Michelle 0000-0001-5715-448X","orcid":"https://orcid.org/0000-0001-5715-448X","contributorId":206269,"corporation":false,"usgs":true,"family":"Collier","given":"Michelle","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bonewell, Lea","contributorId":206270,"corporation":false,"usgs":true,"family":"Bonewell","given":"Lea","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739943,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Klug, Page 0000-0002-0836-3901","orcid":"https://orcid.org/0000-0002-0836-3901","contributorId":206271,"corporation":false,"usgs":false,"family":"Klug","given":"Page","affiliations":[{"id":37295,"text":"USDA APHIS","active":true,"usgs":false}],"preferred":false,"id":739944,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Naretto, Sergio","contributorId":206272,"corporation":false,"usgs":false,"family":"Naretto","given":"Sergio","email":"","affiliations":[{"id":37296,"text":"Instituto de Diversidad y Ecología Animal","active":true,"usgs":false}],"preferred":false,"id":739945,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739946,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70197568,"text":"ofr20181096 - 2018 - Procedures for using the Horiba Scientific Aqualog<sup>®</sup> fluorometer to measure absorbance and fluorescence from dissolved organic matter","interactions":[],"lastModifiedDate":"2018-07-11T10:42:39","indexId":"ofr20181096","displayToPublicDate":"2018-07-05T00:00:00","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":"2018-1096","title":"Procedures for using the Horiba Scientific Aqualog<sup>®</sup> fluorometer to measure absorbance and fluorescence from dissolved organic matter","docAbstract":"<p>Advances in spectroscopic techniques have led to an increase in the use of optical measurements (absorbance and fluorescence) to assess dissolved organic matter composition and infer sources and processing. Although optical measurements are easy to make, they can be affected by many variables rendering them less comparable, including by inconsistencies in sample collection (for example, filter pore size, preservation), the application of corrections for interferences (for example, inner-filtering corrections), differences in holding times, and instrument drift (for example, lamp intensity). A documented, standardized procedure to address these variables ensures that the optical (absorbance and fluorescence) measurements collected by U.S. Geological Survey researchers are useful and widely comparable.</p><p>Rigorous and quantifiable quality assurance and quality control are essential for making these data comparable, particularly because there is no published guideline for the measurement of dissolved organic matter absorbance and fluorescence, and especially because there is no National Institute of Standards and Technology standard for dissolved organic matter. Validation and quality-control samples are analyzed on a monthly basis to determine laboratory and instrument precision and daily (that is, each day samples are run) to ensure repeatability. Data are not considered acceptable unless they meet laboratory criteria: All standards should be within 10 percent of the target value, laboratory replicates should be within 5 percent relative percent difference, and laboratory blanks (that is, laboratory reagent-grade water) should be less than one-tenth of the long-term method detection limit.</p><p>Finally, for data to be useful, they must be accessible to users in a format that can be easily analyzed and interpreted. The Organic Matter Research Laboratory staff has developed a processing routine that extracts a subset of the data, which is made available to the public through the USGS National Water Quality Information System (<a href=\"http://nwis.waterdata.usgs.gov/usa/nwis/qwdata\" target=\"_blank\" data-mce-href=\"http://nwis.waterdata.usgs.gov/usa/nwis/qwdata\">http://nwis.waterdata.usgs.gov/usa/nwis/qwdata</a>), and organizes the full datasets (that is, complete absorbance spectra and fluorescence excitation-emission matrices) in different forms that allow for these data to be analyzed using multi-parameter and multi-way statistical approaches.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181096","usgsCitation":"Hansen, A.M., Fleck, J.A., Kraus, T.E.C., Downing, B.D., von Dessonneck, T., and Bergamaschi, B.A., 2018, Procedures for using the Horiba Scientific Aqualog<sup>®</sup> fluorometer to measure absorbance and fluorescence from dissolved organic matter: U.S. Geological Survey Open-File Report 2018–1096, 31 p., https://doi.org/10.3133/ofr20181096.","productDescription":"Report: vi, 31 p.; 3 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082063","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":355505,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1096/coverthb.jpg"},{"id":355506,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1096/ofr2018.1096.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1096"},{"id":355509,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1096/ofr20181096_appendix3.xlsx","text":"Appendix 3","size":"12.8 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2018-1096 Appendix 3"},{"id":355507,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1096/ofr20181096_appendix1.pdf","text":"Appendix 1","size":"450 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1096 Appendix 1"},{"id":355508,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1096/ofr20181096_appendix2.xlsx","text":"Appendix 2","size":"350 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2018-1096 Appendix 2"}],"contact":"<div><a href=\"mailto:dc_ca@usgs.gov\" target=\"_blank\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,</div><div><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a></div><div><a href=\"https://usgs.gov/\" target=\"_blank\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a></div><div>6000 J Street, Placer Hall</div><div>Sacramento, California 95819</div>","tableOfContents":"<ul><li>Abstract<br></li><li>Purpose and Scope<br></li><li>Background<br></li><li>Sample Collection and Handling<br></li><li>Analytical Method<br></li><li>Data Processing and Corrections<br></li><li>Data Storage<br></li><li>Data Analysis<br></li><li>Summary<br></li><li>Acknowledgments<br></li><li>References Cited<br></li><li>Appendix 1.  Aqualog® Standard Operating Procedure Walkthrough<br></li><li>Appendix 2.  Processed Summary Report for Absorbance Data<br></li><li>Appendix 3.  Processed Summary Report for Fluorescence Data<br></li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2018-07-05","noUsgsAuthors":false,"publicationDate":"2018-07-05","publicationStatus":"PW","scienceBaseUri":"5b46e544e4b060350a15d079","contributors":{"authors":[{"text":"Hansen, Angela M. 0000-0003-0938-7611","orcid":"https://orcid.org/0000-0003-0938-7611","contributorId":204702,"corporation":false,"usgs":true,"family":"Hansen","given":"Angela M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleck, Jacob 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":168694,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downing, Bryan D. 0000-0002-2007-5304 bdowning@usgs.gov","orcid":"https://orcid.org/0000-0002-2007-5304","contributorId":1449,"corporation":false,"usgs":true,"family":"Downing","given":"Bryan","email":"bdowning@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737697,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"von Dessonneck, Travis","contributorId":178352,"corporation":false,"usgs":false,"family":"von Dessonneck","given":"Travis","email":"","affiliations":[],"preferred":false,"id":737698,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":737699,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70199208,"text":"70199208 - 2018 - Defining the risk landscape in the context of pathogen pollution: Toxoplasma gondii in sea otters along the Pacific Rim","interactions":[],"lastModifiedDate":"2018-09-10T13:50:22","indexId":"70199208","displayToPublicDate":"2018-07-04T13:50:13","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3908,"text":"Royal Society Open Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Defining the risk landscape in the context of pathogen pollution: <i>Toxoplasma gondii</i> in sea otters along the Pacific Rim","title":"Defining the risk landscape in the context of pathogen pollution: Toxoplasma gondii in sea otters along the Pacific Rim","docAbstract":"<p><span>Pathogens entering the marine environment as pollutants exhibit a spatial signature driven by their transport mechanisms. The sea otter (</span><i>Enhydra lutris</i><span>), a marine animal which lives much of its life within sight of land, presents a unique opportunity to understand land–sea pathogen transmission. Using a dataset on&nbsp;</span><i>Toxoplasma gondii</i><span>&nbsp;prevalence across sea otter range from Alaska to California, we found that the dominant drivers of infection risk vary depending upon the spatial scale of analysis. At the population level, regions with high&nbsp;</span><i>T. gondii</i><span>&nbsp;prevalence had higher human population density and a greater proportion of human-dominated land uses, suggesting a strong role for population density of the felid definitive host of this parasite. This relationship persisted when a subset of data were analysed at the individual level: large-scale patterns in sea otter&nbsp;</span><i>T. gondii</i><span>&nbsp;infection prevalence were largely explained by individual exposure to areas of high human housing unit density, and other landscape features associated with anthropogenic land use, such as impervious surfaces and cropping land. These results contrast with the small-scale, within-region analysis, in which age, sex and prey choice accounted for most of the variation in infection risk, and terrestrial environmental features provided little variation to help in explaining observed patterns. These results underscore the importance of spatial scale in study design when quantifying both individual-level risk factors and landscape-scale variation in infection risk.</span></p>","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rsos.171178","usgsCitation":"Burgess, T.L., Tinker, M.T., Miller, M.A., Bodkin, J.L., Murray, M.J., Saarinen, J.A., Nichol, L.M., Larson, S.E., Conrad, P.A., and Johnson, C., 2018, Defining the risk landscape in the context of pathogen pollution: Toxoplasma gondii in sea otters along the Pacific Rim: Royal Society Open Science, v. 5, Article  171178; 11 p., https://doi.org/10.1098/rsos.171178.","productDescription":"Article  171178; 11 p.","ipdsId":"IP-091756","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468604,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.171178","text":"Publisher Index Page"},{"id":357204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.618408203125,\n              34.15272698011818\n            ],\n            [\n              -119.24560546875001,\n              34.15272698011818\n            ],\n            [\n              -119.24560546875001,\n              37.69251435532741\n            ],\n            [\n              -122.618408203125,\n              37.69251435532741\n            ],\n            [\n              -122.618408203125,\n              34.15272698011818\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-04","publicationStatus":"PW","scienceBaseUri":"5b98a2a2e4b0702d0e842f94","contributors":{"authors":[{"text":"Burgess, Tristan L.","contributorId":207772,"corporation":false,"usgs":false,"family":"Burgess","given":"Tristan","email":"","middleInitial":"L.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":744678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":744677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Melissa A.","contributorId":57701,"corporation":false,"usgs":false,"family":"Miller","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":39007,"text":"CA Dept of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":744679,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bodkin, James L. 0000-0003-1641-4438 jbodkin@usgs.gov","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":748,"corporation":false,"usgs":true,"family":"Bodkin","given":"James","email":"jbodkin@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":744680,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murray, Michael J.","contributorId":206852,"corporation":false,"usgs":false,"family":"Murray","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":37418,"text":"Monterey Bay Aquarium, Monterey, CA","active":true,"usgs":false}],"preferred":false,"id":744681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saarinen, Justin A.","contributorId":207774,"corporation":false,"usgs":false,"family":"Saarinen","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":35150,"text":"New College of Florida","active":true,"usgs":false}],"preferred":false,"id":744682,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nichol, Linda M.","contributorId":207775,"corporation":false,"usgs":false,"family":"Nichol","given":"Linda","email":"","middleInitial":"M.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":744683,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Larson, Shawn E.","contributorId":149287,"corporation":false,"usgs":false,"family":"Larson","given":"Shawn","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":744684,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Conrad, Patricia A.","contributorId":181937,"corporation":false,"usgs":false,"family":"Conrad","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":744685,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Christine K.","contributorId":23771,"corporation":false,"usgs":false,"family":"Johnson","given":"Christine K.","affiliations":[],"preferred":false,"id":744686,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70197980,"text":"fs20183037 - 2018 - Coastal National Elevation Database","interactions":[],"lastModifiedDate":"2018-07-03T12:42:37","indexId":"fs20183037","displayToPublicDate":"2018-07-03T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3037","title":"Coastal National Elevation Database","docAbstract":"<p>The Coastal National Elevation Database (CoNED) Applications Project develops enhanced topographic (land elevation) and bathymetric (water depth) datasets that serve as valuable resources for coastal hazards research (Danielson and others, 2016; Thatcher and others, 2016). These datasets are used widely for mapping inundation zones from riverine flood events, hurricanes, and sea-level rise and for other Earth science applications, such as sediment transport, erosion, and storm impact models. </p><p>CoNED is a U.S. Geological Survey (USGS) Coastal-Marine Hazards and Resources Program (formerly Coastal and Marine Geology Program) activity centered at the USGS Earth Resources Observation and Science Center and distributed at other USGS Science Centers. As part of the vision for a 3D Nation, the CoNED Project is working collaboratively with the USGS National Geospatial Program, the National Oceanic and Atmospheric Administration, and the U.S. Army Corps of Engineers through the Interagency Working Group on Ocean and Coastal Mapping to build integrated elevation models in the coastal zone by assimilating the land surface topography with littoral zone and continental shelf bathymetry. Several nongovernmental organizations and Federal agencies, including the Department of the Interior Pacific Islands Climate Adaptation Science Center, the National Park Service, the Nature Conservancy, the Louisiana Coastal Protection and Restoration Authority, and numerous academic institutions, partner to make CoNED a success.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183037","usgsCitation":"Danielson, J.J., Poppenga, S.K., Tyler, D.J., Palaseanu-Lovejoy, M., and Gesch, D.B., 2018, Coastal National Elevation Database: U.S. Geological Survey Fact Sheet 2018–3037, 2 p., https://doi.org/10.3133/2018.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-093676","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":355477,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3037/coverthb.jpg"},{"id":355478,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3037/fs20183037.pdf","text":"Report","size":"617 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018–3037"}],"contact":"<p>Director, <a href=\"https://eros.usgs.gov/\" data-mce-href=\"https://eros.usgs.gov/\">Earth Resources Observation and Science Center</a> &nbsp;<br>U.S. Geological Survey <br>47914 252nd Street&nbsp; <br>Sioux Falls, SD 57198</p>","tableOfContents":"<ul><li>Introduction<br></li><li>Goals and Benefits<br></li><li>CoNED TBDEMs<br></li><li>CoNED Scientific Research<br></li><li>References<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-07-03","noUsgsAuthors":false,"publicationDate":"2018-07-03","publicationStatus":"PW","scienceBaseUri":"5b46e544e4b060350a15d07b","contributors":{"authors":[{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":739450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poppenga, Sandra K. 0000-0002-2846-6836 spoppenga@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-6836","contributorId":3327,"corporation":false,"usgs":true,"family":"Poppenga","given":"Sandra","email":"spoppenga@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":739451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tyler, Dean J. 0000-0002-1542-7539 dtyler@usgs.gov","orcid":"https://orcid.org/0000-0002-1542-7539","contributorId":177897,"corporation":false,"usgs":true,"family":"Tyler","given":"Dean","email":"dtyler@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":739452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":739454,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70196994,"text":"ds1087 - 2018 - Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014","interactions":[],"lastModifiedDate":"2018-07-03T12:45:49","indexId":"ds1087","displayToPublicDate":"2018-07-03T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1087","title":"Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014","docAbstract":"<p>Groundwater-quality data were collected from 502 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (500) were sampled from January through December 2015, and 2 of them were sampled in 2013. The data were collected from five types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; and vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Some data from environmental samples collected in 2013 and quality-control samples collected in 2014 also are included in the associated data release; these data are associated with networks described in this report and have not been published previously.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1087","usgsCitation":"Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and Belitz, K., 2018, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014: U.S. Geological Survey Data Series 1087, 68 p., https://doi.org/10.3133/ds1087.","productDescription":"Report: ix, 67 p.; Data Release","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091701","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":355481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1087/coverthb.jpg"},{"id":355482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1087/ds1087.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1087"},{"id":355483,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XK8DHK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2015 and Previously Unpublished Data from 2013–2014"}],"country":"United States","contact":"<p><a href=\"mailto: dc_il@usgs.gov\" data-mce-href=\"mailto: dc_il@usgs.gov\">Director</a>, <a href=\"https://il.water.usgs.gov\" data-mce-href=\"https://il.water.usgs.gov\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>405 N. Goodwin <br>Urbana, IL 61801<br></p>","tableOfContents":"<ul><li>Foreword<br></li><li>Abstract<br></li><li>Introduction<br></li><li>Purpose and Scope<br></li><li>Groundwater Study Design<br></li><li>Sample Collection and Analysis<br></li><li>Data Reporting<br></li><li>Quality-Assurance and Quality-Control Methods<br></li><li>Groundwater-Quality Data<br></li><li>Summary<br></li><li>Acknowledgments<br></li><li>References Cited<br></li><li>Appendix 1. Well Depth and Open Interval by Study Network<br></li><li>Appendix 2. High-Frequency Data from Enhanced Trends Networks<br></li><li>Appendix 3. Quality-Control Data and Analysis<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-07-03","noUsgsAuthors":false,"publicationDate":"2018-07-03","publicationStatus":"PW","scienceBaseUri":"5b46e544e4b060350a15d07d","contributors":{"authors":[{"text":"Arnold, Terri 0000-0003-1406-6054 tlarnold@usgs.gov","orcid":"https://orcid.org/0000-0003-1406-6054","contributorId":1598,"corporation":false,"usgs":false,"family":"Arnold","given":"Terri","email":"tlarnold@usgs.gov","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":false,"id":735215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":735216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musgrove, MaryLynn 0000-0003-1607-3864 mmusgrov@usgs.gov","orcid":"https://orcid.org/0000-0003-1607-3864","contributorId":197013,"corporation":false,"usgs":true,"family":"Musgrove","given":"MaryLynn","email":"mmusgrov@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":739490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X pestack@usgs.gov","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":1069,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","email":"pestack@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":434,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce D.","email":"blindsey@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":739492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739493,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":156272,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":739494,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739495,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
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