{"pageNumber":"626","pageRowStart":"15625","pageSize":"25","recordCount":184717,"records":[{"id":70220898,"text":"70220898 - 2020 - Microbiology and oxidation-reduction geochemistry of the water-table and Memphis aquifers in the Allen well field, Shelby County, Tennessee","interactions":[],"lastModifiedDate":"2021-06-02T12:16:57.62","indexId":"70220898","displayToPublicDate":"2020-04-30T14:14:54","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Microbiology and oxidation-reduction geochemistry of the water-table and Memphis aquifers in the Allen well field, Shelby County, Tennessee","docAbstract":"<p>The shallow and Memphis aquifers in Shelby County, Tennessee, are valuable natural resources that are used for domestic, public-supply, and agricultural water use. The Memphis aquifer is the primary source for public supply in West Tennessee and provides 170 to 175 million gallons of water per day for more than 900,000 people (Robinson, 2018). The shallow aquifer includes the unconfined water table, provides domestic water supplies in Shelby County, and is susceptible to contamination from urban and industrial activities, underground storage tanks, old dumps, and other sources. Both aquifers are likely to be stressed in the future by factors such as population increase, contaminant migration from historical contamination sites, industrial and agricultural activities, climate change, and other competing demands on the water resources.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the 29th Tennessee water resources symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2020 Tennessee Water Resources Symposium","conferenceDate":"April 22-24, 2020","conferenceLocation":"Burns, TN","language":"English","publisher":"Tennessee section of the American Water Resources Association","usgsCitation":"Byl, T.D., and Bradley, M., 2020, Microbiology and oxidation-reduction geochemistry of the water-table and Memphis aquifers in the Allen well field, Shelby County, Tennessee, <i>in</i> Proceedings from the 29th Tennessee water resources symposium, Burns, TN, April 22-24, 2020, p. 2C-3-2C-24.","productDescription":"22 p.","startPage":"2C-3","endPage":"2C-24","ipdsId":"IP-116089","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":386067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386060,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://tnawra.org/library"}],"country":"United States","state":"Tennessee","county":"Shelby County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -90.25749206542969,\n              35.003003395276714\n            ],\n            [\n              -89.77890014648436,\n              35.00637800423346\n            ],\n            [\n              -89.76585388183594,\n              35.31568548101236\n            ],\n            [\n              -90.08583068847656,\n              35.285984736065764\n            ],\n            [\n              -90.05287170410156,\n              35.160898088930104\n            ],\n            [\n              -90.07759094238281,\n              35.117100314572774\n            ],\n            [\n              -90.13595581054688,\n              35.126086394372955\n            ],\n            [\n              -90.17578124999999,\n              35.106428057364255\n            ],\n            [\n              -90.17784118652344,\n              35.056418354320755\n            ],\n            [\n              -90.23071289062499,\n              35.01762569539653\n            ],\n            [\n              -90.25749206542969,\n              35.003003395276714\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byl, Thomas D. 0000-0001-6907-9149 tdbyl@usgs.gov","orcid":"https://orcid.org/0000-0001-6907-9149","contributorId":583,"corporation":false,"usgs":true,"family":"Byl","given":"Thomas","email":"tdbyl@usgs.gov","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradley, Mike 0000-0002-2979-265X mbradley@usgs.gov","orcid":"https://orcid.org/0000-0002-2979-265X","contributorId":582,"corporation":false,"usgs":true,"family":"Bradley","given":"Mike","email":"mbradley@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816645,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70228256,"text":"70228256 - 2020 - Seasonal selection of riverine habitat by Spotted Bass and Shorthead Redhorse in a regulated river in the Midwestern U.S.","interactions":[],"lastModifiedDate":"2022-02-08T20:29:13.325602","indexId":"70228256","displayToPublicDate":"2020-04-30T14:12:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal selection of riverine habitat by Spotted Bass and Shorthead Redhorse in a regulated river in the Midwestern U.S.","docAbstract":"<p><span>Riverine fish populations depend on habitats supporting their resource and life history needs. Dynamic streamflow caused by river regulation or natural events influences the distribution of downstream habitat characteristics. Through studying habitat selection, we can identify the most utilized and valuable habitats for the success of native fishes. We determined seasonal habitat selection of two common, native fish species on the Osage River downstream of Bagnell Dam, a hydroelectric dam in central Missouri, from April 2016 to June 2017 using radio telemetry. Spotted Bass (</span><i>Micropterus punctulatus</i><span>) are nest-guarders, sight feeders, and habitat generalists, whereas Shorthead Redhorse (</span><i>Moxostoma macrolepidotum</i><span>) are fluvial dependent, migratory, and benthic feeders. Bayesian discrete choice analyses determined that both species selected particular water depth, velocity, and presence of submerged cover in some or all seasons, even as available habitat changed. Spotted Bass selected water depths &lt;4.0 m near submerged cover during all seasons, low velocity during spring and summer, and near-bank habitat in all seasons except spring. Shorthead Redhorse used fast flowing habitat during spring, 0.4–1.1 m/s velocity during summer, and low velocity in fall and winter (0.1–0.5 m/s). Shorthead Redhorse used submerged cover in all seasons except summer and selected specific ranges of depth within spring (2.4–4.4 m), summer (3.3–6.7 m), and winter (1.1–2.3 m). Our findings suggest that maintaining habitats with cover and diverse water depths and velocities, particularly both low and high velocity habitats during spring, may promote resilience by providing beneficial habitats for native fishes.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/rra.3637","usgsCitation":"Edge, E., Paukert, C.P., III, L., Landwer, B., and Bonnot, T., 2020, Seasonal selection of riverine habitat by Spotted Bass and Shorthead Redhorse in a regulated river in the Midwestern U.S.: River Research and Applications, v. 36, no. 7, p. 1087-1096, https://doi.org/10.1002/rra.3637.","productDescription":"10 p.","startPage":"1087","endPage":"1096","ipdsId":"IP-109676","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Bagnell Dam,  Lake of the Ozarks, Osage River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.58316040039062,\n              38.10754709314396\n            ],\n            [\n              -92.35107421874999,\n              38.10754709314396\n            ],\n            [\n              -92.35107421874999,\n              38.136716904135376\n            ],\n            [\n              -92.58316040039062,\n              38.136716904135376\n            ],\n            [\n              -92.58316040039062,\n              38.10754709314396\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.977294921875,\n              37.972349871995256\n            ],\n            [\n              -91.99951171875,\n              37.972349871995256\n            ],\n            [\n              -91.99951171875,\n              38.50948995925553\n            ],\n            [\n              -92.977294921875,\n              38.50948995925553\n            ],\n            [\n              -92.977294921875,\n              37.972349871995256\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"36","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Edge, E.N.","contributorId":274981,"corporation":false,"usgs":false,"family":"Edge","given":"E.N.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"III, Lobb","contributorId":274982,"corporation":false,"usgs":false,"family":"III","given":"Lobb","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":833546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landwer, B.","contributorId":274984,"corporation":false,"usgs":false,"family":"Landwer","given":"B.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":833547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonnot, T.W.","contributorId":274985,"corporation":false,"usgs":false,"family":"Bonnot","given":"T.W.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833548,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70211912,"text":"70211912 - 2020 - Forecasting water demand across a rapidly urbanizing region","interactions":[],"lastModifiedDate":"2020-08-11T18:12:44.78815","indexId":"70211912","displayToPublicDate":"2020-04-30T12:57:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting water demand across a rapidly urbanizing region","docAbstract":"<p><span>Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have independently examined the impacts of urban planning and climate change on water demand, but little attention has been given to their combined impact. Here we forecast urban water demand using a Geographically Weighted Regression model informed by socio-economic, environmental and landscape pattern metrics. The purpose of our study is to evaluate how future scenarios of population densities and climate warming will jointly affect water demand across two rapidly growing U.S. states (North Carolina and South Carolina). Our forecasts indicate that regional water demand by 2065 will increase by 37%–383% relative to the baseline in 2010, across all scenarios of change. Our results show future water demand will increase under rising temperatures, but could be ameliorated by policies that promote higher density development and urban infill. These water-efficient land use policies show a 5% regional reduction in water demand and up to 25% reduction locally for counties with the highest expected population growth by 2065. For rural counties experiencing depopulation, the land use policies we considered are insufficient to significantly reduce water demand. For expanding communities seeking to increase their adaptive capacity to changing socio-environmental conditions, our framework can assist in developing sustainable solutions.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.139050","usgsCitation":"Sanchez, G., Terando, A., Smith, J.W., Garcia, A.M., Wagner, C., and Meentemeyer, R.K., 2020, Forecasting water demand across a rapidly urbanizing region: Science of the Total Environment, v. 730, 139050, 13 p., https://doi.org/10.1016/j.scitotenv.2020.139050.","productDescription":"139050, 13 p.","ipdsId":"IP-108370","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":456898,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.139050","text":"Publisher Index Page"},{"id":437009,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95PTP5G","text":"USGS data release","linkHelpText":"Land-use and water demand projections (2012 to 2065) under different scenarios of environmental change for North Carolina, South Carolina, and coastal Georgia"},{"id":377357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, South Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.5419921875,\n              36.491973470593685\n            ],\n            [\n              -81.6064453125,\n              36.66841891894786\n            ],\n            [\n              -84.1552734375,\n              35.06597313798418\n            ],\n            [\n              -82.5732421875,\n              34.08906131584994\n            ],\n            [\n              -80.7275390625,\n              31.87755764334002\n            ],\n            [\n              -77.47558593749999,\n              34.488447837809304\n            ],\n            [\n              -76.2451171875,\n              34.74161249883172\n            ],\n            [\n              -75.0146484375,\n              35.92464453144099\n            ],\n            [\n              -75.5419921875,\n              36.491973470593685\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"730","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sanchez, Georgina M. 0000-0002-2365-6200","orcid":"https://orcid.org/0000-0002-2365-6200","contributorId":210477,"corporation":false,"usgs":false,"family":"Sanchez","given":"Georgina M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":795791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam J. 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":216875,"corporation":false,"usgs":true,"family":"Terando","given":"Adam J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":795792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Jordan W.","contributorId":177326,"corporation":false,"usgs":false,"family":"Smith","given":"Jordan","email":"","middleInitial":"W.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":795793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garcia, Ana Maria 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":2035,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"Maria","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Chad R. 0000-0002-9602-7413 cwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-7413","contributorId":1530,"corporation":false,"usgs":true,"family":"Wagner","given":"Chad R.","email":"cwagner@usgs.gov","affiliations":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":false,"id":795795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":795796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70219116,"text":"70219116 - 2020 - Exploring regional scale metamorphic fabrics in the Yukon Tanana terrane and environs using quantitative domain analyses","interactions":[],"lastModifiedDate":"2021-04-15T16:28:18.487489","indexId":"70219116","displayToPublicDate":"2020-04-30T11:25:18","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Exploring regional scale metamorphic fabrics in the Yukon Tanana terrane and environs using quantitative domain analyses","docAbstract":"<p>Metamorphic rock fabrics such as foliations and lineations provide a rock record of numerous deformational characteristics in the Earth’s crust. When spatial information is combined with fabric data collected at points on geologic maps, the nature and consistency of metamorphic fabrics can be explored through structural domain analysis. This is particularly useful in large regions where there is not well-established stratigraphy and where bedrock exposures are limited. Domains that contain distinctive orientations and patterns of fabrics can be constructed on the basis of several different parameters, but in folded, polydeformational regions cylindricity can be particularly useful. Distinct domains of cylindrical folding can sometimes be determined where poles to foliations show characteristic patterns on equal-area projections and lie perpendicular to a single axis in space. Additionally, the patterns of elements such as fold axes and mineral lineations can be used in conjunction with foliation data to refine domains and confirm parameters such as coaxiality.&nbsp;</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2020 Cordilleran tectonics workshop program and abstracts","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2020 Cordilleran Tectonics Workshop","conferenceDate":"Feb 21-23, 2020","conferenceLocation":"Anchorage, AK","language":"English","publisher":"Cordilleran Tectonics Workshop","usgsCitation":"Caine, J., and Jones, J.V., 2020, Exploring regional scale metamorphic fabrics in the Yukon Tanana terrane and environs using quantitative domain analyses, <i>in</i> 2020 Cordilleran tectonics workshop program and abstracts, Anchorage, AK, Feb 21-23, 2020, p. 11-13.","productDescription":"3 p.","startPage":"11","endPage":"13","ipdsId":"IP-116658","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":385130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384616,"type":{"id":15,"text":"Index Page"},"url":"https://cordillerantectonics.com/program-and-abstracts/"}],"country":"Canada, United States","state":"Alaska, Yukon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -156.59912109375,\n              61.41775026352097\n            ],\n            [\n              -134.14306640625,\n              61.41775026352097\n            ],\n            [\n              -134.14306640625,\n              67.44122869796351\n            ],\n            [\n              -156.59912109375,\n              67.44122869796351\n            ],\n            [\n              -156.59912109375,\n              61.41775026352097\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":812833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":812834,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70210285,"text":"70210285 - 2020 - Correlations along a 140 km transect in the westernmost Peach Spring Tuff, and tracing changing facies through depositional environments","interactions":[],"lastModifiedDate":"2020-05-29T16:07:11.372569","indexId":"70210285","displayToPublicDate":"2020-04-30T10:59:53","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Correlations along a 140 km transect in the westernmost Peach Spring Tuff, and tracing changing facies through depositional environments","docAbstract":"Tephrochronology is the correlation of tephra beds and tuffs by various means, and it is an important tool in refining stratigraphic and structural interpretations.  The 18.78 Ma Peach Spring Tuff (PST) is a large-volume ignimbrite that was deposited across a ~200 km x 360 km area of southeastern California, northwestern Arizona, and southern Nevada.  The PST is a valuable stratigraphic marker in several stratigraphic sequences in this area.  In this study, the field characteristics, mineral abundance, and feldspar composition of eight ignimbrite locations are examined along a 140 km swath across the northwestern extent of the PST in the Mojave Desert.  Based on geochronologic or paleomagnetic data, five of the ignimbrites are PST, and three are possible PST ignimbrites do not have supporting geochronologic or paleomagnetic data.  In 53 regionally dispersed locations of the PST, including the three possible PST ignimbrites in this study, the overlying and underlying sedimentary deposits are described in order to determine the depositional changes, if any, resulting from the geologically instantaneous deposition of the ignimbrite.  Of the 53 locations, 37 locations allow interpretation of the pre- and post-PST depositional environments. Of the 37, 25 have an upward fining-thinning trend  indicating that the deposition of the ignimbrite resulted in (1) disruption and change in local stream gradients and sediment supply, (2) a long period of time for depositional systems to propagate to and regenerate at a location, or (3) a lack of re-establishment of the pre-PST environments.  However, 12 have no significant change, so there was minimal disruption to the depositional system.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Changing facies: The 2020 desert symposium Field guide and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2020 Desert Symposium","conferenceDate":"April 17-20, 2020","conferenceLocation":"Zzyzz, CA","language":"English","publisher":"Desert Symposium","usgsCitation":"Buesch, D.C., 2020, Correlations along a 140 km transect in the westernmost Peach Spring Tuff, and tracing changing facies through depositional environments, <i>in</i> Changing facies: The 2020 desert symposium Field guide and proceedings, Zzyzz, CA, April 17-20, 2020, p. 68-85.","productDescription":"18 p.","startPage":"68","endPage":"85","ipdsId":"IP-116896","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":375148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375143,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org/History.html"}],"country":"United States","state":"Arizona, California, Nevada","otherGeospatial":"Peach Spring Tuff","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.25,\n              34.000\n            ],\n            [\n              -113.25,\n              34.000\n            ],\n            [\n              -113.25,\n              35.75\n            ],\n            [\n              -117.25,\n              35.75\n            ],\n            [\n              -117.25,\n              34.000\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":789953,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70211321,"text":"70211321 - 2020 - 2019 National park visitor spending effects: Economic contributions to local communities, states, and the nation","interactions":[],"lastModifiedDate":"2020-07-27T15:10:54.41551","indexId":"70211321","displayToPublicDate":"2020-04-30T10:06:48","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/EQD/NRR—2020/2110","title":"2019 National park visitor spending effects: Economic contributions to local communities, states, and the nation","docAbstract":"<p>The National Park Service (NPS) manages the Nation’s most iconic destinations that attract millions of visitors from across the Nation and around the world. Trip-related spending by NPS visitors generates and supports economic activity within park gateway communities. This report summarizes the annual economic contribution analysis that measures how NPS visitor spending cycles through local economies, generating business sales and supporting jobs and income. In 2019, the National Park System received over 327.5 million recreation visits. Visitors to national parks spent an estimated \\$21 billion in local gateway regions. The contribution of this spending to the national economy was 340,500 jobs, \\$14.1 billion in labor income, \\$24.3 billion in value added, and \\$41.7 billion in economic output. The lodging sector saw the highest direct effects, with \\$7.1 billion in economic output directly contributed to this sector nationally. The restaurants sector saw the next greatest effects, with $4.2 billion in economic output directly contributed to this sector nationally. Results from the Visitor Spending Effects report series are available online via an interactive tool. Users can view year-by-year trend data and explore current year visitor spending, jobs, labor income, value added, and economic output effects by sector for national, state, and local economies. The interactive tool is available at https://www.nps.gov/subjects/socialscience/vse.htm.</p>","language":"English","publisher":"National Park Service","usgsCitation":"Cullinane Thomas, C., and Koontz, L., 2020, 2019 National park visitor spending effects: Economic contributions to local communities, states, and the nation: Natural Resource Report NPS/NRSS/EQD/NRR—2020/2110, v, 52 p.","productDescription":"v, 52 p.","ipdsId":"IP-117599","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376714,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nps.gov/nature/customcf/NPS_Data_Visualization/docs/NPS_2019_Visitor_Spending_Effects.pdf"}],"country":"United 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,{"id":70212036,"text":"70212036 - 2020 - Effects of flow diversion on Snake Creek and its riparian cottonwood forest, Great Basin National Park","interactions":[],"lastModifiedDate":"2020-08-13T14:59:57.567569","indexId":"70212036","displayToPublicDate":"2020-04-30T09:53:53","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/GRBA/NRR-2020/2104","title":"Effects of flow diversion on Snake Creek and its riparian cottonwood forest, Great Basin National Park","docAbstract":"<p>Snake Creek flows east from the southern Snake Range in Nevada over complex lithology before leaving Great Basin National Park. The river travels over a section of karst limestone where some surface water naturally recharges the groundwater flow system. In 1961 a water diversion pipeline was constructed by downstream water users to transport surface water through the groundwater recharge zone to reduce potential water losses. The diversion pipeline dewaters a 5-km reach for most of the year by transporting water past the recharge zone then returning it to the channel downstream. Snake Creek was incorporated into the newly established Great Basin National Park in 1986, and today park managers and visitors are concerned that the diversion has destabilized Snake Creek’s riparian ecosystem in this arid region where it has high ecological value. The objectives of this study were to 1) document riparian cottonwood forest conditions in the pipeline-dewatered (DW) reach, 2) evaluate Snake Creek water availability and whether it can support a healthy riparian ecosystem, and 3) determine if dewatering has shifted the fluvial system into an unnatural and poorly functioning state. </p><p>We pursued these ecohydrological study objectives in 11 research investigations of Snake Creek’s DW reach and nearby reference reaches. The research investigations analyzed: 1) riparian forest condition, tree age, growth, and death; 2) tree ring chronologies through time and space; 3) hydroclimatic drivers of tree growth; 4) stable carbon isotopes extracted from tree rings; 5) cottonwood ecophysiology related to water transport and water stress; 6) historical aerial photography; 7) stand-level riparian forest production; 8) groundwater availability as related to surface water and plant rooting zones; 9) near-surface geophysics using electrical resistivity imaging; 10) channel and valley geomorphology; and 11) in-channel wood jams caused by fallen trees. Integrating these diverse research topics provided a full perspective of historical and modern conditions along Snake Creek. </p><p>We found that modern hydrological conditions in Snake Creek’s DW reach could not maintain the drought-sensitive ecosystem. The riparian cottonwoods (<i>Populus angustifolia</i> and <i>P. angustifolia</i> x <i>P. trichocarpa</i>) have experienced significant dieback. Tree mortality was 2.4 times higher in the DW reach than in reference reaches, and surviving trees supported only 60% of the live canopy compared to trees in reference reaches. Changes in the DW reach forest began in the 1960s and became more severe during the last two decades. Stable carbon isotope ratios and branch dieback analyses both demonstrated initial forest adjustments related to water stress beginning in the early 1960s. Tree ring width chronologies indicated two periods of growth decline in the DW relative to control reaches. The first decline in the 1960s represented an immediate adjustment to the modified flow regime, and the second decline in the 2000s demonstrated reduced resilience to atmospheric drought. Aerial photos and stand-level forest production calculations indicated that substantial riparian forest decline occurred in the 1990s–2010s in the DW reach compared to reference reaches. Stable carbon isotope ratios and leaf water potentials revealed that trees in the DW reach experienced greater drought stress than those in reference reaches. Monitoring wells and electrical resistivity surveys both showed riparian water tables to be largely supported by in-channel surface water flow, indicating that the flow diversion removed water that recharges alluvial groundwater and sustains riparian plants. Areas of widespread tree mortality in the DW reach also corresponded to a larger and more unstable channel with a high instream wood load from fallen trees. Modern conditions of Snake Creek in the DW reach robustly suggest that dewatering the river and its associated riparian corridor adversely affected the riparian ecosystem. The degraded condition is likely to persist and intensify unless water is returned to the channel. As we documented during the wet 1980s and the scientific literature suggest, a partial recovery of the riparian ecosystem is likely possible with restored flows.</p>","language":"English","publisher":"National Park Service","usgsCitation":"Schook, D.M., Cooper, D.J., Friedman, J.M., Rice, S.E., Hoover, J.D., and Thaxton, R.D., 2020, Effects of flow diversion on Snake Creek and its riparian cottonwood forest, Great Basin National Park: Natural Resource Report NPS/GRBA/NRR-2020/2104, xv, 159 p.","productDescription":"xv, 159 p.","ipdsId":"IP-114048","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":377493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377489,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/DownloadFile/637892"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.3951416015625,\n              38.66406704456943\n            ],\n            [\n              -114.114990234375,\n              38.66406704456943\n            ],\n            [\n              -114.114990234375,\n              39.08956785484934\n            ],\n            [\n              -114.3951416015625,\n              39.08956785484934\n            ],\n            [\n              -114.3951416015625,\n              38.66406704456943\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schook, Derek M.","contributorId":178325,"corporation":false,"usgs":false,"family":"Schook","given":"Derek","email":"","middleInitial":"M.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":796163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cooper, David J.","contributorId":53309,"corporation":false,"usgs":true,"family":"Cooper","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":796164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":796165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rice, Steven E.","contributorId":238179,"corporation":false,"usgs":false,"family":"Rice","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":796166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoover, Jamie D.","contributorId":238180,"corporation":false,"usgs":false,"family":"Hoover","given":"Jamie","email":"","middleInitial":"D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":796167,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thaxton, Richard D.","contributorId":238181,"corporation":false,"usgs":false,"family":"Thaxton","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":796168,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70223287,"text":"70223287 - 2020 - Movement ecology and habitat use differences in Black Scoters wintering along the Atlantic coast","interactions":[],"lastModifiedDate":"2021-08-20T14:19:55.645255","indexId":"70223287","displayToPublicDate":"2020-04-30T09:13:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Movement ecology and habitat use differences in Black Scoters wintering along the Atlantic coast","docAbstract":"<p><span>For migratory species such as Black Scoters (</span><i>Melanitta americana</i><span>) whose range encompasses a variety of habitats, it is especially important to obtain habitat use information across the species’ range to better understand anthropogenic threats, e.g., marine development and climate change. The objective of our study was to investigate the winter movement patterns and habitat use of Black Scoters in the Atlantic Ocean by quantifying the following key movement indices: number of wintering sites, arrival and departure dates to and from the wintering grounds, days at a wintering site, area of a wintering site, distance between wintering sites, and differences in habitat features of wintering sites. We also tested if winter movement patterns varied by sex or along a latitudinal gradient. To quantify winter movement patterns of Black Scoters, we used satellite telemetry data from 2009 to 2012 (n = 29 tagged females and 15 males for a total of 66 winter seasons, 38 female winter seasons, 28 male winter seasons). Our results indicated that the average wintering site area and distance between wintering sites varied with latitude. Wintering sites located at southern latitudes were larger and further apart than wintering sites located at more northern latitudes. Additionally, wintering sites varied in bathymetry, distance to shore, and the slope of the ocean floor at different latitudes; northern wintering sites were in deeper waters, closer to shore, and on steeper slopes than southern wintering sites. Our results suggest that habitat use may differ by latitude, indicating that habitats used in northern locations may not be representative of habitats used in more southern wintering areas. Understanding variation of habitat use along a latitudinal gradient will enable managers to focus sampling effort for Black Scoter abundance and distribution along the Atlantic coast and provide insight on the wintering ecology and movement of Black Scoters.</span></p>","language":"English","publisher":"The Resilience Alliance","doi":"10.5751/ACE-01654-150206","usgsCitation":"Plumpton, H.M., Gilliland, S.G., and Ross, B., 2020, Movement ecology and habitat use differences in Black Scoters wintering along the Atlantic coast: Avian Conservation and Ecology, v. 15, no. 2, 6, https://doi.org/10.5751/ACE-01654-150206.","productDescription":"6","ipdsId":"IP-101476","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-01654-150206","text":"Publisher Index Page"},{"id":388234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Florida, Georgia, Maryland, Massachusetts, New Jersey, New York, North Carolina, Rhode Island, South Carolina, Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.33203125,\n              28.844673680771795\n            ],\n            [\n              -80.4638671875,\n              31.316101383495624\n            ],\n            [\n              -75.4541015625,\n              34.19817309627726\n            ],\n            [\n              -74.5751953125,\n              35.85343961959182\n            ],\n            [\n              -74.794921875,\n              37.54457732085582\n            ],\n            [\n              -73.916015625,\n              39.027718840211605\n            ],\n            [\n              -69.3017578125,\n              41.343824581185686\n            ],\n            [\n              -70.7080078125,\n              42.032974332441405\n            ],\n            [\n              -72.7734375,\n              41.47566020027821\n            ],\n            [\n              -74.3994140625,\n              40.97989806962013\n            ],\n            [\n              -76.728515625,\n              39.36827914916014\n            ],\n            [\n              -77.34374999999999,\n              37.92686760148135\n            ],\n            [\n              -76.46484375,\n              36.527294814546245\n            ],\n            [\n              -76.81640625,\n              35.60371874069731\n            ],\n            [\n              -77.255859375,\n              34.88593094075317\n            ],\n            [\n              -79.2333984375,\n              33.7243396617476\n            ],\n            [\n              -81.123046875,\n              32.13840869677249\n            ],\n            [\n              -81.5625,\n              30.939924331023445\n            ],\n            [\n              -81.0791015625,\n              28.8831596093235\n            ],\n            [\n              -80.33203125,\n              28.844673680771795\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Plumpton, H. M.","contributorId":264502,"corporation":false,"usgs":false,"family":"Plumpton","given":"H.","email":"","middleInitial":"M.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":821619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilliland, S. G.","contributorId":264504,"corporation":false,"usgs":false,"family":"Gilliland","given":"S.","email":"","middleInitial":"G.","affiliations":[{"id":12590,"text":"Canadian Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":821620,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":821621,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70210935,"text":"70210935 - 2020 - 7700-year persistence of an isolated, free-living coral assemblage in the Galápagos Islands: A model for coral refugia?","interactions":[],"lastModifiedDate":"2020-07-07T14:09:51.779689","indexId":"70210935","displayToPublicDate":"2020-04-30T09:05:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"7700-year persistence of an isolated, free-living coral assemblage in the Galápagos Islands: A model for coral refugia?","docAbstract":"In an eastern-Pacific coral assemblage at Devil’s Crown, Galápagos Islands, Ecuador, two coral species, Psammocora stellata and Cycloseris (Diaseris) distorta, form dense populations of unattached colonies on sand and rubble substrata. In the Galápagos, living C. (D.) distorta is found only at this single site, whereas populations of P. stellata are found throughout the archipelago. Six cores dating to 7700 yBP showed P. stellata to be dominant throughout the history of this isolated community, but C. (D.) distorta increased in abundance from ~2200 yBP and reached peak abundance between 1471 yBP and the present. The relative frequency of the two coral species may be linked to millennial-scale climatic variability, and this site may represent a refuge for C. (D.) distorta from unfavorable climatic fluctuations on millennial timescales. Our results demonstrate that some corals can persist in isolated populations for millennia.","language":"English","publisher":"Springer","doi":"10.1007/s00338-020-01935-5","usgsCitation":"Feingold, J., Reigl, B., Hendrickson, K., Toth, L., Cheng, H., Edwards, R.L., and Aronson, R.B., 2020, 7700-year persistence of an isolated, free-living coral assemblage in the Galápagos Islands: A model for coral refugia?: Coral Reefs, v. 39, p. 639-647, https://doi.org/10.1007/s00338-020-01935-5.","productDescription":"9 p.","startPage":"639","endPage":"647","ipdsId":"IP-111819","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":376148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Galápagos Islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.0654296875,\n              -1.8124417945380265\n            ],\n            [\n              -88.868408203125,\n              -1.8124417945380265\n            ],\n            [\n              -88.868408203125,\n              0.5163504323777589\n            ],\n            [\n              -92.0654296875,\n              0.5163504323777589\n            ],\n            [\n              -92.0654296875,\n              -1.8124417945380265\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2020-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Feingold, Joshua","contributorId":228835,"corporation":false,"usgs":false,"family":"Feingold","given":"Joshua","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":792215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reigl, Bernhard","contributorId":228836,"corporation":false,"usgs":false,"family":"Reigl","given":"Bernhard","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":792216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hendrickson, Katie","contributorId":228837,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Katie","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":792217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cheng, Hai 0000-0002-5305-9458","orcid":"https://orcid.org/0000-0002-5305-9458","contributorId":223142,"corporation":false,"usgs":false,"family":"Cheng","given":"Hai","email":"","affiliations":[{"id":40680,"text":"Xi'an Jiaotong University","active":true,"usgs":false}],"preferred":false,"id":792219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, R. Lawrence","contributorId":69760,"corporation":false,"usgs":true,"family":"Edwards","given":"R.","email":"","middleInitial":"Lawrence","affiliations":[],"preferred":false,"id":792245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aronson, Richard B. 0000-0003-0383-3844","orcid":"https://orcid.org/0000-0003-0383-3844","contributorId":212695,"corporation":false,"usgs":false,"family":"Aronson","given":"Richard","email":"","middleInitial":"B.","affiliations":[{"id":17748,"text":"Florida Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":792220,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70213143,"text":"70213143 - 2020 - Vegetation affects timing and location of wetland methane emissions","interactions":[],"lastModifiedDate":"2020-09-10T14:07:15.577182","indexId":"70213143","displayToPublicDate":"2020-04-30T08:58:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation affects timing and location of wetland methane emissions","docAbstract":"<div class=\"col-md-8 col-lg-8 article__content\"><div class=\"article__body \"><div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>Common assumptions about how vegetation affects wetland methane (CH) flux include acting as conduits for CH release, providing carbon substrates for growth and activity of methanogenic organisms, and supplying oxygen to support CH oxidation. However, these effects may change through time, especially in seasonal wetlands that experience drying and re-wetting, or change across space, dependent on proximity to vegetation. In a mesocosm study, we assessed the impacts of on CH flux using clear flux-chamber measurements directly over plants (&amp;lsquo;whole-plant&amp;rsquo;), adjacent to plants (where roots were present but no stems; &amp;lsquo;plant-adjacent&amp;rsquo;), and plant-free soils (&amp;lsquo;control&amp;rsquo;). During the establishment phase of the study (first 30-days), the whole-plant treatment had ~5-times higher CH flux rates (51.78&amp;plusmn;8.16 mg-C md) than plant-adjacent or control treatments, which was primarily due to plant-mediated transport, with little contribution from diffusive-only flux. However, high fluxes from whole-plants depleted porewater CH concentrations both directly below whole-plant and in neighboring plant-adjacent treatments, while controls accumulated a highly concentrated reservoir of porewater CH. When the water table was drawn down to simulate seasonal drying, reserve porewater CH from control soil was released as a pulse, equaling the earlier higher CH emissions from whole-plants. Plant-adjacent treatments, which had neither plant-mediated CH transport nor a concentrated reservoir of porewater CH, had low CH flux throughout the study. Our findings indicate that in seasonal wetlands, vegetation affects the timing and location of CH emissions. These results have important mechanistic and methodological implications for understanding the role of vegetation on wetland CH flux.</p></div></div></div></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JG005777","usgsCitation":"Bansal, S., Johnson, O., Meier, J., and Xiaoyan, Z., 2020, Vegetation affects timing and location of wetland methane emissions: Journal of Geophysical Research: Biogeosciences, v. 125, no. 9, e2020JG005777, 14 p., https://doi.org/10.1029/2020JG005777.","productDescription":"e2020JG005777, 14 p.","ipdsId":"IP-116816","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456904,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jg005777","text":"Publisher Index Page"},{"id":437010,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QT9V3K","text":"USGS data release","linkHelpText":"Greenhouse gas fluxes, dissolved gas concentrations, and water properties of laboratory mesocosms"},{"id":378307,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":798390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Olivia 0000-0002-6839-6617","orcid":"https://orcid.org/0000-0002-6839-6617","contributorId":240088,"corporation":false,"usgs":false,"family":"Johnson","given":"Olivia","affiliations":[{"id":38050,"text":"Contractor","active":true,"usgs":false}],"preferred":false,"id":798391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meier, Jacob 0000-0002-8822-8434","orcid":"https://orcid.org/0000-0002-8822-8434","contributorId":204473,"corporation":false,"usgs":true,"family":"Meier","given":"Jacob","email":"","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":798392,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xiaoyan, Zhu","contributorId":240091,"corporation":false,"usgs":false,"family":"Xiaoyan","given":"Zhu","email":"","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":798393,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70219907,"text":"70219907 - 2020 - Fisheries research and monitoring activities of the Lake Erie Biological Station, 2019","interactions":[],"lastModifiedDate":"2021-04-16T13:31:57.728158","indexId":"70219907","displayToPublicDate":"2020-04-30T08:29:44","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":8434,"text":"Lake Erie Biological Station Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Fisheries research and monitoring activities of the Lake Erie Biological Station, 2019","docAbstract":"<p>A comprehensive understanding of fish populations and their interactions is the cornerstone of modern fishery management and the basis for Fish Community Goals and Objectives for Lake Erie (Ryan et al. 2003). This report is responsive to U.S. Geological Survey (USGS) obligations via Memorandum of Understanding (MOU) with the Great Lakes Council of Lake Committees (CLC) to provide scientific information in support of fishery management. Goals for the USGS Great Lakes Deepwater Fish Assessment and Ecological Studies in 2019 were to monitor long-term changes in the fish community and population dynamics of key fishes of interest to management agencies. Specific to Lake Erie, expectations of this agreement were sustained investigations of native percids, forage (prey) fish populations, and Lake Trout. </p><p>Our 2019 deepwater program operations began in April and concluded in December, and utilized trawl, gillnet, hydroacoustic, lower trophic sampling, and telemetry methods. This work resulted in 88 bottom trawls covering 65 ha of lake-bottom and catching 24,140 fish totaling 3,622 kg during three separate trawl surveys in the West and Central basins of Lake Erie. Overnight gillnet sets (n=44) for cold water species were performed at 42 unique locations in the West and East basins of Lake Erie. A total of 8.0 km of gillnet was deployed during these surveys, which caught 286 fish, 114 of which were native coldwater species: Lake Trout, Burbot, and Lake Whitefish. USGS hydroacoustic surveys in 2019 produced 240 km of transects, and lower trophic sampling provided data from zooplankton samples (n=21) and water quality profiles (n=21) to populate a database maintained by the Ontario Ministry of Natural Resources and Forestry (OMNRF), Ohio Division of Natural Resources (ODNR), Michigan Division of Natural Resources (MDNR), Pennsylvania Fish and Boat Commission (PFBC), and New York State Department of Environmental Conservation (NYSDEC). USGS also assisted CLC member agencies with deployment and maintenance of the Great Lakes Acoustic Telemetry Observation System (GLATOS) throughout all three Lake Erie sub-basins, supporting multiple coordinated telemetry investigations. </p><p>In 2019, Lake Trout investigations included annual gill net surveys and acoustic telemetry of spawning migration and habitat use in coordination with OMNRF, NYSDEC, and PFBC. Results from Lake Trout investigations were reported in the Coldwater Task Group annual report to the Great Lakes Fishery Commission (GLFC) and the CLC (Coldwater Task Group 2020). Likewise, interagency forage fish assessments conducted with hydroacoustics were summarized and reported in the Forage Task Group annual report (Forage Task Group 2020). </p><p>This report presents biomass-based summaries of fish communities in western Lake Erie derived from USGS bottom trawl surveys conducted from 2013 to 2019 during June and September. The survey design provided temporal and spatial coverage that did not exist in the historic interagency trawl database, and thus complemented the August ODNR-OMNRF effort to reinforce stock assessments with more robust data. Analyses herein evaluated trends in: total biomass, abundance of dominant predator and forage species, non-native species composition, biodiversity and community structure. Data from this effort can be explored interactively online (https://lebs.shinyapps.io/western-basin/), and are accessible for download (https://doi.org/10.5066/P9LL6YOR, Keretz et al. 2020). Annual survey data are added to these sources as the data become available.</p>","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Keretz, K.R., Kocovsky, P., Kraus, R., and Schmitt, J., 2020, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2019: Lake Erie Biological Station Annual Report, 12 p.","productDescription":"12 p.","ipdsId":"IP-116726","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385155,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/lake-erie-committee.php"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n    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pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814369,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70224928,"text":"70224928 - 2020 - Research in the refuge constraints to restoring diverse forest ecosystems at Hakalau","interactions":[],"lastModifiedDate":"2021-10-05T13:09:48.371083","indexId":"70224928","displayToPublicDate":"2020-04-30T08:08:00","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"title":"Research in the refuge constraints to restoring diverse forest ecosystems at Hakalau","docAbstract":"<p>No abstract available.&nbsp;</p>","largerWorkType":{"id":25,"text":"Newsletter"},"largerWorkTitle":"Friends of Hakalau Forst National Wildlife Refuge Newsletter","largerWorkSubtype":{"id":30,"text":"Newsletter"},"language":"English","publisher":"Friends of Hakalau Forst National Wildlife Refuge","usgsCitation":"Yelenik, S.G., Rose, E., Paxton, E.H., Rehm, E.M., and D'Antonio, C., 2020, Research in the refuge constraints to restoring diverse forest ecosystems at Hakalau.","ipdsId":"IP-134024","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":390238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390229,"type":{"id":15,"text":"Index Page"},"url":"https://myemail.constantcontact.com/Spring-2020-Newsletter---Friends-of-Hakalau-Forest-NWR.html?soid=1131173118925&aid=kgVVesY7TaQ"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hakalau Forest National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.40023803710938,\n              19.694314241825747\n            ],\n            [\n              -155.07888793945312,\n              19.694314241825747\n            ],\n            [\n              -155.07888793945312,\n              20.019806765982878\n            ],\n            [\n              -155.40023803710938,\n              20.019806765982878\n            ],\n            [\n              -155.40023803710938,\n              19.694314241825747\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":824665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, Eli T.","contributorId":145699,"corporation":false,"usgs":false,"family":"Rose","given":"Eli T.","affiliations":[],"preferred":false,"id":824666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":824667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rehm, Evan M","contributorId":216487,"corporation":false,"usgs":false,"family":"Rehm","given":"Evan","email":"","middleInitial":"M","affiliations":[{"id":39457,"text":"University of California at Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":824668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"D'Antonio, Carla M.","contributorId":27992,"corporation":false,"usgs":false,"family":"D'Antonio","given":"Carla M.","affiliations":[],"preferred":false,"id":824669,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70210390,"text":"70210390 - 2020 - Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging","interactions":[],"lastModifiedDate":"2020-06-02T13:08:40.384665","indexId":"70210390","displayToPublicDate":"2020-04-30T08:03:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2789,"text":"Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging","docAbstract":"Sections of the Upper Cretaceous (Coniacian to Campanian) Niobrara Formation in two cores from Kansas and Colorado, the Amoco Rebecca Bounds and USGS Portland 1, respectively, were examined by hyperspectral core imaging and analysis. A spectral imaging system combining high-resolution photography (50 μm), 3D laser profiling (20 μm), and near-visible + short-wave infrared reflectance spectroscopy (wavelengths from 450 to 2500 nm, 500 μm pixel size) was applied to these cores to provide spectral and textural data facilitating creation of continuous mineral and lithology class maps. In addition, compositing of pixel-based results to group pixels to create mineralogical and lithological logs (0.5 ft resolution) was performed to facilitate comparisons to other geochemical datasets. The results show general correspondence in trends identified by previous geochemistry studies, with some exceptions due to instrumental limitations related to low reflectance of some rock intervals and the limited range of infrared wavelengths examined. This study provides a cursory overview of an extensive dataset meant to demonstrate the utility of hyperspectral core scanning to studies of mudrocks in petroleum systems as well as the kinds of information this technique can provide for detailed examination of stratigraphic features in sedimentary systems more generally.","language":"English","publisher":"Rocky Mountain Association of Geologists","doi":"10.31582/rmag.mg.57.2.121","usgsCitation":"Birdwell, J.E., Fontenot, L.C., and Martini, B., 2020, Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging: Mountain Geologist, v. 57, no. 2, p. 121-143, https://doi.org/10.31582/rmag.mg.57.2.121.","productDescription":"23 p.","startPage":"121","endPage":"143","ipdsId":"IP-115802","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -127.08984375000001,\n              46.255846818480315\n            ],\n            [\n              -121.55273437499999,\n              32.24997445586331\n            ],\n            [\n              -106.5234375,\n              30.29701788337205\n            ],\n            [\n              -99.931640625,\n              25.3241665257384\n            ],\n            [\n              -94.833984375,\n              25.958044673317843\n            ],\n            [\n              -94.833984375,\n              54.826007999094955\n            ],\n            [\n              -109.6875,\n              58.6769376725869\n            ],\n            [\n              -121.728515625,\n              60.19615576604439\n            ],\n            [\n              -139.658203125,\n              61.312451574838214\n            ],\n            [\n              -135.791015625,\n              54.826007999094955\n            ],\n            [\n              -127.08984375000001,\n              46.255846818480315\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"57","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":790140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fontenot, Lionel C.","contributorId":225058,"corporation":false,"usgs":false,"family":"Fontenot","given":"Lionel","email":"","middleInitial":"C.","affiliations":[{"id":41029,"text":"Corescan Pty. Ltd., Ascot, WA Australia","active":true,"usgs":false}],"preferred":false,"id":790141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martini, Brigette","contributorId":225059,"corporation":false,"usgs":false,"family":"Martini","given":"Brigette","email":"","affiliations":[{"id":41030,"text":"North Shore Consulting","active":true,"usgs":false}],"preferred":false,"id":790142,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70210026,"text":"70210026 - 2020 - Parsing complex terrain controls on mountain glacier response to climate forcing","interactions":[],"lastModifiedDate":"2020-08-06T19:14:26.872296","indexId":"70210026","displayToPublicDate":"2020-04-30T07:41:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Parsing complex terrain controls on mountain glacier response to climate forcing","docAbstract":"Glaciers are a key indicator of changing climate in the high mountain landscape.\nGlacier variations across a mountain range are ultimately driven by regional climate\nforcing. However, changes also reflect local, topographically driven processes such as\nsnow avalanching, snow wind-drifting, and radiation shading as well as the initial\nglacier conditions such as hypsometry and ice thickness. Here we assess the role of\nthese various terrain influences on change to Little Ice Age (LIA) glaciers in Glacier\nNational Park, U.S.A . With available data for LIA and modern glacier areas, we\nestimate glacier volumes using simple ice flow assumptions, and topographically\ndriven processes using terrain proxies. At the LIA glacial maxima there were 82\nglaciers larger than 0.1 km 2 ranging from 0.11 to 4.97 km 2 . Over the course of the\n20 th century, every single LIA glacier decreased in area and 60% (49 glaciers)\ndiminished to below the 0.1 km 2 threshold. Glaciers with large initial area (>1.5 km\n2 ) at the end of LIA persisted. Within the intermediate size class (0.5 km 2 < area <\n1.5 km 2 ), LIA glacier persistence is poorly explained by initial glacier volume, ice\nthickness, or elevation. Instead, wind exposure is an important explanatory factor.\nOur analysis demonstrates the complex response of cirque glaciers to post-LIA climate\nchange in this region: individual glaciers have not necessarily undergone equivalent\nand synchronous change. Nevertheless, that all glaciers in this mountain range\nexperienced retreat demonstrates that local processes mediated adjustments of some\nglaciers, but completely decoupled none from the regional climate forcing.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2020.103209","usgsCitation":"Florentine, C., Harper, J.T., and Fagre, D., 2020, Parsing complex terrain controls on mountain glacier response to climate forcing: Global and Planetary Change, v. 191, 103209, 13 p., https://doi.org/10.1016/j.gloplacha.2020.103209.","productDescription":"103209, 13 p.","ipdsId":"IP-112133","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456906,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloplacha.2020.103209","text":"Publisher Index Page"},{"id":374649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.620361328125,\n              48.28319289548349\n            ],\n            [\n              -112.96142578125,\n              48.28319289548349\n            ],\n            [\n              -112.96142578125,\n              49.005447494058096\n            ],\n            [\n              -114.620361328125,\n              49.005447494058096\n            ],\n            [\n              -114.620361328125,\n              48.28319289548349\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"191","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Florentine, Caitlyn Elizabeth 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":224631,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn Elizabeth","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":788858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harper, Joel T.","contributorId":173392,"corporation":false,"usgs":false,"family":"Harper","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":16951,"text":"Department of Geosciences, University of Montana, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":788859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":224632,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":788860,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70219005,"text":"70219005 - 2020 - Automated location correction and spot height generation for named summits in the coterminous United States","interactions":[],"lastModifiedDate":"2021-03-19T12:31:02.918089","indexId":"70219005","displayToPublicDate":"2020-04-30T07:27:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"Automated location correction and spot height generation for named summits in the coterminous United States","docAbstract":"<div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>Spot elevations published on historical U.S. Geological Survey topographic maps were established as needed to enhance information imparted by the quadrangle’s contours. In addition to other features, labels were routinely placed on mountain summits. While some elevations were established through field survey triangulation, many were computed during photogrammetric stereo-compilation. Today, Global Navigation Satellite System (GNSS) receivers have replaced expensive triangulation methods. However, since GNSS measurements require visiting the feature location, a national dataset containing high-accuracy spot elevations has not yet been created. Consequently, modern U.S. Topo maps are devoid of mountain peak or other spot elevations. Still, topographic map users continue to demand the display of spot heights. Therefore, a pilot study was conducted to evaluate the feasibility of automatically generating elevation values at named U.S. summits using available elevation data. The devised method uses an uphill stepping technique to find the most likely highest point in subsequently higher-resolution elevation models. Resulting elevation values are compared to other published sources. Results from 196 summits indicate that values derived from lidar are generally higher, whereas those populated from the one-third arc-second USGS Seamless 3DEP elevation dataset are generally lower. A thorough understanding of these relationships require the evaluation of more points.</p></div></div>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/17538947.2020.1754936","usgsCitation":"Arundel, S., and Sinha, G., 2020, Automated location correction and spot height generation for named summits in the coterminous United States: International Journal of Digital Earth, v. 13, no. 12, p. 1570-1584, https://doi.org/10.1080/17538947.2020.1754936.","productDescription":"15 p.","startPage":"1570","endPage":"1584","ipdsId":"IP-112848","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":499919,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/ef9864c7c44e489185483ba722a1b09b","text":"External Repository"},{"id":384500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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          -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":812444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sinha, Gaurav","contributorId":220051,"corporation":false,"usgs":false,"family":"Sinha","given":"Gaurav","email":"","affiliations":[{"id":12807,"text":"Ohio University","active":true,"usgs":false}],"preferred":false,"id":812445,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70209818,"text":"70209818 - 2020 - Green turtle mitochondrial microsatellites indicate finer-scale natal homing to isolated islands than to continental nesting sites","interactions":[],"lastModifiedDate":"2020-06-12T17:45:25.808564","indexId":"70209818","displayToPublicDate":"2020-04-29T12:40:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2636,"text":"MEPS","active":true,"publicationSubtype":{"id":10}},"title":"Green turtle mitochondrial microsatellites indicate finer-scale natal homing to isolated islands than to continental nesting sites","docAbstract":"<p><span>&nbsp;In highly mobile philopatric species, defining the scale of natal homing is fundamental to characterizing population dynamics and effectively managing distinct populations. Genetic tools have provided evidence of regional natal philopatry in marine turtles, but extensive sharing of maternally inherited mitochondrial control region (CR) haplotypes within regions (&lt;500 km) often impedes identification of population boundaries. Previous CR-based analyses of Florida (USA) green turtle&nbsp;</span><i>Chelonia mydas</i><span>&nbsp;nesting sites detected at least 2 populations, but the ubiquity of haplotype CM-A3.1 among southern rookeries decreased the power to detect differentiation. We reassessed population structure by sequencing the mitochondrial microsatellite (short tandem repeat, mtSTR) in 786 samples from 11 nesting sites spanning 700 km from Canaveral National Seashore through Dry Tortugas National Park. The mtSTR marker subdivided CM-A3.1 into 12 haplotypes that were structured among rookeries, demonstrating independent female recruitment into the Dry Tortugas and Marquesas Keys nesting populations. Combined haplotypes provided support for recognition of at least 4 management units in Florida: (1) central eastern Florida, (2) southeastern Florida, (3) Key West National Wildlife Refuge, and (4) Dry Tortugas National Park. Recapture data indicated female nesting dispersal between islands &lt;15 km apart, but haplotype frequencies demonstrated discrete natal homing to island groups separated by 70 km. These isolated insular rookeries may be more vulnerable to climate change-mediated nesting habitat instability than those along continental coasts and should be monitored more consistently to characterize population status. Broader application of the mtSTR markers holds great promise in improving resolution of stock structure and migratory connectivity for green turtles globally.</span></p>","language":"English","publisher":"Inter-Research Science Press","doi":"10.3354/meps13348","usgsCitation":"Shamblin, B.M., Hart, K., Martin, K.J., Ceriani, S.A., Bagley, D.A., Mansfield, K.L., Ehrhart, L.M., and Nairn, C.J., 2020, Green turtle mitochondrial microsatellites indicate finer-scale natal homing to isolated islands than to continental nesting sites: MEPS, v. 643, p. 159-171, https://doi.org/10.3354/meps13348.","productDescription":"13 p.","startPage":"159","endPage":"171","ipdsId":"IP-112808","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":375563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park, Key West National Wildlife Refuge, Marquesas Keys","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -83.10333251953125,\n              24.35960758535081\n            ],\n            [\n              -82.64190673828125,\n              24.35960758535081\n            ],\n            [\n              -82.64190673828125,\n              24.79670834894575\n            ],\n            [\n              -83.10333251953125,\n              24.79670834894575\n            ],\n            [\n              -83.10333251953125,\n              24.35960758535081\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.9415283203125,\n              24.171813716251364\n            ],\n            [\n              -80.04638671875,\n              24.171813716251364\n            ],\n            [\n              -80.04638671875,\n              26.377106813670053\n            ],\n            [\n              -81.9415283203125,\n              26.377106813670053\n            ],\n            [\n              -81.9415283203125,\n              24.171813716251364\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -82.28759765625,\n              24.404636766948936\n            ],\n            [\n              -81.93603515625,\n              24.404636766948936\n            ],\n            [\n              -81.93603515625,\n              24.65076163520743\n            ],\n            [\n              -82.28759765625,\n              24.65076163520743\n            ],\n            [\n              -82.28759765625,\n              24.404636766948936\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"643","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shamblin, Brian M.","contributorId":138897,"corporation":false,"usgs":false,"family":"Shamblin","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":12573,"text":"Daniel B. Warnell School of Forestry and Natural Resource, Athens Georiga","active":true,"usgs":false}],"preferred":false,"id":788149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":214952,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":788150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Kelly J.","contributorId":168557,"corporation":false,"usgs":false,"family":"Martin","given":"Kelly","email":"","middleInitial":"J.","affiliations":[{"id":25334,"text":"Loggerhead Marinelife Center, 14200 U.S. Highway 1, Juno Beach, Florida, 33408, USA","active":true,"usgs":false}],"preferred":false,"id":788151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ceriani, Simona A.","contributorId":224398,"corporation":false,"usgs":false,"family":"Ceriani","given":"Simona","email":"","middleInitial":"A.","affiliations":[{"id":40873,"text":"Florida Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":788152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bagley, Dean A.","contributorId":138898,"corporation":false,"usgs":false,"family":"Bagley","given":"Dean","email":"","middleInitial":"A.","affiliations":[{"id":12574,"text":"Department of Biology , University of Central Florida, Orlando, Florida","active":true,"usgs":false}],"preferred":false,"id":788153,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mansfield, Katherine L.","contributorId":138887,"corporation":false,"usgs":false,"family":"Mansfield","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":12564,"text":"Department of Biology, University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":788154,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ehrhart, Llewellyn M.","contributorId":138899,"corporation":false,"usgs":false,"family":"Ehrhart","given":"Llewellyn","email":"","middleInitial":"M.","affiliations":[{"id":12574,"text":"Department of Biology , University of Central Florida, Orlando, Florida","active":true,"usgs":false}],"preferred":false,"id":788155,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nairn, Campbell J.","contributorId":138908,"corporation":false,"usgs":false,"family":"Nairn","given":"Campbell","email":"","middleInitial":"J.","affiliations":[{"id":12573,"text":"Daniel B. Warnell School of Forestry and Natural Resource, Athens Georiga","active":true,"usgs":false}],"preferred":false,"id":788156,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70212746,"text":"70212746 - 2020 - Use of strong habitat–abundance relationships in assessing population status of cryptic fishes: An example using the Harlequin Darter","interactions":[],"lastModifiedDate":"2020-08-27T17:09:00.380248","indexId":"70212746","displayToPublicDate":"2020-04-29T12:00:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Use of strong habitat–abundance relationships in assessing population status of cryptic fishes: An example using the Harlequin Darter","docAbstract":"<p><span>Understanding trends in abundance is important to fisheries conservation, but techniques for estimating streamwide abundance of cryptic fishes with strong habitat–abundance relationships are not well established and need further development. We developed techniques for addressing this need using the Harlequin Darter&nbsp;</span><i>Etheostoma histrio</i><span>, a small, cryptic freshwater fish associated with submerged wood in streams. Our objectives were to (1) determine how Harlequin Darter abundance and the amount of submerged wood were related at sampled sites and (2) use this relationship to estimate Harlequin Darter abundance at unsampled sites and extrapolate Harlequin Darter abundance estimates and associated uncertainty streamwide. We conducted a mark–recapture study to estimate abundance of Harlequin Darters in 25‐m stream reaches at 24 sites in Big Escambia Creek (BEC) and 18 sites in Pine Barren Creek (PBC) (Escambia River tributaries in northwestern Florida). The number of wood pieces (submerged wood ≥1.5&nbsp;m long and ≥0.25&nbsp;m in circumference) in both creeks was counted and mapped using side‐scan sonar and a geographic information system. Harlequin Darter and wood data were used in a Bayesian multinomial mixture model to estimate site abundance of Harlequin Darters, to determine the relationship between wood and Harlequin Darter abundance, and to extrapolate Harlequin Darter abundance streamwide. We found a positive relationship between wood and Harlequin Darter abundance in both creeks, and there were more wood pieces in PBC than in BEC. Streamwide abundance of Harlequin Darters was greater in PBC than in BEC. The extrapolated streamwide abundance estimates were 9,369 Harlequin Darters (95% credible interval&nbsp;=&nbsp;6,668–13,402) in PBC and 7,439 Harlequin Darters (95% credible interval&nbsp;=&nbsp;4,493–11,226) in BEC. Our methods effectively estimated abundance of a small, cryptic fish that uses complex wood habitat. In addition, our findings may assist in the conservation of the Harlequin Darter.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10231","usgsCitation":"Holcomb, K.M., Schueller, P., Jelks, H.L., Knight, J.R., and Allen, M., 2020, Use of strong habitat–abundance relationships in assessing population status of cryptic fishes: An example using the Harlequin Darter: Transactions of the American Fisheries Society, v. 149, no. 3, p. 320-334, https://doi.org/10.1002/tafs.10231.","productDescription":"15 p.","startPage":"320","endPage":"334","ipdsId":"IP-107850","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":377944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Big Escambia Creek, Escambia River, Pine Barren Creek","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -87.37220764160156,\n              30.537425073997134\n            ],\n            [\n              -87.09548950195312,\n              30.537425073997134\n            ],\n            [\n              -87.09548950195312,\n              30.994680105042487\n            ],\n            [\n              -87.37220764160156,\n              30.994680105042487\n            ],\n            [\n              -87.37220764160156,\n              30.537425073997134\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"149","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Holcomb, Kathryn M","contributorId":239617,"corporation":false,"usgs":false,"family":"Holcomb","given":"Kathryn","email":"","middleInitial":"M","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":797405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schueller, Paul","contributorId":181829,"corporation":false,"usgs":false,"family":"Schueller","given":"Paul","email":"","affiliations":[],"preferred":false,"id":797406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jelks, Howard L. 0000-0002-0672-6297 hjelks@usgs.gov","orcid":"https://orcid.org/0000-0002-0672-6297","contributorId":168997,"corporation":false,"usgs":true,"family":"Jelks","given":"Howard","email":"hjelks@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":797407,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, John R","contributorId":239619,"corporation":false,"usgs":false,"family":"Knight","given":"John","email":"","middleInitial":"R","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":797408,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allen, Micheal S","contributorId":239622,"corporation":false,"usgs":false,"family":"Allen","given":"Micheal S","affiliations":[{"id":47938,"text":"Fisheries and Aquatic Sciences Program, University of Florida","active":true,"usgs":false}],"preferred":false,"id":797409,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70210810,"text":"70210810 - 2020 - Predicting the floods that follow the flames","interactions":[],"lastModifiedDate":"2020-08-04T14:17:11.386942","indexId":"70210810","displayToPublicDate":"2020-04-29T10:02:39","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the floods that follow the flames","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-20-0040.1","usgsCitation":"Gourley, J.J., Vergara, H., Arthur, A., Clark, R.A., Staley, D.M., Fulton, J.W., Hempel, L.A., Goodrich, D.C., Rowden, K., and Robichaud, P.R., 2020, Predicting the floods that follow the flames: Bulletin of the American Meteorological Society, v. 101, no. 7, p. E1101-E1106, https://doi.org/10.1175/BAMS-D-20-0040.1.","productDescription":"6 p.","startPage":"E1101","endPage":"E1106","ipdsId":"IP-116546","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456911,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/bams-d-20-0040.1","text":"Publisher Index Page"},{"id":375973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"101","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gourley, Jonathan J 0000-0001-7363-3755","orcid":"https://orcid.org/0000-0001-7363-3755","contributorId":225540,"corporation":false,"usgs":false,"family":"Gourley","given":"Jonathan","email":"","middleInitial":"J","affiliations":[{"id":41158,"text":"NOAA/OAR/National Severe Storms Laboratory, Norman, OK, USA 73072","active":true,"usgs":false}],"preferred":false,"id":791536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vergara, Humberto","contributorId":225541,"corporation":false,"usgs":false,"family":"Vergara","given":"Humberto","email":"","affiliations":[{"id":41159,"text":"Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA 73072","active":true,"usgs":false}],"preferred":false,"id":791537,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arthur, Ami","contributorId":225542,"corporation":false,"usgs":false,"family":"Arthur","given":"Ami","affiliations":[{"id":41159,"text":"Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA 73072","active":true,"usgs":false}],"preferred":false,"id":791538,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Robert A","contributorId":225543,"corporation":false,"usgs":false,"family":"Clark","given":"Robert","email":"","middleInitial":"A","affiliations":[{"id":41159,"text":"Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA 73072","active":true,"usgs":false}],"preferred":false,"id":791539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":791540,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791541,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hempel, Laura A. 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":224286,"corporation":false,"usgs":true,"family":"Hempel","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791542,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goodrich, David C.","contributorId":65552,"corporation":false,"usgs":false,"family":"Goodrich","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":791543,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rowden, Katherine","contributorId":225544,"corporation":false,"usgs":false,"family":"Rowden","given":"Katherine","email":"","affiliations":[{"id":41160,"text":"NOAA/National Weather Service Western Region Headquarters, Salt Lake City, UT, USA 84138","active":true,"usgs":false}],"preferred":false,"id":791544,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Robichaud, Peter R.","contributorId":176259,"corporation":false,"usgs":false,"family":"Robichaud","given":"Peter","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":791545,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70211883,"text":"70211883 - 2020 - Comparison of underwater video with electrofishing and dive‐counts for stream fish abundance estimation","interactions":[],"lastModifiedDate":"2021-02-03T23:17:27.387819","indexId":"70211883","displayToPublicDate":"2020-04-29T09:35:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of underwater video with electrofishing and dive‐counts for stream fish abundance estimation","docAbstract":"<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>Advances in video technology enable new strategies for stream fish research. We compared juvenile (age‐0) and adult (age 1+) Brook Trout<span>&nbsp;</span><i>Salvelinus fontinalis<span>&nbsp;</span></i>abundance estimates from underwater video with backpack electrofishing and dive‐count methods across a series of stream pools in Shenandoah National Park, Virginia (<i>n<span>&nbsp;</span></i>= 41). Video methods estimated greater mean abundance of adult trout than 1‐pass electrofishing but were not different than 3‐pass electrofishing or dive‐count methods in this regard. In contrast, videos underestimated abundance of juvenile trout, and we suggest this is because predator avoidance‐behaviors by juvenile trout limit their use of microhabitat locations visible to cameras. Integrated abundance estimates from 2 cameras increased correspondence to comparison methods relative to single cameras, demonstrating the importance of an expanded field of view for video sampling in streams. Geomorphic features helped explain method‐wise differences: more adult trout were estimated with video than 3‐pass electrofishing as riffle crest depth and boulder composition increased, indicating habitat associations with trout escapement from electrofishing. Our results demonstrated that video techniques can provide a robust alternative or supplement to traditional methods for estimating adult trout abundance in stream pools.</p></div></div></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10245","usgsCitation":"Hitt, N.P., Rogers, K.M., Snyder, C.D., and Dolloff, C.A., 2020, Comparison of underwater video with electrofishing and dive‐counts for stream fish abundance estimation: Transactions of the American Fisheries Society, v. 150, no. 1, p. 24-37, https://doi.org/10.1002/tafs.10245.","productDescription":"14 p.","startPage":"24","endPage":"37","ipdsId":"IP-114418","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":456915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10245","text":"Publisher Index Page"},{"id":377329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virgiinia","otherGeospatial":"Shenandoah National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -78.78570556640624,\n              38.09674050228651\n            ],\n            [\n              -78.6785888671875,\n              38.19610083395667\n            ],\n            [\n              -78.56597900390625,\n              38.26945406815749\n            ],\n            [\n              -78.45062255859374,\n              38.370732250376854\n            ],\n            [\n              -78.34762573242188,\n              38.4428334985915\n            ],\n            [\n              -78.24600219726562,\n          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Center","active":true,"usgs":true}],"preferred":true,"id":795653,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snyder, Craig D. 0000-0002-3448-597X csnyder@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-597X","contributorId":2568,"corporation":false,"usgs":true,"family":"Snyder","given":"Craig","email":"csnyder@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dolloff, C. Andrew","contributorId":97405,"corporation":false,"usgs":true,"family":"Dolloff","given":"C.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":795655,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70214673,"text":"70214673 - 2020 - Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","interactions":[],"lastModifiedDate":"2020-10-02T13:24:58.347144","indexId":"70214673","displayToPublicDate":"2020-04-29T08:21:42","publicationYear":"2020","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":"Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","docAbstract":"Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha  2,524 ha (95% C.I.), and 13,012 ha  1,384 ha for WGSW.  Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2153","usgsCitation":"Byrd, K.B., Lorenz, A., Anderson, J., Wallace, C., Kara Moore-O'Leary, Isola, J., Ortega, R., and Reiter, M., 2020, Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing: Ecological Applications, v. 30, no. 7, e02153, 20 p., https://doi.org/10.1002/eap.2153.","productDescription":"e02153, 20 p.","ipdsId":"IP-112842","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":378986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.16796875,\n              40.48038142908172\n            ],\n            [\n              -122.431640625,\n              40.713955826286046\n            ],\n            [\n              -123.00292968749999,\n              40.34654412118006\n            ],\n            [\n              -122.958984375,\n              39.26628442213066\n            ],\n            [\n              -122.431640625,\n              38.58252615935333\n            ],\n            [\n              -121.9482421875,\n              37.33522435930639\n            ],\n            [\n              -120.5419921875,\n              36.06686213257888\n            ],\n            [\n              -119.4873046875,\n              35.02999636902566\n            ],\n            [\n              -119.00390625,\n              34.994003757575776\n            ],\n            [\n              -118.564453125,\n              35.209721645221386\n            ],\n            [\n              -118.95996093749999,\n              36.35052700542763\n            ],\n            [\n              -120.0146484375,\n              37.055177106660814\n            ],\n            [\n              -121.201171875,\n              38.89103282648846\n            ],\n            [\n              -122.16796875,\n              40.48038142908172\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"30","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorenz, Austen 0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, James","contributorId":242025,"corporation":false,"usgs":false,"family":"Anderson","given":"James","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":800395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wallace, Cynthia 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":149179,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kara Moore-O'Leary","contributorId":242031,"corporation":false,"usgs":false,"family":"Kara Moore-O'Leary","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800397,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isola, Jennifer","contributorId":242027,"corporation":false,"usgs":false,"family":"Isola","given":"Jennifer","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ortega, Ricardo","contributorId":242028,"corporation":false,"usgs":false,"family":"Ortega","given":"Ricardo","email":"","affiliations":[{"id":48476,"text":"Grassland Water District","active":true,"usgs":false}],"preferred":false,"id":800399,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reiter, Matt","contributorId":242029,"corporation":false,"usgs":false,"family":"Reiter","given":"Matt","email":"","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":800400,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70206720,"text":"tm7C24 - 2020 - Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences","interactions":[],"lastModifiedDate":"2020-04-29T12:04:07.712559","indexId":"tm7C24","displayToPublicDate":"2020-04-28T15:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C24","displayTitle":"Bayesian Modeling of Non-Stationary, Univariate, Spatial  Data for the Earth Sciences","title":"Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences","docAbstract":"<p>Some Earth science data, such as geochemical measurements of element concentrations, are non-stationary—the mean and the standard deviation vary spatially. It is important to estimate the spatial variations in both statistics because such information is indicative of geological and other Earth processes. To this end, an estimation method is formulated as a Bayesian hierarchical model. The method represents the spatially varying mean and the spatially varying standard deviation with basis functions; this formulation implicitly accounts for a spatially varying covariance function. A unique advantage of this method is that it can map the mean, the standard deviation, quantiles, and exceedance probabilities. The method is demonstrated by mapping titanium concentrations, which are measured in the coastal plain of the southeastern United States. Various checks demonstrate that the model fits the data and that the estimated statistics are geologically plausible.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C24","usgsCitation":"Ellefsen, K.J., and Van Gosen, B.S., 2020, Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences: U.S. Geological Survey Techniques and Methods, book 7, chap. C24, 20 p., https://doi.org/10.3133/tm7C24.","productDescription":"Report: iii, 20 p.; Companion File","onlineOnly":"Y","ipdsId":"IP-098004","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":374242,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c24/tm7c24.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7 C-24"},{"id":374257,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c24/supplementary_materials.zip","text":"Supplementary Materials","size":"12.0 kB","linkFileType":{"id":6,"text":"zip"},"description":"T and M 7 C-24 Supplementary Materials"},{"id":374241,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c24/coverthb.jpg"},{"id":374243,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C20","text":"Techniques and Methods 7-C20—","linkHelpText":"User Guide to Bayesian Modeling of Non-Stationary,  Univariate, Spatial Data Using R-Language Package BMNUS"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/gggsc/\" data-mce-href=\"https://www.usgs.gov/centers/gggsc/\"> Geology, Geophysics, and Geochemistry Science Center</a><br>U.S. Geological Survey<br>Box 25046, Mail Stop 973<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Method</li><li>Demonstration of the Method</li><li>Discussion</li><li>Conclusions</li><li>Acknowledgments</li><li>Data, Software, and Reproducibility</li><li>References Cited</li><li>Appendix 1. Checks of Statistical Model</li><li>Appendix 2. Sensitivity Analysis</li><li>Appendix 3. Covariance Function</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ellefsen, Karl J. 0000-0003-3075-4703 ellefsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3075-4703","contributorId":789,"corporation":false,"usgs":true,"family":"Ellefsen","given":"Karl","email":"ellefsen@usgs.gov","middleInitial":"J.","affiliations":[{"id":82803,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":false}],"preferred":true,"id":775546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":775547,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70200739,"text":"tm7C20 - 2020 - User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS","interactions":[],"lastModifiedDate":"2020-04-29T11:59:05.544535","indexId":"tm7C20","displayToPublicDate":"2020-04-28T15:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C20","displayTitle":"User Guide to Bayesian Modeling of Non-Stationary,  Univariate, Spatial Data Using R-Language Package BMNUS","title":"User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS","docAbstract":"<p>Bayesian modeling of non-stationary, univariate, spatial data is performed using the R-language package BMNUS. A unique advantage of this package is that it can map the mean, standard deviation, quantiles, and probability of exceeding a specified value. The package includes several R-language classes that prepare the data for the modeling, help select suitable model parameters, and help analyze the results. This user guide describes the BMNUS package and presents step-by-step instructions to model data that accompany the package.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C20","collaboration":"","usgsCitation":"Ellefsen, K.J, Goldman, M.A., and Van Gosen, B.S., 2020, User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS: U.S. Geological Survey Techniques and Methods, book 7, chap. 20, 27 p., https://doi.org/10.3133/tm7C20.","productDescription":"Report: iv, 27 p.; 6 Companion Files","onlineOnly":"Y","ipdsId":"IP-096956","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":374236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c20/coverthb.jpg"},{"id":374237,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c20/tm7c20.pdf","text":"Report","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7 C-20"},{"id":374281,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/ScriptsInUsersGuide.R","text":"Scripts in Users Guide","size":"24.0 kB","description":"T & M 7-C20 Scripts in Users Guide"},{"id":374238,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C24","text":"Techniques and Methods 7-C24—","linkHelpText":"Bayesian Modeling of Non-Stationary, Univariate, Spatial  Data for the Earth Sciences"},{"id":374282,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/BMNUS_1.0.0.tar.gz","text":"BMNUS Software Package","size":"308.kB","description":"T & M 7-C20 BMNUS Software Package"},{"id":374286,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/RepeatedMeasurements_1.0.0.tar.gz","text":"RepeatedMeasurements Software Package","size":"28.0 kB","description":"T & M 7-C20  RepeatedMeasurements Software Package"},{"id":374283,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/BasicCodaFunctions_1.0.0.tar.gz","text":"BasicCodaFunctions Software Package","size":"16.0 kB","description":"T & M 7-C20  BasicCodaFunctions Software Package"},{"id":374285,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/PairedMeasurements_1.0.0.tar.gz","text":"PairedMeasurements Software Package","size":"16.0 kB","description":"T & M 7-C20  PairedMeasurements Software Package"},{"id":374284,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/MappingUtilities_1.0.0.tar.gz","text":"MapUtilities Software Package","size":"8.0 kB","description":"T & M 7-C20  MapUtilities Software Package"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/gggsc/\" data-mce-href=\"https://www.usgs.gov/centers/gggsc/\"> Geology, Geophysics, and Geochemistry Science Center</a><br>U.S. Geological Survey<br>Box 25046, Mail Stop 973<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Preparatory Steps</li><li>Statistical Modeling</li><li>Data, Software, and Reproducibility</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Estimate the Standard Deviation of the Measurement Error using Paired Measurements</li><li>Appendix 2. Reading and Writing Data for GIS Programs</li><li>Appendix 3. Cross validation using a validation dataset</li><li>Appendix 4. Troubleshooting Tips</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ellefsen, Karl J. 0000-0003-3075-4703 ellefsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3075-4703","contributorId":789,"corporation":false,"usgs":true,"family":"Ellefsen","given":"Karl","email":"ellefsen@usgs.gov","middleInitial":"J.","affiliations":[{"id":82803,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":false}],"preferred":true,"id":756803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldman, Margaret A. 0000-0003-2232-6362 mgoldman@usgs.gov","orcid":"https://orcid.org/0000-0003-2232-6362","contributorId":176468,"corporation":false,"usgs":true,"family":"Goldman","given":"Margaret","email":"mgoldman@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":787832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":756804,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70209784,"text":"fs20203023 - 2020 - Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2016–17","interactions":[],"lastModifiedDate":"2020-04-30T13:12:13.705126","indexId":"fs20203023","displayToPublicDate":"2020-04-28T14:42:14","publicationYear":"2020","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":"2020-3023","displayTitle":"Continuous Water-Quality and Suspended-Sediment Transport Monitoring in the San Francisco Bay, California, Water Years 2016–17","title":"Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2016–17","docAbstract":"<p><span>The U.S. Geological Survey (USGS) monitors water quality and suspended-sediment transport in the San Francisco Bay (Bay) as part of a multi-agency effort to address estuary management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people, and the Bay teems with plants and both resident and migratory wildlife, and fish. Freshwater mixes with salt water in the Bay and is subject to riverine influences (floods, droughts, managed reservoir releases, and freshwater diversions) and marine influences (tides, waves, and effects of salt water). To understand this environment, the USGS along with its cooperators (see “Acknowledgments”), has been monitoring the Bay’s waters continuously since 1988.</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203023","usgsCitation":"Einhell, D.C., Downing-Kunz, M.A., and Livsey, D.N., 2020, Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2016–17: U.S. Geological Survey Fact Sheet 2020–3023, 4 p., https://doi.org/10.3133/fs20203023.","productDescription":"4 p. ","numberOfPages":"4","ipdsId":"IP-113711","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":374332,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3023/fs20203023.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3023/coverthb.jpg"}],"country":"United States","state":"California","city":"","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.947998046875,\n              37.391981943533544\n            ],\n            [\n              -121.89056396484375,\n              37.391981943533544\n            ],\n            [\n              -121.89056396484375,\n              38.171273439283084\n            ],\n            [\n              -122.947998046875,\n              38.171273439283084\n            ],\n            [\n              -122.947998046875,\n              37.391981943533544\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ca@usgs.gov\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,<br><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>6000 J Street, Placer Hall<br>Sacramento, California 95819</p>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Einhell, Darin C. 0000-0002-3190-7727 deinhell@usgs.gov","orcid":"https://orcid.org/0000-0002-3190-7727","contributorId":220042,"corporation":false,"usgs":true,"family":"Einhell","given":"Darin","email":"deinhell@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":787999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Downing-Kunz, Maureen A. 0000-0002-4879-0318 mdowning-kunz@usgs.gov","orcid":"https://orcid.org/0000-0002-4879-0318","contributorId":3690,"corporation":false,"usgs":true,"family":"Downing-Kunz","given":"Maureen","email":"mdowning-kunz@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788001,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70206310,"text":"sim2932C - 2020 - Geologic map of the southern flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii","interactions":[],"lastModifiedDate":"2024-05-23T22:03:38.745463","indexId":"sim2932C","displayToPublicDate":"2020-04-28T11:59:26","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2932","chapter":"C","displayTitle":"Geologic Map of the Southern Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii","title":"Geologic map of the southern flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii","docAbstract":"<p>On the Island of Hawaiʻi, Mauna Loa, the largest volcano on Earth, has erupted 33 times since written descriptions became available in 1832. Some eruptions began with only brief seismic unrest, whereas others followed several months to a year of increased seismicity. Once underway, its eruptions can produce lava flows that may reach the sea in less than 24 hours, severing roads and utilities. In terms of eruption frequency, pre-eruption warning, and rapid flow emplacement, Mauna Loa has great volcanic-hazard potential for the Island of Hawai‘i. Volcanic hazards on Mauna Loa may be anticipated, and risk substantially mitigated, by documenting the past activity to refine our knowledge of the hazards and by alerting the public and local government officials of our findings and their implications for hazards assessments and risk.</p><p>Although most Mauna Loa eruptions begin in the summit area at 12,000 feet (ft) elevation, the Southwest Rift Zone (SWRZ) was the source of at least 10 flank eruptions since 1843. The SWRZ extends from the summit towards Kalae (South Point) at sea level. The lowermost part of this rift zone, marked by Pu‘uʻoke‘oke‘o to the north at 6,874 ft elevation and extending to the sea, makes up the lower SWRZ. The community of Hawaiian Ocean View Estates, with a population of about 2,500, is the largest in the region. The subdivision is built entirely on flows erupted from southern Mauna Loa, and some source vents are located within the subdivision. Approximately 25 percent of the subdivision is within Hazard Zone 1.</p><p>From east to west, the map covers the area from Punalu‘u to Miloli‘i and, from north to south, extends from north of Pu‘uʻoke‘oke‘o to Kalae (South Point). The map encompasses 1,163 square kilometers of the southwest flank of Mauna Loa, from 7,325 ft elevation to sea level. It shows the distribution of eruptive units (flows), which are separated into 16 age groups, ranging from more than 100,000 years before present to A.D. 1950.</p><p>Lava erupted from the SWRZ typically flows to the west, east, or south (depending upon vent location relative to the rift crest) and generally produces narrow flow lobes. Both morphologic lava flow types—‘a‘ā and pāhoehoe—are present. In general, the northern part of the mapped area is dominated by flows from the middle SWRZ, whereas the southern part contains flows from the lower SWRZ and includes areas adjacent to, and downslope of, the rift zone. The exceptions are flows that originated from the upper SWRZ in the northeastern part of the Punaluu quadrangle.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim2932C","usgsCitation":"Trusdell, F.A., and Lockwood, J.P., 2020, Geologic map of the southern flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii: U.S. Geological Survey Scientific Investigations Map 2932–C, pamphlet 28 p., 2 sheets, scale 1:50,000, https://doi.org/10.3133/sim2932C.","productDescription":"Pamphlet: iv, 28 p.; 2 Sheets: 51.88 x 39.18 inches and 38.20 x 38.05 inches; Read Me; Metadata; Database; 1 Appendix","numberOfPages":"28","additionalOnlineFiles":"Y","ipdsId":"IP-054346","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":429221,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932E","text":"Scientific Investigations Map 2932-E","linkHelpText":"- Geologic Map of the Northwest Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii"},{"id":374326,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932B","text":"Scientific Investigations Map 2932-B","linkHelpText":"- Geologic Map of the Central-Southeast Flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii"},{"id":374327,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2932A","text":"Scientific Investigations Map 2932-A","linkHelpText":"- Geologic Map of the Northeast Flank of Mauna Loa Volcano, Island of Hawai'i, Hawaii"},{"id":374325,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_appendix2.xlsx","text":"Appendix 2","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":374324,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_database.zip","size":"11.5 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Me"},"url":"https://pubs.usgs.gov/sim/2932/c/sim2932c_readme.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Southern flank of Mauna Loa Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.950927734375,\n              18.869904894964883\n            ],\n            [\n              -155.4400634765625,\n              18.869904894964883\n            ],\n            [\n              -155.4400634765625,\n              19.267072569005542\n            ],\n            [\n              -155.950927734375,\n              19.267072569005542\n            ],\n            [\n              -155.950927734375,\n              18.869904894964883\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://hvo.wr.usgs.gov/observatory/contactHVO.html\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://hvo.wr.usgs.gov/observatory/contactHVO.html\">Contact HVO</a><br><a href=\"https://hvo.wr.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://hvo.wr.usgs.gov/\">Volcano Science Center, Hawaiian Volcano Observatory</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br></p>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-04-28","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Trusdell, Frank A. 0000-0002-0681-0528 trusdell@usgs.gov","orcid":"https://orcid.org/0000-0002-0681-0528","contributorId":754,"corporation":false,"usgs":true,"family":"Trusdell","given":"Frank A.","email":"trusdell@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":774137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lockwood, John P. 0000-0002-6562-0222","orcid":"https://orcid.org/0000-0002-6562-0222","contributorId":30976,"corporation":false,"usgs":true,"family":"Lockwood","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":774138,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70210513,"text":"70210513 - 2020 - Stormwater control impacts on runoff volume and peak flow: A meta-analysis of watershed modelling studies","interactions":[],"lastModifiedDate":"2020-07-09T15:05:48.672194","indexId":"70210513","displayToPublicDate":"2020-04-28T10:01:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Stormwater control impacts on runoff volume and peak flow: A meta-analysis of watershed modelling studies","docAbstract":"<p><span>Decades of research has concluded that the percent of impervious surface cover in a watershed is strongly linked to negative impacts on urban stream health. Recently, there has been a push by municipalities to offset these effects by installing structural stormwater control measures (SCMs), which are landscape features designed to retain and reduce runoff to mitigate the effects of urbanisation on event hydrology. The goal of this study is to build generalisable relationships between the level of SCM implementation in urban watersheds and resulting changes to hydrology. A literature review of 185 peer‐reviewed studies of watershed‐scale SCM implementation across the globe was used to identify 52 modelling studies suitable for a meta‐analysis to build statistical relationships between SCM implementation and hydrologic change. Hydrologic change is quantified as the percent reduction in storm event runoff volume and peak flow between a watershed with SCMs relative to a (near) identical control watershed without SCMs. Results show that for each additional 1% of SCM‐mitigated impervious area in a watershed, there is an additional 0.43% reduction in runoff and a 0.60% reduction in peak flow. Values of SCM implementation required to produce a change in water quantity metrics were identified at varying levels of probability. For example, there is a 90% probability (high confidence) of at least a 1% reduction in peak flow with mitigation of 33% of impervious surfaces. However, as the reduction target increases or mitigated impervious surface decreases, the probability of reaching the reduction target also decreases. These relationships can be used by managers to plan SCM implementation at the watershed scale.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13784","usgsCitation":"Bell, C.D., Wolfand, J.M., Panos, C.L., Bhaskar, A.S., Gilliom, R.L., Hogue, T.S., Hopkins, K.G., and Jefferson, A.J., 2020, Stormwater control impacts on runoff volume and peak flow: A meta-analysis of watershed modelling studies: Hydrological Processes, v. 34, no. 14, p. 3134-3152, https://doi.org/10.1002/hyp.13784.","productDescription":"19 p.","startPage":"3134","endPage":"3152","ipdsId":"IP-114115","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":456920,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13784","text":"Publisher Index Page"},{"id":375409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bell, Colin D.","contributorId":215502,"corporation":false,"usgs":false,"family":"Bell","given":"Colin","email":"","middleInitial":"D.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfand, Jordyn M.","contributorId":225130,"corporation":false,"usgs":false,"family":"Wolfand","given":"Jordyn","email":"","middleInitial":"M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panos, Chelsea L.","contributorId":225131,"corporation":false,"usgs":false,"family":"Panos","given":"Chelsea","email":"","middleInitial":"L.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bhaskar, Aditi S.","contributorId":199824,"corporation":false,"usgs":false,"family":"Bhaskar","given":"Aditi","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":790477,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilliom, Ryan L.","contributorId":225132,"corporation":false,"usgs":false,"family":"Gilliom","given":"Ryan","email":"","middleInitial":"L.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790478,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790479,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790480,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jefferson, Anne J.","contributorId":199823,"corporation":false,"usgs":false,"family":"Jefferson","given":"Anne","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":790481,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
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