{"pageNumber":"239","pageRowStart":"5950","pageSize":"25","recordCount":46677,"records":[{"id":70211016,"text":"70211016 - 2020 - An updated genetic marker for detection of Lake Sinai Virus and metagenetic applications","interactions":[],"lastModifiedDate":"2020-07-17T16:11:40.285032","indexId":"70211016","displayToPublicDate":"2020-07-17T10:52:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"An updated genetic marker for detection of Lake Sinai Virus and metagenetic applications","docAbstract":"<h2 class=\"heading\">Background</h2><p>Lake Sinai Viruses (LSV) are common RNA viruses of honey bees (<i>Apis mellifera</i>) that frequently reach high abundance but are not linked to overt disease. LSVs are genetically heterogeneous and collectively widespread, but despite frequent detection in surveys, the ecological and geographic factors structuring their distribution in<span>&nbsp;</span><i>A. mellifera</i><span>&nbsp;</span>are not understood. Even less is known about their distribution in other species. Better understanding of LSV prevalence and ecology have been hampered by high sequence diversity within the LSV clade.</p><h2 class=\"heading\">Methods</h2><p>Here we report a new polymerase chain reaction (PCR) assay that is compatible with currently known lineages with minimal primer degeneracy, producing an expected 365 bp amplicon suitable for end-point PCR and metagenetic sequencing. Using the Illumina MiSeq platform, we performed pilot metagenetic assessments of three sample sets, each representing a distinct variable that might structure LSV diversity (geography, tissue, and species).</p><h2 class=\"heading\">Results</h2><p>The first sample set in our pilot assessment compared cDNA pools from managed<span>&nbsp;</span><i>A. mellifera</i><span>&nbsp;</span>hives in California (<i>n</i>&nbsp;=&nbsp;8) and Maryland (<i>n</i>&nbsp;=&nbsp;6) that had previously been evaluated for LSV2, confirming that the primers co-amplify divergent lineages in real-world samples. The second sample set included cDNA pools derived from different tissues (thorax vs. abdomen,<span>&nbsp;</span><i>n</i>&nbsp;=&nbsp;24 paired samples), collected from managed<span>&nbsp;</span><i>A. mellifera</i><span>&nbsp;</span>hives in North Dakota. End-point detection of LSV frequently differed between the two tissue types; LSV metagenetic composition was similar in one pair of sequenced samples but divergent in a second pair. Overall, LSV1 and intermediate lineages were common in these samples whereas variants clustering with LSV2 were rare. The third sample set included cDNA from individual pollinator specimens collected from diverse landscapes in the vicinity of Lincoln, Nebraska. We detected LSV in the bee<span>&nbsp;</span><i>Halictus ligatus</i><span>&nbsp;</span>(four of 63 specimens tested, 6.3%) at a similar rate as<span>&nbsp;</span><i>A. mellifera</i><span>&nbsp;</span>(nine of 115 specimens, 7.8%), but only one<span>&nbsp;</span><i>H. ligatus</i><span>&nbsp;</span>sequencing library yielded sufficient data for compositional analysis. Sequenced samples often contained multiple divergent LSV lineages, including individual specimens. While these studies were exploratory rather than statistically powerful tests of hypotheses, they illustrate the utility of high-throughput sequencing for understanding LSV transmission within and among species.</p>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.9424","usgsCitation":"Iwanowicz, D.D., Wu-Smart, J.Y., Olgun, T., Smart, A.H., Otto, C., Lopez, D., Evans, J.D., and Cornman, R.S., 2020, An updated genetic marker for detection of Lake Sinai Virus and metagenetic applications: PeerJ, v. 8, e9424, 18 p., https://doi.org/10.7717/peerj.9424.","productDescription":"e9424, 18 p.","ipdsId":"IP-117458","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455972,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.9424","text":"Publisher Index Page"},{"id":436871,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9ZMA1","text":"USGS data release","linkHelpText":"Genetic detection of Lake Sinai Virus in honey bees (Apis mellifera) and other insects"},{"id":436870,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9ZMA1","text":"USGS data release","linkHelpText":"Genetic detection of Lake Sinai Virus in honey bees (Apis mellifera) and other insects"},{"id":376462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Maryland, Nebraska","county":"San Joaquin County","city":"Beltsville, Lincoln","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        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Tugce","contributorId":228877,"corporation":false,"usgs":false,"family":"Olgun","given":"Tugce","email":"","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":792439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smart, Autumn H. 0000-0003-0711-3035","orcid":"https://orcid.org/0000-0003-0711-3035","contributorId":228828,"corporation":false,"usgs":true,"family":"Smart","given":"Autumn","email":"","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792440,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792441,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lopez, Dawn","contributorId":228879,"corporation":false,"usgs":false,"family":"Lopez","given":"Dawn","email":"","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":792442,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Evans, Jay D.","contributorId":168966,"corporation":false,"usgs":false,"family":"Evans","given":"Jay","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":792443,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort 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,{"id":70211283,"text":"70211283 - 2020 - A comparative phylogeographic approach to facilitate recovery of an imperiled freshwater mussel (Bivalvia: Unionida: Potamilus inflatus)","interactions":[],"lastModifiedDate":"2020-07-22T15:30:11.619569","indexId":"70211283","displayToPublicDate":"2020-07-17T10:26:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"A comparative phylogeographic approach to facilitate recovery of an imperiled freshwater mussel (Bivalvia: Unionida: Potamilus inflatus)","docAbstract":"North American freshwaters are among the world’s most threatened ecosystems, and freshwater mussels are among the most imperiled inhabiting these systems. A critical aspect of conservation biology is delineating patterns of genetic diversity, which can be difficult when a taxon has been extirpated from a significant portion of its historical range. In such cases, evaluating conservation and recovery options may benefit by using surrogate species as proxies when assessing overall patterns of genetic diversity. Here, we integrate the premise of surrogate species into a comparative phylogeographic framework to hypothesize genetic relationships between extant and extirpated populations of Potamilus inflatus by characterizing genetic structure in co-distributed congeners with similar life histories and dispersal capabilities. Our mitochondrial and nuclear sequence data exhibited variable patterns of genetic divergence between Potamilus spp. native to the Mobile and Pascagoula + Pearl + Pontchartrain (PPP) provinces. However, hierarchical Approximate Bayesian Computation indicated that the diversification between Mobile and PPP clades was synchronous and represents a genetic signature of a common history of vicariance. Recent fluctuations in sea-level appear to have caused Potamilus spp. in the PPP to form a single genetic cluster, providing justification for using individuals from the Amite River as a source of brood stock to re-establish extirpated populations of P. inflatus. Future studies utilizing eDNA and genome-wide molecular data are essential to better understand the distribution of P. inflatus and establish robust recovery plans. Given the imperilment status of freshwater mussels globally, our study represents a useful methodology for predicting relationships among extant and extirpated populations of imperiled species.","language":"English","publisher":"MDPI","doi":"10.3390/d12070281","usgsCitation":"Smith, C.H., and Johnson, N., 2020, A comparative phylogeographic approach to facilitate recovery of an imperiled freshwater mussel (Bivalvia: Unionida: Potamilus inflatus): Diversity, v. 12, no. 7, 281,21 p., https://doi.org/10.3390/d12070281.","productDescription":"281,21 p.","ipdsId":"IP-119251","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455974,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d12070281","text":"Publisher Index Page"},{"id":436872,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3CFL5","text":"USGS data release","linkHelpText":"Novel genetic resources to facilitate future molecular studies in freshwater mussels (Bivalvia: Unionidae)"},{"id":376638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Alabama, Mississippi, Louisiana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.99951171875,\n              28.998531814051795\n            ],\n            [\n              -85.69335937499999,\n              28.998531814051795\n            ],\n            [\n              -85.69335937499999,\n              31.87755764334002\n            ],\n            [\n              -91.99951171875,\n              31.87755764334002\n            ],\n            [\n              -91.99951171875,\n              28.998531814051795\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Chase H. 0000-0002-1499-0311","orcid":"https://orcid.org/0000-0002-1499-0311","contributorId":225140,"corporation":false,"usgs":false,"family":"Smith","given":"Chase","email":"","middleInitial":"H.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":793502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":793503,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70211624,"text":"70211624 - 2020 - Observations of an extreme atmospheric river storm with a diverse sensor network","interactions":[],"lastModifiedDate":"2021-10-26T16:02:25.708294","indexId":"70211624","displayToPublicDate":"2020-07-17T09:38:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Observations of an extreme atmospheric river storm with a diverse sensor network","docAbstract":"<p><span>Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020EA001129","usgsCitation":"Hatchett, B.J., Cao, Q., Dawson, P.B., Ellis, C.J., Hecht, C.W., Kawzenuk, B., Lancaster, J.T., Osborne, T.C., Wilson, A.M., Anderson, M.L., Dettinger, M., Kalansky, J.F., Kaplan, M.L., Lettenmaier, D.P., Oakley, N.S., Ralph, R., Reynolds, D.W., White, A.B., Sierks, M., and Sumargo, E., 2020, Observations of an extreme atmospheric river storm with a diverse sensor network: Earth and Space Science, v. 7, no. 8, e2020EA001129, 21 p., https://doi.org/10.1029/2020EA001129.","productDescription":"e2020EA001129, 21 p.","ipdsId":"IP-115218","costCenters":[{"id":617,"text":"Volcano Science 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 \"}}]}","volume":"7","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hatchett, Benjamin J. 0000-0003-1066-3601","orcid":"https://orcid.org/0000-0003-1066-3601","contributorId":214405,"corporation":false,"usgs":false,"family":"Hatchett","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":39033,"text":"Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":794839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cao, Q. 0000-0003-3262-2149","orcid":"https://orcid.org/0000-0003-3262-2149","contributorId":236966,"corporation":false,"usgs":false,"family":"Cao","given":"Q.","email":"","affiliations":[{"id":47576,"text":"Department of Geography, University of California, Los Angeles, California, USA","active":true,"usgs":false}],"preferred":false,"id":794840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dawson, Phillip B. 0000-0003-4065-0588 dawson@usgs.gov","orcid":"https://orcid.org/0000-0003-4065-0588","contributorId":206751,"corporation":false,"usgs":true,"family":"Dawson","given":"Phillip","email":"dawson@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":794841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellis, C. J. 0000-0002-0901-1545","orcid":"https://orcid.org/0000-0002-0901-1545","contributorId":236968,"corporation":false,"usgs":false,"family":"Ellis","given":"C.","email":"","middleInitial":"J.","affiliations":[{"id":47577,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego: San Diego, California, US","active":true,"usgs":false}],"preferred":false,"id":794842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hecht, C. W. 0000-0002-8357-3263","orcid":"https://orcid.org/0000-0002-8357-3263","contributorId":236985,"corporation":false,"usgs":false,"family":"Hecht","given":"C.","email":"","middleInitial":"W.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kawzenuk, B. 0000-0003-1194-4296","orcid":"https://orcid.org/0000-0003-1194-4296","contributorId":236969,"corporation":false,"usgs":false,"family":"Kawzenuk","given":"B.","email":"","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794844,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lancaster, J. T. 0000-0003-3662-3181","orcid":"https://orcid.org/0000-0003-3662-3181","contributorId":236970,"corporation":false,"usgs":false,"family":"Lancaster","given":"J.","email":"","middleInitial":"T.","affiliations":[{"id":47579,"text":"California Geological Survey, Sacramento, California, USA","active":true,"usgs":false}],"preferred":false,"id":794845,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Osborne, T. C. 0000-0003-3279-4688","orcid":"https://orcid.org/0000-0003-3279-4688","contributorId":236971,"corporation":false,"usgs":false,"family":"Osborne","given":"T.","email":"","middleInitial":"C.","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794846,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, A. M. 0000-0001-7342-1955","orcid":"https://orcid.org/0000-0001-7342-1955","contributorId":236972,"corporation":false,"usgs":false,"family":"Wilson","given":"A.","email":"","middleInitial":"M.","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794847,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Anderson, M. L.","contributorId":236973,"corporation":false,"usgs":false,"family":"Anderson","given":"M.","email":"","middleInitial":"L.","affiliations":[{"id":47580,"text":"California Department of Water Resources, Sacramento, California, USA","active":true,"usgs":false}],"preferred":false,"id":794848,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dettinger, M. D. 0000-0002-7509-7332","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":236974,"corporation":false,"usgs":false,"family":"Dettinger","given":"M. D.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794849,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kalansky, J. F. 0000-0003-2562-7398","orcid":"https://orcid.org/0000-0003-2562-7398","contributorId":236975,"corporation":false,"usgs":false,"family":"Kalansky","given":"J.","email":"","middleInitial":"F.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794850,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kaplan, M. L. 0000-0003-0072-8758","orcid":"https://orcid.org/0000-0003-0072-8758","contributorId":236976,"corporation":false,"usgs":false,"family":"Kaplan","given":"M.","email":"","middleInitial":"L.","affiliations":[{"id":47581,"text":"Applied Meteorology Program, Embry-Riddle Aeronautical University, Prescott, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":794851,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lettenmaier, D. P. 0000-0002-0914-0726","orcid":"https://orcid.org/0000-0002-0914-0726","contributorId":236977,"corporation":false,"usgs":false,"family":"Lettenmaier","given":"D.","email":"","middleInitial":"P.","affiliations":[{"id":47576,"text":"Department of Geography, University of California, Los Angeles, California, USA","active":true,"usgs":false}],"preferred":false,"id":794852,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Oakley, N. S. 0000-0001-5680-9296","orcid":"https://orcid.org/0000-0001-5680-9296","contributorId":236978,"corporation":false,"usgs":false,"family":"Oakley","given":"N.","email":"","middleInitial":"S.","affiliations":[{"id":47583,"text":"Desert Research Institute and Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794853,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Ralph, R. M. 0000-0002-0870-6396","orcid":"https://orcid.org/0000-0002-0870-6396","contributorId":236979,"corporation":false,"usgs":false,"family":"Ralph","given":"R. M.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794854,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Reynolds, D. W.","contributorId":236980,"corporation":false,"usgs":false,"family":"Reynolds","given":"D.","email":"","middleInitial":"W.","affiliations":[{"id":47584,"text":"Department of Atmospheric and Oceanic Sciences, Colorado University, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":794855,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"White, A. B. 0000-0001-8587-3481","orcid":"https://orcid.org/0000-0001-8587-3481","contributorId":236981,"corporation":false,"usgs":false,"family":"White","given":"A.","email":"","middleInitial":"B.","affiliations":[{"id":47585,"text":"NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":794856,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Sierks, M. 0000-0003-2438-1082","orcid":"https://orcid.org/0000-0003-2438-1082","contributorId":236982,"corporation":false,"usgs":false,"family":"Sierks","given":"M.","email":"","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794857,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sumargo, E. 0000-0002-3671-7498","orcid":"https://orcid.org/0000-0002-3671-7498","contributorId":236983,"corporation":false,"usgs":false,"family":"Sumargo","given":"E.","email":"","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794858,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}}
,{"id":70212478,"text":"70212478 - 2020 - Behavioral response to high temperatures in a desert grassland bird: Use of shrubs as thermal refugia","interactions":[],"lastModifiedDate":"2020-08-17T14:33:37.700025","indexId":"70212478","displayToPublicDate":"2020-07-17T09:28:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral response to high temperatures in a desert grassland bird: Use of shrubs as thermal refugia","docAbstract":"<div class=\"div0\"><div class=\"row ArticleContentRow\"><p id=\"ID0EF\" class=\"first\">Birds inhabiting hot, arid ecosystems contend with trade-offs between heat dissipation and water conservation. As temperatures increase, passerines engage in various behaviors to reduce exposure to heat, solar radiation and insolation, and reradiation of heat from the ground. These responses to rising temperatures may result in subordination of reproductive urgency or nutrient acquisition to the need for thermoregulation. During studies on Arizona Grasshopper Sparrow (<i>Ammodramus savannarum ammolegus</i>) life history and ecology, we noted that these sparrows abandoned territoriality and foraging behaviors under certain circumstances in favor of cooler microsites. In this paper we document the extreme temperatures to which these and other ground-foraging and ground-nesting birds are exposed in southwestern desert grasslands, and we present evidence that<span>&nbsp;</span><i>A. s. ammolegus</i><span>&nbsp;</span>avoids exposure to extreme air and ground temperatures by using shrubs as thermal refugia. Our observations have implications for Arizona Grasshopper Sparrows and other desert grassland passerines in the southwestern United States, where the climate is projected to become hotter and drier. We provide some of the only behavioral data, and associated temperature data, associated with the use of thermal refugia by desert grassland birds. We encourage further studies that use more robust methods to supplement our observational data.</p></div></div>","language":"English","publisher":"BioOne","doi":"10.3398/064.080.0215","usgsCitation":"Ruth, J.M., Talbot, W.A., and Smith, E.K., 2020, Behavioral response to high temperatures in a desert grassland bird: Use of shrubs as thermal refugia: Western North American Naturalist, v. 80, no. 2, p. 265-275, https://doi.org/10.3398/064.080.0215.","productDescription":"11 p.","startPage":"265","endPage":"275","ipdsId":"IP-110841","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":377565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","county":"Santa Cruz County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-111.364,31.4234],[-111.3654,31.5211],[-111.2983,31.5216],[-111.2634,31.5218],[-111.1608,31.522],[-111.1595,31.5403],[-111.1616,31.5508],[-111.1612,31.6389],[-111.1614,31.7242],[-111.0036,31.7247],[-110.9557,31.7247],[-110.8906,31.7255],[-110.8712,31.7257],[-110.8518,31.7255],[-110.8523,31.731],[-110.7941,31.7309],[-110.7042,31.7308],[-110.6902,31.7306],[-110.6838,31.7305],[-110.6692,31.7308],[-110.6644,31.7303],[-110.617,31.7306],[-110.5341,31.7309],[-110.4485,31.7307],[-110.4485,31.702],[-110.4482,31.6883],[-110.4483,31.6536],[-110.448,31.6157],[-110.4561,31.6154],[-110.4558,31.6017],[-110.4555,31.5871],[-110.4562,31.4684],[-110.4561,31.3328],[-110.4611,31.3328],[-110.4888,31.3328],[-110.5574,31.3324],[-110.6259,31.3323],[-110.6645,31.3321],[-110.7229,31.3318],[-110.7915,31.3315],[-110.8238,31.3313],[-110.8261,31.3312],[-110.8351,31.3312],[-110.8659,31.3309],[-110.8787,31.3308],[-110.9721,31.3301],[-111.0496,31.3294],[-111.0664,31.3292],[-111.0728,31.3292],[-111.1604,31.3577],[-111.1676,31.3601],[-111.1705,31.361],[-111.1725,31.3617],[-111.1746,31.3624],[-111.2218,31.3778],[-111.2843,31.3978],[-111.364,31.4234]]]},\"properties\":{\"name\":\"Santa Cruz\",\"state\":\"AZ\"}}]}","volume":"80","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ruth, Janet M. 0000-0003-1576-5957 janet_ruth@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-5957","contributorId":1408,"corporation":false,"usgs":true,"family":"Ruth","given":"Janet","email":"janet_ruth@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":796463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Talbot, William A.","contributorId":238749,"corporation":false,"usgs":false,"family":"Talbot","given":"William","email":"","middleInitial":"A.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":796464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Eric Krabbe","contributorId":238750,"corporation":false,"usgs":false,"family":"Smith","given":"Eric","email":"","middleInitial":"Krabbe","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":796465,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227084,"text":"70227084 - 2020 - Bot fly parasitism of Allegheny woodrats (Neotoma magister) in Virginia","interactions":[],"lastModifiedDate":"2021-12-29T15:11:37.712404","indexId":"70227084","displayToPublicDate":"2020-07-16T09:05:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5153,"text":"The American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Bot fly parasitism of Allegheny woodrats (Neotoma magister) in Virginia","docAbstract":"<div class=\"div0\"><div class=\"row ArticleContentRow\"><p id=\"ID0EF\" class=\"first\">The Allegheny woodrat (<i>Neotoma magister</i>) is a species of high conservation concern and relatively well-studied with respect to habitat use/associations, food habits, conservation genetics, and population trends. However, with the exception of raccoon roundworm (<i>Baylisascaris procyonis</i>) occurrence and etiology in woodrats, most disease and parasite ecology aspects for the woodrat are unknown. Herein, we examined the prevalence of bot flies (<i>Cuterebra</i>) over nearly three decades of woodrat surveys (1990–2018) in the central Appalachian Mountains of western Virginia. We use genetic analyses to identify recent bot fly specimen collections from a woodrat captured in 2017. Though highly variable from year to year, the overall prevalence of parasitism was low (typically &lt; 4% of captures). As such, bot flies do not appear to be a widespread parasitic burden to Allegheny woodrats in Virginia. Genetic analysis of four collected bot fly larvae was inconclusive, as the genetic signature of these woodrat bots did not match any of the six bot species known to parasitize rodents and lagomorphs in the eastern United States. Further collections and genetic analyses will be needed to determine if the genetic database is incomplete or incorrect, or if our find is a new species of bot fly not yet taxonomically recognized.</p></div></div>","language":"English","publisher":"BioOne","doi":"10.1637/0003-0031-184.1.62","usgsCitation":"Powers, K., Mengak, M.T., Sheehy, R.R., Ford, W., and Reynolds, R., 2020, Bot fly parasitism of Allegheny woodrats (Neotoma magister) in Virginia: The American Midland Naturalist, v. 184, no. 1, p. 62-72, https://doi.org/10.1637/0003-0031-184.1.62.","productDescription":"11 p.","startPage":"62","endPage":"72","ipdsId":"IP-112172","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":393586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393585,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://bioone.org/journals/The-American-Midland-Naturalist/volume-184/issue-1/0003-0031-184.1.62/Bot-Fly-Parasitism-of-Allegheny-Woodrats-Neotoma-magister-in-Virginia/10.1637/0003-0031-184.1.62.full#:~:text=As%20such%2C%20bot%20flies%20do%20not%20appear%20to,rodents%20and%20lagomorphs%20in%20the%20eastern%20United%20States."}],"country":"United States","state":"Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -84.111328125,\n              36.33282808737917\n            ],\n            [\n              -74.5751953125,\n              36.33282808737917\n            ],\n            [\n              -74.5751953125,\n              39.50404070558415\n            ],\n            [\n              -84.111328125,\n              39.50404070558415\n            ],\n            [\n              -84.111328125,\n              36.33282808737917\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"184","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Powers, Karen E.","contributorId":270587,"corporation":false,"usgs":false,"family":"Powers","given":"Karen E.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":829592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mengak, Michael T.","contributorId":270588,"corporation":false,"usgs":false,"family":"Mengak","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":829593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sheehy, Robert R.","contributorId":270589,"corporation":false,"usgs":false,"family":"Sheehy","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":829594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":829591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reynolds, Richard J.","contributorId":270590,"corporation":false,"usgs":false,"family":"Reynolds","given":"Richard J.","affiliations":[{"id":35592,"text":"Virginia Department of Game and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":829595,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217192,"text":"70217192 - 2020 - Towards understanding relationships between atmospheric pressure variations and long-period horizontal seismic data: A case study","interactions":[],"lastModifiedDate":"2021-01-12T13:18:51.918256","indexId":"70217192","displayToPublicDate":"2020-07-16T07:17:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Towards understanding relationships between atmospheric pressure variations and long-period horizontal seismic data: A case study","docAbstract":"<p class=\"chapter-para\">Variations in atmospheric pressure have long been known to introduce noise in long-period (&gt;10&nbsp;s) seismic records. This noise can overwhelm signals of interest such as normal modes and surface waves. Generally, this noise is most pronounced on the horizontal components where it arises due to tilting of the seismometer in response to changes in atmospheric pressure. Several studies have suggested methodologies for correcting unwanted pressure-induced noise using collocated microbarograph records. However, how applicable these corrections are to varying geologic settings and installation types (e.g. vault versus post-hole) is unclear. Using coefficients obtained by solving for the residuals of these corrections, we can empirically determine the sensitivity of instruments in a specific location to the influences of pressure. To better understand how long-period, pressure-induced noise changes with time and emplacement, we examine horizontal seismic records along with barometric pressure at five different Global Seismographic Network stations, all with multiple broadband seismometers. We also analyse three Streckeisen STS-2 broadband seismometers, which are collocated with a microbarograph, at the Albuquerque Seismological Laboratory. We observe periods of high magnitude-squared-coherence (γ<sup>2</sup>-coherence; γ<sup>2</sup>&nbsp;&gt;&nbsp;0.8) between the seismic and pressure signals which fluctuate through time, frequency, and even between seismic instruments in the same vault. These observations suggest that these tilt-generated signals are highly sensitive to very local (&lt;10&nbsp;m) site effects. However, we find that in cases where instruments are not located at a large depth (&lt;100&nbsp;m), the pressure-induced noise is polarized in a nearly constant direction that is consistent with local topographic features or the geometry of the vault. We also find that borehole instruments at a large depth (&gt;100&nbsp;m) appear to be unaffected by pressure-loading mechanisms outlined by Sorrells (1971) but possibly by Newtonian attraction. Correlating the induced-noise polarization direction with times of high coherence, we work to identify sensors that are ultimately limited by pressure-induced horizontal noise as well as period bands that can benefit from pressure corrections. We find that while the situation is complex, each sensor appears to have its own unique response to pressure. Our findings suggest that we can determine empirical relationships between pressure and induced tilt on a case by case basis.</p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggaa340","usgsCitation":"Alejandro, A.C., Ringler, A.T., Wilson, D.C., Anthony, R.E., and Moore, S., 2020, Towards understanding relationships between atmospheric pressure variations and long-period horizontal seismic data: A case study: Geophysical Journal International, v. 223, no. 1, p. 676-691, https://doi.org/10.1093/gji/ggaa340.","productDescription":"16 p.","startPage":"676","endPage":"691","ipdsId":"IP-117969","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455992,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaa340","text":"Publisher Index Page"},{"id":382087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"223","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Alejandro, Alexis Casondra Bianca 0000-0002-3401-9303","orcid":"https://orcid.org/0000-0002-3401-9303","contributorId":246023,"corporation":false,"usgs":true,"family":"Alejandro","given":"Alexis","email":"","middleInitial":"Casondra Bianca","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807921,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, S.V. 0000-0003-3059-8261","orcid":"https://orcid.org/0000-0003-3059-8261","contributorId":247564,"corporation":false,"usgs":false,"family":"Moore","given":"S.V.","affiliations":[{"id":49580,"text":"UNLV, Las Vegas, NV","active":true,"usgs":false}],"preferred":false,"id":807922,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70211684,"text":"70211684 - 2020 - Mapping the 3-D extent of the Stillwater Complex, Montana—Implications for potential platinum group element exploration and development","interactions":[],"lastModifiedDate":"2020-08-06T22:38:00.306525","indexId":"70211684","displayToPublicDate":"2020-07-15T17:31:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Mapping the 3-D extent of the Stillwater Complex, Montana—Implications for potential platinum group element exploration and development","docAbstract":"<p><span>Geophysical models characterize the exposed and interpreted buried extent of the Stillwater Complex, critical for understanding the origin of the layered mafic intrusion and its associated high-grade platinum group element resources. The 3D models, constrained by gravity, magnetic, xenolith, seismic, borehole, and rock property data indicate that the likely maximum extent of the Stillwater Complex beneath Phanerozoic cover is ~10 times greater than its outcrop, ~2240&nbsp;km</span><sup>2</sup><span>. The thickness values are poorly constrained but vary from ~7000 to 12,000&nbsp;m, depending on crustal and mantle density variations and depths to the top of the lower crust and mantle. This thickness may include dense metasedimentary units of the basin into which the Stillwater Complex intruded. Using the modeled thickness results in a volume estimate of ~24,700&nbsp;km</span><sup>3</sup><span>, albeit poorly constrained. New analyses of xenoliths from the Cretaceous Sliderock and Suzie Peak intrusions produce ages of 2706–2716&nbsp;Ma, corresponding to the age of the Stillwater Complex, and 2813&nbsp;Ma, corresponding to the age of Archean gneissic basement. Seismic reflectors in inferred Archean crystalline basement, possibly including the Stillwater Complex, dip ~25–30° north, with segments dipping as much as 70° north. Layered reflectors beneath the Phanerozoic sedimentary section and above the inferred Archean crystalline basement may represent metasedimentary units, perhaps a southern extension of the Mesoproterozoic Belt Basin. The potential field models and the seismic reflection data suggest that the Stillwater Complex was dipping northward prior to deposition of Cambrian strata, perhaps uplifted in the late Archean or Proterozoic as previously proposed, and that during Laramide times, faulting and intrusions highly disrupted the complex. Temperature measurements from boreholes help constrain the depths of feasible mining of the complex.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2020.105860","usgsCitation":"Finn, C., Zientek, M., Parks, H.L., and Peterson, D.E., 2020, Mapping the 3-D extent of the Stillwater Complex, Montana—Implications for potential platinum group element exploration and development: Precambrian Research, v. 348, 105860, 13 p., https://doi.org/10.1016/j.precamres.2020.105860.","productDescription":"105860, 13 p.","ipdsId":"IP-114827","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":455995,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2020.105860","text":"Publisher Index Page"},{"id":377138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Stillwater Complex","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.70898437499999,\n              45.0502402697946\n            ],\n            [\n              -108.7646484375,\n              45.0502402697946\n            ],\n            [\n              -108.7646484375,\n              45.98169518512228\n            ],\n            [\n              -111.70898437499999,\n              45.98169518512228\n            ],\n            [\n              -111.70898437499999,\n              45.0502402697946\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"348","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":205010,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":795058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zientek, Michael L. 0000-0002-8522-9626","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":210763,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parks, Heather L. 0000-0002-5917-6866 hparks@usgs.gov","orcid":"https://orcid.org/0000-0002-5917-6866","contributorId":4989,"corporation":false,"usgs":true,"family":"Parks","given":"Heather","email":"hparks@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Dana E. 0000-0002-1941-265X","orcid":"https://orcid.org/0000-0002-1941-265X","contributorId":225536,"corporation":false,"usgs":true,"family":"Peterson","given":"Dana","email":"","middleInitial":"E.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":795061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70262551,"text":"70262551 - 2020 - A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms","interactions":[],"lastModifiedDate":"2025-01-23T17:56:36.098172","indexId":"70262551","displayToPublicDate":"2020-07-15T11:52:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms","docAbstract":"<p><span>Targeted amplicon sequencing methods, such as genotyping-in-thousands by sequencing (GT-seq), facilitate rapid, accurate, and cost-effective analysis of hundreds of genetic loci in thousands of individuals. Development of GT-seq panels is nontrivial, but studies describing trade-offs associated with different steps of GT-seq panel development are rare. Here, we construct a dual-purpose GT-seq panel for walleye (</span><i>Sander vitreus</i><span>), discuss trade-offs associated with different development and genotyping approaches, and provide suggestions for researchers constructing their own GT-seq panels. Our GT-seq panel was developed using an ascertainment set consisting of restriction site-associated DNA data from 954 individuals sampled from 23 populations in Minnesota and Wisconsin, USA. We conducted simulations to test the utility of all loci for parentage analysis and genetic stock identification and designed 600 primer pairs to maximize joint accuracy for these analyses. We then performed three rounds of primer optimization to remove loci that overamplified and our final panel consisted of 436 loci. We also explored different approaches for DNA extraction, multiplexed polymerase chain reaction (PCR) amplification, and cleanup steps during the GT-seq process and discovered the following: (i) inexpensive Chelex extractions performed well for genotyping; (ii) the exonuclease I and shrimp alkaline phosphatase (ExoSAP) procedure included in some current protocols did not improve results substantially and was probably unnecessary; and (iii) it was possible to PCR amplify panels separately and combine them prior to adapter ligation. Well-optimized GT-seq panels are valuable resources for conservation genetics and our findings and suggestions should aid in their construction in myriad taxa.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13226","usgsCitation":"Bootsma, M., Gruenthal, K., McKinney, G., Simmons, L., Miller, L., Sass, G., and Larson, W., 2020, A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms: Molecular Ecology Resources, v. 20, no. 6, p. 1706-1722, https://doi.org/10.1111/1755-0998.13226.","productDescription":"17 p.","startPage":"1706","endPage":"1722","ipdsId":"IP-115320","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481106,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2020.02.13.948331","text":"External 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 \"}}]}","volume":"20","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bootsma, Matthew L.","contributorId":349232,"corporation":false,"usgs":false,"family":"Bootsma","given":"Matthew L.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gruenthal, Kristen","contributorId":349610,"corporation":false,"usgs":false,"family":"Gruenthal","given":"Kristen","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKinney, Garrett","contributorId":270641,"corporation":false,"usgs":false,"family":"McKinney","given":"Garrett","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":924530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simmons, Levi","contributorId":349636,"corporation":false,"usgs":false,"family":"Simmons","given":"Levi","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Loren","contributorId":349233,"corporation":false,"usgs":false,"family":"Miller","given":"Loren","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":924532,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sass, Greg G.","contributorId":279948,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":924533,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924527,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70211326,"text":"70211326 - 2020 - Distribution of earthquakes on a branching fault system using integer programming and greedy sequential methods","interactions":[],"lastModifiedDate":"2020-09-10T20:13:38.032211","indexId":"70211326","displayToPublicDate":"2020-07-15T10:40:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of earthquakes on a branching fault system using integer programming and greedy sequential methods","docAbstract":"A new global optimization method is used to determine the distribution of earthquakes on a complex, connected fault system.  The method, integer programming, has been advanced in the field of operations research, but has not been widely applied to geophysical problems until recently.  In this application, we determine the optimal distribution of earthquakes on mapped faults to minimize the global misfit in slip rates for multi-fault ruptures.  Integer programming solves for a decision vector composed of every possible location that a sample of earthquakes can occur on every fault, subject to slip-rate uncertainty constraints. Step over connections are straightforward to include, whereas branching fault connections are not.  To include branching ruptures, we distinguish between individual multi-fault rupture paths, as opposed to formulating the integer-programming problem based on individual faults as in previous studies.  The new method is applied to the complex fault system in the San Francisco Bay Area as a case study.  Results from the integer-programming method are compared to those from a local optimization method, termed the greedy-sequential method.  Several experiments using these two methods indicate that shape of the on-fault magnitude distributions and which branching faults are involved in multi-fault ruptures depend on how much emphasis is placed on fitting the target slip rate.  In cases where the underlying data are not strong enough to warrant chasing the target slip rate, it is better to focus on the distribution of feasible results that better represents the uncertainty in the solutions imposed by the data.","language":"English","publisher":"AGU","doi":"10.1029/2020GC008964","usgsCitation":"Geist, E.L., and Parsons, T.E., 2020, Distribution of earthquakes on a branching fault system using integer programming and greedy sequential methods: Geochemistry, Geophysics, Geosystems, v. 21, no. 9, e2020GC008964, 22 p., https://doi.org/10.1029/2020GC008964.","productDescription":"e2020GC008964, 22 p.","ipdsId":"IP-115931","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499871,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/b65c6f112d5f46ac9575e46a20357731","text":"External Repository"},{"id":376688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":793799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793800,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70214604,"text":"70214604 - 2020 - Wastewater-based epidemiology pilot study to examine drug use in the Western United States","interactions":[],"lastModifiedDate":"2020-09-30T13:37:52.582629","indexId":"70214604","displayToPublicDate":"2020-07-15T08:28:31","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":"Wastewater-based epidemiology pilot study to examine drug use in the Western United States","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0025\">The extent of prescription and illicit drug abuse in geographically isolated rural and micropolitan communities in the intermountain western United States (US) has not been well tracked. The goal of this pilot study was to accurately measure drug dose consumption rates (DCR) between two select populations, normalize the data and compare the DCRs to similar communities. To learn about patterns of drug abuse between the two disparate communities, we used the emergent field of wastewater-based epidemiology (WBE). A rapid, quantitative and systematic process for the determination of multiple classes of prescribed and illicit drugs was applied to influent wastewater samples. Influent samples were collected over the course of three months (April to June 2019) at two wastewater treatment plants representing a small urban and a rural community. Collection of sewage influent included 24-h composite samples and the use of polar organic chemical integrative samplers (POCIS), time-weighted samplers. Using the results from the composite sampling data, DCRs per 1000 population could be calculated from the concentration data and the use of excretion correction factors. The following 18 compounds: amphetamine, methamphetamine, MDA, MDMA, morphine, 6-acetylmorphine, methadone, EDDP, codeine, benzoylecgonine, hydrocodone, hydromorphone, oxycodone, noroxycodone, ketamine, fluoxetine, tramadol, and ritalinic acid; represent a subset of the targeted analytes that were consistently measured at detectable concentration levels, and present at both sites. Following normalization of the drug measurements to influent flow rates and per capita, the small urban community demonstrated greater collective excretion rates (CER) than the rural community, with the exceptions of amphetamine and methamphetamine.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.140697","usgsCitation":"Bishop, N., Jones-Lepp, T., Margetts, M., Sykes, J., Alvarez, D.A., and Keil, D., 2020, Wastewater-based epidemiology pilot study to examine drug use in the Western United States: Science of the Total Environment, v. 745, 140697, 7 p., https://doi.org/10.1016/j.scitotenv.2020.140697.","productDescription":"140697, 7 p.","ipdsId":"IP-117735","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":456005,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7361084","text":"External Repository"},{"id":378895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"745","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bishop, Nicholas","contributorId":241939,"corporation":false,"usgs":false,"family":"Bishop","given":"Nicholas","email":"","affiliations":[{"id":41008,"text":"Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":800195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones-Lepp, Tammy","contributorId":241940,"corporation":false,"usgs":false,"family":"Jones-Lepp","given":"Tammy","email":"","affiliations":[{"id":48456,"text":"Consultant, Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":800196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Margetts, Miranda","contributorId":241942,"corporation":false,"usgs":false,"family":"Margetts","given":"Miranda","email":"","affiliations":[{"id":41008,"text":"Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":800197,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sykes, Jordan","contributorId":241944,"corporation":false,"usgs":false,"family":"Sykes","given":"Jordan","email":"","affiliations":[{"id":41008,"text":"Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":800198,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alvarez, David A. 0000-0002-6918-2709","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":220763,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800199,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keil, Deborah","contributorId":241945,"corporation":false,"usgs":false,"family":"Keil","given":"Deborah","email":"","affiliations":[{"id":41008,"text":"Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":800200,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70211095,"text":"fs20203029 - 2020 - Hydrologic conditions in Kansas, water year 2019","interactions":[],"lastModifiedDate":"2020-07-15T15:15:41.30338","indexId":"fs20203029","displayToPublicDate":"2020-07-15T06:52:52","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":"2020-3029","displayTitle":"Hydrologic Conditions in Kansas, Water Year 2019","title":"Hydrologic conditions in Kansas, water year 2019","docAbstract":"<p><span>The U.S. Geological Survey Kansas Water Science Center, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in the State of Kansas. These include a&nbsp;network of 217 real-time streamgages and 12 real-time reservoir-level monitoring stations in water year 2019.&nbsp;The data and associated analyses from the streamgages and monitoring stations provide a unique overview of hydrologic conditions and help improve the understanding of Kansas’ water resources. Annual assessments of hydrologic conditions are made by comparing statistical analyses of current and past water year data for the period of record.&nbsp;Long-term monitoring of hydrologic conditions in Kansas provides imperative information for many uses including managing water resources and protecting human life and property and promoting agricultural practices, industrial activities, operation of&nbsp;</span><span>reservoirs</span><span>, development of infrastructure, ecological assessments, and recreational purposes.</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203029","usgsCitation":"Davis, C., 2020, Hydrologic conditions in Kansas, Water year 2019: U.S. Geological Survey Fact Sheet 2020–3029, 6 p., https://doi.org/10.3133/fs20203029.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-117275","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376376,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3029/fs20203029.pdf","text":"Report","size":"5.89 MB","linkFileType":{"id":1,"text":"pdf"},"description":"fs 2020–3029"},{"id":376375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3029/coverthb2.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.541116,36.999573],[-99.648652,36.999604],[-99.657658,37.000197],[-99.875409,37.001659],[-99.995201,37.001631],[-100.115722,37.002206],[-100.193754,37.002133],[-100.552683,37.000735],[-100.734517,36.999059],[-100.756894,36.999357],[-100.855634,36.998626],[-100.904274,36.998745],[-100.945469,36.998153],[-101.012641,36.998176],[-101.359674,36.996232],[-102.04224,36.993083],[-102.041749,37.034397],[-102.041809,37.111973],[-102.042092,37.125021],[-102.041963,37.258164],[-102.041664,37.29765],[-102.042089,37.352819],[-102.041524,37.375018],[-102.042016,37.535261],[-102.041574,37.680436],[-102.042158,37.760164],[-102.042953,37.803535],[-102.044644,38.045532],[-102.044255,38.113011],[-102.044589,38.125013],[-102.044251,38.141778],[-102.044944,38.384419],[-102.044442,38.415802],[-102.044936,38.41968],[-102.045324,38.453647],[-102.045074,38.669617],[-102.045334,38.799463],[-102.046571,39.047038],[-102.04937,39.41821],[-102.049554,39.538932],[-102.050422,39.646048],[-102.050099,39.653812],[-102.050594,39.675594],[-102.051569,39.849805],[-102.051744,40.003078],[-101.904176,40.003162],[-101.841025,40.002784],[-101.409953,40.002354],[-101.324036,40.002696],[-100.937427,40.002145],[-100.75883,40.002302],[-100.66023,40.002162],[-100.645445,40.001883],[-100.196959,40.001494],[-99.990926,40.001503],[-99.948167,40.001813],[-99.930433,40.001516],[-99.813401,40.0014],[-99.772121,40.001804],[-99.756835,40.001342],[-99.746628,40.00182],[-99.49766,40.001912],[-99.423565,40.00227],[-99.412645,40.001868],[-99.282967,40.001879],[-99.018701,40.002333],[-98.710404,40.00218],[-98.690287,40.002548],[-98.652494,40.002245],[-98.64071,40.002493],[-98.560578,40.002274],[-98.274017,40.002516],[-98.250008,40.002307],[-98.193483,40.002614],[-98.099659,40.002227],[-97.838379,40.00191],[-97.777155,40.002167],[-97.510264,40.001835],[-97.369199,40.00206],[-97.20231,40.001442],[-97.142448,40.001495],[-97.137866,40.001814],[-97.049663,40.001323],[-96.916093,40.001506],[-96.622401,40.001158],[-96.610349,40.000881],[-96.467536,40.001035],[-96.125937,40.000432],[-96.02409,40.000719],[-95.30829,39.999998],[-95.308404,39.993758],[-95.30778,39.990618],[-95.307111,39.989114],[-95.302507,39.984357],[-95.289715,39.977706],[-95.274757,39.972115],[-95.269886,39.969396],[-95.261854,39.960618],[-95.257652,39.954886],[-95.250254,39.948644],[-95.241383,39.944949],[-95.236761,39.943931],[-95.231114,39.943784],[-95.220212,39.944433],[-95.21644,39.943953],[-95.213737,39.943206],[-95.204428,39.938949],[-95.201277,39.934194],[-95.20069,39.928155],[-95.20201,39.922438],[-95.205745,39.915169],[-95.206326,39.912121],[-95.206196,39.909557],[-95.205733,39.908275],[-95.201935,39.904053],[-95.199347,39.902709],[-95.193816,39.90069],[-95.189565,39.899959],[-95.179453,39.900062],[-95.172296,39.902026],[-95.159834,39.906984],[-95.156024,39.907243],[-95.149657,39.905948],[-95.146055,39.904183],[-95.143802,39.901918],[-95.142563,39.897992],[-95.142445,39.89542],[-95.143403,39.889356],[-95.142718,39.885889],[-95.140601,39.881688],[-95.137092,39.878351],[-95.134747,39.876852],[-95.128166,39.874165],[-95.105912,39.869164],[-95.090158,39.86314],[-95.085003,39.861883],[-95.081534,39.861718],[-95.052535,39.864374],[-95.042142,39.864805],[-95.037767,39.865542],[-95.032053,39.868337],[-95.027931,39.871522],[-95.025422,39.876711],[-95.025119,39.878833],[-95.025947,39.886747],[-95.02524,39.8897],[-95.024389,39.891202],[-95.018743,39.897372],[-95.013152,39.899953],[-95.00844,39.900596],[-95.003819,39.900401],[-94.990284,39.89701],[-94.986975,39.89667],[-94.977749,39.897472],[-94.963345,39.901136],[-94.959276,39.901671],[-94.95154,39.900533],[-94.943867,39.89813],[-94.934493,39.893366],[-94.929574,39.888754],[-94.927897,39.886112],[-94.927359,39.883966],[-94.927252,39.880258],[-94.928466,39.876344],[-94.931463,39.872602],[-94.938791,39.866954],[-94.940743,39.86441],[-94.942407,39.861066],[-94.942567,39.856602],[-94.939767,39.85193],[-94.937655,39.849786],[-94.92615,39.841322],[-94.916918,39.836138],[-94.909942,39.834426],[-94.903157,39.83385],[-94.892677,39.834378],[-94.889493,39.834026],[-94.886933,39.833098],[-94.881013,39.828922],[-94.878677,39.826522],[-94.877044,39.823754],[-94.876544,39.820594],[-94.875944,39.813294],[-94.876344,39.806894],[-94.880932,39.797338],[-94.884084,39.794234],[-94.890292,39.791626],[-94.892965,39.791098],[-94.925605,39.789754],[-94.929654,39.788282],[-94.932726,39.786282],[-94.935206,39.78313],[-94.935782,39.778906],[-94.935302,39.77561],[-94.934262,39.773642],[-94.929653,39.769098],[-94.926229,39.76649],[-94.916789,39.760938],[-94.912293,39.759338],[-94.906244,39.759418],[-94.899156,39.761258],[-94.895268,39.76321],[-94.883924,39.770186],[-94.88146,39.771258],[-94.871144,39.772994],[-94.869644,39.772894],[-94.867143,39.771694],[-94.865243,39.770094],[-94.863143,39.767294],[-94.860743,39.76309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 \"}}]}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/kswsc\" href=\"https://www.usgs.gov/centers/kswsc\">Kansas Water Science Center</a> <br>U.S. Geological Survey<br>1217 Biltmore Dr.<br>Lawrence, Kansas 66049<br></p>","tableOfContents":"<ul><li>Preceding Conditions and Precipitation</li><li>Streamflow Conditions and Drainage Basin Runoff</li><li>Record Breaking High Water</li><li>Reservoirs</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-07-15","noUsgsAuthors":false,"publicationDate":"2020-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Chantelle 0000-0001-6415-7320","orcid":"https://orcid.org/0000-0001-6415-7320","contributorId":225019,"corporation":false,"usgs":true,"family":"Davis","given":"Chantelle","email":"","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":792760,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70211288,"text":"70211288 - 2020 - Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio","interactions":[],"lastModifiedDate":"2020-07-22T15:10:17.124546","indexId":"70211288","displayToPublicDate":"2020-07-14T10:06:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio","docAbstract":"Cyanobacterial harmful algal blooms and the toxins they produce are a global water-quality problem. Monitoring and prediction tools are needed to quickly predict cyanotoxin action-level exceedances in recreational and drinking waters used by the public. To address this need, data were collected at eight locations in Ohio, USA, to identify factors significantly related to observed concentrations of microcystins (a freshwater cyanotoxin) that could be used in two types of site-specific regression models.  Real-time models include easily- or continuously-measured factors that do not require that a sample be collected; comprehensive models use a combination of discrete sample-based measurements and real-time factors.  The study sites included two recreational sites and six water treatment plant sites.  Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models were useful at some sites with lagged variables for cyanobacterial toxin genes, dissolved nutrients, and (or) nitrogen to phosphorus ratios.  Because models can be used for management decisions, important measures of model performance were sensitivity, specificity, and accuracy of estimates above or below the microcystin concentration threshold standard or action level. Sensitivity is how well the predictive tool correctly predicts exceedance of a threshold, an important measure for water-resource managers. Sensitivities >90% at four Lake Erie water treatment plants indicated that models with continuous monitor data were especially promising.  The planned next steps are to collect more data to build larger site-specific datasets and validate models before they can be used for management decisions.","language":"English","publisher":"Springer","doi":"10.1007/s10661-020-08407-x","usgsCitation":"Francy, D.S., Brady, A.M., Stelzer, E., Cicale, J.R., Hackney, C.P., Dalby, H.D., Struffolino, P., and Dwyer, D.F., 2020, Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio: Environmental Monitoring and Assessment, v. 192, 513, 27 p., https://doi.org/10.1007/s10661-020-08407-x.","productDescription":"513, 27 p.","ipdsId":"IP-095890","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":456015,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-020-08407-x","text":"Publisher Index Page"},{"id":436878,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CDF76E","text":"USGS data release","linkHelpText":"Data for multiple linear regression models for predicting microcystin concentration action-level exceedances in selected lakes in Ohio"},{"id":376635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -84.122314453125,\n              40.97160353279909\n            ],\n            [\n              -82.24365234375,\n              40.97160353279909\n            ],\n            [\n              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]\n}","volume":"192","noUsgsAuthors":false,"publicationDate":"2020-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Francy, Donna S. 0000-0001-9229-3557 dsfrancy@usgs.gov","orcid":"https://orcid.org/0000-0001-9229-3557","contributorId":1853,"corporation":false,"usgs":true,"family":"Francy","given":"Donna","email":"dsfrancy@usgs.gov","middleInitial":"S.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Amie M.G. 0000-0002-7414-0992 amgbrady@usgs.gov","orcid":"https://orcid.org/0000-0002-7414-0992","contributorId":2544,"corporation":false,"usgs":true,"family":"Brady","given":"Amie","email":"amgbrady@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cicale, Jessica R. 0000-0002-0008-4051","orcid":"https://orcid.org/0000-0002-0008-4051","contributorId":208157,"corporation":false,"usgs":true,"family":"Cicale","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hackney, Courtney Paige 0000-0002-7018-9780","orcid":"https://orcid.org/0000-0002-7018-9780","contributorId":228840,"corporation":false,"usgs":true,"family":"Hackney","given":"Courtney","email":"","middleInitial":"Paige","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dalby, Harrison D 0000-0001-6048-3217","orcid":"https://orcid.org/0000-0001-6048-3217","contributorId":228841,"corporation":false,"usgs":true,"family":"Dalby","given":"Harrison","email":"","middleInitial":"D","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793530,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Struffolino, Pamela 0000-0002-9065-3405","orcid":"https://orcid.org/0000-0002-9065-3405","contributorId":229549,"corporation":false,"usgs":false,"family":"Struffolino","given":"Pamela","email":"","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":793531,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dwyer, Daryl F. 0000-0002-5033-0927","orcid":"https://orcid.org/0000-0002-5033-0927","contributorId":229550,"corporation":false,"usgs":false,"family":"Dwyer","given":"Daryl","email":"","middleInitial":"F.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":793532,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70211063,"text":"ds1127 - 2020 - Causes of land change in the U.S. Interior Highlands, 2001–2011","interactions":[],"lastModifiedDate":"2021-06-14T19:49:41.072561","indexId":"ds1127","displayToPublicDate":"2020-07-14T09:50:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1127","displayTitle":"Causes of Land Change in the U.S. Interior Highlands, 2001–2011","title":"Causes of land change in the U.S. Interior Highlands, 2001–2011","docAbstract":"<p>The causes of land change from 2001 through 2011 for the Interior Highlands region of the south-central United States were assessed using satellite imagery, historical land-use and land-cover data, and digital orthophotos. The study was designed to develop improved regional land-use and land-cover change information, including identification of the proximate causes of change. The four leading causes of land change involved various stages of forest change: harvest (376,497 hectares), reforestation (105,150 hectares), stand loss to fire (98,875 hectares), and thinning (54,029 hectares). The study provides baseline spatial data for understanding human and ecological dynamics in the region. The spatial data, including metadata, are available in the data release associated with this report at https://doi.org/10.5066/P9W4SF05.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1127","usgsCitation":"Drummond, M.A., Stier, M.P., McBeth, J.L., Auch, R.F., Taylor, J.L., and Riegle, J.L., 2020, Causes of land change in the U.S. Interior Highlands, 2001–2011: U.S. Geological Survey Data Series 1127, 4 p., https://doi.org/10.3133/ds1127.","productDescription":"Report: iii, 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-102515","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":376322,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1127/ds1127.pdf","text":"Report","size":"4.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1127"},{"id":376321,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1127/coverthb.jpg"},{"id":376325,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W4SF05","text":"USGS data release","linkHelpText":"Data release for the Land Change Causes for the United States Interior Highlands (2001 to 2006 and 2006 to 2011 time intervals)"}],"country":"United States","state":"Missouri, Arkansas, Oklahoma, Kansas","otherGeospatial":"Interior Highlands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -96.06445312499999,\n              34.21634468843465\n            ],\n            [\n              -95.82275390624997,\n              33.943359946578795\n            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Cited</li></ul>","publishedDate":"2020-07-14","noUsgsAuthors":false,"publicationDate":"2020-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Drummond, Mark A. 0000-0001-7420-3503 madrummond@usgs.gov","orcid":"https://orcid.org/0000-0001-7420-3503","contributorId":3053,"corporation":false,"usgs":true,"family":"Drummond","given":"Mark","email":"madrummond@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":792641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stier, Michael P. 0000-0002-8518-9855 mpstier@usgs.gov","orcid":"https://orcid.org/0000-0002-8518-9855","contributorId":3121,"corporation":false,"usgs":true,"family":"Stier","given":"Michael","email":"mpstier@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":792642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McBeth, Jamie L. 0000-0002-7688-7985 jlmcbeth@usgs.gov","orcid":"https://orcid.org/0000-0002-7688-7985","contributorId":1254,"corporation":false,"usgs":true,"family":"McBeth","given":"Jamie","email":"jlmcbeth@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":792643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":792644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, Janis L. 0000-0002-9418-5215 jltaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-9418-5215","contributorId":147371,"corporation":false,"usgs":true,"family":"Taylor","given":"Janis","email":"jltaylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":792645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riegle, Jodi L. 0000-0001-8640-8952 jlriegle@usgs.gov","orcid":"https://orcid.org/0000-0001-8640-8952","contributorId":1789,"corporation":false,"usgs":true,"family":"Riegle","given":"Jodi","email":"jlriegle@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":792646,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70211257,"text":"70211257 - 2020 - Spatial proximity moderates genotype uncertainty in genetic tagging studies","interactions":[],"lastModifiedDate":"2020-08-04T14:28:28.360713","indexId":"70211257","displayToPublicDate":"2020-07-13T15:08:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatial proximity moderates genotype uncertainty in genetic tagging studies","docAbstract":"<p><span>Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (</span><i>Pekania pennanti</i><span>) in which 37% of hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63 and 42%, respectively, using three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.</span></p>","language":"English","publisher":"United States National Academy of Sciences","doi":"10.1073/pnas.2000247117","usgsCitation":"Augustine, B., Royle, A., Linden, D., and Fuller, A.K., 2020, Spatial proximity moderates genotype uncertainty in genetic tagging studies: Proceedings of the National Academy of Sciences, v. 117, no. 30, p. 17903-17912, https://doi.org/10.1073/pnas.2000247117.","productDescription":"10 p.","startPage":"17903","endPage":"17912","ipdsId":"IP-114514","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456020,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2000247117","text":"Publisher Index Page"},{"id":376597,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"30","noUsgsAuthors":false,"publicationDate":"2020-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Augustine, Ben C.","contributorId":229524,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":793443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":793444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Linden, Daniel W.","contributorId":229525,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":793445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuller, Angela K.","contributorId":229526,"corporation":false,"usgs":false,"family":"Fuller","given":"Angela","email":"","middleInitial":"K.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":793446,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70222537,"text":"70222537 - 2020 - Time-evolving surface and subsurface signatures of Quaternary volcanism in the Cascades arc","interactions":[],"lastModifiedDate":"2021-08-03T12:24:29.313995","indexId":"70222537","displayToPublicDate":"2020-07-13T07:22:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Time-evolving surface and subsurface signatures of Quaternary volcanism in the Cascades arc","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p>Increased resolution of data constraining topography and crustal structures provides new quantitative ways to assess province-scale surface-subsurface connections beneath volcanoes. We used a database of mapped vents to extract edifices with known epoch ages from digital elevation models (DEMs) in the Cascades arc (western North America), deriving volumes that likely represent ∼50% of total Quaternary eruptive output. Edifice volumes and spatial vent density correlate with diverse geophysical data that fingerprint magmatic influence in the upper crust. Variations in subsurface structures consistent with volcanism are common beneath Quaternary vents throughout the arc, but they are more strongly associated with younger vents. Geophysical magmatic signatures increase in the central and southern Cascade Range (Cascades), where eruptive output is largest and vents are closely spaced. Vents and correlated crustal structures, as well as temporal transitions in the degree of spatially localized versus distributed eruptions, define centers with lateral extents of ∼100 km throughout the arc, suggesting a time-evolving spatial focusing of magma ascent.</p></div>","language":"English","publisher":"Geological Society of America","doi":"10.1130/G47706.1","usgsCitation":"O’Hara, D., Karlstrom, L., and Ramsey, D.W., 2020, Time-evolving surface and subsurface signatures of Quaternary volcanism in the Cascades arc: Geology, v. 48, no. 11, p. 1088-1093, https://doi.org/10.1130/G47706.1.","productDescription":"6 p.","startPage":"1088","endPage":"1093","ipdsId":"IP-113386","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456023,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g47706.1","text":"Publisher Index Page"},{"id":387646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascades arc","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.04687499999999,\n              40.17887331434696\n            ],\n            [\n              -119.17968749999999,\n              40.17887331434696\n            ],\n            [\n              -119.17968749999999,\n              49.1242192485914\n            ],\n            [\n              -123.04687499999999,\n              49.1242192485914\n            ],\n            [\n              -123.04687499999999,\n              40.17887331434696\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"48","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Hara, Daniel 0000-0002-1630-7985","orcid":"https://orcid.org/0000-0002-1630-7985","contributorId":261727,"corporation":false,"usgs":false,"family":"O’Hara","given":"Daniel","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":820496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlstrom, Leif 0000-0002-2197-2349","orcid":"https://orcid.org/0000-0002-2197-2349","contributorId":261729,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Leif","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":820497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, David W. 0000-0003-1698-2523 dramsey@usgs.gov","orcid":"https://orcid.org/0000-0003-1698-2523","contributorId":3819,"corporation":false,"usgs":true,"family":"Ramsey","given":"David","email":"dramsey@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820498,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70211252,"text":"70211252 - 2020 - Robust age estimation of southern sea otters from multiple morphometrics","interactions":[],"lastModifiedDate":"2020-09-10T20:08:19.720219","indexId":"70211252","displayToPublicDate":"2020-07-12T14:29:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Robust age estimation of southern sea otters from multiple morphometrics","docAbstract":"<p><span>Reliable age estimation is an essential tool to assess the status of wildlife populations and inform successful management. Aging methods, however, are often limited by too few data, skewed demographic representation, and by single or uncertain morphometric relationships. In this study, we synthesize age estimates in southern sea otters&nbsp;</span><i>Enhydra lutris nereis</i><span>&nbsp;from 761 individuals across 34&nbsp;years of study, using multiple noninvasive techniques and capturing all life stages from 0 to 17&nbsp;years of age. From wild, stranded, and captive individuals, we describe tooth eruptions, tooth wear, body length, nose scarring, and pelage coloration across ontogeny and fit sex‐based growth functions to the data. Dental eruption schedules provided reliable and identifiable metrics spanning 0.3–9&nbsp;months. Tooth wear was the most reliable predictor of age of individuals aged 1–15&nbsp;years, which when combined with total length, explained &gt;93% of observed age. Beyond age estimation, dental attrition also indicated the maximum lifespan of adult teeth is 13‒17&nbsp;years, corresponding with previous estimates of life expectancy. Von Bertalanffy growth function model simulations of length at age gave consistent estimates of asymptotic lengths (male&nbsp;</span><i>L<sub>oo</sub></i><span>&nbsp;=&nbsp;126.0‒126.8&nbsp;cm, female&nbsp;</span><i>L<sub>oo</sub></i><span>&nbsp;=&nbsp;115.3‒115.7&nbsp;cm), biologically realistic gestation periods (</span><i>t</i><sub>0</sub><span>&nbsp;=&nbsp;115&nbsp;days,&nbsp;</span><i>SD</i><span>&nbsp;=&nbsp;10.2), and somatic growth (male&nbsp;</span><i>k</i><span>&nbsp;=&nbsp;1.8,&nbsp;</span><i>SD</i><span>&nbsp;=&nbsp;0.1; female&nbsp;</span><i>k</i><span>&nbsp;=&nbsp;2.1,&nbsp;</span><i>SD</i><span>&nbsp;=&nbsp;0.1). Though exploratory, we describe how field radiographic imaging of epiphyseal plate development or fusions may improve aging of immature sea otters. Together, our results highlight the value of integrating information from multiple and diverse datasets to help resolve conservation problems.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6493","usgsCitation":"Nicholson, T.E., Mayer, K.A., Staedler, M.M., Gagne, T.O., Murray, M.J., Young, M.A., Tomoleoni, J.A., Tinker, M., and Van Houtan, K.S., 2020, Robust age estimation of southern sea otters from multiple morphometrics: Ecology and Evolution, v. 10, no. 16, p. 8592-8609, https://doi.org/10.1002/ece3.6493.","productDescription":"18 p.","startPage":"8592","endPage":"8609","ipdsId":"IP-119622","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":456026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6493","text":"Publisher Index Page"},{"id":376584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"16","noUsgsAuthors":false,"publicationDate":"2020-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Nicholson, Teri E.","contributorId":213741,"corporation":false,"usgs":false,"family":"Nicholson","given":"Teri","email":"","middleInitial":"E.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":793418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayer, Karl A.","contributorId":203504,"corporation":false,"usgs":false,"family":"Mayer","given":"Karl","email":"","middleInitial":"A.","affiliations":[{"id":36639,"text":"University of Wisconsin Zoological Museum, 250 North Mills Street, Madison, WI 53706 (PMH)              Sea Otter Research and Conservation Program, Monterey Bay Aquarium, 886 Cannery Row, Monterey, CA 93940","active":true,"usgs":false}],"preferred":false,"id":793419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":213742,"corporation":false,"usgs":false,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":793420,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gagne, Tyler O","contributorId":229513,"corporation":false,"usgs":false,"family":"Gagne","given":"Tyler","email":"","middleInitial":"O","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":793421,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murray, Michael J.","contributorId":206852,"corporation":false,"usgs":false,"family":"Murray","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":37418,"text":"Monterey Bay Aquarium, Monterey, CA","active":true,"usgs":false}],"preferred":false,"id":793422,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Young, Marissa A","contributorId":229514,"corporation":false,"usgs":false,"family":"Young","given":"Marissa","email":"","middleInitial":"A","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":793423,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":793424,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tinker, M. Tim 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":221787,"corporation":false,"usgs":false,"family":"Tinker","given":"M. Tim","affiliations":[{"id":40428,"text":"University of California, Santa Cruz; former USGS PI","active":true,"usgs":false}],"preferred":false,"id":793425,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Van Houtan, Kyle S.","contributorId":213743,"corporation":false,"usgs":false,"family":"Van Houtan","given":"Kyle","email":"","middleInitial":"S.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":793426,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70211255,"text":"70211255 - 2020 - Robust geographical determinants of infection prevalence and a contrasting latitudinal diversity gradient for haemosporidian parasites in Western Palearctic birds","interactions":[],"lastModifiedDate":"2020-09-10T20:06:07.309391","indexId":"70211255","displayToPublicDate":"2020-07-11T15:18:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Robust geographical determinants of infection prevalence and a contrasting latitudinal diversity gradient for haemosporidian parasites in Western Palearctic birds","docAbstract":"<p><span>Identifying robust environmental predictors of infection probability is central to forecasting and mitigating the ongoing impacts of climate change on vector‐borne disease threats. We applied phylogenetic hierarchical models to a data set of 2,171 Western Palearctic individual birds from 47 species to determine how climate and landscape variation influence infection probability for three genera of haemosporidian blood parasites (</span><i>Haemoproteus</i><span>,&nbsp;</span><i>Leucocytozoon</i><span>, and&nbsp;</span><i>Plasmodium</i><span>). Our comparative models found compelling evidence that birds in areas with higher vegetation density (captured by the normalized difference vegetation index [NDVI]) had higher likelihoods of carrying parasite infection. Magnitudes of this relationship were remarkably similar across parasite genera considering that these parasites use different arthropod vectors and are widely presumed to be epidemiologically distinct. However, we also uncovered key differences among genera that highlighted complexities in their climate responses. In particular, prevalences of&nbsp;</span><i>Haemoproteus</i><span>&nbsp;and&nbsp;</span><i>Plasmodium</i><span>&nbsp;showed strong but contrasting relationships with winter temperatures, supporting mounting evidence that winter warming is a key environmental filter impacting the dynamics of host‐parasite interactions. Parasite phylogenetic community diversities demonstrated a clear but contrasting latitudinal gradient, with&nbsp;</span><i>Haemoproteus</i><span>&nbsp;diversity increasing towards the equator and&nbsp;</span><i>Leucocytozoon</i><span>&nbsp;diversity increasing towards the poles.&nbsp;</span><i>Haemoproteus</i><span>&nbsp;diversity also increased in regions with higher vegetation density, supporting our evidence that summer vegetation density is important for structuring the distributions of these parasites. Ongoing variation in winter temperatures and vegetation characteristics will probably have far‐reaching consequences for the transmission and spread of vector‐borne diseases.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/mec.15545","usgsCitation":"Clark, N.J., Drovetski, S.V., and Voelker, G., 2020, Robust geographical determinants of infection prevalence and a contrasting latitudinal diversity gradient for haemosporidian parasites in Western Palearctic birds: Molecular Ecology, v. 29, no. 16, p. 3131-3143, https://doi.org/10.1111/mec.15545.","productDescription":"13 p.","startPage":"3131","endPage":"3143","ipdsId":"IP-116693","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":376602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"16","noUsgsAuthors":false,"publicationDate":"2020-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark, Nicholas J.","contributorId":204867,"corporation":false,"usgs":false,"family":"Clark","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":16755,"text":"University of Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":793434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":793435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voelker, Gary","contributorId":229521,"corporation":false,"usgs":false,"family":"Voelker","given":"Gary","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":793436,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70211178,"text":"70211178 - 2020 - Seismic stratigraphic framework of the continental shelf offshore Delmarva, U.S.A.: Implications for Mid-Atlantic Bight evolution since the Pliocene","interactions":[],"lastModifiedDate":"2020-07-16T17:21:30.337689","indexId":"70211178","displayToPublicDate":"2020-07-10T12:16:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Seismic stratigraphic framework of the continental shelf offshore Delmarva, U.S.A.: Implications for Mid-Atlantic Bight evolution since the Pliocene","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0065\">Understanding how past coastal systems have evolved is critical to predicting future coastal change. Using over 12,000 trackline kilometers of recently collected, co-located multi-channel boomer, sparker and chirp seismic reflection profile data integrated with previously collected borehole and vibracore data, we define the upper (&lt; 115&nbsp;m below mean lower low water) seismic stratigraphic framework offshore of the Delmarva Peninsula, USA. Twelve seismic units and 11 regionally extensive unconformities (U1-U11) were mapped over 5900&nbsp;km<sup>2</sup><span>&nbsp;</span>of North America's Mid-Atlantic continental shelf. We interpret U3, U7, U9, U11 as transgressive ravinement surfaces, while U1,2,4,5,6,8,10 are subaerial unconformities illustrating distinct periods of lower sea-level. Based on areal distribution, stratigraphic relationships and dating results (Carbon 14 and amino acid racemization estimates) from earlier vibracore and borehole studies, we interpret the infilled channels as late Neogene and Quaternary courses of the Susquehanna, Potomac, Rappahannock, York, James rivers and tributaries, and a broad flood plain. These findings indicate that the region's geologic framework is more complex than previously thought and that Pleistocene paleochannels are abundant in the Mid-Atlantic. This study synthesizes and correlates the findings of other Atlantic Margin studies and establishes a large-scale Quaternary framework that enables more detailed stratigraphic analysis in the future. Such work has implications for inner continental shelf systems tract evolution, the relationship between antecedent geology and modern coastal systems, assessments of eustacy, glacial isostatic adjustment, and other processes and forcings that play a role in passive margin evolution.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2020.106287","usgsCitation":"Brothers, L.L., Foster, D.S., Pendleton, E.A., and Baldwin, W.E., 2020, Seismic stratigraphic framework of the continental shelf offshore Delmarva, U.S.A.: Implications for Mid-Atlantic Bight evolution since the Pliocene: Marine Geology, v. 428, 106287, 19 p., https://doi.org/10.1016/j.margeo.2020.106287.","productDescription":"106287, 19 p.","ipdsId":"IP-110610","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456045,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2020.106287","text":"Publisher Index Page"},{"id":436881,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GQY0ZN","text":"USGS data release","linkHelpText":"Geospatial data layers of shallow geology from the inner continental shelf of the Delmarva Peninsula, including Maryland and Virginia state waters"},{"id":376437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Delmarva Peninsula, Mid-Atlantic Bight","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.409912109375,\n              36.98500309285596\n            ],\n            [\n              -73.80615234375,\n              36.98500309285596\n            ],\n            [\n              -73.80615234375,\n              39.29179704377487\n            ],\n            [\n              -76.409912109375,\n              39.29179704377487\n            ],\n            [\n              -76.409912109375,\n              36.98500309285596\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"428","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brothers, Laura L. 0000-0003-2986-5166 lbrothers@usgs.gov","orcid":"https://orcid.org/0000-0003-2986-5166","contributorId":176698,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura","email":"lbrothers@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, David S. 0000-0003-1205-0884 dfoster@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0884","contributorId":1320,"corporation":false,"usgs":true,"family":"Foster","given":"David","email":"dfoster@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pendleton, Elizabeth A. 0000-0002-1224-4892 ependleton@usgs.gov","orcid":"https://orcid.org/0000-0002-1224-4892","contributorId":174845,"corporation":false,"usgs":true,"family":"Pendleton","given":"Elizabeth","email":"ependleton@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baldwin, Wayne E. 0000-0001-5886-0917 wbaldwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5886-0917","contributorId":1321,"corporation":false,"usgs":true,"family":"Baldwin","given":"Wayne","email":"wbaldwin@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792961,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70211891,"text":"70211891 - 2020 - Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2020-08-11T14:07:59.855212","indexId":"70211891","displayToPublicDate":"2020-07-10T09:01:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed","docAbstract":"<div id=\"as0005\"><p id=\"sp0065\">Winter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (&gt;26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass (<i>p</i>&nbsp;≤0.01, R<sup>2</sup>&nbsp;=&nbsp;0.56) and with observed percent vegetative ground cover (p&nbsp;≤0.01, R<sup>2</sup>&nbsp;=&nbsp;0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014–15, 2015–16, and 2017–18) for four Maryland counties. Results from winter 2017–18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit.</p></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111943","usgsCitation":"Thieme, A., Yadav, S., Oddo, P.C., Fitz, J.M., McCartney, S., King, L., Keppler, J., McCarty, G.W., and Hively, W.D., 2020, Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed: Remote Sensing of Environment, v. 248, 111943, 13 p., https://doi.org/10.1016/j.rse.2020.111943.","productDescription":"111943, 13 p.","ipdsId":"IP-106325","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":456059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.111943","text":"Publisher Index Page"},{"id":377323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Gueen Anne's County, Somerset County, Talbot County, Washington County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -78.3544921875,\n              39.715638134796336\n            ],\n            [\n              -78.31054687499999,\n              39.639537564366684\n            ],\n            [\n              -78.145751953125,\n              39.68182601089365\n            ],\n            [\n              -77.607421875,\n              39.232253141714885\n            ],\n            [\n              -77.36572265625,\n              39.7240885773337\n            ],\n            [\n              -78.3544921875,\n              39.715638134796336\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.014404296875,\n              39.68182601089365\n            ],\n            [\n              -76.2890625,\n              39.45316112807394\n            ],\n            [\n              -76.1572265625,\n              39.27478966170308\n            ],\n            [\n              -75.73974609375,\n              39.232253141714885\n            ],\n            [\n              -75.772705078125,\n              39.67337039176558\n            ],\n            [\n              -76.014404296875,\n              39.68182601089365\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.816650390625,\n              37.95286091815649\n            ],\n            [\n              -75.498046875,\n              38.039438891821746\n            ],\n            [\n              -75.65185546874999,\n              38.26406296833961\n            ],\n            [\n              -75.970458984375,\n              38.212288054388175\n            ],\n            [\n              -75.816650390625,\n              37.95286091815649\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":237963,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","email":"","affiliations":[{"id":47661,"text":"University of Maryland, Geographical Sciences","active":true,"usgs":false}],"preferred":false,"id":795689,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yadav, Sunita","contributorId":237964,"corporation":false,"usgs":false,"family":"Yadav","given":"Sunita","email":"","affiliations":[{"id":47662,"text":"USDA Foreign Agricultural Service","active":true,"usgs":false}],"preferred":false,"id":795690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oddo, Perry C.","contributorId":237965,"corporation":false,"usgs":false,"family":"Oddo","given":"Perry","email":"","middleInitial":"C.","affiliations":[{"id":47663,"text":"Universities Space Research Association","active":true,"usgs":false}],"preferred":false,"id":795691,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fitz, John M.","contributorId":237966,"corporation":false,"usgs":false,"family":"Fitz","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":47661,"text":"University of Maryland, Geographical Sciences","active":true,"usgs":false}],"preferred":false,"id":795692,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCartney, Sean","contributorId":237968,"corporation":false,"usgs":false,"family":"McCartney","given":"Sean","email":"","affiliations":[{"id":7239,"text":"Science Systems and Applications, Inc.","active":true,"usgs":false}],"preferred":false,"id":795693,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, LeeAnn","contributorId":237969,"corporation":false,"usgs":false,"family":"King","given":"LeeAnn","email":"","affiliations":[{"id":47664,"text":"Chesapeake Conservancy","active":true,"usgs":false}],"preferred":false,"id":795694,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":795695,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":795696,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795697,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70210987,"text":"sir20205063 - 2020 - Sediment lithology and borehole erosion testing, American and Sacramento Rivers, California","interactions":[],"lastModifiedDate":"2020-07-10T12:28:55.581268","indexId":"sir20205063","displayToPublicDate":"2020-07-09T12:35:39","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5063","displayTitle":"Sediment Lithology and Borehole Erosion Testing, American and Sacramento Rivers, California","title":"Sediment lithology and borehole erosion testing, American and Sacramento Rivers, California","docAbstract":"<h1>Executive Summary</h1><p>A field investigation intended to measure the potential for erosion of sediments beside the American and Sacramento Rivers near Sacramento, California, is described. The study featured two primary components: (1) drilling and soil sampling to reveal lithology, down to depths matching the local river thalweg, where possible, and (2) borehole erosion tests (BETs) as described by Briaud and others (2017) at many of the same locations. The latter test involved drilling a vertical hole, measuring its diameter profile, inserting a hollow drilling rod to almost the bottom of the hole, and pumping fluid through the drilling rod at a known discharge for a chosen time interval. The hole was then resurveyed to establish an erosion rate (change in borehole radius divided by duration of flow event) as a function of depth, and the test was repeated. This test was performed with water as the erosive fluid at 12 locations, with 1 test repeated with drilling mud. Lithology holes were drilled at these same locations and an additional five locations. Drilling operations took place on river left and river right on the American River and river left (left bank, when looking downstream) on the Sacramento River.</p><p>The drilling to acquire sediment samples and reveal lithology involved the use of a mobile drilling rig equipped with a 6-inch (in.) auger, a 140-pound pneumatic hammer to drive split spoon and Calmod samplers, and a piston to push Shelby tube samplers to obtain samples of clayey material. Blow count (hammer blows per 6-in. sampler advance) was recorded while sampling, and the process was logged using standard U.S. Army Corps of Engineers (USACE), Sacramento District procedures. Sediment samples were identified and described in the field per ASTM D2488 and then delivered to a USACE laboratory and to Texas A&amp;M University for additional laboratory analysis.</p><p>The BETs were performed with the same drilling rig that performed the drilling for definition of lithology. In most instances, tests were limited to regions above the water table, to avoid slumping of the borehole and heaving sands pushing into the hole. Most of the tests featured sediments that were primarily silty sand or sandy silt.</p><p>The testing procedure involved comparing borehole profiles before and after passing an assumed constant discharge through a drilling rod to the bottom of the drilled hole. Discharge and water losses were logged during the testing procedure, and water losses into the walls of the drilled hole were typically less than 5 percent of the introduced volume. For the tests performed with water, the coefficient of variation of the discharge ranged from 4.5 to 28 percent, with a mean of 13 percent, but the mean discharge appeared to be reasonably steady over the typical test duration of 10–30 minutes. It was thus assumed that discharge was constant and water losses during the tests were neglected. Coefficients of variation of the discharge for the three tests performed with drilling mud were much higher (20–50 percent), but erosion rates were much smaller.</p><p>Resolution of the borehole caliper-reported diameter was 0.1 in. and several of the tests lasted for 10 minutes. With boreholes measured twice, before and after each test, and averaged, these numbers correspond to an apparent erosion rate (radius change divided by test duration) of 0.3 inches per hour (in/hr), which is a theoretical lower bound on what could be measured with this approach and equipment. In practice, 0.5 in/hr appears to be a more realistic lower bound on the detectable erosion rate, based on inspection of computed changes and erosion rates.</p><p>Three flow speeds (5, 8, and 12 feet per second; ft/s) were targeted for the tests. Because of equipment limitations, it was not possible in the field to reach an average of 12 ft/s throughout any given borehole, although much higher flow speeds were reached locally in some cases. Most tests featured at least two different flow rates, and the borehole was typically surveyed at least twice for each condition, to allow averaging to reduce the influence of random diameter measurement errors. Errors arising from out-of-round boreholes appeared to be uncommon.</p><p>Briaud and others (2017) recommend stepped increases in the flow rate during a borehole test. This approach was taken during initial testing but proved to be problematic. The drilled hole would be enlarged by the first (smaller) discharge, and then it would be difficult to reach the desired higher flow speed because of the larger annulus between the drilled hole and the drilling rod that supplied the water for testing. This was largely solved by starting with a high discharge and, in many cases, maintaining it for subsequent tests with the average flow speed decreasing as the hole enlarged.</p><p>Several different measures of erosion rate were computed and investigated by comparison to lithological profiles. The vertically averaged erosion rate for each hole was computed, but this result does not reveal vertical variability of erodibility; and the mean flow speed within the hole is not a good representation of the speed when attempting to determine a relationship between erosion rate and flow speed. Instead, for each 6-inch layer within the hole, vertically averaged erosion rates and local flow speeds were computed and plotted. Where possible, the soil type for each layer was identified. For later laboratory analysis, project protocol dictated collection of Shelby tube samples whenever clay was encountered.</p><p>Plots of erosion rate versus flow speed displayed scatter that indicate that several other factors influence the erosion potential of the soil. Blow count was not a good predictor variable; it is better correlated with soil type than erodibility.</p><p>Soils were classified as sand, silt, or clay, depending on which soil type dominated within a sample. In general, those classified as sand and silt did not reveal clear patterns allowing erosion rate to be computed directly from flow speed, but the test results define the range and bounds on the erosion rate. Results for clay were slightly clearer with the erosion rate increasing with flow speed, once a threshold had been reached. In this case, the erosion rate appeared to change near a speed of 7 ft/s; above this threshold, erosion rates jumped from less than 2 in/hr to greater than 3 in/hr.</p><p>Even for soils with similar classifications, large differences in erodibility were observed between sites and in different layers within an individual hole. One potential means of dealing with this problem would be to perform more tests at each site to allow establishment of relationships between flow speed and erodibility for individual layers within a borehole. The maximum number of tests performed at a site in this study was four, but in some cases, results are available for only one or two flow events. Comparison of data to a set of Erosion Function Apparatus tests that provide better resolution of the vertical variation in the erosion rate versus flow speed relationship would allow further investigation of this idea.</p><p>It was hypothesized that drilling mud could expand the utility of the test in soft sands by reducing the likelihood of slumping that would be interpreted as erosion. The one test that was performed with drilling mud indicated that it greatly reduced the erosion rate of the soils encountered. It yielded very different results from the test performed at the same site with water.</p><p>Erosion rate is often expressed as a function of shear stress applied to a soil. In order to compute shear stress on the walls of the drilled hole, one must assume a form for the relationship between flow speed and shear stress and select a friction factor that is often estimated empirically from head loss, observed water-surface profiles, surface roughness, or other data not available in this report. One methodology for computing shear stress from flow speed is discussed in this report, but the test results have been presented in terms of erosion rate versus flow speed to avoid assuming values that are not verifiable via the field data collected in this study. Erosion rate was computed from directly measured values (sequential borehole profiles) and flow speed was computed directly from measured quantities (discharge and borehole geometry).</p><p>The BET has seen limited application, primarily in clayey soils, whereas most of the soils encountered in this study were primarily sand or silt. The objective of the BET is to determine the erodibility of in situ soil below the ground or riverbed surface. The BET is simple in principle and has the advantage of revealing erodibility of in situ sediments below the ground or riverbed surface; it appears to be very useful in clayey soils, based on previously published work, but is more difficult to apply in sandy soils where slumping and water losses within the hole during testing are more likely to occur. The BET did reveal a large variation in the results both laterally and vertically, even for the same soil-type classification. It is thus recommended that the results be applied considering these spatial variations rather than attempting to universally assign an erosion-rate relationship to a particular soil type. Results have been provided showing the results by site and by sediment classification (sand, silt, and clay), to allow either approach. Where possible, it is important to rely on site-specific results because the erosion-rate relationship for a given soil type varied by site.</p><p>Data collected during this project have been made publicly available online via the U.S. Geological Survey (USGS) Sciencebase database. The measured borehole profiles, discharge, lithology log sheets, and photos are available in the data release that accompanies this report (see Work and Livsey (2019) in the “Selected References” section for the appropriate link).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205063","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Work, P., and Livsey, D., 2020, Sediment lithology and borehole erosion testing, American and Sacramento Rivers, California: U.S. Geological Survey Scientific Investigations Report 2020–5063, 92 p., https://doi.org/10.3133/sir20205063.","productDescription":"Report: vii, 92 p.; Data Release","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-110364","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376205,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5063/coverthb.jpg"},{"id":376206,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5063/sir20205063.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376207,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96MCT2Q","linkHelpText":"Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 3.0, July 2020)"}],"country":"United States","state":"California","city":"Sacramento","otherGeospatial":"American River, Sacramento Rivers","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.7010498046875,\n              38.38903340675905\n            ],\n            [\n              -121.22589111328126,\n              38.38903340675905\n            ],\n            [\n              -121.22589111328126,\n              38.70694605159386\n            ],\n            [\n              -121.7010498046875,\n              38.70694605159386\n            ],\n            [\n              -121.7010498046875,\n              38.38903340675905\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>","tableOfContents":"<ul><li>Acknowledgments</li><li>Executive Summary</li><li>Introduction</li><li>Site Conditions and Drilling Plan</li><li>Augering and Sediment Sampling Procedure</li><li>Borehole Erosion Test Procedure</li><li>Borehole Erosion Test Results</li><li>Conclusions</li><li>Selected References</li><li>Appendix 1. Individual Borehole Erosion Test Plots and Notes</li><li>Appendix 2. Flowmeter and Water Level Logging</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-07-09","noUsgsAuthors":false,"publicationDate":"2020-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":792349,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70210883,"text":"sir20205046 - 2020 - Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018","interactions":[],"lastModifiedDate":"2020-07-08T15:30:55.392543","indexId":"sir20205046","displayToPublicDate":"2020-07-08T11:40:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5046","displayTitle":"Effects of Stormwater Runoff from Selected Bridge Decks on Conditions of Water, Sediment, and Biological Quality in Receiving Waters in South Carolina, 2013 to 2018","title":"Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, investigated the effects of stormwater runoff from bridge decks on stream water quality conditions in South Carolina. The investigation assessed 5 bridges in 3 physiographic provinces in South Carolina (Piedmont, Upper Coastal Plain, and Lower Coast Plain) that had a range of bridge, traffic, and hydrologic characteristics. The five selected South Carolina bridge sites (coincident with U.S. Geological Survey stations) and corresponding highways were Lynches River at Effingham (station 02132000; U.S. Highway 52), North Fork Edisto River at Orangeburg (station 02173500; U.S. Highway 301), Turkey Creek above Huger (station 02172035; South Carolina Highway 41), South Fork Edisto River near Denmark (station 02173000; U.S. Highway 321), and Fishing Creek at Highway 5 below York (station 021473415; South Carolina Highway 5). Bridge decks at the selected sites used open chutes, scuppers, and downspouts to drain stormwater directly into the receiving water at evenly spaced intervals.</p><p>Stream water, sediment, and biological samples were collected and analyzed for a variety of constituents to evaluate the stream conditions for this study. Five to six stream samples were collected at transects upstream and downstream from each selected bridge site using the equal-width-increment technique during observable stormwater runoff. Routine samples of the receiving waters were collected 12 to 14 times at the upstream transect during nonstorm conditions. Samples were analyzed for physical properties, suspended sediment, nutrients, major ions, trace metals, polycyclic aromatic hydrocarbons, and <i>Escherichia coli</i>. Bridge-deck sediment and streambed sediment at upstream and downstream transects were collected once at each bridge site and analyzed for metals and semivolatile organic compounds that include polycyclic aromatic hydrocarbons. Benthic macroinvertebrate community surveys were conducted once using Hester-Dendy multiplate artificial substrate samplers deployed at multiple upstream and downstream transects concurrently.</p><p>Statistical analysis of the water-quality data determined that stormwater runoff from bridges did not significantly degrade physical properties, nor nutrient, trace-metal, <i>Escherichia coli</i>, and suspended-sediment concentrations at the selected sites beyond the variability at the upstream transect (no bridge influence) during the study period. During storm sampling at the bridge sites, water-quality conditions were statistically similar upstream and downstream from each bridge, except for greater turbidity, total nitrogen, and total organic nitrogen plus ammonia concentrations found downstream from the bridge site on Fishing Creek; higher total chromium concentrations detected downstream from the bridge site on Turkey Creek; and increased <i>Escherichia coli</i> concentrations found downstream from the bridge site on the North Fork Edisto River. Total recoverable lead, cadmium, and copper concentrations were the only trace metals that periodically exceeded the South Carolina Department of Health and Environmental Control freshwater aquatic-life criteria at some bridge sites (lead, copper, and cadmium in Turkey Creek; cadmium and lead in Fishing Creek; lead in the South Fork Edisto River and Lynches River), but the exceedances occurred more frequently during routine sampling upstream from the bridge sites than during storm sampling at upstream and downstream transects. In general, stormwater runoff from the bridge decks did not seem to be the major source of metal enrichment in receiving waters during the study period. North Fork and South Fork Edisto Rivers and Turkey Creek had only one storm sample that exceeded South Carolina Department of Health and Environmental Control recreational criterion for <i>Escherichia coli</i> at both the upstream and downstream locations, while Fishing Creek had more frequent exceedances. Polycyclic aromatic hydrocarbons were detected infrequently in the stream samples.</p><p>In general, sediment trace-metal concentrations were below the threshold and probable effect concentration at all bridge sites, except for the chromium concentration (45.1 milligrams per kilogram) detected upstream from the bridge site on Fishing Creek that exceeded the threshold effect concentration of 43.4 milligrams per kilogram. Based on enrichment ratios less than 1.5, bridge-deck runoff did not seem to be affecting trace-metal accumulation in the streambed sediment downstream from the bridge sites, except for lead at the bridge site on the Lynches River and manganese at the bridge site on Fishing Creek.</p><p>Individual polycyclic aromatic compound concentrations and the sum of 18 compounds did not exceed any threshold and probable effect concentrations, indicating polycyclic aromatic hydrocarbon concentrations in the streambed sediment at downstream and upstream transects were not likely to affect the health of benthic macroinvertebrate communities. Although the cumulative polycyclic aromatic hydrocarbon concentrations in downstream sediment at the sites on Turkey and Fishing Creeks were well below the threshold effect concentration of 1,610 micrograms per kilogram, the 3- to 100-fold increase in downstream concentrations indicated a strong probability of a bridge-deck runoff source.</p><p>Overall, benthic macroinvertebrate community health downstream from the bridge sites did not seem to be affected by bridge-deck runoff based on several multivariate analyses that indicated statistically similar benthic macroinvertebrate communities at upstream and downstream transects. Of the five bridge sites in this study, the site on Turkey Creek seemed to have the least healthy benthic macroinvertebrate communities because of the lowest Ephemeroptera, Plecoptera, and Trichoptera spp. (mayflies, stoneflies, and caddisflies, respectively) taxa, species richness, and diversity; and the highest biotic indices, indicative of poorer ecological health, at upstream and downstream transects. This ecological finding was not unexpected because of seasonal periods of negligible flow when dissolved-oxygen concentrations fell below 4 milligrams per liter during the study period. Of the five bridge sites in this study, the site on the South Fork Edisto River seemed to have healthier benthic macroinvertebrate communities because of the greater mean Ephemeroptera, Plecoptera, and Trichoptera spp. taxa; and lower mean biotic indices at upstream and downstream transects.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205046","collaboration":"Prepared in cooperation with South Carolina Department of Transportation","usgsCitation":"Journey, C.A., Petkewich, M.D., Conlon, K.J., Caldwell, A.W., Clark, J.M., Riley, J.W., and Bradley, P.M., 2020, Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018: U.S. Geological Survey Scientific Investigations Report 2020–5046, 101 p., https://doi.org/10.3133/sir20205046.","productDescription":"xii, 101 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099513","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":376048,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes.xlsx","text":"Appendixes 1-3","size":"312 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":376047,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5046"},{"id":376046,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FXSV2Y","text":"USGS data release","linkHelpText":"Water-, Sediment-, and Biological-Quality Data for Waters Receiving Runoff from Five Bridges in South Carolina, 2013 to 2018"},{"id":376045,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5046/coverthb.jpg"},{"id":376051,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes_csv.zip","text":"Appendixes 1-3 (CSV)","size":"34.5 KB","linkFileType":{"id":6,"text":"zip"}}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/sa-water\" data-mce-href=\"https://www.usgs.gov/centers/sa-water\">South Atlantic Water Science Center</a><br>U.S. Geological Survey <br>720 Gracern Road<br>Columbia, SC 29210</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Approach and Methods</li><li>Quality Assurance and Quality Control</li><li>Description of the Study Areas at Selected Bridge Sites</li><li>Selected Bridge Site Characteristics</li><li>Water Quality at Selected Bridge Sites</li><li>Sediment Quality</li><li>Benthic Macroinvertebrate Assemblages</li><li>Summary</li><li>References Cited</li><li>Appendix 1. U.S. Geological Survey National Water Quality Laboratory methods</li><li>Appendix 2. Quality-Assurance and Quality Control Related to stream Water and Sediment Replicate Analysis and Hydrologic Characterization</li><li>Appendix 3. Water-Quality, Sediment-Quality, and Benthic Macroinvertebrate Data Collected at the Five Selected Bridge Sites in South Carolina, 2013 to 2018</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-07-08","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Journey, Celeste A. 0000-0002-2284-5851 cjourney@usgs.gov","orcid":"https://orcid.org/0000-0002-2284-5851","contributorId":189681,"corporation":false,"usgs":true,"family":"Journey","given":"Celeste","email":"cjourney@usgs.gov","middleInitial":"A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petkewich, Matthew D. 0000-0002-5749-6356 mdpetkew@usgs.gov","orcid":"https://orcid.org/0000-0002-5749-6356","contributorId":982,"corporation":false,"usgs":true,"family":"Petkewich","given":"Matthew","email":"mdpetkew@usgs.gov","middleInitial":"D.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conlon, Kevin J. 0000-0003-0798-368X kjconlon@usgs.gov","orcid":"https://orcid.org/0000-0003-0798-368X","contributorId":2561,"corporation":false,"usgs":true,"family":"Conlon","given":"Kevin","email":"kjconlon@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":791932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell, Andral W. 0000-0003-1269-5463 acaldwel@usgs.gov","orcid":"https://orcid.org/0000-0003-1269-5463","contributorId":3228,"corporation":false,"usgs":true,"family":"Caldwell","given":"Andral","email":"acaldwel@usgs.gov","middleInitial":"W.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791933,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Jimmy M. 0000-0002-3138-5738 jmclark@usgs.gov","orcid":"https://orcid.org/0000-0002-3138-5738","contributorId":4773,"corporation":false,"usgs":true,"family":"Clark","given":"Jimmy","email":"jmclark@usgs.gov","middleInitial":"M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791934,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riley, Jeffrey W. 0000-0001-5525-3134 jriley@usgs.gov","orcid":"https://orcid.org/0000-0001-5525-3134","contributorId":3605,"corporation":false,"usgs":true,"family":"Riley","given":"Jeffrey","email":"jriley@usgs.gov","middleInitial":"W.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791935,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791936,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70212672,"text":"70212672 - 2020 - A maximum rupture model for the southern San Andreas and San Jacinto Faults California, derived from paleoseismic earthquake ages: Observations and limitations","interactions":[],"lastModifiedDate":"2020-08-25T14:02:24.152487","indexId":"70212672","displayToPublicDate":"2020-07-08T08:58:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"A maximum rupture model for the southern San Andreas and San Jacinto Faults California, derived from paleoseismic earthquake ages: Observations and limitations","docAbstract":"<p><span>Paleoseismic rupture histories provide spatiotemporal models of earthquake moment release needed to test numerical models and lengthen the instrumental catalog. We develop a model of the fewest and thus largest magnitude earthquakes permitted by paleoseismic data for the last 1,500&nbsp;years on the southern San Andreas and San Jacinto Faults, California, USA. The largest geometric complexity appears to regulate the system: Only two ruptures break the San Gorgonio Pass region, followed by episodes of ruptures that could bridge the northern San Jacinto Fault and the San Andreas Fault. When tested against independent data on slip per event, the model produces comparable values indicating the end‐member model does not underpredict rupture rates. Rupture of &gt;85% of the fault length in the historic period between 1800 and 1857 and the subsequent quiescence is similar to epochs of activity in the prehistoric model, suggesting that regional clustering of seismicity could be a trait of the system.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088532","usgsCitation":"Scharer, K., and Yule, D., 2020, A maximum rupture model for the southern San Andreas and San Jacinto Faults California, derived from paleoseismic earthquake ages: Observations and limitations: Geophysical Research Letters, v. 47, e2020GL088532, 11 p., https://doi.org/10.1029/2020GL088532.","productDescription":"e2020GL088532, 11 p.","ipdsId":"IP-119093","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088532","text":"Publisher Index Page"},{"id":377818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault, San Jacinto Fault","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.39941406249999,\n              36.59788913307022\n            ],\n            [\n              -120.80566406250001,\n              36.13787471840729\n            ],\n            [\n              -116.89453125,\n              32.76880048488168\n            ],\n            [\n              -115.1806640625,\n              32.80574473290688\n            ],\n            [\n              -115.13671875,\n              33.8339199536547\n            ],\n            [\n              -119.39941406249999,\n              36.59788913307022\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yule, Doug","contributorId":239568,"corporation":false,"usgs":false,"family":"Yule","given":"Doug","email":"","affiliations":[{"id":36305,"text":"CSU Northridge","active":true,"usgs":false}],"preferred":false,"id":797256,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70210991,"text":"70210991 - 2020 - Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide","interactions":[],"lastModifiedDate":"2020-07-10T13:47:57.191861","indexId":"70210991","displayToPublicDate":"2020-07-08T08:46:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide","docAbstract":"After more than 100 years of earthquake research, earthquake forecasting, which relies on knowledge of past fault rupture patterns, has become the foundation for societal defense against seismic natural disasters. A concept that has come into focus more recently is that rupture segmentation and cyclicity can be complex, and that a characteristic earthquake model is too simple to adequately describe much of fault behavior. Nevertheless, recognizable patterns in earthquake recurrence emerge from long, high resolution, spatially distributed chronologies. Researchers now seek to discover the maximum, minimum, and typical rupture areas; the distribution, variability, and spatial applicability of recurrence intervals; and patterns of earthquake clustering in space and time. The term “supercycle” has been used to describe repeating longer periods of elastic strain accumulation and release that involve multiple fault ruptures. However, this term has become very broadly applied, lumping together several distinct phenomena that likely have disparate underlying causes. We divide earthquake cycle behavior into four major classes that have different implications for seismic hazard and fault mechanics: 1) quasi-periodic similar ruptures, 2) clustered similar ruptures, 3) clustered complementary ruptures/rupture cascades, and 4) superimposed cycles. “Segmentation” is likewise an ambiguous term; we identify “master segments” and “asperities” as defined by barriers to fault rupture. These barriers may be persistent (rarely or never traversed), frequent (occasionally traversed), or ephemeral (changing location from cycle to cycle). We compile a catalog of the historical and paleoseismic evidence that currently exists for each of these types of behavior on major well-studied faults worldwide. Due to the unique level of paleoseismic and paleogeodetic detail provided by the coral microatoll technique, the Sumatran Sunda megathrust provides one of the most complete records over multiple earthquake rupture cycles. Long historical records of earthquakes along the South American and Japanese subduction zones are also vital contributors to our catalog, along with additional data compiled from subduction zones in Cascadia, Alaska, and Middle America, as well as the North Anatolian and Dead Sea strike-slip faults in the Middle East. We find that persistent and frequent barriers, rupture cascades, superimposed cycles, and quasi-periodic similar ruptures are common features of most major faults. Clustered similar ruptures do not appear to be common, but broad overlap zones between neighboring segments do occur. Barrier regions accommodate slip through reduced interseismic coupling, slow slip events, and/or smaller more localized ruptures, and are frequently associated with structural features such as subducting seafloor relief or fault trace discontinuities. This catalog of observations provides a basis for exploring and modeling root causes of rupture segmentation and cycle behavior. We expect that researchers will recognize similar behavior styles on other major faults around the world.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106390","usgsCitation":"Philibosian, B.E., and Meltzner, A.J., 2020, Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide: Quaternary Science Reviews, v. 241, 106390, 43 p., https://doi.org/10.1016/j.quascirev.2020.106390.","productDescription":"106390, 43 p.","ipdsId":"IP-103767","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106390","text":"Publisher Index Page"},{"id":376257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"241","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":792358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meltzner, Aron J.","contributorId":193419,"corporation":false,"usgs":false,"family":"Meltzner","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false},{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":792359,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70210949,"text":"ofr20201068 - 2020 - Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington","interactions":[],"lastModifiedDate":"2020-07-09T13:43:08.205679","indexId":"ofr20201068","displayToPublicDate":"2020-07-08T08:31:31","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1068","displayTitle":"Development of a Two-Stage Life Cycle Model for <i>Oncorhynchus kisutch</i> (Coho Salmon) in the Upper Cowlitz River Basin, Washington","title":"Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington","docAbstract":"<p>Recovery of salmon populations in the upper Cowlitz River Basin depends on trap-and-haul efforts owing to impassable dams. Therefore, successful recovery depends on the collection of out-migrating juvenile salmon at Cowlitz Falls Dam (CFD) for transport below downstream dams, as well as the collection of adults for transport upstream from the dams. Tacoma Power began downstream fish collection efforts at CFD in the mid-1990s and has been working consistently since then to improve collection efficiency to support self-sustaining salmon and steelhead (<i>Onchorhynchus</i> spp.) populations in the upper Cowlitz River Basin. Although much work has focused on estimating fish collection efficiency (FCE), there has been relatively little focus on modeling population dynamics to understand how fish collection efficiency and other factors drive production of both juvenile and adult salmon over their life cycle. As a first step towards understanding the factors affecting population dynamics of <i>Oncorhynchus kisutch</i> (coho salmon) in the upper Cowlitz River Basin, we developed a statistical life cycle model using adult escapement and age structure data, juvenile collection data, and juvenile fish collection efficiency estimates. The goal of the statistical life cycle model is to estimate annual production and survival during two critical life-stage transitions: the freshwater production from escapement of adults upstream from CFD to collection of juveniles at CFD, and the juvenile-to-adult survival from the time of collection at the dam to the return of adults. To structure the life cycle model, we used the Ricker stock-recruitment model to estimate juvenile production from the number of parent spawners. This approach allowed us to account for density dependence at high spawner abundances while estimating annual productivity, defined as the number of juveniles produced per spawner at low spawner abundance. We then expressed productivity as a function two key variables affecting the number of juveniles collected and transported at CFD: (1) annual FCE, and (2) the annual number of days that spill occurred at CFD from September 1 to April 30.</p><p>Our key findings were as follows:</p><ol><li>FCE was the primary factor affecting productivity of coho salmon upstream from CFD because FCE affects the number of juveniles that survive to continue downstream migration;</li><li>Juvenile-to-adult return (JAR) rates were relatively high considering that harvest was included in the estimate, averaging about 3.6 percent and ranging as high as 9.1 percent, suggesting that adult coho salmon may be able to return to CFD at sustainable population sizes; and</li><li>Much variation in the estimates of juvenile fish production upriver of CFD was unexplained even after adult escapement and FCE were accounted for, suggesting that the model may be improved by exploring different covariates and model structures for juvenile production as well as JAR rates.</li></ol><p>Additionally, by including FCE in the model, we estimated that the median pre-collection productivity, defined as the number of juveniles produced per spawner when FCE=1, was 108.4 juveniles per spawner. Because this two-stage life cycle model partitions factors that affect fish production in river compared to the ocean environment and fish life stages, the model estimates should help inform fishery managers about the overall role that fish collection at CFD may have on the recovery and sustainability of coho salmon populations.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201068","collaboration":"Prepared in cooperation with Tacoma Power","usgsCitation":"Plumb, J.M., and Perry, R.W., 2020, Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington: U.S. Geological Survey Open-File Report 2020–1068, 25 p., https://doi.org/10.3133/ofr20201068.","productDescription":"iv, 25 p.","onlineOnly":"Y","ipdsId":"IP-117483","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":376162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1068/coverthb.jpg"},{"id":376163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1068/ofr20201068.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1068"}],"country":"United States","state":"Washington","otherGeospatial":"Cowlitz River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.97821044921875,\n              46.09228143052647\n            ],\n            [\n              -121.8548583984375,\n              46.09228143052647\n            ],\n            [\n              -121.8548583984375,\n              46.70596917928676\n            ],\n            [\n              -122.97821044921875,\n              46.70596917928676\n            ],\n            [\n              -122.97821044921875,\n              46.09228143052647\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/wfrc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/wfrc\">Western Fisheries Research Center</a><br>U.S. Geological Survey<br>6505 NE 65th Street<br>Seattle, Washington 98115-5016</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>References Cited</li><li>Appendix 1. Coho Salmon Life Cycle Parameter Estimates</li></ul>","publishedDate":"2020-07-08","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":792271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":792272,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70255612,"text":"70255612 - 2020 - Calibrated simulation of the long-term average surficial groundwater system and derived spatial distributions of its characteristics for the contiguous United States","interactions":[],"lastModifiedDate":"2024-06-26T13:27:34.420421","indexId":"70255612","displayToPublicDate":"2020-07-08T08:23:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Calibrated simulation of the long-term average surficial groundwater system and derived spatial distributions of its characteristics for the contiguous United States","docAbstract":"<p><span>While the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental-scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base-flow discharge, but is not well-characterized at large regional to continental scales. We used a process-based inversion of CONUS-extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base-flow, and (d) long-term average water content in the unsaturated zone. CONUS-extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long-term average transmissivity for each subdomain model calibrated against water-levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first-order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long-term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR026724","usgsCitation":"Zell, W.O., and Sanford, W.E., 2020, Calibrated simulation of the long-term average surficial groundwater system and derived spatial distributions of its characteristics for the contiguous United States: Water Resources Research, v. 56, no. 8, e2019WR026724, 16 p.; Data Release, https://doi.org/10.1029/2019WR026724.","productDescription":"e2019WR026724, 16 p.; Data Release","ipdsId":"IP-117925","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":436888,"rank":0,"type":{"id":30,"text":"Data 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