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Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.</span></p>","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2207537120","usgsCitation":"Shea, K., Borchering, R.K., Probert, W., Howerton, E., Bogich, T.L., Li, S., van Panhuis, W., Viboud, C., Aguas, R., Belov, A.A., Bhargava, S.H., Cavany, S.M., Chang, J.C., Chen, C., Chen, J., Chen, S., Chen, Y., Childs, L.M., Chow, C.C., Crooker, I., Del Valle, S.Y., Espana, G., Fairchild, G., Gerkin, R.C., Germann, T.C., Gu, Q., Guan, X., Guo, L., Hart, G.R., Hladish, T.J., Hupert, N., Janies, D., Kerr, C.C., Klein, D.J., Klein, E.Y., Lin, G., Manore, C., Meyers, L.A., Mittler, J.E., Mu, K., Nunez, R.C., Oidtman, R.J., 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,{"id":70247953,"text":"70247953 - 2023 - Redefinition of the Petersburg batholith and implications for crustal inheritance in the Dinwiddie terrane, Virginia, USA","interactions":[],"lastModifiedDate":"2023-08-29T14:34:25.608068","indexId":"70247953","displayToPublicDate":"2023-04-20T09:21:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Redefinition of the Petersburg batholith and implications for crustal inheritance in the Dinwiddie terrane, Virginia, USA","docAbstract":"<p><span>Field relations as well as geochemical and petrologic studies of metaigneous rocks assigned to the Pennsylvanian–Permian Petersburg batholith identify at least two distinct rock types: foliated metagranitoid gneiss and massive to porphyritic granite. Foliated metagranitoid gneiss of mostly granodioritic composition is geochemically distinct from associated massive and porphyritic granitic rocks. These gneissic rocks yield radiometric ages from ca. 425 Ma to ca. 403 Ma and document that many of the rocks assigned to the late Paleozoic Petersburg batholith are 100 m.y. older than the youngest portions of the composite batholith and are part of an earlier infrastructural terrane. Two samples of massive equigranular granite southwest of Petersburg, Virginia, yield ages of ca. 321 Ma and ca. 317 Ma, which are 15–20 m.y. older than ca. 300 Ma ages for porphyritic granite, massive granite, and monzodiorite near Richmond, Virginia. Geologic mapping shows that the Early Pennsylvanian granite southwest of Petersburg is separated from Late Pennsylvanian to early Permian granite near Richmond by a map-scale septum of Silurian–Devonian foliated metagranitoid gneiss, referred to herein as the informal Pocoshock Creek gneiss. Laser ablation–inductively coupled plasma–mass spectrometry data from one sample of a quartz-muscovite felsic schist xenolith show a peak age mode of ca. 529 Ma that we interpret to be the maximum depositional age. Inherited zircons from foliated metagranitoid gneiss and massive equigranular granite range from ca. 631 Ma to ca. 376 Ma, but many are Cambrian. Neoproterozoic–Cambrian quartz-muscovite felsic schist and amphibolite, Silurian–Devonian Pocoshock Creek gneiss, and Pennsylvanian–Permian granite comprise a fault-bounded terrane referred to herein as the Dinwiddie terrane. Ages of inherited cores in zircon from igneous rocks and limited detrital zircon geochronology suggest the terrane is of peri-Gondwanan affinity. U/Pb ages of healed fractures in zircon grains from foliated metagranitoid gneiss indicate low-grade deformation of the gneiss at ca. 378–376 Ma, while ca. 320–280 Ma rims on many grains record intrusion of late Paleozoic granite. The temperature-time-deformation history of the Dinwiddie terrane is distinct from the adjacent Goochland and Roanoke Rapids terranes. Orogen-scale dextral transpression likely translated the Dinwiddie terrane southward during the Alleghanian orogeny, at which time they were intruded by Pennsylvanian to Permian granite.</span></p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02546.1","usgsCitation":"Carter, M.W., McAleer, R.J., Holm-Denoma, C., Occhi, M.E., Owens, B.E., and Vazquez, J.A., 2023, Redefinition of the Petersburg batholith and implications for crustal inheritance in the Dinwiddie terrane, Virginia, USA: Geosphere, v. 19, no. 3, p. 900-932, https://doi.org/10.1130/GES02546.1.","productDescription":"33 p.","startPage":"900","endPage":"932","ipdsId":"IP-133680","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443798,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1130/ges02546.1","text":"Publisher Index Page"},{"id":435366,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92IZPID","text":"USGS data release","linkHelpText":"Whole Rock Geochemistry and Uranium Lead Isotopic Data from the Dinwiddie Terrane, Virginia, USA"},{"id":420240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Dinwiddie terrane","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -77.49748525226563,\n              37.798681296457076\n            ],\n            [\n              -78.21468653839521,\n              37.79869080070846\n            ],\n            [\n              -78.24158158662492,\n              36.599064764101655\n            ],\n            [\n              -77.49748525226563,\n              36.599064764101655\n            ],\n            [\n              -77.49748525226563,\n              37.798681296457076\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"19","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":881212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":881213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":881214,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Occhi, Marcie E.","contributorId":328758,"corporation":false,"usgs":false,"family":"Occhi","given":"Marcie","email":"","middleInitial":"E.","affiliations":[{"id":78483,"text":"Virginia Energy - Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":881215,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Owens, Brent E.","contributorId":178190,"corporation":false,"usgs":false,"family":"Owens","given":"Brent","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":881216,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":881217,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70244005,"text":"70244005 - 2023 - Karst terrain promotes thermal resiliency in headwater streams","interactions":[],"lastModifiedDate":"2023-05-31T12:02:24.141945","indexId":"70244005","displayToPublicDate":"2023-04-19T06:58:16","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Karst terrain promotes thermal resiliency in headwater streams","docAbstract":"<p>The response of stream ecosystems to climate change will depend in part on groundwater processes that reduce the sensitivity of streams to atmospheric conditions. &nbsp;We investigated the thermal sensitivity of streams across a gradient of groundwater inputs defined by karst terrain (carbonate parent materials) in the headwaters of the Potomac River basin in eastern North America.&nbsp; We collected stream temperature data and quantified thermal sensitivity for 30 sites from the relationship between daily mean water and air temperatures.&nbsp; Our analysis demonstrates that thermal sensitivity is lower for streams in karst terrain than elsewhere, and that the effect of karst terrain is more important than effects of elevation or basin size in this regard.&nbsp; Our study indicates the importance of karstic groundwater for stream thermal resiliency and suggests the importance of riparian vegetation for maintaining stream temperatures elsewhere. Our study also provides a simple and rapid method for climate change research that can be implemented in conjunction with watershed organizations and citizen science networks.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the West Virginia Academy of Science","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"West Virginia Academy of Science","usgsCitation":"Kessler, K.G., Rogers, K.M., Marshak, C., and Hitt, N.P., 2023, Karst terrain promotes thermal resiliency in headwater streams, <i>in</i> Proceedings of the West Virginia Academy of Science, v. 95, no. 3, 8 p.","productDescription":"8 p.","ipdsId":"IP-144871","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":417568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417549,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pwvas.org/index.php/pwvas/article/view/947"}],"country":"United States","state":"West Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -78.43083122514363,\n              39.72194367002291\n            ],\n            [\n              -78.43083122514363,\n              39.468004910183225\n            ],\n            [\n              -77.88158514397257,\n              39.468004910183225\n            ],\n            [\n              -77.88158514397257,\n              39.72194367002291\n            ],\n            [\n              -78.43083122514363,\n              39.72194367002291\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"95","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kessler, Karmann G. 0000-0001-5681-4909","orcid":"https://orcid.org/0000-0001-5681-4909","contributorId":242765,"corporation":false,"usgs":true,"family":"Kessler","given":"Karmann","email":"","middleInitial":"G.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Karli M. 0000-0002-6188-7405","orcid":"https://orcid.org/0000-0002-6188-7405","contributorId":237955,"corporation":false,"usgs":true,"family":"Rogers","given":"Karli","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marshak, Charles","contributorId":292162,"corporation":false,"usgs":false,"family":"Marshak","given":"Charles","email":"","affiliations":[],"preferred":false,"id":874124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":238185,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":874122,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70247308,"text":"70247308 - 2023 - Evaluating methods for applying fouling attenuation shifts to acoustic backscatter data used in suspended-sediment computations","interactions":[],"lastModifiedDate":"2023-07-27T16:53:25.730843","indexId":"70247308","displayToPublicDate":"2023-04-15T11:52:49","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Evaluating methods for applying fouling attenuation shifts to acoustic backscatter data used in suspended-sediment computations","docAbstract":"<p>No abstract available.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Lucena, Z., Lee, M.T., and East, J.W., 2023, Evaluating methods for applying fouling attenuation shifts to acoustic backscatter data used in suspended-sediment computations, <i>in</i> Proceedings of SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 19 p.","productDescription":"19 p.","ipdsId":"IP-149116","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":419403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419374,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s19.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -94,\n              30.5\n            ],\n            [\n              -98,\n              30.5\n            ],\n            [\n              -98,\n              27.5\n            ],\n            [\n              -94,\n              27.5\n            ],\n            [\n              -94,\n              30.5\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lucena, Zulimar 0000-0002-1682-2661 zlucena@usgs.gov","orcid":"https://orcid.org/0000-0002-1682-2661","contributorId":178284,"corporation":false,"usgs":true,"family":"Lucena","given":"Zulimar","email":"zlucena@usgs.gov","affiliations":[],"preferred":true,"id":879169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Michael T. 0000-0002-8260-8794 mtlee@usgs.gov","orcid":"https://orcid.org/0000-0002-8260-8794","contributorId":4228,"corporation":false,"usgs":true,"family":"Lee","given":"Michael","email":"mtlee@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Jeffery W. 0000-0003-1115-3638 jweast@usgs.gov","orcid":"https://orcid.org/0000-0003-1115-3638","contributorId":317723,"corporation":false,"usgs":true,"family":"East","given":"Jeffery","email":"jweast@usgs.gov","middleInitial":"W.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879171,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70232702,"text":"70232702 - 2023 - Numerical model characterization of sediment transport potentials pre- and post-construction of an artificial island in Foggy Island Bay, Alaska","interactions":[],"lastModifiedDate":"2023-04-27T14:19:55.329084","indexId":"70232702","displayToPublicDate":"2023-04-15T10:43:36","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Numerical model characterization of sediment transport potentials pre- and post-construction of an artificial island in Foggy Island Bay, Alaska","docAbstract":"<p><span>The anticipated construction of the Liberty Development Island near Prudhoe Bay, Alaska, has created a need to understand how the island may influence sediment transport patterns and deposition on the nearby Boulder Patch ecosystem. This study uses a numerical model to characterize sediment transport pathways in Foggy Island Bay with and without the artificial island in place. We present the Delft3D-based model setup and application that yields an improved quantification and understanding of the region’s hydrodynamic, wave, and sediment transport patterns. The results for the present show mainly east-west directed, alongshore transport of silt and clay. Insertion of the planned island results in limited changes to the overall hydrodynamic and sediment transport patterns within the Bay but reverses erosional tendencies across a boulder patch, situated within a kilometer of the planned construction location, to being net depositional.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, LA","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0045","usgsCitation":"Nederhoff, C.M., Erikson, L.H., Engelstad, A.C., and Pearson, S., 2023, Numerical model characterization of sediment transport potentials pre- and post-construction of an artificial island in Foggy Island Bay, Alaska, <i>in</i> The proceedings of the coastal sediments 2023, New Orleans, LA, April 11-15, 2023, p. 487-496, https://doi.org/10.1142/9789811275135_0045.","productDescription":"10 p.","startPage":"487","endPage":"496","ipdsId":"IP-142502","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416384,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Foggy Island Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -147.72875527390383,\n              70.2158827003305\n            ],\n            [\n              -147.72875527390383,\n              70.1980805800392\n            ],\n            [\n              -147.66538717065322,\n              70.1980805800392\n            ],\n            [\n              -147.66538717065322,\n              70.2158827003305\n            ],\n            [\n              -147.72875527390383,\n              70.2158827003305\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":846335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":846336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Anita C 0000-0002-0211-4189","orcid":"https://orcid.org/0000-0002-0211-4189","contributorId":268303,"corporation":false,"usgs":true,"family":"Engelstad","given":"Anita","email":"","middleInitial":"C","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":846337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearson, Stuart 0000-0002-3986-4469","orcid":"https://orcid.org/0000-0002-3986-4469","contributorId":245646,"corporation":false,"usgs":false,"family":"Pearson","given":"Stuart","email":"","affiliations":[{"id":49245,"text":"Delft University of Technology; Deltares","active":true,"usgs":false}],"preferred":false,"id":846338,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70242991,"text":"70242991 - 2023 - Processes controlling coastal erosion along Cape Cod Bay, MA","interactions":[],"lastModifiedDate":"2023-04-26T11:00:06.393489","indexId":"70242991","displayToPublicDate":"2023-04-15T08:43:40","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Processes controlling coastal erosion along Cape Cod Bay, MA","docAbstract":"<p><span>Cape Cod Bay, MA, is a semi-enclosed embayment in the northeastern United States, open on the north to the Gulf of Maine. The coastline experiences impacts typically from strong Nor’easter storms that occur in the late fall or winter months, with some sections of this coastline being affected more severely than others. We investigate the processes that cause spatial variability in storm impacts by using geophysical surveys, shoreline-change analysis, and numerical modeling. We simulated the Gulf of Maine and Cape Cod Bay from Jan–April, 2021, using the COAWST modeling system, including ocean, wave, infragravity wave, and sediment transport models. Results identify bay-scale circulation of alongshore sediment fluxes and convergences at regional shoals. Nearshore modeling also revealed zones of increased wave heights that correlate with regions of increased erosion and coastal angle orientation. Modeled and computed shoreline-change have some correlation but the model does not capture all the variability. This overall approach can potentially be used for other coastal locations to identify regions of storm impacts and to manage coastal resources.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, LA","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0171","usgsCitation":"Warner, J.C., Brothers, L.L., Himmelstoss, E.A., Sherwood, C.R., Aretxabaleta, A., Foster, D.S., and Farris, A.S., 2023, Processes controlling coastal erosion along Cape Cod Bay, MA, <i>in</i> The proceedings of the coastal sediments 2023, New Orleans, LA, April 11-15, 2023, p. 1872-1883, https://doi.org/10.1142/9789811275135_0171.","productDescription":"12 p.","startPage":"1872","endPage":"1883","ipdsId":"IP-142387","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -70.64819420423547,\n              42.075734354731\n            ],\n            [\n              -70.64819420423547,\n              41.73638149646493\n            ],\n            [\n              -69.99225888262333,\n              41.73638149646493\n            ],\n            [\n              -69.99225888262333,\n              42.075734354731\n            ],\n            [\n        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Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Himmelstoss, Emily A. 0000-0002-1760-5474 ehimmelstoss@usgs.gov","orcid":"https://orcid.org/0000-0002-1760-5474","contributorId":194838,"corporation":false,"usgs":true,"family":"Himmelstoss","given":"Emily","email":"ehimmelstoss@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870458,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":870460,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Farris, Amy S. 0000-0002-4668-7261 afarris@usgs.gov","orcid":"https://orcid.org/0000-0002-4668-7261","contributorId":196866,"corporation":false,"usgs":true,"family":"Farris","given":"Amy","email":"afarris@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870461,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70243156,"text":"70243156 - 2023 - Hindcast of Hurricane Sally impacts on barrier islands in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2023-05-02T13:26:41.325537","indexId":"70243156","displayToPublicDate":"2023-04-15T08:22:50","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Hindcast of Hurricane Sally impacts on barrier islands in the Gulf of Mexico","docAbstract":"<p><span>We performed XBeach and ADIRC+SWAN model simulations of Hurricane Sally over Dauphin and Petit Bois Islands off the Alabama-Mississippi coast to evaluate the morphologic response. Simulated water levels compared well with NOAA tide gauge observations to the east of Dauphin Island with a high model skill of 0.9. In addition, the XBeach model results of water levels, mean current speeds and significant wave heights agreed with ADCIRC+SWAN simulations near the offshore boundary and in the channel. Qualitative comparisons between the XBeach simulations and post-storm lidar observations confirmed model predictions of overwash. However, XBeach predicted minor breaches in Dauphin Island, which were not observed. This effort is part of a larger project in which several hydrodynamic and morphodynamic models will be coupled to produce hindcasts over a 15-year period for a larger region along the coast. These evaluations will provide local managers with strategic tools to make decisions about various coastal restoration alternatives.</span></p>","largerWorkTitle":"The proceedings of the coastal sediments 2023","conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, LA","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0204","usgsCitation":"Frank-Gilchrist, D.P., Passeri, D., and Bilskie, M.V., 2023, Hindcast of Hurricane Sally impacts on barrier islands in the Gulf of Mexico, <i>in</i> The proceedings of the coastal sediments 2023, New Orleans, LA, April 11-15, 2023, p. 2220-2227, https://doi.org/10.1142/9789811275135_0204.","productDescription":"8 p.","startPage":"2220","endPage":"2227","ipdsId":"IP-147624","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Mississippi","otherGeospatial":"Dauphin and Petit Bois Islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -88.043598091522,\n              30.30917051732709\n            ],\n            [\n              -88.53511493858724,\n              30.30917051732709\n            ],\n            [\n              -88.53511493858724,\n              30.152412222672723\n            ],\n            [\n              -88.043598091522,\n              30.152412222672723\n            ],\n            [\n              -88.043598091522,\n              30.30917051732709\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Frank-Gilchrist, Donya P. 0000-0002-7146-0069","orcid":"https://orcid.org/0000-0002-7146-0069","contributorId":292926,"corporation":false,"usgs":true,"family":"Frank-Gilchrist","given":"Donya","email":"","middleInitial":"P.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":871297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":871298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bilskie, Matthew V.","contributorId":166891,"corporation":false,"usgs":false,"family":"Bilskie","given":"Matthew","email":"","middleInitial":"V.","affiliations":[{"id":16154,"text":"LSU","active":true,"usgs":false}],"preferred":false,"id":871299,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70242712,"text":"pp1837D - 2023 - Evaluation of hydrologic processes in the eastern Snake River Plain aquifer using uranium and strontium isotopes, Idaho National Laboratory, eastern Idaho","interactions":[],"lastModifiedDate":"2026-02-18T22:12:09.217716","indexId":"pp1837D","displayToPublicDate":"2023-04-14T06:48:58","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1837","chapter":"D","displayTitle":"Evaluation of Hydrologic Processes in the Eastern Snake River Plain Aquifer Using Uranium and Strontium Isotopes, Idaho National Laboratory, Eastern Idaho","title":"Evaluation of hydrologic processes in the eastern Snake River Plain aquifer using uranium and strontium isotopes, Idaho National Laboratory, eastern Idaho","docAbstract":"<p>Waste constituents discharged to the eastern Snake River Plain aquifer at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) pose risks to the water quality of the aquifer. To understand these risks, the U.S. Geological Survey, in cooperation with the DOE, is conducting geochemical studies to better understand the hydrologic processes at the INL that affect the movement of groundwater and waste constituents. In this study, we used natural uranium (<sup>234</sup>U/<sup>238</sup>U) and strontium (<sup>87</sup>Sr/<sup>86</sup>Sr) isotope ratios of surface water and groundwater to identify the sources of water, the mixing of different source waters, and the flow directions in the shallow part (upper 250 feet) of the aquifer at the INL.</p><p>Samples were collected from 17 sites at and near the INL that represent the source-water contributions to the aquifer. These source-water sites included surface water, regional groundwater, and springs. Groundwater samples from 63 sites were collected at and near the INL. For all sites, sample collection dates ranged from 1979 to 2019, but groundwater samples collected at the INL are representative of wet climate cycles when the Big Lost River (BLR) was flowing onto the INL.</p><p>The <sup>234</sup>U/<sup>238</sup>U activity ratios and <sup>87</sup>Sr/<sup>86</sup>Sr from groundwater at the INL were plotted on graphs within ternary mixing webs in which the three end members of the mixing web represented specific sources of recharge. The large number of sources of recharge required numerous mixing webs, representing various geographic locations at the INL, so that each mixing web represented an area with just three sources of recharge. Considerations for determining the sources of recharge to groundwater sites included chemical signatures in addition to <sup>234</sup>U/<sup>238</sup>U and <sup>87</sup>Sr/<sup>86</sup>Sr, hydrologic context, and geographic location. The mixing webs were used to estimate the percentage of recharge from specific sources to groundwater at wells.</p><p>The results of this study identified groundwater from the Lemhi Range as a source of recharge to the INL, which was a previously unsuspected source of recharge. The estimated spatial distribution of recharge from the BLR and groundwater from the Lost River Range also decreased and increased, respectively, relative to the spatial distribution estimated from an earlier study. Upwelling geothermal water was identified at only one well, which indicates that the upward movement of deep groundwater to the shallow part of the aquifer is largely nonexistent. Mixing between surface water and groundwater, different groundwater recharge sources, or both is ubiquitous at the INL. Mixing of water fully explains the distribution of <sup>234</sup>U/<sup>238</sup>U and <sup>87</sup>Sr/<sup>86</sup>Sr in groundwater at the INL and thus renders unnecessary the hypothesis that fast and slow flow zones at the INL are required to explain the distribution of <sup>234</sup>U/<sup>238</sup>U and <sup>87</sup>Sr/<sup>86</sup>Sr.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837D","collaboration":"DOE/ID-22259<br />Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Rattray, G.W., and Paces, J.B., 2023, Evaluation of hydrologic processes in the eastern Snake River Plain aquifer using uranium and strontium isotopes, Idaho National Laboratory, eastern Idaho, with contributions by Treinen, K.C.: U.S. Geological Survey Professional Paper 1837–D (DOE/ID-22259), 65 p., https://doi.org/10.3133/pp1837D.","productDescription":"vi, 65 p.","onlineOnly":"Y","ipdsId":"IP-127503","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":415758,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837C","text":"PP 1837 Chapter  C","description":"PP 1837 Chapter  C"},{"id":415757,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837B","text":"PP 1837 Chapter  B","description":"PP 1837 Chapter  B"},{"id":415754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1837/d/coverthb.jpg"},{"id":415755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1837/d/pp1837d.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1837 Chapter D"},{"id":415756,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837A","text":"PP 1837 Chapter  A","description":"PP 1837 Chapter  A"},{"id":500156,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114666.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -113.73997601464795,\n              43.235490275196184\n            ],\n            [\n              -112.19156981148207,\n              43.235490275196184\n            ],\n            [\n              -112.19156981148207,\n              44.2273523624917\n            ],\n            [\n              -113.73997601464795,\n              44.2273523624917\n            ],\n            [\n              -113.73997601464795,\n              43.235490275196184\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_id@usgs.gov\" data-mce-href=\"mailto:dc_id@usgs.gov\">Director</a>, <a href=\" https://www.usgs.gov/centers/idaho-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/idaho-water-science-center\">Idaho Water Science Center</a><br>U.S. Geological Survey<br>230 Collins Road<br>Boise, Idaho 83702-4520</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Previous Investigations</li><li>Data, Methods, and Quality Assurance</li><li>Geochemistry</li><li>Three-Component Mixing</li><li>Interpretation of Isotope Ratios</li><li>Confidence in Results</li><li>Hydrologic Processes</li><li>Comparison of Results with Previous Investigations</li><li>Summary and Conclusions</li><li>Acknowledgments</li><li>References Cited</li><li>Glossary</li></ul>","publishedDate":"2023-04-14","noUsgsAuthors":false,"publicationDate":"2023-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Rattray, Gordon W. 0000-0002-1690-3218 grattray@usgs.gov","orcid":"https://orcid.org/0000-0002-1690-3218","contributorId":2521,"corporation":false,"usgs":true,"family":"Rattray","given":"Gordon","email":"grattray@usgs.gov","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paces, James B. 0000-0002-9809-8493 jbpaces@usgs.gov","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":2514,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"jbpaces@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":869459,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70242711,"text":"pp1837C - 2023 - Determining three-dimensional hydrologic processes in the eastern Snake River Plain aquifer using geochemical mass-balance modeling, Idaho National Laboratory, eastern Idaho, with contributions by Treinen, K.C.","interactions":[],"lastModifiedDate":"2023-04-17T11:04:59.33674","indexId":"pp1837C","displayToPublicDate":"2023-04-14T06:48:18","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1837","chapter":"C","displayTitle":"Determining Three-Dimensional Hydrologic Processes in the Eastern Snake River Plain Aquifer Using Geochemical Mass-Balance Modeling, Idaho National Laboratory, Eastern Idaho","title":"Determining three-dimensional hydrologic processes in the eastern Snake River Plain aquifer using geochemical mass-balance modeling, Idaho National Laboratory, eastern Idaho, with contributions by Treinen, K.C.","docAbstract":"<p>Waste constituents discharged to the eastern Snake River Plain aquifer at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) pose risks to the water quality of the aquifer. To understand these risks, the U.S. Geological Survey, in cooperation with the DOE, used geochemical mass-balance modeling to identify three-dimensional hydrologic processes in that portion of the aquifer underlying the southwestern part of the INL that affect the movement of groundwater and waste constituents. Modeling was performed using water chemistry of 74 water samples collected from 30 wells. Fifty-four of the water samples were collected from 11 wells equipped with multilevel monitoring systems with vertically discrete sampling zones that encompass the upper 750 feet of the aquifer. Water samples from these multilevel wells were collected during 2007‒13, a period when conditions in the aquifer were approximately steady-state because there was little or no recharge from the Big Lost River.</p><p>The primary source of water in groundwater at the multilevel wells during 2007‒13 was the Big Lost River. Other sources of water include groundwater from the Little Lost River valley, precipitation, and wastewater. Horizontal groundwater-flow directions appear to be similar in both the shallow and deep parts of the aquifer, and surface-water sources of water in most deep groundwater shows that groundwater moves downward. Surface-water sources of water in deep groundwater noticeably decrease within and below the Matuyama flow and associated sedimentary interbeds, which indicates that these units are semi-impermeable and retard the downward movement of groundwater.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837C","collaboration":"DOE/ID-22258<br />Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Suggested citation:\n\nRattray, G.W., 2023, Determining three-dimensional hydrologic processes in the eastern Snake River Plain aquifer using geochemical mass-balance modeling, Idaho National Laboratory, eastern Idaho, with contributions by Treinen, K.C.: U.S. Geological Survey Professional Paper 1837–C (DOE/ID-22258), 133 p., https://doi.org/10.3133/pp1837C.","productDescription":"Report: vii, 133 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-118750","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":415747,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837B","text":"PP 1837 Chapter B","description":"PP 1837 Chapter B"},{"id":415748,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837D","text":"PP 1837 Chapter D","description":"PP 1837 Chapter D"},{"id":415743,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1837/c/coverthb2.jpg"},{"id":415744,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1837/c/pp1837c.pdf","text":"Report","size":"9.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1837 Chapter C"},{"id":415745,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92CEFXN","text":"USGS data release","description":"USGS data release","linkHelpText":"Data for tritium deposition in precipitation in the United States, 1953‒2012"},{"id":415746,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837A","text":"PP 1837 Chapter A","description":"PP 1837 Chapter A"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -113.73997601464795,\n              43.235490275196184\n            ],\n            [\n              -112.19156981148207,\n              43.235490275196184\n            ],\n            [\n              -112.19156981148207,\n              44.2273523624917\n            ],\n            [\n              -113.73997601464795,\n              44.2273523624917\n            ],\n            [\n              -113.73997601464795,\n              43.235490275196184\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:id_or@usgs.gov\" data-mce-href=\"mailto:id_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/idaho-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/idaho-water-science-center\">Idaho Water Science Center</a><br>U.S. Geological Survey<br>230 Collins Road<br>Boise, Idaho 83702-4520</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Geochemistry Data</li><li>Sources of Solutes</li><li>Identifying Sources of Water from Water Chemistry</li><li>Geochemical Modeling</li><li>Hydrologic Processes</li><li>Summary and Conclusions</li><li>Acknowledgments</li><li>References Cited</li><li>Glossary</li></ul>","publishedDate":"2023-04-14","noUsgsAuthors":false,"publicationDate":"2023-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Rattray, Gordon W. 0000-0002-1690-3218 grattray@usgs.gov","orcid":"https://orcid.org/0000-0002-1690-3218","contributorId":2521,"corporation":false,"usgs":true,"family":"Rattray","given":"Gordon","email":"grattray@usgs.gov","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869457,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70243363,"text":"70243363 - 2023 - Vital rates of a burgeoning population of Humpback Chub in western Grand Canyon","interactions":[],"lastModifiedDate":"2023-07-24T16:46:47.833694","indexId":"70243363","displayToPublicDate":"2023-04-14T06:41:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Vital rates of a burgeoning population of Humpback Chub in western Grand Canyon","docAbstract":"<div id=\"article__content\" class=\"col-sm-12 col-md-8 col-lg-8 article__content article-row-left\"><div class=\"article__body \"><div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>The Colorado River ecosystem has experienced habitat alterations and non-native species invasions, and as a result, many of its native species have experienced extirpations, abundance declines, and range constrictions. Despite these pitfalls, Humpback Chub,<span>&nbsp;</span><i>Gila cypha</i>, have persisted and, in the last 10-15 years, expanded their range to become abundant in western Grand Canyon, a river segment in which it had been rare for the prior three decades. Here we analyze a 6-year mark-recapture study from a fixed monitoring reach in western Grand Canyon and provide the first estimates of survival and growth (vital rates) for this relatively ‘new’ group of Humpback Chub. We compare vital rates in western Grand Canyon to two life history forms (residents and migrants, which represent fast and slow life history trajectories, respectively) from the more established Little Colorado River (LCR) aggregation in eastern Grand Canyon. Compared to LCR-migrants and LCR-residents, Humpback Chub in western Grand Canyon had intermediate values for apparent survival, growth, and asymptotic length. Relatively high survival of subadults coupled with fast growth allows for rapid population growth in western Grand Canyon. However, a large cohort in 2017 failed to lead to noticeable increases in adults. Seasonal survival patterns were distinct in all three groups, and apparent survival was lowest in western Grand Canyon during spring months. Adult Humpback Chub in western Grand Canyon were mobile and had a high probability of transience (i.e., just passing through the reach) and temporary emigration, demonstrating the need for future movement studies in western Grand Canyon to better distinguish emigration from survival. We discuss how observations are related to disparate temperature regimes experienced by the three groups, and if(how) the relationship between metabolism and temperature influences vital rates within the river network.</p></div></div></div></div>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10415","usgsCitation":"Dzul, M.C., Yackulic, C., Giardina, M.A., Van Haverbeke, D., and Yard, M., 2023, Vital rates of a burgeoning population of Humpback Chub in western Grand Canyon: Transactions of the American Fisheries Society, v. 152, no. 4, p. 443-459, https://doi.org/10.1002/tafs.10415.","productDescription":"17 p.","startPage":"443","endPage":"459","ipdsId":"IP-148437","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498968,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10415","text":"Publisher Index Page"},{"id":435374,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E96ADU","text":"USGS data release","linkHelpText":"Humpback chub (Gila cypha) capture histories and growth data for two areas in the Colorado River network from 2009-2022 and 2017-2022"},{"id":416898,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.601457318797,\n              36.65987844880918\n            ],\n            [\n              -113.91812426407165,\n              36.65987844880918\n            ],\n            [\n              -113.91812426407165,\n              35.63520969136876\n            ],\n            [\n              -111.601457318797,\n              35.63520969136876\n            ],\n            [\n              -111.601457318797,\n              36.65987844880918\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"152","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giardina, Mariah Aurelia 0000-0001-6753-0450","orcid":"https://orcid.org/0000-0001-6753-0450","contributorId":300798,"corporation":false,"usgs":true,"family":"Giardina","given":"Mariah","email":"","middleInitial":"Aurelia","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Haverbeke, David R.","contributorId":83838,"corporation":false,"usgs":false,"family":"Van Haverbeke","given":"David R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":872169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yard, Michael D. 0000-0002-6580-6027","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":291738,"corporation":false,"usgs":false,"family":"Yard","given":"Michael D.","affiliations":[{"id":62744,"text":"Retired, US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":872170,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70254341,"text":"70254341 - 2023 - Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach","interactions":[],"lastModifiedDate":"2024-05-20T11:34:51.897282","indexId":"70254341","displayToPublicDate":"2023-04-14T06:33:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach","docAbstract":"<div id=\"preview-section-abstract\"><div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><p id=\"spara011\">Prediction of short- (i.e., aquifer is near or at saturated conditions) and long-time (i.e., aquifer is not near or at saturated conditions) baseflow recession characteristics at ungauged stream locations is a current challenge that has been primarily addressed by empirical approaches that relate these characteristics to basin attributes. However, the performance of these models is often only fair with coefficient of determination values ranging from 0.5 to 0.7. In this study, we propose a hybrid physical and machine learning approach to predict the long- and short-time baseflow recession characteristics at ungauged stream locations. This approach is compared to a machine learning method, random forest regression, that relates baseflow recession characteristics to basin attributes in 582 basins across the western and eastern United States. The new approach resulted in lower median and inner quartile ranges (IQR) of absolute normalized errors in predicting long-time baseflow recession characteristics (western: 23%, IQR=32%; eastern: 30%, IQR=39%) compared to estimates of those properties based on random forest regressions (western: 27%, IQR=34%; eastern: 38%, IQR=50%). For the short-time baseflow recession characteristics, the hybrid approach resulted in substantially lower median errors and IQR values (western: 79%, IQR=143%; eastern: 83%, IQR=140%) compared to estimates from random forest regressions (western: 1,577%, IQR=8,887%; eastern: 341%, IQR=2,154%). In addition, this approach identified four major regions in the western United States and three in the eastern United States where the baseflow recession characteristics are mostly constant, and these characteristics only vary based on the geometric properties of aquifers. Lastly, the inter-basin variability of the baseflow recession characteristics was not found to be strongly related to metrics measuring interstorm arrival periods, average number of storms, and average length of storms.</p></div></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2023.104440","usgsCitation":"Eng, K., Wolock, D.M., and Wieczorek, M., 2023, Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach: Advances in Water Resources, v. 175, 104440, https://doi.org/10.1016/j.advwatres.2023.104440.","productDescription":"104440","ipdsId":"IP-127260","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":428824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"175","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eng, Ken 0000-0001-6838-5849 keng@usgs.gov","orcid":"https://orcid.org/0000-0001-6838-5849","contributorId":3580,"corporation":false,"usgs":true,"family":"Eng","given":"Ken","email":"keng@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":901033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":901034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wieczorek, Michael 0000-0003-0999-5457","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":207911,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901035,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70242682,"text":"ofr20231026 - 2023 - Assessment of riparian vegetation patterns and change downstream from Glen Canyon Dam from 2014 to 2019","interactions":[],"lastModifiedDate":"2026-02-11T21:04:06.498805","indexId":"ofr20231026","displayToPublicDate":"2023-04-13T12:02:15","publicationYear":"2023","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":"2023-1026","displayTitle":"Assessment of Riparian Vegetation Patterns and Change Downstream from Glen Canyon Dam from 2014 to 2019","title":"Assessment of riparian vegetation patterns and change downstream from Glen Canyon Dam from 2014 to 2019","docAbstract":"<p>Changes in riparian vegetation cover and composition occur in relation to flow regime, geomorphic template, and climate, and can have cascading effects on aquatic and terrestrial ecosystems. Tracking such changes over time is therefore an important part of monitoring the condition and trajectory of riparian ecosystems. Maintaining diverse, self-sustaining riparian vegetation comprised of mostly native species is identified in the Glen Canyon Dam Long-Term Experimental and Management Plan as a key resource objective for the section of the Colorado River between Glen Canyon Dam and Lake Mead. The U.S. Geological Survey Grand Canyon Monitoring and Research Center implemented an annual monitoring program in 2014 to assess the status and trends of riparian vegetation along this section of river, particularly as they relate to flow regime. In this report, we summarize plant species composition and cover data collected under the annual monitoring program from 2014 to 2019, with special consideration given to the hydrologic position, associated geomorphic feature class, local climate patterns, native and nonnative species, and floristic region for key vegetation metrics and species. We divided the study area into four river segments (referred to as Glen Canyon, Marble Canyon, eastern Grand Canyon, and western Grand Canyon) on the basis of geography and floristic composition and calculated each recorded plant species’ relative frequency and foliar cover by river segment. These data were then used to evaluate species composition relationships among river segments, hydrologic zones, geomorphic features, and sampling years through ordination analysis. Temporal trends in our focal resource objectives—species richness, total foliar cover, proportion of native to nonnative species richness, proportion of native to nonnative species cover, <i>Tamarix</i> cover, <i>Pluchea sericea</i> cover, and <i>Baccharis</i> species cover—were assessed using mixed-effects models. Four patterns related to species composition emerged: (1) species composition of fixed-site sandbars differed from that of randomly selected sites (including randomly selected sandbars), (2) species composition of Glen Canyon sites differed from that of other previously identified floristic regions, (3) species composition differed across hydrologic zones related to dam operations, and (4) species composition within river segments did not change across years. For temporal patterns, four main findings emerged: (1) trends differed between fixed-sites and randomly selected sites; (2) although few directional changes were observed from 2014 to 2019, <i>Baccharis</i> species cover increased at randomly selected sites in areas influenced by daily water fluctuations; (3) native species cover and richness were greater than nonnative species cover and richness across all hydrologic zones; and (4) the temporal trend metrics used here can be used across floristic groups, enabling assessment of the Colorado River ecosystem as a whole. In addition to these findings, lists of recorded plant species are included as appendixes. The variations and patterns in vegetation status and trends presented in this report can be used as a baseline against which future monitoring can be compared.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231026","collaboration":"Prepared in cooperation with the Bureau of Reclamation Glen Canyon Adaptive Management Program","usgsCitation":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2023, Assessment of riparian vegetation patterns and change downstream from Glen Canyon Dam from 2014 to 2019: U.S. Geological Survey Open-File Report 2023–1026, 55 p., https://doi.org/10.3133/ofr20231026.","productDescription":"Report: vii, 55 p.; Data Release","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-132835","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499774,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114661.htm","linkFileType":{"id":5,"text":"html"}},{"id":415675,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1026/images"},{"id":415674,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1026/ofr20231026.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415673,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1026/covrthb.jpg"},{"id":415672,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEHY2S","text":"Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019","description":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2022, Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9KEHY2S."}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -114.06028247701303,\n              36.94784441270309\n            ],\n            [\n              -114.06028247701303,\n              35.55756259875736\n            ],\n            [\n              -111.24899178190306,\n              35.55756259875736\n            ],\n            [\n              -111.24899178190306,\n              36.94784441270309\n            ],\n            [\n              -114.06028247701303,\n              36.94784441270309\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<div class=\"street-block\"><div class=\"thoroughfare\"><a href=\"https://www.usgs.gov/centers/sbsc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/sbsc\">Southwest Biological Science Center</a></div><div class=\"thoroughfare\"><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a></div><div class=\"thoroughfare\">2255 N. Gemini Drive</div></div><div class=\"addressfield-container-inline locality-block country-US\"><span class=\"locality\">Flagstaff</span>,&nbsp;<span class=\"state\">AZ</span>&nbsp;<span class=\"postal-code\">86001</span></div>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>References Cited</li><li>Appendix 1. Species List for Randomly Selected Sites</li><li>Appendix 2. Species List for Fixed-Site Sandbars</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-04-13","noUsgsAuthors":false,"publicationDate":"2023-04-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":869339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butterfield, Bradley J.","contributorId":18096,"corporation":false,"usgs":true,"family":"Butterfield","given":"Bradley J.","affiliations":[],"preferred":false,"id":869340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ralston, Barbara E. 0000-0001-9991-8994 bralston@usgs.gov","orcid":"https://orcid.org/0000-0001-9991-8994","contributorId":606,"corporation":false,"usgs":true,"family":"Ralston","given":"Barbara","email":"bralston@usgs.gov","middleInitial":"E.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":false,"id":869341,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70243529,"text":"70243529 - 2023 - Inferring pathogen presence when sample misclassification and partial observation occur","interactions":[],"lastModifiedDate":"2023-05-11T11:57:40.081025","indexId":"70243529","displayToPublicDate":"2023-04-11T06:55:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Inferring pathogen presence when sample misclassification and partial observation occur","docAbstract":"<ol class=\"\"><li>Surveillance programmes are essential for detecting emerging pathogens and often rely on molecular methods to make inference about the presence of a target disease agent. However, molecular methods rarely detect target DNA perfectly. For example, molecular pathogen detection methods can result in misclassification (i.e. false positives and false negatives) or partial detection errors (i.e. detections with ‘ambiguous’, ‘uncertain’ or ‘equivocal’ results). Then, when data are to be analysed, these partial observations are either discarded or censored; this, however, disregards information that could be used to make inference about the true state of the system. There is a critical need for more direction and guidance related to how many samples are enough to declare a unit of interest ‘pathogen free’.</li><li>Here, we develop a Bayesian hierarchal framework that accommodates false negative, false positive and uncertain detections to improve inference related to the occupancy of a pathogen. We apply our modelling framework to a case study of the fungal pathogen<span>&nbsp;</span><i>Pseudogymnoascus destructans</i><span>&nbsp;</span>(Pd) identified in Texas bats at the invasion front of white-nose syndrome. To improve future surveillance programmes, we provide guidance on sample sizes required to be 95% certain a target organism is absent from a site.</li><li>We found that the presence of uncertain detections increased the variability of resulting posterior probability distributions of pathogen occurrence, and that our estimates of required sample size were very sensitive to prior information about pathogen occupancy, pathogen prevalence and diagnostic test specificity. In the Pd case study, we found that the posterior probability of occupancy was very low in 2018, but occupancy probability approached 1 in 2020, reflecting increasing prior probabilities of occupancy and prevalence elicited from the site manager.</li><li>Our modelling framework provides the user a posterior probability distribution of pathogen occurrence, which allows for subjective interpretation by the decision-maker. To help readers apply and use the methods we developed, we provide an interactive RShiny app that generates target species occupancy estimation and sample size estimates to make these methods more accessible to the scientific community (<a class=\"linkBehavior\" href=\"https://rmummah.shinyapps.io/ambigDetect_sampleSize\" data-mce-href=\"https://rmummah.shinyapps.io/ambigDetect_sampleSize\">https://rmummah.shinyapps.io/ambigDetect_sampleSize</a>). This modelling framework and sample size guide may be useful for improving inferences from molecular surveillance data about emerging pathogens, non-native invasive species and endangered species where misclassifications and ambiguous detections occur.</li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.14102","usgsCitation":"Campbell Grant, E.H., Mummah, R.O., Mosher, B.A., Evans, J., and DiRenzo, G.V., 2023, Inferring pathogen presence when sample misclassification and partial observation occur: Methods in Ecology and Evolution, v. 14, no. 5, p. 1299-1311, https://doi.org/10.1111/2041-210X.14102.","productDescription":"13 p.","startPage":"1299","endPage":"1311","ipdsId":"IP-148152","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443886,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14102","text":"Publisher Index Page"},{"id":435379,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDV4LV","text":"USGS data release","linkHelpText":"Inferring pathogen presence when sample misclassification and partial observation occur"},{"id":416954,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":872230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mummah, Riley O.","contributorId":305294,"corporation":false,"usgs":false,"family":"Mummah","given":"Riley","email":"","middleInitial":"O.","affiliations":[{"id":66204,"text":"Massachusetts Cooperative Fish and Wildlife Research Unit, University of Massachusetts, Department of Environmental Conservation, 160 Holdsworth Way, Amherst, Massachusetts 01003","active":true,"usgs":false}],"preferred":false,"id":872231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mosher, Brittany A.","contributorId":189579,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":872232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Evans, Jonah","contributorId":239062,"corporation":false,"usgs":false,"family":"Evans","given":"Jonah","email":"","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":872233,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":872234,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70254971,"text":"70254971 - 2023 - Effects of large-scale disturbance on animal space use: Functional responses by greater sage-grouse after megafire","interactions":[],"lastModifiedDate":"2024-06-11T14:42:09.314762","indexId":"70254971","displayToPublicDate":"2023-04-07T09:37:01","publicationYear":"2023","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":"Effects of large-scale disturbance on animal space use: Functional responses by greater sage-grouse after megafire","docAbstract":"<p><span>Global change has altered the nature of disturbance regimes, and megafire events are increasingly common. Megafires result in immediate changes to habitat available to terrestrial wildlife over broad landscapes, yet we know surprisingly little about how such changes shape space use of sensitive species in habitat that remains. Functional responses provide a framework for understanding and predicting changes in space use following habitat alteration, but no previous studies have assessed functional responses as a consequence of megafire. We studied space use and tested for functional responses in habitat use by breeding greater sage-grouse (</span><i>Centrocercus urophasianus</i><span>) before and after landscape-level changes induced by a &gt;40,000 ha, high-intensity megafire that burned sagebrush steppe in eastern Idaho, USA. We also incorporated functional responses into predictive resource selection functions (RSFs) to map breeding habitat before and after the fire. Megafire had strong effects on the distribution of available resources and resulted in context-dependent habitat use that was heterogeneous across different components of habitat. We observed functional responses in the use and selection of a variety of resources (shrubs and herbaceous vegetation) for both nesting and brood rearing. Functional responses in the use of nesting habitat were influenced by the overarching effect of megafire on vegetation, whereas responses during brood rearing appeared to be driven by individual variation in available resources that were conditional on nest locations. Importantly, RSFs built using data collected prior to the burn also had poor transferability for predicting space use in a post-megafire landscape. These results have strong implications for understanding and predicting how animals respond to a rapidly changing environment, given that increased severity, frequency, and extent of wildfire are consequences of global change with the capacity to reshape ecosystems. We therefore demonstrate a conceptual framework to better understand space use and aid habitat conservation for wildlife in a rapidly changing world.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9933","usgsCitation":"Stevens, B.S., Roberts, S., Conway, C.J., and Engelstead, D.K., 2023, Effects of large-scale disturbance on animal space use: Functional responses by greater sage-grouse after megafire: Ecology and Evolution, v. 13, no. 4, e9933, 30 p., https://doi.org/10.1002/ece3.9933.","productDescription":"e9933, 30 p.","ipdsId":"IP-136242","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443914,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9933","text":"Publisher Index Page"},{"id":429874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -112.30314841246594,\n              44.47748992705084\n            ],\n            [\n              -112.30314841246594,\n              44.05252291063391\n            ],\n            [\n              -111.2985432757669,\n              44.05252291063391\n            ],\n            [\n              -111.2985432757669,\n              44.47748992705084\n            ],\n            [\n              -112.30314841246594,\n              44.47748992705084\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":171809,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":903006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Shane","contributorId":279606,"corporation":false,"usgs":false,"family":"Roberts","given":"Shane","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":903007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engelstead, Devin K.","contributorId":338188,"corporation":false,"usgs":false,"family":"Engelstead","given":"Devin","email":"","middleInitial":"K.","affiliations":[{"id":37086,"text":"U.S. Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":903009,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70242130,"text":"70242130 - 2023 - Hidden in the hills: Phylogeny of the freshwater mussel genus Alasmidonta (Bivalvia: Unionidae) and description of a new species","interactions":[],"lastModifiedDate":"2023-06-08T14:47:03.003874","indexId":"70242130","displayToPublicDate":"2023-04-07T08:33:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3810,"text":"Zoological Journal of the Linnean Society","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Hidden in the hills: Phylogeny of the freshwater mussel genus <i>Alasmidonta</i> (Bivalvia: Unionidae) and description of a new species","title":"Hidden in the hills: Phylogeny of the freshwater mussel genus Alasmidonta (Bivalvia: Unionidae) and description of a new species","docAbstract":"<p><span>Inaccurate taxonomy can lead to species in need of conservation being overlooked, which makes revisionary systematics crucially important for imperilled groups. The freshwater mussel genus&nbsp;</span><i>Alasmidonta</i><span>&nbsp;is one such group in need of study. Here, we take a multilocus phylogenetic approach to assess species-level taxonomy of&nbsp;</span><i>Alasmidonta</i><span>&nbsp;and test monophyly of this genus. Phylogenetic inference resulted in polyphyly of&nbsp;</span><i>Alasmidonta</i><span>.&nbsp;</span><i>Lasmigona</i><span>, which was included to test monophyly of&nbsp;</span><i>Alasmidonta</i><span>, was also polyphyletic. Species delimitation methods disagreed about whether&nbsp;</span><i>Alasmidonta arcula</i><span>,&nbsp;</span><i>Alasmidonta triangulata</i><span>&nbsp;and&nbsp;</span><i>Alasmidonta undulata</i><span>&nbsp;are distinct species, but all delimitation methods agreed that&nbsp;</span><i>Alasmidonta</i><span>&nbsp;harbours an undescribed species that would be considered&nbsp;</span><i>Alasmidonta varicosa</i><span>&nbsp;under current taxonomy. Given conflict among species delimitation methods and geographical separation, we maintain the current taxonomy for&nbsp;</span><i>A. arcula</i><span>&nbsp;and&nbsp;</span><i>A. triangulata</i><span>. The undescribed species is restricted to rivers of the Uwharrie Mountains region in North Carolina, USA that flow into the Pee Dee River from the east and can be distinguished morphologically from&nbsp;</span><i>A. varciosa</i><span>&nbsp;by higher and wider placed adductor mussels and a hooked pseudocardinal tooth. We offer insights into how supraspecific taxonomy of subtribe Alasmidontina might be resolved and formally describe the lineage from the Uwharrie Mountains region as Uwharrie elktoe,&nbsp;</span><i>Alasmidonta uwharriensis</i><span>&nbsp;sp. nov.</span></p>","language":"English","publisher":"Oxford Academic Press","doi":"10.1093/zoolinnean/zlac106","usgsCitation":"Whelan, N., Johnson, N., Williams, A.S., Perkins, M.A., Beaver, C.E., and Mays, J.W., 2023, Hidden in the hills: Phylogeny of the freshwater mussel genus Alasmidonta (Bivalvia: Unionidae) and description of a new species: Zoological Journal of the Linnean Society, v. 198, no. 2, p. 650-676, https://doi.org/10.1093/zoolinnean/zlac106.","productDescription":"27 p.; Data Release","startPage":"650","endPage":"676","ipdsId":"IP-138304","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":443919,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/zoolinnean/zlac106","text":"Publisher Index Page"},{"id":415412,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417812,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P47PUC"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -81.01909716357271,\n              35.05786699652403\n            ],\n            [\n              -78.85956067260797,\n              35.05786699652403\n            ],\n            [\n              -78.85956067260797,\n              36.47874763761361\n            ],\n            [\n              -81.01909716357271,\n              36.47874763761361\n            ],\n            [\n              -81.01909716357271,\n              35.05786699652403\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"198","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Whelan, Nathan V.","contributorId":304024,"corporation":false,"usgs":false,"family":"Whelan","given":"Nathan V.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":868962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nathan 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":210319,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Ashantye’ S.","contributorId":304031,"corporation":false,"usgs":false,"family":"Williams","given":"Ashantye’","email":"","middleInitial":"S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":868964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Michael A.","contributorId":178870,"corporation":false,"usgs":false,"family":"Perkins","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":868965,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beaver, Caitlin E. 0000-0002-9269-7604","orcid":"https://orcid.org/0000-0002-9269-7604","contributorId":268037,"corporation":false,"usgs":true,"family":"Beaver","given":"Caitlin","email":"","middleInitial":"E.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868966,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mays, Jason W.","contributorId":304033,"corporation":false,"usgs":false,"family":"Mays","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":868967,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70242109,"text":"70242109 - 2023 - The stratigraphy and stratigraphic nomenclature of the Goochland Terrane in the Piedmont Province of east-central Virginia","interactions":[],"lastModifiedDate":"2023-11-20T17:05:35.904071","indexId":"70242109","displayToPublicDate":"2023-04-07T08:25:35","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"The stratigraphy and stratigraphic nomenclature of the Goochland Terrane in the Piedmont Province of east-central Virginia","docAbstract":"<p><span>The Goochland terrane is a structurally isolated crustal block in the eastern Piedmont of Virginia. It is composed of the previously named State Farm Gneiss, Montpelier Anorthosite, Sabot Amphibolite, and Maidens Gneiss, but also includes the Scotchtown Gneiss, Teman Gneiss, and Old Bandana Gneiss which are formally named and defined herein. The eastern part of the Goochland terrane is antiformal and cored by Mesoproterozoic rocks (the State Farm Gneiss and the Montpelier Anorthosite). These basement units are overlain by a late Neoproterozoic to early Paleozoic (Ediacaran to Early Cambrian) saprolitic, metavolcanic, and metasedimentary sequence that sequentially includes the Scotchtown Gneiss, Sabot Amphibolite and Maidens Gneiss. The western part of the terrane is synformal and includes in its core two additional units that overlie the Maidens Gneiss: the Teman Gneiss and the Old Bandana Gneiss. Based on mineralogy and zircon grain morphology, the protoliths of the Maidens, Teman, and Old Bandana gneisses were predominantly sedimentary rocks. The protoliths of the Teman Gneiss and Old Bandana Gneiss were deposited unconformably upon the protolith of the Maidens Gneiss. The eastern and western parts of the Goochland terrane are separated by the Dabneys fault, which has considerable east-side-up vertical offset and possibly also significant transverse displacement. Correlation of the upper part of the Goochland terrane (Teman and Old Bandana gneisses) with the Setters and Cockeysville gneisses in the Baltimore region suggests that the Goochland terrane was left about 135 miles (ca. 220 km) southwest of its original North American location, which was to the east of Baltimore, Maryland. This displacement was caused by the oblique collision of the eastern North American continent with the western edge of the Gondwanan craton during the later Carboniferous (Pennsylvanian) Period.</span></p>","language":"English","publisher":"Micropress","doi":"10.29041/strat.20.1.03","usgsCitation":"Weems, R.E., and Robbins, E., 2023, The stratigraphy and stratigraphic nomenclature of the Goochland Terrane in the Piedmont Province of east-central Virginia: Stratigraphy, v. 20, no. 1, p. 39-58, https://doi.org/10.29041/strat.20.1.03.","productDescription":"20 p.","startPage":"39","endPage":"58","ipdsId":"IP-126463","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":415411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Goochland Terrane, Piedmont province","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -78.58432298451764,\n              38.424681988885965\n            ],\n            [\n              -78.58432298451764,\n              37.92609933589563\n            ],\n            [\n              -77.43824965406793,\n              37.92609933589563\n            ],\n            [\n              -77.43824965406793,\n              38.424681988885965\n            ],\n            [\n              -78.58432298451764,\n              38.424681988885965\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weems, Robert E.","contributorId":304011,"corporation":false,"usgs":false,"family":"Weems","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":868914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robbins, Eleanora I.","contributorId":304012,"corporation":false,"usgs":false,"family":"Robbins","given":"Eleanora I.","affiliations":[],"preferred":false,"id":868915,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70242136,"text":"70242136 - 2023 - Predicted aquatic exposure effects from a national urban stormwater study","interactions":[],"lastModifiedDate":"2023-12-04T16:57:15.989156","indexId":"70242136","displayToPublicDate":"2023-04-07T08:18:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13794,"text":"Environmental Science: Water Research and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Predicted aquatic exposure effects from a national urban stormwater study","docAbstract":"<p><span>A multi-agency study of 438 organic and 62 inorganic chemicals measured in urban stormwater during 50 total runoff events at 21 sites across the United States demonstrated that stormwater discharges can generate localized, aquatic exposures to extensive contaminant mixtures, including organics suspected to cause adverse aquatic-health effects. The aggregated risks to multiple aquatic trophic levels (fish, invertebrates, plants) of the stormwater mixture exposures, which were documented in the national study, were explored herein by calculating cumulative ratios of organic-contaminant&nbsp;</span><i>in vitro</i><span>&nbsp;exposure–activity cutoffs (∑</span><small><sub>EAR</sub></small><span>) and health-benchmark-weighted cumulative toxicity quotients (∑</span><small><sub>TQ</sub></small><span>). Both risk assessment approaches indicated substantial (moderate to high) risk for acute adverse effects to aquatic organisms across multiple trophic levels (fish, macroinvertebrates, non-vascular/vascular plants) at or near stormwater discharge points across the United States. The results are interpreted as potential orders of magnitude underestimates of actual aquatic risk in stormwater control wetlands or in the immediate vicinity of such discharges to surface-water receptors, because the 438 organic-compound analytical space assessed in this study is orders of magnitude less than the 350 000 parent compounds estimated to be in current commercial use globally and the incalculable chemical-space of potential metabolites and degradates.</span></p>","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D2EW00933A","usgsCitation":"Bradley, P., Romanok, K., Smalling, K., Masoner, J.R., Kolpin, D., and Gordon, S.E., 2023, Predicted aquatic exposure effects from a national urban stormwater study: Environmental Science: Water Research and Technology, v. 9, p. 3191-3199, https://doi.org/10.1039/D2EW00933A.","productDescription":"9 p.","startPage":"3191","endPage":"3199","ipdsId":"IP-124205","costCenters":[{"id":242,"text":"Eastern Geographic 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States\"}}]}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","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":868974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":868977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":204154,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":868978,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gordon, Stephanie E. 0000-0002-6292-2612 sgordon@usgs.gov","orcid":"https://orcid.org/0000-0002-6292-2612","contributorId":200931,"corporation":false,"usgs":true,"family":"Gordon","given":"Stephanie","email":"sgordon@usgs.gov","middleInitial":"E.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":868979,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70242706,"text":"70242706 - 2023 - Shallow deformation on the Kirby Hills fault, Sacramento–San Joaquin Delta, California (USA), revealed from high-resolution seismic reflection data and coring in a fluvial system","interactions":[],"lastModifiedDate":"2023-06-09T15:15:59.382116","indexId":"70242706","displayToPublicDate":"2023-04-06T06:51:28","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Shallow deformation on the Kirby Hills fault, Sacramento–San Joaquin Delta, California (USA), revealed from high-resolution seismic reflection data and coring in a fluvial system","docAbstract":"<div id=\"136251760\" class=\"article-section-wrapper js-article-section js-content-section  \" data-section-parent-id=\"0\"><p>The Sacramento–San Joaquin Delta (Delta) in California (USA) is an important part of the state’s freshwater system and is also a major source of agricultural and natural resources. However, the Delta is traversed by a series of faults that make up the easternmost part of the San Andreas fault system at this latitude and pose seismic hazard to this region. In this study, we use new high-resolution chirp subbottom data to map and characterize the shallow expression of the Kirby Hills fault, where it has been mapped to cross the Sacramento River at the western extent of the Delta. The fault is buried here, but we document a broad zone of deformation associated with the eastern strand of the fault that changes in character, along strike, across ~600 m of the river channel. Radiocarbon dates from sediment cores collected in the Sacramento River provide some minimum constraints on the age of deformation. We do not observe evidence of the western strand as previously mapped. We also discuss difficulties of conducting a paleoseismologic study in a fluvial environment.</p></div>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02525.1","usgsCitation":"Klotsko, S., Maloney, J., and Watt, J., 2023, Shallow deformation on the Kirby Hills fault, Sacramento–San Joaquin Delta, California (USA), revealed from high-resolution seismic reflection data and coring in a fluvial system: Geosphere, v. 19, no. 3, p. 748-769, https://doi.org/10.1130/GES02525.1.","productDescription":"22 p.","startPage":"748","endPage":"769","ipdsId":"IP-144086","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443936,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1130/ges02525.1","text":"Publisher Index Page"},{"id":415703,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -123.15618476884723,\n              38.36476843145434\n            ],\n            [\n              -123.15618476884723,\n              37.28049028339727\n            ],\n            [\n              -121.05869835179277,\n              37.28049028339727\n            ],\n            [\n              -121.05869835179277,\n              38.36476843145434\n            ],\n            [\n              -123.15618476884723,\n              38.36476843145434\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"19","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Klotsko, Shannon","contributorId":304140,"corporation":false,"usgs":false,"family":"Klotsko","given":"Shannon","affiliations":[{"id":24668,"text":"University of North Carolina, Wilmington","active":true,"usgs":false}],"preferred":false,"id":869423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maloney, Jillian","contributorId":304141,"corporation":false,"usgs":false,"family":"Maloney","given":"Jillian","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":869424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watt, Janet 0000-0002-4759-3814","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":221271,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":869425,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70243528,"text":"70243528 - 2023 - Environmental factors influencing detection efficiency of an acoustic telemetry array and consequences for data interpretation","interactions":[],"lastModifiedDate":"2023-05-11T11:47:29.744393","indexId":"70243528","displayToPublicDate":"2023-04-06T06:40:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Environmental factors influencing detection efficiency of an acoustic telemetry array and consequences for data interpretation","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Background</h3><p>Acoustic telemetry is a commonly used technology to monitor animal occupancy and infer movement in aquatic environments. The information that acoustic telemetry provides is vital for spatial planning and management decisions concerning aquatic and coastal environments by characterizing behaviors and habitats&nbsp;such as spawning aggregations, migrations, corridors, and&nbsp;nurseries,&nbsp;among others. However, performance of acoustic telemetry equipment and resulting detection ranges and efficiencies can vary as a function of environmental conditions, leading to potentially biased interpretations of telemetry data. Here, we characterize variation in detection performance using an acoustic telemetry receiver array deployed in Wellfleet Harbor, Massachusetts, USA from 2015 to 2017. The array was designed to study benthic invertebrate movements and provided an in situ opportunity to identify factors driving variation in detection probability.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>The near-shore location proximate to environmental monitoring allowed for a detailed examination of factors influencing detection efficiency in a range-testing experiment. Detection ranges varied from &lt; 50 to 1,500&nbsp;m and efficiencies varied from 0 to 100% within those detection ranges. Detection efficiency was affected by distance, wind speed and direction, wave height and direction, water temperature, water depth, and water quality.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>Performance of acoustic telemetry systems is strongly contingent on environmental conditions. Our study found that wind, waves, water temperature, water quality, and depth all affected performance to an extent that could seriously compromise a study if these effects were not taken into consideration. Other unmeasured factors may also be important, depending on the characteristics of each site. This information can help guide future telemetry study designs by helping researchers anticipate the density of receivers required to achieve study objectives. Researchers can further refine and document the reliability of&nbsp;their data by incorporating continuously deployed range-testing tags and prior knowledge on varying detection efficiency into movement and occupancy models.</p>","language":"English","publisher":"Springer","doi":"10.1186/s40317-023-00317-2","usgsCitation":"Long, M., Jordaan, A., and Castro-Santos, T.R., 2023, Environmental factors influencing detection efficiency of an acoustic telemetry array and consequences for data interpretation: Animal Biotelemetry, v. 11, 18, 13 p., https://doi.org/10.1186/s40317-023-00317-2.","productDescription":"18, 13 p.","ipdsId":"IP-141767","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-023-00317-2","text":"Publisher Index Page"},{"id":416951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -70.12152378095689,\n              41.97721573790295\n            ],\n            [\n              -70.12152378095689,\n              41.80349781857885\n            ],\n            [\n              -69.90189169540182,\n              41.80349781857885\n            ],\n            [\n              -69.90189169540182,\n              41.97721573790295\n            ],\n            [\n              -70.12152378095689,\n              41.97721573790295\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Michael 0000-0001-6735-6878","orcid":"https://orcid.org/0000-0001-6735-6878","contributorId":261905,"corporation":false,"usgs":false,"family":"Long","given":"Michael","email":"","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":872227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jordaan, Adrian","contributorId":257709,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":872228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":872229,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70242000,"text":"sir20235013 - 2023 - Salinity and selenium yield maps derived from geostatistical modeling in the lower Gunnison River Basin, western Colorado, 1992–2013","interactions":[],"lastModifiedDate":"2026-03-02T21:57:03.940791","indexId":"sir20235013","displayToPublicDate":"2023-04-05T10:35:01","publicationYear":"2023","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":"2023-5013","displayTitle":"Salinity and Selenium Yield Maps Derived from Geostatistical Modeling in the Lower Gunnison River Basin, Western Colorado, 1992–2013","title":"Salinity and selenium yield maps derived from geostatistical modeling in the lower Gunnison River Basin, western Colorado, 1992–2013","docAbstract":"<p>Salinity is known to affect drinking-water supplies and damage irrigated agricultural lands. Selenium in high concentrations is harmful to fish and other wildlife. Land managers, water providers, and agricultural producers in the lower Gunnison River Basin in western Colorado expend resources mitigating the effects of these constituents. The U.S. Geological Survey revised existing salinity (total dissolved solids) and selenium models for the lower Gunnison River Basin in an attempt to better identify areas of greatest salinity and selenium yield. This effort developed maps of yields predicted from multiple linear regression (MLR) models for the lower Gunnison River Basin. The models included data for irrigation and nonirrigation seasons and two periods, 1992–2004 and 2005–13.</p><p>Concentrations of salinity and selenium and discharge measurements made at the time of sampling were used to compute loads for subbasins (component drainages of the larger lower Gunnison River Basin study area), which were adjusted for inflows and outflows of canal loads. Load regression equations were determined from explanatory basin characteristics that included physical properties, precipitation, land use and cover, surficial deposits (soil and unconsolidated geologic materials), and bedrock geology. Loads of salinity and selenium were converted to yields by using the subbasin drainage areas, and an empirical Bayesian kriging procedure was used to produce robust grids of yields for salinity and selenium.</p><p>Salinity yields ranged from 0.00667 to 6.564 tons per year per acre. The highest salinity yields, greater than about 5.0 tons per year per acre, are predicted on the western side of the Uncompahgre River upstream from Delta, Colorado, an area with a high density of irrigated land. The selenium yield map shows a similar pattern, but the highest yields are somewhat more confined to the eastern side of the lower Uncompahgre River and south of the Gunnison River near the confluence with the Uncompahgre River at Delta, Colorado. Selenium yields ranged from 2.6888 x 10<sup>-10</sup> to 0.000445 pounds per day per acre. The highest predicted selenium yields, greater than 0.0003 pounds per day per acre, were in the area downstream from Montrose, Colorado, on the eastern side of the Uncompahgre River.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235013","collaboration":"Prepared in cooperation with the Bureau of Reclamation and the Colorado Water Conservation Board","usgsCitation":"Williams, C.A., Gidley, R.G., and Stevens, M.R., 2023, Salinity and selenium yield maps derived from geostatistical modeling in the lower Gunnison River Basin, western Colorado, 1992–2013: U.S. Geological Survey Scientific Investigations Report 2023–5013, 37 p., https://doi.org/10.3133/sir20235013.","productDescription":"Report: vi, 37 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-127438","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":415136,"rank":3,"type":{"id":30,"text":"Data 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2023-5013"},{"id":415134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5013/coverthb.jpg"},{"id":415137,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":500707,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114651.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -108.66041487616633,\n              38.99638415429618\n            ],\n            [\n              -108.6616273692395,\n              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href=\"https://www.usgs.gov/centers/colorado-water-science-center/\" data-mce-href=\"https://www.usgs.gov/centers/colorado-water-science-center/\">Colorado Water Science Center</a><br>U.S. Geological Survey<br>Box 25046, Mail Stop 415<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Previous Investigations</li><li>Methods</li><li>Salinity and Selenium Yield Maps</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishedDate":"2023-04-05","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Cory A. 0000-0003-1461-7848 cawillia@usgs.gov","orcid":"https://orcid.org/0000-0003-1461-7848","contributorId":689,"corporation":false,"usgs":true,"family":"Williams","given":"Cory","email":"cawillia@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gidley, Rachel G. 0000-0002-9840-8252","orcid":"https://orcid.org/0000-0002-9840-8252","contributorId":259315,"corporation":false,"usgs":true,"family":"Gidley","given":"Rachel","email":"","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stevens, Michael R. 0000-0002-9476-6335","orcid":"https://orcid.org/0000-0002-9476-6335","contributorId":303903,"corporation":false,"usgs":false,"family":"Stevens","given":"Michael R.","affiliations":[{"id":37196,"text":"Retired USGS employee","active":true,"usgs":false}],"preferred":false,"id":868488,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70241590,"text":"sir20235010 - 2023 - Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada","interactions":[],"lastModifiedDate":"2023-04-05T14:53:25.369229","indexId":"sir20235010","displayToPublicDate":"2023-04-05T09:55:00","publicationYear":"2023","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":"2023-5010","displayTitle":"Visualization of Petroleum Exploration Maturity for Six Petroleum Provinces Outside the United States and Canada","title":"Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada","docAbstract":"<p>Outside the United States and Canada, most of the world’s supplies of oil and natural gas are recovered from conventional (or discrete) oil and gas accumulations. This type of hydrocarbon accumulation remains a target for exploration. In this report, exploration and discovery data are used to visually assist in describing the exploration maturity of selected petroleum provinces with respect to conventional oil and natural gas accumulations. The specific provinces are the Campos Basin (Brazil), the Santos Basin (Brazil), the North Sea Graben (northwestern Europe), the Middle Magdelena Basin (Colombia), the Sirte Basin (Libya), and the Kutei Basin (Indonesia). For each province, discovery data and well data through October 2019 are reported; from these data, depth distributions of the oil in oil fields and natural gas in gas fields were computed.</p><p>The concepts of delineated prospective area and explored area include elements of geographic spatial information and statistical data analytics. Graphs showing dynamic growth of discoveries that are tied to the delineated prospective area provide a means of grading prospective area. Visualizations put the results of exploration in the context of geographic and geologic features of the play or basin and can be a tool to assist geologists with the appraisal of the number and sizes of undiscovered petroleum accumulations. Visualizations of exploration drilling and discoveries can (1) assist in conceptualizing a geologic model of the basin, (2) highlight relations among discovered accumulations in different plays or assessment units within the basin, and (3) allow the geologist to identify the missing information needed to complete the geologic model of a basin. Further, if visualization attributes can be quantified, they may be used for formulating quantitative models that predict numbers and sizes of undiscovered oil and gas accumulations. Such modeling approaches include discovery process models, Bayesian network models that characterize play or assessment unit dependencies, and innovative applications of machine learning to complement standard geologic assessments.</p><p>The purpose of this report is to show how visualizations can further the understanding of exploration maturity for the six selected petroleum provinces. It also shows how the geologic framework, geologic data, and drilling and discovery trends can give context to the interpretation of the visualizations that lead to appraisal of exploration maturity.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235010","usgsCitation":"Attanasi, E.D., and Freeman, P.A., 2023, Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada: U.S. Geological Survey Scientific Investigations Report 2023–5010, 38 p., https://doi.org/10.3133/sir20235010.","productDescription":"viii, 38 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119047","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":414671,"rank":3,"type":{"id":39,"text":"HTML 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}\n    }\n  ]\n}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/connect\" data-mce-href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/connect\">Energy Resources Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p><a href=\"mailto:AskEnergyProgram@usgs.gov\" data-mce-href=\"mailto:AskEnergyProgram@usgs.gov\">AskEnergyProgram@usgs.gov</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methodology</li><li>Summary Description of the Six Petroleum Provinces</li><li>Explanation of Tabular Data and Figures</li><li>Provisional Evaluation of Exploration Maturity</li><li>Implications and Conclusions</li><li>References Cited</li><li>Appendix 1. Mean Volume Estimates of the Undiscovered, Technically Recoverable, and Conventional Petroleum Resources for the Six Provinces in This Study</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-04-05","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":867400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":867398,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70242647,"text":"70242647 - 2023 - Population dynamics and harvest management of eastern mallards","interactions":[],"lastModifiedDate":"2023-06-09T15:15:15.702993","indexId":"70242647","displayToPublicDate":"2023-04-03T07:03:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Population dynamics and harvest management of eastern mallards","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Managing sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision-making. Mallards (<i>Anas platyrhynchos</i>) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2-season banding and recovery data, and hunter-harvest data from 1998 to 2018. When developing the model, we used novel model selection methods to test various forms of a sub-model for survival including estimating the degree of harvest additivity and any age-specific trends. The top survival sub-model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.22405","usgsCitation":"Roberts, A.J., Hostetler, J.A., Stiller, J.C., Devers, P.K., and Link, W., 2023, Population dynamics and harvest management of eastern mallards: Journal of Wildlife Management, v. 87, no. 5, e22405, 18 p., https://doi.org/10.1002/jwmg.22405.","productDescription":"e22405, 18 p.","ipdsId":"IP-148216","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":499332,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22405","text":"Publisher Index Page"},{"id":435387,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZMPO0J","text":"USGS data release","linkHelpText":"Data for &amp;quot;Population Dynamics and Harvest Management of Eastern Mallards&amp;quot;"},{"id":415650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, Anthony J.","contributorId":191131,"corporation":false,"usgs":false,"family":"Roberts","given":"Anthony","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":869215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Jeffrey A. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":190248,"corporation":false,"usgs":false,"family":"Hostetler","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":869216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stiller, Joshua C.","contributorId":276124,"corporation":false,"usgs":false,"family":"Stiller","given":"Joshua","email":"","middleInitial":"C.","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":869217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Devers, Patrick K.","contributorId":167173,"corporation":false,"usgs":false,"family":"Devers","given":"Patrick","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":869218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Link, William 0000-0002-9913-0256","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":221718,"corporation":false,"usgs":true,"family":"Link","given":"William","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":869219,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70242089,"text":"70242089 - 2023 - Magnitude conversion and earthquake recurrence rate models for the central and eastern United States","interactions":[],"lastModifiedDate":"2023-04-06T16:37:04.276307","indexId":"70242089","displayToPublicDate":"2023-03-31T11:17:40","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":13787,"text":"Research Information Letter","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"2023-03","title":"Magnitude conversion and earthquake recurrence rate models for the central and eastern United States","docAbstract":"<p>Development of Seismic Source Characterization (SSC) models, which is an essential part of Probabilistic Seismic Hazard Analyses (PSHA), can help forecast the temporal and spatial distribution of future damaging earthquakes (\uD835\uDC40<i><sub>w</sub></i>≥ 5) in seismically active regions. Because it is impossible to associate all earthquakes with known faults, seismic source models for PSHA often include sources of diffuse seismicity in which future earthquake scenarios are not localized on mapped faults. These sources of diffuse seismicity are referred to as area source zones, distributed seismicity zones, or just source zones. During the early years of PSHA studies, it was assumed that earthquakes in seismotectonic zones have (1) uniform spatial distribution, (2) Poisson temporal distribution, and (3) exponential magnitude distribution (NRC, 2012). In seismically active regions (e.g., the Western United States), where active faults are readily identified, models of the spatial distribution of earthquakes include both the fault source geometries and the distributed seismicity (background) source zones. Source characterization of active faults is complemented by paleoseismic studies with estimates of earthquake magnitudes, dates of occurrences, and slip rates, which provide important information for PSHA studies. </p><p>In the Central and Eastern United States (CEUS) very few Quaternary-active faults have the requisite information for use in PSHA (i.e., fault geometry and dimensions, event rates or slip rates, etc.), and we lack knowledge about the causative faults for most observed seismicity in the region. As a result, area source zones are frequently used in site-specific PSHA in the CEUS to represent diffuse seismicity that cannot be associated with faults. However, there are examples of active fault sources in the CEUS, such as the Meers fault, the Cheraw fault, and New Madrid region, where individual faults can be characterized. </p><p>The source characterization models for background seismicity are based, to a large extent, on an assumption that spatial distribution of historical and recorded seismicity will not change substantially for time periods of interest for PSHA (approximately the next 50-100 years for engineered structures). Furthermore, studies such as those by Kafka (2007, 2009) found a correlation between the locations of small- to moderate-magnitude earthquakes and the locations of large-magnitude earthquakes, indicating that we can, with some level of confidence, use the spatial pattern of smaller earthquakes to forecast the future pattern of damaging earthquakes. </p><p>Within background seismicity zones, the earthquake rate forecast is developed using spatial smoothing of the small to moderate magnitude events in earthquake catalogs. Different methodologies are used for this purpose and can predict varying distributions of seismicity rates. This in turn affects the results of a seismic hazard analysis. The U.S. Geological Survey (USGS) and Nuclear Regulatory Commission (NRC) use different methods for computing spatially smoothed seismicity rates in the CEUS; the USGS uses kernel-based spatial smoothing methods in developing the National Seismic Hazard Model (NSHM), and the method adopted in the Central and Eastern United States Seismic Source Characterization (CEUS-SSC) project is used when evaluating seismic hazard for nuclear power plant siting. These methods are described and the impact on seismic hazard are evaluated in this Research Information Letter (RIL). </p><p>Another important input to estimating the rate of distributed seismicity is event magnitudes listed in earthquake catalogs. A substantial source of uncertainty in catalogs is the magnitude assigned to a given earthquake. Numerous different magnitude types exist, with each magnitude type computed in a different way. Therefore, for the sake of consistency, both the CEUS-SSC and the USGS NSHM have attempted to assemble a complete catalog with a uniform magnitude determination. To this end, moment magnitude, \uD835\uDC40<i><sub>w</sub></i>, which is a physics-based measurement, has been adopted as the standard. However, \uD835\uDC40<i><sub>w</sub></i> was not computed routinely until the past few decades. To address this issue, the CEUS-SSC conducted extensive analyses to determine conversion equations from which to take a routinely computed network (e.g., \uD835\uDC40<i><sub>L</sub></i> or \uD835\uDC5A<i><sub>bLg</sub></i> ) and convert it into \uD835\uDC40<i><sub>w</sub></i>. Another issue with using \uD835\uDC40<i><sub>w</sub></i>&nbsp;is that it becomes increasingly difficult to compute for earthquakes with \uD835\uDC40 less than ~4. </p><p>This study investigates the effects of moment magnitude estimation and spatial smoothing methods on estimation of the earthquake rate forecast and on seismic hazard. We investigate the validity of the magnitude conversion equations and their associated uncertainties by applying them to a case study for induced earthquakes in southern Kansas and northern Oklahoma, and summarize the use of the decay of the seismic coda to estimate \uD835\uDC40<i><sub>w</sub></i> for small earthquakes (\uD835\uDC40<i><sub>w</sub></i> &lt; 4. Furthermore, the study documents a comparison and assessment of background seismicity smoothing methods implemented by the USGS for the NSHM and used by the CEUS-SSC for siting nuclear facilities based on probabilistic seismic hazard estimates from multiple source zones in the CEUS and for multiple sites.&nbsp;</p>","language":"English","publisher":"Nuclear Regulatory Commission","usgsCitation":"Anooshehpoor, R., Weaver, T., Ake, J., Munson, C., Moschetti, M.P., Shelly, D.R., and Powers, P.M., 2023, Magnitude conversion and earthquake recurrence rate models for the central and eastern United States: Research Information Letter 2023-03, 81 p.","productDescription":"81 p.","ipdsId":"IP-148166","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":415346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415324,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://adamswebsearch2.nrc.gov/webSearch2/main.jsp?AccessionNumber=ML23073A370","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","otherGeospatial":"central and eastern United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -115,\n              50\n            ],\n            [\n              -115,\n              25\n            ],\n            [\n              -65,\n              25\n            ],\n            [\n              -65,\n              50\n            ],\n            [\n              -115,\n              50\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anooshehpoor, Rasool","contributorId":303980,"corporation":false,"usgs":false,"family":"Anooshehpoor","given":"Rasool","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weaver, Thomas","contributorId":303981,"corporation":false,"usgs":false,"family":"Weaver","given":"Thomas","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ake, Jon","contributorId":303982,"corporation":false,"usgs":false,"family":"Ake","given":"Jon","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868792,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munson, Cliff","contributorId":303983,"corporation":false,"usgs":false,"family":"Munson","given":"Cliff","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868793,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868794,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":868795,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868796,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70263433,"text":"70263433 - 2023 - The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake","interactions":[],"lastModifiedDate":"2025-02-11T15:25:05.142851","indexId":"70263433","displayToPublicDate":"2023-03-31T09:20:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2467,"text":"Journal of Structural Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake","docAbstract":"<p><span>Seismic behavior and performance of the new Self- Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System is studied using response data recorded during the October 14, 2019,&nbsp;</span><span>\uD835\uDC40\uD835\uDC64⁢4.6</span><span>&nbsp;Pleasant Hill earthquake. The new bridge went into service within the last decade as a replacement for the older truss bridge that spanned between Yerba Buena Island and East Bay. During the October 19, 1989, M6.9 Loma Prieta earthquake, which occurred&nbsp;</span><span>∼100  km</span><span>&nbsp;away from the Bay Bridge, a section of the upper deck of the old truss bridge fell onto the lower deck—thus closing this important lifeline between San Francisco and East Bay. The new SAS Bridge (as well as the rest of the Bay Bridge) is instrumented by the California Strong Motion Instrumentation Program (CSMIP). The unique SAS Bridge is suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the eastbound (E) and westbound (W) decks. At both the west and east ends of the SAS, there is a hinge system that connects the W and E decks to the skyways leading to highways. For the west side, the SAS is led to a tunnel at Yerba Buena Island. The response data analyses highlight the complex and yet identifiable coupled response of the deck, tower, and cable system. Using system identification methods including spectral analyses of both acceleration and displacement time history data, the fundamental frequencies (periods) and critical damping percentages are extracted for the main components (tower, deck, and cables) of the bridge where the sensors are deployed. Frequencies (periods) are then compared with the values computed during the design and analysis process of the bridge. The analyses in this paper showed that there is strong evidence of a beating effect attributed to low critical damping percentages and coupled modes. A possible correlation of fundamental periods of such suspension bridges with their span lengths is discussed. The beating effect and period versus span length can be significant topics for further research.</span></p>","language":"English","publisher":"American Society of Civil Engineering","doi":"10.1061/JSENDH.STENG-11725","usgsCitation":"Celebi, M., 2023, The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake: Journal of Structural Engineering, v. 149, no. 6, 05023003, 12 p., https://doi.org/10.1061/JSENDH.STENG-11725.","productDescription":"05023003, 12 p.","ipdsId":"IP-138272","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jsendh.steng-11725","text":"Publisher Index Page"},{"id":481928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Bridge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.30992911723865,\n              37.83469490117358\n            ],\n            [\n              -122.36848380330268,\n              37.83469490117358\n            ],\n            [\n              -122.36848380330268,\n              37.80847229984835\n            ],\n            [\n              -122.30992911723865,\n              37.80847229984835\n            ],\n            [\n              -122.30992911723865,\n              37.83469490117358\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"149","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":926975,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70241955,"text":"70241955 - 2023 - Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling","interactions":[],"lastModifiedDate":"2023-04-03T11:43:32.05906","indexId":"70241955","displayToPublicDate":"2023-03-31T06:40:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling","docAbstract":"<h2 class=\"heading\">Background</h2><p>Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.</p><h2 class=\"heading\">Methods</h2><p>We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.</p><h2 class=\"heading\">Results</h2><p>Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.</p>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.15163","usgsCitation":"Aunins, A.W., Mueller, S.J., Fike, J., and Cornman, R.S., 2023, Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling: PeerJ, v. 11, e15163, 34 p., https://doi.org/10.7717/peerj.15163.","productDescription":"e15163, 34 p.","ipdsId":"IP-146615","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":444004,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.15163","text":"Publisher Index Page"},{"id":435391,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NNZNVH","text":"USGS data release","linkHelpText":"Metabarcode sequencing of aquatic environmental DNA from the Potomac River Watershed, 2015-2020"},{"id":415048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Sara J.","contributorId":303889,"corporation":false,"usgs":false,"family":"Mueller","given":"Sara","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":868370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
]}