U.S. Geological Survey (USGS) Mission: The USGS national mission is to monitor, analyze, and predict the current and evolving dynamics of complex human and natural Earth-system interactions, and to deliver actionable information at scales and timeframes relevant to decision-makers. Consistent with the national mission, the USGS in Alaska provides timely and objective scientific information to help address issues and inform management decisions across five inter-connected themes:
The USGS in Alaska consists of approximately 350 scientists and support staff working in three Alaska-based science centers, a Cooperative Research Unit, and USGS centers outside Alaska, with a combined annual science budget of about $60 million. In the last 5 years, USGS research in Alaska has produced many scientific benefits resulting from more than 1,050 publications. Publications relevant to Alaska can be conveniently searched by keyword through the USGS Publications Warehouse at https://pubs.er.usgs.gov/search?q=Alaska.
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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
We present a model of the lower seismogenic depth of earthquakes in the western United States (WUS) estimated using the hypocentral depths of events M > 1, a crustal temperature model, and historical earthquake rupture depth models. Locations of earthquakes are from the Advanced National Seismic System Comprehensive Earthquake Catalog from 1980 to 2021 supplemented with seismicity in southern California for event hypocenters that were relocated by Hauksson et al. (2012) to obtain higher precision and better resolution in the model. We calculated the average depth of the deepest 10% of the merged catalog using an adaptive radius of 50 km or more. Along the San Andreas fault, the deepest seismogenic depths are located at 23 km around the Cholame segment, whereas the shallowest depths are located at about 10 km along the Rodgers Creek and Maacama faults. For the WUS outside California, the depth generally varies between 10 and 25 km with an average around 14 km but could extend to 35 km along Cascadia subduction zone. We find good agreement between the small‐magnitude depths and rupture depths derived from coseismic slip of large earthquakes across the region. Our estimates are generally deeper than the previous seismogenic depths determined for the Uniform California Earthquake Rupture Forecast, Version 3 model based on work by Petersen et al. (1996) who used seismicity cross sections along major fault zones in California. Our new seismogenic depth distribution correlates closely with crustal temperature derived from WUS heat flow (Blackwell et al., 2011). This correlation allowed us to develop a map of the brittle–ductile transition that we use to replace seismogenic depths in the model east of the Intermountain West Seismic Belt where the seismicity rate is low. This updated depth model is useful for recalibrating the lower geologic fault rupture depths, and constraining deformation and seismicity source models in updates of the U.S. Geological Survey National Seismic Hazard Model.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220174","usgsCitation":"Zeng, Y., Petersen, M.D., and Boyd, O.S., 2022, Lower seismogenic depth model of western U.S. Earthquakes: Seismological Research Letters, v. 93, no. 6, p. 3186-3204, https://doi.org/10.1785/0220220174.","productDescription":"19 p.","startPage":"3186","endPage":"3204","ipdsId":"IP-142152","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":408265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.33203125,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 29.84064389983441\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237484,"text":"sir20225095 - 2022 - Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","interactions":[],"lastModifiedDate":"2024-05-07T20:58:03.278223","indexId":"sir20225095","displayToPublicDate":"2022-10-12T10:35:13","publicationYear":"2022","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":"2022-5095","displayTitle":"Updated Annual and Semimonthly Streamflow Statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Southwestern Idaho, 2021","title":"Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Bureau of Land Management (BLM), continued streamflow data collection in water years 2013–21 to update daily streamflow regressions and annual and semimonthly streamflow statistics initially developed in 2012 for streams designated as “wild,” “scenic,” or “recreational” under the National Wild and Scenic Rivers System in the Owyhee Canyonlands Wilderness in southwestern Idaho. To sustain “outstanding remarkable values” in the Owyhee Canyonlands Wilderness, BLM determined that maintaining specific streamflow conditions in rivers was important for sustaining ecological health, recreational opportunities, and water demands for stock water and irrigation in a region with increased pressure from upstream land development. Streamflow statistics previously developed using regional regressions based on limited number of streamgages and generalized basin characteristics were determined to inaccurately represent hydrologic characteristics in the Owyhee Canyonlands Wilderness.
In this study, updated streamflow regressions and statistics are provided for 11 partial-record sites in the Owyhee Canyonlands Wilderness using 311 additional streamflow measurements. A partial-record Maintenance of Variance Extension, Type 1 (MOVE.1) streamflow regression method was used to relate discrete streamflow measurements collected at partial-record sites with daily mean streamflow at nearby index sites. The updated regressions were used to estimate a synthetic daily mean streamflow record at each partial-record site for the period of record of the selected index site. The computed synthetic streamflow record was then used to determine annual and semimonthly streamflow statistics at each partial-record site. Annual bankfull streamflow statistics were calculated at each partial-record site using the computed bankfull streamflow at the selected index site and the updated streamflow regression.
Additional streamflow measurements representing a larger range of hydrologic conditions since 2012, reevaluation of index site selection, and updated regression techniques improved streamflow statistic estimates in the Owyhee Canyonlands Wilderness. Regression performance was evaluated based on the coefficient of determination (R2) between the partial-record and index sites, percent bias, and similarity of basin characteristics between the selected index site and the partial-record site. Generally, the updated regressions performed well for partial-record sites with an index site located upstream or downstream on the same stream. Regression performance was degraded and less robust for index sites located farther away from the corresponding partial-record site. Additional streamflow measurements at partial-record sites with few measurements over a small range in hydrologic conditions could improve regression performance and reduce prediction intervals. Furthermore, additional index sites in the Owyhee Canyonlands Wilderness could improve the updated streamflow regressions and statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225095","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Dudunake, T.J., and Ducar, S.D., 2022, Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021 (ver. 1.1, May 2024): U.S. Geological Survey Scientific Investigations Report 2022–5095, 31 p., https://doi.org/10.3133/sir20225095.","productDescription":"Report: viii, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128129","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":408220,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.XML"},{"id":408218,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJA24","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow regressions and annual and semimonthly exceedance probability statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Idaho"},{"id":408217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5095"},{"id":408221,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5095"},{"id":408219,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5095/images"},{"id":428468,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5095/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":408216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5095/coverthb2.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Owyhee Canyonlands Wilderness","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.02636718749999,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
In Katmai National Park, Alaska, USA, we have seen changes in the number of brown bears and sea otters. The number of animals of a species a habitat can support is called carrying capacity. Even though bears live on land and sea otters live in the ocean, these two mammals share coastal habitats. Bears eat salmon, other fish, plants, clams, and beached whales. Sea otters feed on clams and other marine invertebrates. All these foods are influenced by the ocean. Recently, we have seen fewer bears but more sea otters! What changed? Many things, but several observations point to the ocean. There are fewer salmon, whales, and clams, so bears rely more on plants for food. Fewer clams mean sea otters must work harder to find food. Our studies are helping us to understand how and why carrying capacity for a given species may change over time.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frym.2022.715993","usgsCitation":"Coletti, H., Hilderbrand, G., Bodkin, J., Ballachey, B., Erlenbach, J., Esslinger, G.G., Hannam, M.P., Kloecker, K.A., Mangipane, B., Miller, A., Monson, D., Pister, B., Griffin, K., Bodkin, K., and Smith, T., 2022, Where land and sea meet: Brown bears and sea otters: Frontiers for Young Minds, HTML Document, https://doi.org/10.3389/frym.2022.715993.","productDescription":"HTML Document","ipdsId":"IP-128728","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":446135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frym.2022.715993","text":"Publisher Index Page"},{"id":408326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park","geographicExtents":"{\n 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P.","contributorId":199775,"corporation":false,"usgs":false,"family":"Hannam","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":854688,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kloecker, Kimberly A. 0000-0002-2461-968X kkloecker@usgs.gov","orcid":"https://orcid.org/0000-0002-2461-968X","contributorId":3442,"corporation":false,"usgs":true,"family":"Kloecker","given":"Kimberly","email":"kkloecker@usgs.gov","middleInitial":"A.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":854689,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mangipane, Buck","contributorId":211731,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":854690,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, 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Service","active":true,"usgs":false}],"preferred":false,"id":854693,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Griffin, K.","contributorId":297945,"corporation":false,"usgs":false,"family":"Griffin","given":"K.","email":"","affiliations":[],"preferred":false,"id":854698,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bodkin, K.","contributorId":297944,"corporation":false,"usgs":false,"family":"Bodkin","given":"K.","email":"","affiliations":[],"preferred":false,"id":854699,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Smith, Tom","contributorId":207440,"corporation":false,"usgs":false,"family":"Smith","given":"Tom","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":854694,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70237377,"text":"70237377 - 2022 - Biological assessments of aquatic ecosystems","interactions":[],"lastModifiedDate":"2022-10-12T14:47:36.289209","indexId":"70237377","displayToPublicDate":"2022-10-12T09:42:24","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Biological assessments of aquatic ecosystems","docAbstract":"The aim of biological assessments (or bioassessments) is to provide decision makers and managers the scientific information and tools needed to protect and restore aquatic life. Biological assessments typically include several critical elements, including development of ecological indicators, indices of ecological status, benchmarks by which to gauge impairment, ways to identify the stressors causing ecological impairment, and biological criteria and standards to protect aquatic life. New scientific tools are emerging that should improve the accuracy and precision of biological assessments, but the major challenges to effective protection and restoration of aquatic life are political and economic rather than scientific.","language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00100-6","usgsCitation":"Hawkins, C.P., and Carlisle, D.M., 2022, Biological assessments of aquatic ecosystems, p. 525-536, https://doi.org/10.1016/B978-0-12-819166-8.00100-6.","productDescription":"12 p.","startPage":"525","endPage":"536","ipdsId":"IP-134353","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":408214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hawkins, Charles P.","contributorId":198331,"corporation":false,"usgs":false,"family":"Hawkins","given":"Charles","email":"","middleInitial":"P.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":854345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":503,"text":"Office of Water Quality","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":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":854346,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237388,"text":"70237388 - 2022 - Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","interactions":[],"lastModifiedDate":"2022-10-17T16:42:25.152014","indexId":"70237388","displayToPublicDate":"2022-10-12T09:07:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","docAbstract":"This study investigates the applicability of the Landsat Dynamic Surface Water Extent (DSWE) science product for waterbird habitat modeling in multiple non-canopied habitat types. We compare surface water distribution estimates derived from DSWE to two site-specific survey methods: visual surveys and digitized aerial imagery. These site-specific surveys were conducted on Poplar Island, a restoration island project in the Chesapeake Bay, USA. Visual surveys were collected bimonthly from 2006 – 2013, and digitized aerial imagery was collected annually from 2006 – 2015. As a restoration island, Poplar Island presents a unique opportunity to analyze DSWE in a rapidly changing site. We structure our analysis based on the procedural development of individual sub-island cells developed from unconsolidated dredge material into fully restored wetlands that have independent hydrologic connection to the surrounding bay. Each development status is analyzed using our three DSWE classifications: Open Water (OW), a conservative estimate; Wetland Inclusive (WI), an aggressive estimate; and Development Dependent (DD), a landcover adaptive estimate. The OW classification consistently underestimates surface water coverage especially in the more complex, fully developed cells. The WI classification is better able to capture the tidal channels in these cells, but marginally overestimates surface water coverage in more sparsely vegetated cells. The DD classification does not significantly improve upon the estimations of the WI classification. Our data indicate that DSWE can be a capable alternative to our site-specific survey methods. However, the product is limited by Landsat’s 30 m spatial resolution, especially in more structurally complex wetlands. A recommended classification method for characterizing waterbird habitats would depend on the goals and targeted scale of analysis, for which DSWE may be a viable option.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2022.100845","usgsCitation":"Taylor, J., Sullivan, J.D., Teitelbaum, C.S., Reese, J.G., and Prosser, D., 2022, Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats: Remote Sensing Applications: Society and Environment, v. 28, 100845, 9 p., https://doi.org/10.1016/j.rsase.2022.100845.","productDescription":"100845, 9 p.","ipdsId":"IP-139932","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446139,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2022.100845","text":"Publisher Index Page"},{"id":435658,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SW505K","text":"USGS data release","linkHelpText":"Surface water estimates for a complex study site derived from traditional and emerging methods"},{"id":408211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Poplar Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.36236190795898,\n 38.74631848708898\n ],\n [\n -76.36373519897461,\n 38.754886481591335\n ],\n [\n -76.36905670166014,\n 38.7564928660758\n ],\n [\n -76.37231826782227,\n 38.754886481591335\n ],\n [\n -76.37884140014648,\n 38.762114927054405\n ],\n [\n -76.37969970703125,\n 38.76519348705099\n ],\n [\n -76.37712478637695,\n 38.76827191422227\n ],\n [\n -76.36356353759766,\n 38.78165483591876\n ],\n [\n -76.35824203491211,\n 38.78580303175893\n ],\n [\n -76.35772705078125,\n 38.793295935073516\n ],\n [\n -76.36510848999022,\n 38.797577240505625\n ],\n [\n -76.36974334716797,\n 38.79650693825768\n ],\n [\n -76.37042999267578,\n 38.79102138696157\n ],\n [\n -76.37128829956055,\n 38.78807774637309\n ],\n [\n -76.37060165405273,\n 38.784331118923134\n ],\n [\n -76.37334823608398,\n 38.78272536117434\n ],\n [\n -76.37695312499999,\n 38.78339443129763\n ],\n [\n -76.38141632080078,\n 38.78138720209366\n ],\n [\n -76.39291763305664,\n 38.767201172010736\n ],\n [\n -76.39120101928711,\n 38.75582354360097\n ],\n [\n -76.38656616210938,\n 38.7500671112174\n ],\n [\n -76.38364791870117,\n 38.743239112884744\n ],\n [\n -76.37678146362305,\n 38.741766321754575\n ],\n [\n -76.36236190795898,\n 38.74631848708898\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, John B.","contributorId":296294,"corporation":false,"usgs":false,"family":"Taylor","given":"John B.","affiliations":[{"id":64012,"text":"Chesapeake Bay Trust","active":true,"usgs":false}],"preferred":false,"id":854370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":854371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teitelbaum, Claire S. 0000-0001-5646-3184","orcid":"https://orcid.org/0000-0001-5646-3184","contributorId":255382,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":854372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reese, Jan G.","contributorId":296295,"corporation":false,"usgs":false,"family":"Reese","given":"Jan","email":"","middleInitial":"G.","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":854373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237375,"text":"70237375 - 2022 - Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","interactions":[],"lastModifiedDate":"2022-10-12T14:07:03.951041","indexId":"70237375","displayToPublicDate":"2022-10-12T08:55:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","docAbstract":"Aim: Anticipating when and where changes in species' demographic rates will lead to range shifts in response to changing climate remains a major challenge. Despite evidence of increasing mortality in dry forests across the globe in response to drought and warming temperatures, the overall impacts on the distribution of dry forests are largely unknown because we lack comparable large-scale data on tree recruitment rates. Here, our aim was to develop range-wide population models for dry forest tree species (pinyon pine and juniper), quantifying both mortality and recruitment, to better understand where and under what conditions species range contractions are occurring.
Location: Western United States.
Major taxa studied: Two pinyon pine (Pinus spp.) and three juniper (Juniperus spp.) species.
Methods: We developed range-wide demographic models for five species using forest inventory data from across the western United States and estimated population trends and climate vulnerability.
Results: We find that four of the five species are declining in parts of their range, with Pinus edulis having the largest proportion of populations declining (24%). Population vulnerability increases with aridity and temperature, with up to ~50% of populations declining in the warmest and driest conditions. Mortality and recruitment were both essential to explaining where populations are declining.
Main conclusions: Our results suggest that dry forest species are undergoing an active range shift driven by both changing recruitment and mortality, and that increasing temperatures and drought threaten the long-term viability of many of these species in their current range. While four of the five species examined were experiencing some declines, P. edulis is currently most vulnerable. Management actions such as reducing tree density may be able to mitigate some of these impacts. The framework we present to estimate range-wide demographic rates can be applied to other species to determine where range contractions are most likely.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.13582","usgsCitation":"Shriver, R., Yackulic, C., Bell, D.M., and Bradford, J., 2022, Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions: Global Ecology and Biogeography, v. 31, no. 11, p. 2259-2269, https://doi.org/10.1111/geb.13582.","productDescription":"11 p.","startPage":"2259","endPage":"2269","ipdsId":"IP-143036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":435659,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FIGKFM","text":"USGS data release","linkHelpText":"Pinyon-juniper basal area, climate and demographics data from National Forest Inventory plots and projected under future density and climate conditions"},{"id":408210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Idaho, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Utah, Washington, Wyoming","otherGeospatial":"Great Basin, Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.5751953125,\n 49.03786794532644\n ],\n [\n -119.64111328125,\n 48.38544219115483\n ],\n [\n -118.63037109375,\n 47.79839667295524\n ],\n [\n -117.44384765625,\n 47.78363463526376\n ],\n [\n -116.806640625,\n 47.14489748555398\n ],\n [\n -117.02636718749999,\n 46.118941506107056\n ],\n [\n -116.56494140625001,\n 45.69083283645816\n ],\n [\n -116.91650390625,\n 44.91813929958515\n ],\n [\n -117.1142578125,\n 44.69989765840318\n ],\n [\n -117.18017578125,\n 44.29240108529005\n ],\n [\n -116.45507812500001,\n 43.77109381775651\n ],\n [\n -115.59814453125001,\n 43.18114705939968\n ],\n [\n -112.96142578125,\n 42.13082130188811\n ],\n [\n -111.20361328125,\n 42.032974332441405\n ],\n [\n -111.29150390625,\n 40.730608477796636\n ],\n [\n -112.08251953125,\n 40.111688665595956\n ],\n [\n -112.30224609374999,\n 39.01064750994083\n ],\n [\n -112.54394531249999,\n 38.13455657705411\n ],\n [\n -111.46728515624999,\n 37.996162679728116\n ],\n [\n -110.23681640625,\n 38.993572058209466\n ],\n [\n -110.06103515625,\n 40.43022363450862\n ],\n [\n -108.984375,\n 40.38002840251183\n ],\n [\n -108.78662109375,\n 38.94232097947902\n ],\n [\n -108.19335937499999,\n 37.33522435930639\n ],\n [\n -106.72119140625,\n 37.00255267215955\n ],\n [\n -106.67724609375,\n 36.54494944148322\n ],\n [\n -106.94091796875,\n 35.746512259918504\n ],\n [\n -106.61132812499999,\n 34.831841149828655\n ],\n [\n -106.3916015625,\n 33.669496972795535\n ],\n [\n -106.6552734375,\n 33.119150226768866\n ],\n [\n -106.5673828125,\n 31.50362930577303\n ],\n [\n -105.16113281249999,\n 30.675715404167743\n ],\n [\n -104.6337890625,\n 29.458731185355344\n ],\n [\n -103.0517578125,\n 28.806173508854776\n ],\n [\n -102.26074218749999,\n 29.649868677972304\n ],\n [\n -101.42578124999999,\n 29.649868677972304\n ],\n [\n -99.36035156249999,\n 32.65787573695528\n ],\n [\n -97.6025390625,\n 33.7243396617476\n ],\n [\n -97.2509765625,\n 34.84987503195418\n ],\n [\n -96.8994140625,\n 37.23032838760387\n ],\n [\n -96.591796875,\n 38.89103282648846\n ],\n [\n -96.94335937499999,\n 41.27780646738183\n ],\n [\n -96.8994140625,\n 42.4234565179383\n ],\n [\n -98.5693359375,\n 43.54854811091286\n ],\n [\n -98.7890625,\n 44.37098696297173\n ],\n [\n -98.87695312499999,\n 47.39834920035926\n ],\n [\n -100.37109375,\n 47.81315451752768\n ],\n [\n -101.9970703125,\n 49.03786794532644\n ],\n [\n -119.5751953125,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Robert K.","contributorId":297511,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert K.","affiliations":[{"id":64419,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno; Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":854333,"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":854334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":854335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237723,"text":"70237723 - 2022 - Probiotics beyond the farm: Benefits, costs, and considerations of using antibiotic alternatives in livestock","interactions":[],"lastModifiedDate":"2022-10-21T13:51:58.019829","indexId":"70237723","displayToPublicDate":"2022-10-12T08:48:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12789,"text":"Frontiers in Antibiotics","active":true,"publicationSubtype":{"id":10}},"title":"Probiotics beyond the farm: Benefits, costs, and considerations of using antibiotic alternatives in livestock","docAbstract":"The increasing global expansion of antimicrobial resistant infections warrants the development of effective antibiotic alternative therapies, particularly for use in livestock production, an agricultural sector that is perceived to disproportionately contribute to the antimicrobial resistance (AMR) crisis by consuming nearly two-thirds of the global antibiotic supply. Probiotics and probiotic derived compounds are promising alternative therapies, and their successful use in disease prevention, treatment, and animal performance commands attention. However, insufficient or outdated probiotic screening techniques may unintentionally contribute to this crisis, and few longitudinal studies have been conducted to determine what role probiotics play in AMR dissemination in animal hosts and the surrounding environment. In this review, we briefly summarize the current literature regarding the efficacy, feasibility, and limitations of probiotics, including an evaluation of their impact on the animal microbiome and resistome and their potential to influence AMR in the environment. Probiotic application for livestock is often touted as an ideal alternative therapy that might reduce the need for antibiotic use in agriculture and the negative downstream impacts. However, as detailed in this review, limited research has been conducted linking probiotic usage with reductions in AMR in agricultural or natural environments. Additionally, we discuss the methods, including limitations, of current probiotic screening techniques across the globe, highlighting approaches aimed at reducing antibiotic usage and ensuring safe and effective probiotic mediated health outcomes. Based on this information, we propose economic and logistical considerations for bringing probiotic therapies to market including regulatory roadblocks, future innovations, and the significant gaps in knowledge requiring additional research to ensure probiotics are suitable long-term options for livestock producers as an antibiotic alternative therapy.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frabi.2022.1003912","usgsCitation":"Leistikow, K.R., Beattie, R.E., and Hristova, K.R., 2022, Probiotics beyond the farm: Benefits, costs, and considerations of using antibiotic alternatives in livestock: Frontiers in Antibiotics, v. 1, 1003912, 18 p., https://doi.org/10.3389/frabi.2022.1003912.","productDescription":"1003912, 18 p.","ipdsId":"IP-143496","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":446144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frabi.2022.1003912","text":"Publisher Index Page"},{"id":408602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Leistikow, Kyle R.","contributorId":298311,"corporation":false,"usgs":false,"family":"Leistikow","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":64527,"text":"Marquette University","active":true,"usgs":false}],"preferred":false,"id":855363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beattie, Rachelle Elaine 0000-0002-9648-4948","orcid":"https://orcid.org/0000-0002-9648-4948","contributorId":298312,"corporation":false,"usgs":true,"family":"Beattie","given":"Rachelle","email":"","middleInitial":"Elaine","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":855364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hristova, Krassimira R.","contributorId":298313,"corporation":false,"usgs":false,"family":"Hristova","given":"Krassimira","email":"","middleInitial":"R.","affiliations":[{"id":64527,"text":"Marquette University","active":true,"usgs":false}],"preferred":false,"id":855365,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237391,"text":"70237391 - 2022 - An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","interactions":[],"lastModifiedDate":"2022-10-12T13:40:56.735739","indexId":"70237391","displayToPublicDate":"2022-10-12T08:20:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5557,"text":"Wader Study","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","title":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","docAbstract":"We used two datasets to investigate the reliability of plumage for sexing adult Ruddy Turnstones Arenaria interpres of the morinella subspecies during May and early June in Delaware Bay, on the Mid-Atlantic Coast of the United States (39.1202°N, 75.2479°W). We first examined 23 years of data on the capture and recapture of 1,818 individual Ruddy Turnstones to assess the consistency of observers with varying levels of expertise in assigning sex using plumage criteria. Among birds recaptured once, the sex recorded for about 10% differed between captures. This increased to about 16% among birds recaptured more than once. Significantly more birds sexed as females early in the season (during 1–12 May) were later sexed as males than vice versa. This suggests that early-season captures may include birds still in non- (or partial) breeding plumage, which can be confused with female breeding plumage. Second, we compared plumage-based and genetic assessments of sex for 66 Ruddy Turnstones captured in Delaware Bay on 29 May 2016 and 19 May 2017; these individuals were sexed in the hand by an expert on shorebird plumages. Plumage-based and molecular assessments differed in only one case. This suggests that fewer birds will be wrongly sexed on plumage if more care is taken and better instruction is given to observers (including how to distinguish non- breeding plumage from female breeding plumage). We suggest simple procedures to reduce field-sexing errors for Ruddy Turnstones based on plumage.
","language":"English","publisher":"International Wader Study Group","doi":"10.18194/ws.00274","usgsCitation":"Fullagar, P.J., Chesser, R., Sitters, H.P., Davey, C.C., Niles, L., Drovetski, S.V., and Cortes-Rodriguez, M., 2022, An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America: Wader Study, v. 129, no. 2, p. 138-147, https://doi.org/10.18194/ws.00274.","productDescription":"10 p.","startPage":"138","endPage":"147","ipdsId":"IP-133440","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":408208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.09979248046875,\n 38.76479194327964\n ],\n [\n -74.94598388671875,\n 38.94232097947902\n ],\n [\n -74.87457275390625,\n 39.095962936305476\n ],\n [\n -74.85260009765625,\n 39.17478791493289\n ],\n [\n -74.8828125,\n 39.22587043822116\n ],\n [\n -75.13824462890625,\n 39.24288969082635\n ],\n [\n -75.23162841796875,\n 39.31517545076218\n ],\n [\n -75.27557373046875,\n 39.32579941789298\n ],\n [\n -75.311279296875,\n 39.36190883564925\n ],\n [\n -75.3662109375,\n 39.37677199661635\n ],\n [\n -75.44586181640625,\n 39.45316112807394\n ],\n [\n -75.51177978515625,\n 39.48284540453334\n ],\n [\n -75.498046875,\n 39.54217596171196\n ],\n [\n -75.47332763671875,\n 39.58240712203527\n ],\n [\n -75.531005859375,\n 39.63319206567459\n ],\n [\n -75.487060546875,\n 39.688167045890395\n ],\n [\n -75.42938232421875,\n 39.75999140525313\n ],\n [\n -75.311279296875,\n 39.82119422647453\n ],\n [\n -75.22064208984375,\n 39.83595916247957\n ],\n [\n -75.08880615234375,\n 39.86969567045658\n ],\n [\n -75.1519775390625,\n 39.91394967016644\n ],\n [\n -75.3662109375,\n 39.86969567045658\n ],\n [\n -75.53924560546875,\n 39.74521015328692\n ],\n [\n -75.5474853515625,\n 39.71141252523694\n ],\n [\n -75.63812255859375,\n 39.614152077002664\n ],\n [\n -75.6024169921875,\n 39.53370327008705\n ],\n [\n -75.62164306640625,\n 39.45528185347343\n ],\n [\n -75.56121826171875,\n 39.40861097325807\n ],\n [\n -75.5145263671875,\n 39.33854604847979\n ],\n [\n -75.42388916015625,\n 39.20246222588238\n ],\n [\n -75.44036865234375,\n 39.16839998800286\n ],\n [\n -75.42938232421875,\n 39.07677595221322\n ],\n [\n -75.38543701171875,\n 39.036252959636606\n ],\n [\n -75.34149169921875,\n 38.974357249228206\n ],\n [\n -75.34423828125,\n 38.9380483825641\n ],\n [\n -75.2947998046875,\n 38.884619201291905\n ],\n [\n -75.29754638671875,\n 38.86323626888358\n ],\n [\n -75.17669677734375,\n 38.77978137804918\n ],\n [\n -75.09979248046875,\n 38.76479194327964\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Fullagar, Peter J.","contributorId":297536,"corporation":false,"usgs":false,"family":"Fullagar","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":64424,"text":"private individual","active":true,"usgs":false}],"preferred":false,"id":854375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chesser, R. 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Terry","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sitters, Humphrey P.","contributorId":297537,"corporation":false,"usgs":false,"family":"Sitters","given":"Humphrey","email":"","middleInitial":"P.","affiliations":[{"id":64424,"text":"private individual","active":true,"usgs":false}],"preferred":false,"id":854377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davey, Christopher C.","contributorId":297538,"corporation":false,"usgs":false,"family":"Davey","given":"Christopher","email":"","middleInitial":"C.","affiliations":[{"id":64424,"text":"private individual","active":true,"usgs":false}],"preferred":false,"id":854378,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Niles, Lawrence J.","contributorId":297539,"corporation":false,"usgs":false,"family":"Niles","given":"Lawrence J.","affiliations":[{"id":64426,"text":"Wildlife Restoration Partnerships","active":true,"usgs":false}],"preferred":false,"id":854379,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854380,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cortes-Rodriguez, M. 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Nandadevi","affiliations":[{"id":18877,"text":"Ithaca College","active":true,"usgs":false}],"preferred":false,"id":854381,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70269055,"text":"70269055 - 2022 - Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2025-07-15T15:43:20.638903","indexId":"70269055","displayToPublicDate":"2022-10-12T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","docAbstract":"It has been nearly a decade since updates to seismic and fault sources in the central and eastern United States (CEUS) were last assessed for the 2012 Central and Eastern United States Seismic Source Characterization for nuclear facilities (CEUS-SSCn) and 2014 United States Geological Survey National Seismic Hazard Model (NSHM) for the conterminous U.S. In advance of the 2023 NSHM update, we created 3 related geospatial databases to summarize and characterize new fault source information for the CEUS. These include fault section, fault-zone polygon, and earthquake geology (fault slip rate, earthquake recurrence intervals) databases which document updates to fault parameters used in prior seismic hazard models in this region. The 2012 CEUS-SSCn and 2014 NSHM fault models served as a foundation, as we revised and added fault sources where new published studies documented significant changes to our understanding of fault location, geometry, or activity. We added 9 new fault sections that meet the criteria of (1) a length ≥7 km, (2) evidence of recurrent Quaternary tectonic activity, and (3) documentation that is publicly available in a peer-reviewed source. The prior CEUS models only included 6 fault sections (sources) and 10 fault-zone polygons (previously called repeating large magnitude earthquake (RLME) polygons). The revised databases include 15 fault sections and 10 fault zone polygons. Updates to the faults constitute a 150% increase in fault sections, but no change in the number of fault-zone polygons, although some fault-zone polygons differ from RLME polygons used in prior models. No faults were removed from past models. Several seismic zones and suspected faults were evaluated but not included in this update due to a lack of information about fault location, geometry, or recurrent Quaternary activity. These updates to the fault sections, fault-zone polygons, and earthquake geology databases will inform fault geometry and activity rates of CEUS sources during the 2023 NSHM implementation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220162","usgsCitation":"Jobe, J.A., Hatem, A.E., Gold, R.D., DuRoss, C., Reitman, N.G., Briggs, R.W., and Collett, C.M., 2022, Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model: Seismological Research Letters, v. 93, no. 6, p. 3100-3120, https://doi.org/10.1785/0220220162.","productDescription":"21 p.","startPage":"3100","endPage":"3120","ipdsId":"IP-138939","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.33762311995545,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 51.85857884546667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatem, Alexandra Elise 0000-0001-7584-2235","orcid":"https://orcid.org/0000-0001-7584-2235","contributorId":225597,"corporation":false,"usgs":true,"family":"Hatem","given":"Alexandra","email":"","middleInitial":"Elise","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collett, Camille Marie 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":257819,"corporation":false,"usgs":true,"family":"Collett","given":"Camille","email":"","middleInitial":"Marie","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943167,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237374,"text":"70237374 - 2022 - Advances in coral immunity ‘omics in response to disease outbreaks","interactions":[],"lastModifiedDate":"2022-10-12T13:56:05.210143","indexId":"70237374","displayToPublicDate":"2022-10-11T14:09:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Advances in coral immunity ‘omics in response to disease outbreaks","docAbstract":"Coral disease has progressively become one of the most pressing issues affecting coral reef survival. In the last 50 years, several reefs throughout the Caribbean have been severely impacted by increased frequency and intensity of disease outbreaks leading to coral death. A recent example of this is stony coral tissue loss disease which has quickly spread throughout the Caribbean, devastating coral reef ecosystems. Emerging from these disease outbreaks has been a coordinated research response that often integrates ‘omics techniques to better understand the coral immune system. ‘Omics techniques encompass a wide range of technologies used to identify large scale gene, DNA, metabolite, and protein expression. In this review, we discuss what is known about coral immunity and coral disease from an ‘omics perspective. We reflect on the development of biomarkers and discuss ways in which coral disease experiments to test immunity can be improved. Lastly, we consider how existing data can be better leveraged to combat future coral disease outbreaks.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.952199","usgsCitation":"Traylor-Knowles, N., Baker, A.C., Beavers, K.M., Garg, N., Guyon, J.R., Hawthorn, A.C., MacKnight, N.J., Medina, M., Mydlarz, L.D., Peters, E.C., Stewart, J.M., Studivan, M.S., and Voss, J.D., 2022, Advances in coral immunity ‘omics in response to disease outbreaks: Frontiers in Marine Science, v. 9, 952199, 26 p., https://doi.org/10.3389/fmars.2022.952199.","productDescription":"952199, 26 p.","ipdsId":"IP-144516","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":446147,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.952199","text":"Publisher Index Page"},{"id":408182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Traylor-Knowles, Nikki","contributorId":297502,"corporation":false,"usgs":false,"family":"Traylor-Knowles","given":"Nikki","email":"","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":854320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Andrew C.","contributorId":297503,"corporation":false,"usgs":false,"family":"Baker","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":854321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beavers, Kelsey M.","contributorId":297504,"corporation":false,"usgs":false,"family":"Beavers","given":"Kelsey","email":"","middleInitial":"M.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":854322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garg, Neha","contributorId":297505,"corporation":false,"usgs":false,"family":"Garg","given":"Neha","email":"","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":854323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guyon, Jeffrey R.","contributorId":297506,"corporation":false,"usgs":false,"family":"Guyon","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":854324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hawthorn, Aine C. 0000-0002-8029-1383","orcid":"https://orcid.org/0000-0002-8029-1383","contributorId":292709,"corporation":false,"usgs":true,"family":"Hawthorn","given":"Aine","email":"","middleInitial":"C.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":854325,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"MacKnight, Nicholas J.","contributorId":297507,"corporation":false,"usgs":false,"family":"MacKnight","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":854326,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Medina, Mónica","contributorId":297508,"corporation":false,"usgs":false,"family":"Medina","given":"Mónica","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":854327,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mydlarz, Laura D.","contributorId":167562,"corporation":false,"usgs":false,"family":"Mydlarz","given":"Laura","email":"","middleInitial":"D.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":854328,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Peters, Esther C.","contributorId":209975,"corporation":false,"usgs":false,"family":"Peters","given":"Esther","email":"","middleInitial":"C.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":854329,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Stewart, Julia Marie","contributorId":297509,"corporation":false,"usgs":false,"family":"Stewart","given":"Julia","email":"","middleInitial":"Marie","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":854330,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Studivan, Michael S.","contributorId":297510,"corporation":false,"usgs":false,"family":"Studivan","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":64418,"text":"University of Miami, NOAA","active":true,"usgs":false}],"preferred":false,"id":854331,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Voss, Joshua D.","contributorId":150551,"corporation":false,"usgs":false,"family":"Voss","given":"Joshua","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":854332,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70237376,"text":"70237376 - 2022 - Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","interactions":[],"lastModifiedDate":"2022-10-11T19:08:25.114099","indexId":"70237376","displayToPublicDate":"2022-10-11T14:04:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","title":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","docAbstract":"Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90) and medium (90-150)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2022.102576","usgsCitation":"Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E., Siler, D.L., Karra, S., and Middleton, R.S., 2022, Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering: Geothermics, v. 106, 102576, 15 p., https://doi.org/10.1016/j.geothermics.2022.102576.","productDescription":"102576, 15 p.","ipdsId":"IP-132590","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1890937","text":"Publisher Index Page"},{"id":408181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Colorado Plateau, Gila Hot Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.05029296875,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 32.008075959291055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vesselinov, Velimir V.","contributorId":260765,"corporation":false,"usgs":false,"family":"Vesselinov","given":"Velimir","email":"","middleInitial":"V.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmmed, Bulbul","contributorId":260767,"corporation":false,"usgs":false,"family":"Ahmmed","given":"Bulbul","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mudunuru, Maruti K.","contributorId":260766,"corporation":false,"usgs":false,"family":"Mudunuru","given":"Maruti","email":"","middleInitial":"K.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":854339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karra, Satish","contributorId":297512,"corporation":false,"usgs":false,"family":"Karra","given":"Satish","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854343,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middleton, Richard S.","contributorId":297513,"corporation":false,"usgs":false,"family":"Middleton","given":"Richard","email":"","middleInitial":"S.","affiliations":[{"id":64420,"text":"Carbon Solutions LLC","active":true,"usgs":false}],"preferred":false,"id":854344,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237371,"text":"70237371 - 2022 - Selecting auditory alerting stimuli for eagles on the basis of auditory evoked potentials","interactions":[],"lastModifiedDate":"2022-10-11T19:00:04.483288","indexId":"70237371","displayToPublicDate":"2022-10-11T13:57:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3919,"text":"Conservation Physiology","onlineIssn":"2051-1434","active":true,"publicationSubtype":{"id":10}},"title":"Selecting auditory alerting stimuli for eagles on the basis of auditory evoked potentials","docAbstract":"Development of wind energy facilities results in interactions between wildlife and wind turbines. Raptors, including bald and golden eagles, are among the species known to incur mortality from these interactions. Several alerting technologies have been proposed to mitigate this mortality by increasing eagle avoidance of wind energy facilities. However, there has been little attempt to match signals used as alerting stimuli with the sensory capabilities of target species like eagles. One potential approach to tuning signals is to use sensory physiology to determine what stimuli the target eagle species are sensitive to even in the presence of background noise, thereby allowing the development of a maximally stimulating signal. To this end, we measured auditory evoked potentials of bald and golden eagles to determine what types of sounds eagles can process well, especially in noisy conditions. We found that golden eagles are significantly worse than bald eagles at processing rapid frequency changes in sounds, but also that noise effects on hearing in both species are minimal in response to rapidly changing sounds. Our findings therefore suggest that sounds of intermediate complexity may be ideal both for targeting bald and golden eagle hearing and for ensuring high stimulation in noisy field conditions. These results suggest that the sensory physiology of target species is likely an important consideration when selecting auditory alerting sounds and may provide important insight into what sounds have a reasonable probability of success in field applications under variable conditions and background noise.","language":"English","publisher":"Oxford University Press","doi":"10.1093/conphys/coac059","usgsCitation":"Goller, B., Baumhardt, P., Dominguez-Villegas, E., Katzner, T., Fernandez-Juricic, E., and Lucas, J.R., 2022, Selecting auditory alerting stimuli for eagles on the basis of auditory evoked potentials: Conservation Physiology, v. 10, no. 1, coac059, 18 p., https://doi.org/10.1093/conphys/coac059.","productDescription":"coac059, 18 p.","ipdsId":"IP-139387","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coac059","text":"Publisher Index Page"},{"id":408179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Goller, Benjamin","contributorId":297485,"corporation":false,"usgs":false,"family":"Goller","given":"Benjamin","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baumhardt, Patrice","contributorId":297486,"corporation":false,"usgs":false,"family":"Baumhardt","given":"Patrice","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dominguez-Villegas, Ernesto","contributorId":223077,"corporation":false,"usgs":false,"family":"Dominguez-Villegas","given":"Ernesto","email":"","affiliations":[{"id":37079,"text":"Wildlife Center of Virginia","active":true,"usgs":false}],"preferred":false,"id":854297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":854298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fernandez-Juricic, Esteban","contributorId":224607,"corporation":false,"usgs":false,"family":"Fernandez-Juricic","given":"Esteban","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lucas, Jeffrey R.","contributorId":297487,"corporation":false,"usgs":false,"family":"Lucas","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854300,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237354,"text":"70237354 - 2022 - Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling","interactions":[],"lastModifiedDate":"2022-10-12T15:04:06.279175","indexId":"70237354","displayToPublicDate":"2022-10-11T12:34:41","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"17","title":"Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling","docAbstract":"This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. Even though PGL formulations result in improvements in the generalization performance and lead to machine learning (ML) predictions that are more physically consistent, simply adding the physics-based loss function in the learning objective does not overcome the black-box nature of neural network architectures, which often involve arbitrary design choices. The temperature of water in a lake is a fundamental driver of lake biogeochemical processes, and it controls the growth, survival, and reproduction of fishes in the lake.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-17","usgsCitation":"Daw, A., Thomas, R.Q., Carey, C.C., Read, J., Appling, A.P., and Karpatne, A., 2022, Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling, chap. 17 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 399-416, https://doi.org/10.1201/9781003143376-17.","productDescription":"18 p.","startPage":"399","endPage":"416","ipdsId":"IP-131612","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination 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The heat budget of most lakes is dominated by heat fluxes at the lake surface, especially shortwave radiation, incoming and outgoing longwave radiation, and the latent heat flux. The seasonality of these fluxes is the most important driver for seasonal mixing processes in lakes. 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It explains termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Data science has become an indispensable tool for knowledge discovery in the era of big data, as the volume of data continues to explode in practically every research domain. Recent advances in data science such as deep learning have been immensely successful in transforming the state-of-the-art in a number of commercial and industrial applications such as natural language translation and image classification, using billions or even trillions of data samples. 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Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-16","usgsCitation":"Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J.A., Steinbach, M., and Kumar, V., 2022, Physics-guided recurrent neural networks for predicting lake water temperature, chap. 16 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 373-398, https://doi.org/10.1201/9781003143376-16.","productDescription":"26 p.","startPage":"373","endPage":"398","ipdsId":"IP-132700","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854186,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237347,"text":"70237347 - 2022 - Planetary-scale change to the biosphere signalled by global species translocations can be used to identify the Anthropocene","interactions":[],"lastModifiedDate":"2022-10-11T17:10:30.606714","indexId":"70237347","displayToPublicDate":"2022-10-11T11:59:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2998,"text":"Palaeontology","active":true,"publicationSubtype":{"id":10}},"title":"Planetary-scale change to the biosphere signalled by global species translocations can be used to identify the Anthropocene","docAbstract":"We examine three distinctive biostratigraphic signatures associated with: hunting and gathering, landscape domestication, and globalisation. All three signatures have significant fossil records of regional importance that can be correlated inter-regionally and help describe the developing pattern of human expansion and appropriation of resources. While none have individual first or last appearances that provide a globally isochronous marker, all three signatures overlap stratigraphically, in that they are part of a continuum of change, with complex regional patterns. Here we show that during the later stages of globalisation, late 19th to 20th century records of species translocations can be used to build an interconnected web of palaeontological correlation with decadal or sub-decadal precision that dovetails with other stratigraphic markers for the Anthropocene. This palaeontological web is also a proxy for accelerating species extinction and of a state shift in the biosphere in the 20th century.","language":"English","publisher":"John Wiley & Sons","doi":"10.1111/pala.12618","usgsCitation":"Williams, M., Leinfelder, R., Barnosky, A.D., Head, M., McCarthy, F.M., Cearreta. Alejandro, Himson, S.J., Holmes, R., Waters, C.N., Zalasiewicz, J., Turner, S., McGann, M., Hadly, E.A., Stegner, M.A., Pilkington, P.M., Kaiser, J., Berrio, J.C., Wilkinson, I.P., Zinke, J., and DeLong, K., 2022, Planetary-scale change to the biosphere signalled by global species translocations can be used to identify the Anthropocene: Palaeontology, v. 65, no. 4, e12618, 25 p., https://doi.org/10.1111/pala.12618.","productDescription":"e12618, 25 p.","ipdsId":"IP-135084","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446162,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://figshare.com/articles/journal_contribution/Planetary-scale_change_to_the_biosphere_signalled_by_global_species_translocations_can_be_used_to_identify_the_Anthropocene/19625769","text":"Publisher Index Page"},{"id":408168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"65","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Mark","contributorId":214696,"corporation":false,"usgs":false,"family":"Williams","given":"Mark","affiliations":[],"preferred":false,"id":854211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leinfelder, Reinhold","contributorId":297457,"corporation":false,"usgs":false,"family":"Leinfelder","given":"Reinhold","email":"","affiliations":[{"id":64399,"text":"Freie University, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":854212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnosky, Anthony D.","contributorId":197553,"corporation":false,"usgs":false,"family":"Barnosky","given":"Anthony","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":854213,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Head, Martin J","contributorId":297458,"corporation":false,"usgs":false,"family":"Head","given":"Martin J","affiliations":[{"id":64401,"text":"Brock University, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":854214,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarthy, Francine M G","contributorId":297459,"corporation":false,"usgs":false,"family":"McCarthy","given":"Francine","email":"","middleInitial":"M G","affiliations":[{"id":64401,"text":"Brock University, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":854215,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cearreta. 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Estimates were generated using a long short-term memory deep learning model and compared to existing process-based and linear regression models. Model training was optimized for prediction on unmonitored lakes through cross-validation that held out lakes to assess generalizability and estimate error. On the held-out lakes with in situ observations, median lake-specific error was 1.24°C, and the overall root mean squared error was 1.61°C. This dataset increases the number of lakes with daily temperature predictions when compared to existing datasets, as well as substantially improves predictive accuracy compared to a prior empirical model and a debiased process-based approach (2.01°C and 1.79°C median error, respectively).
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0000-0003-4434-051X","orcid":"https://orcid.org/0000-0003-4434-051X","contributorId":297456,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","email":"","middleInitial":"D.","affiliations":[{"id":64397,"text":"University of Minnesota Department of Computer Science","active":true,"usgs":false}],"preferred":false,"id":854206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Topp, Simon Nemer 0000-0001-7741-5982","orcid":"https://orcid.org/0000-0001-7741-5982","contributorId":268229,"corporation":false,"usgs":true,"family":"Topp","given":"Simon","email":"","middleInitial":"Nemer","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Gretchen J. A.","contributorId":174557,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J. A.","affiliations":[{"id":27469,"text":"Wisconsin Department of Natural Resources, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":854209,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854210,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237612,"text":"70237612 - 2022 - Identifying nutrient sources and sinks to the South Platte River and Cherry Creek, Denver, CO, during low-flow conditions in 2019–2020","interactions":[],"lastModifiedDate":"2022-12-15T14:54:03.689933","indexId":"70237612","displayToPublicDate":"2022-10-11T09:53:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Identifying nutrient sources and sinks to the South Platte River and Cherry Creek, Denver, CO, during low-flow conditions in 2019–2020","docAbstract":"Elevated concentrations and loads of nutrients in the South Platte River and Cherry Creek in Denver, Colorado, may have adverse effects on those streams and downstream water bodies, including increased production of algae, eutrophication, and decreased recreational opportunities. This article describes streamflow and concentrations and loads of nutrients for the South Platte River and Cherry Creek in Denver based on data collected during two longitudinal Lagrangian sampling campaigns in low-flow conditions in fall of 2019 and 2020. The results are used to assess sources and sinks of nutrients in the study area and help to establish baseline conditions against which future changes in nutrient concentrations and loads can be assessed. Discharges from Chatfield and Cherry Creek Reservoirs, storm drains, and most tributaries to the South Platte River, and Cherry Creek were generally small sources of streamflow and nutrient loads in both years. The Marcy Gulch, South Platte Water Renewal, and Robert W. Hite wastewater treatment plants were larger sources of streamflow and nutrient loads. The Burlington Ditch was a sink for streamflow and nutrient loads, diverting more than 95% of the South Platte River during the two sampling campaigns. Most other sinks were associated with decreases in streamflow between sampling sites. Golf courses were a potential source of nutrients for Cherry Creek but not for the South Platte River.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4060","usgsCitation":"Battaglin, W., and Chapin, T.W., 2022, Identifying nutrient sources and sinks to the South Platte River and Cherry Creek, Denver, CO, during low-flow conditions in 2019–2020: River Research and Applications, v. 38, no. 10, p. 1860-1883, https://doi.org/10.1002/rra.4060.","productDescription":"24 p.","startPage":"1860","endPage":"1883","ipdsId":"IP-131828","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":408322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Denver","otherGeospatial":"Cherry Creek, South Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.23803710937499,\n 39.35978526869001\n ],\n [\n -103.919677734375,\n 39.35978526869001\n ],\n [\n -103.919677734375,\n 40.66397287638688\n ],\n [\n -105.23803710937499,\n 40.66397287638688\n ],\n [\n -105.23803710937499,\n 39.35978526869001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Battaglin, William A. 0000-0001-7287-7096","orcid":"https://orcid.org/0000-0001-7287-7096","contributorId":204638,"corporation":false,"usgs":true,"family":"Battaglin","given":"William A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapin, Tanner William 0000-0003-3905-3241","orcid":"https://orcid.org/0000-0003-3905-3241","contributorId":297923,"corporation":false,"usgs":true,"family":"Chapin","given":"Tanner","email":"","middleInitial":"William","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854653,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240887,"text":"70240887 - 2022 - Decision support for aquatic restoration based on species-specific responses to disturbance","interactions":[],"lastModifiedDate":"2023-02-28T13:05:03.312497","indexId":"70240887","displayToPublicDate":"2022-10-11T07:02:23","publicationYear":"2022","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":"Decision support for aquatic restoration based on species-specific responses to disturbance","docAbstract":"Disturbances to aquatic habitats are not uniformly distributed within the Great Lakes and acute effects can be strongest in nearshore areas where both landscape and within lake effects can have strong influence. Furthermore, different fish species respond to disturbances in different ways. A means to identify and evaluate locations and extent of disturbances that affect fish is needed throughout the Great Lakes. We used partial Canonical Correspondence Analysis to separate “natural” effects on nearshore assemblages from disturbance effects. Species-specific quadratic models of fish abundance as functions of in-lake disturbance or watershed-derived disturbance were developed separately for each of 35 species and lakewide predictions mapped for Lake Erie. Most responses were unimodal and more species decreased in abundance with increasing watershed disturbance than increased. However, eight species increased in abundance with current in-lake disturbance conditions. Optimum Yellow Perch (Perca flavescens) abundance occurred at in-lake disturbance values less than the gradient mean, but decreased continuously from minimum watershed disturbance to higher values. Bands of optimum in-lake conditions occurred throughout the eastern and western portions of the Lake Erie nearshore zone; some areas were less disturbed than desirable. However, watershed-derived disturbance conditions were generally poor for Yellow Perch throughout the lake. In contrast, optimum Smallmouth Bass (Micropterus dolomieu) abundance occurred at in-lake disturbance values greater than the gradient mean and continuously increased with increasing watershed disturbance. Smallmouth Bass responses to disturbance indicated that most of the nearshore zone was less disturbed than is desirable and were most abundant in areas that the Yellow Perch response indicated were highly disturbed. Mapping counts of species response models that agreed on the disturbance level in each spatial unit of the nearshore zone showed a fine-scale mosaic of areas in which habitat restoration may benefit many or few species. This tool may assist managers in prioritizing conservation and restoration efforts and evaluating environmental conditions that may be improved.
Neonicotinoid insecticide use has increased over the last decade, including as agricultural seed treatments (application of chemical in a coating to the seed prior to planting). In California, multiple crops, including lettuce, can be grown using neonicotinoid treated seeds or receive a direct neonicotinoid soil application (drenching) at planting. Using research plots, this study compared pesticide runoff in four treatments: (1) imidacloprid seed treatment; (2) clothianidin seed treatment; (3) imidacloprid drench and an azoxystrobin seed treatment; and (4) a control with no pesticidal treatment. Neonicotinoid and azoxystrobin concentrations were measured in surface water runoff during six irrigations events in the 2020 growing seasons. Results showed runoff concentrations up to 1308 (±1200) ng L−1 for imidacloprid drench treatment, 431 (±100) ng L−1 for clothianidin seed treatment, 135 (±60) ng L−1 for imidacloprid seed treatment, 13 (±10) ng L−1 for azoxystrobin seed treatment (treatments averaged). The percent of applied mass in runoff over the entire sampling period varied by compound; the imidacloprid seed treatment and drench were similar (0.015 and 0.019%, respectively) to the clothianidin seed treatment (0.036%) while the azoxystrobin seed treatment was much higher (15%). Although the proportion of imidacloprid in runoff was similar for imidacloprid treatments, the mass applied during soil drench was > 4x the amount applied from the imidacloprid seed treatment. Surface soils were collected before planting and at the end of the trial. The neonicotinoids were detected in soil throughout the study and average maximum concentrations were 9–13 ng g−1; azoxystrobin was detected in only two soils at concentrations up to 0.57 ng g−1. These results elucidate the comparative mass runoff resulting from planting treated seed and soil drench applications and highlight the value of additional work to characterize off-site transport from the many commodities that may be utilizing treated seeds.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2022.120325","usgsCitation":"Woodward, E., Hladik, M.L., Main, A., Cahn, M., Orlando, J., and Teerlink, J., 2022, Comparing imidacloprid, clothianidin, and azoxystrobin runoff from lettuce fields using a soil drench or treated seeds in the Salinas Valley, California: Environmental Pollution, v. 315, 120325, 8 p., https://doi.org/10.1016/j.envpol.2022.120325.","productDescription":"120325, 8 p.","ipdsId":"IP-141733","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":446170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2022.120325","text":"Publisher Index Page"},{"id":408325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salinas Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.36528015136717,\n 36.42515002455931\n ],\n [\n -121.33575439453126,\n 36.46933726558023\n ],\n [\n -121.61521911621092,\n 36.75594019674357\n ],\n [\n -121.74293518066406,\n 36.75924093413334\n ],\n [\n -121.75529479980467,\n 36.673375615028256\n ],\n [\n -121.6021728515625,\n 36.584106249883554\n ],\n [\n -121.47583007812501,\n 36.47265029399174\n ],\n [\n -121.36528015136717,\n 36.42515002455931\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodward, Emily E. 0000-0001-9196-1349 ewoodward@usgs.gov","orcid":"https://orcid.org/0000-0001-9196-1349","contributorId":177364,"corporation":false,"usgs":true,"family":"Woodward","given":"Emily","email":"ewoodward@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Main, Anson 0000-0001-9539-760X","orcid":"https://orcid.org/0000-0001-9539-760X","contributorId":202852,"corporation":false,"usgs":false,"family":"Main","given":"Anson","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":854615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cahn, Michael","contributorId":297909,"corporation":false,"usgs":false,"family":"Cahn","given":"Michael","email":"","affiliations":[{"id":64448,"text":"Univeristy of California ANR","active":true,"usgs":false}],"preferred":false,"id":854616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orlando, James 0000-0002-0099-7221","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":208413,"corporation":false,"usgs":true,"family":"Orlando","given":"James","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Teerlink, Jennifer","contributorId":297910,"corporation":false,"usgs":false,"family":"Teerlink","given":"Jennifer","email":"","affiliations":[{"id":40320,"text":"California Department of Pesticide Regulation","active":true,"usgs":false}],"preferred":false,"id":854618,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256617,"text":"70256617 - 2022 - The Bathy-drone: An autonomous unmanned drone-tethered sonar system","interactions":[],"lastModifiedDate":"2024-08-27T14:37:31.951355","indexId":"70256617","displayToPublicDate":"2022-10-10T09:32:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18351,"text":"Drones","active":true,"publicationSubtype":{"id":10}},"title":"The Bathy-drone: An autonomous unmanned drone-tethered sonar system","docAbstract":"A unique drone-based system for underwater mapping (bathymetry) was developed at the University of Florida. The system, called the “Bathy-drone”, comprises a drone that drags, via a tether, a small vessel on the water surface in a raster pattern. The vessel is equipped with a recreational commercial off-the-shelf (COTS) sonar unit that has down-scan, side-scan, and chirp capabilities and logs GPS-referenced sonar data onboard or transmitted in real time with a telemetry link. Data can then be retrieved post mission and plotted in various ways. The system provides both isobaths and contours of bottom hardness. Extensive testing of the system was conducted on a 5 acre pond located at the University of Florida Plant Science and Education Unit in Citra, FL. Prior to performing scans of the pond, ground-truth data were acquired with an RTK GNSS unit on a pole to precisely measure the location of the bottom at over 300 locations. An assessment of the accuracy and resolution of the system was performed by comparison to the ground-truth data. The pond ground truth had an average depth of 2.30 m while the Bathy-drone measured an average 21.6 cm deeper than the ground truth, repeatable to within 2.6 cm. The results justify integration of RTK and IMU corrections. During testing, it was found that there are numerous advantages of the Bathy-drone system compared to conventional methods including ease of implementation and the ability to initiate surveys from the land by flying the system to the water or placing the platform in the water. The system is also inexpensive, lightweight, and low-volume, thus making transport convenient. The Bathy-drone can collect data at speeds of 0–24 km/h (0–15 mph) and, thus, can be used in waters with swift currents. Additionally, there are no propellers or control surfaces underwater; hence, the vessel does not tend to snag on floating vegetation and can be dragged over sandbars. An area of more than 10 acres was surveyed using the Bathy-drone in one battery charge and in less than 25 min.
","language":"English","publisher":"MDPI","doi":"10.3390/drones6100294","usgsCitation":"Diaz, A.L., Ortega, A.E., Tingle, H., Pulido, A., Cordero, O., Nelson, M., Cocoves, N.E., Shin, J., Carthy, R., Wilkinson, B.E., and Ifju, P.G., 2022, The Bathy-drone: An autonomous unmanned drone-tethered sonar system: Drones, v. 6, no. 10, 294, 19 p., https://doi.org/10.3390/drones6100294.","productDescription":"294, 19 p.","ipdsId":"IP-144387","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/drones6100294","text":"Publisher Index Page"},{"id":433196,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz, Antonio L.","contributorId":341377,"corporation":false,"usgs":false,"family":"Diaz","given":"Antonio","email":"","middleInitial":"L.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ortega, Andrew E.","contributorId":341378,"corporation":false,"usgs":false,"family":"Ortega","given":"Andrew","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tingle, Henry","contributorId":341379,"corporation":false,"usgs":false,"family":"Tingle","given":"Henry","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pulido, Andres","contributorId":341380,"corporation":false,"usgs":false,"family":"Pulido","given":"Andres","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cordero, Orlando","contributorId":341381,"corporation":false,"usgs":false,"family":"Cordero","given":"Orlando","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Marisa","contributorId":341382,"corporation":false,"usgs":false,"family":"Nelson","given":"Marisa","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cocoves, Nicholas E.","contributorId":341383,"corporation":false,"usgs":false,"family":"Cocoves","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908330,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shin, Jaejeong","contributorId":341384,"corporation":false,"usgs":false,"family":"Shin","given":"Jaejeong","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908331,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908332,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wilkinson, Benjamin E.","contributorId":341385,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Benjamin","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908333,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ifju, Peter G.","contributorId":341386,"corporation":false,"usgs":false,"family":"Ifju","given":"Peter","email":"","middleInitial":"G.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908334,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70259362,"text":"70259362 - 2022 - Return from dormancy: Rapid inflation and seismic unrest driven by transcrustal magma transfer at Mt. Edgecumbe (L’´ux Shaa) Volcano, Alaska","interactions":[],"lastModifiedDate":"2024-10-04T12:18:13.532979","indexId":"70259362","displayToPublicDate":"2022-10-10T07:14:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Return from dormancy: Rapid inflation and seismic unrest driven by transcrustal magma transfer at Mt. Edgecumbe (L’´ux Shaa) Volcano, Alaska","docAbstract":"In April 2022, a seismic swarm near Mt. Edgecumbe in southeast Alaska suggested renewed activity at this transform fault volcano, which was last active ≈800 years ago. Previously, thin rhyolitic tephras were deposited 5 and 4 ka. Satellite radar data from 2014 to 2022 resolves line-of-sight rapid inflation up to 7.1 cm/yr beginning in August 2018. Bayesian modeling suggests a transcrustal system of a deflating (−0.528 km3) dipping sill at 20 km depth recharging a magma chamber at 10 km (0.222 km3). A near-vertical conduit could capture the volume difference without noticeable surface deformation. Reanalyzed seismicity, recorded 25 km away, shows increases since July 2019. Magma ascent through ductile material and brittle strain release in a stressed overburden could explain the time delay. Cloud-native open data and workflows enabled discovery and analysis of this signal within days after going unnoticed for >3 years.
The phylogeography of the American black bear (Ursus americanus) is characterized by isolation into glacial refugia, followed by population expansion and genetic admixture. Anthropogenic activities, including overharvest, habitat loss, and transportation infrastructure, have also influenced their landscape genetic structure. We describe the genetic structure of the American black bear in the American Southwest and northern Mexico and investigate how prehistoric and contemporary forces shaped genetic structure and influenced gene flow. Using a suite of microsatellites and a sample of 550 bears, we identified 14 subpopulations organized hierarchically following the distribution of ecoregions and mountain ranges containing black bear habitat. The pattern of subdivision we observed is more likely a product of postglacial habitat fragmentation during the Pleistocene and Holocene, rather than a consequence of contemporary anthropogenic barriers to movement during the Anthropocene. We used linear mixed-effects models to quantify the relationship between landscape resistance and genetic distance among individuals, which indicated that both isolation by resistance and geographic distance govern gene flow. Gene flow was highest among subpopulations occupying large tracts of contiguous habitat, was reduced among subpopulations in the Madrean Sky Island Archipelago, where montane habitat exists within a lowland matrix of arid lands, and was essentially nonexistent between two isolated subpopulations. We found significant asymmetric gene flow supporting the hypothesis that bears expanded northward from a Pleistocene refugium located in the American Southwest and northern Mexico and that major highways were not yet affecting gene flow. The potential vulnerability of the species to climate change, transportation infrastructure, and the US–Mexico border wall highlights conservation challenges and opportunities for binational collaboration.