A species’ population structure and history are critical pieces of information that can help guide the use of available native plant materials in restoration treatments and decide what new native plant materials should be developed to meet future restoration needs. In the western United States, Pseudoroegneria spicata (bluebunch wheatgrass; Poaceae) is an important component of grassland and shrubland plant communities and commonly used for restoration due to its drought resistance and competitiveness with exotic weeds. We used next‐generation sequencing data to investigate the processes that shaped P. spicata's geographic pattern of genetic variation across the Intermountain West. Pseudoroegneria spicata's genetic diversity is partitioned into populations that likely differentiated since the Last Glacial Maximum. Adjacent populations display varying magnitudes of historical gene flow, with migration rates ranging from multiple migrants per generation to multiple generations per migrant. When considering the commercial germplasm sources available for restoration, genetic identities remain representative of the wildland localities from which germplasm sources were originally developed, and they maintain high levels of heterozygosity and nucleotide diversity. However, the commercial germplasm sources represent a small fraction of the overall genetic diversity of P. spicata in the Intermountain West. Given the low migration rates and long divergence times between some pairs of P. spicata populations, using commercial germplasm sources could facilitate undesirable restoration outcomes when used in certain geographic areas, even if the environment in which the commercial materials thrive is similar to that of the restoration site. As such, population structure and history can be used to provide guidance on what geographic areas may need additional native plant materials so that restoration efforts support species and community resilience and improve outcomes.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.12704","usgsCitation":"Massatti, R., Prendeville, H.R., Larson, S., Richardson, B.A., Waldron, B., and Kilkenny, F.F., 2018, Population history provides foundational knowledge for utilizing and developing native plant restoration materials: Evolutionary Applications, v. 11, no. 10, p. 2025-2039, https://doi.org/10.1111/eva.12704.","productDescription":"15 p.","startPage":"2025","endPage":"2039","ipdsId":"IP-096813","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468403,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.12704","text":"Publisher Index Page"},{"id":357286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Staets","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.4755859375,\n 37.82280243352756\n ],\n [\n -108.984375,\n 37.82280243352756\n ],\n [\n -108.984375,\n 48.99463598353405\n ],\n [\n -122.4755859375,\n 48.99463598353405\n ],\n [\n -122.4755859375,\n 37.82280243352756\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-24","publicationStatus":"PW","scienceBaseUri":"5bc02fa0e4b0fc368eb53923","contributors":{"authors":[{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":744805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prendeville, Holly R.","contributorId":207817,"corporation":false,"usgs":false,"family":"Prendeville","given":"Holly","email":"","middleInitial":"R.","affiliations":[{"id":37638,"text":"U.S.D.A. 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Agricultural Research Service, Logan, UT 84322 USA","active":true,"usgs":false}],"preferred":false,"id":744808,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kilkenny, Francis F.","contributorId":191031,"corporation":false,"usgs":false,"family":"Kilkenny","given":"Francis","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":744810,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227947,"text":"70227947 - 2018 - A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","interactions":[],"lastModifiedDate":"2023-07-24T18:21:04.356707","indexId":"70227947","displayToPublicDate":"2018-09-13T10:28:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","docAbstract":"The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping.
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Center","active":true,"usgs":true}],"preferred":true,"id":832662,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":215192,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":832663,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fry, Joyce 0000-0002-8466-9582 jfry@usgs.gov","orcid":"https://orcid.org/0000-0002-8466-9582","contributorId":3147,"corporation":false,"usgs":true,"family":"Fry","given":"Joyce","email":"jfry@usgs.gov","affiliations":[],"preferred":true,"id":832664,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Funk, Michelle 0000-0002-2772-2041","orcid":"https://orcid.org/0000-0002-2772-2041","contributorId":223239,"corporation":false,"usgs":true,"family":"Funk","given":"Michelle","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":832665,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Granneman, Brian J. 0000-0002-1910-0955","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":273180,"corporation":false,"usgs":true,"family":"Granneman","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":832666,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Liknes, Greg C","contributorId":273181,"corporation":false,"usgs":false,"family":"Liknes","given":"Greg","email":"","middleInitial":"C","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":832667,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":832668,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":832669,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70199563,"text":"70199563 - 2018 - Movements and dive patterns of pygmy killer whales (Feresa attenuata) released in the Gulf of Mexico following rehabilitation","interactions":[],"lastModifiedDate":"2018-09-25T13:21:11","indexId":"70199563","displayToPublicDate":"2018-09-12T10:43:48","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":869,"text":"Aquatic Mammals","active":true,"publicationSubtype":{"id":10}},"title":"Movements and dive patterns of pygmy killer whales (Feresa attenuata) released in the Gulf of Mexico following rehabilitation","docAbstract":"The habits and habitats of pygmy killer whales (Feresa attenuata) in the Gulf of Mexico (GoM) are poorly known outside of strandings and line-transect surveys. Two adult male pygmy killer whales were found live-stranded in the state of Mississippi (USA) on 1 September 2015 and were subsequently rehabilitated and returned to the offshore waters of the GoM on 11 July 2016. To monitor the animals post-release, both were tagged with satellite-linked location and dive behavior tags. Tags were programed to record and transmit dive duration and depth (when dives were ≥ 30 m deep for ≥ 30 s), duration of time spent above 30 m depth, and estimate locations using the Argos system. The tags transmitted for 15 and 88 days, respectively, providing a total of 1,027 filtered locations and 3,150 dive duration and maximum depth records. The animals began diving after two and four days, respectively, post-release. More than 96% of dives occurred at night. The longest recorded dive was more than 9 min in duration, and the deepest was to 368 m. More than 98% of the locations were over the GoM shelf break, spanning water 200 to 1,200 m deep. Diving patterns indicate that this species is most active at night in the GoM, suggesting its prey species are likely diel migrators that are below reachable depths during daylight hours. Near simultaneous location data from both animals confirmed that they stayed in close proximity but did not dive synchronously. Success of the rehabilitation and release was inconclusive for pygmy killer whale ID 30IMMS, whereas 31IMMS met the established criteria for success with ≥ 6 weeks of documented post-release survival. Follow-up monitoring through satellite-linked telemetry provided not only important data for evaluating the success of the rehabilitation but also for documenting the activity and habitat use of these seldom-observed cetaceans.
","language":"English","publisher":"Aquatic Mammals","doi":"10.1578/AM.44.5.2018.555","usgsCitation":"Pulis, E., Wells, R.S., Schorr, G.S., Douglas, D., Samuelson, M.M., and Solangi, M., 2018, Movements and dive patterns of pygmy killer whales (Feresa attenuata) released in the Gulf of Mexico following rehabilitation: Aquatic Mammals, v. 44, no. 5, p. 555-567, https://doi.org/10.1578/AM.44.5.2018.555.","productDescription":"13 p.","startPage":"555","endPage":"567","ipdsId":"IP-094379","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":357658,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","volume":"44","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-15","publicationStatus":"PW","scienceBaseUri":"5bc02fa1e4b0fc368eb53933","contributors":{"authors":[{"text":"Pulis, Eric","contributorId":208090,"corporation":false,"usgs":false,"family":"Pulis","given":"Eric","email":"","affiliations":[{"id":37711,"text":"Institute for Marine Mammal Studies","active":true,"usgs":false}],"preferred":false,"id":745859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wells, Randall S.","contributorId":208091,"corporation":false,"usgs":false,"family":"Wells","given":"Randall","email":"","middleInitial":"S.","affiliations":[{"id":37712,"text":"Chicago Zoological Society’s Sarasota Dolphin Research Program","active":true,"usgs":false}],"preferred":false,"id":745860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schorr, Gregory S.","contributorId":208092,"corporation":false,"usgs":false,"family":"Schorr","given":"Gregory","email":"","middleInitial":"S.","affiliations":[{"id":37713,"text":"Marine Ecology and Telemetry Research","active":true,"usgs":false}],"preferred":false,"id":745861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":745858,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Samuelson, Mystera M.","contributorId":208093,"corporation":false,"usgs":false,"family":"Samuelson","given":"Mystera","email":"","middleInitial":"M.","affiliations":[{"id":37711,"text":"Institute for Marine Mammal Studies","active":true,"usgs":false}],"preferred":false,"id":745862,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Solangi, Moby","contributorId":208094,"corporation":false,"usgs":false,"family":"Solangi","given":"Moby","email":"","affiliations":[{"id":37711,"text":"Institute for Marine Mammal Studies","active":true,"usgs":false}],"preferred":false,"id":745863,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212480,"text":"70212480 - 2018 - Quantifying uncertainty in cumulative surface slip along the Cucamonga Fault, a crustal thrust fault in southern California","interactions":[],"lastModifiedDate":"2020-08-17T14:48:52.189119","indexId":"70212480","displayToPublicDate":"2018-09-12T09:40:03","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying uncertainty in cumulative surface slip along the Cucamonga Fault, a crustal thrust fault in southern California","docAbstract":"Studies of historic earthquake ground surface ruptures show that displacements along strike are spatially variable. As a result, latest Quaternary slip rates developed from a spatially restricted set of cumulative displacement measurements may not accurately represent fault velocity. Here we examine the uncertainties associated with slip on the Cucamonga Fault, which is part of a network of faults that have generated damaging historical earthquakes in and around Los Angeles, California. Numerous scarps along its ~25‐km length are well expressed on alluvial fans. We make 310 measurements of vertical separation across the scarps using lidar data. We show that the dispersion of the vertical separations cannot be explained by our best estimates of analytical uncertainties alone. Additional epistemic uncertainties are required. We find that the magnitude of the required epistemic uncertainty is typically larger than analytical uncertainty by a factor of 3 and typically about 22% of the maximum vertical separation. These relationships appear to hold at several spatial scales. We examine three potential sources of epistemic uncertainty and find that none among surface age uncertainty, fault dip, and anthropogenic landscape alteration is likely sufficient to explain the overdispersion of the data, which suggests differences in cumulative strain along the strike of the fault. We calculate a range of dip‐slip rates between 0.4 and 2.6 mm/year. In light of our results, we suggest that future thrust‐fault slip‐rate studies adopt an epistemic uncertainty of 22% of the maximum value for vertical separation measurements, unless there are sufficient data to demonstrate otherwise.
The Omnibus Public Land Management Act of 2009 (Public Law 111—11) was passed into law on March 30, 2009. Subtitle F, also known as the SECURE Water Act, calls for the establishment of a “national water availability and use assessment program” within the U.S. Geological Survey (USGS). The USGS issued the first report on the program in 2013. Program progress over the period 2013–17 is reported herein to fulfill the requirement to inform Congress on implementation of the national water availability and use assessment program, also referred to as the USGS National Water Census (the Water Census).
Much work has been accomplished during 2013–17 on producing water budgets for the nation, a goal USGS outlined in its first report on program progress to Congress. The USGS has completed three geographic focus area studies and has begun three others. Work has advanced on nationwide efforts in streamflow analysis, groundwater assessment and research, evapotranspiration studies, water use, environmental water science, and drought science. The USGS works with Federal and non-Federal agencies, universities, and other organizations to ensure that the information can be aggregated with other types of water-availability and socioeconomic information, such as data on food and energy production. The USGS has also made great strides in measures for delivering data and information on the Water Census to stakeholders and the public.
Much work remains to be accomplished for the Nation to have a comprehensive, ongoing Water Census. In this report, the USGS lays out activities to be accomplished in the next 5 years (2017–22), based upon current funding levels. These include selecting new focus area studies, conducting hydrologic modeling to complete water budgets for the conterminous United States, expanding groundwater modeling efforts, mapping a national classification system for environmental water science, and developing an inventory of interbasin water transfers. All of these steps are necessary in order for the Water Census to achieve the goals outlined by Congress in the SECURE Water Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1440","usgsCitation":"Evenson, E.J., Jones, S.A., Barber, N.L., Barlow, P.M., Blodgett, D.L., Bruce, B.W., Douglas-Mankin, K., Farmer, W.H., Fischer, J.M., Hughes, W.B., Kennen, J.G., Kiang, J.E., Maupin, M.A., Reeves, H.W., Senay, G.B., Stanton, J.S., Wagner, C.R., and Wilson, J.T., 2018, Continuing progress toward a national assessment of water availability and use: U.S. Geological Survey Circular 1440, 64 p., https://doi.org/10.3133/cir1440.","productDescription":"viii, 64 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-088874","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":354392,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1440/circ1440.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1440"},{"id":354391,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1440/coverthb.jpg"}],"contact":"Coordinator—Water Availability and Use Science Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
While many studies on tribal water resources of individual tribal lands in the United States (US) have been conducted, the importance of tribal water resources at a national scale has largely gone unrecognized because their combined totals have not been quantified. Thus, we sought to provide a numerical estimate of major water budget components on tribal lands within the conterminous US and on USGS hydrologic unit codes (HUC2) regions. Using existing national-scale data and models, we estimated mean annual precipitation, evapotranspiration, excess precipitation, streamflow, and water use for the period 1971–2000. Tribal lands represent about 3.4 percent of the total land area of the conterminous US and on average account for 1.9 percent of precipitation, 2.4 percent of actual evapotranspiration, 0.95 percent of excess precipitation, 1.6 percent of water use, and 0.43 percent of streamflow origination. Additionally, approximately 9.5 and 11.3 percent of US streamflow flows through or adjacent as boundaries to tribal lands, respectively. Streamflow through or adjacent to tribal lands accounts for 42 and 48 percent of streamflow in the Missouri region, respectively; and for 86 and 88 percent in the Lower Colorado region, respectively. On average, 5,600 million cubic meters of streamflow per year was produced on tribal lands in the Pacific Northwest region, nearly five times greater than tribal lands in any other region. Tribal lands in the Great Lakes, Missouri, Arkansas-White-Red, and California regions all produced between 1,000 and 1,400 million cubic meters per year.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0203872","usgsCitation":"Blasch, K.W., Hundt, S., Wurster, P., Sando, R., and Berthelote, A., 2018, Streamflow contributions from tribal lands to major river basins of the United States: PLoS ONE, v. 13, no. 9, p. 1-16, https://doi.org/10.1371/journal.pone.0203872.","productDescription":"e0203872; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-089346","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":468411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0203872","text":"Publisher Index Page"},{"id":357234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"13","issue":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-11","publicationStatus":"PW","scienceBaseUri":"5b98a25fe4b0702d0e842e3e","contributors":{"authors":[{"text":"Blasch, Kyle W. 0000-0002-0590-0724","orcid":"https://orcid.org/0000-0002-0590-0724","contributorId":203415,"corporation":false,"usgs":true,"family":"Blasch","given":"Kyle","email":"","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hundt, Stephen A. 0000-0002-6484-0637","orcid":"https://orcid.org/0000-0002-6484-0637","contributorId":204678,"corporation":false,"usgs":true,"family":"Hundt","given":"Stephen","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wurster, Patrick 0000-0003-2668-2014","orcid":"https://orcid.org/0000-0003-2668-2014","contributorId":207806,"corporation":false,"usgs":false,"family":"Wurster","given":"Patrick","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":false,"id":744766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":744767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berthelote, Antony","contributorId":207807,"corporation":false,"usgs":false,"family":"Berthelote","given":"Antony","email":"","affiliations":[{"id":37636,"text":"Salish Kootenai College","active":true,"usgs":false}],"preferred":false,"id":744768,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199224,"text":"70199224 - 2018 - Collision and displacement vulnerability to offshore wind energy infrastructure among marine birds of the Pacific Outer Continental Shelf","interactions":[],"lastModifiedDate":"2018-09-11T16:34:03","indexId":"70199224","displayToPublicDate":"2018-09-11T16:33:59","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Collision and displacement vulnerability to offshore wind energy infrastructure among marine birds of the Pacific Outer Continental Shelf","docAbstract":"Marine birds are vulnerable to collision with and displacement by offshore wind energy infrastructure (OWEI). Here we present the first assessment of marine bird vulnerability to potential OWEI in the California Current System portion of the U.S. Pacific Outer Continental Shelf (POCS). Using population size, demography, life history, flight heights, and avoidance behavior for 62 seabird and 19 marine water bird species that occur in the POCS, we present and apply equations to calculate Population Vulnerability, Collision Vulnerability, and Displacement Vulnerability to OWEI for each species. Species with greatest Population vulnerability included those listed as species of concern (e.g., Least Tern [Sternula antillarum], Marbled Murrelet [Brachyramphus marmoratus], Pink-footed Shearwater [Puffinus creatopus]) and resident year-round species with small population sizes (e.g., Ashy Storm-Petrel [Oceanodroma homochroa], Brandt's Cormorant [Phalacrocorax penicillatus], and Brown Pelican [Pelecanus occidentalis]). Species groups with the greatest Collision Vulnerability included jaegers/skuas, pelicans, terns and gulls that spend significant amounts of time flying at rotor sweep zone height and don't show macro-avoidance behavior (avoidance of entire OWEI area). Species groups with the greatest Displacement Vulnerability show high macro-avoidance behavior and low habitat flexibility and included loons, grebes, sea ducks, and alcids. Using at-sea survey data from the southern POCS, we combined species-specific vulnerabilities described above with at-sea species densities to assess vulnerabilities spatially. Spatial vulnerability densities were greatest in areas with high species densities (e.g., near-shore areas) and locations where species with high vulnerability were found in abundance. Our vulnerability assessment helps understand and minimize potential impacts of OWEI infrastructure on marine birds in the POCS and could inform management decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2018.08.051","usgsCitation":"Kelsey, E.C., Felis, J.J., Czapanskiy, M., Peresksta, D.M., and Adams, J., 2018, Collision and displacement vulnerability to offshore wind energy infrastructure among marine birds of the Pacific Outer Continental Shelf: Journal of Environmental Management, v. 227, p. 229-247, https://doi.org/10.1016/j.jenvman.2018.08.051.","productDescription":"19 p.","startPage":"229","endPage":"247","ipdsId":"IP-089929","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468412,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2018.08.051","text":"Publisher Index Page"},{"id":357232,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"227","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a25fe4b0702d0e842e40","contributors":{"authors":[{"text":"Kelsey, Emily C. 0000-0002-0107-3530 ekelsey@usgs.gov","orcid":"https://orcid.org/0000-0002-0107-3530","contributorId":206505,"corporation":false,"usgs":true,"family":"Kelsey","given":"Emily","email":"ekelsey@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":744748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":744749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Czapanskiy, Max 0000-0002-6302-905X","orcid":"https://orcid.org/0000-0002-6302-905X","contributorId":207793,"corporation":false,"usgs":false,"family":"Czapanskiy","given":"Max","email":"","affiliations":[{"id":37635,"text":"San Fransciso State University","active":true,"usgs":false}],"preferred":false,"id":744750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peresksta, David M.","contributorId":207794,"corporation":false,"usgs":false,"family":"Peresksta","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":false,"id":744751,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Josh 0000-0003-3056-925X josh_adams@usgs.gov","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":2422,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","email":"josh_adams@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":744747,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201102,"text":"70201102 - 2018 - Chronic wasting disease detection and mortality sources in semi-protected deer population","interactions":[],"lastModifiedDate":"2019-11-13T13:16:32","indexId":"70201102","displayToPublicDate":"2018-09-11T11:03:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Chronic wasting disease detection and mortality sources in semi-protected deer population","docAbstract":"Surveillance for wildlife diseases is essential for assessing population dynamics of ungulates, especially in free-ranging populations where infected animals are difficult to sample. Chronic wasting disease (CWD) is an emerging infectious disease of concern because of the potential for substantial negative effects on populations of cervids. Variability in the likelihood that CWD is detected could invalidate traditional estimators for prevalence. In some instances, deer located after death cannot be tested for infectious diseases, including CWD, because of lack of availability or condition of appropriate tissues. We used various methods to detect infectious diseases that could cause mortality for deer Odocoileus spp. residing in Wind Cave National Park, South Dakota, USA, and we report survival estimates for animals in this population. We included 34 monthly encounters of deer resightings and 67 mortalities. We tested live deer by tonsillar biopsy for CWD and estimated pooled prevalence (mean ± SE) at 5.6 ± 3.0% over the three-year study. Live deer potentially had exposure to several infectious diseases, including bluetongue, epizootic hemorrhagic disease, bovine viral diarrhea, West Nile virus, and malignant catarrhal fever, but no apparent morbidity or mortality from those diseases. We tested survival and influence of covariates, including age and sex, using known-fate analysis in Program MARK. Those data best supported a model with time-invariant encounter probability and an annual survival of 72.8%. Even without direct pressure from hunting within the park, average life expectancy in this population was 3.2 years. Only 68% of mortalities contained sufficient material for CWD sampling (because of predation and scavenger activity) and >42% of these were CWD-positive. These findings underscore the possible biases in postmortem surveillance estimates of disease prevalence because of potential for subclinical infected animals to be removed by predators and not tested.
","language":"English","publisher":"Nordic Board for Wildlife Research","doi":"10.2981/wlb.00437","usgsCitation":"Schuler, K.L., Jenks, J.A., Klaver, R.W., Jennelle, C.S., and Bowyer, R.T., 2018, Chronic wasting disease detection and mortality sources in semi-protected deer population: Wildlife Biology, v. 2018, no. 1, wlb.00437, 7 p., https://doi.org/10.2981/wlb.00437.","productDescription":"wlb.00437, 7 p.","ipdsId":"IP-064498","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468413,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00437","text":"Publisher Index Page"},{"id":359778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Wind Cave National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.6669921875,\n 43.48680489735277\n ],\n [\n -103.304443359375,\n 43.48680489735277\n ],\n [\n -103.304443359375,\n 43.67581809328341\n ],\n [\n -103.6669921875,\n 43.67581809328341\n ],\n [\n -103.6669921875,\n 43.48680489735277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2018","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-11","publicationStatus":"PW","scienceBaseUri":"5c0108d5e4b0815414cc2dfb","contributors":{"authors":[{"text":"Schuler, Krysten L.","contributorId":210886,"corporation":false,"usgs":false,"family":"Schuler","given":"Krysten","email":"","middleInitial":"L.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":752680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenks, Jonathan A.","contributorId":210887,"corporation":false,"usgs":false,"family":"Jenks","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":752681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":752679,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennelle, Christopher S.","contributorId":210888,"corporation":false,"usgs":false,"family":"Jennelle","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":752682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowyer, R. Terry","contributorId":210889,"corporation":false,"usgs":false,"family":"Bowyer","given":"R.","email":"","middleInitial":"Terry","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":752683,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199211,"text":"70199211 - 2018 - Climatically driven changes in primary production propagate through trophic levels","interactions":[],"lastModifiedDate":"2018-09-28T08:52:39","indexId":"70199211","displayToPublicDate":"2018-09-11T10:24:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Climatically driven changes in primary production propagate through trophic levels","docAbstract":"Climate and land‐use change are the major drivers of global biodiversity loss. Their effects are particularly acute for wide‐ranging consumers, but little is known about how these factors interact to affect the abundance of large carnivores and their herbivore prey. We analyzed population densities of a primary and secondary consumer (mule deer, Odocoileus hemionus, and mountain lion, Puma concolor) across a climatic gradient in western North America by combining satellite‐based maps of plant productivity with estimates of animal abundance and foraging area derived from Global Positioning Systems telemetry data (GPS). Mule deer density exhibited a positive, linear relationship with plant productivity (r2 = 0.58), varying by a factor of 18 across the climate‐vegetation gradient (range: 38–697 individuals/100 km2). Mountain lion home range size decreased in response to increasing primary productivity and consequent changes in the abundance of their herbivore prey (range: 20–450 km2). This pattern resulted in a strong, positive association between plant productivity and mountain lion density (r2 = 0.67). Despite varying densities, the ratio of prey to predator remained constant across the climatic gradient (mean ± SE = 363 ± 29 mule deer/mountain lion), suggesting that the determinacy of the effect of primary productivity on consumer density was conserved across trophic levels. As droughts and longer term climate changes reduce the suitability of marginal habitats, consumer home ranges will expand in order for individuals to meet basic nutritional requirements. These changes portend decreases in the abundance of large‐bodied, wide‐ranging wildlife through climatically driven reductions in carrying capacity, as well as increased human–wildlife interactions stemming from anthropogenic land use and habitat fragmentation.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14364","usgsCitation":"Stoner, D.C., Sexton, J.O., Choate, D.M., Nagol, J., Bernales, H.H., Sims, S.A., Ironside, K.E., Longshore, K.M., and Edwards, T., 2018, Climatically driven changes in primary production propagate through trophic levels: Global Change Biology, v. 24, no. 10, p. 4453-4463, https://doi.org/10.1111/gcb.14364.","productDescription":"11 p.","startPage":"4453","endPage":"4463","ipdsId":"IP-090173","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468414,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1463359","text":"Publisher Index Page"},{"id":357221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau, Great Basin, Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03662109374999,\n 32.80574473290688\n ],\n [\n -104.67773437499999,\n 32.80574473290688\n ],\n [\n -104.67773437499999,\n 42.049292638686836\n ],\n [\n -120.03662109374999,\n 42.049292638686836\n ],\n [\n -120.03662109374999,\n 32.80574473290688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-07","publicationStatus":"PW","scienceBaseUri":"5b98a260e4b0702d0e842e42","contributors":{"authors":[{"text":"Stoner, David C.","contributorId":207777,"corporation":false,"usgs":false,"family":"Stoner","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":744695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sexton, Joseph O.","contributorId":191918,"corporation":false,"usgs":false,"family":"Sexton","given":"Joseph","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":744696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choate, David M.","contributorId":207778,"corporation":false,"usgs":false,"family":"Choate","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":744697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagol, Jyothy","contributorId":207779,"corporation":false,"usgs":false,"family":"Nagol","given":"Jyothy","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":744698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernales, Heather H.","contributorId":198513,"corporation":false,"usgs":false,"family":"Bernales","given":"Heather","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":744699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sims, Steven A.","contributorId":207780,"corporation":false,"usgs":false,"family":"Sims","given":"Steven","email":"","middleInitial":"A.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":744700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ironside, Kirsten E. 0000-0003-1166-3793 kironside@usgs.gov","orcid":"https://orcid.org/0000-0003-1166-3793","contributorId":3379,"corporation":false,"usgs":true,"family":"Ironside","given":"Kirsten","email":"kironside@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":744693,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Longshore, Kathleen M. 0000-0001-6621-1271 longshore@usgs.gov","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":2677,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"longshore@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":744694,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Edwards, Thomas C. Jr. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":191916,"corporation":false,"usgs":true,"family":"Edwards","given":"Thomas C.","suffix":"Jr.","email":"tce@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":744692,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263652,"text":"70263652 - 2018 - The 1952 Kern County, California earthquake: A case study of issues in the analysis of historical intensity data for estimation of source parameters","interactions":[],"lastModifiedDate":"2025-02-19T14:24:12.731569","indexId":"70263652","displayToPublicDate":"2018-09-11T10:06:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3071,"text":"Physics of the Earth and Planetary Interiors","active":true,"publicationSubtype":{"id":10}},"title":"The 1952 Kern County, California earthquake: A case study of issues in the analysis of historical intensity data for estimation of source parameters","docAbstract":"Seismic intensity data based on first-hand accounts of shaking give valuable insight into historical and early instrumental earthquakes. Comparing an observed intensity distribution to intensity-prediction models based on modern calibration events allows the magnitude to be estimated for many historic earthquakes. Magnitude estimates can also potentially be refined for earthquakes for which limited instrumental data are available. However, the complicated nature of macroseismic data and the methods used to collect and interpret the data introduce significant uncertainties. In this paper, we illustrate these challenges and possible solutions using the 1952 Kern County, California, earthquake as a case study. Published estimates of its magnitude vary from MW 7.2–7.5, making it possibly the second largest in California during the 20th century. We considered over 1100 first-hand reports of shaking, supplemented with other data, and inferred the magnitude in several ways using intensity prediction equations, yielding a preferred intensity magnitude MI 7.2 ± 0.2, where the uncertainty reflects our judgement. The revised intensity distribution reveals stronger shaking on the hanging wall, south of the surface expression of the White Wolf fault, than on the footwall. Characterizing the magnitude and shaking distribution of this early instrumental earthquake can help improve estimation of the seismic hazard of the region. Such reinterpreted intensities for historic earthquakes, combined with U.S. Geological Survey (USGS) Did You Feel It? data for more recent events, can be used to produce a uniform shaking dataset with which earthquake hazard map performance can be assessed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2018.08.007","usgsCitation":"Salditch, L., Hough, S.E., Stein, S., Spencer, B., Brooks, E., Neely, J.S., and Lucas, M.C., 2018, The 1952 Kern County, California earthquake: A case study of issues in the analysis of historical intensity data for estimation of source parameters: Physics of the Earth and Planetary Interiors, v. 283, p. 140-151, https://doi.org/10.1016/j.pepi.2018.08.007.","productDescription":"12 p.","startPage":"140","endPage":"151","ipdsId":"IP-101209","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.84752123561667,\n 35.795851992110386\n ],\n [\n -119.84752123561667,\n 34.87128790332352\n ],\n [\n -118.27725907195563,\n 34.87128790332352\n ],\n [\n -118.27725907195563,\n 35.795851992110386\n ],\n [\n -119.84752123561667,\n 35.795851992110386\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Salditch, Leah","contributorId":263445,"corporation":false,"usgs":false,"family":"Salditch","given":"Leah","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spencer, Bruce","contributorId":350997,"corporation":false,"usgs":false,"family":"Spencer","given":"Bruce","affiliations":[],"preferred":false,"id":927671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brooks, Edward","contributorId":350999,"corporation":false,"usgs":false,"family":"Brooks","given":"Edward","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927672,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neely, James S.","contributorId":263454,"corporation":false,"usgs":false,"family":"Neely","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927673,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lucas, Madeleine C.","contributorId":336741,"corporation":false,"usgs":false,"family":"Lucas","given":"Madeleine","middleInitial":"C.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":927674,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211581,"text":"70211581 - 2018 - Necropsy-based wild fish health assessment","interactions":[],"lastModifiedDate":"2020-07-31T13:38:52.250692","indexId":"70211581","displayToPublicDate":"2018-09-11T08:36:52","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2498,"text":"Journal of Visualized Experiments","active":true,"publicationSubtype":{"id":10}},"title":"Necropsy-based wild fish health assessment","docAbstract":"Anthropogenic influences from increased nutrients and chemical contaminants, to habitat alterations and climate change, can have significant effects on fish populations. Adverse effects monitoring, utilizing biomarkers from the organismal to the molecular level, can be used to assess the cumulative effects on fishes and other organisms. Fish health has been used worldwide as an indicator of aquatic ecosystem health. The necropsy-based fish health assessment provides data on visible abnormalities and lesions, parasites, condition and organosomatic indices. These can be compared by site, season and sex, as well as temporally, to document change over time. Severity ratings can be assigned to various observations to calculate a fish health index for more quantitative assessment. A drawback of the necropsy-based assessment is that it is based on visual observations and condition factors, which are not as sensitive as tissue and subcellular biomarkers for sublethal effects. Additionally, it is rarely possible to identify causes or risk factors associated with observed abnormalities. So, for instance a raised lesion or \"tumor\" on the fins, lips or body surface may be a neoplasm. However, it could also be a response to a parasite, chronic inflammation or hyperplasia of normal cells in response to an irritant. Conversely, neoplasms, certain parasites, other infectious agents and many tissue changes are not visible and so may be underestimated. However, during the necropsy-based assessment, blood (plasma), tissues for histopathology (microscopic pathology), genomics and other molecular analyses, and otoliths for aging can be collected. These downstream analyses, together with geospatial analyses, habitat assessments, water quality and contaminant analyses can all be important in comprehensive ecosystem evaluations.
","language":"English","publisher":"JOVE","doi":"10.3791/57946","usgsCitation":"Blazer, V., Walsh, H.L., Braham, R.P., and Smith, C.R., 2018, Necropsy-based wild fish health assessment: Journal of Visualized Experiments, v. 139, e57946, 11 p., https://doi.org/10.3791/57946.","productDescription":"e57946, 11 p.","ipdsId":"IP-094627","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":468416,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6235148","text":"Publisher Index Page"},{"id":376942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","noUsgsAuthors":false,"publicationDate":"2018-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walsh, Heather L. 0000-0001-6392-4604","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":213348,"corporation":false,"usgs":false,"family":"Walsh","given":"Heather","email":"","middleInitial":"L.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":794693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Braham, Ryan P. 0000-0002-2102-0989","orcid":"https://orcid.org/0000-0002-2102-0989","contributorId":204542,"corporation":false,"usgs":true,"family":"Braham","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Cheyenne R. 0000-0002-7226-1774","orcid":"https://orcid.org/0000-0002-7226-1774","contributorId":219236,"corporation":false,"usgs":true,"family":"Smith","given":"Cheyenne","email":"","middleInitial":"R.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true},{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":true,"id":794695,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199202,"text":"70199202 - 2018 - Modelling sound attenuation in heterogeneous environments for improved bioacoustic sampling of wildlife populations","interactions":[],"lastModifiedDate":"2018-09-10T13:56:12","indexId":"70199202","displayToPublicDate":"2018-09-10T13:56:09","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Modelling sound attenuation in heterogeneous environments for improved bioacoustic sampling of wildlife populations","docAbstract":"A study was conducted by the U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, to reconstruct streamflows for use in the RiverWare model. Methods and data used to estimate daily reconstructed streamflows at 53 sites in selected subbasins in northern New Jersey and southeastern New York are presented in the report. These subbasins contain one or more surface-water diversions that are operated or have been operated in the past by water purveyors or the New Jersey Department of Environmental Protection. Reconstructed streamflows are estimates of those streamflows that would have occurred without the effects of changes in reservoir storage or surface-water diversions by water purveyors.
Reconstructed flows at 47 sites were determined from monthly observed streamflows, changes in reservoir storage, and surface-water diversions. Monthly reconstructed streamflows were calculated directly for those months with sufficient data. Missing monthly reconstructed flows were estimated from relations between selected calculated values of monthly reconstructed flows and monthly observed flows at selected index gages. Daily reconstructed flows were determined from the disaggregation of monthly reconstructed flows on the basis of daily observed flows at selected streamgages. At six sites, reconstructed flows were determined from relations between discrete measurements of observed streamflows and daily observed streamflows at selected index gages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185068","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Hickman, R.E., and McHugh, A.R., 2018, Methods used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922–2010: U.S. Geological Survey Scientific Investigations Report 2018–5068, 75 p., https://doi.org/10.3133/sir20185068.","productDescription":"viii, 75 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070147","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":437761,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J965CZ","text":"USGS data release","linkHelpText":"Data and equations used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922-2010"},{"id":357153,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5068/coverthb2.jpg"},{"id":357154,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5068/sir20185068.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5068"},{"id":357155,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F7J965CZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and equations used to reconstruct historical daily streamflows in northern New Jersey and southeastern New York, water years 1922–2010"}],"country":"United States","state":"New Jersey, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.049,\n 40.402\n ],\n [\n -73.906,\n 40.402\n ],\n [\n -73.906,\n 41.396\n ],\n [\n -75.049,\n 41.396\n ],\n [\n -75.049,\n 40.402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Use of soil survey information by non-soil-scientists is often limited by their inability to select the correct soil map unit component (COMP). Here, we developed two approaches that can be deployed to smartphones for non-soil-scientists to identify COMP from the location alone or location together with easily observed field data (i.e., slope, depth to the restrictive layer, and soil texture by depth). In addition, we also compared the two newly developed approaches with a traditional approach identifying COMP based on the dominant COMP (DC-based approach). All three approaches were tested with the Rapid Assessment of US Soil Carbon database and the combined USDA- NRCS Soil Survey Geographic database and the USDA-NRCS State Soil Geographic Database. The results indicated that the observation-based approach performed significantly better than the other two approaches, suggesting that a small set of easy-to-measure site-specific observations could significantly improve COMP identification. The location- and DC-based approaches had similar low performance overall. However, the location-based approach slightly improved identifications over the DC-based approach for cases where (i) there were multiple possible components within the soil map unit and (ii) the components were located in close proximity to a boundary of a different soil map unit polygon. The benefit of using the location-based approach may be greater in specific soil survey areas where topography was the major factor leading to the creation of the map unit legend.
","language":"English","publisher":"Soil Science Society of America","doi":"10.2136/sssaj2017.09.0337","usgsCitation":"Fan, Z., Wills, S.A., Herrick, J.E., Nauman, T.W., Brungard, C.W., Beaudette, D.E., Levi, M.R., and O’Geen, A.T., 2018, Approaches for improving field soil identification: Soil Science Society of America Journal, v. 82, no. 4, p. 871-877, https://doi.org/10.2136/sssaj2017.09.0337.","productDescription":"7 p.","startPage":"871","endPage":"877","ipdsId":"IP-091477","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":357148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-17","publicationStatus":"PW","scienceBaseUri":"5b98a263e4b0702d0e842e56","contributors":{"authors":[{"text":"Fan, Zhaosheng","contributorId":199104,"corporation":false,"usgs":false,"family":"Fan","given":"Zhaosheng","email":"","affiliations":[],"preferred":false,"id":744538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wills, Skye A.","contributorId":207736,"corporation":false,"usgs":false,"family":"Wills","given":"Skye","email":"","middleInitial":"A.","affiliations":[{"id":37616,"text":"USDA-NRCS National Soil Survey Center","active":true,"usgs":false}],"preferred":false,"id":744539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrick, Jeffrey E.","contributorId":26054,"corporation":false,"usgs":false,"family":"Herrick","given":"Jeffrey","email":"","middleInitial":"E.","affiliations":[{"id":12627,"text":"USDA-ARS Jornada Experimental Range, New Mexico State University, Las Cruces, NM 88003-8003, USA","active":true,"usgs":false}],"preferred":false,"id":744540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":744537,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brungard, Colby W.","contributorId":207737,"corporation":false,"usgs":false,"family":"Brungard","given":"Colby","email":"","middleInitial":"W.","affiliations":[{"id":37617,"text":"Department of Plant and Environmental Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":744541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beaudette, Dylan E.","contributorId":207738,"corporation":false,"usgs":false,"family":"Beaudette","given":"Dylan","email":"","middleInitial":"E.","affiliations":[{"id":37618,"text":"USDA-NRCS Soil Survey Division, Sonora, CA","active":true,"usgs":false}],"preferred":false,"id":744542,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Levi, Matthew R.","contributorId":207739,"corporation":false,"usgs":false,"family":"Levi","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":37619,"text":"USDA- ARS Research Unit","active":true,"usgs":false}],"preferred":false,"id":744543,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"O’Geen, Anthony T.","contributorId":207740,"corporation":false,"usgs":false,"family":"O’Geen","given":"Anthony","email":"","middleInitial":"T.","affiliations":[{"id":37041,"text":"Department of Land, Air, and Water Resources, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":744544,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70199176,"text":"70199176 - 2018 - Soil property and class maps of the conterminous United States at 100-meter spatial resolution","interactions":[],"lastModifiedDate":"2018-09-09T20:20:06","indexId":"70199176","displayToPublicDate":"2018-09-09T20:16:58","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3420,"text":"Soil Science Society of America Journal","active":true,"publicationSubtype":{"id":10}},"title":"Soil property and class maps of the conterminous United States at 100-meter spatial resolution","docAbstract":"With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with a stack of over 200 environmental datasets and gSSURGO polygon maps to generate complete coverage gridded predictions at 100-m spatial resolution of six soil properties (percentage of organic C, total N, bulk density, pH, and percentage of sand and clay) and two US soil taxonomic classes (291 great groups [GGs] and 78 modified particle size classes [mPSCs]) for the conterminous United States. Models were built using parallelized random forest and gradient boosting algorithms as implemented in the ranger and xgboost packages for R. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Cross validation results indicated an out-of-bag classification accuracy of 60% for GGs and 66% for mPSCs; for soil properties, RMSE for leave-location-out cross-validation was 0.74 (R2 = 0.68), 17.8 wt% (R2 = 0.57), 12 wt% (R2 = 0.46), 3.63 wt% (R2 = 0.41), 0.2 g cm−3 (R2 = 0.42), and 0.27 wt% (R2 = 0.39) for pH, percent sand and clay, weight percentage of organic C, bulk density, and weight percentage of total N, respectively. Nine independent validation datasets were used to assess prediction accuracies for soil class models, and results ranged between 24 and 58% and between 24 and 93% for GG and mPSC prediction accuracies, respectively. Although mapping accuracies were variable and likely lower than gSSURGO in some areas, this modeling approach can enable easier integration of soil information with spatially explicit models compared with multicomponent map units.
","language":"English","publisher":"Soil Science Society of America","doi":"10.2136/sssaj2017.04.0122","usgsCitation":"Ramcharan, A., Hengl, T., Nauman, T.W., Brungard, C.W., Waltman, S., Wills, S.A., and Thompson, J., 2018, Soil property and class maps of the conterminous United States at 100-meter spatial resolution: Soil Science Society of America Journal, v. 82, no. 1, p. 186-201, https://doi.org/10.2136/sssaj2017.04.0122.","productDescription":"16 p.","startPage":"186","endPage":"201","ipdsId":"IP-086724","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/sssaj2017.04.0122","text":"Publisher Index Page"},{"id":357147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Such droughts have been linked with massive tree mortality, and data suggest that warming interacts with drought to aggravate plant performance. Yet how forests will respond to hotter droughts remains unclear, as does the suite of mechanisms trees use to deal with hot droughts. We used an ecosystem‐scale manipulation of precipitation and temperature on piñon pine (Pinus edulis) and juniper (Juniperus monosperma) trees to investigate nitrogen (N) cycling‐induced mitigation processes related to hotter droughts. We found that while negative impacts on plant carbon and water balance are manifest after prolonged drought, performance reductions were not amplified by warmer temperatures. Rather, increased temperatures for 5 years stimulated soil N cycling under piñon trees and modified tree N allocation for both species, resulting in mitigation of hotter drought impacts on tree water and carbon functions. These findings suggest that adjustments in N cycling are likely after multi‐year warming conditions and that such changes may buffer reductions in tree performance during hotter droughts. The results highlight our incomplete understanding of trees' ability to acclimate to climate change, raising fundamental questions about the resistance potential of forests to long‐term, compound climatic stresses.
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2018 - KG²B, a collaborative benchmarking exercise for estimating the permeability of the Grimsel granodiorite - Part 1: measurements, pressure dependence and pore-fluid effects","interactions":[],"lastModifiedDate":"2018-09-07T16:06:00","indexId":"70199150","displayToPublicDate":"2018-09-07T16:05:56","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"KG²B, a collaborative benchmarking exercise for estimating the permeability of the Grimsel granodiorite - Part 1: measurements, pressure dependence and pore-fluid effects","docAbstract":"Measuring the permeability of tight rocks remains a challenging task. In addition to the traditional sources of errors that affect more permeable formations (e.g. sample selection, non-representative specimens, disturbance introduced during sample acquisition and preparation), tight rocks can be particularly prone to solid–fluid interactions and thus more sensitive to the methods, procedures and techniques used to measure permeability. To address this problem, it is desirable to collect, for a single material, measurements obtained by different methods and pore-fluids. For that purpose a collaborative benchmarking exercise involving 24 laboratories was organized for measuring the permeability of a single low permeability material, the Grimsel granodiorite, at a common effective confining pressure (5 MPa). The objectives of the benchmark were: (i) to compare the results for a given method, (ii) to compare the results between different methods, (iii) to analyze the accuracy of each method, (iv) to study the influence of experimental conditions (especially the nature of pore fluid), (v) to discuss the relevance of indirect methods and models and finally (vi) to suggest good practice for low permeability measurements. In total 39 measurements were collected that allowed us to discuss the influence of (i) pore-fluid, (ii) measurement method, (iii) sample size and (iv) pressure sensitivity. Discarding some outliers from the bulk data set (4 out of 39) an average permeability of 1.11 × 10−18 m² with a standard deviation of 0.57 × 10−18 m² was obtained. The most striking result was the large difference in permeability for gas measurements compared to liquid measurements. Regardless of the method used, gas permeability was higher than liquid permeability by a factor approximately 2 (kgas = 1.28 × 10−18 m² compared to kliquid = 0.65 × 10−18 m²). Possible explanations are that (i) liquid permeability was underestimated due to fluid-rock interactions (ii) gas permeability was overestimated due to insufficient correction for gas slippage and/or (iii) gases and liquids do not probe exactly the same porous networks. The analysis of Knudsen numbers shows that the gas permeability measurements were performed in conditions for which the Klinkenberg correction is sufficient. Smaller samples had a larger scatter of permeability values, suggesting that their volume were below the Representative Elementary Volume. The pressure dependence of permeability was studied by some of the participating teams in the range 1–30 MPa and could be fitted to an exponential law k = ko.exp(–γPeff) with γ = 0.093 MPa−1. Good practice rules for measuring permeability in tight materials are also provided.
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America","interactions":[],"lastModifiedDate":"2019-03-26T16:20:45","indexId":"70199170","displayToPublicDate":"2018-09-07T15:49:02","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2497,"text":"Journal of Virology","active":true,"publicationSubtype":{"id":10}},"title":"Genetic evidence supports sporadic and independent introductions of subtype H5 low pathogenic avian influenza A viruses from wild birds to domestic poultry in North America","docAbstract":"Wild bird–origin influenza A viruses (IAVs or avian influenza) have led to sporadic outbreaks among domestic poultry in the United States (US) and Canada, resulting in economic losses through the implementation of costly containment practices and destruction of birds. We used evolutionary analyses of virus sequence data to determine that 78 H5 low pathogenic avian influenza viruses (LPAIVs) isolated from domestic poultry in the US and Canada during 2001–2017 resulted from 18 independent virus introductions from wild birds. Within the wild bird reservoir, the hemagglutinin gene segments of H5 LPAIVs exist primarily as two co-circulating genetic sublineages, and our findings suggest the H5 gene segments flow within each migratory bird flyway and among adjacent flyways, with limited exchange between the non-adjacent Atlantic and Pacific Flyways. Phylogeographic analyses provided evidence that IAVs from dabbling ducks and swans/geese contributed to emergence of viruses among domestic poultry. H5 LPAIVs isolated from commercial farm poultry (i.e. turkey) were descended from a single introduction typically remain a single genotype, whereas those from live bird markets sometimes led to multiple genotypes, reflecting the potential for reassortment with other IAVs circulating within live bird markets. H5 LPAIV introduced from wild birds to domestic poultry represent economic threats to the U.S. poultry industry, and our data suggest that such introductions have been sporadic, controlled effectively through production monitoring and a stamping-out policy, and are, therefore, unlikely to result in sustained detections in commercial poultry operations.
","language":"English","publisher":"American Society of Microbiology","doi":"10.1128/JVI.00913-18","usgsCitation":"Li, L., Bowman, A.S., DeLiberto, T., Killian, M.L., Krauss, S., Nolting, J.M., Torchetti, M.K., Ramey, A.M., Reeves, A.B., Stallknecht, D.E., Webby, R.J., and Wan, X., 2018, Genetic evidence supports sporadic and independent introductions of subtype H5 low pathogenic avian influenza A viruses from wild birds to domestic poultry in North America: Journal of Virology, v. 92, no. 19, p. 1-16, https://doi.org/10.1128/JVI.00913-18.","productDescription":"e00913-18; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-096036","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":468427,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/jvi.00913-18","text":"Publisher Index 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J.","affiliations":[{"id":12749,"text":"USDA APHIS National Wildlife Research Center, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":744513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Killian, Mary L.","contributorId":29685,"corporation":false,"usgs":false,"family":"Killian","given":"Mary","email":"","middleInitial":"L.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":744514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Scott","contributorId":190854,"corporation":false,"usgs":false,"family":"Krauss","given":"Scott","email":"","affiliations":[],"preferred":false,"id":744515,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nolting, Jacqueline M.","contributorId":190855,"corporation":false,"usgs":false,"family":"Nolting","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":744516,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Torchetti, Mia Kim","contributorId":190856,"corporation":false,"usgs":false,"family":"Torchetti","given":"Mia","email":"","middleInitial":"Kim","affiliations":[],"preferred":false,"id":744517,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":744509,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reeves, Andrew B. 0000-0002-7526-0726 areeves@usgs.gov","orcid":"https://orcid.org/0000-0002-7526-0726","contributorId":167362,"corporation":false,"usgs":true,"family":"Reeves","given":"Andrew","email":"areeves@usgs.gov","middleInitial":"B.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":744510,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stallknecht, David E.","contributorId":14323,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":744518,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Webby, Richard J.","contributorId":190857,"corporation":false,"usgs":false,"family":"Webby","given":"Richard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":744519,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wan, Xiu-Feng","contributorId":173959,"corporation":false,"usgs":false,"family":"Wan","given":"Xiu-Feng","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":744520,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70201103,"text":"70201103 - 2018 - Impacts of tidal road-stream crossings on aquatic organism passage","interactions":[],"lastModifiedDate":"2018-11-29T15:11:27","indexId":"70201103","displayToPublicDate":"2018-09-07T15:11:21","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"131-2018","title":"Impacts of tidal road-stream crossings on aquatic organism passage","docAbstract":"ivers and streams are highly vulnerable to fragmentation from roads due to their prevalence in the landscape. Road-stream crossings are far more numerous than other anthropogenic barriers such as dams; these crossing structures (culverts, bridges, fords, and tide gates) have been demonstrated to impede the passage of aquatic organisms. However, road-stream crossings vary widely in the extent to which they serve as a barrier. It is important to identify barrier severity to facilitate prioritization of restoration activities, since proactively addressing all structures is not feasible. In 2015 the North Atlantic Landscape Conservation Collaborative (LCC) funded a project managed by the North Atlantic Aquatic Connectivity Cooperative (NAACC) to develop a unified protocol for assessing aquatic road-stream crossings focusing on aquatic connectivity. The NAACC relied on rapid field-based assessments, which have been shown to be a useful tool for gathering information necessary for prioritization. However, the rapid assessment protocol developed from the NAACC initiative is not applicable to tidal crossings as it does not address two-directional flow, daily water depth fluctuations, or many of the species likely present in coastal habitats.
The goal of this report is to provide the background necessary to create guidelines and rapid assessment tools for assessing risk posed to aquatic organism passage at tidal crossings. To accomplish these goals, this report identifies species present in tidally influenced coastal wetlands, the unique traits they may display that puts them at risk for detrimental impact from impeded passage, and passage threats unique to tidal crossings that are not addressed by protocols designed for non-tidal systems. Species lists were compiled through literature reviews and discussions with regional researchers and managers familiar with coastal ecosystems or fish passage concerns. Life history traits, environmental sensitivities, and movement patterns for each species were compiled to build a database that can be queried to identify species that are highly vulnerable to impeded passage at tidal crossings (Available at: https://umass.box.com/s/w5mhokxjxshyxmr7si2v0gzcypcitu9d). These risk factors for species, combined with passage threats associated with specific crossing characteristics are discussed in this report. The species list is thorough enough to provide a baseline summary of the types of threats experienced by aquatic organisms at tidal road-stream crossings, but it is not exhaustive. Unique ecosystems, species assemblages, management goals, and prioritization models may require different approaches and solutions. Thus, care must be taken to ensure that assessment tools are appropriate to a project’s target species, habitats, and scale.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Becker, S., Jackson, S., Jordaan, A., and Roy, A.H., 2018, Impacts of tidal road-stream crossings on aquatic organism passage: Cooperator Science Series 131-2018, ii, 57 p.","productDescription":"ii, 57 p.","ipdsId":"IP-090405","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":359809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":359808,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalmedia.fws.gov/digital/collection/document/id/2238/"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c0108d5e4b0815414cc2dfd","contributors":{"authors":[{"text":"Becker, Sarah","contributorId":210890,"corporation":false,"usgs":false,"family":"Becker","given":"Sarah","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, Scott","contributorId":210891,"corporation":false,"usgs":false,"family":"Jackson","given":"Scott","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jordaan, Adrian","contributorId":210892,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":752687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":752684,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198107,"text":"sir20185087 - 2018 - Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12","interactions":[],"lastModifiedDate":"2018-09-07T16:38:05","indexId":"sir20185087","displayToPublicDate":"2018-09-07T08:27:21","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5087","title":"Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12","docAbstract":"The sediments of Sinclair Inlet, in Puget Sound, Washington, have elevated levels of contaminants including mercury. The Bremerton Naval Complex is adjacent to Sinclair Inlet, and has known areas of historical soil mercury contamination. The U.S. Geological Survey, in cooperation with the U.S. Navy, has been investigating the potential for mercury sources on the Bremerton Naval Complex to recontaminate recently remediated marine sediment. In 2011–12, the U.S. Geological Survey conducted three tidal- related sampling campaigns to characterize mercury dynamics in the largest stormwater drain system on the Bremerton Naval Complex, which passes through the soils of an area known as Site 2 that has elevated soil mercury concentrations. The sampling campaigns confirmed that the stormwater drain system, PSNS015, serves as a conduit for seawater transport more than 250 m landward of the contaminated soils that subsequently facilitates mercury transport to Sinclair Inlet.
During the December 2011 reconnaissance sampling campaign, no freshwater source of mercury to PSNS015 was identified. There was heavy precipitation preceding and stormwater runoff generated during the reconnaissance survey, which suggests that the primary source of mercury in PSNS015 is not precipitation-induced. During the May 2012 spring-tide sampling campaign, the water in PSNS015 drained to Sinclair Inlet during a negative low tide, and the highest filtered total mercury concentration in the stormwater drain system (60 ng/L) was measured during the lower-low tide in the freshwater flowing into the seaward-most stormwater drain vault from either up-pipe or local groundwater intrusion. Similar conditions were not observed during the June 2012 companion neap-tide sampling campaign, when the water-level elevation of the positive low tide in Sinclair Inlet dropped only slightly below the stormwater drain vault elevation, the water in the seaward-most stormwater vault was brackish rather than fresh, and the filtered total mercury concentration never exceeded 24 ng/L. Particulate total mercury concentrations and dynamics during the spring- and neap-tide sampling campaigns were variable, with higher concentrations (as much as 133 ng/L) measured throughout the neap-tide study compared to those measured during the spring-tide study (as much as 4.34 ng/L). The highest filtered total mercury concentration of all sampling campaigns (1,140 ng/L) was measured during ebb tide in a nearshore monitoring well that represents groundwater discharging from the contaminated soils directly to Sinclair Inlet along an unwalled part of the shoreline.
The results suggest that mercury extracted from Site 2 soils can be carried to Sinclair Inlet during ebb tides by at least two mechanisms: (1) through groundwater directly to Sinclair Inlet along an unwalled part of the shoreline or (2) through the stormwater drain system when the water level in Sinclair Inlet drops below the water level in the stormwater drain system. The data can be used to guide future modifications to the seawall and stormwater drain system that aim to hydraulically disconnect the stormwater drain system from the surrounding contaminated soils.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185087","collaboration":"Prepared in cooperation with U.S. Department of the Navy","usgsCitation":"Conn, K.E., Paulson, A.J., Dinicola, R.S., and DeWild, J.F., 2018, Tidal flushing of mercury from the Bremerton Naval Complex through the PSNS015 stormwater drain system to Sinclair Inlet, Kitsap County, Washington, 2011 -12: U.S. Geological Survey Scientific Investigations Report 2018-5087, 23 p., https://doi.org/10.3133/sir20185087.","productDescription":"vi, 23 p.","onlineOnly":"Y","ipdsId":"IP-097597","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":357094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5087/coverthb.jpg"},{"id":357095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5087/sir20185087.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5087"}],"country":"United States","state":"Washington","county":"Kitsap County","otherGeospatial":"Sinclair Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.86285400390624,\n 47.43737696208075\n ],\n [\n -122.178955078125,\n 47.43737696208075\n ],\n [\n -122.178955078125,\n 48.21003212234042\n ],\n [\n -122.86285400390624,\n 48.21003212234042\n ],\n [\n -122.86285400390624,\n 47.43737696208075\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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U.S. Landsat Analysis Ready Data (ARD) are a revolutionary new U.S. Geological Survey science product that allows the Landsat archive to be more accessible and easier to analyze and reduces the amount of time users spend on data processing for monitoring and assessing landscape change. U.S. Landsat ARD are Level-2 products derived from Landsat Collections Level-1 precision and terrain-corrected scenes that are processed and arranged in spatially consistent tiles and dense temporal stacks for immediate use in monitoring and assessing landscape change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183053","usgsCitation":"U.S. Geological Survey, 2018, U.S. Landsat Analysis Ready Data: U.S. Geological Survey Fact Sheet 2018–3053, 2 p., https://doi.org/10.3133/fs20183053.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-098255","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":357027,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3053/coverthb.jpg"},{"id":357028,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3053/fs20183053.pdf","text":"Report","size":"579 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018–3053"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252d Street
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We used conventional and finite mixture removal models with and without time-varying covariates to evaluate availability given presence for 152 bird species using data from point counts in boreal North America. We found that the choice of model had an impact on the estimability of unknown model parameters and affected the bias and variance of corrected counts. Finite mixture models provided better fit than conventional removal models and better adjusted for count duration. However, reliably estimating parameters and minimizing variance using mixture models required at least 200–1,000 detections. Mixture models with time-varying proportions of infrequent singers were best supported across species, indicating that accounting for date- and time-related heterogeneity is important when combining data across studies over large spatial scales, multiple sampling time frames, or variable survey protocols. Our flexible and continuous time-removal modeling framework can be used to account for such heterogeneity through the incorporation of easily obtainable covariates, such as methods, date, time, and location. Accounting for availability bias in bird surveys allows for better integration of disparate studies at large spatial scales and better adjustment of local, regional, and continental population size estimates.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-18-32.1","usgsCitation":"Solymos, P., Matsuoka, S.M., Cumming, S.G., Stralberg, D., Fontaine, P.C., Schmiegelow, F.K., Song, S.J., and Bayne, E.M., 2018, Evaluating time-removal models for estimating availability of boreal birds during point count surveys: Sample size requirements and model complexity: Condor, v. 120, no. 4, p. 765-786, https://doi.org/10.1650/CONDOR-18-32.1.","productDescription":"22 p.","startPage":"765","endPage":"786","ipdsId":"IP-095119","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":468438,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-18-32.1","text":"Publisher Index Page"},{"id":357080,"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 \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -178.59375,\n 43.197167282501276\n ],\n [\n -53.0859375,\n 43.197167282501276\n ],\n [\n -53.0859375,\n 70.8446726342528\n ],\n [\n -178.59375,\n 70.8446726342528\n ],\n [\n -178.59375,\n 43.197167282501276\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-29","publicationStatus":"PW","scienceBaseUri":"5b98a267e4b0702d0e842e74","contributors":{"authors":[{"text":"Solymos, Peter","contributorId":203718,"corporation":false,"usgs":false,"family":"Solymos","given":"Peter","email":"","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":744118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matsuoka, Steven M. 0000-0001-6415-1885 smatsuoka@usgs.gov","orcid":"https://orcid.org/0000-0001-6415-1885","contributorId":184173,"corporation":false,"usgs":true,"family":"Matsuoka","given":"Steven","email":"smatsuoka@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":744117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cumming, Steven G.","contributorId":207538,"corporation":false,"usgs":false,"family":"Cumming","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":37556,"text":"University of Laval","active":true,"usgs":false}],"preferred":false,"id":744119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stralberg, Diana","contributorId":187413,"corporation":false,"usgs":false,"family":"Stralberg","given":"Diana","email":"","affiliations":[],"preferred":false,"id":744120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fontaine, Patricia C.","contributorId":140676,"corporation":false,"usgs":false,"family":"Fontaine","given":"Patricia","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":744121,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmiegelow, Fiona K. A.","contributorId":207539,"corporation":false,"usgs":false,"family":"Schmiegelow","given":"Fiona","email":"","middleInitial":"K. A.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":744122,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Song, Samantha J.","contributorId":207540,"corporation":false,"usgs":false,"family":"Song","given":"Samantha","email":"","middleInitial":"J.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":744123,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bayne, Erin M.","contributorId":140675,"corporation":false,"usgs":false,"family":"Bayne","given":"Erin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":744124,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70199111,"text":"70199111 - 2018 - Controls on submarine channel-modifying processes identified through morphometric scaling relationships","interactions":[],"lastModifiedDate":"2018-10-04T13:17:39","indexId":"70199111","displayToPublicDate":"2018-09-05T10:26:56","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Controls on submarine channel-modifying processes identified through morphometric scaling relationships","docAbstract":"Submarine channels share morphological similarities with rivers, but observations from modern and ancient systems indicate they are formed under processes and controls unique to submarine settings. Morphologic characteristics of channels—e.g., width, depth, slope, and the relationships among them—can constrain interpretations of channel-forming processes. This work uses morphometric scaling relationships extracted from high-resolution seafloor bathymetry to infer connections between morphology and process in submarine channels. Analysis of 36 modern channels in five geographic regions shows that channel widths vary regionally (from <100 m to >10 km wide) but occupy the same range of aspect ratios (~10:1–100:1). This suggests an autogenic control on aspect ratio, perhaps resulting from feedback processes in levee growth and/or bank erosion, and allogenic (e.g., sediment supply, grain size) controls on channel width. Submarine channel aspect ratios tend to decrease with increasing dimensions, while the opposite relationship has been observed for fluvial channels, likely due to opposing relationships between flow discharge and channel distance. Additionally, observation of an apparent lag between channel thalweg and levee responses to gradient changes suggests that thalweg and levee deposition and erosion may be partially decoupled due to the vertical structure of turbidity currents, with thalweg evolution driven by the basal, higher-shear-stress portion of the flow and levee evolution by the dilute upper portion. The data presented here provide a basis for predicting channel metrics in exploration scenarios, in which data coverage may be sparse. This documentation of a diverse suite of channels also captures the range of scales and variability exhibited globally by submarine channel systems, providing context for local studies.
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The River Basin Restoration Prioritization tool has been developed to incorporate vetted, distributed data models into a catchment scale restoration prioritization framework. Catchment baseline condition and potential improvement with restoration activity is calculated for all National Hydrography Dataset stream reaches and catchments in North Carolina and compared to other catchments within the river subbasin to assess where restoration efforts may best be focused. Hydrologic, water quality, and aquatic habitat quality conditions are assessed with peak flood flow, nitrogen and phosphorus loading, and aquatic species distribution models. The modular nature of the tool leaves ample opportunity for future incorporation of novel and improved datasets to better represent the holistic health of a watershed, and the nature of the datasets used herein allow this framework to be applied at much broader scales than North Carolina.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-018-1100-z","usgsCitation":"Lovette, J.P., Duncan, J.M., Smart, L.S., Fay, J.P., Urban, D.L., Daly, N., Blackwell, J., Hoos, A.B., Garcia, A.M., and Band, L.E., 2018, Leveraging big data towards functionally-based, catchment scale restoration prioritization: Environmental Management, v. 62, no. 6, p. 1007-1024, https://doi.org/10.1007/s00267-018-1100-z.","productDescription":"18 p.","startPage":"1007","endPage":"1024","ipdsId":"IP-094881","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":357074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-31","publicationStatus":"PW","scienceBaseUri":"5b98a268e4b0702d0e842e78","contributors":{"authors":[{"text":"Lovette, John P.","contributorId":207568,"corporation":false,"usgs":false,"family":"Lovette","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":37566,"text":"UNC","active":true,"usgs":false}],"preferred":false,"id":744230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duncan, Jonathan M.","contributorId":207569,"corporation":false,"usgs":false,"family":"Duncan","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":744231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smart, Lindsey S.","contributorId":207570,"corporation":false,"usgs":false,"family":"Smart","given":"Lindsey","email":"","middleInitial":"S.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":744232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fay, John P.","contributorId":207571,"corporation":false,"usgs":false,"family":"Fay","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":744233,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Urban, Dean L.","contributorId":207572,"corporation":false,"usgs":false,"family":"Urban","given":"Dean","email":"","middleInitial":"L.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":744234,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Daly, Nancy","contributorId":207573,"corporation":false,"usgs":false,"family":"Daly","given":"Nancy","email":"","affiliations":[{"id":37567,"text":"Wake County Department of Environmental Services","active":true,"usgs":false}],"preferred":false,"id":744235,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blackwell, Jamie","contributorId":207574,"corporation":false,"usgs":false,"family":"Blackwell","given":"Jamie","email":"","affiliations":[{"id":24615,"text":"North Carolina Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":744236,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoos, Anne B. 0000-0001-9845-7831","orcid":"https://orcid.org/0000-0001-9845-7831","contributorId":207575,"corporation":false,"usgs":true,"family":"Hoos","given":"Anne","email":"","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744237,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Garcia, Ana M. 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":207567,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":744229,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Band, Lawrence E.","contributorId":207576,"corporation":false,"usgs":false,"family":"Band","given":"Lawrence","email":"","middleInitial":"E.","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":744238,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ]}