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","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings World Geothermal Congress 2020+1","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"World Geothermal Congress 2020","conferenceLocation":"Reykjavik, Iceland","language":"English","usgsCitation":"Halldorsson, S.A., Scarsi, P., Abebe, T., Evans, T., Kulongoski, J.T., Castillo, P.R., and Barry, P., 2020, He-CO2-N2 isotope and relative abundance characterization of geothermal fluids from the Ethiopian Rift, in Proceedings World Geothermal Congress 2020+1, Reykjavik, Iceland, 6 p.","productDescription":"6 p.","ipdsId":"IP-112494","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":413534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":413533,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-energy.org/cpdb/record_detail.php?id=33392","linkFileType":{"id":5,"text":"html"}}],"country":"Ethiopia","otherGeospatial":"Ethiopian Rift","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 42,\n 10\n ],\n [\n 36,\n 10\n ],\n [\n 36,\n 6\n ],\n [\n 42,\n 6\n ],\n [\n 42,\n 10\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Halldorsson, S. A.","contributorId":302625,"corporation":false,"usgs":false,"family":"Halldorsson","given":"S.","email":"","middleInitial":"A.","affiliations":[{"id":65518,"text":"NordVulk, Institute of Earth Sciences, University of Iceland, Reykjavík, Iceland","active":true,"usgs":false}],"preferred":false,"id":864915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scarsi, P.","contributorId":302626,"corporation":false,"usgs":false,"family":"Scarsi","given":"P.","email":"","affiliations":[{"id":65519,"text":"Institute of Geosciences and Earth Resources, National Research Council of Italy, Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":864916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abebe, T.","contributorId":302627,"corporation":false,"usgs":false,"family":"Abebe","given":"T.","email":"","affiliations":[{"id":65519,"text":"Institute of Geosciences and Earth Resources, National Research Council of Italy, Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":864917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Evans, T.","contributorId":302628,"corporation":false,"usgs":false,"family":"Evans","given":"T.","affiliations":[{"id":65520,"text":"Scripps Institution of Oceanography, UCSD, La Jolla, CA, USA","active":true,"usgs":false}],"preferred":false,"id":864918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":864919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Castillo, P. R.","contributorId":302629,"corporation":false,"usgs":false,"family":"Castillo","given":"P.","email":"","middleInitial":"R.","affiliations":[{"id":65520,"text":"Scripps Institution of Oceanography, UCSD, La Jolla, CA, USA","active":true,"usgs":false}],"preferred":false,"id":864920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barry, P. H.","contributorId":302630,"corporation":false,"usgs":false,"family":"Barry","given":"P. H.","affiliations":[{"id":65521,"text":"Woods Hole Oceanographic Institution, Woods Hole, MA, USA","active":true,"usgs":false}],"preferred":false,"id":864921,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236284,"text":"70236284 - 2020 - Landslide guide for residents of Puerto Rico","interactions":[],"lastModifiedDate":"2023-10-18T16:13:39.227868","indexId":"70236284","displayToPublicDate":"2020-12-31T08:32:30","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Landslide guide for residents of Puerto Rico","docAbstract":"No abstract available.
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The National Map (TNM) portal provides public access to U.S. Geological Survey (USGS) high-resolution topographic datasets, and maps from the Historical Topographic Map Collection (HTMC). Elevation values shown on HTMC maps were obtained from ground spot elevation measurements, as compared to today’s elevation measurements derived from more efficient methods, such as lidar, radar, or sonar. These spot elevations were collected either by levelling in the field or by photogrammetrists in the office, and are called mass points with post-spacings of two-arc seconds (arcsec), approximately 60 meters depending on latitude, in steep terrain and one-half arcsec, approximately 15 meters, in flat terrain (Federal Geographic Data Committee 1997). The vertical accuracy of spot elevations is ± 10 feet. Most spot elevations were used only in contour derivation to create a more spatially continuous representations of terrain, but some were also labelled on the maps to supply accurate elevations of culturally important features such as mountain peaks, gaps, and road junctions.
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Arthur","contributorId":257861,"corporation":false,"usgs":false,"family":"Chan","given":"Arthur","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":815172,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218699,"text":"70218699 - 2020 - Fault trace mapping and surface-fault-rupture special study zone delineation of the Wasatch Fault Zone, Utah and Idaho","interactions":[],"lastModifiedDate":"2021-03-05T14:13:50.732481","indexId":"70218699","displayToPublicDate":"2020-12-31T08:12:47","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Fault trace mapping and surface-fault-rupture special study zone delineation of the Wasatch Fault Zone, Utah and Idaho","docAbstract":"The Wasatch fault zone (WFZ) is a 220-mile-long (350-km) fault zone divided into 10 structural segments extending from southeastern Idaho to central Utah. The central five segments of the WFZ underlie the densely populated Wasatch Front region, where the majority of Utah’s population and economy are proximal to the fault zone. The West Valley fault zone (WVFZ) is an antithetic structure related to the WFZ and runs through the Salt Lake Valley. Communities on or adjacent to the WFZ are at risk of earthquake damage, due to their proximity to the fault zones. During 2016–2018, the Utah Geological Survey and a U.S. Geological Survey collaborator performed updated fault mapping of 39 7.5' quadrangles along the WFZ using recently acquired high-resolution topographic data derived from airborne light detection and ranging (lidar) elevation data. Previous geologic mapping, paleoseismic investigations, historical aerial photography, and field investigations were also used to identify and map surface fault traces and infer fault locations. Special study zones were delineated around fault traces to facilitate understanding of the surface-rupturing hazard and associated risk. Defining these special study zones encourages the creation and implementation of municipal and county geologic-hazard ordinances dealing with hazardous faults. We identified potential paleoseismic investigation sites where fault scarps appear relatively pristine, are located in geologically favorable settings, and where additional earthquake timing data would be beneficial to the continued earthquake research of the WFZ. The fault geometries, attributes, and special study zones were published in the online Utah Geologic Hazards Portal simultaneously with this Report of Investigation (RI). This report contains supplementary material describing the data and methods used to perform the mapping and in locating potential paleoseismic investigation sites in the study area. This work is critical to raise awareness of earthquake hazards in areas of Utah experiencing rapid growth.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Report of Investigation 280","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Utah Geological Survey","doi":"10.34191/RI-280","collaboration":"Utah Geological Survey","usgsCitation":"McDonald, G.N., Kleber, E.J., Hiscock, A.I., Bennett, S., and Bowman, S.D., 2020, Fault trace mapping and surface-fault-rupture special study zone delineation of the Wasatch Fault Zone, Utah and Idaho, 23 p., https://doi.org/10.34191/RI-280.","productDescription":"23 p.","ipdsId":"IP-102607","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":384068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Utah","otherGeospatial":"Wasatch Fault Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.21435546875,\n 39.2492708462234\n ],\n [\n -111.46728515624999,\n 39.2492708462234\n ],\n [\n -111.46728515624999,\n 42.94033923363181\n ],\n [\n -112.21435546875,\n 42.94033923363181\n ],\n [\n -112.21435546875,\n 39.2492708462234\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McDonald, Greg N.","contributorId":198715,"corporation":false,"usgs":false,"family":"McDonald","given":"Greg","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":811415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleber, Emily J.","contributorId":254373,"corporation":false,"usgs":false,"family":"Kleber","given":"Emily","email":"","middleInitial":"J.","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":811416,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hiscock, Adam I.","contributorId":214811,"corporation":false,"usgs":false,"family":"Hiscock","given":"Adam","email":"","middleInitial":"I.","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":811417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bennett, S. 0000-0002-9772-4122","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":29230,"corporation":false,"usgs":true,"family":"Bennett","given":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":811418,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowman, Steve D.","contributorId":254374,"corporation":false,"usgs":false,"family":"Bowman","given":"Steve","email":"","middleInitial":"D.","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":811419,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217619,"text":"70217619 - 2020 - Estuarine habitat use by White Sturgeon (Acipenser transmontanus)","interactions":[],"lastModifiedDate":"2021-01-25T14:20:17.003115","indexId":"70217619","displayToPublicDate":"2020-12-31T08:11:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7563,"text":"San Francisco Estuary & Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Estuarine habitat use by White Sturgeon (Acipenser transmontanus)","docAbstract":"White Sturgeon (Acipenser transmontanus), a species of concern in the San Francisco Estuary, is in relatively low abundance due to a variety of factors. The purpose of our study was to identify the estuarine habitat used by White Sturgeon to aid in the conservation and management of the species locally and across its range. We seasonally sampled sub-adult and adult White Sturgeon in the central estuary using setlines across a habitat gradient representative of three primary structural elements: shallow wetland channels (mean sample depth = 2 m), shallow open-water shoal (mean sample depth = 2 m), and deep open-water channel (mean sample depth = 7 m). We found that the shallow open-water shoal and deep open-water channel habitats were consistently occupied by White Sturgeon in spring, summer, and fall across highly variable water quality conditions, whereas the shallow wetland channel habitat was essentially unoccupied. We conclude that sub-adult and adult White Sturgeon inhabit estuaries in at least spring, summer, and fall and that small, shallow wetland channels are relatively unoccupied.
","language":"English","publisher":"University of California Davis","doi":"10.15447/sfews.2020v18iss4art4","usgsCitation":"Patton, O., Violette, V.L., Young, M.J., and Feyrer, F.V., 2020, Estuarine habitat use by White Sturgeon (Acipenser transmontanus): San Francisco Estuary & Watershed Science, v. 18, no. 4, 4, 10 p., https://doi.org/10.15447/sfews.2020v18iss4art4.","productDescription":"4, 10 p.","ipdsId":"IP-118307","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":454602,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2020v18iss4art4","text":"Publisher Index Page"},{"id":382537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.07983398437499,\n 37.23907530202184\n ],\n [\n -121.322021484375,\n 37.23907530202184\n ],\n [\n -121.322021484375,\n 38.42777351132902\n ],\n [\n -123.07983398437499,\n 38.42777351132902\n ],\n [\n -123.07983398437499,\n 37.23907530202184\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Oliver 0000-0002-2911-7718","orcid":"https://orcid.org/0000-0002-2911-7718","contributorId":218217,"corporation":false,"usgs":true,"family":"Patton","given":"Oliver","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Violette, Veronica L. 0000-0002-7390-4655 vviolette@usgs.gov","orcid":"https://orcid.org/0000-0002-7390-4655","contributorId":222824,"corporation":false,"usgs":true,"family":"Violette","given":"Veronica","email":"vviolette@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808920,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217284,"text":"70217284 - 2020 - Hatchling emergence ecology of Ouachita map turtles (Graptemys ouachitensis) on the lower Wisconsin River, Wisconsin, USA","interactions":[],"lastModifiedDate":"2021-01-19T12:40:19.075023","indexId":"70217284","displayToPublicDate":"2020-12-31T08:06:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1210,"text":"Chelonian Conservation and Biology","active":true,"publicationSubtype":{"id":10}},"title":"Hatchling emergence ecology of Ouachita map turtles (Graptemys ouachitensis) on the lower Wisconsin River, Wisconsin, USA","docAbstract":"Despite its biological importance in shaping both individual fitness and population structure, much remains to be learned about the hatchling emergence ecology of most freshwater turtles. Here, we provide some of the first details on these early life stages for the Ouachita map turtle (Graptemys ouachitensis) obtained during 2015–2017 along the lower Wisconsin River, Iowa County, Wisconsin, and integrate our results into related research within the genus Graptemys. Dedicated trail cameras over in situ turtle nests provided otherwise difficult to obtain observational data relevant to natural hatchling emergence without disturbing nests or hatchlings. In contrast to some earlier reports for Graptemys, hatchling emergence was mostly diurnal and synchronous, primarily in the morning soon after soil temperatures began to rise from overnight low values. Data suggest a temperature change model of cueing hatchling emergence, which may represent a local or regional adaptation to reduce nocturnal predation risks, mostly from raccoons (Procyon lotor), or may simply reflect default diurnal hatchling activity patterns when not affected by thermal constraints. Aside from predation, hatchlings on this small study site are affected by vegetative shading, leading to relatively long times to first emergence periods (mean, 82.3 d), low mean nest temperatures (25.9°C), and a likely male-biased sex ratio. These findings highlight the value of hatchling emergence studies in revealing important influences on population viability and in guiding appropriate habitat management in conservation efforts.
Stream-obligate amphibians are important indicators of ecosystem health in the Pacific Northwest, but distributional information to improve forest management is lacking in many regions. We analyzed archived DNA extracted from water samples in 60 pools in streams on private timberlands in Mendocino County, California, for 3 California Species of Special Concern—Coastal Tailed Frogs (Ascaphus truei), Foothill Yellow-legged Frogs (Rana boylii), and Southern Torrent Salamanders (Rhyacotriton variegatus)—to better understand their distributions in the region. Detection probabilities for eDNA of Foothill Yellow-legged Frogs and Coastal Tailed Frogs were positively influenced by water temperature. eDNA occurrence for both frogs was affected by whether silt or organic matter was a dominant substrate in the sampled pool, and Foothill Yellow-legged Frog eDNA occurrence was also affected by water temperature. Foothill Yellow-legged Frog eDNA occurrence had a strong, positive association with water temperature, with occurrence unlikely below 14°C and very likely above 16°C, and a positive association with silt or organic substrates in pools, which was likely an indicator of higher-order stream reaches. In contrast, Coastal Tailed Frogs had a negative association with silt or organic substrates. Historical visual detections were generally congruent with findings using eDNA, but differences highlight important areas for further study. We did not detect Southern Torrent Salamanders using eDNA at any sites. Our study reinforces that ecological relationships of these species are varied, and shows the importance of maintaining the integrity of streams with diverse characteristics for conserving stream amphibians.
Map projections are an area of cartography with a firm mathematical foundation for their creation and display providing a basis for a knowledge representation. Using only variations on a single equation set, an infinite number of projections can be created, but less than 100 are in active use. Because each projection preserves specific characteristics, such as area, angles, global look, or a compromise of properties, classifications of map projections have been developed to aid in knowledge representation. These classifications are used for decision-making. They help select the correct projection for the map use. They assist users with determining the correct orientation, standard parallels and meridians. The classifications also inform the user how to adjust the selection based on size, extent, and latitude. Semantics can be used to automate map projections knowledge into a knowledge base that can be accessed by humans and machines. This work details a semantic representation of map projections knowledge and provides a simple example of a use case that exploits the knowledge base.
","language":"English","publisher":"Croatian Cartographic Society","doi":"10.32909/kg.19.33.5","usgsCitation":"Usery, E., 2020, Semantically enabling map projections knowledge: Cartography and Geoinformation, v. 19, no. 33, p. 66-77, https://doi.org/10.32909/kg.19.33.5.","productDescription":"12 p.","startPage":"66","endPage":"77","ipdsId":"IP-116864","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":454604,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.32909/kg.19.33.5","text":"External Repository"},{"id":384692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"33","noUsgsAuthors":false,"publicationDate":"2020-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":813015,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70219239,"text":"70219239 - 2020 - Dispersal of hatchling Ouachita map turtles (Graptemys ouachitensis) from natural nests on the lower Wisconsin River, Wisconsin, USA","interactions":[],"lastModifiedDate":"2021-04-01T12:50:52.960634","indexId":"70219239","displayToPublicDate":"2020-12-31T07:47:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1210,"text":"Chelonian Conservation and Biology","active":true,"publicationSubtype":{"id":10}},"title":"Dispersal of hatchling Ouachita map turtles (Graptemys ouachitensis) from natural nests on the lower Wisconsin River, Wisconsin, USA","docAbstract":"Despite its importance to individual fitness and population dynamics, the dispersal behaviors of most neonate freshwater turtles after nest emergence are poorly known. We studied the initial dispersal tendencies of neonate Ouachita map turtles (Graptemys ouachitensis) exiting natural nests during 2015–2017 along the Wisconsin River, Wisconsin. Overall, dispersal was nonrandom, and hatchlings largely oriented toward the nearest substantial vegetative cover, a woodland north of the nesting area. However, variation sometimes occurred in routes taken among hatchlings within a clutch. Directional changes within an individual's dispersal track, including route reversals, were also observed. As our work appears to be the first to use standalone trail cameras as a primary data-gathering tool for a hatchling dispersal study, it highlights the potential benefits and limitations of this technique for similar research.
No abstract available.
","language":"English","publisher":"Mojave National Preserve","usgsCitation":"Langenheim, V., 2020, Using gravity to map faults and basins in the Mojave Desert, California: Mojave Science Newsletter, p. 9-14.","productDescription":"6 p.","startPage":"9","endPage":"14","ipdsId":"IP-118578","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":383406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383401,"type":{"id":15,"text":"Index Page"},"url":"https://granite.ucnrs.org/wp-content/uploads/2021/02/2020_Science-Newsletter.pdf"}],"country":"United States","state":"Califronia","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.05908203124999,\n 33.770015152780125\n ],\n [\n -114.5654296875,\n 33.770015152780125\n ],\n [\n -114.5654296875,\n 35.98689628443789\n ],\n [\n -118.05908203124999,\n 35.98689628443789\n ],\n [\n -118.05908203124999,\n 33.770015152780125\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":216217,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":810695,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218790,"text":"70218790 - 2020 - Vapor-bubble growth in olivine-hosted melt inclusions","interactions":[],"lastModifiedDate":"2021-03-12T13:34:32.725277","indexId":"70218790","displayToPublicDate":"2020-12-31T07:32:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":738,"text":"American Mineralogist","active":true,"publicationSubtype":{"id":10}},"title":"Vapor-bubble growth in olivine-hosted melt inclusions","docAbstract":"Melt inclusions record the depth of magmatic processes, magma degassing paths, and volatile budgets of magmas. Extracting this information is a major challenge. It requires determining melt volatile contents at the time of entrapment when working with melt inclusions that have suffered post-entrapment modifications. Several processes decrease internal melt inclusion pressure, resulting in nucleation and growth of a vapor bubble and, time permitting, diffusion of volatiles (especially CO2) into the vapor bubble. Methods exist that attempt to reconstruct the entrapped CO2 contents, but they are difficult to apply and yield inconsistent results. Here, we explore bubble growth, evaluate CO2 reconstruction approaches, and develop improved experimental and computational approaches. Piston-cylinder experiments were conducted on olivine-hosted melt inclusions from Seguam (Alaska, USA) and Fuego (Guatemala) volcanoes at the following conditions: 500-800 MPa, 1140-1200 °C for Seguam and 1110-1140 °C for Fuego, 4-8 wt% H2O in the KBr brine, and run durations of 10-120 minutes. Heated melt inclusions form well-defined S-CO2 trends that can be described by degassing models. CO2 contents are enriched by a factor of ~2.5, on average, relative to those of the glasses within unheated melt inclusions, whereas S contents of heated and unheated melt inclusion glasses overlap, indicating insignificant amounts of S partition into the vapor bubble. Low closure temperatures enable CO2 diffusion into vapor bubbles during quench upon eruption, while a higher closure temperature for S limits its loss to vapor bubbles. We evaluate the timescales of post-entrapment processes and use the results to develop a new computational model to restore entrapped CO2 contents: MIMiC (Melt Inclusion Modification Corrections). Heated melt inclusion data are used as a benchmark to evaluate of the results from MIMiC and other published methods of CO2 reconstruction. The methods perform variably well. Key advantages to our experimental rehomogenization technique are that it enables accurate measurements of CO2 contents and allows for large quantities of melt inclusions to be rehomogenized efficiently. Our new computational model produces more accurate results than other computational methods, has similar accuracy to the Raman method of CO2 reconstruction in cases where Raman can be applied (i.e., no C-bearing phases in bubble), and can be applied to the vast body of published melt inclusion data. To obtain the most robust data on bubble-bearing melt inclusions, we recommend taking both experimental- and MIMiC-based approaches.","language":"English","publisher":"De Gruyter","doi":"10.2138/am-2020-7377","usgsCitation":"Rasmussen, D.J., Plank, T., Wallace, P., Newcombe, M., and Lowenstern, J.B., 2020, Vapor-bubble growth in olivine-hosted melt inclusions: American Mineralogist, v. 105, no. 12, p. 1898-1919, https://doi.org/10.2138/am-2020-7377.","productDescription":"22 p.","startPage":"1898","endPage":"1919","ipdsId":"IP-114146","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":384341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"105","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rasmussen, Daniel J.","contributorId":237828,"corporation":false,"usgs":false,"family":"Rasmussen","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":47619,"text":"Lamont-Doherty Earth Observatory, Columbia University, New York, NY 10027","active":true,"usgs":false}],"preferred":false,"id":811886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plank, Terry","contributorId":199797,"corporation":false,"usgs":false,"family":"Plank","given":"Terry","email":"","affiliations":[],"preferred":false,"id":811887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wallace, Paul J.","contributorId":29308,"corporation":false,"usgs":true,"family":"Wallace","given":"Paul J.","affiliations":[],"preferred":false,"id":811888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newcombe, Megan","contributorId":255165,"corporation":false,"usgs":false,"family":"Newcombe","given":"Megan","email":"","affiliations":[{"id":51448,"text":"Lamont Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":811889,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":811890,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217884,"text":"70217884 - 2020 - Assessment of methods for soil monitoring in the Adirondack region of New York","interactions":[],"lastModifiedDate":"2021-02-09T13:33:48.676583","indexId":"70217884","displayToPublicDate":"2020-12-31T07:30:49","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Assessment of methods for soil monitoring in the Adirondack region of New York","docAbstract":"Repeated sampling to detect changes in forest soils was rarely used before 1990, but the value of soil monitoring in understanding environmental change is becoming well established. The growing number of resampling studies has shown that sampling designs and procedures must be adapted to the objectives of the monitoring program and the soils being monitored. In the Adirondack region, current priorities include the response of soils to large increases, and more recently, large decreases in acidic deposition, and changes driven by trending climate such as altered pools of soil organic carbon, as well as other unforeseen factors that will occur in the future. \nTo improve methods and assess the feasibility of long-term soil monitoring in the Adirondack region, the United States Geological Survey (USGS) conducted a pilot project to evaluate a new sampling method for characterizing soils on a watershed basis. Results obtained with this new approach, referred to as the ADK sampling method, was compared to methods used in previous sampling conducted in 2004 as part of the Western Adirondack Stream Survey (WASS), and also to previous high-replication pit sampling in the North and South Tributary watersheds of Buck Creek (North Buck and South Buck). The number of sampling locations and spatial distribution of sampling points within watersheds differed among the methods, although pit excavation was used to obtain samples in all cases. In addition, this investigation evaluated the use of small diameter corers as a means to measure forest floor mass with greater accuracy and precision than commonly used methods such as pit excavation.\nSufficient statistical power to detect ecologically relevant changes in upper profile horizons (Oe, Oa and upper 10 cm of the B) were achieved with the ADK sampling method that utilized 18 pit excavations per watershed. The sampling locations were organized within each watershed into three study areas (six sampling locations per study area) that represented the primary types of landscape within the watershed. Sampling at 18 locations per watershed was found to be nearly as effective at detecting changes as sampling at 28 locations per watershed. Numerous significant changes (P < 0.10) were detected with both 18 and 28 sampling locations at sampling intervals of 12 to 16 years. The relationship between soil data obtained with the ADK method and stream chemistry at the base of the watershed suggested that this approach adequately characterized soil variability within the watershed for the purpose of studying soil-stream linkages. Significant changes in upper B horizon calcium (P < 0.10) and Oa horizon aluminum (P < 0.01) were detected when the data from the four WASS watersheds were combined with the two Buck Creek watersheds, which suggested that there would be value in resampling other WASS watersheds previously sampled in 2004 to support a regional assessment.\nStudy results support small diameter cores as a useful method to monitor changes in the organic matter mass of the forest floor. This method showed high reproducibility in repeated sampling tests and lower spatial variability in sample data than traditional approaches when compared on a watershed basis. Soil coring is also faster and requires less equipment than pit excavation methods, which makes it more conducive to sampling over large areas. However, organic matter mass of the forest floor determined by coring was consistently less than the values obtained by the ADK sampling method that used pit sampling and vertical horizon measurements, and also literature values of a previous Adirondack study that utilized pit sampling in which the entire horizon was collected over a measured area. However, a high correlation (R2 = 0.87) occurred between organic matter content (expressed as Mg ha-1) determined by coring and the ADK sampling method. Differing methods with regard to where sample could be collected, and how organic matter was collected for chemical analysis were the likely reasons for differences in quantification of forest floor organic mass. \nCollection of forest floor cores in conjunction with the ADK method is recommended to provide improved sensitivity in detecting changes in the forest floor in proximity of where full analyses of the soil profile are being done. This duel sampling approach represents an optimized method for measuring and understanding how Adirondack soils will change in the future.","language":"English","publisher":"NYS Energy Research and Development Authority","collaboration":"New York State Energy Research and Development Authority","usgsCitation":"Lawrence, G.B., and Antidormi, M.R., 2020, Assessment of methods for soil monitoring in the Adirondack region of New York, vi, 37 p.","productDescription":"vi, 37 p.","ipdsId":"IP-111655","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":383152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383151,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.nyserda.ny.gov/About/Publications/Research-and-Development-Technical-Reports/Environmental-Research-and-Development-Technical-Reports"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.69580078125001,\n 43.77109381775648\n ],\n [\n -75.06958007812501,\n 42.988576458321816\n ],\n [\n -73.32275390625,\n 43.11702412135048\n ],\n [\n -73.1689453125,\n 45.07352060670971\n ],\n [\n -74.89379882812501,\n 44.91035917458492\n ],\n [\n -75.69580078125001,\n 43.77109381775648\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antidormi, Michael R. 0000-0002-3967-1173 mantidormi@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-1173","contributorId":150722,"corporation":false,"usgs":true,"family":"Antidormi","given":"Michael","email":"mantidormi@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810097,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217150,"text":"70217150 - 2020 - Conservation genetics of imperiled striped whipsnake in Washington","interactions":[],"lastModifiedDate":"2021-01-07T13:30:51.659051","indexId":"70217150","displayToPublicDate":"2020-12-31T07:28:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"title":"Conservation genetics of imperiled striped whipsnake in Washington","docAbstract":"Conservation of wide-ranging species is aided by population genetic information that provides insights into adaptive potential, population size, interpopulation connectivity, and even extinction risk in portions of a species range. The Striped Whipsnake (Masticophis taeniatus) occurs across 11 western U.S. states and into Mexico but has experienced population declines in parts of its range, particularly in the state of Washington. We analyzed nuclear and mitochondrial DNA extracted from 192 shed skins, 63 muscle tissue samples, and one mouth swab to assess local genetic diversity and differentiation within and between the last known whipsnake populations in Washington. We then placed that information in a regional context to better understand levels of differentiation and diversity among whipsnake populations in the northwestern portion of the range of the species. Microsatellite data analyses indicated that there was comparable genetic diversity between the two extant Washington populations, but gene flow may be somewhat limited. We found moderate to high levels of genetic differentiation among states across all markers, including five microsatellites, two nuclear genes, and two mitochondrial genes. Pairwise state-level comparisons and dendrograms suggested that Washington whipsnakes are most closely related to those in Oregon, and distinct from Idaho, Nevada, and Utah, approximately following an isolation by distance model. We conclude that Washington populations of whipsnakes have experienced recent isolating events, but they have yet to lose genetic diversity. The longevity and high vagility of the species may provide opportunity for conservation of whipsnakes in the state as long as shrubland habitat is available","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Pilliod, D.S., Hallock, L.A., Miller, M.P., Mullins, T.D., and Haig, S.M., 2020, Conservation genetics of imperiled striped whipsnake in Washington: Herpetological Conservation and Biology, v. 15, no. 3, p. 597-610.","productDescription":"14 p.","startPage":"597","endPage":"610","ipdsId":"IP-117373","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":381993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381977,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/~herpconb/contents_vol15_issue3.html"}],"country":"United 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\"}}]}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":807741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hallock, Lisa A.","contributorId":247496,"corporation":false,"usgs":false,"family":"Hallock","given":"Lisa","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":807754,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Mark P. 0000-0003-1045-1772 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susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":807757,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228612,"text":"70228612 - 2020 - Inter-individual differences in the foraging behavior of breeding Adélie penguins are driven by individual quality and sex","interactions":[],"lastModifiedDate":"2022-02-14T13:25:50.703049","indexId":"70228612","displayToPublicDate":"2020-12-31T07:22:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2636,"text":"MEPS","active":true,"publicationSubtype":{"id":10}},"title":"Inter-individual differences in the foraging behavior of breeding Adélie penguins are driven by individual quality and sex","docAbstract":"Inter-individual differences in demographic traits of iteroparous species can arise through learning and maturation, as well as from permanent differences in individual ‘quality’ and sex-specific constraints. As the ability to acquire energy determines the resources an individual can allocate to reproduction and self-maintenance, foraging behavior is a key trait to study to better understand the mechanisms underlying these differences. So far, most seabird studies have focused on the effect of maturation and learning processes on foraging performance, while only a few have included measures of individual quality. Here, we investigated the effects of age, breeding experience, sex, and individual breeding quality on the foraging behavior and location of 83 known-age Adélie penguins at Cape Bird, Ross Sea, Antarctica. Over a 2 yr period, we showed that (1) high-quality birds dived deeper than lower quality ones, apparently catching a higher number of prey per dive and targeting different foraging locations; (2) females performed longer foraging trips and a higher number of dives compared to males; (3) there were no significant age-related differences in foraging behavior; and (4) breeding experience had a weak influence on foraging behavior. We suggest that high-quality individuals have higher physiological ability, enabling them to dive deeper and forage more effectively. Further inquiry should focus on determining the physiological differences among penguins of different quality.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps13208","usgsCitation":"Lescroël, A., Lyver, P., Jongsomjit, D., Veloz, S., Dugger, K., Kappes, P., Karl, B., Whitehead, A., Pech, R., Cole, T.L., and Ballard, G., 2020, Inter-individual differences in the foraging behavior of breeding Adélie penguins are driven by individual quality and sex: MEPS, v. 636, p. 189-205, https://doi.org/10.3354/meps13208.","productDescription":"17 p.","startPage":"189","endPage":"205","ipdsId":"IP-112127","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"636","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lescroël, Amelie","contributorId":276366,"corporation":false,"usgs":false,"family":"Lescroël","given":"Amelie","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":834796,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyver, Phil O’B.","contributorId":276368,"corporation":false,"usgs":false,"family":"Lyver","given":"Phil O’B.","affiliations":[{"id":12679,"text":"Landcare Research","active":true,"usgs":false}],"preferred":false,"id":834797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jongsomjit, Dennis","contributorId":276370,"corporation":false,"usgs":false,"family":"Jongsomjit","given":"Dennis","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":834798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Veloz, Sam","contributorId":276372,"corporation":false,"usgs":false,"family":"Veloz","given":"Sam","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":834799,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kappes, Peter","contributorId":276374,"corporation":false,"usgs":false,"family":"Kappes","given":"Peter","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":834800,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karl, Brian J.","contributorId":276377,"corporation":false,"usgs":false,"family":"Karl","given":"Brian J.","affiliations":[{"id":12679,"text":"Landcare Research","active":true,"usgs":false}],"preferred":false,"id":834801,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Whitehead, Amy L.","contributorId":276379,"corporation":false,"usgs":false,"family":"Whitehead","given":"Amy L.","affiliations":[{"id":25457,"text":"NIWA","active":true,"usgs":false}],"preferred":false,"id":834802,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pech, Roger","contributorId":276381,"corporation":false,"usgs":false,"family":"Pech","given":"Roger","email":"","affiliations":[{"id":12679,"text":"Landcare Research","active":true,"usgs":false}],"preferred":false,"id":834803,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cole, Theresa L.","contributorId":276383,"corporation":false,"usgs":false,"family":"Cole","given":"Theresa","email":"","middleInitial":"L.","affiliations":[{"id":12679,"text":"Landcare Research","active":true,"usgs":false}],"preferred":false,"id":834804,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ballard, Grant","contributorId":276385,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":834805,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70220285,"text":"70220285 - 2020 - The next frontier: Making research more reproducible","interactions":[],"lastModifiedDate":"2021-04-30T12:21:02.781787","indexId":"70220285","displayToPublicDate":"2020-12-31T07:20:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2501,"text":"Journal of Water Resources Planning and Management","active":true,"publicationSubtype":{"id":10}},"title":"The next frontier: Making research more reproducible","docAbstract":"Science and engineering rest on the concept of reproducibility. An important question for any study is: are the results reproducible? Can the results be recreated independently by other researchers or professionals? Research results need to be independently reproduced and validated before they are accepted as fact or theory. Across numerous fields like psychology, computer systems, and water resources there are problems to reproduce research results (Aarts et al. 2015; Collberg et al. 2014; Hutton et al. 2016; Stagge et al. 2019; Stodden et al. 2018). This editorial examines the challenges to reproduce research results and suggests community practices to overcome these challenges. Coordination is needed among the authors, journals, funders and institutions that produce, publish, and report research. Making research more reproducible will allow researchers, professionals, and students to more quickly understand and apply research in follow-on efforts and advance the field.","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/(ASCE)WR.1943-5452.0001215","usgsCitation":"Rosenberg, D.E., Filion, Y., Teasley, R., Sandoval-Solis, S., Hecht, J.S., van Zyl, J.E., McMahon, G.F., Horsburgh, J., Kasprzyk, J.R., and Tarboton, D.G., 2020, The next frontier: Making research more reproducible: Journal of Water Resources Planning and Management, v. 146, no. 6, 4 p., https://doi.org/10.1061/(ASCE)WR.1943-5452.0001215.","productDescription":"4 p.","ipdsId":"IP-112233","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":454610,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/(asce)wr.1943-5452.0001215","text":"Publisher Index Page"},{"id":385407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"146","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberg, David E. 0000-0003-2163-2907","orcid":"https://orcid.org/0000-0003-2163-2907","contributorId":257767,"corporation":false,"usgs":false,"family":"Rosenberg","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":815003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Filion, Yves","contributorId":257768,"corporation":false,"usgs":false,"family":"Filion","given":"Yves","email":"","affiliations":[{"id":40753,"text":"Queen's University","active":true,"usgs":false}],"preferred":false,"id":815004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teasley, Rebecca","contributorId":257769,"corporation":false,"usgs":false,"family":"Teasley","given":"Rebecca","email":"","affiliations":[{"id":34699,"text":"University of Minnesota-Duluth","active":true,"usgs":false}],"preferred":false,"id":815005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sandoval-Solis, Samuel 0000-0003-0329-3243","orcid":"https://orcid.org/0000-0003-0329-3243","contributorId":257770,"corporation":false,"usgs":false,"family":"Sandoval-Solis","given":"Samuel","email":"","affiliations":[{"id":7082,"text":"University of California - Davis","active":true,"usgs":false}],"preferred":false,"id":815006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hecht, Jory Seth 0000-0002-9485-3332","orcid":"https://orcid.org/0000-0002-9485-3332","contributorId":257771,"corporation":false,"usgs":true,"family":"Hecht","given":"Jory","email":"","middleInitial":"Seth","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":815007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Zyl, Jakobus E.","contributorId":257774,"corporation":false,"usgs":false,"family":"van Zyl","given":"Jakobus","email":"","middleInitial":"E.","affiliations":[{"id":52116,"text":"Univ. of Auckland","active":true,"usgs":false}],"preferred":false,"id":815008,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McMahon, George F.","contributorId":257776,"corporation":false,"usgs":false,"family":"McMahon","given":"George","email":"","middleInitial":"F.","affiliations":[{"id":36715,"text":"Arcadis","active":true,"usgs":false}],"preferred":false,"id":815009,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Horsburgh, J. S. 0000-0002-0768-3196","orcid":"https://orcid.org/0000-0002-0768-3196","contributorId":248851,"corporation":false,"usgs":false,"family":"Horsburgh","given":"J. S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":815010,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kasprzyk, Joseph R. 0000-0002-6344-6478","orcid":"https://orcid.org/0000-0002-6344-6478","contributorId":257779,"corporation":false,"usgs":false,"family":"Kasprzyk","given":"Joseph","email":"","middleInitial":"R.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":815011,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tarboton, David G. 0000-0002-1998-3479","orcid":"https://orcid.org/0000-0002-1998-3479","contributorId":257780,"corporation":false,"usgs":false,"family":"Tarboton","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":815012,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70217211,"text":"70217211 - 2020 - Multilocus metabarcoding of terrestrial leech bloodmeal iDNA increases species richness uncovered in surveys of vertebrate host biodiversity","interactions":[],"lastModifiedDate":"2021-01-13T13:15:51.987522","indexId":"70217211","displayToPublicDate":"2020-12-31T07:13:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2414,"text":"Journal of Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Multilocus metabarcoding of terrestrial leech bloodmeal iDNA increases species richness uncovered in surveys of vertebrate host biodiversity","docAbstract":"Leech-derived invertebrate DNA (iDNA) has been successfully leveraged to conduct surveys of vertebrate host biodiversity across the Indo Pacific. However, this technique has been limited methodologically, typically only targeting mammalian 16S rDNA, or both 16S and vertebrate 12S rDNA for leech host determination. To improve the taxonomic richness of vertebrate host species in iDNA surveys, we re-analyze datasets from Bangladesh, Cambodia, China, and Madagascar through metabarcoding via next generation sequencing (NGS) of 12S, 16S (2 types, one designed to target mammals and the other, residual eDNA), nicotinamide adenine dinucleotide hydride dehydrogenase 2 (ND2), and cytochrome c oxidase subunit 1 (COI). With our 5 primer sets, we identify 41 unique vertebrate hosts to the species level, among 1,200 leeches analyzed, along with an additional 13 taxa to the family rank. Within our 41 taxa, we note that adding ND2 and COI loci increased species richness detection by 25%. NGS has emerged as more efficient than Sanger sequencing for large scale metabarcoding applications and, with the decline in cost of NGS, our pooled sample multilocus protocol is an attractive option for iDNA biodiversity surveys.
The Washita River alluvial aquifer is a valley-fill and terrace alluvial aquifer along the valley of the Washita River in western Oklahoma that provides a productive source of groundwater for agricultural irrigation and water supply. The Oklahoma Water Resources Board (OWRB) has designated the westernmost section of the aquifer in Roger Mills and Custer Counties, Okla., as reach 1 of the Washita River alluvial aquifer; reach 1 is the focus of this report. The OWRB issued an order on November 13, 1990, that established the maximum annual yield (MAY; 120,320 acre-feet per year [acre-ft/yr]) and equal-proportionate-share (EPS) pumping rate (2.0 acre-feet per acre per year [(acre-ft/acre)/yr]) for reach 1 of the Washita River alluvial aquifer. The MAY and EPS were based on hydrologic investigations that evaluated the effects of potential groundwater withdrawals on groundwater availability in the Washita River alluvial aquifer. Every 20 years, the OWRB is statutorily required to update the hydrologic investigation on which the MAY and EPS were based. Because 30 years have elapsed since the last order was issued, the U.S. Geological Survey, in cooperation with the OWRB, conducted a new hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in the Washita River alluvial aquifer.
The Washita River is the primary source of inflow to Foss Reservoir, a Bureau of Reclamation reservoir constructed in 1961 for flood control, water supply, and recreation. Foss Reservoir provides water for Bessie, Clinton, New Cordell, and Hobart, Okla. Nearly 98 percent of the total groundwater use from the Washita River alluvial aquifer during 1967 to 2015 was for irrigation; other uses of groundwater in the study area include public supply, mining, and agriculture.
A hydrogeologic framework was developed for the Washita River alluvial aquifer and included the physical characteristics of the aquifer, the geologic setting, the hydraulic properties of hydrogeologic units, the potentiometric surface (water table), and groundwater-flow directions at a scale that captures the regional controls on groundwater flow. The Washita River alluvial aquifer consists of alluvium and terrace deposits that were transported primarily by water and range from clay to gravel in size. The terrace includes windblown deposits of silt size and, in some cases, contains gravel laid down at several levels along former courses of present-day rivers.
A conceptual flow model is a simplified description of the aquifer system that includes hydrologic boundaries, major inflow and outflow sources of the groundwater-flow system, and a conceptual water budget with the estimated mean flows between those hydrologic boundaries. During the study period 1980–2015, mean annual groundwater withdrawals, predominantly used for agricultural irrigation, totaled 5,502 acre-ft/yr, or 14 percent of aquifer outflows. When applied across the 132-square-mile aquifer area used for modeling purposes (84,366 acres), mean annual recharge of 3.15 inches per year corresponds to a mean annual recharge volume of 22,169 acre-ft/yr, or 56 percent of aquifer inflows. The annual saturated-zone evapotranspiration outflow was 11,828 acre-ft/yr for the Washita River alluvial aquifer, or about 30 percent of aquifer outflows. For the Washita River alluvial aquifer, lateral flow was 17,157 acre-ft/yr, or 44 percent of the aquifer inflows. The conceptual flow model and hydrogeologic framework were used to conceptualize, design, and build the numerical groundwater-flow model.
A numerical groundwater-flow model of the Washita River alluvial aquifer was constructed by using MODFLOW-2005. The Washita River alluvial aquifer groundwater-model grid was spatially discretized into 350-foot (ft) cells and two layers. Layer 1 represented the undifferentiated alluvium and terrace deposits of Quaternary age, and layer 2 represented the bedrock of Permian age, which was given a uniform nominal thickness of 100 ft. The groundwater-simulation period was temporally discretized into 433 monthly transient stress periods, representing January 1980 to December 2015. An initial 365-day steady-state stress period was configured to represent mean annual inflows and outflows from the Washita River alluvial aquifer for the study period. The groundwater-flow model was calibrated manually and by automated adjustment of model inputs by using PEST++. Calibration targets for the Washita River alluvial aquifer model included groundwater-level observations and reservoir-stage observations, as well as base-flow and stream-seepage estimates.
Three groundwater-availability scenarios were used in the calibrated groundwater model to (1) estimate the EPS pumping rate that retains the saturated thickness that meets the minimum 20-year life of the aquifer, (2) quantify the effects of projected pumping rates on groundwater storage over a 50-year period, and (3) evaluate how projected pumping rates extended 50 years into the future and sustained hypothetical drought conditions over a 10-year period affect base flow and groundwater in storage. The results of the groundwater-availability scenarios could be used by the OWRB to reevaluate the established MAY of groundwater from the Washita River alluvial aquifer.
EPS scenarios for the Washita River alluvial aquifer were run for periods of 20, 40, and 50 years. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were 1.7, 1.6, and 1.6 (acre-ft/acre)/yr, respectively. Given the aquifer area used for modeling purposes (84,366 acres), these rates correspond to annual yields of 142,579, 134,986, and 134,986 acre-ft/yr, respectively. Groundwater storage at the end of the 20-year EPS scenario was about 281,000 acre-feet (acre-ft), or about 306,000 acre-ft (52 percent) less than the starting storage. Considering the land-surface area of the Washita River alluvial aquifer and using a specific yield of 0.12, this decrease in storage was equivalent to a mean groundwater-level decline of about 30 ft. The Washita River downstream from Foss Reservoir and most of the streams in the study area were dry at the end of the 20-year EPS scenario. Foss Reservoir stage was below the dead-pool stage of 1,597 ft after about 7 years of pumping in the 20-year EPS scenario.
Four projected 50-year groundwater-use scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage in the Washita River alluvial aquifer. These four scenarios used (1) no groundwater use, (2) groundwater use at the 2015 pumping rate, (3) mean groundwater use for the simulation period, and (4) increasing groundwater use. Groundwater storage after 50 years with no groundwater use was 545,249 acre-ft, or 693 acre-ft (0.1 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean groundwater-level increase of 0.1 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 543,831 acre-ft, or 723 acre-ft (0.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage after 50 years with the mean pumping rate for the study period was 543,202 acre-ft, or 1,349 acre-ft (0.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage at the end of the 50-year period with an increasing demand groundwater-pumping rate, which was 38 percent greater than the 2015 groundwater-pumping rate, was 542,584 acre-ft, or 1,967 acre-ft (0.4 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.2 ft.
A hypothetical 10-year-drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage in the Washita River alluvial aquifer and Foss Reservoir stage and storage. To simulate the hypothetical drought, recharge in the calibrated model was reduced by 50 percent during the simulated drought period (1983–1992). Groundwater storage at the end of the drought period in December 1992 was 562,000 acre-ft, or 36,000 acre-ft (6 percent) less than the groundwater storage of the calibrated groundwater model (598,000 acre-ft). At the end of the hypothetical drought, the largest changes in saturated thickness (as great as 43.5 ft) were in the area upgradient from Foss Reservoir, particularly in the terrace at the model boundary. Substantial decreases in the Foss Reservoir stage began during the fall of 1985 in conjunction with base-flow decreases of up to 100 percent at U.S. Geological Survey streamgage 07324200 Washita River near Hammon, Okla. These lake-stage declines outpaced groundwater-level declines in the surrounding aquifer. The minimum Foss Reservoir storage simulated during the drought period was 77,954 acre-ft, which was a decrease of 46 percent from the nondrought storage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205118","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Ellis, J.H., Ryter, D.W., Fuhrig, L.T., Spears, K.W., Mashburn, S.L., and Rogers, I.M.J., 2020, Hydrogeology, numerical simulation of groundwater flow, and effects of future water use and drought for reach 1 of the Washita River alluvial aquifer, Roger Mills and Custer Counties, western Oklahoma, 1980–2015: U.S. Geological Survey Scientific Investigations Report 2020–5118, 81 p., https://doi.org/10.3133/sir20205118.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-116035","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":381399,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PKMG6U","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used in simulation of groundwater flow, and analysis of projected water use for the Washita River alluvial aquifer, western Oklahoma"},{"id":381398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5118/sir20205118.pdf","text":"Report","size":"18.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5118"},{"id":381397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5118/coverthb.jpg"}],"country":"United States","state":"Oklahoma","county":"Roger Mills County, Custer County","otherGeospatial":"Washita River alluvial aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.6305,35.812],[-98.6308,35.6387],[-98.6307,35.552],[-98.6199,35.552],[-98.6209,35.4639],[-98.8338,35.4653],[-98.9399,35.4659],[-99.0455,35.4654],[-99.1517,35.4658],[-99.3629,35.4649],[-99.3631,35.508],[-99.5755,35.5085],[-99.576,35.42],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-99.9742,35.8921],[-99.9566,35.8959],[-99.947,35.9009],[-99.938,35.9037],[-99.9272,35.9074],[-99.9228,35.9115],[-99.9177,35.9175],[-99.9132,35.9234],[-99.911,35.928],[-99.9082,35.9325],[-99.9049,35.9371],[-99.9038,35.9462],[-99.9045,35.9562],[-99.9051,35.9589],[-99.899,35.9698],[-99.894,35.9748],[-99.8522,36.0051],[-99.8398,36.0115],[-99.829,36.0107],[-99.8227,36.0089],[-99.8152,36.0026],[-99.8078,35.9949],[-99.8019,35.9827],[-99.8019,35.9737],[-99.8051,35.9618],[-99.809,35.9518],[-99.8111,35.9364],[-99.8099,35.9287],[-99.8087,35.9246],[-99.8007,35.9174],[-99.7938,35.9102],[-99.788,35.8962],[-99.784,35.8921],[-99.7725,35.8867],[-99.76,35.885],[-99.7521,35.8824],[-99.7372,35.8738],[-99.7258,35.8653],[-99.7189,35.8626],[-99.7149,35.854],[-99.6979,35.855],[-99.6774,35.847],[-99.6615,35.847],[-99.6558,35.8457],[-99.6416,35.8444],[-99.6291,35.84],[-99.6149,35.84],[-99.6042,35.8478],[-99.6002,35.8519],[-99.5929,35.8551],[-99.5855,35.8574],[-99.577,35.8588],[-99.5623,35.8621],[-99.5578,35.8675],[-99.5562,35.8825],[-99.5416,35.903],[-99.532,35.9076],[-99.5241,35.9185],[-99.5156,35.9281],[-99.5067,35.9481],[-99.5061,35.9535],[-99.5085,35.9608],[-99.5085,35.9649],[-99.5045,35.9703],[-99.4995,35.974],[-99.4876,35.9795],[-99.4785,35.9899],[-99.4638,35.9995],[-99.4445,36.01],[-99.4303,36.016],[-99.4144,36.0169],[-99.3928,36.017],[-99.3809,36.017],[-99.3808,35.8991],[-99.374,35.8991],[-99.3736,35.8111],[-99.0571,35.8112],[-98.7366,35.8118],[-98.6305,35.812]]]},\"properties\":{\"name\":\"Custer\",\"state\":\"OK\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Arey Lagoon, located in eastern Arctic Alaska, supports a highly productive ecosystem, where soft substrate and coastal wet sedge fringing the shores are feeding grounds and nurseries for a variety of marine fish and waterfowl. The lagoon is partially protected from the direct onslaught of Arctic Ocean waves by a barrier island chain (Arey Island) which in itself provides important habitat for migratory shorebirds and waterfowl. In this work, numerically modeled waves and water levels are computed under the provision of sea-level rise and changing conditions brought about by 21st century climate variability. Model results, supported by observations, are used to assess the stability of the barrier chain and spatiotemporal changes in flood patterns across fringing coastal wet sedge areas. The results aim to support studies that investigate the possibility of new biological succession trajectories and loss or increase of habitat areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201142","collaboration":"Prepared in cooperation with and funded in part by the Arctic Landscape Conservation Cooperation (ALCC)","usgsCitation":"Erikson, L.H., Gibbs, A.E., Richmond, B.M., Storlazzi, C.D., Jones, B.M., and Ohman, K.A., 2020, Changing storm conditions in response to projected 21st century climate change and the potential impact on an arctic barrier island–lagoon system—A pilot study for Arey Island and Lagoon, eastern Arctic Alaska: U.S. Geological Survey Open-File Report 2020–1142, 68, p., https://doi.org/10.3133/ofr20201142.","productDescription":"Report: x, 68 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-079323","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":381735,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGYO2Q","text":"USGS data release","linkHelpText":"Modeled 21st century storm surge, waves, and coastal flood hazards and supporting oceanographic and geological field data (2010 and 2011) for Arey and Barter Islands, Alaska and vicinity"},{"id":381739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1142/coverthb.jpg"},{"id":381740,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1142/ofr20201142.pdf","text":"Report","size":"8.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1142"}],"country":"United States","state":"Alaska","otherGeospatial":"Arey Island and Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -144.09805297851562,\n 70.03559723423488\n ],\n [\n -143.6407470703125,\n 70.03559723423488\n ],\n [\n -143.6407470703125,\n 70.13476515043729\n ],\n [\n -144.09805297851562,\n 70.13476515043729\n ],\n [\n -144.09805297851562,\n 70.03559723423488\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060