Millette et al. (Ecology Letters, 2020, 23:55–67) reported no consistent worldwide anthropogenic effects on animal genetic diversity using repurposed mitochondrial DNA sequences. We reexamine data from this study, describe genetic marker and scale limitations which might lead to misinterpretations with conservation implications, and provide advice to improve future macrogenetic studies.
Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data-intensive methods such as kriging performing better than 3 more scalable methods involving post-processing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens, less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, -0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13683","usgsCitation":"Holmquist, J., Schile-Beers, L., Buffington, K., Lu, M., Mozdzer, T.J., Riera, J., Weller, D.E., Williams, M., and Megonigal, J., 2021, Scalability and performance tradeoffs in quantifying relationships between elevation and tidal wetland plant communities: Marine Progress Series, v. 666, p. 57-72, https://doi.org/10.3354/meps13683.","productDescription":"16 p.","startPage":"57","endPage":"72","ipdsId":"IP-127317","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":452993,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps13683","text":"Publisher Index Page"},{"id":414537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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College","active":true,"usgs":false}],"preferred":false,"id":867020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riera, Jefferson","contributorId":303282,"corporation":false,"usgs":false,"family":"Riera","given":"Jefferson","email":"","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":867021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weller, Donald E.","contributorId":199780,"corporation":false,"usgs":false,"family":"Weller","given":"Donald","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":867022,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williams, Meghan","contributorId":303283,"corporation":false,"usgs":false,"family":"Williams","given":"Meghan","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":867023,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Megonigal, J Patrick","contributorId":228820,"corporation":false,"usgs":false,"family":"Megonigal","given":"J Patrick","affiliations":[{"id":41515,"text":"Smithsonian Env Res Ctr","active":true,"usgs":false}],"preferred":false,"id":867024,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219519,"text":"70219519 - 2021 - A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems","interactions":[],"lastModifiedDate":"2021-06-30T18:00:05.022669","indexId":"70219519","displayToPublicDate":"2021-03-21T08:48:19","publicationYear":"2021","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":"A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems","docAbstract":"Groundwater models have evolved to encompass more aspects of the water cycle, but the incorporation of realistic boundary conditions representing surface water remains time-consuming and error-prone. We present two Python packages that robustly automate this process using readily available hydrography data as the primary input. SFRmaker creates input for the MODFLOW SFR package, while Linesink-maker creates linesink string input for the GFLOW analytic element program. These programs can reduce weeks or even months of manual effort to a few minutes of execution time, and carry the added advantages of reduced potential for error, improved reproducibility and facilitation of step-wise modeling through reduced dependency on a particular conceptual model or discretization. Two real-world examples at the county to multi-state scales are presented.
The U.S. Geological Survey monitors a suite of intertidal black abalone sites at San Nicolas Island, California, in cooperation with the U.S. Navy, which owns the island. The nine rocky intertidal sites were established in 1980 to study the potential impact of translocated sea otters on the intertidal black abalone population at the island. The sites were monitored from 1981 to 1997, usually annually or biennially. Monitoring resumed in 2001 and has been completed annually since then. At the time of this report, the work is conducted by the Western Ecological Research Center’s Santa Cruz Field Station, Santa Cruz, California. The study sites became particularly important, from a management perspective, after a virulent disease decimated black abalone populations throughout southern California beginning in the mid-1980s. The disease, withering syndrome, was first observed on San Nicolas Island in 1992 and during the next few years, it reduced the population there by more than 99 percent. The species was subsequently listed as endangered under the Endangered Species Act in 2009.
The subject of this report is the 2020 monitoring cycle of the sites and how the current status fits into the long-term data at San Nicolas Island. Since 2001, the monitored population has increased twelvefold to approximately 9.6 percent of the pre-disease level. This increase has resulted from generally higher levels of recruitment than seen in the first two decades of monitoring, punctuated by a few unexplained high recruitment events. Most of the population growth has been at two of the nine sites (sites 7 and 8). This pattern continued in 2020, but with increasing numbers at all sites and the highest number of abalone counted and measured island-wide since 1993. Recruitment rates have fallen since a peak in 2017, but 2020 continued to show moderate levels of additional recruitment. The distance between adjacent black abalone has decreased substantially since it was first consistently measured in 2005, potentially indicating that the abalone are close enough to one another to reproduce successfully. Sand burial can have devastating localized consequences to black abalone, but there is evidence suggesting that they may be able to escape periodic sand inundation if suitable refugia exist. These data suggest that monitoring can inform adaptive management of the resource by base resource managers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211023","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., 2021, Black abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2020, annual report: U.S. Geological Survey Open-File Report 2021–1023, 33 p., https://doi.org/10.3133/ofr20211023.","productDescription":"vii, 33 p.","numberOfPages":"33","onlineOnly":"Y","ipdsId":"IP-125069","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":384507,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1023/covrthb.jpg"},{"id":384508,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1023/ofr20211023.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384509,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1023/ofr20211023.xml"},{"id":384510,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1023/images"}],"country":"United States","state":"California","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.59442138671875,\n 33.20996748987798\n ],\n [\n -119.42928314208984,\n 33.20996748987798\n ],\n [\n -119.42928314208984,\n 33.28806392819752\n ],\n [\n -119.59442138671875,\n 33.28806392819752\n ],\n [\n -119.59442138671875,\n 33.20996748987798\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
With declining capture fisheries production, maintaining nutrient supplies largely hinges on substituting wild fish with economically comparable farmed animals. Although such transitions are increasingly commonplace across global inland and coastal communities, their nutritional consequences are unknown. Here, using human demographic and health information, and fish nutrient composition data from the Peruvian Amazon, we show that substituting wild inland fisheries with chicken and aquaculture has the potential to exacerbate iron deficiencies and limit essential fatty acid supplies in a region already experiencing high prevalence of anaemia and malnutrition. Substituting wild fish with chicken, however, can increase zinc and protein supplies. Chicken and aquaculture production also increase greenhouse gas emissions, agricultural land use and eutrophication. Thus, policies that enable access to wild fisheries and their sustainable management while improving the quality, diversity and environmental impacts of farmed species will be instrumental in ensuring healthy and sustainable food systems.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s43016-021-00242-8","usgsCitation":"Heilpern, S., Fiorella, K., Canas, C., Flecker, A., Moya, L., Naeem, S., Sethi, S., Uriarte, M., and DeFries, R., 2021, Substitution of inland fisheries with aquaculture and chicken undermines human nutrition in the Peruvian Amazon: Nature Food, v. 2, p. 192-197, https://doi.org/10.1038/s43016-021-00242-8.","productDescription":"6 p.","startPage":"192","endPage":"197","ipdsId":"IP-121798","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":396547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","otherGeospatial":"Peruvian Amazon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.53125,\n -8.363692651835823\n ],\n [\n -73.212890625,\n -4.915832801313164\n ],\n [\n -70.927734375,\n -4.083452772038619\n ],\n [\n -70.8837890625,\n -2.85526278436657\n ],\n [\n -74.794921875,\n -0.8349313860427057\n ],\n [\n -77.51953125,\n -0.08789059053082422\n ],\n [\n -78.046875,\n -2.8991526985043006\n ],\n [\n -77.5634765625,\n -5.703447982149503\n ],\n [\n -75.1904296875,\n -8.102738577783168\n ],\n [\n -74.53125,\n -8.363692651835823\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2021-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Heilpern, Sebastian A.","contributorId":287013,"corporation":false,"usgs":false,"family":"Heilpern","given":"Sebastian A.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":836433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fiorella, Kathryn","contributorId":287014,"corporation":false,"usgs":false,"family":"Fiorella","given":"Kathryn","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":836434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Canas, Carlos","contributorId":287015,"corporation":false,"usgs":false,"family":"Canas","given":"Carlos","email":"","affiliations":[{"id":34928,"text":"Independent Researcher","active":true,"usgs":false}],"preferred":false,"id":836435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flecker, Alexander S.","contributorId":287016,"corporation":false,"usgs":false,"family":"Flecker","given":"Alexander S.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":836436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moya, Luis","contributorId":287017,"corporation":false,"usgs":false,"family":"Moya","given":"Luis","email":"","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":836437,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Naeem, Shahid","contributorId":287018,"corporation":false,"usgs":false,"family":"Naeem","given":"Shahid","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":836438,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Uriarte, Maria","contributorId":287019,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":836439,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeFries, Ruth","contributorId":287020,"corporation":false,"usgs":false,"family":"DeFries","given":"Ruth","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":836440,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70223703,"text":"70223703 - 2021 - The 2018 update of the US National Seismic Hazard Model: Ground motion models in the central and eastern US","interactions":[],"lastModifiedDate":"2021-09-02T12:58:27.982991","indexId":"70223703","displayToPublicDate":"2021-03-19T07:56:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2018 update of the US National Seismic Hazard Model: Ground motion models in the central and eastern US","docAbstract":"The United States Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous US NSHM includes major changes to the underlying ground motion models (GMMs). Most of the changes are motivated by the new multi-period response spectra requirements of seismic design regulations that use hazard results for 22 spectral periods and 8 site classes. In the central and eastern United States (CEUS), the 2018 NSHM incorporates 31 new GMMs for hard-rock site conditions
The University of Kentucky (U KY) has owned Robinson Forest (37.460723° N, 83.158598° W) since 1923, conducting experiments crucial to understanding the environmental effects of land management in the region. Part of the management of Robinson Forest has been collection of environmental data, including precipitation quantity, bulk‐deposition chemistry, streamflow, stream‐water chemistry, and air and stream temperature. Over the years, these data have been collected and archived using various technologies and have been mostly inaccessible for research use – unedited and uncompiled, scattered across several spreadsheets and paper records. Through a partnership between the U.S. Geological Survey (USGS) and U KY, daily precipitation data for six stations and stream data from four watersheds in Robinson Forest have been compiled for 1971–2018, checked for transcription errors, and annotated for changes in methodologies. These data are available as a USGS data release at https://doi.org/10.5066/P9FPLG1O. Improved accessibility of this data set provides an important research resource for understanding water quality in minimally effected forests in the region. Preliminary results indicate that these data present a valuable opportunity to evaluate linkages among atmospheric deposition and stream chemistry, the effects of environmental policy, such as the Clean Air Act, and effects from nearby land disturbance in the form of surface mining. Furthermore, these data fill a geographic and physiographic gap in what is available to examine deposition and streamflow patterns over the last 45 years, supplementing those long‐term records of research sites in northern (e.g., Hubbard Brook Experimental Forest), central (e.g., Fernow Experimental Forest) and southern Appalachia (e.g., Coweeta Hydrologic Laboratory). As an oasis in the midst of significant surface mining activity, Robinson Forest presents a unique opportunity to understand environmental conditions characteristic of minimally disturbed forests similar to pre‐mining conditions in the Central Appalachian region.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14133","usgsCitation":"Sena, K., Barton, C.D., and Williamson, T.N., 2021, The Robinson Forest environmental monitoring network: Long‐term evaluation of streamflow and precipitation quantity and stream‐water and bulk deposition chemistry in eastern Kentucky watersheds: Hydrological Processes, v. 35, no. 4, e14133, 6 p., https://doi.org/10.1002/hyp.14133.","productDescription":"e14133, 6 p.","ipdsId":"IP-122607","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":385638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"southeast Kentucky","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.27636718749999,\n 36.756490329505176\n ],\n [\n -81.36474609375,\n 36.756490329505176\n ],\n [\n -81.36474609375,\n 37.82280243352756\n ],\n [\n -83.27636718749999,\n 37.82280243352756\n ],\n [\n -83.27636718749999,\n 36.756490329505176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Sena, Kenton 0000-0003-1822-9375","orcid":"https://orcid.org/0000-0003-1822-9375","contributorId":258046,"corporation":false,"usgs":false,"family":"Sena","given":"Kenton","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":815604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barton, Chris D. 0000-0003-0692-3079","orcid":"https://orcid.org/0000-0003-0692-3079","contributorId":236883,"corporation":false,"usgs":false,"family":"Barton","given":"Chris","email":"","middleInitial":"D.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":815605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815606,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219028,"text":"70219028 - 2021 - Organic geochemistry and petrology of Devonian shale in eastern Ohio: Implications for petroleum systems assessment","interactions":[],"lastModifiedDate":"2021-03-22T11:51:02.709168","indexId":"70219028","displayToPublicDate":"2021-03-19T07:07:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":701,"text":"American Association of Petroleum Geologists Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Organic geochemistry and petrology of Devonian shale in eastern Ohio: Implications for petroleum systems assessment","docAbstract":"Recent production of light sweet oil has prompted reevaluation of Devonian petroleum systems in the central Appalachian Basin. Upper Devonian Ohio Shale (lower Huron Member) and Middle Devonian Marcellus Shale organic-rich source rocks from eastern Ohio and nearby areas were examined using organic petrography and geochemical analysis of solvent extracts to test ideas related to organic matter sources, oil–source rock correlation, thermal maturity, and distances of petroleum migration. The data from these analyses indicate organic matter in the Ohio and Marcellus Shales primarily was derived from marine algae and its degradation products, including bacterial biomass. Absence of odd-over-even n-alkane distributions (n-C13 to n-C21 range) in gas chromatograms and low gammacerane index values in Devonian source rocks are similar to those of Devonian-reservoired oils in eastern Ohio, suggesting an oil–source rock correlation. Lower Paleozoic oils from eastern Ohio, in contrast, are characterized by the presence of odd-over-even n-alkane distributions (n-C13 to n-C21 range) and higher gammacerane values, which discriminate them from Devonian shale-derived oils. Thermal maturity estimates from equilibrium(?) biomarker isomerization ratios suggest that some of the Devonian source rock samples are at middle to peak oil window conditions (i.e., approximate vitrinite reflectance values of 0.8%–0.9%). This observation requires local to short-distance (<50 mi) lateral migration for emplacement of Devonian-sourced oils into Devonian reservoirs of eastern Ohio and may impact exploration and assessment of petroleum resources in the Upper Devonian Berea Sandstone.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/08192019076","usgsCitation":"Hackley, P.C., and Ryder, R.T., 2021, Organic geochemistry and petrology of Devonian shale in eastern Ohio: Implications for petroleum systems assessment: American Association of Petroleum Geologists Bulletin, v. 105, no. 3, p. 543-573, https://doi.org/10.1306/08192019076.","productDescription":"31 p.","startPage":"543","endPage":"573","ipdsId":"IP-099052","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":384494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Eastern and central Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.57373046875,\n 41.97582726102573\n ],\n [\n -82.06787109374999,\n 41.5579215778042\n ],\n [\n -82.85888671875,\n 41.46742831254425\n ],\n [\n -82.94677734375,\n 40.763901280945866\n ],\n [\n -82.90283203125,\n 39.791654835253425\n ],\n [\n -82.935791015625,\n 38.75408327579141\n ],\n [\n -82.68310546875,\n 38.831149809348744\n ],\n [\n -82.584228515625,\n 40.17887331434696\n ],\n [\n -82.59521484375,\n 41.1455697310095\n ],\n [\n -80.518798828125,\n 41.73852846935917\n ],\n [\n -80.57373046875,\n 41.97582726102573\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":812493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ryder, Robert T. rryder@usgs.gov","contributorId":211801,"corporation":false,"usgs":false,"family":"Ryder","given":"Robert","email":"rryder@usgs.gov","middleInitial":"T.","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":false,"id":812494,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219029,"text":"70219029 - 2021 - Organic petrology and geochemistry of the Sunbury and Ohio Shales in eastern Kentucky and southeastern Ohio","interactions":[],"lastModifiedDate":"2021-03-22T11:51:52.617715","indexId":"70219029","displayToPublicDate":"2021-03-19T07:03:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Organic petrology and geochemistry of the Sunbury and Ohio Shales in eastern Kentucky and southeastern Ohio","docAbstract":"As part of a study to determine the origin of oil and gas in the Berea Sandstone in northeastern Kentucky and southeastern Ohio, 158 samples of organic-rich shale from the Upper Devonian Olentangy and Ohio Shales and the Lower Mississippian Sunbury Shale, collectively referred to as the “black shale,” were collected and analyzed from 12 cores. The samples were analyzed for total organic carbon (TOC) content, organic petrography, and programmed pyrolysis. Previously acquired analytical data for 11 samples from 2 additional wells in eastern Kentucky were also used.
Most of the samples were organic rich (>5 wt. % TOC), high in sulfur (>2.0 wt. %), and dominated by liptinite macerals. The vitrinite reflectance (VRo) and equivalent vitrinite reflectance (VReq) values, calculated from bitumen reflectance (BRo) measurements, were found to be in close agreement. The calculated reflectance values from programmed pyrolysis temperature at which the maximum release of hydrocarbons occurs (Tmax) showed better agreement with measured VRo after Tmax was corrected for excessive hydrogen index values for several samples. Thermal maturation parameters were found to increase in a northwest–southeast direction, paralleling an increase in black shale thickness and depth of burial. The thermal maturity proxies indicate the northwestern part of the study area to be more thermally mature than previously indicated. Geochemical and biomarker data from Berea oils indicate migration of oil from more thermally mature to less thermally mature areas. As such, the occurrence of petroleum liquids in the Berea Sandstone cannot be predicted directly from conventional thermal maturity proxies (Tmax, VRo, and BRo) because these methods do not account for migrated petroleum.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/09242019089","usgsCitation":"Eble, C.F., Hackley, P.C., Parris, T.M., and Greb, S.F., 2021, Organic petrology and geochemistry of the Sunbury and Ohio Shales in eastern Kentucky and southeastern Ohio: AAPG Bulletin, v. 105, no. 3, p. 493-515, https://doi.org/10.1306/09242019089.","productDescription":"23 p.","startPage":"493","endPage":"515","ipdsId":"IP-100494","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":384493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio, Kentucky","otherGeospatial":"Eastern Kentucky and southeastern Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.814453125,\n 39.21523130910491\n ],\n [\n -84.990234375,\n 37.49229399862877\n ],\n [\n -82.41943359375,\n 37.31775185163688\n ],\n [\n -81.93603515625,\n 37.579412513438385\n ],\n [\n -82.6171875,\n 38.09998264736481\n ],\n [\n -82.50732421875,\n 38.788345355085625\n ],\n [\n -82.4853515625,\n 39.605688178320804\n ],\n [\n -84.83642578125,\n 39.740986355883564\n ],\n [\n -84.814453125,\n 39.21523130910491\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eble, Cortland F.","contributorId":255518,"corporation":false,"usgs":false,"family":"Eble","given":"Cortland","email":"","middleInitial":"F.","affiliations":[{"id":51568,"text":"Kentucky Geological Survey, U. of Kentucky","active":true,"usgs":false}],"preferred":false,"id":812495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parris, Thomas M.","contributorId":255526,"corporation":false,"usgs":false,"family":"Parris","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greb, Stephen F.","contributorId":255517,"corporation":false,"usgs":false,"family":"Greb","given":"Stephen","email":"","middleInitial":"F.","affiliations":[{"id":51568,"text":"Kentucky Geological Survey, U. of Kentucky","active":true,"usgs":false}],"preferred":false,"id":812498,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219034,"text":"70219034 - 2021 - Oil–source correlation studies in the shallow Berea Sandstone petroleum system, eastern Kentucky","interactions":[],"lastModifiedDate":"2021-03-22T11:52:22.896868","indexId":"70219034","displayToPublicDate":"2021-03-19T06:49:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":701,"text":"American Association of Petroleum Geologists Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Oil–source correlation studies in the shallow Berea Sandstone petroleum system, eastern Kentucky","docAbstract":"Shallow production of sweet high-gravity oil from the Upper Devonian Berea Sandstone in northeastern Kentucky has caused the region to become the leading oil producer in the state. Potential nearby source rocks, namely, the overlying Mississippian Sunbury Shale and underlying Ohio Shale, are immature for commercial oil generation according to vitrinite reflectance and programmed pyrolysis analyses. We used organic geochemical measurements from Berea oils and solvent extracts from potential Upper Devonian–Mississippian source rocks to better understand organic matter sources, oil–oil and oil–source rock correlations, and thermal maturity in the shallow Berea oil play. Multiple geochemical proxies suggest Berea oils are from one family and from similar source rocks. Oils and organic matter in the potential source rocks are from a marine source based on pristane-to-phytane (Pr/Ph) and terrestrial-to-aquatic ratios, carbon preference index values, n-alkane maxima, C-isotopic composition, and tricyclic terpane and hopane ratios. Any or all of the Devonian to Mississippian black shale source rocks could be potential source rocks for Berea oils based on similarities in oil and solvent extract Pr/n-C17 and Ph/n-C18 ratios, sterane distributions, C-isotopic values, and sterane/hopane and tricyclic terpane ratios. Multiple biomarker ratios suggest Berea oils formed at thermal maturities of approximately 0.7% –0.9% vitrinite reflectance. These data require significant updip lateral migration of 30–50 mi from a downdip Devonian black shale source kitchen to emplace low-sulfur oils in the shallow updip oil-play area and indicate that immature source rocks nearby to Berea oil production are not contributing to produced hydrocarbons.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/08192019077","usgsCitation":"Hackley, P.C., Parris, T., Eble, C.F., Greb, S.F., and Harris, D., 2021, Oil–source correlation studies in the shallow Berea Sandstone petroleum system, eastern Kentucky: American Association of Petroleum Geologists Bulletin, v. 105, no. 3, p. 517-542, https://doi.org/10.1306/08192019077.","productDescription":"26 p.","startPage":"517","endPage":"542","ipdsId":"IP-098811","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":384491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Northeast Kentucky","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.671875,\n 38.74551518488265\n ],\n [\n -83.81469726562499,\n 37.95286091815649\n ],\n [\n -83.001708984375,\n 37.448696585910376\n ],\n [\n -82.2216796875,\n 37.709899354855125\n ],\n [\n -82.562255859375,\n 38.05674222065296\n ],\n [\n -82.562255859375,\n 38.47079371120379\n ],\n [\n -82.90283203125,\n 38.805470223177466\n ],\n [\n -83.177490234375,\n 38.62545397209084\n ],\n [\n -83.671875,\n 38.74551518488265\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":812510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parris, T.M.","contributorId":255535,"corporation":false,"usgs":false,"family":"Parris","given":"T.M.","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eble, C. F.","contributorId":255536,"corporation":false,"usgs":false,"family":"Eble","given":"C.","email":"","middleInitial":"F.","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greb, S. F.","contributorId":255538,"corporation":false,"usgs":false,"family":"Greb","given":"S.","email":"","middleInitial":"F.","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harris, D.C.","contributorId":255540,"corporation":false,"usgs":false,"family":"Harris","given":"D.C.","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812514,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219046,"text":"70219046 - 2021 - Whole‐genome resequencing reveals persistence of forest‐associated mammals in Late Pleistocene refugia along North America’s North Pacific Coast","interactions":[],"lastModifiedDate":"2021-05-13T15:51:18.012368","indexId":"70219046","displayToPublicDate":"2021-03-18T08:23:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Whole‐genome resequencing reveals persistence of forest‐associated mammals in Late Pleistocene refugia along North America’s North Pacific Coast","docAbstract":"Numerous glacial refugia have been hypothesized along North America's North Pacific Coast that may have increased divergence of refugial taxa, leading to elevated endemism and subsequently clustered hybrid zones following deglaciation. The locations and community composition of these ice‐free areas remains controversial, but whole‐genome sequences now enable detailed analysis of the demographic and evolutionary histories of refugial taxa. Here, we use genomic data to test spatial and temporal processes of diversification among martens with respect to the Coastal Refugium Hypothesis, to understand the role of climate cycling in shaping diversity across complex landscapes.
North America and North Pacific Coast archipelagos.
North American martens (Martes).
Short‐read whole‐genome resequencing data were generated for 11 martens: four M. americana, four M. caurina, two hybrids, and one outgroup (Martes zibellina). Sampling was representative of known genetic clades within New World martens, including sampling within insular and continental hybrid zones and along the North Pacific Coast (five island populations). ADMIXTURE, F‐statistics, and D‐statistics (ABBA‐BABA) were used to identify introgression and infer directionality. Heterozygosity densities, estimated via PSMC, were used to characterize historical demography at and below the species level to infer refugial and colonization processes.
Forest‐associated Pacific martens (M. caurina) are divided into distinct insular and continental clades consistent with the Coastal Refugium Hypothesis. There was no evidence of introgression on islands that received historical translocations of American pine martens (M. americana), but introgression was detected in two active zones of secondary contact: one insular and one continental. Only early‐generational hybrids were identified across multiple hybrid zones, a pattern consistent with potential genetic swamping of M. caurina by M. americana.
Despite an incomplete fossil record, genomic evidence supports the persistence of forest‐associated martens, likely the insular Pacific marten lineage, along the western edges of the Alexander Archipelago during the Last Glacial Maximum. This discovery informs our understanding of refugial paleoenvironments, critical to interpreting refugial timing, duration, and community composition. Genomic reevaluations of other taxa along North America's North Pacific Coast may yield new and deeper perspectives on the history of refugial forest communities and the role of dynamic climate shifts in shaping high‐latitude diversity across complex insular landscapes.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.14068","usgsCitation":"Colella, J.P., Lan, T., Talbot, S.L., Lindqvist, C., and Cook, J.A., 2021, Whole‐genome resequencing reveals persistence of forest‐associated mammals in Late Pleistocene refugia along North America’s North Pacific Coast: Journal of Biogeography, v. 48, no. 5, p. 1153-1169, https://doi.org/10.1111/jbi.14068.","productDescription":"17 p.","startPage":"1153","endPage":"1169","ipdsId":"IP-117301","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia, California, Oregon, Washington, Yukon","otherGeospatial":"North Pacific Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.796875,\n 61.438767493682825\n ],\n [\n -165.58593749999997,\n 53.54030739150022\n ],\n [\n -154.68749999999997,\n 55.3791104480105\n ],\n [\n -147.3046875,\n 58.07787626787517\n ],\n [\n -139.5703125,\n 56.559482483762245\n ],\n [\n -129.375,\n 48.45835188280866\n ],\n [\n -127.61718749999999,\n 38.272688535980976\n ],\n [\n -121.640625,\n 38.54816542304656\n ],\n [\n -120.9375,\n 47.754097979680026\n ],\n [\n -128.671875,\n 58.07787626787517\n ],\n [\n -138.515625,\n 62.75472592723178\n ],\n [\n -156.796875,\n 61.438767493682825\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Colella, Jocelyn P.","contributorId":190332,"corporation":false,"usgs":false,"family":"Colella","given":"Jocelyn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":812554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lan, Tianying","contributorId":207037,"corporation":false,"usgs":false,"family":"Lan","given":"Tianying","email":"","affiliations":[{"id":37434,"text":"Department of Biological Scineces, State University of New York at Buffalo","active":true,"usgs":false}],"preferred":false,"id":812555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","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":812556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindqvist, Charlotte","contributorId":207038,"corporation":false,"usgs":false,"family":"Lindqvist","given":"Charlotte","email":"","affiliations":[{"id":37434,"text":"Department of Biological Scineces, State University of New York at Buffalo","active":true,"usgs":false}],"preferred":false,"id":812557,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Joseph A.","contributorId":8323,"corporation":false,"usgs":false,"family":"Cook","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":812558,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219109,"text":"70219109 - 2021 - Evaluating a laboratory flume microbiome as a window into natural riverbed biogeochemistry","interactions":[],"lastModifiedDate":"2021-03-24T12:13:12.940737","indexId":"70219109","displayToPublicDate":"2021-03-18T07:10:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating a laboratory flume microbiome as a window into natural riverbed biogeochemistry","docAbstract":"Riverbeds are hotspots for microbially-mediated reactions that exhibit pronounced variability in space and time. It is challenging to resolve biogeochemical mechanisms in natural riverbeds, as uncontrolled settings complicate data collection and interpretation. To overcome these challenges, laboratory flumes are often used as proxies for natural riverbed systems. Flumes capture spatiotemporal variability and thus allow for controlled investigations of riverbed biogeochemistry. These investigations implicitly rely on the assumption that the flume microbiome is similar to the microbiome of natural riverbeds. However, this assumption has not been tested and it is unknown how the microbiome of a flume compares to natural aquatic settings, including riverbeds. To evaluate the fundamental assumption that a flume hosts a microbiome similar to natural riverbed systems, we used 16s rRNA gene sequencing and publicly available data to compare the sediment microbiome of a single large laboratory flume to a wide variety of natural ecosystems including lake and marine sediments, river, lake, hyporheic, soil, and marine water, and bank and wetland soils. Richness and Shannon diversity metrics, analyses of variance, Bray-Curtis dissimilarity, and analysis of the common microbiomes between flume and river sediment all indicated that the flume microbiome more closely resembled natural riverbed sediments than other ecosystems, supporting the use of flume experiments for investigating natural microbially-mediated biogeochemical processes in riverbeds.
","language":"English","publisher":"Frontiers","doi":"10.3389/frwa.2021.596260","usgsCitation":"Kaufman, M., Warden, J.G., Cardenas, M.B., Stegen, J.C., Graham, E.B., and Brown, J., 2021, Evaluating a laboratory flume microbiome as a window into natural riverbed biogeochemistry: Frontiers in Water, v. 21, no. 3, 12 p., https://doi.org/10.3389/frwa.2021.596260.","productDescription":"12 p.","ipdsId":"IP-096581","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":453034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.596260","text":"Publisher Index Page"},{"id":384625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaufman, Matthew H.","contributorId":255711,"corporation":false,"usgs":false,"family":"Kaufman","given":"Matthew H.","affiliations":[{"id":29861,"text":"The University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":812810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warden, John G. 0000-0003-1384-458X","orcid":"https://orcid.org/0000-0003-1384-458X","contributorId":215846,"corporation":false,"usgs":true,"family":"Warden","given":"John","email":"","middleInitial":"G.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cardenas, M. Bayani","contributorId":181932,"corporation":false,"usgs":false,"family":"Cardenas","given":"M.","email":"","middleInitial":"Bayani","affiliations":[],"preferred":false,"id":812812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stegen, James C.","contributorId":255712,"corporation":false,"usgs":false,"family":"Stegen","given":"James","email":"","middleInitial":"C.","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":812813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graham, Emily B.","contributorId":202683,"corporation":false,"usgs":false,"family":"Graham","given":"Emily","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":812814,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Joseph","contributorId":255713,"corporation":false,"usgs":false,"family":"Brown","given":"Joseph","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":812815,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241892,"text":"70241892 - 2021 - A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions","interactions":[],"lastModifiedDate":"2023-03-30T12:08:50.610487","indexId":"70241892","displayToPublicDate":"2021-03-18T07:06:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions","docAbstract":"Spatial cross-correlation among flood sequences impacts the accuracy of regional predictors. Our study investigates this impact for two regionalization procedures, generalized least squares (GLS) regression and top-kriging (TK), which deal with cross-correlation in two fundamentally different ways and therefore might be associated with different accuracy and uncertainty of predicted flood quantiles. We perform a Monte Carlo experiment based on a dataset of annual maximum flood series for 20 catchments in a hydrologically homogeneous region. Based on a log-Pearson type III parent distribution, we generate 3000 realizations of the region with different degrees of cross-correlation. For each realization, GLS and TK are applied in leave-one-out cross-validation to predict at-site flood quantiles. Our study shows that (a) TK outperforms GLS when catchment area is the only catchment descriptor used for predicting “true” population (theoretical) flood quantiles, regardless of the level of cross-correlation, and (b) GLS and TK perform similarly when multiple catchment descriptors are used.
The 2018 eruption on the lower East Rift Zone of Kīlauea Volcano produced one of the largest and most destructive lava flows in Hawai’i during the past 200 years. Over the course of more than 3 months, twenty-four fissures erupted, and the rate of lava effusion varied by two orders of magnitude, with significant implications for evolving flow behavior and hazards. Syn-eruptive data were collected to quantify these changes in lava effusion rate, including video of flow through channels and digital elevation models acquired using small unoccupied aircraft systems, airborne lidar, and airborne single-pass interferometric synthetic aperture radar. Topographic data through time allowed calculation of subaerial lava flow volume and time-averaged discharge rate over the course of the eruption, which we integrated with pre- and post-eruption bathymetric surveys. Repeat videos of the near-vent channel were analyzed with particle velocimetry to extract flow velocities, and these were combined with open channel flow theory to calculate a time series of instantaneous effusion rates. Results show a general increase in dense rock equivalent (DRE) effusion rate from ~7 to ~100 m3/s from early to late May for the whole flow field and ≥ 200 m3/s by mid-June after the eruption had focused at a primary vent. By the end of the eruption in August, 0.9–1.4 km3 DRE of lava had erupted, with 0.4 km3 deposited on land and at least 0.5 km3 offshore. The trends in effusion rate through time reflect magmatic processes in the connected summit and rift zone system that controlled eruption rate, with resulting implications for lava flow dynamics and hazards.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-021-01443-6","usgsCitation":"Dietterich, H., Diefenbach, A., Soule, S.A., Zoeller, M.H., Patrick, M.R., Major, J., and Lundgren, P., 2021, Lava effusion rate evolution and erupted volume during the 2018 Kīlauea lower East Rift Zone eruption: Bulletin of Volcanology, v. 83, no. 25, 18 p., https://doi.org/10.1007/s00445-021-01443-6.","productDescription":"18 p.","ipdsId":"IP-122554","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488679,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/gsofacpubs/2493","text":"External Repository"},{"id":384747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano, Hawaii volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.0507354736328,\n 19.321511226817176\n ],\n [\n -155.25054931640625,\n 19.369454073094243\n ],\n [\n -155.35011291503906,\n 19.39082944712291\n ],\n [\n -155.4242706298828,\n 19.204186382298897\n ],\n [\n -155.39749145507812,\n 19.191217165341648\n ],\n [\n -155.12832641601562,\n 19.2748506284423\n ],\n [\n -155.0507354736328,\n 19.321511226817176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","issue":"25","noUsgsAuthors":false,"publicationDate":"2021-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Dietterich, Hannah R. 0000-0001-7898-4343","orcid":"https://orcid.org/0000-0001-7898-4343","contributorId":212771,"corporation":false,"usgs":true,"family":"Dietterich","given":"Hannah R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diefenbach, Angela K. 0000-0003-0214-7818","orcid":"https://orcid.org/0000-0003-0214-7818","contributorId":204743,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Angela K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soule, S. Adam 0000-0002-4691-6300","orcid":"https://orcid.org/0000-0002-4691-6300","contributorId":221052,"corporation":false,"usgs":false,"family":"Soule","given":"S.","email":"","middleInitial":"Adam","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":813191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zoeller, Michael H. 0000-0003-4716-8567","orcid":"https://orcid.org/0000-0003-4716-8567","contributorId":214557,"corporation":false,"usgs":true,"family":"Zoeller","given":"Michael","email":"","middleInitial":"H.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Major, J. J. 0000-0003-2449-4466","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":29461,"corporation":false,"usgs":true,"family":"Major","given":"J. J.","affiliations":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":813194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundgren, Paul 0000-0002-6771-2876","orcid":"https://orcid.org/0000-0002-6771-2876","contributorId":215622,"corporation":false,"usgs":false,"family":"Lundgren","given":"Paul","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":813195,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70218820,"text":"sir20215005 - 2021 - Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator","interactions":[],"lastModifiedDate":"2021-03-18T11:47:02.407154","indexId":"sir20215005","displayToPublicDate":"2021-03-17T12:49:05","publicationYear":"2021","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":"2021-5005","displayTitle":"Supporting Data and Simulation of Hypothetical Bighead Carp Egg and Larvae Development and Transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator","title":"Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator","docAbstract":"Data collection, along with hydraulic and fluvial egg transport modeling, was completed along a 70.9-mile reach of the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam in Kentucky and Indiana. Water-quality data collected in this reach included surface measurements and vertical profiles of water temperature, specific conductance, pH, dissolved oxygen, turbidity, relative chlorophyll, and relative phycocyanin. Data were collected during two surveys: October 27–November 4, 2016, and June 26–29, 2017. Streamflow and velocity data were collected simultaneously with the water-quality data at cross sections and along longitudinal lines (corresponding to the water-quality surface measurements) and at selected stationary locations (corresponding to the water-quality vertical profiles). The data were collected to understand variability of flow and water-quality conditions relative to simulated reaches of the Ohio River and to aid in identifying parts of the reach that may provide conditions favorable to spawning and recruitment habitat for Hypophthalmichthys nobilis (bighead carp).
A copy of an existing step-backwater model of Ohio River flows was obtained from the National Weather Service and used to simulate hydraulic conditions for four different streamflows. Streamflows were selected to represent typical conditions ranging from a high-streamflow event to a seasonal dry-weather event, with two streamflows between these extremes for this reach of the Ohio River. Outputs from the hydraulic model, a range of five water temperatures observed in water-quality data, and four potential spawning locations were used as input to the Fluvial Egg Drift Simulator to simulate the extents and quantile positions of developing bighead carp, from egg hatching to the gas bladder inflation stage, under each scenario. A total of 80 simulations were run.
Results from the Fluvial Egg Drift Simulator scenarios (which include only the hydraulic influences on survival that result from settling, irrespective of mortality from other physical or biological factors such as excess turbulence, fertilization failure, predation, or starvation) indicate that most eggs will hatch, about half will die, and a quarter of the surviving larvae will reach the gas bladder inflation stage within the model reach. The overall mean percentage of embryos surviving to the gas bladder inflation stage was 13.1 percent. Individual simulations have embryo survival percentages as high as 49.1 percent. The highest embryo survival percentages occurred for eggs spawned at a streamflow of 38,100 cubic feet per second and water temperatures of 24 to 30 degrees Celsius. Conversely, embryo survival percentages were lowest for the lowest and highest streamflows regardless of water temperature or spawn location. Under low water temperature and high-streamflow conditions, some of the eggs did not hatch nor did the larvae reach the gas bladder inflation stage until passing beyond the downstream model domain. Although the final quantile positions of the eggs and larvae beyond the downstream model domain are unknown, the outcomes still provide useful information about conditions favorable to spawning and recruitment habitat for bighead carp in the Ohio River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215005","collaboration":"Biological Threats and Invasive Species Research Program","usgsCitation":"Ostheimer, C.J., Boldt, J.A., and Buszka, P.M., 2021, Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator: U.S. Geological Survey Scientific Investigations Report 2021–5005, 30 p., https://doi.org/10.3133/sir20215005.","productDescription":"Report: v, 30 p.; 2 Data Releases","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-116266","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":384390,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5005/coverthb.jpg"},{"id":384391,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5005/sir20215005.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5005"},{"id":384392,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MQHEPU","text":"USGS data release","linkHelpText":"Velocity and water-quality surveys in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, October 27–November 4, 2016, and June 26–29, 2017"},{"id":384393,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JHLGZL","text":"USGS data release","linkHelpText":"Geospatial data and models for the simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator"}],"country":"United States","state":"Indiana, Kentucky","otherGeospatial":"Ohio River, Markland Locks and Dam, McAlpine Locks and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.660400390625,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.40194908237822\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229–1737
Floods are one of the most costly and frequent natural disasters in Nevada. For example, the 1997 New Year’s flood has been estimated to have caused more than $1 billion in damage across northern Nevada (Truckee River Flood Management Authority, 2017). In 2014, more than 2 miles of Interstate 15 in southern Nevada was heavily damaged by the remnants of Hurricane Norbert combined with monsoonal rains (Sutko, 2015). Flooding in Nevada is highly variable in cause and the season of the year. Flooding can be caused by snowmelt, rain on snow, and flash flooding during thunderstorms. Peak streamflow estimates are critical for planning by government agencies; designation of flood zones; and design of infrastructure including culverts, bridges, and roadways. In order to provide accurate estimates of flood frequencies, long-term data collection of peak streamflows would be needed because the accuracy of estimates improves with longer datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213015","collaboration":"Prepared in cooperation with Nevada Department of Transportation","usgsCitation":"Schmidt, K., 2021, Peak streamflow determinations in Nevada: A cooperative program with the USGS and Nevada Department of Transportation: U.S. Geological Survey, Fact Sheet 2021-3015, 4 p., https://doi.org/10.3133/fs20213015.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-112970","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":384413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3015/covrthb.jpg"},{"id":384414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3015/fs20213015.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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\"}}]}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.
Barrier coasts, including barrier islands, beach-ridge plains, and associated landforms, can assume a broad spectrum of morphologies over multi-decadal scales that reflect conditions of sediment availability, accommodation, and relative sea-level rise. However, the quantitative thresholds of these controls on barrier-system behavior remain largely unexplored, even as modern sea-level rise and anthropogenic modification of sediment availability increasingly reshape the world's sandy coastlines. In this study, we conceptualize barrier coasts as sediment-partitioning frameworks, distributing sand delivered from the shoreface to the subaqueous and subaerial components of the coastal system. Using an idealized morphodynamic model, we explore thresholds of behavioral and morphologic change over decadal to centennial timescales, simulating barrier evolution within quasi-stratigraphic morphological cross sections. Our results indicate a wide diversity of barrier behaviors can be explained by the balance of fluxes delivered to the beach vs. the dune or backbarrier, including previously understudied forms of transgression that allow the subaerial system to continue accumulating sediment during landward migration. Most importantly, our results show that barrier state transitions between progradation, cross-shore amalgamation, aggradation, and transgression are controlled largely through balances within a narrow range of relative sea-level rise and sediment flux. This suggests that, in the face of rising sea levels, subtle changes in sediment fluxes could result in significant changes in barrier morphology. We also demonstrate that modeled barriers with reduced vertical sediment accommodation are highly sensitive to the magnitude and direction of shoreface fluxes. Therefore, natural barriers with limited sediment accommodation could allow for exploration of the future effects of sea-level rise and changing flux magnitudes over a period of years as opposed to the decades required for similar responses in sediment-rich barrier systems. Finally, because our model creates stratigraphy generated under different input parameters, we propose that it could be used in combination with stratigraphic data to hindcast the sensitivity of existing barriers and infer changes in prehistoric morphology, which we anticipate will provide a baseline to assess the reliability of forward modeling predictions.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/esurf-9-183-2021","usgsCitation":"Ciarletta, D.J., Miselis, J.L., Shawler, J.L., and Hein, C.J., 2021, Quantifying thresholds of barrier geomorphic change in a cross-shore sediment-partitioning model: Earth Surface Dynamics, v. 9, p. 183-203, https://doi.org/10.5194/esurf-9-183-2021.","productDescription":"21 p.","startPage":"183","endPage":"203","ipdsId":"IP-122455","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453052,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esurf-9-183-2021","text":"Publisher Index Page"},{"id":436457,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O3D29V","text":"USGS data release","linkHelpText":"Python-based Subaerial Barrier Sediment Partitioning (pySBSP) model (ver. 1.0, February 2024)"},{"id":436456,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DE6QCL","text":"USGS data release","linkHelpText":"Subaerial Barrier Sediment Partitioning (SBSP) Model Version 1.0"},{"id":384718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ciarletta, Daniel J. 0000-0002-8555-2239","orcid":"https://orcid.org/0000-0002-8555-2239","contributorId":256700,"corporation":false,"usgs":true,"family":"Ciarletta","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":813078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":813079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shawler, Justin L.","contributorId":256701,"corporation":false,"usgs":false,"family":"Shawler","given":"Justin","email":"","middleInitial":"L.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":813080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hein, Christopher J.","contributorId":256702,"corporation":false,"usgs":false,"family":"Hein","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":813081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223217,"text":"70223217 - 2021 - Development of a simulated lung fluid leaching method to assess the release of potentially toxic elements from volcanic ash","interactions":[],"lastModifiedDate":"2021-08-18T12:49:40.865699","indexId":"70223217","displayToPublicDate":"2021-03-17T07:48:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1226,"text":"Chemosphere","active":true,"publicationSubtype":{"id":10}},"title":"Development of a simulated lung fluid leaching method to assess the release of potentially toxic elements from volcanic ash","docAbstract":"Freshly erupted volcanic ash contains a range of soluble elements, some of which can generate harmful effects in living cells and are considered potentially toxic elements (PTEs). This work investigates the leaching dynamics of ash-associated PTEs in order to optimize a method for volcanic ash respiratory hazard assessment. Using three pristine (unaffected by precipitation) ash samples, we quantify the release of PTEs (Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn) and major cations typical of ash leachates (Mg, Na, Ca, K) in multiple simulated lung fluid (SLF) preparations and under varying experimental parameters (contact time and solid to liquid ratio). Data are compared to a standard water leach (WL) to ascertain whether the WL can be used as a simple proxy for SLF leaching. The main findings are: PTE concentrations reach steady-state dissolution by 24 h, and a relatively short contact time (10 min) approximates maximum dissolution; PTE dissolution is comparatively stable at low solid to liquid ratios (1:100 to 1:1000); inclusion of commonly used macromolecules has element-specific effects, and addition of a lung surfactant has little impact on extraction efficiency. These observations indicate that a WL can be used to approximate lung bioaccessible PTEs in an eruption response situation. This is a useful step towards standardizing in vitro methods to determine the soluble-element hazard from inhaled ash.
Sexual segregation has been intensely studied across diverse ecosystems and taxa, but studies are often limited to periods when animals occupy distinct seasonal ranges. Some avian and marine studies have revealed that habitat segregation, when sexes differ spatially or temporally in use of the physical landscape, is common during the migratory period and characterized by sex-specific differences in migratory behaviors. Recent research highlights the importance of understanding movement patterns in the context of the full annual life cycle and highlights the need to extend relevant theories of sexual segregation to the migratory period. We tested predictions from two leading hypotheses of sexual segregation, the forage-selection hypothesis (FSH) and the reproductive strategy hypothesis (RSH) as applied to the migratory period. We collected global positioning system (GPS) location data for male and female mule deer (Odocoileus hemionus) in south-central Wyoming and northwest Colorado and tested the main predictions of the FSH and RSH. Both sexes showed high fidelity to their migratory routes, but route fidelity was more variable in males. Males also started spring migrations earlier, ended spring and autumn migrations later, and spent 22% more time on stopover sites during spring migrations. Consequently, males took twice as long in spring and 44% longer in autumn to complete migration. Our results revealed clear sex-specific migratory behaviors and supported predictions of the RSH that male foraging behaviors optimize body condition for the autumn rut, and females prioritize foraging while balancing reproductive constraints. Specifically, males timed their movements with spring green-up as optimally as females, and the timing of male migrations and use of stopovers suggested that males prioritized time in areas of high-quality forage. This refutes predictions of the FSH during the migratory period that males should consistently choose habitats with abundant, low-quality forage. Our findings provide an important contribution to sexual segregation theory by extending relevant theories to understand male and female movements during the migratory period.
Western Yellow-billed Cuckoo (Cuckoo; Coccyzus americanus) populations continue to decline in the western United States despite efforts to increase availability of riparian forest. Cuckoos have unique breeding habitat requirements such as large contiguous tracts of riparian forest (>80 ha), large estimated home ranges (20–90 ha), and dense vertical structure around the nest. However, local habitat-scale features may be missing in landscapes of predominantly mature riparian forest that may need to be specifically managed for nesting. We used historical nest data (n = 95) from the South Fork Kern River Valley, California, from 1985 to 1996 to identify important nest site features that may be missing in current riparian forests. We found that increased canopy cover and vertical structure at all levels in the canopy greatly increased the probability of Cuckoo nesting. With smaller estimated effect sizes, the probability of Cuckoo nesting increased with increasing willows and forbs and smaller mean tree dbh. Cuckoos selected plots with disproportionately high percent willow cover relative to availability plots regardless of whether sites had low or high percent willow available. Counts of fledged young were positively related to willow percentage. No vegetation variable influenced daily survival rate which was 0.991 (LCI = 0.980, UCI = 0.996). Overall 17-day nest success was likely high (0.86, LCI = 0.71, UCI = 0.93). In the absence of natural processes that create early successional stage forest, specific management for early successional stage forest is needed to increase the probability of Cuckoo nesting and nest productivity.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13376 ","usgsCitation":"Wohner, P., Laymon, S., Stanek, J., King, S.L., and Cooper, R., 2021, Early successional riparian vegetation is important for western Yellow-billed Cuckoo nesting habitat: Restoration Ecology, v. 29, no. 5, e13376, 13 p., https://doi.org/10.1111/rec.13376 .","productDescription":"e13376, 13 p.","ipdsId":"IP-123208","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South Fork Kern River 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P.J.","contributorId":287172,"corporation":false,"usgs":false,"family":"Wohner","given":"P.J.","affiliations":[{"id":61497,"text":"Cuckoo Conservation Initiative","active":true,"usgs":false}],"preferred":false,"id":836846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laymon, S.A.","contributorId":287173,"corporation":false,"usgs":false,"family":"Laymon","given":"S.A.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":836847,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanek, J.E.","contributorId":287174,"corporation":false,"usgs":false,"family":"Stanek","given":"J.E.","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":836848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836849,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, R.J.","contributorId":287175,"corporation":false,"usgs":false,"family":"Cooper","given":"R.J.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":836850,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219181,"text":"70219181 - 2021 - American Woodcock singing-ground survey: Comparison of four models for trend in population size","interactions":[],"lastModifiedDate":"2021-08-03T13:58:58.411403","indexId":"70219181","displayToPublicDate":"2021-03-16T07:14:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"American Woodcock singing-ground survey: Comparison of four models for trend in population size","docAbstract":"Wildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t-distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t-test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses.
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The USFWS estimated that potential habitat, south of the Interstate-10 highway in Arizona and New Mexico, had a carrying capacity of c. six jaguars, and so focused its recovery programme on areas south of the USA–Mexico border. Here we present a systematic review of the modelling and assessment efforts over the last 25 years, with a focus on areas north of Interstate-10 in Arizona and New Mexico, outside the recovery unit considered by the USFWS. Despite differences in data inputs, methods, and analytical extent, the nine previous studies found support for potential suitable jaguar habitat in the central mountain ranges of Arizona and New Mexico. Applying slightly modified versions of the USFWS model and recalculating an Arizona-focused model over both states provided additional confirmation. Extending the area of consideration also substantially raised the carrying capacity of habitats in Arizona and New Mexico, from six to 90 or 151 adult jaguars, using the modified USFWS models. This review demonstrates the crucial ways in which choosing the extent of analysis influences the conclusions of a conservation plan. More importantly, it opens a new opportunity for jaguar conservation in North America that could help address threats from habitat losses, climate change and border infrastructure.
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