Kīlauea Volcano’s 2018 lower East Rift Zone (LERZ) eruption produced exceptionally high lava effusion rates and record-setting SO2 emissions. The eruption involved a diverse range of magmas, including primitive basalts sourced from Kīlauea’s summit reservoirs. We analyzed LERZ matrix glasses, melt inclusions, and host minerals to identify melt volatile contents and magma storage depths. The LERZ glasses and melt inclusions span nearly the entire compositional range previously recognized at Kīlauea. Melt inclusions in Fo86-89 olivine from the main eruptive vent (fissure 8) underwent 70–170 °C cooling during transport in LERZ carrier melts, causing extensive post-entrapment crystallization and sulfide precipitation. Many of these melt inclusions have low sulfur (400–900 ppm) even after correction for sulfide formation. CO2 and H2O vapor saturation pressures indicate shallow melt inclusion trapping depths (1–5 km), consistent with formation within Kīlauea’s Halemaʻumaʻu and South Caldera reservoirs. Many of these inclusions also have degassed δ34S values (− 1.5 to − 0.5‰). Collectively, these results indicate that some primitive melts experienced near-surface degassing before being trapped into melt inclusions. We propose that decades-to-centuries of repeated lava lake activity and lava drain-back during eruptions (e.g., 1959 Kīlauea Iki) recycled substantial volumes of degassed magma into Kīlauea’s shallow reservoir system. Degassing and magma recycling from the 2008–2018 Halemaʻumaʻu lava lake likely reduced the volatile contents of LERZ fissure 8 magmas, resulting in lower fountain heights compared to many prior Kīlauea eruptions. The eruption’s extreme SO2 emissions were due to high lava effusion rates rather than particularly volatile-rich melts.
Tracing produced water origins from wells hydraulically fractured with freshwater-based fluids is sometimes predicated on assumptions that (1) each geological formation contains compositionally unique brine and (2) produced water from recently hydraulically fractured wells resembles fresher meteoric water more so than produced water from older wells. These assumptions are not valid in Williston Basin oil wells sampled in this study. Although distinct average 228Ra/226Ra ratios were found in water produced from the Bakken and Three Forks Formations, average δ2H, δ18O, specific gravity, and conductivity were similar but exhibited significant variability across five oil fields within each formation. Furthermore, initial produced water (“flowback”) was operationally defined based on the presence of glycol ether compounds and water from wells that had produced <56% of the amount of fluids injected and sampled within 160 days of fracturing. Flowback unexpectedly exhibited higher temperature, specific gravity, conductivity, δ2H, and δ18O, but lower oxidation–reduction potential and δ11B, relative to the wells thought to be producing formation brines (from wells with a produced-to-injected water ratio [PIWR] > 0.84 and sampled more than 316 days after fracturing). As such, establishing an overall geochemical and isotopic signature of produced water compositions based solely on chemical similarity to meteoric water and formation without the consideration of well treatments, well completion depth, or lateral location across the basin could be misleading if these signatures are assumed to be applicable across the entire basin. These findings have implications for using produced water compositions to understand the interbasin fluid flow and trace sources of hydraulic fracturing fluids.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06789","usgsCitation":"Gallegos, T., Doolan, C.A., Caldwell, R.R., Engle, M.A., Varonka, M., Birdwell, J.E., Jolly, G.D., Coplen, T.B., and Oliver, T.A., 2021, Insights on geochemical, isotopic, and volumetric compositions of produced water from hydraulically fractured Williston Basin oil wells: Environmental Science and Technology, v. 55, no. 14, p. 10025-10034, https://doi.org/10.1021/acs.est.0c06789.","productDescription":"10 p.","startPage":"10025","endPage":"10034","ipdsId":"IP-118139","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":488266,"rank":0,"type":{"id":40,"text":"Open Access 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Beginning in 2015, the benthic preyfish survey expanded from US-only to incorporate lake-wide sampling sites which drastically increased the survey’s spatial coverage. In 2020, the collaborative benthic preyfish survey completed 82 bottom trawl tows across main lake and embayment sites at depths from 6 to 226 m. Compared to previous years, the survey was largely confined to ports east of Rochester on the US side, and east of Wesleyville on the Canadian side due to COVID-19 travel constraints. In total, the survey sampled 109,315 fish from 32 species in 2020. Round goby was the most numerically abundant species comprising 59% of total catch, followed by alewife (27%), and deepwater sculpin (7%). Despite being a common species represented in historical catches, slimy sculpin comprised only 0.05% of the total catch, with only 53 fish sampled in 2020. Annual biomass indices were among the lowest reported for slimy sculpin across the entire time series, whereas deepwater sculpin biomass remained high but showed a decrease from values observed in 2019. Deepwater sculpin condition in 2020 was lower than in 2019 but similar to values observed in 2017-2018. Community diversity remained high relative to historically lower values when round goby and deepwater sculpin were not captured in trawl catches. Round goby biomass in 2020 was the highest on the US time series, but not on the lake-wide series, highlighting the need to resume lake-wide sampling to further understand how regional vs lake-wide sampling affects benthic preyfish population estimates.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"NYSDEC Lake Ontario annual report 2020","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"New York Department of Environmental Conservation","usgsCitation":"O’Malley, B., Goretzke, J., and Holden, J.P., 2021, Bottom trawl assessment of benthic preyfish community in Lake Ontario: NYSDEC Lake Ontario Annual Report , 15 p.","productDescription":"15 p.","startPage":"12-1","endPage":"12-15","ipdsId":"IP-127309","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":391511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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The TFHRC was developing a device to measure the erosional properties of clay-rich sediments to be used for in situ testing at locations of bridge pier construction. FBGC scientists selected 15 drilling locations at 14 different field sites across Northern Virginia and Southern Maryland for the investigation of near-surface sediment properties and the development and testing of the TFHRC’s in situ scour testing device (ISTD). This report provides information about the project and summarizes the data collected during fieldwork including sediment descriptions of the borehole cores and the methods used during fieldwork.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211038","collaboration":"Prepared in cooperation with the U.S. Department of Transportation Federal Highway Administration","usgsCitation":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Borehole sampling of surficial sediments in Northern Virginia and Southern Maryland: U.S. Geological Survey Open-File Report 2021–1038, 27 p., https://doi.org/10.3133/ofr20211038.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-120037","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":386418,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1038/ofr20211038.pdf","text":"Report","size":"6.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1038"},{"id":386421,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A8G5LQ","text":"USGS Data Release","linkHelpText":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Datasheets associated with borehole sampling of surficial sediments in Northern Virginia and Southern Maryland conducted by the U.S. Geological Survey for the Federal Highways Administration Turner-Fairbanks Research Center In Situ Scour Testing Device: U.S. Geological Survey data release"},{"id":386417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1038/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.06884765624999,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 37.77071473849609\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Green Lake is the deepest natural inland lake in Wisconsin, USA, with a maximum depth of about 72 meters (m). In the early 1900’s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity), with low dissolved oxygen (DO) concentrations only in the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake increased, which led to increased algal production and low DO concentrations not only occurring in its deepest areas but also in the middle of the water column (metalimnion). Routine monitoring of the lake and its tributaries has been conducted by the U.S. Geological Survey since 2004 and 1988, respectively. Results from this monitoring led to the Wisconsin Department of Natural Resources (WDNR) listing the lake as impaired because of low DO concentrations in the metalimnion, with elevated TP concentrations identified as the cause of impairment.
As part of this study, comprehensive sampling of the lake and its tributaries was conducted in 2017–2018 to augment ongoing monitoring and further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water quality models were then used to determine the causes of the low DO concentrations and the magnitude of P load reductions needed to improve the water quality of the lake to meet multiple water-quality goals, including the WDNR criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900’s, but since the late 1970s have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–2018 (baseline period for this study), the near-surface geometric-mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were below the 0.015 mg/L WDNR criterion for the lake), and the minimum metalimnetic DO concentrations measured in August ranged from 1.0 to 4.7 mg/L. It was believed that the degradation in water quality was caused by excessive P inputs to the lake; therefore, the total P inputs to the lake were estimated. The mean annual external P load during 2014–2018 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. At fall turnover, internal sediment recycling contributed an additional 7,040 kg that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there is a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, indicated by Secchi disk depths, had a larger response to decreases in P loading than to increases in loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration on the east side of the lake, with higher TP concentrations than the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr equates to a 46-percent reduction in the potentially controllable external P sources (all external sources except precipitation, atmospheric deposition, and waterfowl) from that measured during water years (WYs) 2014–2018. The total external P loading would need to be decreased to 7,680 kg/yr (17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to be decreased to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June-September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM-AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that metalimnetic DO minima are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM-AED results indicated that settling of organic material during summer may be slowed by the colder, denser, and more viscous water in the metalimnion and increase DO consumption. Based on empirical evidence comparing minimum metalimnetic DO concentrations with various meteorological, hydrologic, water quality, and in-lake physical factors, lower metalimnetic DO concentrations occurred when there was warmer metalimnetic water temperatures, higher near-surface Chl-a and TP concentrations, and lower Secchi depths during summer. GLM-AED results indicated that the external P load would need to be reduced to about 4,010 kg/yr, a 57-percent reduction from that measured in 2014–2018, to eliminate the occurrence of metalimnetic DO minima of less than 5 mg/L in over 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake. However, it may take several years for the full effects of the external load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Diagnostic and feasibility study findings: Water quality improvements for Green Lake, Wisconsin","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Green Lake Association","usgsCitation":"Robertson, D., Siebers, B.J., Ladwig, R., Hamilton, D., Reneau, P., McDonald, C.P., Prellwitz, S., and Lathrop, R.C., 2021, Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading, vii, 115 p.","productDescription":"vii, 115 p.","ipdsId":"IP-129488","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":389346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388824,"type":{"id":15,"text":"Index Page"},"url":"https://www.greenlakeassociation.org/research/"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07920837402344,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.75894467245554\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siebers, Benjamin J. 0000-0002-2900-5169","orcid":"https://orcid.org/0000-0002-2900-5169","contributorId":206518,"corporation":false,"usgs":true,"family":"Siebers","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":822505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":822506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Cory P. 0000-0002-1208-8471","orcid":"https://orcid.org/0000-0002-1208-8471","contributorId":261754,"corporation":false,"usgs":false,"family":"McDonald","given":"Cory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":822508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prellwitz, Stephanie","contributorId":265281,"corporation":false,"usgs":false,"family":"Prellwitz","given":"Stephanie","email":"","affiliations":[{"id":54642,"text":"Green Lake Association","active":true,"usgs":false}],"preferred":false,"id":822509,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lathrop, Richard C","contributorId":172075,"corporation":false,"usgs":false,"family":"Lathrop","given":"Richard","email":"","middleInitial":"C","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822510,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70226817,"text":"70226817 - 2021 - Characterizing ground motion amplification by extensive flat sediments: The seismic response of the eastern U.S. Atlantic Coastal Plain strata","interactions":[],"lastModifiedDate":"2021-12-14T12:58:24.210836","indexId":"70226817","displayToPublicDate":"2021-06-29T06:56:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing ground motion amplification by extensive flat sediments: The seismic response of the eastern U.S. Atlantic Coastal Plain strata","docAbstract":"We examine the effects that Atlantic Coastal Plain (ACP) strata have on ground motions in the eastern and southeastern United States. The ACP strata consist of widespread, nearly flat‐lying sediments, the upper portions of which are unconsolidated or semiconsolidated. The ACP sediments are deposited primarily on crystalline basement rocks, creating large velocity and density contrasts with the underlying rocks. At 211 sites on ACP strata to thicknesses of 4000 m, we compute spectral ratios relative to the average of four bedrock sites west or northwest of the strata. Sites consist of stations of Earthscope’s USArray Transportable Array (TA), and temporary deployments in the Southeast Suture of the Atlantic Margin Experiment (SESAME), Eastern North American Margin (ENAM) experiment, and the DCShake deployment in Washington, D.C. For the TA and SESAME stations, we use signals from 13 teleseisms and three regional earthquakes as input, combining the north and east components of motion after taking the Fourier transforms. We also include similarly processed site responses from the ENAM and DCShake arrays that were computed in earlier studies. Results show prominent, fundamental resonance peaks at frequencies determined by reverberations in the entire sediment column, and that often define the largest amplifications for each frequency. As frequencies increase, these resonance peaks migrate to thinner ACP strata and increase in amplitude. The peaks are well defined at frequencies below about 1 Hz, but become narrower and less defined regionally at higher frequencies. We develop simple equations to characterize amplification versus ACP thickness, which we approximate by cosine and Gaussian curves with amplifications of 1 on bedrock and rising to the resonance peak, and then decreasing to an average amplification at thicknesses greater than twice the resonance peak. Comparisons with other site corrections for the central and eastern United States based on sediment thickness show similarities on thin ACP strata but divergence on thicker sediments. The results also demonstrate the effectiveness of using teleseismic arrivals to characterize the site responses of sedimentary sequences.
A study was conducted by the U.S. Geological Survey in cooperation with the U.S. Navy (the Navy) to determine the status of volatile organic compounds (VOCs) and per- and polyfluoroalkyl substances (PFASs) in groundwater at the former Naval Air Warfare Center (NAWC) in West Trenton, New Jersey. Wells contaminated with VOCs were sampled in 2014, 2015, 2016, and 2017 as part of the Navy’s long-term monitoring program. The results for trichloroethene (TCE), cis-1,2-dichloroethene (cisDCE), and vinyl chloride (VC) were plotted in map view to determine whether the areal extent of the contamination had changed over the 4-year period. TCE, cisDCE, and VC concentrations were plotted along nine lines of section across the former NAWC site to determine whether the vertical distribution of VOCs had changed during 2014–17. TCE, cisDCE, and VC concentrations over time were plotted on graphs for each well to determine long-term trends and changes in VOC concentrations. Data from 1990 to 2017 were used, if available, to make these graphs.
Results show that the areas of VOC concentrations greater than or equal to 1 microgram per liter decreased slightly on the northwestern side and the northeastern side of the NAWC site from 2014 to 2017 under the influence of a pump-and-treat system, natural attenuation processes, and engineered bioaugmentation experiments ongoing at the site. The pump-and-treat system continued to hydraulically contain the VOC contamination and kept it from moving offsite to the south and west of NAWC. One well northeast of the NAWC site, 50BR, was found to have detectable TCE and cisDCE concentrations. These detections indicated that VOC contamination had migrated offsite and that the pump-and-treat system was not containing the VOC contamination on the eastern side of the facility. Detectable VOC concentrations were present in wells as deep as 200 and 221 feet on the eastern and western sides of the NAWC site. TCE concentrations in most wells were found to be stable or to have slowly decreased since the facility closed in 1999. Only 7 wells, including 3 pump-and-treat extraction wells, showed substantial increases in TCE concentration from 2014 to 2017. Continuing sources of TCE to the system are desorption of TCE from organic materials in the aquifer, back diffusion of TCE from the contaminated bedrock matrix, and dissolution of remaining dense nonaqueous phase TCE in the aquifer.
Wells at the former NAWC site were sampled for PFASs in 2015, 2016, and 2017. Perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) results were plotted in map and cross-section views to determine the areal and vertical extent of the PFAS contamination at the site. PFOS, PFOA, and PFNA concentrations greater than their established maximum contaminant levels were detected in 25, 24, and 21 of the 26 wells sampled, respectively, on the eastern side of NAWC in 2017. Vertically, the highest PFAS concentrations were present in shallow wells along the fence near the firehouse and along the railroad tracks where the aqueous film-forming foam discharge reportedly occurred back in 1990. PFAS concentrations were detected in one well (54BR) as deep as 200 feet on the eastern side of the NAWC site. PFASs were present in wells east of the railroad tracks, indicating that PFAS-contaminated groundwater had moved offsite. In a limited test of five wells, samples collected with regenerated cellulose dialysis membrane (RCDM) passive samplers contained PFAS concentrations equal to those in samples from low-flow purging.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201105","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Imbrigiotta, T.E., and Fiore, A.R., 2021, Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17: U.S. Geological Survey Open-File Report 2020–1105, 107 p., https://doi.org/10.3133/ofr20201105.","productDescription":"Report: xii, 107 p.; Data Release; 4 Appendixes","numberOfPages":"107","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-110205","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":386575,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.xlsx","text":"Appendix 2","size":"288 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 2. Volatile organic compounds, per- and polyfluoroalkyl substances, and 1,4-dioxane concentrations measured in samples from wells at the former Naval Air Warfare Center site, West Trenton, New Jersey, 1990–2017"},{"id":386577,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.csv","text":"Appendix 2 as CSV file","size":"187 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386576,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.csv","text":"Appendix 1 as CSV file","size":"22.9 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386573,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCAQ5N","text":"USGS data release","linkHelpText":"Concentrations of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in groundwater and surface water, former Naval Air Warfare Center, West Trenton, New Jersey"},{"id":386572,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105.pdf","text":"Report","size":"9.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1105"},{"id":386571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1105/coverthb.jpg"},{"id":386574,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.xlsx","text":"Appendix 1","size":"43.7 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 1. Descriptions of boreholes, well locations, and well construction at the former Naval Air Warfare Center, West Trenton, New Jersey"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.80979204177856,\n 40.26746805544402\n ],\n [\n -74.80759263038635,\n 40.27155298671227\n ],\n [\n -74.8130750656128,\n 40.27224060619094\n ],\n [\n -74.81433033943176,\n 40.26832763061523\n ],\n [\n -74.81412649154663,\n 40.268139343654944\n ],\n [\n -74.80979204177856,\n 40.26746805544402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
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Mount Rushmore National Memorial (hereafter referred to as “the memorial”), in western South Dakota, is maintained by the National Park Service (NPS) and includes 1,278 acres of land in the east-central part of the Black Hills. An ongoing challenge for NPS managers at the memorial is providing water from sustainable and reliable sources for operations, staff, and the increasing number of visitors. In 2020, the U.S. Geological Survey (USGS) and NPS completed a hydrological study of the Burnpit well (well 5), a 580-foot-deep open hole groundwater well completed in metamorphic (crystalline) rock at the memorial. The purpose of this study was to estimate the geological and hydraulic properties of the aquifer supplying the well and to determine the water quality of the groundwater from the well. The study provides NPS staff and managers background information for assessing future uses for the well. Methods for data collection and analysis for the study included borehole and video camera analysis in 2020, aquifer testing by the NPS in 2009 and the USGS in 2020, and water-quality sampling in 2020.
Borehole camera video generally matched the lithology recorded in the well log. Fractures recorded in the well log and observed with the borehole camera, including more than 20 less prominent fractures and rough sidewall areas, indicated a fractured aquifer. The fractures are the primary conduits for groundwater flow through the rock and into the well.
Transmissivity was estimated for the upper and lower water-level drawdown zones at the Burnpit well with data from the NPS and USGS using the Theis and Cooper-Jacob methods. Transmissivity for the NPS test using the Theis method was 9.0 and 11 feet squared per day (ft2/d) for the upper and lower drawdown zones, respectively. Using the Cooper-Jacob method, the transmissivity was 22 and 14 ft2/d for the upper and lower drawdown zones of the aquifer, respectively. Transmissivity estimates from data from the USGS test were similar. The Theis method, applied to the upper and lower drawdown zones of the aquifer, produced transmissivity estimates of 7.7 and 10 ft2/d, and the Cooper-Jacob method produced estimates of 9.7 and 12 ft2/d, respectively.
Storativity (specific yield) estimated using the Theis method for the NPS aquifer-test data was 0.85 and 0.92 for the upper and lower drawdown zones of the aquifer, respectively. The Cooper-Jacob method applied to the NPS aquifer-test data produced storativity estimates of 0.11 and 0.50 for the upper and lower drawdown zones, respectively. The Theis method applied to the USGS aquifer-test data estimated storativity values of 0.77 and 1.0 for the upper and lower drawdown zones, respectively. The Cooper-Jacob method estimated storativity of 0.50 and 0.60 for the upper and lower drawdown zones of the USGS aquifer test, respectively. The estimated storativity values from the NPS and USGS aquifer tests for the upper and lower drawdown zones were higher than expected for limestones and schists.
The hypothetical equilibrium drawdown for the Burnpit well was estimated after the NPS test in 2009 at no more, and possibly less, than 35 gallons per minute. The NPS noted that the sustainable yield likely was overestimated because the water level did not stabilize during the NPS aquifer test. The specific capacity for the NPS aquifer test in 2009 was 0.16 gallon per minute per foot ([gal/min]/ft) of drawdown at 3 hours, and the specific capacity for the USGS aquifer test in 2020 was 0.13 (gal/min)/ft of drawdown at 3 hours. The rate of water-level recovery after pumping ceased was 0.017 and 0.013 (gal/min)/ft for the NPS and USGS aquifer tests, respectively. The water-level recovery rate was nearly an order of magnitude less than the specific capacity estimated during pumping, indicating that water levels in the Burnpit well may not recover quickly enough during pumping to provide for a continuous source of water.
Water-quality samples were collected at the Burnpit well on June 24 and July 23, 2020, and analyzed for field-measured properties, major ions, metals, nutrients, and perchlorate. Iron, zinc, and lithium concentrations for unfiltered samples in the well were at least three times greater than the mean filtered sample concentrations reported for crystalline aquifers in the Black Hills. Manganese concentrations were less than the mean concentration for crystalline aquifers but exceeded the U.S. Environmental Protection Agency (EPA) secondary drinking-water standards. The iron concentration from the June 24 sample was about 11 times greater than the EPA secondary drinking-water standards and mean concentrations from crystalline aquifers in the Black Hills. Arsenic concentrations in Burnpit well samples collected in 2020 were greater than the EPA primary drinking-water standard and the mean concentration for crystalline aquifers in the Black Hills. Arsenic occurs naturally in the rock of crystalline aquifers, and concentrations from samples in the Black Hills commonly exceed the EPA primary drinking-water standard of 10 micrograms per liter. High concentrations of arsenic, iron, and manganese metals in the Burnpit well make groundwater from the well in its natural state unusable as a drinking-water source, and water treatment would be necessary to reduce the trace element concentrations to less than the EPA primary and secondary drinking-water standards. However, if the memorial has immediate nonpotable water requirements, such as for construction and fire suppression, groundwater from the Burnpit well could provide water without causing additional stress to current (2021) drinking-water sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215059","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Eldridge, W.G., Hoogestraat, G.K., and Rice, S.E., 2021, Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota: U.S. Geological Survey Scientific Investigations Report 2021–5059, 29 p., https://doi.org/10.3133/sir20215059.","productDescription":"Report: vii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126498","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386673,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98OZQN9","text":"USGS data release","description":"USGS data release","linkHelpText":"Borehole video and aquifer test data for the Burnpit well, Mount Rushmore National Memorial, South Dakota, 2020"},{"id":386672,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5059/sir20215059.pdf","text":"Report","size":"2.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5059"},{"id":386674,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":386671,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5059/coverthb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0625,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 44.52392653654213\n ],\n [\n -104.0625,\n 44.52392653654213\n ],\n [\n -104.0625,\n 43.40903821777055\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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1608 Mountain View Road
Rapid City, SD 57702
Amphibian larvae are commonly used as indicators of aquatic ecosystem health because they are susceptible to contaminants. However, there is limited information on how species characteristics and trophic position influence contaminant loads in larval amphibians. Importantly, there remains a need to understand whether grazers (frogs and toads [anurans]) and predators (salamanders) provide comparable information on contaminant accumulation or if they are each indicative of unique environmental processes and risks. To better understand the role of trophic position in contaminant accumulation, we analyzed composite tissues for 10 metals from larvae of multiple co-occurring anuran and salamander species from 20 wetlands across the United States. We examined how metal concentrations varied with body size (anurans and salamanders) and developmental stage (anurans) and how the digestive tract (gut) influenced observed metal concentrations. Across all wetlands, metal concentrations were greater in anurans than salamanders for all metals tested except mercury (Hg), selenium (Se), and zinc (Zn). Concentrations of individual metals in anurans decreased with increasing weight and developmental stage. In salamanders, metal concentrations were less correlated with weight, indicating diet played a role in contaminant accumulation. Based on batches of similarly sized whole-body larvae compared to larvae with their digestive tracts removed, our results indicated that tissue type strongly affected perceived concentrations, especially for anurans (gut represented an estimated 46–97% of all metals except Se and Zn). This suggests the reliability of results based on whole-body sampling could be biased by metal, larval size, and development. Overall, our data shows that metal concentrations differs between anurans and salamanders, which suggests that metal accumulation is unique to feeding behavior and potentially trophic position. To truly characterize exposure risk in wetlands, species of different life histories, sizes and developmental stages should be included in biomonitoring efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2021.117638","usgsCitation":"Smalling, K., Oja, E.B., Cleveland, D.M., Davenport, J.D., Eagles-Smith, C., Campbell Grant, E.H., Kleeman, P.M., Halstead, B., Stemp, K.M., Tornabene, B., Bunnell, Z.J., and Hossack, B., 2021, Metal accumulation varies with life history, size, and development of larval amphibians: Environmental Pollution, v. 287, 117638, 10 p., https://doi.org/10.1016/j.envpol.2021.117638.","productDescription":"117638, 10 p.","ipdsId":"IP-127103","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":489089,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2021.117638","text":"Publisher Index Page"},{"id":436291,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LM78","text":"USGS data release","linkHelpText":"Metal concentrations in sediment and amphibian tissues from wetlands sampled across the United States"},{"id":387228,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"287","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oja, Emily Bea 0000-0002-8621-9665","orcid":"https://orcid.org/0000-0002-8621-9665","contributorId":261164,"corporation":false,"usgs":true,"family":"Oja","given":"Emily","email":"","middleInitial":"Bea","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":819408,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davenport, Jon D 0000-0002-9911-2779","orcid":"https://orcid.org/0000-0002-9911-2779","contributorId":261166,"corporation":false,"usgs":false,"family":"Davenport","given":"Jon","email":"","middleInitial":"D","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":819409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":819410,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819411,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kleeman, Patrick M. 0000-0001-6567-3239 pkleeman@usgs.gov","orcid":"https://orcid.org/0000-0001-6567-3239","contributorId":3948,"corporation":false,"usgs":true,"family":"Kleeman","given":"Patrick","email":"pkleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":819412,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":819413,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stemp, Kenzi M 0000-0001-7566-8513","orcid":"https://orcid.org/0000-0001-7566-8513","contributorId":261169,"corporation":false,"usgs":false,"family":"Stemp","given":"Kenzi","email":"","middleInitial":"M","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":819414,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":819415,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bunnell, Zachary J 0000-0001-6142-8703","orcid":"https://orcid.org/0000-0001-6142-8703","contributorId":261172,"corporation":false,"usgs":true,"family":"Bunnell","given":"Zachary","email":"","middleInitial":"J","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819416,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819417,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70221438,"text":"tm6G1 - 2021 - Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming","interactions":[],"lastModifiedDate":"2021-06-24T13:59:13.479713","indexId":"tm6G1","displayToPublicDate":"2021-06-23T08:54:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-G1","displayTitle":"Probabilistic Methodology for the Assessment of Original and Recoverable Coal Resources, Illustrated with an Application to a Coal Bed in the Fort Union Formation, Wyoming","title":"Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming","docAbstract":"The U.S. Geological Survey (USGS) has been using its Circular 891 for evaluating uncertainty in coal resource assessments for more than 35 years. Calculated cell tonnages are assigned to four qualitative reliability classes depending exclusively on distance to the nearest drill hole. The main appeal of this methodology, simplicity, is also its main drawback. Reliability may depend so marginally on distance to the nearest drill hole that, over time, it has become evident that Circular 891 is inadequate for modeling reliability and is limited by other shortcomings. The present publication describes the use of geostatistics as an approach allowing a more satisfactory performance than that which is achieved following Circular 891. Geostatistics takes advantage of partly random and partly organized fluctuations in attributes such as coal thickness, coal density, and elevation of the top of a coal bed, borrowing concepts and tools that have been standard features in statistics and risk analysis for decades. Considering that readers interested in this study may not have the background to go directly into the details of the methodology, we start by explaining geostatistical concepts and modeling techniques. The remainder of the publication is devoted to formulating the assessment methodology, applying it to data from the Fillmore Ranch coal bed in the Fort Union Formation in Wyoming, and explaining the computer software applied for performing calculations and displays. The assessment methodology has been designed to report three different forms of resources: coal in place, coal mineable by surface mining methods, and coal mineable by underground mining methods. These three types of resources are reported graphically by displaying both the magnitude and the reliability of total coal resources and resources at the cell scale. In the case of the Fillmore Ranch coal bed example, there is a 90-percent probability that the resources in place are 9.687 ± 0.383 billion short tons (bst), while the coal available for underground mining is 2.279 ± 0.160 bst, and that available for surface mining is only 0.240 ± 0.025 bst because of the steep dip to the west away from the outcrop. These magnitudes are derived from numerical probability distributions not following any specific form.
Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 954
Reston, VA 20192
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Rising global temperatures are expected to decrease the precipitation amount that falls as snow, causing greater risk of water scarcity, groundwater overdraft, and fire in areas that rely on mountain snowpack for their water supply. Streamflow in large river basins varies with the amount, timing, and type of precipitation, evapotranspiration, and drainage properties of watersheds; however, these controls vary in time and space making it difficult to identify the areas contributing most to flow and when. In this study, we separate the evaporative influences from source values of water isotopes from the Snake River Basin in the western United States (US) to relate source area to flow dynamics. We developed isoscapes (δ2H and δ18O) for the basin and found that isotopic composition of surface water in small watersheds is primarily controlled by longitude, latitude, and elevation. To examine temporal variability in source contributions to flow, we present a six-year record of Snake River water isotopes from King Hill, Idaho after removing evaporative influences. During periods of low flow, source water values were isotopically lighter indicating a larger contribution to flow from surface waters in the highest elevation, eastern portion of the basin. River evaporation increases were evident during summer likely reflecting climate, changing water availability, and management strategies within the basin. Our findings present a potential tool for identifying critical portions of basins contributing to river flow as climate fluctuations alter flow dynamics. This tool can be applied in other continental-interior basins where evaporation may obscure source water isotopic signatures.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR029157","usgsCitation":"Windler, G., Brooks, J.R., Johnson, H.M., Comeleo, R., Coulombe, R., and Bowen, G.J., 2021, Climate impacts on source contributions and evaporation to flow in the Snake River Basin using surface water isoscapes (δ2H and δ18O): Water Resources Research, v. 57, no. 7, e2020WR029157, 15 p., https://doi.org/10.1029/2020WR029157.","productDescription":"e2020WR029157, 15 p.","ipdsId":"IP-122721","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":451798,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8328002","text":"External Repository"},{"id":386865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Oregon, Wyoming","otherGeospatial":"Snake River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.73999023437499,\n 42.16340342422401\n ],\n [\n -109.742431640625,\n 42.16340342422401\n ],\n [\n -109.742431640625,\n 45.78284835197676\n ],\n [\n -119.73999023437499,\n 45.78284835197676\n ],\n [\n -119.73999023437499,\n 42.16340342422401\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Windler, Grace","contributorId":260666,"corporation":false,"usgs":false,"family":"Windler","given":"Grace","email":"","affiliations":[{"id":52636,"text":"Department of Geosciences, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":818451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, J. Renee","contributorId":176587,"corporation":false,"usgs":false,"family":"Brooks","given":"J.","email":"","middleInitial":"Renee","affiliations":[],"preferred":false,"id":818452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Henry M. 0000-0002-7571-4994 hjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7571-4994","contributorId":869,"corporation":false,"usgs":true,"family":"Johnson","given":"Henry","email":"hjohnson@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comeleo, Randy","contributorId":217974,"corporation":false,"usgs":false,"family":"Comeleo","given":"Randy","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":818454,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coulombe, Rob","contributorId":260667,"corporation":false,"usgs":false,"family":"Coulombe","given":"Rob","email":"","affiliations":[{"id":52638,"text":"Pacific Ecological Systems Division, Center for Public Health and Environmental Assessment Office of Research and Development, U.S. Environmental Protection Agency, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":818455,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bowen, Gabriel J.","contributorId":138889,"corporation":false,"usgs":false,"family":"Bowen","given":"Gabriel","email":"","middleInitial":"J.","affiliations":[{"id":12566,"text":"Department of Geology and Geophysics, Unviersity of Utah","active":true,"usgs":false}],"preferred":false,"id":818456,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221489,"text":"cir1479 - 2021 - The North American Breeding Bird Survey in Mexico, 2008 to 2018—A status report","interactions":[],"lastModifiedDate":"2021-06-21T17:42:27.497155","indexId":"cir1479","displayToPublicDate":"2021-06-21T08:55:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1479","displayTitle":"The North American Breeding Bird Survey in Mexico, 2008 to 2018—A Status Report","title":"The North American Breeding Bird Survey in Mexico, 2008 to 2018—A status report","docAbstract":"Collection of avian population data has repeatedly been identified as a high priority for bird conservation in Mexico. To meet this need, in 2008 the North American Breeding Bird Survey (BBS), a volunteer-based survey, was expanded to include northern Mexico. The BBS in Mexico (Mexican BBS) is managed by the North American Bird Conservation Initiative (NABCI), Mexico’s National Coordination Office inside the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO).
During 2008–18, 252 surveys were conducted along 68 routes in Mexico, with geographic coverage varying from year to year. Of these 68 routes, 36 were surveyed three or more times. Thirty-one observers conducted the surveys, and 21 of these observers conducted two or more surveys. Just two observers conducted more than one-third of the 252 surveys, and both observers were paid to conduct the surveys. The low availability of local observers who are qualified, willing, and able to volunteer their services to conduct BBS surveys may prove to be the biggest obstacle to the success of the Mexican BBS program, especially in the context of Mexico’s ongoing safety and security concerns.
Apart from the amount of data collected, many surveys did not adhere to pre-established quality-control requirements, and this would result in the exclusion of a large percentage of the data from potential trend analyses. Only 31 percent of the surveys met all the quality-control criteria. Additional observer training may help resolve this issue. Of greater concern is the selection of region-specific sampling date windows during which the surveys are conducted. Observers consistently conducted surveys outside the preliminarily prescribed sampling date window, reflecting the need to re-evaluate the regional appropriateness of this date window.
Regardless of the quality of the data, the quantity of data available from 2008 to 2018 is insufficient for trend analysis using methods typically employed by U.S. Geological Survey BBS analysts. Reaching minimum sample size thresholds for statistical analysis will require a substantial increase in effort. During 2008–18, no strata (defined as the intersection of State and Bird Conservation Region boundaries) reached the suggested minimum of 14 sampled routes, and most routes were not run consistently.
This report provides information needed for an evaluation of the merits of continuing to invest in the Mexican BBS program in its current form. Such an evaluation should consider the likelihood of achieving the primary project goal of producing reliable long-term population trend estimates, a projected timeline for meeting this goal, and include an assessment of the potential value of any additional data products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1479","usgsCitation":"U.S. Geological Survey and Mexican National Commission for the Knowledge and Use of Biodiversity, 2021, The North American Breeding Bird Survey in Mexico, 2008 to 2018—A Status Report: U.S. Geological Survey Circular 1479, 33 p., https://doi.org/10.3133/cir1479.","productDescription":"Report: v, 33 p.; Data Release","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120948","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":436297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L4KBDC","text":"USGS data release","linkHelpText":"The North American Breeding Bird Survey in Mexico, 2008-2018 - unprocessed data"},{"id":386569,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1479/cir1479.pdf","text":"Report","size":"14.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1479"},{"id":386568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1479/coverthb.jpg"},{"id":386570,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.doi.org/10.5066/P9L4KBDC","text":"USGS data release","linkHelpText":"The North American Breeding Bird Survey in Mexico, 2008–2018—unprocessed data"}],"country":"Mexico","state":"Baja California, Baja California Sur, Chihuahua, Coahuila, Nuevo Leon, Sonora, Tamaulipas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.09423828125,\n 26.303264239389534\n ],\n [\n -108.336181640625,\n 27.049341619870376\n ],\n [\n -108.17138671875,\n 26.83387451505858\n ],\n [\n -107.808837890625,\n 26.185018250078308\n ],\n [\n -106.61132812499999,\n 25.54244147012483\n ],\n [\n -106.051025390625,\n 26.814266197561462\n ],\n [\n -104.501953125,\n 26.322960198925365\n ],\n [\n -104.21630859375,\n 26.775039386999605\n ],\n [\n -103.370361328125,\n 26.56887654795065\n ],\n [\n -103.436279296875,\n 25.3241665257384\n ],\n [\n -102.908935546875,\n 24.716895455859337\n ],\n [\n -102.469482421875,\n 25.06569718553588\n ],\n [\n -101.898193359375,\n 24.986058021167594\n ],\n [\n -100.74462890625,\n 24.5271348225978\n ],\n [\n -100.404052734375,\n 23.200960808078566\n ],\n [\n -100.1953125,\n 23.271627053918277\n ],\n [\n -100.08544921874999,\n 22.806567100271522\n ],\n [\n -99.42626953125,\n 22.664709810176827\n ],\n [\n -98.909912109375,\n 22.31958944283391\n ],\n [\n -98.63525390624999,\n 22.370396344320053\n ],\n [\n -98.184814453125,\n 22.451648819126202\n ],\n [\n -97.7947998046875,\n 22.17214491738176\n ],\n [\n -97.7288818359375,\n 24.307053283225915\n ],\n [\n -97.38830566406249,\n 25.18505888358067\n ],\n [\n -97.1246337890625,\n 25.962983554822678\n ],\n [\n -97.37182617187499,\n 25.86416657624641\n ],\n [\n -97.7398681640625,\n 26.03704188651584\n ],\n [\n -98.2342529296875,\n 26.046912801683984\n ],\n [\n -99.085693359375,\n 26.441065564038418\n ],\n [\n -99.50866699218749,\n 27.36201054924028\n ],\n [\n -99.51416015625,\n 27.581329075043357\n ],\n [\n -100.579833984375,\n 28.7965462417692\n ],\n [\n -101.4532470703125,\n 29.76437737516313\n ],\n [\n -102.32666015625,\n 29.854937397596693\n ],\n [\n -102.623291015625,\n 29.72145191669099\n ],\n [\n -103.216552734375,\n 28.98411731593083\n ],\n [\n -104.249267578125,\n 29.501768632523262\n ],\n [\n -104.5513916015625,\n 29.625996273660785\n ],\n [\n -104.6832275390625,\n 30.178373310707887\n ],\n [\n -105.0018310546875,\n 30.680439786468128\n ],\n [\n -106.46301269531249,\n 31.765537409484374\n ],\n [\n -106.5673828125,\n 31.770207631866715\n ],\n [\n -108.1988525390625,\n 31.774877618507386\n ],\n [\n -108.204345703125,\n 31.320794146937374\n ],\n [\n -111.0443115234375,\n 31.325486676506983\n ],\n [\n -114.466552734375,\n 32.37996146435729\n ],\n [\n -114.81811523437501,\n 32.509761735919426\n ],\n [\n -114.71923828124999,\n 32.72721987021932\n ],\n [\n -117.13623046874999,\n 32.54218257955074\n ],\n [\n -117.10876464843749,\n 32.29177633471201\n ],\n [\n -116.73522949218751,\n 31.81689688674699\n ],\n [\n -116.75170898437501,\n 31.63467554954133\n ],\n [\n -116.53198242187499,\n 31.245681880715527\n ],\n [\n -115.64208984374999,\n 29.616445727622548\n ],\n [\n -115.6640625,\n 27.737022779516813\n ],\n [\n -113.25256347656249,\n 26.115985925333536\n ],\n [\n -111.68701171875,\n 23.83560098662095\n ],\n [\n -109.8687744140625,\n 22.71032284205226\n ],\n [\n -109.2041015625,\n 23.427968862308678\n ],\n [\n -110.54443359375,\n 25.27450351782018\n ],\n [\n -109.434814453125,\n 26.367263860129366\n ],\n [\n -109.09423828125,\n 26.303264239389534\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD
Crustal fragments underlain by high-grade rocks represent a challenge to plate reconstructions, and integrated mapping, geochronology, and geochemistry enable the unravelling of the temporal and spatial history of exotic crustal blocks. The Quinebaug-Marlboro belt (QMB) is an enigmatic fragment on the trailing edge of the peri-Gondwanan Ganderian margin of southeastern New England. SHRIMP U-Pb geochronology and geochemistry indicate the presence of Ediacaran to Cambrian metamorphosed volcanic and intrusive rocks dated for the first time between ca. 540–500 Ma. The entire belt may preserve a cryptic, internal stratigraphy that is truncated by subsequent faulting. Detrital zircons from metapelite in the overlying Nashoba and Tatnic Hill Formations indicate deposition between ca. 485–435 Ma, with provenance from the underlying QMB or Ganderian crust. The Preston Gabbro (418 ± 3 Ma) provides a minimum age for the QMB. Mafic rocks are tholeiitic with trace elements that resemble arc and E-MORB sources, and samples with negative Nb-Ta anomalies are similar to arc-like rocks, but others show no negative Nb-Ta anomaly and are similar to rocks from E-MORB to OIB or backarc settings. Geochemistry points to a mixture of sources that include both mantle and continental crust. Metamorphic zircon, monazite, and titanite ages range from 400 to 305 Ma and intrusion of granitoids and migmatization occurred between 410 and 325 Ma. Age and chemistry support correlations with the Ellsworth terrane in Maine and the Penobscot arc and backarc system in Maritime Canada. The arc-rifting zone where the Mariana arc and the Mariana backarc basin converge is a possible modern analog.
The effects of runoff from land on nearshore ecosystems, including coral reef communities, are influenced by both sediment supply and removal by coastal processes. Integrated studies across the land-sea interface describing sources and transport of terrestrial sediment and its nearshore fate allow reef protection initiatives to target key onshore and offshore areas. Geochemical signatures in the fine fraction of terrestrial sediment from watersheds in southwest Puerto Rico were determined by multivariate principal component analysis and used to identify terrestrial sources of sediment runoff to nearshore coral reefs. Sediment settling out of suspension at reefs was collected at approximately 2 month-long intervals in bottom-mounted sediment traps from May 2017 to June 2018, a period that included Hurricanes Irma and Maria. Bulk sediment accumulation rates in traps exceeded a 10 mg/cm2/d threshold found to stress corals at 5 of 7 reef sites throughout the 13 month-long study. Geochemical signatures showed that watersheds 10s km to the east were a predominant, year-round source of fine sediment to reefs offshore of Guánica Bay and could have introduced sediment-bound contaminants due to a higher degree of industrialization and urbanization than the local watershed. Sediment runoff from the local watershed appeared to be constrained to a narrow band close to shore. During the 2.5 months after Hurricanes Irma and Maria, bulk sediment accumulation rates increased substantially and fine sediment geochemical signatures were indicative of predominantly distal sources, except outside of the mouth of Guánica Bay, which was strongly impacted by local runoff. Mass wasting, sediment runoff, and coastal turbidity persisted for months after Hurricane Maria and could account for the appearance of a small fraction of geochemical variance from a distal sediment source that appeared in reef traps 4 months post-hurricane and persisted through the end of the study 9 months post-hurricane. Sediment geochemical sourcing in temporally resolved records from sediment traps showed how landscape-scale changes after a major hurricane affected both near-term and long-term sediment delivery to reef communities. In addition, the importance of fine sediment advection from distal sources indicates that successful reduction of land-based pressures on nearshore ecosystems will require cross-jurisdictional strategies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2021.107476","usgsCitation":"Takesue, R.K., Sherman, C.E., Reyes, A.O., Cheriton, O.M., Ramirez, N.I., Viqueira Rios, R., and Storlazzi, C.D., 2021, Land-based sediment sources and transport to southwest Puerto Rico coral reefs after Hurricane Maria, May 2017 to June 2018: Estuarine, Coastal and Shelf Science, v. 259, 107476, 12 p., https://doi.org/10.1016/j.ecss.2021.107476.","productDescription":"107476, 12 p.","ipdsId":"IP-113468","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451825,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2021.107476","text":"Publisher Index Page"},{"id":386790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.16354370117188,\n 17.770920015568638\n ],\n [\n -66.1761474609375,\n 17.770920015568638\n ],\n [\n -66.1761474609375,\n 18.135411517108345\n ],\n [\n -67.16354370117188,\n 18.135411517108345\n ],\n [\n -67.16354370117188,\n 17.770920015568638\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Takesue, Renee K. 0000-0003-1205-0825 rtakesue@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0825","contributorId":2159,"corporation":false,"usgs":true,"family":"Takesue","given":"Renee","email":"rtakesue@usgs.gov","middleInitial":"K.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherman, Clark E. 0000-0003-0758-7900","orcid":"https://orcid.org/0000-0003-0758-7900","contributorId":259180,"corporation":false,"usgs":false,"family":"Sherman","given":"Clark","middleInitial":"E.","affiliations":[{"id":34129,"text":"University of Puerto Rico Mayaguez","active":true,"usgs":false}],"preferred":false,"id":818371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reyes, Aaron O.","contributorId":260655,"corporation":false,"usgs":false,"family":"Reyes","given":"Aaron","email":"","middleInitial":"O.","affiliations":[{"id":52630,"text":"Westfield State University","active":true,"usgs":false}],"preferred":false,"id":818372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheriton, Olivia M. 0000-0003-3011-9136","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":204459,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramirez, Natalia I.","contributorId":260656,"corporation":false,"usgs":false,"family":"Ramirez","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":52631,"text":"University of Puerto Rico at Mayaguez","active":true,"usgs":false}],"preferred":false,"id":818374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Viqueira Rios, Roberto","contributorId":260657,"corporation":false,"usgs":false,"family":"Viqueira Rios","given":"Roberto","email":"","affiliations":[{"id":52632,"text":"Protectores de Cuencas, Inc.","active":true,"usgs":false}],"preferred":false,"id":818375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818376,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229497,"text":"70229497 - 2021 - The marine terraces of Santa Cruz Island, California: Implications for glacial isostatic adjustment models of last-interglacial sea-level history","interactions":[],"lastModifiedDate":"2022-03-09T12:47:41.369958","indexId":"70229497","displayToPublicDate":"2021-06-18T06:43:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"The marine terraces of Santa Cruz Island, California: Implications for glacial isostatic adjustment models of last-interglacial sea-level history","docAbstract":"Glacial isostatic adjustment (GIA) models hypothesize that along coastal California, last interglacial (LIG, broadly from ~130 to ~115 ka) sea level could have been as high as +11 m to +13 m, relative to present, substantially higher than the commonly estimated elevation of +6 m. Areas with low uplift rates can test whether such models are valid. Marine terraces on Santa Cruz Island have previously been reported to occur at low (<10 m) elevations, but ages of many such localities are not known. Using lidar imagery as a base, marine terraces on Santa Cruz Island were newly mapped, elevations were measured, fossils were collected for U-series dating (corals), strontium isotope compositions and amino acid geochronology (mollusks), and paleozoogeography (all taxa). Sr isotope compositions of mollusks from the highest of three marine terraces give ages of ~2.5 Ma to 1.9 Ma, along with Pliocene ages, from shells interpreted to be reworked. U-series ages of corals from the western part of the island indicate that low-elevation terraces north of the Santa Cruz Island fault correlate to the LIG. Where corals are lacking, amino acid ratios and faunal aspects support terrace correlation to the LIG high stand of sea. Elevations of most terrace localities north of the east-west trending Santa Cruz Island fault, in both the western and eastern parts of the island, range from 5.75 m to 8 m above sea level, well below the modeled paleo-sea-level range. Subsidence is ruled out as a mechanism for explaining the lower-than-modeled elevations, because higher-elevation terraces are present along much of the Santa Cruz Island coast north of the fault, indicating long-term tectonic uplift. The low elevations of the LIG terrace fragments are, however, consistent with a low rate of uplift derived from the higher, ~2.5–1.9 Ma terrace. A number of other localities on the Pacific Coast, also dated to the LIG, have marine terrace elevations below the modeled level. GIA models may have overestimated last interglacial sea level by a substantial amount and need to be revised if used for forecasts for future sea-level rise.
More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture–recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pbio.3001307","usgsCitation":"Nichols, J.D., Bogich, T.L., Howerton, E., Bjornstad, O.N., Borchering, R.K., Ferrari, M.J., Haran, M., Jewell, C., Pepin, K., Probert, W.J., Pulliam, J.R., Runge, M.C., Tildesley, M.J., Viboud, C., and Shea, K., 2021, Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making: PLoS Biology, v. 19, no. 6, e3001307, 9 p., https://doi.org/10.1371/journal.pbio.3001307.","productDescription":"e3001307, 9 p.","ipdsId":"IP-128913","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451848,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pbio.3001307","text":"Publisher Index Page"},{"id":386644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":818000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bogich, Tiffany L. 0000-0002-8143-5289","orcid":"https://orcid.org/0000-0002-8143-5289","contributorId":260459,"corporation":false,"usgs":false,"family":"Bogich","given":"Tiffany","email":"","middleInitial":"L.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":818001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howerton, Emily 0000-0002-0639-3728","orcid":"https://orcid.org/0000-0002-0639-3728","contributorId":258035,"corporation":false,"usgs":false,"family":"Howerton","given":"Emily","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":818002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bjornstad, Ottar N. 0000-0002-1158-3753","orcid":"https://orcid.org/0000-0002-1158-3753","contributorId":217708,"corporation":false,"usgs":false,"family":"Bjornstad","given":"Ottar","email":"","middleInitial":"N.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":818003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borchering, Rebecca K. 0000-0003-4309-2913","orcid":"https://orcid.org/0000-0003-4309-2913","contributorId":258031,"corporation":false,"usgs":false,"family":"Borchering","given":"Rebecca","email":"","middleInitial":"K.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":818004,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":818005,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haran, Murali 0000-0003-4440-8625","orcid":"https://orcid.org/0000-0003-4440-8625","contributorId":260461,"corporation":false,"usgs":false,"family":"Haran","given":"Murali","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":818006,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jewell, Christopher P. 0000-0002-7902-2178","orcid":"https://orcid.org/0000-0002-7902-2178","contributorId":260466,"corporation":false,"usgs":false,"family":"Jewell","given":"Christopher P.","affiliations":[{"id":33563,"text":"Lancaster University","active":true,"usgs":false}],"preferred":false,"id":818012,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pepin, Kim M. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":187441,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim M.","affiliations":[],"preferred":false,"id":818007,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Probert, William J. M. 0000-0002-3437-759X","orcid":"https://orcid.org/0000-0002-3437-759X","contributorId":216183,"corporation":false,"usgs":false,"family":"Probert","given":"William","email":"","middleInitial":"J. M.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":818008,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pulliam, Juliet R. C. 0000-0003-3314-8223","orcid":"https://orcid.org/0000-0003-3314-8223","contributorId":260463,"corporation":false,"usgs":false,"family":"Pulliam","given":"Juliet","email":"","middleInitial":"R. C.","affiliations":[{"id":39919,"text":"Stellenbosch University","active":true,"usgs":false}],"preferred":false,"id":818009,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":818010,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":818011,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Viboud, Cecile 0000-0003-3243-4711","orcid":"https://orcid.org/0000-0003-3243-4711","contributorId":258034,"corporation":false,"usgs":false,"family":"Viboud","given":"Cecile","email":"","affiliations":[{"id":52216,"text":"National Institutes of Health Fogarty International Center","active":true,"usgs":false}],"preferred":false,"id":818013,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":818014,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70221549,"text":"70221549 - 2021 - Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","interactions":[],"lastModifiedDate":"2021-06-23T12:28:09.472652","indexId":"70221549","displayToPublicDate":"2021-06-17T06:59:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","docAbstract":"In contrast to abrupt changes caused by land cover conversion, subtle changes driven by a shift in the condition, structure, or other biological attributes of land often lead to minimal and slower alterations of the terrestrial surface. Accurate mapping and monitoring of subtle change are crucial for an early warning of long-term gradual change that may eventually result in land cover conversion. Freely accessible moderate-resolution datasets such as the Landsat archive have great potential to characterize subtle change by capturing low-magnitude spectral changes in long-term observations. However, past studies have reported limited success in accurately extracting subtle changes from satellite-based time series analysis. In this study, we introduce a supervised framework named ‘PIDS’ to detect subtle forest disturbance from a comprehensive Landsat data archive by leveraging disturbance-based calibration sites. PIDS consists of four components: (1) Parameter optimization; (2) Index selection; (3) Dynamic stratified monitoring; and (4) Spatial consideration. PIDS was applied to map the early stage of bark beetle infestations (i.e., a lower per-pixel fraction of trees cover that show visual signs of infestation), which are a typical example of subtle change in conifer forests. Landsat Analysis Ready Data were used as the time series inputs for mapping mountain pine beetle and spruce beetle disturbance between 2001 and 2019 in Colorado, USA. PIDS-detection map assessment showed that the overall performance of PIDS (namely ‘F1 score’) was 0.86 for mountain pine beetle and 0.73 for spruce beetle, making a substantial improvement (> 0.3) compared to other approaches/products including COntinuous monitoring of Land Disturbance, LandTrendr, and the National Land Cover Database forest disturbance product. A sub-pixel analysis of tree canopy mortality percentage was performed by linking classified high-resolution (0.3- and 1-m) aerial imagery and 30-m PIDS-detection maps. Results show that PIDS typically detects mountain pine beetle infestation when ≥56% of a Landsat pixel is occupied by red-stage canopy mortality (one year after initial infestation), and spruce beetle infestation when ≥55% is occupied by gray-stage mortality (two years after initial infestation). This study addresses an important methodological goal pertinent to the utility of event-based reference samples for detecting subtle forest change, which could be potentially applied to other types of subtle land change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112560","usgsCitation":"Ye, S., Rogan, J., Zhu, Z., Hawbaker, T., Hart, S., Andrus, R.A., Meddens, A.J., Hicke, J.A., Eastman, J.R., and Kulakowski, D., 2021, Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance: Remote Sensing of Environment, v. 263, 112560, 16 p., https://doi.org/10.1016/j.rse.2021.112560.","productDescription":"112560, 16 p.","ipdsId":"IP-124774","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":488056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112560","text":"Publisher Index Page"},{"id":386642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0283203125,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"263","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ye, Su","contributorId":260471,"corporation":false,"usgs":false,"family":"Ye","given":"Su","email":"","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogan, John","contributorId":260472,"corporation":false,"usgs":false,"family":"Rogan","given":"John","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhe","contributorId":260473,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":818019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":818020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Sarah J.","contributorId":260474,"corporation":false,"usgs":false,"family":"Hart","given":"Sarah J.","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":818021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrus, Robert A.","contributorId":260475,"corporation":false,"usgs":false,"family":"Andrus","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818022,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meddens, Arjan J.H.","contributorId":260476,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","middleInitial":"J.H.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818023,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hicke, Jeffery A.","contributorId":260477,"corporation":false,"usgs":false,"family":"Hicke","given":"Jeffery","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":818024,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eastman, J. Ronald","contributorId":260480,"corporation":false,"usgs":false,"family":"Eastman","given":"J.","email":"","middleInitial":"Ronald","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818025,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kulakowski, Dominik","contributorId":260482,"corporation":false,"usgs":false,"family":"Kulakowski","given":"Dominik","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818026,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70221457,"text":"sir20215057 - 2021 - Bathymetry of New York City’s East of Hudson reservoirs and controlled lakes, 2017 to 2019","interactions":[],"lastModifiedDate":"2021-06-17T10:15:19.443423","indexId":"sir20215057","displayToPublicDate":"2021-06-16T15:05:00","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":"2021-5057","displayTitle":"Bathymetry of New York City’s East of Hudson Reservoirs and Controlled Lakes, 2017 to 2019","title":"Bathymetry of New York City’s East of Hudson reservoirs and controlled lakes, 2017 to 2019","docAbstract":"New York City maintains an extensive system of reservoirs and aqueducts to provide drinking water to its residents, including 16 reservoirs and controlled lakes in Westchester and Putnam Counties in southern New York, east of the Hudson River (also called “East of Hudson reservoirs and controlled lakes”). These reservoirs were put into service from 1842 to 1915, and their capacities have likely changed since their original construction. To provide updated bathymetric surface, contour, and capacity data, the U.S. Geological Survey, in cooperation with New York City Department of Environmental Protection, surveyed the bathymetry of the 16 East of Hudson reservoirs and controlled lakes from 2017 to 2019 using a multibeam echosounder. The points measured with the multibeam echosounder were combined with light detection and ranging data to generate 3.28-foot raster grids of the bathymetric surfaces, bathymetric contours at 2-foot intervals of elevation, and elevation-area-capacity tables. The results of the bathymetric survey show that the East of Hudson reservoirs range from about 25 feet deep (Kirk Lake) to about 162 feet deep (Kensico Reservoir) and have a total capacity of 142.9 billion gallons, with a combined surface area of more than 11,600 acres.
The accuracy of the mapped bathymetric data was evaluated using quality assurance datasets collected with a single-beam echosounder; about 284,000 quality assurance points were spatially joined with the mapped raster surface to compute measurement errors. The calculated mean point elevation error for the East of Hudson reservoirs was 0.35 foot, the median error was 0.21 foot, and the 95-percent accuracy was 1.68 feet; the 95-percent accuracy of the computed capacity at spillway elevation was 1.6 percent or less. The largest errors occurred in the steepest areas of the reservoirs and in areas where the data were interpolated. Geospatial files of the bathymetry data, including mapped bathymetric surfaces, contours, and capacity tables, quality assurance points, and associated metadata are available for download as part of an accompanying U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215057","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Nystrom, E.A., Huston, C.J., and Welk, R.J., 2021, Bathymetry of New York City’s East of Hudson reservoirs and controlled lakes, 2017 to 2019: U.S. Geological Survey Scientific Investigations Report 2021–5057, 46 p., https://doi.org/10.3133/sir20215057.","productDescription":"Report: viii, 46 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119563","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":386538,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZZQ2OT","text":"USGS data release","linkHelpText":"Geospatial bathymetry datasets for New York City's East of Hudson Reservoirs and Controlled Lakes"},{"id":386537,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5057/sir20215057.pdf","text":"Report","size":"30.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5057"},{"id":386536,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5057/coverthb2.jpg"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.992919921875,\n 40.88029480552824\n ],\n [\n -73.49853515625,\n 40.88029480552824\n ],\n [\n -73.49853515625,\n 41.40153558289846\n ],\n [\n -73.992919921875,\n 41.40153558289846\n ],\n [\n -73.992919921875,\n 40.88029480552824\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Controlling range expansion of invasive carp (specifically Hypophthalmichthys spp.) on the Tennessee River is important to conserve the ecological and economic benefits provided by the river. We collaborated with State and Federal agencies (the stakeholder group) to develop a decision framework and decision support model to evaluate strategies to control carp expansion in the Tennessee River. Using this decision framework, we assessed the efficacy of various barrier strategies (technologies and locations) on reducing bigheaded carp (Hypophthalmichthys nobilis [bighead carp] and Hypophthalmichthys molitrix [silver carp]) relative abundance under different patterns and magnitudes of population growth and movement. We also assessed whether or not these strategies induced tradeoffs between reducing bigheaded carp relative abundance and other considerations for public satisfaction, effects on lock operation, and native species. For the purpose of comparing options to control carp in a quantitative framework, we codeveloped a carp population dynamics model with the stakeholder group. We then used the model to compare invasive carp management options within the Tennessee River system. The actions we considered included barrier placement at lock and dam systems and targeted removal through harvest, which were believed to impede upstream carp spread and establishment. To account for the uncertainty in carp population growth and movement rates, the group developed four population models that varied in the underlying population dynamics and population growth rates. The models affected population growth through either the stock-recruitment relation or intrinsic density-dependent growth rate. We then tasked the stakeholder group to test various strategies using the model. We then developed a more formal optimization framework and solved for strategies that performed well under scenarios of barrier effectiveness, movement rate, recruitment frequency, fishing mortality, and variation in population growth rate. The results of our qualitative and quantitative analyses indicated that strategies designed to first protect reservoirs just above the leading edge of carp invasion by installing barriers and removing fish below that point would perform best; however, this depended on barrier effectiveness. When barrier effectiveness was high, simply cutting off the presumed source of carp and blocking the leading the edge was enough to stop carp invasion; however, lower effectiveness meant that more barriers would be needed to slow, but not completely stop, carp invasion. We discuss what these findings mean in terms of future monitoring and management efforts to reduce the potential for expanding carp invasion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211068","programNote":"Biological Threats Research Program","usgsCitation":"Post van der Burg, M., Smith, D.R., Cupp, A.R., Rogers, M.W., and Chapman, D.C., 2021, Decision analysis of barrier placement and targeted removal to control invasive carp in the Tennessee River Basin: U.S. Geological Survey Open-File Report 2021–1068, 18 p., https://doi.org/10.3133/ofr20211068.","productDescription":"vi, 18 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-129842","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":386492,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1068/coverthb2.jpg"},{"id":386493,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1068/ofr20211068.pdf","text":"Report","size":"979 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1068"}],"country":"United States","state":"Kentucky","otherGeospatial":"Tennessee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.516845703125,\n 37.01132594307015\n ],\n [\n -87.12158203125,\n 37.42252593456307\n ],\n [\n -85.111083984375,\n 38.212288054388175\n ],\n [\n -84.13330078125,\n 38.74551518488265\n ],\n [\n -84.48486328124999,\n 39.036252959636606\n ],\n [\n -85.078125,\n 39.13006024213511\n ],\n [\n -85.97900390625,\n 38.98503278695909\n ],\n [\n -87.022705078125,\n 38.54816542304656\n ],\n [\n -87.945556640625,\n 38.24680876017446\n ],\n [\n -88.72558593749999,\n 37.70120736474139\n ],\n [\n -88.78051757812499,\n 37.32648861334206\n ],\n [\n -88.472900390625,\n 36.99377838872517\n ],\n [\n -88.516845703125,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Sea otter (Enhydra lutris) populations in southwest Alaska declined substantially between about 1990 and the most recent set of surveys in 2015. Here we report changes in the distribution and abundance of sea otters, and covarying patterns in reproduction, mortality, body size and condition, diet and foraging behavior, food availability, health profiles, and exposure to environmental contaminants over this 25-yr period. The population decline, which resulted in densities on the order of 5% of environmental carrying capacity, ranged from Attu Island in the west to about Castle Cape (on the south side of the Alaska Peninsula) in the east. Remaining sea otters moved closer to shore and into shallow, protected habitats. Reproductive rates appeared unchanged with the decline. Although the demographic cause of the decline was clearly elevated mortality, stranded carcasses were rare or absent. The net rate of energy gain by foraging sea otters, body length and condition, and prey biomass density, all increased after the decline and varied inversely with sea otter population density beyond the area of decline. Sea otters within the area of decline showed no increases in health anomalies, disease, contaminant exposure, or abnormal gene transcription patterns as compared to animals outside the area of decline. These collective findings are inconsistent with nutritional limitation, disease, or environmental contaminants, and consistent with predation (or possibly some other density-independent factor) as the reason for the sea otter population decline. Our approach and analyses provide a broad conceptual template for thinking about and assessing the causes of wildlife population declines.
Population monitoring is critical for informing the management and conservation of rare Hawaiian forest birds. In 2017, we used point-transect distance sampling methods to estimate population densities of birds on Haleakalā Volcano on east Maui island. We estimated the populations and ranges of three island-endemic Hawaiian honeycreepers, including the endangered ‘Ākohekohe (Palmeria dolei), the endangered Kiwikiu (Maui Parrotbill; Pseudonestor xanthophrys), and the Maui ʻAlauahio (Paroreomyza montana newtoni). We examined population trends back to 1980, and our 2017 density estimates were the lowest ever recorded for each species. Most concerning was the status of Kiwikiu, with a 71% decline in population since 2001 to a current population of 157 (95% CI 44–312) birds. The population of ‘Ākohekohe similarly decreased by 78% to a current population of 1768 (1193–2411) birds. For both species, population declines were due to declines in density and contraction of ranges from lower elevations. Both species are now restricted to ranges of less than 3000 ha. We surveyed ~ 91% of the range of Maui ‘Alauahio and estimated a population of 99,060 (88,502–106,954) birds, a 41% decrease since the highest estimate in 1992. Contraction of ranges to higher elevations is consistent with evidence that the impacts of avian malaria are being exacerbated by global warming trends. Our results indicate that the landscape control of either avian malaria transmission or its vector (Culex mosquitoes) will be a pre-requisite to preventing the extinction of endemic forest birds in Hawaii.
The oomycete Aphanomyces invadans (Saprolegniales, Oomycetes), the cause of epizootic ulcerative syndrome (EUS), is an OIE (World Organization for Animal Health) reportable pathogen, capable of infecting many fish species worldwide in both freshwater and estuarine environments (Iberahim et al. 2018). Since the discovery of EUS in Japan in 1971 (Egusa and Masuda 1971), it has spread globally and caused substantial fish mortalities and economic losses (Lilley et al. 1998). Cases in the United States have included lesions initially named ulcerative mycosis of Atlantic menhaden Brevoortia tyrannus (Noga et al. 1988) along the Atlantic coast and later confirmed as A. invadans in menhaden from the Chesapeake Bay (Blazer et al. 1999). It has also been reported in several freshwater and estuarine fishes from Florida, including largemouth bass Micropterus salmoides (Sosa et al. 2007) and channel catfish, black bullhead, and bluegill from recreational ponds in Louisiana (Hawke et al. 2003).This communication reports the finding of A. invadans in smallmouth bass Micropterus dolomieu from the Cheat River, West Virginia. During fish health assessments in October 2020, smallmouth bass with grossly observable skin lesions were determined to be infected with A. invadans. We believe this is the first report of A. invadans infection in smallmouth bass and it was not observed during previous health assessments in this region.
","language":"English","publisher":"Wiley","doi":"10.1111/jfd.13468","usgsCitation":"Walsh, H.L., Blazer, V., and Mazik, P.M., 2021, Identification of Aphanomyces invadans, the cause of epizootic ulcerative syndrome, in smallmouth bass (Micropterus dolomieu) from the Cheat River, West Virginia, USA: Journal of Fish Diseases, v. 44, no. 10, p. 1639-1641, https://doi.org/10.1111/jfd.13468.","productDescription":"3 p.","startPage":"1639","endPage":"1641","ipdsId":"IP-129024","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":386645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Cheat River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.16448974609375,\n 39.39587712612034\n ],\n [\n -79.84039306640625,\n 39.39587712612034\n ],\n [\n -79.84039306640625,\n 39.71775084250472\n ],\n [\n -80.16448974609375,\n 39.71775084250472\n ],\n [\n -80.16448974609375,\n 39.39587712612034\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Walsh, Heather L. 0000-0001-6392-4604 hwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":4696,"corporation":false,"usgs":true,"family":"Walsh","given":"Heather","email":"hwalsh@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":817997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":818038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":818039,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221853,"text":"70221853 - 2021 - A decision-analytical framework for developing harvest regulations","interactions":[],"lastModifiedDate":"2021-07-13T00:44:37.862671","indexId":"70221853","displayToPublicDate":"2021-06-07T19:39:38","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"7","title":"A decision-analytical framework for developing harvest regulations","docAbstract":"The development of harvest regulations for fish or wildlife is a complex decision that needs to weigh multiple objectives, consider a set of alternative regulatory options, integrate scientific understanding about the population dynamics of the harvested species as well as the human response to regulations, account for uncertainty, and provide an avenue for feedback from monitoring programs. The author describes how the field of decision analysis provides a framework for structuring such decisions and tools for navigating the components. At the center of any harvest management endeavor is a set of objectives that may include providing harvest opportunity, conserving the harvested population long into the future, and satisfying hunters, anglers, or trappers; tools from multi-criteria decision analysis are useful in finding the right balance among competing objectives. The population dynamics of harvested populations are often stochastic; tools from risk analysis and dynamic optimization can be used to find state-dependent policies that manage variation. Finally, harvest regulations are often set in the face of uncertainty; value-of-information methods can be used to evaluate the importance of that uncertainty, and adaptive management methods can be used to reduce it.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Harvest of fish and wildlife: New paradigms for sustainable management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","doi":"10.1201/9781003009054","usgsCitation":"Runge, M.C., 2021, A decision-analytical framework for developing harvest regulations, chap. 7 of Harvest of fish and wildlife: New paradigms for sustainable management, 15 p., https://doi.org/10.1201/9781003009054.","productDescription":"15 p.","ipdsId":"IP-123515","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-05-11","publicationStatus":"PW","contributors":{"editors":[{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":819202,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Powell, Larkin A.","contributorId":15100,"corporation":false,"usgs":true,"family":"Powell","given":"Larkin A.","affiliations":[],"preferred":false,"id":819203,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819006,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}