This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences. Satellite imagery can serve as a cost-effective and consistent monitoring technique for surface cyanobacterial blooms in source waters and can provide drinking water treatment operators information for managing their systems. This study uses satellite imagery from the European Space Agency's Ocean and Land Colour Instrument (OLCI) spanning June 2016 through April 2020. At 300-m spatial resolution, OLCI imagery can be used to monitor cyanobacteria in 685 drinking water sources across 285 lakes in 44 states, referred to here as resolvable drinking water sources. First, a subset of satellite data was compared to a subset of responses (n = 84) submitted as part of the U.S. Environmental Protection Agency's fourth Unregulated Contaminant Monitoring Rule (UCMR 4). These UCMR 4 qualitative responses included visual observations of algal bloom presence and absence near drinking water intakes from March 2018 through November 2019. Overall agreement between satellite imagery and UCMR 4 qualitative responses was 94% with a Kappa coefficient of 0.70. Next, temporal frequency of cyanobacterial blooms at all resolvable drinking water sources was assessed. In 2019, bloom frequency averaged 2% and peaked at 100%, where 100% indicated a bloom was always present at the source waters when satellite imagery was available. Monthly cyanobacterial abundances were used to assess short-term trends across all resolvable drinking water sources and effect size was computed to provide insight on the number of years of data that must be obtained to increase confidence in an observed change. Generally, 2016 through 2020 was an insufficient time period for confidently observing changes at these source waters; on average, a decade of satellite imagery would be required for observed environmental trends to outweigh variability in the data. However, five source waters did demonstrate a sustained short-term trend, with one increasing in cyanobacterial abundance from June 2016 to April 2020 and four decreasing.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2021.117377","usgsCitation":"Coffer, M., Schaeffer, B., Foreman, K., Porteous, A., Loftin, K.A., Stumpf, R., Werdell, J., Urquhart, E., Albert, R., and Darling, J., 2021, Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States: Water Research, v. 201, no. 1, 117377, 13 p., https://doi.org/10.1016/j.watres.2021.117377.","productDescription":"117377, 13 p.","ipdsId":"IP-129698","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":451757,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2021.117377","text":"Publisher Index Page"},{"id":387133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"201","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coffer, Megan","contributorId":260848,"corporation":false,"usgs":false,"family":"Coffer","given":"Megan","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaeffer, Blake A.","contributorId":260849,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake A.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foreman, Katherine","contributorId":260850,"corporation":false,"usgs":false,"family":"Foreman","given":"Katherine","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Porteous, Alex","contributorId":260851,"corporation":false,"usgs":false,"family":"Porteous","given":"Alex","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":818970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stumpf, Richard","contributorId":260852,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werdell, Jeremy","contributorId":260853,"corporation":false,"usgs":false,"family":"Werdell","given":"Jeremy","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818972,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Urquhart, Erin","contributorId":260854,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818973,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Albert, Ryan","contributorId":260855,"corporation":false,"usgs":false,"family":"Albert","given":"Ryan","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818974,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Darling, John","contributorId":260856,"corporation":false,"usgs":false,"family":"Darling","given":"John","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818975,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70221576,"text":"sir20215059 - 2021 - Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota","interactions":[],"lastModifiedDate":"2021-06-25T11:51:29.973079","indexId":"sir20215059","displayToPublicDate":"2021-06-24T10:38:00","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":"2021-5059","displayTitle":"Borehole Analysis, Single-Well Aquifer Testing, and Water Quality for the Burnpit Well, Mount Rushmore National Memorial, South Dakota","title":"Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota","docAbstract":"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
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
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 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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":70221878,"text":"70221878 - 2021 - Exploring the potential of electrospray-Orbitrap for stable isotope analysis using nitrate as a model","interactions":[],"lastModifiedDate":"2021-07-12T13:41:12.547617","indexId":"70221878","displayToPublicDate":"2021-06-24T08:37:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":761,"text":"Analytical Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the potential of electrospray-Orbitrap for stable isotope analysis using nitrate as a model","docAbstract":"Widely used isotope ratio mass spectrometers have limited capabilities to measure metabolites, drugs, or small polyatomic ions without the loss of structural isotopic information. A new approach has recently been introduced that uses electrospray ionization Orbitrap to measure multidimensional isotope signatures of intact polar compounds. Using nitrate as a model compound, this study aims to establish performance metrics for comparisons with conventional IRMS at the natural abundance level. We present a framework on how to convert isotopolog intensities to δ values that are commonly used in the isotope geochemistry community. The quantification of seven nitrate isotopologs provides multiple pathways for obtaining the primary N and O δ values including non-mass-dependent O isotope variations, as well as opportunities to explore nonrandom isotopic distributions (i.e., clumping effects) within molecular nitrate. Using automation and the adaptation of measurement principles that are specific to isotope ratio analysis, nitrate δ15NAIR, δ18OVSMOW, and δ17OVSMOW were measured with a long-term precision of 0.4‰ or better for isotopic reference materials and purified nitrate from environmental samples. In addition, we demonstrate promising results for unpurified environmental samples in liquid form. With these new developments, this study connects the two largely disparate mass spectrometry fields of bioanalytical MS and isotope ratio MS, thus providing a route to measure new isotopic signatures in diverse organic and inorganic solutes.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.analchem.1c00944","usgsCitation":"Hilkert, A., Bohlke, J., Mroczkowski, S.J., Fort, K.L., Aizikov, K., Wang, X.T., Kopf, S.H., and Neubauer, C., 2021, Exploring the potential of electrospray-Orbitrap for stable isotope analysis using nitrate as a model: Analytical Chemistry, v. 93, p. 9139-9148, https://doi.org/10.1021/acs.analchem.1c00944.","productDescription":"10 p.","startPage":"9139","endPage":"9148","ipdsId":"IP-126882","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":387105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","noUsgsAuthors":false,"publicationDate":"2021-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hilkert, Andreas","contributorId":260947,"corporation":false,"usgs":false,"family":"Hilkert","given":"Andreas","email":"","affiliations":[{"id":52734,"text":"Thermo-Fisher Scientific","active":true,"usgs":false}],"preferred":false,"id":819178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":819179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mroczkowski, Stanley J. 0000-0001-8026-6025 smroczko@usgs.gov","orcid":"https://orcid.org/0000-0001-8026-6025","contributorId":2628,"corporation":false,"usgs":true,"family":"Mroczkowski","given":"Stanley","email":"smroczko@usgs.gov","middleInitial":"J.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":819180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fort, Kyle L.","contributorId":260948,"corporation":false,"usgs":false,"family":"Fort","given":"Kyle","email":"","middleInitial":"L.","affiliations":[{"id":52734,"text":"Thermo-Fisher Scientific","active":true,"usgs":false}],"preferred":false,"id":819181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aizikov, Konstantin","contributorId":260949,"corporation":false,"usgs":false,"family":"Aizikov","given":"Konstantin","email":"","affiliations":[{"id":52734,"text":"Thermo-Fisher Scientific","active":true,"usgs":false}],"preferred":false,"id":819182,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Xinchen T.","contributorId":260950,"corporation":false,"usgs":false,"family":"Wang","given":"Xinchen","email":"","middleInitial":"T.","affiliations":[{"id":13422,"text":"Boston College","active":true,"usgs":false}],"preferred":false,"id":819183,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kopf, Sebastian H.","contributorId":260951,"corporation":false,"usgs":false,"family":"Kopf","given":"Sebastian","email":"","middleInitial":"H.","affiliations":[{"id":51970,"text":"U Colorado","active":true,"usgs":false}],"preferred":false,"id":819184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Neubauer, Cajetan","contributorId":260952,"corporation":false,"usgs":false,"family":"Neubauer","given":"Cajetan","email":"","affiliations":[{"id":51970,"text":"U Colorado","active":true,"usgs":false}],"preferred":false,"id":819185,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224562,"text":"70224562 - 2021 - Experimental warming across a tropical forest canopy height gradient reveals minimal photosynthetic and respiratory acclimation","interactions":[],"lastModifiedDate":"2021-09-28T12:44:23.554388","indexId":"70224562","displayToPublicDate":"2021-06-24T07:42:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9360,"text":"Plant, Cell, and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Experimental warming across a tropical forest canopy height gradient reveals minimal photosynthetic and respiratory acclimation","docAbstract":"Tropical forest canopies cycle vast amounts of carbon, yet we still have a limited understanding of how these critical ecosystems will respond to climate warming. We implemented in situ leaf-level + 3°C experimental warming from the understory to the upper canopy of two Puerto Rican tropical tree species, Guarea guidonia and Ocotea sintenisii. After approximately 1 month of continuous warming, we assessed adjustments in photosynthesis, chlorophyll fluorescence, stomatal conductance, leaf traits and foliar respiration. Warming did not alter net photosynthetic temperature response for either species; however, the optimum temperature of Ocotea understory leaf photosynthetic electron transport shifted upward. There was no Ocotea respiratory treatment effect, while Guarea respiratory temperature sensitivity (Q10) was down-regulated in heated leaves. The optimum temperatures for photosynthesis (Topt) decreased 3–5°C from understory to the highest canopy position, perhaps due to upper canopy stomatal conductance limitations. Guarea upper canopy Topt was similar to the mean daytime temperatures, while Ocotea canopy leaves often operated above Topt. With minimal acclimation to warmer temperatures in the upper canopy, further warming could put these forests at risk of reduced CO2 uptake, which could weaken the overall carbon sink strength of this tropical forest.
The conservation of native insect pollinators is hampered by a lack of information about environmental factors influencing pollinator communities. We investigated how insect pollinator communities, composed of bees (Hymenoptera), butterflies and moths (Lepidoptera), and flies (Diptera), are influenced by spatial and temporal aspects of the environment in sagebrush steppe shrublands. We assessed hypotheses regarding spatial autocorrelation of communities, inter-annual variability in communities, influence of elevation on timing of emergence, and influence of weather on seasonal changes in relative abundance of different pollinator taxa. We captured 27,310 insects from 6 bee families, 27 butterfly and moth families, and 3 fly families. The occurrence of insect pollinators among sampling plots was not spatially autocorrelated, indicating that insect communities may be structured by habitat variation and microclimates over relatively fine spatial scales. Pollinator familial richness, diversity, abundance, and timing of emergence were most strongly positively associated with spatiotemporal variation in minimum daily temperatures at the ground surface during the active season. Emergence timing was positively correlated with growing degree days and percent humidity, regardless of elevation. All pollinator groups varied in abundance throughout their active season, peaking in early July (bees), late July (flies), or early August (butterflies and moths). Our findings suggest that changes in nighttime temperatures, which have been steadily increasing over the last several decades as a result of climate change, may have strong effects on sagebrush steppe pollinator communities. Also, non-bee pollinators may provide particularly important pollination in this vast ecosystem during the warmest time of the year.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01691","usgsCitation":"Rohde, A., and Pilliod, D.S., 2021, Spatiotemporal dynamics of insect pollinator communities in sagebrush steppe associated with weather and vegetation: Global Ecology and Conservatuin, v. 29, e01691, 16 p., https://doi.org/10.1016/j.gecco.2021.e01691.","productDescription":"e01691, 16 p.","ipdsId":"IP-111292","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":451760,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01691","text":"Publisher Index Page"},{"id":436292,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JL1WEN","text":"USGS data release","linkHelpText":"Insect community responses to climate and weather across elevation gradients in the Sagebrush Steppe, eastern Oregon 2012 and 2013"},{"id":389154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.2840576171875,\n 41.9921602333763\n ],\n [\n -117.00439453125,\n 41.9921602333763\n ],\n [\n -117.00439453125,\n 44.15068115978094\n ],\n [\n -119.2840576171875,\n 44.15068115978094\n ],\n [\n -119.2840576171875,\n 41.9921602333763\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rohde, Ashley 0000-0003-4939-3047 arohde@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-3047","contributorId":5250,"corporation":false,"usgs":true,"family":"Rohde","given":"Ashley","email":"arohde@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":823211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":823212,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221581,"text":"70221581 - 2021 - Sources and risk factors for nitrate and microbial contamination of private household wells in the fractured dolomite aquifer of northeastern Wisconsin","interactions":[],"lastModifiedDate":"2021-06-24T14:54:40.481459","indexId":"70221581","displayToPublicDate":"2021-06-23T09:52:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1542,"text":"Environmental Health Perspectives","active":true,"publicationSubtype":{"id":10}},"title":"Sources and risk factors for nitrate and microbial contamination of private household wells in the fractured dolomite aquifer of northeastern Wisconsin","docAbstract":"Groundwater quality in the Silurian dolomite aquifer in northeastern Wisconsin, USA, has become contentious as dairy farms and exurban development expand.
We investigated private household wells in the region, determining the extent, sources, and risk factors of nitrate and microbial contamination.
Total coliforms, Escherichia coli, and nitrate were evaluated by synoptic sampling during groundwater recharge and no-recharge periods. Additional seasonal sampling measured genetic markers of human and bovine fecal-associated microbes and enteric zoonotic pathogens. We constructed multivariable regression models of detection probability (log-binomial) and concentration (gamma) for each contaminant to identify risk factors related to land use, precipitation, hydrogeology, and well construction.
Total coliforms and nitrate were strongly associated with depth-to-bedrock at well sites and nearby agricultural land use, but not septic systems. Both human wastewater and cattle manure contributed to well contamination. Rotavirus group A, Cryptosporidium, and Salmonella were the most frequently detected pathogens. Wells positive for human fecal markers were associated with depth-to-groundwater and number of septic system drainfield within
These findings may inform policies to minimize contamination of the Silurian dolomite aquifer, a major water supply for the U.S. and Canadian Great Lakes region.
","language":"English","publisher":"Environmental health Perspectives","doi":"10.1289/EHP7813","usgsCitation":"Borchardt, M.A., Stokdyk, J.P., Kieke, B.A., Muldoon, M.A., Spencer, S.K., Firnstahl, A.D., Bonness, D., Hunt, R., and Burch, T., 2021, Sources and risk factors for nitrate and microbial contamination of private household wells in the fractured dolomite aquifer of northeastern Wisconsin: Environmental Health Perspectives, v. 129, no. 6, 067004, 18 p., https://doi.org/10.1289/EHP7813.","productDescription":"067004, 18 p.","ipdsId":"IP-120190","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":451762,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1289/ehp7813","text":"Publisher Index Page"},{"id":386701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]},\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"}}]}","volume":"129","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":818173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kieke, Burney A","contributorId":195802,"corporation":false,"usgs":false,"family":"Kieke","given":"Burney","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":818175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muldoon, Maureen A.","contributorId":198974,"corporation":false,"usgs":false,"family":"Muldoon","given":"Maureen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":818176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spencer, Susan K.","contributorId":181738,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":818177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bonness, Davina","contributorId":260613,"corporation":false,"usgs":false,"family":"Bonness","given":"Davina","email":"","affiliations":[{"id":52618,"text":"Kewaunee County","active":true,"usgs":false}],"preferred":false,"id":818179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818180,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":818181,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70221582,"text":"70221582 - 2021 - Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin","interactions":[],"lastModifiedDate":"2021-06-24T14:50:56.03881","indexId":"70221582","displayToPublicDate":"2021-06-23T09:46:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1542,"text":"Environmental Health Perspectives","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin","docAbstract":"Private wells are an important source of drinking water in Kewaunee County, Wisconsin. Due to the region’s fractured dolomite aquifer, these wells are vulnerable to contamination by human and zoonotic gastrointestinal pathogens originating from land-applied cattle manure and private septic systems.
We determined the magnitude of the health burden associated with contamination of private wells in Kewaunee County by feces-borne gastrointestinal pathogens.
This study used data from a year-long countywide pathogen occurrence study as inputs into a quantitative microbial risk assessment (QMRA) to predict the total cases of acute gastrointestinal illness (AGI) caused by private well contamination in the county. Microbial source tracking was used to associate predicted cases of illness with bovine, human, or unknown fecal sources.
Results suggest that private well contamination could be responsible for as many as 301 AGI cases per year in Kewaunee County, and that 230 and 12 cases per year were associated with a bovine and human fecal source, respectively. Furthermore, Cryptosporidium parvum was predicted to cause 190 cases per year, the most out of all 8 pathogens included in the QMRA.
This study has important implications for land use and water resource management in Kewaunee County and informs the public health impacts of consuming drinking water produced in other similarly vulnerable hydrogeological settings.
","language":"English","doi":"10.1289/EHP7815","usgsCitation":"Burch, T., Stokdyk, J.P., Spencer, S.K., Kieke, B.A., Firnstahl, A.D., Muldoon, M.A., and Borchardt, M.A., 2021, Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin: Environmental Health Perspectives, v. 129, no. 6, 067003, 9 p., https://doi.org/10.1289/EHP7815.","productDescription":"067003, 9 p.","ipdsId":"IP-120047","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":451765,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1289/ehp7815","text":"Publisher Index Page"},{"id":386700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]},\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"}}]}","volume":"129","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":818182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spencer, Susan K.","contributorId":210972,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","email":"","middleInitial":"K.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":818184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kieke, Burney A","contributorId":195802,"corporation":false,"usgs":false,"family":"Kieke","given":"Burney","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":818185,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818186,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muldoon, Maureen A.","contributorId":198974,"corporation":false,"usgs":false,"family":"Muldoon","given":"Maureen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":818187,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":210973,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":818188,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229073,"text":"70229073 - 2021 - Ecological engineering with oysters enhances coastal resilience efforts","interactions":[],"lastModifiedDate":"2022-03-01T12:07:46.755897","indexId":"70229073","displayToPublicDate":"2021-06-23T09:32:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Ecological engineering with oysters enhances coastal resilience efforts","docAbstract":"Coastal areas are especially vulnerable to habitat loss, sea-level rise, and other climate change effects. Oyster-dominated eco-engineered reefs have been promoted as integral components of engineered habitats enhancing coastal resilience through provision of numerous ecological, morphological, and socio-economic services. However, the assessed ‘success’ of these eco-engineered oyster reefs remains variable across projects and locations, with their general efficacy in promoting coastal resilience, along with related services, often mixed at best. Understanding factors influencing the success of these eco-engineered habitats as valuable coastal management tools could greatly inform related future efforts. Here, we review past studies incorporating reef-building oysters for coastal resilience and enhanced ecosystem services. Our aims are to better understand their utility and limitations, along with critical knowledge gaps to better advance future applicability. Success depends largely on site selection, informed by physical, chemical and biological factors, and adjacent habitats and bottom types. Better understanding of oyster metapopulation dynamics, tolerance and adaptation to changing conditions, and interactions with adjacent habitats will help to better identify suitable locations, and design more effective eco-engineered reefs. These eco-engineered reefs provide a useful tool to assist in developing coastal resilience in the face of climate change and sea level rise.
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
The handbooks and synchronized MP4 recordings provide hands-on instruction for creating and analyzing vegetation live fractional cover (LFC) maps. The methods and protocols used in the instruction materials follow those developed and recorded in Rangoonwala and Ramsey (2019). The LFC mapping and geographic information system (GIS) analyses highlight the consortium of rangeland conservancies covering the semiarid central region of Kenya (approximately 44,000 square kilometers).
The instruction materials are separated into two parts: processing and map-product creation based on remote-sensing images and GIS analyses of the created maps for rangeland management. The image processing is conducted using the advanced and professional software package SeNtinels Application Platform (SNAP) that is supported and maintained by the European Space Agency. SNAP is a free image analyses software package available for download. It is largely icon driven but offers simple to advanced program inserts and batch processing. The GIS analyses are conducted using the software package Quantum Geographic Information System (QGIS), another free and downloadable software. QGIS is compatible with numerous software, including the Esri suite of ArcGIS software and database structure. The image data includes both high-spatial-resolution Sentinel-2 optical data and Sentinel-1 synthetic aperture radar (SAR). Both datasets are freely available via a public portal that is maintained by the European Space Agency.
The image-processing instruction handbook covers all aspects of acquiring and processing satellite-image data and importing vector-data sources into SNAP. The GIS analysis handbook covers final creation of map products from the maps created in SNAP and creation of GIS procedures in QGIS that are needed to manage the rangeland resources for wildlife and pastoral grazing. Although focused on the Kenyan conservancies and their semiarid environment, the processing methods and procedures are applicable for similar environments and management, and to a large part, even for integrated mapping and GIS functionality of any managed landscape resource.
The instruction handbooks are synchronized to MP4 training videos created with U.S. Geological Survey-licensed Camtasia 9 software.
The workbook and MP4 video combinations are suitable for a single user or a workshop setting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211001","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Rangoonwala, A., and Ramsey, E., III, 2021, Grassland live fractional cover map creation and Geographic Information System (GIS) analysis for rangeland management supporting Kenya Northern Rangelands Trust Conservancies: U.S. Geological Survey Report 2021–1001, 59 p., https://doi.org/10.3133/ofr20211001.","productDescription":"Report: v, 59 p.; 2 Companion Files","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119513","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":386330,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1001/coverthb.jpg"},{"id":386331,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001.pdf","text":"Report","size":"5.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1001"},{"id":386397,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_SNAP_video/ofr20211001_SNAP_video_player.html","text":"Section I—SNAP Video Player","linkHelpText":"— SeNtinels Application Platform (SNAP)"},{"id":386334,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_video/ofr20211001_QGIS_video_player.html","text":"Section II—QGIS Video Player","description":"OFR 2021–1001 Video Player","linkHelpText":"— Quantum Geographic Information System (QGIS)"},{"id":386578,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_SNAP_video/","text":"Section I—SNAP package"},{"id":391246,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_shapefile","text":"Section II—QGIS Shapefiles"},{"id":386579,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_video/","text":"Section II—QGIS package"}],"country":"Kenya","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[40.993,-0.85829],[41.58513,-1.68325],[40.88477,-2.08255],[40.63785,-2.49979],[40.26304,-2.57309],[40.12119,-3.27768],[39.80006,-3.68116],[39.60489,-4.34653],[39.20222,-4.67677],[37.7669,-3.67712],[37.69869,-3.09699],[34.07262,-1.05982],[33.90371,-0.95],[33.89357,0.10981],[34.18,0.515],[34.6721,1.17694],[35.03599,1.90584],[34.59607,3.05374],[34.47913,3.5556],[34.005,4.24988],[34.6202,4.84712],[35.29801,5.506],[35.81745,5.33823],[35.81745,4.77697],[36.15908,4.44786],[36.85509,4.44786],[38.12091,3.59861],[38.43697,3.58851],[38.67114,3.61607],[38.89251,3.50074],[39.55938,3.42206],[39.85494,3.83879],[40.76848,4.25702],[41.1718,3.91909],[41.85508,3.91891],[40.98105,2.78452],[40.993,-0.85829]]]},\"properties\":{\"name\":\"Kenya\"}}]}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, Louisiana 70506
The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2020, the network consisted of 120 wells and piezometers. A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths. The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at the sites through water year 2020 (October 1, 2019, through September 30, 2020). Water levels that were collected from discontinued wells in previous water years were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1139","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Jurney, E.R., and Bell, M.T., 2021, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2020: U.S. Geological Survey Data Series 1139, 40 p., https://doi.org/10.3133/ds1139.","productDescription":"iv, 40 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-128111","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":386657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1139/coverthb.jpg"},{"id":386658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1139/ds1139.pdf","text":"Report","size":"6.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":386659,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ds/1139/images"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.3583984375,\n 34.261756524459805\n ],\n [\n -106.14990234375,\n 34.261756524459805\n ],\n [\n -106.14990234375,\n 35.65729624809628\n ],\n [\n -107.3583984375,\n 35.65729624809628\n ],\n [\n -107.3583984375,\n 34.261756524459805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Between 2012 and 2016, California suffered one of the most severe droughts on record. During this period Sequoiadendron giganteum (giant sequoias) in the Sequoia and Kings Canyon National Parks (SEKI), California, USA experienced canopy water content (CWC) loss, unprecedented foliage senescence, and, in a few cases, death. We present an assessment of the vulnerability of giant sequoia populations to droughts that is currently lacking and needed for management. We used a temporal trend of remotely sensed CWC obtained between 2015 and 2017, and recently georeferenced giant sequoia crowns to quantify the vulnerability of 7,408 individuals in 10 groves in the northern portion of SEKI. CWC is sensitive to changes in liquid water in tree canopies; therefore, it is a useful metric for quantifying the response of sequoia trees to drought. Temporal trends indicated that 9% of giant sequoias had a significant decline or consistently low CWC, suggesting these trees were likely operating at low photosynthetic capacity and potentially at high risk to drought stress. We also found that 20% of the giant sequoias had an increase or consistently high level of CWC, indicating these trees were at low risk to drought stress. These vulnerability categories were used in a random forest model with a combination of topographic, fire-related, and climate variables to generate high-resolution vulnerability risk maps. These maps show that higher risk is associated with lower elevation and higher climate water deficit. We also found that sequoias at higher elevations but located near meadows had higher vulnerability risk. These results and the vulnerability maps can identify vulnerable sequoias that may be difficult to save or locations of refugia to be protected, and thus may aid forest managers in preparation for future droughts.
Conservation actions to safeguard climate change vulnerable species may not be utilized due to a variety of perceived barriers. Assisted colonization, the intentional movement and release of an organism outside its historical range, is one tool available for species predicted to lose habitat under future climate change scenarios, particularly for single island or single mountain range endemic species. Despite the existence of policies that allow for this action, to date, assisted colonization has rarely been utilized for species of conservation concern in the Hawaiian Islands. Given the potential for climate driven biodiversity loss, the Hawaiian Islands are a prime location for the consideration of adaptation strategies. We used first-person interviews with conservation decision makers, managers, and scientists who work with endangered species in the Hawaiian Islands to identify perceived barriers to the use of assisted colonization. We found that assisted colonization was often not considered or utilized due to a lack of expertize with translocations; ecological risk and uncertainty, economic constraints, concerns regarding policies and permitting, concerns with public perception, and institutional resistance. Therefore, conservation planners may benefit from decision tools that integrate risk and uncertainty into decision models, and compare potential outcomes among conservation actions under consideration, including assisted colonization. Within a decision framework that addresses concerns, all conservation actions for climate sensitive species, including assisted colonization, may be considered in a timely manner.
Understanding of morphodynamic processes associated with large-scale floods has recently improved following significant advances of modern technologies. Nevertheless, a clear link between flood discharge and in-channel sedimentation processes remains to be resolved. The hydrological and geomorphological data available for the meandering Powder River (Montana, USA) since 1977 makes it a perfect laboratory to investigate connections between flood discharge and point-bar sedimentation processes. This study focuses on a point-bar that accreted laterally ca 70 m during a 50-year recurrence flood, which lasted about 14 days in May 1978. In September 2018, a trench ca 2 m deep and 70 m long was excavated through the axial point-bar deposits, and the 1978 flood deposits were delineated based on georeferenced pre-flood and post-flood cross-section surveys. Sedimentological data show that point-bar deposits accumulated at the early and late flood stages, when the flow was confined to the channel, and have similarities with classical facies models in terms of palaeocurrent patterns and vertical grain-size trend. However, during high-stage flood conditions, when the flow overtopped the bar, cross-cutting of the bar and armouring were typical processes. Integration of sedimentological and palaeo-hydrological data highlight that the relation between channel cross-sectional area and flood discharge play a key role in preserving bar deposits. The integrated approach adopted here provides a basis for advancing palaeoflood hydrology beyond the stage of estimating peak discharges to the next stage of estimating palaeoflood hydrographs.
An International Virtual Workshop on Global Seismology and Tectonics (IVWGST‐2020) was organized by the Geoscience and Technology Division of Council of Scientific and Industrial Research—North East Institute of Science and Technology, Jorhat, India from 14 to 25 September 2020. This workshop predominantly catered to undergraduate, postgraduate, and Ph.D. students, scientists, and academicians from across the globe. The primary motive of IVWGST-2020 was to inspire the participating students, perturbed by the unprecedented situation brought about by the COVID-19 pandemic, with quality lecture sessions, so as to lift their spirits. The virtual workshop served as a conduit for the students and researchers to directly interact with several pioneers and prominent geoscience researchers from around the world. Lectures, via Microsoft Teams, were given by 15 eminent speakers from diverse geoscience forums and institutions, and were attended by more than 1000 participants, mostly students and researchers, from 30 different countries. This report briefly summarizes the agenda, describes our experiences hosting the virtual workshop, and documents the challenges faced.
Control methods that target specific traits of an invasive species can produce results contrary to the aims of management. If targeted phenotypes exhibit heritability, then it follows that the invasive species could evolve greater resistance to the applied control measures over time. Additional complications emerge if those traits targeted by control are also inversely related to reproductive success. Given this, prudent considerations for invasive species management are to quantify the heritability of traits selected through control measures and gauge their relationship with reproductive success. Herein we provide a case study utilizing long-term field data and a multi-generational pedigree of an experimentally-closed population of brown treesnakes (N = 426; Boiga irregularis) on Guam. We employed an “animal model” to estimate the narrow-sense heritability (h2) for annual body condition, a trait related to both susceptibility to a primary tool used for brown treesnake control (i.e., live-lure traps) and annual reproductive success. Annual body condition displayed significant heritability [h2 = 0.149 (95% highest posterior density interval: 0.059–0.220)]. Considering a negative effect of body condition on susceptibility to trap capture but positive effect on reproductive success, significant heritability of body condition suggests the potential for live-lure traps to lose efficacy over time while also eliciting an undesirable effect on brown treesnake fecundity. Our results highlight the potential for negative repercussions that can stem from management actions, while also serving to underscore the evolutionary implications that are often overlooked but subsumed within invasive species control.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-021-02588-3","usgsCitation":"Levine, B., Douglas, M.R., Yackel Adams, A.A., Lardner, B., Reed, R., Savidge, J.A., and Douglas, M.E., 2021, Trait heritability and its implications for the management of an invasive vertebrate: Biological Invasions, v. 23, p. 3447-3456, https://doi.org/10.1007/s10530-021-02588-3.","productDescription":"10 p.","startPage":"3447","endPage":"3456","ipdsId":"IP-124543","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":386980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2021-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Levine, Brenna A","contributorId":243207,"corporation":false,"usgs":false,"family":"Levine","given":"Brenna A","affiliations":[{"id":38022,"text":"University of Tulsa","active":true,"usgs":false}],"preferred":false,"id":818727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, Marlis R","contributorId":243208,"corporation":false,"usgs":false,"family":"Douglas","given":"Marlis","email":"","middleInitial":"R","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":818728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lardner, Bjorn","contributorId":225066,"corporation":false,"usgs":false,"family":"Lardner","given":"Bjorn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818731,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818732,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Douglas, Michael E","contributorId":243209,"corporation":false,"usgs":false,"family":"Douglas","given":"Michael","email":"","middleInitial":"E","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":818733,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228553,"text":"70228553 - 2021 - Assessing the robustness of time-to-event models for estimating unmarked wildlife abundance using remote cameras","interactions":[],"lastModifiedDate":"2022-02-14T20:47:10.973293","indexId":"70228553","displayToPublicDate":"2021-06-22T15:46:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the robustness of time-to-event models for estimating unmarked wildlife abundance using remote cameras","docAbstract":"Recently developed methods, including time-to-event and space-to-event models, estimate the abundance of unmarked populations from encounter rates with camera trap arrays, addressing a gap in noninvasive wildlife monitoring. However, estimating abundance from encounter rates relies on assumptions that can be difficult to meet in the field, including random movement, population closure, and an accurate estimate of movement speed. Understanding how these models respond to violation of these assumptions will assist in making them more applicable in real-world settings. We used simulated walk models to test the effects of violating the assumptions of the time-to-event model under four scenarios: (1) incorrectly estimating movement speed, (2) violating closure, (3) individuals moving within simplified territories (i.e., movement restricted to partially overlapping circles), (4) and individuals clustering in preferred habitat. The time-to-event model was robust to closure violations, territoriality, and clustering when cameras were placed randomly. However, the model failed to estimate abundance accurately when movement speed was incorrectly estimated or cameras were placed nonrandomly with respect to habitat. We show that the time-to-event model can provide unbiased estimates of abundance when some assumptions that are commonly violated in wildlife studies are not met. Having a robust method for estimating the abundance of unmarked populations with remote cameras will allow practitioners to monitor a more diverse array of populations noninvasively. With the time-to-event model, placing cameras randomly with respect to animal movement and accurately estimating movement speed allows unbiased estimation of abundance. The model is robust to violating the other assumptions we tested.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2388","usgsCitation":"Loonam, K.E., Lukacs, P.M., Ausband, D.E., Mitchell, M.S., and Robinson, H., 2021, Assessing the robustness of time-to-event models for estimating unmarked wildlife abundance using remote cameras: Ecological Applications, v. 31, no. 6, e02388, 11 p., https://doi.org/10.1002/eap.2388.","productDescription":"e02388, 11 p.","ipdsId":"IP-117335","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Loonam, Kenneth E.","contributorId":276117,"corporation":false,"usgs":false,"family":"Loonam","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":834570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lukacs, Paul M.","contributorId":101240,"corporation":false,"usgs":true,"family":"Lukacs","given":"Paul","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":834571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Hugh S.","contributorId":276113,"corporation":false,"usgs":false,"family":"Robinson","given":"Hugh S.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":834573,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220649,"text":"sim3475 - 2021 - Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado","interactions":[],"lastModifiedDate":"2021-06-24T13:13:16.405477","indexId":"sim3475","displayToPublicDate":"2021-06-22T14:35:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3475","displayTitle":"Surficial Geology of the Northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado","title":"Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado","docAbstract":"The San Luis Valley and associated underlying basin of south-central Colorado and north-central New Mexico is the largest structural and hydrologic basin of the Rio Grande Rift and fluvial system. The surrounding San Juan and Sangre de Cristo Mountains reveal evidence of widespread volcanism and transtensional tectonism beginning in the Oligocene and continuing to the present, as seen in fault displacement of Pleistocene to Holocene deposits along the eastern basin-bounding Sangre de Cristo fault system and fault zones along the western margin of the basin. The San Luis basin can generally be subdivided into northern and southern basins at the structural and physiographic high terrain of the San Luis Hills in the center of the basin, proximal to the Colorado-New Mexico stateline. The northern San Luis Valley can be subdivided into two subbasins at approximately the latitude of the Great Sand Dunes and San Luis Lakes, where the endorheic northern subbasin surface and subsurface flow currently accumulate in a series of playa lakes. To the south of this playa region, the Rio Grande has captured basin hydrology into a through-going fluvial system cutting through the San Luis Hills, carving the Rio Grande gorge, and ultimately flowing into the Gulf of Mexico. This surficial geologic map of the northern San Luis Valley, paired with the Alamosa, CO 1:100,000-scale geologic map (U.S. Geological Survey Scientific Investigations Map 3342) provides new and compiled geologic mapping that characterizes basin deposits and locates the traces of active faults, with the goal to provide geospatial data for future investigations related to western North American neotectonics, Pleistocene paleoclimate, and related geomorphic processes. In addition, present natural and anthropogenic water bodies have been located and updated for hydrologic modeling and water-usage investigations.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3475","usgsCitation":"Ruleman, C.A., and Brandt, T.R., 2021, Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3475, 2 sheets, scale 1:75,000, https://doi.org/10.3133/sim3475.","productDescription":"4 Sheets: 52.81 x 75.84 inches or smaller; ReadMe; Data Release","onlineOnly":"Y","ipdsId":"IP-092739","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":386092,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3436","text":"Scientific Investigations Map 3346—","linkHelpText":"Geologic map of the Poncha Pass area, Chaffee, Fremont, and Saguache Counties, Colorado"},{"id":385874,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3342","text":"Scientific Investigations Map 3342—","linkHelpText":"Geologic map of the Alamosa 30’ × 60’ quadrangle, south-central Colorado"},{"id":385873,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PUTQYK","text":"USGS data release","linkHelpText":"Data release for Surficial Geology of the Northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado"},{"id":385872,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3475/ReadMe.txt","size":"7.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3475 Read Me"},{"id":385875,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3475/sim3475_sheet2.pdf","text":"Sheet 2","size":"527 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3475 Sheet 2"},{"id":385870,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3475/sim3475_sheet1.pdf","text":"Sheet 1. hill shade and topography","size":"63.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3475 Sheet 1"},{"id":385871,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3475/sim3475_sheet1_georeferenced.pdf","text":"Sheet 1, georeferenced","size":"64.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3475 Sheet 1, georeferenced"},{"id":386040,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3475/sim3475_sheet1_hillshade_base.pdf","text":"Sheet 1, hill shade base","size":"22.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3475 Sheet 1, hill shade and base map"},{"id":385869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3475/coverthb2.jpg"}],"country":"United 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Geosciences and Environmental Change Science Center
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Box 25046, MS-980
Denver, CO 80225-0046
The Proximity Visualization Tool is a simple lightweight tool that can be placed on web pages that allows users to identify non-native species near Department of Interior lands. The tool works by accessing the more than 400 million species occurrence records in the Biodiversity Information Serving Our Nation (BISON) database using the BISON Application Programming Interface (API).
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Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Groundwater is an important source of drinking water in California. Water-borne diseases caused by microbial contamination are a growing concern. The MI test, a membrane filtration method for the chromogenic/fluorogenic detection of total coliforms and Escherichia coli, was used for samples collected January to April 2014 from 42 domestic wells in the southeastern San Joaquin Valley. The wells were sampled as part of the Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP), a cooperative study between the U.S. Geological Survey and the California State Water Resources Control Board. Polymerase chain reaction analysis and sequencing of deoxyribonucleic acid (DNA) were used for 34 target and nontarget colonies that grew on the MI media from samples collected from 13 of the domestic wells to identify what genera of bacteria could exist in groundwater used by domestic wells. Gene sequences obtained using the Sanger method were entered into the basic local alignment search tool (BLAST) database, and 17 genera of bacteria were identified. Of these, 13 genera contain species that are human pathogens or opportunistic human pathogens. All the genera that include human pathogens are naturally present in soil, plants, or water; one of the pathogens also can be found in fecal matter. Six of the human pathogens were from non-target colony growth on the MI media. Target and non-target microbial growth on MI media are indicators of the possible presence of pathogenic bacteria even if the bacteria naturally are from soil rather than from a fecal source.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215030","collaboration":"Prepared in cooperation with California State Water Resources Control BoardDirector,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The mangrove forests across the Federated States of Micronesia provide critical resources and contribute to climate resilience. Locally, mangrove forests provide habitat for fish and wildlife, timber, and other cultural resources. Mangrove forests also protect Micronesian communities from tropical cyclones and tsunamis, providing a buffer against powerful waves and winds. Mangrove forests in Micronesia can store 700–1,800 metric tons of carbon per hectare (Donato and others, 2011), contributing to the estimated 5–10 billion metric tons of carbon stored by mangroves around the world (Alongi, 2018). This carbon storage is essential for global climate resilience.
Mangrove forests and the benefits these ecosystems provide are threatened by accelerating sea-level rise and human activities. Healthy mangrove forests are resilient systems and have kept pace with some amounts of sea-level rise, but rapid sea-level rise could outpace the mangroves’ ability to adapt. Degraded mangroves are at greater risk where natural processes have been altered. Overharvest and clearing of timber, infrastructure development, and altered hydrology are just a few of the human activities that can damage mangrove forests.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213030","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture-U.S. Forest Service, Micronesia Conservation Trust, Conservation Society of Pohnpei, Pacific Island Climate Adaptation Science Center, and the U.S. Fish and Wildlife Service","usgsCitation":"Thorne, K.M., and Buffington, K.J., 2021, Sea-level rise vulnerability of mangrove forests on the Micronesian Island of Pohnpei: U.S. Geological Survey Fact Sheet 2021-3030, 4 p., https://doi.org/10.3133/fs20213030","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-122163","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":386653,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3030/images"},{"id":386652,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3030/fs20213030.xml"},{"id":386651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3030/fs20213030.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3030/covrthb.jpg"}],"otherGeospatial":"Island of Pohnpei","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 158.06442260742188,\n 6.770988820924266\n ],\n [\n 158.38577270507812,\n 6.770988820924266\n ],\n [\n 158.38577270507812,\n 7.027297875479451\n ],\n [\n 158.06442260742188,\n 7.027297875479451\n ],\n [\n 158.06442260742188,\n 6.770988820924266\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The Mississippi Alluvial Plain hosts one of the most prolific shallow aquifer systems in the United States but is experiencing chronic groundwater decline. The Reelfoot rift and New Madrid seismic zone underlie the region and represent an important and poorly understood seismic hazard. Despite its societal and economic importance, the shallow subsurface architecture has not been mapped with the spatial resolution needed for effective management. Here, we present airborne electromagnetic, magnetic, and radiometric observations, measured over more than 43,000 flight-line-kilometers, which collectively provide a system-scale snapshot of the entire region. We develop detailed maps of aquifer connectivity and shallow geologic structure, infer relationships between structure and groundwater age, and identify previously unseen paleochannels and shallow fault structures. This dataset demonstrates how regional-scale airborne geophysics can close a scale gap in Earth observation by providing observational data at suitable scales and resolutions to improve our understanding of subsurface structures.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s43247-021-00200-z","usgsCitation":"Minsley, B.J., Rigby, J.R., James, S.R., Burton, B.L., Knierim, K.J., Pace, M., Bedrosian, P.A., and Kress, W., 2021, Airborne geophysical surveys of the lower Mississippi Valley demonstrate system-scale mapping of subsurface architecture: Communications Earth & Environment, v. 2, 131, 14 p., https://doi.org/10.1038/s43247-021-00200-z.","productDescription":"131, 14 p.","ipdsId":"IP-126061","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":451779,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-021-00200-z","text":"Publisher Index Page"},{"id":387124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.47265625,\n 37.405073750176925\n ],\n [\n -91.40625,\n 34.45221847282654\n ],\n [\n -91.7578125,\n 31.653381399664\n ],\n [\n -91.7138671875,\n 30.600093873550072\n ],\n [\n -90.17578124999999,\n 31.615965936476076\n ],\n [\n -89.12109375,\n 35.137879119634185\n ],\n [\n -88.41796875,\n 37.020098201368114\n ],\n [\n -89.47265625,\n 37.405073750176925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2021-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":819076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":260894,"corporation":false,"usgs":true,"family":"Rigby","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James, Stephanie R. 0000-0001-5715-253X","orcid":"https://orcid.org/0000-0001-5715-253X","contributorId":260620,"corporation":false,"usgs":true,"family":"James","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":819078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burton, Bethany L. 0000-0001-5011-7862 blburton@usgs.gov","orcid":"https://orcid.org/0000-0001-5011-7862","contributorId":138925,"corporation":false,"usgs":true,"family":"Burton","given":"Bethany","email":"blburton@usgs.gov","middleInitial":"L.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":819079,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pace, Michael 0000-0003-2770-5724","orcid":"https://orcid.org/0000-0003-2770-5724","contributorId":216678,"corporation":false,"usgs":true,"family":"Pace","given":"Michael","email":"","affiliations":[],"preferred":true,"id":819081,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819082,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819083,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228607,"text":"70228607 - 2021 - Hydrology of annual winter water level drawdown regimes in recreational lakes of Massachusetts, United States","interactions":[],"lastModifiedDate":"2022-02-14T17:30:20.604864","indexId":"70228607","displayToPublicDate":"2021-06-22T11:19:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Hydrology of annual winter water level drawdown regimes in recreational lakes of Massachusetts, United States","docAbstract":"Annual winter water level drawdown (WD) is a common lake management strategy to maintain recreational value by controlling nuisance macrophytes and preventing ice damage to shoreline infrastructure in lakes of the northeastern United States. The state of Massachusetts provides general guidelines for lake managers to implement and practice WDs. However, WD management reporting is not required and as such empirical water level records are scarce, making it difficult to assess guideline adherence and link these management actions to littoral habitat conditions. We monitored water levels bihourly in 18 lakes with ongoing WD regimes and 3 non-drawdown lakes over 3–4 yr. Our results show an interlake drawdown magnitude gradient of 0.07–2.66 m with intralake consistency across years. Corresponding WD magnitudes generated exposure of 1.3–37.6% for entire lakebeds and 9.2–71.1% for littoral zones. WD durations averaged 171 d and ranged widely from 5 to 246 d. Longer recession and refill phase durations and faster recession rates were moderately to strongly correlated with drawdown magnitudes. WDs were predominantly initiated prior to the state of Massachusetts 1 November starting guideline (83.1%) and refilled to summer reference levels after the recommended date of 1 April (70.6%). To minimize ecological impacts while still meeting recreational goals, WD performance guidelines may require a more fine-scale approach that integrates local hydrogeomorphic features and the presence of WD-sensitive littoral biotic assemblages. However, climate change model projections of warmer and wetter winters in the Northeast indicate increasing uncertainty for WD as an effective and worthwhile macrophyte control tool.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10402381.2021.1927268","usgsCitation":"Carmignani, J., Roy, A.H., Stolarski, J., and Richards, T., 2021, Hydrology of annual winter water level drawdown regimes in recreational lakes of Massachusetts, United States: Lake and Reservoir Management, v. 37, no. 4, p. 339-359, https://doi.org/10.1080/10402381.2021.1927268.","productDescription":"21 p.","startPage":"339","endPage":"359","ipdsId":"IP-117681","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451781,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/10402381.2021.1927268","text":"Publisher Index Page"},{"id":395898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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