Raikoke, a small, unmonitored volcano in the Kuril Islands, erupted in June 2019. We integrate data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System (IMS) infrasound network, and global lightning detection network (GLD360) with information from local authorities and social media to retrospectively characterize the eruptive sequence and improve understanding of the pre-, syn- and post- eruptive behavior. We observe six infrasound pulses beginning on 21 June at 17:49:55 UTC as well as the main Plinian phase on 21 June at 22:29 UTC. Each pulse is tracked in space and time using lightning and satellite imagery as the plumes drift eastward. Post-eruption visible satellite imagery shows expansion of the island's surface area, an increase in crater size, and a possibly-linked algal bloom south of the island. We use thermal satellite imagery and plume modeling to estimate plume height at 10–12 km asl and 1.5–2 × 106 kg/s mass eruption rate. Remote infrasound data provide insight into syn-eruptive changes in eruption intensity. Our analysis illustrates the value of interdisciplinary analyses of remote data to illuminate eruptive processes. However, our inability to identify deformation, pre-eruptive outgassing, and thermal signals, which may reflect the relatively short duration (~12 h) of the eruption and minimal land area around the volcano and/or the character of closed-system eruptions, highlights current limitations in the application of remote sensing for eruption detection and characterization.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107354","usgsCitation":"McKee, K., Smith, C.M., Reath, K., Snee, E., Maher, S., Matoza, R.S., Carn, S.A., Mastin, L.G., Anderson, K.R., Damby, D., Roman, D., Degterev, A., Rybin, A., Chibisova, M., Assink, J.D., de Negri Levia, R., and Perttu, A., 2021, Evaluating the state-of-the-art in remote volcanic eruption characterization Part I: Raikoke volcano, Kuril Islands: Journal of Volcanology and Geothermal Research, v. 419, p. 1-14, https://doi.org/10.1016/j.jvolgeores.2021.107354.","productDescription":"107354, 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-131053","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":450671,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2021.107354","text":"Publisher Index 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]\n }\n }\n ]\n}","volume":"419","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Kathleen 0000-0003-3189-9189","orcid":"https://orcid.org/0000-0003-3189-9189","contributorId":265977,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","email":"","affiliations":[{"id":54848,"text":"Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":823913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra Marie 0000-0003-2653-4249 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-2653-4249","contributorId":257000,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","middleInitial":"Marie","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reath, 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USA","active":true,"usgs":false}],"preferred":false,"id":823917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matoza, Robin S.","contributorId":257265,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":823918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carn, Simon A","contributorId":191165,"corporation":false,"usgs":false,"family":"Carn","given":"Simon","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":823919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mastin, Larry G. 0000-0002-4795-1992 lgmastin@usgs.gov","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":555,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"lgmastin@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science 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Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":823923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Degterev, Artem 0000-0001-6284-8830","orcid":"https://orcid.org/0000-0001-6284-8830","contributorId":265980,"corporation":false,"usgs":false,"family":"Degterev","given":"Artem","email":"","affiliations":[{"id":54851,"text":"Sakhalin Volcanic Eruptions Response Team (SVERT), Institute of Marine Geology and Geophysics, Yuzhno-Sakhalinsk, Russia","active":true,"usgs":false}],"preferred":false,"id":823924,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rybin, Alexander 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The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP–III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the skew of the annual peak flows for a given streamgage and a regional skew. This report presents an estimate of regional skew for a study area encompassing parts of eastern New York and Pennsylvania. A total of 232 candidate U.S. Geological Survey streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length (PRL) of at least 36 years, 183 streamgages were selected for use in the study.
Flood frequencies for candidate streamgages were analyzed by employing the expected moments algorithm, which extends the method of moments so that it can accommodate interval, censored, and historical/paleo flow data, as well as the multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Ten basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 1 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model that had a regional skew coefficient of 0.32 and an average variance of prediction at a new streamgage (AVPnew, which corresponds to the mean square error [MSE] of 0.11) was selected. The AVPnew corresponds to an effective record length of 68 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215015","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2021, Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2021–5015, 38 p., https://doi.org/10.3133/sir20215015.","productDescription":"Report: vi, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114558","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5015/coverthb.jpg"},{"id":389080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5015/sir20215015.pdf","text":"Report","size":"6.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5015"},{"id":389081,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PGAL0D","text":"USGS data release","linkHelpText":"Regional flood skew for parts of the mid-Atlantic region (hydrologic unit 02) in eastern New York and Pennsylvania"}],"country":"United States","state":"New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.56396484375,\n 39.740986355883564\n ],\n [\n -75.03662109375,\n 40.04443758460856\n ],\n [\n -74.86083984375,\n 40.34654412118006\n ],\n [\n -75.16845703124999,\n 40.713955826286046\n ],\n [\n -74.970703125,\n 41.062786068733026\n ],\n [\n -74.619140625,\n 41.393294288784865\n ],\n [\n -74.1796875,\n 41.65649719441145\n ],\n [\n -73.54248046875,\n 42.17968819665961\n ],\n [\n -73.23486328124999,\n 43.02071359427862\n ],\n [\n -73.32275390625,\n 43.628123412124616\n ],\n [\n -73.45458984375,\n 43.77109381775651\n ],\n [\n -73.80615234375,\n 43.35713822211053\n ],\n [\n -74.28955078125,\n 43.14909399920127\n ],\n [\n -74.77294921875,\n 42.79540065303723\n ],\n [\n -75.34423828125,\n 42.73087427928485\n ],\n [\n -75.82763671875,\n 42.68243539838623\n ],\n [\n -76.35498046875,\n 42.68243539838623\n ],\n [\n -76.88232421875,\n 42.68243539838623\n ],\n [\n -77.23388671874999,\n 42.45588764197166\n ],\n [\n -77.607421875,\n 42.19596877629178\n ],\n [\n -77.607421875,\n 42.01665183556825\n ],\n [\n -77.6513671875,\n 41.409775832009565\n ],\n [\n -77.80517578125,\n 41.1290213474951\n ],\n [\n -77.9150390625,\n 40.53050177574321\n ],\n [\n -78.11279296875,\n 40.16208338164617\n ],\n [\n -78.46435546875,\n 39.67337039176558\n ],\n [\n -75.56396484375,\n 39.740986355883564\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Integrated Modeling and Prediction Division
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12201 Sunrise Valley Drive
Reston, VA 20192
Geodetically modeled reservoir volume changes during volcanic eruptions are commonly much smaller than the observed eruptive volumes. This discrepancy is thought to be partially due to the compressibility of magma, which is largely controlled by the presence of exsolved volatiles. The 2006 eruption of Augustine Volcano, Alaska, produced an eruptive volume that was ∼3 times larger than the geodetically estimated syn-eruptive subsurface volume change. In this study, we use a multistep methodology that combines constraints from geodetic, volcanic gas, geologic, and petrologic data together with equations relating physical processes to observable parameters. We apply a Monte Carlo approach to quantify uncertainties. Ultimately, we solve for the exsolved volatile volume fraction and the magma compressibility. We estimate Augustine's 2006 pre-eruptive exsolved volatile phase to be ∼5.5 vol% of the magma at storage depths, yielding a bulk magma compressibility of ∼3.8 × 10−10 Pa−1. We develop a novel approach to estimate the H2O/CO2 ratio of the syn-eruptive gas emissions in the absence of direct H2O emission measurements which are hard to obtain due to the high background levels in ambient air. We find a best-fit H2O/CO2 molar ratio of 29. We also investigate the effects of applying different equations of state to our model. We find that the Ideal Gas Law might be used as a first approximation due to its simplicity; however, it overestimates volatile density and compressibility significantly at storage depths. This project capitalizes on the insights that can be gained by integrating multidisciplinary data with models of physical processes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GC009911","usgsCitation":"Wasser, V.K., Lopez, T., Anderson, K.R., Izbekov, P.E., and Freymueller, J., 2021, Multidisciplinary constraints on magma compressibility, the pre-eruptive exsolved volatile fraction, and the H2O/CO2 molar ratio for the 2006 Augustine eruption, Alaska: Geochemistry, Geophysics, Geosystems (G-Cubed), v. 22, no. 9, p. 1-24, https://doi.org/10.1029/2021GC009911.","productDescription":"e2021GC009911, 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-116941","costCenters":[{"id":153,"text":"California Volcano Observatory","active":false,"usgs":true}],"links":[{"id":489770,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gc009911","text":"Publisher Index Page"},{"id":389721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano, Cook Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.38150024414062,\n 59.404209722248545\n ],\n [\n -153.38356018066406,\n 59.411198752750096\n ],\n [\n -153.4295654296875,\n 59.417836996163324\n ],\n [\n -153.46939086914062,\n 59.40840331358838\n ],\n [\n -153.47488403320312,\n 59.39966607911177\n ],\n [\n -153.51470947265625,\n 59.395471406154286\n ],\n [\n -153.52500915527344,\n 59.38987770116238\n ],\n [\n -153.5504150390625,\n 59.39652012307085\n ],\n [\n -153.56483459472653,\n 59.39337387496283\n ],\n [\n -153.57650756835938,\n 59.38218484937568\n ],\n [\n -153.58200073242188,\n 59.37379065648717\n ],\n [\n -153.577880859375,\n 59.36924293446236\n ],\n [\n -153.55934143066406,\n 59.36644403325385\n ],\n [\n -153.5504150390625,\n 59.345795005122895\n ],\n [\n -153.55865478515625,\n 59.33564087770051\n ],\n [\n -153.5504150390625,\n 59.32968704671643\n ],\n [\n -153.52157592773438,\n 59.3167251017617\n ],\n [\n -153.42613220214844,\n 59.32443279994899\n ],\n [\n -153.41720581054688,\n 59.32022881759866\n ],\n [\n -153.39797973632812,\n 59.32583401185328\n ],\n [\n -153.358154296875,\n 59.33669144545403\n ],\n [\n -153.336181640625,\n 59.35734600959442\n ],\n [\n -153.34304809570312,\n 59.380785961506966\n ],\n [\n -153.37051391601562,\n 59.39092659116679\n ],\n [\n -153.38150024414062,\n 59.404209722248545\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Wasser, Valerie K.","contributorId":265989,"corporation":false,"usgs":false,"family":"Wasser","given":"Valerie","email":"","middleInitial":"K.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":823947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopez, Taryn M.","contributorId":265990,"corporation":false,"usgs":false,"family":"Lopez","given":"Taryn M.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":823948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Izbekov, Pavel E.","contributorId":265991,"corporation":false,"usgs":false,"family":"Izbekov","given":"Pavel","email":"","middleInitial":"E.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":823950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freymueller, Jeffrey T.","contributorId":96841,"corporation":false,"usgs":false,"family":"Freymueller","given":"Jeffrey T.","affiliations":[{"id":26875,"text":"Michigan State University, East Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":823951,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262512,"text":"70262512 - 2021 - Upper Grand Coulee: New views of a channeled scabland megafloods enigma","interactions":[],"lastModifiedDate":"2025-01-17T15:29:50.650525","indexId":"70262512","displayToPublicDate":"2021-09-24T09:21:11","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Upper Grand Coulee: New views of a channeled scabland megafloods enigma","docAbstract":"New findings about old puzzles occasion rethinking of the Grand Coulee, greatest of the scabland channels. Those puzzles begin with antecedents of current upper Grand Coulee. By a recent interpretation, the upper coulee exploited the former high-level valley of a preflood trunk stream that had drained to the southwest beside and across Coulee anticline or monocline. In any case, a constriction and sharp bend in nearby Columbia valley steered Missoula floods this direction. Completion of upper Grand Coulee by megaflood erosion captured flood drainage that would otherwise have continued to enlarge Moses Coulee.
Upstream in the Sanpoil valley, deposits and shorelines of last-glacial Lake Columbia varied with the lake’s Grand Coulee outlet while also recording scores of Missoula floods. The Sanpoil evidence implies that upper Grand Coulee had approached its present intake depth early the last glaciation at latest, or more simply during a prior glaciation. An upper part of the Sanpoil section provides varve counts between the last tens of Missoula floods in a stratigraphic sequence that may now be linked to flood rhythmites of southern Washington by a set-S tephra from Mount St. Helens.
On the floor of upper Grand Coulee itself, recently found striated rock and lodgement till confirm the long-held view, which Bretz and Flint had shared, that cutting of upper Grand Coulee preceded its last-glacial occupation by the Okanogan ice lobe. A dozen or more late Missoula floods registered as sand and silt in the lee of Steamboat Rock.
Some of this field evidence about upper Grand Coulee may conflict with results of recent two-dimensional simulations for a maximum Lake Missoula. In these simulations only a barrier high above the present coulee intake enables floods to approach high-water marks near Wenatchee that predate stable blockage of Columbia valley by the Okanogan lobe. Above the walls of upper Grand Coulee, scabland limits provide high-water targets for two-dimensional simulations of watery floods. The recent models sharpen focus on water sources, prior coulee incision, and coulee’s occupation by the Okanogan ice lobe.
Field reappraisal continues downstream from Grand Coulee on Ephrata fan. There, some of the floods exiting lower Grand Coulee had bulked up with fine sediment from glacial Lake Columbia, upper coulee till, and a lower coulee lake that the fan itself impounded. Floods thus of debris-flow consistency carried outsize boulders previously thought transported by watery floods.
Below Ephrata fan, a backflooded reach of Columbia valley received Grand Coulee outflow of small, late Missoula floods. These late floods can—by varve counts in post-S-ash deposits of Sanpoil valley—be clocked now as a decade or less apart. Still farther downstream, Columbia River gorge choked the largest Missoula floods, passing peak discharge only one-third to one-half that released by the breached Lake Missoula ice dam.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From terranes to terrains: Geologic field guides on the construction and destruction of the Pacific Northwest","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.0062(07)","usgsCitation":"Waitt, R.B., Atwater, B., Lehnigk, K., Larsen, I., Bjornstad, B., Hanson, M., and O'Connor, J., 2021, Upper Grand Coulee: New views of a channeled scabland megafloods enigma, chap. of From terranes to terrains: Geologic field guides on the construction and destruction of the Pacific Northwest, v. 62, p. 245-300, https://doi.org/10.1130/2021.0062(07).","productDescription":"56 p.","startPage":"245","endPage":"300","ipdsId":"IP-129810","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":481101,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2021.0062(07)","text":"Publisher Index Page"},{"id":480733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Upper Grand Coulee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.50054736602212,\n 49.462558518080414\n ],\n [\n -125.44572643872874,\n 49.462558518080414\n ],\n [\n -125.44572643872874,\n 44.60810790414203\n ],\n [\n -117.50054736602212,\n 44.60810790414203\n ],\n [\n -117.50054736602212,\n 49.462558518080414\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwater, Brian F.","contributorId":349552,"corporation":false,"usgs":true,"family":"Atwater","given":"Brian F.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lehnigk, Karin","contributorId":349556,"corporation":false,"usgs":false,"family":"Lehnigk","given":"Karin","affiliations":[{"id":83490,"text":"University of Massachusetts, Amherst, Mass.","active":true,"usgs":false}],"preferred":false,"id":924415,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Isaac J.","contributorId":349557,"corporation":false,"usgs":false,"family":"Larsen","given":"Isaac J.","affiliations":[{"id":83490,"text":"University of Massachusetts, Amherst, Mass.","active":true,"usgs":false}],"preferred":false,"id":924416,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bjornstad, Bruce N.","contributorId":349558,"corporation":false,"usgs":false,"family":"Bjornstad","given":"Bruce N.","affiliations":[{"id":83492,"text":"Ice Age Floodscapes, Richland, Wash.","active":true,"usgs":false}],"preferred":false,"id":924417,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hanson, Michelle A.","contributorId":349554,"corporation":false,"usgs":false,"family":"Hanson","given":"Michelle A.","affiliations":[{"id":83488,"text":"Saskatchewan Geological Survey, Regina, Sask.","active":true,"usgs":false}],"preferred":false,"id":924414,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":924418,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70244091,"text":"70244091 - 2021 - Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach","interactions":[],"lastModifiedDate":"2023-06-01T14:04:48.814131","indexId":"70244091","displayToPublicDate":"2021-09-24T08:41:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach","docAbstract":"A continuous-variable Bayesian network (cBN) model is used to link watershed development and climate change to stream ecosystem indicators. A graphical model, reflecting our understanding of the connections between climate change, weather condition, loss of natural land cover, stream flow characteristics, and stream ecosystem indicators is used as the basis for selecting flow metrics for predicting macroinvertebrate-based indicators. Selected flow metrics were then linked to variables representing watershed development and climate change. We fit the model to data from two river basins in southeast US and the resulting model was used to simulate future stream ecological conditions using projected future climate and development scenarios. The three climate models predicted varying ecological condition trajectories, but similar worst-case ecological conditions. The established modeling approach couples mechanistic understanding with field data to develop predictions of management-relevant variables across a heterogeneous landscape. We discussed the transferability of the modeling approach.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2021.117685","usgsCitation":"Qian, S.S., Kennen, J., May, J., Freeman, M., and Cuffney, T.F., 2021, Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach: Water Research, v. 205, 117685, 11 p., https://doi.org/10.1016/j.watres.2021.117685.","productDescription":"117685, 11 p.","ipdsId":"IP-125255","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":450679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2021.117685","text":"Publisher Index Page"},{"id":417645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, South Carolina, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.52179800853753,\n 32.86310990611119\n ],\n [\n -78.95424437482033,\n 33.20732442214225\n ],\n [\n -78.83061042748197,\n 33.62402639579081\n ],\n [\n -78.03123021414571,\n 33.81848745598903\n ],\n [\n -77.49960057459164,\n 34.229007941502374\n ],\n [\n -77.20942075405912,\n 34.57053494448374\n ],\n [\n -78.04925882655441,\n 35.593623760054015\n ],\n [\n -79.61149509648914,\n 36.3847977489354\n ],\n [\n -80.69994996842892,\n 36.980415621762745\n ],\n [\n -81.2368093995407,\n 36.697683304342775\n ],\n [\n -81.66006417146134,\n 35.81807327161229\n ],\n [\n -80.92321025597303,\n 33.67553987083947\n ],\n [\n -80.06864524456462,\n 32.587876038945694\n ],\n [\n -79.52179800853753,\n 32.86310990611119\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"205","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":874463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Jason 0000-0002-5699-2112","orcid":"https://orcid.org/0000-0002-5699-2112","contributorId":224991,"corporation":false,"usgs":false,"family":"May","given":"Jason","affiliations":[{"id":41015,"text":"Deceased (ex-USGS)","active":true,"usgs":false}],"preferred":false,"id":874465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":874466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cuffney, Thomas F 0000-0003-1164-5560","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":306032,"corporation":false,"usgs":false,"family":"Cuffney","given":"Thomas","email":"","middleInitial":"F","affiliations":[],"preferred":false,"id":874467,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225172,"text":"70225172 - 2021 - miR133b microinjection during early development targets transcripts of sardiomyocyte ion channels and induces oil-like cardiotoxicity in zebrafish (Danio rerio) embryos","interactions":[],"lastModifiedDate":"2021-10-18T15:13:36.286802","indexId":"70225172","displayToPublicDate":"2021-09-24T07:40:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9529,"text":"Chemical Research in Toxicology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"miR133b microinjection during early development targets transcripts of sardiomyocyte ion channels and induces oil-like cardiotoxicity in zebrafish (Danio rerio) embryos","title":"miR133b microinjection during early development targets transcripts of sardiomyocyte ion channels and induces oil-like cardiotoxicity in zebrafish (Danio rerio) embryos","docAbstract":"Previous studies have shown that altered expression of a family of small noncoding RNAs (microRNAs, or miRs) regulates the expression of downstream mRNAs and is associated with diseases and developmental disorders. miR133b is highly expressed in mammalian cardiac and skeletal muscle, and aberrant expression is associated with cardiac disorders and electrophysiological changes in cardiomyocytes. Similarly, cardiac dysfunction has been observed in early life-stage mahi-mahi (Coryphaena hippurus) exposed to crude oil, a phenotype that has been associated with an upregulation of miR133b as well as subsequent downregulation of a delayed rectifier potassium channel (IKr) and calcium signaling genes that are important for proper heart development during embryogenesis. To examine the potential role of miR133b in oil-induced early life-stage cardiotoxicity in fish, cleavage-stage zebrafish (Danio rerio) embryos were either (1) microinjected with ∼3 nL of negative control miR (75 μM) or miR133b (75 μM) or (2) exposed to a treatment solution containing 5 μM benzo(a)pyrene (BaP), a model polycyclic aromatic hydrocarbon, as a positive control. At 72 h post fertilization (hpf), miR133b-injected fish exhibited BaP-like cardiovascular malformations, including a significantly increased pericardial area relative to negative control miR-injected embryos, as well as a significantly reduced eye area. qPCR revealed that miR133b microinjection decreased the abundance of cardiac-specific IKrkcnh6 at 5 hpf, which may contribute to action potential elongation in oil-exposed cardiomyocytes. Additionally, ryanodine receptor 2, a crucial calcium receptor in the sarcoplasmic reticulum, was also downregulated by miR133b. These results indicate that an oil-induced increase in miR133b may contribute to cardiac abnormalities in oil-exposed fish by targeting cardiac-specific genes essential for proper heart development.
For species with variable phenology, it is often challenging to produce reliable estimates of population dynamics or changes in occupancy. The Arizona Toad (Anaxyrus microscaphus) is a southwestern USA endemic that has been petitioned for legal protection, but status assessments are limited by a lack of information on population trends. Also, timing and consistency of Arizona Toad breeding varies greatly, making it difficult to predict optimal survey times or effort required for detection. To help fill these information gaps, we conducted breeding season call surveys during 2013–2016 and 2019 at 86 historically occupied sites and 59 control sites across the species' range in New Mexico. We estimated variation in mean dates of arrival and departure from breeding sites, changes in occupancy, and site-level extinction since 1959 with recently developed multi-season staggered-entry models, which relax the within-season closure assumption common to most occupancy models. Optimal timing of surveys in our study areas was approximately 5–30 March. Averaged across years, estimated probability of occupancy was 0.58 (SE = 0.09) for historical sites and 0.19 (SE = 0.08) for control sites. Occupancy increased from 2013 through 2019. Notably, even though observer error was trivial, annual detection probabilities varied from 0.23 to 0.75 and declined during the study; this means naïve occupancy values would have been misleading, indicating apparent declines in toad occupancy. Occupancy was lowest during the first year of the study, possibly due to changes in stream flows and conditions in many waterbodies following extended drought and recent wildfires. Although within-season closure was violated by variable calling phenology, simple multi-season models provided nearly identical estimates as staggered-entry models. Surprisingly, extinction probability was unrelated to the number of years since the first or last record at historically occupied sites. Collectively, our results suggest a lack of large, recent declines in occupancy by Arizona Toads in New Mexico, but we still lack population information from most of the species' range.
Surface-water and groundwater resources in the Big Lost River Basin of south-central Idaho are extensively interconnected; this interchange affects and is affected by water-resource management for irrigated agriculture and other uses in the basin. Concerns from water users regarding declining groundwater levels, declining streamflows, and drought helped motivate an updated evaluation of water resources in the Big Lost River Basin. The hydrogeologic framework presented in this report provides a conceptual basis for understanding groundwater resources in the Big Lost River Basin and comprises three major parts: (1) conceptual description of four hydrogeologic units, (2) development of a three-dimensional hydrogeologic framework model representing the spatial distribution of the hydrogeologic units, and (3) a description of groundwater occurrence and movement. This hydrogeologic framework represents the first of three planned reports describing water resources in the Big Lost River Basin; subsequent reports are intended to present a groundwater budget for the basin and to describe the results of a series of events measuring gains to and losses from streamflow in the Big Lost River. This report was prepared by the U.S. Geological Survey in cooperation with the Idaho Department of Water Resources.
The Big Lost River Basin has four hydrogeologic units. First, the Quaternary unconsolidated sediments unit comprises the basin-fill alluvial aquifer and generally is used within 250 feet of the land surface. The Quaternary unconsolidated sediments unit is spatially heterogeneous, with locally confining conditions in some areas, and is the most heavily used hydrogeologic unit in the basin. Second, the Paleozoic sedimentary rocks unit, composed primarily of carbonates with some siliciclastic rocks, represents the major bedrock aquifer and contributes subsurface recharge at the margins of the alluvial aquifer. Third, the Tertiary volcanic rocks unit, composed primarily of andesite and dacite with lesser tuff, is locally important to water production, particularly in faulted and fractured zones. The Paleozoic sedimentary rocks hydrogeologic unit occurs at the valley margins and underlies tributaries throughout the basin, whereas the Tertiary volcanic rocks hydrogeologic unit primarily occurs in uplands in the western one-half of the basin. Fourth, the Quaternary basalt rocks unit consists of multiple basalt flows that are interbedded with the Quaternary unconsolidated sediments unit in the southern end of the Big Lost River Basin and contains at least three water-bearing zones. Insights gained from this updated hydrogeologic framework will help inform current water-resource management in the Big Lost River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078A","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Zinsser, L.M., 2021, Hydrogeologic framework of the Big Lost River Basin, south-central Idaho, chap. A of Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078–A, 42 p., https://doi.org/10.3133/sir20215078A.","productDescription":"Report: viii, 42 p.; Appendix; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-125228","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":396956,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5078/a/sir20215078A.XML"},{"id":396955,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5078/a/images"},{"id":389624,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P911S9LF","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrogeologic framework of the Big Lost River Basin, south-central Idaho—Hydrogeologic framework model and well data"},{"id":389623,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5078/a/sir20215078A_app1.pdf","text":"Appendix 1","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5078A Appendix 1"},{"id":389622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5078/a/sir20215078A.pdf","text":"Report","size":"8.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5078A"},{"id":389621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/a/coverthb.jpg"},{"id":409279,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5078/a/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5078A Version History"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.169921875,\n 43.229195113965005\n ],\n [\n -112.2802734375,\n 43.229195113965005\n ],\n [\n -112.2802734375,\n 44.15068115978094\n ],\n [\n -114.169921875,\n 44.15068115978094\n ],\n [\n -114.169921875,\n 43.229195113965005\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Unconsolidated boulder-rich diamicton units exposed in sea cliffs at Harriet Point southeast of Redoubt Volcano were evaluated to better understand their provenance relative to the late Quaternary eruptive history of the volcano. A previous study concluded that deposits at Harriet Point were emplaced by a large volcanic landslide originating on the southeast flank of Redoubt Volcano (Begét and Nye, 1994). Field-based analysis of the stratigraphy and sedimentology of the Harriet Point deposits and numerical simulations of the volcanic landslide area of inundation indicate that none of the deposits are volcanogenic. All of the unconsolidated boulder-rich diamicton units at Harriet Point are glacial in origin and can be reconciled using the presently available model for late Quaternary glaciation of Cook Inlet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215071","usgsCitation":"Waythomas, C.F., 2021, Origin of unconsolidated Quaternary deposits at Harriet Point near Redoubt Volcano, Alaska: U.S. Geological Survey Scientific Investigations Report 2021-5071, 14 p., https://doi.org/10.3133/sir20215071.","productDescription":"iv, 14 p.","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-116798","costCenters":[{"id":121,"text":"Alaska Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":389648,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5071/sir20215071.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389649,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5071/covrthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Harriet Point, Redoubt Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.94067382812497,\n 60.303144396154856\n ],\n [\n -152.14279174804688,\n 60.303144396154856\n ],\n [\n -152.14279174804688,\n 60.5923622983958\n ],\n [\n -152.94067382812497,\n 60.5923622983958\n ],\n [\n -152.94067382812497,\n 60.303144396154856\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
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This paper considers mutualistic interactions between two consumers, in which one consumer can consume a resource only by exchange of service for service with the other. By rigorous analysis on the one-resource and two-consumer model with Holling-type I response, we show periodic oscillations and tri-stability in the mutualism system: when their initial densities decrease, the consumers' interaction outcomes would change from coexistence in periodic oscillation, to persistence at a steady state, and to extinction. Under certain conditions, we also show two types of bi-stability in the system: the consumers would change from coexisting in periodic oscillation (resp. at a steady state) to going to extinction when their initial densities decrease. Then we analyze a modified system with Holling-type II response. Based on theoretical analysis and numerical computation, we show that there also exist tri-stability and two types of bi-stability in this system. Moreover, it is shown that varying the degree of obligation can lead to transition of interaction outcomes between coexistence in periodic oscillation (resp. at a steady state) and extinction of both consumers. These results are important in understanding complexity in mutualism.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jmaa.2021.125672","usgsCitation":"Wang, Y., Wu, H., and DeAngelis, D.L., 2021, Periodic oscillation and tri-stability in mutualism systems with two consumers: Journal of Mathematical Analysis and Applications, v. 506, no. 2, 125672, https://doi.org/10.1016/j.jmaa.2021.125672.","productDescription":"125672","ipdsId":"IP-131197","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"506","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Yuanshi","contributorId":207814,"corporation":false,"usgs":false,"family":"Wang","given":"Yuanshi","email":"","affiliations":[{"id":37637,"text":"School of Mathematics and Computational Science Sun Yat-sen University","active":true,"usgs":false}],"preferred":false,"id":855730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Hong","contributorId":207815,"corporation":false,"usgs":false,"family":"Wu","given":"Hong","email":"","affiliations":[{"id":37637,"text":"School of Mathematics and Computational Science Sun Yat-sen University","active":true,"usgs":false}],"preferred":false,"id":855731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":855732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224528,"text":"70224528 - 2021 - Survival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016","interactions":[],"lastModifiedDate":"2021-11-01T16:02:45.091989","indexId":"70224528","displayToPublicDate":"2021-09-23T08:38:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Survival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016","docAbstract":"The Arctic Ocean is undergoing rapid transformation toward a seasonally ice-free ecosystem. As ice-adapted apex predators, polar bears (Ursus maritimus) are challenged to cope with ongoing habitat degradation and changes in their prey base driven by food-web response to climate warming. Knowledge of polar bear response to environmental change is necessary to understand ecosystem dynamics and inform conservation decisions. In the southern Beaufort Sea (SBS) of Alaska and western Canada, sea ice extent has declined since satellite observations began in 1979 and available evidence suggests that the carrying capacity of the SBS for polar bears has trended lower for nearly two decades. In this study, we investigated the population dynamics of polar bears in Alaska's SBS from 2001 to 2016 using a multistate Cormack–Jolly–Seber mark–recapture model. States were defined as geographic regions, and we used location data from mark–recapture observations and satellite-telemetered bears to model transitions between states and thereby explain heterogeneity in recapture probabilities. Our results corroborate prior findings that the SBS subpopulation experienced low survival from 2003 to 2006. Survival improved modestly from 2006 to 2008 and afterward rebounded to comparatively high levels for the remainder of the study, except in 2012. Abundance moved in concert with survival throughout the study period, declining substantially from 2003 and 2006 and afterward fluctuating with lower variation around an average of 565 bears (95% Bayesian credible interval [340, 920]) through 2015. Even though abundance was comparatively stable and without sustained trend from 2006 to 2015, polar bears in the Alaska SBS were less abundant over that period than at any time since passage of the U.S. Marine Mammal Protection Act. The potential for recovery is likely limited by the degree of habitat degradation the subpopulation has experienced, and future reductions in carrying capacity are expected given current projections for continued climate warming.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8139","usgsCitation":"Bromaghin, J.F., Douglas, D.C., Durner, G.M., Simac, K.S., and Atwood, T.C., 2021, Survival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016: Ecology and Evolution, v. 11, no. 20, p. 14250-14267, https://doi.org/10.1002/ece3.8139.","productDescription":"18 p.","startPage":"14250","endPage":"14267","ipdsId":"IP-125254","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":450707,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8139","text":"External Repository"},{"id":389724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.2890625,\n 68.23682270936281\n ],\n [\n -156.4453125,\n 71.24435551310674\n ],\n [\n -140.9765625,\n 69.59589006237648\n ],\n [\n -141.15234374999997,\n 76.24781659441473\n ],\n [\n -166.55273437499997,\n 76.03731657616542\n ],\n [\n -166.2890625,\n 68.23682270936281\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"20","noUsgsAuthors":false,"publicationDate":"2021-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823893,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simac, Kristin S. 0000-0002-4072-1940 ksimac@usgs.gov","orcid":"https://orcid.org/0000-0002-4072-1940","contributorId":131096,"corporation":false,"usgs":true,"family":"Simac","given":"Kristin","email":"ksimac@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":823894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":823895,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228980,"text":"70228980 - 2021 - Modelling presence versus abundance for invasive species risk assessment","interactions":[],"lastModifiedDate":"2022-02-25T14:26:41.787468","indexId":"70228980","displayToPublicDate":"2021-09-23T08:22:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Modelling presence versus abundance for invasive species risk assessment","docAbstract":"Invasive species prevention and management can be guided by comparisons of invasion risk across space and among species. Species distribution models are widely used to assess invasion risk and typically estimate suitability for species presence. However, suitability for presence may not capture patterns of abundance and impact. We asked how models estimating suitability for presence versus suitability for abundance aligned in their implications for risk assessment.
Western United States.
We developed ensembles of species distribution models for presence and for abundance for four invasive plants. We visualized the distribution of presence and abundance in environmental and geographic space and compared model outputs using criteria relevant for decision-making: a comparison of risk across management units for each species, and a ranking of risk among species for each management unit.
We found good overall agreement between models of presence versus abundance in the relative risk across management units and among species. However, the area predicted to be suitable for invasive species presence was often substantially higher than the area predicted to be suitable for abundance, especially within uninvaded management units.
Models of suitability for invasive species presence and abundance yielded similar assessments of relative risk in comparisons across space and species. In addition, we found patterns of presence and abundance in environmental space can guide modelling decisions and model interpretation. Suitability for abundance can improve relative risk assessment when abundance locations occupy a well-defined subset of the environmental space corresponding to presence. Where abundance locations occur throughout this environmental space, as was particularly striking for Taeniatherum caput-medusae, suitability for presence may better reflect risk of ongoing population increases and spread. This species is at risk of becoming abundant across a substantial portion of the western United States.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13414","usgsCitation":"Jarnevich, C.S., Sofaer, H., and Engelstad, P., 2021, Modelling presence versus abundance for invasive species risk assessment: Diversity and Distributions, v. 27, no. 12, p. 2454-2464, https://doi.org/10.1111/ddi.13414.","productDescription":"11 p.","startPage":"2454","endPage":"2464","ipdsId":"IP-123563","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450709,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13414","text":"Publisher Index Page"},{"id":436188,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MVEPP4","text":"USGS data release","linkHelpText":"Presence and abundance data and models for four invasive plant species"},{"id":396476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":836066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sofaer, Helen 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":836067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Peder","contributorId":238758,"corporation":false,"usgs":false,"family":"Engelstad","given":"Peder","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":836068,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227126,"text":"70227126 - 2021 - Evaluating streamwater dissolved organic carbon dynamics in context of variable flowpath contributions with a tracer-based mixing model","interactions":[],"lastModifiedDate":"2022-01-03T15:32:26.574984","indexId":"70227126","displayToPublicDate":"2021-09-23T08:09:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating streamwater dissolved organic carbon dynamics in context of variable flowpath contributions with a tracer-based mixing model","docAbstract":"This study focuses on characterizing the contributions of key terrestrial pathways that deliver dissolved organic carbon (DOC) to streams during hydrological events and on elucidating factors governing variation in water and DOC fluxes from these pathways. We made high-frequency measurements of discharge, specific conductance (SC), and fluorescent dissolved organic matter (FDOM) during 221 events recorded over 2 years within four Vermont (USA) watersheds that range in area from 0.4 to 139 km2. Using the SC measurements, together with statistical information on discharge, we separated the event hydrographs into contributions from three terrestrial pathways, which we refer to as riparian quickflow, subsurface quickflow, and slow-flow groundwater. The pathway discharges were used as input to a mixing model that closely approximated sub-hourly streamwater DOC concentrations as measured with the FDOM sensors. Subsurface quickflow, comprised of pre-event water, was the leading contributor to streamwater DOC fluxes, while riparian quickflow, comprised of event water, was the second-leading contributor to streamwater DOC fluxes, despite comprising the smallest proportion of streamflow yield among the three end-member pathways. Fixed-effects regression analysis revealed that the relationship between DOC fluxes from the end-member pathways and event magnitude was consistent across the four watersheds. This analysis also showed that DOC fluxes from the quickflow pathways increased significantly with temperature and varied inversely, but weakly, with catchment antecedent wetness. We believe that our approach, which leverages in-stream sensors that enable high-frequency measurements over extended periods, may be applicable for evaluating controls on DOC export from other watersheds within and beyond our study region.
Carbon dioxide emissions from active subaerial volcanoes represent 20–50% of the annual global volcanic CO2 flux (Barry et al., 2014). Passive degassing of carbon from the flanks of volcanoes, and the associated accumulation of dissolved inorganic carbon (DIC) within nearby groundwater, also represents a potentially important, yet poorly constrained flux of carbon to the surface (Werner et al., 2019). Here we investigate sources and sinks of DIC in groundwaters in the Lassen Peak region of California. Specifically, we report and interpret the relative abundance and isotopic composition of helium (3He, 4He) and carbon (12C, 13C, 14C) in 37 groundwater samples, from 24 distinct wells, collected between 20 and 60 km from Lassen Peak. Measured groundwater samples have air-corrected 3He/4He values between 0.19 and 7.44 RA (where RA = air 3He/4He = 1.39 × 10−6), all in excess of the radiogenic production value (~0.05 RA), indicating pervasive mantle-derived helium additions to the groundwater system in the Lassen Peak region. Stable carbon isotope ratios of DIC (δ13C) vary between −12.6 and − 27.7‰ (vs. VPDB). Measured groundwater DIC/3He values fall in the range of 2.2 × 1010 to 1.1 × 1012. Using helium and carbon isotope data, we explore several conceptual models to estimate surface carbon contributions and to differentiate between DIC derived from soil CO2 versus DIC derived from external (slab and mantle) carbon sources. Specifically, if we use 14C to identify soil-derived DIC (assuming decadal-to-centennial groundwater ages and a soil CO2 14C activity equal to that of the atmosphere), we calculate that a hypothetical external carbon source would have an apparent δ13C signature between −10.3 and − 59.3‰ (vs. Vienna Pee Dee Belemnite (VPDB)) and an apparent C/3He between 7.0 × 109 and 1.0 × 1012. These apparent δ13C and C/3He values are substantially isotopically lighter than and greater than canonical MORB values, respectively. We suggest that >95% of any external (non-soil-derived) DIC in groundwater must thus be non-mantle in origin (i.e., slab derived or assimilated organic carbon). We further investigate possible sources of external DIC to groundwater using two idealized conceptual approaches: a pure (unfractionated) source mixing model (after Sano and Marty, 1995) and a scenario that invokes fractionation due to calcite precipitation. Because the former model requires carbon contributions from an organic source component with unrealistically low δ13C (~ − 60‰), we suggest that the second scenario is more plausible. Importantly, however, we caution that all conceptual models are dependent on assumptions about initial 14C activity. Thus, we cannot rule out the possibility that the true fraction of non-surface-derived DIC in these samples is lower or negligible, despite the pervasive mantle-derived He isotope signatures throughout the region. Following the 14C approach to deconvolving sources of DIC, we determine that the maximum passive carbon flux could be up to ~2.2 × 106 kg/yr, which is lower than previous magmatic carbon flux estimates from the Lassen region (Rose and Davisson, 1996). We find that the passive dissolved carbon flux could represent a maximum of ~4–18% of the total Lassen geothermal CO2 degassing flux (estimated to be ~3.5 × 107 kg/yr Rose and Davisson, 1996; Gerlach et al., 2008), which is still more than an order of magnitude smaller than soil gas CO2 flux estimates (7.3–11 × 107 kg/yr) for nearby volcanoes (Sorey et al., 1998; Gerlach et al., 1999; Evans et al., 2002; Werner et al., 2014). We conclude that passive dissolved carbon fluxes should be combined with geothermal fluxes and soil gas fluxes to obtain a complete picture of volcanic carbon emissions globally. Our approach highlights the utility of measuring helium isotopes in concert with the full suite of noble gas abundances, tritium, δ13C and 14C, which when interpreted together can be used to better elucidate the various sources of DIC in groundwater.
For the past ∼12 years the Pacific Northwest Seismic Network has been automatically detecting and locating tectonic tremor across the Cascadia subduction zone, resulting in a catalog of more than 500,000 tremor epicenters to date, which has served as a valuable resource for tremor and slip research. This manuscript presents an updated methodology for routine tremor detection in Cascadia and a new catalog of over 180,000 tremor epicenters including amplitudes detected along the subduction zone margin from 2017 to 2021. The events are detected via cross-correlation of continuous vertical envelope data of 128 stations from northern California to northern Vancouver Island. The modified approach results in less scatter and a 55% increase in detected epicenters than previously observed, as well as a newly identified tremor source offset updip from the main tremor and slip region at the southern edge of the subduction zone. Radiated seismic energy in the 1.5–5 Hz band is used to assign epicenters an energy magnitude (MeL), which is calibrated to the ML of local earthquakes. Southern Cascadia is most active, but the highest tremor energy rates occur in northern Cascadia. Tremor in central Cascadia is systematically weaker and less frequent. Individual epicenter magnitudes range from ∼0.5–2 and spatiotemporally cluster into 1,060 swarms with cumulative MeL ranging from ∼0.8 to 3.7. The swarms reflect underlying slow slip events and occur with an earthquake-like energy distribution with a b value ∼1. Tremor epicenters, however, follow a tapered Gutenberg-Richter distribution with high b values, suggesting individual tremor bursts and their constituent low-frequency earthquakes are fault-dimension limited.
Red Knots (Calidris canutus rufa) stop at Delaware Bay during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). The northward migration of C. c. rufa coincides with the spawning of horseshoe crabs whose eggs are the perfect food for a migrating Red Knot (Karpanty et al. 2006, Haramis et al. 2007). Horseshoe crabs are therefore an important food resource for Red Knots as well as other shorebirds at Delaware Bay.
Horseshoe crabs have been harvested since at least 1990 for use as bait in American eel (Anguilla rostrata) and whelk (Busycon) fisheries (Kreamer and Michels 2009). In the late 1990s and early 2000s the number of Red Knots found at Delaware Bay declined dramatically from ~50,000 to ~13,000 (Niles et al. 2008). At the same time the number of horseshoe crabs harvested also declined and avian conservation biologists hypothesized that unregulated harvest of horseshoe crabs from Delaware Bay in the 1990s prevented sufficient refueling during stopover for successful migration to the breeding grounds, nesting, and survival for the remainder of the annual cycle (McGowan et al. 2011).
The harvest of horseshoe crabs in the Delaware Bay region has been managed by the Atlantic States Marine Fisheries Commission (ASMFC) since 2012 using an Adaptive Resource Management (ARM) framework (McGowan et al. 2015b). The ARM framework was designed to constrain the harvest so that number of spawning crabs would not limit the number of Red Knots stopping at Delaware Bay during migration. This management framework to achieve multiple objectives requires an estimate each year of both the crab population and the Red Knot stopover population size to inform harvest recommendations (McGowan et al. 2015a). We have estimated the stopover population size using mark-resight data on individually-marked birds and a Jolly-Seber model for open populations since 2011.
","language":"English","publisher":"Atlantic States Marine Fisheries Commission","usgsCitation":"Lyons, J.E., 2021, Red knot stopover population size and migration ecology at Delaware Bay, USA, 2021, 21 p.","productDescription":"21 p.","ipdsId":"IP-135416","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":427147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398095,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.delaware.gov/fish-wildlife/conservation/shorebirds/research/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Delaware, New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.09403509407363,\n 38.74026331013363\n ],\n [\n -74.92922524720622,\n 38.95518554423097\n ],\n [\n -74.86023507875021,\n 39.17242937484494\n ],\n [\n -75.47348102058082,\n 39.53695640590777\n ],\n [\n -75.45818237996806,\n 39.725833415896574\n ],\n [\n -75.62300710583959,\n 39.7375634879601\n ],\n [\n -75.68813576821344,\n 39.5812414619472\n ],\n [\n -75.61529784001694,\n 39.40677166373757\n ],\n [\n -75.46198775359684,\n 39.16648631014266\n ],\n [\n -75.34700185696957,\n 38.90151720709986\n ],\n [\n -75.09403509407363,\n 38.74026331013363\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":839581,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70224547,"text":"70224547 - 2021 - SiteOpt: An open-source R-package for site selection and portfolio optimization","interactions":[],"lastModifiedDate":"2021-11-16T15:45:42.56053","indexId":"70224547","displayToPublicDate":"2021-09-22T08:35:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"SiteOpt: An open-source R-package for site selection and portfolio optimization","docAbstract":"Conservation planning involves identifying and selecting actions to best achieve objectives for managing natural, social and cultural resources. Conservation problems are often high dimensional when specified as combinatorial or portfolio problems and when multiple competing objectives are considered at varying spatial and temporal scales. Although analytical techniques such as modern portfolio theory (MPT) have been developed to address these complex problems, open source computational platforms for executing these approaches are not readily available. We present a user-friendly R-package called SiteOpt for optimization of binary decisions while explicitly considering environmental or economic uncertainty and the risk tolerance of decision makers. We illustrate the package with spatially-explicit site selection problems (i.e. spatial conservation planning), including an option for divestment (i.e. selling assets), when accounting for future uncertainties in designing conservation areas. The tool is applicable to both spatial and non-spatial problems, such as budget allocation or species selection. Constraints for spatial design and spatial dependencies (e.g. connectivity among sites) can also be specified in SiteOpt. Users can optimize site selection based on two competing objectives by solving for the Nash bargaining solution. Importantly, by quantifying uncertainty and asset spatial correlation, a measure of risk can be included as one such objective to be traded off against portfolio benefits. Thus, SiteOpt can be used to explicitly manage risk in portfolio-based spatial optimization. This tool facilitates decisions in a variety of problem settings, including reserve selection, invasive species management, allocation of law enforcement activities for conservation, budget allocation and asset selection under uncertainty and risk.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.05717","usgsCitation":"Saghand, P.G., Haider, Z., Charkhgard, H., Eaton, M.J., Martin, J., Yurek, S., and Udell, B.J., 2021, SiteOpt: An open-source R-package for site selection and portfolio optimization: Ecography, v. 44, no. 11, p. 1678-1685, https://doi.org/10.1111/ecog.05717.","productDescription":"8 p.","startPage":"1678","endPage":"1685","ipdsId":"IP-119211","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":450726,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.05717","text":"Publisher Index Page"},{"id":436191,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4QV7T","text":"USGS data release","linkHelpText":"Data from SiteOpt: an Open-source R-package for Site Selection and Portfolio Optimization"},{"id":389806,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Saghand, Payman G","contributorId":266005,"corporation":false,"usgs":false,"family":"Saghand","given":"Payman","email":"","middleInitial":"G","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":824023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haider, Zulqarnain","contributorId":216714,"corporation":false,"usgs":false,"family":"Haider","given":"Zulqarnain","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":824024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Charkhgard, Hadi","contributorId":216710,"corporation":false,"usgs":false,"family":"Charkhgard","given":"Hadi","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":824025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":824026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":218445,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":824027,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yurek, Simeon 0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216738,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":824028,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Udell, Bradley J. 0000-0001-5225-4959","orcid":"https://orcid.org/0000-0001-5225-4959","contributorId":223440,"corporation":false,"usgs":false,"family":"Udell","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":40715,"text":"Wildlife Ecology and Conservation Department, University of Florida, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":824029,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70226600,"text":"70226600 - 2021 - Shallow marine ecosystem collapse and recovery during the Paleocene-Eocene Thermal Maximum","interactions":[],"lastModifiedDate":"2021-12-02T14:30:55.839361","indexId":"70226600","displayToPublicDate":"2021-09-21T07:15:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Shallow marine ecosystem collapse and recovery during the Paleocene-Eocene Thermal Maximum","docAbstract":"The Paleocene-Eocene Thermal Maximum (PETM), the most well-studied transient hyperthermal event in Earth history, is characterized by prominent and dynamic changes in global marine ecosystems. Understanding such biotic responses provides valuable insights into future scenarios in the face of anthropogenic warming. However, evidence of the PETM biotic responses is largely biased towards deep-sea records, whereas shallow-marine evidence remains scarce and elusive. Here we investigate a shallow-marine microfaunal record from Maryland, eastern United States, to comprehensively document the shallow-marine biotic response to the PETM. We applied birth-death modeling to estimate the local diversity dynamics, combined with evaluation of time-variable preservation artifacts. We discovered strong increase of species disappearance and appearance predating the onset and at the final recovery phase of the PETM, respectively. Our paleoecological analyses indicate that bathymetric habitat compression due to extreme warmth and oxygen minimum zone expansion caused shallow-marine benthic species extirpation and ecosystem perturbation during the PETM; and that rapid recovery and diversification followed the PETM disaster, thus contributing new understanding to the shallow-marine biotic changes in a broad context of global warming.
Surface effects of sea-level rise (SLR) in permafrost regions are obvious where increasingly iceless seas erode and inundate coastlines. SLR also drives saltwater intrusion, but subsurface impacts on permafrost-bound coastlines are unseen and unclear due to limited field data and the absence of models that include salinity-dependent groundwater flow with solute exclusion and freeze-thaw dynamics. Here, we develop a numerical model with the aforementioned processes to investigate climate change impacts on coastal permafrost. We find that SLR drives lateral permafrost thaw due to depressed freezing temperatures from saltwater intrusion, whereas warming drives top-down thaw. Under high SLR and low warming scenarios, thaw driven by SLR exceeds warming-driven thaw when normalized to the influenced surface area. Results highlight an overlooked feedback mechanism between SLR and permafrost thaw with potential implications for coastal infrastructure, ocean-aquifer interactions, and carbon mobilization.
Avian populations must mount effective immune responses upon exposure to environmental stressors such as avian influenza and xenobiotics. Although multiple immune assays have been tested and applied to various avian species, antibody-mediated immune responses in non-model avian species are not commonly reported due to the lack of commercially available species-specific antibodies. The objectives of the present study were to advance methods for studying wild bird immune responses and to apply these to the evaluation of cytological responses after exposure of American kestrels, Falco sparverius, to a commercial flame retardant mixture containing isopropylated triarylphosphate isomers (ITP). Hatchlings were gavaged daily with safflower oil or 1.5 ug/g bw/day of ITP suspended in safflower oil, then bled on days 9, 17, and 21. The ITP treatment group (n = 18) and a subset of controls (Poly I:C treatment group; n = 10) were injected on days 9 and 15 with a synthetic analog of viral double-stranded RNA, polyinosinic:polycytidylic acid (Poly I:C), a toll-like receptor ligand and synthetic viral mimic, and responses compared to a sham injected control group (n = 8). The hypotheses tested whether kestrels showed immunological differences among treatment groups, genetic sex, and/or white blood cell (WBC) subpopulation type over time. A flow cytometry (FCM) gating strategy categorized heterophils (H), lymphocytes (L), and monocytes (M) and their proportions, and measured relative fluorescence in response to anti-chicken CD4 binding. Fluorescent cell surfaces and some granular/vacuolar inclusions were visualized by epifluorescence microscopy. A fourth subpopulation with higher levels of granularity than M but less than H became increasingly apparent with time and was gated along with the H subpopulation; its frequency of occurrence was lowest in the ITP group (P = 0.0023). The percentages of cells differed among treatment groups, days, and sexes (P = 0.0001). For both sexes, percentages of H and L were higher than M in control and Poly I:C. In the ITP group, L percentages were higher than H and M (P = 0.0457), and H and L were higher than M on days 9 and 21 (P = 0.0001). The ratios of H:L and H:WBC, indicators of robust immunity, were also higher on days 9 and 21 than on 17 (P = 0.0079). For each sex, the highest levels of activity measured by FCM geometric means (GEO) of fluorescence (indicative of antibody binding) were observed on day 9 (P = 0.0001 female, and P = 0.0011 male) in H over both L and M (P < 0.0001 for each). In males, GEO of the Poly I:C group was higher than that of the ITP group (P = 0.0374), with no difference observed among females over all days. By using a FCM algorithm for population comparisons of fluorescence to investigate binding within H, the T(x) scores indicated higher fluorescence in control and Poly I:C groups over ITP (P = 0.0001). Unlike chickens, Gallus gallus, which express CD4 primarily on L, kestrels bound the commercial antibody primarily within the gated H subpopulation, suggesting an immunophenotypic difference between taxa, despite a ~60% identity of Falco CD4 amino acid sequences with chicken CD4. The emergent cell subset within the gated H presented dendritic-like cell (DLC) morphological and functional properties, apparently serving as an effector cell. This study adds interpretive context to ecological investigations of infection and of potential immunomodulation by emerging compounds, whereby the early innate responses are mediated by the various cell subsets serving as useful quantitative markers of immunological condition. Data showed that dietary exposure to ITP was immunosuppressive for male and female kestrels over the course of the experiment, reducing DLC frequency compared to the Poly I:C controls. Heterophils and DLC were important in facilitating innate immunological responses.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envint.2021.106779","usgsCitation":"Jenkins, J., Baudoin, B.A., Johnson, D., Fernie, K.J., Stapelton, H.M., and Karouna-Renier, N., 2021, Establishment of baseline cytology metrics in nestling American kestrels (Falco sparverius): Immunomodulatory effects of the flame retardant isopropylated triarylphosphate isomers: Environment International, v. 157, 106779, 15 p.; Data Release, https://doi.org/10.1016/j.envint.2021.106779.","productDescription":"106779, 15 p.; Data Release","ipdsId":"IP-116785","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450748,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envint.2021.106779","text":"Publisher Index Page"},{"id":436196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P7ZTMU","text":"USGS data release","linkHelpText":"Laboratory analysis assessing immune response after flame retardant exposure in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":436195,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SGX37F","text":"USGS data release","linkHelpText":"Discerning innate immunity in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":390889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417862,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9sgx37f"}],"volume":"157","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Jill 0000-0002-5087-0894","orcid":"https://orcid.org/0000-0002-5087-0894","contributorId":206575,"corporation":false,"usgs":true,"family":"Jenkins","given":"Jill","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baudoin, Brooke A 0000-0003-2874-1604","orcid":"https://orcid.org/0000-0003-2874-1604","contributorId":267938,"corporation":false,"usgs":true,"family":"Baudoin","given":"Brooke","email":"","middleInitial":"A","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":203921,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernie, Kim J.","contributorId":211241,"corporation":false,"usgs":false,"family":"Fernie","given":"Kim","email":"","middleInitial":"J.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":825645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stapelton, Heather M. 0000-0002-9995-6517","orcid":"https://orcid.org/0000-0002-9995-6517","contributorId":267940,"corporation":false,"usgs":false,"family":"Stapelton","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":825646,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":825647,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224957,"text":"70224957 - 2021 - Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration","interactions":[],"lastModifiedDate":"2021-10-11T15:55:41.405172","indexId":"70224957","displayToPublicDate":"2021-09-20T10:49:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration","docAbstract":"RRestoring degraded rivers requires initial assessment of the fluvial landscape to identify stressors and riverine features that can be enhanced. We associated local-scale river habitat data collected using standardized national monitoring tools with modeled regional water temperature and flow data on mid-sized northwest U.S. rivers (30–60 m wide). We grouped these rivers according to quartiles of their modeled mean August water temperature and examined their physical habitat structure and flow. We then used principal components analysis to summarize the variation in several dimensions of physical habitat. We also compared local conditions in the Priest River, a river targeted for restoration of native salmonid habitat in northern Idaho, with those in other rivers of the region to infer potential drivers controlling water temperature. The warmest rivers had physical structure and fluvial characteristics typical of thermally degraded rivers, whereas the coldest rivers had higher mean summer flows and greater channel planform complexity. The Priest River sites had approximately twice as many deep residual pools (>50, >75, and >100 cm) and incision that averaged approximately twice that in the coldest rivers. Percentage fines and natural cover in the Priest were also more typical of the higher-temperature river groups. We found generally low instream cover and low levels of large wood both across the region and within the Priest River. Our approach enabled us to consider the local habitat conditions of a river in the context of other similarly sized rivers in the surrounding region. Understanding this context is important for identifying potential influences on river water temperature within the focal basin and for defining attainable goals for management and restoration of thermal and habitat conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108213","usgsCitation":"Mejia, F.H., Connor, J.M., Kaufmann, P.R., Torgersen, C.E., Berntsen, E.K., and Andersen, T., 2021, Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration: Ecological Indicators, no. 131, 108213, 14 p., https://doi.org/10.1016/j.ecolind.2021.108213.","productDescription":"108213, 14 p.","ipdsId":"IP-119748","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":450752,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108213","text":"Publisher Index Page"},{"id":390391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Washington","otherGeospatial":"Priest River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 48.191725575618726\n ],\n [\n -116.861572265625,\n 48.191725575618726\n ],\n [\n -116.861572265625,\n 48.49840764096433\n ],\n [\n -117.158203125,\n 48.49840764096433\n ],\n [\n -117.158203125,\n 48.191725575618726\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"131","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":824849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, Jason M","contributorId":267258,"corporation":false,"usgs":false,"family":"Connor","given":"Jason","email":"","middleInitial":"M","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":824850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaufmann, Phil R","contributorId":267259,"corporation":false,"usgs":false,"family":"Kaufmann","given":"Phil","email":"","middleInitial":"R","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":824851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","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":824852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berntsen, Eric K","contributorId":214885,"corporation":false,"usgs":false,"family":"Berntsen","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":39131,"text":"Kalispel Tribe of Indians","active":true,"usgs":false}],"preferred":false,"id":824853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andersen, Todd","contributorId":243418,"corporation":false,"usgs":false,"family":"Andersen","given":"Todd","email":"","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":824854,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229695,"text":"70229695 - 2021 - Improving ESRI ArcGIS performance of coastal and seafloor analysis with the Python multiprocessing module","interactions":[],"lastModifiedDate":"2022-03-15T14:27:30.976578","indexId":"70229695","displayToPublicDate":"2021-09-20T09:25:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Improving ESRI ArcGIS performance of coastal and seafloor analysis with the Python multiprocessing module","docAbstract":"Coastal research frequently involves the use of a GIS to analyze large areas for changes in response to major weather events, human action, and other factors. The GIS workflows used to conduct these analyses can be complex and sometimes require multiple days to complete. Long runtimes often exist even on modern high-powered workstations if the GIS software does not use parallel computing techniques, which prevents it from fully utilizing the capabilities of multicore processors. If a GIS application supports a programming interface that allows geoprocessing tools to be called from an external program, then GIS workflows can use parallel functionality embedded in that programming language to divide the load of a large workflow among multiple child processes. In ArcMap and ArcGIS Pro, this technique can be implemented by using the Python programming interface and the multiprocessing module in Python to run geoprocessing tools in child processes. This method was used in the Seafloor Elevation Change Analysis Tool (SECAT), a Python script written for ArcMap and ArcGIS Pro that calculates changes in seafloor elevation over time using two different digital elevation models. Running SECAT with between one and eight child processes on two different datasets improved execution times by at least a factor of 2.4. These results demonstrate that using the Python multiprocessing module can significantly accelerate a variety of time-consuming workflows.
","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/JCOASTRES-D-21-00026.1","usgsCitation":"Zieg, J.A., and Zawada, D., 2021, Improving ESRI ArcGIS performance of coastal and seafloor analysis with the Python multiprocessing module: Journal of Coastal Research, v. 37, no. 6, p. 1288-1293, https://doi.org/10.2112/JCOASTRES-D-21-00026.1.","productDescription":"6 p.","startPage":"1288","endPage":"1293","ipdsId":"IP-117051","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":397108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zieg, Jonathan Andrew 0000-0002-4590-9328","orcid":"https://orcid.org/0000-0002-4590-9328","contributorId":288476,"corporation":false,"usgs":true,"family":"Zieg","given":"Jonathan","email":"","middleInitial":"Andrew","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":1898,"corporation":false,"usgs":true,"family":"Zawada","given":"David G.","email":"dzawada@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837980,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230277,"text":"70230277 - 2021 - Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","interactions":[],"lastModifiedDate":"2023-06-09T14:07:06.207792","indexId":"70230277","displayToPublicDate":"2021-09-20T09:00:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5094,"text":"Regional Studies in Marine Science","onlineIssn":"2352-4855","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","title":"Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","docAbstract":"Evaluating resource use patterns for imperiled species is critical for understanding what supports their populations. Here we established stable isotope (
This report describes a new screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States using estimated nutrient loading and flushing rates with measures of waterbody morphometry. To that end, the report documents the compiled data and methods (R-script) used to categorize waterbodies by Carlson’s Trophic State Index. Assessments were completed for 232 lakes and reservoirs having a surface area greater than or equal to 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Waterbodies were categorized by type—natural lakes, headwater reservoirs, and downstream reservoirs—and were assessed independently. Recursive partitioning and the model-based boosting routine were used to create four-node regression trees to group waterbodies into five endpoints from low-to-high measures of Secchi depth, and concentrations of chlorophyll a and microcystin according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned based on the average value within each of the five endpoints. An application (procedure) is provided using the tool to examine the susceptibility of a given waterbody of interest to eutrophication. Results of this study can aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication relative to nutrient loading, flushing rate, and morphometric characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215075","usgsCitation":"Green, W.R., Hoos, A.B., Wilson, A.E., and Heal, E.N., 2021, Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5075, 59 p., https://doi.org/10.3133/sir20215075.","productDescription":"Report: vi, 59 p.; Data Release","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-097274","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":389348,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5075/coverthb.jpg"},{"id":389349,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5075/sir20215075.pdf","text":"Report","size":"4.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5075"},{"id":389350,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K7EOH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Nutrient loading, flushing rate, and lake morphometry data used to identify trophic states in selected watersheds of the eastern and southeastern United States"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.916015625,\n 40.51379915504413\n ],\n [\n -73.71826171874999,\n 40.896905775860006\n ],\n [\n -73.05908203125,\n 41.19518982948959\n ],\n [\n -71.60888671875,\n 41.343824581185686\n ],\n [\n -70.48828125,\n 41.21172151054787\n ],\n [\n -69.7412109375,\n 41.83682786072714\n ],\n [\n -70.48828125,\n 42.69858589169842\n ],\n [\n -70.09277343749999,\n 43.54854811091286\n ],\n [\n -67.1044921875,\n 44.574817404670306\n ],\n [\n -66.884765625,\n 44.824708282300236\n ],\n [\n -67.236328125,\n 45.182036837015886\n ],\n [\n -67.43408203124999,\n 45.259422036351694\n ],\n [\n -67.39013671875,\n 45.583289756006316\n ],\n [\n -67.8515625,\n 45.706179285330855\n ],\n [\n -67.87353515625,\n 47.15984001304432\n ],\n [\n -68.22509765625,\n 47.338822694822\n ],\n [\n -68.90625,\n 47.234489635299184\n ],\n [\n -69.08203125,\n 47.45780853075031\n ],\n [\n -69.2138671875,\n 47.487513008956554\n ],\n [\n -70.048828125,\n 46.694667307773116\n ],\n [\n -70.224609375,\n 46.17983040759436\n ],\n [\n -70.55419921875,\n 45.66012730272194\n ],\n [\n -70.90576171875,\n 45.27488643704891\n ],\n [\n -71.279296875,\n 45.321254361171476\n ],\n [\n -71.47705078125,\n 44.99588261816546\n ],\n [\n -74.86083984375,\n 45.01141864227728\n ],\n [\n -75.73974609375,\n 44.5435052132082\n ],\n [\n -74.8828125,\n 44.18220395771566\n ],\n [\n -74.33349609375,\n 43.91372326852401\n ],\n [\n -75.12451171875,\n 43.46886761482925\n ],\n [\n -75.9375,\n 43.29320031385282\n ],\n [\n -76.88232421875,\n 42.4234565179383\n ],\n [\n -77.431640625,\n 42.65012181368022\n ],\n [\n -77.9150390625,\n 42.22851735620852\n ],\n [\n -77.76123046875,\n 41.902277040963696\n ],\n [\n -78.57421875,\n 40.64730356252251\n ],\n [\n -79.4970703125,\n 39.791654835253425\n ],\n [\n -79.95849609375,\n 38.013476231041935\n ],\n [\n -80.52978515625,\n 37.24782120155428\n ],\n [\n -81.40869140625,\n 35.37113502280101\n ],\n [\n -83.43017578125,\n 35.35321610123823\n ],\n [\n -85.10009765625,\n 35.38904996691167\n ],\n [\n -86.90185546874999,\n 35.38904996691167\n ],\n [\n -87.802734375,\n 35.17380831799959\n ],\n [\n -89.07714843749999,\n 34.63320791137959\n ],\n [\n -89.05517578125,\n 33.37641235124676\n ],\n [\n -88.87939453125,\n 32.80574473290688\n ],\n [\n -89.8681640625,\n 31.85889704445453\n ],\n [\n -89.93408203124999,\n 31.316101383495624\n ],\n [\n -89.736328125,\n 30.694611546632277\n ],\n [\n -89.45068359374999,\n 30.14512718337613\n ],\n [\n -86.8359375,\n 30.088107753367257\n ],\n [\n -85.62744140625,\n 29.935895213372444\n ],\n [\n -84.9462890625,\n 29.401319510041485\n ],\n [\n -84.287109375,\n 29.878755346037977\n ],\n [\n -82.85888671875,\n 29.036960648558267\n ],\n [\n -83.0126953125,\n 27.97499795326776\n ],\n [\n -81.5625,\n 25.403584973186703\n ],\n [\n -80.88134765625,\n 24.607069137709683\n ],\n [\n -79.89257812499999,\n 25.005972656239187\n ],\n [\n -79.5849609375,\n 26.92206991673282\n ],\n [\n -81.2109375,\n 30.939924331023445\n ],\n [\n -77.431640625,\n 34.21634468843463\n ],\n [\n -75.65185546874999,\n 34.97600151317588\n ],\n [\n -75.60791015625,\n 37.142803443716836\n ],\n [\n -75.12451171875,\n 38.35888785866677\n ],\n [\n -73.80615234375,\n 39.99395569397331\n ],\n [\n -73.916015625,\n 40.51379915504413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
This study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long-Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time-lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process-based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.