Survival rate of the larval stage is an important driver of fish recruitment. To understand mechanisms regulating larval survival it is important to understand the relative importance of abiotic and biotic factors that shape larval spatial distributions. We studied larval Coregonus distributions in surface waters (surface to 1 m) by repeatedly sampling study sites in two lakes that varied greatly in trophic state and regional climate. We evaluated the importance of bathymetric depth, Julian Day, edible zooplankton densities (EZ, ind. L−1) and wind vectors on larval spatial distributions using generalized additive modeling. In both systems, larval counts declined in a negative exponential fashion with bathymetric depth, indicating shallow depths are critical nursery habitat. The north-south wind vectors and Julian Day (which was positively correlated with surface temperature) influenced larval distributions in Lake Geneva with larval counts related to both variables linearly, whereas the east-west wind vector and EZ were unimportant. Highest larval counts were during an offshore south wind and declined slightly with Julian Day. In Lake Superior, bathymetric depth and the east-west wind vector influenced larval distributions and were unrelated to EZ, Julian Day, and the north-south wind vector. Larval counts were highest when onshore southwest winds preceded sampling. Differences in how wind affected larval distribution (offshore vs. onshore) might be related to larval size with Lake Superior larvae considerably smaller (average length 12.9 mm vs. 15.9 mm); thus, more apt to be subjected to advection. Within coastal waters, Julian Day and wind vectors influence distributions, but their importance seemingly varies lake-to-lake.
","language":"English","publisher":"EcoSciences","doi":"10.1051/limn/2024013","usgsCitation":"Dobosenski, J.A., Yule, D.L., Guillard, J., Anneville, O., Isaac, E., Stockwell, J.D., Myers, J., Ackiss, A.S., Chapina, R.J., and Moore, S., 2024, Factors influencing larval coregonine spatial distribution in Lake Geneva (Europe) and Lake Superior (North America) during a single season near known spawning sites: International Journal of Limnology, v. 60, no. 12, 12, 21 p., https://doi.org/10.1051/limn/2024013.","productDescription":"12, 21 p.","ipdsId":"IP-159935","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":432483,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439333,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1051/limn/2024013","text":"Publisher Index Page"}],"country":"France, United States","state":"Minnesota","otherGeospatial":"Lake Geneva, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.78267355691995,\n 47.99897216779462\n ],\n [\n -89.78267355691995,\n 47.90122910288531\n ],\n [\n -89.55798468980855,\n 47.90122910288531\n ],\n [\n -89.55798468980855,\n 47.99897216779462\n ],\n [\n -89.78267355691995,\n 47.99897216779462\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 6.238951771684498,\n 46.427296321727056\n ],\n [\n 6.238951771684498,\n 46.291519022939156\n ],\n [\n 6.534382971374612,\n 46.291519022939156\n ],\n [\n 6.534382971374612,\n 46.427296321727056\n ],\n [\n 6.238951771684498,\n 46.427296321727056\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dobosenski, Jamie A.","contributorId":239602,"corporation":false,"usgs":false,"family":"Dobosenski","given":"Jamie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":909586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":909587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guillard, Jean","contributorId":342064,"corporation":false,"usgs":false,"family":"Guillard","given":"Jean","affiliations":[{"id":81834,"text":"Univ. Savoie Mont Blanc, INRAE, CARRTEL","active":true,"usgs":false}],"preferred":false,"id":909588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anneville, Orlane","contributorId":147752,"corporation":false,"usgs":false,"family":"Anneville","given":"Orlane","affiliations":[{"id":16922,"text":"INRA UMR CARRTEL, Thonon-les-Bains, France","active":true,"usgs":false}],"preferred":false,"id":909589,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Isaac, Edmund J.","contributorId":342065,"corporation":false,"usgs":false,"family":"Isaac","given":"Edmund J.","affiliations":[{"id":81835,"text":"Grand Portage Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":909590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stockwell, Jason D. 0000-0003-3393-6799","orcid":"https://orcid.org/0000-0003-3393-6799","contributorId":61004,"corporation":false,"usgs":false,"family":"Stockwell","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":909591,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Myers, Jared T. 0009-0004-9362-8792","orcid":"https://orcid.org/0009-0004-9362-8792","contributorId":44055,"corporation":false,"usgs":false,"family":"Myers","given":"Jared T.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":909592,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ackiss, Amanda Susanne 0000-0002-8726-7423","orcid":"https://orcid.org/0000-0002-8726-7423","contributorId":272165,"corporation":false,"usgs":true,"family":"Ackiss","given":"Amanda","email":"","middleInitial":"Susanne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":909593,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chapina, Rosaura J.","contributorId":342066,"corporation":false,"usgs":false,"family":"Chapina","given":"Rosaura","email":"","middleInitial":"J.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":909594,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Moore, Seth A.","contributorId":342067,"corporation":false,"usgs":false,"family":"Moore","given":"Seth A.","affiliations":[{"id":81835,"text":"Grand Portage Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":909595,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70255718,"text":"70255718 - 2024 - Metal release from manganese nodules in anoxic seawater and implications for deep-sea mining dewatering operations","interactions":[],"lastModifiedDate":"2024-07-15T16:13:14.868429","indexId":"70255718","displayToPublicDate":"2024-06-27T07:08:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10742,"text":"ACS ES&T Water","active":true,"publicationSubtype":{"id":10}},"title":"Metal release from manganese nodules in anoxic seawater and implications for deep-sea mining dewatering operations","docAbstract":"The potential mining of deep-sea polymetallic nodules has been gaining increasing attention due to their enrichment in metals essential for a low-carbon future. To date, there have been few scientific studies concerning the geochemical consequences of dewatered mining waste discharge into the pelagic water column, which can inform best practices in future mining operations. Here, we report the results of laboratory incubation experiments that simulate mining discharge into anoxic waters such as those that overlie potential mining sites in the North Pacific Ocean. We find that manganese nodules are reductively dissolved, with an apparent activation energy of 42.8 kJ mol–1, leading to the release of associated metals in the order manganese > nickel > copper > cobalt > cadmium > lead. The composition of trace metals released during the incubation allows us to estimate a likely trace metal budget from the simulated dewatering waste plume. These estimates suggest that released cobalt and copper are the most enriched trace metals within the plume, up to ∼15 times more elevated than the background seawater. High copper concentrations can be toxic to marine organisms. Future work on metal toxicity to mesopelagic communities could help us better understand the ecological effects of these fluxes of trace metals.
The logistic equation models single-species population growth with a sigmoid curve that begins as exponential and ends with an asymptotic approach to a final population determined by natural system carrying capacity. But the population of a natural system often does not stabilize as it approaches carrying capacity. Instead, it exhibits periodic change, sometimes with very large amplitudes. The time-delay modification of the logistic equation accounts for this behavior by connecting the present rate of population growth to conditions at an earlier time. The periodic change in population with time can progress from a monotonic approach to the carrying capacity; to oscillation around the carrying capacity; to limit-cycle periodic change; and, finally, to chaotic change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.174061","usgsCitation":"Kurtz, B.E., Landmeyer, J.E., and Culter, J.K., 2024, Detection of periodic peaks in Karenia brevis concentration consistent with the time-delay logistic equation: Science of the Total Environment, v. 946, 174061, 13 p., https://doi.org/10.1016/j.scitotenv.2024.174061.","productDescription":"174061, 13 p.","ipdsId":"IP-156971","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":439336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.174061","text":"Publisher Index Page"},{"id":430887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.22453932882675,\n 28.202140467401122\n ],\n [\n -83.22453932882675,\n 25.11977470858126\n ],\n [\n -80.62077956320161,\n 25.11977470858126\n ],\n [\n -80.62077956320161,\n 28.202140467401122\n ],\n [\n -83.22453932882675,\n 28.202140467401122\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"946","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kurtz, Bruce E.","contributorId":304961,"corporation":false,"usgs":false,"family":"Kurtz","given":"Bruce","email":"","middleInitial":"E.","affiliations":[{"id":35150,"text":"New College of Florida","active":true,"usgs":false}],"preferred":false,"id":905888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landmeyer, James E. 0000-0002-5640-3816","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":216137,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Culter, James K.","contributorId":304962,"corporation":false,"usgs":false,"family":"Culter","given":"James","email":"","middleInitial":"K.","affiliations":[{"id":66192,"text":"Mote Marine Laboratory and Aquarium","active":true,"usgs":false}],"preferred":false,"id":905890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255667,"text":"70255667 - 2024 - Quantitative microbial risk assessment with microbial source tracking for mixed fecal sources contaminating recreational river waters, Iowa, USA","interactions":[],"lastModifiedDate":"2024-07-15T15:45:57.314269","indexId":"70255667","displayToPublicDate":"2024-06-27T06:53:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16870,"text":"Environmental Science & Technology Water","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative microbial risk assessment with microbial source tracking for mixed fecal sources contaminating recreational river waters, Iowa, USA","docAbstract":"Fecal contamination of surface water can cause acute gastrointestinal illness (AGI) among recreators. AGI risk varies among human, livestock, and wildlife fecal sources, but the prevalence of individual sources is unknown for most recreational sites. We estimated AGI risk for six sites near Des Moines, Iowa, using quantitative microbial risk assessment combined with microbial source-tracking (MST). Water samples (n = 147) collected over two years were tested for 36 qPCR assays quantifying waterborne pathogens and MST markers specific to avian, bovine, human, and porcine fecal sources. Average swimming risk across all sites was 5 (95% CI: 0.0030–142) to 67 (16–215) AGI cases per 1,000 recreators. Individual fecal sources were rarely associated with swimming exposures where risk was >36 AGI cases per 1,000 recreators; most high-risk exposures were associated with simultaneous occurrence of multiple fecal sources. Iowa’s beach action value for Escherichia coli (235 MPN/100 mL) identified >90% of high-risk exposures at five of six sites, so was generally protective of public health in this setting. For sites influenced by mixed fecal sources, results illustrate that identifying a single dominant source of risk is less important than recognizing the number of unique fecal sources that impact AGI risk.
North American Black Terns (Chlidonias niger) breed primarily in the Prairie Pothole region of southern Canada and the northern United States, winter in Central and South American waters, and often migrate through the northern Gulf of Mexico (nGoM). This species has exhibited long-term population declines and is exposed to a myriad of anthropogenic threats in the nGoM, including oil spills, with an estimated 800–1,000 injured during the Deepwater Horizon oil spill, yet historical studies of Black Terns' use of the nGoM are sparse, with inconsistent spatial and temporal coverage. Using vessel-based observations collected from 2017 to 2019, we characterize Black Tern spatial and temporal occurrence in marine waters of the nGoM. We develop 2 separate habitat models: one describing spatial and temporal aspects of Black Terns occurrence and the other describing the relative density when present. In 10 months of survey effort, January–October, we observed Black Terns in 7 (Mar–May and Jul–Oct), predominantly on the continental shelf at <200 m depth. Relative densities were greatest in the fall, coinciding with Black Terns' southward migration. Spatial distribution and habitat models suggest an association with river mouths or ports, as well as cool, productive waters, frequently associated near the outflow of the Mississippi River and just off the coast from Corpus Christi, Texas. The enhanced understanding of Black Terns in the nGoM could inform the preparation for, and response to, future oiling events or provide insight into potential interactions with the installation of offshore wind farms and aquaculture.
","language":"English","publisher":"Wilson Ornithological Society","doi":"10.1676/23-00069","usgsCitation":"Michael, P.E., Gleason, J., Haney, J., Hixson, K.M., Satgé, Y., and Jodice, P.G., 2024, Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico: Wilson Journal of Ornithology, v. 136, no. 2, p. 220-236, https://doi.org/10.1676/23-00069.","productDescription":"17 p.","startPage":"220","endPage":"236","ipdsId":"IP-155261","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.06640768994139,\n 25.968336212595545\n ],\n [\n -81.56410467268525,\n 24.655591782200545\n ],\n [\n -81.0728587401813,\n 25.246086829050483\n ],\n [\n -82.48259218249659,\n 27.272254831037472\n ],\n [\n -82.73152989277146,\n 27.960240807935207\n ],\n [\n -82.69786543399637,\n 28.715489724979037\n ],\n [\n -84.01930283028096,\n 30.13642592586335\n ],\n [\n -85.21352357055073,\n 29.683019111608573\n ],\n [\n -86.43868847529218,\n 30.499185485782192\n ],\n [\n -87.48025217176053,\n 30.277107124160494\n ],\n [\n -88.01869425708601,\n 30.521051975468964\n ],\n [\n -89.17037649125658,\n 30.240796867164235\n ],\n [\n -89.90702908193344,\n 29.518977092248832\n ],\n [\n -90.49189050390542,\n 29.36185843606789\n ],\n [\n -91.74270448217844,\n 29.90068888910362\n ],\n [\n -93.01117136756345,\n 29.866650987283208\n ],\n [\n -94.66379808733016,\n 29.61102885408677\n ],\n [\n -94.72573793650419,\n 29.93320591649301\n ],\n [\n -94.98767798427114,\n 29.573223193987033\n ],\n [\n -95.28662091934446,\n 28.98698052519731\n ],\n [\n -96.75825455051769,\n 28.63898925070241\n ],\n [\n -97.57160445145992,\n 27.963607001385867\n ],\n [\n -97.86987981335206,\n 27.03862279886482\n ],\n [\n -97.06640768994139,\n 25.968336212595545\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Michael, Pamela E.","contributorId":341152,"corporation":false,"usgs":false,"family":"Michael","given":"Pamela","email":"","middleInitial":"E.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gleason, Jeffrey S.","contributorId":341153,"corporation":false,"usgs":false,"family":"Gleason","given":"Jeffrey S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haney, J. Christopher","contributorId":341154,"corporation":false,"usgs":false,"family":"Haney","given":"J. Christopher","affiliations":[{"id":81710,"text":"Terra Mar Applied Science","active":true,"usgs":false}],"preferred":false,"id":908008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hixson, Kathy M.","contributorId":341155,"corporation":false,"usgs":false,"family":"Hixson","given":"Kathy","email":"","middleInitial":"M.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908009,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Satgé, Yvan G.","contributorId":341156,"corporation":false,"usgs":false,"family":"Satgé","given":"Yvan G.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908010,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908011,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255599,"text":"sir20245056 - 2024 - Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","interactions":[],"lastModifiedDate":"2026-02-03T19:43:57.736197","indexId":"sir20245056","displayToPublicDate":"2024-06-25T15:43:18","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5056","displayTitle":"Two-Dimensional Hydraulic Model for the Chain of Lakes on the Fox River near McHenry, Illinois","title":"Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","docAbstract":"Forecasts of flows entering and leaving the Chain of Lakes on the Fox River in northeastern Illinois are critical information to water-resource managers operating the Stratton Dam at McHenry, Illinois. These managers determine the optimal operation of the Stratton Dam at McHenry, Ill., to manage Chain of Lakes pool levels and to help mitigate flooding in the Chain of Lakes system. In 2020, the U.S. Geological Survey (USGS) and the Illinois Department of Natural Resources–Office of Water Resources (IDNR–OWR) began a cooperative study to develop a system to enable engineers and planners to simulate and communicate water-surface elevations and flows and to proactively prepare for runoff events forecasted for the Chain of Lakes. The hydraulic model described in this report may be helpful to the IDNR–OWR for optimizing the operation of the Stratton Dam and includes the implementation of three newly installed torque-tube crest gates that became operational in 2020.
The hydraulic model for the Chain of Lakes was developed using the Hydrologic Engineering Center–River Analysis System program (version 6.5). The hydraulic model was used to simulate water-surface elevations and flows through the 18.5-mile Chain of Lakes system to 1.7 miles downstream from the Stratton Dam. Five USGS streamgages within the study area were used as reference points for model calibration and initial water-surface elevations for beginning a simulation. The hydraulic model was calibrated to three runoff events that incorporated the design specifications and observed gate operations of the Stratton Dam; furthermore, the hydraulic model simulated a validation event and a substantial flooding event during July 2017. The July 2017 event predated the torque-tube crest gate installation but nevertheless tested the performance of the model for such a substantial event. The model simulation results were a good fit to observed records at USGS streamgages with simulated peak water-surface elevations within −0.36–0.15 foot of observed events. The hydraulic model was then implemented into a forecast workflow that streamlines implementation of model inputs and documents the model outputs tailored to IDNR–OWS Stratton Dam operations and interpretations of simulated water-surface elevations and flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245056","collaboration":"Prepared in cooperation with the Illinois Department of Natural Resources–Office of Water Resources","usgsCitation":"Cigrand, C.V., and Ament, M.R., 2024, Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois: U.S. Geological Survey Scientific Investigations Report 2024–5056, 20 p., https://doi.org/10.3133/sir20245056.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137180","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499478,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117099.htm","linkFileType":{"id":5,"text":"html"}},{"id":430505,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":430504,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P16H3TDH","text":"USGS data release","linkHelpText":"Archive of the hydraulic model used in the two-dimensional simulation of the Chain of Lakes on the Fox River near McHenry, Illinois:"},{"id":430503,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245056/full"},{"id":430502,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5056/images/"},{"id":430501,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.XML"},{"id":430500,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.pdf","text":"Report","size":"3.5 MB","description":"SIR 2024–5056"},{"id":430499,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5056/coverthb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Fox River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.3061687403806,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.29838954847517\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Detailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image-based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image-based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)-based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image-derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4333","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2024, An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA): River Research and Applications, v. 40, no. 8, p. 1602-1616, https://doi.org/10.1002/rra.4333.","productDescription":"15 p.","startPage":"1602","endPage":"1616","ipdsId":"IP-163908","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.4333","text":"Publisher Index Page"},{"id":462540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":914715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":914716,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255575,"text":"sir20245041 - 2024 - Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","interactions":[],"lastModifiedDate":"2026-02-03T19:22:10.1439","indexId":"sir20245041","displayToPublicDate":"2024-06-25T09:45:01","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5041","displayTitle":"Representation of Surface-Water Flows Using Gradient-Related Discharge in an Everglades Network","title":"Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","docAbstract":"The Everglades Depth Estimation Network interpolates water-level gage data to produce daily water-level elevations for the Everglades in south Florida. These elevations were used to estimate flow vectors (gradients and directions) and volumetric flow rates using the Gradient-Related Discharge in an Everglades Network (GARDEN) application developed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. Flow rates in both the east-west and north-south directions were computed on a 400-meter square grid using modified parameters in the Manning’s equation. The frictional resistance parameter in the Manning’s equation was calibrated to measured flow rates at coastal creeks fed by Everglades Depth Estimation Network boundary flows. Levees and other features that act as barriers to flow were defined as “no-flow” grid cells where vectors were set to zero.
The flow volume magnitudes were calibrated with 2020 daily values of coastal river flows, and verification was performed using 2021 data. Within a given day, the measured coastal river flows fluctuate more than the GARDEN boundary flows because of tidal and wind forcings. Because the GARDEN boundary flows were the upstream water source for the coastal rivers, calibration focused on matching average daily flow volumes rather than daily fluctuations. The Pearson’s correlation coefficient is 0.766 for the 2020 calibration period and 0.566 for the 2021 verification period.
Applying GARDEN to periods with hydraulic-control-structure releases allows the propagation of structure flows to be seen in the daily flow-vector maps along with the multiday response of flows farther downgradient. Flow vectors may be overestimated near control structures because of difficulties in resolving the water gradient downstream from the structure. Flow vectors farther from the structure are more accurate than those near the structure.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The U.S. Geological Survey (USGS) provided technical assistance to the National Park Service (NPS) as part of the USGS-NPS Water-Quality Partnership, by gathering references related to water-quality research conducted in the three units of Gateway National Recreation Area (GATE): Jamaica Bay and Staten Island in New York, and Sandy Hook in New Jersey. As part of this effort, a literature search was performed to compile previous water-quality research conducted within the boundaries of GATE. The resulting bibliography is meant to assist GATE resource managers in understanding the extent of available data and developing plans to close data gaps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241035","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Savoy, P., Marionkova, M., and Schubert, C., 2024, Bibliography of water-quality studies in Gateway National Recreation Area, New York and New Jersey: U.S. Geological Survey Open-File Report 2024–1035, 7 p., https://doi.org/10.3133/ofr20241035.","productDescription":"iii, 7 p.","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161856","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499256,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117097.htm","linkFileType":{"id":5,"text":"html"}},{"id":430428,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1035/images/"},{"id":430427,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1035/ofr20241035.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1035 XML"},{"id":430426,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241035/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1035 HTML"},{"id":430425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1035/ofr20241035.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1035 PDF"},{"id":430424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1035/coverthb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Gateway National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.85513729439843,\n 40.654551507838846\n ],\n [\n -73.89381273583244,\n 40.62388973104002\n ],\n [\n -73.9579556996699,\n 40.537441746266865\n ],\n [\n -73.8554944605876,\n 40.56042638081682\n ],\n [\n -73.85236012115627,\n 40.58267541112733\n ],\n [\n -73.83876809711201,\n 40.58418418537727\n ],\n [\n -73.82517607306771,\n 40.58569296761614\n ],\n [\n -73.7681617378135,\n 40.606390031110664\n ],\n [\n -73.7722763223719,\n 40.62727653623955\n ],\n [\n -73.81698658676503,\n 40.65139922165838\n ],\n [\n -73.85513729439843,\n 40.654551507838846\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.03359844094561,\n 40.48300783959223\n ],\n [\n -74.00884376460158,\n 40.40869619490783\n ],\n [\n -73.96483206082176,\n 40.405554701601744\n ],\n [\n -73.99371377377663,\n 40.48823772192023\n ],\n [\n -74.03359844094561,\n 40.48300783959223\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.13918165624833,\n 40.5462145867333\n ],\n [\n -74.13082225121698,\n 40.54483184944499\n ],\n [\n -74.1300961800887,\n 40.53681505828524\n ],\n [\n -74.1417287594383,\n 40.53488174204816\n ],\n [\n -74.13701209880821,\n 40.526583892279575\n ],\n [\n -74.04424809693685,\n 40.58020576969048\n ],\n [\n -74.03879133708992,\n 40.602581719859955\n ],\n [\n -74.05916439950865,\n 40.60892165585267\n ],\n [\n -74.06716503242002,\n 40.602004600980564\n ],\n [\n -74.0631653942842,\n 40.59593339538253\n ],\n [\n -74.09372089641063,\n 40.56998116301571\n ],\n [\n -74.10679839509868,\n 40.576050498707474\n ],\n [\n -74.10971340310442,\n 40.56970029742831\n ],\n [\n -74.09954055858626,\n 40.56307272953646\n ],\n [\n -74.11445006764852,\n 40.558650678011816\n ],\n [\n -74.1293363756321,\n 40.56141089836322\n ],\n [\n -74.13918165624833,\n 40.5462145867333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
We present a hydroclimate synthesis of the southern Great Basin over the last two glacial-interglacial cycles focused on paleolakes in Death Valley (core DV93-1), Searles Valley (core SLAPP-SRLS17), Owens Valley (core OL92), and the Devils Hole cave. There is close agreement between the occurrence of lakes in Death Valley and the height of the water table in the Devils Hole (50 km east of Death Valley) during the last 200 kyr. Death Valley and Devils Hole have adjacent, partly overlapping, drainage areas and most likely did over the last 200 kyr. When the water table in the Devils Hole was above the threshold level of ∼5 m higher than the modern, permanent lakes existed in Death Valley. At water table elevations less than 5 m above the modern, ephemeral lakes, saline pans, and mudflats occurred in Death Valley. The close temporal agreement between inferred paleoenvironments from the sediments in the Death Valley core and the paleowater table elevation in Devils Hole suggests a common forcing and provides insight into climate variability in the southwestern United States over the last 200 kyr. Owens Valley and Searles Valley, which derived inflow waters from the Sierra Nevada via the Owens River, contain paleohydrologic records which match those from Death Valley and the Devils Hole in terms of timing and direction of water availability over the last 200 kyr, indicating a similar paleohydrologic history for the entire southern Great Basin region. Near the end of Marine Oxygen Isotope Stage 6 (MIS 6), 140 ka - 130 ka, Lake Manly in Death Valley became shallow and hypersaline, and ultimately dried up at 127.1 ka ±4.3 ka. The transition from glacial to interglacial vegetation, which involved the loss of Juniperus pollen and an increase in Quercus (oak) pollen, occurred in Death Valley core DV93-1 at 131.3 ka ±4.0 ka. Following the glacial to interglacial pollen shift, a large alkaline lake formed in Death Valley. Similar conditions (freshwater, high productivity, and a mixed, deeply oxygenated water column indicated by biomarkers) existed in Searles Lake between 135.3 +2.7/-2.9 ka and 130.1+2.7/-2.6 ka, also following the juniper-oak pollen transition. Sr isotopes in calcite and sulfate minerals (gypsum, glauberite, thenardite), and the rare occurrence of the sodium carbonate mineral northupite with a low 87Sr/86Sr ratio in core DV93-1, together with organic geochemical proxies from Searles core SLAPP-SRLS17, all suggest that at this time, late MIS 6 Lake Manly in Death Valley received alkaline water via spillover from Searles Valley into Death Valley through Panamint Valley. The hydrologic connection between Searles Valley, Panamint Valley, and Death Valley at Termination II (130 ka) is documented here for this system of pluvial lakes for the first time. The Devils Hole water table decreased to +6.5 m at 140.8 ka ±3.2 ka, rose briefly to +8 m at 137.6 ka ±0.5 ka, and then dropped 8 m by 120.36 ka ±0.45 ka, when it reached an elevation similar to the modern. The pluvial lakes in Death Valley and Searles Valley may have coincided with the rise of the Devils Hole water table at ∼137.6 ka ±0.5 ka years ago, although the age models for core DV93-1 and core SLAPP-SLRS17 during the end of MIS 6 carry large uncertainties.
Nutrient concentrations in many estuaries have increased over the past century due to increases in wastewater discharge and increased agricultural intensity, contributing to multiple environmental problems. Numerous biogeochemical and physical processes in estuaries influence nutrient concentrations during transport, resulting in complex spatial and temporal variability and challenges identifying predominant processes and their rates. Mechanistic models which require these rates to quantify biogeochemical processes become complex and difficult to calibrate as the number of processes and parameters grows, owing to the high dimensionality of the parameter space and the computational cost of simultaneously modeling the transport and transformations of constituents. We developed a modeling approach that decouples transport from transformations, enabling fast, data-driven exploration of the parameter space. The approach extracted information including water age, cumulative exposure to specific habitats, and mean water depth exposure from a hydrodynamic model. Using this information, a biogeochemical model was implemented to predict ammonium and nitrate concentrations in a Lagrangian frame. The model performed each simulation in milliseconds on a laptop computer, allowing the fitting of rate parameters for key transformations by optimization. The optimization used fixed station nitrate observations and the model was then validated against high-resolution mapping observations of ammonium and nitrate. The results suggest that the observed spatial and temporal variation can be largely represented with five transformation processes and their associated rates. Dissolved inorganic nitrogen (DIN) losses occurred only in shallow vegetated areas in the model, highlighting that biogeochemical processes in these areas should be included in DIN models.
With growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite-based discharge measurements. Satellite-based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite-based discharge measurements because geomorphic river characteristics have strong control over the relationships between discharge and width, water surface elevation (or depth), slope, and velocity. A large ground-truth data set of 8,445 conventional hydraulic measurements, collected by acoustic Doppler current profilers at 503 stations in the United States, was developed and quality assured to examine correlation between river discharge and water surface width, depth, velocity, and cross-sectional area. A separate database of river surface slope and discharge time-series was developed from paired continuous monitoring stations to examine slope-discharge correlations. Results show that discharge correlates most strongly with velocity, cross-sectional area, depth, width, and slope, in that order. Uncertainty of satellite discharge estimates is affected by observed hydraulic variable and reach-specific variability in observed variable(s) characteristics including range of variability, georegistration accuracy, and stability over time of relationships between discharge and observed hydraulic variable.
Scientists and resource managers increasingly use Markdown-based tools to create reproducible reports and manuscripts. These workflows allow people to use standardized methods that are more reproducible, efficient, and transparent than other standard office tools. We present a Quarto template and demonstrate how this template may be used for a journal, the Journal of Fish and Wildlife Management, in our article. This template may also be readily adapted to other journals that use Microsoft Word-based workflows and for other product types such as annual reports. We also provide a high-level overview of Quarto and other Markdown-based workflows as part of the document. Lastly, we provide examples of some features of the Quarto publishing system that may be helpful for authors when customizing Quarto templates for specific journal formatting requirements and other product types.
Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R2) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106124","usgsCitation":"Goodling, P.J., Belitz, K., Stackelberg, P.E., and Fleming, B.J., 2024, A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA: Environmental Modelling & Software, v. 179, 106124, 12 p., https://doi.org/10.1016/j.envsoft.2024.106124.","productDescription":"106124, 12 p.","ipdsId":"IP-160581","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":439361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106124","text":"Publisher Index Page"},{"id":430446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.88099946089724,\n 38.58741180591247\n ],\n [\n -74.71321333503417,\n 39.379784628066616\n ],\n [\n -74.91854009408658,\n 39.623906471535875\n ],\n [\n -74.56682974019151,\n 39.83490997578861\n ],\n [\n -74.83087158557463,\n 40.43445755432647\n ],\n [\n -74.69462804413362,\n 42.31099383658801\n ],\n [\n -75.89851464683143,\n 42.243461978517985\n ],\n [\n -76.67474108243883,\n 40.45538711590305\n ],\n [\n -76.4341425039691,\n 39.732367924983954\n ],\n [\n -75.81256791410406,\n 39.70992285882126\n ],\n [\n -75.68892404289328,\n 38.72600697054469\n ],\n [\n -74.88099946089724,\n 38.58741180591247\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":904793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":904794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fleming, Brandon J. 0000-0001-9649-7485 bjflemin@usgs.gov","orcid":"https://orcid.org/0000-0001-9649-7485","contributorId":4115,"corporation":false,"usgs":true,"family":"Fleming","given":"Brandon","email":"bjflemin@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904795,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256164,"text":"70256164 - 2024 - Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS)","interactions":[],"lastModifiedDate":"2025-01-17T15:55:36.342599","indexId":"70256164","displayToPublicDate":"2024-06-22T06:52:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1248,"text":"Climate Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS)","docAbstract":"The mid-Piacenzian Warm Period (MPWP, ~ 3.264–3.025 Ma) is the most recent example of a persistently warmer climate in equilibrium with atmospheric CO2 concentrations similar to today. Towards studying patterns and dynamics of a warming climate the MPWP is often compared to today. Following the Pliocene Model Intercomparison Project, Phase 2 (PlioMIP2) protocol we prepare a water isotope-enabled Community Earth System Model (iCESM1.2) simulation that is warmer and wetter than the PlioMIP2 multi-model ensemble (MME). While our simulation resembles PlioMIP2 MME in many aspects we find added insights. (1) Considerable warmth at high latitudes exceeds previous simulations. Polar amplification (PA) is comparable to proxies, enabled by iCESM1.2’s high climate sensitivity and a distinct method of ocean initialization. (2) Major driver of warmth is the downward component of clear-sky surface long-wave radiation (ΔTrlds_clearsky). (3) In iCESM1.2 modulated dominance of dynamic (δDY) processes causes different low-latitude (~ 30 S°–10°N) precipitation response than the PlioMIP2 MME, where thermodynamic processes (δTH) dominate. (4) Modulated local condensation leads to lower δ18Op across tropical Indian Ocean and surrounding Asian-African-Australian monsoon regions. (5) We find contrasting changes in tropical atmospheric circulations (Hadley and Walker cells). Anomalous regional meridional (zonal) circulation, forced by changes in tropical-subtropical (tropical) diabatic processes, presents a more comprehensive perspective than explaining weakened and expanded Hadley circulation (strengthened and westward-shifted Walker circulation) via static stability. (6) Enhanced Atlantic meridional overturning circulation owes to a closed Bering Strait.
","language":"English","publisher":"Springer","doi":"10.1007/s00382-024-07304-0","usgsCitation":"Sun, Y., Su, B., Dowsett, H.J., Wu, H., Hu, J., Stepanek, C., Xiong, Z., Yuan, X., and Ramstein, G., 2024, Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS): Climate Dynamics, v. 62, p. 7741-7761, https://doi.org/10.1007/s00382-024-07304-0.","productDescription":"21 p.","startPage":"7741","endPage":"7761","ipdsId":"IP-145565","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":439362,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00382-024-07304-0","text":"Publisher Index Page"},{"id":431436,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationDate":"2024-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Sun, Yong","contributorId":336900,"corporation":false,"usgs":false,"family":"Sun","given":"Yong","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":906957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Su, Baohuang","contributorId":338746,"corporation":false,"usgs":false,"family":"Su","given":"Baohuang","email":"","affiliations":[],"preferred":false,"id":907051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":907052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Haibin","contributorId":338744,"corporation":false,"usgs":false,"family":"Wu","given":"Haibin","email":"","affiliations":[],"preferred":false,"id":907053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hu, Jun","contributorId":340390,"corporation":false,"usgs":false,"family":"Hu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":907054,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stepanek, Christian","contributorId":220691,"corporation":false,"usgs":false,"family":"Stepanek","given":"Christian","email":"","affiliations":[{"id":40240,"text":"Alfred Wegener Institute-Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":907055,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xiong, Zhongyu","contributorId":340391,"corporation":false,"usgs":false,"family":"Xiong","given":"Zhongyu","email":"","affiliations":[],"preferred":false,"id":907056,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yuan, Xiayu","contributorId":338747,"corporation":false,"usgs":false,"family":"Yuan","given":"Xiayu","email":"","affiliations":[],"preferred":false,"id":907057,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ramstein, Gilles","contributorId":269585,"corporation":false,"usgs":false,"family":"Ramstein","given":"Gilles","email":"","affiliations":[{"id":55994,"text":"Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France","active":true,"usgs":false}],"preferred":false,"id":907058,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70273359,"text":"70273359 - 2024 - Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska","interactions":[],"lastModifiedDate":"2026-01-09T17:22:33.919542","indexId":"70273359","displayToPublicDate":"2024-06-21T11:17:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska","docAbstract":"This work evaluates glacial dust as a source of sediment, and associated iron (Fe), to the Fe-limited Gulf of Alaska (GoA). A reanalysis of GoA sediment data, using rare earth elements and thorium as provenance tracers, suggests a flux to the ocean surface of Copper River (AK) glacial dust, and associated Fe, that is comparable to the flux of dust from Asia, at least 1,000 km from the narrow mountain valley glacial dust source area. This work suggests dust from Asia may not be the largest source of Fe to the GoA. Dust models fail to accurately simulate this glacial dust transport because their coarse resolution underestimates wind speeds, and the dust flux. This work suggests that glacial dust fluxes may have been important in the geologic past (e.g., the last glacial maximum) from locations where there was more extensive coverage by glaciers than at present.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GL106778","usgsCitation":"Crusius, J., Lao, C., Holmes, T.M., and Murray, J.W., 2024, Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska: Geophysical Research Letters, v. 51, no. 12, e2023GL106778, 10 p., https://doi.org/10.1029/2023GL106778.","productDescription":"e2023GL106778, 10 p.","ipdsId":"IP-144402","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":498678,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gl106778","text":"Publisher Index Page"},{"id":498516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166,\n 61\n ],\n [\n -166,\n 48\n ],\n [\n -136,\n 48\n ],\n [\n -136,\n 61\n ],\n [\n -166,\n 61\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lao, Carsten","contributorId":364912,"corporation":false,"usgs":false,"family":"Lao","given":"Carsten","affiliations":[{"id":87012,"text":"UW Dept of Chemistry","active":true,"usgs":false}],"preferred":false,"id":953434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Thomas M. 0000-0001-8061-4325","orcid":"https://orcid.org/0000-0001-8061-4325","contributorId":364913,"corporation":false,"usgs":false,"family":"Holmes","given":"Thomas","middleInitial":"M.","affiliations":[{"id":87014,"text":"U. Tasmania","active":true,"usgs":false}],"preferred":false,"id":953435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, J. W. 0000-0002-8577-7964","orcid":"https://orcid.org/0000-0002-8577-7964","contributorId":364914,"corporation":false,"usgs":false,"family":"Murray","given":"J.","middleInitial":"W.","affiliations":[{"id":87015,"text":"UW School of Oceanography","active":true,"usgs":false}],"preferred":false,"id":953436,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257766,"text":"70257766 - 2024 - From eDNA to decisions using a multi-method approach to restoration planning in streams","interactions":[],"lastModifiedDate":"2024-08-26T12:29:25.642251","indexId":"70257766","displayToPublicDate":"2024-06-21T07:24:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"From eDNA to decisions using a multi-method approach to restoration planning in streams","docAbstract":"Reintroduction efforts are increasingly used to mitigate biodiversity losses, but are frequently challenged by inadequate planning and uncertainty. High quality information about population status and threats can be used to prioritize reintroduction and restoration efforts and can transform ad hoc approaches into opportunities for improving conservation outcomes at a landscape scale. We conducted comprehensive environmental DNA (eDNA) and visual encounter surveys to determine the distribution of native and non-native aquatic species in two high-priority watersheds to address key uncertainties—such as the distribution of threats and the status of existing populations—inherent in restoration planning. We then used these occurrence data to develop a menu of potential conservation actions and a decision framework to benefit an endangered vertebrate (foothill yellow-legged frog, Rana boylii) in dynamic stream systems. Our framework combines the strengths of multiple methods, allowing managers and conservation scientists to incorporate conservation science and site-specific knowledge into the planning process to increase the likelihood of achieving conservation goals.
Arsenic toxicity is an environmental health problem. Levels of arsenic in surface waters at some locations in the Klamath River Basin in southern Oregon and northern California can exceed the U.S. Environmental Protection Agency (EPA) standard for drinking water. There are both anthropogenic and natural sources of arsenic. The Klamath River Basin consists primarily of volcanic deposits and contains an underground geothermal system with hot springs and warm water wells, all known natural sources of arsenic. Anthropogenic sources of arsenic are related to the agricultural use of herbicides, fungicides, and insecticides. Surface water arsenic levels can also be affected by fertilizer amendments, evaporative concentration, oxygen-level depletion, and various geochemical transformations that can increase arsenic mobilization.
In this study by the U.S. Geological Survey and the Bureau of Reclamation, dissolved concentrations of arsenic, copper, and lead were measured in surface waters at 39 unique sites within the upper portions of the Klamath River Basin between 2018 and 2022. In every year, except 2022, sites were sampled four times between April and November. Surface-water arsenic concentrations varied up to four-orders of magnitude among sites. Median arsenic concentration was lowest at Cherry Creek (0.03 micrograms per liter [μg/L]) and highest at Wood Kimball Spring (36.7 μg/L), two sites located north of Upper Klamath Lake. The highest arsenic concentrations (17.4±4.9 μg/L, n=3) were found in drain sites (defined here as a waterbody returning used irrigation water) while the lowest arsenic concentrations were found in an artesian well (0.8 μg/L, n=1). The elevated arsenic concentrations of the drain sites suggest that arsenic might be concentrated or mobilized by agricultural activities, water re-use practices, and (or) by geochemical processes occurring around water stored in drains (that is, in the water column and across sediment water boundaries). A source of arsenic in drain water in the Klamath Strait Drain area includes water used for irrigation originating from Ady Canal. Other potential sources include groundwater, geothermal water, and local soils and sediments.
Seasonal differences in surface-water arsenic concentrations were detected at 13 sites, 10 of which had higher arsenic concentrations in summer than in either spring or fall. The sites sampled around Upper Klamath Lake, the impounded rivers, one of the two canal sites, and 5 of the 14 river sites had higher surface-water arsenic concentrations in the summer than in either spring or fall. Surface-water arsenic concentrations from groundwater sources (that is, springs and in the artesian well) did not vary significantly among seasons (p-values greater than 0.1).
Median surface-water concentrations of copper and lead ranged from 0.03 to 3.7 μg/L, and from 0.013 to 0.175 μg/L (n=2–18), respectively. Dissolved concentrations of both metals were below acute toxicity endpoints reported by the EPA for freshwater animals. Surface-water arsenic concentrations varied independently from corresponding changes in surface-water lead or copper concentrations. However, arsenic concentrations measured in bed-sediment samples collected from a subset of sites located north of Upper Klamath Lake correlated strongly and significantly with the corresponding sedimentary lead concentrations (p=0.015).
Aqueous arsenic speciation measured in a subset of sites in 2019 and 2022 showed that all the arsenic existed as arsenic (V), the most oxidized arsenic species, and presumably, the least toxic. The highest proportions of arsenite (As(III)), the presumably most toxic arsenic species, relative to total arsenic concentrations were found at drain sites.
Our assessment of dissolved arsenic concentrations in various surface-water bodies in the Upper Klamath River Basin reveals geographical areas of consistently low (below 2.1 μg/L), moderate (below 10 μg/L) and high (above 10 μg/L) surface-water arsenic concentrations. South of Upper Klamath Lake, surface-water arsenic concentrations were consistently higher than 20 μg/L at two drain sites located in an area of predominant agricultural land use with extensive water re-use practices. North of Upper Klamath Lake, surface-water arsenic concentrations greater than 20 μg/L were consistently measured at sites with limited nearby agricultural activities, suggesting a geogenic source. The consistently high arsenic levels from the Wood River at Jackson F. Kimball State Park, Fort Creek, and Crooked Creek, which are sites located at or near headwater spring sources, suggest a natural background source of arsenic. Water flowing downstream from this area could be a potential source of arsenic to Upper Klamath Lake and the Upper Klamath River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245029","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Croteau, M.N., Topping, B.R., and Carlson, R.A., 2024, Dissolved arsenic concentrations in surface waters within the upper portions of the Klamath River Basin, Oregon and California: U.S. Geological Survey Scientific Investigations Report 2024–5029, 42 p., https://doi.org/10.3133/sir20245029.","productDescription":"Report: viii, 38 p.; Data Release","ipdsId":"IP-149938","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":430399,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P943CWH1","text":"USGS Data Release","description":"Hill, K.L., Croteau, M.N., Topping, B.R., Caro, D.A., Parris, J.L., Zierdt Smith, E.L., and Baesman, S.M., 2021, Dissolved arsenic, copper and lead concentrations in surface water within the Klamath Basin (ver 4.0, April 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P943CWH1.","linkHelpText":"Dissolved arsenic, copper and lead concentrations in surface water within the Klamath Basin (ver 4.0, April 2023)"},{"id":430404,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245029/full"},{"id":430403,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5029/images"},{"id":430402,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5029/sir20245029.xml"},{"id":430401,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5029/sir20245029.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":430400,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5029/covrthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.44639737545533,\n 43\n ],\n [\n -122.44639737545533,\n 41.66727944834608\n ],\n [\n -120.85711312398831,\n 41.66727944834608\n ],\n [\n -120.85711312398831,\n 43\n ],\n [\n -122.44639737545533,\n 43\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
The purpose of this report is to provide the Marine Corps with an annual summary of abundance, breeding activity, demography, and habitat use of endangered Least Bell’s Vireos (Vireo bellii pusillus) at Marine Corps Base Camp Pendleton (MCBCP, or Base). Surveys for the Least Bell's Vireo were conducted at MCBCP, California, between April 1 and July 10, 2020. Core survey areas and a subset of non-core areas in drainages containing riparian habitat suitable for vireos were surveyed 3–4 times. We detected 669 territorial male vireos and 16 transient vireos in core survey areas. An additional 156 territorial male vireos were detected in non-core survey areas. Territorial vireos were detected on all 10 drainages/sites surveyed (core and non-core areas). Of the vireo territories in core areas, 88 percent were on the 4 most populated drainages, with the Santa Margarita River containing 69 percent of all territories. In core areas, 79 percent of male vireos were confirmed as paired; 83 percent of male vireos in non-core areas were confirmed as paired.
The number of documented Least Bell’s Vireo territories in core survey areas on MCBCP (669) increased 39 percent from 2019 to 2020. The number of territories in all core survey area drainages increased by one or more between 2019 and 2020. The substantial increase in vireo numbers on MCBCP (39 percent) was consistent with population changes in surrounding areas, including the lower San Luis Rey River (26 percent), Marine Corps Air Station, Camp Pendleton (58 percent), and the middle San Luis Rey River (7 percent).
Most core-area vireo territories (69 percent of males) occurred in willow (Salix spp.) riparian habitat. An additional 4 percent of birds occupied willow habitat co-dominated by Western sycamores (Platanus racemosa) or Fremont cottonwoods (Populus fremontii). Eighteen percent of territories were found in riparian scrub dominated by mule fat (Baccharis salicifolia) or sandbar willow (S. exigua). Upland scrub was used by 7 percent or fewer vireos; 1 percent of territories occurred in non-native vegetation, and less than 1 percent of vireo territories occurred in habitat co-dominated by coast live oak (Quercus agrifolia) and sycamore.
In 2019, MCBCP began operating an artificial seep along the Santa Margarita River. The artificial seep pumped water to the surface from March through August each year during daylight hours and was designed to increase the amount of surface water present to enhance Southwestern Willow Flycatcher (Empidonax traillii extimus; flycatcher) breeding habitat. Although this enhancement was designed to benefit flycatchers, few flycatchers have inhabited the seep and proposed seep areas within the past several years. Therefore, vireos were selected as a surrogate species to determine effects of the habitat enhancement. This report presents preliminary analyses of vireo and vegetation response to the existing artificial seep.
We sampled vegetation in the Seep site and three Reference sites to determine the effects of a new water diversion dam that was completed in 2019 and a surface water enhancement seep pump installed along the Santa Margarita River. We found minor differences in non-native vegetation cover between Reference sites and the Seep site. However, soil moisture was higher at the Reference sites compared to the Seep site. The effect of the seep pump may have been masked by high precipitation in the bio-year (July 1‒June 30) before 2020, limited time for the water diversion to have an effect, well-draining soil, and the non-operation of two to three of the six seep outlets.
We color banded and resighted color banded Least Bell’s Vireos to evaluate adult site fidelity, between-year movement, and the effect of surface water enhancement on vireo site fidelity and between-year movement. We banded 146 Least Bell's Vireos for the first time during the 2020 season. Birds banded included 27 adult vireos and 119 juvenile vireos. All adult vireos were banded with unique color combinations. The juvenile vireos (all nestlings) were banded with a single gold numbered federal band on the left leg.
We resighted and identified 85 Least Bell's Vireos banded before the 2020 breeding season on Base in 2020. Of the 85, 13 vireos were originally banded on the San Luis Rey River, 2 were banded in Baja California Sur, 1 was banded at Marine Corps Air Station, Camp Pendleton, and the remaining birds were banded at MCBCP. Adult birds of known age ranged from 1 to 8 years old.
Most returning adult vireos showed strong between-year site fidelity. Of the adults present in 2019 and 2020, 74 percent, (79 percent of males; 40 percent of females) returned to within 100 m of their previous territory. The average between-year movement for returning adult vireos was 0.3 plus or minus (±) 0.8 kilometer (km). The average movement of first-year vireos detected in 2020 that fledged from a known nest on MCBCP in 2019 was 4.7±7.0 km. One first-year vireo that originated at MCBCP moved off Base and was detected at Murrieta Creek, 23.0 km from his natal territory.
We monitored Least Bell's Vireo pairs to evaluate the effects of surface water enhancement on nest success and breeding productivity. Vireos were monitored at one Seep site and three Reference sites. Base personnel plan to install a second seep pump at one of the Reference sites in the future, at which time the status of the monitoring site will change from Reference to Seep.
Nesting activity was monitored between March 31 and July 28 in 52 territories within the Seep and Reference sites (12 at the Seep site and 40 at Reference sites). All territories were occupied by pairs, and all but one territory was fully monitored, meaning all nesting attempts were monitored at these territories. One vireo territory within a Reference site was partially monitored. During the monitoring period, 94 nests (25 in the Seep site and 69 in Reference sites) were monitored.
Breeding productivity was similar at the Seep site and Reference sites (3.7 and 2.9 young per pair, respectively), with 75 percent of Seep pairs and 79 percent of Reference pairs successfully fledging at least 1 young in 2020. Compared to Reference sites, the Seep site had a higher proportion of all eggs that hatched and also a higher proportion of nests with eggs that hatched. Conversely, a lower proportion of hatchlings and nests that had hatchlings fledged at the Seep site than at Reference sites. According to the best model, nest survival in 2020 was not affected by treatment (Seep versus Reference), although the second best model that included treatment was also well supported.
Completed nests at the Seep site were likely to be as successful as nests at Reference sites in 2020 (57 percent and 59 percent, respectively). Predation was believed to be the primary source of nest failure at both sites. Predation accounted for 90 percent and 73 percent of nest failures at Seep and Reference sites, respectively. Failure of the remaining eight nests was attributed to the collapse of the nesting substrate, exposure to rain and flooding, and other unknown reasons.
Fourteen plant species were used as hosts for vireo nests in 2020. In 2020, we found that at the Seep site, successful nests were placed in taller host plants and further from the edge of host plants (closer to the center) than unsuccessful nests. We found no difference in nest placement between the Seep site and the Reference sites.
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The wild to domestic bird interface is an important nexus for emergence and transmission of highly pathogenic avian influenza (HPAI) viruses. Although the recent incursion of HPAI H5N1 Clade 2.3.4.4b into North America calls for emergency response and planning given the unprecedented scale, readily available data-driven models are lacking. Here, we provide high resolution spatial and temporal transmission risk models for the contiguous United States. Considering virus host ecology, we included weekly species-level wild waterfowl (Anatidae) abundance and endemic low pathogenic avian influenza virus prevalence metrics in combination with number of poultry farms per commodity type and relative biosecurity risks at two spatial scales: 3 km and county-level. Spillover risk varied across the annual cycle of waterfowl migration and some locations exhibited persistent risk throughout the year given higher poultry production. Validation using wild bird introduction events identified by phylogenetic analysis from 2022 to 2023 HPAI poultry outbreaks indicate strong model performance. The modular nature of our approach lends itself to building upon updated datasets under evolving conditions, testing hypothetical scenarios, or customizing results with proprietary data. This research demonstrates an adaptive approach for developing models to inform preparedness and response as novel outbreaks occur, viruses evolve, and additional data become available.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-64912-w","usgsCitation":"Prosser, D., Kent, C.M., Sullivan, J.D., Patyk, K.A., McCool, M., Torchetti, M.K., Lantz, K., and Mullinax, J.M., 2024, Using an adaptive modeling framework to identify avian influenza spillover risk at the wild-domestic interface: Scientific Reports, v. 14, 14199, 13 p., https://doi.org/10.1038/s41598-024-64912-w.","productDescription":"14199, 13 p.","ipdsId":"IP-160406","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466992,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-64912-w","text":"Publisher Index Page"},{"id":465191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"contiguous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":921226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kent, Cody M.","contributorId":265823,"corporation":false,"usgs":false,"family":"Kent","given":"Cody","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":921227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":921228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patyk, Kelly A.","contributorId":139696,"corporation":false,"usgs":false,"family":"Patyk","given":"Kelly","email":"","middleInitial":"A.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":921229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCool, Mary-Jane","contributorId":347273,"corporation":false,"usgs":false,"family":"McCool","given":"Mary-Jane","email":"","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":921230,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Torchetti, Mia K.","contributorId":252830,"corporation":false,"usgs":false,"family":"Torchetti","given":"Mia","email":"","middleInitial":"K.","affiliations":[{"id":50437,"text":"US Department of Agriculture – Veterinary Services, Ames, Iowa, USA","active":true,"usgs":false}],"preferred":false,"id":921231,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lantz, Kristina","contributorId":317920,"corporation":false,"usgs":false,"family":"Lantz","given":"Kristina","email":"","affiliations":[{"id":69192,"text":"National Veterinary Services Laboratories, Animal and Plant Health Inspection Service, USDA","active":true,"usgs":false}],"preferred":false,"id":921232,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":921233,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70259615,"text":"70259615 - 2024 - Indications of preferential groundwater seepage feeding northern peatland pools","interactions":[],"lastModifiedDate":"2024-10-17T12:07:58.976979","indexId":"70259615","displayToPublicDate":"2024-06-20T07:05:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Indications of preferential groundwater seepage feeding northern peatland pools","docAbstract":"Understanding the role of hydrologic variation in structuring aquatic communities is crucial for successful conservation and sustainable management of native freshwater biodiversity. Partitioning beta diversity into the additive components of spatial turnover and nestedness can provide insight into the forces driving variability in fish assemblages across stream flow regimes. We examined stream fish beta diversity across hydrologic and anthropogenic disturbance gradients using long-term (1916–2016) site occurrence records (n = 17,375) encompassing 252 species. We assessed total beta diversity (Sørensen dissimilarity), spatial turnover, and nestedness of fish assemblages in contrasting stream flow regimes across a gradient of decreasing flow stability: groundwater stable (n = 77), groundwater (n = 67), groundwater flashy (n = 175), perennial runoff (n = 141), runoff flashy (n = 255), and intermittent (n = 63) streams. Differences in total beta diversity among the stream flow regimes were driven predominantly (>86 %) by spatial turnover (i.e. species replacement) as opposed to nestedness (i.e. species loss or gain). Total fish beta diversity and spatial turnover were highest in streams with intermediate flow stability (groundwater flashy), while more flow-stable streams (groundwater stable and groundwater) had lower turnover and higher nestedness. Species turnover was also strongly associated with seasonal variation in hydrology across all flow regimes, but these relationships were most evident for assemblages in intermittent streams. Distance-based statistical comparisons showed significant correlations between beta diversity and anthropogenic disturbance variables, including dam density, dam storage volume and water withdrawals in catchments of groundwater stable streams, while hydrologic variables were more strongly correlated with beta diversity in streams with runoff-dominated and flashy flow regimes. The high spatial turnover of species implies that fish conservation actions would benefit from watershed-focused approaches targeting multiple streams with wide spatial distribution, as opposed to simply focusing on preserving sites with the greatest number of species.
1. The Santa Ana River in the Los Angeles region of California demonstrates common habitat degradation symptoms that are characteristic of the urban stream syndrome. These impacts have altered the Santa Ana River community structure, with few species as impacted as the native Santa Ana sucker (sucker; Pantosteus santaanae). 2. Consequently, a recovery plan developed for sucker identified the need for a population viability analysis (PVA) to assess sucker extirpation risk. However, PVAs can be data-intensive and are subject to several sources of bias when standardized protocols are absent. 3. More than 20 years of sucker and arroyo chub (chub; Gila orcuttii) surveys using different methods were compiled to build an integrated hierarchical multi-population PVA to estimate trends in abundance and extirpation probability of these native fishes from the Santa Ana River. 4. PVA modelling indicated similar patterns in sucker and chub abundance along the Santa Ana River, with the highest abundance of both species in the upper regions of the river during the early 2000s and downstream in recent years (2018–2022). Extirpation risk was estimated to be greatest near wastewater treatment facilities, where native fish abundance estimates have been zero since 2018. Extirpation risk was lower downstream of the wastewater treatment facilities for both species, although extinction risk was higher for sucker than chub throughout the river. 5. As the model evolves and more data are collected, the PVA could be used to assess the effects of various management actions, such as non-native predator removals and native fish re-introductions, on sucker and chub persistence.
","language":"English","publisher":"Wiley","doi":"10.1002/aqc.4164","usgsCitation":"Huntsman, B., Palenscar, K., Russell, K., Mills, B., Jones, C., Ota, W., Anderson, K.E., Dyer, H., Abadi, F., and Wulff, M.L., 2024, Evaluation of extinction risk for stream fishes within an urban riverscape using population viability analysis: Aquatic Conservation: Marine and Freshwater Ecosystems, v. 34, no. 6, e4164, 15 p., https://doi.org/10.1002/aqc.4164.","productDescription":"e4164, 15 p.","ipdsId":"IP-155060","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":490042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aqc.4164","text":"Publisher Index Page"},{"id":430448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Ana River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.91739602273532,\n 33.59806544941986\n ],\n [\n -117.07072671553968,\n 33.928615009582344\n ],\n [\n -116.86491284498888,\n 34.11992851642641\n ],\n [\n -117.15177435882552,\n 34.394478755569835\n ],\n [\n -117.39266048613797,\n 34.37688489281085\n ],\n [\n -117.75593270208347,\n 34.36856513907851\n ],\n [\n -117.94264116351808,\n 33.99522385830353\n ],\n [\n -118.0554774461699,\n 33.74788429741041\n ],\n [\n -118.0424650174655,\n 33.69256809570372\n ],\n [\n -117.91739602273532,\n 33.59806544941986\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":223101,"corporation":false,"usgs":true,"family":"Huntsman","given":"Brock","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palenscar, Kai","contributorId":297131,"corporation":false,"usgs":false,"family":"Palenscar","given":"Kai","email":"","affiliations":[{"id":64298,"text":"San Bernardino Valley Municipal Water District","active":true,"usgs":false}],"preferred":false,"id":904778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russell, Kerwin","contributorId":297133,"corporation":false,"usgs":false,"family":"Russell","given":"Kerwin","email":"","affiliations":[{"id":64299,"text":"Riverside-Corona Resource Conservation District","active":true,"usgs":false}],"preferred":false,"id":904779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mills, Brett","contributorId":297135,"corporation":false,"usgs":false,"family":"Mills","given":"Brett","email":"","affiliations":[{"id":64299,"text":"Riverside-Corona Resource Conservation District","active":true,"usgs":false}],"preferred":false,"id":904780,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Chris","contributorId":297132,"corporation":false,"usgs":false,"family":"Jones","given":"Chris","affiliations":[{"id":64298,"text":"San Bernardino Valley Municipal Water District","active":true,"usgs":false}],"preferred":false,"id":904781,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ota, William","contributorId":339658,"corporation":false,"usgs":false,"family":"Ota","given":"William","email":"","affiliations":[{"id":81373,"text":"Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, CA","active":true,"usgs":false}],"preferred":false,"id":904782,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Anderson, Kurt E.","contributorId":265545,"corporation":false,"usgs":false,"family":"Anderson","given":"Kurt","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":904783,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dyer, Heather","contributorId":297134,"corporation":false,"usgs":false,"family":"Dyer","given":"Heather","email":"","affiliations":[{"id":64298,"text":"San Bernardino Valley Municipal Water District","active":true,"usgs":false}],"preferred":false,"id":904784,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Abadi, Fitsum","contributorId":244779,"corporation":false,"usgs":false,"family":"Abadi","given":"Fitsum","affiliations":[{"id":48968,"text":"New Mexico State University, Department of Fish, Wildlife and Conservation Ecology","active":true,"usgs":false}],"preferred":false,"id":904785,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wulff, Marissa L. 0000-0003-0121-9066","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":229534,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904786,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70257021,"text":"70257021 - 2024 - Bioconcentration of per- and polyfluoroalkyl substances and precursors in fathead minnow tissues environmentally exposed to aqueous film-forming foam-contaminated waters","interactions":[],"lastModifiedDate":"2024-08-07T11:52:46.473301","indexId":"70257021","displayToPublicDate":"2024-06-19T06:51:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Bioconcentration of per- and polyfluoroalkyl substances and precursors in fathead minnow tissues environmentally exposed to aqueous film-forming foam-contaminated waters","docAbstract":"Exposure to per- and polyfluoroalkyl substances (PFAS) has been associated with toxicity in wildlife and negative health effects in humans. Decades of fire training activity at Joint Base Cape Cod (MA, USA) incorporated the use of aqueous film-forming foam (AFFF), which resulted in long-term PFAS contamination of sediments, groundwater, and hydrologically connected surface waters. To explore the bioconcentration potential of PFAS in complex environmental mixtures, a mobile laboratory was established to evaluate the bioconcentration of PFAS from AFFF-impacted groundwater by flow-through design. Fathead minnows (n = 24) were exposed to PFAS in groundwater over a 21-day period and tissue-specific PFAS burdens in liver, kidney, and gonad were derived at three different time points. The ∑PFAS concentrations in groundwater increased from approximately 10,000 ng/L at day 1 to 36,000 ng/L at day 21. The relative abundance of PFAS in liver, kidney, and gonad shifted temporally from majority perfluoroalkyl sulfonamides (FASAs) to perfluoroalkyl sulfonates (PFSAs). By day 21, mean ∑PFAS concentrations in tissues displayed a predominance in the order of liver > kidney > gonad. Generally, bioconcentration factors (BCFs) for FASAs, perfluoroalkyl carboxylates (PFCAs), and fluorotelomer sulfonates (FTS) increased with degree of fluorinated carbon chain length, but this was not evident for PFSAs. Perfluorooctane sulfonamide (FOSA) displayed the highest mean BCF (8700 L/kg) in day 21 kidney. Suspect screening results revealed the presence of several perfluoroalkyl sulfinate and FASA compounds present in groundwater and in liver for which pseudo-bioconcentration factors are also reported. The bioconcentration observed for precursor compounds and PFSA derivatives detected suggests alternative pathways for terminal PFAS exposure in aquatic wildlife and humans.
In August 2020, the 3-m tall Pilchuck River Diversion Dam was removed from the Pilchuck River, allowing free fish passage to the upper third of the watershed for the first time in over a century. The narrow, 300-m long impoundment behind the dam was estimated to hold 4,000–7,500 m3 of sand and gravel, representing less than one year's typical bedload flux. Repeat cross section surveys, stage sensors, and time-lapse cameras were used to document the physical channel response over the first year following dam removal. A total of 7,400 m3 (effectively 100%) of impoundment sediment was eroded in the first year, with 25% accomplished by manual regrading during dam removal. Most river-caused erosion occurred during a sequence of modest flows in October 2020. Downstream deposition totaled 4,300 m3, predominately filling in the first 100 m downstream of the dam site. Deposition tapered below detectable levels within 350 m, and most downstream channel adjustments occurred before November 2020. Multiple high flows after December 2020 caused little upstream or downstream change. The physical river response to this dam removal then appears to have been largely accomplished within several months by modest flows, consistent with pre-removal modeling and observations from other regional dam removals. Efficient sediment evacuation was likely aided by the narrow and steep-walled impoundment geometry. Our observations support existing guidance that the physical river response to small dam removals is typically rapid and modest; the benefits of removal may then often be gained with minimal negative downstream geomorphic impacts.
","language":"English","publisher":"Northwest Scientific Association","doi":"10.3955/046.097.0113","usgsCitation":"Anderson, S.W., Shattuck, B., Shea, N., Seguin, C.M., Miles, J.J., Marks, D., and Coumou, N., 2024, River channel response to the removal of the Pilchuck River Diversion Dam, Washington State: Northwest Science, v. 97, no. 1-2, p. 134-145, https://doi.org/10.3955/046.097.0113.","productDescription":"12 p.","startPage":"134","endPage":"145","ipdsId":"IP-144271","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":427589,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Pilchuck River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.904,\n 48.02\n ],\n [\n -121.916,\n 48.02\n ],\n [\n -121.916,\n 48.016\n ],\n [\n -121.904,\n 48.016\n ],\n [\n -121.904,\n 48.02\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"97","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shattuck, Brett","contributorId":335415,"corporation":false,"usgs":false,"family":"Shattuck","given":"Brett","email":"","affiliations":[{"id":80397,"text":"Tulalip Indian Tribe","active":true,"usgs":false}],"preferred":false,"id":898314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shea, Neil","contributorId":335416,"corporation":false,"usgs":false,"family":"Shea","given":"Neil","email":"","affiliations":[{"id":80397,"text":"Tulalip Indian Tribe","active":true,"usgs":false}],"preferred":false,"id":898315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seguin, Catherine M.","contributorId":332787,"corporation":false,"usgs":false,"family":"Seguin","given":"Catherine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":898316,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miles, Joe J. 0009-0003-4960-6783","orcid":"https://orcid.org/0009-0003-4960-6783","contributorId":337064,"corporation":false,"usgs":true,"family":"Miles","given":"Joe","email":"","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901867,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marks, Derek","contributorId":225112,"corporation":false,"usgs":false,"family":"Marks","given":"Derek","email":"","affiliations":[],"preferred":false,"id":898318,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Coumou, Natasha","contributorId":335418,"corporation":false,"usgs":false,"family":"Coumou","given":"Natasha","email":"","affiliations":[{"id":80397,"text":"Tulalip Indian Tribe","active":true,"usgs":false}],"preferred":false,"id":898319,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}