Mississippi has a dispersed population of nearly three million residents in an area of approximately 48,400 square miles and has a favorable climate for agriculture, with abundant precipitation and minimal extreme temperatures. The topography consists mostly of low hills and lowland plains, with the highest elevation about 800 feet above sea level. An exception is the nearly flat Mississippi Alluvial Plain, or “Delta,” in the northwestern part of the State. Agriculture and forestry are Mississippi’s major industries. With 65 percent of its area forested, the State is one of the country’s top producers of lumber and wood-related products. In addition to agriculture and forest resources management, other important economic activities are infrastructure and construction management, flood risk management, and water supply and quality assessment. High-quality elevation data can help to support these activities. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Animal movement is a fundamental mechanism that shapes communities and ecosystems. Ungulates alter the ecosystems they inhabit and understanding their movements and distribution is critical for linking habitat with population dynamics. Predation risk has been shown to strongly influence ungulate movement patterns, such that ungulates may select habitat where predation risk is lower (refugia), adjust movement rates, temporal patterns, or selection of cover variables in areas with greater predation risk. We evaluated potential predation avoidance behavior in a population of plains bison inhabiting the north rim of Grand Canyon National Park (GRCA) and adjacent Kaibab National Forest (KNF). The KNF has year-round hunting managed by Arizona Game and Fish Department, whereas hunting is not allowed in GRCA. Human-maintained water sources on the KNF are particularly important resources for bison wherein they may be exposed to higher predation risk to access these resources. We used 2-h GPS locations for three years from 31 bison (n = 9 males; n = 22 females), and integrative step selection analysis to test four hypotheses about the potential for bison to reduce their risk from human predation by avoiding areas of high predation risk; moving faster in areas with high predation risk; entering high-risk areas at night when risk is reduced; and entering high-risk areas in habitats that provide cover (coniferous forest). The highest performing model indicated bison movement was 1.3 times faster per 2-h step interval than in areas with no hunting across all vegetation classes (coniferous forest, shrub, quaking aspen, grass-forb meadow) and across all topography classes (valley, slope, ridge). Bison moved more slowly in grass-forb meadows than all other vegetation types, and in valleys relative to slopes and ridges. Several radio-collared individuals had no GPS locations in KNF for the duration of the study. Bison avoided predation risk using two strategies: moving faster while in the KNF, and fully avoiding high-risk areas by remaining within GRCA. Management that manipulates or reduces timing of hunting seasons may reduce perceived predation risk and encourage bison to distribute into the KNF and across a broader range of available habitat.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4909","usgsCitation":"Salganek, S., Schoenecker, K., and Terwilliger, M., 2024, Using integrated step selection to determine effects of predation risk on bison habitat selection and movement: Ecosphere, v. 15, no. 7, e4909, 16 p., https://doi.org/10.1002/ecs2.4909.","productDescription":"e4909, 16 p.","ipdsId":"IP-148082","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":492488,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4909","text":"Publisher Index Page"},{"id":492199,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","county":"Coconino County","otherGeospatial":"Kaibab Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.68098786953385,\n 36.999074018413296\n ],\n [\n -112.68098786953385,\n 35.85596339767277\n ],\n [\n -111.67893358079623,\n 35.85596339767277\n ],\n [\n -111.67893358079623,\n 36.999074018413296\n ],\n [\n -112.68098786953385,\n 36.999074018413296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Salganek, Skye","contributorId":357945,"corporation":false,"usgs":false,"family":"Salganek","given":"Skye","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":942896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":942897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terwilliger, Miranda L.N.","contributorId":357947,"corporation":false,"usgs":false,"family":"Terwilliger","given":"Miranda L.N.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":942898,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256058,"text":"70256058 - 2024 - Remote sensing of volcano deformation and surface change","interactions":[],"lastModifiedDate":"2024-07-17T12:07:08.404231","indexId":"70256058","displayToPublicDate":"2024-07-14T07:06:11","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Remote sensing of volcano deformation and surface change","docAbstract":"Volcanic unrest and eruptions are associated with surface deformation and landscape change that can be detected, characterized, and tracked via remote sensing measurements. Subsurface processes, including magma accumulation, withdrawal, and transport, can cause displacements at the surface that are best tracked at subaerial volcanoes with interferometric synthetic aperture radar (InSAR) and Global Navigation Satellite System (GNSS) measurements, although non-volcanic activity, like hydrothermal and tectonic sources, can complicate interpretations. Surface change is often associated with the emplacement of volcanic deposits, which modify the landscape and can experience post-emplacement deformation or morphological changes over time. Measurement of surface topography at volcanoes via remote means is a particularly important capability, given the control that topography exerts on many volcanic hazards and the potential for topographic change measurements to provide information about eruption rates. A much broader set of tools is available to investigate surface change at volcanoes, including not only InSAR and GNSS, but also synthetic aperture radar amplitude data, visible imagery, and lidar, acquired from airborne, ground-based, and satellite platforms. These data can also be used to identify instability of volcanic flanks and even have potential for use in detecting airborne ash plumes. Although hidden from traditional airborne and space-based remote sensing, deformation and surface change associated with submarine volcanism can be investigated with pressure sensors and bathymetric measurements—the below-water remote sensing analogs of GNSS and InSAR, respectively.
The freshwater salinization syndrome (FSS), a concomitant watershed-scale increase in salinity, alkalinity, and major-cation and trace-metal concentrations, over recent decades, has been described for major rivers draining extensive urban areas, yet few studies have evaluated temporal and spatial FSS variations, or causal factors, at the subwatershed scale in mixed-use landscapes. This study examines the potential influence of land-use practices and wastewater treatment plant (WWTP) effluent on the export of major ions and trace metals from the mixed-use East Branch Brandywine Creek watershed in southeastern Pennsylvania, during the 2019 water year. Separate analysis of baseflow and stormflow subsets revealed similar correlations among land-use characteristics and streamwater chemistry. Positive associations between percent impervious surface cover, which ranged from 1.26 % to 21.9 % for the 13 sites sampled, and concentrations of Ca2+, Mg2+, Na+, and Cl− are consistent with road-salt driven reverse cation exchange and weathering of the built environment. The relative volume of upstream WWTP was correlated with Cu and Zn, which may be derived in part from corroded water-conveyance infrastructure; chloride to sulfate mass ratios (CSMR) ranged from ~6.3 to ~7.7× the 0.5 threshold indicating serious corrosivity potential. Observed exceedances of U.S. Environmental Protection Agency Na+ and Cl− drinking water and aquatic life criteria occurred in winter months. Finally, correlations between percent cultivated cropland and As and Pb concentrations may be explained by the persistence of agricultural pesticides that had been used historically. Study results contribute to the understanding of FSS solute origin, fate, and transport in mixed-use watersheds, particularly those in road salt-affected regions. Study results also emphasize the complexity of trace-metal source attribution and explore the potential for FSS solutes to affect human health, aquatic life, and infrastructure.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.174266","usgsCitation":"Marks, N.K., Cravotta, C., Rossi, M.L., Silva, C., Kremer, P., and Goldsmith, S.T., 2024, Exploring spatial and temporal symptoms of the freshwater salinization syndrome in a rural to urban watershed: Science of the Total Environment, v. 947, 174266, 17 p., https://doi.org/10.1016/j.scitotenv.2024.174266.","productDescription":"174266, 17 p.","ipdsId":"IP-154332","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":466983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.174266","text":"Publisher Index Page"},{"id":464716,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"East Branch Brandywine Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.83342045572547,\n 40.01916009274666\n ],\n [\n -75.72337369661636,\n 39.97446847844611\n ],\n [\n -75.66768738477772,\n 39.96227478065106\n ],\n [\n -75.61332693750671,\n 40.00088069477613\n ],\n [\n -75.6106752083719,\n 40.09425729037662\n ],\n [\n -75.62260798947986,\n 40.11555358035895\n ],\n [\n -75.71011505094027,\n 40.1621792980624\n ],\n [\n -75.83474632029366,\n 40.14090269654602\n ],\n [\n -75.8519825596717,\n 40.078027071145215\n ],\n [\n -75.84535323683392,\n 40.05570674139187\n ],\n [\n -75.83342045572547,\n 40.01916009274666\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"947","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marks, Nicole K.","contributorId":346882,"corporation":false,"usgs":false,"family":"Marks","given":"Nicole","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":920112,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":258816,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920113,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rossi, Marissa Lee 0000-0003-2341-0312","orcid":"https://orcid.org/0000-0003-2341-0312","contributorId":310430,"corporation":false,"usgs":true,"family":"Rossi","given":"Marissa","email":"","middleInitial":"Lee","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920114,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silva, Camila","contributorId":346883,"corporation":false,"usgs":false,"family":"Silva","given":"Camila","email":"","affiliations":[],"preferred":false,"id":920115,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kremer, Peleg","contributorId":296521,"corporation":false,"usgs":false,"family":"Kremer","given":"Peleg","email":"","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":920116,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goldsmith, Steven T.","contributorId":193458,"corporation":false,"usgs":false,"family":"Goldsmith","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":920117,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256033,"text":"70256033 - 2024 - Palaeontological signatures of the Anthropocene are distinct from those of previous epochs","interactions":[],"lastModifiedDate":"2024-07-16T11:56:38.958876","indexId":"70256033","displayToPublicDate":"2024-07-13T06:54:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14252,"text":"Earth Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Palaeontological signatures of the Anthropocene are distinct from those of previous epochs","docAbstract":"The “Great Acceleration” beginning in the mid-20th century provides the causal mechanism of the Anthropocene, which has been proposed as a new epoch of geological time beginning in 1952 CE. Here we identify key parameters and their diagnostic palaeontological signals of the Anthropocene, including the rapid breakdown of discrete biogeographical ranges for marine and terrestrial species, rapid changes to ecologies resulting from climate change and ecological degradation, the spread of exotic foodstuffs beyond their ecological range, and the accumulation of reconfigured forest materials such as medium density fibreboard (MDF) all being symptoms of the Great Acceleration. We show: 1) how Anthropocene successions in North America, South America, Africa, Oceania, Europe, and Asia can be correlated using palaeontological signatures of highly invasive species and changes to ecologies that demonstrate the growing interconnectivity of human systems; 2) how the unique depositional settings of landfills may concentrate the remains of organisms far beyond their geographical range of environmental tolerance; and 3) how a range of settings may preserve a long-lived, unique palaeontological record within post-mid-20th century deposits. Collectively these changes provide a global palaeontological signature that is distinct from all past records of deep-time biotic change, including those of the Holocene.
Des Moines Water Works (DMWW) is a regional municipal water utility that provides residential and commercial water resources to about 600,000 customers in Des Moines, Iowa, and surrounding municipalities in central Iowa. DMWW has identified a need for increased water supply and is exploring the potential for expanding groundwater production capabilities in the Des Moines River alluvial aquifer, where it operates two radial collector wells (RCWs). The U.S. Geological Survey, in cooperation with DMWW, completed a study of the Des Moines River alluvial aquifer and interactions of the RCWs with the aquifer; no previously published model has included the existing well locations, which is the focus of this model. A conceptual and numerical groundwater flow model have been developed to characterize the Des Moines River alluvial aquifer under existing conditions, to simulate water levels observed in the RCWs, and to provide publicly accessible hydrologic data and research that advance understanding of the regional hydrologic system and can potentially be used in the future to evaluate groundwater production scenarios. Model performance was assessed by comparing observed and simulated groundwater levels that included water level elevations, water level changes, water level inequality observations, surface water streamflow, and change in surface water volume from upstream to downstream. Water table elevation in the aquifer layers is on average slightly overestimated with average absolute value error less than 1.5 meters at both RCWs and less than 2.5 meters for all observation wells in the alluvial aquifer layers. The model also accurately simulated water tables greater than the RCW design minimum (a water level threshold at which RCW pumping is reduced) in all timesteps for which water level observation data existed. Water table elevation error was higher in other model layers that were not the focus of the study, and the model did not accurately match streamflow targets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245059","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"Bristow, E.L., and Davis, K.W., 2024, Groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa: U.S. Geological Survey Scientific Investigations Report 2024–5059, 47 p., https://doi.org/10.3133/sir20245059.","productDescription":"Report: ix, 47 p.; 3 Data Releases; 1 Dataset","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154246","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":430905,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5059/sir20245059.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5059"},{"id":430904,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5059/coverthb.jpg"},{"id":430906,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5059/sir20245059.XML"},{"id":430907,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5059/images/"},{"id":430908,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245059/full"},{"id":430909,"rank":6,"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":430910,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZDDVY","text":"USGS data release","linkHelpText":"MODFLOW 6 groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":430911,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B9AVKJ","text":"USGS data release","linkHelpText":"Geophysical data collected in the Des Moines River, Beaver Creek, and the Des Moines River floodplain, Des Moines, Iowa, 2018"},{"id":430912,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":499480,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117123.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Iowa","city":"Des Moines","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.75578446475713,\n 41.70743368403336\n ],\n [\n -93.75578446475713,\n 41.53433869670215\n ],\n [\n -93.54349781702975,\n 41.53433869670215\n ],\n [\n -93.54349781702975,\n 41.70743368403336\n ],\n [\n -93.75578446475713,\n 41.70743368403336\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The 2-aminoethanol salt of niclosamide (2′,5-dichloro-4′-nitrosalicylanilide) is a pesticide known as Bayluscide that is used in conjunction with TFM (4-nitro-3-[trifluoromethyl]phenol), also known as 3-trifluoromethyl-4-nitrophenol) to treat tributaries to the Great Lakes infested with invasive parasitic Petromyzon marinus (sea lamprey). Adding 0.5 to 2 percent Bayluscide with TFM can substantially reduce the amount of TFM required to achieve effective control. Currently, an emulsifiable concentrate (EC) formulation of Bayluscide is used in combination with TFM during some stream treatments completed by the Great Lakes Fishery Commission’s binational sea lamprey control program. The Bayluscide EC formulation is highly effective; however, it degrades application tubing, adheres to application equipment, and raises concerns for worker safety because of the solvent in the formulation, N-methyl-2-pyrrolidone.
We collaborated with a pesticide formulation company to develop a Bayluscide 20-percent suspension concentrate (SC) formulation as a potential replacement for the Bayluscide 20-percent EC formulation. The 20-percent SC formulation was specifically developed using inert ingredients approved for use by the U.S. Environmental Protection Agency and the Health Canada Pest Management Regulatory Agency. Although approved for use, the inclusion of a small quantity of an antimicrobial in the formulation warranted evaluating the toxicological profile to sea lamprey and select nontarget fish species. We evaluated and compared the toxicity of the 20-percent SC formulation to the 20-percent EC formulation using continuous-flow diluter systems and larval sea lamprey and select cold-, cool-, and warm-water fish as test animals. Our results demonstrate comparable toxicological profiles between the two formulations with the 20-percent SC formulation being slightly less toxic to the nontarget species evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241037","usgsCitation":"Luoma, J.A., Schueller, J.R., Schloesser, N.A., Kirkeeng, C.A., and Wolfe, S.L., 2024, Comparative toxicity of emulsifiable concentrate and suspension concentrate formulations of 2′,5-dichloro-4′-nitrosalicylanilide ethanolamine salt: U.S. Geological Survey Open-File Report 2024–1037, 10 p., https://doi.org/10.3133/ofr20241037.","productDescription":"Report: vii, 10 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161507","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":430974,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1473X4B","text":"USGS data release","linkHelpText":"Data and code release—Comparative toxicity of emulsifiable concentrate and suspension concentrate formulations of 2′,5-dichloro-4′-nitrosalicylanilide ethanolamine salt"},{"id":430973,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241037/full"},{"id":430971,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1037/ofr20241037.XML"},{"id":430969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1037/coverthb.jpg"},{"id":430970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1037/ofr20241037.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1037"},{"id":430972,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1037/images/"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
International Ocean Discovery Program (IODP) Expedition 374 sailed to the Ross Sea in 2018 to reconstruct paleoenvironments, track the history of key water masses, and assess model simulations that show warm-water incursions from the Southern Ocean led to the loss of marine-based Antarctic ice sheets during past interglacials. IODP Site U1523 (water depth 828 m) is located at the continental shelf break, northeast of Pennell Bank on the southeastern flank of Iselin Bank, where it lies beneath the Antarctic Slope Current (ASC). This site is sensitive to warm-water incursions from the Ross Sea Gyre and modified Circumpolar Deep Water (mCDW) today and during times of past warming climate. Multiple incursions of subpolar or temperate planktic foraminifera taxa occurred at Site U1523 after 3.8 Ma and prior to ∼ 1.82 Ma. Many of these warm-water taxa incursions likely represent interglacials of the latest Early Pliocene and Early Pleistocene, including Marine Isotope Stage (MIS) Gi7 to Gi3 (∼ 3.72–3.65 Ma), and Early Pleistocene MIS 91 or 90 (∼ 2.34–2.32 Ma) and MIS 77–67 (∼ 2.03–1.83 Ma) and suggest warmer-than-present conditions and less ice cover in the Ross Sea. However, a moderately resolved age model based on four key events prohibits us from precisely correlating with Marine Isotope Stages established by the LR04 Stack; therefore, these correlations are best estimates. Diatom-rich intervals during the latest Pliocene at Site U1523 include evidence of anomalously warm conditions based on the presence of subtropical and temperate planktic foraminiferal species in what likely correlates with interglacial MIS G17 (∼ 2.95 Ma), and a second interval that likely correlates with MIS KM3 (∼ 3.16 Ma) of the mid-Piacenzian Warm Period. Collectively, these multiple incursions of warmer-water planktic foraminifera provide evidence for polar amplification during super-interglacials of the Pliocene and Early Pleistocene. Higher abundances of planktic and benthic foraminifera during the Mid- to Late Pleistocene associated with interglacials of the MIS 37–31 interval (∼ 1.23–1.07 Ma), MIS 25 (∼ 0.95 Ma), MIS 15 (∼ 0.60 Ma), and MIS 6–5e transition (∼ 0.133–0.126 Ma) also indicate a reduced ice shelf and relatively warm conditions, including multiple warmer interglacials during the Mid-Pleistocene Transition (MPT). A decrease in sedimentation rate after ∼ 1.78 Ma is followed by a major change in benthic foraminiferal biofacies marked by a decrease in Globocassidulina subglobosa and a decrease in mud (< 63 µm) after ∼ 1.5 Ma. Subsequent dominance of Trifarina earlandi biofacies beginning during MIS 15 (∼ 600 ka) indicate progressive strengthening of the Antarctic Slope Current along the shelf edge of the Ross Sea during the mid to Late Pleistocene. A sharp increase in foraminiferal fragmentation after the MPT (∼ 900 ka) and variable abundances of T. earlandi indicate higher productivity, a stronger but variable ASC during interglacials, and/or corrosive waters, suggesting changes in water masses entering (mCDW) and exiting (High Salinity Shelf Water or Dense Shelf Water) the Ross Sea since the MPT.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/jm-43-211-2024","usgsCitation":"Seidenstein, J.L., Leckie, R., McKay, R., De Santis, L., Harwood, D., and IODP Expedition 374 Scientists, 2024, Pliocene–Pleistocene warm-water incursions and water mass changes on the Ross Sea continental shelf (Antarctica) based on foraminifera from IODP Expedition 374: Journal of Micropalaeontology, v. 43, no. 2, p. 211-238, https://doi.org/10.5194/jm-43-211-2024.","productDescription":"28 p.","startPage":"211","endPage":"238","ipdsId":"IP-154696","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":488299,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/jm-43-211-2024","text":"Publisher Index Page"},{"id":483338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Ross Ice Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n -70\n ],\n [\n -179.9,\n -78\n ],\n [\n -150,\n -78\n ],\n [\n -150,\n -70\n ],\n [\n -179.9,\n -70\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 170,\n -70\n ],\n [\n 170,\n -78\n ],\n [\n 179.9,\n -78\n ],\n [\n 179.9,\n -70\n ],\n [\n 170,\n -70\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Seidenstein, Julia Lynn 0000-0002-0585-1977","orcid":"https://orcid.org/0000-0002-0585-1977","contributorId":290625,"corporation":false,"usgs":true,"family":"Seidenstein","given":"Julia","email":"","middleInitial":"Lynn","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":930738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leckie, R. Mark","contributorId":352312,"corporation":false,"usgs":false,"family":"Leckie","given":"R. Mark","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":930739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKay, Robert","contributorId":9546,"corporation":false,"usgs":true,"family":"McKay","given":"Robert","affiliations":[],"preferred":false,"id":930752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"De Santis, L.","contributorId":96471,"corporation":false,"usgs":true,"family":"De Santis","given":"L.","email":"","affiliations":[],"preferred":false,"id":930753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harwood, David","contributorId":352313,"corporation":false,"usgs":false,"family":"Harwood","given":"David","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":930740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"IODP Expedition 374 Scientists","contributorId":352319,"corporation":true,"usgs":false,"organization":"IODP Expedition 374 Scientists","id":930754,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256053,"text":"70256053 - 2024 - Sero-epidemiology of Highly Pathogenic Avian Influenza viruses among wild birds in subarctic intercontinental transition zones","interactions":[],"lastModifiedDate":"2024-07-17T13:22:14.276022","indexId":"70256053","displayToPublicDate":"2024-07-11T08:20:23","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18008,"text":"Research Square","active":true,"publicationSubtype":{"id":32}},"title":"Sero-epidemiology of Highly Pathogenic Avian Influenza viruses among wild birds in subarctic intercontinental transition zones","docAbstract":"Background: The geographic expansion and evolution of A/Goose/Guangdong/1/1996(H5N1) (Gs/GD) lineage H5Nx highly pathogenic avian influenza (HPAI) viruses since 1996 have raised awareness of enzootic circulation among migratory birds and the potential for intercontinental transport and spread. Recent Pacific- and Atlantic-route introductions of HPAI to North America were facilitated by avian migration through subarctic zones, specifically Alaska and Iceland. This study aimed to identify recent historical patterns of exposure to HPAI viruses among birds within and migrating through both regions and evaluate how geographic, demographic, and taxonomic differences contribute to exposure risk at two intercontinental staging locations.
Methods: During 2010-2019, blood samples were obtained from captured wild migratory seabirds and waterfowl in Alaska and Iceland. All live birds were released following completion of sampling. Sampling date, species, sampling location, and age class was documented for each bird, and sex was documented when possible. Lentiviral pseudoviruses that express the influenza hemagglutinin surface glycoprotein for H5Nx HPAI and H5 low-pathogenicity avian influenza (LPAI) were constructed for use in serological assays to screen for and quantify titers of antibodies against the latter viruses. Data were analyzed to compare (a) categorical baseline ecological traits between Iceland and Alaska, and (b) ecological traits between birds identified to be seropositive and suggestive/seronegative/fully cross-reactive birds to H5Nx HPAI in Iceland and Alaska. Factors associated with seroreactivity to H5Nx HPAI and H5 LPAI were assessed.
Results:The seroprevalence of HPAI among birds in both locations was 7.3% (112/1526). Findings reveal variability in seroprevalence by year, higher rates of exposure to H5 LPAI than H5Nx HPAI overall, and significantly more seropositive and suggestive exposure of birds to H5Nx HPAI in Alaska as compared to Iceland. Geographic, demographic, and taxonomic differences contribute to exposure risk between Alaska and Iceland. Most tested birds were immuno-naïve to HPAI in both locations, which indicates many migratory birds in the subarctic are susceptible to HPAI infection, demonstrating substantial risk for intercontinental transmission between Asia, Europe, and North America.
Conclusions: Our findings provide further justification for increased viral and serosurveillance in Alaska and Iceland to monitor subarctic movements of migratory birds and intercontinental transmission dynamics of currently circulating and new strains of HPAI globally.
","language":"English","publisher":"Research Square","doi":"10.21203/rs.3.rs-4233804/v2","usgsCitation":"Gass, J.D., Dusek, R.J., Hill, N.J., Borkenhagen, L., Hall, J.S., Hallgrimsson, G.T., Bishop, M., Ramey, A.M., Timothy J. Spivey, Vignisson, S.R., Ragnarsdottir, S.B., Halldorsson, H.P., Jonsson, J.E., Simulynas, A.D., Nutter, F.B., Puryear, W., and Runstadler, J.A., 2024, Sero-epidemiology of Highly Pathogenic Avian Influenza viruses among wild birds in subarctic intercontinental transition zones: Research Square, https://doi.org/10.21203/rs.3.rs-4233804/v2.","productDescription":"27 p.","ipdsId":"IP-165563","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":466985,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.21203/rs.3.rs-4233804/v2","text":"External Repository"},{"id":431127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gass, Jonathon D.","contributorId":340193,"corporation":false,"usgs":false,"family":"Gass","given":"Jonathon","email":"","middleInitial":"D.","affiliations":[{"id":81502,"text":"Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University","active":true,"usgs":false}],"preferred":false,"id":906519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":174374,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":906520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hill, Nichola J.","contributorId":189563,"corporation":false,"usgs":false,"family":"Hill","given":"Nichola","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":906521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Borkenhagen, Laura","contributorId":292318,"corporation":false,"usgs":false,"family":"Borkenhagen","given":"Laura","email":"","affiliations":[{"id":62870,"text":"Department of Infectious Disease and Global Health, Tufts University, North Grafton, MA 01536, USA","active":true,"usgs":false}],"preferred":false,"id":906522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hall, Jeffrey S. 0000-0001-5599-2826 jshall@usgs.gov","orcid":"https://orcid.org/0000-0001-5599-2826","contributorId":2254,"corporation":false,"usgs":true,"family":"Hall","given":"Jeffrey","email":"jshall@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":906523,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hallgrimsson, Gunnar Thor","contributorId":298374,"corporation":false,"usgs":false,"family":"Hallgrimsson","given":"Gunnar","email":"","middleInitial":"Thor","affiliations":[{"id":64545,"text":"Institute of Biology, University of Iceland","active":true,"usgs":false}],"preferred":false,"id":906524,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bishop, Mary Anne","contributorId":258847,"corporation":false,"usgs":false,"family":"Bishop","given":"Mary Anne","affiliations":[{"id":13600,"text":"Prince William Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":906525,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":906526,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Timothy J. Spivey","contributorId":340194,"corporation":false,"usgs":false,"family":"Timothy J. Spivey","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":906527,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vignisson, Solvi Runar","contributorId":298376,"corporation":false,"usgs":false,"family":"Vignisson","given":"Solvi","email":"","middleInitial":"Runar","affiliations":[{"id":64547,"text":"University of Iceland’s Research Centre in Suðurnes","active":true,"usgs":false}],"preferred":false,"id":906528,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ragnarsdottir, Sunna Bjork","contributorId":298377,"corporation":false,"usgs":false,"family":"Ragnarsdottir","given":"Sunna","email":"","middleInitial":"Bjork","affiliations":[{"id":40188,"text":"Icelandic Institute of Natural History","active":true,"usgs":false}],"preferred":false,"id":906529,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Halldorsson, Halldor Palmar","contributorId":298375,"corporation":false,"usgs":false,"family":"Halldorsson","given":"Halldor","email":"","middleInitial":"Palmar","affiliations":[{"id":64547,"text":"University of Iceland’s Research Centre in Suðurnes","active":true,"usgs":false}],"preferred":false,"id":906530,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Jonsson, Jon Einar","contributorId":156367,"corporation":false,"usgs":false,"family":"Jonsson","given":"Jon","email":"","middleInitial":"Einar","affiliations":[{"id":20328,"text":"University of Iceland, Snæfellsnes Research Centre, Stykkishólmur, Iceland 245.","active":true,"usgs":false}],"preferred":false,"id":906531,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Simulynas, Alexa D.","contributorId":340195,"corporation":false,"usgs":false,"family":"Simulynas","given":"Alexa","email":"","middleInitial":"D.","affiliations":[{"id":81502,"text":"Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University","active":true,"usgs":false}],"preferred":false,"id":906532,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Nutter, Felicia B.","contributorId":8070,"corporation":false,"usgs":false,"family":"Nutter","given":"Felicia","email":"","middleInitial":"B.","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":906533,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Puryear, Wendy B.","contributorId":292313,"corporation":false,"usgs":false,"family":"Puryear","given":"Wendy B.","affiliations":[{"id":62870,"text":"Department of Infectious Disease and Global Health, Tufts University, North Grafton, MA 01536, USA","active":true,"usgs":false}],"preferred":false,"id":906534,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Runstadler, Jonathan A.","contributorId":24706,"corporation":false,"usgs":false,"family":"Runstadler","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":906535,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70256416,"text":"70256416 - 2024 - A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps","interactions":[],"lastModifiedDate":"2024-08-01T14:20:43.087876","indexId":"70256416","displayToPublicDate":"2024-07-10T09:19:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps","docAbstract":"Wildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver-Factor-Stressor-Effect (DFSE) conceptual framework to assess the current state of the science related to post-wildfire water-quality. We reviewed 64 peer-reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water-quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long-term monitoring, multi-factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta-analyses by exploring causal linkages between influential variables and overall effects in post-wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post-wildfire water quality.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR036260","usgsCitation":"Elliott, S.M., Hornberger, M.I., Rosenberry, D.O., Frus, R., and Webb, R.M., 2024, A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps: Water Resources Research, v. 60, no. 7, e2023WR036260, 20 p.; Data Release, https://doi.org/10.1029/2023WR036260.","productDescription":"e2023WR036260, 20 p.; Data Release","ipdsId":"IP-156361","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439286,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr036260","text":"Publisher Index Page"},{"id":434931,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97JZOVY","text":"USGS data release","linkHelpText":"Annotated bibliography of 64 papers reviewed and summarized in a conceptual framework to assess post-wildfire water quality"},{"id":432026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":907311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hornberger, Michelle I. 0000-0002-7787-3446 mhornber@usgs.gov","orcid":"https://orcid.org/0000-0002-7787-3446","contributorId":1037,"corporation":false,"usgs":true,"family":"Hornberger","given":"Michelle","email":"mhornber@usgs.gov","middleInitial":"I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":907312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":907313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frus, Rebecca J. 0000-0002-2435-7202","orcid":"https://orcid.org/0000-0002-2435-7202","contributorId":340187,"corporation":false,"usgs":false,"family":"Frus","given":"Rebecca J.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":907314,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webb, Richard M. 0000-0001-9531-2207 rmwebb@usgs.gov","orcid":"https://orcid.org/0000-0001-9531-2207","contributorId":1570,"corporation":false,"usgs":true,"family":"Webb","given":"Richard","email":"rmwebb@usgs.gov","middleInitial":"M.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":907315,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257502,"text":"70257502 - 2024 - Same streams in a different forest? Investigations of forest harvest legacies and future trajectories across 30 years of stream habitat monitoring on the Tongass National Forest, Alaska","interactions":[],"lastModifiedDate":"2024-09-09T16:16:56.834676","indexId":"70257502","displayToPublicDate":"2024-07-10T09:07:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Same streams in a different forest? Investigations of forest harvest legacies and future trajectories across 30 years of stream habitat monitoring on the Tongass National Forest, Alaska","docAbstract":"The effects of timber harvest practices and climate change have altered forest ecosystems in southeast Alaska. However, quantification of patterns and trends in stream habitats associated with these forests is limited owing to a paucity of data available in remote watersheds. Here, we analyzed a 30-year dataset from southeast Alaska's Tongass National Forest to understand how these factors shape stream habitats. First, we examined differences between broad management classes (i.e., harvested and non-harvested) that have been used to guide stream channel restoration goals. Second, we assessed associations between intrinsic landscape characteristics, watershed management, and timber harvest legacies on aquatic habitat metrics. And third, we examined trends in stream habitat metrics over the duration of the dataset to anticipate future management challenges for these systems. Small effect sizes for some harvest-related predictors suggest that some stream habitat metrics, such as pool densities, are less responsive than others, and management practices such as protecting riparian buffers as well as post-harvest restoration may help conserve fish habitats. Large wood densities increased with time since harvest at sites harvested >50 years ago, indicating that multiple decades of post-harvest forest regrowth may contribute large wood to streams (possibly alder), but that it is not enough time for old-growth trees (e.g., spruce, Picea, or hemlock, Tsuga,), classified as key wood, to develop and be delivered to streams. The declining trend in key wood (i.e., the largest size class of wood) regardless of management history may reflect that pre-harvest legacy old-growth trees are declining along streams, with low replacement. The introduction of wood to maintain complex stream habitats may fill this gap until riparian stands again contribute structural key wood to streams. Trend analyses indicate an increasing spatial extent of undercut banks that may also be influenced by shifting hydrologic regimes under climate change.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0301723","usgsCitation":"Moore, M.J., Flitcroft, R., Tucker, E., Prussian, K.K., and Claeson, S.M., 2024, Same streams in a different forest? Investigations of forest harvest legacies and future trajectories across 30 years of stream habitat monitoring on the Tongass National Forest, Alaska: PLoS ONE, v. 19, no. 7, e0301723, 28 p., https://doi.org/10.1371/journal.pone.0301723.","productDescription":"e0301723, 28 p.","ipdsId":"IP-146418","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439287,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1371/journal.pone.0301723","text":"Publisher Index Page"},{"id":433632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Tongass National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.79598611853271,\n 35.785590501974994\n ],\n [\n -120.79598611853271,\n 35.758103908925094\n ],\n [\n -120.7544854283608,\n 35.758103908925094\n ],\n [\n -120.7544854283608,\n 35.785590501974994\n ],\n [\n -120.79598611853271,\n 35.785590501974994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -136.02998750758678,\n 57.464783614185876\n ],\n [\n -136.02998750758678,\n 55.846134928392786\n ],\n [\n -133.47293766914498,\n 55.846134928392786\n ],\n [\n -133.47293766914498,\n 57.464783614185876\n ],\n [\n -136.02998750758678,\n 57.464783614185876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Michael J. 0000-0002-5495-7049","orcid":"https://orcid.org/0000-0002-5495-7049","contributorId":304258,"corporation":false,"usgs":true,"family":"Moore","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flitcroft, R.","contributorId":342974,"corporation":false,"usgs":false,"family":"Flitcroft","given":"R.","email":"","affiliations":[{"id":81962,"text":"Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":910553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, E.","contributorId":342975,"corporation":false,"usgs":false,"family":"Tucker","given":"E.","email":"","affiliations":[{"id":81965,"text":"Tongass National Forest","active":true,"usgs":false}],"preferred":false,"id":910554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prussian, K. K.","contributorId":204860,"corporation":false,"usgs":false,"family":"Prussian","given":"K.","email":"","middleInitial":"K.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":910555,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Claeson, S. M.","contributorId":342976,"corporation":false,"usgs":false,"family":"Claeson","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":81962,"text":"Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":910556,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255973,"text":"70255973 - 2024 - Decoding paleomire conditions of Paleogene superhigh-organic-sulfur coals","interactions":[],"lastModifiedDate":"2024-07-11T13:45:51.459538","indexId":"70255973","displayToPublicDate":"2024-07-10T08:38:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Decoding paleomire conditions of Paleogene superhigh-organic-sulfur coals","docAbstract":"Superhigh-organic‑sulfur (SHOS) coals (coals with organic sulfur content >4 wt%) are unique coal deposits found at a few notable locations in the world. Specific peat accumulation and preservation conditions must be met to form SHOS coals. Organic sulfur is a major constituent of such coals, and it may have various sources depending on the prevailing paleomire conditions. Understanding such paleomire conditions sheds light on the formation mechanisms of SHOS coals. This investigation decodes the paleomire conditions of the Paleogene SHOS coals from Meghalaya, India, using sulfur isotopic compositions (δ34S) of organic sulfur (δ34SOS) and pyritic sulfur (δ34SPy) along with organic petrography, pyrite morphology and trace element ratios. Thirty coal samples were collected from the Jaintia Hills in the east, Khasi Hills in the middle, and Garo Hills in the west of Meghalaya. The organic sulfur content in the Garo, Khasi, and Jaintia coals varies from 1.0 to 3.3 wt%, 1.4 to 13.8 wt%, and 1.0 to 7.2 wt%, respectively. Further, after separation from pyritic sulfur and sulfate sulfur phases, the organic sulfur content ranges from 54.4 to 69.2%, 63.8 to 79.9%, and 59.3 to 73.8%, in the Garo, Khasi, and Jaintia Hills, respectively, suggesting the SHOS nature of these coal samples. The δ34SPy varies from −29.3 ‰ to +5.7 ‰, −21.3 ‰ to +27.3 ‰, and −12.1 ‰ to −4.3 ‰, in the Jaintia, Khasi, and Garo Hills, respectively, while the δ34SOS fluctuates from −4.6 ‰ to +3.7 ‰, −9.3 ‰ to +7.8 ‰, and − 9.0 ‰ to −5.0 ‰, respectively. The δ34S values of pyrite and organic sulfur (OS) in Jaintia coals are 34S depleted compared to seawater sulfate (+22 ‰), leading to fractionations in the range of −51.3 ‰ to −16.3 ‰ (mean − 31.6 ‰) and − 26.6 ‰ to −18.3 ‰ (mean − 23.1 ‰) for pyritic and organic sulfur (OS), respectively. Pyrite in Khasi coals show a relatively heavier δ34S composition averaging at −20.5 ‰, whereas organic sulfur (OS) isotope compositions range from −31.3 ‰ to −14.2 ‰ with a mean of −22.6 ‰. Pyrite and OS in the Garo coals are depleted compared to seawater sulfate. Isotope variations in the Jaintia, Khasi, and Garo coals indicate microbial sulfate reduction (MSR) of seawater sulfate. Large isotopic fractionations between Eocene seawater sulfate and pyritic sulfur (Δ34SSO4Eocene – pyrite = up to −51.3 ‰; mean − 31.6 ‰) in Jaintia coals indicate their possible formation in the water column/near the sediment-seawater interface (open system) and also hint toward dissimilatory sulfate reduction pathways that prevailed under anoxic redox conditions. However, mean values of Δ34SSO4Eocene – pyrite (−20.5 ‰) in the Khasi coals imply pyrite formation deeper in the sediments (more closed system) under dysoxic conditions. The dominance of OS over pyritic sulfur, framboidal pyrite, and its microcrystal size distributions in Jaintia coals may suggest syngenetic pyrite formation in open water reducing/anoxic conditions under paralic environments. Elevated Sr/Ba and U/Th values in these coals further confirm the anoxic conditions. Nevertheless, the presence of euhedral pyrite with the alleviated pyrite framboids in the Khasi coals and their complete absence in the Garo coals may suggest dysoxic-suboxic and suboxic-oxic depositional conditions, respectively. The isotopic signatures of the Garo coals suggest sulfur contribution from the parent paleobiota and MSR under a freshwater-oxic environment. Insignificant fractionations between δ34SPy and δ34SOS indicate limited iron and sulfate availability for additional sulfur cycling and disproportionation reactions, typical of oxic conditions. The absence of framboidal pyrite, elevated sulfate concentration, and mean Sr/Ba and U/Th values of 0.5 and 0.3, respectively, further suggest the freshwater peat deposition in the Garo Hills under limnotelmatic to telmatic freshwater conditions. Moreover, high inertinite content (Immf = 9.77–33.16 vol%), possibly induced by atmospheric peat exposure, supports the interpretation of suboxic-oxic paleomire conditions in Garo Hills. Gradually decreasing mineral matter content from Jaintia (mean 13.6 vol%) to Garo coals (mean 7.4 vol%) additionally projects a transition from mesotrophic brackish to freshwater limnotelmatic environment, complementing the shift in the paleomire condition from eastern (Jaintia) to western (Garo) Meghalayan Hills.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2024.104559","usgsCitation":"Adsul, T., O’Beirne, M.D., Fike, D., Ghosh, S., Werne, J.P., Gilhooly, W., Hackley, P.C., Hatcherian, J.J., Philip, B., Hazra, B., Bhattachryya, S., Konar, R., and Varma, A.K., 2024, Decoding paleomire conditions of Paleogene superhigh-organic-sulfur coals: International Journal of Coal Geology, v. 290, 104559, 19 p., https://doi.org/10.1016/j.coal.2024.104559.","productDescription":"104559, 19 p.","ipdsId":"IP-158499","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":500066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2024.104559","text":"Publisher Index Page"},{"id":430956,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 90,\n 26\n ],\n [\n 90,\n 25.25\n ],\n [\n 92.55,\n 25.25\n ],\n [\n 92.55,\n 26\n ],\n [\n 90,\n 26\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"290","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adsul, Tushar","contributorId":330815,"corporation":false,"usgs":false,"family":"Adsul","given":"Tushar","email":"","affiliations":[{"id":79028,"text":"Indian Institute of Technology (Indian School of Mines), India","active":true,"usgs":false}],"preferred":false,"id":906187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Beirne, Molly D.","contributorId":340100,"corporation":false,"usgs":false,"family":"O’Beirne","given":"Molly","email":"","middleInitial":"D.","affiliations":[{"id":81466,"text":"Organic and Stable Isotope Biogeochemistry Laboratory, Department of Geology and Environmental Science, University of Pittsburgh, 200 Space Research Coordination Center, 4107 O'Hara Street, Pittsburgh, PA 15260, USA","active":true,"usgs":false}],"preferred":false,"id":906188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, David","contributorId":340101,"corporation":false,"usgs":false,"family":"Fike","given":"David","email":"","affiliations":[{"id":81467,"text":"Department of Earth and Planetary Sciences, Washington University in St. Louis, 1 Brookings Drive, St. Louis, MO 63130-4899","active":true,"usgs":false}],"preferred":false,"id":906189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ghosh, Santanu","contributorId":330824,"corporation":false,"usgs":false,"family":"Ghosh","given":"Santanu","email":"","affiliations":[{"id":79037,"text":"Mizoram University, India","active":true,"usgs":false}],"preferred":false,"id":906190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Werne, Josef P.","contributorId":340102,"corporation":false,"usgs":false,"family":"Werne","given":"Josef","email":"","middleInitial":"P.","affiliations":[{"id":81466,"text":"Organic and Stable Isotope Biogeochemistry Laboratory, Department of Geology and Environmental Science, University of Pittsburgh, 200 Space Research Coordination Center, 4107 O'Hara Street, Pittsburgh, PA 15260, USA","active":true,"usgs":false}],"preferred":false,"id":906191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gilhooly, William P. 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Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per-capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm-season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.
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Donald 0000-0001-5913-2372 domartin@usgs.gov","orcid":"https://orcid.org/0000-0001-5913-2372","contributorId":4450,"corporation":false,"usgs":true,"family":"Martin","given":"Donald","email":"domartin@usgs.gov","affiliations":[],"preferred":true,"id":912937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herbert, Deidre Mary 0000-0001-8707-3218","orcid":"https://orcid.org/0000-0001-8707-3218","contributorId":302591,"corporation":false,"usgs":true,"family":"Herbert","given":"Deidre","email":"","middleInitial":"Mary","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912919,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buchwald, Cheryl A. 0000-0001-8968-5023 cabuchwa@usgs.gov","orcid":"https://orcid.org/0000-0001-8968-5023","contributorId":1943,"corporation":false,"usgs":true,"family":"Buchwald","given":"Cheryl","email":"cabuchwa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912920,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dieter, Cheryl A. 0000-0002-5786-4091","orcid":"https://orcid.org/0000-0002-5786-4091","contributorId":220502,"corporation":false,"usgs":true,"family":"Dieter","given":"Cheryl A.","affiliations":[],"preferred":true,"id":912921,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miller, Lisa D. 0000-0002-3523-0768 ldmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-3523-0768","contributorId":1125,"corporation":false,"usgs":true,"family":"Miller","given":"Lisa","email":"ldmiller@usgs.gov","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912938,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stewart, Jana S. 0000-0002-8121-1373","orcid":"https://orcid.org/0000-0002-8121-1373","contributorId":211037,"corporation":false,"usgs":true,"family":"Stewart","given":"Jana","middleInitial":"S.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912922,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Paulinski, Scott R. 0000-0001-6548-8164","orcid":"https://orcid.org/0000-0001-6548-8164","contributorId":204240,"corporation":false,"usgs":true,"family":"Paulinski","given":"Scott R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912924,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kristen Valseth","contributorId":344134,"corporation":false,"usgs":false,"family":"Kristen Valseth","affiliations":[],"preferred":false,"id":912925,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70255702,"text":"dr1195 - 2024 - Pesticide concentrations of surface water and suspended sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021","interactions":[],"lastModifiedDate":"2026-01-27T17:31:25.509382","indexId":"dr1195","displayToPublicDate":"2024-07-09T07:40:19","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1195","displayTitle":"Pesticide Concentrations of Surface Water and Suspended Sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021","title":"Pesticide concentrations of surface water and suspended sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021","docAbstract":"Managed flow pulses in the north Sacramento-San Joaquin Delta are an adaptive management tool used in efforts to enhance food availability in delta smelt (Hypomesus transpacificus) habitat as part of the North Delta Food Subsidies Action. The California Department of Water Resources (DWR) monitors non-managed seasonal and local flow pulses and managed flow pulses from agricultural drainage or main stem Sacramento River water redirected through Yolo By-Pass. Augmented flow pulses are hypothesized to improve net positive flow during summer and fall in Yolo By-Pass and enhance plankton availability in delta smelt habitat in Cache Slough complex. However, flow pulses may also result in unintended negative effects of increased pesticides that are transported through Yolo By-Pass. Here, we evaluate pesticides in surface water and suspended sediment correlated with flow pulses in Yolo By-Pass during the 2019–21 calendar years.
Surface-water and suspended-sediment samples were collected by DWR personnel. Water samples were analyzed at the U.S. Geological Survey Organic Chemistry Research Laboratory in Sacramento, California, for a suite of as many as 178 current-use pesticides and pesticide degradates using gas chromatography with mass spectrometry (GC/MS), gas chromatography with tandem mass spectrometry, and liquid chromatography with tandem mass spectrometry. Suspended sediments filtered from water samples were analyzed for a suite of as many as 173 current-use pesticides and pesticide degradates.
There were 52 different current-use pesticides and pesticide degradates detected in water samples collected throughout the study. Concentrations ranged from below method detection limits to 4,070 nanograms per liter. Five different compounds in water samples were detected with concentrations above U.S. Environmental Protection Agency aquatic life benchmarks. In suspended-sediment samples collected throughout the study, eight different current-use pesticides and pesticide degradates were detected.
Total pesticide concentrations were highest at surface-water sites in the northern end of Yolo By-Pass and decreased farther downstream during the same sampling events. Total pesticide concentrations generally were higher for most surface-water sites immediately before or during the managed flow pulse in 2019 versus after the flow pulse. Finally, mean total pesticide concentrations for each surface-water site generally were higher during all of 2019 than 2021, regardless of sampling period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1195","collaboration":"Prepared in cooperation with the California Department of Water Resources","programNote":"Water Availability and Use Science Program","usgsCitation":"Uychutin, M., Orlando, J.L., Hladik, M.L., Sanders, C.J., Gross, M.S., De Parsia, M.D., LaBarbera, E.M., Twardochleb, L., and Davis, B.E., 2024, Pesticide concentrations of surface water and suspended sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021: U.S. Geological Survey Data Report 1195, 24 p., https://doi.org/10.3133/dr1195.","productDescription":"v, 24 p.","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-139194","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499108,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117119.htm","linkFileType":{"id":5,"text":"html"}},{"id":430682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1195/covrthb.jpg"},{"id":430683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1195/dr1195.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":430684,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1195/dr1195.xml"},{"id":430686,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1195/full"},{"id":430685,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1195/images/"}],"country":"United States","state":"California","otherGeospatial":"Yolo By-Pass and Cache Slough Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.916667,\n 38.8333\n ],\n [\n -121.916667,\n 38.1667\n ],\n [\n -121.33,\n 38.1667\n ],\n [\n -121.333,\n 38.8333\n ],\n [\n -121.916667,\n 38.8333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This cooperative study between the City of Durham Public Works Department, Stormwater Division and U.S. Geological Survey evaluated whether alternate monitoring strategies that incorporated samples collected across an increased range of streamflows would improve nutrient load estimates for Ellerbe and Sandy Creeks, two small, highly urbanized streams in the City of Durham, North Carolina. Water-quality and streamflow data collected between January 2009 and December 2020 were used to develop instream nutrient-load models using the U.S. Geological Survey R-LOADEST program. This study compared model results from two sampling scenarios: routine monthly (fixed frequency) sampling combined with targeted high-streamflow sampling (scenario A), and fixed frequency sampling only (scenario B).
Calibration diagnostic results were used to select the final, or most optimal, models. Most final models included seasonality terms to compensate for intra-annual variability in the data. Storm-runoff samples provided better definition at higher streamflows and improved the overall concentration versus flow relations for all constituents, except nitrate + nitrite. Uncertainties in the nutrient load estimates were lower and less variable for the scenario A tests compared to the scenario B tests.
Five time steps representing 12-, 9-, 7-, 6-, and 5-year subsets of the overall dataset were used to examine the effect of prediction period length on the computed loads and uncertainties. In focusing on the scenario A results, nutrient loads tended to be higher for the shorter time steps. These shorter time steps also produced higher errors, or uncertainty, in the load estimates compared to longer time steps. Evaluations of annual nutrient loads during 2016–20 indicated that the most consistent load estimates and tightest confidence intervals were obtained for longer 12- and 9-year time steps. Estimated loads were more variable and uncertain when based on the shorter 6- and 5-year time steps. The degree of uncertainty (standard error of prediction) in the nutrient load estimation results was influenced by sampling approach, calibration time step, and hydrologic characteristics during the model period of interest.
Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, suite 500
Norcross, GA 30093
Contact Us- USGS Publications Warehouse
","tableOfContents":"In central Virginia, the Pamunkey Indian Tribe and Reservation are facing increasingly complex water resource issues related to quantity and quality. Documentation of surface-water, groundwater, water quality, land subsidence, sea-level rise, and river ecology issues in the Pamunkey River watershed and incorporation of traditional ecological knowledge into these research topics may improve understanding of the water resources broadly. This report summarizes the relevant traditional ecological knowledge and scientific literature to elucidate gaps in the total combined knowledge of a suite of water science topics concerning the Pamunkey River watershed. This suite of water science topics includes some of the issues that the Pamunkey Indian Tribe and Reservation are facing within the watershed: fragmentation of streams and management of streamflow, flooding, degrading water quality, groundwater extraction, relative sea-level rise, rapid changes in ecosystems, loss of ecological and biological diversity, and climate change. Gaps in total combined knowledge are pervasive throughout each of these water resource topics. Identifying these knowledge gaps can help inform future research and management strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245024","collaboration":"Prepared in cooperation with The Pamunkey Indian Tribe","usgsCitation":"Foster, B.M., Lopez, R., Crawford, E.R., Cook, W., Krigsvold, J., Langston, J.H., Langston, T., Miles, G., Moore, K., Garman, G.C., Rice, K.C., and Jastram, J.D., 2024, Characterization of the water resources of the Pamunkey River watershed in Virginia—A review of water science, management, and traditional ecological knowledge: U.S. Geological Survey Scientific Investigations Report 2024–5024, 64 p., https://doi.org/10.3133/sir20245024.","productDescription":"ix, 64 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150381","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":430658,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5024/images/"},{"id":430656,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5024 HTML"},{"id":430654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5024/coverthb.jpg"},{"id":430655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5024/sir20245024.pdf","text":"Report","size":"32.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5024 PDF"},{"id":430657,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5024/sir20245024.XML","description":"SIR 2024-5024 XML"}],"country":"United States","state":"Virginia","otherGeospatial":"Pamunkey River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.37200097289723,\n 38.37682664861029\n ],\n [\n -78.37200097289723,\n 37.038722790200765\n ],\n [\n -76.04924547820508,\n 37.038722790200765\n ],\n [\n -76.04924547820508,\n 38.37682664861029\n ],\n [\n -78.37200097289723,\n 38.37682664861029\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virgnia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106133","usgsCitation":"Alzraiee, A.H., and Niswonger, R.G., 2024, A probabilistic approach to training machine learning models using noisy data: Environmental Modelling & Software, v. 179, 106133, 15 p., https://doi.org/10.1016/j.envsoft.2024.106133.","productDescription":"106133, 15 p.","ipdsId":"IP-151600","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":439292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106133","text":"Publisher Index Page"},{"id":433694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":912927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259332,"text":"70259332 - 2024 - Summer 2024","interactions":[],"lastModifiedDate":"2024-10-04T14:40:26.857377","indexId":"70259332","displayToPublicDate":"2024-07-08T09:39:29","publicationYear":"2024","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10554,"text":"Watermarks New England Water Science Center Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"Summer 2024","docAbstract":"No abstract available.
","language":"English","publisher":"New England Water Science Center","usgsCitation":"Rossos, K., 2024, Summer 2024: Watermarks New England Water Science Center Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-167734","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":462600,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/watermarks-new-england-wsc-newsletters/watermarks-newsletter-summer-2024","linkFileType":{"id":5,"text":"html"}},{"id":462601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossos, Katrina 0000-0002-3819-4344","orcid":"https://orcid.org/0000-0002-3819-4344","contributorId":331723,"corporation":false,"usgs":true,"family":"Rossos","given":"Katrina","email":"","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914963,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70257506,"text":"70257506 - 2024 - Integrating ecological value and charismatic species habitats to prioritize habitats for conservation: A case study from Greater Yellowstone","interactions":[],"lastModifiedDate":"2024-09-10T11:27:55.620233","indexId":"70257506","displayToPublicDate":"2024-07-08T06:24:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Integrating ecological value and charismatic species habitats to prioritize habitats for conservation: A case study from Greater Yellowstone","docAbstract":"Expanding human pressure has reduced natural habitats globally and motivated strategies to conserve remaining natural habitats. Decisions about conservation on private lands, however, are typically made by local stakeholders who are motivated by the elements of nature they most highly value. Thus, national prioritization for conservation should be complemented by local analysis of species or habitats that most influence local landowner decisions. We demonstrate within the Greater Yellowstone Ecosystem how quantitative mapping of wildlife species that are highly valued by local residents can be integrated with indices of ecosystem integrity to prioritize private lands for conservation. We found that natural vegetation cover (NVC) comprised 81% of the private lands. Some watersheds have lost 6% of NVC since 2001 and developed lands now cover >40% of their areas. Locations high in ecological value, elk habitat, and grizzly habitat occurred in different biophysical settings. Consequently, only 2% of the NVC supports high levels of all three biodiversity measures and 26% of this area was within conservation easements. The remaining areas of high biodiversity value that are unprotected are priorities for conservation. We suggest that national-scale conservation planning will be most effective on private lands if additional within-ecoregion analyses are done on the elements of biodiversity that are most valued by local people.
Global Li production will require a ~500 % increase to meet 2050 projected energy storage demands. One potential source is oil and gas wastewater (i.e., produced water or brine), which naturally has high total dissolved solids (TDS) concentrations, that can also be enriched in Li (>100 mg/L). Understanding the sources and mechanisms responsible for high naturally-occurring Li concentrations can aid in efficient targeting of these brines. The isotopic composition (δ7Li, δ11B, δ138Ba) of produced water and core samples from the Utica Shale and Point Pleasant Formation (UPP) in the Appalachian Basin, USA indicates that depth-dependent thermal maturity and water-rock interaction, including diagenetic clay mineral transformations, likely control Li concentrations. A survey of Li content in produced waters throughout the USA indicates that Appalachian Basin brines from the Marcellus Shale to the UPP have the potential for economic resource recovery.
Climate change is anticipated to cause species to shift their ranges upward and poleward, yet space for tracking suitable habitat conditions may be limited for range-restricted species at the highest elevations and latitudes of the globe. Consequently, range-restricted species inhabiting Arctic freshwater ecosystems, where global warming is most pronounced, face the challenge of coping with changing abiotic and biotic conditions or risk extinction. Here, we use an extensive fish community and environmental dataset for 1762 lakes sampled across Scandinavia (mid-1990s) to evaluate the climate vulnerability of Arctic char (Salvelinus alpinus), the world's most cold-adapted and northernly distributed freshwater fish. Machine learning models show that abiotic and biotic factors strongly predict the occurrence of Arctic char across the region with an overall accuracy of 89 percent. Arctic char is less likely to occur in lakes with warm summer temperatures, high dissolved organic carbon levels (i.e., browning), and presence of northern pike (Esox lucius). Importantly, climate warming impacts are moderated by habitat (i.e., lake area) and amplified by the presence of competitors and/or predators (i.e., northern pike). Climate warming projections under the RCP8.5 emission scenario indicate that 81% of extant populations are at high risk of extirpation by 2080. Highly vulnerable populations occur across their range, particularly near the southern range limit and at lower elevations, with potential refugia found in some mountainous and coastal regions. Our findings highlight that range shifts may give way to range contractions for this cold-water specialist, indicating the need for pro-active conservation and mitigation efforts to avoid the loss of Arctic freshwater biodiversity.
Harmful algal blooms (HABs) are a persistent and increasing problem globally, yet we still have limited knowledge about how they affect wildlife. Although semi-aquatic and aquatic amphibians and reptiles have experienced large declines and occupy environments where HABs are increasingly problematic, their vulnerability to HABs remains unclear. To inform monitoring, management, and future research, we conducted a literature review, synthesized the studies, and report on the mortality events describing effects of cyanotoxins from HABs on freshwater herpetofauna. Our review identified 37 unique studies and 71 endpoints (no-observed-effect and lowest-observed-effect concentrations) involving 11 amphibian and 3 reptile species worldwide. Responses varied widely among studies, species, and exposure concentrations used in experiments. Concentrations causing lethal and sublethal effects in laboratory experiments were generally 1 to 100 µg/L, which contains the mean value of reported HAB events but is 70 times less than the maximum cyanotoxin concentrations reported in the environment. However, one species of amphibian was tolerant to concentrations of 10,000 µg/L, demonstrating potentially immense differences in sensitivities. Most studies focused on microcystin-LR (MC-LR), which can increase systemic inflammation and harm the digestive system, reproductive organs, liver, kidneys, and development. The few studies on other cyanotoxins illustrated that effects resembled those of MC-LR at similar concentrations, but more research is needed to describe effects of other cyanotoxins and mixtures of cyanotoxins that commonly occur in the environment. All experimental studies were on larval and adult amphibians; there were no such studies on reptiles. Experimental work with reptiles and adult amphibians is needed to clarify thresholds of tolerance. Only nine mortality events were reported, mostly for reptiles. Given that amphibians likely decay faster than reptiles, which have tissues that resist decomposition, mass amphibian mortality events from HABs have likely been under-reported. We propose that future efforts should be focused on seven major areas, to enhance our understanding of effects and monitoring of HABs on herpetofauna that fill important roles in freshwater and terrestrial environments. Environ Toxicol Chem 2024;00:1–14. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Flow alteration and riparian vegetation encroachment are causing habitat simplification with severe consequences for native fishes. To assess the effectiveness of enhancing simplified habitat in a large dryland river, we experimentally added invasive wood at 19 paired treatment and reference (no wood added) subreaches (50–100 m) within the main channel of the San Juan River. Using a before-after-control-impact design, we sampled fishes and macroinvertebrates, and quantified habitat complexity. After wood addition, total native fish densities were 2.2× higher in treatments compared with references, whereas total nonnative fish densities exhibited no response. Macroinvertebrate densities were 6.8× higher, and habitat complexity increased in treatments. Counts of geomorphic features in treatments increased from 1 to a maximum of 11 following wood addition, while the number of features in references remained unchanged. Wood addition has potential to instigate natural riverine processes, ultimately enhancing native fish habitat by increasing macroinvertebrate densities and habitat complexity in dryland rivers. Water overallocation and increasing aridity will continue to challenge efforts to improve habitat conditions with environmental flows alone, and managers might consider integrating non-flow alternatives like addition of abundant, invasive wood to reduce habitat simplification.
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