In urbanized areas, the “freshwater salinization syndrome” (FSS), which pertains to long-term increases in concentrations of major ions and metals in fresh surface waters, has been attributed to road salt application. In addition to FSS, the water composition changes as an influx of sodium (Na+) in recharge may displace calcium (Ca2+), magnesium (Mg2+), potassium (K+), and trace metals by reverse cation exchange. These changing ion fluxes can result in adverse impacts on groundwater and surface waters used for municipal supplies. Few datasets exist to quantify the FSS on a watershed scale or link its manifestation to potential controlling factors such as changes in urban development, land use/land cover (LULC), or wastewater treatment plant (WWTP) discharges in upstream areas. Here, we use two decades (1999–2019) of monthly streamwater quality data combined with daily streamflow for six exurban and suburban watersheds in southeastern Pennsylvania to examine the relations among Ca2+, Mg2+, K+, Na+, chloride (Cl−), sulfate (SO42-), and alkalinity (HCO3−) concentrations and upstream controlling factors. Flow-normalized annual and baseflow (August ̶ November) concentrations for Ca2+, Mg2+, Na+, and Cl− increased in all six watersheds over the 20-year study, providing evidence of FSS’s impacts on groundwater that sustains streamflow. Additionally, a redundancy analysis using 2019 flow-normalized values identified the following positive associations between solute concentrations and controlling variables: 1) Cl−, Mg2+, and Ca2+ with impervious surface cover (ISC), 2) Na+ and SO42- with ISC and total WWTP discharge volume, and 3) HCO3− with agriculture and total WWTP discharge volume. From a human health perspective, 2019 flow-normalized Na+ concentrations exceeded the U.S. Environmental Protection Agency’s 20 mg L-1 threshold for individuals restricted to a low sodium diet. Furthermore, indices used to evaluate the corrosivity of source waters to drinking water infrastructure and inform municipal water treatment practices, such as the Chloride to Sulfate Mass Ratio and Larson Ratio, increased between two- and seven-fold over the 20-year time. Collectively, the results elucidate the causal factors of the FSS in suburban and exurban watersheds and its potential impacts on human health and drinking water infrastructure.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2023.1153133","usgsCitation":"Rossi, M.L., Kremer, P., Cravotta, C., Seng, K.E., and Goldsmith, S.T., 2023, Land development and road salt usage drive long-term changes in major-ion chemistry of streamwater in six exurban and suburban watersheds, southeastern Pennsylvania, 1999-2019: Frontiers in Environmental Science, v. 11, 1153133, 21 p ., https://doi.org/10.3389/fenvs.2023.1153133.","productDescription":"1153133, 21 p .","ipdsId":"IP-147665","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":443021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2023.1153133","text":"Publisher Index Page"},{"id":418218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Chester Creek, Crum Creek, East Branch Brandywine Creek, Neshaminy Creek, Perkiomen Creek, Ridley Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.5237058720174,\n 39.713850779802925\n ],\n [\n -74.67929211102248,\n 39.713850779802925\n ],\n [\n -74.67929211102248,\n 40.9985953543042\n ],\n [\n -76.5237058720174,\n 40.9985953543042\n ],\n [\n -76.5237058720174,\n 39.713850779802925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-05-26","publicationStatus":"PW","contributors":{"authors":[{"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":875662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":875663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. 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In fishery-independent surveys, zeros that come from reduced susceptibility to sample gears and reduced availability of the population to the survey impact survey catchability and negatively bias relative abundance indices. A zero-inflated negative binomial model was used to standardize a juvenile Atlantic Sturgeon (Acipenser oxyrinchus oxyrinchus) relative abundance index (Hudson River, New York) that included a high proportion (42%) of zero observations and intra- and interannually variable covariates. Reduced susceptibility was related to low water temperature, with the percentage of zeroes increasing rapidly below 7°C. Availability was influenced by temperature and distance to salt front, as catch rates increased with temperature and peaked in mesohaline waters ~27 km downstream of the predicted salt front. An alternative index suggested significant population growth (r = 0.15; p-value = 0.007) occurred from 2004 to 2015. The zero-inflated model helped better understand Hudson River juvenile Atlantic Sturgeon ecology and relative trends in abundance, to better inform future management and monitoring decisions along the Atlantic Coast.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12638","usgsCitation":"Dufour, M.R., and Qian, S.S., 2023, Evaluating population trends of juvenile Atlantic Sturgeon at low abundance in a dynamic estuarine environment (Hudson River, New York): Fisheries Management and Ecology, v. 30, no. 5, p. 507-520, https://doi.org/10.1111/fme.12638.","productDescription":"14 p.","startPage":"507","endPage":"520","ipdsId":"IP-133677","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":499248,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fme.12638","text":"Publisher Index Page"},{"id":418213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Haverstraw Bay, Hudson River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.98965367137986,\n 41.27278785874276\n ],\n [\n -73.98810103364949,\n 41.21441540680195\n ],\n [\n -73.96481146769594,\n 41.172354958942265\n ],\n [\n -73.92754816217085,\n 41.15131460173012\n ],\n [\n -73.85923210204054,\n 41.15365275277256\n ],\n [\n -73.87010056615213,\n 41.1898834296822\n ],\n [\n -73.9042585962173,\n 41.222590688889795\n ],\n [\n -73.93686398855216,\n 41.24477558908072\n ],\n [\n -73.94152190174316,\n 41.26578591767401\n ],\n [\n -73.98965367137986,\n 41.27278785874276\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"30","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":875670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":875671,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70245183,"text":"70245183 - 2023 - A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data","interactions":[],"lastModifiedDate":"2023-08-08T14:19:03.582056","indexId":"70245183","displayToPublicDate":"2023-06-19T07:06:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data","docAbstract":"Nearshore bathymetry is difficult to measure using survey methods when wave heights approach the breaking limit. Remote sensing using digital cameras offers a way to observe the surf zone and calculate water depths based on phase speed but comes with its challenges of potentially noisy data that can introduce error into estimates of frequency and wavenumber used in phase speed calculation. This study investigates the robustness of a new version of a bathymetric inversion algorithm (cBathy, version 2.0) in moderate to energetic wave conditions by comparing depth estimates from timeseries’ of pixel intensity with depth estimates from synthetic water level data. The synthetic data are generated by the phase-resolving numerical model, SWASH, and optical data were collected during a field experiment in 2015. Model results from SWASH computed with known bathymetry are used as input to cBathy, and depth estimates are compared to nearshore surveys. Argus camera observations are also used as input to cBathy for the same times as the SWASH simulations. The SWASH simulations resolve breaking waves and do not include (optical) changes to the relation between water surface slope and pixel intensity, termed modulation transfer function, that occur during wave breaking, enabling better estimates from bathymetric inversion algorithms near morphologic features like sand bars. The results indicate that improvements result from eliminating of disruptions to the modulation transfer function caused by wave breaking and residual foam. We show that the use of synthetic wave data is a valuable means of isolating errors in bathymetric inversion algorithms.
Freshwater is a vital natural resource. New York is a water-rich State; however, even here, the economical use of water resources is needed to ensure there is enough water of adequate quality for human and ecological needs—now and into the future. Nowhere in New York is this more evident than on Long Island where public-water supply is obtained from the sole-source aquifers directly beneath the 3 million people that live there. The U.S. Geological Survey works in partnership with the Nassau County Department of Public Works and Department of Health to monitor streamflow, groundwater, water quality, and water use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233024","usgsCitation":"Breault, R.F., Masterson, J.P., Busciolano, R., and Fisher, I., 2023, A century of hydrologic data collection prepares western Long Island for current and future water-resources challenges (ver. 1.1, July 2023): U.S. Geological Survey Fact Sheet 2023–3024, 4 p., https://doi.org/10.3133/fs20233024.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-145029","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":419071,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2023/3024/versionHist.txt","size":"2.88 KB","linkFileType":{"id":2,"text":"txt"}},{"id":419068,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3024"},{"id":418370,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3024/coverthb3.jpg"},{"id":499688,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114776.htm","linkFileType":{"id":5,"text":"html"}},{"id":419070,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3024/fs20233024.XML"},{"id":419069,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3024/images/"},{"id":419067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3024/fs20233024.pdf","text":"Report","size":"4.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3024"}],"country":"United States","state":"New York","otherGeospatial":"western Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.47830294208853,\n 40.943716469005324\n ],\n [\n -73.78304279806949,\n 40.943716469005324\n ],\n [\n -73.78304279806949,\n 40.52975156287121\n ],\n [\n -73.47830294208853,\n 40.52975156287121\n ],\n [\n -73.47830294208853,\n 40.943716469005324\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: June 2023; Version 1.1: July 2023","contact":"Director, New York Water Science Center
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This report describes status and trends in watershed condition across the Northwest Forest Plan (NWFP) area over the first 25 years since its inception in 1994. The program charged with this task is the Aquatic and Riparian Effectiveness Monitoring Program (AREMP), which has assembled information from field data collection, spatial datasets, and a host of landscape models to evaluate the status and trends in aquatic resources in streams and watersheds. Field data included hydrologic measurements (stream wetted widths and temperatures), geomorphic responses (instream wood and sediment), and biological responses (macroinvertebrates and aquatic organism passage). Novel statistical models were used to estimate trends in these measured responses. A suite of complementary modeled results was also employed to describe hydrometeorological drivers (e.g., drought indices and stream discharge), forest cover (upslope and riparian vegetation), and geomorphic conditions (e.g., road-related estimates of chronic and shallow landslide sediment delivery risk). Collectively, information on these responses allowed us to rigorously evaluate instream responses and hypothesize watershed drivers of those responses across the NWFP area and over time. The majority of responses we observed indicated widespread and incremental improvements from active management of forests, forest roads, and road-stream crossings as envisioned by the aquatic conservation strategy of the NWFP. Additionally, many of the responses we observed were consistent with those expected under the influences of changing climates in the Pacific Northwest. Ultimately, the long-term, broad-scale information provided by AREMP is a critical foundation for evaluating the effectiveness of federal land management and the effects of changing climates on water resources that sustain the Pacific Northwest’s human and natural landscapes.
","language":"English","publisher":"U.S. Department of Agriculture, Forest Service","doi":"10.2737/PNW-GTR-1010","usgsCitation":"Dunham, J., Hirsch, C., Gordon, S., Flitcroft, R.L., Chelgren, N., Snyder, M.N., Hockman-Wert, D.P., Reeves, G.H., Andersen, H.V., Anderson, S.K., Battaglin, W., Black, T.A., Brown, J., Claeson, S., Hay, L., Heaston, E.D., Luce, C., Nelson, N., Penn, C., and Raggon, M., 2023, Northwest Forest Plan — The first 25 years (1994–2018): Watershed condition status and trends: Technical Report PNW-GTR-1010, 165 p., https://doi.org/10.2737/PNW-GTR-1010.","productDescription":"165 p.","numberOfPages":"184","ipdsId":"IP-133075","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":443056,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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Engineers","active":false,"usgs":false}],"preferred":false,"id":875467,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Brown, Jason","contributorId":310353,"corporation":false,"usgs":false,"family":"Brown","given":"Jason","email":"","affiliations":[],"preferred":false,"id":875468,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Claeson, Shannon 0000-0003-3839-1871","orcid":"https://orcid.org/0000-0003-3839-1871","contributorId":310354,"corporation":false,"usgs":false,"family":"Claeson","given":"Shannon","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":875469,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hay, Lauren","contributorId":310356,"corporation":false,"usgs":false,"family":"Hay","given":"Lauren","affiliations":[],"preferred":false,"id":875470,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Heaston, Emily D. 0000-0002-3949-391X","orcid":"https://orcid.org/0000-0002-3949-391X","contributorId":236919,"corporation":false,"usgs":false,"family":"Heaston","given":"Emily","email":"","middleInitial":"D.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":false,"id":875471,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Luce, Charles H.","contributorId":245593,"corporation":false,"usgs":false,"family":"Luce","given":"Charles H.","affiliations":[{"id":40027,"text":"United States Forest Service","active":true,"usgs":false}],"preferred":false,"id":875472,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Nelson, Nathan","contributorId":310359,"corporation":false,"usgs":false,"family":"Nelson","given":"Nathan","email":"","affiliations":[],"preferred":false,"id":875473,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Penn, Colin 0000-0002-5195-2744","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":310361,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","affiliations":[],"preferred":false,"id":875474,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Raggon, Mark","contributorId":310363,"corporation":false,"usgs":false,"family":"Raggon","given":"Mark","affiliations":[],"preferred":false,"id":875475,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70244323,"text":"sim3504 - 2023 - Potentiometric surface map of the Southern High Plains aquifer in the Cannon Air Force Base area, Curry County, New Mexico, 2020","interactions":[],"lastModifiedDate":"2026-02-19T17:56:46.734655","indexId":"sim3504","displayToPublicDate":"2023-06-15T09:19:26","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3504","displayTitle":"Potentiometric Surface Map of the Southern High Plains Aquifer in the Cannon Air Force Base Area, Curry County, New Mexico, 2020","title":"Potentiometric surface map of the Southern High Plains aquifer in the Cannon Air Force Base area, Curry County, New Mexico, 2020","docAbstract":"Declining water levels and the potential impact on water resources on and around Cannon Air Force Base (AFB), New Mexico, has necessitated an up-to-date review of the potentiometric surface to evaluate the availability of water resources for future use. Analysis of groundwater-flow directions and hydraulic gradients can provide an understanding of depletion by heavy groundwater pumping and recharge through playa lakes, as well as the relationship between the groundwater levels and underlying geology. The objectives of this study, conducted by the U.S. Geological Survey in cooperation with the U.S. Air Force Civil Engineer Center, are to assist Cannon AFB in understanding and interpreting current and local hydrologic conditions and to evaluate groundwater-level change from 2015 to 2020 using new and historical data. A groundwater potentiometric surface contour map was constructed to better understand the Southern High Plains aquifer around Cannon AFB and to show the altitude of the water-table surface and groundwater-flow patterns. Four hydrographs were created from periodic measurements of groundwater levels in wells on and around Cannon AFB to provide information about historical groundwater-level changes and visualize trends from the water-level records. The long-term trend present in all four hydrographs is a steady decline in groundwater levels, with some areas declining faster than others. The groundwater-level change map presented in this study provides a visual representation of the change in groundwater level from the winter 2015 to winter 2020 measuring events. Results show that among corresponding wells measured in 2015 and 2020, 50.7 percent indicated a decline in water levels, 29.9 percent indicated neutral water levels, and 19.4 percent indicated a rise in water levels. The region to the north of the groundwater trough on Cannon AFB contained most of the groundwater-level rises, whereas the regions located near the trough and just west of Clovis, N. Mex., contained most of the declines. These results suggest that continued monitoring of declining groundwater levels in the area would provide valuable decision-support information for assessing the sustainability of this water resource.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The Machine Learning Asset Aggregation of the Preliminary Determination of Epicenters (MLAAPDE) dataset is a labeled waveform archive designed to enable rapid development of machine learning (ML) models used in seismic monitoring operations. MLAAPDE consists of more than 5.1 million recordings of 120 s long three‐component broadband waveform data (raw counts) for P, Pn, Pg, S, Sn, and Sg arrivals. The labeled catalog is collected from the U.S. Geological Survey National Earthquake Information Center’s (NEIC) Preliminary Determination of Epicenters bulletin, which includes local to teleseismic observations for earthquakes ∼M 2.5 and larger. Each arrival in the labeled dataset has been manually reviewed by NEIC staff. An accompanying Python module enables users to develop customized training datasets, which includes different time‐series lengths, distance ranges, sampling rates, and/or phase lists. MLAAPDE is distinct from other publicly available datasets in containing local (14%), regional (36%), and teleseismic (50%) observations, in which local, regional, and teleseismic distance are 0°–3°, 3°–30°, and 30°+, respectively. A recent version of the dataset is publicly available (see Data and Resources), and user‐specific versions can be generated locally with the accompanying software. MLAAPDE is an NEIC supported, curated, and periodically updated dataset that can contribute to seismological ML research and development.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220230021","usgsCitation":"Cole, H.M., Yeck, W.L., and Benz, H.M., 2023, MLAAPDE: A machine learning dataset for determining global earthquake source parameters: Seismological Research Letters, v. 94, no. 5, p. 2489-2499, https://doi.org/10.1785/0220230021.","productDescription":"11 p.","startPage":"2489","endPage":"2499","ipdsId":"IP-135344","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":427310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Cole, Hank M. 0000-0003-1684-9116","orcid":"https://orcid.org/0000-0003-1684-9116","contributorId":335228,"corporation":false,"usgs":true,"family":"Cole","given":"Hank","email":"","middleInitial":"M.","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":897818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":897819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897820,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70245088,"text":"70245088 - 2023 - The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology","interactions":[],"lastModifiedDate":"2023-06-15T14:07:24.755563","indexId":"70245088","displayToPublicDate":"2023-06-15T08:59:44","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":15221,"text":"Methods in Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"title":"The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology","docAbstract":"The Deepwater Horizon Open Ocean Trustee Implementation Group (OO TIG) released a Final Open Ocean Restoration Plan 2 in 2019, which included a project titled Developing a Gulf-wide Comprehensive Plan for In-water Sea Turtle Data Collection. This document, A Comprehensive Plan for In-water Sea Turtle Data Collection in the US Gulf of Mexico (Plan), is the culmination of that OO TIG project. This Plan serves as the OO TIG project’s technical report as well as a framework for a biologically and statistically-sound plan to support coordinated in-water sea turtle data collection in the United States (US) Gulf of Mexico (GoM) to determine sea turtle abundance and population trends.
The purpose of this Plan is to act as a guide for collecting biologically and statistically robust, in-water sea turtle data in a comprehensive, coordinated, and standardized fashion in the US GoM. Several sea turtle in-water monitoring efforts are underway in the GoM; however, additional coordination and standardization of these efforts will benefit current restoration and recovery objectives. These efforts will aid in restoration project design, assess long-term effectiveness of restoration activities, and create abundance and distribution baselines across the GoM. This Plan provides guidance for researchers investigating sea turtle abundance and demographic questions, as well as for management agencies and restoration planners.
A Steering Committee (SC) was assembled to develop this Plan and to recommend a coordinated approach to the formulation of an improved understanding of sea turtle population baselines in the GoM, from which determination of large-scale population changes, effects of specific threats (e.g., oil spills, anthropogenic hazards), and effects of changes in ocean conditions (e.g., climate change) can later be evaluated. In crafting this guidance, the SC considered species distribution and life history characteristics, spatial and logistical considerations, level of effort required to detect trends, methods available and the pros and cons of each, associated assumptions and biases with suggested monitoring methods, and standardization of data collection.
Given the current level of data available, the SC has recommended species monitoring in two main phases in neritic and oceanic waters, with additional recommended sampling for surface pelagic drift communities.
The two phases in this Plan focus on 1) monitoring a limited number of sites in the first 5 to 8 years, followed by 2) a refined monitoring design. To support implementation of this Plan, the SC also considered broader programmatic needs, including supplemental data collection, program and data management, potential international partnerships, program expansion, and applications including future technology.
","language":"English","publisher":"National Oceanic and Atmospheric Administration","usgsCitation":"NOAA, Department of the Interior, Hart, K., Plotkin, P.T., Sasso, C., and Witherington, B.E., 2023, A comprehensive plan for in-water sea turtle data collection in the US Gulf of Mexico, v, 69 p.","productDescription":"v, 69 p.","ipdsId":"IP-152105","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":418360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418340,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.gulfspillrestoration.noaa.gov/sites/default/files/In-Water%20Sea%20Turtle%20Plan_FINAL_v2.pdf"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.5264253558096,\n 30.24489967358521\n ],\n [\n -86.64298031887166,\n 30.53597189734414\n ],\n [\n -88.10641276491391,\n 30.749707588041076\n ],\n [\n -89.79330787057165,\n 30.154537825734877\n ],\n [\n -90.08670236321969,\n 29.96433178516294\n ],\n [\n -90.79587522647664,\n 29.472155439209743\n ],\n [\n -91.18028281506544,\n 29.819820106118726\n ],\n [\n -91.98695954730219,\n 30.005352165065304\n ],\n [\n -93.39181791789512,\n 30.19566964652458\n ],\n [\n -95.05441013086163,\n 29.88977578604954\n ],\n [\n -96.86686746427104,\n 28.69085626965058\n ],\n [\n -97.70099252595101,\n 27.766320123817778\n ],\n [\n -97.87263263903903,\n 27.175400696917563\n ],\n [\n -97.38684906161815,\n 25.941789648432064\n ],\n [\n -93.94956884192511,\n 25.988756403609997\n ],\n [\n -87.10787230454817,\n 26.888051400543716\n ],\n [\n -85.9227099016574,\n 29.639041703135902\n ],\n [\n -86.5264253558096,\n 30.24489967358521\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"NOAA","contributorId":257934,"corporation":true,"usgs":false,"organization":"NOAA","id":876026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Department of the Interior","contributorId":128058,"corporation":true,"usgs":false,"organization":"Department of the Interior","id":876027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":222407,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":875942,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plotkin, Pamela T.","contributorId":200818,"corporation":false,"usgs":false,"family":"Plotkin","given":"Pamela","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":876028,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sasso, Christopher","contributorId":209797,"corporation":false,"usgs":false,"family":"Sasso","given":"Christopher","affiliations":[{"id":37992,"text":"NOAA, National Marine Fisheries Service, Southeast Fisheries Science Center, Miami, FL, USA 33149","active":true,"usgs":false}],"preferred":false,"id":876029,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Witherington, Blair E.","contributorId":60117,"corporation":false,"usgs":true,"family":"Witherington","given":"Blair","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":876030,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248351,"text":"70248351 - 2023 - Modeling the spatial distribution of carcasses of eagles killed by wind turbines","interactions":[],"lastModifiedDate":"2023-09-08T12:09:37.770416","indexId":"70248351","displayToPublicDate":"2023-06-15T07:07:30","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the spatial distribution of carcasses of eagles killed by wind turbines","docAbstract":"Currently, the US Fish and Wildlife Service makes eagle permitting and management decisions nationwide based on a limited understanding of the impacts of wind power generation on eagles, and the factors that influence risk at a given facility. Accurate estimates of eagle mortality at wind power facilities form the basis for comparing the magnitudes of mortality rates in different areas and for measuring the benefits of proposed methods of minimizing the collision-caused impacts to eagle populations. Simple counts of observed eagle carcasses at wind facilities are almost certainly underestimates of the true mortality because fatalities can be removed by scavengers, be missed by searchers, or fall outside searched areas. For the latter, models of relative carcass density as a function of distance from the turbine can be fit to observed carcass locations and used to estimate the proportion of carcasses expected to land within an area of any configuration beneath a turbine. In the USA, however, it has been difficult to estimate these models for large birds such as Bald Eagles (Haliaeetus leucocephalus) and Golden Eagles (Aquila chrysaetos) due to inadequate numbers of dead eagles found at any single facility. In this case, analysis of a surrogate species might be useful to inform carcass distributions. We chose to model the carcass distribution of White-tailed Eagles (Haliaeetus albicilla) in Norway as an informative surrogate for Bald Eagles and Golden Eagles in the USA. Our three best-fitting parametric models were very consistent in estimating that 50% (95% CI: 40–60%) of White-tailed Eagle carcasses land within approximately 42 m of the turbines that had 70-m hubs and approximately 40-m blades. Although our models were fit to data from White-tailed Eagles and not Bald or Golden Eagles, applying these models when calculating mortality impacts of wind developments on both eagle species will likely improve the accuracy of post-construction mortality estimates, particularly at sites where substantial areas may be unsearchable. Accurate post-construction mortality estimates can inform pre-construction fatality prediction models. Resource managers can determine whether their conditions are sufficiently similar to those we modeled to warrant the use of these models for Bald and Golden Eagle carcass distributions.
","language":"English","publisher":"BioOne","doi":"10.3356/JRR-21-53","usgsCitation":"Huso, M., Dalthorp, D., Mintz, J.M., Nygard, T., and May, R., 2023, Modeling the spatial distribution of carcasses of eagles killed by wind turbines: Journal of Raptor Research, v. 57, no. 3, p. 456-467, https://doi.org/10.3356/JRR-21-53.","productDescription":"12 p.","startPage":"456","endPage":"467","ipdsId":"IP-132647","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":420655,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Huso, Manuela 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":223969,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":882653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalthorp, Daniel 0000-0002-4815-6309","orcid":"https://orcid.org/0000-0002-4815-6309","contributorId":329585,"corporation":false,"usgs":false,"family":"Dalthorp","given":"Daniel","affiliations":[{"id":78668,"text":"PowerStats","active":true,"usgs":false}],"preferred":false,"id":882654,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mintz, Jeffrey Michael 0000-0003-4345-366X","orcid":"https://orcid.org/0000-0003-4345-366X","contributorId":225149,"corporation":false,"usgs":true,"family":"Mintz","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":882655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nygard, Torgeir","contributorId":203220,"corporation":false,"usgs":false,"family":"Nygard","given":"Torgeir","email":"","affiliations":[{"id":36585,"text":"Norwegian Institute for Natural Research","active":true,"usgs":false}],"preferred":false,"id":882656,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"May, Roel","contributorId":329586,"corporation":false,"usgs":false,"family":"May","given":"Roel","email":"","affiliations":[{"id":33046,"text":"Norwegian Institute for Nature Research","active":true,"usgs":false}],"preferred":false,"id":882657,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247936,"text":"70247936 - 2023 - Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat","interactions":[],"lastModifiedDate":"2023-08-24T11:54:15.452353","indexId":"70247936","displayToPublicDate":"2023-06-15T06:48:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat","docAbstract":"Citizen science is a cost-effective potential source of invasive species occurrence data. However, data quality issues due to unstructured sampling approaches may discourage the use of these observations by science and conservation professionals. This study explored the utility of low-structure iNaturalist citizen science data in invasive plant monitoring. We first examined the prevalence of invasive taxa in iNaturalist plant observations and sampling biases associated with these data. Using four invasive species as examples, we then compared iNaturalist and professional agency observations and used the two datasets to model suitable habitat for each species.
Hawai'i, USA.
To estimate the prevalence of invasive plant data, we compared the number of species and observations recorded in iNaturalist to botanical checklists for Hawai'i. Sampling bias was quantified along gradients of site accessibility, protective status and vegetation disturbance using a bias index. Habitat suitability for four invasive species was modelled in Maxent, using observations from iNaturalist, professional agencies and stratified subsets of iNaturalist data.
iNaturalist plant observations were biased towards invasive species, which were frequently recorded in areas with higher road/trail density and vegetation disturbance. Professional observations of four example invasive species tended to occur in less accessible, native-dominated sites. Habitat suitability models based on iNaturalist versus professional data showed moderate overlap and different distributions of suitable habitat across vegetation disturbance classes. Stratifying iNaturalist observations had little effect on how suitable habitat was distributed for the species modelled in this study.
Opportunistic iNaturalist observations have the potential to complement and expand professional invasive plant monitoring, which we found was often affected by inverse sampling biases. Invasive species represented a high proportion of iNaturalist plant observations, and were recorded in environments that were not captured by professional surveys. Combining the datasets thus led to more comprehensive estimates of suitable habitat.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13749","usgsCitation":"Dimson, M., Fortini, L., Tingley, M.W., and Gillespie, T., 2023, Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat: Diversity and Distributions, v. 29, no. 9, p. 1141-1156, https://doi.org/10.1111/ddi.13749.","productDescription":"16 p.","startPage":"1141","endPage":"1156","ipdsId":"IP-139923","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":443085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13749","text":"Publisher Index Page"},{"id":420108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"29","issue":"9","noUsgsAuthors":false,"publicationDate":"2023-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Dimson, Monica","contributorId":304630,"corporation":false,"usgs":false,"family":"Dimson","given":"Monica","email":"","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":881118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":881119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tingley, Morgan W","contributorId":328728,"corporation":false,"usgs":false,"family":"Tingley","given":"Morgan","email":"","middleInitial":"W","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":881120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillespie, Thomas W","contributorId":304639,"corporation":false,"usgs":false,"family":"Gillespie","given":"Thomas W","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":881121,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255122,"text":"70255122 - 2023 - Integrating community science and agency-collected monitoring data to expand monitoring capacity at large spatial scales","interactions":[],"lastModifiedDate":"2024-06-14T11:24:02.625488","indexId":"70255122","displayToPublicDate":"2023-06-15T06:22:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Integrating community science and agency-collected monitoring data to expand monitoring capacity at large spatial scales","docAbstract":"Monitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks. We describe a framework for integrating data from the eBird community science platform with agency-collected monitoring data using a multistate occupancy model. Our model accounts for the structural differences across datasets and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons (Gavia immer) in Washington State. A total of 766 sites had observation effort, of which 713 sites had only eBird effort, 26 sites had only Washington Department of Fish and Wildlife (WDFW) effort, and 27 sites had both. We predicted that the probability of occupancy was only 0.07 (95% Bayesian credible interval, BCI = 0.02–0.51) at the 2324 sites in our sampling frame, though the probability that Common Loons were breeding at occupied sites was 0.95 (95% BCI = 0.71–1.00). We found that probability of occupancy was positively related to waterbody size (probability of a positive effect = 0.88) and negatively related to an index of human influence (probability of a negative effect = 0.94). We found that probability of breeding at occupied sites was positively related to tree canopy cover (0.86), negatively related to elevation (0.99), and negatively related to barren, scrub/shrub, and herbaceous land cover (0.98). We found that state agency biologists were 16 times more likely to detect breeding Common Loons at a site than were eBird users (0.94, 95% BCI = 0.78–0.99 for agency biologists vs. 0.08, 95% BCI = 0.06–0.10 for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites, although evidence of reproduction was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency-collected data, if appropriate frameworks are developed to integrate these data sources.
When a notable earthquake occurs in the United States, a range of familiar real‐ and near‐real‐time products are produced by the U.S. Geological Survey (USGS) Advanced National Seismic System (ANSS), and made available via the ANSS Comprehensive Earthquake Catalog. For historical and early instrumental earthquakes, similar results and products are developed depending on data availability and event significance, drawing from published later studies. The year 2023 marked the ninetieth anniversary of the 11 March 1933 Long Beach, California, earthquake. This anniversary provided the impetus to update ANSS products, drawing on archived and published data. Here, we describe the updated ShakeMap, shaking recordings and intensities, and retrospective aftershock forecast for the Long Beach, California, earthquake. In effect we have developed standard, modern ANSS products for an earthquake that occurred 90 yr ago. Our results show that the distributions of both the ground motions, anchored by three strong‐motion recordings, and aftershock magnitudes are consistent with expectations for an M 6.4 mainshock in Southern California. We show that, notwithstanding possible limitations, instrumentally recorded accelerations from the closest station are consistent with predicted shaking and directly estimated macroseismic intensities. Updated data products have been added to the USGS event page, where they are available for download. Public‐facing products were also created for the anniversary and are freely available on the USGS website.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230015","usgsCitation":"Hough, S.E., Blair, J.L., Ellison, S., Graves, R., Haefner, S., Thompson, E.M., van der Elst, N., Page, M.T., and Wald, D.J., 2023, Modern products for a vintage event: An update on the 1933 Long Beach, California, earthquake: The Seismic Record, v. 3, no. 2, p. 171-181, https://doi.org/10.1785/0320230015.","productDescription":"11 p.","startPage":"171","endPage":"181","ipdsId":"IP-152269","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":487650,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230015","text":"Publisher Index Page"},{"id":482220,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Long Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.10057757657458,\n 34.481723829510074\n ],\n [\n -119.10057757657458,\n 33.30206417955914\n ],\n [\n -117.44400100782676,\n 33.30206417955914\n ],\n [\n -117.44400100782676,\n 34.481723829510074\n ],\n [\n -119.10057757657458,\n 34.481723829510074\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blair, J. 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This information could aide in the management and understanding of the Galena River and the contributions from the intervening 58-square-mile study area watershed. Constituent concentrations were characteristic for contemporary midwestern agricultural watersheds and did not display any notable high or low values. Concentrations of nitrogen were generally higher at the upstream station, whereas concentrations of phosphorus and suspended sediment were generally higher at the downstream station. Decreases in nutrient concentrations were observed at both stations during the study period, but there was no appreciable pattern in suspended-sediment concentrations. Constituent fluxes, particularly nitrogen, were higher at the downstream station, whereas fluxes of phosphorus and suspended sediment were higher at the upstream station during several high-flow events, indicating substantial contribution of particulate material upstream from the study area and potential sequestration within the study area reach of the Galena River. For all constituents, yields were typically higher at the upstream station during periods of increased streamflow and lower at the upstream station during periods of reduced streamflow. These data indicate that the constituent contributions are greater from within the study area than from the watershed upstream from the study area during periods of normal to low streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235044","collaboration":"Prepared in cooperation with the City of Galena, Illinois","usgsCitation":"Terrio, P.J., and Garcia, L.A., 2023, Nutrient and suspended-sediment concentrations, flux, and yields in the Galena River, Illinois, 2019–21: U.S. Geological Survey Scientific Investigations Report 2023–5044, 26 p., https://doi.org/10.3133/sir20235044.","productDescription":"Report: v, 26 p.; Dataset","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-146048","costCenters":[],"links":[{"id":500921,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114778.htm","linkFileType":{"id":5,"text":"html"}},{"id":418087,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235044/full"},{"id":418069,"rank":5,"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":418068,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5044/images/"},{"id":418067,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5044/sir20235044.XML"},{"id":418066,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5044/sir20235044.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":418065,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5044/coverthb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Galena River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.5,\n 42.5\n ],\n [\n -90.5,\n 42.3\n ],\n [\n -90.20,\n 42.3\n ],\n [\n -90.20,\n 42.5\n ],\n [\n -90.5,\n 42.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Birds are often obligate to specific habitats which can result in study areas with complex boundaries due to sudden changes in vegetation or other features. This can result in study areas with concave arcs or that include holes of unsuitable habitat such as lakes or agricultural fields. Spatial models used to produce species’ distribution and density estimates need to respect such boundaries to make informed decisions for species conservation and management. The soap film smoother is one model for complex study regions which controls the boundary behaviour, ensuring realistic values at the edges of the region. We apply the soap film smoother to account for boundary effects and compare it with thin plate regression spline (TPRS) smooth and design-based conventional distance sampling methods to produce abundance estimates from point-transect distance sampling collected data on Hawai‘i ‘Ākepa Loxops coccineus in the Hakalau Forest Unit of the Big Island National Wildlife Refuge Complex, Hawai‘i Island, USA. The soap film smoother predicted zero or near zero densities in the northern part of the domain and two hotspots (in the southern and central parts of the domain). Along the boundary the soap film model predicted relatively high densities where ‘Ākepa occur in the adjacent forest and near zero elsewhere. The design-based and soap film abundance estimates were nearly identical. The width of the soap film confidence interval was 16.5% and 0.8% wider than the width of the TPRS smooth and design-based confidence intervals, respectively. The peaks in predicted densities along the boundary indicates leakage by the TPRS smooth. We provide a discussion of the statistical methods, biological findings and management implications of applying soap film smoothers to estimate forest bird population status.
Biodiversity is responsible for important ecological processes like productivity and ecosystem stability, and rare species are a major component of biodiversity. Rarity increases a species' vulnerability to disturbances and also makes them difficult to study. Globally, species of freshwater systems are some of the most threatened, and evaluation of rare freshwater species and their habitats is needed to help preserve natural flexibility and ecological function. We conducted an analysis of full fish communities of the upper St. Lawrence River and its major US tributaries, with the goals of determining species locations and abundances, associated environmental conditions, the distribution of distinct fish assemblages across the landscape (with emphasis on communities supporting rare species), and potential threats. From 2009 to 2015, the US Geological Survey (USGS) and Saint Regis Mohawk Tribe (SRMT) worked together using standardized methods to collect community samples within 4 different aquatic realms (shallow and deep lentic, and small and large lotic systems) and determine species-specific fish abundances, frequencies of occurrence, and associated habitat signatures and spatial distributions. Distinct fish assemblages and associated habitat conditions were objectively identified by multivariate and hypothesis-testing methods. We used a geographic information system (GIS) to spatially associate habitat, biotic, and landscape attributes within each stream reach throughout the study area, facilitating quantification of distribution patterns. Comparisons with historical data provided estimates of loss or gain of threatened and endangered species (T&E) colonies. We developed a disturbance index to highlight potential threats to aquatic species. More than 140,000 fishes of 87 species were collected from a total of 1140 sample sites, covering 278 stream reaches, including the endangered Notropis anogenus (Pugnose Shiner), and threatened Hiodon tergisus (Mooneye), Etheostoma pellucidum (Eastern Sand Darter), and Acipenser fulvescens (Lake Sturgeon). We identified 50 distinct fish assemblages differing in species composition, abundance, and/or diversity, but only 13 of those assemblages included a T&E species. The rareness, extent, and patchiness of fish assemblages created a mosaic of fish communities across the landscape, from headwaters to the mainstem of the St. Lawrence River. Comparisons with historic surveys (1978–2008) showed a stable number of T&E species colonies or an increase for some species. The geographic distribution of multimetric disturbance index values showed where combinations of disturbances to fish habitats might affect rare fish species and aquatic communities in the region. The species–habitat associations and fish assemblage distributions can be used for evaluation of species, communities, or habitats that may need protection or restoration.
Offutt Air Force Base, south of Omaha, Nebraska, experienced major flooding during the March 2019 flood event because of the proximity of the base to the confluence of the Missouri River and nearby tributaries, which exceeded flood stages. Postflood, standing water remained through much of the year, attracting waterfowl and other birds and posing a major safety risk to aircraft. The U.S. Geological Survey, in cooperation with the U.S. Air Force, began a study in 2020 to describe the hydrologic processes that affect the persistence of standing water on Offutt Air Force Base.
Existing site data, reviewed in concert with groundwater and surface-water elevation data collected for the study, indicate varying hydrologic responses between two areas of concern (AOCs), which can be linked to differences in subsurface geology and changes in flows of the Missouri River. An inundation map indicated that standing water would be present throughout Papillion Creek Ditch in AOC 1 and would extend upstream to AOC 2 during flow events greater than 771 cubic feet per second. A U.S. Army Corps of Engineers Hydrologic Engineering Center-River Analysis System model and a flow-duration analysis were used to infer that many of the surface-water drainage problems experienced in 2019 were the result of backwater conditions caused by higher streamflows in the Missouri River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235053","collaboration":"Prepared in cooperation with the U.S. Air Force, Offutt Air Force Base","usgsCitation":"Hobza, C.M., and Strauch, K.R., 2023, Floodwater drainage assessment of Offutt Air Force Base, Nebraska, 2020–22: U.S. Geological Survey Scientific Investigations Report 2023–5053, 31 p., https://doi.org/10.3133/sir20235053.","productDescription":"Report: vii, 31 p.; Data Release; Dataset","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-138559","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":418093,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235053/full"},{"id":417735,"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":417734,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KYD1CX","text":"USGS data release","linkHelpText":"Water-surface and groundwater-level elevations on and near Offutt Air Force Base, Nebraska, summer 2020 and spring 2021"},{"id":417718,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5053/images"},{"id":417717,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5053/sir20235053.XML"},{"id":417707,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5053/coverthb1.jpg"},{"id":417713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5053/sir20235053.pdf","text":"Report","size":"5.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5053"}],"country":"United States","state":"Nebraska","otherGeospatial":"Offutt Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.09742410665552,\n 41.18555144700602\n ],\n [\n -96.09742410665552,\n 41.06019915604605\n ],\n [\n -95.87125037103594,\n 41.06019915604605\n ],\n [\n -95.87125037103594,\n 41.18555144700602\n ],\n [\n -96.09742410665552,\n 41.18555144700602\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
In recent years, Arkansas has coordinated with Federal and local partners to improve the quality and availability of high-resolution elevation data for the State. With high-quality elevation data, Arkansas can improve services offered to the public and within government, resulting in better quality of life, improved public safety, and higher return on investments. Elevation data are beneficial in numerous business activities, including agriculture and precision farming, flood risk management, geologic resource assessment and hazard mitigation, infrastructure and construction management, urban and regional planning, and natural resources conservation. 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. The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Arkansas. This fact sheet shows the status of available and in-progress 3DEP baseline lidar data in Arkansas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233018","programNote":"National Geospatial Program","usgsCitation":"Cretini, C., 2023, The 3D Elevation Program—Supporting Arkansas's economy: U.S. Geological Survey Fact Sheet 2023–3018, 2 p., https://doi.org/10.3133/fs20233018.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-119442","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":417809,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3018/fs20233018.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3018"},{"id":417808,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3018/coverthb.jpg"},{"id":499684,"rank":3,"type":{"id":36,"text":"NGMDB Index 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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Ol Doinyo Lengai, located in the southern Eastern Branch of the East African Rift had several eruptive episodes with ash falls and lava flows (VEI 3) that caused damage to the nearby communities between 2007 and 2010. The volcano is remote and access is difficult. Although this volcano has been studied for decades, its plumbing system is still poorly understood, in part, because of the lack of precise observations of surface deformation during periods of quiet and unrest. This study investigates the volcanic plumbing system of Ol Doinyo Lengai and its surroundings using data from the network of permanent Global Navigation Satellite System (GNSS) sites monitoring the volcano (the TZVOLCANO network) around the flanks of the volcano and Interferometric Synthetic Aperture Radar (InSAR) observations. We constrain surface motions using 6 GNSS sites distributed around Ol Doinyo Lengai, operating between 2016 and 2021, and InSAR data covering nearly the same time period. Because of the complex local tectonics, the interpretation of the deformation pattern is not straightforward. We first invert the GNSS deformation and InSAR observations independently to infer potential deformation sources. Then we perform a joint inversion of both GNSS and InSAR datasets to verify our findings. We compare the results from the joint inversion with the results from inverting each dataset independently. The GNSS, InSAR, and joint inversion results point to a deflating source, located east of Ol Doinyo Lengai and southwest of the dormant volcano Gelai at a depth of 3.49 ± 0.03 km (GNSS inversion), 5.2 ± 1.2 km (InSAR inversion) and 3.49 ± 0.06 km (joint inversion) relative to the summit (vent) and with a volume change ∆V of −0.04 ± 0.05 × 106 m3 (GNSS inversion), −0.39 ± 0.29 × 106 m3 (InSAR inversion), and − 0.04 ± 0.01 × 106 m3 (joint inversion). Although this is non-unique modeling of geodetic datasets with small signals, the inversion results suggest that Ol Doinyo Lengai could be fed by an offset multi-reservoir system that includes a shallow magma reservoir (<5 km) east of Ol Doinyo Lengai, possibly connected to a deeper magma reservoir.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2023.107821","usgsCitation":"Daud, N., Stamps, S., Battaglia, M., Huang, M., Saria, E., and Ji, K., 2023, Elucidating the magma plumbing system of Ol Doinyo Lengai (Natron Rift, Tanzania) Using satellite geodesy and numerical modeling: Journal of Volcanology and Geothermal Resources, v. 438, 107821, 16 p., https://doi.org/10.1016/j.jvolgeores.2023.107821.","productDescription":"107821, 16 p.","ipdsId":"IP-145102","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":443108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2023.107821","text":"Publisher Index Page"},{"id":418585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tanzania","otherGeospatial":"Natron Rift, Ol Doinyo Lengai","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 35.69259436508915,\n -2.339521063494246\n ],\n [\n 35.69259436508915,\n -3.369145554578978\n ],\n [\n 36.21613235881256,\n -3.369145554578978\n ],\n [\n 36.21613235881256,\n -2.339521063494246\n ],\n [\n 35.69259436508915,\n -2.339521063494246\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"438","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daud, Ntambila","contributorId":224260,"corporation":false,"usgs":false,"family":"Daud","given":"Ntambila","email":"","affiliations":[],"preferred":false,"id":876465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stamps, Sarah 0000-0002-3531-1752","orcid":"https://orcid.org/0000-0002-3531-1752","contributorId":299923,"corporation":false,"usgs":false,"family":"Stamps","given":"Sarah","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":876466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":876467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huang, Mong-Han","contributorId":192699,"corporation":false,"usgs":false,"family":"Huang","given":"Mong-Han","email":"","affiliations":[],"preferred":false,"id":876468,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Saria, Elifuraha","contributorId":315415,"corporation":false,"usgs":false,"family":"Saria","given":"Elifuraha","email":"","affiliations":[{"id":68311,"text":"Ardhi University, Tanzania","active":true,"usgs":false}],"preferred":false,"id":876469,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ji, Kang-Hyeun","contributorId":315416,"corporation":false,"usgs":false,"family":"Ji","given":"Kang-Hyeun","email":"","affiliations":[{"id":68313,"text":"Korea Institute for Geoscience","active":true,"usgs":false}],"preferred":false,"id":876470,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70244311,"text":"70244311 - 2023 - One Ranney well can make a difference: The impacts of a radial collector well on groundwater level and quality in the Cedar River alluvial aquifer","interactions":[],"lastModifiedDate":"2023-06-13T12:46:49.181649","indexId":"70244311","displayToPublicDate":"2023-06-13T07:33:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1866,"text":"Groundwater Monitoring & Remediation","active":true,"publicationSubtype":{"id":10}},"title":"One Ranney well can make a difference: The impacts of a radial collector well on groundwater level and quality in the Cedar River alluvial aquifer","docAbstract":"The City of Cedar Rapids, Iowa, depends on groundwater from the Cedar River alluvial aquifer for residential and industrial use. In 2020, the city completed an additional radial collector well, or Ranney well, and was concerned that pumping from the well at high rates may lower water level elevations in the aquifer, reduce yields from nearby production wells, and change the quality of produced water. During an operational test of the well's pumps, the U.S. Geological Survey and the city collected water level and water quality data to evaluate the effects of increased pumping rates on the aquifer and nearby production wells. Results indicated that a high rate of pumping from the new well caused sustained declines in water levels near the well and other nearby production wells, and, if maintained, the aquifer water level in the vicinity would continue to decline to levels observed during the 2012 drought. Aquifer specific conductance and temperature were altered and matched trends and values of the river, and river-to-well travel time was shortened from 7-17 days to about 3 days. Results may also provide insights to other municipal water resource managers when considering wellfield design, production expectations, and long-term management strategies for radial collector well production during drought, low streamflow, and times when high concentrations of nitrate-N or organic pesticide compounds in the river may limit production options.","language":"English","publisher":"John Wiley & Sons, Inc","doi":"10.1111/gwmr.12560","usgsCitation":"Haj, A.E., Gruhn, L.R., and Kalkhoff, S.J., 2023, One Ranney well can make a difference: The impacts of a radial collector well on groundwater level and quality in the Cedar River alluvial aquifer: Groundwater Monitoring & Remediation, v. 43, no. 2, p. 51-60, https://doi.org/10.1111/gwmr.12560.","productDescription":"10 p.","startPage":"51","endPage":"60","ipdsId":"IP-134157","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":443114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwmr.12560","text":"Publisher Index Page"},{"id":418049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","city":"Cedar Rapids","otherGeospatial":"Cedar River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.75569134574914,\n 42.02224155975952\n ],\n [\n -91.75569134574914,\n 41.99154441740589\n ],\n [\n -91.70486017024997,\n 41.99154441740589\n ],\n [\n -91.70486017024997,\n 42.02224155975952\n ],\n [\n -91.75569134574914,\n 42.02224155975952\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Haj, Adel E. 0000-0002-3377-7161 ahaj@usgs.gov","orcid":"https://orcid.org/0000-0002-3377-7161","contributorId":147631,"corporation":false,"usgs":true,"family":"Haj","given":"Adel","email":"ahaj@usgs.gov","middleInitial":"E.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875328,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gruhn, Lance R. 0000-0002-7120-3003 lgruhn@usgs.gov","orcid":"https://orcid.org/0000-0002-7120-3003","contributorId":219710,"corporation":false,"usgs":true,"family":"Gruhn","given":"Lance","email":"lgruhn@usgs.gov","middleInitial":"R.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kalkhoff, Stephen J. 0000-0003-4110-1716 sjkalkho@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-1716","contributorId":1731,"corporation":false,"usgs":true,"family":"Kalkhoff","given":"Stephen","email":"sjkalkho@usgs.gov","middleInitial":"J.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875329,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70244266,"text":"dr1179 - 2023 - Quality of surface water in Missouri, water year 2021","interactions":[],"lastModifiedDate":"2026-02-04T20:11:44.929282","indexId":"dr1179","displayToPublicDate":"2023-06-12T09:58:17","publicationYear":"2023","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":"1179","displayTitle":"Quality of Surface Water in Missouri, Water Year 2021","title":"Quality of surface water in Missouri, water year 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, maintains a statewide group of stations known as the Ambient Water-Quality Monitoring Network, which includes selected streams and springs in Missouri. During water year 2021 (October 1, 2020, through September 30, 2021), the U.S. Geological Survey collected water-quality data at 72 stations: 70 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Water Quality Network stations. Four of the stations have data from additional sampling completed in cooperation with the U.S. Army Corps of Engineers. Water-quality data provided in this report include dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds. Monitoring stations have been classified based on the physiographic province or primary land use in the drainage basin or based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) monitored. A summary of hydrologic conditions, including peak streamflows, monthly mean streamflows, and 7-day low flows, also is provided for representative streamgages in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1179","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Markland, K.M., 2023, Quality of surface water in Missouri, water year 2021: U.S. Geological Survey Data Report 1179, 24 p., https://doi.org/10.3133/dr1179.","productDescription":"Report: vii, 24 p.; Dataset","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-142715","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499554,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114770.htm","linkFileType":{"id":5,"text":"html"}},{"id":418003,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1179/full"},{"id":417977,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
To study population dynamics, ecologists and wildlife biologists typically use relative abundance data, which may be subject to temporal preferential sampling. Temporal preferential sampling occurs when the times at which observations are made and the latent process of interest are conditionally dependent. To account for preferential sampling, we specify a Bayesian hierarchical abundance model that considers the dependence between observation times and the ecological process of interest. The proposed model improves relative abundance estimates during periods of infrequent observation and accounts for temporal preferential sampling in discrete time. Additionally, our model facilitates posterior inference for population growth rates and mechanistic phenometrics. We apply our model to analyze both simulated data and mosquito count data collected by the National Ecological Observatory Network. In the second case study, we characterize the population growth rate and relative abundance of several mosquito species in the Aedes genus. Supplementary materials accompanying this paper appear on-line.
Freshwater mussels (order Unionida) play a key role in freshwater systems as ecosystem engineers and indicators of aquatic ecosystem health. The fauna is globally imperilled due to a diversity of suspected factors; however, causes for many population declines and mortality events remain unconfirmed due partly to limited health assessment tools. Mussel-monitoring activities often rely on population-level measurements, such as abundance and age structure, which reflect delayed responses to environmental conditions. Measures of organismal health would enable preemptive detection of declining condition before population-level effects manifest. Metabolomic analysis can identify shifts in biochemical pathways in response to stressors and changing environmental conditions; however, interpretation of the results requires information on inherent variability of metabolite concentrations in mussel populations. We targeted metabolites in the haemolymph of two common mussels, Lampsilis cardium and Lampsilis siliquoidea, from three Indiana streams (USA) using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectroscopy. The influence of species, stream and sex on metabolite variability was examined with distance-based redundancy analysis. Metabolite variability was most influenced by species, followed by site and sex. Inter- and intraspecies metabolite variability among sexes was less distinct than differences among locations. We further categorized metabolites by occurrence and variability in mussel populations. Metabolites with high occurrence (Categories 1 and 2) included those indicative of energy status (catabolism versus anabolism; arginine, proline, carnitine, nicotinic acid, pantothenic acid), oxidative stress (proline, glutamine, glutamate) and protein metabolism (thymidine, cytidine, inosine). Metabolites with lower occurrence (Category 3) are constituents of assorted metabolic pathways and can be important biomarkers with additional temporal sampling to characterize their variability. These data provide a reference for future temporal (before/after) monitoring and for studies of stressor–metabolite linkages in freshwater mussels.
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