Water is an increasingly precious resource in California as years of drought, climate change, pollution, as well as an expanding population have all stressed the state's drinking water supplies. Currently, there are increasing concerns about whether regulated and unregulated contaminants in drinking water are linked to a variety of human-health outcomes particularly in socially disadvantaged communities with a history of health risks. To begin to address this data gap by broadly assessing contaminant mixture exposures, the current study was designed to collect tapwater samples from communities in Gold Country, the San Francisco Bay Area, two regions of the Central Valley (Merced/Fresno and Kern counties), and southeast Los Angeles for 251 organic chemicals and 32 inorganic constituents. Sampling prioritized low-income areas with suspected water quality challenges and elevated breast cancer rates. Results indicated that mixtures of regulated and unregulated contaminants were observed frequently in tapwater throughout the areas studied and the types and concentrations of detected contaminants varied by region, drinking-water source, and size of the public water system. Multiple exceedances of enforceable maximum contaminant level(s) (MCL), non-enforceable MCL goal(s) (MCLG), and other health advisories combined with frequent exceedances of benchmark-based hazard indices were also observed in samples collected in all five of the study regions. Given the current focus on improving water quality in socially disadvantaged communities, our study highlights the importance of assessing mixed-contaminant exposures in drinking water at the point of consumption to adequately address human-health concerns (e.g., breast cancer risk). Data from this pilot study provide a foundation for future studies across a greater number of communities in California to assess potential linkages between breast cancer rates and tapwater contaminants.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2024.122485","usgsCitation":"Smalling, K., Romanok, K., Bradley, P., Hladik, M.L., Gray, J.L., Kanagy, L.K., McCleskey, R., Stavreva, D.A., Alexander-Ozinskas, A.K., Alonso, J., Avila, W., Breitmeyer, S.E., Bustillo, R., Gordon, S.E., Hager, G.L., Jones, R.R., Kolpin, D., Newton, S., Reynolds, P., Sloop, J., Ventura, A., Von Behren, J., Ward, M.H., and Solomon, G.M., 2024, Mixed contaminant exposure in tapwater and the potential implications for human-health in disadvantaged communities in California: Water Research, v. 267, 122485, 18 p., https://doi.org/10.1016/j.watres.2024.122485.","productDescription":"122485, 18 p.","ipdsId":"IP-132707","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":466881,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2024.122485","text":"Publisher Index Page"},{"id":462746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Callifornia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.4780141117476,\n 33.38214013511279\n ],\n [\n -117.0137634893847,\n 34.63804038963741\n ],\n [\n -118.9178740720775,\n 36.41544295333733\n ],\n [\n -121.31207993232795,\n 39.36561371857371\n ],\n [\n -123.45430122708109,\n 38.55271441652263\n ],\n [\n -122.39081480148963,\n 37.237131631622205\n ],\n [\n -121.82420632584103,\n 36.28237723543127\n ],\n [\n -119.35559571042745,\n 34.63804973561979\n ],\n [\n -118.74189049260241,\n 34.120808464956255\n ],\n [\n -118.30821804827679,\n 33.74109773655418\n ],\n [\n -117.4780141117476,\n 33.38214013511279\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"267","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":205651,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":205658,"corporation":false,"usgs":true,"family":"Gray","given":"James","email":"","middleInitial":"L.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":915373,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kanagy, Leslie K. 0000-0001-5073-8538 lkkanagy@usgs.gov","orcid":"https://orcid.org/0000-0001-5073-8538","contributorId":4543,"corporation":false,"usgs":true,"family":"Kanagy","given":"Leslie","email":"lkkanagy@usgs.gov","middleInitial":"K.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":915374,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McCleskey, R. 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2024 - Ore mineralization in the Mofete and San Vito geothermal fields, Campi Flegrei volcanic complex, Naples, Italy","interactions":[],"lastModifiedDate":"2025-02-11T15:12:16.677353","indexId":"70263442","displayToPublicDate":"2024-10-04T08:06:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Ore mineralization in the Mofete and San Vito geothermal fields, Campi Flegrei volcanic complex, Naples, Italy","docAbstract":"The Mofete and San Vito geothermal fields, located west of Naples, Italy, are part of the Campi Flegrei volcanic complex. In the 1970s, exploratory wells were drilled to a depth of ~3000 m in an attempt to locate high-enthalpy fluids for potential power production. Drill core samples from Mofete wells (MF1, MF2, and MF5) and from San Vito wells (SV1 and SV3) contain authigenic ore mineralization. Pyrite, pyrrhotite, and galena are abundant. Less common are chalcopyrite, sphalerite, arsenopyrite, and scheelite; rare are millerite, violarite, native bismuth, tellurobismuthite, cassiterite, molybdenite, and acanthite. Mineral chemistry was determined by electron microprobe wavelength dispersive spectroscopy aided by a scanning electron microscope equipped with energy-dispersive spectroscopy. The mineral assemblage suggests a low sulfidation environment and the absence of pyrrhotite in the MF1 well and upper part of the SV1 well indicates variable sulfur activity. Both molybdenite and scheelite were identified in samples SV1–2860 and SV3–2353 and scheelite in the SV3 well is zoned with variable Mo6+ content; low Mo6+ zones show blue cathodoluminescence, whereas, zones with high Mo6+ content are yellow to brown. Zoned scheelite and the occurrence of both Mo-bearing minerals attest to the variability of ƒO2 and ƒS2 in the geothermal fluid.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2024.107556","usgsCitation":"Belkin, H.E., McAleer, R.J., and De Vivo, B., 2024, Ore mineralization in the Mofete and San Vito geothermal fields, Campi Flegrei volcanic complex, Naples, Italy: Journal of Geochemical Exploration, v. 265, 107556, 16 p., https://doi.org/10.1016/j.gexplo.2024.107556.","productDescription":"107556, 16 p.","ipdsId":"IP-022383","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":488062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gexplo.2024.107556","text":"Publisher Index Page"},{"id":481925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","city":"Naples","otherGeospatial":"Campi Flegrei volcanic complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 14.083578245698646,\n 40.89040097650229\n ],\n [\n 14.083578245698646,\n 40.79646405677639\n ],\n [\n 14.291918908312113,\n 40.79646405677639\n ],\n [\n 14.291918908312113,\n 40.89040097650229\n ],\n [\n 14.083578245698646,\n 40.89040097650229\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"265","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Belkin, Harvey E. 0000-0001-7879-6529","orcid":"https://orcid.org/0000-0001-7879-6529","contributorId":190267,"corporation":false,"usgs":false,"family":"Belkin","given":"Harvey","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":927008,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":927009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Vivo, Benedetto","contributorId":350796,"corporation":false,"usgs":false,"family":"De Vivo","given":"Benedetto","affiliations":[{"id":83833,"text":"3Pegaso Online University, Piazza Trieste e Trento 48, 80132 Naples, Italy devivob@libero.it","active":true,"usgs":false}],"preferred":false,"id":927010,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259395,"text":"70259395 - 2024 - Arctic fishes reveal patterns in radiocarbon age across habitats and with recent climate change","interactions":[],"lastModifiedDate":"2024-11-22T16:13:19.150412","indexId":"70259395","displayToPublicDate":"2024-10-04T06:30:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Arctic fishes reveal patterns in radiocarbon age across habitats and with recent climate change","docAbstract":"Climate change alters the sources and age of carbon in Arctic food webs by fostering the release of older carbon from degrading permafrost. Radiocarbon (14C) traces carbon sources and age, but data before rapid warming are rare and limit assessments over time. We capitalized on 14C data collected ~ 40 years ago that used fish as natural samplers by resampling the same species today. Among resampled fish, those using freshwater food webs had the oldest 14C ages (> 1000 yr BP), while those using marine food webs had the youngest 14C ages (near modern). One migratory species encompassed the entire range of 14C ages because juveniles fed in freshwater streams and adults fed in offshore marine habitats. Over ~ 40 yr, average 14C ages of freshwater and marine feeding fish shifted closer to atmospheric values, suggesting a potential influence from “greening of the Arctic.”
Data from wadeable streams collected by monitoring programs are used to assess watershed condition status and trends. Federally managed programs collect a suite of similar habitat measurements using compatible methods and produce individual program datasets for their prescribed geographic and temporal range. We identified four programs that produce similar data: the Bureau of Land Management Assessment, Inventory, and Monitoring lotic division, the U.S. Environmental Protection Agency National Aquatic Resource Surveys National Rivers and Streams Assessment survey section, the Federal interagency Aquatic and Riparian Effectiveness Monitoring Program, and the PacFish/InFish Biological Opinion Monitoring Program. Their datasets answer agency-specific management questions and fulfill reporting requirements, but the datasets are not released in full, or at all, and in some cases, there was no method to integrate data from the four programs to provide data at a larger spatial scale.
The Pacific Northwest Aquatic Monitoring Partnership (PNAMP) led a working group of experts from the four monitoring programs to determine data compatibility, develop a Stream Habitat Metrics Integration (SHMI) data exchange standard, and integrate compatible wadeable stream data. The resulting SHMI data exchange standard contains a data mapping file used to transform data from the source program data to a conformed format based on a controlled vocabulary. After extensive discussions assessing and comparing program collection and analyses methods, the working group found 26 stream habitat metrics to be sufficiently comparable to be integrated into a meaningful dataset. Furthermore, a subset of PIBO MP data previously available only by request and AREMP data available only as a proprietary ESRI ArcGIS geodatabase were made publicly available in non-proprietary formats via the integrated SHMI dataset.
A selection of data from the four programs determined to be compatible among 14 datasets were filtered, transformed, standardized, and combined using R code to create the integrated SHMI dataset containing about 12,000 locations, 19,000 events, and 200,000 measurements from 2000 to 2022.
This report describes the SHMI data exchange standard and its development, the metric compatibility assessment, and the data integration process, so that others may reuse the SHMI data exchange standard and its components as well as the data integration processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm16B2","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Bureau of Land Management, and the U.S. Forest Service","usgsCitation":"Scully, R.A., Dlabola, E.K., Bayer, J.M., Heaston, E., Courtwright, J., Snyder, M.N., Hockman-Wert, D., Saunders, W.C., Blocksom, K.A., Hirsch, C., and Miller, S.W., 2024, A data exchange standard for wadeable stream habitat monitoring data: U.S. Geological Survey Techniques and Methods, book 16, chap. B2, 28 p., https://doi.org/10.3133/tm16B2.","productDescription":"Report: vii, 28 p.; Data Release; Software Release","onlineOnly":"Y","ipdsId":"IP-139478","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":497946,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117643.htm","linkFileType":{"id":5,"text":"html"}},{"id":462494,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/16/b2/images"},{"id":462493,"rank":5,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9KON2PK","text":"USGS software release","description":"USGS software release","linkHelpText":"- SHMI-DES"},{"id":462492,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J3P7SN","text":"USGS data release","description":"USGS data release","linkHelpText":"Wadeable stream habitat data integrated from multiple monitoring programs for the US from 2000–2022"},{"id":462491,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm16B2/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"TM 16-B2"},{"id":462490,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/16/b2/tm16b2.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 16-B2"},{"id":462495,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/16/b2/tm16b2.XML"},{"id":462489,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/16/b2/tm16b2.jpg"}],"contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th Street, Suite 400
Corvallis, Oregon 97330
This Research Topic compiles contemporary studies on cold seeps, hydrothermal vents, mud volcanoes, and related seafloor features that are associated with focused fluid emissions and the transfer of carbon, other chemical species, and sometimes heat from the geosphere to the ocean. Because these features sometimes tap fluids and gas originating kilometers below the seafloor, they provide an important window into deep processes that are otherwise inaccessible to scientists. At the shallow portion of their journey, migrating fluids nearing the seafloor contribute to a range of unique biological, physical, and chemical processes within the sediments themselves and at the sediment-water interface.
Seafloor fluid emissions play a critical role in global biogeochemical cycles, ocean chemistry, and possibly even climate change. Seafloor leakage points often emit hydrocarbon gases (especially methane and CO2) and are sometimes the loci for deposition of seafloor minerals that have economic value. A burgeoning area of research focuses on natural products generated at these features, seeking compounds with potential pharmaceutical or other applications.
Multidisciplinary studies have become routine for characterization of seafloor fluid emission sites, attesting to the inseparability of geologic, physical, chemical, and biological processes in these settings. It is increasingly common for researchers to combine in a single research cruise: subbottom imaging and seafloor mapping; porewater and water column geochemistry and gas sampling; sediment retrieval for lithologic, biostratigraphic, and solid phase analyses; and studies of benthic and subseafloor communities at the microbial to macrofaunal scales. This multidisciplinary approach has the advantage of ensuring the spatial and temporal coincidence of surveys and samples, an important factor at highly dynamic seafloor fluid emission sites. In addition, researchers often use remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), or human-occupied vehicles (HOVs) to record video of the seafloor, compile photomosaics, collect targeted samples, and survey with high-resolution geophysical near-seafloor systems, providing a degree of detail about seafloor fluid emission sites that is unprecedented compared to most areas of the deep ocean. While rarer, long-term cabled observatories or shorter-term deployments of portable observatories are also used at some loci for seafloor fluid flux and are particularly helpful for capturing temporal variations at these dynamic features.
Here we summarize the Research Topic’s contribution to multidisciplinary seafloor emission studies in the categories of cold seeps, mud volcanoes, and hydrothermal vents. Figure 1 shows the geographic distribution of the studies in this Research Topic and key features referred to in this Introduction.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2024.1496572","usgsCitation":"Snyder, G.T., Thurber, A.R., Dupre, S., Ketzer, M., and Ruppel, C.D., 2024, Editorial: From cold seeps to hydrothermal vents: Geology, chemistry, microbiology, and ecology in marine and coastal environments: Frontiers in Earth Science, v. 12, 1496572, 5 p., https://doi.org/10.3389/feart.2024.1496572.","productDescription":"1496572, 5 p.","ipdsId":"IP-170113","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466883,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2024.1496572","text":"Publisher Index Page"},{"id":464121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2024-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Snyder, Glen T.","contributorId":299211,"corporation":false,"usgs":false,"family":"Snyder","given":"Glen","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":918541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thurber, Andrew R.","contributorId":346259,"corporation":false,"usgs":false,"family":"Thurber","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":918542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dupre, Stephanie","contributorId":346260,"corporation":false,"usgs":false,"family":"Dupre","given":"Stephanie","email":"","affiliations":[{"id":82806,"text":"Institut Français de Recherche pour l'Exploitation de la Mer","active":true,"usgs":false}],"preferred":false,"id":918543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ketzer, Marcelo","contributorId":346261,"corporation":false,"usgs":false,"family":"Ketzer","given":"Marcelo","email":"","affiliations":[{"id":49394,"text":"Linnaeus University","active":true,"usgs":false}],"preferred":false,"id":918544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918545,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263272,"text":"70263272 - 2024 - New insights on the origin of the Richardson-Richards equation","interactions":[],"lastModifiedDate":"2025-02-04T15:15:13.461055","indexId":"70263272","displayToPublicDate":"2024-10-03T08:11:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"New insights on the origin of the Richardson-Richards equation","docAbstract":"The Richardson-Richards equation (RRE), despite known shortcomings especially in regard to preferential flow, provides the basis of the vast majority of unsaturated flow models in use today. L.F. Richardson published this equation in 1922, nine years before L.A. Richards. Whereas Richards approached this problem directly from the groundbreaking developments of Edgar Buckingham, Richardson, surprisingly, cited as his starting point only the earlier work of L.J. Briggs. Collectively, these four scientists’ published and unpublished work reveals that: (1) Briggs’ work, though qualitative, captured the essential physical principles needed for quantifying unsaturated flow; (2) Buckingham came very close to deriving the RRE and explained why he stopped short of doing so; (3) derivation of the RRE from the work of either Briggs or Buckingham required only modest developmental work; and (4) besides deriving the RRE, Richards carried through much of the experimental agenda that Buckingham considered a necessary precursor to mathematical treatment.","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02626667.2024.2404714","usgsCitation":"Nimmo, J.R., 2024, New insights on the origin of the Richardson-Richards equation: Hydrological Sciences Journal, v. 69, no. 15, p. 2153-2158, https://doi.org/10.1080/02626667.2024.2404714.","productDescription":"6 p.","startPage":"2153","endPage":"2158","ipdsId":"IP-168093","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":487616,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02626667.2024.2404714","text":"Publisher Index Page"},{"id":481662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"15","noUsgsAuthors":false,"publicationDate":"2024-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":926109,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70260487,"text":"70260487 - 2024 - Dust in the Critical Zone: North American case studies","interactions":[],"lastModifiedDate":"2024-11-27T16:03:50.34186","indexId":"70260487","displayToPublicDate":"2024-10-02T10:06:04","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Dust in the Critical Zone: North American case studies","docAbstract":"The dust cycle facilitates the exchange of particles among Earth's major systems, enabling dust to traverse ecosystems, cross geographic boundaries, and even move uphill against the natural flow of gravity. Dust in the atmosphere is composed of a complex and ever-changing mixture that reflects the evolving human footprint on the landscape. The emission, transport, and deposition of dust interacts with and connects Critical Zone processes at all spatial and temporal scales. Landscape properties, land use, and climatic factors influence the wind erosion of soil and nutrient loss, which alters the long-term ecological dynamics at erosional locations. Once in the atmosphere, dust particles influence the amount of solar radiation reaching Earth, and interact with longwave (terrestrial) radiation, with cascading effects on the climate system. Finally, the wet and dry deposition of particles influences ecosystem structure, composition, and function over both short and long-term scales. Tracking dust particles from source to sink relies on monitoring and measurement of the geochemical composition and size distribution of the particles, space-borne and ground-based remote sensing, and dust modeling. Dust is linked to human systems via land use and policies that contribute to dust emissions and the health-related consequences of particulate loads and composition. Despite the significant influence dust has in shaping coupled natural-human systems, it has not been considered a key component of the Critical Zone. Here, we demonstrate that dust particles should be included as a key component of the Critical Zone by outlining how dust interacts with and shapes Earth System processes from generation, through transport, to deposition. We synthesize current understanding from global research and identify critical data and knowledge gaps while showcasing case studies from North America.
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2018–21","interactions":[],"lastModifiedDate":"2024-12-03T19:55:55.265095","indexId":"sir20245077","displayToPublicDate":"2024-10-01T15:01:27","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5077","displayTitle":"Quantifying Fine Sediment Infiltration in Spawning Gravel Used by Chinook Salmon (Oncorhynchus tshawytscha) in the Sauk River Basin, Washington, 2018–21","title":"Quantifying fine sediment infiltration in spawning gravel used by Chinook salmon (Oncorhynchus tshawytscha) in the Sauk River Basin, Washington, 2018–21","docAbstract":"Fine sediment can infiltrate into river substrate that salmonid fish species (Oncorhynchus spp.) use to spawn. High levels of sediment infiltration can increase egg-to-fry mortality, which corresponds to the period when salmonids are still residing in the subsurface gravels. This study quantifies fine sediment infiltration of Chinook salmon (Oncorhynchus tshawytscha) spawning habitat during the egg-to-fry emergence period over three years in the Sauk River, which has naturally high fine sediment loads and important native salmon populations. Additionally, this study qualitatively assesses how grain size distribution of the riverbed and adjacent gravel bars compare to grain size distribution following fine sediment infiltration to evaluate if riverbed or gravel bar grain size distributions may provide information on the potential for fine sediment infiltration in spawning gravels.
Fine sediment infiltration into spawning gravels was quantified using sediment boxes and infiltration bags that were installed in artificial redds constructed at known Chinook salmon spawning locations at three study sites on the Sauk River over the expected egg-to-fry period. Over the three-year study period (August 2018–April 2021), fraction finer of sediment (grain sizes of less than two millimeters), ranged from 0.12 to 0.23 across the three study sites and years. Based on a comparison of field observations from this study and percentage egg-to-fry survival curves found in the literature, the expected survival for Chinook salmon eggs in the Sauk River is roughly 30 percent. Expected survival increases to approximately 90 percent if eggs are eyed and thus farther along in their development. Our field study did not evaluate the progression of infiltration, so it is unknown if observed fine sediment infiltration was at this relatively high rate during the period that corresponded to early egg development, when eggs are more sensitive to fine sediment infiltration. Dissolved oxygen in the gravels is largely above critically low levels (4 milligrams per liter) during sensitive periods corresponding to egg development and is interpreted not to affect egg-to-fry mortality. Active channel morphology in the middle reaches of the Sauk River may pose an additional challenge to pre-emergence survival. Channel change, deposition, and potential scour at the middle Sauk River study site likely contributed to low recovery rates of both sediment boxes and infiltration bags in two of the three study years.
In terms of grain size distributions of riverbed sediment and adjacent gravel bars representing the potential for fine sediment infiltration into spawning gravels, both riverbed and gravel bar bulk subsurface sediment samples had higher fraction finer for the representative fine grain size of two millimeters compared to the sediment boxes and infiltration bags. Therefore, riverbed and gravel bar samples may serve as a conservative first order proxy for potential fine sediment infiltration into spawning gravels, with the understanding that these samples may overestimate fine sediment infiltration by up to 15 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245077","collaboration":"Prepared in cooperation with the Sauk-Suiattle Indian Tribe","usgsCitation":"Jaeger, K.L., Anderson, S.W., Leach, A.C., and Morris, S.T., 2024, Quantifying fine sediment infiltration in spawning gravel used by Chinook salmon (Oncorhynchus tshawytscha) in the Sauk River Basin, Washington, 2018–21: U.S. Geological Survey Scientific Investigations Report 2024–5077, 36 p., https://doi.org/10.3133/sir20245077.","productDescription":"Report: viii, 36 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-156004","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":462435,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5077/sir20245077.jpg"},{"id":462436,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5077/sir20245077.pdf","text":"Report","size":"6.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5077"},{"id":462437,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5077"},{"id":462438,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RV73FO","text":"USGS data release","description":"USGS data release","linkHelpText":"Sediment and dissolved oxygen data to support fine sediment intrusion in Chinook salmon spawning gravels, Sauk River, Washington"},{"id":462439,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5077/images"},{"id":462440,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5077/sir20245077.XML"}],"country":"United States","state":"Washington","otherGeospatial":"Sauk River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.30874041064037,\n 48.47425854928386\n ],\n [\n -121.30874041064037,\n 47.81450909416341\n ],\n [\n -120.27602556689018,\n 47.81450909416341\n ],\n [\n -120.27602556689018,\n 48.47425854928386\n ],\n [\n -121.30874041064037,\n 48.47425854928386\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The State of Delaware has encouraged agricultural conservation practices to improve nutrient uptake by crops and mitigate nutrient transport to groundwater in the surficial aquifer. To study recent changes in groundwater quality, the U.S. Geological Survey and the Delaware Department of Agriculture (DDA) developed a network of shallow wells near agricultural areas throughout the Delaware Coastal Plain. This network was designed to characterize water quality related to agricultural practices and to detect any recent changes in shallow groundwater quality, in particular groundwater nitrate concentrations. The shallow well network was first sampled in 2014 and resampled in 2019. In 2019, field parameters (including dissolved oxygen, pH, specific conductance, and temperature), major ions, nutrients, stable isotopes of water, and isotopes of nitrate were measured in groundwater samples collected between October and December. Wells were organized into three groups based on their geochemical characteristics measured in 2014: the Agricultural, Urban, and Mixed Groups. Results from the 2019 sampling showed little change in water quality from the 2014 sampling. Land-use factors continued to be the driving influence between groups. Groundwater moves slowly and changes in groundwater quality are likely to respond slowly to changes in conservation practices. Continued sampling of both groundwater quality in this network and monitoring land management practices can help detect groundwater quality trends in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245076","collaboration":"Prepared in cooperation with Delaware Department of Agriculture and the Delaware Geological Survey","usgsCitation":"Soroka, A.M., Reyes, B., Fleming, B., and Brownley, M., 2024, Comparison of water quality in shallow groundwater near agricultural areas in the Delaware Coastal Plain, 2014 and 2019: U.S. Geological Survey Scientific Investigations Report 2024–5076, 23 p., https://doi.org/10.3133/sir20245076.","productDescription":"viii, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126969","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":462286,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5076/sir20245076.XML","description":"SIR 2024-5076 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\"}}]}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Dr.
Baltimore, Maryland 21228
We review the functioning and sustainability of coastal marshes and mangroves. Urbanized humans have a 7,000-year-old enduring relationship to coastal wetlands. Wetlands include marshes, salt flats, and saline and freshwater forests. Coastal wetlands occur in all climate zones but are most abundant in deltas. Mangroves are tropical, whereas marshes occur from tropical to boreal areas. Quantification of coastal wetland areas has advanced in recent years but is still insufficiently accurate. Climate change and sea-level rise are predicted to lead to significant wetland losses and other impacts on coastal wetlands and the humans associated with them. Landward migration and coastal retreat are not expected to significantly reduce coastal wetland losses. Nitrogen watershed inputs are unlikely to alter coastal marsh stability because watershed loadings are mostly significantly lower than those in fertilization studies that show decreased belowground biomass and increased decomposition of soil organic matter. Blue carbon is not expected to significantly reduce climate impacts. The high values of ecosystem goods and services of wetlands are expected to be reduced by area losses. Humans have had strong impacts on coastal wetlands in the Holocene, and these impacts are expected to increase in the Anthropocene.
","language":"English","publisher":"Annual Reviews","doi":"10.1146/annurev-environ-121922-041109","usgsCitation":"Day, J.W., Anthony, E., Costanza, R., Edmonds, D., Gunn, J., Hopkinson, C., Mann, M., Morris, J., Osland, M., Quirk, T., Rovai, A.S., Rybczyk, J.M., Spencer, T., Stephens, J., Syvitski, J., Twilley, R.R., Visser, J., and White, J.R., 2024, Coastal wetlands in the Anthropocene: Annual Review of Environment and Resources, v. 49, p. 105-135, https://doi.org/10.1146/annurev-environ-121922-041109.","productDescription":"31 p.","startPage":"105","endPage":"135","ipdsId":"IP-158374","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":466885,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1146/annurev-environ-121922-041109","text":"Publisher Index Page"},{"id":463196,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Day, John W.","contributorId":200323,"corporation":false,"usgs":false,"family":"Day","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":916785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Edward","contributorId":345502,"corporation":false,"usgs":false,"family":"Anthony","given":"Edward","email":"","affiliations":[{"id":82607,"text":"Aix Marseille U, Aix-en-Provence, France","active":true,"usgs":false}],"preferred":false,"id":916786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costanza, Robert","contributorId":345503,"corporation":false,"usgs":false,"family":"Costanza","given":"Robert","affiliations":[{"id":82609,"text":"Institute for Global Prosperity, University College London, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":916787,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edmonds, Douglas","contributorId":345504,"corporation":false,"usgs":false,"family":"Edmonds","given":"Douglas","email":"","affiliations":[{"id":82610,"text":"Dept. of Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana, USA","active":true,"usgs":false}],"preferred":false,"id":916788,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gunn, Joel","contributorId":345505,"corporation":false,"usgs":false,"family":"Gunn","given":"Joel","email":"","affiliations":[{"id":82611,"text":"Dept. of Anthropology, University of North Carolina Greensboro, North Carolina, USA","active":true,"usgs":false}],"preferred":false,"id":916789,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hopkinson, Charles","contributorId":224758,"corporation":false,"usgs":false,"family":"Hopkinson","given":"Charles","affiliations":[{"id":40936,"text":"U. 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Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
We investigated the feasibility of using two analytical methods, N‐mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long‐term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions.
When compared with abundance estimates from mark–recapture methods, N‐mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N‐mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N‐mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models.
N‐mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long‐term data sets.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11026","usgsCitation":"Briggs, M., Glassic, H.C., Guy, C.S., Opitz, S., Rotella, J., and Schmetterling, D., 2024, Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA: North American Journal of Fisheries Management, v. 44, no. 5, p. 947-961, https://doi.org/10.1002/nafm.11026.","productDescription":"15 p.","startPage":"947","endPage":"961","ipdsId":"IP-172896","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.11026","text":"Publisher Index Page"},{"id":486239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"upper Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.27790695947738,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.77750283140904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Michelle A.","contributorId":355621,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glassic, Hayley Corrine 0000-0001-6839-1026","orcid":"https://orcid.org/0000-0001-6839-1026","contributorId":305858,"corporation":false,"usgs":true,"family":"Glassic","given":"Hayley","email":"","middleInitial":"Corrine","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":937792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":937793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Opitz, Scott T.","contributorId":355622,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott T.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rotella, Jay J.","contributorId":355623,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmetterling, David A.","contributorId":355624,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David A.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264248,"text":"70264248 - 2024 - Onset of aftershocks: Constraints on the Rate-and-State model","interactions":[],"lastModifiedDate":"2025-03-10T14:39:45.261636","indexId":"70264248","displayToPublicDate":"2024-10-01T09:39:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Onset of aftershocks: Constraints on the Rate-and-State model","docAbstract":"Aftershock rates typically decay with time t after the mainshock according to the Omori–Utsu law, R(t)=K(c+t)−p, with parameters K, c, and p. The rate‐and‐state (RS) model, which is currently the most popular physics‐based seismicity model, also predicts an Omori–Utsu decay with p = 1 and a c‐value that depends on the size of the coseismic stress change. Because the mainshock‐induced stresses strongly vary in space, the c‐value should vary accordingly. Short‐time aftershock incompleteness (STAI) in earthquake catalogs has prevented a detailed test of this prediction so far, but the newly developed a‐positive method for reconstructing the true earthquake rate now allows its testing. Using previously published slip models, we calculate the coseismic stress changes for the six largest mainshocks in Southern California in recent decades and estimate the maximum shear as a scalar proxy of the coseismic stress tensor. Aftershock rates reconstructed for events in different stress ranges show that the rates follow a power law with p = 1 independent of stress with no clear sign of a c‐value. The onset of the power‐law decay is abrupt and more delayed in areas with smaller stress changes. The observations do not necessarily contradict the RS model, as STAI limits the resolution for early aftershocks, and the RS model can reproduce the observations for specific Aσ values. However, the observations lead to strong constraints, namely Aσ<10 kPa and a power‐law decay of the background rate with distance to the fault, with exponent 2.7.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240176","usgsCitation":"Hainzl, S., Page, M.T., and van der Elst, N., 2024, Onset of aftershocks: Constraints on the Rate-and-State model: Seismological Research Letters, v. 95, no. 6, p. 3507-3516, https://doi.org/10.1785/0220240176.","productDescription":"10 p.","startPage":"3507","endPage":"3516","ipdsId":"IP-165790","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":502579,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://gfzpublic.gfz-potsdam.de/pubman/item/item_5029403","text":"External Repository"},{"id":483137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hainzl, Sebastian","contributorId":352178,"corporation":false,"usgs":false,"family":"Hainzl","given":"Sebastian","affiliations":[{"id":84127,"text":"Univ. of Potsdam","active":true,"usgs":false}],"preferred":false,"id":930194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":930195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van der Elst, Nicholas 0000-0002-3812-1153 nvanderelst@usgs.gov","orcid":"https://orcid.org/0000-0002-3812-1153","contributorId":147858,"corporation":false,"usgs":true,"family":"van der Elst","given":"Nicholas","email":"nvanderelst@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":930196,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262336,"text":"70262336 - 2024 - Understanding the role of recreational angling technology in angler expectations of catch, trip catch, and angler satisfaction","interactions":[],"lastModifiedDate":"2025-01-17T16:35:42.615984","indexId":"70262336","displayToPublicDate":"2024-10-01T09:30:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the role of recreational angling technology in angler expectations of catch, trip catch, and angler satisfaction","docAbstract":"Rapid technological advancement often receives a mix of criticism and welcome implementation. Fishing technologies, such as sonar, are believed to enable anglers to be more efficient and effective in their angling. There are concerns from anglers and managers of increased catch by technology users. We assessed the relationships between technology use—defined as the use of imaging technology such as sonar and underwater cameras—and catch, angler expectations of catch, and trip satisfaction using a dual intercept creel survey. Angling technologies were used by 80% and 79% of intercepted boat and ice anglers, respectively, but only 3.9% of shore anglers. Fishing technologies increased expected catch for game fish anglers, but not panfish anglers, and had no effect on actual catch for either group. Most anglers caught fewer fish than expected, and technology did not improve their ability to meet expectations. Technology use was associated with decreased overall satisfaction among panfish and game fish anglers. These results suggest that concerns about fishing technology increasing catch may not be warranted. Rather, technology use may affect angler expectations and negatively impact angler satisfaction, potentially influencing angler behavior.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.11157","usgsCitation":"Kerkhove, A., Trudeau, A., Jensen, O., Isermann, D.A., Dombrowski, P., Latimer, A., and Feiner, Z., 2024, Understanding the role of recreational angling technology in angler expectations of catch, trip catch, and angler satisfaction: Fisheries Magazine, v. 49, no. 10, p. 463-474, https://doi.org/10.1002/fsh.11157.","productDescription":"12 p.","startPage":"463","endPage":"474","ipdsId":"IP-158366","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fsh.11157","text":"Publisher Index Page"},{"id":480748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Dane County, Vilas 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","language":"English","publisher":"National Park Service","doi":"10.36967/2302417","usgsCitation":"Pavlovic, N., Plampin, B., Tonkovich, G.S., and Hamilla, D.R., 2024, Special flora and vegetation of Indiana Dunes National Park: Natural Resource Report NPS/INDU/NRR—2024/262, xxvii, 771 p., https://doi.org/10.36967/2302417.","productDescription":"xxvii, 771 p.","startPage":"802","ipdsId":"IP-069641","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":463119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Dunes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.14259682399684,\n 41.64975697846441\n ],\n [\n -87.12126914485442,\n 41.630763565962894\n ],\n [\n -87.09722021804414,\n 41.60920060097345\n ],\n [\n -87.04638795765823,\n 41.5948273396551\n ],\n [\n -86.93033845468838,\n 41.630254643981544\n ],\n [\n -86.88982018901976,\n 41.65899026034268\n ],\n [\n -86.89049275682176,\n 41.69951612637291\n ],\n [\n -86.91725614199466,\n 41.72158907366915\n ],\n [\n -87.14259682399684,\n 41.64975697846441\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pavlovic, Noel B. 0000-0002-2335-2274","orcid":"https://orcid.org/0000-0002-2335-2274","contributorId":266174,"corporation":false,"usgs":true,"family":"Pavlovic","given":"Noel B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":916588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plampin, Barbara","contributorId":345432,"corporation":false,"usgs":false,"family":"Plampin","given":"Barbara","email":"","affiliations":[{"id":82581,"text":"Botanist","active":true,"usgs":false}],"preferred":false,"id":916589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tonkovich, Gayle S.","contributorId":345433,"corporation":false,"usgs":false,"family":"Tonkovich","given":"Gayle","email":"","middleInitial":"S.","affiliations":[{"id":82582,"text":"Northwest Indiana Restoration Monitoring Inventory, Department of Biology, Indiana University","active":true,"usgs":false}],"preferred":false,"id":916611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilla, David R.","contributorId":345439,"corporation":false,"usgs":false,"family":"Hamilla","given":"David","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":916612,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259320,"text":"70259320 - 2024 - Preliminary observations of the April 5th, 2024, Mw4.8 New Jersey earthquake","interactions":[],"lastModifiedDate":"2024-10-04T11:54:34.426806","indexId":"70259320","displayToPublicDate":"2024-10-01T06:52:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Preliminary observations of the April 5th, 2024, Mw4.8 New Jersey earthquake","docAbstract":"On 5 April 2024, 10:23 a.m. local time, a moment magnitude 4.8 earthquake struck Tewksbury Township, New Jersey, about 65 km west of New York City. Millions of people from Virginia to Maine and beyond felt the ground shaking, resulting in the largest number (>180,000) of U.S. Geological Survey (USGS) “Did You Feel It?” reports of any earthquake. A team deployed by the Geotechnical Extreme Events Reconnaissance Association and the National Institute of Standards and Technology documented structural and nonstructural damage, including substantial damage to a historic masonry building in Lebanon, New Jersey. The USGS National Earthquake Information Center reported a focal depth of about 5 km, consistent with a lack of signal in Interferometric Synthetic Aperture Radar data. The focal mechanism solution is strike slip with a substantial thrust component. Neither mechanism’s nodal plane is parallel to the primary northeast trend of geologic discontinuities and mapped faults in the region, including the Ramapo fault. However, many of the relocated aftershocks, for which locations were augmented by temporary seismic deployments, form a cluster that parallels the general northeast trend of the faults. The aftershocks lie near the Tewksbury fault, north of the Ramapo fault.
Objective
Long-term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark-recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the last two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
Methods
We investigated the feasibility of using two analytical methods, N-mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River; these methods allow abundance to be estimated when a mark-recapture estimate cannot be obtained due to hydrologic conditions.
Result
When compared to abundance estimates from mark-recapture methods, N-mixture models most often resulted in negatively biased abundance estimates while mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N-mixture models produced negatively biased estimates compared to true abundance values from simulated datasets. Bias in N-mixture model estimates was caused by poor model fit and variation in capture probability. Bias in mean capture probability estimates was caused by heterogeneity in capture probability that was not accounted for in the models.
Conclusion
N-mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining long-term datasets.
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Elk (Cervus canadensis) ranged widely across diverse ecoregions in North America prior to European settlement and subsequent eastern extirpation. Most habitat selection studies have occurred within their contemporary western range, even after eastern reintroductions began. As habitat selection can vary by geographic location, available cover, season, and diel period, it is important to understand how a non-migratory, reintroduced population in northern Wisconsin, USA, is limited by the lack of variation in topography, elevation, and vegetation. We tested scale-dependent habitat selection on 79 adult elk from 2017 to 2020 using resource selection functions across temporal (i.e., seasonal) and spatial scales (i.e., landscape and home range). We found that selection varied both spatially and temporally, and elk selected areas with the greatest potential to influence fitness at larger scales, meaning elk selected areas closer to escape cover and further from “risky” features (e.g., annual wolf territory centers, county roads, and highways). We found stronger avoidance of annual wolf territory centers during spring, suggesting elk were selecting safer habitats during calving season. Elk selected habitats with less canopy cover across both spatial scales and all seasons, suggesting that elk selected areas with better access to forage as early seral stage stands have greater forage biomass than closed-canopy forests and direct solar radiation to provide warmth in the cooler seasons. This study provides insight into the complexity of hierarchical decision-making, such as how risky habitat features and land cover type influence habitat selection differently across seasons and spatial scales, influencing the decision-making of elk. Scale-dependent behavior is crucial to understand within specific geographic regions, as these decisions scale up to influence population dynamics.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.70346","usgsCitation":"Merems, J., Brose, A., Tack, J., Crimmins, S.M., and Van Deelen, T., 2024, Scale-dependence in elk habitat selection for a reintroduced population in Wisconsin, USA: Ecology and Evolution, v. 14, no. 10, e70346, 13 p., https://doi.org/10.1002/ece3.70346.","productDescription":"e70346, 13 p.","ipdsId":"IP-151404","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488270,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.70346","text":"Publisher Index Page"},{"id":485840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Chequamegon-Nicolet National Forest, Flambeau River State Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.44038392352694,\n 46.55749714177122\n ],\n [\n -91.44038392352694,\n 45.89642997708481\n ],\n [\n -90.69624903990778,\n 45.89642997708481\n ],\n [\n -90.69624903990778,\n 46.55749714177122\n ],\n [\n -91.44038392352694,\n 46.55749714177122\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Merems, Jennifer L.","contributorId":354971,"corporation":false,"usgs":false,"family":"Merems","given":"Jennifer L.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":936740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brose, Anna L.","contributorId":354972,"corporation":false,"usgs":false,"family":"Brose","given":"Anna L.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":936741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tack, Jennifer Price","contributorId":354973,"corporation":false,"usgs":false,"family":"Tack","given":"Jennifer Price","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crimmins, Shawn M. 0000-0001-6229-5543 scrimmins@usgs.gov","orcid":"https://orcid.org/0000-0001-6229-5543","contributorId":5498,"corporation":false,"usgs":true,"family":"Crimmins","given":"Shawn","email":"scrimmins@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":936743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Deelen, Timothy R.","contributorId":354974,"corporation":false,"usgs":false,"family":"Van Deelen","given":"Timothy R.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":936744,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}