The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, investigated the hydrogeology of the unconsolidated and bedrock aquifers along the Salmon River corridor in northern Franklin County, New York. The study area covers roughly 147 square miles and includes the village of Malone and parts of the Towns of Malone, Bellmont, Burke, Constable, Westville, Bangor, Duane, and Franklin, New York. Groundwater is the primary source for water supply within the study area. Eighty-three percent of all residents use public water supplied from two production wells that draw water from a thick, highly productive sand-and-gravel aquifer likely receiving induced infiltration from the Salmon River. Twenty-four percent of the 187 verified domestic wells in the study area outside of the production well service area boundary are screened in typically discontinuous deposits of stratified sand and sand and gravel. Seventy-six percent of the wells are completed in bedrock aquifers including the Potsdam Sandstone, metamorphic rocks, Theresa Formation, and unknown bedrock. Characterizing and understanding potential groundwater resources is critical for protecting the quality of the groundwater. The information in this report may be used to guide delineation of groundwater contributing areas, assess potential threats to aquifers from both point and nonpoint sources, respond to contamination from spills or leaks from underground storage facilities or other sources, and to support assessments for future development of municipal water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255048","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Fisher, S.R., Van Hoesen, J.G., Heisig, P.M., and Woda, J.C., 2025, Hydrogeology of unconsolidated and bedrock aquifers along the Salmon River, including Malone, Franklin County, New York: U.S. Geological Survey Scientific Investigations Report 2025–5048, 28 p., https://doi.org/10.3133/sir20255048.","productDescription":"Report: vii, 28 p.; 2 Data Releases: Application Site","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140296","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":494165,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118731.htm","linkFileType":{"id":5,"text":"html"}},{"id":493534,"rank":8,"type":{"id":4,"text":"Application Site"},"url":"https://ny.water.usgs.gov/maps/aquifer/","text":"Upstate New York Aquifer Viewer 2.0"},{"id":493493,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P946DA5G","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio (HVSR) measurements and depth to bedrock estimates along the Salmon River corridor from the Adirondack Foothills to the St. Lawrence Lowlands, Franklin County, New York, 2018-2023"},{"id":493491,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5048/images/"},{"id":493489,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255048/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5048 HTML"},{"id":493488,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5048/sir20255048.pdf","text":"Report","size":"21.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5048 PDF"},{"id":493486,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5048/coverthb.jpg"},{"id":493492,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SX7MV2","text":"USGS data release","linkHelpText":"Geospatial datasets for the glacial geology and hydrogeology of the Salmon River corridor from the Adirondack Foothills to the St. Lawrence Lowlands, including Malone, Franklin County, New York"},{"id":493490,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5048/sir20255048.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5048 XML"}],"country":"United States","state":"New York","county":"Franklin County","city":"Malone","otherGeospatial":"Salmon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.14023040503912,\n 44.66985439246068\n ],\n [\n -74.08731731901575,\n 44.72813115388607\n ],\n [\n -74.30478428794912,\n 44.91578732942074\n ],\n [\n -74.39491097293471,\n 44.86800332653027\n ],\n [\n -74.16465182935801,\n 44.64669273826843\n ],\n [\n -74.14023040503912,\n 44.66985439246068\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The influence of streamflow can be highly heterogeneous around lake edges, making it challenging to predict how benthic productivity in the littoral zone responds to hydroclimatic change. The degree to which streamflow affects nearshore productivity varies as a function of catchment characteristics, internal lake morphometry, and processes. This study investigates the relative influence of streamflow on nearshore metabolism (e.g., gross primary productivity [GPP], ecosystem respiration [ER], and net ecosystem productivity [NEP]) for shores with large, small, or no stream inflows (four locations across two shores) during two contrasting water years (one drought and one wet) in Lake Tahoe (Nevada/California, USA). Using Bayesian structural equation modeling, we found streamflow decreased water temperature, benthic light, and GPP across both years. Compared to the drought year, the subsequent wet year had 54% higher annual streamflow, 37% less light, and lower NEP at locations with large or small inflows (39% Δ −0.32 mmol O₂ m−3 d−1% and 49% Δ −1.19 mmol O₂ m−3 d−1, respectively). During the wet year, we observed a 68% increase in the negative association between streamflow and nearshore GPP at the large inflow and a 62% decrease in the positive association between streamflow and GPP at the small inflow. This work demonstrates how oligotrophic littoral productivity varies across shorelines and in response to hydrological conditions, with streamflow and precipitation exerting contrasting effects depending on the proximity to inflowing streams. Our results suggest future lake responses to climate volatility depend on spatial and temporal hydrologic connectivity to catchments and upland processes.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.70157","usgsCitation":"Loria, K., Lowman, H., Krause, J., Katona, L.R., Naranjo, R.C., Scordo, F., Harpold, A., Chandra, S., and Blaszczak, J., 2025, The influence of mountain streamflow on nearshore ecosystem metabolism in a large, oligotrophic lake across a drought and a wet year: Limnology and Oceanography, v. 70, no. 9, p. 2645-2659, https://doi.org/10.1002/lno.70157.","productDescription":"15 p.","startPage":"2645","endPage":"2659","ipdsId":"IP-171346","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":493851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.22907288918867,\n 39.27733095987708\n ],\n [\n -120.22907288918867,\n 38.90788242474909\n ],\n [\n -119.85664507888565,\n 38.90788242474909\n ],\n [\n -119.85664507888565,\n 39.27733095987708\n ],\n [\n -120.22907288918867,\n 39.27733095987708\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Loria, Kelly 0000-0002-0067-0413","orcid":"https://orcid.org/0000-0002-0067-0413","contributorId":359371,"corporation":false,"usgs":false,"family":"Loria","given":"Kelly","affiliations":[{"id":38163,"text":"UNR","active":true,"usgs":false}],"preferred":false,"id":945205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowman, Heili 0000-0002-2939-9225","orcid":"https://orcid.org/0000-0002-2939-9225","contributorId":359373,"corporation":false,"usgs":false,"family":"Lowman","given":"Heili","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":945206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krause, Jasimine 0009-0002-2017-0229","orcid":"https://orcid.org/0009-0002-2017-0229","contributorId":359376,"corporation":false,"usgs":false,"family":"Krause","given":"Jasimine","affiliations":[{"id":38163,"text":"UNR","active":true,"usgs":false}],"preferred":false,"id":945207,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Katona, Leon R. 0000-0001-5323-1871","orcid":"https://orcid.org/0000-0001-5323-1871","contributorId":331458,"corporation":false,"usgs":true,"family":"Katona","given":"Leon","email":"","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945208,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945209,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scordo, Facundo 0000-0001-6182-7368","orcid":"https://orcid.org/0000-0001-6182-7368","contributorId":359380,"corporation":false,"usgs":false,"family":"Scordo","given":"Facundo","affiliations":[{"id":85780,"text":"Universidad Nacional del Sur, Argentina","active":true,"usgs":false}],"preferred":false,"id":945210,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harpold, Adrian A. 0000-0002-2566-9574","orcid":"https://orcid.org/0000-0002-2566-9574","contributorId":353577,"corporation":false,"usgs":false,"family":"Harpold","given":"Adrian A.","affiliations":[{"id":84439,"text":"Dept. of Natural Resources and Environmental Science, Univ. of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":945211,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chandra, Sudeep 0000-0003-1724-5154","orcid":"https://orcid.org/0000-0003-1724-5154","contributorId":359381,"corporation":false,"usgs":false,"family":"Chandra","given":"Sudeep","affiliations":[{"id":38163,"text":"UNR","active":true,"usgs":false}],"preferred":false,"id":945212,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blaszczak, Joanna 0000-0001-5122-0829","orcid":"https://orcid.org/0000-0001-5122-0829","contributorId":225159,"corporation":false,"usgs":false,"family":"Blaszczak","given":"Joanna","email":"","affiliations":[{"id":41055,"text":"Natural Resources and Environmental Science, University of Nevada, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":945213,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70271932,"text":"70271932 - 2025 - Warming Alaskan rivers affect first-year growth in critical northern food fishes","interactions":[],"lastModifiedDate":"2025-09-24T15:09:37.960929","indexId":"70271932","displayToPublicDate":"2025-08-06T10:05:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Warming Alaskan rivers affect first-year growth in critical northern food fishes","docAbstract":"Arctic and subarctic rivers are warming rapidly, with unknown consequences for migratory fishes and the human communities dependent on them. To date, few studies have provided a comprehensive assessment of possible climate change impacts on the hydrology and temperature of Arctic rivers at the regional scale, and even fewer have connected those changes to multiple fish species with input and guidance from Indigenous communities. We used climate, hydrologic, and fish-growth simulations of historical (1990–2021) and future (2034–2065) young-of-year (YOY) growth potential of Chinook salmon (Oncorhynchus tshawytscha) and Dolly Varden (Salvelinus malma) for seven river basins in the Arctic-Yukon-Kuskokwim (AYK) region of Alaska, USA and Yukon Territory, Canada. Historically, summer water temperatures of all river basins remained below thresholds regarded as deleterious for Chinook salmon (14.6 °C) and Dolly Varden (16 °C), even in the warmest years. However, by the mid-century, Chinook salmon growth was limited, with declines in the warmest years in most river basins. Conversely, Dolly Varden are expected to benefit, with a near-doubling in growth projections in all river basins. This suggests that there may be an increase in suitable habitat for Dolly Varden by mid-century. The results highlight species-specific consequences of climate change and can guide future research on refugia for these species of cultural and subsistence importance to Indigenous communities in the AYK region and throughout the Arctic.
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Branch","active":true,"usgs":true}],"preferred":true,"id":949424,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Musselman, Keith N.","contributorId":361776,"corporation":false,"usgs":false,"family":"Musselman","given":"Keith","middleInitial":"N.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":949425,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70271738,"text":"70271738 - 2025 - Reproductive condition of an invasive snake in urban, savanna, and forest habitats","interactions":[],"lastModifiedDate":"2025-09-23T15:02:11.953765","indexId":"70271738","displayToPublicDate":"2025-08-06T09:59:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive condition of an invasive snake in urban, savanna, and forest habitats","docAbstract":"Predation by Brown Treesnakes (Boiga irregularis) has caused the decline or extinction of all native forest vertebrates on Guam, including birds, lizards, and bats. Loss of the highest-quality prey has caused shifts in Brown Treesnake size and life history. We sought to understand how reproductive condition varies among habitats island-wide, particularly comparing urban and savanna habitats (where some primarily non-native large prey remain) with prey-depauperate forest habitats. We dissected 100 snakes from each of 18 sites stratified by 6 habitat types and calculated a body condition index, relative follicle size (for females; N = 817) and relative testes size (for males; N = 992) for each. Low detection of reproductive females continues to hamper research on their biology, as only 11% of females had secondary vitellogenic follicles (N = 89). Although variation among sites within habitats was high, above-average reproductive development of both males and females was most frequently observed in urban and savanna habitats, emphasizing the potential for prey subsidies in these habitats to fuel population growth and affect invasive snake management objectives. Furthermore, the trade-off between body condition and reproductive condition appeared to be more severe in forested habitats and less severe in urban habitats for both males and females. There were few obvious seasonal patterns, although there was a slightly lower probability of females having secondary vitellogenic follicles in the wet season compared to the dry season.
","language":"English","publisher":"Pensoft","doi":"10.3897/neobiota.100.148230","usgsCitation":"Durso, A.M., Siers, S.R., Reed, R., and Savidge, J.A., 2025, Reproductive condition of an invasive snake in urban, savanna, and forest habitats: NeoBiota, v. 100, p. 109-134, https://doi.org/10.3897/neobiota.100.148230.","productDescription":"26 p.","startPage":"109","endPage":"134","ipdsId":"IP-173485","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":496146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.100.148230","text":"Publisher Index Page"},{"id":495900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 145.06721828854353,\n 13.664307921525207\n ],\n [\n 144.53613617158783,\n 13.664307921525207\n ],\n [\n 144.53613617158783,\n 13.168663979518698\n ],\n [\n 145.06721828854353,\n 13.168663979518698\n ],\n [\n 145.06721828854353,\n 13.664307921525207\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"100","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Durso, Andrew M.","contributorId":361697,"corporation":false,"usgs":false,"family":"Durso","given":"Andrew","middleInitial":"M.","affiliations":[{"id":40458,"text":"Florida Gulf Coast University","active":true,"usgs":false}],"preferred":false,"id":949243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siers, Shane R. 0000-0001-7961-5072","orcid":"https://orcid.org/0000-0001-7961-5072","contributorId":361698,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","middleInitial":"R.","affiliations":[{"id":86329,"text":"USDA-APHIS-WS National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":949244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Robert 0000-0001-8349-6168","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":267796,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":949245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Savidge, Julie A.","contributorId":361700,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":949246,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269997,"text":"70269997 - 2025 - Wetlands, groundwater and seasonality influence the spatial distribution of stream chemistry in a low-relief catchment","interactions":[],"lastModifiedDate":"2025-08-07T14:21:54.933339","indexId":"70269997","displayToPublicDate":"2025-08-06T09:20:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Wetlands, groundwater and seasonality influence the spatial distribution of stream chemistry in a low-relief catchment","docAbstract":"Evaluating stream water chemistry patterns provides insight into catchment ecosystem and hydrologic processes. Spatially distributed patterns and controls of stream solutes are well-established for high-relief catchments where solute flow paths align with surface topography. However, the controls on solute patterns are poorly constrained for low-relief catchments where hydrogeologic heterogeneities and river corridor features, like wetlands, may influence water and solute transport. Here, we provide a data set of solute patterns from 58 synoptic surveys across 28 sites and over 32 months in a low-relief wetland-rich catchment to determine the major surface and subsurface controls along with wetland influence across the catchment. In this low-relief catchment, the expected wetland storage, processing, and transport of solutes is only apparent in solute patterns of the smallest subcatchments. Meanwhile, downstream seasonal and wetland influence on observed chemistry can be masked by large groundwater contributions to the main stream channel. These findings highlight the importance of incorporating variable groundwater contributions into catchment-scale studies for low-relief catchments, and that understanding the overall influence of wetlands on stream chemistry requires sampling across various spatial and temporal scales. Therefore, in low-relief wetland-rich catchments, given the mosaic of above and below ground controls on stream solutes, modeling efforts may need to include both surface and subsurface hydrological data and processes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JG008989","usgsCitation":"Weidner, C., Zarnestke, J., Kendall, A., Martin, S., Nesheim, S., and Shogren, A., 2025, Wetlands, groundwater and seasonality influence the spatial distribution of stream chemistry in a low-relief catchment: JGR Biogeosciences, v. 130, no. 8, e2025JG008989, 19 p., https://doi.org/10.1029/2025JG008989.","productDescription":"e2025JG008989, 19 p.","ipdsId":"IP-179047","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":494438,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jg008989","text":"Publisher Index Page"},{"id":493705,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Sates","state":"Michigan","otherGeospatial":"Augusta Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.37501891108799,\n 42.37891153154604\n ],\n [\n -85.37501891108799,\n 42.32980992829573\n ],\n [\n -85.34485255012329,\n 42.32980992829573\n ],\n [\n -85.34485255012329,\n 42.37891153154604\n ],\n [\n -85.37501891108799,\n 42.37891153154604\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Weidner, Caroline R. 0009-0008-6994-0021","orcid":"https://orcid.org/0009-0008-6994-0021","contributorId":359353,"corporation":false,"usgs":false,"family":"Weidner","given":"Caroline R.","affiliations":[{"id":85775,"text":"Michigan State University Department of Earth and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":945168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarnestke, Jay P. 0000-0001-7194-5245","orcid":"https://orcid.org/0000-0001-7194-5245","contributorId":359354,"corporation":false,"usgs":false,"family":"Zarnestke","given":"Jay P.","affiliations":[{"id":85775,"text":"Michigan State University Department of Earth and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":945169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendall, Anthony D.","contributorId":357745,"corporation":false,"usgs":false,"family":"Kendall","given":"Anthony D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":945170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Sherry Lynn 0000-0001-7471-0476","orcid":"https://orcid.org/0000-0001-7471-0476","contributorId":343444,"corporation":false,"usgs":true,"family":"Martin","given":"Sherry Lynn","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":945171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nesheim, Samuel","contributorId":359355,"corporation":false,"usgs":false,"family":"Nesheim","given":"Samuel","affiliations":[{"id":85775,"text":"Michigan State University Department of Earth and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":945172,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shogren, Arial J.","contributorId":359356,"corporation":false,"usgs":false,"family":"Shogren","given":"Arial J.","affiliations":[{"id":85776,"text":"The University of Alabama Biological Sciences Department","active":true,"usgs":false}],"preferred":false,"id":945173,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269769,"text":"sim3534 - 2025 - Geologic map of the Greater Antilles and Virgin Islands","interactions":[{"subject":{"id":70202933,"text":"ofr20191036 - 2019 - Preliminary geologic map of the Greater Antilles and the Virgin Islands","indexId":"ofr20191036","publicationYear":"2019","noYear":false,"displayTitle":"Preliminary Geologic Map of the Greater Antilles and the Virgin Islands","title":"Preliminary geologic map of the Greater Antilles and the Virgin Islands"},"predicate":"SUPERSEDED_BY","object":{"id":70269769,"text":"sim3534 - 2025 - Geologic map of the Greater Antilles and Virgin Islands","indexId":"sim3534","publicationYear":"2025","noYear":false,"title":"Geologic map of the Greater Antilles and Virgin Islands"},"id":1}],"lastModifiedDate":"2026-02-03T14:47:36.789022","indexId":"sim3534","displayToPublicDate":"2025-08-06T08:15:00","publicationYear":"2025","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":"3534","displayTitle":"Geologic Map of the Greater Antilles and Virgin Islands","title":"Geologic map of the Greater Antilles and Virgin Islands","docAbstract":"The geologic map of the Greater Antilles and Virgin Islands is a compilation of information from the literature, integrated to provide a seamless geologic map of the region. This map was prepared to serve as a base map for a mineral resource assessment of the region. Several small-scale regional geologic maps of the region have been prepared in the past. This report supersedes an earlier version of the geologic map of the Greater Antilles and the Virgin Islands that was released as U.S. Geological Survey (USGS) Open-File Report 2019–1036.
For this report, the regional geologic overview shown on sheet 1 covers Cuba; the island of Hispaniola, which includes Haiti and Dominican Republic; Jamaica; the Cayman Islands; Puerto Rico; and the U.S. and British Virgin Islands. Sheet 2 shows the geology of Cuba and Cayman Islands, sheet 3 the geology of Jamaica, sheet 4 the geology of Hispaniola, and sheet 5 the geology of Puerto Rico and Virgin Islands. Accompanying the maps are three tables: table 1 lists the map units (in alphabetical order by map-unit label), their ages, and the countries or territories in which they can be found. These are listed in alphabetical order for ease in locating unit names from the symbols on the maps. Table 2 is a list of sources for the geologic map compilation of Puerto Rico. Table 3 shows formations in the Devil’s Race Course Group of Jamaica.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3534","programNote":"Mineral Resources Program","usgsCitation":"Wilson, F.H., and Labay, K.A., comps., 2025, Geologic map of the Greater Antilles and Virgin Islands: U.S. Geological Survey Scientific Investigations Map 3534, 5 sheets, scales 1:2,500,000, 1:1,000,000, 1:250,000, 1:650,000, 1:300,000, and 1:140,000, 102-p. pamphlet, https://doi.org/10.3133/sim3534. [Supersedes USGS Open-File Report 2019–1036.]","productDescription":"Pamphlet: vi, 102 p.; 5 Sheets: 53.68 x 35.41 inches or smaller; 3 Data Releases","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119640","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":499037,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118719.htm","linkFileType":{"id":5,"text":"html"}},{"id":493264,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QGKUBG","text":"USGS data release","linkHelpText":"Age determinations from various geochronological methods of rock samples in the Greater Antilles and Virgin Islands"},{"id":493263,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1ZN39XQ","text":"USGS data release","linkHelpText":"U-Pb isotopic data and zircon age determinations from the Island of Puerto Rico, United States"},{"id":493262,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13X7BHY","text":"USGS data release","linkHelpText":"Data release for the geologic map of the Greater Antilles and Virgin Islands"},{"id":493261,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3534/sim3534_sheet5.pdf","text":"Sheet 5","size":"26.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3534 Sheet 5","linkHelpText":"- Geologic Map of Puerto Rico and the Virgin Islands"},{"id":493260,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3534/sim3534_sheet4.pdf","text":"Sheet 4","size":"6.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3534 Sheet 4","linkHelpText":"- Geologic Map of Hispaniola"},{"id":493259,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3534/sim3534_sheet3.pdf","text":"Sheet 3","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3534 Sheet 3","linkHelpText":"- Geologic Map of Jamaica"},{"id":493258,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3534/sim3534_sheet2.pdf","text":"Sheet 2","size":"22.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3534 Sheet 2","linkHelpText":"- Geologic Map of Cuba and the Cayman Islands"},{"id":493257,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3534/sim3534_sheet1.pdf","text":"Sheet 1","size":"30.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3534 Sheet 1","linkHelpText":"- Geologic Map of the Greater Antilles and Virgin Islands"},{"id":493255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3534/coverthb.jpg"},{"id":493256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3534/sim3534_pamphlet.pdf","text":"Pamphlet","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3534 Pamphlet"}],"country":"Cayman Islands, Cuba, Dominican Republic, Great Britain, Haiti, Jamaica, United States","otherGeospatial":"Greater Antilles, Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.41689796960137,\n 23.68158826700146\n ],\n [\n -85.41689796960137,\n 16.88696307754377\n ],\n [\n -63.546232446293374,\n 16.88696307754377\n ],\n [\n -63.546232446293374,\n 23.68158826700146\n ],\n [\n -85.41689796960137,\n 23.68158826700146\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Passive acoustic monitoring is a cost-effective, minimally invasive technology commonly used to study behavior and population dynamics of soniferous fish species. To understand the strengths and limitations of acoustic monitoring for this purpose at fish spawning aggregations (FSA) requires an assessment of the variability in aggregation-associated sounds (AAS) as a function of time, space, and proximity for spawning fishes of interest. Here, we evaluate temporal and spatial trends in the detection of AAS by Nassau Grouper (Epinephelus striatus) using an array of six hydrophones deployed across a large Nassau Grouper FSA at Little Cayman, Cayman Islands. We collected continuous data for nine days during a winter spawning season and subsequently used an automatic classifier to extract the embedded Nassau Grouper AAS. Using these data, we analyzed variability in spatiotemporal AAS detection rates across the array with a Bayesian mixed effects model. We found high variability in the detection of AAS across the spawning site, with positive correlations among neighboring hydrophone pairs trending toward negative correlations with distances exceeding 350 m. Indeed, temporal trends in AAS rates at the spawning site were approximately inverted at the two most distant hydrophones (~600 m). Across the hydrophone network, our model predicted strong positive effects of fish proximity, spawning behavior, and crepuscular periods on detected AAS. Our findings suggest hydrophone placement can strongly influence AAS detection rates and even basic temporal patterns in AAS across the spawning season. Given both the vagaries of movement and behavior of aggregating fish at spawning sites and the limits of AAS detection using standard monitoring tools, we suggest spawning site acoustic monitoring programs deploy hydrophone arrays of sufficient size to capture the site-wide trends in AAS rates if possible; this is particularly true if researchers hope to compare/contrast AAS rates between spawning sites or across seasons for the purpose of population assessment.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70081","usgsCitation":"Van Horn, C.J., Candelmo, A.C., Heppell, S.A., McCoy, C.R., Pattengill-Semmens, C.V., Waterhouse, L., Cherubin, L.M., Taylor, J., Michaels, W., Locascio, J., Ibrahim, A.K., and Semmens, B.X., 2025, Hydrophone placement yields high variability in detection of Epinephelus striatus calls at a spawning site.: Ecological Applications, v. 35, no. 5, e70081, 21 p., https://doi.org/10.1002/eap.70081.","productDescription":"e70081, 21 p.","ipdsId":"IP-170566","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494455,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70081","text":"Publisher Index Page"},{"id":494311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Little Cayman, Cayman Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.1384616529091,\n 19.741226725935803\n ],\n [\n -80.1384616529091,\n 19.647682249769503\n ],\n [\n -79.94337474038241,\n 19.647682249769503\n ],\n [\n -79.94337474038241,\n 19.741226725935803\n ],\n [\n -80.1384616529091,\n 19.741226725935803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Horn, Cameron J.","contributorId":359810,"corporation":false,"usgs":false,"family":"Van Horn","given":"Cameron","middleInitial":"J.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":946323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Candelmo, Alli C.","contributorId":359814,"corporation":false,"usgs":false,"family":"Candelmo","given":"Alli","middleInitial":"C.","affiliations":[{"id":13188,"text":"Reef Environmental Education Foundation (REEF)","active":true,"usgs":false}],"preferred":false,"id":946325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heppell, Scott A.","contributorId":359816,"corporation":false,"usgs":false,"family":"Heppell","given":"Scott","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":946326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCoy, Croy R.M.","contributorId":359818,"corporation":false,"usgs":false,"family":"McCoy","given":"Croy","middleInitial":"R.M.","affiliations":[{"id":85923,"text":"Department of Environment","active":true,"usgs":false}],"preferred":false,"id":946327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pattengill-Semmens, Christine V.","contributorId":359819,"corporation":false,"usgs":false,"family":"Pattengill-Semmens","given":"Christine","middleInitial":"V.","affiliations":[{"id":13188,"text":"Reef Environmental Education Foundation (REEF)","active":true,"usgs":false}],"preferred":false,"id":946328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waterhouse, Lynn 0000-0002-7455-7632","orcid":"https://orcid.org/0000-0002-7455-7632","contributorId":348524,"corporation":false,"usgs":true,"family":"Waterhouse","given":"Lynn","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":946329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cherubin, Laurent M.","contributorId":359820,"corporation":false,"usgs":false,"family":"Cherubin","given":"Laurent","middleInitial":"M.","affiliations":[{"id":65664,"text":"Harbor Branch Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":946330,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Taylor, J. Christopher","contributorId":359821,"corporation":false,"usgs":false,"family":"Taylor","given":"J. Christopher","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":946331,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Michaels, William","contributorId":359822,"corporation":false,"usgs":false,"family":"Michaels","given":"William","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":946332,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Locascio, James","contributorId":359823,"corporation":false,"usgs":false,"family":"Locascio","given":"James","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":946333,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ibrahim, Ali K.","contributorId":359812,"corporation":false,"usgs":false,"family":"Ibrahim","given":"Ali","middleInitial":"K.","affiliations":[{"id":65664,"text":"Harbor Branch Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":946324,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Semmens, Brice X.","contributorId":359824,"corporation":false,"usgs":false,"family":"Semmens","given":"Brice","middleInitial":"X.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":946334,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70270735,"text":"70270735 - 2025 - Impacts of flowering rush (Butomus umbellatus L.) on macrophyte diversity and composition in the Upper Mississippi River","interactions":[],"lastModifiedDate":"2025-08-22T14:51:12.417579","indexId":"70270735","displayToPublicDate":"2025-08-06T07:45:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of flowering rush (Butomus umbellatus L.) on macrophyte diversity and composition in the Upper Mississippi River","docAbstract":"Flowering rush (Butomus umbellatus L.), a perennial plant native to Eurasia, made a widespread appearance in the Upper Mississippi River in the United States in 2020, following extremely high river discharge during the previous year. Flowering rush expanded rapidly and was found at 1–10% of sites (n = 6,630 total sites) across a 400 km river reach within the first 4 years of invasion. Flowering rush invaded at least 12 of 31 wetland vegetation classes, including submersed aquatic, rooted-floating, deep marsh, and shallow marsh. Analysis of long-term macrophyte data and our targeted field study revealed that plant diversity declined with greater abundance of flowering rush over a 4-year early invasion period, suggesting that native species were displaced. Furthermore, species correlation plots showed a significant negative correlation (r < -0.1) between flowering rush and several native species, including wild celery, water stargrass, and wild rice. Non-metric multi-dimensional scaling (NMDS) ordination placed flowering rush near the center of the plot, which may indicate tolerance to a wide range of environmental conditions such as water depth, flow, and substrate. Centering on the NMDS plot also shows that flowering rush invades many types of vegetated aquatic land cover classes, which was also supported by our geographic information systems analysis of land cover invasion. These habitat associations and ecological impacts of the recent, widespread invasion of flowering rush in the Upper Mississippi River can help inform restoration and management actions during early invasion. Continuing long-term data collection can break limitations on modeling cause-effect relationships and provide insights to the future ecological trajectory of the macrophyte community to this non-native invasive species.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10530-025-03643-z","usgsCitation":"Carhart, A., Larson, D.M., Froehly, J., Lund, E., Szura, S., and Fopma, S., 2025, Impacts of flowering rush (Butomus umbellatus L.) on macrophyte diversity and composition in the Upper Mississippi River: Biological Invasions, v. 27, 188, 16 p., https://doi.org/10.1007/s10530-025-03643-z.","productDescription":"188, 16 p.","ipdsId":"IP-176660","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":494516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.21798186280225,\n 45.16720641933907\n ],\n [\n -93.21798186280225,\n 41.95055183534416\n ],\n [\n -89.4311724348378,\n 41.95055183534416\n ],\n [\n -89.4311724348378,\n 45.16720641933907\n ],\n [\n -93.21798186280225,\n 45.16720641933907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Carhart, Alicia 0000-0002-9977-8124","orcid":"https://orcid.org/0000-0002-9977-8124","contributorId":223884,"corporation":false,"usgs":false,"family":"Carhart","given":"Alicia","email":"","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":946926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Danelle M. 0000-0001-6349-6267","orcid":"https://orcid.org/0000-0001-6349-6267","contributorId":228838,"corporation":false,"usgs":true,"family":"Larson","given":"Danelle","email":"","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":946927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Froehly, Jennifer","contributorId":360275,"corporation":false,"usgs":false,"family":"Froehly","given":"Jennifer","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":946928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lund, Eric","contributorId":221777,"corporation":false,"usgs":false,"family":"Lund","given":"Eric","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":946929,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Szura, Stephanie","contributorId":360278,"corporation":false,"usgs":false,"family":"Szura","given":"Stephanie","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":946930,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fopma, Seth","contributorId":360281,"corporation":false,"usgs":false,"family":"Fopma","given":"Seth","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":946931,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269823,"text":"fs20253036 - 2025 - Applying U.S. Geological Survey science to understand effects to water supply in the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2026-02-03T14:46:41.794993","indexId":"fs20253036","displayToPublicDate":"2025-08-05T16:10:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3036","displayTitle":"Applying U.S. Geological Survey Science to Understand Effects to Water Supply in the Upper Colorado River Basin","title":"Applying U.S. Geological Survey science to understand effects to water supply in the Upper Colorado River Basin","docAbstract":"The Colorado River Basin is a vital source of water to more than 40 million people in the Western United States and Mexico, including in major cities like Denver, Las Vegas, Phoenix, Tucson, Los Angeles, and San Diego, and supports irrigation for about 16,000 square kilometers of agricultural land. Since 2000, the southwestern United States has been unusually dry due to low precipitation and warm air temperatures, contributing to extreme water level declines of the two large reservoirs on the Colorado River, Lake Mead and Lake Powell. In 2021, these reservoirs reached their lowest levels on record, resulting in unprecedented restrictions on water usage in the basin. As much as 90 percent of the annual runoff in the Colorado River Basin originates in areas upstream from Lake Powell (hereafter, these areas will be referred to collectively as the “Upper Basin”). Consequently, understanding the processes that can affect water supply in the Upper Basin could be crucial for supporting human, agricultural, and ecological needs across a large spatial scale.
The U.S. Geological Survey (USGS) does a wide variety of science in cooperation with resource managers, municipalities, tribes, and local, State, and Federal agencies to help improve understanding of processes, such as streamflow and water quality, potentially affecting water supply in the Upper Basin. This fact sheet describes three key potential factors affecting water supply in the Upper Basin—snow processes and water storage, wildfire and basin hydrology, and salinity concentrations and water quality—and highlights associated USGS research activities in the basin.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
The U.S. Geological Survey (USGS) cooperates with resource managers, municipalities, tribes, and local, State, and Federal agencies to help improve understanding of processes potentially affecting water supply in the Colorado River Basin. This fact sheet describes three key potential factors affecting water supply in the upper portion of the basin—snow processes and water storage, wildfire and basin hydrology, and salinity concentrations and water quality—and highlights associated USGS research activities in the basin. The Colorado River Basin is an important water source for more than 40 million people in the Western United States and Mexico, providing water to major cities and irrigating agricultural land. However, since 2000, the region has faced prolonged drought conditions, leading to record low levels in Lake Mead and Lake Powell and resulting in water usage restrictions. The USGS plays a key role in studying the Colorado River Basin water supply. Understanding the processes that can affect water supply in the upper portion of the basin could be crucial for supporting human, agricultural, and ecological needs across a large spatial scale.
","publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Day, Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":944726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Cory A. 0000-0003-1461-7848 cawillia@usgs.gov","orcid":"https://orcid.org/0000-0003-1461-7848","contributorId":689,"corporation":false,"usgs":true,"family":"Williams","given":"Cory","email":"cawillia@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944727,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270729,"text":"70270729 - 2025 - Harmless tags or hazardous ads? Investigating the potential for ear tags to increase predation on neonatal ungulates","interactions":[],"lastModifiedDate":"2026-01-05T16:38:47.312452","indexId":"70270729","displayToPublicDate":"2025-08-05T12:31:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1176,"text":"Canadian Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"Harmless tags or hazardous ads? Investigating the potential for ear tags to increase predation on neonatal ungulates","docAbstract":"Studies involving individually marked animals provide insights predicated on the assumption marked individuals are accurate models of their unmarked counterparts. Taxa-specific and marker-specific examinations are needed to determine if marked animals are suitable models for the parameter(s) being measured. Our objective was to determine if brightly colored ear tags influenced the probability of predation for neonatal ungulates. We captured 94 neonatal pronghorn (Antilocapra americana (Ord, 1815)), fitted each neonate with a tracking collar, and attached a yellow ear tag to 49 (52.1%) of the captured neonates. We monitored the survival of each neonate during 2023–2024 in Oklahoma, USA. Predation was the leading cause of mortality during our monitoring period and accounted for 29 (82.9%) of the 35 mortalities with a known cause. Coyotes (Canis latrans Say, 1823) were the predominant predator of neonatal pronghorn in our study area. Presence of a yellow ear tag seemingly did not influence the probability of predation, even though coyotes can distinguish yellow objects from most natural backgrounds. A larger sample size may be needed to validate our results, but neonatal ungulates with an ear tag appear to be accurate models of neonatal ungulates without an ear tag when examining predation risk.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjz-2025-0007","usgsCitation":"Turnley, M.T., Fairbanks, W.S., Lonsinger, R.C., Cherry, M.J., Dart, M.M., DeYoung, R.W., Hahn, D.P., Heffelfinger, L.J., Rickels, C.M., Tanner, E.P., Wang, H.G., and Chitwood, M.C., 2025, Harmless tags or hazardous ads? Investigating the potential for ear tags to increase predation on neonatal ungulates: Canadian Journal of Zoology, v. 82, p. 1-8, https://doi.org/10.1139/cjz-2025-0007.","productDescription":"8 p.","startPage":"1","endPage":"8","ipdsId":"IP-173266","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":496186,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjz-2025-0007","text":"Publisher Index Page"},{"id":494570,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.95879483531866,\n 37.01702846766675\n ],\n [\n -103.00366471233622,\n 36.465192148761275\n ],\n [\n -100.0382639035881,\n 36.444047652380554\n ],\n [\n -99.99355342380954,\n 34.62260870076\n ],\n [\n -97.82482106377475,\n 33.870423470954286\n ],\n [\n -94.34667345797635,\n 33.69831711826369\n ],\n [\n -94.5394914752001,\n 36.95468680737966\n ],\n [\n -102.95879483531866,\n 37.01702846766675\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Turnley, Matthew T.","contributorId":360236,"corporation":false,"usgs":false,"family":"Turnley","given":"Matthew","middleInitial":"T.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":946911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fairbanks, W. 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George","contributorId":360263,"corporation":false,"usgs":false,"family":"Wang","given":"H.","middleInitial":"George","affiliations":[{"id":85772,"text":"East Central University","active":true,"usgs":false}],"preferred":false,"id":946921,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chitwood, M. Colter","contributorId":360265,"corporation":false,"usgs":false,"family":"Chitwood","given":"M.","middleInitial":"Colter","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":946922,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70269614,"text":"70269614 - 2025 - Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","interactions":[],"lastModifiedDate":"2025-08-06T15:04:23.764097","indexId":"70269614","displayToPublicDate":"2025-08-05T09:57:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","docAbstract":"Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne-field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation (r = 0.85) than solely using elevation (r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70356","usgsCitation":"Silva, G., Roberts, D., Byrd, K.B., Chadwick, D., Walker, I., and King, J., 2025, Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands: Ecosphere, v. 16, no. 8, e70356, 22 p., https://doi.org/10.1002/ecs2.70356.","productDescription":"e70356, 22 p.","ipdsId":"IP-172039","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":494433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70356","text":"Publisher Index Page"},{"id":493643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.86741425943123,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.4236991476653\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Silva, German","contributorId":358801,"corporation":false,"usgs":false,"family":"Silva","given":"German","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Dar","contributorId":358803,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":944181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chadwick, Dana","contributorId":358806,"corporation":false,"usgs":false,"family":"Chadwick","given":"Dana","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":944182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, Ian","contributorId":358809,"corporation":false,"usgs":false,"family":"Walker","given":"Ian","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, Jennifer","contributorId":358812,"corporation":false,"usgs":false,"family":"King","given":"Jennifer","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944184,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271386,"text":"70271386 - 2025 - A global dataset of terrestrial biological nitrogen fixation","interactions":[],"lastModifiedDate":"2025-09-11T14:43:06.92465","indexId":"70271386","displayToPublicDate":"2025-08-05T09:39:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"A global dataset of terrestrial biological nitrogen fixation","docAbstract":"Biological nitrogen fixation (BNF) is the main natural source of new nitrogen inputs in terrestrial ecosystems, supporting terrestrial productivity, carbon uptake, and other Earth system processes. We assembled a comprehensive global dataset of field measurements of BNF in all major N-fixing niches across natural terrestrial biomes derived from the analysis of 376 BNF studies. The dataset comprises 32 variables, including site location, biome type, N-fixing niche, sampling year, quantification method, BNF rate (kg N ha−1 y−1), the percentage of nitrogen derived from the atmosphere (%Ndfa), N fixer or N-fixing substrate abundance, BNF rate per unit of N fixer abundance, and species identity. Overall, the dataset combines 1,207 BNF rates for trees, shrubs, herbs, soil, leaf litter, woody litter, dead wood, mosses, lichens, and biocrusts, 152 herb %Ndfa values, 1,005 measurements of N fixer or N-fixing substrate abundance, and 762 BNF rates per unit of N fixer abundance for a total of 424 species across 66 countries. This dataset facilitates synthesis, meta-analysis, upscaling, and model benchmarking of BNF fluxes at multiple spatial scales.
","language":"English","publisher":"Nature","doi":"10.1038/s41597-025-05131-4","usgsCitation":"Reis Ely, C., Perakis, S.S., Cleveland, C., Menge, D., Reed, S.C., Batterman, S., Crews, T., Dynarski, K.A., Gei, M., Gundale, M., Jovan, S.E., Kou-Giesbrecht, S., Peoples, M., Rodriguez-Caballero, E., Salmon, V., Soper, F.M., Staccone, A., Taylor, B., Weber, B., and Wurzburger, N., 2025, A global dataset of terrestrial biological nitrogen fixation: Scientific Data, v. 12, 1362, 10 p., https://doi.org/10.1038/s41597-025-05131-4.","productDescription":"1362, 10 p.","ipdsId":"IP-169686","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-025-05131-4","text":"Publisher Index 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Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":948337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cleveland, Cory C. 0000-0002-8804-4248","orcid":"https://orcid.org/0000-0002-8804-4248","contributorId":353556,"corporation":false,"usgs":false,"family":"Cleveland","given":"Cory C.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":948338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Menge, Duncan 0000-0003-4736-9844","orcid":"https://orcid.org/0000-0003-4736-9844","contributorId":241126,"corporation":false,"usgs":false,"family":"Menge","given":"Duncan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":948339,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Sasha C. 0000-0002-8597-8619 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0000-0003-2447-609X","orcid":"https://orcid.org/0000-0003-2447-609X","contributorId":353564,"corporation":false,"usgs":false,"family":"Gundale","given":"Michael J.","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":948345,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jovan, Sarah E. 0000-0001-7860-4005","orcid":"https://orcid.org/0000-0001-7860-4005","contributorId":361180,"corporation":false,"usgs":false,"family":"Jovan","given":"Sarah","middleInitial":"E.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":948346,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kou-Giesbrecht, Sian 0000-0002-4086-0561","orcid":"https://orcid.org/0000-0002-4086-0561","contributorId":261258,"corporation":false,"usgs":false,"family":"Kou-Giesbrecht","given":"Sian","email":"","affiliations":[{"id":52786,"text":"Columbia 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0000-0002-2188-551X","orcid":"https://orcid.org/0000-0002-2188-551X","contributorId":353567,"corporation":false,"usgs":false,"family":"Salmon","given":"Verity G.","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":948350,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Soper, Fiona M. 0000-0002-9910-9377","orcid":"https://orcid.org/0000-0002-9910-9377","contributorId":361183,"corporation":false,"usgs":false,"family":"Soper","given":"Fiona","middleInitial":"M.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":948351,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Staccone, Anika P. 0000-0002-2558-8715","orcid":"https://orcid.org/0000-0002-2558-8715","contributorId":353568,"corporation":false,"usgs":false,"family":"Staccone","given":"Anika P.","affiliations":[{"id":84436,"text":"Earthshot 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loss"},"id":1}],"lastModifiedDate":"2025-08-07T14:14:57.928515","indexId":"70269975","displayToPublicDate":"2025-08-05T09:14:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Elk personality and anthropogenic food subsidy: Managing conflict and migration loss","docAbstract":"The continued decline of long-distance ungulate migrations threatens to decouple important ecological processes that increase biodiversity and wildlife abundance. Past research has focused on preserving migration paths where habitat fragmentation and loss disrupt movement corridors. However, shifting residency-migration trade-offs are the stronger driver of migration loss in some populations. Suburban residential developments may provide ungulates with anthropogenic food sources and refuge from predators, which can increase population growth among short-distance migrants relative to long-distance migrants. This trend can increase wildlife vehicle collisions and other human–wildlife conflicts while simultaneously reducing hunting opportunities. Yet, individual animals vary in their tolerance of human disturbance. We investigated how interindividual variation relative to conflict and human habituation influences elk migration and space use on shared winter range. We used a clustering algorithm applied to GPS collar data to identify elk use of anthropogenic food resources in suburban habitat. Cluster locations identified all known anthropogenic subsidy locations during the study period. Elk that used suburban anthropogenic food sources also migrated 60% shorter distances between summer and winter ranges than elk with no known use of these food subsidies. Elk use of protected wintering grounds was spatially structured such that conflict-prone, short-distance migrants disproportionately used areas with more human activity. Clustering algorithms applied to GPS collar data may allow managers to identify foci of concentrated use that generates human–wildlife conflict, and where prion deposition and environmental contamination facilitate the spread of chronic wasting disease, particularly in suburban areas with anthropogenic food subsidies. The apparent spatial structuring of shared winter range according to the conflict potential and migration strategy of individual elk may also permit managers to assess relative recruitment among cryptic population segments using different migration strategies and facilitate targeted, adaptive management actions. These associations between conflict, human habituation, and migration shed light on the urbanization of wildlife species, inform efforts to manage human–wildlife conflict and disease spread, and emphasize that a multipronged approach beyond maintaining habitat corridors may be necessary to conserve long-distance migrations for species that can become human-habituated.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ECS2.70344","usgsCitation":"Cotterill, G.G., Cole, E., Cross, P., Dewey, S., Wise, B., and Graves, T., 2025, Elk personality and anthropogenic food subsidy: Managing conflict and migration loss: Ecosphere, v. 16, no. 8, e70344, 12 p., https://doi.org/10.1002/ECS2.70344.","productDescription":"e70344, 12 p.","ipdsId":"IP-159240","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":494437,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70344","text":"Publisher Index Page"},{"id":493704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"National Elk Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111,\n 43.7\n ],\n [\n -111,\n 43.4\n ],\n [\n -110.5,\n 43.4\n ],\n [\n -110.5,\n 43.7\n ],\n [\n -111,\n 43.7\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Cotterill, Gavin G. 0000-0002-1408-778X","orcid":"https://orcid.org/0000-0002-1408-778X","contributorId":346534,"corporation":false,"usgs":true,"family":"Cotterill","given":"Gavin","middleInitial":"G.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":945107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cole, Eric K.","contributorId":302890,"corporation":false,"usgs":false,"family":"Cole","given":"Eric K.","affiliations":[{"id":65572,"text":"U.S. Fish and Wildlife Service, National Elk Refuge","active":true,"usgs":false}],"preferred":false,"id":945108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":945109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dewey, Sarah R.","contributorId":342391,"corporation":false,"usgs":false,"family":"Dewey","given":"Sarah R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":945110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wise, Ben L.","contributorId":359279,"corporation":false,"usgs":false,"family":"Wise","given":"Ben L.","affiliations":[{"id":83136,"text":"Wyoming Game & Fish Department","active":true,"usgs":false}],"preferred":false,"id":945111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":945112,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269632,"text":"fs20253038 - 2025 - Global maps of critical mineral production in 2023","interactions":[],"lastModifiedDate":"2026-02-03T14:45:36.109669","indexId":"fs20253038","displayToPublicDate":"2025-08-05T09:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3038","displayTitle":"Global Maps of Critical Mineral Production in 2023","title":"Global maps of critical mineral production in 2023","docAbstract":"The global production of many mineral commodities, especially critical minerals, is concentrated in a few countries that have mineral resources and the infrastructure necessary to mine and process those resources. For this reason, the type and amount of mineral production differ by country. For example, many countries produce such metallic ores as gold and silver, whereas only a few countries produce magnesium, niobium, platinum-group metals, and rare earths. The concentration of mining and processing in certain countries necessitates the existence of a global supply chain.
A mineral supply chain is the sequence of mining and processing of minerals and manufacturing of products. Mineral supply chains are global in scale, complex, and dynamic. Supply chain data can be used to understand how a country’s mineral resources and various economic, technical, and environmental factors affect the complexity of global supply chains.
This fact sheet summarizes the world’s leading countries (those accounting for 5 percent or more of a commodity’s global production in 2023) for production of select mineral commodities (mainly critical minerals) in the mining and processing stages. These countries and the minerals they produce are synthesized on global maps to communicate the status of, and potential risk to, mineral commodity supply chains from geographic production concentration. Trade data from United Nations Statistics Division (2025) is used to support assessments of the observed production data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253038","usgsCitation":"Chung, J., Xun, S., and Textoris, S.D., 2025, Global maps of critical mineral production in 2023: U.S. Geological Survey Fact Sheet 2025–3038, 5 p., https://doi.org/10.3133/fs20253038.","productDescription":"5 p.","numberOfPages":"5","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-179511","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":494162,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118718.htm","linkFileType":{"id":5,"text":"html"}},{"id":493043,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3038/images/"},{"id":493042,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3038/fs20253038.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2025-3038 XML"},{"id":493041,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253038/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3038 HTML"},{"id":493039,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3038/coverthb.jpg"},{"id":493040,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3038/fs20253038.pdf","text":"Report","size":"5.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3038 PDF"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
","tableOfContents":"Fourier amplitude spectra from regional earthquakes in the eastern United States are used in a parametric inversion for source, path, and site effects. Five earthquakes are selected for analysis during the installation of the United States National Seismic Network (US), Earthscope’s USArray Transportable Array (TA), and other temporary arrays to maximize station coverage. A global search algorithm is used to solve for site response from 0.1 to 15 Hz, corner frequency, geometrical spreading (r-γ), and frequency dependent anelastic attenuation in the form Q(f) = Qof α. Tradeoff between moment and geometric spreading is handled by fixing the moment. The tradeoff between corner frequency and Q(f) is solved by selecting the value of corner frequency that minimizes an objective function defined over all stations. Values of site response and attenuation parameters show a strong spatial correlation with the physiographic provinces of the eastern United States. Site response for the Atlantic Coastal Plain is consistent with previous work using spectral ratios relative to a reference site, defined by strong resonance peaks correlated with the thickness of sediments. Site response for the other physiographic provinces is markedly different from the coastal plain, with a lack of distinct resonance peaks and a broad moderate high at frequences from 0.1 to 0.5 Hz consistent with the hard-rock geology of the regions. Like site response, Q(f) has a strong correlation with physiographic province, showing lower values on the coastal plain and higher values inland. Geometric spreading exponent, γ, decreases with increasing hypocenter distance from just above 1 at a few tens of kilometers to 0.9 at 500 km. The limited range in geometric spreading values is attributed to starting the Fourier transform window at the S‐wave arrival for all distances and averaging over multiple wave types.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0120250066","usgsCitation":"Hartzell, S.H., Martinetti, L., Mendoza, C., and Schmitt, R.G., 2025, Site response and wave propagation effects in the eastern United States: Bulletin of the Seismological Society of America, v. 115, no. 5, p. 2485-2506, https://doi.org/10.1785/0120250066.","productDescription":"22 p.","startPage":"2485","endPage":"2506","ipdsId":"IP-174795","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":493930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.07834108451145,\n 43.347066936734876\n ],\n [\n -83.07190566074532,\n 41.15850918749996\n ],\n [\n -84.72974599179413,\n 38.49572818331108\n ],\n [\n -87.6040403599389,\n 37.557442613196955\n ],\n [\n -90.35205007389611,\n 32.79825745697784\n ],\n [\n -81.70066250884963,\n 32.45943477635677\n ],\n [\n -76.33120640624651,\n 37.93006949063302\n ],\n [\n -74.11435019028751,\n 44.90250530044207\n ],\n [\n -74.96201722230823,\n 45.03628280801624\n ],\n [\n -78.07834108451145,\n 43.347066936734876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinetti, Luis B.","contributorId":359446,"corporation":false,"usgs":false,"family":"Martinetti","given":"Luis B.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":945457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mendoza, Carlos 0000-0002-2428-7064","orcid":"https://orcid.org/0000-0002-2428-7064","contributorId":343872,"corporation":false,"usgs":false,"family":"Mendoza","given":"Carlos","email":"","affiliations":[{"id":18923,"text":"Universidad Nacional Autonoma de Mexico","active":true,"usgs":false}],"preferred":false,"id":945458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Robert G. 0000-0001-8060-1954 rschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-1954","contributorId":5611,"corporation":false,"usgs":true,"family":"Schmitt","given":"Robert","email":"rschmitt@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945459,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269896,"text":"70269896 - 2025 - Contributions of Great Salt Lake playa- and industrially-sourced priority pollutant metals in dust contribute to possible health hazards in the communities of northern Utah","interactions":[],"lastModifiedDate":"2025-08-06T14:53:59.285549","indexId":"70269896","displayToPublicDate":"2025-08-05T07:48:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16135,"text":"GeoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Contributions of Great Salt Lake playa- and industrially-sourced priority pollutant metals in dust contribute to possible health hazards in the communities of northern Utah","docAbstract":"Communities and ecosystems of northern Utah, USA receive particulate pollution from anthropogenic activity and dust emissions from sources including the Great Salt Lake (“the Lake”) playa. In addition to affecting communities, anthropogenic pollution is delivered to the Lake's playa sediments, which are eroded during dust events. Yet, spatial variability in dust flux and composition and their risks to human health are poorly understood. We analyzed dust in 17 passive samplers proximal to the Lake during fall 2022 for dust flux, the dust fraction of particulate matter, 87Sr/86Sr, and elemental geochemistry. We evaluated spatial patterns of 11 priority pollutant metals and estimated the hypothetical non-cancer dust and soil ingestion health hazard for six age cohorts. We observed the highest dust fluxes proximal to the Lake's playa. The highest concentrations of and greatest number of metals occurred in and south of Ogden, UT. Sites to the northeast of Farmington Bay had the highest fluxes. Metal concentrations and 87Sr/86Sr suggest that the dust composition near Bountiful represents contributions from anthropogenic sources, whereas the dust composition to the northeast of Farmington Bay reflects the Lake's playa emissions. Evaluations of potential health hazards from dust ingestion suggest that children between birth and 6 years are vulnerable at higher ingestion rates. Thallium, As, Pb, Co and Cr contributed most to the estimated hazard. Among these, As and sometimes Pb are likely derived from the Lake's playa emissions. Thus, suppression of dust emissions from the Lake's playa may decrease possible health risks for children in northern Utah.
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Spatial analysis of predicted grain-sizes in the context of Chinook salmon spawning habitat suggests that suitable size sediment patches in the upper, unregulated reaches the study basin is typically small and unorganized. In contrast, the larger rivers downstream of high-head dams typically have larger areas of suitable spawning gravels. This method may be useful for quantification of fish and macroinvertebrates habitats, surface grain-size metrics for sediment transport models, and monitoring of natural and anthropogenic changes in river systems.
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Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey (USGS) operates a National Water Quality Network (NWQN) to monitor trends in groundwater quality and assess emerging contaminants of concern. It is a “network of networks” with 81 subnetworks being sampled on a decadal time scale. Each year, 8 of the subnetworks are sampled. Subnetworks have 20–30 wells each and include studies of domestic supply wells or shallow groundwater (20–50 feet deep) underlying urban land use or agricultural land use. Currently there are 2,089 wells in the network. All wells are sampled for physical properties, nutrients, major ions, trace elements, per- and polyfluoroalkyl substances (PFAS), and a subset of wells are sampled for pesticides, volatile organic compounds, radionuclides, and microbiological contaminants.
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Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
More than 10 years following the onset of the sea star wasting disease (SSWD) epidemic, affecting over 20 asteroid species from Mexico to Alaska, the causative agent has been elusive. SSWD killed billions of the most susceptible species, sunflower sea stars (Pycnopodia helianthoides), initiating a trophic cascade involving unchecked urchin population growth and the widespread loss of kelp forests. Identifying the causative agent underpins the development of recovery strategies. Here we induced disease and subsequent mortality in exposure experiments using tissue extracts, coelomic fluid and effluent water from wasting sunflower sea stars, with no mortality in controls. Deep sequencing of diseased sea star coelomic fluid samples from experiments and field outbreaks revealed a dominant proportion of reads assigned to the bacterium Vibrio pectenicida. Fulfilling Koch’s postulates, V. pectenicida strain FHCF-3, cultured from the coelomic fluid of a diseased sunflower sea star, caused disease and mortality in exposed sunflower sea stars, demonstrating that it is a causative agent of SSWD. This discovery will enable recovery efforts for sea stars and the ecosystems affected by their decline by facilitating culture-based experimental research and broad-scale screening for pathogen presence and abundance in the laboratory and field.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-025-02797-2","usgsCitation":"Prentice, M.B., Crandall, G., Chan, A.M., Davis, K.M., Hershberger, P., Finke, J.F., Hodin, J., McCracken, A., Kellogg, C.T., Carvalho, R., Prentice, C., Zhong, K.X., Harvell, D., Suttle, C.A., and Gehman, A.M., 2025, Vibrio pectenicida strain FHCF-3 is a causative agent of sea star wasting disease: Nature Ecology & Evolution, v. 9, p. 1739-1751, https://doi.org/10.1038/s41559-025-02797-2.","productDescription":"13 p.","startPage":"1739","endPage":"1751","ipdsId":"IP-174859","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":496267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2025-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Prentice, Melanie B.","contributorId":361921,"corporation":false,"usgs":false,"family":"Prentice","given":"Melanie","middleInitial":"B.","affiliations":[{"id":86390,"text":"The University of British Columbia, Vancouver, Canada; The Hakai Institute","active":true,"usgs":false}],"preferred":false,"id":949664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crandall, Grace","contributorId":361922,"corporation":false,"usgs":false,"family":"Crandall","given":"Grace","affiliations":[{"id":86393,"text":"University of Washington; Seattle, USA.","active":true,"usgs":false}],"preferred":false,"id":949665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chan, Amy M.","contributorId":361923,"corporation":false,"usgs":false,"family":"Chan","given":"Amy","middleInitial":"M.","affiliations":[{"id":86394,"text":"The University of British Columbia, Vancouver, Canada","active":true,"usgs":false}],"preferred":false,"id":949666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Katherine M.","contributorId":361924,"corporation":false,"usgs":false,"family":"Davis","given":"Katherine","middleInitial":"M.","affiliations":[{"id":86390,"text":"The University of British Columbia, Vancouver, Canada; The Hakai Institute","active":true,"usgs":false}],"preferred":false,"id":949667,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hershberger, Paul 0000-0002-2261-7760","orcid":"https://orcid.org/0000-0002-2261-7760","contributorId":203322,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":949668,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Finke, Jan F.","contributorId":361925,"corporation":false,"usgs":false,"family":"Finke","given":"Jan","middleInitial":"F.","affiliations":[{"id":86390,"text":"The University of British Columbia, Vancouver, Canada; The Hakai Institute","active":true,"usgs":false}],"preferred":false,"id":949669,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hodin, Jason","contributorId":295360,"corporation":false,"usgs":false,"family":"Hodin","given":"Jason","email":"","affiliations":[{"id":63853,"text":"Friday Harbor Labs","active":true,"usgs":false}],"preferred":false,"id":949670,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCracken, Andrew","contributorId":361926,"corporation":false,"usgs":false,"family":"McCracken","given":"Andrew","affiliations":[{"id":86395,"text":"Department of Biology, University of Vermont; Burlington, USA.","active":true,"usgs":false}],"preferred":false,"id":949671,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kellogg, Colleen T.","contributorId":361927,"corporation":false,"usgs":false,"family":"Kellogg","given":"Colleen","middleInitial":"T.","affiliations":[{"id":86396,"text":"The Hakai Institute; Campbell River, Canada.","active":true,"usgs":false}],"preferred":false,"id":949672,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Carvalho, Rute","contributorId":361928,"corporation":false,"usgs":false,"family":"Carvalho","given":"Rute","affiliations":[{"id":86396,"text":"The Hakai Institute; Campbell River, Canada.","active":true,"usgs":false}],"preferred":false,"id":949673,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Prentice, Carolyn","contributorId":361929,"corporation":false,"usgs":false,"family":"Prentice","given":"Carolyn","affiliations":[{"id":86396,"text":"The Hakai Institute; Campbell River, Canada.","active":true,"usgs":false}],"preferred":false,"id":949674,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Zhong, Kevin X.","contributorId":361930,"corporation":false,"usgs":false,"family":"Zhong","given":"Kevin","middleInitial":"X.","affiliations":[{"id":86394,"text":"The University of British Columbia, Vancouver, Canada","active":true,"usgs":false}],"preferred":false,"id":949675,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Harvell, Drew","contributorId":149982,"corporation":false,"usgs":false,"family":"Harvell","given":"Drew","email":"","affiliations":[{"id":17869,"text":"Department of Ecology & Evolutionary Biology, Cornell University, Ithaca, NY 14853","active":true,"usgs":false}],"preferred":false,"id":949676,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Suttle, Curtis A.","contributorId":361931,"corporation":false,"usgs":false,"family":"Suttle","given":"Curtis","middleInitial":"A.","affiliations":[{"id":86394,"text":"The University of British Columbia, Vancouver, Canada","active":true,"usgs":false}],"preferred":false,"id":949677,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Gehman, Alyssa-Lois M.","contributorId":361932,"corporation":false,"usgs":false,"family":"Gehman","given":"Alyssa-Lois","middleInitial":"M.","affiliations":[{"id":86390,"text":"The University of British Columbia, Vancouver, Canada; The Hakai Institute","active":true,"usgs":false}],"preferred":false,"id":949678,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70271149,"text":"70271149 - 2025 - Three decades of declines restructure butterfly communities in the Midwestern United States","interactions":[],"lastModifiedDate":"2025-08-29T13:36:55.913684","indexId":"70271149","displayToPublicDate":"2025-08-04T08:31:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Three decades of declines restructure butterfly communities in the Midwestern United States","docAbstract":"Insects are declining worldwide, yet gaps remain in our understanding of how declines are distributed across species within communities. Using three decades of butterfly monitoring data aggregated from the Midwestern United States, we found that no butterfly species increased in abundance from 1992 to 2023. 59 out of 136 species declined (annual mean trend: −1.2 to −6.9% per year) with losses distributed across all functional groups including residents, migrants, rare, and common species. Community composition changed such that abundance is now more even across species, driven by more severe losses in abundance—but not richness—of common species compared to rare species. These widespread declines are likely cascading across ecosystems. Conservation efforts that focus on entire communities could mitigate butterfly biodiversity loss.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2501340122","usgsCitation":"Leuenberger, W., Doser, J.W., Belitz, M.W., Ries, L., Haddad, N.M., Thogmartin, W.E., and Zipkin, E.F., 2025, Three decades of declines restructure butterfly communities in the Midwestern United States: PNAS, v. 122, no. 33, e2501340122, 8 p., https://doi.org/10.1073/pnas.2501340122.","productDescription":"e2501340122, 8 p.","ipdsId":"IP-173907","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":495176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2501340122","text":"Publisher Index Page"},{"id":495078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, 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