Bottled water facilities exist across the United States (U.S.) in all 50 states and have the potential to affect localities in which they are located. This study aims to understand how water bottling facilities are portrayed in news media in the U.S., focusing on economic and environmental tradeoffs, by using Natural Language Processing techniques, specifically Structural Topic Modeling and Lexicon-based Categorization, across different U.S. states and time periods. Through our stratified analysis, we identified key environmental topics and natural resources, as well as companies attracting media attention in different regions and time periods. Results suggest that: (1) the increase in news media publications were correlated with current events such as drought or the start or change in operations of bottling facilities, and (2) these current events also influenced whether the coverage focused on economic topics or environmental concerns. The balance of water availability and economic development is a theme prevalent among the results of both forms of analysis. This study demonstrates the importance of understanding the unique values of a locality before making decisions that may affect residents.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/2515-7620/adf1e1","usgsCitation":"Chan, A., and Christenson, C., 2025, Understanding economic and environmental tradeoffs of bottled water facilities using Structural Topic Modeling and Lexicon-based categorization of public news media: Environmental Research Communications, v. 7, no. 8, 085003, 14 p., https://doi.org/10.1088/2515-7620/adf1e1.","productDescription":"085003, 14 p.","ipdsId":"IP-176981","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":495366,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2515-7620/adf1e1","text":"Publisher Index Page"},{"id":495314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Chan, Alisha Yee 0000-0001-5652-8013","orcid":"https://orcid.org/0000-0001-5652-8013","contributorId":302874,"corporation":false,"usgs":true,"family":"Chan","given":"Alisha Yee","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christenson, Catherine 0000-0001-5944-2186 cchristenson@usgs.gov","orcid":"https://orcid.org/0000-0001-5944-2186","contributorId":200263,"corporation":false,"usgs":true,"family":"Christenson","given":"Catherine","email":"cchristenson@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948364,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269892,"text":"sir20255066 - 2025 - Simulated hydrologic responses to proposed wastewater-returnflow scenarios in Falmouth, Massachusetts","interactions":[],"lastModifiedDate":"2026-04-01T14:28:13.392829","indexId":"sir20255066","displayToPublicDate":"2025-08-08T08:55:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5066","displayTitle":"Simulated Hydrologic Responses to Proposed Wastewater-Return-Flow Scenarios in Falmouth, Massachusetts","title":"Simulated hydrologic responses to proposed wastewater-returnflow scenarios in Falmouth, Massachusetts","docAbstract":"The Cape Cod aquifer is the sole source of drinking water for communities on Cape Cod, Massachusetts, including the Town of Falmouth, where the aquifer is currently threatened by contamination from septic-system-derived nitrogen. To address this problem, the Town is proposing to sewer areas of Falmouth, treat the wastewater at the Town’s Main Wastewater Treatment Facility (a nitrogen removing/tertiary treatment facility), and discharge the treated wastewater to an ocean outfall pipe in Nantucket Sound.
The U.S. Geological Survey, in cooperation with the Town of Falmouth, updated a three-dimensional steady-state groundwater flow model to represent current (defined as 2019–23) average hydrologic conditions and to simulate the long-term average freshwater hydrologic response to two wastewater-return-flow scenarios. Scenario 1 involves the sewering of all properties south of Route 28 in Falmouth, which approximates the Town’s possible sewer expansion over the next 20–30 years. Scenario 2 involves sewering of all properties in Falmouth to demonstrate the maximum potential effect of sewering on the aquifer.
Overall, the simulated hydrologic response of water-table altitudes and streamflow in both scenarios was relatively small compared to fluctuations from natural recharge. In scenario 1, the water-table altitude decreased by about 0.1 feet south of Route 28, where the conversion to municipal sewers removed wastewater-return flow from onsite septic systems. The water-table altitude decreased by about 0.1–0.2 feet over a larger area in Falmouth under town-wide sewering in scenario 2. The greatest decrease in water-table altitude in both scenarios occurred near the Main Wastewater Treatment Facility, with a decrease of about 1.1 feet in scenario 1 and about 1.3 feet in scenario 2.
Simulated decreases in streamflow also were estimated for six selected streams in Falmouth and Mashpee. In both scenarios, the largest simulated decreases in streamflow were at the Coonamessett River, which is the closest stream to the Main Wastewater Treatment Facility. In scenario 1, the average annual decrease in flow at the Coonamessett River was 0.1 cubic feet per second, a 1.1 percent decrease from current (2019–23) conditions. In scenario 2, streamflow at the Coonamessett River decreased by 0.6 cubic feet per second, a 5.4 percent decrease from current (2019–23) conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255066","collaboration":"Prepared in cooperation with the Town of Falmouth","usgsCitation":"Goldstein, K.M.F., and McCobb, T.D., 2025, Simulated hydrologic responses to proposed wastewater-returnflow scenarios in Falmouth, Massachusetts (ver. 1.1, 2026): U.S. Geological Survey Scientific Investigations Report 2025–5066, 19 p., https://doi.org/10.3133/sir20255066.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172502","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501695,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5066/versionHist.txt","size":"694 B","linkFileType":{"id":2,"text":"txt"}},{"id":493661,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1O3SSE5","text":"USGS data release","linkHelpText":"MODFLOW-2005 groundwater flow model used to simulate wastewater-return-flow scenarios in Falmouth, Massachusetts"},{"id":493660,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5066/images/"},{"id":493659,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5066/sir20255066.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5066 XML"},{"id":493658,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255066/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5066 HTML"},{"id":493657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5066/sir20255066.pdf","text":"Report","size":"4.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5066 PDF"},{"id":493656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5066/coverthb2.jpg"}],"country":"United States","state":"Massachusetts","city":"Falmouth","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.69386874698256,\n 41.790542081897485\n ],\n [\n -70.69386874698256,\n 41.50620936893142\n ],\n [\n -70.2313991423423,\n 41.50620936893142\n ],\n [\n -70.2313991423423,\n 41.790542081897485\n ],\n [\n -70.69386874698256,\n 41.790542081897485\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 8, 2025; Version 1.1: April 1, 2026","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Water level drawdowns are common in reservoirs and can affect dissolved oxygen (DO) dynamics via several pathways. In large storage reservoirs, inflow deltas are often important sites for sediment deposition, with some sediment laden rivers forming highly dynamic delta regions as they enter the reservoir. As water levels change, deposited sediment may be remobilized and affect pelagic DO dynamics. Here, we analyze a long-term set of DO profiles to ask how water levels have interacted with both reservoir age and spring inflow volumes to affect metalimnion low DO events in Lake Powell, desert Southwest, USA. The most supported model suggests that declining water levels interact with reservoir age, such that an older and lower elevation reservoir leads to more metalimnion DO consumption, with larger spring snowmelt inflows furthering DO declines. We also conducted incubations to understand how sediment source, monsoon inputs, and water temperature affect DO demand and nutrient cycling. Incubation oxygen demand varied significantly by sediment source, exhibiting modest temperature dependence at the nonmonsoonal sites. We observed the highest oxygen demand from monsoonal inputs and substantial phosphorus release from 2 of 3 sediment types. Our findings emphasize how reservoir aging and hydrological dynamics can combine to reduce DO availability.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/20442041.2025.2476309","usgsCitation":"Deemer, B., Andrews, C.M., Reibold, R.H., Mihalevich, B.A., Sabol, T.A., Drewel, J., and Yackulic, C., 2025, Low water levels interact with reservoir aging to increase the severity of summertime metalimnion dissolved oxygen minima in Lake Powell, desert Southwest, USA: Inland Waters, v. 15, no. 1, 2476309, 16 p., https://doi.org/10.1080/20442041.2025.2476309.","productDescription":"2476309, 16 p.","ipdsId":"IP-169658","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.11073805318645,\n 37.26333469404298\n ],\n [\n -111.74162726575902,\n 36.997266633380335\n ],\n [\n -111.35931664510963,\n 36.87613235542568\n ],\n [\n -110.32254234263192,\n 37.24143878879368\n ],\n [\n -110.31017219601101,\n 37.95477774275503\n ],\n [\n -111.11073805318645,\n 37.26333469404298\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andrews, Caitlin M.","contributorId":361011,"corporation":false,"usgs":false,"family":"Andrews","given":"Caitlin","middleInitial":"M.","affiliations":[{"id":86147,"text":"National Park Service, Southern Florida and Caribbean Network, Flagstaff AZ","active":true,"usgs":false}],"preferred":false,"id":948108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reibold, Robin H. 0000-0002-3323-487X","orcid":"https://orcid.org/0000-0002-3323-487X","contributorId":207499,"corporation":false,"usgs":true,"family":"Reibold","given":"Robin","email":"","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mihalevich, Bryce A.","contributorId":361012,"corporation":false,"usgs":false,"family":"Mihalevich","given":"Bryce","middleInitial":"A.","affiliations":[{"id":86149,"text":"Bureau of Reclamation, Upper Colorado Basin, Salt Lake City UT","active":true,"usgs":false}],"preferred":false,"id":948110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drewel, Jeremiah","contributorId":361013,"corporation":false,"usgs":false,"family":"Drewel","given":"Jeremiah","affiliations":[{"id":86150,"text":"Oregon Water Science Center, U.S. Geological Survey, Klamath Falls OR","active":true,"usgs":false}],"preferred":false,"id":948112,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948113,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271124,"text":"70271124 - 2025 - Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition","interactions":[],"lastModifiedDate":"2026-03-27T17:34:41.462684","indexId":"70271124","displayToPublicDate":"2025-08-08T07:55:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14424,"text":"Applied Computing and Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition","docAbstract":"The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2025.100274","usgsCitation":"Goldman, M.A., Lederer, G.W., Rosera, J.M., Graham, G.E., Mishra, A., and Yepremyan, A., 2025, Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition: Applied Computing and Geosciences, v. 27, 100274, 15 p., https://doi.org/10.1016/j.acags.2025.100274.","productDescription":"100274, 15 p.","ipdsId":"IP-164764","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":501736,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FXSPT1","text":"Data Release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Training and validation data from the AI for Critical Mineral Assessment Competition (ver. 2.0, July 2025)"},{"id":495004,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":495070,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2025.100274","text":"Publisher Index Page"}],"volume":"27","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldman, Margaret A. 0000-0003-2232-6362 mgoldman@usgs.gov","orcid":"https://orcid.org/0000-0003-2232-6362","contributorId":176468,"corporation":false,"usgs":true,"family":"Goldman","given":"Margaret","email":"mgoldman@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":947494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lederer, Graham W. 0000-0002-9505-9923","orcid":"https://orcid.org/0000-0002-9505-9923","contributorId":202407,"corporation":false,"usgs":true,"family":"Lederer","given":"Graham","email":"","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":947495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosera, Joshua Mark 0000-0003-3807-5000","orcid":"https://orcid.org/0000-0003-3807-5000","contributorId":270284,"corporation":false,"usgs":true,"family":"Rosera","given":"Joshua","email":"","middleInitial":"Mark","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":947496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":947497,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mishra, Asitang","contributorId":301178,"corporation":false,"usgs":false,"family":"Mishra","given":"Asitang","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":947498,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yepremyan, Alice","contributorId":358951,"corporation":false,"usgs":false,"family":"Yepremyan","given":"Alice","affiliations":[{"id":85724,"text":"NASA - JPL","active":true,"usgs":false}],"preferred":false,"id":947499,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271344,"text":"70271344 - 2025 - Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network","interactions":[],"lastModifiedDate":"2025-09-08T14:58:50.690039","indexId":"70271344","displayToPublicDate":"2025-08-08T07:48:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network","docAbstract":"Smallmouth bass (Micropterus dolomieu) is an important recreational sportfish and destructive non-native species when introduced into freshwater habitats. There is therefore a need to understand the drivers of, and uncertainties in, smallmouth bass population dynamics for various management objectives. We combined long-term smallmouth bass catch-effort and early life history data from a non-native population in the Green River sub-basin of the upper Colorado River to develop a demographic model that links interannual variability in environmental conditions to recruitment in three river reaches. We used the model to quantify how hydrology, river temperature, and exploitation drive smallmouth bass population dynamics. Early life stages were influenced by timing of hatching and discharge. Dispersal of age-0 fish and density-dependent dynamics were identified as primary sources of uncertainty. Determining the true nature of density-dependent dynamics is important, as the impact of exploitation-based management actions is dependent on the strengths of any density-dependent feedbacks. Our model provides a framework to predict smallmouth bass population responses to future climate conditions, reservoir operations, and exploitation levels.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0183","usgsCitation":"Bruckerhoff, L.A., Yackulic, C., Eppehimer, D.E., Bestgen, K.R., Jones, M.T., and Michaud, C., 2025, Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-24, https://doi.org/10.1139/cjfas-2024-0183.","productDescription":"24 p.","startPage":"1","endPage":"24","ipdsId":"IP-166028","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":502659,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":495217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.07791103370728,\n 38.47445328611764\n ],\n [\n -110.07791103370728,\n 38.06202716185106\n ],\n [\n -109.72266590720243,\n 38.06202716185106\n ],\n [\n -109.72266590720243,\n 38.47445328611764\n ],\n [\n -110.07791103370728,\n 38.47445328611764\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bruckerhoff, Lindsey A.","contributorId":361014,"corporation":false,"usgs":false,"family":"Bruckerhoff","given":"Lindsey","middleInitial":"A.","affiliations":[{"id":86151,"text":"Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio","active":true,"usgs":false}],"preferred":false,"id":948117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eppehimer, Drew Elliot 0000-0003-0076-1494","orcid":"https://orcid.org/0000-0003-0076-1494","contributorId":333633,"corporation":false,"usgs":true,"family":"Eppehimer","given":"Drew","email":"","middleInitial":"Elliot","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bestgen, Kevin R.","contributorId":361015,"corporation":false,"usgs":false,"family":"Bestgen","given":"Kevin","middleInitial":"R.","affiliations":[{"id":86153,"text":"Larval Fish Laboratory, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":948120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, M. Tildon","contributorId":361016,"corporation":false,"usgs":false,"family":"Jones","given":"M.","middleInitial":"Tildon","affiliations":[{"id":86154,"text":"U.S. Fish and Wildlife Service, Upper Colorado River Endangered Fish Recovery Program, Vernal","active":true,"usgs":false}],"preferred":false,"id":948121,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Michaud, Chris","contributorId":361017,"corporation":false,"usgs":false,"family":"Michaud","given":"Chris","affiliations":[{"id":86156,"text":"U.S. Fish and Wildlife Service, Upper Colorado River Endangered Fish Recovery Program, Lakewood, Colorado","active":true,"usgs":false}],"preferred":false,"id":948122,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271324,"text":"70271324 - 2025 - Rupture continuity through intermittent pauses in Cascadia slow slip events","interactions":[],"lastModifiedDate":"2025-09-05T15:05:56.488062","indexId":"70271324","displayToPublicDate":"2025-08-07T08:00:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Rupture continuity through intermittent pauses in Cascadia slow slip events","docAbstract":"Cascadia slow slip events (SSEs) are often envisioned as smooth, continuous ruptures, progressively activating tremor asperities as they propagate. Macroscopically, geodetic inversions and spatiotemporal maps of tremor epicenters show steady, uniform migration. In detail tremor is more chaotic and discontinuous. Larger long-term SSEs observed in daily geodetic solutions are inferred to exhibit intermittent pauses that reflect temporary re-locking of the fault, but this temporal resolution limits tests for similar re-locking on shorter timescales. We use temporal measurements of the areal growth and radiated energy of tremor clusters to investigate SSE intermittence. We find that ruptures mirror tremor pauses. Areal growth rate, however, does not reset, and removing the pauses results in smoother and more similar growth measurements among all SSEs. The rupture similarity occurs regardless of size or location and hints at an underlying uniformity and lack of predeterminism in eventual SSE size. Epicentral uncertainty precludes quantifying early rupture stages, but for larger events areal growth follows a power-law and slows with increasing size. Temporal correlations in tremor energy with inferred SSE propagation velocities and tremor rates suggest its use as a proxy for slip velocity. We find that tremor energy is tidally modulated at daily and sub-daily frequencies, and this modulation is continuous through pauses, suggesting a memory of slip state is sustained through them. We argue these pauses reflect unsteady propagation of the slip front, marked by rapid re- and un-locking, and excluding them removes rupture complexity to reveal a diffusive-like slip process and underlying universality in growth.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JB031501","usgsCitation":"Wech, A., and Gomberg, J.S., 2025, Rupture continuity through intermittent pauses in Cascadia slow slip events: Journal of Geophysical Research: Solid Earth, v. 130, no. 8, e2025JB031501, 18 p., https://doi.org/10.1029/2025JB031501.","productDescription":"e2025JB031501, 18 p.","ipdsId":"IP-173639","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":495198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -128.395483482341,\n 50.865572536238915\n ],\n [\n -125.60869035746057,\n 46.041667735587566\n ],\n [\n -124.98377466492585,\n 38.57668752987752\n ],\n [\n -120.96754220889056,\n 38.41486403323435\n ],\n [\n -121.34435422163615,\n 48.16096017318148\n ],\n [\n -123.77679244748882,\n 51.47194946290554\n ],\n [\n -128.395483482341,\n 50.865572536238915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":948053,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":948054,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270897,"text":"70270897 - 2025 - Mapping ecological states in the upper Colorado River basin: Implications for fire management","interactions":[],"lastModifiedDate":"2025-08-26T14:55:55.492663","indexId":"70270897","displayToPublicDate":"2025-08-07T07:47:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22185,"text":"Environmental Research: Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Mapping ecological states in the upper Colorado River basin: Implications for fire management","docAbstract":"Spatially explicit information on ecosystem dynamics that offers a mechanistic understanding of ecological processes can benefit environmental management. Broad-scale maps based on state-and-transition models provide valuable insight into transitions among ecological states resulting from specific drivers within areas sharing similar climatic and edaphic characteristics ecological sites (ES). We aimed to quantify ecological dynamics of two ES groups in the Upper Colorado River Basin from 1986 to 2022 through annual maps of ecological states and assess potential drivers of observed state change. This region comprises important sagebrush shrublands and pinyon-juniper woodlands affected by non-native annual grass invasion, wildfires, and drought-induced tree mortality. Using field-based and remote sensing data, we modeled vegetation states using random forest models and mapped the states annually from 1986 to 2022. To demonstrate the utility of the state maps for monitoring and management, we used this time series of maps to investigate the influences of fire and drought on state occurrence. Our findings revealed a statistically significant increase in states invaded by non-native annual species (Invaded state), which replaced Grassland and Shrubland states, while Shrubland states decreased significantly, transitioning to invaded and Woodland states. Invaded states had the highest likelihood of burning, followed by Woodlands. Drought was associated with increased area of Grassland and Bare states, but with decreased area of invaded and Shrubland states. These results indicate an accelerating fire cycle is potentially leading to ongoing regional environmental degradation. Despite increasing drought conditions during the study period, the invaded states continued to increase in area, indicating additional underlying mechanisms. Our reproducible, broad-scale, ecologically-driven state mapping process enhances understanding of how drought, fire, and invasion by non-native plants can transform semiarid landscapes of the western USA.
","language":"English","publisher":"IOPscience","doi":"10.1088/2752-664X/adf55f","usgsCitation":"Severson, J.P., Bishop, T.B., Knight, A.C., Nauman, T.W., McNellis, B.E., Villarreal, M.L., Reed, S.C., Young, K.E., Brunson, M., and Duniway, M.C., 2025, Mapping ecological states in the upper Colorado River basin: Implications for fire management: Environmental Research: Ecology, v. 4, no. 3, 035004, 21 p., https://doi.org/10.1088/2752-664X/adf55f.","productDescription":"035004, 21 p.","ipdsId":"IP-177340","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2752-664x/adf55f","text":"Publisher Index Page"},{"id":494895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.86831536697369,\n 43.42056013100884\n ],\n [\n -113.9651578898021,\n 35.88773718647083\n ],\n [\n -113.42515034117945,\n 35.250820583313256\n ],\n [\n -107.51033502507522,\n 34.06562974390198\n ],\n [\n -106.78782386602788,\n 39.68976401424576\n ],\n [\n -108.89167762624004,\n 42.868819570877264\n ],\n [\n -110.86831536697369,\n 43.42056013100884\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":947311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bishop, Tara B.","contributorId":360618,"corporation":false,"usgs":false,"family":"Bishop","given":"Tara","middleInitial":"B.","affiliations":[{"id":86058,"text":"Utah Valley University, Department of Earth Science, Orem, UT, USA","active":true,"usgs":false}],"preferred":false,"id":947312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W.","contributorId":360619,"corporation":false,"usgs":false,"family":"Nauman","given":"Travis","middleInitial":"W.","affiliations":[{"id":86060,"text":"USDA Natural Resources Conservation Service, Soil and Plant Science Division, Moab, UT, USA","active":true,"usgs":false}],"preferred":false,"id":947314,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McNellis, Brandon E.","contributorId":360620,"corporation":false,"usgs":false,"family":"McNellis","given":"Brandon","middleInitial":"E.","affiliations":[{"id":86061,"text":"Agricultural Research Service, USDA Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":947315,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Villarreal, Miguel L.","contributorId":360621,"corporation":false,"usgs":false,"family":"Villarreal","given":"Miguel","middleInitial":"L.","affiliations":[{"id":86063,"text":"US Geological Survey, Western Geographic Science Center, Moffett Field, CA, USA","active":true,"usgs":false}],"preferred":false,"id":947316,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":217604,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947317,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Young, Kristina E.","contributorId":360622,"corporation":false,"usgs":false,"family":"Young","given":"Kristina","middleInitial":"E.","affiliations":[{"id":86061,"text":"Agricultural Research Service, USDA Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":947318,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brunson, Mark","contributorId":178263,"corporation":false,"usgs":false,"family":"Brunson","given":"Mark","affiliations":[],"preferred":false,"id":947319,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947320,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70270683,"text":"70270683 - 2025 - Density dependence and weather drive dabbling duck spatiotemporal distributions and intercontinental migration","interactions":[],"lastModifiedDate":"2025-08-22T15:44:31.027186","indexId":"70270683","displayToPublicDate":"2025-08-06T10:42:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5641,"text":"Avian Research","active":true,"publicationSubtype":{"id":10}},"title":"Density dependence and weather drive dabbling duck spatiotemporal distributions and intercontinental migration","docAbstract":"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":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 L. 0000-0001-7471-0476","orcid":"https://orcid.org/0000-0001-7471-0476","contributorId":343444,"corporation":false,"usgs":true,"family":"Martin","given":"Sherry","middleInitial":"L.","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":70270407,"text":"70270407 - 2025 - Hydrophone placement yields high variability in detection of Epinephelus striatus calls at a spawning site.","interactions":[],"lastModifiedDate":"2025-08-19T15:06:05.613398","indexId":"70270407","displayToPublicDate":"2025-08-06T07:52:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Hydrophone placement yields high variability in detection of Epinephelus striatus calls at a spawning site.","docAbstract":"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. 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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":70270729,"text":"70270729 - 2025 - Harmless tags or hazardous ads? 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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.
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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. 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Zaffaroni","given":"Paula","affiliations":[{"id":63280,"text":"Universidad de Buenos Aires","active":true,"usgs":false}],"preferred":false,"id":947378,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Van Oevelen, Peter","contributorId":360670,"corporation":false,"usgs":false,"family":"Van Oevelen","given":"Peter","affiliations":[{"id":86080,"text":"GEWEX","active":true,"usgs":false}],"preferred":false,"id":947379,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Wang, Jinbo","contributorId":360671,"corporation":false,"usgs":false,"family":"Wang","given":"Jinbo","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":947380,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Wegiel, Jerry","contributorId":360672,"corporation":false,"usgs":false,"family":"Wegiel","given":"Jerry","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":947381,"contributorType":{"id":1,"text":"Authors"},"rank":32}]}} ,{"id":70270102,"text":"70270102 - 2025 - Site response and wave propagation effects in the eastern United States","interactions":[],"lastModifiedDate":"2025-09-22T15:59:29.316903","indexId":"70270102","displayToPublicDate":"2025-08-05T07:53:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Site response and wave propagation effects in the eastern United States","docAbstract":"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":70269910,"text":"70269910 - 2025 - High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon","interactions":[],"lastModifiedDate":"2025-08-06T14:47:01.818797","indexId":"70269910","displayToPublicDate":"2025-08-05T07:41:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon","docAbstract":"The distribution of river-bed grain sizes plays a foundational role in river morphology and ecology. River-bed grain size is a key driver of channel form and process, and has first order effects on aquatic macroinvertebrate assemblages, fish nesting, and biogeochemical processes. Despite this importance, tools to spatially quantify grain-size distributions, particularly submerged grain-size distributions, are lacking. Efforts to address this knowledge gap include developing optical and sonographic tools, however, these approaches have limitations, especially in shallow rivers and over large spatial extents. This study quantifies submerged grain size at high resolution (1 m2) across 260 km of geomorphically diverse river corridors in the Santiam River Basin, Oregon, by pairing bathymetric Lidar point clouds with georeferenced pebble counts. Results suggest that derivatives of Lidar point clouds are able to accurately estimate measured median grain size across seven of the eight river reaches investigated, including reaches above and below high-head dams. 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.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR039219","usgsCitation":"White, J., Bartelt, K., Overstreet, B., and Kelley, J.R., 2025, High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon: Water Resources Research, v. 61, no. 8, e2024WR039219, 18 p., https://doi.org/10.1029/2024WR039219.","productDescription":"e2024WR039219, 18 p.","ipdsId":"IP-171337","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":494431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr039219","text":"Publisher Index Page"},{"id":493640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Santiam Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.97986671577394,\n 44.89670062678684\n ],\n [\n -122.97986671577394,\n 44.49916161145734\n ],\n [\n -121.95839049307452,\n 44.49916161145734\n ],\n [\n -121.95839049307452,\n 44.89670062678684\n ],\n [\n -122.97986671577394,\n 44.89670062678684\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":944920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartelt, Karen Michelle 0000-0003-4012-1694","orcid":"https://orcid.org/0000-0003-4012-1694","contributorId":316325,"corporation":false,"usgs":true,"family":"Bartelt","given":"Karen Michelle","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelley, Jacob Ryan 0000-0002-0316-679X","orcid":"https://orcid.org/0000-0002-0316-679X","contributorId":300600,"corporation":false,"usgs":true,"family":"Kelley","given":"Jacob","email":"","middleInitial":"Ryan","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":944923,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270065,"text":"70270065 - 2025 - Coral restoration can drive rapid increases in reef accretion potential","interactions":[],"lastModifiedDate":"2025-08-08T14:51:53.917168","indexId":"70270065","displayToPublicDate":"2025-08-04T07:46:37","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":"Coral restoration can drive rapid increases in reef accretion potential","docAbstract":"Coral-reef degradation is disrupting the balance between reef accretion and erosion and threatening the persistence of essential coral-reef habitats. In south Florida, most reefs are already net eroding, and without intervention, valuable ecosystem services may be lost. Coral restoration holds the potential to reverse those trends; however, typical restoration monitoring does not adequately capture key geo-ecological functions. We addressed this knowledge gap using carbonate budgets and Structure-from-Motion models to evaluate the impact of coral restoration on reef-accretion potential and structural complexity at eight offshore and three inshore coral reefs in the Lower Florida Keys. Within 2–6 years following outplanting, restoration of rapidly growing A. cervicornis populations increased reef-accretion potential to 2.8 mm y− 1 and drove significant increases in structural complexity. There was no measurable impact of restoring slower-growing, massive corals on reef-accretion potential inshore; however, whereas the severe 2023 coral-bleaching event immediately following our study caused near-complete mortality of A. cervicornis, 59% of massive corals survived, highlighting potential trade-offs between coral growth and survival on future restoration efficacy. We conclude that although restoration can produce rapid, small-scale increases in reef-accretion potential, there remain important uncertainties about how and whether ecosystem-scale benefits of restoration on important geo-ecological reef functions can persist long term.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-04818-3","usgsCitation":"Toth, L., Johnson, S.A., Lyons, E.O., Spadaro, J., Stathakopoulos, A., Bloomer, S., Mallon, J., Jenkins, C., Williams, S., Combs, I., Craig, Z., and Muller, E., 2025, Coral restoration can drive rapid increases in reef accretion potential: Scientific Reports, v. 15, 28353, 15 p., https://doi.org/10.1038/s41598-025-04818-3.","productDescription":"28353, 15 p.","ipdsId":"IP-176248","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494441,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-04818-3","text":"Publisher Index Page"},{"id":493839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Florida","otherGeospatial":"south Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6585939793311,\n 25.015548783169194\n ],\n [\n -81.6585939793311,\n 24.57435107315807\n ],\n [\n -80.43635167922672,\n 24.57435107315807\n ],\n [\n -80.43635167922672,\n 25.015548783169194\n ],\n [\n -81.6585939793311,\n 25.015548783169194\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyons, Erin O. 0000-0001-9829-6476","orcid":"https://orcid.org/0000-0001-9829-6476","contributorId":316708,"corporation":false,"usgs":true,"family":"Lyons","given":"Erin","email":"","middleInitial":"O.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spadaro, Jason","contributorId":359407,"corporation":false,"usgs":false,"family":"Spadaro","given":"Jason","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":945276,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945277,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bloomer, Sierra Kathleen 0009-0005-1742-6221","orcid":"https://orcid.org/0009-0005-1742-6221","contributorId":359409,"corporation":false,"usgs":true,"family":"Bloomer","given":"Sierra Kathleen","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945278,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mallon, Jennifer","contributorId":357345,"corporation":false,"usgs":false,"family":"Mallon","given":"Jennifer","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":945279,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jenkins, Connor Monroe 0000-0003-1807-3665","orcid":"https://orcid.org/0000-0003-1807-3665","contributorId":357343,"corporation":false,"usgs":true,"family":"Jenkins","given":"Connor Monroe","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945280,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, Sara D.","contributorId":359411,"corporation":false,"usgs":false,"family":"Williams","given":"Sara D.","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":945281,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Combs, Ian","contributorId":357347,"corporation":false,"usgs":false,"family":"Combs","given":"Ian","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":945282,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Craig, Zachary","contributorId":359415,"corporation":false,"usgs":false,"family":"Craig","given":"Zachary","affiliations":[{"id":85793,"text":"DLRN – Division of Aquatic Resources","active":true,"usgs":false}],"preferred":false,"id":945283,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Muller, Erinn","contributorId":149012,"corporation":false,"usgs":false,"family":"Muller","given":"Erinn","affiliations":[],"preferred":false,"id":945284,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70273115,"text":"70273115 - 2025 - Variable partitioning of lithium in rhyolitic melt during decompression and ascent","interactions":[],"lastModifiedDate":"2025-12-16T15:54:26.382187","indexId":"70273115","displayToPublicDate":"2025-08-01T09:48:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Variable partitioning of lithium in rhyolitic melt during decompression and ascent","docAbstract":"The partitioning behavior of Li in magmatic systems is increasingly being investigated due to the economic importance of Li in the transition to sustainable energy resources (e.g., batteries). However, at upper crustal pressures, it remains uncertain whether Li preferentially partitions into the vapor or liquid (brine) phase or remains in the silicate melt. This complicates our ability to determine where Li resides—silicate melt, minerals, or fluid phase—upon eruption, a crucial factor for understanding its postdepositional movement and concentration into a brine or volcano-sedimentary deposit. Here, we present a novel investigation into the behavior of Li within natural evolved melts during continuous magma decompression and ascent using melt embayments (open melt inclusions). Mineral-hosted melt embayments preserve records of the evolving composition of the exterior melt, including degassing pathways and ascent timescales, when paired with appropriate diffusion coefficients. Lithium concentration profiles were measured in quartz-hosted melt embayments from the rapidly quenched eruptive phases of five rhyolitic, caldera-forming eruptions to investigate the behavior of Li during magma decompression and ascent, where vapor partitioning and ascent dynamics were previously established by investigating H2O and CO2 profiles. We find that in four systems, embayments contain lower interior Li concentrations than the coerupted melt inclusions; the fifth system contains the same Li concentrations in embayments and melt inclusions. However, many of these embayments contain gradients, with 84% preserving Li enrichment near the melt-bubble interface, as compared to their interior concentration. We interpret these characteristics to represent two distinct stages of Li partitioning during magma decompression and ascent, in contrast to existing literature that proposes only one type of partitioning behavior. The first stage is interpreted as melt depletion of Li, likely driven by partitioning into an exsolved supercritical fluid phase, supported by the strong correlation between the extent of Li depletion and Cl concentration in the melt, as well as the decompression rate. This behavior then fundamentally shifts, where Li reenriches in the melt, postulated to be driven by the unmixing of the supercritical fluid phase at shallow pressures. For the one system that did not develop Li gradients through decompression, we attribute this to the lower values of Na and Cl in the melt, potentially inhibiting the partitioning of Li into a fluid phase. Importantly, the behavior of Li during decompression is not consistent within or between volcanic centers, highlighting the need for systematic experimental investigation in variable composition melts at pressures relevant to conduit dynamics. This knowledge would improve our ability to model Li profiles to understand magma decompression, and predict where Li resides (e.g., stored in volcanic glass, gas, or crystals) upon eruption prior to any later extraction.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.5171","usgsCitation":"Myers, M., Spallanzani, R., Schwartz, D., Mercer, C.N., and Hosseini, B., 2025, Variable partitioning of lithium in rhyolitic melt during decompression and ascent: Economic Geology, v. 120, no. 5, p. 1191-1206, https://doi.org/10.5382/econgeo.5171.","productDescription":"16 p.","startPage":"1191","endPage":"1206","ipdsId":"IP-169836","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":497728,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.5171","text":"Publisher Index Page"},{"id":497573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Myers, Madison 0000-0003-2271-4445","orcid":"https://orcid.org/0000-0003-2271-4445","contributorId":331812,"corporation":false,"usgs":false,"family":"Myers","given":"Madison","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":952376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spallanzani, Roberta","contributorId":364231,"corporation":false,"usgs":false,"family":"Spallanzani","given":"Roberta","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":952377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwartz, Darin","contributorId":364233,"corporation":false,"usgs":false,"family":"Schwartz","given":"Darin","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":952378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mercer, Celestine N. 0000-0001-8359-4147 cmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-8359-4147","contributorId":4006,"corporation":false,"usgs":true,"family":"Mercer","given":"Celestine","email":"cmercer@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":952379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hosseini, Behnaz","contributorId":364237,"corporation":false,"usgs":false,"family":"Hosseini","given":"Behnaz","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":952380,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269626,"text":"sir20255060 - 2025 - Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island","interactions":[],"lastModifiedDate":"2026-04-08T14:23:42.870821","indexId":"sir20255060","displayToPublicDate":"2025-08-01T09:30:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5060","displayTitle":"Random Forest Regression Models for Estimating Low-Streamflow Statistics at Ungaged Locations in New York, Excluding Long Island","title":"Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island","docAbstract":"Models to estimate low-streamflow statistics at ungaged locations in New York, excluding Long Island and including hydrologically connected basins from bordering States, were developed for the first time by the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation. A total of 224 basin characteristics were developed for 213 unaltered streamgages (locations where the human effects on streamflow were limited), across the following categories: basin geometry, climate, land cover, soils, surficial geology, and other characteristics. The basins with unaltered streamgages were evaluated for potential redundancy, and streamgages in close proximity and with similar drainage areas were flagged and removed from the testing and cross-validation datasets to prevent data leaking from the training dataset to the testing dataset.
Random forest regression models were created by using basin characteristics as predictor variables and by developing a workflow to train, tune, and test the model. Models were developed to estimate the ungaged lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10). The top four basin characteristics used for the 7Q10 and 30Q10 models were drainage area, total stream length, perimeter of the basin, and length of the longest flow path. Results for the 7Q10 and 30Q10 models had coefficients of determination (R2) of 0.796 and 0.853, respectively. The output model results were bias-corrected for ungaged locations across New York and are available within the interactive StreamStats tool.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255060","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stagnitta, T.J., Woda, J.C., and Graziano, A.P., 2025, Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island: U.S. Geological Survey Scientific Investigations Report 2025–5060, 23 p., https://doi.org/10.3133/sir20255060.","productDescription":"Report: v, 23 p.; 2 Data Releases","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167540","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492987,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P146MTRS","text":"USGS data release","linkHelpText":"Random forest regression model archive for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island"},{"id":492986,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NOM6FR","text":"USGS data release","linkHelpText":"Low-flow statistics for New York State, excluding Long Island, computed through March 2022"},{"id":492985,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5060/images/"},{"id":492988,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245055","text":"Scientific Investigations Report 2024–5055","linkHelpText":"- Low-Flow Statistics for Selected Streams in New York, Excluding Long Island"},{"id":492984,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5060 XML"},{"id":492983,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5060 HTML"},{"id":492981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5060/coverthb.jpg"},{"id":492982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.pdf","text":"Report","size":"8.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5060 PDF"},{"id":492989,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://streamstats.usgs.gov/ss/","text":"StreamStats"}],"country":"United States","state":"New York","otherGeospatial":"New York excluding Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.8668360740237,\n 40.82174116460561\n ],\n [\n -73.64101370770591,\n 40.97341618058218\n ],\n [\n -73.641675229166,\n 41.369110620239354\n ],\n [\n -73.44442715633755,\n 41.42737341824312\n ],\n [\n -73.23400688811246,\n 42.735562128301694\n ],\n [\n -73.28689303884832,\n 45.063167069767246\n ],\n [\n -74.92263898531354,\n 45.049241271408505\n ],\n [\n -76.59790269295956,\n 44.152600935032865\n ],\n [\n -76.27472597825448,\n 43.63979148650773\n ],\n [\n -76.9594903941915,\n 43.33322253590592\n ],\n [\n -78.50182350844328,\n 43.459456447836146\n ],\n [\n -79.39789785747713,\n 43.19859290777666\n ],\n [\n -78.90328998597391,\n 42.86522759603616\n ],\n [\n -79.82520290314585,\n 42.24436339597355\n ],\n [\n -79.76467975802953,\n 41.964093469327736\n ],\n [\n -75.37056382699097,\n 42.00382656329654\n ],\n [\n -74.98586389700988,\n 41.54435731278656\n ],\n [\n -73.999903714982,\n 41.001270702844295\n ],\n [\n -73.96336292917533,\n 40.82357491793678\n ],\n [\n -73.8668360740237,\n 40.82174116460561\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
Background
Physics-based three-dimensional (3D) fire behavior models improve planning for prescribed fire application and wildfire mitigation, but require high spatial resolution 3D fuel models as inputs. While multiple methods and data sources for realistically representing 3D, heterogeneous fuels are available, no unifying framework exists to guide the use of these tools to create 3D fuel models across gradients of vegetation characteristics and data availability. Existing data and methods are most uncertain for mid-level fuels (e.g., shrubs and small trees), due to canopy obstruction of remotely sensed data and a relative lack of modeling efforts. Yet, mid-level fuels are especially important as potential ladder fuels and increasingly common as the dominant fuel in type-converted, post-fire, shrub-dominated landscapes.
Results
Here we introduce the Framework for Representing 3D Fuels (FR3D), a general framework for combining multiple data sources and methods to construct 3D fuel models for forested and unforested landscapes. We then demonstrate FR3D in a case study to build a 3D fuelbed model in a post-fire, shrub-dominated landscape using three new methods for deriving mid-level shrub fuels from: (1) Airborne Laser Scanning (ALS), (2) imputation of Terrestrial Laser Scanning (TLS), and (3) generative modeling of TLS. We compare the resulting fuel models and examine how they affected simulated 3D fire behavior using QUIC-Fire. While each method represented the broad landscape patterning of shrubs, differences in shrub loading, height, and cover highlighted advantages and drawbacks of the different methods. Modeled fire behavior was realistic for all fuel representation methods, but rate of spread and fine fuel consumption was sensitive to the different arrangements of shrubs.
Conclusions
The sensitivity of fire behavior to shrub modeling methods emphasizes the need for fuel models that faithfully represent local fuelbed characteristics and conditions, and highlights the value in testing a range of modeled fuels to understand the potential range of prescribed fire outcomes. FR3D and novel methods of modeling mid-level fuel provide a foundation for tool integration efforts and increased site-specificity of fuel representation for physics-based fire models.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-025-00383-2","usgsCitation":"Tutland, N., Wion, A.P., May, C.J., Hutchings, G.C., Nowak, H., Gattiker, J.R., Hiers, J.K., Linn, R.R., Pokswinski, S.M., and Margolis, E.Q., 2025, Representing 3-dimensional fuels for physics-based fire behavior models: A general framework and case study in a type-converted post-fire shrubfield: Fire Ecology, v. 21, 43, 18 p., https://doi.org/10.1186/s42408-025-00383-2.","productDescription":"43, 18 p.","ipdsId":"IP-176508","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":495062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-025-00383-2","text":"Publisher Index Page"},{"id":494909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Sanchez Canyon, Santa Fe National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.03368207720807,\n 36.27913419354171\n ],\n [\n -107.03368207720807,\n 35.6434439241678\n ],\n [\n -106.074466969809,\n 35.6434439241678\n ],\n [\n -106.074466969809,\n 36.27913419354171\n ],\n [\n -107.03368207720807,\n 36.27913419354171\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2025-08-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Tutland, Niko","contributorId":360588,"corporation":false,"usgs":false,"family":"Tutland","given":"Niko","affiliations":[{"id":86045,"text":"New Mexico Consortium","active":true,"usgs":false}],"preferred":false,"id":947236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wion, Andreas Paul 0000-0002-0701-2843","orcid":"https://orcid.org/0000-0002-0701-2843","contributorId":335166,"corporation":false,"usgs":true,"family":"Wion","given":"Andreas","email":"","middleInitial":"Paul","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":947237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Carolina Jasmine 0009-0005-1667-109X","orcid":"https://orcid.org/0009-0005-1667-109X","contributorId":360589,"corporation":false,"usgs":true,"family":"May","given":"Carolina","middleInitial":"Jasmine","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":947238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutchings, Grant C.","contributorId":360590,"corporation":false,"usgs":false,"family":"Hutchings","given":"Grant","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowak, Hope","contributorId":360591,"corporation":false,"usgs":false,"family":"Nowak","given":"Hope","affiliations":[{"id":7197,"text":"Unaffiliated","active":true,"usgs":false}],"preferred":false,"id":947240,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gattiker, James R.","contributorId":360592,"corporation":false,"usgs":false,"family":"Gattiker","given":"James","middleInitial":"R.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947241,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hiers, J. 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The Obed Wild and Scenic River is managed by the National Park Service and covers a protected area of the Obed River headwaters (including four contributing tributaries). The Obed Wild and Scenic River supports a unique ecosystem with eight federally listed species. The National Park Service is responsible for preserving the baseline free-flowing condition of the river and associated outstandingly remarkable values (ORVs). Previous studies have been mostly project-based with differing methods, thus complicating efforts to quantify long-term changes in environmental conditions. This report presents a science plan summarizing (1) ORV conditions, (2) recent results of a decision-support hydrologic model for OBRI, and (3) possible future research priorities. The decision-support model was created to model streamflow conditions and changes in the ORVs since park establishment in 1976 and during three additional time periods. Established baseline conditions could help with management of ORVs not dependent on streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251035","issn":"2331-1258","collaboration":"Prepared in cooperation with the National Park Service","programNote":"Water Availability and Use Science Program","usgsCitation":"Crowley-Ornelas, E.R., Schapansky, R., Blount, T., and Nicholas, N.S., 2025, Decision-support modeling and research priorities for establishing baseline conditions for outstandingly remarkable values, Obed Wild and Scenic River, Tennessee: U.S. Geological Survey Open-File Report 2025–1035, 18 p., https://doi.org/10.3133/ofr20251035.","productDescription":"viii, 18 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-160489","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":493199,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251035/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1035 HTML"},{"id":493198,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1035/ofr20251035.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1035 XML"},{"id":493197,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1035/ofr20251035.pdf","size":"1.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1035"},{"id":493200,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1035/images"},{"id":493196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1035/coverthb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Obed Wild and Scenic River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.95125744767695,\n 36.150994941624745\n ],\n [\n -84.95125744767695,\n 36.049079144332424\n ],\n [\n -84.64800767968804,\n 36.049079144332424\n ],\n [\n -84.64800767968804,\n 36.150994941624745\n ],\n [\n -84.95125744767695,\n 36.150994941624745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
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640 Grassmere Park, Suite 100
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Contact Us- USGS Publications Warehouse
","tableOfContents":"Population growth and recurring droughts in the Black Hills region raised interest in water resources and future availability. The Black Hills hydrology study (BHHS) was initiated in the early 1990s to address questions regarding water resources. Since completion of the BHHS in the early 2000s, the population of the Black Hills region increased by about 39 percent, which has renewed interest in water demand and availability in the Black Hills. The U.S. Geological Survey, in cooperation with the Western Dakota Regional Water System, completed a study to update hydrologic budgets from the BHHS for six of the most used aquifers in the Black Hills. Water availability was determined by comparing results from hydrologic budgets to modern well withdrawals (2003–22) and water rights information. Key updates to the BHHS budgets included adding available data from 1999 to 2022 and determining hydrologic budgets for six aquifers in nine smaller areas (called “subareas”).
Inflows for the hydrologic budget included recharge from precipitation and streamflow losses to aquifers. Total mean annual recharge for the six aquifers in the study area was estimated at 278,900 acre-feet, with 205,100 acre-feet from precipitation recharge and 73,800 acre-feet from streamflow recharge. Mean annual precipitation recharge for the Madison and Minnelusa aquifers together accounted for 76 percent of the total mean annual precipitation recharge, with the Madison aquifer contributing 57,000 acre-feet and the Minnelusa aquifer contributing 98,100 acre-feet. Outflow components estimated for the hydrologic budget include artesian springflow and well withdrawals. Total mean annual artesian springflow in the study area was estimated as 166,100 acre-feet for the combined Madison and Minnelusa aquifers. Mean total annual well withdrawals for 2003–22 in the study area were about 50,000 acre-feet. No increased well withdrawal patterns corresponding to population increases were observed between 2003 and 2022.
Water availability was determined by comparing total annual appropriations and mean and maximum annual well withdrawals for 2003–22 to mean annual recharge for 1931–2022 for each aquifer in subareas 1–9. Modern well withdrawals (mean and maximum for 2003–22) exceeded mean annual recharge for only the Deadwood and Inyan Kara aquifers in subareas 9 and 4, respectively. Additionally, total annual appropriations did not exceed mean annual recharge in most subareas, except most notably in subarea 4 (Rapid City area) where appropriations exceeded recharge for the Madison, Minnelusa, and Inyan Kara aquifers. Total annual appropriations also exceeded mean annual recharge for the Inyan Kara aquifer in subareas 3 and 5. In addition to recharge, water availability includes the water stored in pore spaces of aquifer materials. Estimates of total volume of recoverable water in storage were updated as part of this study to include the portion of aquifers in Wyoming, which were omitted during the BHHS. In total, the estimated total amount of recoverable water in storage in the study area was 356.9 million acre-feet for six major aquifers in the Black Hills area of South Dakota and Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255067","collaboration":"Prepared in cooperation with the Western Dakota Regional Water System","usgsCitation":"Medler, C.J., Anderson, T.M., and Eldridge, W.G., 2025, Hydrologic budgets and water availability of six bedrock aquifers in the Black Hills area, South Dakota and Wyoming, 1931–2022: U.S. Geological Survey Scientific Investigations Report 2025–5067, 87 p., https://doi.org/10.3133/sir20255067.","productDescription":"Report: ix, 87 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-169475","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":493206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QWKUKP","text":"USGS data release","linkHelpText":"Datasets used in constructing hydrologic budgets for six bedrock aquifers in the Black Hills area of South Dakota and Wyoming, 1931–2022"},{"id":493201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5067/coverthb.jpg"},{"id":493202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.pdf","text":"Report","size":"27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Sir 2025–5067"},{"id":493203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.XML"},{"id":493204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5067/images/"},{"id":493205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255067/full"}],"country":"United States","state":"South Dakota, Wyoming","otherGeospatial":"Black Hills area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.5,\n 44.75\n ],\n [\n -104.5,\n 43.25\n ],\n [\n -103,\n 43.25\n ],\n [\n -103,\n 44.75\n ],\n [\n -104.5,\n 44.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
The State of Tennessee has an area of approximately 42,100 square miles and includes six physiographic regions: Blue Ridge, Valley and Ridge, Appalachian Plateaus, Highland Rim, Nashville Basin, and the Gulf Coastal Plains. Up-to-date elevation data support key activities across the State, such as economic development, infrastructure and construction management, agriculture and precision farming, forest resources management, natural resources conservation, flood risk management, emergency management, and urban and regional planning. The State experiences frequent landslides affecting major roadways. High-resolution elevation data can help identify potential landslide-prone areas. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
The 3D Elevation Program (3DEP; refer to sidebar) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at qual-ity level 2 or better to meet the many needs of the Nation and Tennessee. The status of available and in-progress 3DEP baseline lidar data in Tennessee is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $6.32 million in new benefits annually to the State. The top 10 Tennessee business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"