The U.S. Geological Survey, in cooperation with the Suffolk County Water Authority, evaluated the aquifer transmissivity and storage properties at the Alvahs Lane well field north of the village of Cutchogue, New York. This analysis of aquifer properties provides the Suffolk County Water Authority with hydrogeologic information needed to develop water supplies to meet the increasing water demands of the residents of Suffolk County, New York.
An aquifer test was conducted at the Alvahs Lane well field from October 18 through October 21, 2022, when a production well was pumped at 550 gallons per minute for about 24 hours, and groundwater-level drawdown and recovery were measured in two monitoring wells. The three wells are screened in a glaciofluvial aquifer under unconfined (water table) conditions. Drawdown and recovery data were analyzed with an analytical solution for partial penetration and delayed yield in an unconfined aquifer to provide estimates of the glaciofluvial aquifer properties. Inclusion of lateral aquifer boundaries was not necessary for the analysis to result in satisfactory matches with the observed water-level responses. Aquifer transmissivity was estimated at 32,000 feet squared per day. Assuming a saturated aquifer thickness of 120 feet, this result is equivalent to a horizontal hydraulic conductivity value of 270 feet per day. Specific yield was estimated at 0.15 (dimensionless). The estimated properties are consistent with those of a highly transmissive unconfined aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245128","collaboration":"Prepared in cooperation with the Suffolk County Water Authority","usgsCitation":"Misut, P.E., 2025, Analysis of aquifer framework and properties, Alvahs Lane well field, Cutchogue, New York: U.S. Geological Survey Scientific Investigations Report 2024–5128, 9 p., https://doi.org/10.3133/sir20245128.","productDescription":"iv, 9 p.","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154731","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492784,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118489.htm","linkFileType":{"id":5,"text":"html"}},{"id":483275,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5128/images/"},{"id":483274,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5128 XML"},{"id":483273,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/sir20245128/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5128 HTML"},{"id":483272,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5128 PDF"},{"id":483271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5128/coverthb.jpg"}],"country":"United States","state":"New York","city":"Cutchogue","otherGeospatial":"Alvahs Lane well field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.5463729228096,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 41.03435851242847\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
Excessive nitrogen discharge is a major concern for the Long Island Sound. Programs have been implemented to reduce point sources of nitrogen to the sound, but little is known about the nonpoint sources. This study aims to better understand the current groundwater contributions of nitrogen from nonpoint sources in the Long Island Sound watershed.
During the spring and summer of 2022, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, collected water-quality samples to analyze nutrients (nitrogen and phosphorus), chloride, and bromide at 45 stations in the Long Island Sound watershed in Connecticut, New York, and Rhode Island. The stations were in small drainage watersheds (5 to 30 square kilometers) in the southern part of the Long Island Sound watershed. During two separate synoptic sampling events, water-quality samples and instantaneous streamflow measurements were collected under base-flow conditions (where the streamflow is dominated by groundwater inputs rather than overland flow or runoff flow). One sampling event was in the nongrowing season (April 24–25, 2022), and the other was in the growing season (June 30–July 1, 2022). To calculate instantaneous nitrogen loads and yields, streamflow was measured at the time of sample collection.
Nitrogen concentrations, loads, and yields varied among sampling stations and by season. Total filtered nitrogen concentrations were generally lower in the nongrowing season (from less than 0.14 to 1.9 milligrams per liter) than in the growing season (from less than 0.23 to 3.0 milligrams per liter). Nitrate plus nitrite concentrations showed little variation between the nongrowing and growing seasons. Unfiltered ammonia plus organic nitrogen concentrations were generally lower in the nongrowing season (from less than 0.07 to 0.83 milligram per liter) than in the growing season (from 0.11 to 0.98 milligram per liter). In contrast, total filtered and unfiltered nitrogen loads and yields were higher in the nongrowing season than during the growing season, likely because streamflows were higher during the nongrowing season. Total unfiltered nitrogen yields during the nongrowing season ranged from less than 0.15 to 5.0 kilograms per square kilometer per day. Total unfiltered nitrogen yields during the growing season ranged from less than 0.12 to 2.5 kilograms per square kilometer per day. Total filtered nitrogen yields during the nongrowing season ranged from less than 0.13 to 5.2 kilograms per square kilometer per day. Total filtered nitrogen yields during the growing season ranged from less than 0.06 to 2.5 kilograms per square kilometer per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1206","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Laabs, K.L., Barclay, J.R., and Mullaney, J.R., 2025, Base-flow sampling to enhance understanding of the groundwater flow component of nitrogen loading in small watersheds draining into Long Island Sound: U.S. Geological Survey Data Report 1206, 23 p., https://doi.org/10.3133/dr1206.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161925","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":492783,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118484.htm","linkFileType":{"id":5,"text":"html"}},{"id":483188,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1206/full","linkFileType":{"id":5,"text":"html"},"description":"DR 1206 HTML"},{"id":483186,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1206/coverthb.jpg"},{"id":483187,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.pdf","text":"Report","size":"7.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1206 PDF"},{"id":483189,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.XML","linkFileType":{"id":8,"text":"xml"},"description":"DR 1206 XML"},{"id":483190,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1206/images/"},{"id":483191,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99IAXUI","text":"USGS data release","linkHelpText":"Nitrogen loads, yields, and associated field data collected during baseflow conditions and site attributes for small basins draining to Long Island Sound"}],"country":"United States","state":"Connecticut, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.91277546117404,\n 40.875888202775144\n ],\n [\n -73.5833363685162,\n 40.98693216297761\n ],\n [\n -72.40741106161018,\n 41.2504046305547\n ],\n [\n -71.4646381344502,\n 41.35702020040523\n ],\n [\n -71.40893145316154,\n 41.57350712794573\n ],\n [\n -71.49014822027934,\n 41.724945464467424\n ],\n [\n -72.43277552970126,\n 41.78157938825299\n ],\n [\n -72.757106016586,\n 41.53934120640511\n ],\n [\n -73.08115837726446,\n 41.93608782758082\n ],\n [\n -73.30909534441503,\n 41.8870827192506\n ],\n [\n -73.51694453574096,\n 41.73992349657644\n ],\n [\n -73.50192428145502,\n 41.280885844515296\n ],\n [\n -73.88228764759546,\n 40.963994488494365\n ],\n [\n -73.91277546117404,\n 40.875888202775144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean conventional resources of 47 million barrels of oil and 876 billion cubic feet of gas in upper Paleozoic reservoirs of the Wind River Basin, Bighorn Basin, and Powder River Basin Provinces.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20243049","programNote":"National and Global Petroleum Assessment","usgsCitation":"Schenk, C.J., Mercier, T.J., Le, P.A., Cicero, A.D., Drake, R.M., II, Gelman, S.E., Hearon, J.S., Johnson, B.G., Lagesse, J.H., Leathers-Miller, H.M., and Timm, K.K., 2025, Assessment of undiscovered conventional oil and gas resources in upper Paleozoic reservoirs of the Wind River Basin, Bighorn Basin, and Powder River Basin Provinces, 2024: U.S. Geological Survey Fact Sheet 2024–3049, 4 p., https://doi.org/10.3133/fs20243049.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-155711","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":492782,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118483.htm","linkFileType":{"id":5,"text":"html"}},{"id":482979,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95BL3S4","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Paleozoic Reservoirs of the Wind River Basin, Big Horn Basin, and Powder River Basin Provinces: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":482978,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3049/fs20243049.pdf","text":"Report","size":"796 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3049"},{"id":482977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3049/coverthb.jpg"},{"id":483263,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3049/fs20243049.xml"},{"id":483262,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3049/images"},{"id":483287,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243049/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana, Nebraska, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.58694947776809,\n 45.44307137234259\n ],\n [\n -110.58694947776809,\n 42.18474590335302\n ],\n [\n -103.24001465931977,\n 42.18474590335302\n ],\n [\n -103.24001465931977,\n 45.44307137234259\n ],\n [\n -110.58694947776809,\n 45.44307137234259\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
In June 2022, a historic flood event occurred in the headwaters of the Yellowstone River Basin. The flood resulted in millions of dollars in damages and substantial interruptions to Yellowstone National Park. The 2022 flood event was substantially higher in magnitude than other high-peak flow events over the last 30 years. The high discharge was primarily due to the combination of hydrologic mechanisms initiated by rain-on-snow, including a high-elevation snowpack that peaked later than average. However, the contributions of each hydrologic driver, rain and snow, have not been quantified and could be important for understanding future flood events in the region. The contribution of snowmelt to the total terrestrial water input (TWI) varied throughout the area, yet was concentrated in the headwaters of the Yellowstone, Stillwater, and Boulder rivers, along with the headwaters of Rock Creek in Wyoming and Montana. The primary atmospheric contributions to the TWI during the 2022 event were precipitation from moisture transported from the Pacific Ocean that converged over the Greater Yellowstone Area (GYA) and snowmelt from residual snowpack in the northeast part of Yellowstone National Park.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70099","usgsCitation":"Giovando, J., Reis, W., Zhang, W., and Barth, N.A., 2025, Hydrologic mechanisms for 2022 Yellowstone River flood and comparisons to recent historic floods: Hydrological Processes, v. 39, no. 3, e70099, 10 p., https://doi.org/10.1002/hyp.70099.","productDescription":"e70099, 10 p.","ipdsId":"IP-164578","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":483479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.8645863474894,\n 45.53668572000356\n ],\n [\n -111.8645863474894,\n 43.909029192960446\n ],\n [\n -108.86233230449172,\n 43.909029192960446\n ],\n [\n -108.86233230449172,\n 45.53668572000356\n ],\n [\n -111.8645863474894,\n 45.53668572000356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Giovando, Jeremy","contributorId":352388,"corporation":false,"usgs":false,"family":"Giovando","given":"Jeremy","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":931074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reis, Wyatt","contributorId":352389,"corporation":false,"usgs":false,"family":"Reis","given":"Wyatt","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":931075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Wei","contributorId":352390,"corporation":false,"usgs":false,"family":"Zhang","given":"Wei","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":931076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barth, Nancy A. 0000-0002-7060-8244 nabarth@usgs.gov","orcid":"https://orcid.org/0000-0002-7060-8244","contributorId":298020,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":931077,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273052,"text":"70273052 - 2025 - Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest","interactions":[],"lastModifiedDate":"2025-12-15T14:48:17.221433","indexId":"70273052","displayToPublicDate":"2025-03-13T10:58:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7509,"text":"The Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest","title":"Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest","docAbstract":"Saltmarsh Sparrows (Ammospiza caudacuta), a tidal-marsh specialist, face severe population declines due to habitat loss, sea-level rise, and predation. While previous research suggests that predation pressure increases at the southern extent of the species’ breeding range, data on local predator communities remain limited. To address this, we deployed game cameras at 16 Saltmarsh Sparrow nests across four salt marshes on Virginia’s eastern shore, the southern-most extent of their breeding range. Our study provides camera-documented evidence of red fox (Vulpes vulpes) predation on Saltmarsh Sparrow nests. We detected a suspected predation event by white-tailed deer (Odocoileus virginianus) and four other potential nest predators. Additionally, we detected a Willet (Tringa semipalmata) aggressively displacing a nesting female, suggesting interspecific interactions may contribute to nest failure.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15594491.2025.2535832","usgsCitation":"Re, B., Karpanty, S.M., and Hunter, E.A., 2025, Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest: The Wilson Journal of Ornithology, v. 137, no. 4, p. 647-654, https://doi.org/10.1080/15594491.2025.2535832.","productDescription":"8 p.","startPage":"647","endPage":"654","ipdsId":"IP-176752","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497494,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.22610873430784,\n 38.20480113736551\n ],\n [\n -77.22610873430784,\n 36.57433812738637\n ],\n [\n -76.01620307129923,\n 36.57433812738637\n ],\n [\n -76.01620307129923,\n 38.20480113736551\n ],\n [\n -77.22610873430784,\n 38.20480113736551\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"137","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Re, Bridget","contributorId":364008,"corporation":false,"usgs":false,"family":"Re","given":"Bridget","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":952164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karpanty, Sarah M.","contributorId":364009,"corporation":false,"usgs":false,"family":"Karpanty","given":"Sarah","middleInitial":"M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":952165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952166,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264582,"text":"70264582 - 2025 - Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","interactions":[],"lastModifiedDate":"2025-08-19T15:28:24.757921","indexId":"70264582","displayToPublicDate":"2025-03-13T10:03:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20503,"text":"Journal of Fish of Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","title":"Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","docAbstract":"The mummichog Fundulus heteroclitus is a trophically important fish inhabiting Atlantic coastal salt marshes, with few in situ estimates of overwinter survival throughout the species range. We estimated overwinter apparent survival rates of F. heteroclitus at the approximate mid-latitudinal species range [coastal North Carolina (USA)] in four tidal creeks that experience variable winter water temperatures. To estimate apparent survival, we fitted a Cormack-Jolly-Seber model to daily mark-resight data autonomously obtained from fish marked with passive integrated transponder tags. Creek, year, mean daily water temperature, change in mean daily temperature, fish length and fish condition were considered for effects on the modelled parameters: apparent survival (Φ) (product of true survival and site fidelity) and detection probability (p). Modelling showed that water temperature and fish metrics were not related to Φ. Water temperature was directly related to p, indicating reduced fish activity and thus reduced detection probability or poor antenna detection performance at low temperatures. Creek was related to Φ and p, and the creek most open to its downstream estuary (lacking a culvert) had lower rates than the others. Greater loss (fish mortality plus emigration) in this one creek may more effectively transfer production of F. heteroclitus to larger waterbodies via emigration or predation. Conversely, lower Φ may reflect reduced detection efficiency. The results suggest that F. heteroclitus survival is insensitive to variable winter water temperatures typical of thermal dynamics in shallow estuaries in this region of its range. Median creek-specific overwinter Φ rates (range of median values, 2 × 10−8, 0.04) were roughly equal to previously published rates for these creeks during the growing season (April–October). At these latitudes and with increasingly moderate winters, the results indicate that natural mortality could arise equally or more so from predation during the growing season than mechanisms such as starvation, direct mortality, thermal morbidity and stress-related susceptibility to predation resulting from intermittently low water temperatures during the overwinter season.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.70020","usgsCitation":"Rudershausen, P.J., and O'Donnell, M.J., 2025, Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks: Journal of Fish of Biology, v. 107, no. 1, p. 188-200, https://doi.org/10.1111/jfb.70020.","productDescription":"13 p.","startPage":"188","endPage":"200","ipdsId":"IP-168105","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488323,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.70020","text":"Publisher Index Page"},{"id":483454,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.61589538108844,\n 34.739737828981234\n ],\n [\n -76.80342045072118,\n 34.74057409854757\n ],\n [\n -76.80219234343787,\n 34.68681714274115\n ],\n [\n -76.61593931817359,\n 34.68548980814643\n ],\n [\n -76.61589538108844,\n 34.739737828981234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rudershausen, P. J.","contributorId":352331,"corporation":false,"usgs":false,"family":"Rudershausen","given":"P.","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":930816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930817,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264664,"text":"70264664 - 2025 - Exposure of wild mammals inhabiting Alaska to influenza A(H5N1) virus","interactions":[],"lastModifiedDate":"2025-03-26T16:09:33.212626","indexId":"70264664","displayToPublicDate":"2025-03-13T09:40:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1493,"text":"Emerging Infectious Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Exposure of wild mammals inhabiting Alaska to influenza A(H5N1) virus","docAbstract":"Serum samples from wild mammals inhabiting Alaska, USA, showed that 4 species, including Ursus arctos bears and Vulpes vulpes foxes, were exposed to influenza A(H5N1) viruses. Results indicated some mammals in Alaska survived H5N1 virus infection. Surveillance efforts may be improved by incorporating information on susceptibility and detectable immune responses among wild mammals.
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Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":931165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beckmen, Kimberlee B. 0000-0003-0747-7883","orcid":"https://orcid.org/0000-0003-0747-7883","contributorId":347502,"corporation":false,"usgs":false,"family":"Beckmen","given":"Kimberlee B.","affiliations":[],"preferred":false,"id":931166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saafeld, David T.","contributorId":347504,"corporation":false,"usgs":false,"family":"Saafeld","given":"David T.","affiliations":[],"preferred":false,"id":931167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nicholson, Kerry 0000-0001-9951-9897","orcid":"https://orcid.org/0000-0001-9951-9897","contributorId":329033,"corporation":false,"usgs":false,"family":"Nicholson","given":"Kerry","email":"","affiliations":[{"id":50377,"text":"Alaska Dept of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":931168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mangipane, Buck A.","contributorId":288781,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck","email":"","middleInitial":"A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":931169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Laura Celeste 0000-0003-0303-5340","orcid":"https://orcid.org/0000-0003-0303-5340","contributorId":306143,"corporation":false,"usgs":true,"family":"Scott","given":"Laura","email":"","middleInitial":"Celeste","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":931170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stallknecht, David E.","contributorId":225107,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David E.","affiliations":[{"id":36701,"text":"Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":931171,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":931172,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70269914,"text":"70269914 - 2025 - Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","interactions":[],"lastModifiedDate":"2025-08-07T17:05:49.242218","indexId":"70269914","displayToPublicDate":"2025-03-13T09:31:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","docAbstract":"1. Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce.
2. Here, we present a successful case study demonstrating both space-for-time and time-for-space substitutions using emperor penguins (Aptenodytes forsteri) as the focal species. Using abundance-based species distribution models (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average fledgling abundance across 50 colonies.
3. Our analysis reveals that distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. aSDM’s space-for-time substitution performance, as measured by Pearson correlation coefficient was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar time-for-space substitution performance when predicting abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58.
4. We posit that such space-time equivalence arises because: 1) emperor penguins colonies conform to their existing fundamental niche; 2) there is not yet any environmental novelty when comparing the spatial vs temporal variation of distance to nearest open water; and 3) models of more specific components of life histories, such as fledgling abundance, rather than occurrence or total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.70025","usgsCitation":"Şen, B., Che-Castaldo, C., LaRue, M., Krumhardt, K., Landrum, L., Holland, M., Lynch, H., Delord, K., Barbraud, C., and Jenouvrier, S., 2025, Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change: Journal of Animal Ecology, v. 94, no. 5, p. 932-942, https://doi.org/10.1111/1365-2656.70025.","productDescription":"11 p.","startPage":"932","endPage":"942","ipdsId":"IP-170330","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":496436,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.70025","text":"Publisher Index Page"},{"id":493730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Pointe Géologie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 154.44140675588028,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -67.88310899484881\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"94","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Şen, Bilgecan","contributorId":359058,"corporation":false,"usgs":false,"family":"Şen","given":"Bilgecan","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":944928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Che-Castaldo, Christian Joseph 0000-0002-7670-2178","orcid":"https://orcid.org/0000-0002-7670-2178","contributorId":347906,"corporation":false,"usgs":true,"family":"Che-Castaldo","given":"Christian Joseph","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":944929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaRue, Michelle A.","contributorId":348627,"corporation":false,"usgs":false,"family":"LaRue","given":"Michelle A.","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":false,"id":944930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krumhardt, Kristen M.","contributorId":359059,"corporation":false,"usgs":false,"family":"Krumhardt","given":"Kristen M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landrum, Laura","contributorId":359060,"corporation":false,"usgs":false,"family":"Landrum","given":"Laura","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holland, Marika M.","contributorId":359062,"corporation":false,"usgs":false,"family":"Holland","given":"Marika M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944933,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lynch, Heather J.","contributorId":347911,"corporation":false,"usgs":false,"family":"Lynch","given":"Heather J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":944934,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Delord, Karine 0000-0001-6720-951X","orcid":"https://orcid.org/0000-0001-6720-951X","contributorId":197702,"corporation":false,"usgs":false,"family":"Delord","given":"Karine","email":"","affiliations":[],"preferred":false,"id":944935,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barbraud, Christophe","contributorId":197701,"corporation":false,"usgs":false,"family":"Barbraud","given":"Christophe","email":"","affiliations":[],"preferred":false,"id":944936,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jenouvrier, Stéphanie","contributorId":359063,"corporation":false,"usgs":false,"family":"Jenouvrier","given":"Stéphanie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":944937,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70264400,"text":"70264400 - 2025 - MTAB 111, March 2025","interactions":[],"lastModifiedDate":"2025-03-14T14:08:10.828956","indexId":"70264400","displayToPublicDate":"2025-03-13T09:05:42","publicationYear":"2025","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":13451,"text":"Memo to All Banders (MTAB)","active":true,"publicationSubtype":{"id":30}},"title":"MTAB 111, March 2025","docAbstract":"This Memo to All Banders (MTAB 111) was released in March 2025. Subjects in this this memo are 1. The Chief’s Chirp; 2. Alerts – Highly Pathogenic Avian Influenza; 3. Staff updates – celebrating Karen Jone’s remarkable career and retirement, meeting reports and a field trip; 4. News – BandIt end of life! (starting February 1st, 2025 the BBL will no longer be accepting BandIt files), Notes From the Field: Black-bellied Whistling Ducks, Longevity records update, ABA Bird of the Year the Common Loon, EESC signs partnership with Audubon Society, and what 100 years of USGS bird monitoring data tells us about hummingbirds; 5. A note from the permitting shelves – changes to the application process for new master personal or station permits and don’t wait to submit authorization requests; 6. A note from the supply room – best practices for band supply; 7. Data management – banding data submission for birds released from rehabilitation; 8. Frequently asked questions – I had to replace a federal metal band or auxiliary marker, how should I submit this data to the BBL? 9. Banding and encounter highlights; 10. Message to the Flyways; 11. Recent literature; 12. Moments in history; 13. Upcoming events; and 14. Request for information.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Harvey, K., and McKay, J.L., 2025, MTAB 111, March 2025: Memo to All Banders (MTAB), 14 p.","productDescription":"14 p.","ipdsId":"IP-176859","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/mtab-111-march-2025"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Kyra 0000-0003-4781-1874","orcid":"https://orcid.org/0000-0003-4781-1874","contributorId":296250,"corporation":false,"usgs":true,"family":"Harvey","given":"Kyra","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKay, Jennifer L. 0000-0002-8893-0231","orcid":"https://orcid.org/0000-0002-8893-0231","contributorId":296562,"corporation":false,"usgs":true,"family":"McKay","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930751,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70266046,"text":"70266046 - 2025 - Movements and habitat use of Silver Carp in the Arkansas and White rivers","interactions":[],"lastModifiedDate":"2025-04-24T14:57:58.143549","indexId":"70266046","displayToPublicDate":"2025-03-13T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Movements and habitat use of Silver Carp in the Arkansas and White rivers","docAbstract":"Silver Carp Hypophthalmichthys molitrix is an invasive species found throughout the Mississippi River basin. Efforts have been made to control Silver Carp populations through removal programs and movement barrier implementation. Up to date information on diel, seasonal, and annual movements and habitat use by Silver Carp will benefit these efforts. Studies of Silver Carp movement are prevalent in the upper Mississippi River, Ohio River, and tributaries, but rare in tributaries of the lower Mississippi River. Between June 2021 and May 2022, we quantified average movement rates and residency periods of 48 Silver Carp in the free-flowing lower White River and lock-and-dam fragmented lower Arkansas River using passive acoustic telemetry arrays and internal implant acoustic transmitters. We also manually tracked Silver Carp in the two rivers during the four seasons to estimate diel movement rates and use of different habitats. On an annual scale, Silver Carp in the White River moved at faster rates than Silver Carp in the Arkansas River and were recorded more times by acoustic receivers. Diel movement rates varied by season in both rivers but were low overall. Silver Carp used lentic habitats more often than lotic habitats. Overall, results suggest the numerous locks and dams of the McClellan-Kerr Arkansas River Navigation System may limit large-scale, annual movement of Silver Carp in the Arkansas River compared to the White River. Low hourly diel movement rates and high occupancy of lentic habitats also should enable effective harvest of Silver Carp using active gears in those lentic habitats.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-23-066","usgsCitation":"Althoff, A., Kindschuh, J., Lochmann, S., Owens, D., Spurgeon, J.J., and Stevens, J., 2025, Movements and habitat use of Silver Carp in the Arkansas and White rivers: Journal of Fish and Wildlife Management, v. 15, no. 2, p. 493-509, https://doi.org/10.3996/jfwm-23-066.","productDescription":"17 p.","startPage":"493","endPage":"509","ipdsId":"IP-155044","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":490100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-066","text":"Publisher Index Page"},{"id":484978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Arkansas River, White 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of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"id":1}],"isPartOf":{"id":70228323,"text":"ofr20211104 - 2022 - Effects of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"lastModifiedDate":"2025-07-23T16:56:40.604898","indexId":"ofr20211104F","displayToPublicDate":"2025-03-12T15:03:12","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1104","chapter":"F","displayTitle":"Potential Effects of Sea Level Rise and High Tide Flooding on Laterallus jamaicensis jamaicensis (Eastern Black Rail) Coastal Breeding Areas","title":"Potential effects of sea level rise and high tide flooding on Laterallus jamaicensis jamaicensis (eastern black rail) coastal breeding areas","docAbstract":"Laterallus jamaicensis jamaicensis (eastern black rails; Gmelin, 1789) are facing increasing risk from flooding in coastal breeding habitats because of rising sea levels combined with standard high tide flooding. In this report, we examine regional differences in relative rates of sea level rise, days in the breeding season above historical high tide flooding thresholds, future inundation of current (2021) emergent wetlands, and potential marsh resiliency for the breeding distribution of the eastern black rail across the Atlantic and U.S. Gulf coasts. By midcentury (2050), two sea level rise scenarios (intermediate low and intermediate) indicate that areas analyzed in Texas and the Mid-Atlantic will experience at least minor flood levels for more than half of the breeding season. By the end of the century (2100), all tidal gages in the Atlantic and U.S. Gulf coasts are projected to experience at least moderate flood levels for most of the current (April–September) eastern black rail breeding season. In some areas like New Jersey, this translates to inundation for most of the emergent wetlands in the representative parishes and counties analyzed in this report. In other parts of the coastal distribution, estimates of increases in inundation are lower or more variable, stemming from differences in the elevation of existing emergent marsh, especially at the herbaceous wetland/woody wetland transition zone. Sea level rise and tidal flooding are not projected to pose an equal risk across the coastal distribution of the eastern black rail, leading to variation in risk of nest loss because of flooding. The degree to which these wetlands and birds will adapt to changing sea level and salinity depends on a range of factors including future expansion of developed areas and the ability of marsh areas to move inland. Restoration and active management of coastal wetland areas may be necessary to maintain appropriate breeding habitat.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211104F","usgsCitation":"Nikiel, C.A., and Lyons, M.P., 2025, Potential effects of sea level rise and high tide flooding on Laterallus jamaicensis jamaicensis (eastern black rail) coastal breeding areas: U.S. Geological Survey Open-File Report 2021–1104–F, 40 p., https://doi.org/10.3133/ofr20211104F.","productDescription":"vii, 40 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172341","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":483246,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211104F/full"},{"id":483244,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1104/f/ofr20211104f.XML"},{"id":483243,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1104/f/ofr20211104f.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1104-F"},{"id":483242,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1104/f/coverthb.jpg"},{"id":483245,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1104/f/images/"},{"id":492780,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118482.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Atlantic Coast, Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.73369411465748,\n 44.87861692762931\n ],\n [\n -67.9395715369188,\n 45.91302389167819\n ],\n [\n -68.52891205677287,\n 46.14233382856975\n ],\n [\n -69.66609739851674,\n 44.904840367756236\n ],\n [\n -71.8455845133013,\n 43.01632555370077\n ],\n [\n -75.76042364090121,\n 40.73258218965182\n ],\n [\n -77.41422284991526,\n 39.32302567933269\n ],\n [\n -77.11953264135055,\n 36.58955309284522\n ],\n [\n -79.0153316043617,\n 34.430624116007294\n ],\n [\n -82.3380440342791,\n 31.342877305678\n ],\n [\n -87.14213042595998,\n 31.473505392669267\n ],\n [\n -87.74548189802837,\n 32.39195075385649\n ],\n [\n -89.00813665013129,\n 32.354083649747\n ],\n [\n -89.30755056658234,\n 31.271920212222028\n ],\n [\n -92.00443786768639,\n 31.222304379727817\n ],\n [\n -95.98918412828937,\n 30.505582509882984\n ],\n [\n -98.19210772787893,\n 28.060885389466677\n ],\n [\n -98.10578570219339,\n 26.094479941263742\n ],\n [\n -97.05626247225148,\n 25.880720528127156\n ],\n [\n -97.21103649173618,\n 27.232424454623654\n ],\n [\n -95.67794712232511,\n 28.598718054492764\n ],\n [\n -93.37262411904577,\n 29.53246193068931\n ],\n [\n -90.09201823404798,\n 28.868540112379208\n ],\n [\n -88.84955647397418,\n 28.787680093703898\n ],\n [\n -88.58294319870708,\n 29.755309328858473\n ],\n [\n -86.26626920949336,\n 30.023219777079703\n ],\n [\n -84.98796116952035,\n 29.29748111241001\n ],\n [\n -83.97817016398578,\n 29.677155423717906\n ],\n [\n -82.92684471060598,\n 28.711279338050616\n ],\n [\n -83.33453111961292,\n 28.007674975959418\n ],\n [\n -81.38398690858428,\n 24.942184191892167\n ],\n [\n -81.48743115456318,\n 24.146104924998653\n ],\n [\n -79.34128084180263,\n 25.993764942081356\n ],\n [\n -81.14405051637162,\n 30.851402909345737\n ],\n [\n -74.95193417969506,\n 35.431852837245344\n ],\n [\n -75.10789103469754,\n 37.38614632113877\n ],\n [\n -73.3440560833726,\n 40.10806605773732\n ],\n [\n -69.15888557248354,\n 41.691566830140545\n ],\n [\n -70.25945555475435,\n 43.01474979449651\n ],\n [\n -66.73369411465748,\n 44.87861692762931\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Midwest Climate Adaptation Science Center
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Drought and its effect on streamflow are important to understand because of the potential to adversely affect water supply, agricultural production, and ecological conditions. The Red River of the North Basin in north-central United States and central Canada is susceptible to dry conditions. During an extended drought, streamflow conditions in the Red River of the North may become inadequate to support existing water supply needs in the basin for agriculture, industry, human use, and aquatic life. To understand potential future low-streamflow conditions in the Red River of the North Basin, the U.S. Geological Survey, in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board, developed a water-balance model of the Red River of the North Basin upstream from Emerson, Manitoba, Canada, and coupled the model with stochastic weather inputs to simulate possible future low-streamflow conditions.
Historical changes in low-streamflow conditions were characterized across the Red River of the North Basin using multiple change-point analysis for 12 streamgages. Across these stations, significant change-point years in 1943 and 1994 marked increases in the magnitude of low-streamflow conditions. During 1920–2015, conversion of primary land (not affected by human use) to agricultural and secondary land was followed by a conversion from smalls grains to corn and soybeans as the dominant crop type. From land-use analysis, 1940–2000 was determined to have relatively stable land use and therefore was used as the calibration period for the water-balance model.
A deterministic water-balance model was developed for the Red River of the North Basin upstream from Emerson, Manitoba. The water-balance model was calibrated with data from 37 U.S. Geological Survey streamgages for 1940–2000 and verified using data for 2001–15. The calibrated water-balance model simulated streamflow distributions that mirrored the seasonal patterns of the observed mean monthly streamflow and the standard deviation of the monthly streamflow data, especially during the fall and winter months when streamflow was lowest. For the verification period, during the low-streamflow months of December through January, the difference between simulated and observed data was similar to the calibration comparison and successfully reproduced seasonal trends in the distribution of streamflow, even when using weather data that were outside the calibration period.
To determine the future risk of low-streamflow conditions in the Red River of the North Basin, a block-bootstrap method was used to generate multiple possible future climates. These stochastically generated weather time series were then input to a water-balance model to simulate a distribution of possible streamflows. Three sets of experiments were performed, with each experiment containing a set of scenarios. The first set of experiments from the stochastic streamflow model were designed to investigate how changes in reservoir management would affect the distribution of low streamflow. Relative to scenario 1 (present-day [2023] reservoir operation), scenario 2 (no reservoir operation) shifted the low-streamflow frequency curves downward, reducing the annual minimum monthly streamflow for the Emerson subbasin. Subbasins were defined by the contributing area upstream from a selected streamgage station. Relative to scenario 1, scenario 3 (regulated streamflow with an increased reservoir capacity of 10 percent) shifted the low-streamflow frequency curves upward for the Emerson subbasin. The magnitude of this upward shift, caused by increased reservoir capacity, was lower than the magnitude of the shift caused by the absence of the reservoirs, which indicates that the streamflow was most affected when the reservoirs were first constructed.
The second set of experiments from the stochastic streamflow model included two scenarios that were performed to better understand how the Red River of the North Basin responds to long periods of low or high precipitation. The results indicate that the model consistently overestimated streamflow, but the relative change between a wet and dry climate state of simulated streamflow distribution reasonably matched the relative change of historical streamflow. Across the subbasins, the model was most accurate for low-streamflow conditions associated with nonexceedance probabilities between 20 and 40 percent.
The third set of experiments from the stochastic streamflow model were done to investigate low-streamflow response across the basin to several drought events. Low-end streamflow was reduced when the basin was exposed to a drought, and the magnitude of the reduction increased with longer or more intense droughts. Compared to the low-intensity drought scenarios, the range of percent reductions (as indicated by the interquartile range) was larger for the high-intensity drought scenarios for all subbasins, and the subbasins of Grand Forks and Emerson had a smaller range of reductions compared to the other three subbasins. The larger drainage area—combined with the large contribution of the Red Lake River and several other Minnesota tributaries that generally experience wetter climate conditions—upstream from the Emerson and Grand Forks subbasins may contribute to the smaller range in reductions under the high intensity scenarios. Comparison of the percent reduction in low-end streamflow among subbasins also indicated that the effects of drought duration and intensity could be cumulative. Combining factors of time and intensity produced a larger reduction in streamflow than when each effect was isolated. The array of drought scenarios can be used to determine how a subbasin would respond to multiple possible future conditions. Based on climate predictions, the drought scenario that best matches a future anticipated drought scenario can be used to estimate a low streamflow response for a given subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255002","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board","usgsCitation":"Redoloza, F.S., Glas, R.L., Nustad, R.A., and Ryberg, K.R., 2025, Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba: U.S. Geological Survey Scientific Investigations Report 2025–5002, 58 p., https://doi.org/10.3133/sir20255002.","productDescription":"Report: viii, 58 p.; Data Release; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160450","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":492779,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118481.htm","linkFileType":{"id":5,"text":"html"}},{"id":483206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13XEN8U","text":"USGS data release","linkHelpText":"Red River of the North low flow water-balance model and supporting data"},{"id":483203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.XML"},{"id":483202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5002"},{"id":483201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5002/coverthb.jpg"},{"id":483204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5002/images/"},{"id":483207,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":483205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255002/full"}],"country":"Canada, United States","state":"Manitoba, Minnesota, North Dakota","otherGeospatial":"Red River of the North Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.08881710576394,\n 49.037592614274644\n ],\n [\n -97.61189514667993,\n 49.237246321777064\n ],\n [\n -97.98430773681044,\n 49.25792746144549\n ],\n [\n -98.47156466708684,\n 49.30311814027007\n ],\n [\n -98.18869515177104,\n 48.806510092341696\n ],\n [\n -98.92510212635796,\n 48.563256003263746\n ],\n [\n -98.41426650704827,\n 48.10818045779425\n ],\n [\n -97.49283622534884,\n 48.05240253638604\n ],\n [\n -97.3523048621935,\n 47.017146908752025\n ],\n [\n -97.10429716720503,\n 45.72316825195222\n ],\n [\n -95.52291599413297,\n 45.59412827523792\n ],\n [\n -94.77240339220764,\n 46.995377381657505\n ],\n [\n -94.76454577568065,\n 47.45566932791883\n ],\n [\n -93.68493211560182,\n 47.83688445747143\n ],\n [\n -94.11770692662546,\n 48.33322840096503\n ],\n [\n -95.73784376997673,\n 48.92757937919353\n ],\n [\n -97.08881710576394,\n 49.037592614274644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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This report addresses system characterization of the Environmental Mapping and Analysis Program hyperspectral sensor by the DLR (German Aerospace Center, ground segment project management), GFZ (Deutsches Geoforschungszentrum, science lead) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the EnMAP hyperspectral sensor; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), and radiometric performances of the EnMAP hyperspectral sensor. Results of these analyses indicate that the Environmental Mapping and Analysis Program has a band-to-band geometric performance in the range of −0.135 to 0.15 pixel, geometric performance relative to the Operational Land Imager in the range of −27.716 meters (−0.92 pixel) to 32.892 meters (1.09 pixels) offset in comparison to Landsat 8 Operational Land Imager, offset of a radiometric comparison in the range of −0.012 to 0.020, slope of a radiometric comparison in the range of 0.947 to 1.031.
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1. Patterns of migratory connectivity are increasingly used to understand and manage threats throughout the annual cycle of migratory species. Strong migratory connectivity refers to when individuals from different populations remain spatially separated across the annual cycle, which may expose populations to unique sets of threats and conditions that cause differential population trends. However, the populations or groups used for species’ management are often defined a priori based on expert knowledge and/or management units, which may mask important population segregation and obscure differential population trends and their drivers.
2. We compared three approaches to defining management groups of a declining shorebird, the long-billed curlew (Numenius americanus), for annual cycle management: by expert-opinion, according to management flyways, and with unsupervised clustering of satellite tracking data that maximizes the strength of migratory connectivity.
3. Despite the curlews having a continuous breeding range and a pattern of parallel migration, all three approaches identified groups with different population trends, movement behaviours and habitat selection across the annual cycle, suggesting these are meaningful ecological groups. The expert and clustering approaches resulted in similar group structure, strong estimates of migratory connectivity (measured as MC = 0.64 across seasons), movement behaviour and habitat selection; however, the expert approach identified an additional divide between the easternmost grouping, which revealed strongly negative population trends in the group occupying the Chihuahuan desert during the stationary nonbreeding season. In contrast, the flyway delineation resulted in weaker estimates of migratory connectivity, marginal differences in population trends and less between-group differences in movement behaviour and habitat selection.
4. Synthesis and applications. Using measurements of migratory connectivity in concert with expert opinion can define ecologically distinct groups for wildlife management that differ in the environmental conditions they experience across seasons of the annual cycle, which is a key component for understanding and reversing declines of migratory species.
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The U.S. Geological Survey has a new decadal-scale coastal change assessment product that synthesizes nearly two dozen coastal datasets. A supervised machine-learning framework is used to combine existing datasets that describe the landscape and the hazards that affect it to determine the coastal change likelihood (CCL) in the coming decade at a resolution of 10 m per pixel for the NE United States from Maine to Virginia. Here, results from a series of statistical tests conducted on source data, the supervised classification, and the CCL outcomes as compared with historical land-cover change are presented. The overall accuracy of the aggregated land-cover dataset that serves as the foundation to which other source datasets are appended is 94%. The supervised learning classification that determines the final CCL output has an overall accuracy of 92%. The CCL predictions of high expected coastal change were consistent with 95% of the coastal and low-elevation landscape change in the last 20 years, as recorded by the Coastal Change Analysis Program land-cover change atlas. Results suggest that CCL provides accurate estimates of coastal landscape change in the next decade that are consistent with recent observed change. Additionally, best practices for applying CCL for planning purposes are outlined, and citing limitations, knowledge gaps, and opportunities for improved accuracy and further investigation are considered.
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The simplest rating relates discharge to the stage of a river (the stage-discharge relation). From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because flow becomes unsteady as a flood wave passes. The stage-discharge relation is unable to represent hysteresis. However, a dynamic rating method can capture hysteresis, which is driven by the variable energy slope of a flood wave.
A dynamic rating method called DYNPOUND, which accommodates compact and compound channel geometry, was developed by simplifying the one-dimensional Saint-Venant equations. The DYNPOUND method was developed in the Python programming language and computes discharge from stage and stage from discharge. Stage and discharge time series computed with this dynamic rating method were compared to the U.S. Geological Survey (USGS) published stage and discharge time series. The results from the DYNPOUND method were also compared to in-person field measurements of stage and discharge made at 10 USGS streamgages.
DYNPOUND was calibrated for 10 USGS streamgages using published discharge time-series data computed with a simple rating method. The calibration objective was to minimize the mean squared logarithmic error (MSLE) of the DYNPOUND-computed discharge with respect to the discharge time series computed by a simple rating method. For each site, the calibration process also included comparing all field measurements within a selected water year to the corresponding DYNPOUND-computed discharge data points. The MSLE of the DYNPOUND-computed discharge time series for the 10 sites ranged from 8.51×10−4 to 1.36×10−1. For each site, an event-based period was selected to compare the discharge time series computed with the dynamic rating method to discharge field measurements made at the streamgages; the range of MSLE for the 10 DYNPOUND-computed discharge sites was from 4.79×10−4 to 2.30×10−2.
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U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The umbrella species concept is often used as a tool to guide management decisions and focus efforts towards one focal species whose habitat needs overlap that of other species. We assessed this concept in the context of an agriculturally dominant landscape using one of the most well-studied avian species in North America as a target for conservation efforts: Northern Bobwhite (Colinus virginianus). This species is often viewed as an umbrella species for grassland and shrubland bird conservation throughout its native range due to its complex, year-round habitat requirements. We assessed the influence of Northern Bobwhite habitat management on six songbird species of conservation concern in Iowa by evaluating similarities and differences in habitat associations between each species. Our objectives were to (1) assess which vegetation characteristics were most strongly associated with Northern Bobwhite occupancy and (2) evaluate whether those characteristics were also associated with abundance of the focal songbird species. We used occupancy and N-mixture models to assess relationships between vegetation characteristics and Northern Bobwhite occupancy and songbird abundance, respectively. We found that the vegetation characteristics most strongly associated with Northern Bobwhite occupancy probability were the amounts of closed canopy forest, early successional woody vegetation, non-vegetated areas, and percent cover of bare ground. We found that for some of these covariates, including the amounts of forest and non-vegetated area, the effect on focal songbird species abundance aligned with Northern Bobwhite occupancy. For others, including the amount of early successional woody vegetation, the effects differed. This assessment of overlap and variability in habitat associations suggests that Northern Bobwhite-targeted management can provide benefits to other grassland and shrubland birds, but may also come with some trade-offs. This work adds to existing literature, further highlighting the nuances of the umbrella species concept in that land management benefits from the assessment of trade-offs and inclusion of local community dynamics.
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Production capacity can limit future supply, depending on how rapidly that capacity is able to expand. Current capacity can be evaluated on the basis of past production. Decreases to future capacity can be taken into account from announcements of planned shutdowns of mines or processing facilities, which are frequently publicized well in advance of such closures. Likewise, capacity expansions, which usually involve multiple stages—such as permitting, financing, and construction (all of which take time)—can also be estimated. As such, it is possible to evaluate midterm future capacity based on estimates of today’s capacities along with consideration of future investment plans. This World Minerals Outlook provides estimated capacities for cobalt, gallium, helium, lithium, magnesium, palladium, platinum, and titanium for 2025 through 2029.
The results of this analysis indicate that two mineral commodities important to the manufacture of lithium-ion batteries—cobalt and lithium—are expected to have large capacity growth in the next few years, likely owing to expectations for increased demand for these batteries. For gallium, helium, palladium, and platinum, capacity is expected to remain stable or exhibit moderate growth. Still, these expected capacity levels are higher than current production, allowing for future production growth. The production capacity outlook is opaque for magnesium and titanium metal, which have a significant fraction of current production in nonmarket economies, such as China and Russia. Ultimately, though, where free market conditions prevail, full utilization of capacity potential for those commodities is likely to depend on supply deficits and prices that are above production costs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255021","usgsCitation":"Alonso, E., Brioche, A.S., Schulte, R.F., Trimmer, L.M., Kim, J.-E., Gulley, A.L., and Pineault, D.G., 2025, World minerals outlook—Cobalt, gallium, helium, lithium, magnesium, palladium, platinum, and titanium through 2029 (ver. 1.1, March 14, 2025): U.S. Geological Survey Scientific Investigations Report 2025–5021, 19 p., https://doi.org/10.3133/sir20255021.","productDescription":"Report: vi, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-173545","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":483354,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5021/versionHist.txt","size":"4.64 KB","linkFileType":{"id":2,"text":"txt"}},{"id":483159,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HTTCWN","text":"USGS data release","linkHelpText":"World minerals outlook to 2029—Cobalt, gallium, helium, lithium, magnesium, palladium, platinum, and titanium data"},{"id":483408,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5021//images/"},{"id":483406,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255021/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5021 HTML"},{"id":492776,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118478.htm","linkFileType":{"id":5,"text":"html"}},{"id":483407,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5021/sir20255021.XML","description":"SIR 2025-5021 XML"},{"id":483145,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5021/sir20255021.pdf","text":"Report","size":"2.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5021 PDF"},{"id":483144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5021/coverthb2.jpg"}],"edition":"Version 1.0: March 11, 2025; Version 1.1: March 14, 2025","contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
","tableOfContents":"Understanding mine production and potential capacity growth can help inform the growing need for the minerals that support economic growth, technological change, and national security for businesses and policy makers. How much a mine can produce affects future supply, especially as capacities can change over time. This report estimates production capacities for cobalt, gallium, helium, lithium, magnesium, palladium, platinum, and titanium through 2029. The results of the analysis suggest that cobalt and lithium, which are key for lithium-ion batteries, are likely to see significant increases in production capacity owing to rising demand, whereas gallium and platinum are expected to see stable or moderate growth, exceeding current production levels. However, the future for magnesium and titanium is less clear because much of their production comes from countries with nonmarket economies, like China and Russia. In free markets, using full production capacity is likely to depend on supply shortages and prices being above production costs.
","publicationDate":"2025-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Alonso, Elisa 0000-0002-0090-8284","orcid":"https://orcid.org/0000-0002-0090-8284","contributorId":223015,"corporation":false,"usgs":true,"family":"Alonso","given":"Elisa","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":930300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brioche, Amanda Sarah 0000-0002-9650-2456","orcid":"https://orcid.org/0000-0002-9650-2456","contributorId":332784,"corporation":false,"usgs":true,"family":"Brioche","given":"Amanda Sarah","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":930301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulte, Ruth 0000-0003-4724-5905","orcid":"https://orcid.org/0000-0003-4724-5905","contributorId":201973,"corporation":false,"usgs":true,"family":"Schulte","given":"Ruth","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":930302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trimmer, Loyd M. III 0000-0003-4121-7874 ltrimmer@usgs.gov","orcid":"https://orcid.org/0000-0003-4121-7874","contributorId":194120,"corporation":false,"usgs":true,"family":"Trimmer","given":"Loyd","suffix":"III","email":"ltrimmer@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":false,"id":930303,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kim, Ji-Eun 0000-0002-7668-5072","orcid":"https://orcid.org/0000-0002-7668-5072","contributorId":331665,"corporation":false,"usgs":true,"family":"Kim","given":"Ji-Eun","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":930304,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gulley, Andrew L. 0000-0003-4717-2080","orcid":"https://orcid.org/0000-0003-4717-2080","contributorId":203953,"corporation":false,"usgs":true,"family":"Gulley","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":930305,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pineault, David 0009-0001-6801-4711","orcid":"https://orcid.org/0009-0001-6801-4711","contributorId":352217,"corporation":false,"usgs":true,"family":"Pineault","given":"David","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":930306,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270419,"text":"70270419 - 2025 - Evidence for size‐based predation risk during Atlantic salmon (Salmo salar) smolt migration.","interactions":[],"lastModifiedDate":"2025-08-19T14:52:48.36751","indexId":"70270419","displayToPublicDate":"2025-03-11T09:48:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evidence for size-based predation risk during Atlantic salmon (Salmo salar) smolt migration","title":"Evidence for size‐based predation risk during Atlantic salmon (Salmo salar) smolt migration.","docAbstract":"Hatchery supplementation is frequently employed during the conservation and recovery of imperilled salmon populations. At the smolt stage, hatchery rearing practices often produce individuals that are larger than wild conspecifics. Under this ‘bigger is better’ strategy, it is assumed that larger fish are less susceptible to predation during migration. We tested this hypothesis on hatchery-reared Atlantic salmon (Salmo salar) smolts with fork lengths representative of those of natural and hatchery origins, allowing us to isolate the influence of size from rearing history. From May to June 2023 we characterized predation risk for acoustic-tagged (n = 50) and tethered (n = 192) smolts of various sizes through a mostly free-flowing section of the Penobscot River, Maine, USA. Across both methods, more than 50% of smolts were predated, with the majority of predation events being attributed to smallmouth bass (Micropterus dolomieu). Tethered smolts of all sizes experienced similar predation risk. In the acoustic telemetry component of this study, smaller, wild-sized smolts incurred greater overall mortality relative to standard hatchery sizes (95% vs. 75%), the majority of which occurred within 3 km of the release site. Collectively, these results allude to a strong predation influence imposed by smallmouth bass on smolts in freshwater sections of the Penobscot River and small-bodied migrants may incur greater predation risk, particularly near stocking sites.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.70011","usgsCitation":"Mensinger, M., Mortelliti, A., and Zydlewski, J.D., 2025, Evidence for size‐based predation risk during Atlantic salmon (Salmo salar) smolt migration.: Journal of Fish Biology, https://doi.org/10.1111/jfb.70011.","ipdsId":"IP-167997","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":496392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.70011","text":"Publisher Index Page"},{"id":494310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mensinger, Matthew A.","contributorId":287641,"corporation":false,"usgs":false,"family":"Mensinger","given":"Matthew A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":946384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mortelliti, Alessio","contributorId":342757,"corporation":false,"usgs":false,"family":"Mortelliti","given":"Alessio","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":946385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":946386,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264079,"text":"sir20245096 - 2025 - Determining low-flow conditions at select streams to Barnegat Bay-Little Egg Harbor as the first step towards the development of ecological-flow targets","interactions":[],"lastModifiedDate":"2025-07-23T16:43:49.357537","indexId":"sir20245096","displayToPublicDate":"2025-03-11T09:00:00","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":"2024-5096","displayTitle":"Determining Low-Flow Conditions at Select Streams to Barnegat Bay-Little Egg Harbor as the First Step Towards the Development of Ecological-Flow Targets","title":"Determining low-flow conditions at select streams to Barnegat Bay-Little Egg Harbor as the first step towards the development of ecological-flow targets","docAbstract":"Maintaining streamflow to support human water needs and ecosystem services requires a fundamental understanding of the relations between changes in streamflow processes and ecosystem responses. Changes in the natural patterns in flow, geology, and topography alter the habitats that aquatic organisms rely on for food, shelter, and reproduction. The U.S. Geological Survey (USGS) implemented an ecological-flow framework that encapsulates the basic principles of the Ecological Limits of Hydrologic Alteration (ELOHA) to compare the relations between hydrologic metrics and stream conditions and estimate ecological flow needs in the Barnegat Bay-Little Egg Harbor watershed. As a first step in the ELOHA process, streamflow from two historical time periods (occurring between 1933 and 1988) was compared to streamflow for a recent time period (from 2004-2020) for four major streams in the Barnegat Bay-Little Egg Harbor watershed (North Branch Metedeconk River, Toms River, Cedar Creek, and Westecunk Creek), to evaluate if there were statistically significant differences in streamflow metrics. Analysis of monthly, seasonal, and annual low-flow metrics; patterns in the streamflow record; and general land-use changes were used to develop a better understanding of flow conditions in the watershed.
The comparative streamflow analysis indicated that notable changes in flow processes for the study streams occurred between the three periods of record (PORs) evaluated in this study: period of record 1 (POR1, from water years 1933–1958), period of record 2 (POR2, from water years 1974–1988), and period of record 3 (POR3, from water years 2004–2020). For example, the mean of the daily streamflow decreased between the historical POR to the current POR in Cedar Creek but increased in North Branch Metedeconk and Toms Rivers. Larger and more significant changes (p-value <0.10) occurred during specific months or were related to the variability or seasonality of flow. North Branch Metedeconk River and Toms River, the two northern and most developed sites, exhibited changes in low-flow metrics and decreases in minimum n-day moving averages. Decreases in the normalized 75th-percentile exceedance flows were evident at three of the four study sub-basins during POR2 and POR3. In comparison, there was little to no evidence of negative changes to low-flow metrics at Westecunk Creek, the southernmost and least developed site, where all low-flow duration metrics increased as well as seasonal minimum consecutive 7-day average flows. Significant increases in monthly minimums (p-value <0.05) at Cedar Creek for spring months (April, May, and June) also were observed.
Natural and anthropogenic processes can alter the landscape resulting in concomitant changes in the streamflow regime. There is a need to assess these changes and synthesize the results into a scientifically defensible set of goals and standards that help support the management of environmental flows. This study represents the initial steps in building the hydrologic foundation to inform management and develop future ecological flow targets that balance water availability for human and ecosystem needs in the Barnegat Bay-Little Egg Harbor watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245096","collaboration":"Prepared in cooperation with the Barnegat Bay Partnership","usgsCitation":"Wieben, C.M., Kennen, J.G., and Suro, T.P., 2025, Determining low-flow conditions at select streams to Barnegat Bay-Little Egg Harbor as the first step towards the development of ecological-flow targets: U.S. Geological Survey Scientific Investigations Report 2024–5096, 39 p., https://doi.org/10.3133/sir20245096.","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149405","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":492774,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118479.htm","linkFileType":{"id":5,"text":"html"}},{"id":482893,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5096/coverthb.jpg"},{"id":482897,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5096/images/"},{"id":482896,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5096/sir20245096.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5096 XML"},{"id":482895,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245096/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5096 HTML"},{"id":482894,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5096/sir20245096.pdf","text":"Report","size":"7.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5096 PDF"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay-Little Egg Harbor watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.5,\n 40.1667\n ],\n [\n -74.5,\n 39.5\n ],\n [\n -73.8333,\n 39.5\n ],\n [\n -73.8333,\n 40.1667\n ],\n [\n -74.5,\n 40.1667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Birds are vital to our economy, ecosystems, and cultural heritage. Investing in bird conservation benefits communities, businesses, and working lands while reinforcing our nation’s legacy of stewardship and biodiversity. By valuing birds, we ensure a richer, healthier, and more vibrant future for all Americans. The USGS leads two national bird monitoring programs Thriving bird populations contribute over $100 billion in related purchases to the U.S. economy annually, helping to support 1.4 million jobs and $90 billion in labor-related income. Across our nation, Federal and State wildlife agencies, Flyway Councils, non-governmental organizations, and more consider data from the U.S. Geological Survey’s (USGS) Bird Banding Laboratory (BBL) and Breeding Bird Survey (BBS) to be critical to meeting their mandates to set healthy harvest levels and in identifying species of conservation need. However, without stable and sufficient resources for the BBL and BBS, the capacity to monitor and address the rapidly evolving needs of migratory bird populations is at risk, jeopardizing the foundation of collaborative conservation efforts across North America.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253011","programNote":"Species Management Research Program","usgsCitation":"Ziolkowski, D., Celis-Murillo, A., Malpass, J., Pardieck, K., Martin, J., and Walker, L., 2025, Foundational science in flight—USGS bird programs support conservation, culture, and a thriving U.S. economy: U.S. Geological Survey Fact Sheet 2025–3011, 4 p., https://doi.org/10.3133/fs202533011.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-175892","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483152,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3011/fs20253011_print.pdf","text":"Report","size":"3.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3011 PDF","linkHelpText":"Printer-friendly version"},{"id":483073,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3011/images"},{"id":483072,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3011/fs20253011.XML","description":"FS 2025-3011 XML"},{"id":483070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3011/fs20253011.pdf","text":"Report","size":"2.61 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3011 PDF"},{"id":483069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3011/coverthb2.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.02943301109039,\n 15.410306981738572\n ],\n [\n -86.44265063211188,\n 21.842039182358832\n ],\n [\n -79.453241061223,\n 26.64186789245565\n ],\n [\n -78.50306906661305,\n 32.979705227993094\n ],\n [\n -50.77847439442888,\n 48.892922136315434\n ],\n [\n -66.2635829932419,\n 75.41047680539205\n ],\n [\n -74.57496425238357,\n 78.3457781585499\n ],\n [\n -58.13072469895427,\n 83.1029782866932\n ],\n [\n -168.32855790630842,\n 70.40594475176928\n ],\n [\n -166.93127284689004,\n 55.679378483117404\n ],\n [\n -121.64076199951083,\n 32.65515564988158\n ],\n [\n -109.15060725080741,\n 19.965527030937324\n ],\n [\n -93.8127347824803,\n 12.893183633264144\n ],\n [\n -92.02943301109039,\n 15.410306981738572\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Rd.
Kearneysville, WV 25430
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Freshwater megafishes are among some of the most commercially and ecologically important aquatic organisms yet are disproportionately threatened with range and population reduction. Anthropogenic alterations of rivers influencing migrations are among the most significant causes for these declines. However, migratory fishes do not always respond similarly to movement barriers and thus it is necessary to develop models to predict movements of freshwater migratory fishes in the face of anthropogenic alteration. Predicting movement of freshwater fishes is often investigated using statistical packages. However, empirical studies assessing these packages have led to mixed results, questioning its applicability to all taxa. We argue that spatial, temporal, and environmental attributes are more influential for movement of a migratory megafish, the Alligator Gar (Atractosteus spatula), than the current parameters explored in a globally relevant fish movement model.
This study explored two independent mobile telemetry datasets investigating Alligator Gar movement on the Brazos and Trinity rivers. Environmental associations were investigated to predict Alligator Gar displacement and dispersal using generalized additive models, generalized linear models, and model selection. Leptokurtosis of Alligator Gar populations was also assessed. Predictability of the movement model was tested by comparing observed to model derived stationary and mobile components making up a leptokurtic movement distribution.
Our study suggests that current and antecedent measures of discharge and water temperature are positively correlated with Alligator Gar displacement and dispersal. However, these patterns are only detectable when monthly relocation intervals are explored rather than seasonal scales. Leptokurtosis was observed in both Alligator Gar populations. However, movement was normally distributed (i.e., mesokurtic) under tracking events following high flood pulses. Additionally, predicted Alligator Gar movement was significantly farther under modeled values compared to observed values, in part because the species undergoes cyclical migrations for reproduction that are sensitive to water temperature and discharge.
In conclusion, this study provides an alternative framework to assess the movement patterns of migratory fishes, which could be tested on additional freshwater fishes, and suggests that assessing spatial, environmental, and temporal processes simultaneously are necessary to capture the complexities of fish movement which currently are unavailable for the movement model we investigated.
","language":"English","publisher":"BMC","doi":"10.1186/s40462-025-00544-7","usgsCitation":"Roberts, H.C., Kappen, F., Acre, M.R., Daugherty, D.J., Smith, N.G., and Perkin, J., 2025, Meta-analysis of a megafish: Assessing patterns and predictors of Alligator Gar movement across multiple populations: Movement Ecology, v. 13, no. 1, 15, 18 p., https://doi.org/10.1186/s40462-025-00544-7.","productDescription":"15, 18 p.","ipdsId":"IP-172438","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-025-00544-7","text":"Publisher Index Page"},{"id":483713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.70591756751243,\n 30.894440659055704\n ],\n [\n -96.70591756751243,\n 29.622760974861365\n ],\n [\n -94.82999949548378,\n 29.622760974861365\n ],\n [\n -94.82999949548378,\n 30.894440659055704\n ],\n [\n -96.70591756751243,\n 30.894440659055704\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, Hayden C.","contributorId":335083,"corporation":false,"usgs":false,"family":"Roberts","given":"Hayden","email":"","middleInitial":"C.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":931580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kappen, Florian","contributorId":352518,"corporation":false,"usgs":false,"family":"Kappen","given":"Florian","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":931581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Acre, Matthew Ross 0000-0002-5417-9523","orcid":"https://orcid.org/0000-0002-5417-9523","contributorId":268034,"corporation":false,"usgs":true,"family":"Acre","given":"Matthew","email":"","middleInitial":"Ross","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931582,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daugherty, Daniel J.","contributorId":335084,"corporation":false,"usgs":false,"family":"Daugherty","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":931583,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Nathan G.","contributorId":268036,"corporation":false,"usgs":false,"family":"Smith","given":"Nathan","email":"","middleInitial":"G.","affiliations":[{"id":55541,"text":"Heart of the Hills Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":931584,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perkin, Joshuah S.","contributorId":238286,"corporation":false,"usgs":false,"family":"Perkin","given":"Joshuah S.","affiliations":[{"id":47708,"text":"Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX","active":true,"usgs":false}],"preferred":false,"id":931585,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264306,"text":"70264306 - 2025 - 6PPD-quinone in water from the San Francisco-San Joaquin Delta, California, 2018-2024","interactions":[],"lastModifiedDate":"2025-03-11T14:21:30.245946","indexId":"70264306","displayToPublicDate":"2025-03-10T09:15:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"6PPD-quinone in water from the San Francisco-San Joaquin Delta, California, 2018-2024","docAbstract":"The Sacramento-San Joaquin Delta (Delta) is an expansive river delta supplying a large portion of California’s fresh water for agriculture and residential use, and it is also an area of critical habitat for numerous state and federally listed species of concern. In many locations, urban stormwater flows directly into the Delta. 6PPD-quinone (6PPD-Q), an ozonation byproduct of a tire antiozonant 6PPD, has been shown to enter surface water via these pathways and can cause various toxicological effects, including acute urban mortality syndrome to coho salmon (Oncorhynchus kisutch) at low levels (LC50 = 41 and 95 ng/L for juveniles and adults, respectively). Here, we quantified 6PPD-Q in 61 archived Delta water extracts collected between 2018 and 2024 and found concentrations up to 21 ng/L. Currently, no 6PPD-Q presence and/or quantitative data is available for this complex and diverse ecosystem. Little is known regarding long-term storage of 6PPD-quinone in solvent extracts, so 6PPD-Q observations document its presence in the study area and provide evidence that further sampling may be warranted to better quantify environmental concentrations. Consistent with the general understanding of 6PPD-Q transport, all detections observed were in samples collected during or immediately after a precipitation event. This work provides environmentally relevant concentration data to complement ongoing toxicological investigations of 6PPD-Q in Delta organisms and suggests there are research opportunities for a more robust survey of 6PPD-Q inputs into the Delta.
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