The goal of this project was to assess the vulnerability to climate change impacts for a sample set of hatchery programs representing different geographic areas and primary anadromous species raised in state-managed Oregon hatcheries (Summary Figure 1). Freshwater and marine ecosystem processes can significantly influence salmon and steelhead survival, and understanding how these factors have affected historical returns can help managers evaluate the climate vulnerability of hatchery stocks. We examined stock-specific trends in smolt-to-adult returns (SARs), which represent the proportion of smolts released from the hatchery that are recovered in fisheries or as returning adult spawners. SARs are among the most consistent long term estimators of survival for hatchery-origin stocks. Depending on the stock, adult recoveries could occur in marine fisheries, freshwater fisheries, returns to the hatchery or another collection facility, and spawning ground surveys. We collected time series data on relevant ecological indicators and used generalized additive models (GAMs) to explore both univariate and multivariate relationships with SARs for each hatchery stock.
An additional aspect of this assessment was to evaluate the climate vulnerability of resident trout stocking programs in the Department's East and West regions, incorporating insights from Oregon Department of Fish and Wildlife (ODFW) staff interviews and published data on the thermal tolerance of hatchery trout stocks. This assessment is provided in the ‘Climate vulnerability of trout stocking programs’ subsection below.
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\"}}]}","noUsgsAuthors":false,"publicationDate":"2024-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrett, Hannah","contributorId":332245,"corporation":false,"usgs":false,"family":"Barrett","given":"Hannah","email":"","affiliations":[{"id":79431,"text":"Oregon State University, Department of Fisheries, Wildlife, and Conservation Sciences","active":true,"usgs":false}],"preferred":false,"id":947579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":947212,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70266294,"text":"70266294 - 2024 - Cycles in adult steelhead length suggest interspecific competition in the North Pacific Ocean","interactions":[],"lastModifiedDate":"2025-05-02T18:04:07.763361","indexId":"70266294","displayToPublicDate":"2024-12-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Cycles in adult steelhead length suggest interspecific competition in the North Pacific Ocean","docAbstract":"Anadromous fishes rely on abundant prey in the ocean to grow large quickly, but prey limitation leads to interspecific competition. When species interactions are difficult to observe, growth can be studied to detect otherwise cryptic signals of competition. We describe a previously undocumented two-year cycle in the lengths of adult natural-origin steelhead (Oncorhynchus mykiss) returning to spawn in the Snake River Basin. Returning steelhead were 38 mm shorter on odd return years, not accounting for sex, stock, and years of ocean residence. The well-known cycle in abundance of Pink Salmon from eastern Kamchatka and North America had statistically significant negative effects on returning steelhead lengths that depended on sex and the number of years of ocean residence. These results suggest that competition for limited resources occurs well after initial ocean entry and that interactions during later stages of ocean residence can be influential. Interspecific competition has implications for future returns of steelhead from the ocean, especially as metabolic demands for steelhead increase as the ocean warms.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2023-0374","usgsCitation":"Vosbigian, R., Wendling, L., Copeland, T., and Falcy, M.R., 2024, Cycles in adult steelhead length suggest interspecific competition in the North Pacific Ocean: Canadian Journal of Fisheries and Aquatic Sciences, v. 81, no. 12, p. 1666-1675, https://doi.org/10.1139/cjfas-2023-0374.","productDescription":"10 p.","startPage":"1666","endPage":"1675","ipdsId":"IP-152505","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":498002,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2023-0374","text":"Publisher Index Page"},{"id":485357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"North Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -185.32689526996586,\n 61.64416165867908\n ],\n [\n -197.030190866797,\n 55.22105865874343\n ],\n [\n -195.44967843647362,\n 49.2111608346834\n ],\n [\n -129.9559003927209,\n 50.23530897305639\n ],\n [\n -142.44277509138277,\n 59.52776817399416\n ],\n [\n -163.55581623425473,\n 58.827510215872856\n ],\n [\n -167.43488725913025,\n 62.05267202630721\n ],\n [\n -185.32689526996586,\n 61.64416165867908\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"81","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vosbigian, Ryan A.","contributorId":354342,"corporation":false,"usgs":false,"family":"Vosbigian","given":"Ryan A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":935412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wendling, Logan","contributorId":354343,"corporation":false,"usgs":false,"family":"Wendling","given":"Logan","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":935413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Copeland, Timothy","contributorId":354344,"corporation":false,"usgs":false,"family":"Copeland","given":"Timothy","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":935414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falcy, Matthew Richard 0000-0002-3332-2239","orcid":"https://orcid.org/0000-0002-3332-2239","contributorId":288500,"corporation":false,"usgs":true,"family":"Falcy","given":"Matthew","email":"","middleInitial":"Richard","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935415,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263976,"text":"70263976 - 2024 - Wintering grounds leave their mark: Where birds winter influences genomic structure in Arctic nesting common eiders","interactions":[],"lastModifiedDate":"2025-03-04T15:19:16.728961","indexId":"70263976","displayToPublicDate":"2024-11-28T08:12:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Wintering grounds leave their mark: Where birds winter influences genomic structure in Arctic nesting common eiders","docAbstract":"Information about the distribution of genomic variation within and between nesting locations of waterfowl can aid in defining populations and conservation plans and inform harvest management. Identification of locations where shifts in allelic diversity occur is particularly important for Arctic nesting species nesting, a region currently experiencing rapid change associated with climate and other factors. Here, we build upon previous investigations on spatial patterns of genetic variation in a largely migratory, circumpolar breeding sea duck, the common eider (Somateria mollissima) through inclusion of additional sample locations and genomic markers. We analyzed double-digest restriction site-associated DNA sequences from 309 common eiders to estimate genetic variation within, and differentiation among, 21 nesting locations across North America. Our results corroborate previous evidence of four genetic groups that coincide with colonies that share similar winter areas. We found deeper genomic differences between common eiders wintering in the west (Pacific Ocean) versus the east (Atlantic Ocean and Hudson Bay). We also identified subtle structure, not previously recognized, within Hudson Bay nesting common eiders. Our findings add to evidence linking wintering areas and genomic differentiation in waterfowl, offering further insights for management of this culturally important sea duck species.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10592-024-01654-2","usgsCitation":"Sonsthagen, S.A., Wilson, R., Turner, R., Fortin, M., Gilchrist, G., and Friesen, V., 2024, Wintering grounds leave their mark: Where birds winter influences genomic structure in Arctic nesting common eiders: Conservation Genetics, v. 26, p. 89-101, https://doi.org/10.1007/s10592-024-01654-2.","productDescription":"13 p.","startPage":"89","endPage":"101","ipdsId":"IP-164460","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":482795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United 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Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","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":929399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Robert E.","contributorId":340822,"corporation":false,"usgs":false,"family":"Wilson","given":"Robert E.","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":929400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turner, Russell R.","contributorId":351758,"corporation":false,"usgs":false,"family":"Turner","given":"Russell R.","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":929401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fortin, Marie-Josee","contributorId":197532,"corporation":false,"usgs":false,"family":"Fortin","given":"Marie-Josee","email":"","affiliations":[],"preferred":false,"id":929402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilchrist, Grant","contributorId":221288,"corporation":false,"usgs":false,"family":"Gilchrist","given":"Grant","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":929403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Friesen, Vicki L.","contributorId":351067,"corporation":false,"usgs":false,"family":"Friesen","given":"Vicki L.","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":929404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273449,"text":"70273449 - 2024 - Updating the crustal fault model for the 2023 National Seismic Hazard Model for Alaska","interactions":[],"lastModifiedDate":"2026-01-14T15:36:32.289864","indexId":"70273449","displayToPublicDate":"2024-11-27T09:29:20","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"4","title":"Updating the crustal fault model for the 2023 National Seismic Hazard Model for Alaska","docAbstract":"We present the crustal fault model for Alaska, based on geologic observations, as a primary input for the 2023 revision of the U.S. Geological Survey National Seismic Hazard Model. We update the 2013 Alaska Quaternary fault and fold database to produce a simplified model of 105 fault sections and four fault zone polygons with basic geologic parameters including slip sense and rate. Significant updates include the following: (1) a slip rate of ∼53 mm/year on the Queen Charlotte Fault indicating it accommodates all of the plate boundary motion; (2) quantified slip rates on megathrust splay faults in the southern Prince William Sound region and near Kodiak Island; (3) improved details of structures in the Chugach-St. Elias orogen; (4) revision of the Castle Mountain Fault from right-lateral slip to a predominantly reverse fault; (5) improved Interior Alaska tectonic models that clarify relationships between the Denali, Totschunda, and thrust faults on both sides of the Alaska Range; (6) identified large earthquake sources in the eastern Brooks Range; and (7) omission of the Chatham Strait section of the Denali Fault. The fault model underscores that the collision of the Yakutat microplate is the dominant driver of active crustal faulting in most of Alaska.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Tectonics and seismic structure of Alaska and northwestern Canada: EarthScope and beyond","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/9781394195947.ch4","usgsCitation":"Haeussler, P., Bender, A., Powers, P.M., Koehler, R.D., and Brothers, D., 2024, Updating the crustal fault model for the 2023 National Seismic Hazard Model for Alaska, chap. 4 of Tectonics and seismic structure of Alaska and northwestern Canada: EarthScope and beyond, p. 85-127, https://doi.org/10.1002/9781394195947.ch4.","productDescription":"43 p.","startPage":"85","endPage":"127","ipdsId":"IP-154998","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":498612,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.2930153790011,\n 62.12227221887332\n ],\n [\n -157.2930153790011,\n 51.969062626141636\n ],\n [\n -131.04252619969355,\n 51.969062626141636\n ],\n [\n -131.04252619969355,\n 62.12227221887332\n ],\n [\n -157.2930153790011,\n 62.12227221887332\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-12-13","publicationStatus":"PW","contributors":{"editors":[{"text":"Ruppert, Natalia A. 0000-0003-0589-1159","orcid":"https://orcid.org/0000-0003-0589-1159","contributorId":351514,"corporation":false,"usgs":true,"family":"Ruppert","given":"Natalia A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":953793,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Jadamec, M.","contributorId":83326,"corporation":false,"usgs":true,"family":"Jadamec","given":"M.","email":"","affiliations":[],"preferred":false,"id":953794,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Freymueller, Jeffery T. 0000-0003-0614-0306","orcid":"https://orcid.org/0000-0003-0614-0306","contributorId":244609,"corporation":false,"usgs":false,"family":"Freymueller","given":"Jeffery","email":"","middleInitial":"T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":953795,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bender, Adrian 0000-0001-7469-1957","orcid":"https://orcid.org/0000-0001-7469-1957","contributorId":219952,"corporation":false,"usgs":true,"family":"Bender","given":"Adrian","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":953740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koehler, Rich D.","contributorId":365135,"corporation":false,"usgs":false,"family":"Koehler","given":"Rich","middleInitial":"D.","affiliations":[{"id":87051,"text":"Nevada Bureau of Mines and Geology, University of Nevada, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":953741,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brothers, Daniel S. 0000-0001-7702-157X","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":210199,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":953742,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263921,"text":"70263921 - 2024 - Stopover population estimate and migration ecology of Red Knots C. c. rufa at Delaware Bay, USA, 2024","interactions":[],"lastModifiedDate":"2026-03-17T15:06:47.161953","indexId":"70263921","displayToPublicDate":"2024-11-23T10:03:08","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"displayTitle":"Stopover population estimate and migration ecology of Red Knots C. c. rufa at Delaware Bay, USA, 2024","title":"Stopover population estimate and migration ecology of Red Knots C. c. rufa at Delaware Bay, USA, 2024","docAbstract":"Red Knots (Calidris canutus rufa) stop at Delaware Bay on the mid-Atlantic coast of North America during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). Horseshoe crabs have been harvested for use as bait in eel (Anguilla rostrata) and whelk (Busycotypus canaliculatus and Busycon carica) fisheries since at least 1990. In the late 1990s and early 2000s, the number of Red Knots counted during aerial surveys at Delaware Bay declined, leading to conservation concern for Red Knots and shorebirds at Delaware Bay. In 2013, the Atlantic States Marine Fisheries Commission began using an Adaptive Resource Management (ARM) framework to manage the harvest of horseshoe crabs in the Delaware Bay region. The objective of the ARM framework is to manage sustainable harvest of Delaware Bay horseshoe crabs while maintaining ecosystem integrity and supporting Red Knot recovery with adequate stopover habitat. The ARM framework thus requires annual estimates of horseshoe crab population size and Red Knot stopover population size to recommend annual harvest quotas. We estimated the passage population of Red Knots at Delaware Bay in 2024 using a mark-recapture-resight investigation. We used a Bayesian analysis of a Jolly-Seber model, which accounts for turnover in the population and the probability of detection during surveys. The estimated passage population size in 2024 was 46,127 (95% credible interval: 39,286–57,799), an increase from 2023 (39,361 [33,724–47,556]). Since 2019, the stopover population has fluctuated between approximately 39,000 and 46,000, and appears stable given the broad overlap in the confidence intervals of the annual population estimates. The 2024 Red Knot stopover population estimate will inform decision making in the next horseshoe crab management cycle of the Atlantic States Marine Fisheries Commission.
","language":"English","publisher":"Delaware Division of Fish and Wildlife","usgsCitation":"Lyons, J.E., 2024, Stopover population estimate and migration ecology of Red Knots C. c. rufa at Delaware Bay, USA, 2024, 16 p.","productDescription":"16 p.","ipdsId":"IP-172353","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":482621,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.delaware.gov/fish-wildlife/conservation/shorebirds/research/"},{"id":501216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.10844894246691,\n 38.71094419142639\n ],\n [\n -74.84547301245848,\n 39.09891530924247\n ],\n [\n -74.90299899714816,\n 39.19451507998161\n ],\n [\n -75.48099817664571,\n 39.50167475761344\n ],\n [\n -75.52756683091825,\n 39.65791141521865\n ],\n [\n -75.60974680904543,\n 39.66423790391954\n ],\n [\n -75.65905479592207,\n 39.60516815618203\n ],\n [\n -75.60974680904543,\n 39.429772303548646\n ],\n [\n -75.44812618539433,\n 39.24332702987286\n ],\n [\n -75.45360485060331,\n 39.05638485196263\n ],\n [\n -75.2810268965351,\n 38.82841171381057\n ],\n [\n -75.10844894246691,\n 38.71094419142639\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929098,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70261071,"text":"sir20245089 - 2024 - Mapping karst groundwater flow paths and delineating recharge areas for springs in the Little Sequatchie and Pryor Cove watersheds, Tennessee","interactions":[],"lastModifiedDate":"2025-12-22T20:39:12.793493","indexId":"sir20245089","displayToPublicDate":"2024-11-22T16:23:22","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5089","displayTitle":"Mapping Karst Groundwater Flow Paths and Delineating Recharge Areas for Springs in the Little Sequatchie and Pryor Cove Watersheds, Tennessee","title":"Mapping karst groundwater flow paths and delineating recharge areas for springs in the Little Sequatchie and Pryor Cove watersheds, Tennessee","docAbstract":"The Little Sequatchie River and Pryor Cove Branch, in southern Tennessee, drain the eastern escarpment of the Cumberland Plateau to the Sequatchie River near the southern end of the Sequatchie Valley. The Little Sequatchie River is the largest tributary to the Sequatchie River by drainage area, covering over 120 square miles. The hydrology of the two drainage areas has been largely altered by karst processes, which has caused the majority of the streams to sink at the contact between the Mississippian Pennington Formation and the underlying Mississippian Bangor Limestone. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service and Tennessee Department of Environment and Conservation, initiated a study in 2021 to map the karst groundwater pathways in both watersheds in order to delineate recharge areas for several springs. One of these springs, Sequatchie Cave, represents a significant habitat for two Species of Greatest Conservation Need, the Glyphopsyche sequatchie (Sequatchie caddisfly) and the federally endangered Marstonia ogmorhaphe (royal marstonia). Springs and springflow-dominated streams in the Little Sequatchie River valley and Pryor Cove also provide water for agricultural practices and serve as a drinking water source for nearby communities. During the study, a total of 25 dye injections were conducted over eight rounds from January 2022 through March 2023. Dye traces from these injections helped to delineate recharge areas for six major springs, ranging from 7.3 to 65.2 square miles in area. The majority of the dye traces remained subsurface (from sinkpoint to recovery site) for long distances, with karst groundwater travelling nearly 8 miles before resurfacing. The dye traces also had rapid traveltimes, often travelling hundreds to thousands of feet per hour. The goal of this project was to provide scientific data related to karst groundwater pathways and spring recharge areas to aid State and Federal agencies in making informed decisions to protect and preserve this unique and vulnerable karst system.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"This report describes a compilation of brood observations for Anatidae species breeding in Maine during an 18-year period (1977–94) that were made by the U.S. Geological Survey’s Patuxent Wildlife Research Center while it was operated by the U.S. Fish and Wildlife Service. During four focused studies, variables affecting the declining Anas rubripes (Brewster, 1902) (American black duck, hereafter black duck) population were assessed. Broods were observed on seven geographical study sites within four study areas located in three of Maine’s five biophysical regions. For combined studies, 168 wetlands were monitored for broods.
The 1,907 recorded broods were distributed among study areas: 185 in Dixmont, 241 in Cherryfield and Beddington, 411 in Moosehorn, Baring Unit and Edmunds Unit, and 849 in Aroostook County, Agricultural and Forested sites. Additionally, 221 broods were recorded in the 117-hectare Downing Bog wetland at the Cherryfield site during annual evening visits made between 1985 and 1991. Twelve Anatidae species, mostly black duck (676), Aix sponsa (Linnaeus, 1758) (wood duck; 265), Aythya collaris (Donovan, 1809) (ring-necked duck; 246), and Lophodytes cucullatus (Linnaeus, 1758) (hooded merganser; 163) were observed. Only 139 broods of Anas platyrhynchos (Linnaeus, 1758) (mallard) were found; all but 9 broods were at the Aroostook County, Agricultural site. Branta canadensis (Linnaeus, 1758) (Canada goose) broods were found at the Aroostook County, Agricultural site (58) and Moosehorn, Baring Unit (97).
For the combined studies, 468 of 676 (69.2 percent) of black duck broods reached fledging age (Class IIc-III), whereas 93 of 139 (66.9 percent) of mallard broods reached fledging age. Black ducks used predominantly palustrine, emergent wetland; palustrine, forested wetland; and palustrine, scrub-shrub wetland. Of the 134 mallard broods observed at the Agricultural and Forested sites in Aroostook County, 130 (97.0 percent) were observed at the Agricultural site and 93 (71.5 percent) of them were recorded on two palustrine and two lacustrine, unconsolidated bottom class wetlands. Mean size of black duck, wood duck, and ring-necked duck broods in this report were similar to those reported from historic Maine data.
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\"}}]}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Shallow shear‐wave velocities (Vs) sometimes are estimated from joint inversions of horizontal‐to‐vertical (H/V) spectral ratios and surface‐wave dispersion curves derived from ambient noise or small active sources. Here, we evaluate carrying out these inversions using Rayleigh‐wave dispersion curves computed from crustal‐scale P‐wave seismic refraction data. We use data from the 2014–2015 Eastern North American Margin (ENAM) experiment in Virginia and North Carolina, but similar seismic refraction data sets have been acquired over sedimentary basins of interest for seismic hazard studies, including in major urban areas. The ENAM project deployed a pair of ∼215 km long, northwest–southeast linear arrays with ∼300 m receiver spacing to record 11 dynamite shots, and 80 continuously recording seismometers with 5–6 km spacing along the same arrays to record offshore airguns. The arrays crossed the onland portion of the Atlantic Coastal Plain sediments, which are a seaward‐thickening wedge of Cretaceous and younger sediments deposited mostly on crystalline bedrock. We compute Rayleigh‐wave dispersion curves from 3 to 9 km long portions of the receiver arrays on each side of the dynamite shots, and we compute ambient‐noise H/V ratios from the continuously recording seismometers. We use a genetic inversion algorithm in which forward velocity models in each “generation” are evaluated for misfits compared to the observed data, with subsequent generations constructed from the models with the smallest misfits. Velocities to depths of 500 m are defined well, as shown by a narrow range of velocities in the best‐fit models, by the consistency between multiple inversion runs at a site, and by forward modeling of site responses. The resulting velocity cross‐section of the Coastal Plain strata has seaward‐dipping contours in the thinner portions of the Coastal Plain but smaller dips in the deeper portions. We interpret these results as showing that velocity contours in the ACP strata are influenced by a combination of lithology and overburden pressure. Results demonstrate that existing seismic refraction data have the potential for determining detailed shallow shear‐wave velocity profiles.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230241","usgsCitation":"Pratt, T., Parolai, S., Poggi, V., and Dreossi, I., 2024, Using crustal-scale refraction data of joint inversions of Rayleigh-wave dispersion curves and H/V spectral ratios for Atlantic Coastal Plain velocity structure, eastern U.S.: Bulletin of the Seismological Society of America, v. 115, no. 1, p. 270-295, https://doi.org/10.1785/0120230241.","productDescription":"26 p.","startPage":"270","endPage":"295","ipdsId":"IP-167122","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.88300039003173,\n 39.731394919591565\n ],\n [\n -90.88300039003173,\n 24.876206992183768\n ],\n [\n -74.40455609054004,\n 24.876206992183768\n ],\n [\n -74.40455609054004,\n 39.731394919591565\n ],\n [\n -90.88300039003173,\n 39.731394919591565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Pratt, Thomas 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":201084,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","affiliations":[],"preferred":true,"id":947394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parolai, Stefano 0000-0002-9084-7488","orcid":"https://orcid.org/0000-0002-9084-7488","contributorId":296105,"corporation":false,"usgs":false,"family":"Parolai","given":"Stefano","email":"","affiliations":[{"id":63989,"text":"Instituto Nazionale di Oceonografia","active":true,"usgs":false}],"preferred":false,"id":947395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poggi, Valerio","contributorId":360682,"corporation":false,"usgs":false,"family":"Poggi","given":"Valerio","affiliations":[{"id":86081,"text":"Trieste, Italy","active":true,"usgs":false}],"preferred":false,"id":947396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dreossi, Ilaria","contributorId":296107,"corporation":false,"usgs":false,"family":"Dreossi","given":"Ilaria","email":"","affiliations":[{"id":63991,"text":"National Institute of Oceanography and Applied Geophysics – OGS, Udine, Italy","active":true,"usgs":false}],"preferred":false,"id":947397,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70260965,"text":"70260965 - 2024 - Brittle regime slip partitioned damage and deformation mechanisms along the eastern Denali fault zone in southwestern, Yukon","interactions":[],"lastModifiedDate":"2024-11-18T15:26:34.743256","indexId":"70260965","displayToPublicDate":"2024-11-18T08:26:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Brittle regime slip partitioned damage and deformation mechanisms along the eastern Denali fault zone in southwestern, Yukon","docAbstract":"Rare bedrock exposures of the eastern Denali fault zone in southwestern Yukon allow for the measurement, sampling, and analyses of brittle regime fault slip data and deformation mechanisms to explore relations to far field, oblique plate motions. Host rock lithologies and associated slip surfaces show episodic damage zone‐related deformation and calcite ± hematite ± chlorite related hydrothermal fluid flow. This regional scale network of asymmetric fault damage is spatially and kinematically linked to a discrete and narrow fault core. Fault network observations, orientations, slip data, and strain inversions document a slip partitioned strike‐slip fault system with locally and mutually overprinting strike‐, oblique‐, and dip‐slip components. Microstructural analyses reveal crystal plastic and co‐seismic brittle deformation mechanisms active in a narrow range of upper crustal temperature, pressure, fluid, and chemical conditions. The net damage related slip is not exclusively formed by a single kinematic system, but rather a fully partitioned, time integrated system likely operative for much of the fault's brittle regime evolution temporally constrained by previously published thermochronometric data. Although the fault slip data was collected from outcrop‐scale exposures at sites tens of kilometers apart, results show remarkable correlation between fault kinematics and plate motions along the ∼580 km long eastern Denali fault segment. End member, subhorizontal, northeast directed reverse and north directed dextral strike slip fault strain axes closely reflect relative plate motion interactions over at least the last 30 m.y. and act as a proxy for far‐field stresses compatible with the kinematics of the damage zone network.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JB029506","usgsCitation":"Caine, J., Orlandini, O.F., Vollmer, F.W., and Lowers, H.A., 2024, Brittle regime slip partitioned damage and deformation mechanisms along the eastern Denali fault zone in southwestern, Yukon: Journal of Geophysical Research: Solid Earth, v. 129, no. 11, e2024JB029506, 35 p., https://doi.org/10.1029/2024JB029506.","productDescription":"e2024JB029506, 35 p.","ipdsId":"IP-149623","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":466757,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jb029506","text":"Publisher Index Page"},{"id":464228,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","otherGeospatial":"British Columbia, southwest Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -161.0212169953091,\n 60.265114667913366\n ],\n [\n -161.0212169953091,\n 52.584549776442685\n ],\n [\n -131.33133076901765,\n 52.584549776442685\n ],\n [\n -131.33133076901765,\n 60.265114667913366\n ],\n [\n -161.0212169953091,\n 60.265114667913366\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":918724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Orlandini, Omero F. 0000-0002-9578-1203","orcid":"https://orcid.org/0000-0002-9578-1203","contributorId":346333,"corporation":false,"usgs":false,"family":"Orlandini","given":"Omero","email":"","middleInitial":"F.","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":918725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vollmer, Frederick W. 0000-0002-0385-8489","orcid":"https://orcid.org/0000-0002-0385-8489","contributorId":271263,"corporation":false,"usgs":false,"family":"Vollmer","given":"Frederick","email":"","middleInitial":"W.","affiliations":[{"id":56326,"text":"State University of New York at New Paltz","active":true,"usgs":false}],"preferred":false,"id":918726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":918727,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263369,"text":"70263369 - 2024 - Seismicity zoning at Coso geothermal field and stress changes from fluid production and migration","interactions":[],"lastModifiedDate":"2025-02-07T18:50:48.949723","indexId":"70263369","displayToPublicDate":"2024-11-15T11:43:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Seismicity zoning at Coso geothermal field and stress changes from fluid production and migration","docAbstract":"As part of U.S. Geological Survey's (USGS) efforts to identify and assess geothermal energy resources of the US, a three-dimensional (3D) geologic and thermal model has been constructed for the Williston Basin, USA. The geologic model consists of all sedimentary units above the Proterozoic and Archean crystalline rock (called basement herein), with a total sedimentary thickness of up to 5 km near the basin center. Twenty-nine geologic units were mapped from interpreted formation tops from 16,465 wells. A 3D temperature model was constructed to a depth of 7 km by constructing a 3D heat flow model for the sedimentary units, followed by estimating underlying temperature using a one-dimensional (1D) analytic solution for heat flow within the underlying crystalline basement. Using the sedimentary basin model, heat flow was simulated in 3D and was calibrated using three temperature datasets: 1) 24 high-confidence static temperature logs (equilibrium thermal profiles), 2) more than15,000 drill stem test (DST) measurements from >7,000 wells, and 3) more than 45,000 bottomhole temperature (BHT) measurements from >14,000 wells. The DST and BHT datasets provide broad spatial coverage, but are lower confidence, primarily because measurements were made prior to attaining thermal equilibrium. DST and BHT measurements were binned regionally to develop representative thermal profiles that generally agree with these lower quality data (hereafter called pseudowell temperature profiles). Layer properties (primarily thermal conductivity and compaction curves) were set to best estimate values, then the heat flow model was calibrated to fit pseudowell and static temperature logs primarily by adjusting basal heat flow to approximate the overall temperature profile. Minor adjustments to thermal conductivity allowed adjusting changes in slope at lithologic contacts. Resulting maps include 3D temperature and basal (bottom of sedimentary units) heat flow estimates, which are used as input for the temperature model of the basement. The crystalline basement temperature model uses an analytic 1D solution to the heat flow equation that requires estimates of heat flow and temperature at the upper boundary (i.e., the sediment/basement contact), radiogenic heat production within the crystalline basement, and reference thermal conductivity (i.e., uncorrected for temperature). Two regions of high heat flow are identified: 1) in western North Dakota along the North American Central Plains Conductivity Anomaly and 2) in eastern Montana near the Poplar dome. Within the sedimentary column in the center of the basin of the basin, an area of approximately 100,000 km2 is predicted to have moderate- to high-temperature geothermal resources (>90 °C) under the thickest sequences of sediments. Where thick insulation and high heat flow coincide, electric-grade resources can be less than 4 km deep. Assuming a maximum feasible drilling depth of 7 km, temperatures are predicted to be as high as 175 °C. The geologic model may be used to identify strata at sufficient temperatures that may have natural permeability or that may have conditions that favor development of enhanced/engineered geothermal systems resources.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2024.103196","usgsCitation":"Gelman, S.E., and Burns, E.R., 2024, Three-dimensional temperature maps of the Williston Basin, USA: Implications for deep hot sedimentary and enhanced geothermal resources: Geothermics, v. 125, 103196, 9 p., https://doi.org/10.1016/j.geothermics.2024.103196.","productDescription":"103196, 9 p.","ipdsId":"IP-165645","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":466763,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geothermics.2024.103196","text":"Publisher Index Page"},{"id":464292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.05487036081263,\n 49.09401622161886\n ],\n [\n -107.05487036081263,\n 45.9204646960259\n ],\n [\n -100.81731222757732,\n 45.9204646960259\n ],\n [\n -100.81731222757732,\n 49.09401622161886\n ],\n [\n -107.05487036081263,\n 49.09401622161886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gelman, Sarah E. 0000-0003-2549-9509","orcid":"https://orcid.org/0000-0003-2549-9509","contributorId":270004,"corporation":false,"usgs":true,"family":"Gelman","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918758,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70260707,"text":"sir20235064I - 2024 - Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70260707,"text":"sir20235064I - 2024 - Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064I","publicationYear":"2024","noYear":false,"chapter":"I","displayTitle":"Peak Streamflow Trends in South Dakota and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2025-12-22T21:31:08.991933","indexId":"sir20235064I","displayToPublicDate":"2024-11-08T10:53:13","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5064","chapter":"I","displayTitle":"Peak Streamflow Trends in South Dakota and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020","docAbstract":"Peak-flow (flood) frequency analysis is essential to water-resources management applications, including the design of critical infrastructure such as bridges and culverts, and floodplain mapping. Federal guidelines for performing peak-flow flood frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C, which documents the guidelines for determining annual peak streamflow frequency, is that, for basins without major hydrologic alterations (for example, regulation, diversion, and urbanization), statistical properties of the distribution of annual peak streamflows are stationary; that is, the mean, variance, and skew are constant through time. Nonstationarity is a statistical property of a peak-flow series such that the long-term (on the order of decades) distributional properties change one or more times either gradually or abruptly through time. Individual nonstationarities may be attributed to one source such as flow regulation, land-use change, or climate but are often the result of a combination of sources, making detection and attribution of nonstationarities challenging.
In response to a growing concern regarding nonstationarity in peak streamflows in the region, the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, assessed the potential nonstationarity in peak streamflows in the north-central United States. This chapter characterizes the effects of natural hydroclimatic shifts and potential climate change on annual peak streamflows in the State of South Dakota. Annual peak and daily streamflow as well as model-simulated gridded climatic data were examined for temporal monotonic trends, change points, and other statistical properties indicative of changing climatic and environmental conditions.
Changes in annual peak and daily flows were evaluated among 13, 35, and 81 qualifying U.S. Geological Survey streamgages for the 75-, 50-, and 30-year trend periods through water year 2020 (the period from October 1, 2019, to September 30, 2020) in South Dakota, respectively. No qualifying streamgages were in the 100-year trend period in the State. Statistical tests for autocorrelation (independent and identically distributed assumption), monotonic trends, and change points in the median and scale are analyzed to evaluate potential stationarity violations (nonstationarity) for performing at-site peak-flow flood-frequency analysis. The trends are reported using a likelihood approach as an alternative to simply reporting significant trends with an arbitrary p-value cutoff point.
A distinct east-west spatial pattern of likely upward and downward monotonic trends and change points, respectively, was detected in 75- and 50-year trend periods, but an inconsistent spatial pattern was detected in the 30-year trend period. Additionally, change points in the median annual peak streamflows were detected in the late 1970s and early 1980s in the western part of the State, but in the east, the change point was more commonly detected in 1992–93. A similar east-west spatial pattern of likely upward and downward trends was detected in the annual peak-flow timing, the day of the year of the annal peak streamflow. In the western part of the State, the annual peak streamflows are arriving earlier, but in the east, the annual peak streamflows are arriving later. A peaks-over-threshold (POT) analysis where, on average, there are two events per year (POT2) and four events per year (POT4) was also used to evaluate changes in the frequency (count) of daily streamflows exceeding the threshold. Similar to detected changes in the annual peak streamflow, an east-west likely upward or downward change corresponding to an increase or decrease, respectively, in the frequency of daily streamflow greater than a POT2 and POT4 threshold was detected.
A monthly water-balance model was used to evaluate hydroclimatic variation in annual and seasonal precipitation, snowfall, potential evapotranspiration, and soil moisture storage for all qualifying streamgages in the 75-, 50-, and 30-year trend periods. Detected trends in the annual hydroclimatic metrics for the 75- and 50-year trend periods indicate a spatially consistent statewide increase in precipitation, decrease in snowfall, increase in potential evapotranspiration, and increase in soil moisture storage. Furthermore, detected trends in seasonal precipitation in the 75- and 50-year trend periods highlight a pronounced change in precipitation in winter and later into the summer season, especially in the 50-year trend period in the eastern part of the State. Statewide increases in seasonal soil moisture storage were also detected, highlighting year-round increasing flood magnitudes, particularly in the eastern part of the State.
Based on the results of these stationarity tests for the qualifying streamgages in South Dakota among the 75-, 50-, and 30-year trend periods, consistent temporal and spatial patterns of nonstationarity were detected among the 75- and 50-year trend periods. Furthermore, when nonstationarity is detected in daily streamflow, increased streamflow and volume (increasing frequency in POT), as well as potentially bridge scour, may have implications on culvert and highway design in the eastern part of South Dakota. Thus, when performing at-site peak-flow flood-frequency analyses in South Dakota, potential nonstationarities and alternative approaches are important considerations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064I","collaboration":"Prepared in cooperation with the South Dakota Department of Transportation","usgsCitation":"Barth, N.A., and Sando, S.K., 2024, Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020, chap. I of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 70 p., https://doi.org/10.3133/sir20235064I.","productDescription":"Report: x, 70 p.; Data Release; Dataset","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146340","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":497916,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117775.htm","linkFileType":{"id":5,"text":"html"}},{"id":463794,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064I/full"},{"id":463793,"rank":6,"type":{"id":30,"text":"Data 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U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Fluid inclusion microthermometry and Raman spectroscopy of fluid inclusions in quartz veins from the Pennsylvanian rocks of the Anthracite belt, eastern Pennsylvania support a deep burial model of coalification in favor of focused orogenic hot fluid flow. High-temperature (250 to 255 °C) trapping of CH4 ± CO2 saturated aqueous fluids and CH4 ± CO2 inclusions indicate fluid trapping at depths of 11.5 to 13.4 km under a cover of Pennsylvanian to Permian(?) syntectonic load. In the folded rocks to the south of the Anthracite belt, CH4 ± CO2 fluid inclusions indicate a sediment load that was up to 16.3 km thick. Re-equilibrated aqueous fluid inclusions from veins in Silurian through Devonian rocks give the same range of trapping conditions but a wide range of fluid salinities suggesting that folding, fracturing, and meteoric recharge resulted in the intermixing of fluids from throughout the stratigraphic succession.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2024.104646","usgsCitation":"Evans, M.A., and Jubb, A., 2024, Deep syntectonic burial of the Anthracite belt, Eastern Pennsylvania: International Journal of Coal Geology, v. 295, 104646, 27 p., https://doi.org/10.1016/j.coal.2024.104646.","productDescription":"104646, 27 p.","ipdsId":"IP-164265","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75,\n 41.75\n ],\n [\n -80,\n 41.75\n ],\n [\n -80,\n 39.5\n ],\n [\n -75,\n 39.5\n ],\n [\n -75,\n 41.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Mark A.","contributorId":197411,"corporation":false,"usgs":false,"family":"Evans","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":918370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918369,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70260964,"text":"70260964 - 2024 - Depths in a day - A new era of rapid-response Raman-based barometry using fluid inclusions","interactions":[],"lastModifiedDate":"2024-12-10T15:38:41.563856","indexId":"70260964","displayToPublicDate":"2024-11-07T09:57:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Depths in a day - A new era of rapid-response Raman-based barometry using fluid inclusions","docAbstract":"Rapid-response petrological monitoring is a major advance for volcano observatories, allowing them to build and validate models of plumbing systems that supply eruptions in near-real-time. The depth of magma storage has recently been identified as high-priority information for volcanic observatories, yet this information is not currently obtainable via petrological monitoring methods on timescales relevant to eruption response. Fluid inclusion barometry (using micro-thermometry or Raman spectroscopy) is a well-established petrological method to estimate magma storage depths and has been proposed to have potential as a rapid-response monitoring tool, although this has not been formally demonstrated. To address this deficiency, we performed a near-real-time rapid-response simulation for the September 2023 eruption of Kīlauea, Hawaiʻi. We show that Raman-based fluid inclusion barometry can robustly determine reservoir depths within a day of receiving samples — a transformative timescale that has not previously been achieved by petrological methods. Fluid inclusion barometry using micro-thermometric techniques has typically been limited to systems with relatively deep magma storage (>0.4 g/cm3 or >7 km) where measurements of CO2 density are easy and accurate because the CO2 fluid homogenizes into the liquid phase. Improvements of the accuracy of Raman spectroscopy measurements of fluids with low CO2 density over the past couple of decades has enabled measurements of fluid inclusions from shallower magmatic systems. However, one caveat of examining shallower systems is that the fraction of H2O in the fluid may be too high to reliably convert CO2 density to pressure. To test the global applicability of rapid response fluid inclusion barometry, we compiled a global melt inclusion dataset (>4000 samples) and calculate the fluid composition at the point of vapor saturation (XH2O). We show that fluid inclusions in crystal-hosts from mafic compositions (<57 wt. % SiO2) — likely representative of magmas recharging many volcanic systems worldwide — trap fluids with XH2O low enough to make fluid inclusion barometry useful at many of the world’s most active and hazardous mafic volcanic systems (e.g., Iceland, Hawaiʻi, Galápagos Islands, East African Rift, Réunion, Canary Islands, Azores, Cabo Verde).
","language":"English","publisher":"Oxford Academic","doi":"10.1093/petrology/egae119","usgsCitation":"DeVitre, C., Wieser, P.E., Bearden, A.T., Richie, A., Rangel, B., Gleeson, M., Grimsich, J., Lynn, K.J., Downs, D.T., Deligne, N.I., and Mulliken, K.M., 2024, Depths in a day - A new era of rapid-response Raman-based barometry using fluid inclusions: Journal of Petrology, v. 65, no. 11, egae119, 15 p., https://doi.org/10.1093/petrology/egae119.","productDescription":"egae119, 15 p.","ipdsId":"IP-158109","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466776,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egae119","text":"Publisher Index Page"},{"id":464235,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea","geographicExtents":"{\n \"type\": 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Berkeley","active":true,"usgs":false}],"preferred":false,"id":918719,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918720,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918721,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918722,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mulliken, Katherine M. 0000-0003-4190-5060","orcid":"https://orcid.org/0000-0003-4190-5060","contributorId":217810,"corporation":false,"usgs":false,"family":"Mulliken","given":"Katherine","email":"","middleInitial":"M.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":918723,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70260627,"text":"70260627 - 2024 - Trends and environmental impacts of virtual water trade","interactions":[],"lastModifiedDate":"2024-12-10T15:33:04.350122","indexId":"70260627","displayToPublicDate":"2024-11-05T09:58:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7460,"text":"Nature Reviews Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Trends and environmental impacts of virtual water trade","docAbstract":"Virtual water describes water embedded in the production of goods and offers meaningful insights about the complex interplay between water, trade and sustainability. In this Review, we examine the trends, major players, traded products and key drivers of virtual water trade (VWT). Roughly 20% of water used in global food production is traded virtually rather than domestically consumed. As such, agriculture dominates VWT, with livestock products, wheat, maize, soybean, oil palm, coffee and cocoa contributing over 70% of total VWT. These products are also driving VWT growth, the volume of which has increased 2.9 times from 1986 to 2022. However, the countries leading VWT contributions (with China, the United States, the Netherlands, Germany and India accounting for 34% of the global VWT in 2022) have remained relatively stable over time, albeit with China becoming an increasingly important importer. VWT can mitigate the effects of water scarcity and food insecurity, although there are concerns about the disconnect between consumers and the environmental impacts of their choices, and unsustainable resource exploitation. Indeed, approximately 16% of unsustainable water use and 11% of global groundwater depletion are virtually traded. Future VWT analyses must consider factors such as water renewability, water quality, climate change impacts and socioeconomic implications.
","language":"English","publisher":"Nature","doi":"10.1038/s43017-024-00605-2","usgsCitation":"Mekonnen, M.M., Kebede, M.M., Demeke, B.W., Carr, J., Chapagain, A., Dalin, C., Debaere, P., D’Odorico, P., Marston, L., Ray, C., Rosa, L., and Zhuo, L., 2024, Trends and environmental impacts of virtual water trade: Nature Reviews Earth & Environment, v. 5, p. 890-905, https://doi.org/10.1038/s43017-024-00605-2.","productDescription":"16 p.","startPage":"890","endPage":"905","ipdsId":"IP-160482","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":496765,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-05368702","text":"External Repository"},{"id":463764,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Mekonnen, Mesfin M.","contributorId":346068,"corporation":false,"usgs":false,"family":"Mekonnen","given":"Mesfin","email":"","middleInitial":"M.","affiliations":[{"id":37195,"text":"The University of Alabama","active":true,"usgs":false}],"preferred":false,"id":917929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kebede, Mahlet M.","contributorId":346069,"corporation":false,"usgs":false,"family":"Kebede","given":"Mahlet","email":"","middleInitial":"M.","affiliations":[{"id":37195,"text":"The University of Alabama","active":true,"usgs":false}],"preferred":false,"id":917930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Demeke, Betelhem W.","contributorId":346070,"corporation":false,"usgs":false,"family":"Demeke","given":"Betelhem","email":"","middleInitial":"W.","affiliations":[{"id":37195,"text":"The University of Alabama","active":true,"usgs":false}],"preferred":false,"id":917931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":917932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapagain, Ashok","contributorId":346073,"corporation":false,"usgs":false,"family":"Chapagain","given":"Ashok","email":"","affiliations":[{"id":28034,"text":"Pacific Institute","active":true,"usgs":false}],"preferred":false,"id":917933,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dalin, Carole","contributorId":215134,"corporation":false,"usgs":false,"family":"Dalin","given":"Carole","email":"","affiliations":[{"id":39184,"text":"Institute for Sustainable Resources, University College, London, UK","active":true,"usgs":false}],"preferred":false,"id":917934,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Debaere, Peter","contributorId":346078,"corporation":false,"usgs":false,"family":"Debaere","given":"Peter","email":"","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":917935,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"D’Odorico, Paolo","contributorId":209957,"corporation":false,"usgs":false,"family":"D’Odorico","given":"Paolo","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":917936,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Marston, Landon 0000-0001-9116-1691","orcid":"https://orcid.org/0000-0001-9116-1691","contributorId":239626,"corporation":false,"usgs":false,"family":"Marston","given":"Landon","email":"","affiliations":[{"id":47941,"text":"Department of Civil Engineering, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":917937,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ray, Chittaranjan","contributorId":194209,"corporation":false,"usgs":false,"family":"Ray","given":"Chittaranjan","email":"","affiliations":[],"preferred":false,"id":917938,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rosa, Lorenzo","contributorId":209959,"corporation":false,"usgs":false,"family":"Rosa","given":"Lorenzo","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":917939,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Zhuo, La","contributorId":346083,"corporation":false,"usgs":false,"family":"Zhuo","given":"La","email":"","affiliations":[{"id":82762,"text":"Northwest A&F University, Yangling; Chinese Academy of Sciences & Ministry of Water Resources","active":true,"usgs":false}],"preferred":false,"id":917940,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70260957,"text":"70260957 - 2024 - Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy","interactions":[],"lastModifiedDate":"2024-12-10T15:37:31.896607","indexId":"70260957","displayToPublicDate":"2024-11-05T09:14:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy","docAbstract":"Making timely management decisions is often hindered by uncertainty. Monitoring reduces two key types of uncertainty. First, it serves to reduce structural uncertainty of how the system works and provides support for expectations of how a system works. Second, it serves to reduce parametric uncertainty of the drivers of system dynamics. By combining monitoring data and quantitative models, we can reduce structural and parametric uncertainty. To demonstrate this, we focus on the Shenandoah salamander (Plethodon shenandoah), a United States Federally Endangered Species. Early work suggested that P. shenandoah extinction risk results from competition with a conspecific (Plethodon cinereus). However, more recent work has found equivocal support for this claim, instead suggesting that abiotic factors, such as moisture and temperature, drive P. shenandoah persistence. Using long-term monitoring data, we find that while competition may play a part in P. shenandoah extinction risk, measures of surface moisture are better predictors of occupancy dynamics. Further, we find decreased detection rates of P. shenandoah when P. cinereus is present, suggesting a conflation of detection probability with actual competition, which cautions against making inference from unadjusted observations of occurrence. Using multiple lines of inquiry allows for more robust understanding of system drivers in the face of high uncertainty, increasing opportunities to manage extinction risk.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00442-024-05631-x","usgsCitation":"Werba, J.A., DiRenzo, G.V., Brand, A., and Campbell Grant, E.H., 2024, Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy: Oecologia, v. 206, p. 305-316, https://doi.org/10.1007/s00442-024-05631-x.","productDescription":"12 p.","startPage":"305","endPage":"316","ipdsId":"IP-148195","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":464231,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Blue Ridge Mountains, Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.60935855780521,\n 38.68748637317097\n ],\n [\n -78.60935855780521,\n 38.31093059502825\n ],\n [\n -78.13191468279777,\n 38.31093059502825\n ],\n [\n -78.13191468279777,\n 38.68748637317097\n ],\n [\n -78.60935855780521,\n 38.68748637317097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"206","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Werba, Jo Avital 0000-0002-5295-7790","orcid":"https://orcid.org/0000-0002-5295-7790","contributorId":338728,"corporation":false,"usgs":true,"family":"Werba","given":"Jo","email":"","middleInitial":"Avital","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":918691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":918692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brand, Adrianne 0000-0003-2664-0041","orcid":"https://orcid.org/0000-0003-2664-0041","contributorId":304281,"corporation":false,"usgs":true,"family":"Brand","given":"Adrianne","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":918693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":918694,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261018,"text":"70261018 - 2024 - Ceanothus: Taxonomic patterns in life history responses to fire","interactions":[],"lastModifiedDate":"2024-12-10T15:36:47.620254","indexId":"70261018","displayToPublicDate":"2024-11-05T08:14:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Ceanothus: Taxonomic patterns in life history responses to fire","docAbstract":"Premise: Ceanothus (Rhamnaceae) is a large genus of shrubs that dominate California chaparral and are resilient to fires. Persistence is ensured by resprouting and/or seedling recruitment from dormant seed banks. Some species do both and others, the obligate seeders, are entirely dependent on seedling recruitment. The distribution of these two modes within the genus is poorly documented.
Methods: We used all available publications that document species responses to fire and filled most gaps in the literature based on extensive field studies of more than 60 recent wildfires in California.
Results: The genus is divided into two subgenera, Ceanothus and Cerastes. Ceanothus is widely considered to comprise mostly resprouting species and Cerastes to consist of only obligate seeders. The subgenus Ceanothus includes resprouting species throughout their range from the eastern United States and Midwest to western United States. Within the California Floristic Province (CFP), a few species are unique in producing massive lignotubers that develop from repeated fires; however, within the CFP, the majority of species in this subgenus do not resprout and are obligate seeders. Two have disjunct subspecies that are facultative seeders or obligate seeders.
Conclusions: Previously, speciation in this genus was contended to have occurred in the late Miocene within the CFP. The syndrome of obligate seeding is most strongly represented in this region, and we hypothesize that evolution of this syndrome was a response to increased predictability of fire driven by the Mediterranean climate and the long interval between fires.
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\"}}]}","volume":"111","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, V. 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Brandon","affiliations":[{"id":17822,"text":"CA State U., Bakersfield, CA","active":true,"usgs":false}],"preferred":false,"id":918941,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70260170,"text":"sim3521 - 2024 - Geologic map of the southern Stillwater Range, Nevada","interactions":[],"lastModifiedDate":"2025-07-23T16:57:56.517005","indexId":"sim3521","displayToPublicDate":"2024-11-01T14:25:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3521","displayTitle":"Geologic Map of the Southern Stillwater Range, Nevada","title":"Geologic map of the southern Stillwater Range, Nevada","docAbstract":"The southern Stillwater Range in west-central Nevada contains the western part of the Oligocene Stillwater-Clan Alpine caldera complex, which extends about 55 kilometers (km) east from the west side of the Stillwater Range to the northwestern Desatoya Mountains. The complex consists of at least seven nested ignimbrite calderas and subjacent plutonic rocks emplaced into a complex basement composed of Mesozoic metasedimentary and metavolcanic rocks and Cretaceous granitic plutons. The calderas formed during large-volume (100s to greater than (>) 2,500 cubic kilometers [km3]) eruptions of silicic ignimbrites between about 30.4 and 25.1 million years before present (Ma). The Job Canyon and Poco Canyon calderas and the western part of the much larger Elevenmile Canyon caldera, and their plutonic roots, are exposed in the southern Stillwater Range. There, the caldera complex was steeply tilted during large-magnitude crustal extension in the middle Miocene, and further exhumed during the late Miocene to Holocene Basin and Range extension that formed the modern Stillwater Range. This tilted crustal section affords an exceptional opportunity to view structural cross sections of ignimbrite calderas and their plutonic roots to paleodepths as much as 9–10 km.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sim3521","collaboration":"Prepared in cooperation with the Nevada Bureau of Mines and Geology","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"John, D.A., Colgan, J.P., Berry, M.E., Henry, C.D., and Silberling, N.J., 2024, Geologic map of the southern Stillwater Range, Nevada: U.S. Geological Survey Scientific Investigations Map 3521, 2 sheets, scale 1:24,000, 39-p. pamphlet, https://doi.org/10.3133/sim3521.","productDescription":"Report: iv, 39 p.; 3 Sheets: 42.23 x 64.92 inches or smaller; 2 Data Releases","ipdsId":"IP-123012","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":463560,"rank":9,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3521/sim3521.xml"},{"id":463559,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3521/images"},{"id":463384,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26X2V","text":"USGS data release","linkHelpText":"Geochemical and geochronologic data from the Stillwater Range, Clan Alpine, and Desatoya Mountains, Nevada (ver. 3.0, December 2023)"},{"id":463383,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WW1EUF","text":"USGS data release","linkHelpText":"Digital database of the geologic map of the southern Stillwater Range, Nevada"},{"id":463382,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3521/sim3521_sheet2.pdf","text":"Sheet 2—Correlation and list of map units and explanation of map symbols","size":"524 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Sheet 2"},{"id":463381,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3521/sim3521_sheet1-geospatial.pdf","text":"Sheet 1— Georeferenced geologic map","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3521 sheet 1 geospatial"},{"id":463380,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3521/sim3521_sheet1.pdf","text":"Sheet 1—Geologic map","size":"15.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3521 sheet 1"},{"id":463379,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3521/sim3521_pamphlet.pdf","text":"Pamphlet","size":"2.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3521 pamphlet"},{"id":483247,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3521/versionHist.txt","text":"Version History","size":"4.0 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3521 version history"},{"id":463378,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3521/coverthb2.jpg"},{"id":492781,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117751.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","otherGeospatial":"Southern Stillwater Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.5,\n 39.75\n ],\n [\n -118.5,\n 39.375\n ],\n [\n -118,\n 39.375\n ],\n [\n -118,\n 39.75\n ],\n [\n -118.5,\n 39.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: November 1, 2024; Version 1.1: March 12, 2025","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
More than 2 million Californians rely on groundwater from privately owned domestic wells for drinking-water supply. This report summarizes a water-quality survey of domestic and small-system drinking-water supply wells in the eastern Sacramento Valley and adjacent foothills where more than 25,000 residents are estimated to use privately owned domestic wells. Study results show that inorganic and organic constituents in groundwater were present above regulatory (maximum contaminant level, MCL) benchmarks for public drinking-water quality in 8 and 3 percent, respectively, of the aquifer area used for domestic drinking-water supply (herein, “domestic groundwater resources”; fig. 1).
The only inorganic constituent detected above regulatory benchmarks was arsenic. The only organic constituent exceeding regulatory benchmarks was the fumigant 1,2,3-trichloropropane (1,2,3-TCP). Three additional organic constituents—the disinfection by-product chloroform, the gasoline oxygenate methyl tert-butyl ether (MTBE), and the solvent tetrachloroethene (PCE)—were detected at low concentrations below one-tenth of regulatory benchmarks in 34, 10, and 10 percent of domestic groundwater resources, respectively. Total dissolved solids (TDS), iron, and manganese exceeded non-regulatory aesthetic guidelines for drinking water in 5, 10, and 26 percent of domestic groundwater resources, respectively. Per- and polyfluoroalkyl substances (PFASs) were detected in 29 percent of domestic groundwater resources,with 5 percent exceeding the recently enacted (April 2024) U.S. Environmental Protection Agency MCLs. Total coliform and enterococci bacteria were detected in 13 and 8 percent of domestic groundwater resources, respectively.
Redox sensitive constituents in this study included arsenic, manganese, nitrate, and iron. In the lower elevation portions of the eastern Sacramento Valley study area, reducing conditions in groundwater aquifers promote elevated arsenic, iron, and manganese, and conversely lower concentrations of nitrate. The presence of the volatile organic compound (VOC) 1,2,3-TCP was related to its past history in select agricultural land uses (on orchards or vineyards) in the Sacramento Valley; however, unlike in the San Joaquin Valley where orchards and vineyards are more common, its detection frequency was low (only detected in one well in this study). Chloroform was frequently detected in this study at low levels. Chloroform is a disinfection byproduct commonly found in domestic wells treated by shock chlorination. The solvent PCE is among the most frequently detected VOCs in groundwater, which is primarily related to its long history of use and its persistence in groundwater in oxic conditions. The gasoline oxygenate MTBE was a contaminant introduced to groundwater through atmospheric exchange when it was used as a fuel additive to decrease smog inducing emissions from vehicles. Its occurrence in groundwater at low levels is expected and makes it a potentially useful tracer of relatively recent recharge water being withdrawn from wells. The PFASs are anthropogenic chemicals with hundreds of uses, and they have been incorporated into many different products, processes, and applications worldwide. Like MTBE, the occurrence of PFASs in groundwater may be in part due to atmospheric exchange, but there are several other pathways that contribute PFASs to the environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241061","collaboration":"Prepared in cooperation with California State Water Resources Control Board","usgsCitation":"Bennett, G.L., V, 2024, Quality of groundwater used for domestic supply in the eastern Sacramento Valley and adjacent foothills, California: U.S. Geological Survey Open-File Report 2024–1061, 15 p., https://doi.org/10.3133/ofr20241061.","productDescription":"15 p.","numberOfPages":"15","onlineOnly":"Y","ipdsId":"IP-150528","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":497891,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117769.htm","linkFileType":{"id":5,"text":"html"}},{"id":463494,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1061/images"},{"id":463493,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1061/ofr20241061.xml"},{"id":463495,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241061/full"},{"id":463492,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1061/ofr20241061.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463491,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1061/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eastern Sacramento Valley and adjacent foothills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.25,\n 40\n ],\n [\n -122.25,\n 38.666\n ],\n [\n -120.5,\n 38.666\n ],\n [\n -120.5,\n 40\n ],\n [\n -122.25,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Since the onset of the COVID-19 pandemic in early 2020, people have experienced food insecurity challenges because of increased prices of staple food commodities and loss of income or livelihood. Globally, countries with limited capacity to adapt have struggled to recover from pandemic-related disruptions and are further challenged to address adverse effects of climate change on agricultural production (United Nations [UN], 2022). Ukraine, a key agricultural exporter of staple food commodities, has a vital role in contributing to global food security, in particular through its wheat exports to countries in the Middle East, North Africa, and Europe (Martyshev and others, 2023). However, Ukraine’s role as a stable source of global wheat has been disrupted by the ongoing Russia-Ukraine war—a conflict which began in February of 2022.
Given the fragile state of global and local markets and food systems, and the increasing risk climate change poses to agricultural production globally, Ukraine has prioritized adopting efficient agricultural practices to contribute to stabilizing crop yields and to increase its capacity to export wheat and other staple crops. According to Ukraine’s Ministry of Agrarian Policy and Food (MINAGRO), along with addressing climate change, a contributing driver for this prioritization is the desire to join the European Union (EU) and the need to meet the requirements for the EU’s Common Agricultural Policy (CAP) for acceptance as a union member state (Markiyan Dmytrasevych, a former deputy minister of MINAGRO, oral commun., 2023). As a result, MINAGRO is considering climate-smart agricultural practices to secure future crop yields and build resilience within its agricultural sector, especially as the war has impeded millions of tons of crops from reaching domestic and global markets. This report employs the climate-smart agriculture framework to provide Ukrainian agricultural policy- and decision makers and others in technical and development assistance roles with an overview of relevant climate, environmental, and agricultural policy and market factors, and projections on climate and environmental resources that could influence the implementation of climate-smart agricultural practices in Ukraine, and aid Ukraine in successfully joining the EU.
","language":"English","publisher":"Department of Interior International Technical Assistance Program (DOI ITAP)","usgsCitation":"Romero, V., Schultz, A.R., Powlen, K., and Shah, S.D., 2024, Climate-smart agriculture for Ukraine: Winter wheat breeding for food security and climate adaptation, 62 p.","productDescription":"62 p.","ipdsId":"IP-160321","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":465376,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.doi.gov/media/document/report-agriculture-ukraine-winter-wheat-breeding-food-security-and-climate"},{"id":465406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ukraine","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[31.786,52.10168],[32.15941,52.06127],[32.41206,52.28869],[32.71576,52.23847],[33.7527,52.33507],[34.39173,51.76888],[34.14198,51.56641],[34.22482,51.25599],[35.02218,51.20757],[35.37792,50.77396],[35.35612,50.5772],[36.62617,50.22559],[37.39346,50.38395],[38.01063,49.91566],[38.59499,49.92646],[40.06906,49.60106],[40.08079,49.30743],[39.67466,48.78382],[39.89563,48.23241],[39.73828,47.89894],[38.77058,47.82561],[38.25511,47.5464],[38.22354,47.10219],[37.42514,47.02222],[36.75985,46.6987],[35.82368,46.64596],[34.96234,46.2732],[35.02079,45.65122],[35.51001,45.40999],[36.53,45.46999],[36.33471,45.11322],[35.24,44.94],[33.88251,44.36148],[33.32642,44.56488],[33.54692,45.03477],[32.45417,45.32747],[32.6308,45.51919],[33.58816,45.85157],[33.29857,46.0806],[31.74414,46.33335],[31.67531,46.70625],[30.74875,46.5831],[30.37761,46.03241],[29.60329,45.29331],[29.14972,45.46493],[28.67978,45.30403],[28.23355,45.48828],[28.48527,45.59691],[28.65999,45.93999],[28.93372,46.25883],[28.86297,46.43789],[29.07211,46.51768],[29.17065,46.37926],[29.75997,46.34999],[30.02466,46.42394],[29.83821,46.52533],[29.90885,46.67436],[29.55967,46.92858],[29.41514,47.34665],[29.05087,47.51023],[29.1227,47.8491],[28.67089,48.11815],[28.25955,48.15556],[27.52254,48.46712],[26.85782,48.36821],[26.61934,48.22073],[26.19745,48.22088],[25.94594,47.98715],[25.20774,47.89106],[24.86632,47.73753],[24.40206,47.98188],[23.76096,47.9856],[23.14224,48.09634],[22.71053,47.88219],[22.64082,48.15024],[22.08561,48.42226],[22.28084,48.82539],[22.55814,49.08574],[22.77642,49.0274],[22.51845,49.47677],[23.42651,50.30851],[23.92276,50.42488],[24.02999,50.70541],[23.52707,51.57845],[24.00508,51.61744],[24.55311,51.88846],[25.32779,51.91066],[26.33796,51.83229],[27.45407,51.5923],[28.24162,51.57223],[28.61761,51.42771],[28.99284,51.60204],[29.25494,51.36823],[30.15736,51.41614],[30.55512,51.3195],[30.61945,51.82281],[30.92755,52.04235],[31.786,52.10168]]]},\"properties\":{\"name\":\"Ukraine\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Romero, Veronica 0000-0002-8124-4386","orcid":"https://orcid.org/0000-0002-8124-4386","contributorId":302660,"corporation":false,"usgs":true,"family":"Romero","given":"Veronica","email":"","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schultz, August Raleigh 0000-0002-5016-827X","orcid":"https://orcid.org/0000-0002-5016-827X","contributorId":302948,"corporation":false,"usgs":true,"family":"Schultz","given":"August","email":"","middleInitial":"Raleigh","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powlen, Kathryn 0000-0002-9685-0063","orcid":"https://orcid.org/0000-0002-9685-0063","contributorId":328833,"corporation":false,"usgs":true,"family":"Powlen","given":"Kathryn","email":"","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shah, Sachin D. 0000-0002-5440-5535 sdshah@usgs.gov","orcid":"https://orcid.org/0000-0002-5440-5535","contributorId":194450,"corporation":false,"usgs":true,"family":"Shah","given":"Sachin","email":"sdshah@usgs.gov","middleInitial":"D.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921609,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70260810,"text":"70260810 - 2024 - Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing","interactions":[],"lastModifiedDate":"2024-12-10T15:33:45.478559","indexId":"70260810","displayToPublicDate":"2024-10-31T06:56:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing","docAbstract":"Many freshwater forested wetlands along the southeastern United States coastline are rapidly transitioning from forest to marsh or open water, due to climate change-related disturbances. Recent studies have found early warning signals (EWS) of regime shifts in other ecosystems, but it is unclear if these can be detected for coastal wetlands. In this study, we examined the ability to detect EWS of regime shifts in coastal wetlands within the Albemarle Pamlico peninsula (APP), North Carolina, U.S.A. We used the Landsat record (1985–2021) to examine trends of normalized difference vegetation index (NDVI) time series for selected areas known to have undergone regime shifts. We found that while 77% of the APP was either stable or revegetating, 22% of the landscape underwent a decrease in NDVI that would indicate a transition from forest to marsh or open water. Of the areas that transitioned, about half (11%) experienced an abrupt decrease in NDVI and 10% experienced a gradual decline. Increasing standard deviation and skewness of time series could serve as EWS of abrupt transitions, but can also provide false negative and positives. Our results suggest that ecosystem transitions from a forest to a marsh or open water can occur both rapidly and slowly, and remote sensing of NDVI time series can help identify EWS for some areas, but not all. Our results allow for prioritization of conservation/restoration of coastlines which will become important in the face of climate change and sea level rise.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-024-00938-5","usgsCitation":"Martinez, M., Ardon, M.L., and Gray, J., 2024, Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing: Ecosystems, v. 27, p. 1060-1075, https://doi.org/10.1007/s10021-024-00938-5.","productDescription":"16 p.","startPage":"1060","endPage":"1075","ipdsId":"IP-133828","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":463847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Albemarle Pamlico Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.63541246515713,\n 36.06271479689754\n ],\n [\n -77.19335586830498,\n 36.06271479689754\n ],\n [\n -77.19335586830498,\n 35.278844140439915\n ],\n [\n -75.63541246515713,\n 35.278844140439915\n ],\n [\n -75.63541246515713,\n 36.06271479689754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","noUsgsAuthors":false,"publicationDate":"2024-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Martinez, Melinda 0000-0001-6652-9220","orcid":"https://orcid.org/0000-0001-6652-9220","contributorId":290467,"corporation":false,"usgs":true,"family":"Martinez","given":"Melinda","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":918159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ardon, Marcelo L","contributorId":346120,"corporation":false,"usgs":false,"family":"Ardon","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":918160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Joshua","contributorId":346121,"corporation":false,"usgs":false,"family":"Gray","given":"Joshua","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":918161,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259868,"text":"fs20243034 - 2024 - Assessment of undiscovered conventional oil and gas resources in the Norphlet Formation, U.S. Gulf Coast region, 2023","interactions":[],"lastModifiedDate":"2025-12-22T20:21:53.699711","indexId":"fs20243034","displayToPublicDate":"2024-10-30T09:45:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3034","displayTitle":"Assessment of Undiscovered Conventional Oil and Gas Resources in the Norphlet Formation, U.S. Gulf Coast Region, 2023","title":"Assessment of undiscovered conventional oil and gas resources in the Norphlet Formation, U.S. Gulf Coast region, 2023","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey (USGS) estimated undiscovered, technically recoverable mean resources of 16 million barrels of oil and 348 billion cubic feet of gas in conventional reservoirs of the Norphlet Formation in the U.S. Gulf Coast region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20243034","programNote":"National and Global Petroleum Assessment","usgsCitation":"Counts, J.W., Craddock, W.H., Gooley, J.T., Buursink, M., Mercier, T.J., Woodall, C.A., and Schenk, C.J., 2024, Assessment of undiscovered conventional oil and gas resources in the Norphlet Formation, U.S. Gulf Coast region, 2023: U.S. Geological Survey Fact Sheet 2024–3034, 4 p., https://doi.org/10.3133/fs20243034.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-157745","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463490,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243034/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3034"},{"id":463435,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3034/fs20243034.xml"},{"id":463434,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3034/images"},{"id":463200,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3034/fs20243034.pdf","text":"Report","size":"1.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3034"},{"id":463207,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P132OJPA","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Norphlet Formation: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":463199,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3034/coverthb.jpg"},{"id":497885,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117741.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alabama, Florida, Mississippi","otherGeospatial":"Norphlet Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.11177492185513,\n 34.53401166961133\n ],\n [\n -91.11177492185513,\n 29.73879554891593\n ],\n [\n -85.30780523167681,\n 29.73879554891593\n ],\n [\n -85.30780523167681,\n 34.53401166961133\n ],\n [\n -91.11177492185513,\n 34.53401166961133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geology, Energy & Minerals Science Center
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956 National Center
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The connectivity and genetic structuring of populations throughout a region influence a species’ resilience and probability of recovery from anthropogenic impacts. By gaining a comprehensive understanding of population connectivity, more effective management can be prioritized. To assess the connectivity and population genetic structure of a common cold-water coral species, Desmophyllum pertusum (Lophelia pertusa), we performed Restriction-site Associated DNA Sequencing (RADseq) on individuals from nine sites ranging from submarine canyons off New England to the southeastern coast of the United States (SEUS) and the Gulf of Mexico (GOM). Fifty-seven individuals and 3,180 single-nucleotide polymorphisms (SNPs) were used to assess genetic differentiation.
High connectivity exists among populations along the SEUS, yet these populations were differentiated from those to the north off New England and in Norfolk Canyon along the North Atlantic coast of the United States, as well as those in the GOM. Interestingly, Norfolk Canyon, located just north of North Carolina, and GOM populations exhibited low levels of genetic differentiation, corroborating previous microsatellite analyses and signifying gene flow between these populations. Increasing sample sizes from existing populations and including additional sampling sites over a larger geographic range would help define potential source populations and reveal fine-scale connectivity patterns among D. pertusum populations.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13104-024-06977-4","usgsCitation":"Weinnig, A.M., Aunins, A.W., Salamone, V.J., Quattrini, A., Nizinski, M.S., and Morrison, C., 2024, Population structure of Desmophyllum pertusum found along the United States eastern continental margin: BMC Research Notes, v. 17, no. 1, 326, 7 p., https://doi.org/10.1186/s13104-024-06977-4.","productDescription":"326, 7 p.","ipdsId":"IP-157145","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466805,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-024-06977-4","text":"Publisher Index Page"},{"id":463535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Unites States","otherGeospatial":"Atlantic coastal margin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.71142930127395,\n 28.923532210772052\n ],\n [\n -87.20770794927337,\n 29.778273658658676\n ],\n [\n -88.12002541768051,\n 29.69258193898787\n ],\n [\n -89.23879681191622,\n 28.44134714528691\n ],\n [\n -86.44254662826377,\n 27.912389547448754\n ],\n [\n -85.71142930127395,\n 28.923532210772052\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -64.9239384814065,\n 42.20254040148441\n ],\n [\n -67.63438293096546,\n 43.58902192305143\n ],\n [\n -69.21544486232233,\n 42.68212319523596\n ],\n [\n -69.57393034494278,\n 40.17025194363276\n ],\n [\n -73.58827669330255,\n 38.91808684375454\n ],\n [\n -74.60625646365936,\n 36.588016013722694\n ],\n [\n -75.5834673460983,\n 34.46068678372583\n ],\n [\n -79.47920624257769,\n 31.99090849493018\n ],\n [\n -80.4000384261637,\n 31.00478268172816\n ],\n [\n -79.478750607929,\n 26.481859079129094\n ],\n [\n -63.15489504288122,\n 42.1372460985159\n ],\n [\n -64.9239384814065,\n 42.20254040148441\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Weinnig, Alexis Marie 0000-0001-8858-4837","orcid":"https://orcid.org/0000-0001-8858-4837","contributorId":331476,"corporation":false,"usgs":true,"family":"Weinnig","given":"Alexis","email":"","middleInitial":"Marie","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":917476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":917477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Salamone, Veronica J. 0000-0002-6274-6401","orcid":"https://orcid.org/0000-0002-6274-6401","contributorId":293174,"corporation":false,"usgs":true,"family":"Salamone","given":"Veronica","email":"","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":917478,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quattrini, Andrea M.","contributorId":333886,"corporation":false,"usgs":false,"family":"Quattrini","given":"Andrea M.","affiliations":[{"id":80003,"text":"Department of Invertebrate Zoology, Smithsonian Institution, Washington DC, United States of America","active":true,"usgs":false}],"preferred":false,"id":917479,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nizinski, Martha S.","contributorId":174770,"corporation":false,"usgs":false,"family":"Nizinski","given":"Martha","email":"","middleInitial":"S.","affiliations":[{"id":27510,"text":"NMFS National Systematics Laboratory, Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":917480,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morrison, Cheryl 0000-0001-9425-691X cmorrison@usgs.gov","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":202644,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","email":"cmorrison@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":917481,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70260180,"text":"70260180 - 2024 - Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (Micropterus dolomieu) in the mid-Atlantic United States","interactions":[],"lastModifiedDate":"2024-10-30T13:35:44.534951","indexId":"70260180","displayToPublicDate":"2024-10-28T08:27:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (Per- and polyfluoroalkyl substances (PFAS) have become an environmental issue worldwide. A first step to assessing potential adverse effects on fish populations is to determine if concentrations of concern are present in a region and if so, in which watersheds. Hence, plasma from adult smallmouth bass Micropterus dolomieu collected at 10 sites within 4 river systems in the mid-Atlantic region of the United States, from 2014 to 2019, was analyzed for 13 PFAS. These analyses were directed at better understanding the presence and associations with land use attributes in an important sportfish. Four substances, PFOS, PFDA, PFUnA, and PFDoA, were detected in every plasma sample, with PFOS having the highest concentrations. Sites with mean plasma concentrations of PFOS below 100 ng/ml had the lowest percentage of developed landcover in the upstream catchments. Sites with moderate plasma concentrations (mean PFOS concentrations between 220 and 240 ng/ml) had low (< 7.0) percentages of developed land use but high (> 30) percentages of agricultural land use. Sites with mean plasma concentrations of PFOS > 350 ng/ml had the highest percentage of developed land use and the highest number PFAS facilities that included military installations and airports. Four of the sites were part of a long-term monitoring project, and PFAS concentrations of samples collected in spring 2017, 2018, and 2019 were compared. Significant annual differences in plasma concentrations were noted that may relate to sources and climatic factors. Samples were also collected at two sites for tissue (plasma, whole blood, liver, gonad, muscle) distribution analyses with an expanded analyte list of 28 PFAS. Relative tissue distributions were not consistent even within one species of similar ages. Although the long-chained legacy PFAS were generally detected more frequently and at higher concentrations, emerging compounds such as 6:2 FTS and GEN X were detected in a variety of tissues.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-024-35097-6","usgsCitation":"Blazer, V., Walsh, H.L., Smith, C.R., Gordon, S.E., Keplinger, B.J., and Wertz, T., 2024, Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (Micropterus dolomieu) in the mid-Atlantic United States: Environmental Science and Pollution Research, v. p., no. 31, p. 59302-59319, https://doi.org/10.1007/s11356-024-35097-6.","productDescription":"18","startPage":"59302","endPage":"59319","ipdsId":"IP-164838","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11356-024-35097-6","text":"Publisher Index Page"},{"id":463428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, 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model of electrical-potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP-monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock-lined riverbed. The algorithm combined stochastic source-localization by particle-swarm-optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least-squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical-model data to evaluate the algorithm performance. Results indicated that true parameters of single-source models were inverted and approximated with small residual error, whereas inversion of analytical-model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non-uniqueness of the inverse-model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point-scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP-monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.
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