The geologic framework of the Seattle fault zone (SFZ) has been extensively studied, but the structure and fault strand locations in the central portion of the fault zone through the city of Seattle have remained controversial. Much of what is known about the SFZ has come from light detection and ranging (lidar)‐topographic surveys and paleoseismic investigations of fault scarps primarily west of Puget Sound, regional gravity and aeromagnetic modeling, and multiscale marine seismic imaging in waters both west and east of Seattle. We analyze ∼24 km of land‐based P‐wave seismic‐reflection data that fill in a critical gap in our understanding of the SFZ beneath the urban areas of West Seattle, south‐central Seattle, and Mercer Island. These data image deformed strata in the upper 1 km, including upwarped Tertiary rock and younger sediments. Collectively, these data provide evidence for multiple Quaternary‐active thrust faults, back thrusts, and sub‐basins within the SFZ beneath the city of Seattle. The results indicate that multiple and potentially active back thrusts in the upper ∼500 m extend across the length of the SFZ and the entire urban corridor that may be analogous to those on Bainbridge Island west of Puget Sound.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230050","usgsCitation":"Stephenson, W.J., Odum, J.K., and Pratt, T.L., 2024, Shallow faulting and folding beneath south‐central Seattle, Washington State, from land‐based high‐resolution seismic‐reflection imaging: The Seismic Record, v. 4, no. 3, p. 184-193, https://doi.org/10.1785/0320230050.","productDescription":"10 p.","startPage":"184","endPage":"193","ipdsId":"IP-159620","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487948,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230050","text":"Publisher Index Page"},{"id":483230,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","city":"Seattle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.72997503110628,\n 47.62004863663242\n ],\n [\n -122.72997503110628,\n 47.4020813981619\n ],\n [\n -122.11956470840835,\n 47.4020813981619\n ],\n [\n -122.11956470840835,\n 47.62004863663242\n ],\n [\n -122.72997503110628,\n 47.62004863663242\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":930477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Odum, Jack K. 0000-0002-3162-0355","orcid":"https://orcid.org/0000-0002-3162-0355","contributorId":97900,"corporation":false,"usgs":true,"family":"Odum","given":"Jack","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":930478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":930479,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262363,"text":"70262363 - 2024 - Clustering and unconstrained ordination with Dirichlet process mixture models","interactions":[],"lastModifiedDate":"2025-01-16T17:42:37.701078","indexId":"70262363","displayToPublicDate":"2024-08-02T11:33:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Clustering and unconstrained ordination with Dirichlet process mixture models","docAbstract":"Little is known about the post-nesting migration and foraging areas of hawksbill turtles (Eretmochelys imbricata) nesting on St. Kitts and Nevis, an important nesting site for hawksbills in the eastern Caribbean. To elucidate internesting, migration and foraging patterns of hawksbills from Nevis, we satellite tagged 28 post-nesting turtles between 2006 and 2022. Internesting, migrating and foraging activity periods were determined using a switching state–space model to estimate the behavioral state of the turtle’s locations. Twenty-five turtles (83–2,171 tracking days) established a foraging area, migrating between 5.3 and 2,799.5 km from the nesting beach. Twenty-one turtles were tracked during internesting movements with internesting areas ranging between 1.9 and 28.2 km2. Nearly half of the internesting centroids were located closer to a different beach than the beach where the turtle was originally encountered nesting. Hawksbills crossed through 29 different Exclusive Economic Zones (EEZs), including zones with legal sea turtle fisheries or traditional subsistence use. Core foraging areas (KDE 50%) ranged between 3.8 and 69.0 km2. Nearly a third of foraging centroids were within a Marine Protected Area (MPA), while nearly a quarter were within a legal sea turtle fishery EEZ. Hawksbills nesting on Nevis disperse to local, regional, and Caribbean wide foraging grounds, emphasizing the necessity of cooperative efforts to protect turtles and their habitats to ensure support of the recovery of hawksbill turtles throughout the wider Caribbean.
","language":"English","publisher":"Springer","doi":"10.1007/s00227-024-04491-6","usgsCitation":"Evans, D.R., Pemberton, L., and Carthy, R., 2024, Wide-ranging migration of post-nesting hawksbill sea turtles (Eretmochelys imbricata) from the Caribbean island of Nevis: Marine Biology, v. 171, 171, 16 p., https://doi.org/10.1007/s00227-024-04491-6.","productDescription":"171, 16 p.","ipdsId":"IP-161346","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":489134,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00227-024-04491-6","text":"Publisher Index Page"},{"id":480988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Saint Kitts and Nevis","otherGeospatial":"Caribbean, Nevis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.72100737406826,\n 25.303247272191882\n ],\n [\n -89.72100737406826,\n 10.779361764328613\n ],\n [\n -57.461761139581384,\n 10.779361764328613\n ],\n [\n -57.461761139581384,\n 25.303247272191882\n ],\n [\n -89.72100737406826,\n 25.303247272191882\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"171","noUsgsAuthors":false,"publicationDate":"2024-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Daniel R.","contributorId":331390,"corporation":false,"usgs":false,"family":"Evans","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":79193,"text":"Sea Turtle Conservancy, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":924801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pemberton, Lemuel","contributorId":349785,"corporation":false,"usgs":false,"family":"Pemberton","given":"Lemuel","affiliations":[{"id":83514,"text":"Nevis Turtle Group","active":true,"usgs":false}],"preferred":false,"id":924802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":924803,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257086,"text":"70257086 - 2024 - Peri-Gondwanan sediment in the Arkoma Basin derived from the north: The detrital zircon record of a uniquely concentrated non-Laurentian source signal in the late Paleozoic","interactions":[],"lastModifiedDate":"2024-10-07T16:14:46.422867","indexId":"70257086","displayToPublicDate":"2024-08-02T06:46:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Peri-Gondwanan sediment in the Arkoma Basin derived from the north: The detrital zircon record of a uniquely concentrated non-Laurentian source signal in the late Paleozoic","docAbstract":"During the assembly of Pangea, peri-Gondwanan terranes collided with the eastern and southern margins of Laurentia and brought with them unique detrital zircon U-Pb signatures. Discriminating between individual peri-Gondwanan terranes in the detrital record is difficult due to their similar geologic histories. However, characterization of this provenance is critical for understanding late Paleozoic sediment routing during development of Pangea. Along southeastern Laurentia, in the Arkoma Basin (present-day Arkansas and eastern Oklahoma, southeastern United States), we identified Middle Pennsylvanian (Desmoinesian) strata that exhibit a concentrated peri-Gondwanan detrital zircon signature (e.g., ca. 800–550 Ma). Although several southern peri-Gondwanan terranes (e.g., Maya, Suwannee) are closer to the Arkoma Basin, geologic data, such as predominantly north-to-south paleocurrents and proximal-to-distal facies relationships in these Desmoinesian strata, support a northern source (e.g., Ganderia, Avalonia, Meguma). Further evidence of a northern source comes from detrital zircon source mapping, which reveals the persistence of this peri-Gondwanan signal in depocenters to the north of the basin after the signal had diminished in the Arkoma Basin. To this end, bottom-up detrital zircon source modeling, source mapping, regional stratigraphy, paleocurrent data, and sandstone petrography allow us to reconstruct the evolution of this Middle Pennsylvanian (Desmoinesian) sediment pathway in the context of intraplate and plate-margin tectonic activity. This reconstruction documents processes affecting Earth’s surface (e.g., tectonics, climate) during the assembly of Pangea and describes in detail part of a dynamic continental-scale drainage system.
Quantifying migratory connectivity and annual movement is key to sound conservation planning for migratory species. Hermit Warblers (Setophaga occidentalis) are an endemic-breeding species in the Pacific Northwest that winters in Mexico and the Central Americas. This species faces threats from mature forest loss and climate change throughout its range, but we know little about its migration ecology. To understand the annual movements and migratory connectivity of Hermit Warblers, we tracked 22 adult male Hermit Warblers from six breeding sites across the species’ breeding range using geolocators to examine migratory connectivity, spatiotemporal patterns, and migration routes. We found a high degree of mixing on the wintering grounds among birds from different breeding locations, indicating low migratory connectivity. However, birds breeding in Yosemite, the southernmost breeding location in our study, wintered farther east and south than birds from more northern breeding locations, providing weak evidence for potential chain migration. All birds showed much shorter and faster movements during spring migration than during fall migration. Birds arrived at breeding grounds from late April to mid-May and left breeding ranges from late June to mid-July. In fall, birds moved slowly from the breeding locations to montane regions in southern Oregon and California, which may indicate post-breeding molt before swiftly migrating to wintering grounds. Low migratory connectivity in this species implies that habitat and climate change across the broad wintering range may affect breeding populations throughout the species’ breeding range. A particularly compressed breeding schedule and departure of birds from the breeding grounds in early July may indicate that breeding is limited by a short window of favorable climatic conditions for breeding, which could signal heightened vulnerability under future climatic scenarios.
","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ACE-02622-190206","usgsCitation":"Kim, H., Siegel, R., Stephens, J.L., Hagar, J., Furnas, B., Jeong, M., McComb, B., and Betts, M., 2024, Annual migratory movement, apparent molt-migration, migration schedule, and diffuse migratory connectivity of Hermit Warblers: Avian Conservation and Ecology, v. 19, no. 2, 6, 19 p., https://doi.org/10.5751/ACE-02622-190206.","productDescription":"6, 19 p.","ipdsId":"IP-149339","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":439234,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.5751/ace-02622-190206","text":"Publisher Index Page"},{"id":432941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.11203028252265,\n 47.82187188181675\n ],\n [\n -123.78944793706691,\n 46.93753054676185\n ],\n [\n -124.6414125408566,\n 41.58908295445511\n ],\n [\n -123.76636171157838,\n 38.898156250751896\n ],\n [\n -121.01118030676412,\n 34.73923370076069\n ],\n [\n -114.41027429470861,\n 28.70764099274254\n ],\n [\n -110.85644692068149,\n 27.899656621073362\n ],\n [\n -105.51137356648343,\n 22.512656408826786\n ],\n [\n -105.3528752882118,\n 19.65270323671625\n ],\n [\n -96.847740450098,\n 15.062683530657822\n ],\n [\n -94.78046172958133,\n 16.381874246929584\n ],\n [\n -88.93617423399866,\n 12.987506247141695\n ],\n [\n -82.96824120792775,\n 8.601482016581201\n ],\n [\n -84.2473837733847,\n 12.296450374282543\n ],\n [\n -90.84763601057068,\n 17.58036685913001\n ],\n [\n -97.19436678836715,\n 19.53440699783762\n ],\n [\n -106.6740211752392,\n 31.781536902714677\n ],\n [\n -116.89399027637168,\n 39.9200429404375\n ],\n [\n -119.15894422131151,\n 44.206880348869106\n ],\n [\n -120.11203028252265,\n 47.82187188181675\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Hankyu","contributorId":340621,"corporation":false,"usgs":false,"family":"Kim","given":"Hankyu","email":"","affiliations":[{"id":81639,"text":"University of Wisconsin – Madison","active":true,"usgs":false}],"preferred":false,"id":911018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siegel, Rodney","contributorId":195415,"corporation":false,"usgs":false,"family":"Siegel","given":"Rodney","affiliations":[],"preferred":false,"id":911019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephens, Jaime L","contributorId":343422,"corporation":false,"usgs":false,"family":"Stephens","given":"Jaime","email":"","middleInitial":"L","affiliations":[{"id":33317,"text":"Klamath Bird Observatory","active":true,"usgs":false}],"preferred":false,"id":911020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hagar, Joan 0000-0002-3044-6607 joan_hagar@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-6607","contributorId":3369,"corporation":false,"usgs":true,"family":"Hagar","given":"Joan","email":"joan_hagar@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":911021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Furnas, Brett","contributorId":343423,"corporation":false,"usgs":false,"family":"Furnas","given":"Brett","email":"","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":911022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jeong, Min-Su","contributorId":343424,"corporation":false,"usgs":false,"family":"Jeong","given":"Min-Su","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":911023,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McComb, Brenda C","contributorId":343426,"corporation":false,"usgs":false,"family":"McComb","given":"Brenda C","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":911024,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Betts, Matthew G.","contributorId":340630,"corporation":false,"usgs":false,"family":"Betts","given":"Matthew G.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":911025,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70258271,"text":"70258271 - 2024 - Abundance and distribution of white-tailed deer on First State National Historical Park and surrounding lands","interactions":[],"lastModifiedDate":"2024-09-11T15:14:35.971177","indexId":"70258271","displayToPublicDate":"2024-08-01T10:13:51","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18517,"text":"Science Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SR—2024/176","title":"Abundance and distribution of white-tailed deer on First State National Historical Park and surrounding lands","docAbstract":"We estimated both abundance and distribution of white-tailed deer (Odocoileus virginianus) on the Brandywine Valley unit of First State National Historical Park (FRST) and the Brandywine Creek State Park (BCSP) during 2020 and 2021 with two widely used field methods — a road-based count and a network of camera traps. We conducted 24 road-based counts, covering 260 km of roadway, and deployed up to 16 camera traps, processing over 82,000 images representing over 5,000 independent observations.
In both years, we identified bucks based on their body and antler characteristics, tracking their movements between baited camera trap locations. We tested seven estimators commonly reported in the literature, comparing the relative merits for managers of small, protected natural areas like FRST.
Deer densities estimated from conventional road-based distance sampling were approximately 10 deer/km2 lower than densities estimated from camera-trapping surveys. We attribute the bias in roadbased distance sampling to the difficulty of recording the precise effort expended to obtain the counts. Modifying the distance sampling method addressed many of the issues associated with the conventional approach. Despite little substantive differences in land cover types between the two methods, a clear spatial segregation of male and female deer at camera trap locations could bias roadbased counts if the sexes are not encountered in proportion to their abundances. There was a distinct gradient in deer distribution across the study area, with higher proportions of deer recorded in camera traps at FRST than BCSP, which harvests 20–60 deer annually during a regulated, hunting season.
The most reliable (i.e., low bias, acceptable precision) methods, Spatial Capture Recapture (SCR) and Density Surface Modeling (DSM), produced deer densities of approximately 50 deer/km2 in each year — a number which is consistent with previous estimates for New Castle County, Delaware, and our experience in similar, unhunted natural areas. Across both FRST and BCSP, these densities translated into area-wide (~1000 ha) population sizes between 650–1000 deer, with about one-half to two-thirds comprising the FRST population.
Density surface modeling of mapped locations of deer detected during surveys, combined with camera-trapping and a time-to-event data analysis might be the only practical means of reliably assessing white-tailed deer abundance in small (<2000 ha), protected natural areas like FRST. Most other approaches are either too time-consuming, require identification and tracking of individual deer, the use of bait, or require intervention by a subject-area expert.
","language":"English","publisher":"National Park Service","doi":"10.36967/2305428","usgsCitation":"Underwood, H.B., Hand, M.R., and Leopold, D.J., 2024, Abundance and distribution of white-tailed deer on First State National Historical Park and surrounding lands: Science Report NPS/SR—2024/176, x, 76 p., https://doi.org/10.36967/2305428.","productDescription":"x, 76 p.","ipdsId":"IP-154880","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":433697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Pennsylvania","otherGeospatial":"Brandywine Creek State Park, Brandywine Valley unit of First State National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.57893855864407,\n 39.84864721075371\n ],\n [\n -75.57893855864407,\n 39.79988968093724\n ],\n [\n -75.54431018453508,\n 39.79988968093724\n ],\n [\n -75.54431018453508,\n 39.84864721075371\n ],\n [\n -75.57893855864407,\n 39.84864721075371\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Underwood, H. Brian 0000-0002-2064-9128 hbunderw@usgs.gov","orcid":"https://orcid.org/0000-0002-2064-9128","contributorId":140185,"corporation":false,"usgs":true,"family":"Underwood","given":"H.","email":"hbunderw@usgs.gov","middleInitial":"Brian","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":912809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hand, Madison R.","contributorId":344097,"corporation":false,"usgs":false,"family":"Hand","given":"Madison","email":"","middleInitial":"R.","affiliations":[{"id":13404,"text":"SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":912810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leopold, Donald J.","contributorId":220142,"corporation":false,"usgs":false,"family":"Leopold","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":13404,"text":"SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":912811,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257603,"text":"70257603 - 2024 - Joint pilot fish habitat framework","interactions":[],"lastModifiedDate":"2024-08-20T15:11:01.81526","indexId":"70257603","displayToPublicDate":"2024-08-01T10:04:45","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18345,"text":"NOAA Story Map","active":true,"publicationSubtype":{"id":1}},"title":"Joint pilot fish habitat framework","docAbstract":"This story map will take you through the process of exploring and testing methods necessary for a higher resolution, seamless fish habitat assessment across both inland and estuarine waters through the lens of our joint pilot assessment
Fish habitat assessments attempt to relate past, current, or future landscape conditions to the state of fish species occurrence, distribution, abundance, or community and habitat condition in streams, rivers, or estuaries. Previous fish habitat assessments, such as the National Fish Habitat Assessment, conducted separate and disconnected assessments for inland waters and estuaries. In this project, National Oceanic and Atmospheric Administration ( NOAA ) and U.S. Geological Survey ( USGS ) researchers created a seamless spatial framework to allow assessments that integrate influences on fish habitat from headwaters to the estuary. This effort began when the Chesapeake Bay Program Fish Habitat Action Team expressed interest in a Baywide fish habitat assessment spanning tidal salt, tidal fresh, warm non-tidal, and cold non-tidal waters. However, the complexity of the myriad of implementation details to consider when developing such an assessment necessitated the need for a tributary-specific pilot assessment. To conduct this pilot assessment, a NOAA/USGS joint partnership was formed with cooperation and support from the Chesapeake Bay Agreement and Chesapeake Bay Fish Habitat Action Team (FHAT).
We compared elevation trajectories from 14 rod surface elevation table (RSET) stations and 60 real-time kinematic (RTK) global positioning system (GPS) transects within the Blackwater National Wildlife Refuge (BNWR) from 2010–2013. The results were similar, 7.3 ± 0.9 (mean ± standard error; RSET) versus 6.2 ± 0.6 mm year−1 (RTK) (P = 0.216), and were greater than relative sea level rise (RSLR) computed at the nearest long-term tide station (3.9 ± 0.29 mm year−1). Despite having shown elevation gain, these wetlands continue to drown and convert to open water. Episodic, multi-day GPS measurements on geodetic control marks at BNWR between 2005 and 2023 revealed a substantial vertical land motion (VLM) signal. From 2005 to 2015, three reference marks used to control the 2010–2013 RTK study lost on average 6.0 ± 0.7 mm year−1, corresponding to 80% and 94% of the elevation gain measured by the RSET and RTK techniques, respectively. The longer 18-year subsidence trend measured on one of these marks was lower, 3.9 ± 0.7 mm year−1, highlighting important interannual variability. Wetland elevation change measurements need to account for VLM occurring below the reference marks used to measure elevation change. Estimates from the nearest long-term tide station may not be applicable to the wetland if the tide station is in a different geological setting. At BNWR, VLM was higher than the VLM at the Cambridge tide station, which helps explain why wetlands at BNWR are not keeping pace with RSLR despite the measured high rates of elevation gain.
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We computed annual low-flow period duration and timing metrics from 1951 to 2020 for 1032 conterminous United States (CONUS) streamgages and analyzed spatial patterns, trends through time, and relationships to climate. Results show northwestern and eastern CONUS streamgages had longer and more inter-annually consistent low-flow period durations, while central CONUS periods were shorter and more variable. Low-flow periods most often occurred in summer months but start and end dates occurred later in north-central and mountainous western CONUS, which have the greatest number of low flows during cold seasons. Low-flow periods are becoming longer in southeastern and northwestern CONUS but shorter in much of the rest of CONUS. Temperature was correlated with low-flow period duration in southeastern and northwestern CONUS, and precipitation was correlated with duration everywhere, but most strongly in eastern CONUS.
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River","interactions":[],"lastModifiedDate":"2025-08-28T14:42:57.760006","indexId":"70270775","displayToPublicDate":"2024-07-29T09:33:19","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-157-2024","displayTitle":"Environmental DNA (eDNA) surveillance of the federally threatened Slender Chub (Erimystax cahni) in the Clinch River and Powell River","title":"Environmental DNA (eDNA) surveillance of the federally threatened Slender Chub (Erimystax cahni) in the Clinch River and Powell River","docAbstract":"The slender chub (Erimystax cahni) is a federally threatened fish native to and geographically restricted to eastern North America. More specifically, the Powell, Clinch, and lower Holston Rivers in Tennessee were historical collection areas. Habitat degradation from multiple sources, including surface mining, agriculture, dams, and urbanization, is associated with the decline of this species - an obligate inhabitant of gravel shoals in large rivers. As of 1964, only 15 voucher specimens were known and no living specimens were documented for decades. A federal recovery plan and Species Status Assessment were developed to determine if slender chub still exists and could be restored. Given the lack of recent observations using conventional sampling to search for its presence, we used environmental DNA sampling to determine their potential presence. Our specific objectives were to (1) develop a quantitative polymerase chain reaction (qPCR) assay aimed at species-specific detection and (2) sample historically known areas of collection. We sampled 43 sites in the Clinch and Powell Rivers. For the first time in almost two decades, we provide evidence for the continued existence of a putatively extinct species. We detected evidence of E. cahni in both the Clinch and Powell Rivers, but only at a few sites. We cannot confirm the presence of E. cahni, and positive eDNA matches could be attributed to amplification from a hybrid. Our results do indicate slender chub DNA perseveres in the Clinch and Powell Rivers and can inform resource agencies of localities to pursue on-the-ground searches for the slender chub with conventional methods (e.g., snorkeling) and potential restoration and recovery sites.
","language":"English","publisher":"U.S. Fish and Wildlife","doi":"10.3996/css55463605","usgsCitation":"Paine, R.T., Rogers, M.W., and Rosenberger, A.E., 2024, Environmental DNA (eDNA) surveillance of the federally threatened Slender Chub (Erimystax cahni) in the Clinch River and Powell River: Cooperator Science Series CSS-157-2024, ii, 23 p., https://doi.org/10.3996/css55463605.","productDescription":"ii, 23 p.","ipdsId":"IP-156546","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":496395,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3996/css55463605","text":"Publisher Index Page"},{"id":495000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee, Virginia","otherGeospatial":"Clinch River, Powell River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.34951874387174,\n 36.84953633730622\n ],\n [\n -84.34254978688318,\n 36.84953633730622\n ],\n [\n -84.34254978688318,\n 35.91039358207085\n ],\n [\n -82.34951874387174,\n 35.91039358207085\n ],\n [\n -82.34951874387174,\n 36.84953633730622\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Paine, Robert T.R.","contributorId":360425,"corporation":false,"usgs":false,"family":"Paine","given":"Robert","middleInitial":"T.R.","affiliations":[{"id":56209,"text":"Tennessee Tech University","active":true,"usgs":false}],"preferred":false,"id":947047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Mark W. 0000-0001-7205-5623","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":245525,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":947049,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264785,"text":"70264785 - 2024 - Estimating species-specific U.S. waterfowl harvest","interactions":[],"lastModifiedDate":"2025-03-24T13:55:27.652976","indexId":"70264785","displayToPublicDate":"2024-07-29T08:51:12","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19846,"text":"BioRxiv","active":true,"publicationSubtype":{"id":32}},"title":"Estimating species-specific U.S. waterfowl harvest","docAbstract":"The U.S. Fish and Wildlife Service monitors species-specific waterfowl (ducks, seaducks, geese, and brant) harvest through two hunter surveys, one that estimates the total harvest for each waterfowl group, and a second that estimates the species composition of each waterfowl group. Point estimates for species-specific harvest can be computed by multiplying the estimated total harvest by the estimated proportion of the total harvest of each species. However, to date, no uncertainty estimates have been available. Here, we combine these two data sources to provide species-specific harvest estimates at the state and flyway level while characterizing the uncertainty via Bayesian estimation. We take a similar approach to Smith et al. (2022), providing both estimates that treat yearly data as independent and estimates that share information across years via a random walk process. We then discuss the advantages and disadvantages of each approach.
","language":"English","publisher":"bioRxiv","doi":"10.1101/2024.07.27.603620","usgsCitation":"Augustine, B., and Royle, A., 2024, Estimating species-specific U.S. waterfowl harvest: BioRxiv, https://doi.org/10.1101/2024.07.27.603620.","productDescription":"18 p.","ipdsId":"IP-166476","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488364,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1101/2024.07.27.603620","text":"Publisher Index Page"},{"id":483704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Augustine, Ben 0000-0001-6935-6361","orcid":"https://orcid.org/0000-0001-6935-6361","contributorId":245736,"corporation":false,"usgs":true,"family":"Augustine","given":"Ben","email":"","affiliations":[{"id":49304,"text":"Department of Natural Resources, Cornell University","active":true,"usgs":false}],"preferred":false,"id":931676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":931677,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257184,"text":"70257184 - 2024 - Insights on using solid bitumen reflectance as a thermal maturity proxy in the Bakken Formation, Williston Basin, USA","interactions":[],"lastModifiedDate":"2024-08-13T12:01:50.854485","indexId":"70257184","displayToPublicDate":"2024-07-26T06:55:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18329,"text":"ACS Omega","active":true,"publicationSubtype":{"id":10}},"title":"Insights on using solid bitumen reflectance as a thermal maturity proxy in the Bakken Formation, Williston Basin, USA","docAbstract":"To further refine the use of solid bitumen reflectance (BRo in %) as a measurement of thermal maturity in source-rock reservoirs, we examined its relationship to other thermal proxies in the Bakken Formation. Comparisons included criteria from programmed temperature pyrolysis, gas chromatography (GC), and Fourier transform infrared (FTIR) spectroscopy. Thirty-two organic-rich samples from the lower and upper shale members of the Devonian–Lower Carboniferous Bakken Formation were collected from eight cores across the Williston Basin, USA, at depths (∼7575–11,330 ft) representing immature through post peak oil/early condensate thermal maturity conditions based on proximity to current hydrocarbon production. Unmodified BRo values were correlated to programmed temperature pyrolysis parameters (hydrogen index, production index, and Tmax), normal hydrocarbon and isoprenoid analysis of extractable organic matter (pristane/n-C17 and phytane/n-C18) from GC analysis, and peak ratios from FTIR spectroscopy (branching ratio and A-factor). Strong correlations between unmodified BRo values (not corrected to a vitrinite reflectance equivalent, VRe) and other thermal proxies suggest that BRo can be used as a direct thermal proxy in marine Paleozoic source-rock reservoirs where vitrinite is rare or absent. Moreover, an apparent overestimation of VRe at the lowest thermal maturity investigated herein may argue against the application of BRo conversion to VRe in the Bakken Formation. Solvent extraction caused a consistent decrease in BRo when average post-extraction values from a given well were compared to BRo prior to extraction, although the decrease in mean value was not statistically significant. These results are discussed in the context of advocating for the use of unmodified BRo values as a best practice for thermal maturity determination in Paleozoic marine source-rock reservoirs.
In 2020, natural resource managers at Camp Edwards, Barnstable County, MA, observed Terrapene carolina carolina (Eastern Box Turtle) individuals infected by myiasis, where parasitic flesh flies larviposit into the living tissue of a host. The hypothesized parasite was Dexosarcophaga cistudinis, but its impacts on the host's body condition, movement, and habitat use were unknown. Our objectives were to identify the parasite at Camp Edwards and to compare the body condition, movement, and habitat characteristics at capture locations of Eastern Box Turtles for infected and noninfected individuals. We radio-tracked turtles weekly and encountered 48 individuals from May to August 2022 at Camp Edwards, MA. Upon capture, we recorded turtle infection status, mass, carapace length, shell surface temperature, GPS location, and habitat characteristics of the capture location. We confirmed D. cistudinis as the parasite and found that myiasis-infected turtles had a significantly higher shell temperature (27.92 ± 5.28 °C) than noninfected turtles (26.77 ± 5.64 °C). However, we did not find an effect of myiasis on body condition, habitat use, or average daily distance moved. Collectively, our results suggest that infected turtles may exhibit behavioral fever, a mechanism by which ectotherms move to warmer microclimates to raise their body temperature in response to infections. Eastern Box Turtles at Camp Edwards may be able to use behavioral fever in response to myiasis infection because of the habitat mosaic made available through detailed habitat-management regimes.
In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km2), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km2/yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km2 scales under conditions without debris flows.
The environmental ubiquity of tire and road wear particles (TRWP) underscores the need to understand the occurrence, persistence, and environmental effects of tire-related chemicals in aquatic ecosystems. One such chemical is 6PPD-quinone (6PPD-Q), a transformation product of the tire antioxidant 6PPD. In urban stormwater runoff 6PPD-Q can exceed acute toxicity thresholds for several salmonid species and is being implicated in significant coho salmon losses in the Pacific Northwest. There is a critical need to understand the prevalence of 6PPD-Q across watersheds to identify habitats heavily affected by TRWPs. We conducted a reconnaissance of 6PPD and 6PPD-Q in surface waters across the United States from sites (N = 94) with varying land use (urban, agricultural, and forested) and streamflow to better understand stream exposures. A rapid, low-volume direct-inject, liquid chromatography mass spectrometry method was developed for the quantitation of 6PPD-Q and screening for 6PPD. Laboratory holding times, bottle material, headspace, and filter materials were investigated to inform best practices for 6PPD-Q sampling and analysis. Glass bottles with PTFE-lined caps minimized sorption and borosilicate glass fiber filters provided the highest recovery. 6PPD-Q was stable for at least 5 months in pure laboratory solutions and for 75 days at 5 °C with minimal headspace in the investigated surface water and stormwaters. Results also indicated samples can be frozen to extend holding times. 6PPD was not detected in any of the 526 analyzed samples and there were no detections of 6PPD-Q at agricultural or forested sites. 6PPD-Q was frequently detected in stormwater (57%, N = 90) and from urban impacted sites (45%, N = 276) with concentrations ranging from 0.002 to 0.29 μg/L. The highest concentrations, above the lethal level for coho salmon, occurred during stormwater runoff events. This highlights the importance of capturing episodic runoff events in urban areas near ecologically relevant habitat or nursery grounds for sensitive species.
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aspanjer@usgs.gov","orcid":"https://orcid.org/0000-0002-7288-2722","contributorId":150395,"corporation":false,"usgs":true,"family":"Spanjer","given":"Andrew","email":"aspanjer@usgs.gov","middleInitial":"R.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910859,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910860,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263545,"text":"70263545 - 2024 - Slip rate for the Rose Canyon fault through San Diego, California, based on analysis of GPS data: Evidence for a potential Rose Canyon–San Miguel-Vallecitos fault connection?","interactions":[],"lastModifiedDate":"2025-02-13T16:56:38.677011","indexId":"70263545","displayToPublicDate":"2024-07-16T10:52:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Slip rate for the Rose Canyon fault through San Diego, California, based on analysis of GPS data: Evidence for a potential Rose Canyon–San Miguel-Vallecitos fault connection?","docAbstract":"The Rose Canyon fault is the southern extension of the larger Newport–Inglewood–Rose Canyon fault system, which represents a major structural boundary in the Inner Continental Borderland (ICB) offshore of southern California. Ten to fifteen percent of total plate boundary motion in southern California is thought to be accommodated by the faults of the ICB, but the exact distribution of slip is uncertain. With an onshore segment, the Rose Canyon fault offers an opportunity to measure the slip rate using traditional geodetic methods. In this study, we use Global Positioning System (GPS) surface velocities from a combined campaign and continuous GPS network to constrain elastic models of the Rose Canyon fault. We then compare the observed surface velocities with proposed conceptual models of regional fault connections that facilitate the transfer of slip into the Rose Canyon fault to assess how well the observations are explained by the models. The results of elastic half‐space models suggest that the Rose Canyon fault may be slipping toward the higher end of geologic estimates, with the preferred model indicating a slip rate of 2.4 ± 0.5 mm/yr. Although limited in terms of near‐fault benchmarks, we find an improved model fit using an asymmetrical elastic half‐space model and a higher slip rate, suggesting a potential rheological contrast across the Rose Canyon fault, similar to observations from the northern Newport–Inglewood fault segments. Observed GPS surface velocities, background seismicity, and gravity anomalies south of San Diego Bay point toward a more easterly trace for the Rose Canyon fault, suggesting a possible connection with the San Miguel–Vallecitos fault system. Such a connection could increase the potential rupture lengths of future earthquakes and have important consequences for regional seismic hazards.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230278","usgsCitation":"Singleton, D.M., Maloney, J., Agnew, D., and Rockwell, T., 2024, Slip rate for the Rose Canyon fault through San Diego, California, based on analysis of GPS data: Evidence for a potential Rose Canyon–San Miguel-Vallecitos fault connection?: Bulletin of the Seismological Society of America, v. 114, no. 5, p. 2751-2766, https://doi.org/10.1785/0120230278.","productDescription":"16 p.","startPage":"2751","endPage":"2766","ipdsId":"IP-149537","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.03132304321895,\n 32.37326157121886\n ],\n [\n -114.89786575651428,\n 32.75462583665147\n ],\n [\n -114.93333927794629,\n 33.64921849858126\n ],\n [\n -118.29120454417611,\n 35.815309260323346\n ],\n [\n -121.93695511034596,\n 35.53619205111963\n ],\n [\n -121.70925414240213,\n 34.55709712444637\n ],\n [\n -118.03132304321895,\n 32.37326157121886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"114","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Singleton, Drake Moore 0000-0001-5346-0623","orcid":"https://orcid.org/0000-0001-5346-0623","contributorId":261207,"corporation":false,"usgs":true,"family":"Singleton","given":"Drake","email":"","middleInitial":"Moore","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":927318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maloney, Jillian","contributorId":304141,"corporation":false,"usgs":false,"family":"Maloney","given":"Jillian","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":927319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Agnew, Duncan 0000-0002-2360-7783","orcid":"https://orcid.org/0000-0002-2360-7783","contributorId":178605,"corporation":false,"usgs":false,"family":"Agnew","given":"Duncan","email":"","affiliations":[],"preferred":false,"id":927320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rockwell, Thomas","contributorId":175454,"corporation":false,"usgs":false,"family":"Rockwell","given":"Thomas","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":927321,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261249,"text":"70261249 - 2024 - Exploring spatial and temporal symptoms of the freshwater salinization syndrome in a rural to urban watershed","interactions":[],"lastModifiedDate":"2024-12-03T22:47:40.612449","indexId":"70261249","displayToPublicDate":"2024-07-13T16:36:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Exploring spatial and temporal symptoms of the freshwater salinization syndrome in a rural to urban watershed","docAbstract":"The freshwater salinization syndrome (FSS), a concomitant watershed-scale increase in salinity, alkalinity, and major-cation and trace-metal concentrations, over recent decades, has been described for major rivers draining extensive urban areas, yet few studies have evaluated temporal and spatial FSS variations, or causal factors, at the subwatershed scale in mixed-use landscapes. This study examines the potential influence of land-use practices and wastewater treatment plant (WWTP) effluent on the export of major ions and trace metals from the mixed-use East Branch Brandywine Creek watershed in southeastern Pennsylvania, during the 2019 water year. Separate analysis of baseflow and stormflow subsets revealed similar correlations among land-use characteristics and streamwater chemistry. Positive associations between percent impervious surface cover, which ranged from 1.26 % to 21.9 % for the 13 sites sampled, and concentrations of Ca2+, Mg2+, Na+, and Cl− are consistent with road-salt driven reverse cation exchange and weathering of the built environment. The relative volume of upstream WWTP was correlated with Cu and Zn, which may be derived in part from corroded water-conveyance infrastructure; chloride to sulfate mass ratios (CSMR) ranged from ~6.3 to ~7.7× the 0.5 threshold indicating serious corrosivity potential. Observed exceedances of U.S. Environmental Protection Agency Na+ and Cl− drinking water and aquatic life criteria occurred in winter months. Finally, correlations between percent cultivated cropland and As and Pb concentrations may be explained by the persistence of agricultural pesticides that had been used historically. Study results contribute to the understanding of FSS solute origin, fate, and transport in mixed-use watersheds, particularly those in road salt-affected regions. Study results also emphasize the complexity of trace-metal source attribution and explore the potential for FSS solutes to affect human health, aquatic life, and infrastructure.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.174266","usgsCitation":"Marks, N.K., Cravotta, C., Rossi, M.L., Silva, C., Kremer, P., and Goldsmith, S.T., 2024, Exploring spatial and temporal symptoms of the freshwater salinization syndrome in a rural to urban watershed: Science of the Total Environment, v. 947, 174266, 17 p., https://doi.org/10.1016/j.scitotenv.2024.174266.","productDescription":"174266, 17 p.","ipdsId":"IP-154332","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":466983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.174266","text":"Publisher Index Page"},{"id":464716,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"East Branch Brandywine Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.83342045572547,\n 40.01916009274666\n ],\n [\n -75.72337369661636,\n 39.97446847844611\n ],\n [\n -75.66768738477772,\n 39.96227478065106\n ],\n [\n -75.61332693750671,\n 40.00088069477613\n ],\n [\n -75.6106752083719,\n 40.09425729037662\n ],\n [\n -75.62260798947986,\n 40.11555358035895\n ],\n [\n -75.71011505094027,\n 40.1621792980624\n ],\n [\n -75.83474632029366,\n 40.14090269654602\n ],\n [\n -75.8519825596717,\n 40.078027071145215\n ],\n [\n -75.84535323683392,\n 40.05570674139187\n ],\n [\n -75.83342045572547,\n 40.01916009274666\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"947","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marks, Nicole K.","contributorId":346882,"corporation":false,"usgs":false,"family":"Marks","given":"Nicole","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":920112,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. 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One potential source is oil and gas wastewater (i.e., produced water or brine), which naturally has high total dissolved solids (TDS) concentrations, that can also be enriched in Li (>100 mg/L). Understanding the sources and mechanisms responsible for high naturally-occurring Li concentrations can aid in efficient targeting of these brines. The isotopic composition (δ7Li, δ11B, δ138Ba) of produced water and core samples from the Utica Shale and Point Pleasant Formation (UPP) in the Appalachian Basin, USA indicates that depth-dependent thermal maturity and water-rock interaction, including diagenetic clay mineral transformations, likely control Li concentrations. A survey of Li content in produced waters throughout the USA indicates that Appalachian Basin brines from the Marcellus Shale to the UPP have the potential for economic resource recovery.
The U.S. Geological Survey, in cooperation with the Ohio Department of Natural Resources, performed a two-part study to (1) assess water use and temporal trends and changes in streamflow, and to (2) characterize 2021–23 baseline water quality, as they relate to oil and gas extraction activities in selected eastern Ohio watersheds. Between calendar years 2010 and 2019, hydraulic fracturing water withdrawals totaling about 27,168 million gallons were reported at 643 locations in Ohio. In 2021, wells developed with hydraulic fracturing were the source of most of the oil and gas produced in Ohio.
Daily streamflow time-series data from seven study gages and two reference gages were used to assess temporal trends and changes in streamflow. The study gages were in basins with reported water withdrawals for hydraulic fracturing. The reference gages, which have long periods of record and were subject to minimal streamflow regulation, were in nearby basins with no hydraulic fracturing water withdrawals.
Trend slopes for the period of record annual minimum and median daily streamflows and for annual daily streamflow nonexceedance probabilities less than 0.9 were all uniformly positive at the study and reference gages. This trend indicates a consistently increasing pattern over the periods of record, except for high flows. In addition, analyses of annual streamflow statistics showed no general indication that low flows or extreme low flows at the reference or study gages have lowered, become more frequent, or lengthened in duration since 2010, when records for hydraulic fracturing water withdrawals began in Ohio. In fact, in almost all cases, the opposite was indicated.
Nonexceedance percentiles of daily streamflows were compared between the full and pre-2012 periods of record for reference and study gages. The streamflows associated with nonexceedance percentiles in the lower quartile of daily streamflows determined for the full period of record were larger than or equal to those determined for the pre-2012 period of record for all study and reference gages. This indicates that low flows did not decrease during the post-2011 period of record when water was withdrawn for hydraulic fracturing.
Water-quality data were collected eight times at each of eight sampling sites (six of which were colocated with the study gages). Sampling was done during a variety of flow conditions to assess baseline water quality. In 2021, the 8 sampling sites had drainage basins that were wholly or partially within 7 of the 10 most active counties in Ohio for oil and gas development. As part of the record of baseline conditions, water-quality data were used to assess (1) water types based on major-ion chemistry; (2) sources of salinity to streams; (3) exceedances of aquatic life use criteria; and (4) the correlations between water chemistry and drainage-basin characteristics, such as density of oil and gas wells, density of wastewater treatment plants, or the percentage of different types of land cover (agriculture, developed, forest).
Seven of the water-quality sampling sites were designated as coal-mine impacted based on criteria developed for assessing mine-drainage impacts in Ohio. Mine drainage from historical coal mining in the region likely affected the quality of these streams and complicated the use of some constituents typically used as indicators of oil and gas influence. Based on major-ion chemistry, three main types of water were in the study area―sulfate (three sites), calcium-bicarbonate (one site), and mixed bicarbonate-chloride (four sites) type waters. One site had samples with a higher proportion of sodium and chloride ions than other stream samples, indicating potential contamination with oil-field brine or road salt. Binary mixing curves revealed that 11 samples from 4 of the sampling sites likely contained a component of brine. The results of the baseline assessment of surface-water quality in the study area showed no exceedances of Ohio Environmental Protection Agency aquatic life use criteria. Spearman’s rank correlation coefficients indicated no significant positive correlations with the density of vertical or horizontal oil and gas wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245045","collaboration":"Prepared in cooperation with the Ohio Department of Natural Resources","usgsCitation":"Covert, S.A., and Koltun, G.F., 2024, Analysis of water use associated with hydraulic fracturing and determination of baseline water quality in watersheds within the shale play of eastern Ohio, 2021–23: U.S. Geological Survey Scientific Investigations Report 2024–5045, 61 p., https://doi.org/10.3133/sir20245045.","productDescription":"Report: viii, 61 p.; 2 Data Releases","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-159681","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":430672,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1G2W3JQ","text":"USGS data release","linkHelpText":"Annual streamflow statistics for selected streamgages in and near the shale play area of eastern Ohio (through water year 2021)"},{"id":499467,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117118.htm","linkFileType":{"id":5,"text":"html"}},{"id":430670,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5045/images/"},{"id":430668,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245045/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5045 HTML"},{"id":430667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5045/sir20245045.pdf","text":"Report","size":"28.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5045 PDF"},{"id":430669,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5045/sir20245045.XML","description":"SIR 2024-5045 XML"},{"id":430666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5045/coverthb.jpg"},{"id":430671,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EDHXB9","text":"USGS data release","linkHelpText":"Data from quality-control equipment blanks, field blanks, and field replicates for baseline water quality in watersheds within the shale play of eastern Ohio, 2021–23"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.333,\n 41\n ],\n [\n -82.333,\n 39.125\n ],\n [\n -80.666,\n 39.125\n ],\n [\n -80.666,\n 41\n ],\n [\n -82.333,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use.
Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.
","language":"English","publisher":"NOAA","doi":"10.25923/4jqw-mw29","usgsCitation":"Nisonson, H., Kiser, A.H., Gressler, B.P., Leight, A., and Young, J.A., 2024, Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin: NOAA Technical Memorandum NOS NCCOS 332, vi, 41 p., https://doi.org/10.25923/4jqw-mw29.","productDescription":"vi, 41 p.","ipdsId":"IP-163665","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":432484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patuxent River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.5870885061828,\n 38.29653642168418\n ],\n [\n -76.39765972760698,\n 38.2523876845089\n ],\n [\n -76.39469990294168,\n 38.39635283845547\n ],\n [\n -76.5219723635473,\n 38.51224538633858\n ],\n [\n -76.5930081555134,\n 38.75962947245472\n ],\n [\n -76.57524920752218,\n 38.93252018914461\n ],\n [\n -76.82683430406816,\n 39.192214789667304\n ],\n [\n -77.06635333905636,\n 39.45429197245687\n ],\n [\n -77.25054644375115,\n 39.48452671490274\n ],\n [\n -77.41561427713579,\n 39.40769859848646\n ],\n [\n -77.04882238288116,\n 39.139433495010024\n ],\n [\n -76.96002764292388,\n 39.04065023841653\n ],\n [\n -76.82979535765293,\n 38.90718862957951\n ],\n [\n -76.82091588365701,\n 38.66493779010759\n ],\n [\n -76.74100152907371,\n 38.412588059675414\n ],\n [\n -76.5870885061828,\n 38.29653642168418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nisonson, H","contributorId":342011,"corporation":false,"usgs":false,"family":"Nisonson","given":"H","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiser, Alexander H. 0000-0002-2871-0640","orcid":"https://orcid.org/0000-0002-2871-0640","contributorId":342012,"corporation":false,"usgs":true,"family":"Kiser","given":"Alexander","middleInitial":"H.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gressler, Benjamin P. 0000-0001-6639-8558","orcid":"https://orcid.org/0000-0001-6639-8558","contributorId":270167,"corporation":false,"usgs":true,"family":"Gressler","given":"Benjamin","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leight, A","contributorId":342013,"corporation":false,"usgs":false,"family":"Leight","given":"A","email":"","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":909481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263829,"text":"70263829 - 2024 - Upper crustal seismic velocity structure of the Hayward fault zone, San Francisco Bay, California, USA: Results from the 2016 East Bay Seismic Experiment (EBSI-16)","interactions":[],"lastModifiedDate":"2025-02-25T16:27:11.284452","indexId":"70263829","displayToPublicDate":"2024-07-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Upper crustal seismic velocity structure of the Hayward fault zone, San Francisco Bay, California, USA: Results from the 2016 East Bay Seismic Experiment (EBSI-16)","docAbstract":"We developed Vp, Vs, Vp/Vs ratio, and Poisson’s ratio models of the uppermost crust (<4 km depth) from the eastern San Francisco (SF) Bay (California, USA) to near the Calaveras fault along a 15-km-long, linear profile. Upper crustal velocities are highly variable beneath, west, and well east of the Hayward fault. We observe eight notable features, from west to east: (1) Near San Francisco Bay, there is an ~2-km-wide structure with high Vp/Vs ratios (up to 5) and Poisson’s ratios (up to 0.48) extending from the surface to the base of our model, which we suggest the structure is a near-vertical fault that lies along a straight-line projection between the Silver Creek fault to the south and the Point Richmond fault to the north. The structure may be part of an ~90-km-long fault along the eastern SF Bay. (2) The western East Bay Plain, the lower lying area between the bay and the hills, includes up to 800 m of low-velocity sediments (Vp ~1600–3000 m/s, Vs ~500 m/s to ~1000 m/s), underlain by higher velocity basement rocks (Vp ~3000–5800 m/s; Vs ~1000–1500 m/s). (3) Between ~1 km and 3 km east of the Bay shoreline, sediments thin in a series of steps (likely faults) toward the Hayward fault. (4) Between ~3 km west and ~1 km east of the Hayward fault (at the East Chabot fault) at depths greater than 1 km, basement Vp (up to 6000 m/s) and Vs (up to 2800 m/s) are high, and Vp/Vs ratios (<2) and Poisson’s ratios (<0.3) are low, suggesting crystalline rocks. Furthermore, a near-vertical zone of low Vp/Vs ratios and Poisson’s ratios is between near-surface traces of the Hayward and East Chabot faults, likely corresponding to the San Leandro Gabbro of Ponce et al. (2003). (5) Eastward of the East Chabot fault in the upper 1.5 km, basement Vp (~3000 m/s to ~4200 m/s) and Vs (~1200–2000 m/s) are lower than those west of the fault. (6) In the eastern Hayward/Oakland Hills, there are zones of laterally varying, high- and low-velocity (Vp ~2500–3000 m/s) Jurassic–Cretaceous and Tertiary sediments in the shallow subsurface that likely extend much deeper than imaged. (7) Seismic energy that propagates westward from sources east of the Hayward fault (HF) appear weaker than energy that propagates eastward from sources west of the HF, suggesting that the HF acts as a partial barrier to shallow seismic energy propagation into the more populated eastern SF Bay area. (8) Unlike many fault zones, it appears that the active trace of the Hayward fault (in our study area) is not cored by a prominent, low-velocity zone relative to rocks to the east and west of the active trace. However, the active trace does mark a prominent change from relatively higher velocities to the west and lower velocities to the east.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1130/B36919.1","usgsCitation":"Catchings, R.D., Strayer, L.M., Chan, J.H., Goldman, M., McEvilly, A., and Suppe, J., 2024, Upper crustal seismic velocity structure of the Hayward fault zone, San Francisco Bay, California, USA: Results from the 2016 East Bay Seismic Experiment (EBSI-16): GSA Bulletin, v. 136, no. 7-8, p. 3261-3276, https://doi.org/10.1130/B36919.1.","productDescription":"16 p.","startPage":"3261","endPage":"3276","ipdsId":"IP-124842","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.49518270211846,\n 37.855388424415295\n ],\n [\n -122.49518270211846,\n 37.389789695119845\n ],\n [\n -121.96137507277817,\n 37.389789695119845\n ],\n [\n -121.96137507277817,\n 37.855388424415295\n ],\n [\n -122.49518270211846,\n 37.855388424415295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2024-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strayer, Luther M.","contributorId":139930,"corporation":false,"usgs":false,"family":"Strayer","given":"Luther","email":"","middleInitial":"M.","affiliations":[{"id":13318,"text":"California State University East Bay","active":true,"usgs":false}],"preferred":false,"id":928585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chan, Joanne H. 0000-0002-2065-2423 jchan@usgs.gov","orcid":"https://orcid.org/0000-0002-2065-2423","contributorId":178625,"corporation":false,"usgs":true,"family":"Chan","given":"Joanne","email":"jchan@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McEvilly, Andrian T.","contributorId":351006,"corporation":false,"usgs":false,"family":"McEvilly","given":"Andrian T.","affiliations":[],"preferred":false,"id":928588,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suppe, J.","contributorId":61178,"corporation":false,"usgs":true,"family":"Suppe","given":"J.","email":"","affiliations":[{"id":68365,"text":"Department of Earth and Atmospheric Sciences, University of Houston","active":true,"usgs":false}],"preferred":false,"id":928589,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256555,"text":"70256555 - 2024 - Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution","interactions":[],"lastModifiedDate":"2024-10-08T16:28:41.215656","indexId":"70256555","displayToPublicDate":"2024-06-27T11:22:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution","title":"Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution","docAbstract":"The Plains Spotted Skunk (Spilogale interrupta) is a small carnivore native to central North America that has experienced significant population reductions, and there is a lack of information about the species that could inform conservation. Our study aimed to address knowledge gaps about the distribution and habitat associations of the species in South Dakota using species distribution modeling. We used species location data collected from state natural resource managers, trappers, and members of online social media groups dedicated to hunting and wildlife conservation; environmental predictors; and 6 predictive modeling algorithms (i.e., artificial neural networks, artificial classification tree analysis, generalized boosting models, maximum entropy, multivariate adaptive regression splines, and random forests) to develop climate and landcover ensemble distribution models. The most important climate and landcover predictors were mean temperature diurnal range (i.e., average monthly differences between daily high and low temperatures) and proportion of area classified as pasture. Ensemble model concordance identified approximately 31,300 km2 of potential Plains Spotted Skunk habitat primarily in eastern South Dakota and between the watersheds of the Missouri and James rivers. Our results offer insights that can guide conservation and inform effective management strategies for conserving Plains Spotted Skunk populations in the northern Great Plains. The promotion of low-intensity agricultural practices such as maintaining pastures, farm buildings, fences rows, and the management of woodland encroachment may improve habitat suitability and facilitate the recovery of plains spotted skunks in the region.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyae063","usgsCitation":"White, K.M., Cheeseman, A.E., Stafford, J.D., and Lonsinger, R.C., 2024, Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution: Journal of Mammalogy, https://doi.org/10.1093/jmammal/gyae063.","ipdsId":"IP-154327","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":462702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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jstafford@usgs.gov","orcid":"https://orcid.org/0000-0001-7590-8708","contributorId":267260,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907965,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257547,"text":"70257547 - 2024 - Climate, food and humans predict communities of mammals in the United States","interactions":[],"lastModifiedDate":"2024-09-10T15:02:17.761832","indexId":"70257547","displayToPublicDate":"2024-06-27T10:18:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Climate, food and humans predict communities of mammals in the United States","docAbstract":"The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities.
Conterminous United States.
2010–2021.
Twenty-five species of mammals.
We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities.
Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west.
Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.
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Lonsinger, R.C., Kuhn, K.M., Hasstedt, S.C., Zimova, M., Moore, S.M., Herrera, D.J., Fritts, S., Edelman, A.J., Flaherty, E.A., Petroelje, T.R., Neiswenter, S.A., Risch, D.R., Iannarilli, F., van der Merwe, M., Maher, S.P., Farris, Z.J., Webb, S.L., Mason, D.S., Lashley, M.A., Wilson, A.M., Vanek, J.P., Wehr, S.R., Conner, L.M., Beasley, J.C., Bontrager, H.L., Baruzzi, C., Ellis-Felege, S.N., Proctor, M.D., Schipper, J., Weiss, K., Darracq, A.K., Barr, E.G., Alexander, P.D., Sekercioglu, C.H., Bogan, D.A., Schalk, C.M., Fantle-Lepczyk, J.E., Lepczyk, C.A., LaPoint, S., Whipple, L.S., Rowe, H.I., Mullen, K., Bird, T., Zorn, A., Brandt, L., Lathrop, R., McCain, C., Crupi, A.P., Clark, J., and Parsons, A., 2024, Climate, food and humans predict communities of mammals in the United States: Diversity and Distributions, v. 30, e13900, 16 p., https://doi.org/10.1111/ddi.13900.","productDescription":"e13900, 16 p.","ipdsId":"IP-154598","costCenters":[{"id":199,"text":"Coop Res Unit 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To understand mechanisms regulating larval survival it is important to understand the relative importance of abiotic and biotic factors that shape larval spatial distributions. We studied larval Coregonus distributions in surface waters (surface to 1 m) by repeatedly sampling study sites in two lakes that varied greatly in trophic state and regional climate. We evaluated the importance of bathymetric depth, Julian Day, edible zooplankton densities (EZ, ind. L−1) and wind vectors on larval spatial distributions using generalized additive modeling. In both systems, larval counts declined in a negative exponential fashion with bathymetric depth, indicating shallow depths are critical nursery habitat. The north-south wind vectors and Julian Day (which was positively correlated with surface temperature) influenced larval distributions in Lake Geneva with larval counts related to both variables linearly, whereas the east-west wind vector and EZ were unimportant. Highest larval counts were during an offshore south wind and declined slightly with Julian Day. In Lake Superior, bathymetric depth and the east-west wind vector influenced larval distributions and were unrelated to EZ, Julian Day, and the north-south wind vector. Larval counts were highest when onshore southwest winds preceded sampling. Differences in how wind affected larval distribution (offshore vs. onshore) might be related to larval size with Lake Superior larvae considerably smaller (average length 12.9 mm vs. 15.9 mm); thus, more apt to be subjected to advection. Within coastal waters, Julian Day and wind vectors influence distributions, but their importance seemingly varies lake-to-lake.
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