The Quaternary Clear Lake Volcanic Field (CLVF) in the Northern California Coast Range is the youngest of a string of northward-younging volcanic centers in the state. The CLVF is located within the broad San Andreas Transform Fault System and has been active intermittently for ∼2 million years. Heat beneath the CLVF supports The Geysers, one of the largest producing geothermal fields in the world.
Previous geophysical studies proposed the existence of a magma reservoir beneath Mount Hannah, which is northeast of The Geysers, near the geographic center of the CLVF. The lateral extent, depth, and presence of melt within this reservoir are poorly constrained, as is the relationship between this body and the broader magmatic plumbing of the CLVF. To gain a clearer and more comprehensive picture of the CLVF magma source region, a gravity dataset was compiled and the first 3-D gravity inversions of the CLVF were completed.
Field and synthetic model inversions from the current study both indicate that the gravity low roughly centered on Mount Hannah is not accurately explained by a 5–7 km thick lens of Mesozoic Great Valley Sequence (
The prolonged eruption history of the CLVF, coupled with the compositional variation of erupted rocks over time and space, is consistent with the existence of several, potentially ephemeral, melt-bearing bodies as opposed to one large melt body. Given the density and location of the recovered anomaly, rhyolite-MELTS thermodynamic modeling suggests the existence of 10–30% rhyodacitic melt within the proposed silicic magma reservoir at about 700 °C and 8 km depth (210 MPa). Independent petrologic, geochemical, and seismic evidence indicates that this silicic partial melt zone is underlain by basaltic melt in the lower to middle crust (13 to 21 km depth), which is fed by a mantle source.
Eruptions in the past ∼8.5–13.5 thousand years; high regional heat flow; 3He enrichment of hydrothermal fluids; and our modeling, which suggests the presence of a mid-crustal, silicic partial melt zone, point to a still-active CLVF. The relatively low estimates of partial melt (10–30%) predicted by thermodynamic modeling indicates that an injection of new magma into the imaged partial melt zone is needed to generate sufficient melt to incite future eruptions. Despite the low percent melt estimates within the proposed silicic partial melt zone the potential for future volcanic eruption remains. Due to the proximity of the CLVF to cities surrounding Clear Lake and the densely populated San Francisco Bay Area, continued research and monitoring of the volcanic field are warranted. The geophysical and petrologic modeling presented here improves our understanding of the CLVF magma plumbing system and allows us to better characterize its associated volcanic hazards.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2023.107758","usgsCitation":"Mitchell, M.A., Peacock, J., and Burgess, S.D., 2023, Imaging the magmatic plumbing of the Clear Lake Volcanic Field using 3-D gravity inversions: Journal of Volcanology and Geothermal Research, v. 435, 107758, 41 p., https://doi.org/10.1016/j.jvolgeores.2023.107758.","productDescription":"107758, 41 p.","ipdsId":"IP-137378","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":444342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2023.107758","text":"Publisher Index Page"},{"id":415702,"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 -123.25068446644411,\n 39.211549683768425\n ],\n [\n -123.25068446644411,\n 38.316770047400155\n ],\n [\n -122.29803029534756,\n 38.316770047400155\n ],\n [\n -122.29803029534756,\n 39.211549683768425\n ],\n [\n -123.25068446644411,\n 39.211549683768425\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"435","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Michael Albert 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":299110,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"","middleInitial":"Albert","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":869389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":869390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgess, Seth D. 0000-0002-4238-3797 sburgess@usgs.gov","orcid":"https://orcid.org/0000-0002-4238-3797","contributorId":200371,"corporation":false,"usgs":true,"family":"Burgess","given":"Seth","email":"sburgess@usgs.gov","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":869391,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256611,"text":"70256611 - 2023 - A review of lethal thermal tolerance among freshwater mussels (Bivalvia: Unionida) within the North American faunal region","interactions":[],"lastModifiedDate":"2024-08-07T11:10:46.111024","indexId":"70256611","displayToPublicDate":"2023-03-01T06:08:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5056,"text":"Environmental Reviews","active":true,"publicationSubtype":{"id":10}},"title":"A review of lethal thermal tolerance among freshwater mussels (Bivalvia: Unionida) within the North American faunal region","docAbstract":"Hierarchical conservation and management of Indiana bat (Myotis sodalis) habitat may benefit from use of species distribution models. White-nose syndrome has caused additional declines for this endangered bat, requiring use of historical presence locations for habitat-related analy- ses. We created random forest presence/pseudo-absence models to assess the distribution and availability of Indiana bat habitat across the 670,000-ha Monongahela National Forest (MNF), West Virginia, USA. We collated historical roost and capture locations, both individually and in combination, to examine impacts of various biotic and abiotic predictors on roosting and foraging habitat of Indiana bats. Our final concordance map suggests that Indiana bat habitat was abundant (37.2% of the MNF) but localized, with predicted suitable areas often associated with edges of dry-calcareous forests. We observed significant variation between models, with the capture-only model independently identifying the greatest amount of potential habitat (47.8%). However, 21.9% of all potential Indiana bat habitat was identified by complete inter-model agreement. Our SDM outputs may assist land managers in identifying avoidance areas and new survey sites (i.e., capture and acoustic sampling) to support forest management activities.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"De La Cruz, J., Ford, W., Jones, S.B., Johnson, J., and Silvis, A., 2023, Distribution of summer-habitat for the Indiana bat on the Monongahela National Forest, West Virginia: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 125-134.","productDescription":"10 p.","startPage":"125","endPage":"134","ipdsId":"IP-142588","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481122,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/distribution-summer-habitat-indiana-bat-monongahela-national-forest-west-virginia"},{"id":481155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Viginia","otherGeospatial":"Monongahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.92561978482631,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.52966246222408\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"De La Cruz, J.L.","contributorId":349847,"corporation":false,"usgs":false,"family":"De La Cruz","given":"J.L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":924947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, S. Beaux","contributorId":346278,"corporation":false,"usgs":false,"family":"Jones","given":"S.","email":"","middleInitial":"Beaux","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":924949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, J.R.","contributorId":349849,"corporation":false,"usgs":false,"family":"Johnson","given":"J.R.","affiliations":[{"id":32872,"text":"John Hopkins University, Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":924950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silvis, A.","contributorId":349851,"corporation":false,"usgs":false,"family":"Silvis","given":"A.","affiliations":[{"id":40299,"text":"West Virginia Division of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924951,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262166,"text":"70262166 - 2023 - Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir","interactions":[],"lastModifiedDate":"2025-01-15T20:01:12.656734","indexId":"70262166","displayToPublicDate":"2023-03-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir","docAbstract":"Understanding walleye (Sander vitreus) spawning behavior is important for managing walleye fisheries, but such information is limited for Appalachian reservoirs. We assessed spawning movements and spawning locations for a reestablished walleye population in Cheat Lake, West Virginia. We tagged fifty-two walleye with acoustic telemetry transmitters to evaluate environmental correlates associated with pre-spawn movements and to deter- mine spawning locations. Using an information-theoretic approach, we compared candidate logistic regression models to determine which environmental variables best explained upstream movements to spawning areas. The two models with the most support both included additive effects of year and water temperature, with sex also included in the second of these models. Water temperature had a significant positive relationship with pre-spawn movements in each model. Other environmental covariates such as river discharge and water elevation were not significant predictors of upstream pre-spawn move- ments. Walleye made pre-spawn upstream movements in late winter/early spring to spawning areas in the headwaters of Cheat Lake during periods of el- evated water temperatures (75 % of movement events occurred at water temperatures >4.1 C) where spawning occurred in shallow (<1.5 m), rocky habitat. Male walleye generally made upstream pre-spawn movements earlier than females. Our results also suggested the timing of walleye spawning with respect to water-level fluctuations could influence reproductive success due to stranding of eggs or reducing suitable spawning habitat. Knowledge of pre-spawn movement patterns and spawning locations could aid management of this recovering population. Benefits to management may include the prediction of spawning timing and locations for broodstock surveys and influences of water-level fluctuations and other environmental stressors on spawning success.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Smith, D., Welsh, S.A., and Hilling, C.D., 2023, Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 36-44.","productDescription":"9 p.","startPage":"36","endPage":"44","ipdsId":"IP-145288","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":466453,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/environmental-correlates-walleye-spawning-movements-appalachian-hydropower-reservoir"}],"country":"United States","state":"Pennsylvania, West Virginia","otherGeospatial":"Cheat Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.90059403320035,\n 39.7348398274502\n ],\n [\n -79.90059403320035,\n 39.665062724725885\n ],\n [\n -79.82498808610146,\n 39.665062724725885\n ],\n [\n -79.82498808610146,\n 39.7348398274502\n ],\n [\n -79.90059403320035,\n 39.7348398274502\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Dustin M.","contributorId":272979,"corporation":false,"usgs":false,"family":"Smith","given":"Dustin M.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":923331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":1483,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart","email":"swelsh@usgs.gov","middleInitial":"A.","affiliations":[{"id":205,"text":"Cooperative Research Units","active":false,"usgs":true}],"preferred":false,"id":923332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923333,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262830,"text":"70262830 - 2023 - Sources of yearly variation in gray bat activity in the Clinch River watershed, Virginia","interactions":[],"lastModifiedDate":"2025-01-24T16:58:20.707551","indexId":"70262830","displayToPublicDate":"2023-03-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Sources of yearly variation in gray bat activity in the Clinch River watershed, Virginia","docAbstract":"The gray bat (Myotis grisescens) is a cave-obligate species that has been listed as federally endangered since 1976, following population declines from human disturbance at hibernation and maternity caves. However, with cave protection, most gray bat populations have increased. As part of a project examining bat use of transportation structures as day-roosts, we continuously acoustically monitored 12 riparian sites within the Clinch River Watershed of southwest Virginia from March through November, 2018–2020. We used 15 different landscape and weather-related variables in generalized linear mixed models to determine factors influencing gray bat presence and activity. Seasonal activity patterns were similar among years, but the number of nightly gray bat calls increased with each passing year, consistent with positive population trends observed at winter hibernacula. Year and average nightly temperatures were positively correlated with gray bat activity, as was, unexpectedly, average nightly wind speed. Total nightly precipitation, distance to the nearest hibernaculum in Tennessee, percent forested area within 2 km of a detector, mean elevation within 2 km of a detector, detector type, and amount of urban development within 2 km of a detector were negatively correlated with gray bat activity. Our findings show where and when gray bat presence is likely in southwest Virginia, thereby helping managers avoid negative impacts from activities such as bridge repair or replacement and planning of future monitoring to track population trends.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Taylor, H., Powers, K., Orndorff, W., Reynolds, R., Hallerman, E.M., and Ford, W., 2023, Sources of yearly variation in gray bat activity in the Clinch River watershed, Virginia: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 107-113.","productDescription":"7 p.","startPage":"107","endPage":"113","ipdsId":"IP-142692","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481149,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/sources-yearly-variation-gray-bat-activity-clinch-river-watershed-virginia","linkFileType":{"id":5,"text":"html"}},{"id":481151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Clinch River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6898822712355,\n 37.244895334911206\n ],\n [\n -82.48049233355506,\n 36.59887550386596\n ],\n [\n -81.6898822712355,\n 36.59053043712693\n ],\n [\n -81.38353788654133,\n 36.697772476555386\n ],\n [\n -81.0769253267201,\n 37.244895334911206\n ],\n [\n -81.6898822712355,\n 37.244895334911206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, H.","contributorId":195324,"corporation":false,"usgs":false,"family":"Taylor","given":"H.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":924942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powers, K.","contributorId":349843,"corporation":false,"usgs":false,"family":"Powers","given":"K.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":924943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orndorff, W.","contributorId":349845,"corporation":false,"usgs":false,"family":"Orndorff","given":"W.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":924944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reynolds, Rick","contributorId":267215,"corporation":false,"usgs":false,"family":"Reynolds","given":"Rick","email":"","affiliations":[{"id":55446,"text":"Virginia Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":925006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hallerman, E. M.","contributorId":280251,"corporation":false,"usgs":false,"family":"Hallerman","given":"E.","email":"","middleInitial":"M.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":924945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924946,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240897,"text":"fs20233001 - 2023 - Flood warning toolset for the Sabinal River near Utopia, Texas","interactions":[],"lastModifiedDate":"2026-02-05T14:42:08.024436","indexId":"fs20233001","displayToPublicDate":"2023-02-28T12:30:00","publicationYear":"2023","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":"2023-3001","displayTitle":"Flood Warning Toolset for the Sabinal River Near Utopia, Texas","title":"Flood warning toolset for the Sabinal River near Utopia, Texas","docAbstract":"Floods are one of the most frequent and expensive natural disasters that occur across the United States. Rapid, high-water events that occur in local areas—flash floods—are especially difficult for emergency managers to predict and provide advance warning to the public, and insufficient data can hamper postflood recovery efforts. Central Texas is hilly, and it is known as a “flash flood alley” because of its high-intensity rains, shallow soils, and steep terrain, all of which combined can result in loss of life and property damage. For example, the flash flood event during July 2002 claimed 12 lives in central Texas, including 1 in the town of Utopia, which is on the east bank of the Sabinal River in a flash-flood-prone area along the Balcones Escarpment. During the flood event, the peak discharge recorded on July 5, 2002, at U.S. Geological Survey (USGS) streamgage 08198000 Sabinal River near Sabinal, Tex. (hereinafter referred to as the “Sabinal gage”), was 108,000 cubic feet per second (corresponding to a stream stage [also called gage height] of 33.74 feet). To put the 2002 flood into context, during a typical year the median daily discharge in the Sabinal River at the Sabinal gage is only about 23 cubic feet per second. In 2021, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood warning toolset for the Sabinal River near Utopia. This study builds on earlier USGS flood work on the Medina River in Bandera County. The newly developed toolset consists of a newly installed USGS streamgage to collect continuous stream stage data (streamgage 08197970 Sabinal River at Utopia, Tex.; hereinafter referred to as the “Utopia gage”) 13 miles upstream from the Sabinal gage, a hydraulic model developed for the Sabinal River near Utopia, and an online library of digital flood-inundation maps referenced to the stream stage at the Utopia gage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233001","issn":"2327-6932 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood warning toolset for the Sabinal River near Utopia, Texas: U.S. Geological Survey Fact Sheet 2023–3001 (ver. 2.0, September 2023), 4 p., https://doi.org/10.3133/fs20233001.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136337","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":421082,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2023/3001/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":499562,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114429.htm","linkFileType":{"id":5,"text":"html"}},{"id":421081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20235001","text":"Scientific Investigations Report 2023–5001","description":"SIR 2023-5001","linkHelpText":"- Flood-Inundation Maps Created Using a Synthetic Rating Curve for a 10-Mile Reach of the Sabinal River and a 7-Mile Reach of the West Sabinal River Near Utopia, Texas, 2021"},{"id":421080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3001/fs20233001.pdf","text":"Report","size":"1.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3001"},{"id":414773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3001/coverthb.jpg"}],"country":"United States","state":"Texas","city":"Utopia","otherGeospatial":"Sabinal River, West Sabinal River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.6333,\n 29.75\n ],\n [\n -99.6333,\n 29.6\n ],\n [\n -99.5,\n 29.6\n ],\n [\n -99.5,\n 29.75\n ],\n [\n -99.6333,\n 29.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Overview
Creation of Flood Warning Toolset
References Cited
Coastal resources are increasingly affected by erosion, extreme weather events, sea level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying effects on coastal landscapes because of the compounding of geologic, oceanographic, ecologic, and socioeconomic factors that exist at a given location. An assessment framework is introduced in this report that synthesizes existing datasets that cover the variability of the landscape, and hazards that may act on the landscape, to evaluate the likelihood of coastal change along the U.S. coastline on a decadal scale. The pilot study that aided in the development of the framework was run in the northeastern United States (from Maine to Virginia) and consists of datasets derived from a variety of Federal, State, and local sources.
First, a decision-tree-based dataset was built that describes the resistance or integrity of the coastal landscape (called the fabric dataset for the purposes of this report) and includes land cover, elevation, slope, long-term (more than 50 years) shoreline change, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (for example, flooding, wave power, and probability of storm overwash) and persistent or perpetual hazards (for example, relative sea level rise rate, short-term [about 30-year] shoreline erosion rate, and storm recurrence interval). The fabric dataset was then merged with the coastal hazards databases, and a model training dataset made up of hundreds of polygons was generated from these combined data to support machine learning.
The pilot study resulted in location-specific, 10-meter-resolution data classified into five raster datasets that include intrinsic characteristics of the coast used to determine the resistance of the landscape to change, the persistent and event hazards that act on the coast, the machine learning output (coastal change likelihood) based on the cumulative effects of the fabric and hazards datasets, and an estimate of the hazard type (event or persistent) that is the most likely to influence coastal change. Final outcomes are intended to be used as a first-order planning tool to determine which areas of the coast are more likely to change in response to future potential coastal hazards and to examine elements and drivers that make change in a location more likely.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1169","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Pendleton, E.A., Lentz, E.E., Sterne, T.K., and Henderson, R.E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: U.S. Geological Survey Data Report 1169, 56 p., https://doi.org/10.3133/dr1169.","productDescription":"Report: viii, 56 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-141482","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":413447,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96A2Q5X","text":"USGS data release","linkHelpText":"Coastal change likelihood in the U.S. northeast region—Maine to Virginia"},{"id":413449,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1169/images/"},{"id":499552,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114426.htm","linkFileType":{"id":5,"text":"html"}},{"id":413448,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.XML"},{"id":413446,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1169/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1169"},{"id":413445,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1169"},{"id":413444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1169/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.69060371478722,\n 36.642989353191695\n ],\n [\n -75.63687331767586,\n 36.61725767144067\n ],\n [\n -73.59796884586575,\n 40.01648840470193\n ],\n [\n -70.80274185094413,\n 41.01068598755887\n ],\n [\n -69.57806852142825,\n 41.099580604299234\n ],\n [\n -69.90622192003855,\n 42.11383028198776\n ],\n [\n -70.57455282622757,\n 43.02711001288796\n ],\n [\n -67.01522918769722,\n 44.713652472389384\n ],\n [\n -67.51934985246183,\n 45.183064414796405\n ],\n [\n -71.15470019361379,\n 43.73883250370727\n ],\n [\n -71.3079883194934,\n 41.86654548530123\n ],\n [\n -73.98393265041173,\n 41.230559521628294\n ],\n [\n -76.3263141198747,\n 39.710722533017474\n ],\n [\n -77.25368632549163,\n 38.78457338969852\n ],\n [\n -76.72175177570705,\n 36.72418776523644\n ],\n [\n -75.97037108334082,\n 36.81887134243617\n ],\n [\n -75.94893824149692,\n 36.817996199366135\n ],\n [\n -76.69060371478722,\n 36.642989353191695\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
The U.S. Army Corps of Engineers (USACE) is considering changes to the management of water surface elevation in four lakes in the Mahoning River Basin. These changes would affect the timing and amounts of water released to the Mahoning River and could affect the water quality of those releases. To provide information on possible water-quality effects from these operational changes, flow and water-quality models were constructed for Berlin Lake, Lake Milton, Michael J Kirwan Reservoir, Mosquito Creek Lake, Mosquito Creek, and the Mahoning River from the dams downstream to Lowellville, Ohio.
The models were calibrated for two calendar years each, with model years selected depending on the availability of water-quality data. Models were developed with CE-QUAL-W2 version 4.2 (Wells, S.A., 2020, CE-QUAL-W2—A two-dimensional, laterally averaged, hydrodynamic and water quality model [version 4.2]: Portland State University, variously paged), a two-dimensional, laterally averaged hydrodynamic and water-quality model. Modeled constituents included flow, velocity, ice cover, water temperature, total dissolved solids (TDS), sulfate, chloride, inorganic suspended sediment, nitrate, ammonia, total Kjeldahl nitrogen, orthophosphate, total phosphorus, dissolved and particulate organic matter, algae, and dissolved oxygen. Iron was included for the lake models, but not the river.
A whole-basin model, with the four lake models and river model, was used to run model scenarios to examine the effects of altered lake water surface elevations on flow and water quality in the lakes, the lake outflows, and the Mahoning River. The initial whole-basin model, with calendar year 2013 hydrology and measured or typical water quality, was designated as scenario 0. Mahoning River flows for calendar year 2013 were close to a 20-year median flow. Four additional scenarios were constructed based on reservoir operations model (RES-SIM) model water surface elevations for the four lakes as provided by USACE. Scenario 1 was the RES-SIM base case, scenario 2 kept Berlin Lake water surface elevations higher in summer, scenario 3 allowed 25 percent of summer flood storage to extend the guide curve, and scenario 4 allowed more flexibility in lake management by removing any downstream Mahoning River minimum flow requirements. The Mahoning River model was not changed in any scenarios but received altered flows from the lakes. Significant findings from this study include the following:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Bigheaded carp Hypophthalmichthys spp. are invasive species native to Asia expanding in the Mississippi River Basin in North America. An understanding of spatiotemporal distribution and aggregation of invasive carp is key to establishing when and where to focus surveillance designed to monitor expansion, and to managing harvest programs designed to curb population densities. We applied a two-stage hurdle model to assess three aspects of bigheaded carp ecology: distribution, relative abundance, and aggregation. Stage 1 was a binary 0/1 model that represented fish presence (p), and stage 2 was a truncated count distribution that had no zeros and included counts ≥ 1 only (C). Estimates of p and C varied temporally and spatially, but not in harmony and sometimes in opposing directions, indicating temporal and spatial swings in fish distributions and aggregations. Intense fish aggregations in channels in spring shown by low p’s and high C’s, eventually scattered by summer and fall as shown by high p’s and low C’s. An alternative but complementary interpretation of our observations is that p indexes incidence of aggregations and C indexes size of aggregations. Partitioning catch into its zero and nonzero components provided insight into population ecology that can inform development of monitoring and management of harvesting programs targeted at lessening potential effects of the invasion.
","language":"English","publisher":"Invasives.net","doi":"10.3391/mbi.2023.14.2.12","usgsCitation":"Miranda, L.E., Tompkins, J., Dunn, C.G., Morris, J.L., and Combs, M.C., 2023, Patterns of zero and nonzero counts suggest spatiotemporal distributions, aggregation, and dispersion of invasive carp: Management of Biological Invasions, v. 14, no. 2, p. 363-377, https://doi.org/10.3391/mbi.2023.14.2.12.","productDescription":"15 p.","startPage":"363","endPage":"377","ipdsId":"IP-130231","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2023.14.2.12","text":"Publisher Index Page"},{"id":433157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, Kentucky, Mississippi, North Carolina, Tennessee","otherGeospatial":"Cumberland River basin, Kentucky Lake, Lake Barkley, Tennessee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.6579330031289,\n 36.91383372177731\n ],\n [\n -89.3365987950857,\n 36.48519895640891\n ],\n [\n -90.06194179414129,\n 35.00234638850921\n ],\n [\n -88.19060222341875,\n 34.1587395703324\n ],\n [\n -84.99319129439488,\n 34.42656666413427\n ],\n [\n -82.51455380820883,\n 35.573734179364564\n ],\n [\n -81.53907530047877,\n 36.20934135136643\n ],\n [\n -83.66465109105143,\n 36.726966972925695\n ],\n [\n -86.6579330031289,\n 36.91383372177731\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tompkins, J.","contributorId":341343,"corporation":false,"usgs":false,"family":"Tompkins","given":"J.","email":"","affiliations":[{"id":53972,"text":"Kentucky Department of Fish and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":908268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morris, J. L.","contributorId":255439,"corporation":false,"usgs":false,"family":"Morris","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":908267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Combs, Matthew C.","contributorId":343671,"corporation":false,"usgs":false,"family":"Combs","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":911638,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240990,"text":"70240990 - 2023 - Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA","interactions":[],"lastModifiedDate":"2023-05-12T14:54:16.678942","indexId":"70240990","displayToPublicDate":"2023-02-27T08:19:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA","docAbstract":"Graphite Creek is an unusual flake graphite deposit located on the Seward Peninsula, Alaska, USA. We present field observations, uranium-lead (U–Pb) monazite and titanite geochronology, carbon (C) and sulfur (S) stable isotope geochemistry, and graphite Raman spectroscopy data from this deposit that support a new model of flake graphite ore genesis in high-grade metamorphic environments. The Graphite Creek deposit is within the second sillimanite metamorphic zone of the Kigluaik Mountains gneiss dome. Flake graphite, hosted in sillimanite-gneiss and quartz-biotite paragneiss, occurs as disseminations and in sets of very high grade (up to 50 wt.% graphite), semi-massive to massive graphite lenses 0.2 to 1 m wide containing quartz, sillimanite, inclusions of garnet porphyroblasts, K-feldspar, and tourmaline. Restitic garnet, sillimanite, graphite, and biotite accumulations indicate a high degree of anatexis and melt loss. Strong yttrium depletion in monazite, high europium ratios (Eu/Eu*), and excursions of high strontium and thorium concentrations are consistent with biotite dehydration melting. Monazite and titanite U–Pb ages record peak metamorphism from ~ 97 to 92 million years ago (Ma) and a retrograde event at ~ 85 Ma. Raman spectroscopy confirms the presence of carbonaceous material and highly ordered, crystalline graphite. Graphite δ13CVPDB values of − 30 to − 12‰ and pyrrhotite δ34SVCDT values of − 14 to 10‰ are consistent with derivation from organic carbon and sulfur in sedimentary rocks, respectively. These data collectively suggest that formation of massive graphite lenses occurred approximately synchronously with high-temperature metamorphism and anatexis of a highly carbonaceous pelitic protolith. Melt extraction and fluid release associated with anatexis were likely crucial for concentrating graphite. High-temperature, graphitic migmatite sequences within high-strain shear zones may be favorable for the occurrence of high-grade flake graphite deposits.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-023-01161-3","usgsCitation":"Case, G.N., Karl, S.M., Regan, S., Johnson, C.A., Ellison, E.T., Caine, J., Holm-Denoma, C., Pianowski, L., and Benowitz, J.A., 2023, Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA: Mineralium Deposita, v. 58, p. 939-962, https://doi.org/10.1007/s00126-023-01161-3.","productDescription":"24 p.","startPage":"939","endPage":"962","ipdsId":"IP-135671","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"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}],"links":[{"id":444354,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00126-023-01161-3","text":"Publisher Index Page"},{"id":435431,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J50EKX","text":"USGS data release","linkHelpText":"Data for Uranium-Lead Geochronology, Carbon and Sulfur Stable Isotopes, and Raman Spectroscopy from Graphite Creek, Alaska"},{"id":413658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Graphite Creek graphite deposit, Seward Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.1974510679629,\n 65.1\n ],\n [\n -166.1974510679629,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 65.1\n ],\n [\n -166.1974510679629,\n 65.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","noUsgsAuthors":false,"publicationDate":"2023-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":865622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":865623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Regan, Sean P.","contributorId":219815,"corporation":false,"usgs":false,"family":"Regan","given":"Sean P.","affiliations":[{"id":13599,"text":"University of Alaska - Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":865625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellison, Eric T 0000-0002-6761-1397","orcid":"https://orcid.org/0000-0002-6761-1397","contributorId":302853,"corporation":false,"usgs":false,"family":"Ellison","given":"Eric","email":"","middleInitial":"T","affiliations":[{"id":52978,"text":"Department of Geological Sciences, University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":865626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":865627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":865628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pianowski, Laura 0000-0002-5346-8251","orcid":"https://orcid.org/0000-0002-5346-8251","contributorId":218817,"corporation":false,"usgs":true,"family":"Pianowski","given":"Laura","email":"","affiliations":[],"preferred":true,"id":865629,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Benowitz, Jeff A. 0000-0003-2294-9172","orcid":"https://orcid.org/0000-0003-2294-9172","contributorId":229570,"corporation":false,"usgs":false,"family":"Benowitz","given":"Jeff","email":"","middleInitial":"A.","affiliations":[{"id":41671,"text":"Geophysical Institute and Geochronology Laboratory, University of Alaska–Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865630,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262037,"text":"70262037 - 2023 - A big data–model integration approach for predicting epizootics and population recovery in a keystone species","interactions":[],"lastModifiedDate":"2025-01-10T14:56:44.15352","indexId":"70262037","displayToPublicDate":"2023-02-27T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"A big data–model integration approach for predicting epizootics and population recovery in a keystone species","docAbstract":"Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host–pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2827","usgsCitation":"Barrile, G., Augustine, D.J., Porensky, L., Duchardt, C., Shoemaker, K., Hartway, C., Derner, J.D., Hunter, E.A., and Davidson, A.D., 2023, A big data–model integration approach for predicting epizootics and population recovery in a keystone species: Ecological Applications, v. 33, no. 4, e2827, 23 p., https://doi.org/10.1002/eap.2827.","productDescription":"e2827, 23 p.","ipdsId":"IP-142779","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2827","text":"Publisher Index Page"},{"id":465980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.72151393816772,\n 49.06842429079816\n ],\n [\n -106.74407678276975,\n 44.935250564946244\n ],\n [\n -105.02441274593369,\n 40.78587530801761\n ],\n [\n -105.18708495048385,\n 35.23907808129579\n ],\n [\n -110.74352872625728,\n 31.51616164533567\n ],\n [\n -101.12343743446304,\n 28.915776651344572\n ],\n [\n -100.73547916940133,\n 38.76206177537206\n ],\n [\n -100.82267130820097,\n 44.32291601997803\n ],\n [\n -101.89899715272732,\n 47.61230649548153\n ],\n [\n -111.72151393816772,\n 49.06842429079816\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":288734,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":922769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, David J.","contributorId":189957,"corporation":false,"usgs":false,"family":"Augustine","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":922770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":922771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duchardt, Courtney J.","contributorId":347959,"corporation":false,"usgs":false,"family":"Duchardt","given":"Courtney J.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":922772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shoemaker, Kevin T.","contributorId":288541,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":61793,"text":"University of Nevada – Reno","active":true,"usgs":false}],"preferred":false,"id":922773,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartway, Cynthia R.","contributorId":347961,"corporation":false,"usgs":false,"family":"Hartway","given":"Cynthia R.","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":922774,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Derner, Justin D.","contributorId":195928,"corporation":false,"usgs":false,"family":"Derner","given":"Justin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":922775,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922776,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davidson, Ana D. 0000-0003-4701-5923","orcid":"https://orcid.org/0000-0003-4701-5923","contributorId":304176,"corporation":false,"usgs":false,"family":"Davidson","given":"Ana","email":"","middleInitial":"D.","affiliations":[{"id":65991,"text":"CNHP","active":true,"usgs":false}],"preferred":false,"id":922777,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241408,"text":"70241408 - 2023 - Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","interactions":[],"lastModifiedDate":"2023-03-17T11:42:04.586294","indexId":"70241408","displayToPublicDate":"2023-02-26T06:39:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","docAbstract":"Paleoclimate reconstructions can provide a window into the environmental conditions in Earth history when atmospheric carbon dioxide concentrations were higher than today. In the eastern USA, paleoclimate reconstructions are sparse, because terrestrial sedimentary deposits are rare. Despite this, the eastern USA has the largest population and population density in North America, and understanding the effects of current and future climate change is of vital importance. Here, we provide terrestrial paleoclimate reconstructions of the eastern USA from Miocene fossil floras. Additionally, we compare proxy paleoclimate reconstructions from the warmest period in the Miocene, the Miocene Climatic Optimum (MCO), to those of an MCO Earth System Model. Reconstructed Miocene temperatures and precipitation north of 35°N are higher than modern. In contrast, south of 35°N, temperatures and precipitation are similar to today, suggesting a poleward amplification effect in eastern North America. Reconstructed Miocene rainfall seasonality was predominantly higher than modern, regardless of latitude, indicating greater variability in intra-annual moisture transport. Reconstructed climates are almost uniformly in the temperate seasonal forest biome, but heterogeneity of specific forest types is evident. Reconstructed Miocene terrestrial temperatures from the eastern USA are lower than modeled temperatures and coeval Atlantic sea surface temperatures. However, reconstructed rainfall is consistent with modeled rainfall. Our results show that during the Miocene, climate was most different from modern in the northeastern states, and may suggest a drastic reduction in the meridional temperature gradient along the North American east coast compared to today.
Fish that exhibit high foraging activity or bold behavior can be particularly vulnerable to angling. If these traits are heritable, selection through harvest can drive phenotypic change, eventually rendering a target population less vulnerable to angling and consequently impacting the quality of the fishery. In this study, we used parental-based tags to investigate whether vulnerability to angling might be heritable in steelhead trout (Oncorhynchus mykiss) spawned at a hatchery in western Oregon, USA. We found modest evidence to support the hypothesis that vulnerability to angling is a heritable trait in steelhead. However, our data unexpectedly revealed that steelhead collected with in-river traps produced nearly twice as many adult offspring as steelhead collected by anglers. This difference in adult-to-adult production is explained in part through lower egg-to-fry survival of steelhead produced with angler-caught broodstock, possibly related to collection stress and greater time in captivity experienced by angler-caught broodstock. Our findings suggest that managers could improve broodstock fitness and program efficiencies by preferentially spawning fish collected with traps, and limiting use of broodstock collected by anglers. Additional research is needed to identify mechanisms contributing to higher juvenile mortality of steelhead produced with angler-caught broodstock.
Diet plasticity is a common behavior exhibited by piscivores to sustain predator biomass when preferred prey biomass is reduced. Invasive piscivore diet plasticity could complicate suppression success; thus, understanding invasive predator consumption is insightful to meeting conservation targets. Here, we determine if diet plasticity exists in an invasive apex piscivore and whether plasticity could influence native species recovery benchmarks and invasive species suppression goals. We compared diet and stable isotope signatures of invasive lake trout and native Yellowstone cutthroat trout (cutthroat trout) from Yellowstone Lake, Wyoming, U.S.A. as a function of no, low-, moderate-, and high-lake trout density states. Lake trout exhibited plasticity in relation to their density; consumption of cutthroat trout decreased 5-fold (diet proportion from 0.89 to 0.18) from low- to high-density state. During the high-density state, lake trout switched to amphipods, which were also consumed by cutthroat trout, resulting in high diet overlap (Schoener’s index value, D = 0.68) between the species. As suppression reduced lake trout densities (moderate-density state), more cutthroat trout were consumed (proportion of cutthroat trout = 0.42), and diet overlap was released between the species (D = 0.30). A shift in lake trout δ13C signatures from the high- to the moderate-density state also corroborated increased consumption of cutthroat trout and lake trout diet plasticity. Observed declines in lake trout are not commensurate with expected cutthroat trout recovery due to lake trout diet plasticity. The abundance of the native species in need of conservation may take longer to recover due to the diet plasticity of the invasive species. The changes observed in diet, diet overlap, and isotopes associated with predator suppression provides more insight into conservation and suppression dynamics than using predator and prey biomass alone. By understanding these dynamics, we can better prepare conservation programs for potential feedbacks caused by invasive species suppression.
To assess the status and trends of conditions of surface waters throughout Mississippi, the U.S. Geological Survey, in cooperation with the Mississippi Department of Environmental Quality (MDEQ), summarized concentrations and estimated loads, yields, trends, and spatial and temporal patterns of total nitrogen (TN) and total phosphorus (TP) at 20 stream sites in MDEQ’s ambient water-quality monitoring network and 2 stream sites in the U.S. Geological Survey’s National Water-Quality Assessment Project’s monitoring network.
Comparison of streamflow at the time of water-quality sample collection to flow-duration curves for each site showed that samples were relatively evenly spread over a wide range of flows, indicating that load estimations were representative of a wide range of flows. Relation of streamflow to concentrations of TN and TP varied among sites and land use. Sites with high agriculture land use in the drainage basin tended to have a positive correlation between streamflow and concentration, suggesting influence of event-driven nonpoint-source runoff. Sites near urban (developed) areas tended to have a negative correlation between streamflow and concentration, suggesting chronic point-source influences during low-flow conditions. Sites with high forest land use and lower agriculture and urban (developed) land use showed little to no association between streamflow and concentration.
Seasonal distributions of concentrations of TN and TP also corresponded closely with variations in land use. Sites near urban (developed) land had the highest concentrations in late summer and fall, sites with a high percentage of agricultural land had the highest concentrations in the spring, and sites that were primarily forested or with little developed land did not exhibit substantial changes in concentration across seasons.
Eight sites had statistical likelihoods for upward trends of TN loads, and seven sites had statistical likelihoods for downward trends. Trends in TN loads at six sites were considered “about as likely as not,” meaning that a site has an equal chance of having an upward or downward trend. Trend results of mean annual flow-normalized loads of TP for the period of analysis (2008–18) showed that 16 sites had upward trends, 3 sites had downward trends, and 2 sites were considered “about as likely as not.”
Results from our study were compared to results from existing regional models to assess accuracy of predictions at a local scale. Comparisons of yields predicted from 2012 regional-scale SPAtially Referenced Regressions on Watershed attributes (SPARROW) to results from this study showed the 2012 SPARROW-predicted estimates varied in consistency with results from this study. The 2012 SPARROW-prediction model underestimated TN yields, more often and by a slightly larger degree, more than it overestimated TN yields. The 2012 SPARROW-predicted model tended to underestimate yields at study sites with higher yields. All four sites in the predominantly agricultural area of northwest Mississippi, locally known as the Mississippi Delta, were underestimated by 2012 SPARROW. For TP, yield comparisons at sites with lower yields were consistent, yields at sites with midrange yields tended to be overestimated by SPARROW, and yields at sites with high yields tended to be underestimated by SPARROW. TP yields at four sites in the Mississippi Delta were underestimated by the 2012 SPARROW-predicted model.
Results of select sites from our study were also compared to other published load estimates from an earlier time period to evaluate possible trends. Comparison of TN yields at four sites and TP yields at three sites from the study-derived estimates to estimates made from data spanning 1993–2004 showed decreasing TN yields at all four sites and decreasing TP yields at two of three sites, with increasing yields of TP at the Yazoo River lower site. Also, a third comparison of the TN and TP yields of the Yazoo River lower site of this study to estimates made from data spanning 1996–97 showed decreasing TN yields but similar TP yields. This suggests that TN yields may have decreased over the last 20–30 years, but TP yields remain constant or are increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235003","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality","usgsCitation":"Hicks, M.B., Crain, A.S., and Segrest, N.G., 2023, Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18: U.S. Geological Survey Scientific Investigations Report 2023–5003, 77 p., https://doi.org/10.3133/sir20235003.","productDescription":"Report: x, 77 p.; Data Release; Dataset","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-130707","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":413300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VV2U70","text":"USGS data release","linkHelpText":"Datasets of streamflow, nutrient concentrations, loads 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Coupled paleomagnetic and geochronologic data derived from mafic dykes provide valuable records of continental movement. To reconstruct the Proterozoic paleogeographic history of Peninsular India, we report paleomagnetic directions and U-Pb zircon ages from twenty-nine mafic dykes in the Eastern Dharwar Craton near Hyderabad. Paleomagnetic analysis yielded clusters of directional data that correspond to dyke swarms at 2.37 Ga, 2.22 Ga, 2.08 Ga, 1.89–1.86 Ga, 1.79 Ga, and a previously undated dual polarity magnetization. We report new positive baked contact tests for the 2.08 Ga swarm and the 1.89–1.86 Ga swarm(s), and a new inverse baked contact test for the 2.08 Ga swarm. Our results promote the 2.08 Ga Dharwar Craton paleomagnetic pole (43.1° N, 184.5° E; A95 = 4.3°) to a reliability score of R = 7 and suggest a position for the Dharwar Craton at 1.79 Ga based on a virtual geomagnetic pole (VGP) at 33.0° N, 347.5° E (a95 = 16.9°, k = 221, N = 2). The new VGP for the Dharwar Craton provides support for the union of the Dharwar, Singhbhum, and Bastar Cratons in the Southern India Block by at least 1.79 Ga. Combined new and published northeast-southwest moderate-steep dual polarity directions from Dharwar Craton dykes define a new paleomagnetic pole at 20.6° N, 233.1° E (A95 = 9.2°, N = 18; R = 5). Two dykes from this group yielded 1.05–1.01 Ga 207Pb/206Pb zircon ages and this range is taken as the age of the new paleomagnetic pole. A comparison of the previously published poles with our new 1.05–1.01 Ga pole shows India shifting from equatorial to higher (southerly) latitudes from 1.08 Ga to 1.01 Ga as a component of Rodinia.
Land management priorities and decisions may result in population declines for non-target wildlife species. In the western United States, large-scale removal of conifer from sagebrush ecosystems (Artemisia spp.) is occurring to recover greater sage-grouse (Centrocercus urophasianus) populations and may result in pinyon jay (Gymnorhinus cyanocephalus) habitat loss. Jay populations have experienced long-term declines, due to unknown causes, resulting in a recent petition for listing under the Endangered Species Act of 1973. We developed a Bayesian hierarchical model of jay abundance, using 13 years of point count data (2008–2020) collected across the western United States, to estimate regional population trends, model habitat requirements, assess conifer removal effects on jays, and generate hypotheses regarding jay population declines. Our model included climate and landcover covariates and regional trends in pinyon jay density. We applied our modeled habitat relationships to map predicted pinyon jay density, given 2008 and 2020 resource conditions, and map density changes from 2008 to 2020. Our results indicate pinyon jay populations are declining within Bird Conservation Region 16. Jay density was positively associated with sagebrush cover, Palmer Drought Severity Index, and pinyon-juniper cover. Conversely, jay populations were negatively associated with Normalized Difference Vegetation Index (NDVI). We found higher pinyon jay densities within locations possessing both sagebrush and pinyon-juniper cover; conditions characteristic of phase I and II conifer encroachment which are preferentially targeted for conifer removal to restore sagebrush communities. Conifer removal, if conducted at locations with high pinyon jay densities, is therefore likely to negatively affect jay abundance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.109959","usgsCitation":"Van Lanen, N.J., Monroe, A., and Aldridge, C.L., 2023, A hidden cost of single species management: Habitat-relationships reveal potential negative effects of conifer removal on a non-target species: Biological Conservation, v. 280, 109959, 10 p., https://doi.org/10.1016/j.biocon.2023.109959.","productDescription":"109959, 10 p.","ipdsId":"IP-138764","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444374,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2023.109959","text":"Publisher Index Page"},{"id":435435,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NIG4UW","text":"USGS data release","linkHelpText":"Predicted Pinyon Jay (Gymnorhinus cyanocephalus) densities across the western United States, 2008-2020"},{"id":413855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Kansas, Montana, Nebraska, Nevada, North Dakota, South Dakota, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.41395617933225,\n 35.71066116858752\n ],\n [\n -111.67021215517931,\n 35.905346739347536\n ],\n [\n -108.77088674515161,\n 36.96389200169858\n ],\n [\n -101.86862285960521,\n 37.125401887525115\n ],\n [\n -101.51491491061196,\n 37.52404629916971\n ],\n [\n -101.90911987227537,\n 41.291485987900245\n ],\n [\n -103.26229497324951,\n 42.46761717574853\n ],\n [\n -101.97643578414241,\n 43.25420173811844\n ],\n [\n -102.55180019651098,\n 49.041860323717856\n ],\n [\n -117.14265326477982,\n 49.014048521848\n ],\n [\n -116.95141239209565,\n 46.12283190981066\n ],\n [\n -116.941510874859,\n 40.99815769875613\n ],\n [\n -120.0102098874165,\n 38.93737098892768\n ],\n [\n -120.02540765639762,\n 38.10974034171085\n ],\n [\n -115.41395617933225,\n 35.71066116858752\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"280","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":865861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241942,"text":"70241942 - 2023 - Changes in mangrove blue carbon under elevated atmospheric CO2","interactions":[],"lastModifiedDate":"2023-03-31T13:41:21.573249","indexId":"70241942","displayToPublicDate":"2023-02-23T08:38:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5075,"text":"Ecosystem Health and Sustainability","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Changes in mangrove blue carbon under elevated atmospheric CO2","title":"Changes in mangrove blue carbon under elevated atmospheric CO2","docAbstract":"While there is consensus that blue carbon ecosystems, such as mangroves, have an important role in mitigating some aspects of global climate change, little is known about mangrove carbon cycling under elevated atmospheric CO2 concentrations (eCO2). Here, we review studies in order to identify pathways for how eCO2 might influence mangrove ecosystem carbon cycling. In general, eCO2 alters plant productivity, species community composition, carbon fluxes, and carbon deposition in ways that enhance mangrove carbon storage with eCO2. As a result, a negative feedback to climate change exists whereby eCO2 adds to mangrove’s ability to sequester additional carbon, which in turn reduces the rate by which CO2 builds. Furthermore, eCO2 affects warming and sea-level rise (SLR) through alternate pathways, which coinfluence the mangrove response in both antagonistic (i.e., warming = greater carbon loss to decomposition) and synergistic (i.e., SLR = greater soil carbon burial) ways. eCO2 is projected to become a more prominent driver in the future before reaching a steady state. However, given the complexity of the interactions of biological and environmental factors with eCO2, long-term field observations and in situ simulation experiments can help to better understand the mechanisms for proper model initialization to predict future changes in mangrove carbon sequestration.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.34133/ehs.0033","usgsCitation":"Gu, X., Qiao, P., Krauss, K., Lovelock, C.E., Adams, J.B., Chapman, S.K., Jennerjahn, T.C., Lin, Q., and Chen, L., 2023, Changes in mangrove blue carbon under elevated atmospheric CO2: Ecosystem Health and Sustainability, v. 9, 0033, 12 p., https://doi.org/10.34133/ehs.0033.","productDescription":"0033, 12 p.","ipdsId":"IP-146217","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.34133/ehs.0033","text":"Publisher Index Page"},{"id":415007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gu, Xiaoxuan","contributorId":296950,"corporation":false,"usgs":false,"family":"Gu","given":"Xiaoxuan","email":"","affiliations":[{"id":64251,"text":"College of the Environment and Ecology, Xiamen University","active":true,"usgs":false}],"preferred":false,"id":868298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qiao, Peiyang","contributorId":303861,"corporation":false,"usgs":false,"family":"Qiao","given":"Peiyang","email":"","affiliations":[{"id":47617,"text":"Xiamen University, China","active":true,"usgs":false}],"preferred":false,"id":868299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":222378,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lovelock, Catherine E.","contributorId":215562,"corporation":false,"usgs":false,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":39280,"text":"School of Biological Sciences, The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":868301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Janine B.","contributorId":303863,"corporation":false,"usgs":false,"family":"Adams","given":"Janine","email":"","middleInitial":"B.","affiliations":[{"id":65919,"text":"Nelson Mandela University (South Africa)","active":true,"usgs":false}],"preferred":false,"id":868302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Samantha K.","contributorId":303864,"corporation":false,"usgs":false,"family":"Chapman","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":868303,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jennerjahn, Tim C.","contributorId":303865,"corporation":false,"usgs":false,"family":"Jennerjahn","given":"Tim","email":"","middleInitial":"C.","affiliations":[{"id":65921,"text":"Leibniz Centre for Tropical Marine Research (ZMT), Germany","active":true,"usgs":false}],"preferred":false,"id":868304,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lin, Qiulian","contributorId":294476,"corporation":false,"usgs":false,"family":"Lin","given":"Qiulian","email":"","affiliations":[{"id":63579,"text":"Xiamen University","active":true,"usgs":false}],"preferred":false,"id":868305,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chen, Luzhen","contributorId":194706,"corporation":false,"usgs":false,"family":"Chen","given":"Luzhen","email":"","affiliations":[],"preferred":false,"id":868306,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241240,"text":"70241240 - 2023 - Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints","interactions":[],"lastModifiedDate":"2023-05-25T15:52:09.064418","indexId":"70241240","displayToPublicDate":"2023-02-23T08:30:12","publicationYear":"2023","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":"Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints","docAbstract":"We determine optimal on‐fault earthquake spatial distributions using a combinatorial method that minimizes the long‐term cumulative stress resolved on the fault. An integer‐programming framework was previously developed to determine the optimal arrangement of a millennia‐scale earthquake sample that minimizes the misfit to a target slip rate determined from geodetic data. The resulting cumulative stress from just slip‐rate optimization, however, can greatly exceed fault strength estimates. Therefore, we add an objective function that minimizes cumulative stress and broad stress constraints to limit the solution space. We find that there is a trade‐off in the two objectives: minimizing the cumulative stress on a fault within fault strength limits concentrates earthquakes in specific areas of the fault and results in excursions from the target slip rate. Both slip‐rate and stress objectives can be combined in either a weighted or lexicographic (hierarchical) method. Using a combination of objectives, we demonstrate that a Gutenberg–Richter sample of earthquakes can be arranged on a constant slip‐rate finite fault with minimal stress and slip‐rate residuals. We apply this method to determine the optimal arrangement of earthquakes on the variable slip‐rate Nankai megathrust over 5000 yr. The sharp decrease in slip rate at the Tokai section of the fault results in surplus cumulative stress under all scenarios. Using stress optimization alone restricts this stress surplus to the northeast end of the fault at the expense of decreasing the slip rate away from the target slip rate at the southwest end of the fault. A combination of both slip‐rate and stress objectives provides an adequate fit to the data, although alternate model formulations for the fault are needed at the Tokai section to explain persistent excess cumulative stress. In general, incorporating stress objectives and constraints into the integer‐programming framework adds an important aspect of fault physics to the resulting earthquake rupture forecasts.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220175","usgsCitation":"Geist, E.L., and Parsons, T.E., 2023, Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1025-1038, https://doi.org/10.1785/0120220175.","productDescription":"14 p.","startPage":"1025","endPage":"1038","ipdsId":"IP-144689","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":414280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":15543,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":866627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":866628,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241040,"text":"70241040 - 2023 - Incorporation of real-time earthquake magnitudes estimated via peak ground displacement scaling in the ShakeAlert Earthquake Early Warning system","interactions":[],"lastModifiedDate":"2023-05-25T15:50:57.475532","indexId":"70241040","displayToPublicDate":"2023-02-23T07:19:56","publicationYear":"2023","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":"Incorporation of real-time earthquake magnitudes estimated via peak ground displacement scaling in the ShakeAlert Earthquake Early Warning system","docAbstract":"The United States earthquake early warning (EEW) system, ShakeAlert®, currently employs two algorithms based on seismic data alone to characterize the earthquake source, reporting the weighted average of their magnitude estimates. Nonsaturating magnitude estimates derived in real time from Global Navigation Satellite System (GNSS) data using peak ground displacement (PGD) scaling relationships offer complementary information with the potential to improve EEW reliability for large earthquakes. We have adapted a method that estimates magnitude from PGD (Crowell et al., 2016) for possible production use by ShakeAlert. To evaluate the potential contribution of the modified algorithm, we installed it on the ShakeAlert development system for real‐time operation and for retrospective analyses using a suite of GNSS data that we compiled. Because of the colored noise structure of typical real‐time GNSS positions, observed PGD values drift over time periods relevant to EEW. To mitigate this effect, we implemented logic within the modified algorithm to control when it issues initial and updated PGD‐derived magnitude estimates (
The U.S. Geological Survey, in cooperation with the Macoupin County Soil and Water Conservation District and the American Farmland Trust, undertook a monitoring effort from 2017 to 2021 in the upper Macoupin Creek watershed. The monitoring effort was to determine and characterize nitrogen, phosphorus, and suspended-sediment concentrations, loads, and yields for a 566.7 square kilometer area of the Macoupin Creek watershed at two locations on upper Macoupin Creek bracketing a segment of the watershed where increased implementation of conservation land-use practices was planned. Two monitoring stations were established, consisting of an upstream site (Macoupin Creek at Highway 108 near Carlinville, Illinois; U.S. Geological Survey streamgage 05586647) and a downstream site (Macoupin Creek at Highway 111 near Summerville, Ill.; U.S. Geological Survey streamgage 05586745). Data collected at these stations included continuous stream discharge and periodic samples for nutrients and suspended sediment. A Weighted Regressions on Time, Discharge, and Season–Kalman model was implemented to estimate daily concentrations for nitrate plus nitrite, total phosphorus, and suspended sediment for both monitoring stations. These daily concentrations were used in conjunction with the continuous stream discharge data to derive estimates of constituent flux, loads, and yields.
During the study period, the study area subbasin of the upper Macoupin Creek watershed reduced downstream nitrate and total phosphorus cummulative yields by approximately 54 and 21 percent, respectively; however, the cummulative yield of suspended sediment increased by approximately 10 percent from inputs within the study area. These data indicate that nitrate and phosphorus transport is greater from the upstream subbasin and being diluted in the combined subbasin by lower transport from the study area, whereas suspended sediment is being contributed from the study area reach, presumably through surface runoff and streambank and streambed erosion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225131","collaboration":"Prepared in cooperation with the Macoupin County Soil and Water Conservation District and American Farmland Trust","usgsCitation":"Garcia, L.A., Terrio, P.J., and Manaster, A.E., 2023, Nutrient and suspended-sediment concentrations, loads, and yields in upper Macoupin Creek, Illinois, 2017–21: U.S. Geological Survey Scientific Investigations Report 2022–5131, 17 p., https://doi.org/10.3133/sir20225131.","productDescription":"Report: vii, 17 p.; Data Release; Dataset","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-144304","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":413286,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5131/images"},{"id":413285,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5131/sir20225131.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":413284,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5131/sir20225131.pdf","text":"Report","size":"2.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5131"},{"id":499487,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114379.htm","linkFileType":{"id":5,"text":"html"}},{"id":413345,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225131/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":413289,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":413283,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5131/coverthb.jpg"},{"id":413288,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95IC7QS","text":"USGS data release","linkHelpText":"Nutrient and sediment concentrations, loads, and yields in the Upper Macoupin Creek watershed, water years 2018–2021"}],"country":"United States","state":"Illinois","otherGeospatial":"Upper Macoupin Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.666,\n 39.5\n ],\n [\n -90.666,\n 39\n ],\n [\n -89.5,\n 39\n ],\n [\n -89.5,\n 39.5\n ],\n [\n -90.666,\n 39.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries.
Understanding plant community response to environmental change is a crucial aspect of biological conservation and restoration, but species-based approaches are limited in that they do not reveal the underlying mechanisms driving vegetation dynamics. An understanding of trait-environment relationships is particularly important in the case of invasive species which may alter abiotic conditions and available resources. This study is the first to measure the functional response of riparian plant communities to biocontrol of an invasive species. We focused on an invasive shrub, Tamarix (saltcedar), that is defoliated by a beetle that was released by the US Department of Agriculture along the Upper Colorado River (southwestern United States). We calculated community weighted means and functional dispersion of individual traits, multivariate functional dispersion and species diversity. We used linear mixed effect models (LME) to compare these metrics at paired vegetation patches dominated and not dominated by Tamarix during cycles of defoliation and refoliation over eight years. We found that community-weighted average trait values, species diversity and functional dispersion changed little in response to defoliation, and instead seemed to be responding to fluctuations in yearly precipitation. Average height and seed weight were greater in Tamarix-dominated patches relative to control patches. Functional dispersion followed a similar trajectory to species diversity, but was a more sensitive indicator of plant community change. We showed that riparian vegetation can be resilient to Tamarix biocontrol, and that defoliation might not necessarily always lead to substantial changes in ecosystem function.