Lead mining in the Southeast Missouri Lead Mining District began in the 1700s and continued for nearly 300 years; the waste piles associated with smelting, mining, and milling of lead ores have released metal residues that have contaminated soil and water in the region. Previous studies in the district have indicated potential harm to wildlife, including birds, because of elevated lead concentrations associated with mining. Exposure to soil-borne lead was correlated with elevated lead concentrations in tissues, inhibition of δ-aminolevulinic acid dehydratase (δALAD), and renal lesions in birds foraging on ground-dwelling invertebrates at contaminated sites (compared to reference sites) in the Southeast Missouri Lead Mining District.
This study assessed reproductive outcomes for songbirds exposed to soil-borne lead in the district, examined the relation between lead concentrations in soils and in tissues of ground-feeding birds and prey species, and compared the results to literature-based toxicity thresholds for lead that are associated with negative effects in birds. Three lead-contaminated sites and three reference sites (with background concentrations of lead and no known mining inputs) were compared in two ways: individually to all other sites or by site type. Additional effects of lead exposure were evaluated by examining concentrations of biomarkers (oxidative stress, lipid peroxidation, and deoxyribonucleic acid damage) in liver tissues, δALAD inhibition, and renal and hepatic microscopic lesions in birds from lead-contaminated and reference sites.
Lead concentrations in soil were site-dependent and were also generally heterogeneous within the lead-contaminated sites. Between 17 and 74 percent of all soil samples at contaminated sites had lead concentrations that exceeded a threshold (1,000 milligrams per kilogram [mg/kg] lead in soil) previously associated with adverse physiological effects in birds in the Southeast Missouri Lead Mining District. Lead concentrations in mixed invertebrates from lead-contaminated sites (282 to 2,230 mg/kg dry weight [dw]) indicated that consuming soil-dwelling prey species is a potential exposure pathway for adult birds and their broods. At lead-contaminated sites, lead concentrations in 40.5 percent of blood samples (adults and their broods) were within a subclinical effects range (0.9 to 2.3 mg/kg dw), and 18.7 percent of samples had lead concentrations that exceeded clinical effects criteria (greater than 2.3 mg/kg dw). In contrast, only 2.6 percent of blood samples from reference sites were within the subclinical effects range for lead; all other blood samples from the reference sites had lead concentrations representative of background concentrations (less than 0.9 mg/kg dw). Subclinical and clinical threshold exceedances for lead concentrations in livers and kidneys were similarly more prevalent at the contaminated sites compared to the reference sites.
Lead concentrations in blood were positively correlated with lead concentrations in soil, livers, and kidneys. Lead concentrations in blood were negatively correlated with δALAD activity; greater than 50 percent of the birds collected at lead-contaminated sites exhibited injury via greater than 50 percent inhibition of δALAD in blood compared to birds at reference sites. Birds with elevated lead concentrations in tissues also exhibited enhanced oxidative stress. Microscopic lesions in the livers and kidneys of birds had similar rates of occurrence at the contaminated and reference sites, and lesion prevalence could not be directly linked to lead exposure. Reproductive success was monitored at 585 nests, and 3 out of 5 species had reduced nest success associated with elevated lead concentrations in soil; habitat measures did not help explain nest success. Reduced nest success may have resulted from greater nest predation resulting from neurological and behavioral effects of lead exposure. Ultimately, these lines of evidence indicate that bird health and reproduction have been negatively affected by exposure to lead-contaminated soils in the Southeast Missouri Lead Mining District.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235032","usgsCitation":"Brasso, R., Cleveland, D., Thompson, F.R., III, Mosby, D.E., Hixson, K., Roach, M., Rattner, B.A., Karouna-Renier, N.K., and Lankton, J.S., 2023, Effects of lead exposure on birds breeding in the Southeast Missouri Lead Mining District: U.S. Geological Survey Scientific Investigations Report 2023–5032, 127 p., https://doi.org/10.3133/sir20235032.","productDescription":"Report: xi, 127 p.; Data Release","numberOfPages":"144","onlineOnly":"Y","ipdsId":"IP-134142","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419666,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RV1D60","text":"USGS data release","linkHelpText":"Breeding songbird tissue analyses and metal concentrations in tissues, soil and invertebrates collected near nesting sites within the Southeast Missouri Lead Mining District, 2016–19"},{"id":419662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5032/coverthb.jpg"},{"id":419663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5032/sir20235032.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5032"},{"id":419664,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5032/sir20235032.XML"},{"id":500894,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115182.htm","linkFileType":{"id":5,"text":"html"}},{"id":419755,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235032/full"},{"id":419665,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5032/images"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.66252506041675,\n 39.51237957711933\n ],\n [\n -92.66252506041675,\n 36.74875309799043\n ],\n [\n -89.01662840507522,\n 36.74875309799043\n ],\n [\n -89.01662840507522,\n 39.51237957711933\n ],\n [\n -92.66252506041675,\n 39.51237957711933\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
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Invasive carp (Hypophthalmichthys nobilis [Bighead Carp], Mylopharyngodon piceus [Black Carp], Ctenopharyngodon idella [Grass Carp], and H. molitrix [Silver Carp]) continue to spread in the United States and deterrents at river navigation locks are one emerging control strategy for slowing the spread. High-head navigation dams on large rivers serve as impediments to the upstream spread of these populations. One possible control technique is using a multimodal deterrent that utilizes a combination of lights, sound, and air bubbles to guide fish away from a location. Laboratory tests and small-scale field deployments of similar multi-stimuli deterrents have demonstrated potential for deterring invasive carp. These earlier studies led to the deployment and initiation of a field study of a multimodal deterrent in 2019 at the downstream lock approach at Barkley Lock and Dam on the Cumberland River near Grand Rivers, Kentucky. We are using two types of telemetry systems to evaluate how the multimodal deterrent affects movement and behavior of Silver Carp, Grass Carp, and four native species: Aplodinotus grunniens (Freshwater Drum), Polyodon spathula (Paddlefish), Ictiobus bubalus (Smallmouth Buffalo), and Acipenser fulvescens (Lake Sturgeon). We are evaluating fish movements in response to the multimodal deterrent by using a study design that cycles between 1 week with the multimodal deterrent on and 1 week with it off. This weekly cycling provides baseline comparisons across variable environmental conditions. When the multimodal deterrent is on, the number of Silver Carp completing upstream passage through Barkley Lock is reduced by about one-half. This study that began in 2019 can provide insights into the effectiveness of a multimodal deterrent for limiting upstream movement of invasive carp or effects on native fishes through strategic navigation locks on large rivers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231051","usgsCitation":"Fritts, A., Gibson-Reinemer, D., Stanton, J., Mosel, K., Brey, M., Vallazza, J., Appel, D., Faulkner, J., Tompkins, J., Castro-Santos, T., Sholtis, M., Turnpenny, A., Sorensen, P., and Simmonds, R., 2023, Multimodal invasive carp deterrent study at Barkley Lock and Dam—Status update through 2022: U.S. Geological Survey Open-File Report 2023–1051, 7 p., https://doi.org/10.3133/ofr20231051.","productDescription":"Report: vi, 7 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-152060","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419719,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231051/full"},{"id":419570,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKGK94","text":"USGS data release","linkHelpText":"Data release for multimodal invasive carp deterrent study at Barkley Lock and Dam—Status update through 2022"},{"id":419569,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1051/images/"},{"id":419568,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1051/ofr20231051.XML"},{"id":419567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1051/ofr20231051.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1051"},{"id":419566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1051/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Barkley Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.06563176427669,\n 36.91369487844129\n ],\n [\n -88.06563176427669,\n 36.769782774646316\n ],\n [\n -87.88100333801793,\n 36.769782774646316\n ],\n [\n -87.88100333801793,\n 36.91369487844129\n ],\n [\n -88.06563176427669,\n 36.91369487844129\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
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In response to the New England Governor and Eastern Canadian Premier 2017 Climate Change Action Plan recommendation to “manage blue carbon resources to preserve and enhance their existing carbon reservoirs,” the U.S. Environmental Protection Agency (EPA) convened a New England Blue Carbon Inventory Workgroup, comprised of a variety of federal, state, academic, and non-profit organizations to develop an inventory of blue carbon stocks from Maine to Long Island, New York. The Workgroup focused its inventory efforts on salt marshes and eelgrass meadows, leveraging existing habitat maps for geographic data. Existing data for soil organic carbon stocks were then used to calculate blue carbon stock estimates. For visual display purposes, sediment carbon heat maps were developed to highlight areas of greatest carbon accumulation. The habitat distribution and sediment carbon heat maps can be accessed on the Northeast Ocean Data Portal (www.northeastoceandata.org/eelgrass) which is a public source of expert-reviewed, interactive maps and data on the ocean ecosystem, economy, and culture of the northeastern United States and can be used to facilitate decision making by government agencies, tribal nations, businesses, non-governmental organizations (NGOs), academic institutions, and individuals. Based on available data and Workgroup calculations, the target geographic area has an estimated 218,222 acres of eelgrass meadows, salt marsh and saline Phragmites, which are estimated to provide a reservoir of 7,523,568 megagrams of blue carbon, or the equivalent to the annual carbon emissions from over 5,944,024 passenger vehicles. Due to data limitations, the carbon stock estimate represents a mere fraction of the actual quantity of accumulated carbon in these habitats. The findings from the Workgroup’s efforts and the resulting map products can help inform land and coastal management policies, fisheries management, and climate change mitigation practices. Further refinements and expansion of data are needed, including more detailed habitat maps, deeper soil core data for soil organic carbon content, and inclusion of more marine flora into calculations.
","language":"English","publisher":"Environmental Protection Agency","usgsCitation":"Colarusso, P., Libohova, Z., Shumchenia, E., Eagle, M.J., Christian, M., Vincent, R., and Johnson, B., 2023, The blue carbon reservoirs from Maine to Long Island, NY, 31 p.","productDescription":"31 p.","ipdsId":"IP-146585","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":421823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421698,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.northeastoceandata.org/files/metadata/Themes/Habitat/EPABlueCarbonReport.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.61511760727777,\n 45.22754478031186\n ],\n [\n -69.46772958832273,\n 44.45391256542621\n ],\n [\n -70.41808310369574,\n 44.0249641018155\n ],\n [\n -71.18235185260586,\n 43.43128866248264\n ],\n [\n -71.38130070335747,\n 41.926291445574776\n ],\n [\n -72.88727252479508,\n 41.59585766120409\n ],\n [\n -74.23639888538038,\n 41.298612606793824\n ],\n [\n -74.24300744809933,\n 40.29712569967327\n ],\n [\n -72.41592450378879,\n 40.62059607271837\n ],\n [\n -69.64618289257739,\n 41.250157632666884\n ],\n [\n -69.77860402969628,\n 42.24177812265074\n ],\n [\n -70.24692368777268,\n 42.525861412000836\n ],\n [\n -70.24992804020442,\n 43.441149255128096\n ],\n [\n -67.02233592375215,\n 44.65254677637256\n ],\n [\n -67.61511760727777,\n 45.22754478031186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Colarusso, Philip D.","contributorId":218700,"corporation":false,"usgs":false,"family":"Colarusso","given":"Philip D.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":885521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Libohova, Zamir","contributorId":330648,"corporation":false,"usgs":false,"family":"Libohova","given":"Zamir","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shumchenia, Emily","contributorId":330649,"corporation":false,"usgs":false,"family":"Shumchenia","given":"Emily","email":"","affiliations":[{"id":78947,"text":"Northeast Regional Ocean Council","active":true,"usgs":false}],"preferred":false,"id":885523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christian, Megan","contributorId":330651,"corporation":false,"usgs":false,"family":"Christian","given":"Megan","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vincent, Robert","contributorId":330652,"corporation":false,"usgs":false,"family":"Vincent","given":"Robert","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":885526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Beverly","contributorId":330653,"corporation":false,"usgs":false,"family":"Johnson","given":"Beverly","email":"","affiliations":[{"id":33413,"text":"Bates College","active":true,"usgs":false}],"preferred":false,"id":885527,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247452,"text":"70247452 - 2023 - Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources","interactions":[],"lastModifiedDate":"2023-08-08T11:41:46.30057","indexId":"70247452","displayToPublicDate":"2023-08-06T06:39:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources","docAbstract":"Geostatistics is the most commonly used probabilistic approach for modeling earth systems, including quality parameters of various geoenergy resources. In geostatistics, estimates, either on a point or block support, are generated as a spatially-weighted average of surrounding samples. The optimal weights are determined through the stationary variogram model which accounts for the spatial structure of the samples. Recently, efficient modeling workflows using various machine learning algorithms (MLAs) have been expanded to the spatial context for modeling geological heterogeneity. The flexible use of MLAs as a spatial estimation tool stems mainly from the fact that unlike kriging, they do not require any variogram, nor do they depend strongly on a prior stationarity assumption (i.e., second order stationarity). This study evaluates the performance of two MLAs (ensemble super learner and elliptical radial basis neural network), ordinary kriging, and hybrid spatial modeling approaches using ordinary intrinsic collocated cokriging. The aforementioned modeling techniques are compared for estimating resources for four coal variables (wash yield, ash yield, calorific value and thickness) as an example. The results suggest that MLAs, when implemented alone, do not outperform ordinary kriging, but the estimation accuracy of the final model, measured by the root mean squared error tends to subtly improve (
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2023.104328","usgsCitation":"Erdogan Erten, G., Erten, O., Karacan, C.O., Boisvert, J., and Deutsch, C.V., 2023, Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources: International Journal of Coal Geology, v. 276, 104328, 16 p., https://doi.org/10.1016/j.coal.2023.104328.","productDescription":"104328, 16 p.","ipdsId":"IP-149602","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":419585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.33693408888138,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.563642070051216\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erdogan Erten, Gamze","contributorId":317909,"corporation":false,"usgs":false,"family":"Erdogan Erten","given":"Gamze","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erten, Oktay","contributorId":300145,"corporation":false,"usgs":false,"family":"Erten","given":"Oktay","email":"","affiliations":[],"preferred":false,"id":879701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":879702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boisvert, Jeff","contributorId":317910,"corporation":false,"usgs":false,"family":"Boisvert","given":"Jeff","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deutsch, Clayton V.","contributorId":317911,"corporation":false,"usgs":false,"family":"Deutsch","given":"Clayton","email":"","middleInitial":"V.","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879704,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247418,"text":"sir20235093 - 2023 - Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico","interactions":[],"lastModifiedDate":"2026-03-12T21:16:59.60233","indexId":"sir20235093","displayToPublicDate":"2023-08-04T08:46:47","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5093","displayTitle":"Analysis of High-Resolution Single Channel Seismic Data for Use in Sediment Resource Evaluation, Eastern Texas and Western Louisiana Continental Shelf, Gulf of Mexico","title":"Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico","docAbstract":"Shallow subsurface geologic data recorded as high-resolution seismic profiles are used to interpret the geology of coastal and marine systems. These data were originally recorded on paper rolls that are stored in geophysical archives. Data collection has since converted to entirely digital formats, yet the analog data are still useful for geologic interpretation. This report describes the process of recovering analog copies of seismic profiles from physical archives, electronically scanning, and converting them to industry-standard digital format. The recovered data are also reviewed and assessed for potential sediment resources. The data recovered in this study were collected from the Gulf of Mexico continental shelf offshore of East Texas and West Louisiana. The project is a collaborative study between the U.S. Geological Survey and the Bureau of Ocean Energy Management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235093","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management","programNote":"Coastal and Marine Hazards and Resources Program","usgsCitation":"Flocks, J., Forde, A., and Bosse, S., 2023, Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5093, 18 p., https://doi.org/10.3133/sir20235093.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-144157","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":419532,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235093/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5093 HTML"},{"id":419531,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5093/sir20235093.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5093 XML"},{"id":419530,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5093/sir20235093.pdf","size":"4.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5093 pdf"},{"id":419529,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5093/images"},{"id":419528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5093/coverthb.jpg"},{"id":419533,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFUAN3","text":"USGS data release—Archive of digitized analog boomer seismic reflection data collected from the northern Gulf of Mexico—Intersea 1980"},{"id":501060,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115126.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.86243995309941,\n 29.6\n ],\n [\n -94.1,\n 29.6\n ],\n [\n -94.1,\n 28.4\n ],\n [\n -91.86243995309941,\n 28.4\n ],\n [\n -91.86243995309941,\n 29.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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St. Petersburg, FL 33701
Drought and intensive land use can interact as stressors on riparian vegetation, especially along rivers flowing through seasonally dry landscapes. Knowledge of past riparian vegetation response to drought and land use change can provide land managers with a better understanding of changes induced by upstream management actions, climate change, and chronic stressors. To investigate the response of riparian vegetation productivity to drought and land use, we developed a 21-year time series (2000–2020) of growing season vegetation dynamics using near-infrared reflectance of vegetation (NIRV) derived from satellite data across 30 watershed subbasins that drain into the San Francisco Bay Delta in central California, USA. We observed a strong response of riparian vegetation to drought, but rapid recovery and very few long-term declines in productivity. At a local level, vegetation communities' response to drought and post-drought productivity dynamics were highly variable across biophysical settings and land use gradients. Most of the riparian areas with long-term declines in NIRV were located in the lower elevation Coast Range on the western side of the study area where there is little to no water engineering or agricultural irrigation runoff to subsidize riparian vegetation. Riparian areas with the greatest long-term increase were along rivers draining the higher elevation Sierra Nevada range to the east. Our results suggest that river systems with a high proportion of water originating as snowmelt may be more buffered against long-term drought-driven declines in productivity than those dependent exclusively on winter rainfall. The long-term increase in NIRV in the vast majority of riparian areas within our study area may also have been driven in part by increasing atmospheric CO2 concentrations, which have been shown to increase plant water use efficiency.
Invasive annual grasses can promote ecosystem state changes and habitat loss in the American Southwest. Non-native annual grasses such as Bromus spp. and Schismus spp. have invaded the Mojave Desert and degraded habitat through increased fire occurrence, severity, and shifting plant community composition. Thus, it is important to identify and characterize the areas where persistent invasion has occurred, identifying where subsequent habitat degradation has increased. Previous plot and landscape-scale analyses have revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these studies have been limited in spatial and temporal scales. Here we use Landsat imagery validated using an extensive network of plot data to map persistent and productive populations of invasive annual grass, called hot spots, across the entire Mojave Desert ecoregion over 12 years (2009–2020). We also identify important variables for predicting hot spot distribution using the Random Forest algorithm and identifying the most invaded subregions. We identified hot spots in over 5% of the Mojave Desert mostly on the western and eastern edges of the ecoregion, and invasive grasses were detected in over 90% of the Mojave Desert at least once in that time. Across the entire Mojave Desert, our results indicate that soil texture, aspect, winter precipitation, and elevation are the highest-ranking predictive variables of invasive grass hot spots, while anthropogenic variables contributed the least to the accuracy of the predictive model. The total area covered by hot spots varied significantly among subregions of the Mojave Desert. We found that anthropogenic variables became more important in explaining invasive annual establishment and persistence as spatial scale was reduced to the subregional level. Our findings have important implications for informing where land management actions can prioritize reducing invasive annual persistence and promoting restoration efforts.
This pamphlet and accompanying geologic and geophysical maps are the products of cooperative efforts by the California Geological Survey (CGS) and United States Geological Survey (USGS) to compile a comprehensive, digital representation of the bedrock geology, Quaternary surficial deposits, and potential-field anomalies within the boundaries of the Stockton 30’ × 60’ quadrangle. The Stockton 30’ × 60’ quadrangle covers approximately 4,890 km2 of Contra Costa, Alameda, San Joaquin, and Stanislaus Counties, California. From the rugged hillsides of the northern Diablo Range in the west to the San Joaquin Valley in the east, the map extends roughly 88 km across growing suburban communities of the eastern San Francisco Bay Area and Livermore Valley, grass-covered ranchlands along eastern slopes of the Diablo Range, and into the low farmlands of the San Joaquin Valley and Sacramento-San Joaquin River Delta (Figure 1). The elevation ranges from near sea level in the Delta to 1,173 meters on Mt. Diablo, the most prominent peak of the San Francisco Bay region.
","language":"English","publisher":"California Geological Survey","usgsCitation":"Delattre, M.P., Graymer, R.W., Langenheim, V., Knudsen, K.L., Dawson, T.E., Brabb, E., Wentworth, C.M., and Raymond, L.A., 2023, Geologic and geophysical maps of the Stockton 30’ × 60’ quadrangle, California: Preliminary Regional Geologic Maps, Report: iv, 73 p.; 1 Plate: 60.00 x 35.00 inches.","productDescription":"Report: iv, 73 p.; 1 Plate: 60.00 x 35.00 inches","ipdsId":"IP-114090","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":420909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":420880,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.conservation.ca.gov/cgs/rgm"}],"country":"United States","state":"California","otherGeospatial":"Stockton quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122,\n 38\n ],\n [\n -122,\n 37.5\n ],\n [\n -121,\n 37.5\n ],\n [\n -121,\n 38\n ],\n [\n -122,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Delattre, M. P.","contributorId":329772,"corporation":false,"usgs":false,"family":"Delattre","given":"M.","email":"","middleInitial":"P.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":883270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graymer, Russell W. 0000-0003-4910-5682","orcid":"https://orcid.org/0000-0003-4910-5682","contributorId":329773,"corporation":false,"usgs":true,"family":"Graymer","given":"Russell","email":"","middleInitial":"W.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":883271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":217101,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":883272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knudsen, Keith L. 0000-0003-2826-5812 kknudsen@usgs.gov","orcid":"https://orcid.org/0000-0003-2826-5812","contributorId":3758,"corporation":false,"usgs":true,"family":"Knudsen","given":"Keith","email":"kknudsen@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":883273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dawson, T. E. 0000-0002-2554-8449","orcid":"https://orcid.org/0000-0002-2554-8449","contributorId":329778,"corporation":false,"usgs":false,"family":"Dawson","given":"T.","email":"","middleInitial":"E.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":883274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brabb, Earl E.","contributorId":329781,"corporation":false,"usgs":false,"family":"Brabb","given":"Earl E.","affiliations":[{"id":78715,"text":"deceased (formerly U.S. Geological Survey)","active":true,"usgs":false}],"preferred":false,"id":883275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wentworth, Carl M. 0000-0003-2569-569X cwent@usgs.gov","orcid":"https://orcid.org/0000-0003-2569-569X","contributorId":1178,"corporation":false,"usgs":true,"family":"Wentworth","given":"Carl","email":"cwent@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":883276,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raymond, Loren A.","contributorId":177083,"corporation":false,"usgs":false,"family":"Raymond","given":"Loren","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883277,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247813,"text":"70247813 - 2023 - A review of geology and mining in the Marble Mountains, southeastern California","interactions":[],"lastModifiedDate":"2023-08-18T12:14:11.623262","indexId":"70247813","displayToPublicDate":"2023-08-01T07:10:42","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A review of geology and mining in the Marble Mountains, southeastern California","docAbstract":"Mining in the Marble Mountains of southeastern California was active in the earliest 1900s and gradually declined to very few active mines by 1959. Most mining consisted of hard-rock prospects and mines, with a few soft-rock prospects and one mine. The Marble Mountains are a 10 km by 30 km, gently NE-dipping dipping structural block composed of Proterozoic plutonic and metamorphic rocks, Paleozoic sedimentary rocks, and Jurassic granitoids exposed along the western anti-dip slopes, and Miocene volcanic and sedimentary rocks exposed along the crest of the range and eastern dip slopes. Mineralization occurred in metamorphic aureoles of intrusions, along dikes, as contact metasomatic replacement bodies in carbonate rocks, or adjacent to or along thrust faults. Mineralization locally formed gold, copper, malachite, azurite, bornite, chalcopyrite, magnetite, specularite, limonite, quartz, epidote, actinolite, and garnet. Hard-rock prospects and mines are clustered into five locations. The mines are small open pits, a few consist of a shaft or two with a few adits, most are just a single shaft or adit. There are a few small open-pit marble mines, and one is an open pit fossil mine. Eight prospects were developed in the Miocene tuffaceous deposits; three in the northwest and five prospects, and a single adit, in the south. Most of the Marble Mountains are now in the Trilobite Wilderness or adjacent Areas of Critical Environmental Concern, and the area of the Golden Cycle district (Castle Mine area) and prospect areas in the southern part of the range are in the Mojave Trails National Monument. The Iron Hat mine, several nearby areas, and the Trilobite mine area are privately owned. No rock or mineral mines are currently active, but the Trilobite mine in the south end of the range is still open to the public.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2023 Desert Symposium Field Guide and Proceedings: Mines of the Mojave","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Desert Studies, Inc.","usgsCitation":"Buesch, D.C., and Bridenbecker, B.W., 2023, A review of geology and mining in the Marble Mountains, southeastern California, in 2023 Desert Symposium Field Guide and Proceedings: Mines of the Mojave, p. 110-120.","productDescription":"11 p.","startPage":"110","endPage":"120","ipdsId":"IP-149739","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":419925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419919,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org/pages/publications.html"}],"country":"United States","state":"California","otherGeospatial":"Marble Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.93436580364333,\n 35.01678002843924\n ],\n [\n -115.93436580364333,\n 34.222228671143824\n ],\n [\n -114.89844871439354,\n 34.222228671143824\n ],\n [\n -114.89844871439354,\n 35.01678002843924\n ],\n [\n -115.93436580364333,\n 35.01678002843924\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":880556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bridenbecker, Bruce W.","contributorId":328541,"corporation":false,"usgs":false,"family":"Bridenbecker","given":"Bruce","email":"","middleInitial":"W.","affiliations":[{"id":78393,"text":"Retired from Copper Mountain College","active":true,"usgs":false}],"preferred":false,"id":880557,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70247382,"text":"sir20235083 - 2023 - Evaluation of alternative groundwater-withdrawal scenarios on water levels in Kingsbury Pond, upper Charles River Basin, eastern Massachusetts","interactions":[],"lastModifiedDate":"2026-03-12T20:58:29.716193","indexId":"sir20235083","displayToPublicDate":"2023-07-31T20:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5083","displayTitle":"Evaluation of Alternative Groundwater-Withdrawal Scenarios on Water Levels in Kingsbury Pond, Upper Charles River Basin, Eastern Massachusetts","title":"Evaluation of alternative groundwater-withdrawal scenarios on water levels in Kingsbury Pond, upper Charles River Basin, eastern Massachusetts","docAbstract":"Kingsbury Pond is a glacial kettle pond in the town of Norfolk, Massachusetts, in the Mill River Basin, which is part of the Upper Charles River Basin in eastern Massachusetts. The pond is hydraulically connected to the surrounding groundwater-flow system, and water levels in the pond fluctuate in response to recharge to the aquifer from precipitation and wastewater return flows through septic systems, to withdrawals from the aquifer at nearby wells, and to precipitation directly on the pond surface. Concerns about the effects of withdrawals on water levels in the pond prompted an investigation to better understand the hydrology of Kingsbury Pond and its response to groundwater withdrawals and to determine if withdrawals from wells in Franklin, Mass., can be modified to simultaneously reduce the effect on water levels in the pond and yet meet the water-supply demands of the Town of Franklin.
An existing, transient groundwater-flow model of the Upper Charles River Basin was modified for this study in the area near Kingsbury Pond to improve representation of the hydrologic system near the pond. The mean annual water-level altitude simulated for the pond for nonpumping conditions using the modified model is 136 feet (ft), which falls within the range of likely annual pond-altitude fluctuations of 135 to 140 ft estimated for average hydrologic conditions before the beginning of withdrawals at two nearby wells operated by the Town of Franklin (wells FR–04 and FR–05). The mean annual water-level altitude at the pond decreased by 3.8 ft to 132.2 ft for simulated mean monthly withdrawal rates at all wells within the Upper Charles River Basin from 2010 to 2019 (referred to as the baseline withdrawal condition).
A groundwater management model that links the groundwater-flow model with a mathematical optimization method was developed to evaluate the effects of three alternative groundwater-withdrawal scenarios for the Franklin public-water system on water levels in Kingsbury Pond. In the first scenario, monthly withdrawal rates at wells FR–04 and FR–05 were increased from the baseline withdrawal rates to their maximum authorized rates for all months of the year; all other Franklin wells were specified at their baseline withdrawal rates. This scenario resulted in a mean annual water-level altitude at the pond of 129.3 ft, or a mean annual decline of 6.7 ft compared with nonpumping conditions and a decline of 2.9 ft compared with baseline conditions.
The results of the second scenario showed that water levels in the pond can be increased relative to 2010–19 conditions while meeting Franklin’s 2010–19 monthly water-supply demands if withdrawals at wells FR–04 and FR–05 were shifted to other Franklin wells. In this scenario, monthly withdrawal rates at wells FR–04 and FR–05 were decreased from their baseline rates to one-third their maximum practical rates for all months of the year; increased withdrawal rates at other Franklin wells were determined by the management model. The decrease in withdrawal rates at wells FR–04 and FR–05 resulted in a mean water-level altitude at the pond of 134.1 ft, which was equivalent to a 51 percent increase (improvement) in the mean annual water level of the pond relative to the baseline condition.
A third scenario was done to determine if Franklin’s existing water-supply system has the capacity to meet the mean annual maximum permitted withdrawal rate of the system of 3.45 million gallons per day while maintaining monthly withdrawal rates at wells FR–04 and FR–05 at their 2010–19 rates and water levels in Kingsbury Pond at baseline conditions. The analysis indicated that the capacity of the system cannot meet the increased demand during some months of the year with withdrawal rates at the two wells fixed at their monthly 2010–19 rates; however, the existing system is capable of meeting about 90 percent of the maximum permitted rate (3.10 million gallons per day) by increasing withdrawal rates at other Franklin wells above their 2010–19 rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235083","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Barlow, P.M., Friesz, P.J., and Barbaro, J.R., 2023, Evaluation of alternative groundwater-withdrawal scenarios on water levels in Kingsbury Pond, upper Charles River Basin, eastern Massachusetts: U.S. Geological Survey Scientific Investigations Report 2023–5083, 36 p., https://doi.org/10.3133/sir20235083.","productDescription":"Report: viii, 36 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-141684","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501046,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115124.htm","linkFileType":{"id":5,"text":"html"}},{"id":419446,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V49N3R","text":"USGS data release","linkHelpText":"MODFLOW–2000 and management-optimization models used to evaluate alternative groundwater-withdrawal scenarios on water levels in Kingsbury Pond, upper Charles River Basin, eastern Massachusetts"},{"id":419445,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5083/images/"},{"id":419444,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5083/sir20235083.XML"},{"id":419442,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5083/sir20235083.pdf","text":"Report","size":"3.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5083"},{"id":419441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5083/coverthb.jpg"},{"id":419443,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235083/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5083"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Upper Charles River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.61642753245717,\n 42.29469041974528\n ],\n [\n -71.61642753245717,\n 41.90357856449015\n ],\n [\n -71.16069045053953,\n 41.90357856449015\n ],\n [\n -71.16069045053953,\n 42.29469041974528\n ],\n [\n -71.61642753245717,\n 42.29469041974528\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Rattlesnakes play important roles in their ecosystems by regulating prey populations, are involved in complex coevolutionary dynamics with their prey, and exhibit a variety of unusual adaptations, including maternal care, heat-sensing pit organs, hinged fangs, and medically-significant venoms. The western rattlesnake (Crotalus oreganus) is one of the widest ranging rattlesnake species, with a distribution from British Columbia, where it is listed as threatened, to Baja California and east across the Great Basin to western Wyoming, Colorado and New Mexico. Here, we report a new reference genome assembly for one of six currently recognized subspecies, C. oreganus helleri, as part of the California Conservation Genomics Project (CCGP). Consistent with the reference genomic sequencing strategy of the CCGP, we used Pacific Biosciences HiFi long reads and Hi-C chromatin-proximity sequencing technology to produce a de novo assembled genome. The assembly comprises a total of 698 scaffolds spanning 1,564,812,557 base pairs, has a contig N50 of 64.7 Mb, a scaffold N50 of 110.8 Mb, and BUSCO complete score of 90.5%. This reference genome will be valuable for studies on the genomic basis of venom evolution and variation within Crotalus, in resolving the taxonomy of C. oreganus and its relatives, and for the conservation and management of rattlesnakes in general.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jhered/esad045","usgsCitation":"Westeen, E.P., Escalona, M., Holding, M., Beraut, E., Fairbairn, C., Marimuthu, M.P., Nguyen, O., Perri, R., Fisher, R., Toffelmier, E., Shaffer, H., and Wang, I.J., 2023, A genome assembly for the southern Pacific rattlesnake, Crotalus oreganus helleri, in the western rattlesnake species complex: Journal of Heredity, v. 114, no. 6, p. 681-689, https://doi.org/10.1093/jhered/esad045.","productDescription":"9 p.","startPage":"681","endPage":"689","ipdsId":"IP-153896","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":442647,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jhered/esad045","text":"Publisher Index Page"},{"id":422778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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The CBLCM simulates infill development, residential and commercial development, natural land and agricultural land conversion, and growth served by sewer or septic wastewater treatment. The CBLCM is unique among land change models by simulating multiple types of development and explicitly accounting for infill development and the spatial patterns of development densities. The CBLCM was used to simulate five future land use scenarios, holding population constant, for all counties within and adjacent to the Chesapeake Bay watershed from 2013 to 2055. Results are presented here for the state of Maryland over the period 2013–2025 to illustrate model functionality and validation. The growth management (GM) scenario achieved the least development and potential impacts to natural and agricultural lands while accommodating the same amount of population growth as the other four scenarios. Scenarios focusing exclusively on natural or agricultural land protection shifted development to unprotected areas resulting in unforeseen water quality consequences. Simultaneously achieving more compact development while protecting the most valued natural and agricultural lands requires a combination of GM and land conservation policies and actions.
","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.13131","usgsCitation":"Claggett, P., Ahmed, L., Irani, F., McDonald, S., and Thompson, R., 2023, The Chesapeake Bay Land Change Model (CBLCM): Simulating future land use scenarios and potential impacts on water quality: Journal of the American Water Resources Association, 21-0151, 26 p., https://doi.org/10.1111/1752-1688.13131.","productDescription":"21-0151, 26 p.","ipdsId":"IP-134156","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":442650,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13131","text":"Publisher Index Page"},{"id":419413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","noUsgsAuthors":false,"publicationDate":"2023-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Claggett, Peter 0000-0002-5335-2857","orcid":"https://orcid.org/0000-0002-5335-2857","contributorId":238920,"corporation":false,"usgs":true,"family":"Claggett","given":"Peter","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":879320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmed, Labeeb 0000-0003-4524-9611","orcid":"https://orcid.org/0000-0003-4524-9611","contributorId":303117,"corporation":false,"usgs":true,"family":"Ahmed","given":"Labeeb","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irani, Frederick 0000-0002-2424-0135 firani@usgs.gov","orcid":"https://orcid.org/0000-0002-2424-0135","contributorId":303119,"corporation":false,"usgs":true,"family":"Irani","given":"Frederick","email":"firani@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDonald, Sarah 0000-0003-3534-325X","orcid":"https://orcid.org/0000-0003-3534-325X","contributorId":303116,"corporation":false,"usgs":true,"family":"McDonald","given":"Sarah","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Renee 0000-0003-1463-5173 rthompson1@usgs.gov","orcid":"https://orcid.org/0000-0003-1463-5173","contributorId":303115,"corporation":false,"usgs":true,"family":"Thompson","given":"Renee","email":"rthompson1@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879324,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70246624,"text":"ofr20231047 - 2023 - Multiple-well monitoring site within the Poso Creek Oil Field, Kern County, California","interactions":[],"lastModifiedDate":"2026-02-11T21:28:50.272733","indexId":"ofr20231047","displayToPublicDate":"2023-07-18T11:37:02","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1047","displayTitle":"Multiple-Well Monitoring Site Within the Poso Creek Oil Field, Kern County, California","title":"Multiple-well monitoring site within the Poso Creek Oil Field, Kern County, California","docAbstract":"The Poso Creek Oil Field is one of the many fields selected for regional groundwater mapping and monitoring by the California State Water Resources Control Board as part of the Oil and Gas Regional Monitoring Program (RMP; California State Water Resources Control Board, 2015, 2022b; U.S. Geological Survey, 2022a). The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations to groundwater and the geologic materials between them; (3) evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2022a). As part of this evaluation, the USGS installed a multiple-well monitoring site within the administrative boundary of the Poso Creek Oil Field about 12 miles north of Bakersfield, California (fig. 1). Data collected at the Poso Creek multiple-well monitoring site (PCCT) provide information about the geology, hydrology, geophysical properties, and water quality of the aquifer system overlying the oil-bearing zone, thus enhancing understanding of relations between adjacent groundwater and the Poso Creek Oil Field in an area where groundwater data are limited, particularly at different depths in the aquifer. This report presents construction information for the PCCT and initial geohydrologic data collected from the site. Similar sites installed on the east side of the Lost Hills Oil Field and North and South Belridge Oil Fields were described by Everett and others (2020a, b).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231047","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Everett, R.R., McMahon, P.B., Stephens, M.J., Gillespie, J.M., Shepherd, M.M., and Fenton, N.C., 2023, Multiple-well monitoring site within the Poso Creek Oil Field, Kern County, California: U.S. Geological Survey Open-File Report 2023-1047, 11 p., https://doi.org/10.3133/ofr20231047.","productDescription":"11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-143467","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499783,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114968.htm","linkFileType":{"id":5,"text":"html"}},{"id":418877,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/ofr20231047/full"},{"id":418876,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1047/images"},{"id":418875,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1047/ofr20231047.xml"},{"id":418874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1047/ofr20231047.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":418873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1047/covrthb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Poso Creek Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.02751948230053,\n 35.55715768005527\n ],\n [\n -119.02751948230053,\n 35.37156425616723\n ],\n [\n -118.8051417495576,\n 35.37156425616723\n ],\n [\n -118.8051417495576,\n 35.55715768005527\n ],\n [\n -119.02751948230053,\n 35.55715768005527\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The iconic and threatened Caribbean coral, Acropora palmata, is an essential reef-ecosystem engineer. Understanding the processes underpinning this coral’s survival and growth is essential to restoring this foundational species. Here, we compared replicate A. palmata colonies transplanted along 350 km of Florida’s offshore coral reef to determine holobiont and/or environmental variables that predict transplant success. We found a west-to-east gradient in coral physiology coupled with site-specific coral-associated microbiomes. Interestingly, no variables were linked to coral genet. Our results suggest that the unique oceanographic conditions with periodic upwelling events in the Dry Tortugas provide corals with greater opportunity for heterotrophy that in turn enhances coral growth and survivorship, and positively influences the microbiome. Our findings indicate that restoration efforts in the Dry Tortugas, and other places exhibiting higher food availability, could be most effective for A. palmata.
Climate change may induce mismatches between wildlife reproductive phenology and temporal occurrence of resources necessary for reproductive success. Verifying and elucidating the causal mechanisms behind potential mismatches requires large-scale, longer-duration data. We used eastern wild turkey (Meleagris gallopavo silvestris) nesting data collected across the southeastern U.S. over eight years to investigate potential climatic drivers of variation in nest initiation dates. We investigated climactic relationships with two datasets, one inclusive of successful and unsuccessful nests (full dataset) and another of just successful nests (successfully hatched dataset), to determine whether successfully hatched nests responded differently to weather changes than all nests did. In the full dataset, each 10 cm increase in January precipitation was associated with nesting occurring 0.46-0.66 days earlier, and each 10 cm increase in precipitation during the 30 days preceding nesting was associated with nesting occurring 0.17-0.21 days later. In the successfully hatched dataset, a 10 cm increase in March precipitation was associated with nesting occurring 0.67-0.74 days earlier, and an increase of one unit of variation in February maximum temperature was associated with nesting occurring 0.02 days later. We combined the results of these modeled relationships with multiple climate scenarios to understand potential implications of future climate change on wild turkey nesting phenology; results indicated that mean nest initiation date is projected to change by <0.1 day by 2040-2060. Wild turkey nesting phenology did not track changes in spring green-up timing, which could result in phenological mismatch between the timing of nesting and the availability of resources critical for successful reproduction.
The Ibex Hollow Tuff, 12.08 ± 0.03 Ma (40Ar/39Ar), is a widespread tephra layer erupted from the Bruneau-Jarbidge volcanic field of southern Idaho. Tephra from this eruption was deposited across much of western and central North America and adjacent ocean areas. We identified the Ibex Hollow Tuff at Trapper Creek, Idaho, near its eruption site, and at 15 distal sites, from the Pacific Ocean to the Gulf of Mexico, by the chemical composition of its glass shards, using electron-microprobe analysis, instrumental neutron activation analysis, and laser-ablation–inductively coupled plasma–mass spectrometry. By these methods, we distinguished the Ibex Hollow Tuff from overlying and underlying tephra layers near its source and at distal sites. Fluvially reworked Ibex Hollow Tuff ash was transported by the ancestral Mississippi River drainage from the interior of the North American continent to the Gulf of Mexico, where it is present within an ~50-m-thick deposit in marine sediments in the subsurface. The minimum fallout area covered by the ash is ~2.7 million km2, with a minimum volume of ~800 km3, and potential dispersal farther to the north and northeast. The areal distribution for the Ibex Hollow Tuff is similar to that of the Lava Creek B (0.63 Ma) supereruption. The Ibex Hollow Tuff represents a unique chronostratigraphic marker allowing a synoptic view of paleoenvironments at a virtual moment in time across a large terrestrial and marine region. The Ibex Hollow Tuff is also an important marker bed for North American Land Mammal Ages, and it coincides with climatic cooling in the middle to late Miocene documented in marine cores.
The steep, tectonically active terrain along the Central California (USA) coast is well known to produce deadly and destructive debris flows. However, the extent to which fire affects debris-flow susceptibility in this region is an open question. We documented the occurrence of postfire debris floods and flows following the landfall of a storm that delivered intense rainfall across multiple burn areas. We used this inventory to evaluate the predictive performance of the US Geological Survey M1 likelihood model, a tool that presently underlies the emergency assessment of postfire debris-flow hazards in the western USA. To test model performance, we used the threat score skill statistic and found that the rainfall thresholds estimated by the M1 model for the Central California coast performed similarly to training (Southern California) and testing (Intermountain West) data associated with the original model calibration. Model performance decreased when differentiating between “minor” and “major” postfire hydrologic response types, which weigh effects on human life and infrastructure. Our results underscore that the problem of false positives is a major challenge for developing accurate rainfall thresholds for the occurrence of postfire debris flows. As wildfire activity increases throughout the western USA, so too will the demand for the assessment of postfire debris-flow hazards. We conclude that additional collection of field-verified inventories of postfire hydrologic response will be critical to prioritize which model variables may be suitable candidates for regional calibration or replacement.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-023-02106-7","usgsCitation":"Thomas, M.A., Kean, J.W., McCoy, S., Lindsay, D.N., Kostelnik, J., Cavagnaro, D.B., Rengers, F.K., East, A.E., Schwartz, J., Smith, D.P., and Collins, B.D., 2023, Postfire hydrologic response along the central California (USA) coast: Insights for the emergency assessment of postfire debris-flow hazards: Landslides, v. 20, p. 2421-2436, https://doi.org/10.1007/s10346-023-02106-7.","productDescription":"16 p.","startPage":"2421","endPage":"2436","ipdsId":"IP-139528","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":442830,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10346-023-02106-7","text":"Publisher Index Page"},{"id":435262,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91O03Y7","text":"USGS data release","linkHelpText":"Field-verified inventory of postfire hydrologic response for the 2020 CZU Lightning Complex, River, Camel, and Dolan Fires following a 26-29 January 2021 atmospheric river storm sequence"},{"id":418804,"rank":1,"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 -121.47368381570269,\n 35.81897449008355\n ],\n [\n -120.78708136085193,\n 36.2836437903476\n ],\n [\n -121.84398626326276,\n 37.24464732874951\n ],\n [\n -122.37243871446816,\n 36.97394608796073\n ],\n [\n -121.47368381570269,\n 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aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":877150,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schwartz, Jonathan","contributorId":312505,"corporation":false,"usgs":false,"family":"Schwartz","given":"Jonathan","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":877151,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Smith, Douglas P.","contributorId":201716,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas","email":"","middleInitial":"P.","affiliations":[{"id":35924,"text":"California State University, Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":877152,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":877153,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70246616,"text":"70246616 - 2023 - A detailed view of the 2020-2023 southwestern Puerto Rico seismic sequence with deep learning","interactions":[],"lastModifiedDate":"2023-12-04T16:59:36.832347","indexId":"70246616","displayToPublicDate":"2023-07-06T08:36:24","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":"A detailed view of the 2020-2023 southwestern Puerto Rico seismic sequence with deep learning","docAbstract":"The 2020–2023 southwestern Puerto Rico seismic sequence, still ongoing in 2023, is remarkable for its multiple‐fault rupture complexity and elevated aftershock productivity. We applied an automatic workflow to continuous data from 43 seismic stations in Puerto Rico to build an enhanced earthquake catalog with ∼180,000 events for the 3+ yr sequence from 28 December 2019 to 1 January 2023. This workflow contained the EQTransformer (EQT) deep learning model for event detection and phase picking, the EikoNet‐Hypocenter Inversion with Stein Variational Inference probabilistic earthquake location approach with a neural network trained to solve the eikonal wave equation, and relocation with event‐pair waveform cross correlation. EQT increased the number of catalog events in the sequence by about seven times, though its performance was not quite as good as thorough analyst review. The enhanced catalog revealed new structural details of the sequence space–time evolution, including sudden changes in activity, on a complex system of many small normal and strike‐slip faults. 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Macrophage aggregate (MA) abundance in fish is a useful general biomarker of contaminant exposures and environmental stress. Hepatic and splenic MAs were evaluated in semi-anadromous white perch Morone americana (Gmelin, 1789) from the urbanized Severn River (S) and the more rural Choptank River (C), Chesapeake Bay. Fish were collected from different sites in the annual migratory circuit in each river that corresponded to active spawning in late winter-early spring, summer regenerating, autumn developing, and winter spawning-capable phases. An age-associated progressive increase in the total volume of MAs (MAV) was evident in the liver and spleen. Mean hepatic MAV (range in seasonal means, C: 6.4-23.1 mm3; S: 15.7-48.7 mm3) and mean splenic MAV (C: 7.3-12.6 mm3; S: 16.0-33.0 mm3) differed significantly among seasons and were significantly greater in females and in Severn River fish. Age and river were the most influential factors, suggesting that increased MAV in Severn River fish resulted from chronic exposures to higher concentrations of environmental contaminants. Hepatic MAV was directly related to the relative volume of copper granules in the liver. Less influential factors on splenic MAV included fish condition, trematode infections, and granulomas, indicating possible functional differences in MAs by organ. While organ volumes were strongly linked to gonadosomatic index (GSI) and reproductive phase, the reason for seasonal differences in MAV was less clear. Water temperature, salinity, and dissolved oxygen were not significantly related to MAV, and indicators of reproductive phase (hepatosomatic index and GSI) were significant but less important in explaining variation in MAV.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03734","usgsCitation":"Blazer, V., Matsche, M.A., and Pulster, E.L., 2023, White perch health relative to urbanization and habitat degradation in Chesapeake Bay tributaries. II. 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I. Biliary neoplasms and hepatic lesions","interactions":[],"lastModifiedDate":"2023-07-19T11:43:15.216964","indexId":"70246797","displayToPublicDate":"2023-07-06T06:38:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"title":"White perch health relative to urbanization and habitat degradation in Chesapeake Bay tributaries. I. Biliary neoplasms and hepatic lesions","docAbstract":"White perch Morone americana (Gmelin, 1789) from the Chesapeake Bay (USA) watershed have a high incidence of liver disease, including neoplasms of bile duct origin. Fish collected seasonally from spring 2019 to winter 2020 from the urban Severn River and the more rural Choptank River were evaluated for hepatic lesions. Biliary hyperplasia (64.1%), neoplasms (cholangioma and cholangiocarcinoma, 27%), and dysplasia (24.9%) were significantly higher in Severn River fish compared to Choptank River fish (52.9, 16.2, and 15.8%, respectively). Hepatocellular lesions were less common, including foci of hepatocellular alteration (FHA, 13.3%) and hepatocellular neoplasms (1%). There was also a progressive age-related increase in copper-laden granules in hepatocytes, which was a significant risk factor for FHA and could be a source of oxidative stress in the liver. Significant risk factors for biliary neoplasms included age, bile duct fibrosis, and infections by the myxozoan parasite Myxidium murchelanoi, but the prevalence and relative intensity of M. murchelanoi infections did not differ significantly between fish populations. Hepatic disease in this species appears to be chronic and may stem from an age-related accumulation of damage, possibly from parasitic infections and contaminants such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and copper. Watershed development and exposures to PCBs and PAHs were generally higher for white perch in the Severn River, but similar suites of chemical contaminants were detected in the Choptank River. A broader survey of white perch within and outside Chesapeake Bay may allow determination of the extent of biliary neoplasia in this species.
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sturgeon Acipenser oxyrinchus oxyrinchus was once of great commercial importance in many coastal rivers of the eastern USA. Over the 19th and 20th centuries, most historical stocks of Atlantic sturgeon were depleted by human activities. Estimating recruitment for the remaining populations is challenging due to sampling constraints, limited age data, and natural variability. However, recruitment estimates could inform recovery efforts. The objectives of this study were to compare 2 modeling approaches to estimate recruitment of age-1 Atlantic sturgeon and provide an updated index of abundance across more than a decade of sampling in the Altamaha River, Georgia. First, we constructed capture histories of river-resident juveniles, using capture-mark-recapture data collected from 2008 to 2020, and assigned ages based on length-frequency analysis. Second, we compared more traditional Huggins closed population models and a recent nonlinear extension of Huggins models—vector generalized additive models (VGAMs)—to estimate abundance of age-1 fish. Both model types indicated similar yearly age-1 abundance estimates (Huggins: 163 in 2017 to 3839 in 2010; VGAM: 312 in 2020 to 4448 in 2010), but the VGAMs provided more direct interpretation for factors that might affect capture probability (e.g. sampling effort, temperature, fish length). This study indicates that the age-1 Altamaha River Atlantic sturgeon population has remained relatively stable over the past decade and provides a long-term baseline which will better enable managers to assess the effects of either future restoration actions or environmental disturbances on the population.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01250","usgsCitation":"Baker, M., Ingram, E., Higginbotham, D., Irwin, B., and Fox, A., 2023, Refining capture-recapture recruitment estimation methods for Atlantic sturgeon: Endangered Species Research, v. 51, p. 203-214, https://doi.org/10.3354/esr01250.","productDescription":"12 p.","startPage":"203","endPage":"214","ipdsId":"IP-143737","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432972,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":442856,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01250","text":"Publisher Index Page"}],"country":"United States","state":"Georgia","otherGeospatial":"Altamaha River estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.48760060177855,\n 31.490539509150565\n ],\n [\n -81.48760060177855,\n 31.14793158914553\n ],\n [\n -81.23488559221977,\n 31.14793158914553\n ],\n [\n -81.23488559221977,\n 31.490539509150565\n ],\n [\n -81.48760060177855,\n 31.490539509150565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baker, M.A.","contributorId":340977,"corporation":false,"usgs":false,"family":"Baker","given":"M.A.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ingram, E.C.","contributorId":340978,"corporation":false,"usgs":false,"family":"Ingram","given":"E.C.","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":907757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Higginbotham, D.L.","contributorId":340979,"corporation":false,"usgs":false,"family":"Higginbotham","given":"D.L.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Brian J. 0000-0002-0666-2641","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":280043,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907759,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, A.G.","contributorId":340980,"corporation":false,"usgs":false,"family":"Fox","given":"A.G.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907760,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262161,"text":"70262161 - 2023 - Biotic and abiotic factors shaping bat activity in Maryland soybean fields","interactions":[],"lastModifiedDate":"2025-01-15T16:06:14.466194","indexId":"70262161","displayToPublicDate":"2023-07-03T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Biotic and abiotic factors shaping bat activity in Maryland soybean fields","docAbstract":"Bats are important pest control agents in agriculture. Yet, the underlying fine-scale biotic and abiotic mechanisms that drive their foraging behaviors and responses to insect outbreaks are unclear. Herbivore-induced plant volatiles (HIPVs) can attract both invertebrate and vertebrate natural enemies that use the chemical plant cues to locate insect prey. The ability of HIPVs to attract multiple species raises the question of whether they may also be a biotic factor influencing insectivorous bat activity. Additionally, abiotic factors, such as weather conditions, can affect bat activity in agricultural settings, but little is known about how bats respond to shifting environmental conditions on short timescales in this landscape context. Using a model crop system, soybean (Glycine max), our study asked three questions: (1) Which bat species are active in eastern Maryland soybean fields? (2) Is insectivorous bat activity affected by naturally occurring soybean HIPVs and/or synthetic soybean HIPVs (indole or farnesene)? (3) How is insectivorous bat activity affected by hourly weather conditions in this landscape? In soybean fields in eastern Maryland, we created paired treatment plots: HIPV plots (damaged plants or synthetic HIPV dispensers) and control plots (undamaged plants or empty dispensers). We measured bat activity using ultrasonic recorders, summarizing hourly and nightly activity, and detected 10 total species. The most abundant species were big brown/silver-haired bats (Eptesicus fuscus/Lasionycteris noctivagans). Bat activity did not significantly differ between control and HIPV plots in any of the three experiments. Thus, our results do not support our expectation that bats in eastern Maryland use soybean HIPVs to locate insect prey. However, bat activity did increase with increasing average hourly temperature and wind speed. This initial study of bats and HIPVs, as well as the fine-scale examination of weather conditions on bat activity, may serve as a guide for future research on bat–plant interactions that can support the development of new strategies for sustainable pest management.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4575","usgsCitation":"Maynard, L., Ford, W., Parker, J., and Whitehead, S., 2023, Biotic and abiotic factors shaping bat activity in Maryland soybean fields: Ecosphere, v. 14, no. 7, e4575, 13 p., https://doi.org/10.1002/ecs2.4575.","productDescription":"e4575, 13 p.","ipdsId":"IP-145893","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467104,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4575","text":"Publisher Index Page"},{"id":466422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":22,\"properties\":{\"name\":\"Maryland\",\"nation\":\"USA 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Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":923305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parker, John D.","contributorId":348247,"corporation":false,"usgs":false,"family":"Parker","given":"John D.","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":923306,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitehead, Susan R.","contributorId":348249,"corporation":false,"usgs":false,"family":"Whitehead","given":"Susan R.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":923307,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247321,"text":"70247321 - 2023 - Cryptic tolerant fish species and their potential effect on index of biotic integrity (IBI) scores","interactions":[],"lastModifiedDate":"2023-07-27T16:32:22.397158","indexId":"70247321","displayToPublicDate":"2023-06-28T11:29:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Cryptic tolerant fish species and their potential effect on index of biotic integrity (IBI) scores","docAbstract":"Indices such as the Index of Biotic Integrity (IBI) are often used by management agencies to estimate the abstract property of stream health. These indices are usually predicated on the belief that certain fish species are tolerant to environmental perturbation while others are sensitive. Species are usually designated as either tolerant or sensitive in these analyses based on inherent ecological or taxonomic characteristics. However, previous literature has shown that certain species from ecological or taxonomic “sensitive” groups experience increased abundance in degraded streams. We term such species “cryptic tolerants”. Using a stream fish assemblage dataset of 433 unique sample locations across the state of Alabama and the National Landcover Dataset, our objectives were to 1) identify the most common cryptic tolerant species, 2) investigate how cryptic tolerant species might inflate indices of stream health, and 3) compare an alternative measure of stream health in which species are statistically defined rather than defined using the traditional trait-based approach. We identified cryptic tolerants using Nonmetric Multidimensional Scaling in six ecoregions. A series of regressions revealed that the proportion of cryptic tolerant species decreased in response to an increasing proportion of forested land in catchments while the proportion of true sensitives increased in all ecoregions except for the Cumberland Plateau. An index that simply used the percentage of statistically defined, non-tolerant species generally had lower p-values and higher r2 values than IBI scores when both were regressed against percentage of forest in catchment. However, both indices had low degrees of correlation with expected disturbance, indicating a univariate index may be inadequate to characterize stream health. Our results highlight a potential issue with applying the IBI to diverse southeastern systems in the United States, which may be alleviated by designating species sensitivity based on empirical response to disturbance rather than taxonomic or ecological characteristics.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2023.110546","usgsCitation":"Hilburn, B.G., Freeman, M., Lawson, K., Rider, S.J., and Johnston, C.E., 2023, Cryptic tolerant fish species and their potential effect on index of biotic integrity (IBI) scores: Ecological Indicators, v. 154, 110546, 13 p., https://doi.org/10.1016/j.ecolind.2023.110546.","productDescription":"110546, 13 p.","ipdsId":"IP-150939","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2023.110546","text":"Publisher Index Page"},{"id":419401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"154","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hilburn, Bryson G.","contributorId":317735,"corporation":false,"usgs":false,"family":"Hilburn","given":"Bryson","email":"","middleInitial":"G.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":879202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":879203,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawson, Katelyn M.","contributorId":201981,"corporation":false,"usgs":false,"family":"Lawson","given":"Katelyn M.","affiliations":[{"id":36314,"text":"University of Florida/IFAS","active":true,"usgs":false}],"preferred":false,"id":879204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rider, Steven J.","contributorId":317736,"corporation":false,"usgs":false,"family":"Rider","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":35940,"text":"Alabama Division of Wildlife and Freshwater Fisheries","active":true,"usgs":false}],"preferred":false,"id":879205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnston, Carol E.","contributorId":317737,"corporation":false,"usgs":false,"family":"Johnston","given":"Carol","email":"","middleInitial":"E.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":879206,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70246263,"text":"70246263 - 2023 - Modeling the maturation history of the stacked petroleum systems of the Williston Basin, USA","interactions":[],"lastModifiedDate":"2023-06-29T11:44:43.799768","indexId":"70246263","displayToPublicDate":"2023-06-27T06:41:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the maturation history of the stacked petroleum systems of the Williston Basin, USA","docAbstract":"A three-dimensional petroleum systems model was built to support U.S. Geological Survey assessments of undiscovered oil and gas resources in the Williston Basin of North Dakota, Montana, and South Dakota. Numerous Paleozoic source rocks have been proven or postulated in the basin, of which five were the focus of maturation and migration modeling: the Ordovician Icebox Formation, the kukersite beds of the Ordovician Red River Formation, the shales of the Devonian–Mississippian Bakken Formation, the Mississippian Madison Group, and the Pennsylvanian Tyler Formation. Calibration of the three-dimensional model to present-day temperature data indicates the existence of a north-south trend of high heat flow in western North Dakota, along with a region of high heat flow in eastern Montana. These high heat flow trends strongly control the maturity of all studied source intervals. A Bakken-specific hydrocarbon generation kinetic model was developed to match the calibrated time-temperature history of the basin to spatial trends in hydrogen index from programmed pyrolysis data. Generation of hydrocarbons occurred in the Cretaceous through Paleogene due to increased burial. Subsequent uplift and erosion in the Neogene cooled the basin, ending hydrocarbon generation for all source rocks. The cumulative volume of hydrocarbons generated by each of the source rocks was calculated and used to compare their relative robustness. The shales of the Bakken Formation are estimated to have generated approximately 460 billion barrels of oil equivalent (BBOE), while the Red River Formation generated approximately 130 BBOE, the Tyler Formation 94 BBOE, the Madison Group 44 BBOE, and the Icebox Formation 28 BBOE. Gross migration trends were analyzed with respect to historical oil and gas production in the basin and generally indicate segregation of petroleum systems throughout the stratigraphic column. However, most modeled scenarios indicated significant loss of Bakken oil to the Madison Group, suggesting that mixing of Madison and Bakken oils may be more prevalent than has recently been recognized in the U.S. portion of the Williston Basin and is particularly likely in fractured regions of the basin.
The Upper Cretaceous (Turonian?) Middendorf Formation is a sand-rich stratigraphic unit of fluvial origin that forms a large aquifer in the U.S. Atlantic Coastal Plain. In Chesterfield County (South Carolina), which is the site of the type locality, the formation ranges in thickness from 66.5 to > 119.7 meters. The base of the formation is an unconformity above Paleozoic metasiltstone, and the upper contact is an unconformity above which lies sand of the Quaternary Pinehurst Formation. Outcrops display the following five facies assemblages: 1) sandstone to conglomeratic sandstone (fluvial bar and channel deposits), 2) beds of alternating laminae of sandstone and mudstone (fluvial overbank or floodplain deposits), 3) ≥ 1 m-thick beds of clay (swamp deposits, floodplain deposits, and/or sediment that accumulated in abandoned fluvial channels), 4) 0.2–0.5 m-thick planar to slightly undulatory beds of framework-supported sandstone with a mud matrix (traction-dominated current deposits at the top of fluvial bars, upper-flow-regime bedform deposits in subsidiary fluvial channels, or coarse-grained overbank deposits), and 5) sandstone to conglomeratic sandstone cemented by iron (interpreted as fluvial bar and channel deposits, with the iron cement being a diagenetic “groundwater ferricrete” that formed via the circulation of shallow groundwater and the oxidation of iron-bearing minerals). Kaolinite in various forms is pervasive throughout the formation and is interpreted as an early diagenetic phenomenon that formed by prolonged postdepositional weathering and flushing by meteoric water under a warm and humid paleoclimate.
The fluvial system that formed the Middendorf Formation prograded into the area from the west or northwest from uplifted margins of Mesozoic rift basins and/or the Appalachian Mountains. This progradation was a response to a base-level fall and the sediment accumulated during base-level lowstand and subsequent early transgression. In Chesterfield County, the Middendorf Formation can be subdivided into three fining-upward sequences. Each sequence consists predominantly of medium to coarse sand with a greater abundance of gravel in the lower part of the sequence and a greater abundance of clay and silt beds in the upper part. Each sequence is interpreted as either a response to autogenic processes or a response to allogenic sea-level changes, specifically a higher-order (higher-frequency) progression from relative lowstand conditions to early transgression whereby coarse sand and gravel (e.g., fluvial bar and channel deposits) were preserved during initial lowstand conditions and a greater proportion of mud and finer-grained sand (floodplain deposits) were preserved during subsequent early transgression. The Middendorf Formation is correlative with several other kaolinite-rich fluvial sandstones in North America including the Raritan Formation in New Jersey, the Tuscaloosa Formation of the eastern Gulf of Mexico (Alabama, Mississippi, Louisiana), the Woodbine Formation of the central Gulf of Mexico (Texas), and the Frontier Formation of Wyoming. The accumulation and preservation of these formations occurred in response to a Turonian eustatic sea-level fall and subsequent transgression, and the early diagenetic kaolinite in these formations is attributed to similar warm and humid paleoclimate conditions.
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