The U.S. Geological Survey (USGS) National Cooperative Geologic Mapping Program (NCGMP) has published a strategic plan entitled “Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge”. This plan provides the following vision, mission, and goals for the program for the years 2020–30:
To achieve the goal outlined in the strategic plan, the NCGMP has developed an Implementation Plan. This Implementation Plan will guide annual reviews of the FEDMAP component (that is, the component of the USGS NCGMP that funds geologic mapping by USGS geologists) of the NCGMP projects described in the plan and the development of the annual FEDMAP prospectus, which will ensure the application of the NCGMP strategy.
This publication is part of the Implementation Plan of the NCGMP strategy and addresses the following three major topics:
The regions used in this chapter correspond with physiographic divisions of the United States as defined by Fenneman. Physiographic divisions are delineated on the basis of topography, and to a lesser extent, the geologic structure and history. The physiographic divisions are subdivided into physiographic provinces, and the physiographic provinces are subdivided into physiographic sections. Fenneman’s physiographic divisions of the United States provide a robust and useful spatial organization for delineating large geographic regions of the United States for various scientific and industrial applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211120","usgsCitation":"Swezey, C.S., Blome, C.D., Kincare, K.A., Lundstrom, S.C., Stone, B.D., Sweetkind, D.S., Berg, R.C., Brown, S.E., and Yellich, J.A., 2022, Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces): U.S. Geological Survey Open-File Report 2021–1120, 24 p., https://doi.org/10.3133/ofr20211120.","productDescription":"iv, 24 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128891","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501534,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112124.htm","linkFileType":{"id":5,"text":"html"}},{"id":394416,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20211120/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":394244,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1120/coverthb.jpg"},{"id":394245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.pdf","text":"Report","size":"3.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1120"},{"id":394246,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1120/images/"},{"id":394247,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.XML"}],"country":"Canada, United States","otherGeospatial":"Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.701171875,\n 34.016241889667015\n ],\n [\n -75.234375,\n 34.016241889667015\n ],\n [\n -75.234375,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
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12201 Sunrise Valley Drive
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No abstract available.
","language":"English","publisher":"The Capital Area Ground Water Conservation Commission","usgsCitation":"Di Leonardo, D., Dausman, A., Clark, R., Runge, M.C., Dalyander, S., Hemmerling, S., Grismore, A., Afinowicz, J., Taucer, P., Skipwith, J., and Tsai, F.T., 2022, Long-term strategic plan for the Capital Area Ground Water Conservation Commission: Phase 2A final report, 441 p.","productDescription":"441 p.","ipdsId":"IP-146051","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419375,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://thewaterinstitute.org/reports/long-term-strategic-plan-for-the-capital-area-ground-water-conservation-commission-phase2a-final-report"}],"country":"United States","state":"Louisiana","otherGeospatial":"Southern Hills aquifer syttem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.8,\n 31.1\n ],\n [\n -91.8,\n 30.4\n ],\n [\n -90.5,\n 30.4\n ],\n [\n -90.5,\n 31.1\n ],\n [\n -91.8,\n 31.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Di Leonardo, Diana","contributorId":317725,"corporation":false,"usgs":false,"family":"Di Leonardo","given":"Diana","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":879172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dausman, Alyssa","contributorId":223766,"corporation":false,"usgs":false,"family":"Dausman","given":"Alyssa","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":879173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Ryan","contributorId":193538,"corporation":false,"usgs":false,"family":"Clark","given":"Ryan","email":"","affiliations":[],"preferred":false,"id":879174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":879175,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dalyander, Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221905,"corporation":false,"usgs":false,"family":"Dalyander","given":"Soupy","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":879176,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hemmerling, Scott","contributorId":221274,"corporation":false,"usgs":false,"family":"Hemmerling","given":"Scott","affiliations":[],"preferred":false,"id":879177,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grismore, Audrey","contributorId":317726,"corporation":false,"usgs":false,"family":"Grismore","given":"Audrey","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":879178,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Afinowicz, Jason","contributorId":317729,"corporation":false,"usgs":false,"family":"Afinowicz","given":"Jason","email":"","affiliations":[{"id":69136,"text":"Freese and Nichols, Inc.","active":true,"usgs":false}],"preferred":false,"id":879180,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Taucer, Philip","contributorId":317730,"corporation":false,"usgs":false,"family":"Taucer","given":"Philip","email":"","affiliations":[{"id":69136,"text":"Freese and Nichols, Inc.","active":true,"usgs":false}],"preferred":false,"id":879181,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Skipwith, Jordan","contributorId":317731,"corporation":false,"usgs":false,"family":"Skipwith","given":"Jordan","email":"","affiliations":[{"id":69136,"text":"Freese and Nichols, Inc.","active":true,"usgs":false}],"preferred":false,"id":879182,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Tsai, Frank T.-C.","contributorId":305938,"corporation":false,"usgs":false,"family":"Tsai","given":"Frank","email":"","middleInitial":"T.-C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":879183,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70230318,"text":"70230318 - 2022 - Distinct gut microbiomes in two polar bear subpopulations inhabiting different sea ice ecoregions","interactions":[],"lastModifiedDate":"2022-04-08T11:11:21.587662","indexId":"70230318","displayToPublicDate":"2022-01-11T08:21:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Distinct gut microbiomes in two polar bear subpopulations inhabiting different sea ice ecoregions","docAbstract":"Gut microbiomes were analyzed by 16S rRNA gene metabarcoding for polar bears (Ursus maritimus) from the southern Beaufort Sea (SB), where sea ice loss has led to increased use of land-based food resources by bears, and from East Greenland (EG), where persistent sea ice has allowed hunting of ice-associated prey nearly year-round. SB polar bears showed a higher number of total (940 vs. 742) and unique (387 vs. 189) amplicon sequence variants and higher inter-individual variation compared to EG polar bears. Gut microbiome composition differed significantly between the two subpopulations and among sex/age classes, likely driven by diet variation and ontogenetic shifts in the gut microbiome. Dietary tracer analysis using fatty acid signatures for SB polar bears showed that diet explained more intrapopulation variation in gut microbiome composition and diversity than other tested variables, i.e., sex/age class, body condition, and capture year. Substantial differences in the SB gut microbiome relative to EG polar bears, and associations between SB gut microbiome and diet, suggest that the shifting foraging habits of SB polar bears tied to sea ice loss may be altering their gut microbiome, with potential consequences for nutrition and physiology.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-021-04340-2","usgsCitation":"Franz, M., White, L., Atwood, T.C., Laidre, K.L., Roy, D., Watson, S., Gongora, E., and McKinney, M., 2022, Distinct gut microbiomes in two polar bear subpopulations inhabiting different sea ice ecoregions: Scientific Reports, v. 12, 522, 15 p., https://doi.org/10.1038/s41598-021-04340-2.","productDescription":"522, 15 p.","ipdsId":"IP-131826","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":449203,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-04340-2","text":"Publisher Index Page"},{"id":436001,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97KU2YD","text":"USGS data release","linkHelpText":"Southern Beaufort Sea Polar Bear Diet and Gut Microbiota Data, 2015-2019"},{"id":398307,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -39.375,\n 61.270232790000634\n ],\n [\n -23.203125,\n 68.78414378041504\n ],\n [\n -35.15625,\n 70.1403642720717\n ],\n [\n -48.1640625,\n 62.59334083012024\n ],\n [\n -39.375,\n 61.270232790000634\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.328125,\n 69.53451763078358\n ],\n [\n -142.734375,\n 70.72897946208789\n ],\n [\n -156.796875,\n 71.96538769913127\n ],\n [\n -158.203125,\n 68.78414378041504\n ],\n [\n -140.625,\n 68.00757101804004\n ],\n [\n -141.328125,\n 69.53451763078358\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Franz, Megan","contributorId":289877,"corporation":false,"usgs":false,"family":"Franz","given":"Megan","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Lyle","contributorId":289878,"corporation":false,"usgs":false,"family":"White","given":"Lyle","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":839973,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":839974,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roy, Denis","contributorId":289880,"corporation":false,"usgs":false,"family":"Roy","given":"Denis","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839975,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watson, Sophie","contributorId":222143,"corporation":false,"usgs":false,"family":"Watson","given":"Sophie","email":"","affiliations":[{"id":17940,"text":"Cardiff University","active":true,"usgs":false}],"preferred":false,"id":839976,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gongora, Esteban","contributorId":289882,"corporation":false,"usgs":false,"family":"Gongora","given":"Esteban","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839977,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McKinney, Melissa","contributorId":222146,"corporation":false,"usgs":false,"family":"McKinney","given":"Melissa","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839978,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227380,"text":"70227380 - 2022 - Highly pathogenic avian influenza is an emerging disease threat to wild birds in North America","interactions":[],"lastModifiedDate":"2022-03-15T16:53:15.858946","indexId":"70227380","displayToPublicDate":"2022-01-11T06:52:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Highly pathogenic avian influenza is an emerging disease threat to wild birds in North America","docAbstract":"Prior to the emergence of the A/goose/Guangdong/1/1996 (Gs/GD) H5N1 influenza A virus, the long-held and well-supported paradigm was that highly pathogenic avian influenza (HPAI) outbreaks were restricted to poultry, the result of cross-species transmission of precursor viruses from wild aquatic birds that subsequently gained pathogenicity in domestic birds. Therefore, management agencies typically adopted a prevention, control, and eradication strategy that included strict biosecurity for domestic bird production, isolation of infected and exposed flocks, and prompt depopulation. In most cases, this strategy has proved sufficient for eradicating HPAI. Since 2002, this paradigm has been challenged with many detections of viral descendants of the Gs/GD lineage among wild birds, most of which have been associated with sporadic mortality events. Since the emergence and evolution of the genetically distinct clade 2.3.4.4 Gs/GD lineage HPAI viruses in approximately 2010, there have been further increases in the occurrence of HPAI in wild birds and geographic spread through migratory bird movement. A prominent example is the introduction of clade 2.3.4.4 Gs/GD HPAI viruses from East Asia to North America via migratory birds in autumn 2014 that ultimately led to the largest outbreak of HPAI in the history of the United States. Given the apparent maintenance of Gs/GD lineage HPAI viruses in a global avian reservoir; bidirectional virus exchange between wild and domestic birds facilitating the continued adaptation of Gs/GD HPAI viruses in wild bird hosts; the current frequency of HPAI outbreaks in wild birds globally, and particularly in Eurasia where Gs/GD HPAI viruses may now be enzootic; and ongoing dispersal of AI viruses from East Asia to North America via migratory birds, HPAI now represents an emerging disease threat to North American wildlife. This recent paradigm shift implies that management of HPAI in domestic birds alone may no longer be sufficient to eradicate HPAI viruses from a given country or region. Rather, agencies managing wild birds and their habitats may consider the development or adoption of mitigation strategies to minimize introductions to poultry, to reduce negative impacts on wild bird populations, and to diminish adverse effects to stakeholders using wildlife resources. The main objective of this review is, therefore, to provide information that will assist wildlife managers in developing mitigation strategies or approaches for dealing with outbreaks of Gs/GD HPAI in wild birds in the form of preparedness, surveillance, research, communications, and targeted management actions. Resultant outbreak response plans and actions may represent meaningful steps of wildlife managers toward the use of collaborative and multi-jurisdictional One Health approaches when it comes to the detection, investigation, and mitigation of emerging viruses at the human-domestic animal-wildlife interface.
The COVID-19 pandemic has disrupted field research programs, making conservation and management decision-making more challenging. However, it may be possible to conduct population assessments using integrated models that combine community science data with existing data from structured surveys. We developed a space-time integrated model to characterize spatial and temporal variability in population distribution. We fit our integrated model to 10 years of eBird (2010-2020) and 9 years of aerial survey (2010-2019) mottled duck count data to forecast 2020 population size along the western Gulf Coast of Texas and Louisiana. Estimates of mottled duck abundance were similar in magnitude to estimates calculated using previous methods, but were more precise and showed evidence of a declining population. The spatial distribution for mottled ducks each year was characterized by several concentrations of relatively high abundance, although the location of these abundance ‘hotspots’ varied over time. Expected abundance was higher for areas with a higher proportion of area covered by marsh habitat. By leveraging large-scale community science data, we were able to conduct a population assessment despite the disruption in structured surveys caused by the pandemic. As participation in community science platforms continues to increase, we anticipate modeling frameworks, like the integrated model we developed here, will become increasingly useful for informing conservation and management decision-making.
Tree fecundity and recruitment have not yet been quantified at scales needed to anticipate biogeographic shifts in response to climate change. By separating their responses, this study shows coherence across species and communities, offering the strongest support to date that migration is in progress with regional limitations on rates. The southeastern continent emerges as a fecundity hotspot, but it is situated south of population centers where high seed production could contribute to poleward population spread. By contrast, seedling success is highest in the West and North, serving to partially offset limited seed production near poleward frontiers. The evidence of fecundity and recruitment control on tree migration can inform conservation planning for the expected long-term disequilibrium between climate and forest distribution.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2116691118","usgsCitation":"Sharma, S., Andrus, R., Bergeron, Y., Bogdziewicz, M., Bragg, D.C., Brockway, D.G., Cleavitt, N.L., Courbaud, B., Das, A., Dietze, M., Fahey, T.J., Franklin, J.F., Gregory, G.S., Greenberg, C.H., Guo, Q., Lambers, J., Ibanez, I., Johnstone, J.F., Kilner, C.L., Knops, J., Koenig, W.D., Kunstler, G., LaMontagne, J.M., Macias, D., Moran, E.V., Myers, J.A., Parmenter, R., Pearse, I., Poulton-Kamakura, R., Redmond, M.D., Reid, C.D., Rodman, K., Scher, C., Schlesinger, W.H., Steele, M.A., Stephenson, N.L., Swenson, J., Swift, M., Veblen, T.T., Whipple, A., Whitham, T.G., Wion, A.P., Woodall, C.W., Zlotin, R., and Clark, J.S., 2022, North American tree migration paced by climate in the West, lagging in the East: PNAS, v. 119, no. 3, e2116691118, 8 p., https://doi.org/10.1073/pnas.2116691118.","productDescription":"e2116691118, 8 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We examined the influence of past and current landscapes on genomic connectivity in cold-adapted galliformes as a critical first step to assess the vulnerability of Alaska ptarmigan and grouse to environmental change. We hypothesize that the mosaic of physical features and habitat within Alaska promoted the formation of genetic structure across species.
Alaska, United States of America.
Ptarmigan and Grouse (Galliformes: Tetraoninae).
We collected double digest restriction-site-associated DNA sequence data from six ptarmigan and grouse species (N = 13–145/species) sampled across multiple ecosystems up to ~10 degrees of latitude. Spatial genomic structure was analysed using methods that reflect different temporal scales: (1) principal components analysis to identify major trends in the distribution of genomic variation; (2) maximum likelihood clustering analyses to test for the presence of multiple genomic groupings; (3) shared co-ancestry analyses to assess contemporary relationships and (4) effective migration surfaces to identify regions that deviate from a null model of isolation by distance.
Levels of genomic structure varied across species (ΦST =0.009–0.042). Three general patterns of structure emerged: (1) east-west partition located near the Yukon-Tanana uplands; (2) north-south split coinciding with the Alaska Range and (3) northern group near the Brooks Range. Species-specific patterns were observed; not all landscape features were barriers to gene flow for all ptarmigan and grouse and temporal contrasts were detected at the Brooks Range.
Within Alaska galliformes, patterns of genomic structure coincide with physiographic features and highlight the importance of physical and ecological barriers in shaping how genomic diversity is arrayed across the landscape. Lack of concordance in spatial patterns indicates that species behaviour and habitat affinities play key roles in driving the contrasting patterns of genomic structure.
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Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":830649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":830650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":830651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227173,"text":"sir20215126 - 2022 - Hydrology and water quality in 15 watersheds in DeKalb County, Georgia, 2012–16","interactions":[],"lastModifiedDate":"2026-04-02T20:03:05.696911","indexId":"sir20215126","displayToPublicDate":"2022-01-05T16:40:00","publicationYear":"2022","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":"2021-5126","displayTitle":"Hydrology and Water Quality in 15 Watersheds in DeKalb County, Georgia, 2012–16","title":"Hydrology and water quality in 15 watersheds in DeKalb County, Georgia, 2012–16","docAbstract":"The U.S. Geological Survey, in cooperation with DeKalb County Department of Watershed Management, established a long-term water-quantity and water-quality monitoring program in 2012 to monitor and analyze the hydrologic and water-quality conditions of 15 watersheds in DeKalb County, Georgia—an urban and suburban area located in north-central Georgia that includes the easternmost part of the City of Atlanta. This report synthesizes the watershed characteristics and monitoring data collected for the first 5 years of the program, 2012 through 2016. The study area was predominantly medium-density residential (43.9 percent), commercial/industrial/institutional (21.4 percent), forest/park/agriculture (13.6 percent), and high-density residential (11.5 percent) land uses. Land-surface slope averaged 8.7 percent, imperviousness averaged 25.3 percent, and population density averaged 2,936 people per square mile. Watershed imperviousness ranged from 8.7 to 36.6 percent.
In the study area for 2014 to 2016 (when streamflow data were available for all watersheds), runoff represented 40.9 percent of precipitation. Hydrograph separations indicated that 43 percent of runoff occurred as base flow, whereas the remainder occurred as stormflow. Higher watershed imperviousness was significantly related to higher amounts of runoff (Pearson product-moment correlation coefficient [r] = 0.517), higher runoff ratios (r = 0.646), and lower amounts (r = −0.637) and proportions (r = −0.898) of base-flow runoff. Stormwater best management practices have been implemented in the study watersheds; however, these practices do not appear to fully mitigate the effects of urban development and land use on stream hydrology.
Total copper, lead, and zinc concentrations in base-flow and stormflow samples exceeded the national recommended aquatic life criteria for chronic and acute conditions, respectively, to varying degrees. Escherichia coli density predictive regression models indicated that the U.S. Environmental Protection Agency’s Beach Action Value was exceeded at individual watersheds between 44.6 and 100 percent of the time. Exceedance of the Beach Action Value indicates possible unsafe conditions for primary contact recreation and could be used for timely notification of the potential health risks. Annual loads and yields were estimated for 15 constituents. Loads were typically higher for years with higher runoff while variations among watershed yields appear associated with watershed and land use characteristics. The lowest yields for almost all constituents occurred in the Stone Mountain Creek watershed—likely the result of the retention of sediment and reduction of nutrients in Stone Mountain Lake and two smaller downstream reservoirs within the watershed. The Little Stone Mountain Creek watershed also had some of the lowest yields for most constituents, likely due to the lack of many pollutant sources associated with its predominantly medium-density residential land use (95.5 percent), but had the highest total nitrate plus nitrite yields. The Intrenchment Creek watershed consistently had some of the highest yields across all constituents except for total nitrate plus nitrite. The high yields may be related to its high percentage of impervious area (36.0 percent) and high amount of heavily developed land use (high-density residential, 29.9 percent and commercial/industrial/institutional, 26.0 percent). Mean watershed constituent yields in this study were significantly higher than those from a similar analysis of 13 suburban to urban watersheds in adjacent Gwinnett County for 6 of the 10 constituents compared.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions of the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215126","collaboration":"Prepared in cooperation with DeKalb County Department of Watershed Management","usgsCitation":"Aulenbach, B.T., Kolb, K., Joiner, J.K., and Knaak, A.E., 2022, Hydrology and water quality in 15 watersheds in DeKalb County, Georgia, 2012–16: U.S. Geological Survey Scientific Investigations Report 2021–5126, 105 p., https://doi.org/10.3133/sir20215126.","productDescription":"Report: xii, 105 p.; Data Release; Database","numberOfPages":"105","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-117184","costCenters":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":393756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M6WCRH","text":"USGS data release","linkHelpText":"Streamwater constituent load data, models, and estimates for 15 watersheds in DeKalb County, Georgia, 2012–2016"},{"id":393757,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5126/sir20215126.pdf","text":"Report","size":"7.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5126"},{"id":393754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5126/coverthb.jpg"},{"id":502127,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112069.htm","linkFileType":{"id":5,"text":"html"}},{"id":393759,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5126/sir20215126.XML"},{"id":393758,"rank":5,"type":{"id":34,"text":"Image 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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
The Niobrara River of northern Nebraska is a valuable water resource that sustains irrigated agriculture and recreation, as well as a diverse ecosystem. Large-quantity withdrawals from the source aquifer system have the potential to reduce the flow into the river and to adversely affect the free-flowing condition of the Niobrara National Scenic River (NSR). Therefore, to understand the magnitude and characteristics of those flows, the U.S. Geological Survey (USGS), in cooperation with the National Park Service, began a study to quantify seepage gains/losses along the eastern half of the Niobrara NSR and to create a map characterizing the base-flow recession time constant (tau) in the Niobrara NSR study area.
In 2016, a seepage study was completed to quantify seepage gains/losses along the eastern half of the Niobrara NSR. The seepage study results indicated that the main-stem streamflow on the Niobrara River increases 375 cubic feet per second (ft3/s) in the 39.9-mile study reach (river mile 119.3 to river mile 79.4). Although most of the streamflow increases are attributed to tributary inflows (297 ft3/s, 79 percent), 78 ft3/s are attributed to seepage gains within the reach. Seepage rates in the study reach ranged from 1.41 cubic feet per second per mile ([ft3/s]/mi) to 2.56 (ft3/s)/mi, with a mean seepage rate of 2 (ft3/s)/mi.
Tau values were calculated at 10 sites in the Niobrara NSR study area, and kriging geostatistical techniques were used to develop a contour map to estimate tau values at locations where streamflow was not measured. The minimum tau value was 12.1 days at Willow Creek at Atwood Road near Carns, Nebraska (USGS station 06463670), and the maximum value was 45.5 days at Tyler Falls at Fort Niobrara National Wildlife Refuge near Valentine, Nebr. (USGS station 06461150).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215102","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Strauch, K.R., and Soenksen, P.J., 2022, Main-stem seepage and base-flow recession time constants in the Niobrara National Scenic River Basin, Nebraska, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5102, 17 p., https://doi.org/10.3133/sir20215102.","productDescription":"Report: vi, 17 p.; Data Release; Dataset","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125025","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":393921,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDP1BI","text":"USGS data release","linkHelpText":"Datasets used to map the base-flow recession time constants in the Niobrara National Scenic River in Nebraska, 2016–18"},{"id":502117,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112068.htm","linkFileType":{"id":5,"text":"html"}},{"id":393922,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5102/sir20215102.pdf","text":"Report","size":"2.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5102"},{"id":393919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5102/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Niobrara National Scenic River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.974609375,\n 41.64007838467894\n ],\n [\n -100.08544921874999,\n 41.64007838467894\n ],\n [\n -100.08544921874999,\n 42.956422511073335\n ],\n [\n -103.974609375,\n 42.956422511073335\n ],\n [\n -103.974609375,\n 41.64007838467894\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Throughout their range, Brook Trout (Salvelinus fontinalis) occupy thousands of disjunct drainages with varying levels of disturbance, which presents substantial challenges for conservation. Within the southern Appalachian Mountains, fragmentation and genetic drift have been identified as key threats to the genetic diversity of the Brook Trout populations. In addition, extensive historic stocking of domestic lineages of Brook Trout to augment fisheries may have eroded endemic diversity and impacted locally adapted populations. We used 12 microsatellite loci to describe patterns of genetic diversity within 108 populations of wild Brook Trout from Tennessee and used linear models to explore the impacts of land use, drainage area, and hatchery stockings on metrics of genetic diversity, effective population size, and hatchery introgression. We found levels of within-population diversity varied widely, although many populations showed very limited diversity. The extent of hatchery introgression also varied across the landscape, with some populations showing high affinity to hatchery lineages and others appearing to retain their endemic character. However, we found relatively weak relationships between genetic metrics and landscape characteristics, suggesting that contemporary landscape variables are not strongly related to observed patterns of genetic diversity. We consider this result to reflect both the complex history of these populations and the challenges associated with accurately defining drainages for each population. Our study highlights the importance of genetic data to guide management decisions, as complex processes interact to shape the genetic structure of populations and make it difficult to infer the status of unsampled populations.
The Jerusalem cricket subfamily Stenopelmatinae is distributed from south-western Canada through the western half of the United States to as far south as Ecuador. Recently, the generic classification of this subfamily was updated to contain two genera, the western North American Ammopelmatus, and the Mexican, and central and northern South American Stenopelmatus. The taxonomy of the latter genus was also revised, with 5, 13 and 14 species being respectively validated, declared as nomen dubium and described as new. Despite this effort, the systematics of Stenopelmatus is still far from complete. Here, we generated sequences of the mitochondrial DNA barcoding locus and performed two distinct DNA sequence-based approaches to assess the species’ limits among several populations of Stenopelmatus, with emphasis on populations from central and south-east Mexico. We reconstructed the phylogenetic relationships among representative species of the main clades within the genus using nuclear 3RAD data and carried out a molecular clock analysis to investigate its biogeographic history. The two DNA sequence-based approaches consistently recovered 34 putative species, several of which are apparently undescribed. Our estimates of phylogeny confirmed the recent generic update of Stenopelmatinae and revealed a marked phylogeographic structure within Stenopelmatus. Based on our results, we propose the existence of four species-groups within the genus (the faulkneri, talpa, Central America and piceiventris species-groups). The geographic distribution of these species-groups and our molecular clock estimates are congruent with the geological processes that took place in mountain ranges along central and southern Mexico, particularly since the Neogene. Our study emphasises the necessity to continue performing more taxonomic and phylogenetic studies on Stenopelmatus to clarify its actual species richness and evolutionary history in Mesoamerica.
Gas hydrate distributions on the marine margins of the U.S. state of Alaska are more poorly known than those on other U.S. margins, where bottom simulating reflections have been systematically mapped on marine seismic data to support modern, quantitative assessments of gas-in-place in gas hydrates. The extent of bottom simulating reflections in the U.S. Beaufort Sea has been known since the late 1970s, and researchers have investigated the possibility that remnant gas hydrate persists in association with decaying subsea permafrost on both the U.S. and Canadian Beaufort continental shelves. In the Bering Sea, possible gas hydrate-related features have been widely mapped, revealing zones of free gas and concentrated gas hydrate within the hydrate stability zone in features called velocity amplitude anomalies (VAMPs). However, there are few reports on bottom simulating reflections along the more than 2500 km of the Aleutian arc and along the transform plate margin in southeast Alaska. Here we examine selected seismic profiles from southeast Alaska, along the Aleutian margin, and on the Bering continental slope, emphasizing surveys acquired with large airgun arrays, and review the results obtained from Bering Sea’s Aleutian Basin and from the U.S. Beaufort Sea. In the new analyses, we detect hydrate-related bottom simulating reflections in southeastern Alaska and the eastern and central parts of the Aleutian arc, but not in the western Aleutian arc or beneath the continental slope from the island arc north into the Aleutian Basin. In the Bering Sea, recognition of hydrate-related bottom simulating reflections is complicated by the widespread existence of a bottom simulating reflector associated with a diagenetic transition (opal CT). Our detection of continental slope hydrate-related bottom simulating reflections in southeast Alaska and the eastern and central Aleutian arcs expands the area of potential gas hydrate distribution on Alaskan margins and underscores the need for more systematic analysis of existing seismic data to inform quantitative evaluation of gas-in-place.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"World atlas of submarine gas hydrates in continental margins","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-81186-0_17","usgsCitation":"Ruppel, C.D., and Hart, P.E., 2022, Gas hydrates on Alaskan marine margins, chap. of World atlas of submarine gas hydrates in continental margins, p. 209-223, https://doi.org/10.1007/978-3-030-81186-0_17.","productDescription":"15 p.","startPage":"209","endPage":"223","ipdsId":"IP-122791","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":394384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea’s Aleutian basin, U.S. Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.169921875,\n 69.59589006237648\n ],\n [\n -140.18554687499997,\n 69.59589006237648\n ],\n [\n -140.18554687499997,\n 73.42842364106816\n ],\n [\n -159.169921875,\n 73.42842364106816\n ],\n [\n -159.169921875,\n 69.59589006237648\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -177.01171875,\n 47.87214396888731\n ],\n [\n -153.017578125,\n 47.87214396888731\n ],\n [\n -153.017578125,\n 61.39671887310411\n ],\n [\n -177.01171875,\n 61.39671887310411\n ],\n [\n -177.01171875,\n 47.87214396888731\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":830832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":830833,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230305,"text":"70230305 - 2022 - Caretta caretta (Loggerhead Sea Turtle) nesting exchange","interactions":[],"lastModifiedDate":"2022-04-07T13:32:18.48214","indexId":"70230305","displayToPublicDate":"2022-01-01T08:27:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Caretta caretta (Loggerhead Sea Turtle) nesting exchange","title":"Caretta caretta (Loggerhead Sea Turtle) nesting exchange","docAbstract":"The Northwest Atlantic population of Loggerhead Sea Turtles (Caretta caretta) is one of the largest C. caretta populations in the world and is listed as threatened. This population was divided into five genetically distinct subpopulations, including the Northern Gulf of Mexico (NGoM) subpopulation (Shamblin et al. 2017 Mar. Bio. 164:138). Across the NGoM, the majority of C. caretta nesting occurs in Franklin and Gulf Counties, Florida, USA (Florida Fish and Wildlife Conservation Commission, https://myfwc.com/research/wildlife/sea-turtles/nesting/nesting-atlas/). Few C. caretta nests are documented on Texas, USA, beaches and as such, less is known about the individuals that nest in Texas (see Shaver et al. 2020 Front. Mar. Sci. 7:1, Frandsen et al. 2020 Herp. Review 51:825) as compared to those that nest on beaches in the eastern part of the range (Lamont et al. 2014 Mar. Bio. 161:2659). Although C. caretta individuals have been tracked to Texas from nesting beaches throughout the Southeastern USA (Hart et al. 2014, PLoS One, 9), movements of C. caretta away from Texas beaches are rare. Here we detail the exchange of an adult female C. caretta that emerged and was tagged on the beach in Texas and then subsequently documented nesting in Northwest Florida.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Lamont, M., Walker, J.S., and Shaver, D.J., 2022, Caretta caretta (Loggerhead Sea Turtle) nesting exchange: Herpetological Review, v. 52, no. 3, p. 626-627.","productDescription":"2 p.","startPage":"626","endPage":"627","ipdsId":"IP-128942","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.086181640625,\n 26.15543796871355\n ],\n [\n -97.31689453125,\n 27.21555620902969\n ],\n [\n -96.207275390625,\n 28.372068829631633\n ],\n [\n -94.6142578125,\n 29.334298230315675\n ],\n [\n -93.85620117187499,\n 29.69759650228319\n ],\n [\n -94.22973632812499,\n 29.897805610155874\n ],\n [\n -95.020751953125,\n 29.850173125689896\n ],\n [\n -95.44921875,\n 29.152161283318915\n ],\n [\n -96.45996093749999,\n 28.76765910569123\n ],\n [\n -97.03125,\n 28.38173504322308\n ],\n [\n -97.72338867187499,\n 27.332735136859146\n ],\n [\n -97.503662109375,\n 26.086388149394875\n ],\n [\n -97.086181640625,\n 26.15543796871355\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":206817,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jennifer S.","contributorId":289853,"corporation":false,"usgs":false,"family":"Walker","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":839928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":839929,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70246520,"text":"70246520 - 2022 - Reconstructing the paleoceanographic and redox conditions responsible for variations in uranium content in North American Devonian black shales","interactions":[],"lastModifiedDate":"2023-07-07T12:17:22.507283","indexId":"70246520","displayToPublicDate":"2021-12-27T07:13:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2996,"text":"Palaeogeography, Palaeoclimatology, Palaeoecology","printIssn":"0031-0182","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing the paleoceanographic and redox conditions responsible for variations in uranium content in North American Devonian black shales","docAbstract":"The uranium (U) content, and more recently, the ratio between 238U and 235U in black shales are commonly applied as a proxy to determine redox conditions and infer organic-richness. Uranium contents typically display a linear relationship with total organic carbon (TOC) in shales. This relationship is due to the processes and mechanisms responsible for the incorporation of U into the sediment during the deposition and remineralization of organic matter. This U/TOC relationship can vary, however, and some shales display uncharacteristically low U content despite having high TOC content, while others show large enrichments of U relative to TOC. Here we examine the U to TOC ratios and U-isotope compositions of three Upper Devonian-Lower Mississippian shales: the Woodford Shale, the Cleveland Shale, and the Bakken Shale, with two study sites in Oklahoma, one site in eastern Kentucky, and three sites in eastern Montana and western North Dakota, respectively. The U/TOC ratios of each shale are distinct from one another exhibiting average ratios ranging from 3 in the Cleveland Shale, to over 10 in the Bakken Shale. The distinct geochemical composition of the three shales suggests that, although lithologically similar, each study site represents a markedly different and dynamic depositional environment. The low average U/TOC (~3) along with the relatively high δ238U values (~0.03‰) of the Cleveland Shale core suggests deposition along the basin margin under normal marine conditions with periods of reduced bottom water oxygenation, likely due to fluctuations in the location of the pycnocline. The Woodford Shale on the other hand, shows higher U/TOC ratios (~4, George core, ~9, Poe core) and δ238U (~0.02‰ average, George core, ~0.06‰ average, Poe core), which suggests an unrestricted setting with intermittent euxinic conditions. In contrast, high U/TOC ratios (2–15), and very high δ238U values (up to 0.55‰) in the Bakken Shale cores indicate intense metal draw-down into sediments under sulfidic waters. The results show that when the U/TOC ratios and U-isotopic compositions of each studied shale are compared to modern anoxic basins and upwelling areas, it allows for an enhanced understanding of the paleoenvironmental conditions such as basin restriction and redox state of waters within the Late Devonian epicontinental seas of North America.
For the sea otter (Enhydra lutris), genetic population structure is an area of research that has not received significant attention, especially in Southwest Alaska where that distinct population segment has been listed as threatened since 2005 pursuant to the U.S. Endangered Species Act. In this study, 501 samples from 14 locations from Prince William Sound, Alaska to the Commander Islands in Russia were analyzed for variation at 13 microsatellite loci. Our results indicate a high degree of genetic divergence among the 14 locations (FST = 0.120) with gene flow conforming to the isolation by distance (IBD) model (r2 = 0.491, p < .05). The 14 sampling locations formed six geographic associations in clustering and ordination analyses that likely correspond to remnant population lineages: (1) Southcentral Alaska, (2) Kodiak and North Alaska Peninsula, (3) South Alaska Peninsula and Bristol Bay, (4) Eastern Aleutian, (5) Western Aleutian, and (6) the Commander Islands. Except for South Alaska Peninsula and Bristol Bay, these clusters closely agree with previously defined stock and management unit boundaries. Our results reveal significant genetic population structure and are generally congruent with current management strategies for the threatened Southwest Alaska distinct population segment.
Stream intermittency is predicted to increase where water withdrawals and climate warming are increasing. In regions coupled with high fish diversity, understanding how intermittency influences fish trophic ecology is critical for informing ecosystem function. This study compared fish diets across seasons in perennial and intermittent streams to estimate the immediate and cumulative effects of stream drying on fish foraging patterns. We used gut content analysis to compare the diets of small-bodied, secondary consumer fishes, including two minnow and three darter species found in the lower Flint River Basin of southwestern Georgia, during both the summer (before stream dry-down) and fall (post flow resumption) seasons. Fish communities in perennial streams had greater diet richness compared to fishes in intermittent streams for both seasons. Darter diets were characterised by rheophilic aquatic insects in perennial streams and by benthic crustaceans (copepods, cladocerans and isopods) and predatory aquatic insects in intermittent streams. Minnow diets were typified by freshwater sponges, eggs and organic detritus in intermittent streams and by terrestrial insects and diatoms in perennial streams. Fishes in intermittent streams consumed significantly more benthic crustaceans in the fall (37% increase in proportional volume) compared to preflow cessation conditions in the summer, suggesting these organisms play an important, yet relatively unrecognised role in supporting fish communities in southeastern streams. Our findings enhance our understanding of how stream intermittency influences the trophic dynamics of secondary consumer fishes in an agricultural watershed increasingly affected by water scarcity.
The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where “good” and “poor” conditions occur and provide insight into landscape contexts supporting such conditions. However, as assessments are conducted at large extents crossing jurisdictional boundaries, combined datasets are likely not suited for traditional assessment approaches which rely on jurisdictionally-specific reference sites. Here, we used a large dataset compiled from multiple providers to assess the condition of fish habitat for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA. We concurrently used community and species-level analyses to provide a more holistic view of habitat conditions by using random forest models to predict selected metrics and species occurrence with landscape data for inland CBW stream reaches. Community analyses included metrics describing composition, tolerances, habitat preferences, and functional traits of fish communities whereas species-level analyses consisted of distribution models for key sensitive and gamefish species. For community analyses, a final index was calculated as the average of selected metric deciles with higher scores inferring less biologically altered (i.e., better) conditions, providing an alternative to using reference sites. For species analyses, species occurrence was predicted for stream reaches, with presence indicating suitable habitat. Uncertainty was calculated for both approaches using model prediction intervals. Results indicated different numbers of suitable metrics for each region, with most in the Northern Appalachian (15) and least in the Southern Appalachian Piedmont (3). Four species (three sensitive) were suitable for modeling. At the CBW scale, predictions did not vary greatly among deciles for the community or species analyses for 2001, 2006, 2011, and 2016. Most stream reaches did not vary in mean decile rank or in species occurrence between 2001 and 2016; however, the largest community changes occurred in large rivers in the Coastal Plains ecoregion and the largest species occurrence changes occurred in Torrent Suckers in medium-sized rivers. When compared, results from community analyses agreed for one sensitive species (Brook Trout) but not the other three, potentially due to regionally inappropriate tolerance assignment. Comparisons also demonstrated substantial variation among approaches suggesting a lack of redundancy. While each approach traditionally has its targeted audience and respective strengths and weaknesses, concurrent use of these approaches permits direct comparisons and may assuage shortcomings of each approach when considered separately.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108488","usgsCitation":"Maloney, K.O., Krause, K.P., Cashman, M.J., Daniel, W., Gressler, B.P., Wieferich, D.J., and Young, J.A., 2022, Using fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA: Ecological Indicators, v. 134, 108488, 17 p., https://doi.org/10.1016/j.ecolind.2021.108488.","productDescription":"108488, 17 p.","ipdsId":"IP-133787","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449408,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219320,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":828573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gressler, Benjamin P. 0000-0001-6639-8558","orcid":"https://orcid.org/0000-0001-6639-8558","contributorId":270167,"corporation":false,"usgs":true,"family":"Gressler","given":"Benjamin","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":828574,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wieferich, Daniel J. 0000-0003-1554-7992 dwieferich@usgs.gov","orcid":"https://orcid.org/0000-0003-1554-7992","contributorId":176205,"corporation":false,"usgs":true,"family":"Wieferich","given":"Daniel","email":"dwieferich@usgs.gov","middleInitial":"J.","affiliations":[{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true},{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":828575,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":828576,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227659,"text":"70227659 - 2022 - A review of algal toxin exposures on reserved federal lands and among trust species in the United States","interactions":[],"lastModifiedDate":"2023-06-21T16:31:50.809151","indexId":"70227659","displayToPublicDate":"2021-12-10T07:03:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1345,"text":"Critical Reviews in Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"A review of algal toxin exposures on reserved federal lands and among trust species in the United States","docAbstract":"Associated health effects from algal toxin exposure are a growing concern for human and animal health. Algal toxin poisonings may occur from contact with or consumption of water supplies or from ingestion of contaminated animals. The U.S. Federal Government owns or holds in trust about 259 million hectares of land, in addition to the Trust species obligations. We completed the first comprehensive review of potential toxin-producing algal blooms in surface waters on Federal lands and Trust species exposed to algal toxins. Events were sorted into three tiers based on potentially toxic algae abundance or toxin concentration and related effects on animal morbidity and mortality. At least 11.1% of Federal lands are known to have been affected by algal events, but exposure is likely underreported. The occurrence of potential toxin producers and their toxins (Tier 1) have been documented 337 times, health advisory threshold exceedances (Tier 2) were reported 943 times, and 86 events involved animal sickness or death linked to cyanobacteria or marine toxins (Tier 3). Trust species exposed to cyano- or algal toxins included marine mammals, migratory birds, threatened and endangered species, and species of concern. We report numerous data gaps ranging from potential effects on human health from consuming intoxicated animals to the infrequency of measuring and reporting certain toxins. Improvements to field and laboratory methods, more consistent evaluation of toxin exposure, decreased latency on data analysis, delivery and interpretation will be necessary to improve response and management strategies for protecting human and animal health where issues persist.
We present a new catalog of calibrated earthquake relocations from the 2019–2020 Puerto Rico earthquake sequence related to the 7 January 2020 Mw 6.4 earthquake that occurred offshore of southwest Puerto Rico at a depth of 15.9 km. Utilizing these relocated earthquakes and associated moment tensor solutions, we can delineate several distinct fault systems that were activated during the sequence and show that the Mw 6.4 mainshock may have resulted from positive changes in Coulomb stress from earlier events. Seismicity and mechanisms define (1) a west–southwest (∼260°) zone of seismicity comprised of largely sinistral strike‐slip and oblique‐slip earthquakes that mostly occurs later in the sequence and to the west of the mainshock, (2) an area of extensional faulting that includes the mainshock and occurs largely within the mainshock’s rupture area, and (3) an north–northeast (∼30°)‐striking zone of seismicity, consisting primarily of dextral strike‐slip events that occurs before and following the mainshock and generally above (shallower than) the normal‐faulting events. These linear features intersect within the Mw 6.4 mainshock’s fault plane in southwest Puerto Rico. In addition, we show that earthquake relocations for M 4+ normal‐faulting events, when traced along their fault planes, daylight along east–west‐trending bathymetric features offshore of southwest Puerto Rico. Correlation of these normal‐faulting events with bathymetric features suggests an active fault system that may be a contributor to previously uncharacterized seismic hazards in southwest Puerto Rico.
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America","active":true,"publicationSubtype":{"id":10}},"title":"Review of ESA SYMP 7: A dynamic perspective on ecosystem restoration–establishing temporal connectivity at the intersection between paleoecology and restoration ecology","docAbstract":"Landscape connectivity is vital not only spatially, but also temporally; as ecosystems change, it is important to be aware of past, present, and future variables that may impact ecosystem function and biodiversity. As climate and environments continue to change, choosing appropriate restoration targets is becoming more challenging. By considering the paleoecological and paleoenvironmental record for a given region, restoration practitioners are not only able to bear witness to that region’s dynamic history, but also potentially identify multiple, alternative natural ecosystem states. Indeed, one of the deliverables of conservation paleobiology, a field that applies paleontological data and methods to present-day conservation, is to inform restoration targets. Consideration of future change is equally important, and paleoecological and paleoclimatological data are essential for informing models that can help us understand how climate change is affecting species and ecosystems at different temporal scales. The symposium “A dynamic perspective on ecosystem restoration: Establishing temporal\nconnectivity at the intersection between paleoecology and restoration ecology” gathered representatives from macroecology, paleoecology, and restoration ecology to share their perspectives on temporal connectivity and how consideration of an ecosystem’s past, present, and future can positively impact restoration and conservation. Some speakers approached the topic theoretically, while others considered it from a more practical and applied standpoint. The goals of the symposium were to build a stronger relationship among the subdisciplines, stimulate new ideas, and identify data and/or products that would be useful to share across subdisciplines.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/bes2.1954","usgsCitation":"Reid, R., McGuire, J., Svenning, J., Wingard, G.L., and Moreno-Mateos, D., 2022, Review of ESA SYMP 7: A dynamic perspective on ecosystem restoration–establishing temporal connectivity at the intersection between paleoecology and restoration ecology: Bulletin Ecological Society of America, v. 103, no. 1, e01954, 6 p., https://doi.org/10.1002/bes2.1954.","productDescription":"e01954, 6 p.","ipdsId":"IP-134688","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467212,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/bes2.1954","text":"External Repository"},{"id":392566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Reid, Rachel","contributorId":269802,"corporation":false,"usgs":false,"family":"Reid","given":"Rachel","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":827949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jenny","contributorId":269803,"corporation":false,"usgs":false,"family":"McGuire","given":"Jenny","email":"","affiliations":[{"id":56035,"text":"GA Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":827950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Svenning, Jens-Christiane","contributorId":269804,"corporation":false,"usgs":false,"family":"Svenning","given":"Jens-Christiane","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":827951,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wingard, G. Lynn 0000-0002-3833-5207 lwingard@usgs.gov","orcid":"https://orcid.org/0000-0002-3833-5207","contributorId":605,"corporation":false,"usgs":true,"family":"Wingard","given":"G.","email":"lwingard@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":827952,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moreno-Mateos, David","contributorId":269806,"corporation":false,"usgs":false,"family":"Moreno-Mateos","given":"David","email":"","affiliations":[{"id":16810,"text":"Harvard Univ.","active":true,"usgs":false}],"preferred":false,"id":827953,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227326,"text":"70227326 - 2022 - Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA","interactions":[],"lastModifiedDate":"2022-01-10T12:59:36.251299","indexId":"70227326","displayToPublicDate":"2021-11-24T06:56:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA","docAbstract":"Factors affecting groundwater quality used for domestic supply within the Marcellus Shale footprint in north-central and north-east Pennsylvania are identified using a combination of spatial, statistical, and geochemical modeling. Untreated groundwater, sampled during 2011–2017 from 472 domestic wells within the study area, exhibited wide ranges in pH (4.5–9.3), total dissolved solids (TDS, 22–1960 mg/L), sodium (0.3–760 mg/L), chloride (0.3–1020 mg/L), bromide (<0.01–8.6 mg/L), and methane (<0.001–77 mg/L). The wells had depths ranging from 10 to 394 m; 69.5 percent were completed in sandstone bedrock, 19.3 percent in shale, 4.2 percent in siltstone, 4 percent in carbonate, and 3 percent in unconsolidated alluvial or glacial deposits. Groundwater quality in the Delaware River watershed, in the eastern part of the study area where Marcellus gas has not been developed, was similar to that in the Susquehanna, Allegheny, and Genesee River watersheds in the western part of the study area where natural gas production from Marcellus Shale has been ongoing since 2008. Most groundwaters were calcium/bicarbonate type with near-neutral pH; approximately 10 percent were sodium/bicarbonate and 1 percent were sodium/chloride types. Sodium-enriched waters, which were mostly from shale and siltstone aquifers, had the greatest frequency of elevated pH (>8.5) and elevated concentrations of TDS (>250 mg/L), bromide (>0.15 mg/L), methane (>7.0 mg/L), and lithium (>60 μg/L). Geochemical models indicate these characteristics could result from progressive mineral dissolution combined with cation exchange, plus mixing with locally important salinity sources, including as much as 0.7 percent Appalachian Basin brine and/or road-deicing salt. Multivariate correlation models suggest the observed variability in methane concentrations may be attributed to several environmental factors, such as geochemical evolution along groundwater flow paths, redox conditions, and/or mixing with saline groundwater or brine. Most samples having elevated methane were from shale aquifers, which were mainly in the Susquehanna River basin and had the greatest density of gas wells compared to other lithologies. Samples having elevated methane were also observed in the Delaware River watershed and other areas outside gas development. Isotopic compositions of methane for a subset of 39 samples (selected because of elevated methane) and relatively high ratios of methane to ethane in those samples indicated methane could be derived from microbial gas mixed with thermogenic gas that may have undergone degradation and/or fractionation during migration. The methods used in this study could be broadly applicable to understanding major factors affecting groundwater quality, particularly for explaining variations in ionic composition with pH and identifying sources of salinity and associated constituents (e.g. sodium, chloride, bromide, lithium, methane) that may have geogenic or anthropogenic origins.
Historical mine and mineral deposit datasets are routinely used to inform quantitative mineral assessment models, but they also can contain a wealth of supplementary qualitative information that is generally underutilized. We present a workflow that uses correspondence analysis, an exploratory tool commonly applied to multivariate abundance data, to better utilize qualitative data in these historical datasets. The workflow involves extraction of qualitative information on ore mineralogy from a mineral deposit database, attaches those data to a target geological feature, and analyzes the underlying data structure with correspondence analysis and hierarchical clustering. The output of correspondence analysis is inversely weighted to the relative frequency of ore minerals, and therefore rare mineral species (i.e., those with unusually low frequencies) can disproportionately contribute to the total variance of the dataset. We present a novel technique for aggregating frequencies of rare mineral species that minimizes this effect. We apply this workflow to evaluate how ore mineral assemblages in former and active mines vary in spatial relation to silicic calderas in the southwestern United States. The most common ore mineral associations observed spatially and genetically associated to calderas include those related to polymetallic, base metal-rich systems and epithermal Au–Ag systems. Three other groups of mineralized calderas were identified, including: (1) Hg–Sb mineralized calderas in the northern Great Basin and western Nevada volcanic field; (2) calderas associated with elevated abundances of Mn oxides/hydroxides, fluorite, and Be-minerals, mostly in eastern Utah and New Mexico; and (3) calderas with numerous U ± F deposits, which are located in central Colorado, the eastern Great Basin and in northern Nevada. The latter three groups are associated with economically significant critical mineral resources, including the Li resources of the McDermitt complex and Be associated with the Spor Mountain on the margin of the Thomas caldera complex. We conclude that correspondence analysis is a promising technique that can enhance data exploration of the qualitative information held within mineral deposit datasets. Consequently, it could have numerous applications for mineral potential mapping, resource assessment projects, and characterization of mineral systems.
The East Rift Zone (ERZ) of Kīlauea Volcano, Hawai'i, represents one of the most volcanically active regions in the world. The 2007 Father's Day (FD) dike intrusion, eruption, and accompanying slow-slip event (SSE) has been previously modeled using geodetic data to constrain the geometry of the intrusion and the timing and magnitude of the SSE. Here, we perform inversions of three interferometric synthetic aperture radar (InSAR) datasets and a new intensity offset tracking dataset to assess the effect of integrating intensity cross-correlation offsets into inversion problems and explore additional potential models for the intrusion geometry of the FD event based on this additional data. The overall lowest misfit single Okada model for all datasets opens 2.3 m, strikes 73 degrees while dipping sub-vertically at 83 degrees, and extends approximately 2.9 km to the ENE and 2.4 km downdip. The differences are minor between complex en-echelon distributed Okada and decollement model of (Montgomery-Brown et al., 2010) or 3D-MBEM breaching models including multiple surface breaches and free-slipping decollement movement. Finally, we examine the static Coulomb stress changes for the proposed decollement fault created by our preferred model and a representative model of deep rift opening and find that deep rift zones dilation, not shallow ERZ intrusions, are likely modulating slip on the decollement.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107425","usgsCitation":"Leeburn, J., Wauthier, C., Montgomery-Brown, E.K., and Gonzalez-Santana, J., 2022, New insights on faulting and intrusion processes during the June 2007, East Rift Zone eruption of Kilauea volcano, Hawai'i: Journal of Volcanology and Geothermal Research, v. 421, 107425, 14 p., https://doi.org/10.1016/j.jvolgeores.2021.107425.","productDescription":"107425, 14 p.","ipdsId":"IP-125424","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488262,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2021.107425","text":"Publisher Index Page"},{"id":484584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.2908512348302,\n 19.41341967491155\n ],\n [\n -155.2908512348302,\n 19.401793724963014\n ],\n [\n -155.27525765813795,\n 19.401793724963014\n ],\n [\n -155.27525765813795,\n 19.41341967491155\n ],\n [\n -155.2908512348302,\n 19.41341967491155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"421","noUsgsAuthors":false,"publicationDate":"2021-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Leeburn, J.","contributorId":353406,"corporation":false,"usgs":false,"family":"Leeburn","given":"J.","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":933489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wauthier, C.","contributorId":353409,"corporation":false,"usgs":false,"family":"Wauthier","given":"C.","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":933490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":933491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gonzalez-Santana, J.","contributorId":353412,"corporation":false,"usgs":false,"family":"Gonzalez-Santana","given":"J.","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":933492,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}