Lahars, or volcanic mudflows, are the most hazardous eruption-related phenomena that will affect communities living along rivers that originate on Mount Baker. In the past 15,000 years, the largest lahars from Mount Baker have affected the Middle Fork Nooksack River drainage and beyond. Here we use the physics-based D-Claw software package to model nine lahar scenarios that are initiated as water-saturated landslides between Sherman Crater and the Roman Wall on the Mount Baker edifice and flow down the Middle Fork Nooksack River. The scenarios range in volume from 1 to 260 million cubic meters and have an initial hydraulic permeability from 10−12 to 10−10 meters squared. Model output includes data such as flow depth, velocity, runout distance, area inundated, arrival time, and sediment concentration as well as information that allows scientists to calculate other important hydrologic characteristics such as lahar discharge. These data are important to officials who have the responsibility to plan for, or take mitigation measures against, future Mount Baker lahars. To check the validity of the D-Claw results, we compare the scenarios to known geologic information. We also compare D-Claw results with empirical models that have been used in the past to determine potential inundation areas, runout distances, and arrival times. These comparisons highlight similarities and differences between empirical and physics-based models. We also present D-Claw scenario-based animations to help scientists, officials, and lay people alike to visualize how future lahars could affect communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245133","usgsCitation":"Gardner, C.A., Benage, M.C., Cannon, C., and George, D.L., 2025, Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5133, 47 p., https://doi.org/10.3133/sir20245133.","productDescription":"Report: vii, 47 p.; 9 Animation Videos; Data Release","numberOfPages":"47","ipdsId":"IP-151680","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":485743,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioC2.mp4","text":"Appendix 4 - Scenario C2","size":"35.9 MB","description":"Scenario C2","linkHelpText":"- Scenario C2"},{"id":485742,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB3.mp4","text":"Appendix 4 - Scenario B3","size":"47 MB","description":"Scenario B3","linkHelpText":"- Scenario B3"},{"id":485741,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB2.mp4","text":"Appendix 4 - Scenario B2","size":"37.6 MB","description":"Scenario B2","linkHelpText":"- Scenario B2"},{"id":485740,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB1.mp4","text":"Appendix 4 - Scenario B1","size":"25.6 MB","description":"Scenario B1","linkHelpText":"- Scenario B1"},{"id":485739,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA3.mp4","text":"Appendix 4 - Scenario A3","size":"50.4 MB","description":"Scenario A3","linkHelpText":"- Scenario A3"},{"id":485737,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA1.mp4","text":"Appendix 4 - Scenario A1","size":"35.4 MB","description":"Scenario A1","linkHelpText":"- Scenario A1"},{"id":485736,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1PEX7FS","text":"USGS data release","description":"George, D.L., Cannon, C.M., Benage, M.C., and Gardner, C.A., 2025, Simulated lahar extents and dynamics in the Middle Fork Nooksack River drainage, resulting from hypothetical landslide sources on the western summit of Mount Baker, Washington: U.S. Geological Survey data release, https://doi.org/10.5066/P1PEX7FS.","linkHelpText":"Simulated lahar extents and dynamics in the Middle Fork Nooksack River drainage, resulting from hypothetical landslide sources on the western summit of Mount Baker, Washington"},{"id":485734,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5133/images"},{"id":485733,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133.XML","description":"SIR 2024-5133 XML"},{"id":485731,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5133 PDF"},{"id":485730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5133/coverthb.jpg"},{"id":485745,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioE2.mp4","text":"Appendix 4 - Scenario E2","size":"22.1 MB","description":"Scenario E2","linkHelpText":"- Scenario E2"},{"id":485744,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioD2.mp4","text":"Appendix 4 - Scenario D2","size":"26.5 MB","description":"Scenario D2","linkHelpText":"- Scenario D2"},{"id":485732,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245133/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5133 HTML"},{"id":485738,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA2.mp4","text":"Appendix 4 - Scenario A2","size":"41.7 MB","description":"Scenario A2","linkHelpText":"- Scenario A2"},{"id":485848,"rank":16,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118573.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Middle Fork Nooksack River, Mount Baker","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.667,\n 49\n ],\n [\n -122.667,\n 49\n ],\n [\n -122.667,\n 48.6667\n ],\n [\n -121.667,\n 48.6667\n ],\n [\n -121.667,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"David A. Johnston Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Building 10, Suite 100
Vancouver, WA 98683
Email: askCVO@usgs.gov
","tableOfContents":"Protecting lives and livelihoods during volcanic eruptions is the key challenge in volcanology, conducted primarily by volcano monitoring and emergency management organisations, but it is complicated by scarce knowledge of how communities respond in times of crisis. Social sensing is a rapidly developing practice that can be adapted for volcanology. Here we use social sensing of Twitter (currently known as X) posts to track changes in social action and reaction throughout the 2018 eruption of Kīlauea on the island of Hawai`i. The volume of relevant posts very rapidly increases in early May, coincident with the beginning of the eruption; automated sentiment analysis shows a simultaneous shift towards more negative emotions being expressed in post text. Substantial negative trends in sentiment are evident in reaction to high-impact events, including the destruction of a popular residential area and injuries sustained by tourists viewing the eruption. Topics of local Twitter conversation reveal societal actions, including the sharing of hazard warnings, mitigation actions, and aid announcements. Temporal trends in societal actions reflect patterns in volcanic activity (e.g. the peak and waning of eruptive activity), civil protection actions (e.g. risk mitigation actions and the communication of official warnings), and socioeconomic pressures (e.g. the destruction of homes). Local tweets detailing eruption damage and disruption display a similar temporal trend to independent estimates of the number of buildings in contact with lava. We show how hazard and risk information is discussed and reacted to on Twitter, which helps inform our understanding of community response actions and aids situational awareness, and outline how our approach could be adapted for use in real time.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/nhess-25-1681-2025","usgsCitation":"Hickey, J., Young, J., Spruce, M., Pandit, R., Williams, H., Arthur, R., Stovall, W., and Head, M., 2025, Social sensing a volcanic eruption: Application to Kīlauea, 2018: Natural Hazards and Earth System Sciences, v. 25, no. 5, p. 1681-1696, https://doi.org/10.5194/nhess-25-1681-2025.","productDescription":"16 p.","startPage":"1681","endPage":"1696","ipdsId":"IP-168133","costCenters":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":488089,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-25-1681-2025","text":"Publisher Index Page"},{"id":486513,"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 -155.34909539011298,\n 19.523234716050013\n ],\n [\n -155.34909539011298,\n 19.22327113116394\n ],\n [\n -154.805338823834,\n 19.22327113116394\n ],\n [\n -154.805338823834,\n 19.523234716050013\n ],\n [\n -155.34909539011298,\n 19.523234716050013\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hickey, James","contributorId":355777,"corporation":false,"usgs":false,"family":"Hickey","given":"James","affiliations":[{"id":84830,"text":"Department of Earth and Environmental Sciences, University of Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":938130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, James","contributorId":355778,"corporation":false,"usgs":false,"family":"Young","given":"James","affiliations":[{"id":84831,"text":"Department of Computer Science, University of Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":938131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spruce, Michelle","contributorId":355779,"corporation":false,"usgs":false,"family":"Spruce","given":"Michelle","affiliations":[{"id":84833,"text":"Liverpool Business School, Liverpool John Moores University, UK","active":true,"usgs":false}],"preferred":false,"id":938132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pandit, Ravi","contributorId":355780,"corporation":false,"usgs":false,"family":"Pandit","given":"Ravi","affiliations":[{"id":84834,"text":"Institute for Data Science and Artificial Intelligence, University of Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":938133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Hywel","contributorId":355781,"corporation":false,"usgs":false,"family":"Williams","given":"Hywel","affiliations":[{"id":84831,"text":"Department of Computer Science, University of Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":938134,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arthur, Rudy","contributorId":355782,"corporation":false,"usgs":false,"family":"Arthur","given":"Rudy","affiliations":[{"id":84831,"text":"Department of Computer Science, University of Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":938135,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stovall, Wendy K. 0000-0003-2518-2595","orcid":"https://orcid.org/0000-0003-2518-2595","contributorId":214673,"corporation":false,"usgs":true,"family":"Stovall","given":"Wendy K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":938136,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Head, Matthew","contributorId":331805,"corporation":false,"usgs":false,"family":"Head","given":"Matthew","email":"","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":938137,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266906,"text":"70266906 - 2025 - Assessing potential collateral effects on amphibians from insecticide applications for flea control and plague mitigation","interactions":[],"lastModifiedDate":"2025-05-15T14:58:27.853948","indexId":"70266906","displayToPublicDate":"2025-05-12T09:46:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Assessing potential collateral effects on amphibians from insecticide applications for flea control and plague mitigation","docAbstract":"Ideal disease mitigation measures for wildlife are safe and benign for target species, non-target organisms, the environment, and humans. Identifying collateral (i.e., unintended) effects is a key consideration in implementing such actions. Deltamethrin dust and fipronil-laced baits represent a group of insecticides that target fleas (pulicides) and are used to control flea (Siphonaptera) vectors of the plague bacterium Yersinia pestis to protect prairie dogs (Cynomys spp.) and their plague-susceptible obligate predators, endangered black-footed ferrets (Mustela nigripes). A variety of animals use prairie dog burrows as refuge, which potentially exposes them to deltamethrin, and to fipronil and its metabolites in fecal pellets excreted by prairie dogs and other mammals that have eaten fipronil baits. We assessed the potential effects of deltamethrin and fipronil residues on survival, body mass, and activity of western tiger salamanders (Ambystoma mavortium), a burrow-inhabiting amphibian. Pulicides were applied at realistic concentrations in mesocosms mimicking burrows. Treatments included (1) deltamethrin dust and non-treated prairie dog fecal pellets, (2) prairie dog fecal pellets containing fipronil and fipronil sulfone, and (3) un-treated prairie dog fecal pellets as controls. All 29 salamanders survived the experiment. We did not detect pulicide residues in any control salamanders. Fipronil sulfone was detected in tissues from 3 of 10 salamanders in the fipronil treatment and deltamethrin was detected in tissues from 9 of 11 salamanders in the deltamethrin treatment. Salamanders were observed outside of burrows more frequently after treatments than before. Deltamethrin concentrations in whole body samples correlated positively with the amount of time salamanders were inside burrows. Acute, lethal effects were not detected, but uptake of deltamethrin and, to a lesser extent fipronil sulfone, into salamander tissues indicated the potential for long-term effects on this non-target species. Identifying potential collateral effects is an important aspect of evaluating mitigation actions implemented to protect endangered species.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0320382","usgsCitation":"Eads, D.A., Shriner, S.A., Ellis, J.W., Cryan, P.M., Hladik, M.L., Dooley, G.P., and Muths, E., 2025, Assessing potential collateral effects on amphibians from insecticide applications for flea control and plague mitigation: PLoS ONE, v. 20, no. 5, e0320382, 15 p., https://doi.org/10.1371/journal.pone.0320382.","productDescription":"e0320382, 15 p.","ipdsId":"IP-160537","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488598,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0320382","text":"Publisher Index Page"},{"id":486312,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NR46X4","text":"USGS data release","linkHelpText":"Data on tiger salamander body mass, behavioral activity, and insecticide residues"},{"id":485994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":937098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shriner, Susan A.","contributorId":168690,"corporation":false,"usgs":false,"family":"Shriner","given":"Susan","email":"","middleInitial":"A.","affiliations":[{"id":13407,"text":"Colorado State Univ.","active":true,"usgs":false}],"preferred":false,"id":937099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellis, Jeremy W.","contributorId":212846,"corporation":false,"usgs":false,"family":"Ellis","given":"Jeremy","email":"","middleInitial":"W.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":937100,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":937101,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937102,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dooley, Gregory P.","contributorId":347021,"corporation":false,"usgs":false,"family":"Dooley","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937103,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":245922,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":937104,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70267384,"text":"70267384 - 2025 - The complete mitochondrial genomes of the freshwater mussel Ortmanniana ligamentina (Lamarck, 1819): male and female mitotypes","interactions":[],"lastModifiedDate":"2025-05-21T14:17:25.275471","indexId":"70267384","displayToPublicDate":"2025-05-12T09:11:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5471,"text":"Mitochondrial DNA Part B","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The complete mitochondrial genomes of the freshwater mussel Ortmanniana ligamentina (Lamarck, 1819): male and female mitotypes","title":"The complete mitochondrial genomes of the freshwater mussel Ortmanniana ligamentina (Lamarck, 1819): male and female mitotypes","docAbstract":"Freshwater mussels of the Unionida order are important to freshwater ecosystems but are highly imperiled worldwide. Improving our understanding of these species is crucial to their continued conservation. Some Unionid mussels exhibit double uniparental inheritance (DUI) in which individuals have two mitochondrial genomes. Of those species with DUI, sequences of the female mitotype are most prevalent in genetic databases. Here, we demonstrate the ability to recover both mitotypes of Ortmanniana ligamentina (Lamarck, 1819) from a non-lethal collection method coupled with high-throughput sequencing. Increased male mitotype sequence representation facilitates understanding Unionid genetic diversity and development of molecular tools for species detection.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23802359.2025.2500528","usgsCitation":"Klymus, K.E., Coombs, J., Ruiz-Ramos, D., Maloy, A., and Barnhart, C.M., 2025, The complete mitochondrial genomes of the freshwater mussel Ortmanniana ligamentina (Lamarck, 1819): male and female mitotypes: Mitochondrial DNA Part B, v. 10, no. 6, p. 430-436, https://doi.org/10.1080/23802359.2025.2500528.","productDescription":"7 p.","startPage":"430","endPage":"436","ipdsId":"IP-169543","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":486910,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/23802359.2025.2500528","text":"Publisher Index Page"},{"id":486282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Pennsylvania","otherGeospatial":"Allegheny River, Saint Francis River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.51182908381749,\n 37.19162849485569\n ],\n [\n -90.51182908381749,\n 37.14017784562941\n ],\n [\n -90.46325971350004,\n 37.14017784562941\n ],\n [\n -90.46325971350004,\n 37.19162849485569\n ],\n [\n -90.51182908381749,\n 37.19162849485569\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.42977164664136,\n 41.496369262616895\n ],\n [\n -79.61497330981219,\n 41.496369262616895\n ],\n [\n -79.61497330981219,\n 41.42903968922411\n ],\n [\n -79.42977164664136,\n 41.42903968922411\n ],\n [\n -79.42977164664136,\n 41.496369262616895\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":938050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coombs, Jason","contributorId":299021,"corporation":false,"usgs":false,"family":"Coombs","given":"Jason","affiliations":[{"id":37062,"text":"UMASS","active":true,"usgs":false}],"preferred":false,"id":938052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruiz-Ramos, Dannise","contributorId":332474,"corporation":false,"usgs":false,"family":"Ruiz-Ramos","given":"Dannise","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":938051,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloy, Aaron","contributorId":343773,"corporation":false,"usgs":false,"family":"Maloy","given":"Aaron","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Christopher M.","contributorId":331084,"corporation":false,"usgs":false,"family":"Barnhart","given":"Christopher","email":"","middleInitial":"M.","affiliations":[{"id":16806,"text":"Missouri State University","active":true,"usgs":false}],"preferred":false,"id":938054,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267484,"text":"70267484 - 2025 - A framework for guiding management decisions for amphibians in an uncertain future","interactions":[],"lastModifiedDate":"2026-03-17T14:12:06.671127","indexId":"70267484","displayToPublicDate":"2025-05-12T09:04:58","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"A framework for guiding management decisions for amphibians in an uncertain future","docAbstract":"Managing species in a rapidly changing climate requires knowledge of how species will respond to climate change and other threats while simultaneously developing management actions to reduce threats. Amphibians are one of the most threatened taxa on earth and often serve as the ‘canary in the coalmine’ for the health of ecosystems that countless other species and humans rely on. To understand the status of and guide management for the boreal toad (Anaxyrus boreas boreas), an imperiled amphibian species in the North Central region, we coproduced several products with the Boreal Toad Conservation Team. These products included 1) reconstructed seasonal hydrology patterns for historical boreal toad breeding wetlands and high elevation watersheds in the Southern Rocky Mountain Region (SRMR) from remotely sensed data, 2) current and future predictions of drying rates for historical breeding wetlands, 3) current and future predictions on the status of the boreal toad in the SRMR, and 4) a web tool to guide management actions. While the boreal toad is considered a ‘data rich’ species given data collection efforts that span multiple decades, many amphibian species are considered ‘data poor’, meaning managers lack data on the biology, ecology, or status of the species needed to make sound decisions. To address this knowledge gap, we also quantified drying patterns across watersheds for two ‘data poor’ species in the North Central region at risk from climate change: the Great Basin spadefoot toad (Spea intermontana) and the wood frog (Lithobates sylvaticus). These new data can guide management decisions for these species by allowing managers to understand habitat changes with respect to water availability, a crucial element for amphibian survival and persistence. Together, these products demonstrate how cutting-edge technology and analytical methods can produce a range of useful information to support amphibian conservation.
","language":"English","publisher":"Nrrth Central Climate Adaptation Center","usgsCitation":"Kissel, A.M., Muths, E., Lacey, M., Popescu, V.D., Dyck, M., and Littlefield, C., 2025, A framework for guiding management decisions for amphibians in an uncertain future, 56 p.","productDescription":"56 p.","ipdsId":"IP-174467","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":486563,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/6009c26fd34e162231fb2333","linkFileType":{"id":5,"text":"html"}},{"id":501210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Wyoming","otherGeospatial":"southern Rocky Mountain region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108,\n 41.25\n ],\n [\n -108,\n 36.5\n ],\n [\n -105,\n 36.5\n ],\n [\n -105,\n 41.25\n ],\n [\n -108,\n 41.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kissel, Amanda Marie 0000-0002-6346-7455","orcid":"https://orcid.org/0000-0002-6346-7455","contributorId":334356,"corporation":false,"usgs":true,"family":"Kissel","given":"Amanda","email":"","middleInitial":"Marie","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":938369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":245922,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":938370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lacey, Mae","contributorId":355913,"corporation":false,"usgs":false,"family":"Lacey","given":"Mae","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":938371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Popescu, Viorel D.","contributorId":169697,"corporation":false,"usgs":false,"family":"Popescu","given":"Viorel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":938372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dyck, Marissa","contributorId":355915,"corporation":false,"usgs":false,"family":"Dyck","given":"Marissa","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":938373,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Littlefield, Caitlin","contributorId":352216,"corporation":false,"usgs":false,"family":"Littlefield","given":"Caitlin","affiliations":[],"preferred":false,"id":938374,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266500,"text":"ofr20251019 - 2025 - The feasibility of using lidar-derived digital elevation models for gravity data reduction","interactions":[],"lastModifiedDate":"2025-07-07T14:15:33.584578","indexId":"ofr20251019","displayToPublicDate":"2025-05-12T08:40:00","publicationYear":"2025","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":"2025-1019","displayTitle":"The Feasibility of Using Lidar-Derived Digital Elevation Models for Gravity Data Reduction","title":"The feasibility of using lidar-derived digital elevation models for gravity data reduction","docAbstract":"Gravity data require submeter elevation accuracy for data processing, and differential global navigation satellite system (dGNSS) equipment is commonly used to acquire three-dimensional positional data to achieve such accuracy. However, lidar (light detection and ranging) data are commonly used to develop digital elevation models (DEMs) of Earth’s surface. Therefore, using elevations from lidar-derived DEMs for gravity-data acquisition and reduction may improve field efficiency and reduce cost. This study examines the feasibility of using DEMs for gravity-data reduction by comparing dGNSS elevation data from 435 gravity stations in Michigan, Wyoming, and Colorado with their respective DEM elevations. The results show that the average difference between DEM and dGNSS elevations is 13 centimeters (cm) and that 93 percent of those differences are less than 50 cm, even in areas with steep terrain. Because an elevation discrepancy of 50 cm corresponds to an error of roughly 0.1 milligals (mGal) in the simple Bouguer gravity anomaly, the results suggest that lidar-derived DEMs are a viable source for acquiring the elevation data needed to process gravity data, thus improving both the cost and efficiency of data collection for regional surveys where an accuracy of less than 1.0 mGal is desired.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251019","programNote":"Mineral Resources Program","usgsCitation":"Murchek, J.T., Drenth, B.J., Reitman, J.J., Anderson, E.D., Magnin, B.P., and DeGraff, J.M., 2025, The feasibility of using lidar-derived digital elevation models for gravity data reduction (ver. 1.1, July 2025): U.S. Geological Survey Open-File Report 2025–1019, 33 p., https://doi.org/10.3133/ofr20251019.","productDescription":"vii, 33 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163043","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":491565,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1019/coverthb2.jpg"},{"id":491633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118572.htm"},{"id":491634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1019 PDF"},{"id":491636,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1019 XML"},{"id":491635,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251019/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1019 HTML"},{"id":491638,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2025/1019/versionHist.txt","size":"654 B","linkFileType":{"id":2,"text":"txt"}},{"id":491637,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1019/images/"}],"edition":"Version 1.0: May 12, 2025; Version 1.1: July 1, 2025","contact":"Director, Energy and Minerals Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192-0002
Subterranean estuaries (STE) in salt marshes are biogeochemically active zones where interactions between terrestrial groundwater and seawater drive complex cycling of carbon and trace elements, influenced by mineral dissolution. These systems, characterized by fine-grained organic-rich peat overlying permeable coastal aquifers, play a crucial role as a blue carbon sink, yet their geochemical dynamics remain poorly understood. We investigated dissolved trace elements, carbon, silica, and radium isotopes in a salt marsh STE (Sage Lot Pond, Waquoit Bay, MA) over seasonal and annual cycles. Our results reveal that groundwater and estuarine water circulation through marsh peat and aquifer sediments leads to enrichments of dissolved organic and inorganic carbon (DOC and DIC), Si, Ba, and Mn, with variable source/sink behavior of Fe and net removal of U. Submarine groundwater discharge dominated Ba fluxes, whereas pore water drainage from marsh peat acted as the main sink for U and source of Si. Fe cycling was variable, with terrestrial Fe largely removed as groundwater passed through the STE, consistent with Fe-sulfide and amorphous phase formation. Radium isotope ratios identified two distinct subsurface flow pathways, influenced by metal-oxide cycling and organic matter breakdown. Si production was decoupled from DIC, suggesting Si originates from mineral alteration, whereas DIC results from both mineral weathering and microbial respiration. Silicate mineral alteration, coupled with marsh pore water drainage, accounts for up to 16% of annual DIC exports (66 g C m−2 y−1), highlighting the importance of STEs in coastal carbon and trace element cycling, especially as marshes face environmental change.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JG008758","usgsCitation":"Tamborski, J., Eagle, M.J., Thorpe, M., Charette, M., Kurylyk, B., Rahman, S., Kroeger, K.D., O’Keefe Suttles, J.A., Mann, A.G., Brooks, T.W., and Wang, Z., 2025, Evidence of mineral alteration in a salt marsh subterranean estuary: Implications for carbon and trace element cycling: JGR Biogeosciences, v. 130, no. 5, e2025JG008758, 19 p., https://doi.org/10.1029/2025JG008758.","productDescription":"e2025JG008758, 19 p.","ipdsId":"IP-174900","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jg008758","text":"Publisher Index Page"},{"id":490747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod, Sage Lot Pond salt marsh observatory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.52056663105348,\n 41.559066095910765\n ],\n [\n -70.52056663105348,\n 41.55128306872197\n ],\n [\n -70.49785805589167,\n 41.55128306872197\n ],\n [\n -70.49785805589167,\n 41.559066095910765\n ],\n [\n -70.52056663105348,\n 41.559066095910765\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Tamborski, J.J.","contributorId":350271,"corporation":false,"usgs":false,"family":"Tamborski","given":"J.J.","affiliations":[{"id":36518,"text":"Old Dominion University","active":true,"usgs":false}],"preferred":false,"id":940379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":940380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorpe, M.T.","contributorId":356885,"corporation":false,"usgs":false,"family":"Thorpe","given":"M.T.","affiliations":[{"id":85269,"text":"University of Maryland, NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":940381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Charette, M.A.","contributorId":192860,"corporation":false,"usgs":false,"family":"Charette","given":"M.A.","affiliations":[],"preferred":false,"id":940382,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kurylyk, B.","contributorId":222758,"corporation":false,"usgs":false,"family":"Kurylyk","given":"B.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":940383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rahman, S.","contributorId":356886,"corporation":false,"usgs":false,"family":"Rahman","given":"S.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":940384,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":940385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"O’Keefe Suttles, Jennifer A. 0000-0003-2345-5633","orcid":"https://orcid.org/0000-0003-2345-5633","contributorId":202609,"corporation":false,"usgs":true,"family":"O’Keefe Suttles","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940386,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mann, Adrian G. 0000-0003-1689-8524 adriangreen@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-8524","contributorId":4328,"corporation":false,"usgs":true,"family":"Mann","given":"Adrian","email":"adriangreen@usgs.gov","middleInitial":"G.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940387,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Brooks, Thomas W. 0000-0002-0555-3398 wallybrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-0555-3398","contributorId":5989,"corporation":false,"usgs":true,"family":"Brooks","given":"Thomas","email":"wallybrooks@usgs.gov","middleInitial":"W.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940388,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wang, Z.A.","contributorId":350270,"corporation":false,"usgs":false,"family":"Wang","given":"Z.A.","affiliations":[{"id":83704,"text":"Woods Hole Oceanography Institution","active":true,"usgs":false}],"preferred":false,"id":940389,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70265625,"text":"70265625 - 2025 - A geospatial analysis of water-quality threats from orphan wells in principal and secondary aquifers of the United States","interactions":[],"lastModifiedDate":"2025-04-14T16:22:56.080636","indexId":"70265625","displayToPublicDate":"2025-05-10T09:19:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A geospatial analysis of water-quality threats from orphan wells in principal and secondary aquifers of the United States","docAbstract":"Throughout the history of oil and gas production in the United States, millions of wells have been drilled for exploration and energy production. Hundreds of thousands of unplugged wells are no longer actively producing and are currently under orphan status, with no responsible party obligated for plugging. Orphan wells can pose threats to water resources by providing pathways for contaminants such as hydrocarbons and brines to migrate into water-supply aquifers. In this study, we investigate the potential threats to groundwater resources posed by orphan wells at the national scale. Water-quality data is extremely sparse in relation to orphan wells nationally and may not be suitable for identifying contamination from oil and gas development. We used geospatial and statistical methods to evaluate which principal and secondary aquifer systems may be most susceptible to contamination from orphan wells. Analysis involved three sets of susceptibility factors including: 1) factors related to the number and density of orphan wells; 2) factors that can threaten well integrity and contribute to transport of contaminants; and 3) factors related to groundwater withdrawal rates and the affected populations/communities in the event of water quality disturbances. From a dataset of 117,672 documented orphan wells, 64,203 fall within a principal aquifer system, while the remainder fall within a secondary aquifer system.
By assessing the combination of well integrity and hydrogeologic factors within these aquifer systems, five groupings of principal aquifers were identified, where groups ranged from aquifer systems with high numbers of orphan wells, multiple well integrity threats and high withdrawals, to aquifers with a relatively low number of orphan wells, limited well integrity threats and minimal water use. Three regions of the country emerge containing aquifers with higher susceptibility to contamination from orphan oil and gas wells. These regions include 1) The Appalachian Basin (including the Pennsylvanian Aquifer System), 2) The Gulf Coast Aquifers (including the Coastal Lowlands Aquifer system) and 3) The California Aquifers (including the California Coastal Basin Aquifer system). This work is the first multivariate geospatial investigation of orphan wells and groundwater resources on a national scale, and sheds light on which aquifers are most susceptible to groundwater contamination from orphan wells.
","language":"English","publisher":"ScienceDirect","doi":"10.1016/j.scitotenv.2025.179246","usgsCitation":"Woda, J., Haase, K., Gianoutsos, N.J., Jahn, K., and Gutchess, K., 2025, A geospatial analysis of water-quality threats from orphan wells in principal and secondary aquifers of the United States: Science of the Total Environment, v. 976, 179246, 20 p., https://doi.org/10.1016/j.scitotenv.2025.179246.","productDescription":"179246, 20 p.","ipdsId":"IP-170391","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":490096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.179246","text":"Publisher Index Page"},{"id":484512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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ngianoutsos@usgs.gov","orcid":"https://orcid.org/0000-0002-6510-6549","contributorId":3607,"corporation":false,"usgs":true,"family":"Gianoutsos","given":"Nicholas","email":"ngianoutsos@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":933139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jahn, Kalle 0000-0002-4976-0137","orcid":"https://orcid.org/0000-0002-4976-0137","contributorId":333053,"corporation":false,"usgs":true,"family":"Jahn","given":"Kalle","email":"","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gutchess, Kristina 0000-0002-9745-5049","orcid":"https://orcid.org/0000-0002-9745-5049","contributorId":353190,"corporation":false,"usgs":true,"family":"Gutchess","given":"Kristina","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267797,"text":"70267797 - 2025 - Identification of representative earthquakes for probabilistic tsunami hazard analysis (PTHA) using earthquake rupture forecasts and machine learning","interactions":[],"lastModifiedDate":"2025-06-02T14:20:21.988597","indexId":"70267797","displayToPublicDate":"2025-05-10T09:13:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Identification of representative earthquakes for probabilistic tsunami hazard analysis (PTHA) using earthquake rupture forecasts and machine learning","docAbstract":"As probabilistic tsunami hazard analysis (PTHA) focuses more on assessments for localized, populous regions, techniques are needed to identify a subsample of representative earthquake ruptures to make the computational requirements for producing high-resolution hazard maps tractable. Moreover, the greatest epistemic uncertainty in seismic PTHA is related to source characterization, which is often poorly defined and subjective. We address these two salient issues by applying streamlined earthquake rupture forecasts (ERFs), based on combinatorial optimization methods, to an unsupervised machine learning workflow for identifying representative ruptures. ERFs determine the optimal distribution of a millennia-scale sample of earthquakes by inverting the observed slip rate on major faults. We use two previously developed combinatorial optimization ERFs, integer programming and greedy sequential, to produce the optimal location of ruptures with seismic moments sampled from a regional Gutenberg–Richter magnitude–frequency distribution. These ruptures in turn are used to calculate peak nearshore tsunami amplitude, using computationally efficient tsunami Green's functions. An unsupervised machine learning workflow is then used to identify a small subsample of the earthquakes input to ERFs for onshore PTHA analysis. We eliminate epistemic uncertainty related to source distribution under traditional PTHA analysis; in its place, a quantifiable, less subjective and generally smaller uncertainty related to the input to ERFs is included. The Nankai subduction zone is used as a test case, where previous ERFs have been conducted. Results indicate that the locations of representative earthquakes are sensitive to choice of magnitude–area relation and to whether a minimum cumulative stress objective is imposed on the fault. In general, incorporating ERFs into PTHA provide a physically self-consistent method to incorporate fault slip information in determining representative earthquakes for onshore PTHA, eliminating a major source of epistemic uncertainty.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggaf173","usgsCitation":"Geist, E.L., and Parsons, T.E., 2025, Identification of representative earthquakes for probabilistic tsunami hazard analysis (PTHA) using earthquake rupture forecasts and machine learning: Geophysical Journal International, v. 242, no. 1, ggaf173, 22 p., https://doi.org/10.1093/gji/ggaf173.","productDescription":"ggaf173, 22 p.","ipdsId":"IP-174860","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":490659,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaf173","text":"Publisher Index Page"},{"id":489360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","otherGeospatial":"Nankai subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 131,\n 37\n ],\n [\n 131,\n 31\n ],\n [\n 139,\n 31\n ],\n [\n 139,\n 37\n ],\n [\n 131,\n 37\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"242","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":15543,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":938927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":938928,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70267771,"text":"70267771 - 2025 - Antigone canadensis (Sandhill Crane) foraging patterns influenced by crop type, roost distance, and tillage intensity during spring and autumn migration at a primary stopover area","interactions":[],"lastModifiedDate":"2025-05-30T15:43:43.66654","indexId":"70267771","displayToPublicDate":"2025-05-10T08:38:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Antigone canadensis (Sandhill Crane) foraging patterns influenced by crop type, roost distance, and tillage intensity during spring and autumn migration at a primary stopover area","docAbstract":"The San Luis Valley in Colorado, USA, an agriculturally dominated stopover area, is used by the Rocky Mountain population of Antigone canadensis tabida (Greater Sandhill Crane) and some midcontinental individuals of A. c. canadensis (Lesser Sandhill Crane) during migration. While the numbers of both subspecies are stable, the effects of continued water scarcity and declines in grain output on the energetics of cranes in the San Luis Valley are unclear. We conducted roadside counts of A. c. tabida and A. c. canadensis on agricultural fields to determine the effects of crop type, roost distance, and tillage intensity on their selection and abundance on crop fields. Antigone canadensis varied in their use of the San Luis Valley for foraging. In autumn, both subspecies selected barley and other grains over other crop types. In spring, cranes preferred to forage in barley fields, and selection declined as distance to roosts increased. Both subspecies also selected barley fields that were lightly or not tilled. We modeled covariates on abundance for A. c. tabida only and found that more cranes were found close to roosts early in the season in autumn. As the season progressed, the number of A. c. tabida increased as roost distance increased. In spring, abundance was influenced by an interaction between time and crop, with the highest numbers found on barley and pasture around mid-March. Our results suggest that A. canadensis may switch to other crop types as resources are depleted near roosts but appear to prefer to fly farther for grains. Grains that are left idle or moderately tilled and are located near roosts will help ensure A. canadensis are able to maintain adequate nutrient reserves at agriculturally dominated stopover areas during migration.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duaf027","collaboration":"U. S. Fish and Wildlife Service","usgsCitation":"Vanausdall, R., Kendall, W.L., and Collins, D., 2025, Antigone canadensis (Sandhill Crane) foraging patterns influenced by crop type, roost distance, and tillage intensity during spring and autumn migration at a primary stopover area: Ornithological Applications, v. 127, duaf027, 17 p., https://doi.org/10.1093/ornithapp/duaf027.","productDescription":"duaf027, 17 p.","ipdsId":"IP-170214","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490643,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duaf027","text":"Publisher Index Page"},{"id":489266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.19980544186409,\n 37.483870494045064\n ],\n [\n -106.19980544186409,\n 36.996256317805006\n ],\n [\n -105.66654470738654,\n 36.996256317805006\n ],\n [\n -105.66654470738654,\n 37.483870494045064\n ],\n [\n -106.19980544186409,\n 37.483870494045064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","noUsgsAuthors":false,"publicationDate":"2025-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanausdall, Rachel A.","contributorId":356156,"corporation":false,"usgs":false,"family":"Vanausdall","given":"Rachel A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":938810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Daniel P.","contributorId":356157,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938812,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274028,"text":"70274028 - 2025 - Properties of new flows indicate that Martian gullies form via CO2 frost-fluidization processes","interactions":[],"lastModifiedDate":"2026-02-20T14:52:42.327468","indexId":"70274028","displayToPublicDate":"2025-05-10T07:46:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Properties of new flows indicate that Martian gullies form via CO2 frost-fluidization processes","docAbstract":"Martian gully landforms are widely seen as evidence of liquid water, often attributed to snowmelt during high-obliquity periods within the last few million years. However, widespread present-day flows within existing gullies are caused by CO2 frost, presenting an alternative formation mechanism. Entrained frost vapourizes to fluidize flows, allowing them to behave similarly to wet debris flows on Earth. The slopes where present-day flows erode and deposit provide insights into the landforms that many such flows could create. The shallowest slopes eroded by the flows are similar to slopes at existing channel mouths, and the most mobile flows reach final slopes similar to the outer reaches of existing gully aprons. This is consistent with formation of gullies entirely by CO2 frost-driven flows, assuming their intensity and frequency varies in space and time. Geologically recent snowmelt cannot be ruled out, but is not required to explain the observed gully morphology.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL112434","usgsCitation":"Dundas, C., Conway, S.J., Pasquon, K., Noblet, A., Roelofs, L., 2025, Properties of new flows indicate that Martian gullies form via CO2 frost-fluidization processes: Geophysical Research Letters, v. 52, no. 9, e2024GL112434, 8 p., https://doi.org/10.1029/2024GL112434.","productDescription":"e2024GL112434, 8 p.","ipdsId":"IP-141673","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":500823,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl112434","text":"Publisher Index Page"},{"id":500335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"52","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":956208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Susan J.","contributorId":366778,"corporation":false,"usgs":false,"family":"Conway","given":"Susan","middleInitial":"J.","affiliations":[{"id":82106,"text":"Nantes Universite","active":true,"usgs":false}],"preferred":false,"id":956209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pasquon, Kelly","contributorId":343526,"corporation":false,"usgs":false,"family":"Pasquon","given":"Kelly","email":"","affiliations":[{"id":82106,"text":"Nantes Universite","active":true,"usgs":false}],"preferred":false,"id":956210,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noblet, Axel","contributorId":366779,"corporation":false,"usgs":false,"family":"Noblet","given":"Axel","affiliations":[{"id":82106,"text":"Nantes Universite","active":true,"usgs":false}],"preferred":false,"id":956211,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roelofs, Lonneke","contributorId":343523,"corporation":false,"usgs":false,"family":"Roelofs","given":"Lonneke","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":956212,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266649,"text":"sir20255028 - 2025 - Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2024","interactions":[],"lastModifiedDate":"2025-06-25T14:36:40.924869","indexId":"sir20255028","displayToPublicDate":"2025-05-09T14:49:22","publicationYear":"2025","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":"2025-5028","displayTitle":"Status of Water-Level Altitudes and Long-Term and Short-Term Water-Level Changes in the Chicot and Evangeline (Undifferentiated) and Jasper Aquifers, Greater Houston Area, Texas, 2024","title":"Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2024","docAbstract":"Since the early 1900s, groundwater withdrawn from the primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers—has been an important source of water in the greater Houston area, Texas. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of water-level altitudes and water-level changes in these aquifers in the greater Houston area.
In this report, the Chicot and Evangeline aquifers are treated as a single aquifer for the purposes of providing annual assessments of regional-scale water-level altitudes and water-level changes over time. In 2024, shaded depictions of estimated water-level altitudes for the Chicot and Evangeline aquifers (undifferentiated) ranged from about 301 feet (ft) below the North American Vertical Datum of 1988 (NAVD 88) to about 184 ft above NAVD 88. The largest decline in water-level altitudes depicted by the 1977–2024 long-term water-level-change map was in south-central Montgomery County. In comparison, the 1990–2024 long-term water-level-change map depicts the largest declines in water-level altitudes in an area northwest of The Woodlands and in an area of northern Waller County. The largest rise in water-level altitudes for 1977–2024 is depicted in an area of east-central Harris County, whereas the largest rise in water-level altitudes for 1990–2024 is depicted in an area of central Harris County. The 5-year short-term water-level-change map depicts the largest declines in several parts of the study area, but these declines are concentrated primarily in northern Fort Bend County, southwestern Harris County, and south-central Montgomery County. The largest rise for 2019–24 is depicted at a well in northern Fort Bend County. The 1-year short-term water-level-change map depicts the largest declines at a well in northern Fort Bend County and a well in west-central Harris County. The largest rise for 2023–24 is depicted at a well in east-central Fort Bend County.
In 2024, shaded depictions of estimated water-level altitudes for the Jasper aquifer ranged from about 255 ft below NAVD 88 to about 321 ft above NAVD 88. The 2000–24 long-term water-level-change map depicts the largest water-level decline in an area of central San Jacinto County; the largest rise is depicted in an area of central Grimes County. The 5-year short-term water-level-change map depicts the largest declines across parts of central and southern Montgomery County and at one well in north-central Harris County. The largest rise for 2019–24 is depicted at a well centered on the Montgomery-Grimes County line. The 1-year short-term water-level-change map depicts the largest declines at two wells in south-central Montgomery County and one well in northwestern Montgomery County on the west side of Lake Conroe. The largest rises during 2023–24 are depicted at one well in northwestern Montgomery County and one well in south-central Montgomery County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255028","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Ramage, J.K., and Adams, A.C., 2025, Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2024: U.S. Geological Survey Scientific Investigations Report 2025–5028, 27 p., https://doi.org/10.3133/sir20255028.","productDescription":"Report: v; 27 p.; 2 Data Releases; Database","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-165210","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":489426,"rank":9,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5028/sir20255028.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025–5028 XML"},{"id":489425,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255028/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025–5028 HTML"},{"id":485802,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118569.htm","linkFileType":{"id":5,"text":"html"}},{"id":485676,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS Database","linkHelpText":"U.S. Geological Survey, 2024, USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":485675,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13NLJ7T","text":"USGS Data Release","linkHelpText":"Adams, A.C., and Ramage, J.K., 2024, Groundwater-level altitudes and long-term groundwater-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2024: U.S. Geological Survey data release"},{"id":485668,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13XXB5O","text":"USGS Data Release","linkHelpText":"Adams, A.C., and Ramage, J.K., 2024, Depth to groundwater measured from wells in the greater Houston area, Texas, 2024: U.S. Geological Survey data release"},{"id":485665,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5028/sir20255028.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025–5028"},{"id":485664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5028/coverthb.jpg"},{"id":485667,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5028/Images"}],"country":"United States","state":"Texas","city":"Houston","otherGeospatial":"Chicot, Evangeline, and Jasper aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.25,\n 30.875\n ],\n [\n -96.25,\n 28.9\n ],\n [\n -94.4,\n 28.9\n ],\n [\n -94.4,\n 30.875\n ],\n [\n -96.25,\n 30.875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"Nature-based solutions are receiving increasing attention as a cost-effective climate adaptation strategy. Horizontal levees are nature-based adaptation solutions that include a sloping wetland habitat buffer fronting a levee. They can offer a hybrid solution to reinforce traditional levees in estuarine areas—plants on the horizontal levee can provide wave attenuation benefits as well as habitat benefits, but how the design of horizontal levees influences risk of levee failure remains unquantified. We use a hydrodynamic model, XBeach non-hydrostatic (XB-NH), to assess the stability and sustainability of existing levees and determine how hybrid nature-based climate adaptation measures can reduce the risk of overtopping on levees in San Francisco Bay. We compare overtopping rates in the existing levee system and in a variety of nature-based adaptation scenarios using a range of widths and slopes of horizontal levees to assess how horizontal levees perform in reducing risk of flooding, both with present day conditions and sea level rise. We show that climate change will challenge existing levee flood defenses in San Francisco Bay and increase the risk of overtopping, and that the nature-based solution of horizontal levees can meaningfully reduce risk of overtopping while simultaneously supporting marsh habitat. Flood risk reduction and habitat provision are both maximized with more gradually sloping and wider horizontal levee designs. Results show that the risk of overtopping can be reduced by up to 30% with horizontal levees. This analysis provides insight into horizontal levee design considerations and a methodological approach to adapt levees to prepare for climate change in urban wave-exposed estuaries. We show that horizontal levees can support preparation for the projected impacts of sea level rise (SLR) while simultaneously providing new intertidal wetland habitat.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-99762-7","usgsCitation":"Taylor-Burns, R.M., Reguero, B.G., Barnard, P.L., and Beck, M.W., 2025, Nature-based solutions extend the lifespan of a regional levee system under climate change: Nature Scientific Reports, v. 15, no. 1, 16218, 11 p., https://doi.org/10.1038/s41598-025-99762-7.","productDescription":"16218, 11 p.","ipdsId":"IP-162956","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-99762-7","text":"Publisher Index Page"},{"id":485822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.52930652577294,\n 37.836817243666715\n ],\n [\n -122.52930652577294,\n 37.38775011750823\n ],\n [\n -121.90448236358971,\n 37.38775011750823\n ],\n [\n -121.90448236358971,\n 37.836817243666715\n ],\n [\n -122.52930652577294,\n 37.836817243666715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor-Burns, Rae M. 0000-0003-4963-6643","orcid":"https://orcid.org/0000-0003-4963-6643","contributorId":312507,"corporation":false,"usgs":false,"family":"Taylor-Burns","given":"Rae","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":936805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reguero, Borja G. 0000-0001-5526-7157","orcid":"https://orcid.org/0000-0001-5526-7157","contributorId":193831,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","email":"","middleInitial":"G.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":936807,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":936806,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":936808,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267264,"text":"70267264 - 2025 - Dendroseismological investigation of redwood trees along the North Coast section of the San Andreas Fault","interactions":[],"lastModifiedDate":"2025-05-19T17:18:35.149557","indexId":"70267264","displayToPublicDate":"2025-05-09T10:12:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7169,"text":"Quaternary Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Dendroseismological investigation of redwood trees along the North Coast section of the San Andreas Fault","docAbstract":"Sequoia sempervirens (coast redwood) tree rings have the potential to annually resolve late-Holocene earthquakes on the northern San Andreas Fault based on direct (e.g., physical damage) and indirect (e.g., co-seismic environmental change) impacts, but scarcity of suitable samples and challenges crossdating this long-lived species have limited progress. More precise dating of the pre-1906 (penultimate) earthquake can improve hazard assessment and understanding of rupture segmentation. We target old trees (maximum >815 yr) along the North Coast section of the fault (increment cores via rope-climbing, 11 living trees; plunge cuts, 23 stumps) and employ complementary disturbance detection methods including radial-growth averaging (tree- and series-level), cataloging anatomical indicators (e.g., traumatic resin ducts, TRD), and dating structural components (e.g., reiterated trunks, leans). Multi-centennial ring-width chronologies at Fort Ross (1569−2023) and Gualala (1397−2023) promote continued study with incomplete crossdating limiting utilization of some series. Growth pulses (reductions, releases) and TRD dispersed across the record reflect dynamic environments that obfuscate detection of earthquake signals. The 1906 earthquake did not leave strong signatures on most trees, and when it did, within-tree response varied from normal presentation to discoloration, TRD, and missing rings. Synchrony of indicators at both locations identified 1678−1680 (6 of 15 trees) and 1698−1700 (8 of 16 trees) as the strongest disturbances among dated rings in the time range of the penultimate earthquake, peaking at 1698 (15.7 % of possible growth and anatomical indicators), but the triggering mechanisms for these events are unknown.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.qsa.2025.100283","usgsCitation":"Carroll, A.L., Philibosian, B.E., Sillett, S., Antoine, M., and Kozaci, Ö., 2025, Dendroseismological investigation of redwood trees along the North Coast section of the San Andreas Fault: Quaternary Science Advances, v. 18, 100283, 19 p., https://doi.org/10.1016/j.qsa.2025.100283.","productDescription":"100283, 19 p.","ipdsId":"IP-173541","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489166,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.qsa.2025.100283","text":"Publisher Index Page"},{"id":486168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"northern San Andreas Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.83814092270626,\n 38.92679649622329\n ],\n [\n -121.46229105607921,\n 35.267427833783145\n ],\n [\n -120.77467242000347,\n 35.50061264805049\n ],\n [\n -121.62057934578094,\n 36.93729771345596\n ],\n [\n -123.19764758041177,\n 39.037877559747045\n ],\n [\n -123.83814092270626,\n 38.92679649622329\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Allyson L.","contributorId":171539,"corporation":false,"usgs":false,"family":"Carroll","given":"Allyson","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":937544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":937545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sillett, Stephen C. 0000-0002-7147-759X","orcid":"https://orcid.org/0000-0002-7147-759X","contributorId":355531,"corporation":false,"usgs":false,"family":"Sillett","given":"Stephen C.","affiliations":[{"id":63943,"text":"Cal Poly Humboldt","active":true,"usgs":false}],"preferred":false,"id":937546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Antoine, Marie E. 0009-0004-1771-1519","orcid":"https://orcid.org/0009-0004-1771-1519","contributorId":355534,"corporation":false,"usgs":false,"family":"Antoine","given":"Marie E.","affiliations":[{"id":63943,"text":"Cal Poly Humboldt","active":true,"usgs":false}],"preferred":false,"id":937547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kozaci, Özgür 0000-0001-9439-0190","orcid":"https://orcid.org/0000-0001-9439-0190","contributorId":355536,"corporation":false,"usgs":false,"family":"Kozaci","given":"Özgür","affiliations":[{"id":39608,"text":"Pacific Gas & Electric Company","active":true,"usgs":false}],"preferred":false,"id":937548,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271374,"text":"70271374 - 2025 - Global methane budget 2000-2020","interactions":[],"lastModifiedDate":"2025-09-10T14:32:19.040545","indexId":"70271374","displayToPublicDate":"2025-05-09T09:24:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Global methane budget 2000-2020","docAbstract":"Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020.
The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches.
For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters.
The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified.
For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget.
We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-17-1873-2025","usgsCitation":"Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P.A., Regnier, P., Canadell, J.G., Jackson, R.B., Patra, P.K., Bousquet, P., Ciais, P., Dlugokencky, E.J., Lan, X., Allen, G.H., Bastviken, D., Beerling, D.J., Belikov, D., Blake, D.R., Castaldi, S., Crippa, M., Deemer, B., Dennison, F., Etiope, G., Gedney, N., Höglund-Isaksson, L., Holgerson, M.A., Hopcroft, P.O., Hugelius, G., Ito, A., Jain, A.K., Janardanan, R., Johnson, M.S., Kleinen, T., Krummel, P.B., Lauerwald, R., Li, T., Liu, X., McDonald, K.C., Melton, J.R., Mühle, J., Müller, J., Murguia-Flores, F., Niwa, Y., Noce, S., Pan, S., Parker, R.J., Peng, C., Ramonet, M., Riley, W.J., Rocher-Ros, G., Rosentreter, J.A., Sasakawa, M., Segers, A., Smith, S.J., Stanley, E.H., Thanwerdas, J., Tian, H., Tsuruta, A., Tubiello, F.N., Weber, T.S., van der Werf, G.R., Worthy, D.E., Xi, Y., Yoshida, Y., Zhang, W., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q., 2025, Global methane budget 2000-2020: Earth System Science Data, v. 17, no. 5, p. 1873-1958, https://doi.org/10.5194/essd-17-1873-2025.","productDescription":"86 p.","startPage":"1873","endPage":"1958","ipdsId":"IP-163722","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":497355,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-12-1561-2020","text":"Publisher Index Page"},{"id":495276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Saunois, Marielle","contributorId":217394,"corporation":false,"usgs":false,"family":"Saunois","given":"Marielle","email":"","affiliations":[{"id":39615,"text":"Universite 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Small waterbody conservation has been supported by different stakeholders aiming at improving water quality, enhancing floodwater storage, and supporting migratory bird breeding habitat. Conservation agencies are using hydrological and biological monitoring, modeling, and mapping to adaptively manage small waterbodies in the face of stressors such as invasive species and climate change. As remote sensing estimates of small waterbody surface water extent have become easier to access, understanding the capabilities and limitations of using remote sensing, especially in areas lacking surface water monitoring, is important for conservation decision making. Here, we used in situ monitoring and process-based hydrological modeling to explore remote sensing accuracy, especially related to waterbody size, emergent aquatic vegetation cover, and climatic conditions. Overall, we found that the accuracy of satellite and aerial imagery surface water mapping approaches vastly decreased for waterbodies smaller than 2 ha. We also found emergent vegetation could be masking surface water in waterbodies larger than 2 ha and that accuracy of some remote sensing estimates may decrease during wetter climatic periods. These results indicate that sensors commonly used for surface water applications alone may not be able to accurately detect small waterbody surface water, which supports the need for combining monitoring and modeling to understand how small waterbodies may respond to future changes in climate and land use.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2025.113525","usgsCitation":"McKenna, O.P., Lothspeich, A., Vacek, S., MacDonald, D., Eash, J., Vanderhoof, M.K., McCulloch, E., Ross, C., Sabrina, S., and Knight, J., 2025, Small waterbodies of large conservation concern: Towards an integrated approach to more accurately measuring surface water dynamics: Ecological Indicators, v. 175, 113525, 13 p., https://doi.org/10.1016/j.ecolind.2025.113525.","productDescription":"113525, 13 p.","ipdsId":"IP-156979","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488103,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2025.113525","text":"Publisher Index Page"},{"id":486573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Nelson Lake Waterfowl Protection Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.298889,\n 45.492\n ],\n [\n -95.298889,\n 45.4889\n ],\n [\n -95.295,\n 45.4889\n ],\n [\n -95.295,\n 45.492\n ],\n [\n -95.298889,\n 45.492\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"175","noUsgsAuthors":false,"publicationDate":"2025-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":938404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lothspeich, Audrey Claire 0000-0002-5460-6142","orcid":"https://orcid.org/0000-0002-5460-6142","contributorId":355935,"corporation":false,"usgs":true,"family":"Lothspeich","given":"Audrey Claire","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":938405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vacek, Sara","contributorId":178445,"corporation":false,"usgs":false,"family":"Vacek","given":"Sara","email":"","affiliations":[],"preferred":false,"id":938406,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacDonald, Dawn","contributorId":355936,"corporation":false,"usgs":false,"family":"MacDonald","given":"Dawn","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":938407,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eash, Josh D.","contributorId":267175,"corporation":false,"usgs":false,"family":"Eash","given":"Josh D.","affiliations":[{"id":55428,"text":"U.S. Fish and Wildlife Service, 5600 American Blvd. W., Bloomington, MN","active":true,"usgs":false}],"preferred":false,"id":938408,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - 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However, if there are only a few samples within each region, then the map should be smoothed in an appropriate way to mitigate the problems that arise from having only a few samples. A smoothing algorithm based on a Bayesian hierarchical model is developed and presented in this report. This algorithm has several features that make it especially suitable for mapping earth science data: it can account for measurements that are censored, it can process multiple datasets with different measurement errors and different censoring thresholds, and it can calculate the uncertainty in any statistic that is mapped. The algorithm is demonstrated by mapping gold concentrations that are measured in streambed sediments in the Taylor Mountains quadrangle in southwestern Alaska.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7: Bayesian Mapping of Regionally Grouped, Sparse, Univariate Earth Science Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm7C29","programNote":"Mineral Resources Program","usgsCitation":"Ellefsen, K.J., Wang, B., and Goldman, M.A., 2025, Bayesian mapping of regionally grouped, sparse, univariate earth science data: U.S. Geological Survey Techniques and Methods, book 7, chap. C29, 20 p., https://doi.org/10.3133/tm7C29.","productDescription":"iv, 20 p.","onlineOnly":"Y","ipdsId":"IP-148248","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":485233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c29/coverthb2.jpg"},{"id":485714,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm7C29/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 7C29"},{"id":485567,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/07/c29/tm7c29.xml"},{"id":485566,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/07/c29/images"},{"id":485235,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P14X4CKG","text":"USGS software release","linkHelpText":"Software for Bayesian mapping of regionally grouped, sparse, univariate earth science data (program BMRGSU)"},{"id":485234,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c29/tm7c29.pdf","text":"Report","size":"9.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7C29"}],"country":"United States","state":"Alaska","otherGeospatial":"Taylor Mountains quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159,\n 61\n ],\n [\n -159,\n 60\n ],\n [\n -156,\n 60\n ],\n [\n -156,\n 61\n ],\n [\n -159,\n 61\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Emerging wildlife pathogens often display geographic variability due to landscape heterogeneity. Modeling approaches capable of learning complex, non-linear spatial dynamics of diseases are needed to rigorously assess and mitigate the effects of pathogens on wildlife health and biodiversity. We propose a novel machine learning (ML)-guided approach that leverages prior physical knowledge of ecological systems, using partial differential equations. We present our approach, taking advantage of the universal function approximation property of neural networks for flexible representation of the underlying dynamics of the geographic spread and growth of wildlife diseases. We demonstrate the benefits of our approach by comparing its forecasting power with commonly used methods and highlighting the obtained insights on disease dynamics. Additionally, we show the theoretical guarantees for the approximation error of our model. We illustrate the implementation of our ML-guided approach using data from white-nose syndrome (WNS) outbreaks in bat populations across the US. WNS is an infectious fungal disease responsible for significant declines in bat populations. Our results on WNS are useful for disease surveillance and bat conservation efforts. Our methods can be broadly used to assess the effects of environmental and anthropogenic drivers impacting wildlife health and biodiversity.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/eds.2025.3","usgsCitation":"Reyes, J., Oh, G., McGahan, I., Ma, T., Russell, R., Walsh, D.P., and Zhu, J., 2025, Learning complex spatial dynamics of wildlife diseases with machine learning-guided partial differential equations: Environmental Data Science, v. 4, e28, 23 p., https://doi.org/10.1017/eds.2025.3.","productDescription":"e28, 23 p.","ipdsId":"IP-160182","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490167,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/eds.2025.3","text":"Publisher Index Page"},{"id":489410,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":356170,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oh, Gina","contributorId":333634,"corporation":false,"usgs":false,"family":"Oh","given":"Gina","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian","contributorId":333637,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ma, Ting Fung","contributorId":356257,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":938923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russell, Robin 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":333621,"corporation":false,"usgs":false,"family":"Russell","given":"Robin","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938925,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zhu, Jun","contributorId":356177,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938926,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266550,"text":"70266550 - 2025 - A partner-driven decision support model to inform the reintroduction of bull trout","interactions":[],"lastModifiedDate":"2025-05-09T15:23:24.336355","indexId":"70266550","displayToPublicDate":"2025-05-08T10:13:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A partner-driven decision support model to inform the reintroduction of bull trout","docAbstract":"Assessments of species reintroductions involve a series of complex decisions that include human perspectives and ecological contexts. Here, we present a reintroduction assessment involving bull trout (Salvelinus confluentus) using a structured decision-making process. We approached this assessment by engaging partners representing public utilities, government agencies, and Tribes with shared interests in a potential reintroduction. These individuals identified objectives, decision alternatives, and ecological scenarios that were incorporated into a co-produced simulation-based model of potential reintroduction outcomes. The model included mathematical representations of habitat availability, life history expression, and assumptions regarding constraints on potential bull trout populations. Within each recipient stream, partners chose to explore a wide range of decision alternatives and simulated scenarios affecting reintroduction success. Results suggested that 1) reintroductions using eggs or adults were most optimal, 2) adding more individuals resulted in diminishing returns, 3) access to migratory habitat could improve success, and 4) the diversity of opportunities for life history expression led to improved reintroduction opportunities. In addition, modeled scenarios indicated some recipient streams consistently produced lower abundance of reintroduced bull trout. This work contributes a novel example to a growing portfolio of reintroduction assessments that may inform future conservation for bull trout and many other species facing similar challenges.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0323427","usgsCitation":"Benjamin, J.R., Neibauer, J., Anthony, H., Vazquez, J., Rawhouser, A., and Dunham, J., 2025, A partner-driven decision support model to inform the reintroduction of bull trout: PLoS ONE, v. 20, no. 5, e0323427, 17 p., https://doi.org/10.1371/journal.pone.0323427.","productDescription":"e0323427, 17 p.","ipdsId":"IP-172949","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":490112,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0323427","text":"Publisher Index Page"},{"id":485650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Chelan watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.49036241599774,\n 48.04634063295097\n ],\n [\n -120.37067822007936,\n 48.16552254778642\n ],\n [\n -120.75794320314404,\n 48.538515779248854\n ],\n [\n -120.80213597240349,\n 48.5303354671282\n ],\n [\n -121.11319724267128,\n 48.54549059352783\n ],\n [\n -121.18989609066836,\n 48.38742171862049\n ],\n [\n -121.0025710146728,\n 48.255137103181255\n ],\n [\n -120.49036241599774,\n 48.04634063295097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":936553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neibauer, Judith","contributorId":354836,"corporation":false,"usgs":false,"family":"Neibauer","given":"Judith","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":936554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anthony, Hugh","contributorId":354839,"corporation":false,"usgs":false,"family":"Anthony","given":"Hugh","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":936555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vazquez, Jose","contributorId":354841,"corporation":false,"usgs":false,"family":"Vazquez","given":"Jose","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":936556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rawhouser, Ashley","contributorId":243429,"corporation":false,"usgs":false,"family":"Rawhouser","given":"Ashley","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":936557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":936558,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266306,"text":"70266306 - 2025 - Variability in hydrologic response to wildfire between snow zones in forested headwaters","interactions":[],"lastModifiedDate":"2025-05-15T15:08:04.001368","indexId":"70266306","displayToPublicDate":"2025-05-08T10:02:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Variability in hydrologic response to wildfire between snow zones in forested headwaters","docAbstract":"Rising temperatures and shifting fire regimes in the western United States are pushing fires upslope into areas of deep winter snowpack, where we have little knowledge of the likely hydrologic impacts of wildfire. We quantified differences in the timing and magnitude of stormflow responses to summer rainstorms among six catchments of varying levels of burn severity and seasonal snowpack cover for years 1–3 after the 2020 Cameron Peak fire. Our objectives were to (1) examine whether responsiveness, magnitude, and timing of stormflow responses to rainfall vary between burned and unburned catchments and between snow zones, and (2) identify the factors that affect these responses. We evaluated whether differences in storm hydrograph peak flow, total flow, stage rise, and lag to peak time differed by snow zone and burn category using generalised linear models. Additional predictors in these models are the maximum 60-min rainfall intensity for each storm, the cumulative potential water deficit prior to the storm, and the year post-fire. These models showed that the high snow zone (HSZ) has higher total stormflow than the low snow zone (LSZ), likely due to the higher soil moisture content in that area. In both snow zones, the biggest driver of the magnitude of the stormflow response was MI60. Burn category did not have a clear impact on stormflow response in the HSZ, but it did impact stage rise at the severely burned catchment in the LSZ. This was the only site that had widespread overland flow post-fire. These results demonstrate that the stormflow responses to fire vary between snow zones, indicating a need to account for elevation and snow persistence in post-fire risk assessments.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70151","usgsCitation":"Miller, Q., Barnard, D.M., Sears, M., Hammond, J., and Kampf, S., 2025, Variability in hydrologic response to wildfire between snow zones in forested headwaters: Hydrological Processes, v. 39, no. 5, e70151, 16 p., https://doi.org/10.1002/hyp.70151.","productDescription":"e70151, 16 p.","ipdsId":"IP-172047","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":490124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70151","text":"Publisher Index Page"},{"id":485996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106,\n 41\n ],\n [\n -106,\n 40.333\n ],\n [\n -105,\n 40.333\n ],\n [\n -105,\n 41\n ],\n [\n -106,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Quinn","contributorId":354373,"corporation":false,"usgs":false,"family":"Miller","given":"Quinn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, David M 0000-0003-1877-3151","orcid":"https://orcid.org/0000-0003-1877-3151","contributorId":222833,"corporation":false,"usgs":false,"family":"Barnard","given":"David","email":"","middleInitial":"M","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":935515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sears, Megan","contributorId":354374,"corporation":false,"usgs":false,"family":"Sears","given":"Megan","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":935517,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kampf, Stephanie","contributorId":346221,"corporation":false,"usgs":false,"family":"Kampf","given":"Stephanie","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935518,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267309,"text":"70267309 - 2025 - Leveraging detection uncertainty to estimate Renibacterium salmoninarum infection status among multiple tissues and assays","interactions":[],"lastModifiedDate":"2025-05-20T16:55:57.977539","indexId":"70267309","displayToPublicDate":"2025-05-08T09:45:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging detection uncertainty to estimate Renibacterium salmoninarum infection status among multiple tissues and assays","docAbstract":"Effective disease surveillance relies on accurate pathogen testing and robust prevalence estimates. Diagnostic specificity (DSp), the probability that an uninfected animal tests negative, is high when false positives are low. Diagnostic sensitivity (DSe) is the probability an infected animal tests positive; higher DSe means fewer false negatives. However, sensitivity and false negatives are harder to estimate without a \"gold standard\", an assay that can detect between 90 - 100% of true positive infections. Occupancy estimation of infection prevalence offers one solution by allowing for imperfect detection of the pathogen. Testing potentially infected tissues multiple times allows for the use of a Bayesian multistate occupancy model to estimate the probability of pathogen infection in tissues [Formula: see text] and detection probabilities [Formula: see text] for different assays. Using [Formula: see text] and [Formula: see text] from the posterior distribution, the conditional probability of detecting the pathogen can be modeled, allowing for the calculation of DSe. Renibacterium salmoninarum is a bacterial pathogen causing bacterial kidney disease among salmonid species and was the model pathogen we used to train our model. The current testing standard for salmonids combines initial screening for antibodies using direct fluorescent antibody test (DFAT) with polymerase chain reaction (PCR) confirmation to detect R. salmoninarum. However, detection of R. salmoninarum still varies between species, tissues, and assays. Here, a multi-state occupancy model was used to estimate detection probability among individual and dual kidney/liver infections with DFAT and qPCR in fish with an unknown infection status. Both assays produced false negatives, but qPCR had fewer than DFAT and a higher DSe. Infection state was often misclassified, but multiple surveys per individual or combining tissues for testing improved DSe for both assays.
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R.","contributorId":355584,"corporation":false,"usgs":false,"family":"Fetherman","given":"Eric R.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":937689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huyvaert, Kathryn P.","contributorId":355585,"corporation":false,"usgs":false,"family":"Huyvaert","given":"Kathryn P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drennan, John D.","contributorId":355587,"corporation":false,"usgs":false,"family":"Drennan","given":"John D.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":937691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brock, Rebecca E.","contributorId":355589,"corporation":false,"usgs":false,"family":"Brock","given":"Rebecca E.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937692,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yeatts, Brooke","contributorId":355591,"corporation":false,"usgs":false,"family":"Yeatts","given":"Brooke","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937693,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937694,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70269669,"text":"70269669 - 2025 - Ultrasonic deterrents provide no additional benefit over curtailment in reducing bat fatalities at an Ohio wind energy facility","interactions":[],"lastModifiedDate":"2025-07-29T14:36:03.820212","indexId":"70269669","displayToPublicDate":"2025-05-08T09:27:14","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Ultrasonic deterrents provide no additional benefit over curtailment in reducing bat fatalities at an Ohio wind energy facility","docAbstract":"Wind energy is important for achieving net-zero greenhouse gas emissions but also contributes to global bat mortality. Current strategies to minimize bat mortality due to collision with wind-turbine blades fall broadly into two categories: curtailment (limiting turbine operation during high-risk periods) and deterrence (discouraging bat activity near turbines). Recently, there has been interest in combining these strategies to achieve greater reductions in bat fatalities than either strategy might achieve in isolation. To investigate the effectiveness of combining curtailment with ultrasonic deterrent minimization strategies, we deployed six ultrasonic deterrents at nacelle height on 16 experimental turbines at Avangrid Renewables’ Blue Creek Wind Energy Facility. We rotated between four conditions (normal operations, curtailment only, deterrent only, curtailment and deterrent) randomly assigned to four wind turbines each night between 15 June and 3 October 2017. We found that bat mortality at wind turbines was independent of wind speed. The effectiveness of ultrasonic acoustic deterrents varied between high-frequency-calling species (eastern red bats) and low-frequency-calling species (hoary bats, silver-haired bats, and big brown bats). When deterrents were active, mortality was twice as high for eastern red bats compared to the control. Conversely, deterrents had a weak dampening effect on bat mortality for low-frequency species. We found no additive effects on mortality reduction for turbines operating both curtailment and deterrents compared to either approach in isolation. Our findings suggest that ultrasonic acoustic deterrents may not be effective for both high and low frequency echolocating bats. The increase in fatalities of eastern red bats is alarming and underscores the importance of considering site- and species-specific effects of minimization solutions.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0318451","usgsCitation":"Clerc, J., Huso, M., Schirmacher, M.R., Whitby, M.D., and Hein, C.D., 2025, Ultrasonic deterrents provide no additional benefit over curtailment in reducing bat fatalities at an Ohio wind energy facility: PLoS ONE, v. 20, no. 5, e0318451, 16 p., https://doi.org/10.1371/journal.pone.0318451.","productDescription":"e0318451, 16 p.","ipdsId":"IP-169209","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":493320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0318451","text":"Publisher Index Page"},{"id":493094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","county":"Paulding County, Van Wert County","otherGeospatial":"Blue Creek Wind Energy Facility","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.7984601140799,\n 41.01667721426381\n ],\n [\n -84.79663300607753,\n 40.89732795018247\n ],\n [\n -84.50522800681905,\n 40.89802092865145\n ],\n [\n -84.50431462783759,\n 41.0173716521324\n ],\n [\n -84.7984601140799,\n 41.01667721426381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Clerc, Jeffrey 0000-0002-3331-9507","orcid":"https://orcid.org/0000-0002-3331-9507","contributorId":348189,"corporation":false,"usgs":false,"family":"Clerc","given":"Jeffrey","affiliations":[{"id":33782,"text":"National Renewable Energy Laboratory","active":true,"usgs":false}],"preferred":false,"id":944346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huso, Manuela 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":223969,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":944347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schirmacher, Michael R.","contributorId":76635,"corporation":false,"usgs":false,"family":"Schirmacher","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":944348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitby, Michael D. 0000-0002-0694-3830","orcid":"https://orcid.org/0000-0002-0694-3830","contributorId":345180,"corporation":false,"usgs":false,"family":"Whitby","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":82508,"text":"Bat Conservation International, 500 N Capital of Texas Highway, Austin, TX, 78746 USA","active":true,"usgs":false}],"preferred":false,"id":944349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hein, Cris D.","contributorId":73910,"corporation":false,"usgs":false,"family":"Hein","given":"Cris","email":"","middleInitial":"D.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":944350,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266525,"text":"70266525 - 2025 - Marginalizing time in habitat selection and species distribution models improves inference","interactions":[],"lastModifiedDate":"2025-05-09T15:11:57.95249","indexId":"70266525","displayToPublicDate":"2025-05-08T08:01:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Marginalizing time in habitat selection and species distribution models improves inference","docAbstract":"Aim
Recent methodological advances for studying how animals move and use space with telemetry data have focused on fine-scale, more mechanistic inference. However, in many cases, researchers and managers remain interested in larger scale questions regarding species distribution and habitat use across study areas, landscapes, or seasonal ranges. Point processes offer a unified framework for many methods applied in studies of species distribution and resource selection; however, challenges remain in terms of dealing with temporal autocorrelation common in many types of telemetry data collected from animal locations.
Innovation
Space–time point processes (STPPs) have a unique property, in that marginalising time offers a connection between individual animal movement and broader point processes, yet this property has seen little attention in both statistical and applied research. In this paper, we first present some of the details of this marginalisation property and methods for applying marginalised STPPs (mSTTPs) to autocorrelated telemetry data and then apply a mSTTP in a case study on the summer space use and habitat selection of female caribou (Rangifer tarandus) in Denali National Park and Preserve, Alaska.
Main Conclusions
The case study demonstrated that an mSTPP approach can improve inference over other commonly used methods in terms of its ability to account for temporal autocorrelation and offers greater precision in parameter estimates and improved predictions of space use. As this method fits conveniently into the existing point process frameworks, it offers a practical solution to dealing with temporal autocorrelation inherent to many types of telemetry data when research questions center around broader scale patterns of animal habitat selection and space use.
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253021","programNote":"National and Global Petroleum Assessment","usgsCitation":"Burke, L.A., Paxton, S.T., Kinney, S.A., Gianoutsos, N.J., Dubiel, R.F., Pitman, J.K., Schenk, C.J., Mercier, T.J., Le, P.A., and Leathers-Miller, H.M., 2025, Assessment of undiscovered oil and gas resources in the Lower Cretaceous Hosston and Travis Peak Formations, U.S. Gulf Coast, 2024: U.S. Geological Survey Fact Sheet 2025–3021, 4 p., https://doi.org/10.3133/fs20253021.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-170167","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":485851,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118567.htm","linkFileType":{"id":5,"text":"html"}},{"id":485513,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3021/fs20253021.xml"},{"id":485512,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3021/images"},{"id":485342,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1JPWRAY","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Gulf Coast Mesozoic Province, Lower Cretaceous Travis Peak and Hosston Formations: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":485561,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3021"},{"id":485340,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3021/fs20253021.pdf","text":"Report","size":"2.89 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3021"},{"id":485339,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3021/coverthb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Georgia, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101,\n 33.75\n ],\n [\n -101,\n 25.99278122819011\n ],\n [\n -80.66288679220173,\n 25.99278122819011\n ],\n [\n -80.66288679220173,\n 33.75\n ],\n [\n -101,\n 33.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Wetland and permafrost soils contain some of Earth's largest reservoirs of organic carbon, and these stores are threatened by rapid warming across the Arctic. Nearly half of northern wetlands are affected by permafrost. As these ecosystems warm, the cycling of dissolved organic matter (DOM) and the opportunities for microbial degradation are changing. This is particularly evident as the relationship between wetland and permafrost DOM dynamics evolves, especially with the introduction of permafrost-derived DOM into wetland environments. Thus, understanding the interplay of DOM composition and microbial communities from wetlands and permafrost is critical to predicting the impact of released carbon on global carbon cycling. As little is understood about the interactions between wetland active layer and permafrost-derived sources as they intermingle, we conducted experimental bioincubations of mixtures of DOM and microbial communities from two fen wetland depths (shallow: 0–15 cm, and deep: 15–30 cm) and two ages of permafrost soil (Holocene and Pleistocene). We found that the source of microbial inoculum was not a significant driver of dissolved organic carbon (DOC) degradation across treatments; rather, DOM source and specifically, DOM molecular composition, controlled the rate of DOC loss over 100 days of bioincubations. DOC loss across all treatments was negatively correlated with modified aromaticity index, O/C, and the relative abundance of condensed aromatic and polyphenolic formula, and positively correlated with H/C and the relative abundance of aliphatic and peptide-like formula. Pleistocene permafrost-derived DOC exhibited ∼70% loss during the bioincubation driven by its initial molecular-level composition, highlighting its high bioavailability irrespective of microbial source.
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