The Rocky Mountain Population (RMP) of trumpeter swans Cygnus buccinator (hereafter, swans) in North America includes breeders in the Greater Yellowstone Area (GYA) and other western states (together, United States segment) and western provinces of Canada (Canada segment). Conservation concern for the United States segment stems from its slow population growth and the resident nature of GYA swans, which intermingle with migrating Canada segment swans in wintering habitats. Thus, understanding variation in migratory behavior and habitat use by swans in the two population segments can inform how management actions may affect the RMP. We used telemetry data from 55 RMP swans captured in the western United States to understand their movements and habitat use. For 45 swans (60 swan-years) that spent the summer in the United States, distance traveled between breeding and wintering areas ranged from 0 km (i.e., no migration in 22% of swan-years) to 473 km, with an average of nonzero movements of 118 ± 95 km (SD). Swans traveled farther distances when maximum temperatures were lower. For 10 swans (16 swan-years) that spent the summer in Canada, five appeared to molt but not to nest, and four appeared to nest in one or more years. Migration timing was similar for molting and nesting swans. All five molting swans and one nesting swan spent at least one previous summer in the GYA. Migratory connectivity of all birds was weaker in years when more swans migrated to Canada for the summer. During the breeding season, Canada swans used low-elevation lakes, but United States swans used high-elevation lakes. Both groups of swans increased use of crop fields outside of the breeding season. Our study shows interchange between the United States and Canada segments, a finding that challenges the efficacy of existing population designations. Furthermore, variation in movement behavior of GYA swans suggests possible actions, such as restoring winter habitats to increase swan distribution and migration, to support swan conservation.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70096","usgsCitation":"Poessel, S.A., Sanders, T., Long, W., Kristof, A., Reishus, B., Proett, M., Gower, C., Ibrahim, N., and Katzner, T.E., 2025, Movements and habitat use vary across the Rocky Mountain Population of trumpeter swans: Journal of Wildlife Management, v. 89, no. 8, e70096, 18 p., https://doi.org/10.1002/jwmg.70096.","productDescription":"e70096, 18 p.","ipdsId":"IP-160496","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":496640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -138.95754939868905,\n 61.560322423115394\n ],\n [\n -118.55526728999622,\n 51.12368923776994\n ],\n [\n -119.05891663327156,\n 42.82280620245445\n ],\n [\n -113.19458823923267,\n 41.306395580626145\n ],\n [\n -110.6147155407601,\n 41.23543486615125\n ],\n [\n -111.72918253654919,\n 48.78609382462486\n ],\n [\n -114.68487126300441,\n 52.72581807685856\n ],\n [\n -131.60641572663843,\n 61.0005751873353\n ],\n [\n -138.95754939868905,\n 61.560322423115394\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":950525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders, Todd","contributorId":357733,"corporation":false,"usgs":false,"family":"Sanders","given":"Todd","affiliations":[{"id":85545,"text":"U.S. Fish and Wildlife Service, Division of Migratory Bird Management","active":true,"usgs":false}],"preferred":false,"id":950526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, William","contributorId":242920,"corporation":false,"usgs":false,"family":"Long","given":"William","affiliations":[{"id":48582,"text":"(deceased)","active":true,"usgs":false}],"preferred":false,"id":950527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kristof, Andrea","contributorId":362455,"corporation":false,"usgs":false,"family":"Kristof","given":"Andrea","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":950528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reishus, Brandon","contributorId":362456,"corporation":false,"usgs":false,"family":"Reishus","given":"Brandon","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":950529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Proett, Matt","contributorId":362457,"corporation":false,"usgs":false,"family":"Proett","given":"Matt","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":950530,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gower, Claire","contributorId":362458,"corporation":false,"usgs":false,"family":"Gower","given":"Claire","affiliations":[{"id":39047,"text":"Montana Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":950531,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ibrahim, Nicole","contributorId":362461,"corporation":false,"usgs":false,"family":"Ibrahim","given":"Nicole","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":950532,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191909,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd","email":"tkatzner@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":950533,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70272169,"text":"70272169 - 2025 - Assessing survey design for long-term population trend detection in piping plovers","interactions":[],"lastModifiedDate":"2025-11-18T15:45:14.370821","indexId":"70272169","displayToPublicDate":"2025-09-10T08:39:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Assessing survey design for long-term population trend detection in piping plovers","docAbstract":"Determining appropriate spatio-temporal scales for monitoring migratory shorebirds is challenging. Effective surveys must detect population trends without excessive or insufficient sampling, yet many programs lack formal evaluations of survey effectiveness. Using data from 2012 to 2019 on Louisiana’s barrier islands (Whiskey, west Raccoon, east Raccoon, and Trinity), we assessed how spatial and temporal scales influence population trend inference for piping plovers (Charadrius melodus). Point count data were aggregated to grid sizes from 50 to 200 m and analyzed using Bayesian dynamic occupancy models. We found occupancy and colonization estimates varied by spatial resolution, with space–time autocorrelation common across scales. Smaller islands (east and west Raccoon) yielded higher trend detection power due to better detectability, while larger islands (Trinity and Whiskey) showed lower power. Detectability, more than sampling frequency, drove trend inference. Models incorporating spatial autocorrelation outperformed traditional Frequentist approaches but showed poorer fit at coarser scales. These findings underscore how matching analytical scale to ecological processes and selecting appropriate models can influence predictions. Power analysis revealed that increasing survey frequency may improve inference, especially in low-detectability areas. Overall, our study highlights how careful scale selection, model diagnostics, and survey design can enhance monitoring efficiency and support long-term conservation of migratory shorebirds.
","language":"English","publisher":"MDPI","doi":"10.3390/land14091846","usgsCitation":"Bohnett, E., Schulz, J., Dobbs, R., Hoctor, T., Ahmad, B., Rashid, W., and Waddle, J., 2025, Assessing survey design for long-term population trend detection in piping plovers: Land, v. 14, no. 9, 1846, 25 p., https://doi.org/10.3390/land14091846.","productDescription":"1846, 25 p.","ipdsId":"IP-180225","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":496734,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land14091846","text":"Publisher Index Page"},{"id":496588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Isles Dernieres, Raccoon Island, Trinity Island, Whiskey Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.35300936814708,\n 29.370915739158065\n ],\n [\n -91.35300936814708,\n 29.010416129236035\n ],\n [\n -90.53724293701912,\n 29.010416129236035\n ],\n [\n -90.53724293701912,\n 29.370915739158065\n ],\n [\n -91.35300936814708,\n 29.370915739158065\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Bohnett, Eve","contributorId":272548,"corporation":false,"usgs":false,"family":"Bohnett","given":"Eve","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":950294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schulz, Jessica","contributorId":330111,"corporation":false,"usgs":false,"family":"Schulz","given":"Jessica","affiliations":[{"id":52994,"text":"New Hampshire Department of Environmental Services","active":true,"usgs":false}],"preferred":false,"id":950295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dobbs, Robert C. 0000-0002-9079-7249 rdobbs@usgs.gov","orcid":"https://orcid.org/0000-0002-9079-7249","contributorId":200300,"corporation":false,"usgs":false,"family":"Dobbs","given":"Robert C.","email":"rdobbs@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":950296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoctor, Thomas","contributorId":330115,"corporation":false,"usgs":false,"family":"Hoctor","given":"Thomas","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":950297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahmad, Bilal","contributorId":330120,"corporation":false,"usgs":false,"family":"Ahmad","given":"Bilal","email":"","affiliations":[{"id":78816,"text":"University of Swat, Pakistan","active":true,"usgs":false}],"preferred":false,"id":950298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rashid, Wajid","contributorId":330121,"corporation":false,"usgs":false,"family":"Rashid","given":"Wajid","email":"","affiliations":[{"id":78816,"text":"University of Swat, Pakistan","active":true,"usgs":false}],"preferred":false,"id":950299,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waddle, J. 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The U.S. Geological Survey conducted high-resolution nearshore mapping surveys using boat-mounted multiparameter sondes and nitrate sensors during the summer and fall of 2019 on Owasco Lake and Seneca Lake, two lakes with documented CyanoHABs in the Finger Lakes region of New York State. Discrete sensor measurements and water-quality samples were collected at fixed points along survey routes and continuous data were generated at open-water monitoring platforms. This investigation examined whether water-quality information from nearshore surveys was representative of open-water conditions and if nearshore surveys could be used to identify areas with localized nearshore CyanoHABs and potential sources of nutrients not captured by tributary sampling.
In addition to comparisons across methods, nearshore concentrations of nitrate and chlorophyll were evaluated relative to tributary outlets, cyanobacterial abundance and biovolume at discrete locations, and the locations of near-surface CyanoHABs that were designated as “confirmed with high toxins” by the New York State Department of Environmental Conservation. Nitrate and chlorophyll concentrations were comparable across methods for each lake, although concentration ranges were typically higher for nearshore mapping datasets than for nearshore discrete datasets. Nearshore surveys indicated areas of nitrate enrichment that varied temporally in both lakes. Orthophosphate was not routinely detected. Across methods, median chlorophyll concentrations were higher for the summer survey than for the fall survey in Owasco Lake. Nearshore chlorophyll concentrations varied more temporally in Owasco Lake than in Seneca Lake.
Phytoplankton and cyanobacterial abundance and biovolume were about five times higher in Owasco Lake than in Seneca Lake. Cyanobacteria dominated the phytoplankton community in most samples, and Microcystis comprised the bulk of the cyanobacterial biovolume. The most abundant potential cyanotoxin-producing (specifically microcystins) genera were Microcystis, Synechococcus, Aphanocapsa, and Pseudanabaena. The cyanobacterial community composition was comparable between open-water monitoring platforms and nearshore samples. Microcystins were detected in seven survey samples from Owasco Lake, in one survey sample from Seneca Lake, and in one sample each from the open-water monitoring platforms on Owasco and Seneca Lakes that were collected about 7 days before the fall surveys. Microcystin detections were not consistently associated with high cyanobacterial cell counts or cyanotoxin-producing genera.
Results from nearshore surveys were comparable to open-water monitoring platforms and discrete nearshore observations in the absence of nearshore or open-water CyanoHABs in these systems during the study. Patterns of nearshore concentrations of nitrate and chlorophyll from nearshore surveys may aid in the identification of areas with localized nitrate loading and shifts in phytoplankton abundance and community composition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245118","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health","usgsCitation":"Stouder, M.D.W., Gifford, S.R., Gutchess, K.M., Finkelstein, K.M., Johnston, B.D., Beaulieu, K.M., Rosen, J.J., Essig, M.L., and Foster, G.M., 2025, Spatiotemporal variability of algal biomass and nitrate in Owasco and Seneca Lakes in the Finger Lakes Region, New York, in 2019: U.S. Geological Survey Scientific Investigations Report 2024–5118, 39 p., https://doi.org/10.3133/sir20245118.","productDescription":"Report: viii, 39 p.; 2 Appendixes; 2 Data Releases","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-136099","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":495115,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5118/sir20245118_app2.pdf","text":"Appendix 2","size":"397 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5118 Appendix 2","linkHelpText":"- Standard Operating Procedure for the Analysis of Total Microcystins and Nodularins in Discrete Water-Quality Samples for the Cyanobacterial Harmful Algal Blooms Advanced Monitoring Pilot Study"},{"id":495113,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9046YOS","text":"USGS data release","linkHelpText":"Field data for an evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes, New York, 2018–2020"},{"id":495129,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98P1VV6","text":"USGS data release","linkHelpText":"High-resolution spatial water-quality and discrete phytoplankton data, Owasco Lake, Seneca Lake, and Skaneateles Lake, Finger Lakes Region, New York, 2018–2019"},{"id":495205,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5118/sir20245118.XML"},{"id":495111,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5118/images"},{"id":495110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5118/sir20245118.pdf","text":"Report","size":"9.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5118 PDF"},{"id":495155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5118/coverthb.jpg"},{"id":495204,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245118/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5118 HTML"},{"id":495114,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5118/sir20245118_app1.pdf","text":"Appendix 1","size":"657 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5118 Appendix 1","linkHelpText":"- Quality Assurance Project Plan for Discrete Water-Quality Samples, Measurements, and Shoreline Surveys Conducted for the Cyanobacterial Harmful Algal Blooms Advanced Monitoring Pilot Study"}],"country":"United States","state":"New York","otherGeospatial":"Finger Lakes region, Owasco Lake, Seneca Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.99541024728366,\n 42.88134266146028\n ],\n [\n -76.99541024728366,\n 42.3766690451576\n ],\n [\n -76.82099577233303,\n 42.3766690451576\n ],\n [\n -76.82099577233303,\n 42.88134266146028\n ],\n [\n -76.99541024728366,\n 42.88134266146028\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.55679821172299,\n 42.9076046331341\n ],\n [\n -76.55679821172299,\n 42.75261670559294\n ],\n [\n -76.44184136620188,\n 42.75261670559294\n ],\n [\n -76.44184136620188,\n 42.9076046331341\n ],\n [\n -76.55679821172299,\n 42.9076046331341\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Urbanization reshapes dissolved organic matter (DOM) sources, transport, and transformations through changes in vegetation, hydrology, and management of waste and water. Yet the impacts of urbanization on DOM are variable within and among cities. Predicting heterogeneous responses to urbanization is challenged by diverse human activities and underlying biophysical variation along stream networks. Using data from the 486 largest urban areas in the continental United States and seven focal cities, we identified macro and local scale urban gradients in social, built, and biophysical factors that are expected to shape DOM. We used these gradients and the literature to develop hypotheses about heterogeneity in DOM quantity and quality within and among cities. Interactions among landscape and infrastructure attributes across spatial and temporal scales result in heterogeneous responses in DOM. Characterizing and quantifying these inconsistent responses to urbanization in contrasting settings may help to better understand heterogeneity and identify generalities among urban watersheds.
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modelling","interactions":[],"lastModifiedDate":"2025-12-12T15:25:24.213331","indexId":"70273049","displayToPublicDate":"2025-09-09T09:08:26","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":"Catchment prioritization for freshwater mussel conservation in the Northeastern United States based on distribution modelling","docAbstract":"Freshwater mussels are critical to the health of freshwater systems, but their populations are declining dramatically throughout the world. The limited resources available for freshwater mussel conservation necessitates the geographic prioritization of conservation-related actions. However, lack of knowledge about freshwater mussel spatial distributions hinders decision making in this context. In this study, we assessed the distribution of twelve native freshwater mussel species across six Northeastern states (Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine) in the United States using data collected from lentic and lotic environments by eight state agencies. We first modeled individual distributions using a maximum entropy (MaxEnt) model and then compiled distribution models to assess the distribution of freshwater mussel species richness. We also determined geographic prioritization for three conservation-related actions: species surveys, land protection, and population restoration of species of high conservation concern. We found that the percent of catchments predicted to have species occurrence (based on a probability threshold) varied across species, with Elliptio complanata (Eastern elliptio) predicted to occur in the greatest percent of available catchments (33.92%) and Alasmidonta heterodon (Dwarf wedgemussel) expected in the smallest percent (5.30%). The predicted overall species richness within our modeled catchments ranged from zero to all twelve species, with an average of two species per catchment. Although conservation priorities vary depending on the conservation action of interest, we found some areas of consistent importance including much of Maine and the southern reaches of the Connecticut River. An improved understanding of freshwater mussel distribution in a landscape framework will enable managers to implement more precise and efficient conservation interventions for these essential aquatic species.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0324387","usgsCitation":"O’Brien, R.S., DiRenzo, G.V., Roy, A.H., Carmignani, J., Quinones, R.M., Rogers, J.B., and Swartz, B.I., 2025, Catchment prioritization for freshwater mussel conservation in the Northeastern United States based on distribution modelling: PLoS ONE, v. 20, no. 9, e0324387, 20 p., https://doi.org/10.1371/journal.pone.0324387.","productDescription":"e0324387, 20 p.","ipdsId":"IP-175157","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497699,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0324387","text":"Publisher Index Page"},{"id":497466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, 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\"}}]}","volume":"20","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Brien, Rebecca S.M.","contributorId":363993,"corporation":false,"usgs":false,"family":"O’Brien","given":"Rebecca","middleInitial":"S.M.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":952157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":952158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carmignani, Jason","contributorId":360465,"corporation":false,"usgs":false,"family":"Carmignani","given":"Jason","affiliations":[{"id":86008,"text":"Natural Heritage and Endangered Species Program","active":true,"usgs":false}],"preferred":false,"id":952160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quinones, Rebecca M.","contributorId":120271,"corporation":false,"usgs":true,"family":"Quinones","given":"Rebecca","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":952161,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Jennifer B.","contributorId":359344,"corporation":false,"usgs":false,"family":"Rogers","given":"Jennifer","middleInitial":"B.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":952162,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Swartz, Beth I.","contributorId":364001,"corporation":false,"usgs":false,"family":"Swartz","given":"Beth","middleInitial":"I.","affiliations":[{"id":39965,"text":"Maine Department of Inland Fisheries and Wildlife","active":true,"usgs":false}],"preferred":false,"id":952163,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271439,"text":"70271439 - 2025 - Evaluating mass flow meter measurements from chambers for greenhouse gas emissions from orphan wells and other point sources","interactions":[],"lastModifiedDate":"2025-09-15T14:10:51.273265","indexId":"70271439","displayToPublicDate":"2025-09-09T09:07:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":926,"text":"Atmospheric Measurement Techniques","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating mass flow meter measurements from chambers for greenhouse gas emissions from orphan wells and other point sources","docAbstract":"This study evaluates the performance of a rigid gas flux chamber equipped with a mass flow meter (MFM) for measuring gas emissions from leaking orphan wells and similar pressure-driven gas point sources. We conducted a series of laboratory and field experiments to evaluate the sensitivity, stability, and dynamic range of an MFM chamber system and found an optimal method for sealing the chamber to the ground to isolate the emission source. From these results, we estimate the effects of different soil gas permeabilities on measurements and identify the uncertainty of environmental processes that can impact measurements. Simulations of an MFM chamber are compared to those of a dynamic flux chamber to contrast the data derived with both methodologies and illustrate the potential for measuring high variability leaks with the MFM chamber. Using a low flow resistance MFM and a chamber well-sealed to the ground, it is possible to measure leaks down to 1.08 x 10-3 cubic meters per hour (m3 h−1) (refenced to 25°/1 atm), corresponding to 0.77 grams per hour (g h−1) methane or 2.11 g h−1 carbon dioxide, with a mean uncertainty of 0.89 % relative standard deviation. Environmental processes such as heated gas inside the chamber from solar gain, wind blowing across the chamber vent, and changing humidity in the chamber, can cause variation in MFM measurements. Over 11 d of continuous monitoring under varying weather conditions, the standard deviation of the environmentally sourced signals was found to be 7.40 x 10-3 m3 h−1 (equivalent to or 5.27 g h−1 methane or 14.45 g h−1 carbon dioxide). Strategies to obtain the highest quality data from MFM chambers include burying the edges of the chamber below the surface sufficiently deep to seal the chamber edges against gas flow and soaking the dirt with water to lower the chances of escaping gases, while monitoring the gas flow and adjusting the chamber seal to achieve a maximum flow rate.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/amt-18-4207-2025","usgsCitation":"Haase, K., and Gianoutsos, N.J., 2025, Evaluating mass flow meter measurements from chambers for greenhouse gas emissions from orphan wells and other point sources: Atmospheric Measurement Techniques, v. 18, p. 4207-4226, https://doi.org/10.5194/amt-18-4207-2025.","productDescription":"20 p.","startPage":"4207","endPage":"4226","ipdsId":"IP-174760","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":495731,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/amt-18-4207-2025","text":"Publisher Index Page"},{"id":495492,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationDate":"2025-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Haase, Karl B. 0000-0002-6897-6494 khaase@usgs.gov","orcid":"https://orcid.org/0000-0002-6897-6494","contributorId":205943,"corporation":false,"usgs":true,"family":"Haase","given":"Karl","email":"khaase@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":948759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gianoutsos, Nicholas J. 0000-0002-6510-6549 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":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":948760,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70271356,"text":"70271356 - 2025 - Changes in aeolian saltation cloud properties with wind speed and ripples","interactions":[],"lastModifiedDate":"2025-09-10T15:09:10.458291","indexId":"70271356","displayToPublicDate":"2025-09-08T08:05:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"Changes in aeolian saltation cloud properties with wind speed and ripples","docAbstract":"Aeolian sediment transport shapes landscapes on Earth and other planetary surfaces, yet key uncertainties remain in how the near-bed saltation cloud responds to changing wind and surface conditions. Leveraging recent advances in image-based particle tracking, we conducted wind tunnel experiments using high-speed imaging and Particle Tracking Velocimetry to quantify sand grain trajectories in saturated saltation clouds over both flat and rippled beds. Our open-source PTV workflow resolved particle motions within millimeters of the bed across a range of wind speeds. Supporting previous results, we find that mean particle velocities do not scale linearly with wind speed; instead, changes in particle velocity distributions—including skewness and kurtosis—emerge as wind strength and sediment flux increase. At higher transport rates, distinctions among saltation, reptation, and creep within the particle distribution become more smoothed, suggesting a continuum spectrum of particle behavior rather than discrete transport modes. Our new dataset of particle trajectories over an active rippled bed shows distinctions in particle speed across the aspects. On ripple stoss slopes, fast saltating grains co-occur with slow creeping particles, while lee slopes are depleted of slower grains, consistent with shadowing effects. These observations support a feedback between ripple morphology and near-bed particle trajectories, with implications for how splash events redistribute sediment momentum. This study contributes new high-resolution empirical data that illuminate how saltation cloud structure evolves with wind forcing and bedform development, advancing our understanding of aeolian sediment transport under complex, dynamic conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aeolia.2025.100996","usgsCitation":"Kelley, M., Walker, I.J., Schmeeckle, M.W., Swann, C., Dorn, R., Roberts, M., and O'Brien, P., 2025, Changes in aeolian saltation cloud properties with wind speed and ripples: Aeolian Research, v. 74, 100996, 16 p., https://doi.org/10.1016/j.aeolia.2025.100996.","productDescription":"100996, 16 p.","ipdsId":"IP-171990","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"74","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kelley, Madeline Margaret 0009-0003-6406-2307","orcid":"https://orcid.org/0009-0003-6406-2307","contributorId":353253,"corporation":false,"usgs":true,"family":"Kelley","given":"Madeline Margaret","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Ian J. 0000-0001-5719-5310","orcid":"https://orcid.org/0000-0001-5719-5310","contributorId":361056,"corporation":false,"usgs":false,"family":"Walker","given":"Ian","middleInitial":"J.","affiliations":[{"id":86173,"text":"Department of Geography, UC Santa Barbara, Santa Barbara, CA 93106-4060, USA","active":true,"usgs":false}],"preferred":false,"id":948200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmeeckle, Mark W.","contributorId":178432,"corporation":false,"usgs":false,"family":"Schmeeckle","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":948201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swann, Christy","contributorId":258305,"corporation":false,"usgs":false,"family":"Swann","given":"Christy","email":"","affiliations":[{"id":40754,"text":"Naval Research Lab","active":true,"usgs":false}],"preferred":false,"id":948202,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dorn, Ron 0000-0003-1343-4556","orcid":"https://orcid.org/0000-0003-1343-4556","contributorId":361057,"corporation":false,"usgs":false,"family":"Dorn","given":"Ron","affiliations":[{"id":86175,"text":"School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, U.S.A","active":true,"usgs":false}],"preferred":false,"id":948203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, Michaela","contributorId":361058,"corporation":false,"usgs":false,"family":"Roberts","given":"Michaela","affiliations":[{"id":86175,"text":"School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, U.S.A","active":true,"usgs":false}],"preferred":false,"id":948204,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O'Brien, Patrick 0000-0002-8956-2741","orcid":"https://orcid.org/0000-0002-8956-2741","contributorId":361059,"corporation":false,"usgs":false,"family":"O'Brien","given":"Patrick","affiliations":[{"id":86177,"text":"School of the Environment, Trent University, Peterborough, ON, K9L 0G2, Canada","active":true,"usgs":false}],"preferred":false,"id":948205,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271463,"text":"70271463 - 2025 - Speleothem evidence for Late Miocene extreme Arctic amplification – An analogue for near-future anthropogenic climate change?","interactions":[],"lastModifiedDate":"2025-09-17T14:00:53.292778","indexId":"70271463","displayToPublicDate":"2025-09-08T07:56:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"Speleothem evidence for Late Miocene extreme Arctic amplification – An analogue for near-future anthropogenic climate change?","docAbstract":"The Miocene provides an excellent climatic analogue for near-future runaway anthropogenic warming, with atmospheric CO2 concentrations and global average temperatures similar to those projected for the coming century under extreme-emissions scenarios. However, the magnitude of Miocene Arctic warming remains unclear due to the scarcity of reliable proxy data. Here we use stable oxygen isotope and trace element analyses, alongside clumped isotope and fluid inclusion palaeothermometry of speleothems to reconstruct palaeo-environmental conditions near the Siberian Arctic coast during the Tortonian (8.68 ± 0.09 Ma). Stable oxygen isotope records suggest warmer-than-present temperatures. This is supported by temperature estimates based on clumped isotopes and fluid inclusions giving mean annual air temperatures between +6.6 and +11.1 °C, compared with −12.3 °C today. Trace elements records reveal a highly seasonal hydrological environment.
Our estimate of > 18 °C of Arctic warming supports the wider consensus of a warmer-than-present Miocene and provides a rare palaeo-analogue for future Arctic amplification under high-emissions scenarios. The reconstructed increase in mean surface temperature far exceeds temperatures projected in fully coupled global climate models, even under extreme-emissions scenarios. Given that climate models have consistently underestimated the extent of recent Arctic amplification, our proxy data suggest Arctic warming may exceed current projections.
An unknown occurrence of oil was detected near Avak Creek on Native lands on the North Slope of Alaska. Determining the source of oil was imperative for allowing stakeholders (Federal, State, and local government agencies and the landowner, an Alaska Native corporation) to make timely and informed decisions and mount a mitigation response, if required. The regional and local geological framework of the Avak Creek site was constructed using seismic surveys, well data, and basin modeling results, to identify local petroleum systems, map structural geometry and faults, define source rock thermal maturity distributions, and infer likely oil-migration pathways. Molecular hydrocarbon fingerprints (biomarkers, diamondoids, compound-specific isotopes) of the oil were compared to those of local and regional oil seeps, exploration well tests, and produced oils. Biomarker acid distributions characterized the history and extent of petroleum biodegradation. Integrating subsurface and geochemical parameters, the oil is interpreted to be a natural seep generated locally, predominantly from the Brookian Lower Cretaceous Hue Shale/gamma-ray zone, rather than an anthropogenic source of pollution. Results highlight sophisticated analytical technologies used to characterize complex, compositionally altered hydrocarbons. Results also advance our understanding of Brookian source rock distribution, subsurface petroleum migration pathways, and Arctic Alaska petroleum systems.
","conferenceTitle":"32nd International Meeting on Organic Geochemistry (IMOG) 2025","conferenceDate":"September 7-11, 2025","conferenceLocation":"Porto, Portugal","language":"English","publisher":"European Association of Geoscientists & Engineers","doi":"10.3997/2214-4609.202533156","usgsCitation":"Botterell, P.J., Houseknecht, D.W., Wycech, J.B., Moldowan, J.M., Lillis, P.G., Smith, R.A., and Maher, K., 2025, Avak Creek oil occurrence, North Slope, Alaska: Newly discovered oil seep on Native lands, near village of Utqiagvik, 32nd International Meeting on Organic Geochemistry (IMOG) 2025, v. 2025, Porto, Portugal, September 7-11, 2025, 2 p., https://doi.org/10.3997/2214-4609.202533156.","productDescription":"2 p.","ipdsId":"IP-175494","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":495630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":495609,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.earthdoc.org/content/papers/10.3997/2214-4609.202533156","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alaska","city":"Utqiagvik","volume":"2025","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Botterell, Palma J. 0000-0001-7140-0915 pjarboe@usgs.gov","orcid":"https://orcid.org/0000-0001-7140-0915","contributorId":5805,"corporation":false,"usgs":true,"family":"Botterell","given":"Palma","email":"pjarboe@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":948885,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houseknecht, David W 0000-0002-9633-6910","orcid":"https://orcid.org/0000-0002-9633-6910","contributorId":361485,"corporation":false,"usgs":false,"family":"Houseknecht","given":"David","middleInitial":"W","affiliations":[{"id":86299,"text":"USGS Geology, Energy & Minerals Science Center (RET)","active":true,"usgs":false}],"preferred":false,"id":948886,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wycech, Jody Brae 0000-0002-7073-3037","orcid":"https://orcid.org/0000-0002-7073-3037","contributorId":303104,"corporation":false,"usgs":true,"family":"Wycech","given":"Jody","email":"","middleInitial":"Brae","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":948887,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moldowan, J. 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This process encompasses model ideation, model selection, and determining appropriate use of statistical methods. Literature on models for animal movement emerging in the past two decades has yielded a rich collection of statistical methods garnering much deserved positive attention. Among such efforts, there is limited investigation of the broader place for simple machine learning methodology in animal movement modeling. We propose a bagged (i.e., bootstrap aggregated) animal movement model using simple, off-the-shelf machine learning algorithms. The model is intuitive, retains statistical inference about characteristics of animal movement (i.e., estimated from model-based summary statistics), and only requires knowledge of elementary statistical and machine learning analysis to understand. We show by simulation that our model can provide unbiased estimates of pertinent characteristics of animal movement (e.g., daily displacement) in the presence of large and realistic location error. We believe that increasing accessible literature on simple machine learning animal movement models provides valuable pedagogical and practical support for researchers using statistical models to study animal movement.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72060","usgsCitation":"Whetten, A.B., Hefley, T.J., Haukos, D.A., and Brewer, D.E., 2025, Simple bagged movement models for telemetry data: Ecology and Evolution, v. 15, no. 9, e72060, 14 p., https://doi.org/10.1002/ece3.72060.","productDescription":"e72060, 14 p.","ipdsId":"IP-178248","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":496760,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72060","text":"Publisher Index Page"},{"id":496692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Whetten, Andrew B.","contributorId":362668,"corporation":false,"usgs":false,"family":"Whetten","given":"Andrew","middleInitial":"B.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":950708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hefley, Trevor J.","contributorId":362671,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","middleInitial":"J.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":950709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":950710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brewer, Dustin E.","contributorId":362674,"corporation":false,"usgs":false,"family":"Brewer","given":"Dustin","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":950711,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274542,"text":"70274542 - 2025 - Small fish, big implications: Considerations for an ecosystem approach to capelin fisheries management","interactions":[],"lastModifiedDate":"2026-04-01T22:23:26.635675","indexId":"70274542","displayToPublicDate":"2025-09-06T15:09:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Small fish, big implications: Considerations for an ecosystem approach to capelin fisheries management","docAbstract":"Climate-driven changes in the Subarctic will directly impact capelin populations and the ecosystem they inhabit, including their predators, prey, and physical habitats. Consequently, incorporating ecosystem considerations in future capelin fisheries management is crucial. In this study, a multidisciplinary group of experts critically evaluated whether the current capelin stock assessment and management frameworks for the four main capelin stocks in the Barents Sea (BS), Iceland-East Greenland-Jan Mayen (IEGJM), Newfoundland and Labrador shelf (NL) and Alaska (AK) align with the principles of an Ecosystem Approach to Fisheries Management (EAFM). An evidence-based ranking of our knowledge on current capelin dynamics across ecological, economic, and social dimensions was conducted, using expert knowledge supported by literature. This exercise also identified data currently used for assessment and management, which highlighted that the existing capelin assessment frameworks include varying degrees of EAFM elements across stocks, such as considerations of trophic interactions, bottom-up processes, accounting for ecosystem uncertainty, and stakeholder engagement in the advisory process. Nonetheless, there is room for improvement where data and knowledge are lacking. We provide some key tactical (short-term) and strategic (long-term) recommendations from our perspective on what is required to ensure the sustainable management of capelin in the circumpolar region over the coming decades.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11160-025-09986-z","usgsCitation":"Singh, W., Trochta, J.T., Hannah M. Murphy, H.M., McGowan, D.W., Adamack, A.T., Arimitsu, M.L., Barðarson, B., Björnsson, H., Bogstad, B., Boudreau, M., Chambers, C., Gjøsæter, H., Jansen, T., Jónsson, S.Þ., Kvamsdal, S., Lewis, R.S., Mikkelsen, N., Pedersen, T., Olafsdottir, A.H., Oostdijk, M., Silva, T., Skaret, G., Suryan, R.M., and Subbey, S., 2025, Small fish, big implications: Considerations for an ecosystem approach to capelin fisheries management: Reviews in Fish Biology and Fisheries, v. 35, p. 1899-1934, https://doi.org/10.1007/s11160-025-09986-z.","productDescription":"36 p.","startPage":"1899","endPage":"1934","ipdsId":"IP-173579","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":502063,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11160-025-09986-z","text":"Publisher Index Page"},{"id":501975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationDate":"2025-09-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Singh, Warsha","contributorId":368963,"corporation":false,"usgs":false,"family":"Singh","given":"Warsha","affiliations":[{"id":40381,"text":"Marine and Freshwater Research Institute, Iceland","active":true,"usgs":false}],"preferred":false,"id":958188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trochta, John T.","contributorId":368964,"corporation":false,"usgs":false,"family":"Trochta","given":"John","middleInitial":"T.","affiliations":[{"id":87684,"text":"2Institute of Marine Research, Norway","active":true,"usgs":false}],"preferred":false,"id":958189,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hannah M. 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In this study, we investigate mechanisms that determine residual sediment fluxes using continuous measurements of bay-marsh sediment exchange conducted in a tidal creek spanning 13 months (753 tidal cycles) in an intertidal marsh recently subsidized with sediment via thin-layer placement. The maximum water level in each tidal cycle varied over seasonal and fortnightly timescales and was driven by a combination of the seasonal cycle in mean sea level (maximum in September, minimum in January) and the fortnightly spring-neap cycle. Residual water fluxes tended to be ebb-directed during overbank tides, possibly due to water crossing creekshed boundaries in the intertidal zone when water levels were sufficiently high. Sediment concentrations on the ebb of overbank tides exceeded those of their corresponding floods, but only for tidal cycles in which water temperatures exceeded 14°C. The interaction of these dynamics resulted in over 90% of the net sediment export from the creek occurring during overbank tides during warmer months—conditions met in 30% of the observed tidal cycles. These findings exemplify the importance of accounting for seasonality in sediment fluxes when assessing sediment budgets of salt marshes and illustrate how sediment budgets assessed with shorter duration datasets may exhibit seasonal bias. Additionally, they suggest that sediment retention for thin-layer sediment placement projects may be high over the course of the first year after sediment subsidies are deployed.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.70193","usgsCitation":"Snedden, G., and Smith, S.J., 2025, Seasonal variation in bay-marsh sediment exchange through a back-barrier salt marsh tidal creek: Limnology and Oceanography, v. 70, no. 11, p. 3143-3154, https://doi.org/10.1002/lno.70193.","productDescription":"12 p.","startPage":"3143","endPage":"3154","ipdsId":"IP-173834","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":496142,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.70193","text":"Publisher Index Page"},{"id":495837,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Gull Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.78334779722779,\n 39.09081569270708\n ],\n [\n -74.78334779722779,\n 39.071155740770365\n ],\n [\n -74.76930530056791,\n 39.071155740770365\n ],\n [\n -74.76930530056791,\n 39.09081569270708\n ],\n [\n -74.78334779722779,\n 39.09081569270708\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Snedden, Gregg A. 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":212275,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":949207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, S. Jarrell 0000-0002-8649-5598","orcid":"https://orcid.org/0000-0002-8649-5598","contributorId":361683,"corporation":false,"usgs":false,"family":"Smith","given":"S.","middleInitial":"Jarrell","affiliations":[{"id":37304,"text":"U.S. Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":949208,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70271315,"text":"dr1215 - 2025 - Framework developed for geomorphic mapping of Fern Ridge Lake, Oregon, 2023","interactions":[],"lastModifiedDate":"2026-02-03T15:20:52.051701","indexId":"dr1215","displayToPublicDate":"2025-09-04T09:21:48","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1215","displayTitle":"Framework Developed for Geomorphic Mapping of Fern Ridge Lake, Oregon, 2023","title":"Framework developed for geomorphic mapping of Fern Ridge Lake, Oregon, 2023","docAbstract":"The construction and operation of large reservoirs in the Willamette River Basin, Oregon, influences important cultural, biological, and other natural or economic resources in affected river corridors. The present-day landforms and cover within the reservoirs have been shaped by a variety of processes, including the pre-dam valley setting and geomorphic processes related to dam operations. Maps of reservoir geomorphic process domains, landforms, and cover provide a foundation for understanding how erosion and deposition processes in or near the reservoirs may affect cultural resources. Detailed geomorphic mapping of Fern Ridge Lake in 2023 provides a basis for evaluating geomorphic processes and patterns of sediment transfer within the reservoir. These processes are related to geomorphic and hydroclimatic conditions as well as annual lake level fluctuation for seasonal flood-control operations. This geomorphic mapping also provides an inventory of existing landforms from which to evaluate the spatial and temporal geomorphic change over time. Digital maps based on high-resolution digital surface models and orthophotographs acquired during low-pool conditions in 2023 extend over an area of about 30 square kilometers (km) upstream of the Fern Ridge Dam. The mapping framework has 3 main components consisting of several subtypes: 5 process domains, 18 landforms, and 7 cover categories. The overarching classification structure is tied to the process domains, which correspond to dissimilar regions of the reservoir that have distinct landforms and broadly similar suites of geomorphic processes. This document describes the geomorphic mapping framework for the reservoir at Fern Ridge Lake and provides mapping unit descriptions including delineation criteria, hypothesized formation processes inferred from remote-sensing and field observations and the literature, and relevance during drawdown operations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1215","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Keith, M.K., and Bervid, H.D., 2025, Framework developed for geomorphic mapping of Fern Ridge Lake, Oregon, 2023: U.S. Geological Survey Data Report 1215, 33 p., https://doi.org/10.3133/dr1215.","productDescription":"Report: viii, 33 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-160706","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":496027,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118829.htm","linkFileType":{"id":5,"text":"html"}},{"id":495173,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1215/dr1215.XML"},{"id":495168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1215/coverthb.jpg"},{"id":495169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1215/dr1215.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1215"},{"id":495170,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1215/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1215"},{"id":495171,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13MHC5P","text":"USGS data release","description":"USGS data release","linkHelpText":"Geomorphic Mapping of Fern Ridge Lake, Oregon, 2023"},{"id":495172,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1215/images"}],"country":"United States","state":"Oregon","otherGeospatial":"Fern Ridge Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.36193413601275,\n 44.13431042059929\n ],\n [\n -123.36193413601275,\n 44.0351205618081\n ],\n [\n -123.23640004823704,\n 44.0351205618081\n ],\n [\n -123.23640004823704,\n 44.13431042059929\n ],\n [\n -123.36193413601275,\n 44.13431042059929\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
The U.S. Geological Survey (USGS)—a Federal civilian agency—and U.S. military and intelligence agencies collaborate on mapping and remote sensing and have since the establishment of the USGS. The organizations exchange data and information and share technology to further their respective missions in service to the American people. Often referred to as examples of “good government” or “whole of government,” the collaboration avoids costly duplication and maximizes time and effort for the government sectors. Collaboration between these sectors started with the original mapping of the United States and evolved to include remote sensing after the advent of aerial photography and satellite imagery.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1552","isbn":"978-1-4113-4627-7","programNote":"National Land Imaging Program","usgsCitation":"Young, P.M., 2025, The U.S. Geological Survey, the U.S. Department of Defense, and the U.S. Intelligence Community—100 years of mapping and remote sensing collaboration, 1879–1979: U.S. Geological Survey Circular 1552, 64 p., https://doi.org/10.3133/cir1552.","productDescription":"xi, 64 p.","numberOfPages":"64","onlineOnly":"N","ipdsId":"IP-160464","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":495094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1552/coverthb.jpg"},{"id":495095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1552/cir1552.pdf","text":"Report","size":"42.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1552"},{"id":495096,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1552/full","linkFileType":{"id":5,"text":"html"},"description":"CIRC 1552 HTML"},{"id":495097,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1552/cir1552.XML","description":"CIRC 1552 XML"},{"id":495098,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1552/images"}],"contact":"Director, National Civil Applications Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 562
Reston, VA 20192
https://www.usgs.gov/programs/national-land-imaging-program
Traditional remote sensing retrieval models for water quality have historically relied on limited, localized data sets due to the prohibitive costs of extensive field campaigns and logistical challenges of collecting match-up data with satellite overpasses. As a result, these models often lack generalizability across seasons, tides, and sites. Furthermore, small field data sets limit the utility of modern machine learning techniques to advance remote sensing retrieval models. In situ optical sensors deployed in a sensor network to continuously monitor larger water bodies can drastically increase the number of measurements, providing the opportunity to develop new approaches for building robust remote sensing retrieval models by leveraging both remote sensing data and in situ networks as an integrated monitoring system. This study leverages a large “ground-to-space” sensor network that combines an in situ optical sensor network with satellite-based remote sensing to overcome these limitations. Utilizing a large-scale data set from the U.S. Geological Survey's Sacramento—San Joaquin River Delta monitoring network, of dissolved organic matter fluorescence measurements, and remote sensing data from the European Space Agency's Sentinel-2A and -2B satellites, this study implemented a data driven approach for dissolved organic matter models. The data set, consisting of 982 samples collected between 2018 and 2021 was used to train and validate a random forest model (R2 = 0.76, RMSE = 6.1 Quinine Sulfate Equivalents), with demonstrated applicability across diverse site conditions, tidal stages, and seasons. This work provides a scalable solution to address critical challenges in water quality monitoring and offers a replicable framework for global water quality management.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024EA004048","usgsCitation":"Avouris, D., Hestir, E.L., Fleck, J., Hansen, J.A., and Bergamaschi, B.A., 2025, An integrated sensor network and data driven approach to satellite remote sensing of dissolved organic matter: Earth and Space Science, v. 12, no. 12, e2024EA004048, 19 p., https://doi.org/10.1029/2024EA004048.","productDescription":"e2024EA004048, 19 p.","ipdsId":"IP-172592","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":495389,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024ea004048","text":"Publisher Index Page"},{"id":495247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calfornia","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.34190472520478,\n 37.92367880802452\n ],\n [\n -121.34190472520478,\n 38.5486708617789\n ],\n [\n -121.98676504474714,\n 38.5486708617789\n ],\n [\n -121.98676504474714,\n 37.92367880802452\n ],\n [\n -121.34190472520478,\n 37.92367880802452\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Avouris, Dulcinea Marie 0000-0001-5797-3960","orcid":"https://orcid.org/0000-0001-5797-3960","contributorId":335170,"corporation":false,"usgs":true,"family":"Avouris","given":"Dulcinea Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hestir, Erin L","contributorId":361027,"corporation":false,"usgs":false,"family":"Hestir","given":"Erin","middleInitial":"L","affiliations":[{"id":38695,"text":"University of California Merced","active":true,"usgs":false}],"preferred":false,"id":948142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleck, Jacob 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":168694,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948143,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Jeffrey A. 0000-0002-2185-1686","orcid":"https://orcid.org/0000-0002-2185-1686","contributorId":205441,"corporation":false,"usgs":true,"family":"Hansen","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948144,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":948145,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271333,"text":"70271333 - 2025 - Impacts of lake elevation decline on spawning habitat of a critical, native forage species","interactions":[],"lastModifiedDate":"2025-12-01T16:30:30.860959","indexId":"70271333","displayToPublicDate":"2025-09-03T08:18:53","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of lake elevation decline on spawning habitat of a critical, native forage species","docAbstract":"Objective
Lake elevation decline is a global phenomenon with pronounced effects in arid regions that changes the characteristics of nearshore habitat area available to lacustrine spawners, potentially impacting recruitment and whole-lake food web dynamics. Our objective was to understand the potential effects of lake elevation decline on spawning habitat for the Tui Chub Siphateles bicolor, a lacustrine spawner and critical component of the native food web in Pyramid Lake, Nevada.
Methods
We explored the distribution of ripe Tui Chub in nearshore habitat by associating habitat characteristics to ripe Tui Chub CPUE from a custom gill-net configuration, with data analyzed using generalized linear mixed-effects models. We then explored potential spawning habitat availability at all potential lake elevations using an elevation-explicit model of the basin that we developed based on several bathymetric and geospatial data sets and the knowledge of spawner distribution gained in the first component of the study.
Results
Ripe Tui Chub catch was primarily predicted by temperature, reaching a maximum between 14.2°C and 24.8°C found at less than 15 m of depth in Pyramid Lake throughout the summer spawning period. We estimated that with a contemporary decline in lake elevation of 8 m, Pyramid Lake will host the minimum area of spawning habitat based on morphometry alone at a 40% decrease from a theoretical maximum.
Conclusions
A decrease in lake elevation or an increase in lake temperatures—both of which are probable events based on future climate scenarios and estimates of water extraction upstream of Pyramid Lake—is likely to further restrict Tui Chub spawning habitat area. Our results have important implications for ecological water demand in Pyramid Lake and provide managers with information facilitating a science-based approach to managing the fish community.
","language":"English","publisher":"American Fisheries Society","doi":"10.1093/tafafs/vnaf034","usgsCitation":"Barnes, S., Al-Chokhachy, R., and Budy, P., 2025, Impacts of lake elevation decline on spawning habitat of a critical, native forage species: Transactions of the American Fisheries Society, v. 154, no. 6, p. 640-656, https://doi.org/10.1093/tafafs/vnaf034.","productDescription":"17 p.","startPage":"640","endPage":"656","ipdsId":"IP-161347","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":495220,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Pyramid Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.72328588180102,\n 40.234157713420586\n ],\n [\n -119.72328588180102,\n 39.842701658708876\n ],\n [\n -119.32079294757592,\n 39.842701658708876\n ],\n [\n -119.32079294757592,\n 40.234157713420586\n ],\n [\n -119.72328588180102,\n 40.234157713420586\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"154","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnes, Sarah","contributorId":360982,"corporation":false,"usgs":false,"family":"Barnes","given":"Sarah","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":948072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Al-Chokhachy, Robert 0000-0002-2136-5098","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":216140,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":948073,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678 pbudy@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":140028,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra","email":"pbudy@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":948074,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70270764,"text":"sir20255064 - 2025 - Grammar to graph—An approach for semantic transformation of annotations to triples","interactions":[],"lastModifiedDate":"2026-02-03T15:19:08.178992","indexId":"sir20255064","displayToPublicDate":"2025-09-02T11:30:00","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":"2025-5064","displayTitle":"Grammar to Graph—An Approach for Semantic Transformation of Annotations to Triples","title":"Grammar to graph—An approach for semantic transformation of annotations to triples","docAbstract":"Data annotation is the process of labeling data to show the outcome that a related data model should predict. In this study, annotation data were transformed into semantic graph triples, mainly for use with the Resource Description Framework (RDF), a type of entity-relationship-attribute data model for graph databases. The transformation of annotation data to semantic graph triples provides complex linguistic meaning with data handling advantages such as reduced data storage needs, improved logical specification of relations between objects, and reusable classes and properties that support logic and inference. A grammar-based framework in graph form supports user questions and queries.
The words defining approximately 334 topographic feature types compiled by the U.S. Geological Survey were tokenized as units of analysis and grouped by part of speech. Their dependency relations were identified for this study using natural language processing libraries. Dependency concepts are used as structured semantic relations among part-of-speech classes. Tokens, units equivalent to words, form instances of classes and were quantified within a tabular output format using PostgreSQL data storage software. Table data were logically aligned as triples following a mapping file and stored with an ontology file using Ontop virtual triplestore software. A grammar ontology schema for the data was synchronized to match queries whose results validated the graph’s structure. The text analysis produced 8 part-of-speech classes of content words for object representations and 4 classes of function words for operational applications. Dependency relations formed 27 ontology properties for topographic subgraph structures. Token occurrences shaped overall ontology salience and formed a lexicon of syntactic terms for subgraph objects and properties. The schema ontology of class and property population shapes formed the lexicon of English terms. SPARQL Protocol and RDF Query Language (SPARQL) was used with the lexicon to conform data to RDF guidelines.
This study confirms the hypothesis that although linguistic logic varies from description logic, its approximation applies to ontology design. Property and query use case patterns extracted from the analysis support queries concerning complex topographic relations and patterns normally embedded within text definitions. The method used in this study could be applied to text forms in other domains, such as survey notes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255064","programNote":"National Geospatial Program","usgsCitation":"Varanka, D.E., and Abbott, E., 2025, Grammar to graph—An approach for semantic transformation of annotations to triples: U.S. Geological Survey Scientific Investigations Report 2025–5064, 20 p., https://doi.org/10.3133/sir20255064.","productDescription":"Report: vi, 20 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-150174","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":494546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5064/sir20255064.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5064"},{"id":494604,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5064/sir20255064.xml"},{"id":494603,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5064/images"},{"id":494547,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1BDPXKZ","text":"USGS data release","description":"Data release associated with SIR 2025-5064","linkHelpText":"Grammar transformations of topographic feature type annotations of the U.S. to structured graph data"},{"id":494545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5064/coverthb.jpg","text":"Report"},{"id":495126,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255064/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5064"}],"contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
Box 25046, Mail Stop 510
Denver, Colorado 80225-0046
Florida hosts a regionally important nesting aggregation of green turtles (Chelonia mydas) in the North Atlantic, yet internesting and post-nesting movements for this rookery remain poorly understood. Here, we used satellite telemetry to track 23 green turtles nesting on southeast Florida beaches from 2017 to 2021 to investigate their spatial ecology and use of marine protected areas (MPAs) during internesting, migration, and foraging. Marine protected areas are widely used in marine conservation and can be powerful tools for managing species and protecting biodiversity. During internesting, turtles used nearshore, unprotected coastal waters adjacent to the study site. After the nesting season, turtles migrated 24.1 to 203.5 km to previously identified foraging grounds, including areas within Biscayne National Park and Florida Keys National Marine Sanctuary, as well as a high-use but unprotected area off Cape Sable, Florida. Throughout the internesting and foraging periods, turtles exhibited little spatial overlap of core-use areas, suggesting limited space-use sharing even in high-density regions. This study provides the first satellite telemetry dataset for green turtles from southeast Florida and reveals their strong reliance on a relatively small MPA network along southwest Florida. Our findings underscore how these MPAs can support conservation efforts for Florida’s overall green turtle nesting population, while further emphasizing the potential benefits of expanded protections in key areas to safeguard regionally important green turtle habitat.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00227-025-04694-5","usgsCitation":"Goodwin, G.D., Hart, K., Evans, A.C., and Burkholder, D.A., 2025, Satellite tracking reveals heavy use of local MPAs by green turtles (Chelonia mydas) nesting in southeast Florida, USA: Marine Biology, v. 172, no. 10, 153, 13 p., https://doi.org/10.1007/s00227-025-04694-5.","productDescription":"153, 13 p.","ipdsId":"IP-173964","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":495795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83,\n 26.5\n ],\n [\n -83,\n 24\n ],\n [\n -80,\n 24\n ],\n [\n -80,\n 26.5\n ],\n [\n -83,\n 26.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"172","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Goodwin, Glenn D.","contributorId":361599,"corporation":false,"usgs":false,"family":"Goodwin","given":"Glenn","middleInitial":"D.","affiliations":[{"id":81512,"text":"Halmos College of Arts and Sciences, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":949064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":222407,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":949065,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Abby C.","contributorId":361600,"corporation":false,"usgs":false,"family":"Evans","given":"Abby","middleInitial":"C.","affiliations":[{"id":81512,"text":"Halmos College of Arts and Sciences, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":949066,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burkholder, Derek A. 0000-0001-6315-6932","orcid":"https://orcid.org/0000-0001-6315-6932","contributorId":289783,"corporation":false,"usgs":false,"family":"Burkholder","given":"Derek","email":"","middleInitial":"A.","affiliations":[{"id":62249,"text":"Halmos College of Natural Sciences and Oceanography, Department of Marine and Environmental Science, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":949067,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271160,"text":"ofr20251048 - 2025 - ECCOE Landsat quarterly calibration and validation report—Quarter 1, 2025","interactions":[],"lastModifiedDate":"2026-02-03T15:18:16.668024","indexId":"ofr20251048","displayToPublicDate":"2025-09-02T08:01:24","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":"2025-1048","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 1, 2025","title":"ECCOE Landsat quarterly calibration and validation report—Quarter 1, 2025","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 1 (January–March) of 2025. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251048","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Steinwand, D., Bresnahan, P., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Kaita, E., Angal, A., Levy, R., Miller, J.,\nDing, L., and Teixeira Pinto, C., 2025, ECCOE Landsat quarterly calibration and validation report—Quarter 1, 2025: U.S. Geological Survey Open-File Report 2025–1048, 56 p., https://doi.org/10.3133/ofr20251048.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-178690","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":495088,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":495089,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251048/full"},{"id":495087,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1048/images/"},{"id":495084,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1048/coverthb.jpg"},{"id":495086,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1048/ofr20251048.XML"},{"id":495085,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1048/ofr20251048.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1048"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation Center of Excellence Team assesses and calibrates Landsat remote-sensing data to ensure high-quality data products are publicly available. These data products are used to make informed decisions about natural resources and the environment. This report is part of a series of quarterly reports intended to provide updated observed geometric and radiometric analysis results for Landsats 8 and 9.
","publicationDate":"2025-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Haque, Md Obaidul 0000-0002-0914-1446","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":290335,"corporation":false,"usgs":false,"family":"Haque","given":"Md Obaidul","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":947607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hasan, Nahid 0000-0002-0463-601X","orcid":"https://orcid.org/0000-0002-0463-601X","contributorId":292342,"corporation":false,"usgs":false,"family":"Hasan","given":"Nahid","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":947608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shrestha, Ashish 0000-0002-9407-5462","orcid":"https://orcid.org/0000-0002-9407-5462","contributorId":298063,"corporation":false,"usgs":false,"family":"Shrestha","given":"Ashish","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":947609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":947610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lubke, Mark 0000-0002-7257-2337","orcid":"https://orcid.org/0000-0002-7257-2337","contributorId":261911,"corporation":false,"usgs":false,"family":"Lubke","given":"Mark","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":947611,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steinwand, Daniel 0009-0008-6588-9775","orcid":"https://orcid.org/0009-0008-6588-9775","contributorId":357557,"corporation":false,"usgs":false,"family":"Steinwand","given":"Daniel","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":947612,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bresnahan, Paul 0000-0002-3491-0956","orcid":"https://orcid.org/0000-0002-3491-0956","contributorId":306120,"corporation":false,"usgs":false,"family":"Bresnahan","given":"Paul","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":947613,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaw, Jerad L. 0000-0002-8319-2778","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":270396,"corporation":false,"usgs":false,"family":"Shaw","given":"Jerad L.","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":947614,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ruslander, Kathryn 0000-0003-3036-1731","orcid":"https://orcid.org/0000-0003-3036-1731","contributorId":330181,"corporation":false,"usgs":false,"family":"Ruslander","given":"Kathryn","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":947615,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":947616,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":268248,"corporation":false,"usgs":true,"family":"Choate","given":"Michael J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":947617,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":947618,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Clauson, Jeff 0000-0003-3406-4988 jclauson@usgs.gov","orcid":"https://orcid.org/0000-0003-3406-4988","contributorId":5230,"corporation":false,"usgs":true,"family":"Clauson","given":"Jeff","email":"jclauson@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":947619,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Thome, Kurt","contributorId":140792,"corporation":false,"usgs":false,"family":"Thome","given":"Kurt","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":947620,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Kaita, Ed","contributorId":251782,"corporation":false,"usgs":false,"family":"Kaita","given":"Ed","email":"","affiliations":[{"id":50397,"text":"SSAI","active":true,"usgs":false}],"preferred":false,"id":947621,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Angal, Amit","contributorId":360771,"corporation":false,"usgs":false,"family":"Angal","given":"Amit","affiliations":[{"id":78842,"text":"SSAI, under contract to NASA","active":true,"usgs":false}],"preferred":false,"id":947622,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Levy, Raviv","contributorId":131008,"corporation":false,"usgs":false,"family":"Levy","given":"Raviv","email":"","affiliations":[{"id":7209,"text":"SSAI / NASA / GSFC","active":true,"usgs":false}],"preferred":false,"id":947623,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Miller, Jeff","contributorId":204570,"corporation":false,"usgs":false,"family":"Miller","given":"Jeff","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":947624,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Ding, Leibo","contributorId":330182,"corporation":false,"usgs":false,"family":"Ding","given":"Leibo","email":"","affiliations":[{"id":78842,"text":"SSAI, under contract to NASA","active":true,"usgs":false}],"preferred":false,"id":947625,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Teixeira Pinto, Cibele","contributorId":357558,"corporation":false,"usgs":false,"family":"Teixeira Pinto","given":"Cibele","affiliations":[{"id":78842,"text":"SSAI, under contract to NASA","active":true,"usgs":false}],"preferred":false,"id":947626,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70272004,"text":"70272004 - 2025 - Assessing diet and genotyping success of goat pellet surveys from 2019 in Glacier National Park","interactions":[],"lastModifiedDate":"2025-09-30T15:06:54.506965","indexId":"70272004","displayToPublicDate":"2025-09-01T10:03:46","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"displayTitle":"Assessing Diet and Genotyping Success of Goat Pellet Surveys from 2019 in Glacier National Park","title":"Assessing diet and genotyping success of goat pellet surveys from 2019 in Glacier National Park","docAbstract":"Fecal pellets contain genetic information and can be used to identify individuals, their diet, and more. Individual identification can be useful in understanding movements of individuals, developing population estimates, assessing vital rates, genetic diversity and structure, and evaluating trends over time (e.g., Epps et al 2024). Successful genotyping depends on the quality of the sample, which can be influenced by many things including the initial state of the sample, environmental conditions that the pellets were exposed to, and the method of storage.
The diet of mountain goats (Oreamnos americanus) is hard to study as they are habitat specialists in some of the most remote and rugged alpine environments (Festa-Bianchet & Côté 2012). These iconic ungulates epitomize the renowned wilderness character of Glacier National Park (GNP), yet the population has declined by 45% since 2008 likely due at least in part to changing climate (Graves et al. 2025). This is well above the International Union for the Conservation of Nature’s population decline criterion used to designate a species as vulnerable (IUCN 2012).
Mountain goats are known as dietary generalists employing highly variable diets across systems, but diets have not been identified for GNP. Identifying goat diet composition in GNP can provide the information needed to determine whether this population decline could be related to climate-change induced shifts in forage phenology, diversity, productivity, and plant community structure. Other anthropogenic changes in GNP, such as increases in exotic and invasive plant species, will continue to affect plant community composition (Lesica et al. 1993). These alterations may reduce optimal forage availability and quality, which could have a deleterious effect on mountain goat survival. To examine these possibilities, we must first ask- What precisely are the mountain goats of Glacier National Park eating?
DNA metabarcoding of fecal pellets provides a cutting-edge, non-invasive method for assessing herbivore diet. Previous studies of mountain goat diet have used methods such as fecal crude protein analysis (Festa-Bianchet & Côté 2012), rumen analysis (Saunders 1955), fecal microhistology (Cobb et al. 2012), and bite-for-bite feeding observations (Daily et al. 1984). Many of these studies have only been able to identify diet components to functional plant type and the methods used are biased towards less digestible diet components such as grasses (McInnis et al. 1983). Metabarcoding of fecal pellets can provide a higher taxonomic resolution than previous methods (Scasta et al. 2019, Stapleton et al. 2022) and open the field for increased participation of volunteers, also called community or citizen scientists.
Here, we use mountain goat fecal pellets to conduct 1) an evaluation of which conditions might influence genotyping success and 2) a preliminary assessment of diet using metabarcoding. This pilot study allowed us to evaluate which pellets to focus on collecting, identify the best storage methods, explore approaches for analyzing metabarcoding data, examine the utility of metabarcoding for acquiring taxonomically specific diet information, and to identify genetic sequences that could not be resolved to genus or species levels using these methods. We examined the frequency of occurrence and relative diet composition of identified forbs, shrubs, graminoids, mosses, and trees in mountain goat diets.
","language":"English","publisher":"National Park Service","usgsCitation":"Scoresby, S., Dose, L.M., Belt, J., and Graves, T., 2025, Assessing diet and genotyping success of goat pellet surveys from 2019 in Glacier National Park, 23 p.","productDescription":"23 p.","ipdsId":"IP-174564","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":496262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":496254,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2311387"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.60109929508316,\n 49.00250961037867\n ],\n [\n -114.47247108032799,\n 49.00250961037867\n ],\n [\n -114.08980516405627,\n 48.48262143393191\n ],\n [\n -113.89155655683145,\n 48.4856773542281\n ],\n [\n -113.53655323691696,\n 48.22526656841134\n ],\n [\n -113.20921251335938,\n 48.41534459946189\n ],\n [\n -113.60109929508316,\n 49.00250961037867\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Scoresby, Salix","contributorId":352228,"corporation":false,"usgs":false,"family":"Scoresby","given":"Salix","affiliations":[{"id":84134,"text":"Contractor, USGS (Northern Arizona University)","active":true,"usgs":false}],"preferred":false,"id":949691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dose, Lindsay M","contributorId":361945,"corporation":false,"usgs":false,"family":"Dose","given":"Lindsay","middleInitial":"M","affiliations":[{"id":27609,"text":"Contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":949692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belt, Jami","contributorId":177314,"corporation":false,"usgs":false,"family":"Belt","given":"Jami","affiliations":[],"preferred":false,"id":949693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":949694,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271905,"text":"70271905 - 2025 - A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed","interactions":[],"lastModifiedDate":"2025-09-24T15:03:35.249606","indexId":"70271905","displayToPublicDate":"2025-09-01T07:53:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed","docAbstract":"Tsunami hazards are substantial threats to coastal communities across the United States (U.S.) and its territories. U.S. states and territories collaborate through the National Tsunami Hazard Mitigation Program (NTHMP) to develop their own tsunami-hazard information for outreach and evacuation planning. An effort to curate this tsunami-hazard information to support comprehensive risk analysis at the national level has not yet been completed. In support of this effort, the Federal Emergency Management Agency (FEMA) collaborated with the NTHMP, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS) starting in 2023. This collaboration included the collection and analysis of existing tsunami hazard data and methods in the U.S. Tsunami subject matter experts identified and selected scientifically defensible methods for estimating the risks to buildings and populations in coastal communities. These efforts may support decision making regarding resilience policies, priorities, strategies and funding levels.
Tsunamis can be triggered by earthquakes, subaerial or submarine landslides, volcanic eruptions, glacial calving, near-earth objects, weather or other events. These events can cause severe destruction, injuries, and loss of life due to powerful currents and flooding. Tsunamis pose a substantial threat to the western United States and all U.S. territories, as described below.
■ Hawaii is threatened by distant tsunamis due to its central location in the Pacific Ocean basin and has a history of local events.
■ Alaska, particularly the Aleutian Islands, faces local tsunami threats due to proximity to the Alaska-Aleutian Subduction Zone, as well as distant tsunamis from around the Pacific Ocean basin.
■ The western coast of the U.S. is threatened by distant tsunamis from around the Pacific Ocean basin and local source tsunamis from earthquakes generated within the Cascadia Subduction Zone in the Pacific Northwest.
■ American Samoa faces local tsunami threats from earthquakes generated in the nearby Tonga Trench, as well as distant tsunami threats.
■ Guam and the Commonwealth of the Northern Mariana Islands are threatened by local tsunamis from the nearby Mariana Subduction Zone, as well as distant sources from around the Pacific Ocean Basin.
■ Puerto Rico and the United States Virgin Islands are threatened by multiple local and distant tsunami sources, such as the Puerto Rico Trench (PRT), given their location in the complex seismic region of the Caribbean Sea.
Several historical events stand out because of their catastrophic impacts.
■ In the Pacific Northwest, the 1700 Cascadia earthquake caused a tsunami that affected coastal Native American communities, though the extent of the damage is not fully documented (Ludwin, et al., 2005).
■ In Puerto Rico, the 1918 earthquake triggered a tsunami that caused $77 million in damage in 2022 dollars and 116 fatalities, primarily along the western coast (Coffman et al., 1982).
■ The 1946 Aleutian Islands earthquake triggered a massive tsunami that devastated Hilo, Hawaii, killing 158 people and resulting in approximately $375 million in damage (adjusted to 2022 dollars) (Fisher et al., 2023).
■ The 1964 Alaska earthquake (M 9.2) generated tsunamis that caused severe destruction in some communities across Alaska, Oregon, and California. This disaster led to a total of 124 fatalities and approximately $2.9 billion in property damage (adjusted to 2022 dollars) (Brocher et al., 2014) (Alaska Science Center, 2024).
■ In American Samoa, a tsunami generated by the 2009 Samoa earthquake (Mw 8.1) caused widespread devastation, resulting in 34 confirmed fatalities (Apatu et al., 2013) and economic losses exceeding $160 million (adjusted to 2022 dollars) (DHS, 2011).
More recent events, including the 2010 Chile earthquake, the 2011 Japan earthquake, and the 2022 Tonga volcanic eruption, resulted in millions of dollars in damage to numerous ports and harbors in the U.S. South Pacific territories, Hawaii, and along the west coast of the U.S. (Lynett, et al., 2022) (Wilson, et al., 2013). Since these events, the expansion of the built environment in lowlying areas along the coast has increased the exposure of buildings and people, thereby further escalating community risk from tsunamis.
This report provides a comprehensive national assessment of earthquake-generated tsunami risk. It does not include impacts from tsunamis generated by landslides, volcanic eruptions, glacial calving, near-earth objects, weather, or other events. This study is based on the best available hazard data from the U.S. Pacific Coast (California, Oregon and Washington), Alaska, Hawaii, U.S. Pacific Territories (American Samoa, Guam and Commonwealth of the Northern Mariana Islands) and Caribbean Territories (Puerto Rico and United States Virgin Islands). Tsunami risks associated with states along the East Coast, Gulf Coast, and Great Lakes are not included in this study because Hazus 6.1 software (FEMA 2024a) does not currently include the ability to analyze tsunami risk in those states. Once modeling capabilities and tsunami hazard data become available for additional states, FEMA may incorporate these data into future editions of this study.
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County, Wyoming","title":"Fluvial sediment dynamics in the Shoshone River and tributaries around Willwood Dam, Park County, Wyoming","docAbstract":"Sedimentation affects many of the aging reservoirs in the United States. Dams and water diversions from rivers have been central elements of infrastructure supporting agricultural irrigation in the arid and semiarid regions of the Western United States for more than a century. The Willwood Irrigation District diversion dam (hereafter referred to as “Willwood Dam”) in Park County, Wyoming, is approximately 12 miles northeast of Cody, Wyo.; has a structural height of 70 feet; and impounds the Shoshone River for diversion into the Willwood Canal. Willwood Dam is part of a larger irrigation scheme supported by water storage in the much larger Buffalo Bill Dam, which is approximately 20 miles upstream. In October 2016, renovation construction activities at Willwood Dam and the Willwood Canal caused an unplanned evacuation of nearly 96,000 cubic yards of fine sediment.
The fine sediment release in 2016 raised concerns that ongoing sediment management at Willwood Dam could impose limits on the long-term health of the aquatic ecosystem and fish populations. The U.S. Geological Survey, in cooperation with Wyoming Department of Environmental Quality and Willwood Work Groups 2 and 3, initiated an investigation of the dynamics of sediment transport in the Shoshone River and selected tributaries between Buffalo Bill Dam and Willwood Dam. The goal of the study was to quantify sediment transport into and out of Willwood Dam on an annual, seasonal, and event basis to better understand the relative quantities of sediment coming from natural sources and human activities on the landscape. The study ran from March 2019 through October 2021 and used observations of streamflow, turbidity, and acoustic backscatter collected at streamgages upstream and downstream from Willwood Dam to quantify suspended-sediment loads into and out of the dam during irrigation and fallow seasons, precipitation-runoff events, and deliberate sediment releases. Each tributary’s relative contribution to the sediment load upstream from Willwood Dam was examined using discrete measurements of suspended-sediment concentration and bedload during irrigation and fallow seasons, precipitation events, and stable conditions.
Analysis of daily precipitation and temperature data indicated that conditions in the study area during the 2019 agricultural year were wetter and colder than period of record normal, and drier and near normal temperatures for the 2020 and 2021 agricultural years. Not all sediment load records between 2019 and 2021 are complete because of rejected observations (outliers), instrument failures or fouling, and instrument removal for calibrations.
Statistical modeling of suspended-sediment concentration using paired values of turbidity and acoustic backscatter produced four models that, after refinement, had coefficients of determination indicating that more than 84 percent of the variance was explained by either turbidity or acoustic backscatter. A system of rules was developed to select the model predictions based on the seasonal operations of Willwood Dam, assumptions about the grain sizes mobilized during these operations, and assumed accuracy of the models at the downstream streamgage (Shoshone River below Willwood Dam, near Ralston, Wyo. [streamgage 06284010]) under different operational conditions. The sediment budget between upstream and downstream estimates of loads was interpreted using the mean predicted values bound by their respective model prediction intervals. When mean predicted loads of one streamgage were contained in the prediction intervals of the other streamgage, and vice-versa, difference in the sediment budget were interpreted as “indeterminate.”
Modeled sediment load balances demonstrated the depositional and erosional behaviors expected from the conceptual model of dam operations whereby sediment tends to accumulate during irrigation seasons when the dam is spilling over the top, and sediment tends to evacuate during the fallow seasons when it is flowing through the sluice gates at the base of the dam. The sediment load calculations using the rules-based model criteria indicated that between 14,200 and 380,000 tons of suspended sediment moved through the Shoshone River around Willwood Dam during the irrigation seasons of 2019, 2020, and 2021; 380,000 tons of suspended sediment were transported during the cool, wet year of 2019, and 14,200 tons of suspended sediment were transported in 2020, which was relatively dry. During fallow seasons 2019, 2020, and 2021, which had fewer complete records, between 1,140 and 106,000 tons of suspended sediment was estimated to have moved through the river.
For all seasons except fallow season 2022, the models estimated that more sediment was released from the dam than entered the dam, but the modeled mean loads at each streamgage were nearly always within the prediction intervals of each other, making the sediment balance indeterminant. Examination of suspended-sediment loads during irrigation seasons indicated that between 65 and 85 percent of fine sediment was transported during annual high flows and storm events, with the remainder transported during steady, lower streamflows. Examination of suspended loads during fallow seasons indicated that deliberate sediment releases through Willwood Dam accounted for between 39 and 67 percent of the total sediment moved during the fallow seasons. Deliberate sediment releases from Willwood Dam had estimated net exports of between 1,360 and 22,400 tons.
Between August 2017 and July 2023, suspended-sediment concentration and bedload sediment samples were collected from 9 tributaries to the Shoshone River during 137 sampling events, including stable and precipitation-runoff conditions. During irrigation season precipitation events, the mean total sediment yields ranged from 0.33 to 9.51 tons per day per square mile; during fallow season precipitation events, the mean total yields ranged from 0.04 to 0.95 ton per day per square mile. The mean total sediment yield per unit area across all samples at each tributary site ranged from 0.26 to 3.08 tons per day per square mile. Bedload was a minor fraction of the total load, constituting a mean of 4 percent across all samples; 3 and 6 percent for events and nonevents, respectively, during irrigation season; and 3 and 1 percent for events and nonevents, respectively, during the fallow season. With the exception of one tributary, Dry Creek, these mean yield values were within the range of watershed-scale background sediment yield values estimated from reservoir surveys and previous suspended-sediment studies.
Imagery from irrigation seasons 2012, 2015, 2017, 2019, and 2022 was used to determine the planimetric backwater extent of the pool area in the Shoshone River behind Willwood Dam to identify any changes in sediment storage. Active river channel widths in the Shoshone River upstream from Willwood Dam were all similar between years except 2015, which was determined to be statistically different from all other years. Bathymetric data taken in the pool behind Willwood Dam during three different surveys between November 2017 and April 2022 indicated no statistically significant differences in bed elevations between the years. Results from the planimetric and bathymetric survey data provide multiple lines of evidence indicating that sediment did not accumulate behind the dam within the error of the methods used.
Examination of how precipitation affects sediment transport in the Shoshone River upstream from Willwood Dam indicated that accumulated rainfall from the natural runoff events captured during the study period varied from a trace to as much as 4.26 inches, with associated predicted suspended-sediment loads varying from 112 to 232,000 tons of suspended sediment. The behavior of the sediment loads relative to accumulated precipitation did not appear to change depending on irrigation or fallow season. A model of suspended-sediment concentrations relative to the 2-day accumulated precipitation indicated that suspended-sediment concentrations in the Shoshone River upstream from Willwood Dam increased exponentially for accumulations of 0.3 inch or more; such storms accounted for 10 percent or less of precipitation events observed during the 1981 to 2018 period of record.
The gaps in records, precision of the instrumentation, and large variation in grain sizes in suspended-sediment mixtures downstream from the dam made closing the sediment budgets for most seasons unattainable. The biggest recent change in sediment storage measured using the planimetric area of deposits behind Willwood Dam took place between 2015 and 2017. The main event between these two measurements was the installation of new Willwood Canal gates in October 2016, which resulted in the large unplanned sediment release. Because the sediment budgets were nearly always indeterminate and the planimetric and bathymetric data indicated little change in the bed and bank material, it is likely that the change in sediment storage behind the dam during the study period was small relative to the precision of the statistical models and other uncertainties.
This body of evidence suggests that, averaged during the 3-year study period, no major changes in storage took place, and that the current operations may be keeping storage at near-equilibrium. This condition could have been initiated because the middle sluice gate has now been operational since 2014, and the sediment release in October 2016 evacuated a large amount of legacy sediment from storage. Although the uncertainties are large, sluicing events allow for controlled releases of sediment that contributed to the near equilibrium conditions observed over an annual basis during this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255077","collaboration":"Prepared in cooperation with the Wyoming Department of Environmental Quality","usgsCitation":"Alexander, J.S., Brown, H., Eddy-Miller, C.A., Burckhardt, J., Burckhardt, L., Ellison, C., McIntyre, C., Moger, T., Patterson, L., Tavelli, C., Waterstreet, D., and Williams, M., 2025, Fluvial sediment dynamics in the Shoshone River and tributaries around Willwood Dam, Park County, Wyoming: U.S. Geological Survey Scientific Investigations Report 2025–5077, 70 p., https://doi.org/10.3133/sir20255077.","productDescription":"Report: x, 70 p.; Data Release; Dataset","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-164415","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":494651,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5077"},{"id":494674,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":494673,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13VHDRG","text":"USGS data release","linkHelpText":"Shapefiles of digitized backwater extent behind Willwood Dam on the Shoshone River, near Cody, Wyoming, derived from 2012, 2015, 2017, 2019, and 2022 National Agriculture Imagery Program imagery"},{"id":494652,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5077/images"},{"id":494654,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5077/sir20255077.XML","linkFileType":{"id":8,"text":"xml"}},{"id":494650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5077/sir20255077.pdf","text":"Report","size":"9.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5077"},{"id":494649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5077/coverthb.jpg"}],"country":"United States","state":"Wyoming","county":"Park County","otherGeospatial":"Shoshone River and tributaries around Willwood Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.66170194279013,\n 44.80967182289373\n ],\n [\n -109.31267227648169,\n 44.80967182289373\n ],\n [\n -109.31267227648169,\n 44.39309612019585\n ],\n [\n -108.66170194279013,\n 44.39309612019585\n ],\n [\n -108.66170194279013,\n 44.80967182289373\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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Future water availability depends on understanding the responses of constituent concentrations to hydrologic change. Projecting future water quality remains a methodological challenge, particularly when using discrete observations with limited temporal resolution. This study introduces Weighted Regression on Time, Discharge, and Season for Projection (WRTDS-P), a novel, computationally efficient method that enables the projection of daily stream water quality under varying hydrologic conditions using commonly available discrete monitoring data. WRTDS-P model performance was validated using 39 sites in the Delaware River Basin (DRB) and four key constituents: specific conductance (SC), nitrate (NO3−), magnesium (Mg2+) and calcium (Ca2+). Projections were tested against holdout data from the final 1 to 5 years of each time series, demonstrating robust predictive capability, with median Nash-Sutcliffe efficiencies of 0.67 for SC, 0.56 for NO3−, 0.65 for Ca2+, and 0.79 for Mg2+. Model uncertainty was correlated with indicators of hydrologic or geochemical mass-sinks, such as groundwater storage and adsorption in wetland soils. Drought scenario analyses for SC used ranges of reduced discharge including flows from the 1965 drought of record. Scenarios predicted widespread increases of SC, especially in southern DRB streams where baseline SC levels are already elevated. Fractional increases of SC were more uniformly distributed, indicating potential risk to sensitive ecosystems. Notably, drought-induced SC increases were positively correlated with interannual SC trends, indicating that hydrologic extremes could exacerbate ongoing salinization. This work provides a transferable and interpretable framework for projecting future water quality and assessing hydrologic risk to water resources and aquatic ecosystems.
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