{"pageNumber":"37","pageRowStart":"900","pageSize":"25","recordCount":46619,"records":[{"id":70263177,"text":"70263177 - 2025 - Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches","interactions":[],"lastModifiedDate":"2025-01-31T15:18:02.303715","indexId":"70263177","displayToPublicDate":"2025-03-03T08:10:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches","docAbstract":"

Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. Conventionally, water use data are created based on reports and surveys from water users, whereas manual records may not be complete due to lacking flow meters, measurement gaps, inconsistent methods across regions, and time- and cost-consuming data processing. Alternatively, spatially explicit estimation of irrigation water use can be conducted efficiently using remote sensing evapotranspiration (ET) modeling approaches. In this study, we created a gridded blue water evapotranspiration (BWET) dataset to estimate historical irrigation water consumption (1986 – 2020) in the croplands across the United States High Plains aquifer region. The BWET data were generated by integrating an energy-balance ET model [Operational Simplified Surface Energy Balance model (SSEBop)] and a water-balance ET model [Vegetation ET model (VegET)]. BWET in croplands indicates crop consumptive use of irrigation water extracted from surface water and groundwater resources. The BWET estimates were compared with reported irrigation water use data for all counties within the aquifer region. The results revealed high agreement between growing season (May – September) BWET and annual water withdrawal at county level. Specifically, correlation coefficients of volumetric BWET and water withdrawal were 0.90 and 0.96, respectively, for the entire aquifer region and western Kansas. The timeseries of BWET and water withdrawal showed similar temporal trends and high covariations. The BWET estimates were systematically lower than the water withdrawal measurements, which was primarily attributed to blue water losses in the irrigation system. The irrigation efficiency, calculated as the ratio of BWET to water withdrawal depth, was 0.57 and 0.74 for the entire aquifer region and western Kansas, respectively. This study demonstrates the capability of using satellite-based ET models (e.g., SSEBop and VegET) to efficiently estimate crop water consumption and evaluate irrigation efficiency at landscape, county, and regional scales.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2025.109308","usgsCitation":"Ji, L., Senay, G.B., Friedrichs, M., and Kagone, S., 2025, Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches: Agricultural Water Management, v. 309, 109308, 17 p., https://doi.org/10.1016/j.agwat.2025.109308.","productDescription":"109308, 17 p.","ipdsId":"IP-163904","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489921,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agwat.2025.109308","text":"Publisher Index Page"},{"id":481548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, Wyoming","otherGeospatial":"High Plains","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.057698,44.997431],[-104.043814,45.868385],[-103.668479,45.945242],[-96.571871,45.871846],[-96.82616,45.654164],[-96.452315,45.208986],[-96.453049,43.500415],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.396107,42.484095],[-96.272901,42.047281],[-96.129186,41.965136],[-96.081843,41.580407],[-95.850188,41.184798],[-95.885349,40.721093],[-95.41932,40.048442],[-94.916918,39.836138],[-95.113077,39.559133],[-94.615834,39.160003],[-94.617919,36.499414],[-94.431822,35.397652],[-94.485528,33.663388],[-94.386086,33.544923],[-94.070395,33.574561],[-94.0427,32.056012],[-93.523248,31.037842],[-93.765822,30.333318],[-93.702436,30.112721],[-93.922744,29.818808],[-93.852868,29.675885],[-94.731047,29.369141],[-94.532348,29.5178],[-94.767246,29.525523],[-94.724616,29.774766],[-94.965963,29.70033],[-94.894234,29.338],[-95.16525,29.113566],[-94.73132,29.338066],[-94.803695,29.279237],[-96.341617,28.417334],[-95.983106,28.641942],[-96.221784,28.580364],[-96.287942,28.683164],[-96.473694,28.57324],[-96.664534,28.696904],[-96.481836,28.407844],[-96.790235,28.383926],[-96.898123,28.152881],[-97.21535,28.076575],[-97.040618,28.028708],[-97.183455,27.833231],[-97.354614,27.849572],[-97.296598,27.613947],[-97.399398,27.344735],[-97.640111,27.270943],[-97.485149,27.250841],[-97.552325,26.867633],[-97.145567,25.971132],[-97.36542,25.849826],[-99.110855,26.426278],[-99.452316,27.062669],[-99.556812,27.614336],[-99.841708,27.766464],[-100.280518,28.267969],[-100.785521,29.228137],[-101.441059,29.753451],[-102.341033,29.869305],[-102.698347,29.695591],[-102.944911,29.18882],[-103.227801,28.991532],[-104.46652,29.609296],[-104.924796,30.604832],[-106.158218,31.438885],[-106.381039,31.73211],[-108.208394,31.783599],[-108.208573,31.333395],[-109.050044,31.332502],[-109.050076,41.000659],[-111.046723,40.997959],[-111.055199,45.001321],[-104.057698,44.997431]]],[[[-97.240849,26.411504],[-97.383531,26.875521],[-97.366771,27.333276],[-96.946988,28.026522],[-96.403206,28.371475],[-96.929053,27.99044],[-97.276091,27.472145],[-97.370731,26.909706],[-97.161471,26.088705],[-97.240849,26.411504]]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"309","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":272078,"corporation":false,"usgs":false,"family":"Ji","given":"Lei","affiliations":[{"id":56342,"text":"ASRC Federal Data Solutions, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":925791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":925792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":199093,"corporation":false,"usgs":false,"family":"Friedrichs","given":"MacKenzie","affiliations":[],"preferred":false,"id":925793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":199091,"corporation":false,"usgs":false,"family":"Kagone","given":"Stefanie","affiliations":[],"preferred":false,"id":925794,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264422,"text":"70264422 - 2025 - Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin","interactions":[],"lastModifiedDate":"2025-03-14T14:59:53.935548","indexId":"70264422","displayToPublicDate":"2025-03-03T07:53:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin","docAbstract":"

As high-frequency sensor networks increasingly enhance data-driven models of water quality, process-based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient-boosted decision tree algorithm (LightGBM) trained on 5+ years of high-frequency monitoring data to forecast in-stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM-based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country.

","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70011","usgsCitation":"Kemper, J.T., Underwood, K.L., Hamshaw, S.D., Davis, D., Siemion, J., Shanley, J.B., and Schroth, A.W., 2025, Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin: Journal of the American Water Resources Association (JAWRA), v. 61, no. 2, e70011, 20 p., https://doi.org/10.1111/1752-1688.70011.","productDescription":"e70011, 20 p.","ipdsId":"IP-165362","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":488305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.70011","text":"Publisher Index Page"},{"id":483342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Catskill Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.54611692479963,\n 42.29523893577249\n ],\n [\n -74.54611692479963,\n 42.01259179761368\n ],\n [\n -74.01248762206035,\n 42.01259179761368\n ],\n [\n -74.01248762206035,\n 42.29523893577249\n ],\n [\n -74.54611692479963,\n 42.29523893577249\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Kemper, John T.","contributorId":270040,"corporation":false,"usgs":false,"family":"Kemper","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":930705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Underwood, Kristen L. 0000-0003-3008-3057","orcid":"https://orcid.org/0000-0003-3008-3057","contributorId":340200,"corporation":false,"usgs":false,"family":"Underwood","given":"Kristen","email":"","middleInitial":"L.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":930706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":930707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Dany","contributorId":352304,"corporation":false,"usgs":false,"family":"Davis","given":"Dany","affiliations":[{"id":84168,"text":"NYC Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":930708,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siemion, Jason 0000-0001-5635-6469 jsiemion@usgs.gov","orcid":"https://orcid.org/0000-0001-5635-6469","contributorId":127562,"corporation":false,"usgs":true,"family":"Siemion","given":"Jason","email":"jsiemion@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930709,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930710,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schroth, Andrew W.","contributorId":192042,"corporation":false,"usgs":false,"family":"Schroth","given":"Andrew","email":"","middleInitial":"W.","affiliations":[{"id":17809,"text":"University of Vermont, Burlington","active":true,"usgs":false}],"preferred":false,"id":930711,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70275008,"text":"70275008 - 2025 - Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska","interactions":[],"lastModifiedDate":"2026-04-10T15:08:45.046416","indexId":"70275008","displayToPublicDate":"2025-03-01T09:55:18","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5709,"text":"OCS Study","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"BOEM 2025-019","title":"Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska","docAbstract":"

Following near extirpation from the fur trade, sea otters (Enhydra lutris) have returned to occupy lower Cook Inlet since the 1950s, or earlier, with numbers increasing to ~11,000 and ~9,000 on the west and east side, respectively, by 2017. Northward range expansion on the west side has been negligible for decades with few animals found north of Kamishak Bay, while northward expansion on the east side has been more pronounced in recent decades. The reasons for these contrasting distribution patterns are not certain. Possible explanations for lack of expansion on the west side included 1) poor sea otter habitat north of Kamishak Bay; 2) adequate habitat north of Kamishak Bay but no incentive for sea otters to move north because of abundant food in Kamishak Bay, and/or sea otters discouraged from moving north of Kamishak Bay; 3) seasonal ice formation; or 4) seasonal presence of killer whales. This project was designed to document current sea otter abundance and distribution in lower Cook Inlet, including seasonal variation, and evaluate drivers of habitat use and foraging conditions, including how these overlap with Bureau of Ocean Energy Management (BOEM) Lease Sale blocks. 

We found that the probability of sea otter presence in lower Cook Inlet was significantly related to depth and distance from shore with the highest probabilities of sea otter presence in areas ~7–8 kilometers (km) from shore in water ~20–30 meters (m) deep. Historical survey data suggest sea otter distribution has not changed dramatically since 2002, and while we detected significant seasonal changes including due to presence of heavy sea ice, the seasonal changes were modest and primarily related to locations with consistent winter ice formation on the west side of lower Cook Inlet. Overall, throughout the year, sea otters appeared to be utilizing most of the habitat within the 40-m depth contour on the east side of lower Cook Inlet, including Kachemak Bay. Sea otters on the west side reside largely within Kamishak Bay. Because of the shallow bathymetry of lower Cook Inlet, sea otters consistently occurred farther offshore than in many other areas of Alaska. The presence of sea otters, including females with pups, in these offshore waters indicates that sea otters can forage productively in these open water areas. Importantly, we documented that sea otters, including females with pups, occurred in most of the eastern BOEM lease blocks including the original Lease Sale 244 blocks 7064, 7114, 6162, 6310, 6360, 6410, 6458 and 6457. The relinquishments for these 7 leases were effective September 17, 2024: OCS-Y-02434 (block # 7064), OCS-Y-02435 (7114), OCS-Y-02436 (6162), OCS-Y-02438 (6357), OCS-Y-02442 (6407), OCS-Y02446 (6457), OCS-Y-02447 (6458) https://www.boem.gov/sites/default/files/documents/environment/Map%20of%20Active%20Leases%20 Cook%20Inlet%20OCS_0.pdf 

Regarding differential northward expansion on the east and west side of lower Cook Inlet, we found that wind and water circulation patterns make winter sea ice more prominent on the west side, but this had minimal effects on sea otter distribution. In addition, although there have been observations of killer whale (Orcinus orca) predation on sea otters in Cook Inlet, we did not see behavioral or distributional evidence that it was prevalent enough to have strong effects on sea otter habitat use. Benthic surveys using a remotely operated vehicle (ROV) indicated that epibenthic substrate and biological community heterogeneity differed between areas with and without otters, suggesting that these factors may explain the current distribution of otters within lower Cook Inlet. However, ROV surveys cannot assess infaunal prey abundance, making assessments of the role of prey availability difficult. The existence of a healthy Pacific razor clam (Siliqua patula) fishery along western lower Cook Inlet along the Lake Clark coast north of Kamishak Bay suggests there may still be a resource base for eventual expansion of sea otters into this area. 

Our shore-based forage observations indicated that sea otters exist near carrying capacity densities relative to nearshore prey resources. However, our distribution model suggests the bulk of the population lives offshore beyond our ability to observe their feeding activity. The fact that sea otters are a consistent presence in offshore areas suggests that prey resources in these areas were relatively abundant in comparison to nearshore prey resources, making foraging in offshore areas, with water depths of 0–40 m, energetically profitable. Prey types in these offshore areas of lower Cook Inlet likely included epifauna such as crabs and large urchins, in addition to infaunal clams based on the soft substrate habitat types that characterize lower Cook Inlet.  

Collectively, our results suggest that sea otters occupy most areas of lower Cook Inlet with appropriate benthic habitat types and prey resources, which includes areas within BOEM Lease Sale blocks. With the exception of the Lake Clark coast, sea otters may be approaching a food- and habitat-limited distribution and carrying capacity, suggesting that the current status of sea otters in lower Cook Inlet is likely to remain similar in the absence of significant changes to prey, habitat, predation, or anthropogenic disturbance. 

","language":"English","publisher":"Bureau of Ocean Energy Management (BOEM)","usgsCitation":"Monson, D., Kloecker, K.A., LaRoche, N., Power, C., Geissinger, L., Hasan, E., Jones, T., and Weitzman, B., 2025, Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska: OCS Study BOEM 2025-019, viii, 73 p.","productDescription":"viii, 73 p.","ipdsId":"IP-176659","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":502683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502670,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://espis.boem.gov/final%20reports/BOEM_2025-019.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Cook Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.3155651543558,\n 60.79849962080206\n ],\n [\n -154.3155651543558,\n 58.54443596757534\n ],\n [\n -149.77946397731765,\n 58.54443596757534\n ],\n [\n -149.77946397731765,\n 60.79849962080206\n ],\n [\n -154.3155651543558,\n 60.79849962080206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":959185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kloecker, Kimberly A. 0000-0002-2461-968X kkloecker@usgs.gov","orcid":"https://orcid.org/0000-0002-2461-968X","contributorId":3442,"corporation":false,"usgs":true,"family":"Kloecker","given":"Kimberly","email":"kkloecker@usgs.gov","middleInitial":"A.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":959186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaRoche, Nicole 0000-0003-3737-5714","orcid":"https://orcid.org/0000-0003-3737-5714","contributorId":178395,"corporation":false,"usgs":false,"family":"LaRoche","given":"Nicole","email":"","affiliations":[{"id":18890,"text":"formerly USGS Western Ecological Research Center, Santa Cruz Field Station","active":true,"usgs":false}],"preferred":false,"id":959187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Power, Collin","contributorId":303954,"corporation":false,"usgs":false,"family":"Power","given":"Collin","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":959188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geissinger, Laura","contributorId":369804,"corporation":false,"usgs":false,"family":"Geissinger","given":"Laura","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":959189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hasan, Elizabeth","contributorId":369805,"corporation":false,"usgs":false,"family":"Hasan","given":"Elizabeth","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":959190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Tahzay","contributorId":369806,"corporation":false,"usgs":false,"family":"Jones","given":"Tahzay","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":959191,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weitzman, Ben","contributorId":252838,"corporation":false,"usgs":false,"family":"Weitzman","given":"Ben","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":959192,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70265965,"text":"70265965 - 2025 - Summary of results from monitoring the Geysers with continuous passive seismic and repeat magnetotelluric measurements (2021-2023)","interactions":[],"lastModifiedDate":"2025-04-22T14:54:19.271208","indexId":"70265965","displayToPublicDate":"2025-03-01T09:47:53","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Summary of results from monitoring the Geysers with continuous passive seismic and repeat magnetotelluric measurements (2021-2023)","docAbstract":"

Understanding temporal variations in a geothermal field can support operators in decision making that pertains to optimizing production and mitigating hazards. Between 2021 and 2023, The Geysers geothermal field in northern California was monitored with an array of continuous passive seismic sensors and annual repeat magnetotelluric (MT) measurements. Each of these data sets were analyzed and modelled separately to understand the data, sensitivity, and any observable changes. Then, the data were inverted jointly using a crossgradient method to further constrain temporal changes in geophysical properties within the geothermal field. Multiple permutations of annual datasets were used as inputs to the joint inversion. Results demonstrate seismic data constrain smooth inversion of the MT data, and the MT data provide supplementary information about the location of temporal fluid changes. Estimating relative changes in steam saturation for various time intervals of the joint models shows compartmentalized changes in the field, and good spatial correlation with the location of injection wells. These results demonstrate that collecting both passive seismic and MT measurements then modeling them jointly provide complementary information and a relatively inexpensive method for monitoring temporal changes in an active geothermal field that provides results to support operators.

","conferenceTitle":"50th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 10-12, 2025","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Peacock, J., Alumbaugh, D., Gritto, R., Um, E., Ulrich, C., Mitchell, M., and Hartline, C., 2025, Summary of results from monitoring the Geysers with continuous passive seismic and repeat magnetotelluric measurements (2021-2023), 50th Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 10-12, 2025, 4 p.","productDescription":"4 p.","ipdsId":"IP-174899","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":484833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":484832,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=38093"}],"country":"United States","state":"California","otherGeospatial":"the Geysers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.883333,\n 38.866667\n ],\n [\n -122.883333,\n 38.716667\n ],\n [\n -122.6833,\n 38.716667\n ],\n [\n -122.6833,\n 38.866667\n ],\n [\n -122.883333,\n 38.866667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alumbaugh, David 0000-0002-6975-7197","orcid":"https://orcid.org/0000-0002-6975-7197","contributorId":299109,"corporation":false,"usgs":false,"family":"Alumbaugh","given":"David","email":"","affiliations":[{"id":64775,"text":"Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":934177,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gritto, Roland","contributorId":194798,"corporation":false,"usgs":false,"family":"Gritto","given":"Roland","email":"","affiliations":[],"preferred":false,"id":934178,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Um, Evan","contributorId":345396,"corporation":false,"usgs":false,"family":"Um","given":"Evan","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":934179,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ulrich, Craig","contributorId":336922,"corporation":false,"usgs":false,"family":"Ulrich","given":"Craig","affiliations":[{"id":80913,"text":"3. Lawrence Berkeley National Laboratory, Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":934180,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitchell, Michael A. 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":353628,"corporation":false,"usgs":false,"family":"Mitchell","given":"Michael A.","affiliations":[{"id":78718,"text":"formerly U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":934181,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hartline, Craig","contributorId":213429,"corporation":false,"usgs":false,"family":"Hartline","given":"Craig","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":934182,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264818,"text":"70264818 - 2025 - Preliminary depth to basement modeling at Salton Sea, California","interactions":[],"lastModifiedDate":"2025-03-25T14:26:02.871178","indexId":"70264818","displayToPublicDate":"2025-03-01T09:23:47","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary depth to basement modeling at Salton Sea, California","docAbstract":"

The San Andreas Fault – Imperial Fault (SAF-IF) transtensional step-over zone along the southern margin of the Salton Sea hosts substantial geothermal production and lithium brine resources. Recent volcanism at the Salton Buttes and active seismicity along the SAFIF fault system highlight active tectonic and magmatic processes that pose natural hazards and may impact energy and mineral production. Characterizing the subsurface architecture and extent of concealed alteration associated with this tectono-magmatic system enhances understanding of these active processes, associated hazards, and resources.

We have compiled a gravity database, consisting of new and re-processed existing data, from which we have constructed a new isostatic residual gravity anomaly map of the Salton trough. We have used this new gravity dataset together with a compilation of publicly available borehole data to develop new depth to basement inversion models for the region. These depth to basement models help to constrain basin geometries, inform alteration mapping, and reveal variations in basement rocks. Due to the concealed nature of the complex tectonic framework at the Salton trough, it is necessary to utilize geophysical methods for subsurface characterization. These new depth to basement models are a first step toward constructing 2D and 3D geophysical and geologic models of the Imperial Valley and Salton Sea geothermal area. This analysis complements other geophysical initiatives, including magnetotelluric (MT) modeling (Tokmakoff et al., 2024), magnetic mapping (Glen and Earney, 2023, 2024) and potential field modeling, and seismic studies focused on hazard and resource investigations in the Imperial Valley.

","conferenceTitle":"50th Stanford Geothermal Workshop","conferenceDate":"February 12, 2025","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Anderson, J.E., Glen, J.M., Schermerhorn, W.D., Earney, T.E., and Morbeck, B., 2025, Preliminary depth to basement modeling at Salton Sea, California, 50th Stanford Geothermal Workshop, Stanford, CA, February 12, 2025, 9 p.","productDescription":"9 p.","ipdsId":"IP-175199","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":483761,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=37957","linkFileType":{"id":5,"text":"html"}},{"id":483774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salton Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.17095889118062,\n 33.553526286643006\n ],\n [\n -116.17095889118062,\n 32.69509941552114\n ],\n [\n -115.1118650865392,\n 32.69509941552114\n ],\n [\n -115.1118650865392,\n 33.553526286643006\n ],\n [\n -116.17095889118062,\n 33.553526286643006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Jacob Elliott 0000-0002-0709-2548","orcid":"https://orcid.org/0000-0002-0709-2548","contributorId":329989,"corporation":false,"usgs":true,"family":"Anderson","given":"Jacob","email":"","middleInitial":"Elliott","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931832,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931833,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morbeck, Benjamin Lyter 0009-0000-6043-0481","orcid":"https://orcid.org/0009-0000-6043-0481","contributorId":335638,"corporation":false,"usgs":true,"family":"Morbeck","given":"Benjamin Lyter","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931834,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264760,"text":"70264760 - 2025 - Fisheries research and monitoring activities of the Lake Erie Biological Station, 2024","interactions":[],"lastModifiedDate":"2025-03-24T14:18:44.046889","indexId":"70264760","displayToPublicDate":"2025-03-01T09:07:02","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Fisheries research and monitoring activities of the Lake Erie Biological Station, 2024","docAbstract":"A comprehensive understanding of fish populations and their interactions is the cornerstone of modern fishery management and the basis for Lake Erie’s Fish Community Objectives (FCOs) developed in 2020 (Francis et al. 2020). The 2024 U.S. Geological Survey (USGS) Lake Erie Biological Station Annual Report is responsive to these FCOs and the USGS obligations via a Memorandum of Understanding (MOU 2017) with the Great Lakes Fishery Commission (GLFC) Council of Lake Committees (CLC) to provide scientific information in support of fishery management. Goals for the USGS Great Lakes Deepwater Fish Assessment and Ecological Studies were to monitor long-term changes in the fish community and track population dynamics of key fishes of interest to management agencies. Specific to Lake Erie, expectations were sustained investigations of native percids, prey fish populations, and Lake Trout. All work was conducted as part of the Deepwater Science Program under the authority of the Great Lakes Fishery Research Authorization Act of 2019 (16 USC §941h).\n\nThe USGS 2024 Deepwater Science Program fieldwork began in Lake Erie in March and concluded in December, using trawl, gill net, hydroacoustic, lower trophic sampling devices, and telemetry methods. This work resulted in 44 bottom trawls covering 41 ha of lake bottom and catching 48,936 fish totaling 995 kg in the West Basin of Lake Erie, with detailed results described below. Overnight gill net sets (n=25) for coldwater species were performed in the East Basin of Lake Erie. A total of 8 km of gillnet was deployed during these surveys, which caught 106 fish, 92 of which were native coldwater species: Lake Trout, Burbot, and Lake Whitefish. Results from coldwater species assessments will be reported in the Coldwater Task Group report to the GLFC and the CLC (CTG 2025). These reports are used to inform Lake Trout stocking decisions and direct lamprey control measures (16 USC §939a). USGS hydroacoustic sampling included twenty-six 5-km transects (130 km total) in the Central Basin as part of a collaborative lake-wide survey with details and results reported by the Forage Task Group (FTG 2025). Lower trophic sampling provided data from zooplankton samples (n=12) and water quality profiles (n=12) to populate a database maintained by the Michigan Department of Natural Resources (MDNR), Ontario Ministry of Natural Resources (OMNR), Ohio Department of Natural Resources (ODNR), Pennsylvania Fish and Boat Commission (PFBC), and New York State Department of Environmental Conservation (NYSDEC). USGS also assisted CLC member agencies with deployment and maintenance of Great Lakes Acoustic Telemetry Observation System (GLATOS) infrastructure throughout all three Lake Erie basins and tributaries, supporting multiple coordinated telemetry investigations.\n\nThis report presents biomass-based summaries of fish communities in western Lake Erie derived from USGS bottom trawl surveys conducted from 2013 to 2024 during June and September. The survey design compliments the August ODNR- OMNRF effort by reinforcing stock assessments with more robust data. Analyses herein evaluated trends in total biomass, abundance of dominant predator and forage species, non-native species composition, biodiversity, and community structure. Data from this effort are accessible for download (Keretz et al. 2025)","language":"English","publisher":"Great Lakes Fisheries Commission","usgsCitation":"Dufour, M.R., Guzzo, F., Hilling, C.D., Keretz, K.R., Kraus, R., Oldham, R.C., Roberts, J., and Schmitt, J., 2025, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2024, 18 p.","productDescription":"18 p.","ipdsId":"IP-176058","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":483683,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://glfc.org/publication-media-search.php","linkFileType":{"id":5,"text":"html"}},{"id":483708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.27940568089508,\n 42.15760057509911\n ],\n [\n -83.54993583176982,\n 42.15760057509911\n ],\n [\n -83.54993583176982,\n 41.240913888913184\n ],\n [\n -82.27940568089508,\n 41.240913888913184\n ],\n [\n -82.27940568089508,\n 42.15760057509911\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guzzo, Francesco 0000-0003-2374-0514","orcid":"https://orcid.org/0000-0003-2374-0514","contributorId":351294,"corporation":false,"usgs":true,"family":"Guzzo","given":"Francesco","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keretz, Kevin R. 0000-0002-4808-8350 kkeretz@usgs.gov","orcid":"https://orcid.org/0000-0002-4808-8350","contributorId":5859,"corporation":false,"usgs":true,"family":"Keretz","given":"Kevin","email":"kkeretz@usgs.gov","middleInitial":"R.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":931562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oldham, Richard Cole 0000-0002-2331-7612","orcid":"https://orcid.org/0000-0002-2331-7612","contributorId":294345,"corporation":false,"usgs":true,"family":"Oldham","given":"Richard","email":"","middleInitial":"Cole","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931564,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931565,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931566,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70267332,"text":"70267332 - 2025 - Urbanization and host relatedness shape virome composition in a widespread, generalist carnivore","interactions":[],"lastModifiedDate":"2025-05-20T15:58:47.136322","indexId":"70267332","displayToPublicDate":"2025-03-01T08:51:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization and host relatedness shape virome composition in a widespread, generalist carnivore","docAbstract":"

Urban wildlife species have the potential to serve as links in disease transmission between wildlife, humans and domestic animals at the wildland–urban interface (WUI), contributing to both sustained cross-species transmission of pathogens and the emergence of diseases in susceptible populations. However, the relative roles of host and environmental factors in shaping the composition of pathogen communities in urban wildlife is understudied. In this study, we integrated DNA and RNA virome data with host genomic and GPS datasets to investigate factors shaping virome composition in bobcats (Lynx rufus ) at the WUI in the Tucson Mountains, Arizona, USA. Using a hybrid-capture approach for 31 scats and 17 buccal swabs, we identified multiple viruses that could affect carnivore health at the WUI, including canine parvovirus, feline astrovirus, Felis catus papillomaviruses 2 and 3 and Lyon-IARC polyomavirus. Models of virome composition and distribution of viral taxa indicated contributions of host genetic relatedness and factors relating to urbanisation (such as percentages of urban land cover, road and building densities and distances to roads). Genetic associations with virome compositions were particularly influenced by females. While females exhibit significant isolation by distance, partial Mantel tests revealed a significant correlation between beta diversity and host genetic distance in females only. To our knowledge, this study represents the first assessment of factors shaping virome composition in a wild felid. Our finding of known feline and canine pathogens in bobcats underscores the potential of the WUI to facilitate cross-species transmission between wild and domestic animals.

","language":"English","publisher":"Wiley","doi":"10.1111/mec.17681","usgsCitation":"Payne, N., Andersen, D., Mollohan, C., Van Doorslaer, K., Combrink, L., and Culver, M., 2025, Urbanization and host relatedness shape virome composition in a widespread, generalist carnivore: Molecular Ecology, v. 34, no. 6, e17681, 17 p., https://doi.org/10.1111/mec.17681.","productDescription":"e17681, 17 p.","ipdsId":"IP-174506","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","city":"Tucson","otherGeospatial":"Tucson Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.20811415902976,\n 32.32028515103218\n ],\n [\n -111.20811415902976,\n 32.11964386691881\n ],\n [\n -111.03231033562199,\n 32.11964386691881\n ],\n [\n -111.03231033562199,\n 32.32028515103218\n ],\n [\n -111.20811415902976,\n 32.32028515103218\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Payne, Natalie","contributorId":355612,"corporation":false,"usgs":false,"family":"Payne","given":"Natalie","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, Desiree","contributorId":355613,"corporation":false,"usgs":false,"family":"Andersen","given":"Desiree","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mollohan, Cheryl","contributorId":355614,"corporation":false,"usgs":false,"family":"Mollohan","given":"Cheryl","affiliations":[{"id":84784,"text":"Bobcats in Tucson Research Project ©","active":true,"usgs":false}],"preferred":false,"id":937760,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Doorslaer, Koenraad","contributorId":355615,"corporation":false,"usgs":false,"family":"Van Doorslaer","given":"Koenraad","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937761,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Combrink, Leigh","contributorId":355616,"corporation":false,"usgs":false,"family":"Combrink","given":"Leigh","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937762,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":197693,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":937763,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266296,"text":"70266296 - 2025 - Development and evaluation of the remote passive integrated transponder tag reader for customizable monitoring of wildlife","interactions":[],"lastModifiedDate":"2025-05-02T18:09:54.530775","indexId":"70266296","displayToPublicDate":"2025-03-01T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Development and evaluation of the remote passive integrated transponder tag reader for customizable monitoring of wildlife","docAbstract":"

Increasing the quantity and quality of mark-reencounter data can be important when individual identification data are needed to address research or management questions. Physical recapture methods tend to be labor-intensive and therefore expensive, while remote detection methods are not easily applied to all taxa or are proprietary and thus difficult to customize. However, partnerships between wildlife scientists and engineers have the potential to provide innovative solutions that improve data collection while reducing costs. We describe a collaborative effort to improve the collection of mark-reencounter data on an invasive reptile, the brown treesnake (Boiga irregularis). In laboratory trials conducted on Guåhan (CHamoru; Guam in English) in 2021, we assessed the feasibility of detecting snakes fitted with passive integrated transponder (PIT) tags using customized remote PIT-tag readers (RePTaR) paired with baited snake traps. We evaluated differences in scanning success as a function of snake traits, location of PIT tags within snakes, and the distance of the PIT tag from the reader. We successfully scanned all individuals, on average 529 times but ranging from 3 to 6,436 times, during 12-hour trials using a designed customizable RePTaR unit. Snake characteristics and marking location explained little of the variability in scanning success, with distance from the reader as the best-supported covariate. By building cross-disciplinary partnerships, we can develop customizable technological solutions and provide additional tools with which to address challenges in wildlife research and management.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1569","collaboration":"USGS, Department of Defense, US Department of Agriculture","usgsCitation":"Amburgey, S., Prakash, A., Yackel Adams, A.A., Siers, S., and Converse, S.J., 2025, Development and evaluation of the remote passive integrated transponder tag reader for customizable monitoring of wildlife: Wildlife Society Bulletin, v. 49, no. 1, e1569, 16 p., https://doi.org/10.1002/wsb.1569.","productDescription":"e1569, 16 p.","ipdsId":"IP-161941","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1569","text":"Publisher Index Page"},{"id":485358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.58089598320413,\n 13.72087583284113\n ],\n [\n 144.58089598320413,\n 13.236656345472099\n ],\n [\n 145.0547651347528,\n 13.236656345472099\n ],\n [\n 145.0547651347528,\n 13.72087583284113\n ],\n [\n 144.58089598320413,\n 13.72087583284113\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Amburgey, Staci M.","contributorId":354345,"corporation":false,"usgs":false,"family":"Amburgey","given":"Staci M.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":935425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prakash, Aadithya","contributorId":354346,"corporation":false,"usgs":false,"family":"Prakash","given":"Aadithya","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":935426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":935427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siers, Shane R.","contributorId":354347,"corporation":false,"usgs":false,"family":"Siers","given":"Shane R.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":935428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263901,"text":"ofr20251006 - 2025 - ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2024","interactions":[],"lastModifiedDate":"2025-02-28T16:23:31.312068","indexId":"ofr20251006","displayToPublicDate":"2025-02-28T09:39:35","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":"2025-1006","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 3, 2024","title":"ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2024","docAbstract":"

Executive Summary 

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 3 (July–September) of 2024. 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 at https://earthexplorer.usgs.gov.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251006","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Levy, R., Miller, J., and Ding, L., 2025, ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2024: U.S. Geological Survey Open-File Report 2025–1006, 56 p., https://doi.org/10.3133/ofr20251006.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172164","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":482589,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251006/full"},{"id":482584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1006/coverthb.jpg"},{"id":482585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1006/ofr20251006.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025–1006"},{"id":482586,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1006/ofr20251006.XML"},{"id":482587,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1006/images/"},{"id":482588,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"}],"contact":"

Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2025-02-28","noUsgsAuthors":false,"publicationDate":"2025-02-28","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":929023,"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":929024,"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":929025,"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":929026,"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":929027,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":929028,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":929029,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":929030,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":251780,"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":929031,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"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":929032,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"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":929033,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"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":929034,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"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":929036,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"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":929037,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"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":929038,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"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":929039,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70269357,"text":"70269357 - 2025 - ARCHI: A new R package for automated imputation of regionally correlated hydrologic records","interactions":[],"lastModifiedDate":"2025-07-21T14:22:11.345476","indexId":"70269357","displayToPublicDate":"2025-02-28T09:18:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"ARCHI: A new R package for automated imputation of regionally correlated hydrologic records","docAbstract":"

Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.

","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13474","usgsCitation":"Levy, Z., Glas, R.L., Stagnitta, T.J., and Terry, N., 2025, ARCHI: A new R package for automated imputation of regionally correlated hydrologic records: Groundwater, v. 62, no. 4, p. 595-610, https://doi.org/10.1111/gwat.13474.","productDescription":"16 p.","startPage":"595","endPage":"610","ipdsId":"IP-166664","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":492870,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13474","text":"Publisher Index Page"},{"id":492796,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P17NXGHV","text":"USGS data release","linkHelpText":"Example Groundwater-Level Datasets and Benchmarking Results for the Automated Regional Correlation Analysis for Hydrologic Record Imputation (ARCHI) Software Package"},{"id":492616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, New York","otherGeospatial":"Central Valley, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.13447900247726,\n 37.3805353859038\n ],\n [\n -121.13447900247726,\n 35.88363374499093\n ],\n [\n -118.93035501208706,\n 35.88363374499093\n ],\n [\n -118.93035501208706,\n 37.3805353859038\n ],\n [\n -121.13447900247726,\n 37.3805353859038\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.76788606860265,\n 41.23528810034301\n ],\n [\n -74.12298971233533,\n 41.23528810034301\n ],\n [\n -74.12298971233533,\n 40.43286561377994\n ],\n [\n -71.76788606860265,\n 40.43286561377994\n ],\n [\n -71.76788606860265,\n 41.23528810034301\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glas, Robin L. 0000-0002-7394-1667","orcid":"https://orcid.org/0000-0002-7394-1667","contributorId":300625,"corporation":false,"usgs":true,"family":"Glas","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stagnitta, Timothy J. 0000-0001-8903-428X","orcid":"https://orcid.org/0000-0001-8903-428X","contributorId":304230,"corporation":false,"usgs":true,"family":"Stagnitta","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":true,"id":943533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":943534,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263997,"text":"70263997 - 2025 - A practical decision tool for marine bird mortality assessments","interactions":[],"lastModifiedDate":"2025-03-04T14:44:08.99165","indexId":"70263997","displayToPublicDate":"2025-02-28T08:35:15","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18754,"text":"EcoEvoRxiv","active":true,"publicationSubtype":{"id":32}},"title":"A practical decision tool for marine bird mortality assessments","docAbstract":"

Given the rise in anthropogenic, environmental, and disease events contributing to marine bird mortality, there is a critical need to improve the rigor of mortality assessments. Deficits in data collection and mortality estimation can hinder a manager’s ability to document event scales and inform population level impacts. Therefore, to inform decisions required during activities such as conservation status assessments or harvest management, organizations may choose to incorporate mortality assessments into response plans. Resources, capacity, and assets to assess mortality vary across jurisdictions (federal, state, Indigenous, local, etc.), and clear guidance to support mortality assessments is often unavailable or not clearly addressed. Here, we present a decision support tool to help managers identify and evaluate survey options to assess bird mortality in a diverse array of scenarios. The objective of the decision tool is to improve data collection and availability which will increase the ability to robustly estimate mortality, given situation-specific attributes and constraints. This decision tool is designed to guide the response when a mortality event is initially encountered and offers suggestions for assessment and reporting procedures in the absence of other guidance or to complement existing protocols. The decision tool is also meant to inform decision making for response determination and resource allocation. The tool facilitates examination of options for further assessment and monitoring which users determine by examining questions pertaining to species prioritization, mortality spatial extent, and the potential magnitude of impacts on affected species. Finally, identification of appropriate survey methods, that address imperfect detection when a complete census is not possible, are determined by exploring location, spatial and temporal extent, and the type of species affected. Ultimately, this tool aims to facilitate and improve the standardization of mortality assessments, equipping managers with a practical resource to navigate the decision-making process for marine bird mortality estimation.

","language":"English","publisher":"EcoEvoRxiv","doi":"10.32942/X23S6R","usgsCitation":"Harvey, J., Ramey, A.M., Avery-Gomm, S., Robertson, G., Romano, M., Mullinax, J.M., Boldenow, M., Atkinson, P., and Prosser, D.J., 2025, A practical decision tool for marine bird mortality assessments: EcoEvoRxiv, https://doi.org/10.32942/X23S6R.","productDescription":"48 p.","ipdsId":"IP-168193","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":487279,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.32942/x23s6r","text":"Publisher Index Page"},{"id":482790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Johanna Alexandra 0000-0003-4504-6777","orcid":"https://orcid.org/0000-0003-4504-6777","contributorId":351781,"corporation":false,"usgs":true,"family":"Harvey","given":"Johanna Alexandra","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":929443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":929444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avery-Gomm, Stephanie","contributorId":213093,"corporation":false,"usgs":false,"family":"Avery-Gomm","given":"Stephanie","email":"","affiliations":[{"id":12552,"text":"University of Queensland","active":true,"usgs":false}],"preferred":false,"id":929445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, Gregory","contributorId":331077,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory","affiliations":[],"preferred":false,"id":929446,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Romano, Marc","contributorId":200806,"corporation":false,"usgs":false,"family":"Romano","given":"Marc","affiliations":[],"preferred":false,"id":929447,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":929448,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boldenow, Megan","contributorId":169336,"corporation":false,"usgs":false,"family":"Boldenow","given":"Megan","affiliations":[],"preferred":false,"id":929449,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Atkinson, Philip W.","contributorId":295425,"corporation":false,"usgs":false,"family":"Atkinson","given":"Philip W.","affiliations":[{"id":38864,"text":"British Trust for Ornithology","active":true,"usgs":false}],"preferred":false,"id":929450,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929451,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263870,"text":"ofr20251003 - 2025 - Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska","interactions":[],"lastModifiedDate":"2025-07-21T18:27:29.016147","indexId":"ofr20251003","displayToPublicDate":"2025-02-27T11:50:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1003","displayTitle":"Phase 1 Technical Implementation Plan for the Expansion of the ShakeAlert Earthquake Early Warning System to Alaska","title":"Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska","docAbstract":"

Executive Summary

The conference report accompanying the fiscal year (FY) 2022 Consolidated Appropriations Act (Public Law 117–103) for the U.S. Department of the Interior and related agencies directed the U.S. Geological Survey (USGS) to “work with the State of Alaska to develop an implementation plan to be completed within two years in order to put ShakeAlert/Earthquake Early Warning in Alaska” (p. 29). Congress included $1 million in the FY 2022 appropriation to conduct this effort.

The USGS Earthquake Hazards Program, along with partner organizations, has developed the ShakeAlert earthquake early warning (EEW) system for the West Coast, which currently operates in California, Oregon, and Washington. The purpose of the system and its alert delivery partners is to reduce the impact of earthquakes and save lives and property by delivering ShakeAlert-powered alerts that are transmitted to the public via mass notification technologies, and by providing more detailed data streams to institutional users and commercial service providers to trigger automated, user-specific, protective actions.

ShakeAlert was designed in such a way that it could be expanded to other U.S. regions with high earthquake risk, after the build-out of seismic and geodetic networks to support ShakeAlert in a specified region is completed and the necessary funding is secured for long-term operation and maintenance.

When an earthquake occurs, seismic waves radiate from the rupturing fault like waves on a pond. It is these waves that people feel as earthquake shaking and that can cause damage to structures. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Early warnings of impending shaking are then sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking. There will almost always be a region near the earthquake epicenter where alerts arrive after damaging shaking has begun. The ShakeAlert system updates its ground-motion estimates as an earthquake grows larger.

In response to the FY 2022 congressional direction, the USGS worked with the State of Alaska to devise this implementation plan for ShakeAlert expansion to Alaska. The USGS engaged with the Alaska Division of Homeland Security and Emergency Management (DHS&EM) and the Alaska Division of Geological and Geophysical Surveys (DGGS). A cooperative agreement was awarded to the Alaska Earthquake Center (AEC) at the University of Alaska Fairbanks (UAF) for their contributions to the plan and their work coordinating with other networks in Alaska. The USGS engaged with the Alaska Seismic Hazards Safety Commission (ASHSC) throughout the process. The USGS also held a series of Alaska stakeholder engagements. The process of developing the implementation plan was facilitated by contracted staff from Corner Alliance, which is a government consulting firm.

This implementation plan describes the details and estimates the costs for a Phase 1 expansion of the ShakeAlert system to Alaska. A geographically limited Phase 1 goal was chosen that covers the highest risk and most populated areas of Alaska. The areas proposed encompass the State’s main population centers and 90 percent of the State’s population. This Phase 1 design is considered very challenging and ambitious from the viewpoint of network operators. The lessons learned if this plan is implemented could be used to consider subsequent phases to expand EEW beyond Phase 1 in Alaska in the future.

ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System (ANSS). This implementation plan calls for a total of 450 high-quality, real-time EEW-capable ANSS seismic stations in Alaska: 270 new stations, 160 upgraded stations, and 20 existing stations. These seismic station numbers are based on a station spacing of 10 kilometers (km) in urban areas, 20 km in seismic source areas that endanger population centers, and 40 km in other areas. The associated costs also include support for some EEW-capable global navigation satellite system (GNSS) stations, with a focus on improving warnings for large subduction zone earthquakes. For effective EEW, ShakeAlert requires low-latency, high-availability, robust telemetry links to deliver continuous, real-time data from field stations to the data centers.

The Alaska data processing hardware infrastructure would follow the general design for fail-safe operation that is used for the ShakeAlert system on the West Coast. The ShakeAlert architecture uses two independent layers: the production layer for earthquake processing and the alert layer to make alerting decisions and serve alerts to users. This implementation plan includes two geographically separated data centers in Alaska, each with two fully independent production and alert layers using the same system design developed for the West Coast. As of March 2024, the ShakeAlert system is at version 3.0.1, with more advanced versions in the development and testing pipeline. ShakeAlert originally used two algorithms to determine the location and magnitude of earthquakes using seismic data. A third algorithm that can calculate very large magnitudes of very large earthquakes with geodetic data was added in March 2024.

ShakeAlert publishes several data and alert products to meet the needs of different users. All messages include the location of the earthquake, either as a point or a line, and its magnitude. Ground-shaking estimates are published in two forms, as ground-motion contours and a map grid. Providing adequate warning time for strong shaking (the “target threshold”) requires sending alerts at a threshold lower than that strong shaking level (the “alert threshold”). The thresholds for public alerting in Alaska would be a joint USGS and State decision.

To have the greatest benefit, ShakeAlert-powered alerts would be delivered to institutional users and individuals by all practical pathways. The USGS alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission nor the infrastructure and expertise to perform mass notifications to the public or implement automatic actions for end users of the alerts. To meet this need, ShakeAlert recruits private sector “technology enablers” that have the necessary expertise to develop end-user implementations using EEW alerts with the goal of stimulating an EEW industry.

Earthquake early warning alerts are useless if people do not know how to respond to them. Although the alert messages include instructions about what to do (drop, cover, and hold on), alerts are more effective if people have been trained in advance. Messages about ShakeAlert’s capabilities, limitations, and benefits could be integrated with existing earthquake education programs, including State-run programs. Therefore, ShakeAlert would coordinate with both public and private partners and stakeholders through various partnerships and agreements to accomplish consistent and ongoing public earthquake hazard education.

The estimated capital cost of completing the computing infrastructure and sensor networks for the Phase 1 ShakeAlert expansion to Alaska is approximately $66 million in 2024 dollars. The annual operation and maintenance cost of the completed system is estimated to be $12 million per year in 2024 dollars when fully built out.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20251003","programNote":"Earthquake Hazards Program","usgsCitation":"Wolfe, C.J., Ruppert, N.A., Given, D.D., West, M.E., Thomas, V.I., Murray, J.R., and Grapenthin, R., 2025, Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska: U.S. Geological Survey Open-File Report 2025–1003, 32 p., https://doi.org/10.3133/ofr20251003.","productDescription":"viii, 32 p.","onlineOnly":"Y","ipdsId":"IP-169264","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":482514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1003/coverthb.jpg"},{"id":482516,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1003/ofr20251003.pdf","text":"Report","size":"7.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1003"},{"id":492693,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118457.htm","linkFileType":{"id":5,"text":"html"}},{"id":482829,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251003/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1003"},{"id":482578,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1003/ofr20251003.xml"},{"id":482577,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1003/images"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -132.95812081792852,\n 56\n ],\n [\n -132.95812081792852,\n 63\n ],\n [\n -163.75172419269705,\n 63\n ],\n [\n -163.75172419269705,\n 56\n ],\n [\n -132.95812081792852,\n 56\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Senior Science Advisor for Earthquake and Geologic Hazards
Earthquake Hazards Program
U.S. Geological Survey
Mail Stop 905
12201 Sunrise Valley Drive
Reston, VA 20192

","tableOfContents":"","publishedDate":"2025-02-27","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfe, Cecily J. 0000-0003-3144-5697 cwolfe@usgs.gov","orcid":"https://orcid.org/0000-0003-3144-5697","contributorId":191613,"corporation":false,"usgs":true,"family":"Wolfe","given":"Cecily","email":"cwolfe@usgs.gov","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":928765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruppert, Natalia A. 0000-0003-0589-1159","orcid":"https://orcid.org/0000-0003-0589-1159","contributorId":351514,"corporation":false,"usgs":true,"family":"Ruppert","given":"Natalia A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928766,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Given, Douglas D.","contributorId":351515,"corporation":false,"usgs":false,"family":"Given","given":"Douglas D.","affiliations":[{"id":84003,"text":"New Solutions","active":true,"usgs":false}],"preferred":false,"id":928767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"West, Michael E.","contributorId":351516,"corporation":false,"usgs":false,"family":"West","given":"Michael E.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":928768,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thomas, Valerie 0000-0001-6170-5563","orcid":"https://orcid.org/0000-0001-6170-5563","contributorId":222022,"corporation":false,"usgs":true,"family":"Thomas","given":"Valerie","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928769,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928770,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grapenthin, Ronni","contributorId":351517,"corporation":false,"usgs":false,"family":"Grapenthin","given":"Ronni","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":928771,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264313,"text":"70264313 - 2025 - Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model","interactions":[],"lastModifiedDate":"2025-03-11T14:33:16.317819","indexId":"70264313","displayToPublicDate":"2025-02-27T09:28:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5021,"text":"Environments","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model","docAbstract":"

Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.

","language":"English","publisher":"MDPI","doi":"10.3390/environments12030075","usgsCitation":"KarimiDermani, B., Green, C., Tick, G., Gholizadeh, H., Wei, W., and Zhang, Y., 2025, Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model: Environments, v. 12, no. 3, 75, 20 p., https://doi.org/10.3390/environments12030075.","productDescription":"75, 20 p.","ipdsId":"IP-169838","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487829,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/environments12030075","text":"Publisher Index Page"},{"id":483197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Branch","active":true,"usgs":true}],"preferred":true,"id":930396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tick, Geoffrey","contributorId":352233,"corporation":false,"usgs":false,"family":"Tick","given":"Geoffrey","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gholizadeh, Hossein","contributorId":352234,"corporation":false,"usgs":false,"family":"Gholizadeh","given":"Hossein","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930398,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wei, Wei","contributorId":352235,"corporation":false,"usgs":false,"family":"Wei","given":"Wei","affiliations":[{"id":84137,"text":"School of Environment, Nanjing Normal University, Nanjing 210023, China","active":true,"usgs":false}],"preferred":false,"id":930399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Yong","contributorId":352236,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930400,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263949,"text":"70263949 - 2025 - Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","interactions":[],"lastModifiedDate":"2025-03-26T15:59:01.447164","indexId":"70263949","displayToPublicDate":"2025-02-27T09:07:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","docAbstract":"

Thermoelectric power generation accounts for over 41% of total U.S. freshwater withdrawals, making understanding the determinants of power plants’ water withdrawals (WW) and consumption (WC) critical for reducing the sector’s reliance on increasingly scarce water resources. However, reported data inconsistencies and incomplete analysis of potential determinants of thermoelectric water use hinder such understanding. We address these challenges by introducing a novel data filtering method and a more complete assessment of water use determinants. First, we applied a power-cooling ratio as an operations-based data filter that removed operationally implausible records while retaining more original data, outperforming previous statistical filtering methods. Second, we found that different water use reporting methods (WURMs) provided statistically significantly different WW and WC values, revealing the importance of this previously unrecognized feature in reported water use records. Third, our data-driven approach showed that traditionally emphasized features─such as cooling technology and gross generation─are of primary importance but can be surpassed by other, often overlooked, features when modeling WW or WC individually. The plant configuration, cooling technology, and gross generation were the most important features of WW, whereas WURM, cooling technology, and reporting month were the most important for WC. These findings can improve thermoelectric power plant management, water use reporting accuracy, and water use modeling.

","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c02024","usgsCitation":"Sjostedt, E., Rushforth, R., Tidwell, V., Harris, M.A., McManamay, R., and Marston, L., 2025, Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use: Environmental Science & Technology, v. 59, no. 9, p. 4482-4492, https://doi.org/10.1021/acs.est.4c02024.","productDescription":"11 p.","startPage":"4482","endPage":"4492","ipdsId":"IP-160568","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":488675,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.4c02024","text":"Publisher Index Page"},{"id":482736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Sjostedt, Eric 0000-0002-7755-8091","orcid":"https://orcid.org/0000-0002-7755-8091","contributorId":351702,"corporation":false,"usgs":false,"family":"Sjostedt","given":"Eric","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":929309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":929310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tidwell, Vincent 0000-0002-4954-897X","orcid":"https://orcid.org/0000-0002-4954-897X","contributorId":351704,"corporation":false,"usgs":false,"family":"Tidwell","given":"Vincent","affiliations":[{"id":84033,"text":"Pacific Northwest National Laboratories","active":true,"usgs":false}],"preferred":false,"id":929311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Melissa A. 0000-0003-2659-9763 mharris@usgs.gov","orcid":"https://orcid.org/0000-0003-2659-9763","contributorId":1903,"corporation":false,"usgs":true,"family":"Harris","given":"Melissa","email":"mharris@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McManamay, Ryan","contributorId":205277,"corporation":false,"usgs":false,"family":"McManamay","given":"Ryan","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":929313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marston, Landon 0000-0001-9116-1691","orcid":"https://orcid.org/0000-0001-9116-1691","contributorId":239626,"corporation":false,"usgs":false,"family":"Marston","given":"Landon","email":"","affiliations":[{"id":47941,"text":"Department of Civil Engineering, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":929314,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264266,"text":"70264266 - 2025 - Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","interactions":[],"lastModifiedDate":"2025-03-10T13:53:35.39942","indexId":"70264266","displayToPublicDate":"2025-02-27T08:49:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","docAbstract":"

Greater understanding of the survival, travel time, and spatial distribution of juvenile salmonids among migration routes between their natal streams and the ocean is critical to the recovery of these threatened species. In the Sacramento–San Joaquin River Delta (Delta), a highly modified estuary in central California, USA, there is a critical need to evaluate how water management (e.g., water pumping) and environmental factors (e.g., water flow) impact these populations. While management actions can affect some environmental variables in the Delta, only recently have studies begun to uncover associations between these variables and key demographic parameters. In this study, we examine the effects of freshwater flows, water exports, tidal environment, and a temporary barrier on juvenile steelhead (Oncorhynchus mykiss) survival, travel times, and migration routing using a multiyear acoustic telemetry dataset and recent advancements in Bayesian multistate mark-recapture modeling. We found that no single covariate explained variation in juvenile steelhead population dynamics across the entire Delta, but that separate regions within the Delta showed association with specific environmental factors.

","language":"English","publisher":"Springer","doi":"10.1007/s12237-025-01493-5","usgsCitation":"Pope, A., Perry, R., Hance, D., and Buchanan, R., 2025, Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary: Estuaries and Coasts, v. 48, 75, 18 p., https://doi.org/10.1007/s12237-025-01493-5.","productDescription":"75, 18 p.","ipdsId":"IP-156547","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.03238643466244,\n 38.07227139327773\n ],\n [\n -122.03238643466244,\n 37.61108647879182\n ],\n [\n -121.16713739842452,\n 37.61108647879182\n ],\n [\n -121.16713739842452,\n 38.07227139327773\n ],\n [\n -122.03238643466244,\n 38.07227139327773\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buchanan, Rebecca A.","contributorId":300601,"corporation":false,"usgs":false,"family":"Buchanan","given":"Rebecca A.","affiliations":[{"id":65208,"text":"Columbia Basin Research, School of Aquatic and Fishery Sciences, University of Washington 1325 Fourth Avenue, Suite 1515, Seattle, Washington 98101-2540","active":true,"usgs":false}],"preferred":false,"id":930220,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266263,"text":"70266263 - 2025 - When the wild things are: Defining mammalian diel activity and plasticity","interactions":[],"lastModifiedDate":"2025-05-05T17:01:22.120034","indexId":"70266263","displayToPublicDate":"2025-02-26T09:51:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"When the wild things are: Defining mammalian diel activity and plasticity","docAbstract":"

Circadian rhythms are a mechanism by which species adapt to environmental variability and fundamental to understanding species behavior. However, we lack data and a standardized framework to accurately assess and compare temporal activity for species during rapid ecological change. Through a global network representing 38 countries, we leveraged 8.9 million mammalian observations to create a library of 14,587 standardized diel activity estimates for 445 species. We found that less than half the species’ estimates were in agreement with diel classifications from the reference literature and that species commonly used more than one diel classification. Species diel activity was highly plastic when exposed to anthropogenic change. Furthermore, body size and distributional extent were strongly associated with whether a species is diurnal or nocturnal. Our findings provide essential knowledge of species behavior in an era of rapid global change and suggest the need for a new, quantitative framework that defines diel activity logically and consistently while capturing species plasticity.

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Williamson, J., Wilting, A., Wittemyer, G., Zavaleta, A., Zellmer, A., and Gerber, B., 2025, When the wild things are: Defining mammalian diel activity and plasticity: Science Advances, v. 11, no. 9, eado3843, 15 p., https://doi.org/10.1126/sciadv.ado3843.","productDescription":"eado3843, 15 p.","ipdsId":"IP-160515","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.ado3843","text":"Publisher Index Page"},{"id":485398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Devarajan, 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  1. The nesting ecology of wild bumble bees is not well resolved, but information learned from discovered nests can be of great conservation value.
  2. Data collected at nests on foraging patterns, caste-specific behaviour and health (e.g., pathogens) are invaluable for understanding bumble bee behaviour and ecology, but difficult to decipher solely from foraging observations away from the nest.
  3. Post-senescence nest excavation allows the estimation of colony size, caste numbers, documents pest incidence and provides opportunities to examine nest material for stressors (e.g., pesticides) and to use nest material for training purposes (e.g., conservation dogs).
  4. Wild nests are often found opportunistically, and there is an absence of standardised guidance on data collection.
  5. We provide an action plan to ensure the data collection is comparable across studies.
  6. This framework includes key conservation questions and methodological guidelines for both for in situ and post-season nest data collection and is ordered by increasing complexity of data collection methods.
  7. To illustrate our framework, we provide an example with recently discovered Bombus affinis (rusty patched bumble bee) nests.
  8. Through observations at B. affinis nests, we discovered novel patterns of activity, changing activity levels over time, the timing of male and gyne production, variable timing in nest senescence, and associations of nests with past rodent activity.
  9. Although individual nest discoveries may be of limited value in forwarding conservation strategies, the aggregate collections of many similar datasets can be of critical importance for species of conservation concern.
","language":"English","publisher":"Royal Entomological Society","doi":"10.1111/icad.12808","usgsCitation":"Smith, T., Boone, M., Choy, S., Evans, E., Everett, J., Palmer, J., Pearse, I., Pugesek, G., Sadd, B.M., Szymanski, J., Tessenow, A., Watson, J., and Mola, J., 2025, Answering key bumble bee conservation questions by studying discovered wild nests: A Bombus affinis case study: Insect Conservation and Diversity, v. 18, no. 3, p. 303-318, https://doi.org/10.1111/icad.12808.","productDescription":"16 p.","startPage":"303","endPage":"318","ipdsId":"IP-160042","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":501359,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/icad.12808","text":"Publisher Index Page"},{"id":501141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Tamara","contributorId":351890,"corporation":false,"usgs":false,"family":"Smith","given":"Tamara","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":956985,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boone, Michelle","contributorId":335436,"corporation":false,"usgs":false,"family":"Boone","given":"Michelle","affiliations":[{"id":40035,"text":"U Minnesota","active":true,"usgs":false}],"preferred":false,"id":956986,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choy, Stephen","contributorId":367188,"corporation":false,"usgs":false,"family":"Choy","given":"Stephen","affiliations":[{"id":87593,"text":"US Fish & Widllife Service","active":true,"usgs":false}],"preferred":false,"id":956987,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Evans, Elaine","contributorId":335437,"corporation":false,"usgs":false,"family":"Evans","given":"Elaine","email":"","affiliations":[{"id":40035,"text":"U Minnesota","active":true,"usgs":false}],"preferred":false,"id":956988,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Everett, Jeffrey","contributorId":302932,"corporation":false,"usgs":false,"family":"Everett","given":"Jeffrey","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":956989,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Palmer, Justin","contributorId":367189,"corporation":false,"usgs":false,"family":"Palmer","given":"Justin","affiliations":[{"id":87595,"text":"Illinois State U","active":true,"usgs":false}],"preferred":false,"id":956990,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":956991,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pugesek, Genevieve","contributorId":367225,"corporation":false,"usgs":false,"family":"Pugesek","given":"Genevieve","affiliations":[{"id":37554,"text":"Xerces Society","active":true,"usgs":false}],"preferred":false,"id":956992,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sadd, Ben M.","contributorId":168980,"corporation":false,"usgs":false,"family":"Sadd","given":"Ben","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":956993,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":956994,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Tessenow, Ashley","contributorId":367191,"corporation":false,"usgs":false,"family":"Tessenow","given":"Ashley","affiliations":[{"id":87595,"text":"Illinois State U","active":true,"usgs":false}],"preferred":false,"id":956995,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Watson, Jay","contributorId":335443,"corporation":false,"usgs":false,"family":"Watson","given":"Jay","email":"","affiliations":[{"id":80407,"text":"Winsconsin DNR","active":true,"usgs":false}],"preferred":false,"id":956996,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Mola, John","contributorId":335435,"corporation":false,"usgs":false,"family":"Mola","given":"John","affiliations":[{"id":80402,"text":"Colorado State U","active":true,"usgs":false}],"preferred":false,"id":956997,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70263858,"text":"70263858 - 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Grass carp (Ctenopharyngodon idella) are nonnative, herbivorous freshwater fish that represent an ecological threat in North American waters. However, data are limited on reproductive biology specific to wild populations in midwestern North America, despite recent concern for grass carp establishment within the Great Lakes. Basic information on reproductive traits could be useful to determine reproductive potential, inform future population modelling and provide information to aid control efforts. Our objectives were to evaluate grass carp age-at-maturity, spawning season timing, fecundity type, developmental timing and spawning strategy. Additionally, we evaluated the reliability of the gonadosomatic index (GSI) as a proxy for identifying mature grass carp and compared body condition across study areas. We sampled grass carp in portions of the Mississippi River watershed within the state of Missouri and within the Lake Erie basin. Based on gonad histological samples (n = 274), grass carp are batch spawners with indeterminate fecundity and asynchronous ovarian developmental timing. This allows flexibility in their spawning, exemplified by a protracted spawning season in Lake Erie (April to November) in temperatures ranging from 12.3 to 27.3°C. Minimum observed age-at-maturity for females in Missouri and for both males and females in the Lake Erie population was age-3 and age-2 for males in Missouri. Accuracy of GSI as a measure of maturity during the spawning season was 89.7% and 87.5%, for females and males, respectively. Compared to Missouri, grass carp in Lake Erie had a significantly higher body condition, and females ready to spawn had significantly higher GSI values, suggesting that the grass carp in Lake Erie are healthier and more fecund relative to Missouri and, if left uncontrolled, potentially have a high probability of establishment within the Great Lakes. The results of this study will allow managers to identify the reproductive status of grass carp in the field and more accurately estimate populations and reproductive potential.

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Toledo","active":true,"usgs":false}],"preferred":false,"id":928722,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mapes, Robert L.","contributorId":339890,"corporation":false,"usgs":false,"family":"Mapes","given":"Robert L.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":928723,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kemp, Chris M.","contributorId":351499,"corporation":false,"usgs":false,"family":"Kemp","given":"Chris M.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":928724,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Young, Ryan T.","contributorId":351500,"corporation":false,"usgs":false,"family":"Young","given":"Ryan T.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928725,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Byrne, Michael E. 0000-0001-9190-2728 mbyrne@usgs.gov","orcid":"https://orcid.org/0000-0001-9190-2728","contributorId":272077,"corporation":false,"usgs":false,"family":"Byrne","given":"Michael","email":"mbyrne@usgs.gov","middleInitial":"E.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":928726,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70264599,"text":"70264599 - 2025 - Spatiotemporal interactions facilitate sympatry in a diverse mammalian community","interactions":[],"lastModifiedDate":"2025-03-17T14:50:01.007495","indexId":"70264599","displayToPublicDate":"2025-02-25T09:43:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal interactions facilitate sympatry in a diverse mammalian community","docAbstract":"

Understanding mechanisms underlying coexistence among potential competitors, and between predators and prey, is a persistent challenge in community ecology. Using 6 years (2013–2018) of camera-trapping data and species interaction models, we investigated the spatiotemporal patterns of inter- and intra-guild interspecific interactions in a diverse terrestrial mammalian community in Pakke Wildlife Sanctuary and Tiger Reserve (PTR), Northeast India. We found no evidence of spatial interaction among apex predators (tiger Panthera tigris, leopard Panthera pardus, and dhole Cuon alpinus). However, dholes temporally separated themselves from tigers and leopards. Among small carnivores, marbled cat (Pardofelis marmorata) and leopard cat (Prionailurus bengalensis) exhibited temporal separation, whereas leopard cat overlapped spatially and temporally with other small carnivores. Herbivores exhibited neither spatial nor temporal separation with each other. All apex predators exhibited diel activity and space-use patterns to overlap with their preferred prey. Our results suggest that the assembly of the diverse mammalian community of PTR is a complex process, and coexistence among potential competitors, and predators and prey is likely facilitated by several mechanisms including spatial and temporal segregation, and potentially dietary separation.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70125","usgsCitation":"Chaudharya, V., Goswami, V., Ri, C., Hines, J.E., and Oli, M., 2025, Spatiotemporal interactions facilitate sympatry in a diverse mammalian community: Ecosphere, v. 16, no. 2, e70125, 19 p., https://doi.org/10.1002/ecs2.70125.","productDescription":"e70125, 19 p.","ipdsId":"IP-157451","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70125","text":"Publisher Index Page"},{"id":483450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","state":"Arunachal Pradesh","otherGeospatial":"Pakke Wildlife Sanctuary and Tiger Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 92.57841423739944,\n 27.27002678200266\n ],\n [\n 92.57841423739944,\n 26.882944743097184\n ],\n [\n 93.4,\n 26.882944743097184\n ],\n [\n 93.4,\n 27.27002678200266\n ],\n [\n 92.57841423739944,\n 27.27002678200266\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Chaudharya, Vratika","contributorId":352356,"corporation":false,"usgs":false,"family":"Chaudharya","given":"Vratika","affiliations":[{"id":84184,"text":"Department of Wildlife Ecology and Conservation, Newins-Zeigler Hall, University of Florida, Gainesville, FL 32611; Corresponding author current affiliation and address: NatureServe, 2550 South Clark Street, Suite 930, Arlington, VA 22202  ","active":true,"usgs":false}],"preferred":false,"id":930927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goswami, Varun R.","contributorId":352357,"corporation":false,"usgs":false,"family":"Goswami","given":"Varun R.","affiliations":[{"id":84185,"text":"Conservation Initiatives, Guwahati 781022, Assam, India","active":true,"usgs":false}],"preferred":false,"id":930928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ri, Chandan","contributorId":352358,"corporation":false,"usgs":false,"family":"Ri","given":"Chandan","affiliations":[{"id":84186,"text":"Pakke Tiger Reserve Forest Department, Seijosa Range, Lower Seijosa, Arunachal Pradesh, India","active":true,"usgs":false}],"preferred":false,"id":930929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930930,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oli, Madan K.","contributorId":352359,"corporation":false,"usgs":false,"family":"Oli","given":"Madan K.","affiliations":[{"id":84187,"text":"Department of Wildlife Ecology and Conservation, Newins-Zeigler Hall, University of Florida, Gainesville, FL 32611","active":true,"usgs":false}],"preferred":false,"id":930931,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263914,"text":"70263914 - 2025 - Integrating data to assess occupancy patterns of an endangered bumble bee","interactions":[],"lastModifiedDate":"2025-08-04T15:35:01.969665","indexId":"70263914","displayToPublicDate":"2025-02-25T09:34:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating data to assess occupancy patterns of an endangered bumble bee","docAbstract":"

There is growing interest in integrating community science data with structured monitoring data to estimate changes in distribution patterns of imperiled species, including pollinators. However, significant challenges remain in determining how unstructured community science data should be incorporated into formal analyses of species distributions. We developed a dynamic framework for combining community science and structured monitoring data of bumble bees to estimate changes in occupancy of rusty-patched bumble bees (Bombus affinis), a federally endangered species in the United States. We applied traditional metapopulation theory and accounted for imperfect detection to estimate site-specific extirpation risk and colonization rates across the known distribution of B. affinis in the Upper Midwest (USA). Despite a 144% increase in presence-only detections from 2017 to 2022, occupancy probabilities and the estimated number of occupied sites remained static or declined slightly across a 4-state region during this period. Our results provide preliminary evidence that the probability of local extirpation risk of B. affinis increased in response to drought, but that effect was tempered with a high number of neighboring patches occupied by B. affinis (i.e., rescue effect). Our framework can be used by managers to track population recovery goals for B. affinis and other bumble bees of conservation concern. In addition, our study highlights the importance of accounting for imperfect detection and addressing spatial sampling biases in bumble bee monitoring efforts, particularly those for which a portion of the monitoring data are generated from community science projects.

","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.14458","usgsCitation":"Ellis, K.S., Otto, C., Bailey, L.L., Smith, T.A., Choy, S.J., and Hatch, L., 2025, Integrating data to assess occupancy patterns of an endangered bumble bee: Conservation Biology, v. 39, no. 4, e14458, 11 p., https://doi.org/10.1111/cobi.14458.","productDescription":"e14458, 11 p.","ipdsId":"IP-166043","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":482639,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":487711,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.14458","text":"Publisher Index Page"}],"country":"United States","state":"Illinois, Iowa, Minnesota, 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library for time resolved analysis of laser ablation inductively coupled plasma mass spectrometry data","docAbstract":"

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data has a wide variety of uses in the geosciences for in-situ chemical analysis of complex natural materials. Improvements to instrument capabilities and operating software have drastically reduced the time required to generate large volumes of data relative to previous methodologies. Raw data from LA-ICP-MS, however, is in counts per unit time (typically counts per second), not elemental concentrations and converting these count ratesto concentrations requires additional processing. For complex materials where the ablated volume may contain a range of material compositions, a moderate amount of user input is also required if appropriate concentrations are to be accurately calculated. In geologic materials such as glasses and minerals that potentially have numerous heterogeneities (e.g., microlites or other inclusions) within them, this is typically determiningwhether the total ablation signal should be filtered to remove these heterogeneities. This necessitates that the LA-ICP-MS data processing pipeline is one that is not automated, but is also designed to enable rapid and efficient processing of large volumes of data.

Here we introduce \"\" , a Python library for the time resolved analysis of LA-ICP-MS data. We outline its mathematical theory, code structure, and provide an example of how it can be used to provide the time resolved analysis necessitated by LA-ICP-MS data of complex geologic materials. Throughout the \"\" pipeline we show how metadata and data are incrementally added to the objects created such that virtually any aspect of an experiment may be interrogated and its quality assessed. We also show, that when combined with other Python libraries for building graphical user interfaces, it can be utilized outside of a pure scripting environment. \"\" can be found at https://doi.org/10.5066/P1DZUR3Z

","language":"English","publisher":"Elseiver","doi":"10.1016/j.acags.2025.100225","usgsCitation":"Lubbers, J.E., Kent, A., and Russo, C., 2025, lasertram: A Python library for time resolved analysis of laser ablation inductively coupled plasma mass spectrometry data: Applied Computing and Geosciences, v. 25 p., 100225, 16 p., https://doi.org/10.1016/j.acags.2025.100225.","productDescription":"100225, 16 p.","ipdsId":"IP-168201","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488954,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2025.100225","text":"Publisher Index Page"},{"id":482732,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25 p.","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lubbers, Jordan Edward 0000-0002-3566-5091","orcid":"https://orcid.org/0000-0002-3566-5091","contributorId":330466,"corporation":false,"usgs":true,"family":"Lubbers","given":"Jordan","email":"","middleInitial":"Edward","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":929191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kent, Adam J.R.","contributorId":351642,"corporation":false,"usgs":false,"family":"Kent","given":"Adam J.R.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":929192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russo, Chris","contributorId":351643,"corporation":false,"usgs":false,"family":"Russo","given":"Chris","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":929193,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269046,"text":"70269046 - 2025 - Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds.","interactions":[],"lastModifiedDate":"2025-07-15T15:10:57.061077","indexId":"70269046","displayToPublicDate":"2025-02-24T09:31:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds.","docAbstract":"
Climate change is predicted to intensify lake algal blooms globally and result in regime shifts. However, observed increases in algal biomass do not consistently correlate with air temperature or precipitation, and evidence is lacking for a causal effect of climate or the nonlinear dynamics needed to demonstrate regime shifts. We modeled the causal effects of climate on annual lake chlorophyll (a measure of algal biomass) over 34 y for 24,452 lakes across broad ecoclimatic zones of the United States and evaluated the potential for regime shifts. We found that algal biomass was causally related to climate in 34% of lakes. In these cases, 71% exhibited abrupt but mostly temporary shifts as opposed to persistent changes, 13% had the potential for regime shifts. Climate was causally related to algal biomass in lakes experiencing all levels of human disturbance, but with different likelihood. Climate causality was most likely to be observed in lakes with minimal human disturbance and cooler summer temperatures that have increased over the 34 y studied. Climate causality was variable in lakes with low to moderate human disturbance, and least likely in lakes with high human disturbance, which may mask climate causality. Our results explain some of the previously observed heterogeneous climate responses of lake algal biomass globally and they can be used to predict future climate effects on lakes.
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terrains","interactions":[],"lastModifiedDate":"2025-03-19T13:12:59.597752","indexId":"70264627","displayToPublicDate":"2025-02-23T08:21:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"James Buttle review: The characteristics of baseflow resilience across diverse ecohydrological terrains","docAbstract":"The dynamic storage of aquifers is the portion of groundwater that can potentially drain to any given point along a stream to create baseflow. Baseflow typically occurs year-round in perennial streams, though the characteristics and stability of dynamic storage are often most important to instream processes during extended dry periods (without precipitation and snowmelt) when runoff and quickflows are minimised. The term ‘baseflow resilience’ is defined for this review as the tendency of baseflow in streams to maintain a consistent volume and water quality year to year while under stress from climate variability and extremes, along with anthropogenic stressors such as water withdrawals, land use change, and water quality degradation. ‘Baseflow resilience’ has, in part, a user-defined meaning spanning water supply and water quality variables of primary interest. Watershed characteristics that directly impact resilience can often produce non-intuitive feedbacks that enhance some attributes of baseflow while simultaneously impairing others. For example, permeable stream corridor geology creates strong stream-groundwater hydrologic connectivity, yet fast groundwater drainage via preferential high-permeability flowpaths can lead to streamflow not being sustained during extended dry periods. Also, shallow groundwater sources are generally more immediately vulnerable to extreme events, warming, salinization, transpiration, and precipitation drought, compared to deeper groundwater. Yet baseflow drought in streams influenced by deeper groundwater can lag precipitation drought by years, and contaminant legacies may propagate through deep groundwater flowpaths to receiving waters for decades to centuries. Finally, irrigation withdrawals can intercept groundwater that would have drained to streams, and the application of irrigation may leach contaminants from the soil zone by unnaturally raising water tables, yet irrigation return flows can sustain baseflow and groundwater-dependent habitats in semiarid areas. This review covers the concept of hydrologic resilience in the context of stream baseflow processes and summarises the common hydrogeological controls on, and multiscale stressors of, dynamic groundwater storage. Further, we present several quantitative metrics to assess a range of water supply to water quality baseflow characteristics using both broadly available and boutique data types, a subset of which are demonstrated using data from the Delaware River Basin, USA.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70101","usgsCitation":"Briggs, M., Newman, C.P., Benton, J., Rey, D., Konrad, C., Ouellet, V., Torgersen, C.E., Gruhn, L.R., Fleming, B.J., Gazoorian, C.L., and Doctor, D.H., 2025, James Buttle review: The characteristics of baseflow resilience across diverse ecohydrological terrains: Hydrological Processes, v. 39, e70101, 21 p., https://doi.org/10.1002/hyp.70101.","productDescription":"e70101, 21 p.","ipdsId":"IP-172740","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":488334,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70101","text":"Publisher Index Page"},{"id":483482,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.72740040176602,\n 40.21569215354026\n ],\n [\n -75.72740040176602,\n 39.17926830473752\n ],\n [\n -74.59478267456883,\n 39.17926830473752\n ],\n [\n -74.59478267456883,\n 40.21569215354026\n ],\n [\n -75.72740040176602,\n 40.21569215354026\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2025-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - 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2025 - Heterogeneity of locked‐pasture snow conditions modulate habitat and movement choices of a facultative migrant","interactions":[],"lastModifiedDate":"2025-05-21T13:59:10.296747","indexId":"70267360","displayToPublicDate":"2025-02-22T08:53:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Heterogeneity of locked‐pasture snow conditions modulate habitat and movement choices of a facultative migrant","docAbstract":"

Habitat selection and movement are key mechanisms by which animals can respond to and potentially cope with highly variable environmental conditions. Optimal responses likely vary, however, depending on the severity and scope of conditions. We tested this hypothesis using a facultative migrant species, the Great Gray Owl (Strix nebulosa), which exhibits high inter- and intra-individual variation in the timing, direction, and distance of winter movements. Specifically, we evaluated whether episodic, spatiotemporally variable “locked-pasture” snow conditions, which restrict access to subnivean food, prompted shifts in habitat selection or long-distance movements by owls. We quantified the movement of 42 owls using global positioning system (GPS) data within the Greater Yellowstone Ecosystem, USA, during 2017–2022. We used a novel ecological application of SnowModel, a snow evolution modeling system, to estimate fine-scale, physical snow properties likely to influence access to prey. Variables included snow depth, snow crusts produced by wind, and ice crusts produced by melt-freeze and rain-on-snow events. Owls avoided heterogeneously distributed wind crusts via local shifts in habitat selection. More homogenous ice crusts elicited long-distance movements away from affected home ranges. Finally, owls employed both proximate shifts in habitat selection and long-distance movements to avoid deeper snow. Ultimately, owls exhibited behavioral flexibility in response to limiting snow conditions that can vary in terms of severity, spatial extent, and duration. Such behavioral responses determine species distribution, with implications for population and community dynamics in spatiotemporally variable systems. Understanding the effects of, and responses to, environmental controls is increasingly important given the scope of on-going global change.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.70925","usgsCitation":"Gura, K., Liston, G.E., Reinking, A., Bedrosian, B., Elder, K., and Chalfoun, A.D., 2025, Heterogeneity of locked‐pasture snow conditions modulate habitat and movement choices of a facultative migrant: Ecology and Evolution, v. 15, no. 2, e70925, 18 p., https://doi.org/10.1002/ece3.70925.","productDescription":"e70925, 18 p.","ipdsId":"IP-174996","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.70925","text":"Publisher Index Page"},{"id":486280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.012,\n 44.672\n ],\n [\n -112.012,\n 42.92\n ],\n [\n -109.63,\n 42.92\n ],\n [\n -109.63,\n 44.672\n ],\n [\n -112.012,\n 44.672\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Gura, Katherine","contributorId":333836,"corporation":false,"usgs":false,"family":"Gura","given":"Katherine","email":"","affiliations":[],"preferred":false,"id":937966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liston, Glen E.","contributorId":26244,"corporation":false,"usgs":true,"family":"Liston","given":"Glen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":937967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reinking, Adele K.","contributorId":348037,"corporation":false,"usgs":false,"family":"Reinking","given":"Adele K.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bedrosian, Bryan","contributorId":199738,"corporation":false,"usgs":false,"family":"Bedrosian","given":"Bryan","affiliations":[{"id":35591,"text":"Teton Raptor Center","active":true,"usgs":false}],"preferred":false,"id":937969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elder, Kelly","contributorId":174398,"corporation":false,"usgs":false,"family":"Elder","given":"Kelly","email":"","affiliations":[{"id":5121,"text":"U.S. Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843","active":true,"usgs":false}],"preferred":false,"id":937970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937971,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264062,"text":"70264062 - 2025 - Did the Aleutian Basin form by plate capture or backarc basin opening?","interactions":[],"lastModifiedDate":"2025-07-09T15:56:31.474959","indexId":"70264062","displayToPublicDate":"2025-02-22T08:34:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2020,"text":"International Geology Review","active":true,"publicationSubtype":{"id":10}},"title":"Did the Aleutian Basin form by plate capture or backarc basin opening?","docAbstract":"

The origin of the Aleutian Basin is unresolved because its crust is deeply buried beneath sediments. It has been interpreted as forming in the Eocene when the Beringian convergent margin jumped seaward to south of the Aleutian arc, thereby capturing a large sector of Cretaceous Pacific crust. Alternatively, it may have formed by backarc spreading. We present new magnetic and seismic reflection data compilations and review other pertinent data to evaluate these two possibilities. Arguments for entrapment are: 1) Palaeomagnetic and geologic data document that the Aleutian arc formed approximately in place and as a seaward, on-strike continuation of the Alaska Peninsula; 2) basin-central spreading anomalies trend N-S, normal to the Aleutian arc and exhibit amplitudes and lengths typically formed at mid-ocean ridges; 3) seismic reflection profiles document that cross-basin depositional sequences are like those expected of filling a fixed-width basin. Arguments for a backarc spreading origin include: 1) The perpendicular orientation of magnetic anomalies in the Aleutian Basin and the northern Pacific Plate suggest different origins; 2) the sub-parallel orientation of Aleutian Basin spreading magnetic fabric and Palaeogene rift basins on the Bering Shelf suggest a common extensional regime; 3) thinner continental crust beneath outer-shelf basins is consistent with extension; 4) lineated magnetic fabrics on the margins of the basin are similar to early rifting magnetic fabrics found in other backarc basins; 5) basin heat flow is consistent with Palaeogene seafloor spreading; 6) its marginal basin setting is analogous to marginal basins elsewhere that formed by extension and seafloor spreading, not entrapment; and 7) entrapment requires formation of the Aleutian subduction zone by transference, a mode of subduction initiation that has not yet been documented. We present evidence for both interpretations and underscore the need for continued data collection (e.g. scientific drilling) to test competing hypotheses. Similar approaches may be useful for understanding thickly sedimented marginal basins elsewhere.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00206814.2025.2467447","usgsCitation":"Stern, R.J., Scholl, D., Malkowski, M., Martin, K., Barth, G., and Scheirer, D.S., 2025, Did the Aleutian Basin form by plate capture or backarc basin opening?: International Geology Review, v. 67, no. 13, p. 1697-1719, https://doi.org/10.1080/00206814.2025.2467447.","productDescription":"23 p.","startPage":"1697","endPage":"1719","ipdsId":"IP-157041","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":482901,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","state":"Alaska","otherGeospatial":"Bering Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -176.25963109659818,\n 70.19147740706583\n ],\n [\n -176.25963109659818,\n 58.60834104749597\n ],\n [\n -163.92859263594565,\n 58.60834104749597\n ],\n [\n -163.92859263594565,\n 70.19147740706583\n ],\n [\n -176.25963109659818,\n 70.19147740706583\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"67","issue":"13","noUsgsAuthors":false,"publicationDate":"2025-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Robert J.","contributorId":204361,"corporation":false,"usgs":false,"family":"Stern","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":929633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scholl, David W.","contributorId":351869,"corporation":false,"usgs":false,"family":"Scholl","given":"David W.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":929634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Malkowski, Matthew A.","contributorId":221753,"corporation":false,"usgs":false,"family":"Malkowski","given":"Matthew A.","affiliations":[{"id":40415,"text":". Department of Geological Sciences, Stanford University, Stanford CA 94305","active":true,"usgs":false}],"preferred":false,"id":929635,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Kylara M.","contributorId":351871,"corporation":false,"usgs":false,"family":"Martin","given":"Kylara M.","affiliations":[{"id":64648,"text":"California State University, East Bay","active":true,"usgs":false}],"preferred":false,"id":929636,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barth, Ginger 0000-0003-0867-7799 gbarth@usgs.gov","orcid":"https://orcid.org/0000-0003-0867-7799","contributorId":264955,"corporation":false,"usgs":true,"family":"Barth","given":"Ginger","email":"gbarth@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":929637,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scheirer, Daniel S. 0000-0001-8015-7072 dscheirer@usgs.gov","orcid":"https://orcid.org/0000-0001-8015-7072","contributorId":214825,"corporation":false,"usgs":true,"family":"Scheirer","given":"Daniel","email":"dscheirer@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":929638,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}