{"pageNumber":"48","pageRowStart":"1175","pageSize":"25","recordCount":41025,"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":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of 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":70275608,"text":"70275608 - 2025 - Mammalian responses to select Type 3 Watershed Experiment prescriptions in the Olympic Experimental State Forest: A camera-based monitoring approach","interactions":[],"lastModifiedDate":"2026-05-05T14:44:15.804205","indexId":"70275608","displayToPublicDate":"2025-03-01T09:32:24","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":24026,"text":"Study Plan","active":true,"publicationSubtype":{"id":2}},"title":"Mammalian responses to select Type 3 Watershed Experiment prescriptions in the Olympic Experimental State Forest: A camera-based monitoring approach","docAbstract":"The Type 3 Watershed Experiment is a landscape-scale management experiment designed to assess the ecological, economic, and social benefits of timber harvest and post-harvest management prescriptions in upland and riparian systems of the Washington Coast Range ecoregion. The experiment is being conducted on state trust lands in the Olympic Experimental State Forest (OESF), which are managed by the Washington Department of Natural Resources (WADNR). A wide variety of abiotic and biotic responses are being studied for the experiment, but no current studies include mammalian responses. The goal of this study is to use camera traps to assess patterns of medium and large mammal use of treatment blocks associated with the Type 3 Watershed Experiment, including the Complex Early Seral (CES) prescription and Variable Retention Harvest (VRH) prescription, in comparison to unharvested second growth forests.
We are using a block design to assess responses of the medium and large mammal community to the CES and VRH prescriptions. Target taxa include Columbia black-tailed deer, Roosevelt elk, black bear, cougar, coyote, bobcat, fisher, and other medium sized mammals of interest. Mammalian use at the management prescription sites will be compared to use at the unharvested control sites (second growth forest stands). We will conduct an initial study in 2024─2025 for proof of concept after a pilot period of camera deployment. We anticipate that the cameras will be deployed continuously year-round for at least three years and possibly longer to assess longer-term patterns of use of these prescriptions. The project includes 48 cameras deployed evenly across CES, VRH, and control units. All our camera deployments are collocated at sites where avian monitoring is also occurring, and several also overlap with areas where the amphibian community is being surveyed, providing spatial linkages among different wildlife datasets.
We will assess mammal community responses to forest management prescriptions using a variety of approaches. For the different prescriptions, we will examine the occurrence of a suite of species, measure species richness (alpha and beta diversity), estimate relative use, and quantify species persistence (i.e., the amount of time a species spends in a given prescription). We will estimate the influence of select habitat conditions on individual species use and on total species richness using mixed-effects models.
The results of this study will provide estimates of initial responses of the mammalian community to the focal management prescriptions and serve as baseline data for long-term monitoring. This research will provide natural resource managers with a better understanding of how the mammalian community responds to and uses different types of habitats after timber harvest operations and will assess how those patterns change over time as vegetation develops under different silviculture prescriptions.
","language":"English","publisher":"Washington State Department of Natural Resources","usgsCitation":"Goodwin, K.R., Martens, K.D., Minkova, T.V., and McCaffery, R.M., 2025, Mammalian responses to select Type 3 Watershed Experiment prescriptions in the Olympic Experimental State Forest: A camera-based monitoring approach: Study Plan, 28 p.","productDescription":"28 p.","ipdsId":"IP-187709","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":503990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":503977,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.onrc.washington.edu/t3-watershed-experiment/"}],"country":"United States","state":"Washington","otherGeospatial":"Olympic Experimental State Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4257290574183,\n 47.831205210887674\n ],\n [\n -123.85941261029481,\n 47.831205210887674\n ],\n [\n -123.85941261029481,\n 47.48687438181494\n ],\n [\n -124.4257290574183,\n 47.48687438181494\n ],\n [\n -124.4257290574183,\n 47.831205210887674\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goodwin, Katy R 0000-0003-3991-3141","orcid":"https://orcid.org/0000-0003-3991-3141","contributorId":334647,"corporation":false,"usgs":true,"family":"Goodwin","given":"Katy","email":"","middleInitial":"R","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":961039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martens, Kyle D","contributorId":371104,"corporation":false,"usgs":false,"family":"Martens","given":"Kyle","middleInitial":"D","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":961040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minkova, Teodora V","contributorId":371105,"corporation":false,"usgs":false,"family":"Minkova","given":"Teodora","middleInitial":"V","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":961041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCaffery, Rebecca M. 0000-0002-0396-0387","orcid":"https://orcid.org/0000-0002-0396-0387","contributorId":211539,"corporation":false,"usgs":true,"family":"McCaffery","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":961042,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"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":70264813,"text":"70264813 - 2025 - Natural capital accounting on forested lands: An application to the Colorado River basin","interactions":[],"lastModifiedDate":"2025-03-25T14:20:01.525675","indexId":"70264813","displayToPublicDate":"2025-03-01T09:10:39","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Natural capital accounting on forested lands: An application to the Colorado River basin","docAbstract":"This paper creates a first set of forest natural capital accounts and demonstrates how these accounts can be integrated with general equilibrium models of the economy. Focusing on the Colorado River Basin, we show that deforestation has direct implications for the forest industry and indirect impacts on the economy through water treatment costs and carbon stock. 327,000 acres of forest are projected to be lost to development by 2100, representing a loss of 1.3 million tons of carbon stored in forests. The direct economic impacts associated with forest loss are estimated to be over $30 million, with $28 million of that coming directly from the value of lost carbon.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Measuring and accounting for environmental public goods: A national accounts perspective","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"University of Chicago Press","collaboration":"USDA Forest Service, North Carolina State University, USDA ERS, U of Tennessee Knoxville","usgsCitation":"Warziniack, T., Bagstad, K.J., Knowles, M., Mihiar, C., Nehra, A., Rhodes, C., Sanchez, L., Sichko, C., and Sims, C., 2025, Natural capital accounting on forested lands: An application to the Colorado River basin, in Measuring and accounting for environmental public goods: A national accounts perspective, p. 191-214.","productDescription":"24 p.","startPage":"191","endPage":"214","ipdsId":"IP-150771","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":483773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Muller, Nicholas Z.","contributorId":352667,"corporation":false,"usgs":false,"family":"Muller","given":"Nicholas Z.","affiliations":[],"preferred":false,"id":931958,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Fenichel, Eli P.","contributorId":177324,"corporation":false,"usgs":false,"family":"Fenichel","given":"Eli","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":931959,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Bohman, Mary","contributorId":352668,"corporation":false,"usgs":false,"family":"Bohman","given":"Mary","affiliations":[],"preferred":false,"id":931960,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Warziniack, Travis 0000-0002-9431-2288","orcid":"https://orcid.org/0000-0002-9431-2288","contributorId":217841,"corporation":false,"usgs":false,"family":"Warziniack","given":"Travis","email":"","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":931807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":931808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knowles, Michael","contributorId":352586,"corporation":false,"usgs":false,"family":"Knowles","given":"Michael","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":931809,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mihiar, Christopher","contributorId":352587,"corporation":false,"usgs":false,"family":"Mihiar","given":"Christopher","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":931810,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nehra, Arpita","contributorId":352588,"corporation":false,"usgs":false,"family":"Nehra","given":"Arpita","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":931811,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rhodes, Charles 0000-0002-9040-3684","orcid":"https://orcid.org/0000-0002-9040-3684","contributorId":245881,"corporation":false,"usgs":true,"family":"Rhodes","given":"Charles","email":"","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":931812,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sanchez, Leslie","contributorId":352589,"corporation":false,"usgs":false,"family":"Sanchez","given":"Leslie","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":931813,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sichko, Christopher","contributorId":352590,"corporation":false,"usgs":false,"family":"Sichko","given":"Christopher","affiliations":[{"id":84276,"text":"USDA ERS","active":true,"usgs":false}],"preferred":false,"id":931814,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sims, Charles B.","contributorId":352591,"corporation":false,"usgs":false,"family":"Sims","given":"Charles B.","affiliations":[{"id":63836,"text":"University of Tennessee, Knoxville","active":true,"usgs":false}],"preferred":false,"id":931815,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70263980,"text":"70263980 - 2025 - Pesticide contamination detected across five wildlife refuges in the Sacramento Valley of California","interactions":[],"lastModifiedDate":"2025-03-04T15:11:12.24565","indexId":"70263980","displayToPublicDate":"2025-03-01T09:07:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Pesticide contamination detected across five wildlife refuges in the Sacramento Valley of California","docAbstract":"An important goal for the applied ecological sciences is to understand the extent to which the biodiversity on conserved or managed lands is exposed to anthropogenic stressors. Among the various categories of conserved lands in the United States, the National Wildlife Refuge System is focused on the protection and management of native wildlife and plant populations. Refuge lands can be associated with wetlands and, especially in California, tend to be near areas of intense human use, including agriculture. The proximity of refuges to cultivated lands results in potential exposure to pesticide drift, which increases the possibility of non-target effects on plants and wildlife. We used a passive sampler approach to quantify aerial pesticide deposition during 28 days in spring of 2023, across five refuges in the Sacramento Valley of California. Pesticides were detected across all refuges and plots within the study area, with 36 individual compounds identified, including insecticides, herbicides, and fungicides, from 176 silicone bands from 49 plots. Distance to agriculture was not an important predictor in our models for most indices of contamination. However, our models revealed that the nearest crop type was an important mediator of the importance of distance to agriculture, as fungicide contamination decreased with distance to agriculture only when orchards were the nearest crop type. The overall lack of distance effects suggests that the interior of refuges in the study area do not necessarily provide more protection from pesticides than refuge edges, although larger refuges could offer more protection. We currently lack information on how the level of contamination found herein is biologically relevant to wildlife, and ongoing monitoring efforts have found productive communities of insects in the study area. The results of this study provide a foundation for future investigations on refuges to analyze impacts of specific pesticide compounds on plants and wildlife.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.178991","usgsCitation":"Lenard, A., Burns, T., Hladik, M.L., Keller, K., Marcum, S., Thogmartin, W.E., and Forister, M.L., 2025, Pesticide contamination detected across five wildlife refuges in the Sacramento Valley of California: Science of the Total Environment, v. 969, 178991, 11 p., https://doi.org/10.1016/j.scitotenv.2025.178991.","productDescription":"178991, 11 p.","ipdsId":"IP-172823","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":487280,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.178991","text":"Publisher Index Page"},{"id":482793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.37300689853365,\n 39.68687752157848\n ],\n [\n -122.37300689853365,\n 38.24719803978664\n ],\n [\n -121.17657922197687,\n 38.24719803978664\n ],\n [\n -121.17657922197687,\n 39.68687752157848\n ],\n [\n -122.37300689853365,\n 39.68687752157848\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"969","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lenard, Angie 0000-0002-0085-0473","orcid":"https://orcid.org/0000-0002-0085-0473","contributorId":344593,"corporation":false,"usgs":false,"family":"Lenard","given":"Angie","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":929408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Therese 0000-0003-0213-6094","orcid":"https://orcid.org/0000-0003-0213-6094","contributorId":344594,"corporation":false,"usgs":false,"family":"Burns","given":"Therese","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keller, Kaylene","contributorId":351762,"corporation":false,"usgs":false,"family":"Keller","given":"Kaylene","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marcum, Samantha","contributorId":351763,"corporation":false,"usgs":false,"family":"Marcum","given":"Samantha","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929412,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":929413,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Forister, Matthew L.","contributorId":173758,"corporation":false,"usgs":false,"family":"Forister","given":"Matthew","email":"","middleInitial":"L.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":929414,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"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":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"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 J. 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","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes 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":70267311,"text":"70267311 - 2025 - Living on the edge: Identifying demographic bottlenecks in an isolated sage-grouse population","interactions":[],"lastModifiedDate":"2025-05-20T16:04:38.811631","indexId":"70267311","displayToPublicDate":"2025-03-01T08:59:27","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Living on the edge: Identifying demographic bottlenecks in an isolated sage-grouse population","docAbstract":"The greater sage-grouse (Centrocercus urophasianus: hereafter sage-grouse) population in Modoc County California is geographically isolated and contains a single lek (from 56 leks in the 1940s), despite significant efforts to increase the population through translocations and habitat improvement. Repeated wildfire within the landscape has led to an increase in invasive annual grasses and a decrease in sagebrush (Artemisia sp.) cover in important nesting and brood-rearing habitat. We estimated survival for adult females, nests, and chicks and assessed biotic characteristics that may influence these survival estimates to identify factors that may be limiting population growth. We monitored 37 female sage-grouse marked with GPS PTTS, 39 nests, and 8 broods for 3 years (2019–2021). We measured vegetation characteristics for nests at the microsite and landscape scale to evaluate effects on daily nest survival (DNS). We used survival rates from all life stages to parameterize matrix models and estimate population growth rate. Mean nest success over 3 years was 29% (95% CI: 17.1–44.8) across a 29-day incubation period and DNS declined as the proportion of both medusahead Taeniatherum caput-medusae and Japanese brome Bromus japonicus around the nest increased. Across all three years, mean chick survival across a 54-day period was 44% (95% CI: 0.9–72.3) and mean annual survival for adult females was 29% (95% CI: 17.8–43.7). Our estimated vital rates were 45–55% lower than distribution-wide estimates and the projected population growth rate was strongly declining (0.411, 95% CI: 0.30–0.52). Our results suggested recent cover changes associated with wildfire on the study area may have had a detrimental effect on this population across all life stages, and if attention is not given to preventing the transition from sagebrush communities into invasive grasslands, this population and others in degraded landscapes may not persist.
","language":"English","publisher":"Wiley","doi":"10.1002/wlb3.01242","usgsCitation":"Sink, C., Dugger, K., Hagen, C., and Vradenburg, J., 2025, Living on the edge: Identifying demographic bottlenecks in an isolated sage-grouse population: Wildlife Biology, v. 2025, no. 2, e01242, 18 p., https://doi.org/10.1002/wlb3.01242.","productDescription":"e01242, 18 p.","ipdsId":"IP-158671","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489733,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wlb3.01242","text":"Publisher Index Page"},{"id":486226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Modoc County","otherGeospatial":"Clear LakeNational Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.24706523722875,\n 41.936621403090356\n ],\n [\n -121.24706523722875,\n 41.792308909369126\n ],\n [\n -121.02561950743748,\n 41.792308909369126\n ],\n [\n -121.02561950743748,\n 41.936621403090356\n ],\n [\n -121.24706523722875,\n 41.936621403090356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2025","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Sink, Chelsea E.","contributorId":355595,"corporation":false,"usgs":false,"family":"Sink","given":"Chelsea E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":937695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, Christian A.","contributorId":355597,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":937697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vradenburg, John N.","contributorId":355598,"corporation":false,"usgs":false,"family":"Vradenburg","given":"John N.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":937698,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"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":70267776,"text":"70267776 - 2025 - Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids","interactions":[],"lastModifiedDate":"2025-05-30T15:24:10.593599","indexId":"70267776","displayToPublicDate":"2025-03-01T08:17:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1577,"text":"Environmetrics","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids","docAbstract":"Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer (Odocoileus virginianus) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.
","language":"English","publisher":"Wiley","doi":"10.1002/env.2901","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2025, Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids: Environmetrics, v. 36, no. 2, e2901, 14 p., https://doi.org/10.1002/env.2901.","productDescription":"e2901, 14 p.","ipdsId":"IP-166539","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490648,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/env.2901","text":"Publisher Index Page"},{"id":489259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.82700363741058,\n 43.768147806390715\n ],\n [\n -90.82700363741058,\n 42.50633570600206\n ],\n [\n -89.0890731993092,\n 42.50633570600206\n ],\n [\n -89.0890731993092,\n 43.768147806390715\n ],\n [\n -90.82700363741058,\n 43.768147806390715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"36","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":356170,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":356171,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":938823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":356173,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":356175,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":938825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":938826,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":356177,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938827,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"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 C. 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","middleInitial":"C.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","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":70264011,"text":"70264011 - 2025 - The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought","interactions":[],"lastModifiedDate":"2025-03-04T15:45:33.410255","indexId":"70264011","displayToPublicDate":"2025-02-28T08:38:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought","docAbstract":"Quantitative understanding is lacking on how the depth of active groundwater circulation in\nbedrock affects mountain streamflow response to a multi‐year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage‐limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL112927","usgsCitation":"Carroll, R., Manning, A.H., and Williams, K., 2025, The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought: Geophysical Research Letters, v. 52, no. 4, e2024GL112927, 12 p., https://doi.org/10.1029/2024GL112927.","productDescription":"e2024GL112927, 12 p.","ipdsId":"IP-171471","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":487740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl112927","text":"Publisher Index Page"},{"id":482802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Copper Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.1775889108842,\n 39.499761316913094\n ],\n [\n -106.1775889108842,\n 39.45001492350738\n ],\n [\n -106.1485831457982,\n 39.45001492350738\n ],\n [\n -106.1485831457982,\n 39.499761316913094\n ],\n [\n -106.1775889108842,\n 39.499761316913094\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Rosemary W.H.","contributorId":336921,"corporation":false,"usgs":false,"family":"Carroll","given":"Rosemary W.H.","affiliations":[{"id":55475,"text":"Desert Research Institute, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":929470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":929471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Kenneth H.","contributorId":336926,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth H.","affiliations":[{"id":80914,"text":"Rocky Mountain Biological Laboratory, Gothic, CO","active":true,"usgs":false}],"preferred":false,"id":929472,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"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.
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Division","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":70263881,"text":"70263881 - 2025 - Pan-amphibia distribution of the fungal parasite Batrachochytrium dendrobatidis varies with species and temperature","interactions":[],"lastModifiedDate":"2025-02-27T15:37:49.020444","indexId":"70263881","displayToPublicDate":"2025-02-26T09:24:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Pan-amphibia distribution of the fungal parasite Batrachochytrium dendrobatidis varies with species and temperature","title":"Pan-amphibia distribution of the fungal parasite Batrachochytrium dendrobatidis varies with species and temperature","docAbstract":"Batrachochytrium dendrobatidis (Bd) is a globally distributed fungal pathogen of amphibians that has contributed to one of the largest disease-related biodiversity losses in wildlife. Bd is regularly viewed through the lens of a global wildlife epizootic because the spread of highly virulent genetic lineages has resulted in well-documented declines and extinctions of multiple amphibian species. However, the current state of Bd occurrence, host range, host impacts, and ecological drivers remains poorly understood outside of the most negatively affected amphibian species and regions. Our objective was to describe the macroecology of Bd occurrence and infection intensity on caudates (salamanders) across the United States and to compare these patterns with better-studied anurans (frogs and toads). We collected swabs from 11,183 amphibians at 609 sites from 54 species across the United States from 2015 to 2017. We analyzed the prevalence and intensity of Bd infection jointly using a Bayesian hurdle model with covariates of site-level temperature and precipitation, as well as individual characteristics and species identification. Bd was distributed widely across sites and species sampled across the spatial extent of the conterminous United States. We found that Bd prevalence and intensity were most strongly influenced by temperature in the month preceding sampling and by differences among taxon groups. We estimated that temperature had a strong and nonlinear influence on both Bd prevalence and intensity with peak infection at intermediate temperatures and lower infection at low and high temperatures. We found Caudate hosts tended to have higher prevalence than Anuran hosts and Anuran hosts tended to have higher intensity at optimal temperatures for Bd infection. Our findings suggest that Bd has an amphibian-wide host range, temperature gradients exert a strong influence on Bd, and enzootic transmission likely encompasses a much larger spatial and species distribution than previously recognized across North America.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.70001","usgsCitation":"Grear, D.A., Adams, M.J., Backlin, A.R., Barichivich, W., Brand, A., Bucciarelli, G.M., Calhoun, D.L., Chestnut, T., Davenport, J.D., Dietrich, A., DiRenzo, G.V., Fisher, R., Glorioso, B., Campbell Grant, E.H., Halstead, B., Hayes, M.P., Hossack, B., Kain, M., Kleeman, P.M., Lorch, J., McCreary, B., Miller, D., Mosher, B., Muths, E., Pearl, C., Robinson, C., Roth, M., Rowe, J., Sadinski, W., Sigafus, B., Stasiak, I., Sweet, S., Waddle, H., Walls, S., Watkins-Colwell, G.J., Williams, L.A., and Winzeler, M., 2025, Pan-amphibia distribution of the fungal parasite Batrachochytrium dendrobatidis varies with species and temperature: Ecological Monographs, v. 95, no. 1, e70001, 27 p., https://doi.org/10.1002/ecm.70001.","productDescription":"e70001, 27 p.","ipdsId":"IP-156782","costCenters":[{"id":456,"text":"National Wildlife Health 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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":70265842,"text":"70265842 - 2025 - Management and natural regeneration in multiple ponderosa pine forests of the southwestern United States","interactions":[],"lastModifiedDate":"2025-06-12T15:36:39.107884","indexId":"70265842","displayToPublicDate":"2025-02-25T08:51:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1688,"text":"Forest Science","active":true,"publicationSubtype":{"id":10}},"title":"Management and natural regeneration in multiple ponderosa pine forests of the southwestern United States","docAbstract":"Management treatments in ponderosa pine forests of the southwestern United States (SWUS) are largely done for wildfire mitigation and restoration to lower tree densities. However, lack of natural ponderosa pine regeneration in undisturbed forests (i.e., no occurrence of stand-replacing events) may require management treatments to promote regeneration. We conducted a field and modeling study in 77 ponderosa pine forests across 7 SWUS locations, with the goal of evaluating management impacts on recent natural regeneration (∼\">∼20 y). We categorized management into 3 broad categories: unmanaged, thinned from above and/or below (thinning), and thinned +\">+ understory burned (burning). Although climate suitability declined from 1981-2020, management treatments – especially burning – promoted natural regeneration. High density regeneration, an undesirable outcome, occurred in 21%\">% of managed sites. In addition to effects on near-surface temperature and soil moisture, management conducive to natural regeneration was associated with the density of competing tree species, understory litter and debris cover, and adult tree cone production. Natural regeneration occurred ∼\">∼5-10 y following management, underscoring sustained effects of management treatments on tree reproduction success. Our results show that forest management treatments have the potential to promote natural ponderosa pine regeneration in the SWUS, sometimes at undesirable high densities. Study Implications: Natural ponderosa pine regeneration is declining in forests of the southwestern United States (SWUS), and may increasingly be incorporated as a goal of forest management treatments. Across a diverse set of managed and unmanaged SWUS forest sites, we found that contemporary management treatments – especially thinning +\">+ prescribed understory burning – supported natural ponderosa pine regeneration over the past two decades, which were climatically unfavorable in much of the region. Our results show that existing forest management treatments have the potential to promote natural ponderosa pine regeneration in the SWUS, but will require assessment and modification through time to remain effective.
","language":"English","publisher":"Springer","doi":"10.1007/s44391-025-00013-z","collaboration":"US Forest Service","usgsCitation":"Petrie, M., Hubbard, R.M., Bradford, J., Kolb, T.E., Noel, A.R., Schlaepfer, D.R., Bowen, M., Fuller, L., and Moser, W., 2025, Management and natural regeneration in multiple ponderosa pine forests of the southwestern United States: Forest Science, v. 71, p. 203-230, https://doi.org/10.1007/s44391-025-00013-z.","productDescription":"28 p.","startPage":"203","endPage":"230","ipdsId":"IP-153306","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":484676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","noUsgsAuthors":false,"publicationDate":"2025-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Petrie, Matthew D.","contributorId":206328,"corporation":false,"usgs":false,"family":"Petrie","given":"Matthew D.","affiliations":[{"id":37312,"text":"Department of Plant & Environmental Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":933720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, Robert M. 0000-0003-2601-1798","orcid":"https://orcid.org/0000-0003-2601-1798","contributorId":334944,"corporation":false,"usgs":false,"family":"Hubbard","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":80290,"text":"USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80521, USA","active":true,"usgs":false}],"preferred":false,"id":933721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":933722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolb, Tom E.","contributorId":340095,"corporation":false,"usgs":false,"family":"Kolb","given":"Tom","email":"","middleInitial":"E.","affiliations":[{"id":39356,"text":"School of Forestry, Northern Arizona University, Flagstaff, AZ, 86011, USA","active":true,"usgs":false}],"preferred":false,"id":933723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noel, Adam Roy 0000-0002-0891-4005","orcid":"https://orcid.org/0000-0002-0891-4005","contributorId":294761,"corporation":false,"usgs":true,"family":"Noel","given":"Adam","email":"","middleInitial":"Roy","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":933724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":933725,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bowen, M.A.","contributorId":340096,"corporation":false,"usgs":false,"family":"Bowen","given":"M.A.","email":"","affiliations":[{"id":81462,"text":"USDA Forest Service, Lincoln National Forest, Cloudcroft, NM, USA","active":true,"usgs":false}],"preferred":false,"id":933726,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fuller, L.R.","contributorId":340098,"corporation":false,"usgs":false,"family":"Fuller","given":"L.R.","email":"","affiliations":[{"id":81463,"text":"USDA Forest Service, Apache-Sitgreaves National Forest, Springerville, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":933727,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moser, W. Keith","contributorId":298271,"corporation":false,"usgs":false,"family":"Moser","given":"W. Keith","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":933728,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70263938,"text":"70263938 - 2025 - lasertram: A Python library for time resolved analysis of laser ablation inductively coupled plasma mass spectrometry data","interactions":[],"lastModifiedDate":"2025-03-11T15:25:06.047162","indexId":"70263938","displayToPublicDate":"2025-02-25T07:46:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14424,"text":"Applied Computing and Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"lasertram: A Python 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 ![\"\"](\"https://ars.els-cdn.com/content/image/1-s2.0-S2590197425000072-fx1001.jpg\")
, 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 ![\"\"](\"https://ars.els-cdn.com/content/image/1-s2.0-S2590197425000072-fx1002.jpg\")
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. ![\"\"](\"https://ars.els-cdn.com/content/image/1-s2.0-S2590197425000072-fx1003.jpg\")
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":70267774,"text":"70267774 - 2025 - Declining marine survival of steelhead trout linked to climate and ecosystem change","interactions":[],"lastModifiedDate":"2025-05-30T16:04:28.613244","indexId":"70267774","displayToPublicDate":"2025-02-24T11:01:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1652,"text":"Fish and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Declining marine survival of steelhead trout linked to climate and ecosystem change","docAbstract":"Species with complex life cycles, such as anadromous fish that perform spawning migrations between freshwater and the ocean, may be particularly sensitive to global change because freshwater and marine habitats experience distinct shifts in climate and ecosystem dynamics. Abundances of wild steelhead trout (Oncorhynchus mykiss) have declined across most of their range over the past 40–50 years. We examined whether declines in steelhead survival can be linked to changing climate conditions and species interactions. A novel hierarchical integrated population model that accounts for the species' complex life history was fitted to data from multiple wild steelhead populations on the Washington coast, U.S.A. The model estimates recruitment residuals and kelt survival rates as time-varying processes, which reflect annual variation in survival before and after first maturation. We found that survival rates of immature steelhead (recruits) and adult steelhead (kelts) have declined over time and that survival trends across populations were strongly associated with climate and ecosystem change, specifically summer sea surface temperature and pink salmon abundance in the North Pacific Ocean, the NPGO index and river flows. Including these drivers in the model reduced unexplained annual variation in shared recruitment and kelt survival anomalies and largely accounted for their negative long-term trends. Our findings provide evidence that rising temperatures and increased interspecific competition at sea have contributed to declines in steelhead survival over the last five decades. Considering projected warming and high pink salmon abundances in the ocean, steelhead will likely continue to experience low marine survival rates.
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,{"id":70264699,"text":"70264699 - 2025 - Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems","interactions":[],"lastModifiedDate":"2025-03-20T14:50:08.933728","indexId":"70264699","displayToPublicDate":"2025-02-24T09:42:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems","docAbstract":"The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.
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,{"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.