Highly pathogenic avian influenza viruses (HPAIV) persistently threaten wild waterfowl, domestic poultry, and public health. The East Asian–Australasian Flyway plays a crucial role in HPAIV dynamics due to its large populations of migratory waterfowl and poultry. Over recent decades, this flyway has undergone substantial landscape changes, including both losses and gains of waterfowl habitats. These changes can affect waterfowl distributions, increase contact with poultry, and consequently alter ecological conditions that favor avian influenza virus (AIV) evolution. However, limited research has assessed these likely impacts. Here, we integrated empirical data and an individual-based model to simulate AIV transmission in migratory waterfowl and domestic poultry, including wild-to-poultry spillover and reassortment dynamics in poultry, across landscapes representing the years 2000 and 2015. We used the reassortment incidence as a proxy for ecological and transmission conditions that support viral diversification and the emergence of novel subtypes. Our simulations show that landscape change reshaped the waterfowl distribution, facilitated bird aggregation at improved habitats, increased coinfection, and raised reassortment rate by 1,593%, indicating a substantially higher potential for viral diversification and emergence. Model-generated risk maps show expanded and increased reassortment risk in southeastern China, the Yellow River Basin, and northeastern China. These findings suggest the importance of landscape change as a driver of potential AIV diversification and subtype emergence. This underscores the need for interdisciplinary approaches that integrate landscape dynamics, host movement, and viral evolution to better assess and mitigate future risk.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2503427122","usgsCitation":"Yin, S., Zhang, C., Teitelbaum, C.S., Si, Y., Zhang, G., Wang, X., Mao, D., Huangh, Z.Y., de Boer, W.F., Takekawa, J., Prosser, D.J., and Xiao, X., 2025, Landscape changes elevate the risk of avian influenza virus diversification and emergence in the East Asian–Australasian Flyway: Proceedings of the National Academy of Sciences, v. 122, no. 34, e2503427122, 9 p., https://doi.org/10.1073/pnas.2503427122.","productDescription":"e2503427122, 9 p.","ipdsId":"IP-176107","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":494467,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2503427122","text":"Publisher Index Page"},{"id":494397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China, Korea, Mongolia, Russia","otherGeospatial":"East Asian–Australasian Flyway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 75,\n 20\n ],\n [\n 175,\n 20\n ],\n [\n 175,\n 75\n ],\n [\n 75,\n 75\n ],\n [\n 75,\n 20\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"122","issue":"34","noUsgsAuthors":false,"publicationDate":"2025-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Yin, Shenglai","contributorId":223544,"corporation":false,"usgs":false,"family":"Yin","given":"Shenglai","email":"","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":946706,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Chenchen","contributorId":360042,"corporation":false,"usgs":false,"family":"Zhang","given":"Chenchen","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":946707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teitelbaum, Claire Stewart 0000-0001-5646-3184","orcid":"https://orcid.org/0000-0001-5646-3184","contributorId":295336,"corporation":false,"usgs":true,"family":"Teitelbaum","given":"Claire","email":"","middleInitial":"Stewart","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":946708,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Si, Yali","contributorId":223542,"corporation":false,"usgs":false,"family":"Si","given":"Yali","email":"","affiliations":[{"id":40738,"text":"Tsinghua University","active":true,"usgs":false}],"preferred":false,"id":946709,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Geli","contributorId":206235,"corporation":false,"usgs":false,"family":"Zhang","given":"Geli","email":"","affiliations":[],"preferred":false,"id":946710,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Xinxin","contributorId":304701,"corporation":false,"usgs":false,"family":"Wang","given":"Xinxin","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":946711,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mao, Dehua","contributorId":360045,"corporation":false,"usgs":false,"family":"Mao","given":"Dehua","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":946712,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Huangh, Zheng Y.X.","contributorId":360047,"corporation":false,"usgs":false,"family":"Huangh","given":"Zheng","middleInitial":"Y.X.","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":946713,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"de Boer, Willem Frederik","contributorId":360049,"corporation":false,"usgs":false,"family":"de Boer","given":"Willem","middleInitial":"Frederik","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":946714,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Takekawa, John","contributorId":330942,"corporation":false,"usgs":false,"family":"Takekawa","given":"John","affiliations":[{"id":32931,"text":"USGS - Retired","active":true,"usgs":false}],"preferred":false,"id":946715,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":946716,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Xiao, Xiangming","contributorId":150759,"corporation":false,"usgs":false,"family":"Xiao","given":"Xiangming","affiliations":[{"id":18095,"text":"Center for Spatial Analysis, U of OK, Norman, OK","active":true,"usgs":false}],"preferred":false,"id":946717,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70270422,"text":"70270422 - 2025 - Integrating hunter dynamics and waterfowl dynamics to inform harvest management","interactions":[],"lastModifiedDate":"2025-08-19T13:52:50.613209","indexId":"70270422","displayToPublicDate":"2025-08-17T08:50:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Integrating hunter dynamics and waterfowl dynamics to inform harvest management","docAbstract":"The successful conservation and management of North American waterfowl relies upon an adaptive harvest management framework that accounts for changes in the system state and critical uncertainties related to the dynamics of waterfowl populations and habitats. Increasing recognition of the importance of the human dimensions of the harvest process, particularly those related to hunters, has motivated calls for the integration of social objectives into waterfowl conservation programs and decision making. We introduce a framework for modeling the dynamics of hunter populations and behavior alongside those of waterfowl populations. Using Bayesian estimation, we fit a dynamic state space model to observational data from the Mid-Continent mallard (Anas platyrhynchos) system over a 20-year period and estimated parameter values for the relative effects of a set of hypothesized drivers of hunter recruitment, retention, reactivation, participation, and success rates. We then made projections across 3 future scenarios to examine the continuation of current trends, the potential effects of a shift to a moderate regulatory framework as informed by expert elicitation, and increased hunter recruitment to maintain a stable hunter population. We found that a theoretical stable state exists for Mid-Continent mallard and hunter populations but that the influence of broader sociocultural shifts and increasing hunter mortality from an aging base pose significant challenges for efforts to stabilize the ongoing decline of active hunter numbers, even under favorable regulatory conditions. This modeling framework and results from it can be used to inform decision processes in the management of game populations that seek to include social objectives and assess the potential trade-offs of prioritizing across social and ecological objectives. We emphasize the need for focused human dimensions expertise throughout the process of fully integrating goals for waterfowl populations, habitats, and people in waterfowl conservation.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70084","usgsCitation":"Berl, R.E., Devers, P.K., Boomer, G.S., and Runge, M., 2025, Integrating hunter dynamics and waterfowl dynamics to inform harvest management: Journal of Wildlife Management, no. Online First, https://doi.org/10.1002/jwmg.70084.","ipdsId":"IP-177503","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":498234,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70084","text":"Publisher Index Page"},{"id":494296,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"Online First","noUsgsAuthors":false,"publicationDate":"2025-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Berl, Richard Eugene Waggaman 0000-0002-4154-1319","orcid":"https://orcid.org/0000-0002-4154-1319","contributorId":336851,"corporation":false,"usgs":true,"family":"Berl","given":"Richard","email":"","middleInitial":"Eugene Waggaman","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":946388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Devers, Patrick K. 0000-0002-5281-6875","orcid":"https://orcid.org/0000-0002-5281-6875","contributorId":359900,"corporation":false,"usgs":false,"family":"Devers","given":"Patrick","middleInitial":"K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":946389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boomer, G. Scott 0000-0001-5854-3604","orcid":"https://orcid.org/0000-0001-5854-3604","contributorId":261408,"corporation":false,"usgs":false,"family":"Boomer","given":"G.","email":"","middleInitial":"Scott","affiliations":[{"id":7199,"text":"US FWS","active":true,"usgs":false}],"preferred":true,"id":946390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":214737,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":946391,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270821,"text":"70270821 - 2025 - Evaluating the performance of multiple precipitation datasets over the transboundary Ili River Basin between China and Kazakhstan","interactions":[],"lastModifiedDate":"2025-08-25T15:21:07.451208","indexId":"70270821","displayToPublicDate":"2025-08-16T08:15:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3504,"text":"Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the performance of multiple precipitation datasets over the transboundary Ili River Basin between China and Kazakhstan","docAbstract":"The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin.
","language":"English","publisher":"MDPI","doi":"10.3390/su17167418","usgsCitation":"Duisebek, B., Senay, G., Ojima, D.S., Zhang, T., Sagin, J., and Wang, X., 2025, Evaluating the performance of multiple precipitation datasets over the transboundary Ili River Basin between China and Kazakhstan: Sustainability, v. 17, no. 16, 7418, 26 p., https://doi.org/10.3390/su17167418.","productDescription":"7418, 26 p.","ipdsId":"IP-181853","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":495056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su17167418","text":"Publisher Index Page"},{"id":494743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China, Kazakhstan","otherGeospatial":"Ili River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 46.71910209135149,\n 50.09770097338648\n ],\n [\n 46.71910209135149,\n 44.15010644271524\n ],\n [\n 84.48446379774907,\n 44.15010644271524\n ],\n [\n 84.48446379774907,\n 50.09770097338648\n ],\n [\n 46.71910209135149,\n 50.09770097338648\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"16","noUsgsAuthors":false,"publicationDate":"2025-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Duisebek, Baktybek","contributorId":360498,"corporation":false,"usgs":false,"family":"Duisebek","given":"Baktybek","affiliations":[{"id":86016,"text":"Kazakh British Technical University","active":true,"usgs":false}],"preferred":false,"id":947122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":947123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ojima, Dennis S.","contributorId":208511,"corporation":false,"usgs":false,"family":"Ojima","given":"Dennis","email":"","middleInitial":"S.","affiliations":[{"id":37812,"text":"Colorado State University; North Central Climate Science Center","active":true,"usgs":false}],"preferred":false,"id":947124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Tibin","contributorId":360499,"corporation":false,"usgs":false,"family":"Zhang","given":"Tibin","affiliations":[{"id":86019,"text":"State Key Laboratory of Soil and Water Conservation Science and Engineering, Northwest A&F University","active":true,"usgs":false}],"preferred":false,"id":947125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sagin, Janay","contributorId":360500,"corporation":false,"usgs":false,"family":"Sagin","given":"Janay","affiliations":[{"id":86016,"text":"Kazakh British Technical University","active":true,"usgs":false}],"preferred":false,"id":947126,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Xuejiao","contributorId":271179,"corporation":false,"usgs":false,"family":"Wang","given":"Xuejiao","email":"","affiliations":[{"id":12433,"text":"China University of Geosciences","active":true,"usgs":false}],"preferred":false,"id":947127,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270388,"text":"70270388 - 2025 - Relationships between water quality, stream metabolism, and water stargrass growth in the lower Yakima River, 2018 to 2020","interactions":[],"lastModifiedDate":"2025-08-18T13:32:56.593268","indexId":"70270388","displayToPublicDate":"2025-08-15T08:30:05","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Relationships between water quality, stream metabolism, and water stargrass growth in the lower Yakima River, 2018 to 2020","docAbstract":"Since the early 2000s, water clarity on the lower Yakima River has improved. Changes in best management practices combined with a total maximum daily load for suspended sediment led to these improved conditions. As water clarity improved, so did conditions for aquatic plants; the clearer the water, the better the light penetration, and dramatic increases in plant biomass were observed. In the lower Yakima River, beds of native water stargrass (grass-leaf mud-plantain, Heteranthera dubia) are prolific and can extend bank to bank in some locations. Increased primary productivity can alter local water quality by increasing daily swings of dissolved oxygen (DO) and pH from photosynthesis. In this study, we collected continuous water quality data for 2.5 years at three sites on the lower Yakima River to provide a detailed examination of water quality conditions. These sites were located just below the Prosser Dam (Prosser site, USGS station 12509489), at a long-term USGS streamgage in Benton County (Kiona site, USGS station 12510500), and in West Richland, WA (Van Giesen site; USGS station 12511800). In addition to the continuous water quality data collected, estimates of water stargrass biomass were made through the growing season (June through September) during water years 2018–2020. The main objectives of this study were to document water quality conditions on the lower Yakima River and to analyze if there was a statistical relation between the amount of water stargrass biomass and the observed daily cycles of water quality.
During summer, frequent exceedances of established water quality criteria were documented each year during this study. Maximum daily temperatures exceeded 21o C, minimum DO concentrations were below 8 milligrams per liter (mg/L), and maximum pH surpassed 8.5 almost every day from June through August each water year across all three monitoring locations. Water stargrass biomass tended to increase from June through August and September but was ‘reset’ by the following summer likely from high winter and spring streamflows and natural die-off. Results from this study suggest that spring peak discharge and average spring discharge affects late-season water stargrass biomass. In 2018, the highest peak discharge of the study took place, and the August water stargrass biomass values were lower in 2018 than in 2019 and 2020.
Seven different water quality metrics were computed for a 7-day and 28-day period prior to each water stargrass sample to examine possible correlations between the plant biomass and water quality. We examined daily maximum temperature, DO minimum, DO range, pH maximum, pH range, mean nitrate, and nitrate range. While there were some statistically significant correlations among the seven water quality metrics and median water stargrass biomass, the correlations were not consistent across all three sites. At the Prosser site, the 7-day average daily maximum pH and average daily pH range showed significant correlations with median water stargrass biomass. At the Kiona site, both the 7-day and 28-day mean nitrate values showed a significant relationship to median water stargrass biomass. At the Van Giesen site, there were no significant correlations between the seven water quality metrics and median water stargrass biomass. However, whole-stream estimates of gross primary productivity at the Kiona site, which incorporate the entire river community, were related to temperature, DO, and pH indicating the whole river community is influencing surface water quality to some extent.
Additional data on water stargrass biomass and continuous water quality could help elucidate the complex interactions between growth and water quality. At a minimum, collection of water stargrass biomass data near the end of the growing season (mid to late August) could be added to locations where continuous water quality and streamflow discharge measurements are also being collected. In addition, experimental removal of water stargrass and its effects on local water quality could provide insight into the complex relationships between water stargrass growth and water quality. Finally, further investigations into streamflow and its effects on water stargrass could be improved. Our data showed a qualitative relationship between spring peak discharge, average spring discharge, and August water stargrass biomass, but more data are needed to confirm this. If spring high streamflows are important for late-season biomass, then targeted flow releases from reservoirs in the upper watershed could be used to slow down water stargrass growth during summer months.
This report was developed to evaluate the performance of a set of proposed alternatives for Cervus elaphus canadensis (elk) and Bison bison (bison) management at the National Elk Refuge (NER) in Wyoming, U.S.A., and to inform a National Environmental Policy Act Environmental Impact Statement focused on developing the next “Bison and Elk Management Plan” (BEMP). The U.S. Geological Survey facilitated a structured decision-making process for the U.S. Fish and Wildlife Service to develop the alternatives and the criteria (performance metrics) for evaluating the alternatives. Chapter A provides scoping details of the report, a summary of the 19 metrics that are used to evaluate the performance of each of 6 alternatives, and methodological details of 2 performance metrics that were not covered in other technical chapters. Chapter B analyzes elk population and chronic wasting disease dynamics under the five initial alternatives. Chapter C evaluates elk space-use based on data collected from global positioning system collars on elk and expert elicitation for scenarios with limited data. Chapter D evaluates bison population dynamics, conflict, and harvest patterns under the five initial alternatives. Chapter E assesses social and economic consequences. Chapter F is newly added to this superseding report and details the analysis and results of a new alternative that was developed after discussion among the lead and cooperating agencies working on the BEMP. The full set of six alternatives are anticipated to have varying affects on bison and elk population abundance and private land use, wildlife-related recreation and tourism, and hunters and outfitters in the region. Each chapter was developed under advisement of a technical team, made up science experts from U.S. Fish and Wildlife Service, National Park Service, U.S. Forest Service, and Wyoming Game and Fish Department.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255076","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture, National Park Service, U.S. Fish and Wildlife Service, and Wyoming Game and Fish Department","programNote":"Ecosystems Mission Area—Biological Threats & Invasive Species Research Program, Environmental Health Program, and the Species Management Research Program","usgsCitation":"Cook, J.D., and Cross, P.C., eds., 2025, Decision analysis in support of the National Elk Refuge bison and elk management plan: U.S. Geological Survey Scientific Investigations Report 2025–5076, 6 chap. (A–F), variously paged, https://doi.org/10.3133/sir20255076. [Supersedes USGS Scientific Investigations Report 2024–5119.]","productDescription":"Report: 166 p.; 4 Software Releases","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":494039,"rank":6,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P1DZS7MW","text":"USGS software release","linkHelpText":"- Socioeconomic effects of bison and elk management alternatives on National Elk Refuge"},{"id":494038,"rank":5,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P14EPY3U","text":"USGS software release","linkHelpText":"- CWD software code for simulating elk and chronic wasting disease dynamics on the National Elk Refuge (version 0.1.0)"},{"id":494037,"rank":4,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P14FF6E6","text":"USGS software release","linkHelpText":"- Supporting code for—Evaluating elk distribution and conflict under proposed management alternatives at the National Elk Refuge in Jackson, Wyoming"},{"id":494036,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P1QZGZSN","text":"USGS software release","linkHelpText":"- Jackson bison population projections"},{"id":493987,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5076/sir20255076.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5076 PDF"},{"id":493986,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5076/coverthb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"National Elk Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.78225266437141,\n 43.46790181387567\n ],\n [\n -110.72445411600302,\n 43.480764457816235\n ],\n [\n -110.70981181708308,\n 43.502568770827935\n ],\n [\n -110.6897749869821,\n 43.51262964481907\n ],\n [\n -110.6628023310768,\n 43.52213004158219\n ],\n [\n -110.64353614828745,\n 43.53665716685947\n ],\n [\n -110.62349931818646,\n 43.55620738705895\n ],\n [\n -110.5972973095928,\n 43.598637460483474\n ],\n [\n -110.59883860421601,\n 43.62653567653447\n ],\n [\n -110.6520132687147,\n 43.62207283164909\n ],\n [\n -110.6897749869821,\n 43.60477617840215\n ],\n [\n -110.73216058911865,\n 43.56458411181504\n ],\n [\n -110.74680288803886,\n 43.517100606095084\n ],\n [\n -110.76067453964718,\n 43.50480466552287\n ],\n [\n -110.78225266437141,\n 43.46790181387567\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Rd., Ste 4039
Laurel, MD 20708-4039
Catadromous American eel ( Anguilla rostrata ) are native to Maine's Penobscot River watershed and historically have migrated through many of its tributaries prior to extensive damming. Recent restoration efforts, including dam removals, have improved connectivity in the lower reaches of the Penobscot River. Characterizing the extent of the American eel's distribution is important to inform restoration and identify extant barriers to migrations within the watershed. In the summer of 2023, we conducted eDNA surveys throughout the Penobscot River watershed to estimate the current distribution of the American eel and identify barriers to inland waters. Water samples were collected from 70 sites representing 37 rivers and streams; the presence or absence of American eel genetic markers within those samples was assessed using qPCR. We have shown that American eel are present in virtually the full extent of the area surveyed (68/70 sites). The results suggest that the majority of the main-stem dams may be passed by American eels at some level, with eel DNA being confirmed upstream of six dams. We confirmed the presence of American eels throughout the lower watershed with just 1 week of eDNA sampling and have highlighted this method for determining the species' access to habitat upstream of dams. The use of eDNA to sample locally (or regionally) for American eel may provide cost-effective information in data deficient areas and help assess the permeability of dam structures to diadromous species.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.70018","usgsCitation":"Snyder, S., Dillingham, C., Katz, L., Kinnison, M.T., and Zydlewski, J.D., 2025, Assessing American eel (Anguilla rostrata) distribution in a heavily dammed watershed using eDNA : The Penobscot River watershed, Maine, USA: River Research and Applications, v. 41, no. 9, p. 1970-1981, https://doi.org/10.1002/rra.70018.","productDescription":"12 p.","startPage":"1970","endPage":"1981","ipdsId":"IP-176205","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":498284,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.70018","text":"Publisher Index Page"},{"id":497481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.79016932992536,\n 44.47116951424118\n ],\n [\n -67.52367258627547,\n 45.25105363241147\n ],\n [\n -67.8686701761885,\n 45.76334994488934\n ],\n [\n -67.98054023471954,\n 46.34090601333176\n ],\n [\n -69.2665286181527,\n 46.68076665138372\n ],\n [\n -69.97891918507078,\n 46.553060617496385\n ],\n [\n -70.25483843412886,\n 46.20814713139498\n ],\n [\n -70.29358331647212,\n 45.903994178555536\n ],\n [\n -69.92178249014444,\n 45.73610232051021\n ],\n [\n -69.41971638316107,\n 45.71547445614357\n ],\n [\n -69.57280286278458,\n 45.32482188278985\n ],\n [\n -68.79016932992536,\n 44.47116951424118\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Snyder, Shawn","contributorId":302899,"corporation":false,"usgs":false,"family":"Snyder","given":"Shawn","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":952080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dillingham, Cody","contributorId":342595,"corporation":false,"usgs":false,"family":"Dillingham","given":"Cody","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":952081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katz, Lara S.","contributorId":348223,"corporation":false,"usgs":false,"family":"Katz","given":"Lara S.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":952082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinnison, Michael T.","contributorId":363875,"corporation":false,"usgs":false,"family":"Kinnison","given":"Michael","middleInitial":"T.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":952083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":952084,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270751,"text":"70270751 - 2025 - Magnitude, depth and methodological variations of spectral stress drop within the SCEC/USGS Community Stress Drop Validation Study using the 2019 Ridgecrest Earthquake Sequence","interactions":[],"lastModifiedDate":"2025-12-01T16:26:29.56428","indexId":"70270751","displayToPublicDate":"2025-08-14T08:10:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Magnitude, depth and methodological variations of spectral stress drop within the SCEC/USGS Community Stress Drop Validation Study using the 2019 Ridgecrest Earthquake Sequence","docAbstract":"We present the first ensemble analysis of the 56 different sets of results submitted to the ongoing Community Stress Drop Validation Study using the 2019 Ridgecrest, California, earthquake sequence. Different assumptions and methods result in different estimation of the source contribution to recorded seismograms, and hence to the source parameters (principally corner frequency, fc, spectral stress drop, Δσ, and seismic moment, M0) obtained from modeling calculated source spectra. For earthquakes smaller than magnitude (M) 2.5 there is negligible correlation between the fc values obtained by different studies, implying that no present method is reliable using available data. For larger magnitude events, correlation between fc measurements of different studies, within even a small M range is always higher than spectral Δσ, because the fc measurements simply reflect the underlying physical decrease in fc with increasing M. We model the observed trends of submitted fc with both magnitude and depth. Most methods report an increase in spectral Δσ with M, although a magnitude‐invariant spectral Δσ is within the confidence limits. The depth dependence is smaller and depends on whether a study allows attenuation to vary with source depth; a combination of depth‐dependent attenuation correction, and depth‐dependent shear‐wave velocity can compensate for reported depth trends. We model the submitted values to remove differing M and depth variation to investigate the relative interevent variability. We find consistent relative variation between individual events, and also lower relative spectral Δσ in the northwest of the aftershock sequence, and higher on the cross fault and in the region of main fault intersection. This large‐scale comparison implies that absolute spectral Δσ estimates are dependent on the methods used; studies of different regions or using different methods should not be directly compared and improved constraints on path and site corrections are needed to resolve these absolute spectral Δσ differences.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120250056","usgsCitation":"Abercrombie, R., and Baltay Sundstrom, A.S., 2025, Magnitude, depth and methodological variations of spectral stress drop within the SCEC/USGS Community Stress Drop Validation Study using the 2019 Ridgecrest Earthquake Sequence: Bulletin of the Seismological Society of America, v. 115, no. 6, p. 2741-2768, https://doi.org/10.1785/0120250056.","productDescription":"28 p.","startPage":"2741","endPage":"2768","ipdsId":"IP-177230","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":496944,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0120250056","text":"Publisher Index Page"},{"id":494533,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Ridgecrest earthquake sequence","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.8,\n 36\n ],\n [\n -117.8,\n 35.5\n ],\n [\n -117.3,\n 35.5\n ],\n [\n -117.3,\n 36\n ],\n [\n -117.8,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Abercrombie, Rachel E.","contributorId":293131,"corporation":false,"usgs":false,"family":"Abercrombie","given":"Rachel E.","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":946996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":946997,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270300,"text":"70270300 - 2025 - Turning trash into treasure: Leveraging discarded filters for national-scale aquatic eDNA biomonitoring","interactions":[],"lastModifiedDate":"2025-08-14T14:55:37.921682","indexId":"70270300","displayToPublicDate":"2025-08-13T09:48:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12812,"text":"Aquaculture, Fish and Fisheries","onlineIssn":"2693-8847","active":true,"publicationSubtype":{"id":10}},"title":"Turning trash into treasure: Leveraging discarded filters for national-scale aquatic eDNA biomonitoring","docAbstract":"Monitoring biodiversity changes over large spatiotemporal scales is critical for effective ecosystem conservation and management. This study investigates the potential of environmental DNA (eDNA) metabarcoding to enhance national-scale biomonitoring of freshwater diversity by leveraging discarded filters associated with routine water quality sampling from the U.S. Geological Survey's (USGS) National Water Quality Network (NWQN). We tested 375 samples from 103 NWQN sites for eDNA of native and non-native fish and found that 52% of the filters yielded fish eDNA for a total of 70 fish species detections. Of the filters that had fish eDNA present, an average of 3.7 species were detected. Benchmarking these results to USGS's Aquatic Gap Analysis Project (AGAP)—which includes both field-verified observations along with predictive models derived from fish capture and landscape predictor datasets—we found that eDNA from these filters detected only a fraction of the observed and expected fish diversity for these sites. Our results indicate that these discarded filters may not be sufficient for eDNA sampling of fish communities and posit that alternative filter types more appropriate for eDNA sampling may yield more valuable biomonitoring data. Nevertheless, we tested the efficacy of two novel approaches to facilitate large-scale biomonitoring. Though these filters did not yield adequate fish eDNA, the AGAP database provides a useful method for ground truthing fish species presence. The potential of integrating eDNA sampling into existing monitoring frameworks, which, when paired with more optimal eDNA methods, could be a cost-effective strategy to enhance biodiversity monitoring at large scales.
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0000-0001-6561-3244","orcid":"https://orcid.org/0000-0001-6561-3244","contributorId":359618,"corporation":false,"usgs":false,"family":"Crane","given":"Joseph","affiliations":[{"id":85882,"text":"Jonah Ventures","active":true,"usgs":false}],"preferred":false,"id":945959,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":945960,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270656,"text":"70270656 - 2025 - Multi-scale habitat characteristics influence Paleback Darter occupancy and detection probability","interactions":[],"lastModifiedDate":"2025-09-09T14:59:39.006613","indexId":"70270656","displayToPublicDate":"2025-08-13T08:34:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale habitat characteristics influence Paleback Darter occupancy and detection probability","docAbstract":"Objective
The limited distribution of the Paleback Darter Etheostoma pallididorsum, which is often associated with dynamic headwater streams, makes the species vulnerable to changes in its environment in west-central Arkansas. A detailed understanding of habitat characteristics that support the species at multiple spatial scales is limited. This project assessed the relative influences of local- and broadscale habitat characteristics on Paleback Darter occupancy and detection probability.
Methods
Backpack electrofishing was performed, and a mix of in situ and remote habitat characteristics was linked to the Paleback Darter data. A single-season occupancy model was used to examine factors influencing Paleback Darter occupancy. Candidate models were ranked using Akaike's information criterion corrected for small sample sizes.
Results
A total of 158 Paleback Darters were collected at 13 sites during 35 of the 150 surveys. Paleback Darter occupancy and detection probability were estimated to be 0.26 (95% CI = 0.16–0.40) and 0.90 (95% CI = 0.75–0.96), respectively. Distance from springs was negatively related to Paleback Darter occupancy.
Conclusions
Springs appear to be a key in the Paleback Darter's life history strategy, and spring preservation is likely vital to the species’ conservation.
","language":"English","publisher":"American Fisheries Society","doi":"10.1093/tafafs/vnaf033","usgsCitation":"Hartman, M.L., Morris, K.M., Spurgeon, J.J., and Lochmann, S.E., 2025, Multi-scale habitat characteristics influence Paleback Darter occupancy and detection probability: Transactions of the American Fisheries Society, v. 154, no. 5, p. 585-594, https://doi.org/10.1093/tafafs/vnaf033.","productDescription":"10 p.","startPage":"585","endPage":"594","ipdsId":"IP-170555","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494526,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, Maxwell L.","contributorId":360092,"corporation":false,"usgs":false,"family":"Hartman","given":"Maxwell","middleInitial":"L.","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":946780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morris, Katie M.","contributorId":360095,"corporation":false,"usgs":false,"family":"Morris","given":"Katie","middleInitial":"M.","affiliations":[{"id":85969,"text":"Arkansas Natural Heritage Commission","active":true,"usgs":false}],"preferred":false,"id":946781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spurgeon, Jonathan J. 0000-0002-6888-5867","orcid":"https://orcid.org/0000-0002-6888-5867","contributorId":304259,"corporation":false,"usgs":true,"family":"Spurgeon","given":"Jonathan","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lochmann, Steve E.","contributorId":360096,"corporation":false,"usgs":false,"family":"Lochmann","given":"Steve","middleInitial":"E.","affiliations":[{"id":81661,"text":"University of Arkansas at Pine Bluff","active":true,"usgs":false}],"preferred":false,"id":946783,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271346,"text":"70271346 - 2025 - Sparse genetic data limit biodiversity assessments in protected areas globally","interactions":[],"lastModifiedDate":"2025-11-21T22:05:56.926432","indexId":"70271346","displayToPublicDate":"2025-08-12T08:43:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1701,"text":"Frontiers in Ecology and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Sparse genetic data limit biodiversity assessments in protected areas globally","docAbstract":"Global conservation targets include protecting genetic diversity within species. Yet few studies have assessed whether protected areas (PAs) include genetically diverse populations across species globally. A first step is understanding the availability of population genetic data that could be used in these assessments. We surveyed georeferenced population-level nuclear (as opposed to mitochondrial or plastid-based) genetic data across continents and marine biomes (36,354 populations, 2809 species) and found substantial geographic and taxonomic gaps. Most data were concentrated in Europe and North America, with major gaps in Africa and Asia. For most taxonomic groups, data were available for <1% of described species. Globally, 52.08% of the total areal extent of PAs lacked genetically sampled populations. These gaps in data availability highlight the need for targeted genetic data collection, harmonization, and sharing to improve genetic diversity monitoring and conservation planning. Combined with proxy-based genetic indicators, such data are needed to inform PA assessments, bolster area-based conservation initiatives like 30 × 30, and support achievement of global genetic conservation targets.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2867","usgsCitation":"Paz-Vinas, I., Vandergast, A.G., Schmidt, C., Leigh, D.M., Blanchet, S., Clark, R.D., Crandall, E.D., De Kort, H., Falgout, J.T., Garroway, C.J., Karachaliou, E., Kershaw, F., O’Brien, D., Pinsky, M.L., Segelbacher, G., Toczydlowski, R.H., and Hunter, M., 2025, Sparse genetic data limit biodiversity assessments in protected areas globally: Frontiers in Ecology and the Environment, v. 23, no. 8, e2867, 8 p., https://doi.org/10.1002/fee.2867.","productDescription":"e2867, 8 p.","ipdsId":"IP-157823","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":498223,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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Expérimentale, Moulis, France","active":true,"usgs":false}],"preferred":false,"id":948129,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clark, René D.","contributorId":361022,"corporation":false,"usgs":false,"family":"Clark","given":"René","middleInitial":"D.","affiliations":[{"id":86159,"text":"Department of Biology, Drexel University","active":true,"usgs":false}],"preferred":false,"id":948130,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crandall, Eric D.","contributorId":361023,"corporation":false,"usgs":false,"family":"Crandall","given":"Eric","middleInitial":"D.","affiliations":[{"id":86160,"text":"Department of Biology, the Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":948131,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"De Kort, Hanne","contributorId":361024,"corporation":false,"usgs":false,"family":"De Kort","given":"Hanne","affiliations":[{"id":86161,"text":"Division of Ecology, Evolution and Biodiversity Conservation, Biology Department KU Leuven, Leuven, Belgium","active":true,"usgs":false}],"preferred":false,"id":948132,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Falgout, Jeff T. 0000-0002-7108-477X jfalgout@usgs.gov","orcid":"https://orcid.org/0000-0002-7108-477X","contributorId":4957,"corporation":false,"usgs":true,"family":"Falgout","given":"Jeff","email":"jfalgout@usgs.gov","middleInitial":"T.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":948133,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Garroway, Colin J.","contributorId":361025,"corporation":false,"usgs":false,"family":"Garroway","given":"Colin","middleInitial":"J.","affiliations":[{"id":86162,"text":"Department of Biological Sciences, University of Manitoba, Winnipeg, Canada","active":true,"usgs":false}],"preferred":false,"id":948134,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Karachaliou, Eleana","contributorId":352516,"corporation":false,"usgs":false,"family":"Karachaliou","given":"Eleana","affiliations":[{"id":62682,"text":"Department of Biological Sciences, University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":948135,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kershaw, Francine","contributorId":260831,"corporation":false,"usgs":false,"family":"Kershaw","given":"Francine","email":"","affiliations":[{"id":52686,"text":"Natural Resources Defense Council, New York","active":true,"usgs":false}],"preferred":false,"id":948136,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"O’Brien, David","contributorId":261145,"corporation":false,"usgs":false,"family":"O’Brien","given":"David","affiliations":[{"id":52749,"text":"Scottish Natural Heritage, Leachkin Road, Inverness IV3 8NW, UK","active":true,"usgs":false}],"preferred":false,"id":948137,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pinsky, Malin L.","contributorId":361026,"corporation":false,"usgs":false,"family":"Pinsky","given":"Malin","middleInitial":"L.","affiliations":[{"id":86163,"text":"Department of Ecology and Evolution, University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":948138,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":948139,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Toczydlowski, Rachel H.","contributorId":361046,"corporation":false,"usgs":false,"family":"Toczydlowski","given":"Rachel","middleInitial":"H.","affiliations":[],"preferred":false,"id":948173,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214948,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":948140,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70269818,"text":"ofr20251044 - 2025 - Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","interactions":[{"subject":{"id":70269818,"text":"ofr20251044 - 2025 - Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","indexId":"ofr20251044","publicationYear":"2025","noYear":false,"displayTitle":"Insights and Strategic Opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","title":"Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop"},"predicate":"IS_ADDENDUM_TO","object":{"id":70226853,"text":"cir1490 - 2021 - Integrated science for the study of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in the environment—A strategic science vision for the U.S. Geological Survey","indexId":"cir1490","publicationYear":"2021","noYear":false,"title":"Integrated science for the study of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in the environment—A strategic science vision for the U.S. Geological Survey"},"id":1}],"lastModifiedDate":"2026-02-03T15:00:44.018949","indexId":"ofr20251044","displayToPublicDate":"2025-08-11T13:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1044","displayTitle":"Insights and Strategic Opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","title":"Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","docAbstract":"In 2021, the U.S. Geological Survey (USGS) published Circular 1490 titled, “Integrated Science for the Study of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Environment: A Strategic Science Vision for the U.S. Geological Survey” (Tokranov and others, 2021). Circular 1490 was created to be a resource for USGS scientists prioritizing and planning research related to per- and polyfluoroalkyl substances (PFAS) and to be a guide for developing partnerships with other scientists, State and Federal agencies, and stakeholders engaged in PFAS research and management and mitigation of the environmental and human-health effects of PFAS. This USGS PFAS Strategic Science Vision document was intended to be the foundation for a “living strategic vision,” periodically providing updates on the state of USGS PFAS research, emerging PFAS data gaps and needs, and progress on interagency and stakeholder PFAS partnerships and priorities. To meet this objective, the USGS planned to host an Interagency and Stakeholder PFAS Workshop every 2–3 years.
During September 10–12, 2024, the USGS hosted the first Interagency and Stakeholder PFAS Workshop in Reston, Virginia. The Workshop brought together experts from other Federal agencies (U.S. Environmental Protection Agency, National Institute of Environmental Health Sciences, Food and Drug Administration, Department of Defense [Air Force, Army]), State agencies (Washington Fish and Wildlife, Virginia Department of Transportation), and academia (Harvard University, University of Maryland) to address key challenges relating to the measurement and modeling of PFAS and the implications for environmental health. Participants engaged in in-depth discussions centered around six pivotal topics related to PFAS: (1) sampling protocols, methods and interpretation; (2) environmental sources, source apportionment, and occurrence; (3) environmental fate and transport; (4) human and wildlife exposure routes and risk; (5) bioconcentration, bioaccumulation, and biomagnification; and (6) ecotoxicology and effects. Each topic had three breakout sessions.
A recurrent theme of workshop discussions was how data on a nationwide scale for PFAS occurrence in various environmental matrices, including air, water, food crops, biota, soil, and streambed sediment could help to advance scientific understanding. Participants noted significant geospatial data gaps, particularly in the midwestern and southern United States and the Pacific Northwest. PFAS data collection tends to be more robust along the eastern seaboard and in California.
Participants stressed how enhancing the integration of large and small datasets across various agencies could help to support national scale understanding of PFAS. To address these gaps, attendees suggested leveraging datasets from Federal entities like the USGS and the U.S. Department of Defense, State agencies, and municipal utility services to develop predictive contaminant detection and transport models. Improved coordination between water quality programs and USGS research could help to facilitate access to valuable data, leading to comprehensive databases that inform PFAS point (wastewater treatment plants and landfills) and nonpoint (runoff from land, atmospheric deposition, food packaging) sources, environmental transport mechanisms, environmental detection and concentrations, potential exposure routes, and health effects on different biota, including humans. A specific request was made to develop a map demarking the depth of modern (1953 or later) groundwater, which is susceptible to surface-derived anthropogenic (that is, human-made) contamination, based on tritium-age dating. Emphasis was placed on incorporation of hydrology, groundwater flow paths, groundwater–surface water interactions, and landscape factors in predictive statistical models as a step to improve contaminant source identification and tracking.
Molecular fingerprinting approaches garnered attention as techniques to link specific PFAS mixtures detected in a sample to environmental sources and levels in biota (Dávila-Santiago and others, 2022). Integrating data from abiotic (that is, water, soil, and air) and biotic (that is, living organisms) systems identified as a research opportunity. For example, understanding the composition of soils and sediments, which include a mixture of mineral, plant, and animal components, could advance understanding of exposure pathways.
The discussions highlighted opportunities to explore and understand the potential redistribution and biotic exposures of PFAS from biosolid and wastewater treatment plant effluent land application practices, in addition to atmospheric releases and discharges from landfill and wastewater treatment plants. Participants identified research gaps surrounding how these sources may contribute to contamination and may affect surrounding ecosystems, including a better definition of anthropogenic background concentrations.
Moving forward, the collection of co-occurrence data was noted as a means to improve understanding of complex mixtures and to leverage companion modeling efforts focused on areas with high and low contamination levels to identify areas of concern and unaffected resources. Participants emphasized how centralized USGS databases and the establishment of sample-metadata archives can help to ensure that samples are preserved and accessible for future research.
In conclusion, the workshop participants identified opportunities to bridge data gaps and improve measurement techniques, modeling frameworks, databases, and communication, to enhance the understanding of PFAS and their effects on environmental and human health. Upon completion of the workshop, participants indicated an interest in developing strategic data collection, modeling, and analytical approaches to address these challenges.
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The U.S. Geological Survey (USGS) National Water Quality Network for surface water (NWQN-SW) was established in 2013 to develop long-term, comparable assessments of surface-water quality in support of national, regional, State, and local needs related to water-quality management and policy. Water-quality samples are collected at each site and measured for a variety of constituents. In 2024, the NWQN-SW consisted of 105 sites, each of them paired with a streamgage, operated by the USGS or other agencies that provide continuous information on streamflow conditions. The water-quality data and the streamflow information from the NWQN-SW are then used to assess the status and trends of water-quality conditions and potential impacts on human and aquatic health.
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Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Remote Pacific islands (RPI) are characterized by ecological isolation, diverse endemic species, and vulnerability to invasive organisms due to globalization-driven connectivity. Among these species, Aedes albopictus, a highly invasive vector of flaviviruses, has spread extensively across the RPI via human-mediated dispersal, posing significant health and economic burdens. While the population structure and the degree of gene flow between mosquito populations can inform the dispersal pathways critical for disease vector management, the population genetics of Ae. albopictus in Northern RPI remains understudied. The present work investigated the population structure and connectivity of Ae. albopictus populations from Guam, Hawaiian Islands, and the Republic of the Marshall Islands (RMI) to inform disease and vector-based biosecurity risks and develop targeted management strategies. This is the first assessment to develop and analyze whole genome sequences of Ae. albopictus for RPI, enabling more accurate estimates of differentiation, admixture, and ancestry. We found distinct genetic clustering between regions, distinct ancestry of populations across RPI, and potential invasions that originated from Hawaii and spread into the RMI, and invasions from North America that spread to Guam. These findings can inform biosecurity protocols to limit the invasion of Ae. albopictus and their associated diseases within Hawaii and around the Pacific. Given the significant degree of genetic differentiation, we found between islets, islands, and regions, the genome data from this study can be used to enable the development of locally confined geographically isolated gene drives. These drives may be used to prevent and control outbreaks of dengue, chikungunya, and Zika, diseases that have had devastating consequences in these remote island communities.
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In postglacial valleys, many landslides are posited to occur in relation to lateral river erosion, but the dynamics of fluvial-hillslope interactions are not well understood. Here, we investigate a section of the Nooksack River in western Washington State where the channel is flanked by landslide-prone glacial terraces similar to those that failed in the 2014 State Route 530 “Oso” landslide. We map 216 landslides through time across 17 aerial imagery data sets (1933–2022) and analyze them in relation to river meandering and curvature. We observe dynamic feedbacks between lateral river meandering and valley-adjacent landsliding. Terrace lateral retreat rates of up to 25 m/year owing to combined fluvial erosion and slope failure occur on pinned, outer meander bends immediately downstream from peaks in river curvature (>0.0075 1/m); these locations are predisposed to both shallow and deep-seated landslides. Deep-seated landslides extending 17%–32% of the active valley width into the floodplain can displace the river away from the floodplain margin and change the channel planform. River-displacing landslides relocate meanders up- or downstream, thereby conditioning the location of subsequent landslides. This conceptual model of coupled landslide-driven meander displacement and valley-adjacent landsliding is exemplified across western Washington river systems. The distance between up- and downstream valley-adjacent landsliding scales with valley width, meander wavelength, and terrace height. Our results can advance our understanding of the river-hillslope interface in landscape evolution and can be used to inform hazard management in river corridors.
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The OpenET collaboration of six remotely sensed ET modeling teams has demonstrated that an ensemble approach to ET estimation generally provides improved accuracy relative to individual ensemble members. The performance of individual models has been shown to vary by land cover type and climate zone, but a thorough study of the variables that influence model performance differences has not yet been conducted. In this paper, we model the performance of OpenET models relative to flux tower data as a function of variables such as land cover type and precipitation. These performance models are used to map estimated OpenET model performance across the conterminous United States. We develop relative weights based on these modeled performance metrics and show that a performance-weighted ensemble improves accuracy relative to the current OpenET ensemble method to varying degrees. The monthly mean absolute error of the weighted ensemble is reduced relative to the current method by 8% in agricultural settings, by 23% in shrublands and mixed forests, and by 5% in grasslands and evergreen forests. We produce weight maps that can be used to generate performance-weighted ensemble values for OpenET data. The results can be used to inform model selection and provide insight about the controls on model performance that could lead to model refinement.
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However, quantitative understanding of its Quaternary origin, architecture, and hydrologic function remains incomplete. Here we develop a three-dimensional hydrostratigraphic model to characterize the deposition of clay and silt, fine-medium sands, and graveliferous sands using lithologic data from 75,000 boreholes compiled across the Lower Mississippi Valley and a geostatistical method—interval kriging. We find that cyclic glacial entrenchments, evidenced by remnants of pre-Wisconsinan postglacial sediments, alongside geodynamic activities shaped the MRVA basal configuration. Stratal weakening from faulting and salt diapirism enhanced glacial incision and thereby produced abrupt aquifer thickening. We demarcate the top of graveliferous sands as the regional marker of the Pleistocene-Holocene transition. The MRVA hydrostratigraphy reveals hydrologic function and geologic controls on groundwater storage and quality, advancing the assessment of aquifer sustainability under a changing climate, with implications for alluvial aquifers globally.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-025-02545-1","usgsCitation":"Song, Y., Tsai, F.T., Minsley, B.J., Wu, C., and Heggy, E., 2025, Quantitative subsurface characterization illuminates the origin of the Quaternary Mississippi River Valley alluvial aquifer: Communications Earth and Environment, v. 6, 646, 16 p., https://doi.org/10.1038/s43247-025-02545-1.","productDescription":"646, 16 p.","ipdsId":"IP-172339","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":497110,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-025-02545-1","text":"Publisher Index Page"},{"id":497056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River Valley alluvial aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94,\n 38\n ],\n [\n -94,\n 28.5\n ],\n [\n -88,\n 28.5\n ],\n [\n -88,\n 38\n ],\n [\n -94,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2025-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Yuqi","contributorId":363220,"corporation":false,"usgs":false,"family":"Song","given":"Yuqi","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":951315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tsai, Frank T.-C.","contributorId":305938,"corporation":false,"usgs":false,"family":"Tsai","given":"Frank","email":"","middleInitial":"T.-C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":951316,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951317,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Chenliang","contributorId":363221,"corporation":false,"usgs":false,"family":"Wu","given":"Chenliang","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":951318,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heggy, Essem","contributorId":363223,"corporation":false,"usgs":false,"family":"Heggy","given":"Essem","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":951319,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270110,"text":"70270110 - 2025 - Deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in fish communities of the Chesapeake Bay watershed, USA: A regional assessment and potential landscape drivers","interactions":[],"lastModifiedDate":"2025-08-11T15:28:06.323665","indexId":"70270110","displayToPublicDate":"2025-08-08T08:22:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in fish communities of the Chesapeake Bay watershed, USA: A regional assessment and potential landscape drivers","docAbstract":"Fish diseases in freshwater ecosystems pose significant ecological and socioeconomic challenges, yet monitoring them in wild populations is complex due to interactions between pathogens, hosts, and environmental conditions. We examine the prevalence and watershed-scale landscape drivers of external deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in 57 riverine fish species using a large dataset (577,266 individuals collected 2008–2019) from the Chesapeake Bay watershed that originated from state and federal agencies. Overall, DELT prevalence was low (1.4%), but was higher in larger, longer-lived species, including Channel Catfish (Ictalurus punctatus) (18.9%), Rock Bass (Ambloplites rupestris) (7.6%), Smallmouth Bass (Micropterus dolomieu) (7.3%), Brown Bullhead (Ameiurus nebulosus) (5.6%), and Yellow Bullhead (Ameiurus natalis) (5.1%), signifying their potential as regional environmental health indicators. Spatial analysis indicated warmer temperatures increased the estimated probability of DELT occurrence, whereas higher precipitation often mitigated the probability of DELT occurrence. Conservation strategies (e.g., best management practices) had mixed effectiveness in reducing DELT occurrence probability across agricultural and urban landscapes. Across the landscape, various drivers, including harvested forest, impervious land, and pesticide use, influenced DELT occurrence probability differently across species. However, uncertainty remains partly due to low prevalence and variability in sampling methods across agencies. Despite low overall prevalence, DELT occurrence is a rapid fish health indicator. Future research could emphasize species-specific responses and longitudinal studies that incorporate life stages and health indicators. Understanding these intricate, multi-scale interactions is vital for effective monitoring, conservation, and adaptive management of freshwater ecosystems.
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2025 - Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition","interactions":[],"lastModifiedDate":"2026-03-27T17:34:41.462684","indexId":"70271124","displayToPublicDate":"2025-08-08T07:55:30","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":"Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition","docAbstract":"The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2025.100274","usgsCitation":"Goldman, M.A., Lederer, G.W., Rosera, J.M., Graham, G.E., Mishra, A., and Yepremyan, A., 2025, Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition: Applied Computing and Geosciences, v. 27, 100274, 15 p., https://doi.org/10.1016/j.acags.2025.100274.","productDescription":"100274, 15 p.","ipdsId":"IP-164764","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":501736,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FXSPT1","text":"Data Release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Training and validation data from the AI for Critical Mineral Assessment Competition (ver. 2.0, July 2025)"},{"id":495004,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":495070,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2025.100274","text":"Publisher Index Page"}],"volume":"27","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldman, Margaret A. 0000-0003-2232-6362 mgoldman@usgs.gov","orcid":"https://orcid.org/0000-0003-2232-6362","contributorId":176468,"corporation":false,"usgs":true,"family":"Goldman","given":"Margaret","email":"mgoldman@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":947494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lederer, Graham W. 0000-0002-9505-9923","orcid":"https://orcid.org/0000-0002-9505-9923","contributorId":202407,"corporation":false,"usgs":true,"family":"Lederer","given":"Graham","email":"","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":947495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosera, Joshua Mark 0000-0003-3807-5000","orcid":"https://orcid.org/0000-0003-3807-5000","contributorId":270284,"corporation":false,"usgs":true,"family":"Rosera","given":"Joshua","email":"","middleInitial":"Mark","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":947496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":947497,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mishra, Asitang","contributorId":301178,"corporation":false,"usgs":false,"family":"Mishra","given":"Asitang","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":947498,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yepremyan, Alice","contributorId":358951,"corporation":false,"usgs":false,"family":"Yepremyan","given":"Alice","affiliations":[{"id":85724,"text":"NASA - JPL","active":true,"usgs":false}],"preferred":false,"id":947499,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271344,"text":"70271344 - 2025 - Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network","interactions":[],"lastModifiedDate":"2025-09-08T14:58:50.690039","indexId":"70271344","displayToPublicDate":"2025-08-08T07:48:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network","docAbstract":"Smallmouth bass (Micropterus dolomieu) is an important recreational sportfish and destructive non-native species when introduced into freshwater habitats. There is therefore a need to understand the drivers of, and uncertainties in, smallmouth bass population dynamics for various management objectives. We combined long-term smallmouth bass catch-effort and early life history data from a non-native population in the Green River sub-basin of the upper Colorado River to develop a demographic model that links interannual variability in environmental conditions to recruitment in three river reaches. We used the model to quantify how hydrology, river temperature, and exploitation drive smallmouth bass population dynamics. Early life stages were influenced by timing of hatching and discharge. Dispersal of age-0 fish and density-dependent dynamics were identified as primary sources of uncertainty. Determining the true nature of density-dependent dynamics is important, as the impact of exploitation-based management actions is dependent on the strengths of any density-dependent feedbacks. Our model provides a framework to predict smallmouth bass population responses to future climate conditions, reservoir operations, and exploitation levels.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0183","usgsCitation":"Bruckerhoff, L.A., Yackulic, C., Eppehimer, D.E., Bestgen, K.R., Jones, M.T., and Michaud, C., 2025, Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-24, https://doi.org/10.1139/cjfas-2024-0183.","productDescription":"24 p.","startPage":"1","endPage":"24","ipdsId":"IP-166028","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.07791103370728,\n 38.47445328611764\n ],\n [\n -110.07791103370728,\n 38.06202716185106\n ],\n [\n -109.72266590720243,\n 38.06202716185106\n ],\n [\n -109.72266590720243,\n 38.47445328611764\n ],\n [\n -110.07791103370728,\n 38.47445328611764\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bruckerhoff, Lindsey A.","contributorId":361014,"corporation":false,"usgs":false,"family":"Bruckerhoff","given":"Lindsey","middleInitial":"A.","affiliations":[{"id":86151,"text":"Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio","active":true,"usgs":false}],"preferred":false,"id":948117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eppehimer, Drew Elliot 0000-0003-0076-1494","orcid":"https://orcid.org/0000-0003-0076-1494","contributorId":333633,"corporation":false,"usgs":true,"family":"Eppehimer","given":"Drew","email":"","middleInitial":"Elliot","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bestgen, Kevin R.","contributorId":361015,"corporation":false,"usgs":false,"family":"Bestgen","given":"Kevin","middleInitial":"R.","affiliations":[{"id":86153,"text":"Larval Fish Laboratory, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":948120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, M. Tildon","contributorId":361016,"corporation":false,"usgs":false,"family":"Jones","given":"M.","middleInitial":"Tildon","affiliations":[{"id":86154,"text":"U.S. Fish and Wildlife Service, Upper Colorado River Endangered Fish Recovery Program, Vernal","active":true,"usgs":false}],"preferred":false,"id":948121,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Michaud, Chris","contributorId":361017,"corporation":false,"usgs":false,"family":"Michaud","given":"Chris","affiliations":[{"id":86156,"text":"U.S. Fish and Wildlife Service, Upper Colorado River Endangered Fish Recovery Program, Lakewood, Colorado","active":true,"usgs":false}],"preferred":false,"id":948122,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271489,"text":"70271489 - 2025 - Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca)","interactions":[],"lastModifiedDate":"2025-12-01T16:36:48.962288","indexId":"70271489","displayToPublicDate":"2025-08-07T08:26:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca)","title":"Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca)","docAbstract":"Freshwater mussels are among the most sensitive species to a variety of chemicals in water exposures. However, few studies have been conducted to evaluate the effect of toxicants in sediments on mussels. Industrial discharges containing polyaromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and metals entered the Kanawha River surrounding Blaine Island, South Charleston, West Virginia, USA; a river which supports eight federally endangered mussel species. We collected sediment samples from a highly contaminated site, a nearby upstream site, and a further upstream reference site to assess the effects of contaminated sediment on the survival and growth of a unionid mussel (fatmucket, Lampsilis siliquoidea) and a commonly tested benthic organism (amphipod, Hyalella azteca) using standard 28-d sediment toxicity tests. We also determined mussel toxicity in a serial dilution of the highly contaminated sediment. Results showed that concentrations of PAHs, VOCs, and metals in the contaminated sediment were consistently greater than the other two sites. The mean survival of mussels and amphipods in the reference sediment was 100% and 95%, respectively, whereas the mean survival of both test species in the contaminated sediment was 0%. In the sediment dilution study, mean survival and biomass of mussels in the ≥6.25% treatment were significantly reduced relative to the control, with a 25% inhibition concentration of 4.1% for survival and 3.6% for biomass. We used sediment screening values and equilibrium partitioning sediment benchmarks to determine that nickel, mercury, and PAH mixture were likely responsible for the toxicity observed to mussels and amphipods and will provide critical data to identify and mitigate the sources of the mixture in contaminated sediment.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1093/etojnl/vgaf200","usgsCitation":"Ivey, C.D., Steevens, J.A., Wang, N., Patnode, K., Kunz, J.L., and Besser, J.M., 2025, Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca): Environmental Toxicology and Chemistry, v. 44, no. 11, p. 3202-3211, https://doi.org/10.1093/etojnl/vgaf200.","productDescription":"10 p.","startPage":"3202","endPage":"3211","ipdsId":"IP-168278","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":495715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Blaine Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.69768088630148,\n 38.375274297576794\n ],\n [\n -81.69768088630148,\n 38.36648034085218\n ],\n [\n -81.67359328193788,\n 38.36648034085218\n ],\n [\n -81.67359328193788,\n 38.375274297576794\n ],\n [\n -81.69768088630148,\n 38.375274297576794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948950,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patnode, Kathleen","contributorId":361533,"corporation":false,"usgs":false,"family":"Patnode","given":"Kathleen","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":948951,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948952,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948953,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270897,"text":"70270897 - 2025 - Mapping ecological states in the upper Colorado River basin: Implications for fire management","interactions":[],"lastModifiedDate":"2025-08-26T14:55:55.492663","indexId":"70270897","displayToPublicDate":"2025-08-07T07:47:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22185,"text":"Environmental Research: Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Mapping ecological states in the upper Colorado River basin: Implications for fire management","docAbstract":"Spatially explicit information on ecosystem dynamics that offers a mechanistic understanding of ecological processes can benefit environmental management. Broad-scale maps based on state-and-transition models provide valuable insight into transitions among ecological states resulting from specific drivers within areas sharing similar climatic and edaphic characteristics ecological sites (ES). We aimed to quantify ecological dynamics of two ES groups in the Upper Colorado River Basin from 1986 to 2022 through annual maps of ecological states and assess potential drivers of observed state change. This region comprises important sagebrush shrublands and pinyon-juniper woodlands affected by non-native annual grass invasion, wildfires, and drought-induced tree mortality. Using field-based and remote sensing data, we modeled vegetation states using random forest models and mapped the states annually from 1986 to 2022. To demonstrate the utility of the state maps for monitoring and management, we used this time series of maps to investigate the influences of fire and drought on state occurrence. Our findings revealed a statistically significant increase in states invaded by non-native annual species (Invaded state), which replaced Grassland and Shrubland states, while Shrubland states decreased significantly, transitioning to invaded and Woodland states. Invaded states had the highest likelihood of burning, followed by Woodlands. Drought was associated with increased area of Grassland and Bare states, but with decreased area of invaded and Shrubland states. These results indicate an accelerating fire cycle is potentially leading to ongoing regional environmental degradation. Despite increasing drought conditions during the study period, the invaded states continued to increase in area, indicating additional underlying mechanisms. Our reproducible, broad-scale, ecologically-driven state mapping process enhances understanding of how drought, fire, and invasion by non-native plants can transform semiarid landscapes of the western USA.
","language":"English","publisher":"IOPscience","doi":"10.1088/2752-664X/adf55f","usgsCitation":"Severson, J.P., Bishop, T.B., Knight, A.C., Nauman, T.W., McNellis, B.E., Villarreal, M.L., Reed, S.C., Young, K.E., Brunson, M., and Duniway, M.C., 2025, Mapping ecological states in the upper Colorado River basin: Implications for fire management: Environmental Research: Ecology, v. 4, no. 3, 035004, 21 p., https://doi.org/10.1088/2752-664X/adf55f.","productDescription":"035004, 21 p.","ipdsId":"IP-177340","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2752-664x/adf55f","text":"Publisher Index Page"},{"id":494895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.86831536697369,\n 43.42056013100884\n ],\n [\n -113.9651578898021,\n 35.88773718647083\n ],\n [\n -113.42515034117945,\n 35.250820583313256\n ],\n [\n -107.51033502507522,\n 34.06562974390198\n ],\n [\n -106.78782386602788,\n 39.68976401424576\n ],\n [\n -108.89167762624004,\n 42.868819570877264\n ],\n [\n -110.86831536697369,\n 43.42056013100884\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":947311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bishop, Tara B.","contributorId":360618,"corporation":false,"usgs":false,"family":"Bishop","given":"Tara","middleInitial":"B.","affiliations":[{"id":86058,"text":"Utah Valley University, Department of Earth Science, Orem, UT, USA","active":true,"usgs":false}],"preferred":false,"id":947312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis 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USA","active":true,"usgs":false}],"preferred":false,"id":947316,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":217604,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947317,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Young, Kristina E.","contributorId":360622,"corporation":false,"usgs":false,"family":"Young","given":"Kristina","middleInitial":"E.","affiliations":[{"id":86061,"text":"Agricultural Research Service, USDA Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":947318,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brunson, Mark","contributorId":178263,"corporation":false,"usgs":false,"family":"Brunson","given":"Mark","affiliations":[],"preferred":false,"id":947319,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947320,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70269795,"text":"sir20255070 - 2025 - Water-resources inventory and assessment at Katahdin Woods and Waters National 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This compilation includes published and publicly accessible hydrologic data and resource assessments of streams, rivers, ponds, lakes, wetlands, vernal pools, and groundwater in and near KAWW. It also includes reports and datasets summarizing attributes of KAWW’s hydrologic infrastructure, such as stream crossings, dams, wastewater discharge plants, groundwater monitoring wells, and U.S. Geological Survey streamflow-gaging stations. Descriptions of data and details of current limitations in available datasets are included. Wetland, groundwater, streamflow, and water-quality information are all limited. Hydrography data are available; however, there are limited ground-truth data. Accurate streamlines within KAWW were developed from light detection and ranging (lidar) as a part of this work. Hydrologic infrastructure information is available from multiple sources; however, differences exist among the datasets. Datasets are summarized in appendix 1.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255070","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Tudor, A.L., 2025, Water-resources inventory and assessment at Katahdin Woods and Waters National Monument: U.S. Geological Survey Scientific Investigations Report 2025–5070, 16 p., https://doi.org/10.3133/sir20255070.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172915","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":493343,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5070/coverthb.jpg"},{"id":493344,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5070/sir20255070.pdf","text":"Report","size":"3.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5070 PDF"},{"id":493345,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255070/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5070 HTML"},{"id":493346,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5070/sir20255070.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5070 XML"},{"id":493347,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5070/images/"},{"id":493348,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94QSSSP","text":"USGS data release","linkHelpText":"Lidar-derived hydrography of Katahdin Woods and Waters National Monument, Maine, 2023"},{"id":494169,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118733.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maine","otherGeospatial":"Katahdin Woods and Waters National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.72130102300684,\n 46.47955962684003\n ],\n [\n -69.77140212088534,\n 46.34053986409191\n ],\n [\n -68.86469443278118,\n 45.69306934376334\n ],\n [\n -68.55553399953126,\n 45.50754141763335\n ],\n [\n -68.4003426211154,\n 46.253578073434255\n ],\n [\n -68.8194818178918,\n 46.353192713029614\n ],\n [\n -69.3168268626853,\n 46.282299294777914\n ],\n [\n -69.72130102300684,\n 46.47955962684003\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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10 Bearfoot Road
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Passive acoustic monitoring is a cost-effective, minimally invasive technology commonly used to study behavior and population dynamics of soniferous fish species. To understand the strengths and limitations of acoustic monitoring for this purpose at fish spawning aggregations (FSA) requires an assessment of the variability in aggregation-associated sounds (AAS) as a function of time, space, and proximity for spawning fishes of interest. Here, we evaluate temporal and spatial trends in the detection of AAS by Nassau Grouper (Epinephelus striatus) using an array of six hydrophones deployed across a large Nassau Grouper FSA at Little Cayman, Cayman Islands. We collected continuous data for nine days during a winter spawning season and subsequently used an automatic classifier to extract the embedded Nassau Grouper AAS. Using these data, we analyzed variability in spatiotemporal AAS detection rates across the array with a Bayesian mixed effects model. We found high variability in the detection of AAS across the spawning site, with positive correlations among neighboring hydrophone pairs trending toward negative correlations with distances exceeding 350 m. Indeed, temporal trends in AAS rates at the spawning site were approximately inverted at the two most distant hydrophones (~600 m). Across the hydrophone network, our model predicted strong positive effects of fish proximity, spawning behavior, and crepuscular periods on detected AAS. Our findings suggest hydrophone placement can strongly influence AAS detection rates and even basic temporal patterns in AAS across the spawning season. Given both the vagaries of movement and behavior of aggregating fish at spawning sites and the limits of AAS detection using standard monitoring tools, we suggest spawning site acoustic monitoring programs deploy hydrophone arrays of sufficient size to capture the site-wide trends in AAS rates if possible; this is particularly true if researchers hope to compare/contrast AAS rates between spawning sites or across seasons for the purpose of population assessment.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70081","usgsCitation":"Van Horn, C.J., Candelmo, A.C., Heppell, S.A., McCoy, C.R., Pattengill-Semmens, C.V., Waterhouse, L., Cherubin, L.M., Taylor, J., Michaels, W., Locascio, J., Ibrahim, A.K., and Semmens, B.X., 2025, Hydrophone placement yields high variability in detection of Epinephelus striatus calls at a spawning site.: Ecological Applications, v. 35, no. 5, e70081, 21 p., https://doi.org/10.1002/eap.70081.","productDescription":"e70081, 21 p.","ipdsId":"IP-170566","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494455,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70081","text":"Publisher Index Page"},{"id":494311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Little Cayman, Cayman Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.1384616529091,\n 19.741226725935803\n ],\n [\n -80.1384616529091,\n 19.647682249769503\n ],\n [\n -79.94337474038241,\n 19.647682249769503\n ],\n [\n -79.94337474038241,\n 19.741226725935803\n ],\n [\n -80.1384616529091,\n 19.741226725935803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Horn, Cameron J.","contributorId":359810,"corporation":false,"usgs":false,"family":"Van Horn","given":"Cameron","middleInitial":"J.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":946323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Candelmo, Alli C.","contributorId":359814,"corporation":false,"usgs":false,"family":"Candelmo","given":"Alli","middleInitial":"C.","affiliations":[{"id":13188,"text":"Reef Environmental Education Foundation (REEF)","active":true,"usgs":false}],"preferred":false,"id":946325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heppell, Scott A.","contributorId":359816,"corporation":false,"usgs":false,"family":"Heppell","given":"Scott","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":946326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCoy, Croy R.M.","contributorId":359818,"corporation":false,"usgs":false,"family":"McCoy","given":"Croy","middleInitial":"R.M.","affiliations":[{"id":85923,"text":"Department of Environment","active":true,"usgs":false}],"preferred":false,"id":946327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pattengill-Semmens, Christine V.","contributorId":359819,"corporation":false,"usgs":false,"family":"Pattengill-Semmens","given":"Christine","middleInitial":"V.","affiliations":[{"id":13188,"text":"Reef Environmental Education Foundation (REEF)","active":true,"usgs":false}],"preferred":false,"id":946328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waterhouse, Lynn 0000-0002-7455-7632","orcid":"https://orcid.org/0000-0002-7455-7632","contributorId":348524,"corporation":false,"usgs":true,"family":"Waterhouse","given":"Lynn","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":946329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cherubin, Laurent M.","contributorId":359820,"corporation":false,"usgs":false,"family":"Cherubin","given":"Laurent","middleInitial":"M.","affiliations":[{"id":65664,"text":"Harbor Branch Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":946330,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Taylor, J. Christopher","contributorId":359821,"corporation":false,"usgs":false,"family":"Taylor","given":"J. Christopher","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":946331,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Michaels, William","contributorId":359822,"corporation":false,"usgs":false,"family":"Michaels","given":"William","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":946332,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Locascio, James","contributorId":359823,"corporation":false,"usgs":false,"family":"Locascio","given":"James","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":946333,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ibrahim, Ali K.","contributorId":359812,"corporation":false,"usgs":false,"family":"Ibrahim","given":"Ali","middleInitial":"K.","affiliations":[{"id":65664,"text":"Harbor Branch Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":946324,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Semmens, Brice X.","contributorId":359824,"corporation":false,"usgs":false,"family":"Semmens","given":"Brice","middleInitial":"X.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":946334,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70269614,"text":"70269614 - 2025 - Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","interactions":[],"lastModifiedDate":"2025-08-06T15:04:23.764097","indexId":"70269614","displayToPublicDate":"2025-08-05T09:57:40","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":"Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","docAbstract":"Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne-field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation (r = 0.85) than solely using elevation (r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70356","usgsCitation":"Silva, G., Roberts, D., Byrd, K.B., Chadwick, D., Walker, I., and King, J., 2025, Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands: Ecosphere, v. 16, no. 8, e70356, 22 p., https://doi.org/10.1002/ecs2.70356.","productDescription":"e70356, 22 p.","ipdsId":"IP-172039","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":494433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70356","text":"Publisher Index Page"},{"id":493643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.86741425943123,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.4236991476653\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Silva, German","contributorId":358801,"corporation":false,"usgs":false,"family":"Silva","given":"German","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Dar","contributorId":358803,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":944181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chadwick, Dana","contributorId":358806,"corporation":false,"usgs":false,"family":"Chadwick","given":"Dana","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":944182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, Ian","contributorId":358809,"corporation":false,"usgs":false,"family":"Walker","given":"Ian","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, Jennifer","contributorId":358812,"corporation":false,"usgs":false,"family":"King","given":"Jennifer","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944184,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}