Individual behaviors are influenced by environmental, genetic, and demographic factors. Some animals choose to live in groups and cooperatively breed, and their behaviors can change depending on dynamic factors such as group size and composition that affect group persistence. In Idaho, USA, gray wolves (Canis lupus) are harvested annually, providing an opportunity to investigate the effects of harvest and seasonal behaviors on a population of cooperative breeders. These annual hunting and trapping seasons overlap with the dispersal and breeding periods for wolves and we do not know how harvest affects the vulnerability of different sex and age classes during these important biological periods. We applied 9 years (2009–2018) of genetic, age, and harvest data from harvested wolves to investigate how behaviors (dispersal and breeding) and biological drivers might influence the vulnerability of wolves to harvest. We created pedigrees from genotypes of non-invasively collected scats to estimate the expected proportion of the wolf population composed of 3 different age classes (pup, yearling, and sexually mature or ≥2 years old) and compared them to the observed number of each age class harvested during biologically significant periods (i.e., dispersal and breeding). We found that pups were more vulnerable to harvest in December when wolf harvest transitioned largely to trapping (accounts for 66% of harvest), and found evidence that adults were more vulnerable to harvest during their breeding season in January and February. In contrast, we found no difference in the expected versus observed number of wolves ≥2 years old in the harvest during peak dispersal season (December), or in the expected versus observed number of yearlings in the harvest during September and October when pups are mobile and groups of wolves abandon the use of pup-rearing sites. Some age classes were disproportionally harvested during certain periods for specific years, but this was not consistent across all years, suggesting there is more to learn about the vulnerability of different age classes to harvest. We found harvest can disproportionally affect some demographic classes of individuals depending on year, biological period, and harvest type. With wolves continuing to recolonize historical ranges, our approach can benefit managers and future studies with the goal of identifying how interannual harvest affects groups of wolves.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70024","usgsCitation":"Rebholz, P., Bassing, S., Waits, L., and Ausband, D.E., 2025, Linking age and social status of cooperative breeders to vulnerability throughout the harvest season: Journal of Wildlife Management, v. 89, no. 5, e70024, 14 p., https://doi.org/10.1002/jwmg.70024.","productDescription":"e70024, 14 p.","ipdsId":"IP-158746","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485834,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"89","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Rebholz, Peter F.","contributorId":355083,"corporation":false,"usgs":false,"family":"Rebholz","given":"Peter F.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":936841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bassing, Sarah B.","contributorId":275330,"corporation":false,"usgs":false,"family":"Bassing","given":"Sarah B.","affiliations":[{"id":342,"text":"Idaho Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":936970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waits, Lisette P.","contributorId":355084,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":936843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268481,"text":"70268481 - 2025 - A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities","interactions":[],"lastModifiedDate":"2025-06-27T15:13:12.47879","indexId":"70268481","displayToPublicDate":"2025-04-24T08:09:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities","docAbstract":"As wildfires increasingly affect water-supply watersheds, the demand for models to predict water-quality responses is increasing. This work reviews and synthesizes existing post-wildfire applications of water-quality models in the context of geographic and ecohydrological distribution, hydrologic and water-quality response process representation, model parameterization, model and input data scales, model calibration data availability, as well as calibration and performance evaluation approaches. Emphasis is placed on models that simulate water-quality output, rather than sediment and erosional response as the primary focus. Here, identified gaps and opportunities to advance the post-wildfire application of water-quality models include: 1. applying models in under-represented geographic and ecohydrologic regions, 2. simulating multiple streamflow generation mechanisms, including groundwater, with an emphasis on shifting dominant flow pathways as the landscape recovers following wildfire, 3. adding studies that include the simulation of metals, 4. incorporating more biogeochemical and in-stream processes to model applications, 5. applying finer spatial and temporal resolution of precipitation data input as well as finer spatial resolution hydrologic response units, 6. implementing fully distributed grid or element models or finer resolution response units to capture burn severity heterogeneity, 7. collecting enhanced water-quality data for model calibration and validation, 8. conducting model-intercomparison studies, and 9. developing model parameter value guidance in post-wildfire applications. These identified gaps and opportunities may assist users in deciding on key processes and approaches to consider in modeling post-wildfire water-quality conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179435","usgsCitation":"Shephard, Z.M., Partridge, T.F., Murphy, S.F., Walvoord, M.A., and Ebel, B., 2025, A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities: Science of the Total Environment, v. 979, 179435, 15 p., https://doi.org/10.1016/j.scitotenv.2025.179435.","productDescription":"179435, 15 p.","ipdsId":"IP-165381","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":491530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"979","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shephard, Zachary M. 0000-0003-2994-3355","orcid":"https://orcid.org/0000-0003-2994-3355","contributorId":222581,"corporation":false,"usgs":true,"family":"Shephard","given":"Zachary","email":"","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":941497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941499,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266130,"text":"70266130 - 2025 - HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","interactions":[],"lastModifiedDate":"2025-04-30T14:55:18.955047","indexId":"70266130","displayToPublicDate":"2025-04-24T07:42:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12552,"text":"Scientific Data - Nature","active":true,"publicationSubtype":{"id":10}},"title":"HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","docAbstract":"Sub-Saharan Africa (SSA) faces severe agricultural data scarcity amidst high food insecurity and a large agricultural yield gap, making crop production data crucial for understanding and enhancing food systems. To address this gap, HarvestStat Africa presents the largest compilation of open-access subnational crop statistics and time-series across SSA. Based on agricultural statistics collated by USAID’s Famine Early Warning Systems Network, the subnational crop statistics are standardized and calibrated across changing administrative units to produce consistent and continuous time-series. The dataset includes 546,605 records, primarily spanning from 1980 to 2022, detailing crop production, harvested areas, and yields for 33 countries and 90 crop types, including key cereals in SSA such as wheat, maize, rice, sorghum, barley, millet, and fonio. This new dataset enhances our understanding of how climate variability and change influence agricultural production, supports subnational food system analysis, and aids in operational yield forecasting. As an open-source resource, it sets an important precedent for sharing subnational crop statistics to inform decision-making and modeling efforts.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-025-05001-z","usgsCitation":"Lee, D., Anderson, W., Chen, X., Davenport, F., Shukla, S., Sahajpal, R., Budde, M., Rowland, J., Verdin, J., You, L., Ahouangbenon, M., Frankel Davis, K., Kebede, E., Ehrmann, S., Justice, C., and Meyer, C., 2025, HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa: Scientific Data - Nature, v. 12, 690, 13 p., https://doi.org/10.1038/s41597-025-05001-z.","productDescription":"690, 13 p.","ipdsId":"IP-171185","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":487839,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-025-05001-z","text":"Publisher Index Page"},{"id":485134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"sub-Saharan Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -17.424723015261094,\n 17.16083963368456\n ],\n [\n -14.217821051176188,\n 5.539623170864749\n ],\n [\n 4.256539808755463,\n -0.30131645411445973\n ],\n [\n 12.866067481920282,\n -36.38147254759492\n ],\n [\n 38.836875517610935,\n -36.542196807270734\n ],\n [\n 51.14863109990904,\n 4.494257003339012\n ],\n [\n 38.836875517610935,\n 17.16083963368456\n ],\n [\n -17.424723015261094,\n 17.16083963368456\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Lee, Donghoon 0000-0001-5438-903X","orcid":"https://orcid.org/0000-0001-5438-903X","contributorId":292417,"corporation":false,"usgs":false,"family":"Lee","given":"Donghoon","email":"","affiliations":[{"id":62899,"text":"Climate Hazards Center, University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":934708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Weston","contributorId":353902,"corporation":false,"usgs":false,"family":"Anderson","given":"Weston","affiliations":[{"id":84523,"text":"NASA Goddard Space Flight Center, Greenbelt, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":934709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Xuan","contributorId":204821,"corporation":false,"usgs":false,"family":"Chen","given":"Xuan","email":"","affiliations":[{"id":36987,"text":"Louisiana State University, College of Coast and 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":934715,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Verdin, James 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":145830,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":934716,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"You, Liangzhi","contributorId":353904,"corporation":false,"usgs":false,"family":"You","given":"Liangzhi","affiliations":[{"id":84527,"text":"International Food Policy Research Institute, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":934717,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ahouangbenon, Matthieu","contributorId":353905,"corporation":false,"usgs":false,"family":"Ahouangbenon","given":"Matthieu","affiliations":[{"id":84528,"text":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716 USA","active":true,"usgs":false}],"preferred":false,"id":934718,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Frankel Davis, Kyle","contributorId":209958,"corporation":false,"usgs":false,"family":"Frankel Davis","given":"Kyle","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":934719,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kebede, Endalkachew","contributorId":353906,"corporation":false,"usgs":false,"family":"Kebede","given":"Endalkachew","affiliations":[{"id":84528,"text":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716 USA","active":true,"usgs":false}],"preferred":false,"id":934720,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ehrmann, Steffen","contributorId":353907,"corporation":false,"usgs":false,"family":"Ehrmann","given":"Steffen","affiliations":[{"id":13099,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":934721,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Justice, Christina","contributorId":347086,"corporation":false,"usgs":false,"family":"Justice","given":"Christina","email":"","affiliations":[{"id":37106,"text":"Cherokee Nation","active":true,"usgs":false}],"preferred":false,"id":934722,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Meyer, Carsten","contributorId":193124,"corporation":false,"usgs":false,"family":"Meyer","given":"Carsten","email":"","affiliations":[],"preferred":false,"id":934723,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","interactions":[{"subject":{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","indexId":"ofr20211030T","publicationYear":"2025","noYear":false,"chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2025-04-24T14:11:03.291909","indexId":"ofr20211030T","displayToPublicDate":"2025-04-23T12:23:04","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":"2021-1030","chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","docAbstract":"This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence since 2021. These reports present and detail the methodology and procedures for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances.
To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing.
We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager.
The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030T","usgsCitation":"Park, S., Shrestha, M., Kim, M., Sampath, A., and Clauson, J., 2025, System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor, chap. T of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 17 p., https://doi.org/10.3133/ofr20211030T.","productDescription":"v, 17 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170097","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":484900,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/t/images/"},{"id":484899,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.XML"},{"id":484898,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-T"},{"id":484897,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/t/coverthb.jpg"},{"id":484901,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030T/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Black Carp Mylopharyngodon piceus were imported into the United States in the 1970s and 1980s for use in aquaculture; escape occurred and reported wild captures increased. Lacking species-specific capture methods, we assessed fisheries dependent incidental Black Carp catches for a common method, hoop nets, by kernel density analysis to identify an area of increased reporting and compare frequency of reports for water temperature, river stage, and capture date to identify seasonality. We then used fisheries independent effort to identify co-occurrence of species via non-metric multi-dimensional scaling and fit Black Carp catch and environmental covariates by generalized linear models to assess site-specific environmental covariates facilitating capture. The best approximating distribution was refitted for predictions and inference. The greatest density of fisheries dependent hoop net captures (39 %) was near the confluence of the Missouri and Mississippi rivers, primarily from July-September. Captures were characterized by median water temperature 26.7°C, river stage 5.02 m, and 223 day-of-year (DOY; mid-August). Ordination of fisheries independent catch identified similarity in environmental covariates of Smallmouth Buffalo Ictiobus bubalus and Black Carp. The probability of capturing ≥ 1 Black Carp increased with DOY, decreased with increasing current velocity, and increased with depth. Most captures occurred in outside bends (87 %) or side channels (12 %). Probability of Black Carp capture was low but increased in summer and early fall when stage is lower, facilitating reduced current velocity and access to deeper areas. Results may be validated beyond this river segment to test if site-specific hydrology or habitat characteristics facilitated increased commercial and biologist capture and for replication.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2025.107368","usgsCitation":"Kroboth, P., Colvin, M.E., and Broaddus, C., 2025, Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus: Fisheries Research, v. 285, 107368, 12 p., https://doi.org/10.1016/j.fishres.2025.107368.","productDescription":"107368, 12 p.","ipdsId":"IP-167531","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":487576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2025.107368","text":"Publisher Index Page"},{"id":485444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Missouri","otherGeospatial":"MIssissippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.22145042868262,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.90746978465282\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"285","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroboth, Patrick 0000-0002-9447-4818","orcid":"https://orcid.org/0000-0002-9447-4818","contributorId":216578,"corporation":false,"usgs":true,"family":"Kroboth","given":"Patrick","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colvin, Michael E. 0000-0002-6581-4764","orcid":"https://orcid.org/0000-0002-6581-4764","contributorId":331490,"corporation":false,"usgs":true,"family":"Colvin","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broaddus, Courtney 0000-0003-3851-3584","orcid":"https://orcid.org/0000-0003-3851-3584","contributorId":354595,"corporation":false,"usgs":true,"family":"Broaddus","given":"Courtney","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935822,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","interactions":[{"subject":{"id":70257569,"text":"70257569 - 2024 - Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington","indexId":"70257569","publicationYear":"2024","noYear":false,"title":"Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington"},"predicate":"SUPERSEDED_BY","object":{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","indexId":"sir20255029","publicationYear":"2025","noYear":false,"title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington"},"id":1}],"lastModifiedDate":"2025-08-07T21:05:21.759632","indexId":"sir20255029","displayToPublicDate":"2025-04-23T07:58:02","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5029","displayTitle":"Spatial Stream Network Modeling of Water Temperature within the White River Basin, Mount Rainier National Park, Washington","title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","docAbstract":"Water temperature is a primary control on the occurrence and distribution of fish and other ectothermic aquatic species. In the Pacific Northwest, cold-water species such as Pacific salmon (Oncorhynchus spp.) and bull trout (Salvelinus confluentus) have specific temperature requirements during different life stages that must be met to ensure the viability of their populations. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support a number of cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer baseflow during August and September, and the climatic, hydrologic, and physical processes regulating it are not well constrained. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and 7-day average daily maximum water temperature for August and September for the White River Basin. The SSN models were calibrated using water temperature measurements collected in 2010 through 2020. The extent of the models included the White River and its tributaries upstream from its confluence with Silver Creek in Mount Rainier National Park, Washington. SSN models incorporated covariates hypothesized to represent the climatic, hydrologic, and physical processes that influence water temperature. SSN models were fit to the measured data and compared to generalized linear models that lacked spatial autocovariance structures. Statistically significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow-water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. Additionally, models of mean August and September water temperature had better predictive performance than those of 7-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of the mainstem White River compared to cooler water temperatures in the high-elevation headwater streams. The proportion of ice cover emerged as an inversely related significant covariate to both mean August and September water temperature because streams that receive glacial meltwater are colder than non-glaciated streams. Water temperatures of the upper White River increased downstream and are attributed to warming of water temperature from accumulated solar radiation and inflow of non-glaciated tributaries. Estimated water temperatures for the upper White River model are 3–4 degrees Celsius (°C) warmer for tributaries, but 1–2 °C cooler for the mainstem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale NorWeST model are attributed to the fact that the upper White River SSN included water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream from the Mount Rainer National Park boundary were used in the regional scale model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255029","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Gendaszek, A.S., Leach, A.C., and Jaeger, K.L., 2025, Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington (ver. 1.1, May 2025): U.S. Geological Survey\nScientific Investigations Report 2025–5029, 17 p., https://doi.org/10.3133/sir20255029. [Supersedes preprint https://doi.org/10.31223/X5712P.]","productDescription":"Report: vi, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-168299","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":484931,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/6542802dd34ee4b6e05bd2cb","text":"USGS data release","description":"USGS data release","linkHelpText":"Stream Temperature Models of White River Watershed, Mount Rainier National Park, Washington"},{"id":484872,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.XML"},{"id":484871,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5029/images"},{"id":484870,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255029/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5029"},{"id":484869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5029"},{"id":484868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5029/coverthb2.jpg"},{"id":486241,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5029/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":493767,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118576.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park, upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.75,\n 47\n ],\n [\n -121.75,\n 46.8333\n ],\n [\n -121.5,\n 46.8333\n ],\n [\n -121.5,\n 47\n ],\n [\n -121.75,\n 47\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: April 23.2025; Version 1.1: May 20, 2025","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Objective
Translocation is a tool being explored to restart extirpated populations or facilitate new populations of endangered spring-dependent fish populations. Our objective was to provide information on habitat requirements for endangered White River Spinedace Lepidomeda albivallis during all seasons of the year and the population demographics that are necessary to plan conservation translocations of this species
Methods
We tagged and released White River Spinedace with passive integrated transponders during four twice-a-year events. Fish were subsequently recaptured or detected on six passive antennas placed throughout the Flag Springs Complex, Nevada. We evaluated movement data to understand seasonal habitat use patterns, used a Barker model to estimate monthly survival rates, adjusted counts to account for capture probability and estimate abundance, and applied reverse-time mark–recapture models to estimate recruitment to 70 mm total length.
Results
White River Spinedace were more active but used similar habitats during spawning seasons than during nonspawning seasons. Median life expectancy was about 5 months after tagging, and only 1% of adult White River Spinedace survived 3–4 years posttagging. The estimated population size in the Flag Springs Complex during our sampling period (November 2020 to June 2022) was fewer than a thousand White River Spinedace, and this estimate has been steady or slightly increasing.
Conclusions
Complex spring habitats with water temperatures ranging about 13°C to 21°C that are free from piscivorous fish are appropriate for White River Spinedace. The White River Spinedace population at Flag Springs is small but stable or increasing in size.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/tafafs/vnaf007","usgsCitation":"Burdick, S.M., Harter, J.F., Beckstrand, M., Paul-Wilson, R.K., Hayes, B., Perry, R.W., and Smith, C.D., 2025, Seasonal movements and demographics of the endangered White River Spinedace to inform restoration and translocation: Transactions of the American Fisheries Society, v. 154, no. 3, p. 246-261, https://doi.org/10.1093/tafafs/vnaf007.","productDescription":"16 p.","startPage":"246","endPage":"261","ipdsId":"IP-165644","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harter, James F.","contributorId":365736,"corporation":false,"usgs":false,"family":"Harter","given":"James","middleInitial":"F.","affiliations":[{"id":87201,"text":"United States Fish and Wildlife Service, Las Vegas, Nevada","active":true,"usgs":false}],"preferred":false,"id":954696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beckstrand, Mark","contributorId":365737,"corporation":false,"usgs":false,"family":"Beckstrand","given":"Mark","affiliations":[{"id":87202,"text":"Nevada Department of Wildlife, Eli, Nevada","active":true,"usgs":false}],"preferred":false,"id":954697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul-Wilson, Rachael Katelyn 0000-0002-8213-1084","orcid":"https://orcid.org/0000-0002-8213-1084","contributorId":298894,"corporation":false,"usgs":true,"family":"Paul-Wilson","given":"Rachael","email":"","middleInitial":"Katelyn","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Brian S. 0000-0001-8229-4070","orcid":"https://orcid.org/0000-0001-8229-4070","contributorId":37022,"corporation":false,"usgs":true,"family":"Hayes","given":"Brian S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":954699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Russell W. 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":214553,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Collin D. 0000-0003-4184-5686 cdsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-4184-5686","contributorId":3111,"corporation":false,"usgs":true,"family":"Smith","given":"Collin","email":"cdsmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954701,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270100,"text":"70270100 - 2025 - Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?","interactions":[],"lastModifiedDate":"2025-08-11T15:39:28.971692","indexId":"70270100","displayToPublicDate":"2025-04-22T08:35:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5781,"text":"The Depositional Record","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?","docAbstract":"Emblematic of global coral-reef ecosystem decline, the coral ecosystem-engineer Acropora palmata is now rare throughout much of the western Atlantic. Understanding when and where this foundation species occurred during the past can provide information about the environmental limits defining its distribution through space and time. In this paper, the present, historical and newly dated geological records of A. palmata are compared to reveal novel insights into the environmental constraints on its occurrence in Dry Tortugas National Park, a subtropical reef system at the south-western terminus of the Florida reef tract. Although past geological investigation found little evidence of the species in the park, a single, moderately sized A. palmata reef existed throughout historical times (1881 Common Era [CE] to present day; ‘historical population’, termed herein). Over the last 140 years, repeated population declines occurred with little to no recovery, culminating in the extirpation of A. palmata from the area during the 2023–2024 CE global coral bleaching event. Reported here for the first time is a significant record of Late Holocene A. palmata populations that existed from ca 4500 to 375 years before present (‘Late Holocene population,’ termed herein) in three broadly distributed areas of the shallow Dry Tortugas platform. This discovery challenges previous assumptions regarding the species' limited contribution to reef development in the area by providing data that extend the known spatial and stratigraphic extent of Holocene populations in this location. It is posited that, although the Late Holocene climate largely suppressed regional reef development, the new records provide evidence for centennial-scale periods of more favourable and stable climate that allowed for short-term expansions of A. palmata populations in the Dry Tortugas. In conclusion, the species' prospects for future success in this and other subtropical location
","language":"English","publisher":"Wiley","doi":"10.1002/dep2.70005","usgsCitation":"Stathakopoulos, A., Toth, L., Modys, P.A., Johnson, S.A., and Kuffner, I.B., 2025, Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?: The Depositional Record, v. 11, no. 3, p. 808-828, https://doi.org/10.1002/dep2.70005.","productDescription":"21 p.","startPage":"808","endPage":"828","ipdsId":"IP-169190","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494189,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/dep2.70005","text":"Publisher Index Page"},{"id":493935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.9661549521622,\n 24.68040740481777\n ],\n [\n -82.9661549521622,\n 24.595463709079198\n ],\n [\n -82.8127098632192,\n 24.595463709079198\n ],\n [\n -82.8127098632192,\n 24.68040740481777\n ],\n [\n -82.9661549521622,\n 24.68040740481777\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Modys, Peter Alexander Bacon 0000-0002-2948-5983","orcid":"https://orcid.org/0000-0002-2948-5983","contributorId":336719,"corporation":false,"usgs":true,"family":"Modys","given":"Peter","email":"","middleInitial":"Alexander Bacon","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945454,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269704,"text":"70269704 - 2025 - Regional patterns in U.S. wildfire activity: The critical role of ignition sources","interactions":[],"lastModifiedDate":"2025-07-30T15:23:30.059551","indexId":"70269704","displayToPublicDate":"2025-04-22T08:17:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Regional patterns in U.S. wildfire activity: The critical role of ignition sources","docAbstract":"As extreme wildfires increase globally, understanding their causes is critical for effective management. While climate and housing growth are commonly linked to rising fire activity, the role of specific ignition sources—particularly human-caused—remains understudied. Analyzing a 79-year dataset (1940–2019) from U.S. Forest Service regions across the continental United States, we found that different ignition sources in different regions have been a major driver of wildfire trends, accounting for 60%–80% of the interannual variation in fire frequency and approximately 20% in area burned across most U.S. regions. Lightning and campfires were the dominant sources in western regions, while arson drove fire activity east of the Mississippi River. Trends also varied significantly by region and over time, with housing growth explaining more in terms of fire frequency and climate primarily influencing area burned. Importantly, frequent fires often originated from different sources than those causing the largest areas burned. Prevention of human-caused ignitions, such as campfires and arson, could offer efficient and effective strategies to mitigate wildfire impacts on human and natural systems under changing climate and land-use conditions.
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0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":69082,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon E.","affiliations":[],"preferred":false,"id":944482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conlisk, Erin","contributorId":298237,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":944483,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gough, Mike","contributorId":296907,"corporation":false,"usgs":false,"family":"Gough","given":"Mike","email":"","affiliations":[{"id":64235,"text":"Conservation Biology Institute, 136 SW Washington Ave., Suite 202, Corvallis, OR 97333, USA","active":true,"usgs":false}],"preferred":false,"id":944484,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265917,"text":"ofr20251021 - 2025 - Groundwater budget for the surficial aquifer surrounding Lake Nokomis, Minneapolis, Minnesota","interactions":[],"lastModifiedDate":"2025-08-07T21:01:41.485745","indexId":"ofr20251021","displayToPublicDate":"2025-04-21T13:44:28","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":"2025-1021","displayTitle":"Groundwater Budget for the Surficial Aquifer Surrounding Lake Nokomis, Minneapolis, Minnesota","title":"Groundwater budget for the surficial aquifer surrounding Lake Nokomis, Minneapolis, Minnesota","docAbstract":"During prolonged periods of above-average precipitation, rising groundwater levels have the potential to cause damage to and interfere with underground infrastructure and building foundations. To understand the relations between precipitation and groundwater in the vicinity of Lake Nokomis, the U.S. Geological Survey, in collaboration with the University of Minnesota, quantified five components of the groundwater budget: groundwater recharge, change in surficial aquifer storage, surficial aquifer groundwater discharge to Lake Nokomis, groundwater evapotranspiration, and groundwater discharge to underlying bedrock aquifers. Field data, geologic records, and empirical calculation methods were used to quantify groundwater budget components for April 2023 through April 2024. Lake water budget data indicate that Lake Nokomis is a flowthrough system during periods with no outflow through the weir, with groundwater inputs equal to outputs. Roughly 40 percent of precipitation that fell in the study area was added to the surficial aquifer as recharge. Uncertainty in the vertical hydraulic conductivity resulted in wide-ranging estimates (spanning three orders of magnitude) of water discharging from the surficial aquifer to the underlying bedrock aquifer. Drought conditions persisted for the duration of this study and were not representative of the conditions that motivated this study. This study is a start towards understanding relations between precipitation, Lake Nokomis levels, and groundwater levels that could affect local underground infrastructure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251021","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and in collaboration with the University of Minnesota","usgsCitation":"Livdahl, C.T., 2025, Groundwater budget for the surficial aquifer surrounding Lake Nokomis, Minneapolis, Minnesota: U.S. Geological Survey Open-File Report 2025–1021, 15 p., https://doi.org/10.3133/ofr20251021.","productDescription":"Report: vi, 15 p.; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-166630","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":493763,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118543.htm","linkFileType":{"id":5,"text":"html"}},{"id":484783,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":484781,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1021/images/"},{"id":484780,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1021/ofr20251021.XML"},{"id":484782,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251021/full"},{"id":484779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1021/ofr20251021.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1021"},{"id":484778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1021/coverthb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Lake Nokomis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.23325811481614,\n 44.91617052326589\n ],\n [\n -93.2517175215867,\n 44.91617052326589\n ],\n [\n -93.2517175215867,\n 44.901128494318755\n ],\n [\n -93.23325811481614,\n 44.901128494318755\n ],\n [\n -93.23325811481614,\n 44.91617052326589\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
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Freshwater mussels have a unique life history in which larval mussels (glochidia) act as obligate parasites to fish hosts. Host selectivity may be species specific, and identification of host fish is a critical step in conservation planning for individual mussel species. The Flint River harbors ~23% of the freshwater mussel (order Unionida) diversity in the state of Georgia, USA. Nine species in the basin are state or federally listed, and local diversity is threatened by shifting hydrologic conditions, increasing habitat loss, and sedimentation. However, knowledge on host species is lacking for nearly 40% of mussel species in the Flint River, limiting the efforts of conservation managers. In this study, we assessed the use of host fish by mussels by identifying the species of naturally encysted mussel larvae and transformed juveniles found on wild-caught fishes. Infested fishes were collected in the summers of 2021 and 2022 and held in laboratory conditions. Glochidia and juvenile mussels were collected as they excised from live hosts and were identified by DNA barcoding with the cytochrome oxidase c subunit I locus. Twenty-eight unique mussel–host relationships were identified, 27 of which were considered novel when cross-referenced to the existing mussel–host databases and peer-reviewed literature. Our data build upon knowledge of host use in unionids and further demonstrate the importance of understanding patterns in wild host use.
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Wild pigs (Sus scrofa) are a highly detrimental invasive species in North America, directly and indirectly affecting native species. Co-occurrence of wild pigs and native species may lead to interspecific interactions that alter ecological communities. Accordingly, we investigated spatial and temporal factors influencing detection and occupancy of Eurasian Wild Pig and Nine-banded Armadillo (Dasypus novemcinctus) before examining interspecific effects. We analyzed camera-trap data collected from August to September 2021 using a hierarchical modeling framework to estimate detection and occupancy of both species individually (single-species analyses) and concurrently (conditional co-occurrence analyses). We observed higher Wild Pig detection rates and space use in late summer and in areas with greater riparian cover, respectively. Armadillo detection increased linearly throughout our sampling season and in response to precipitation. Moreover, armadillo detection was 3.5 to 5.1× higher at sites used by wild pigs, regardless of whether wild pigs were detected during a survey period. Occupancy of armadillo was best explained by a quadratic trend in site elevation but did not depend on the presence of wild pigs. Our results indicate that wild pigs may influence armadillo detection (or site-use intensity), but not occupancy, therefore revealing nuanced interspecific interactions. Between species, we observed high overlap in diel activity but significantly different activity peaks, with armadillos being strictly nocturnal and wild pigs being crepuscular but with more cathemeral activity, suggesting that fine-scale temporal partitioning may have occurred. Our results provide insights into the influence of a large-bodied and destructive invasive species (Wild Pig) on a smaller, ecologically important native species (Nine-banded Armadillo).
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Hawaiian coastal wetlands provide important habitat for endangered waterbirds, invertebrates, plants, and the Hawaiian hoary bat (ʻōpeʻapeʻa; Lasiurus semotus) as well as support Indigenous food systems. Currently, numerous agencies and groups in Hawaiʻi collect data on coastal wetlands, but information is not typically shared and methods are not standardized. A statewide, standardized, coastal wetland monitoring program with a centralized database would allow managers to keep better track of progress toward restoration goals, population changes of conservation-reliant species, outbreaks and impacts of avian botulism, rates of coastal salinization, and many other critical issues across space and time. Monitoring combined with targeted research could fill critical knowledge gaps about the types, functions, values, and biodiversity of Hawaiian coastal wetlands. Ultimately, the improved knowledge gained from long-term coastal wetland monitoring could inform landscape-scale restoration actions and adaptive management of coastal wetlands under sea-level rise and climate change.
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While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.
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Electroanesthesia, an alternative to traditional chemical anesthetics, uses weak electrical current to immobilize fish while current is applied, resulting in near-immediate induction and recovery. The physiological effects of electroanesthesia appear to be minimal or comparable to those of chemical anesthetics, but knowledge gaps exist on cardiac responses during treatment and behavioral responses in a controlled setting.
Lake Trout Salvelinus namaycush were immobilized by one of four treatments: control (physical restraint); eugenol (AQUI-S 20E); and continuously applied, low-voltage electroanesthesia with either constant DC or transcutaneous electrical nerve stimulation (TENS). We evaluated the heart rate while fish were immobilized, reflexes at 1 h posttreatment, and survival and movements in a laboratory setting over a 6-d posttreatment period.
The heart rates of fish immobilized with either electroanesthesia treatment (constant DC: mean ± SE = 12.6 ± 1.1 beats/min; TENS: 13.1 ± 1.2 beats/min) were not significantly different from those of control fish (10.0 ± 1.2 beats/min) while immobilized. However, fish immobilized with eugenol exhibited heart rates that were more than three times higher (32.1 ± 1.2 beats/min) than those of control fish while immobilized. Treatments had no effect on reflex scores at 1 h posttreatment, the 6-d survival rate of fish (100%), or movement behavior during the hours (1–24 h) or days (6 d) after treatment.
This study adds to a growing body of research finding a lack of negative physiological or behavioral responses resulting from continuously applied electroanesthesia, suggesting that it is at least as safe as many chemical anesthetics in this respect. The significant cardiac response of Lake Trout while immobilized with eugenol warrants further investigation. While the body of research on immobilizing agents in fish grows, species-specific data remain sparse, and users would benefit from pilot testing before wider application.
Stream ecosystems face continuous pressures from multiple anthropogenic stressors that reshape biological communities and impact ecosystem health and services. Managers can encounter challenges in stewarding ecosystems threatened by multiple stressors, in part because most multiple stressor studies are experimental and, while valuable, offer limited management relevance in targeting these stressors on the landscape. Recent advances in causal inference coupled with large biomonitoring data sets could further understanding of observational stressor-response relationships, aiding management. In this study, we use bioassessment data in the Chesapeake Bay watershed in the mid-Atlantic region of the United States to identify how water quality and physical habitat stressors influence key benthic macroinvertebrate response metrics, considering hierarchical relationships using Bayesian networks. Results suggest water temperature and specific conductivity were prevalent stressors in a mountainous region (northern Appalachians), whereas in an agriculturally dominated region (southern Appalachians) physical habitat alterations were the predominant stressor. In mixed-land use regions (Piedmont & Coastal Plains), specific conductivity was a key stressor, but habitat heterogeneity was important for macroinvertebrate metrics. To illustrate how these stressor-response relationships can be used to guide management decisions, we applied the resist-accept-direct (RAD) framework to develop a portfolio of management options based on predicted changes in macroinvertebrate metrics in response to physical habitat and water quality stressors. For example, accepting changes in areas with co-occurring stressors may be the most feasible option, whereas directing changes through stream restoration or water quality improvements may be effective in areas with single stressor groups. By leveraging observational bioassessment data and causal inference to identify key stressor-response relationships, this research supports decision making by building a simple, strategic management portfolio.
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2022","interactions":[],"lastModifiedDate":"2025-08-07T20:58:26.866569","indexId":"sim3532","displayToPublicDate":"2025-04-16T10:12:51","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3532","displayTitle":"Altitude of the Potentiometric Surface and Depth to Water in the Mississippi River Valley Alluvial Aquifer, Spring 2022","title":"Altitude of the potentiometric surface and depth to water in the Mississippi River Valley alluvial aquifer, spring 2022","docAbstract":"Potentiometric-surface and depth-to-water maps for spring 2022 were created for the Mississippi River Valley alluvial aquifer (MRVA) using groundwater-altitude data from 1,136 wells completed in the MRVA and from the altitude of the top of the water surface in area rivers from 160 streamgages. The potentiometric-surface and depth-to-water maps for 2022 were created to support investigations to characterize the MRVA as part of the U.S. Geological Survey Water Availability and Use Science Program. Sufficient data were available to map the potentiometric surface and depth to water of the MRVA for spring 2022 for about 83 percent of the aquifer area. The potentiometric contours ranged from 0 to 340 feet (ft) above the North American Vertical Datum of 1988. The regional direction of groundwater gradient was generally to the south-southwest, except in areas of groundwater-altitude depressions, where the groundwater gradient was into the depression, and near rivers, where the groundwater gradient can be from aquifer to the river or from the river into the aquifer. There are large depressions in the potentiometric-surface map in the lower one-half of the Cache region and in much of the Grand Prairie and Delta regions. Depth to water in the MRVA, spring 2022, by well ranged from 5.00 ft above land surface to 145.66 ft below land surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3532","programNote":"Water Availability and Use Science Program","usgsCitation":"McGuire, V.L., Strauch, K.R., Wojtylko, E.A., Asquith, W.H., Nottmeier, A.M., Thomas, J.C., Tollett, R.W., and Kress, W.H., 2025, Altitude of the potentiometric surface and depth to water in the Mississippi River Valley alluvial aquifer, spring 2022: U.S. Geological Survey Scientific Investigations Map 3532, 5 sheets, scales 1:1,000,000 and 1:2,000,000, 19-p. pamphlet, https://doi.org/10.3133/sim3532.","productDescription":"Pamphlet: ix, 19 p.; 5 Sheets: 30.00 x 45.00 inches or smaller; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-137606","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":493761,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118540.htm","linkFileType":{"id":5,"text":"html"}},{"id":484091,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3532/full"},{"id":484092,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3532/downloads/sim3532_sheet1.pdf","text":"Sheet 1","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":484094,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3532/downloads/sim3532_sheet2.pdf","text":"Sheet 2","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":484095,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3532/downloads/sim3532_sheet3.pdf","text":"Sheet 3","size":"7.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":484096,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3532/downloads/sim3532_sheet4.pdf","text":"Sheet 4","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":484097,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3532/downloads/sim3532_sheet5.pdf","text":"Sheet 5","size":"7.6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":484098,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95883TJ","text":"USGS data release","linkHelpText":"Datasets used to map the potentiometric surface and depth to water, Mississippi River Valley alluvial aquifer, spring 2022"},{"id":484099,"rank":12,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":484087,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3532/coverthb.jpg"},{"id":484088,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3532/sim3532.pdf","text":"Pamphlet","size":"14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3532"},{"id":484089,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3532/sim3532.XML"},{"id":484090,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3532/images/"}],"country":"United States","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 -91.7960849146639,\n 39.04358924105398\n ],\n [\n -91.7960849146639,\n 28.527159743704843\n ],\n [\n -88.44541245375156,\n 28.527159743704843\n ],\n [\n -88.44541245375156,\n 39.04358924105398\n ],\n [\n -91.7960849146639,\n 39.04358924105398\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.
","language":"English","publisher":"MDPI","doi":"10.3390/w17081200","usgsCitation":"Lucas, L., Brown, C., Robertson, D., Baker, N.T., Johnson, Z., Green, C., Cho, J., Erickson, M., Gellis, A.C., Jasmann, J.R., Knowles, N., Prein, A., and Stackelberg, P.E., 2025, Gaps in water quality modeling of hydrologic systems: Water, v. 17, no. 8, 1200, 98 p., https://doi.org/10.3390/w17081200.","productDescription":"1200, 98 p.","ipdsId":"IP-157684","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488460,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17081200","text":"Publisher Index Page"},{"id":484764,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":933941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baker, Nancy T. 0000-0002-7979-5744","orcid":"https://orcid.org/0000-0002-7979-5744","contributorId":222870,"corporation":false,"usgs":true,"family":"Baker","given":"Nancy","email":"","middleInitial":"T.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":933945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":933946,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cho, Jong 0000-0001-5514-6056","orcid":"https://orcid.org/0000-0001-5514-6056","contributorId":291384,"corporation":false,"usgs":true,"family":"Cho","given":"Jong","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":933947,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933948,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933949,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jasmann, Jeramy Roland 0000-0002-5251-6987","orcid":"https://orcid.org/0000-0002-5251-6987","contributorId":238713,"corporation":false,"usgs":true,"family":"Jasmann","given":"Jeramy","email":"","middleInitial":"Roland","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":933950,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knowles, Noah 0000-0001-5652-1049","orcid":"https://orcid.org/0000-0001-5652-1049","contributorId":206338,"corporation":false,"usgs":true,"family":"Knowles","given":"Noah","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":933951,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Prein, Andreas","contributorId":352146,"corporation":false,"usgs":false,"family":"Prein","given":"Andreas","affiliations":[{"id":24610,"text":"NCAR","active":true,"usgs":false}],"preferred":false,"id":933952,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":933953,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70268972,"text":"70268972 - 2025 - Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska","interactions":[],"lastModifiedDate":"2025-07-11T13:56:07.175736","indexId":"70268972","displayToPublicDate":"2025-04-16T08:51:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska","docAbstract":"The 2018–2023 phreatomagmatic eruptions at Semisopochnoi Island, Alaska produced abundant long-period (LP) seismicity, harmonic and broadband tremor, and explosion signals over several well-monitored periods of eruption and quiescence. The corresponding dataset provides an excellent opportunity to investigate precursory and syn-eruptive geophysical signals of long-lived phreatomagmatic eruptions using multiparameter observations. We generated explosion and LP event catalogs through novel implementations of the REDPy (Hotovec-Ellis, 2024) repeating event detector in mid-2021 following a network upgrade and the onset of a new phase of the eruption. The hundreds of detected explosions show a high degree of infrasound waveform similarity over more than a year, indicating a repeating source mechanism likely associated with explosive magma-water interaction. The seismic LP catalog shows that events began over a month prior to renewed explosive activity at the beginning of August 2021, and that lower frequency index (FI) LPs were generated in the week prior to the onset of explosions. We applied a recently developed machine learning tool (VOISS-Net, Tan et al., 2024) to catalog abundant broadband and harmonic seismic tremor recorded before and during the renewed explosive activity, along with LPs and explosions. The tremor catalogs complement the LP and explosion catalogs by filling out the seismic sequence with the dominant signal types. Together, these catalogs reveal a seismic sequence of renewed unrest that started with several weeks of LP events, followed by LPs with lower FI values and harmonic tremor in the days prior to explosive activity, and finally the onset of discrete explosions and broadband eruption tremor. We interpret this sequence as the ascent of a new pulse of magma that first interacted with the hydrothermal/groundwater system to produce LPs, followed by harmonic tremor, and that ultimately drove explosive magma-water interactions and periods of continuous ash emissions. The 2021 seismic sequence, in combination with long-term records of satellite SO2 emissions, deformation from interferometric synthetic aperture radar (InSAR) analysis, ash sample analysis, infrasound, and volcano tectonic seismicity, allows us to interpret the entire 9-year period of unrest and eruption that began with an intrusion and earthquake swarm in 2014.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2025.108329","usgsCitation":"Lyons, J.J., Tan, D., Angarita, M., Loewen, M.W., Lopez, T., Grapenthin, R., Hotovec-Ellis, A.J., Fee, D., and Haney, M.M., 2025, Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska: Journal of Volcanology and Geothermal Research, v. 463, 108329, 20 p., https://doi.org/10.1016/j.jvolgeores.2025.108329.","productDescription":"108329, 20 p.","ipdsId":"IP-176345","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":492468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2025.108329","text":"Publisher Index Page"},{"id":492125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Semisopochnoi Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.45548299275004,\n 52.040459864240546\n ],\n [\n 179.45548299275004,\n 51.86149191597676\n ],\n [\n 179.7966363352781,\n 51.86149191597676\n ],\n [\n 179.7966363352781,\n 52.040459864240546\n ],\n [\n 179.45548299275004,\n 52.040459864240546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"463","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards 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0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942759,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":942760,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942761,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70270107,"text":"70270107 - 2025 - What is the lowest latitude of discrete aurorae during superstorms?","interactions":[],"lastModifiedDate":"2025-08-11T15:47:09.174324","indexId":"70270107","displayToPublicDate":"2025-04-16T08:41:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"What is the lowest latitude of discrete aurorae during superstorms?","docAbstract":"From a survey of published accounts of visual sightings of aurorae, a compilation is presented of the lowest identified geomagnetic latitude at which discrete aurorae were seen at local zenith during magnetic storms having intensities with maximum −Dst > 200 nT. The compilation includes data for the superstorms of 2 September 1859, 4 February 1872, and 15 May 1921. A statistical model is developed representing the equatorward boundary of discrete aurorae versus storm intensity. The model indicates that a once-per-century storm would likely induce discrete aurorae at zenith down to a geomagnetic latitude of 34°. Insofar as aurorae can be taken as a proxy for electrojet currents, such a storm would expose many nighttime electric-power systems, in the contiguous United States or Europe, to high levels of geomagnetic disturbance. A Carrington-class storm would induce discrete aurorae down to 24°. These exposures are much greater than those indicated in recent numerical simulations of extreme magnetic storms. Using the model to infer storm intensity from reports of low-latitude aurorae, a storm on 28 August 1859, likely had maximum −Dst = 673 nT. That this storm occurred just a few days before the Carrington storm of 2 September (maximum −Dst = 964 nT) deserves attention. A storm that occurred on 17 September 1770 is estimated to have had maximum −Dst = 928 nT. The vision of Ezekiel could have been inspired by aurorae from a storm with maximum −Dst = 550 nT.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024SW004286","usgsCitation":"Love, J.J., Mann, I., Qvick, T., and Mursula, K., 2025, What is the lowest latitude of discrete aurorae during superstorms?: Space Weather, v. 23, no. 4, e2024SW004286, 22 p., https://doi.org/10.1029/2024SW004286.","productDescription":"e2024SW004286, 22 p.","ipdsId":"IP-173082","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024sw004286","text":"Publisher Index Page"},{"id":493936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mann, Ian R.","contributorId":359451,"corporation":false,"usgs":false,"family":"Mann","given":"Ian R.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":945474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qvick, Timo","contributorId":359452,"corporation":false,"usgs":false,"family":"Qvick","given":"Timo","affiliations":[{"id":82926,"text":"University of Oulu","active":true,"usgs":false}],"preferred":false,"id":945475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mursula, Kalevi","contributorId":344048,"corporation":false,"usgs":false,"family":"Mursula","given":"Kalevi","affiliations":[{"id":82280,"text":"Space Climate Group, Space Physics and Astronomy Research Unit, University of Oulu, PO Box 3000, 90014 Oulu, Finland","active":true,"usgs":false}],"preferred":false,"id":945476,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266349,"text":"70266349 - 2025 - The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset","interactions":[],"lastModifiedDate":"2025-05-07T13:11:44.831779","indexId":"70266349","displayToPublicDate":"2025-04-16T08:13:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset","docAbstract":"Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 reflectance data showed that the between-sensor VI difference is below 4.5% for most of the indices. These results suggest that the harmonization is robust and the HLS V2.0 data are adequate for quantitative terrestrial applications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2025.114723","usgsCitation":"Ju, J., Zhou, Q., Freitag, B., Roy, D., Zhang, H., Sridhar, M., Mandel, J., Arab, S., Schmidt, G.L., Crawford, C., Gascon, F., Strobl, P., Masek, J.G., and Neigh, C., 2025, The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset: Remote Sensing of Environment, v. 324, 114723, 17 p., https://doi.org/10.1016/j.rse.2025.114723.","productDescription":"114723, 17 p.","ipdsId":"IP-178601","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488127,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2025.114723","text":"Publisher Index Page"},{"id":485453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"324","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ju, Junchang","contributorId":354466,"corporation":false,"usgs":false,"family":"Ju","given":"Junchang","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":935736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhou, Qiang","contributorId":354468,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":935737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freitag, Brian","contributorId":354470,"corporation":false,"usgs":false,"family":"Freitag","given":"Brian","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roy, David P.","contributorId":294404,"corporation":false,"usgs":false,"family":"Roy","given":"David P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":935739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Hankui","contributorId":354472,"corporation":false,"usgs":false,"family":"Zhang","given":"Hankui","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":935740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sridhar, Madhu","contributorId":350383,"corporation":false,"usgs":false,"family":"Sridhar","given":"Madhu","affiliations":[{"id":83729,"text":"University of Alabama Huntsville","active":true,"usgs":false}],"preferred":false,"id":935741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mandel, John","contributorId":354474,"corporation":false,"usgs":false,"family":"Mandel","given":"John","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935742,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":354476,"corporation":false,"usgs":true,"family":"Arab","given":"Saeed","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":935743,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schmidt, Gail L. 0000-0002-9684-8158 gschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":3475,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","email":"gschmidt@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":935744,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":935745,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gascon, Ferran","contributorId":173965,"corporation":false,"usgs":false,"family":"Gascon","given":"Ferran","email":"","affiliations":[{"id":27013,"text":"European Space Agency, Belgium","active":true,"usgs":false}],"preferred":false,"id":935746,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Strobl, Peter A.","contributorId":354478,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter A.","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":935747,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Masek, Jeffrey G.","contributorId":197725,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":935748,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Neigh, Christopher S.R.","contributorId":354481,"corporation":false,"usgs":false,"family":"Neigh","given":"Christopher S.R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935749,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70267832,"text":"70267832 - 2025 - Mercury trophic transfer to a freshwater biosentinel: Quantifying controlled bioaccumulation in larval dragonflies","interactions":[],"lastModifiedDate":"2025-07-10T14:48:56.758985","indexId":"70267832","displayToPublicDate":"2025-04-16T07:53:40","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}},"title":"Mercury trophic transfer to a freshwater biosentinel: Quantifying controlled bioaccumulation in larval dragonflies","docAbstract":"Mercury bioavailability and biomagnification in freshwater systems can be highly variable; thus, tissue data from biosentinel taxa can be useful to assess risk. Dragonfly larvae have emerged as biological indicators of mercury impairment, yet their mercury biodynamics over time and across exposure levels are not well understood. Evaluating these attributes using controlled experimental approaches is an important step to validate larval dragonflies as biosentinels for spatial and temporal trends in mercury risk. We conducted an experimental series quantifying methylmercury trophic transfer from dosed prey to predatory dragonfly larvae at environmentally relevant concentrations. Dragonfly total mercury concentrations increased proportionally by factors of 2.7 to 4.2 with each doubling of prey methylmercury concentration, responding to dietary treatments in 7–28 days and reaching equilibrium in as little as 40 days, supporting their utility to indicate changing mercury exposure regimes. Dosed dragonflies biomagnified methylmercury by factors of 1.0 ± 0.1 to 3.4 ± 0.2 relative to their prey, and biomagnification efficiency decreased by over 40% for each doubling of prey methylmercury concentration. Dragonfly development had dose-dependent effects on bioaccumulation: mercury concentrations increased with growth and decreased with age in higher exposure treatments, whereas they decreased with growth and increased with age in lower exposure treatments. Bioaccumulation also varied taxonomically; within treatments, mean mercury concentrations for each genus varied up to 10% from family-level means. Dragonfly sex, size, and body condition did not significantly affect mercury concentrations. These results help validate and expand the utility of dragonfly larvae as biosentinels to monitor mercury risk and better protect wildlife and human health.","language":"English","publisher":"Oxford Academic","doi":"10.1093/etojnl/vgaf100","usgsCitation":"Sinclair, C.A., Garcia, T.S., Vasta, R., and Eagles-Smith, C., 2025, Mercury trophic transfer to a freshwater biosentinel: Quantifying controlled bioaccumulation in larval dragonflies: Environmental Toxicology and Chemistry, v. 44, no. 7, p. 1824-1834, https://doi.org/10.1093/etojnl/vgaf100.","productDescription":"11 p.","startPage":"1824","endPage":"1834","ipdsId":"IP-172263","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":490664,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/etojnl/vgaf100","text":"Publisher Index Page"},{"id":489458,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","county":"Benton County","otherGeospatial":"William L. Finley National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.38434279122359,\n 44.500601858064925\n ],\n [\n -123.38434279122359,\n 44.43616065782959\n ],\n [\n -123.30603623577454,\n 44.43616065782959\n ],\n [\n -123.30603623577454,\n 44.500601858064925\n ],\n [\n -123.38434279122359,\n 44.500601858064925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sinclair, Cailin A","contributorId":340137,"corporation":false,"usgs":false,"family":"Sinclair","given":"Cailin","email":"","middleInitial":"A","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":939073,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Tiffany S.","contributorId":171591,"corporation":false,"usgs":false,"family":"Garcia","given":"Tiffany","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":939074,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vasta, Rachel","contributorId":356293,"corporation":false,"usgs":false,"family":"Vasta","given":"Rachel","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":939075,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":939076,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265700,"text":"sir20255003 - 2025 - Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","interactions":[],"lastModifiedDate":"2025-08-07T20:57:16.247704","indexId":"sir20255003","displayToPublicDate":"2025-04-16T07:09:29","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5003","displayTitle":"Estimation of Baseflow and Flooding Characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","title":"Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","docAbstract":"An improved understanding of hydrologic responses to changing climatic conditions is needed to better inform water management practices. East Canyon Creek, a perennial, snowmelt-dominated stream in the Wasatch Mountains of northern Utah, is subjected to increasing development and demands on water in the Snyderville Basin and adjacent areas. In this study, streamflow and specific conductance measured at three U.S. Geological Survey streamgages on East Canyon Creek were used to estimate daily baseflow for water years 2011–22. Trends in these estimates and correlations with climate data from two Natural Resource Conservation Service snow telemetry (SNOTEL) stations within the Snyderville Basin above East Canyon Reservoir, were quantified and reported. Peak annual streamflow also was assessed for flood potential on the study reach of East Canyon Creek. The hydrograph separations showed consistent baseflow indices among all sites, with a larger baseflow component during the fall–spring period (September–April; baseflow indices approximately equal to [≈] 0.751–0.835) and smaller component during the summer period (May–August; baseflow indices ≈ 0.428–0.532). In-stream specific conductance during spring (February–April) was influenced by road salt application, limiting the utility of the hydrograph separation approach. Annual streamflow and climate data were evaluated for trends using the nonparametric Mann–Kendall test, with inconclusive results. Related tests for trends, the Seasonal and Regional Kendall tests, were used to evaluate data at monthly timesteps and indicated a decreasing trend in total streamflow and baseflow at all streamgages. The rank-based Kendall’s tau test for correlation was used to measure the ordinal association with climatic data at co-located SNOTEL stations. Total streamflow and baseflow were strongly correlated with precipitation and snow-water equivalent. By incorporating a predictive regression model, the nonparametric Theil–Sen line, these correlations could support the development of streamflow forecast models using climate data from SNOTEL stations. Such models would provide water managers with tools to help make proactive decisions, such as reservoir or water reclamation releases and curtailment of withdrawals, in response to regional drought or varying snowpack and spring runoff in a given year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255003","collaboration":"Prepared in cooperation with Snyderville Basin Water Reclamation District","usgsCitation":"Root, J.C., and Rumsey, C.A., 2025, Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah: U.S. Geological Survey Scientific Investigations Report 2025–5003, 29 p., https://doi.org/10.3133/sir20255003.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-162488","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":493759,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118539.htm","linkFileType":{"id":5,"text":"html"}},{"id":484540,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14SJDMX","text":"USGS data release","description":"Root, J.C., 2025, Baseflow estimation and trend and correlation analysis results for East Canyon Creek, Summit and Morgan Counties, Utah, 2010–2022: U.S. Geological Survey data release, https://doi.org/10.5066/P14SJDMX.","linkHelpText":"Baseflow estimation and trend and correlation analysis results for East Canyon Creek, Summit and Morgan Counties, Utah, 2010–2022"},{"id":484539,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5003/images"},{"id":484538,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5003/sir20255003.XML","description":"SIR 2025-5003 XML"},{"id":484537,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255003/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5003 HTML"},{"id":484536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5003/sir20255003.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5003 PDF"},{"id":484535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5003/coverthb.jpg"}],"country":"United States","state":"Utah","county":"Morgan County, Summit County","otherGeospatial":"East Canyon Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.85739630382633,\n 41.2514958778022\n ],\n [\n -111.85739630382633,\n 40.5798335667547\n ],\n [\n -110.91729451616551,\n 40.5798335667547\n ],\n [\n -110.91729451616551,\n 41.2514958778022\n ],\n [\n -111.85739630382633,\n 41.2514958778022\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
Marine birds in the U.S. Gulf of Mexico have long been poorly studied. Given statutory obligations to protect migratory birds and endangered species, three broad-scale vessel and aerial programs initiated since 2010 have now surveyed the entire northern Gulf. Vessel coverage alone exceeds 700 d and 74,000 km of observer effort using 300-m strip transects. We supplemented these survey data with earlier, smaller-scale studies, eBird checklists, literature reviews, and other less accessible sources to create snapshot summaries of relative abundance, seasonal occurrence, and regional distribution for 117 taxa of marine and water birds reported from the northern Gulf (113 of which were substantiated with physical evidence). Using taxonomic and functional criteria, we identified 56 taxa characteristic of open shelf, slope, and pelagic waters (federal jurisdiction), 41 taxa with primarily coastal affinities (state and federal jurisdiction), and 20 taxa of sea and diving ducks. High species richness of marine birds in the northern Gulf is attributed to (1) a temperate-to-tropical gradient facilitating diverse marine environments year-round; (2) varied geographic origins of marine bird species using the Gulf; and (3) a mostly enclosed sea basin acting as a vagrant trap for wide-ranging species. Our taxonomic list and status updates seek to bridge information gaps for marine birds now subject to accelerated commercial uses of this region's continental shelf, including newly proposed offshore wind energy development. Other applications include guiding risk and vulnerability assessments of Gulf marine birds, providing core content for seabird observer training, and prioritizing environmental impact reviews and monitoring programs in offshore energy construction and operations plans.
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