{"pageNumber":"36","pageRowStart":"875","pageSize":"25","recordCount":68802,"records":[{"id":70264379,"text":"70264379 - 2025 - Flow cytometric detection of waterborne bacteria metabolic response to anthropogenic chemical inputs to aquatic ecosystems","interactions":[],"lastModifiedDate":"2025-03-14T14:46:04.514094","indexId":"70264379","displayToPublicDate":"2025-02-28T09:45:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20352,"text":"Cells","active":true,"publicationSubtype":{"id":10}},"title":"Flow cytometric detection of waterborne bacteria metabolic response to anthropogenic chemical inputs to aquatic ecosystems","docAbstract":"<p><span>Typical investigations into the biological consequences of suspected xenobiotics or nutrients introduced in watersheds include analytical chemistry screens of environmental samples—such as periphyton responses or studies of fish condition—which are all costly in terms of equipment, reagents, time, and human resources. An alternative is to assess pollutant effects on waterborne bacteria. A flow cytometric method was developed to yield rapid, same-day results that could be used to proactively screen for suspected chemical inputs into watersheds using water sampling methods that are identical to those in standard use. The analytical methods are microbe cultivation-independent, for use with waterborne bacteria that are typically viable but not culturable. The procedure is quick and inexpensive, generating measures of bacterial esterase that reflect metabolic activity and are sensitive and statistically robust. After phosphate-EDTA incubation to increase cell wall permeability, staining was performed with 5(6) carboxyfluorescein diacetate (enzyme activity) and propidium iodide (cell viability) with three bacterial species in exponential phase growth having been incubated with organic wastewater compounds (atrazine, pharmaceuticals [17α-ethynylestradiol and trenbolone], and antimicrobials [tylosin and butylparaben]). This method successfully detected metabolic changes in all bacterial species, with atrazine inducing the greatest change. Additional fluorescent stains can target specific microbial structures or functions of interest in a particular watershed. This biotechnology can inform analytical chemistry and study of biota at sites of interest and has the potential to be automated.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/cells14050352","usgsCitation":"Jenkins, J., Mize, S., Johnson, D., and Brown, B.L., 2025, Flow cytometric detection of waterborne bacteria metabolic response to anthropogenic chemical inputs to aquatic ecosystems: Cells, v. 14, no. 5, 352, 15 p., https://doi.org/10.3390/cells14050352.","productDescription":"352, 15 p.","ipdsId":"IP-175016","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488303,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/cells14050352","text":"Publisher Index Page"},{"id":483340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Jill 0000-0002-5087-0894","orcid":"https://orcid.org/0000-0002-5087-0894","contributorId":222865,"corporation":false,"usgs":true,"family":"Jenkins","given":"Jill","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":930587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mize, Scott 0000-0001-6751-5568","orcid":"https://orcid.org/0000-0001-6751-5568","contributorId":218508,"corporation":false,"usgs":true,"family":"Mize","given":"Scott","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":205688,"corporation":false,"usgs":false,"family":"Johnson","given":"Darren","affiliations":[{"id":37106,"text":"Cherokee Nation","active":true,"usgs":false}],"preferred":false,"id":930589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Bonnie L.","contributorId":23083,"corporation":false,"usgs":false,"family":"Brown","given":"Bonnie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":930590,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70269357,"text":"70269357 - 2025 - ARCHI: A new R package for automated imputation of regionally correlated hydrologic records","interactions":[],"lastModifiedDate":"2025-07-21T14:22:11.345476","indexId":"70269357","displayToPublicDate":"2025-02-28T09:18:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"ARCHI: A new R package for automated imputation of regionally correlated hydrologic records","docAbstract":"<p><span>Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13474","usgsCitation":"Levy, Z., Glas, R.L., Stagnitta, T.J., and Terry, N., 2025, ARCHI: A new R package for automated imputation of regionally correlated hydrologic records: Groundwater, v. 62, no. 4, p. 595-610, https://doi.org/10.1111/gwat.13474.","productDescription":"16 p.","startPage":"595","endPage":"610","ipdsId":"IP-166664","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":492870,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13474","text":"Publisher Index Page"},{"id":492796,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P17NXGHV","text":"USGS data release","linkHelpText":"Example Groundwater-Level Datasets and Benchmarking Results for the Automated Regional Correlation Analysis for Hydrologic Record Imputation (ARCHI) Software Package"},{"id":492616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, New York","otherGeospatial":"Central Valley, Long Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.13447900247726,\n              37.3805353859038\n            ],\n            [\n              -121.13447900247726,\n              35.88363374499093\n            ],\n            [\n              -118.93035501208706,\n              35.88363374499093\n            ],\n            [\n              -118.93035501208706,\n              37.3805353859038\n            ],\n            [\n              -121.13447900247726,\n              37.3805353859038\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -71.76788606860265,\n              41.23528810034301\n            ],\n            [\n              -74.12298971233533,\n              41.23528810034301\n            ],\n            [\n              -74.12298971233533,\n              40.43286561377994\n            ],\n            [\n              -71.76788606860265,\n              40.43286561377994\n            ],\n            [\n              -71.76788606860265,\n              41.23528810034301\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"62","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glas, Robin L. 0000-0002-7394-1667","orcid":"https://orcid.org/0000-0002-7394-1667","contributorId":300625,"corporation":false,"usgs":true,"family":"Glas","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stagnitta, Timothy J. 0000-0001-8903-428X","orcid":"https://orcid.org/0000-0001-8903-428X","contributorId":304230,"corporation":false,"usgs":true,"family":"Stagnitta","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":true,"id":943533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":943534,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70263941,"text":"70263941 - 2025 - Characterizing pyrethroid and fipronil concentrations in biosolids","interactions":[],"lastModifiedDate":"2025-03-03T15:06:16.51444","indexId":"70263941","displayToPublicDate":"2025-02-28T09:03:11","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":"Characterizing pyrethroid and fipronil concentrations in biosolids","docAbstract":"<p><span>Pesticides are prevalent in wastewater, yet few studies have measured pesticides in biosolids and aqueous media from samples collected concurrently. Seventeen California wastewater treatment plants (WWTPs) were sampled in May 2020. Biosolids samples were analyzed for 27 analytes, and paired aqueous samples (influent and effluent) were analyzed for 23 analytes. Analytes included fipronil and its transformation products (fiproles), pyrethroids, novaluron, and several other pesticides with down-the-drain transport potential. Of the 27 compounds analyzed in biosolids samples, 16 were detected in at least one sample, and 10 had a detection frequency (DF) of at least 25&nbsp;%. Fipronil sulfone, fipronil sulfide, and fipronil were the most frequently detected fiproles (DF&nbsp;=&nbsp;100&nbsp;%, 94&nbsp;%, and 67&nbsp;%, respectively); permethrin was the most frequently detected pyrethroid (DF&nbsp;=&nbsp;100&nbsp;%), followed by bifenthrin (DF&nbsp;=&nbsp;94&nbsp;%), cyhalothrin (DF&nbsp;=&nbsp;89&nbsp;%), and etofenprox (DF&nbsp;=&nbsp;78&nbsp;%). To elucidate fipronil transformation pathways within the treatment system, data from the three sample types were compared; findings were generally consistent with transformation pathways reported previously (e.g., some fiproles were rarely detected in influent or biosolids, but frequently detected in effluent, indicating their formation during the treatment process). No correlations were found between WWTP characteristics and pesticide concentrations in biosolids. The fraction of organic carbon (</span><i>f</i><sub><i>OC</i></sub><span>) of each biosolids sample was measured, and a statistically significant negative correlation was observed between&nbsp;</span><i>f</i><sub><i>OC</i></sub><span>&nbsp;and some fiproles, but not fipronil; possible explanations are discussed. Additional analysis for two major agricultural pesticides (bifenthrin and permethrin) indicated that estimated mass loads of these pesticides in biosolids applied to land as a soil amendment are minimal (approximately 2 to 3 orders of magnitude lower) compared to inputs from agricultural applications. This study provides insight on the magnitude of pesticides entering the environment via land-applied biosolids; existing regulations surrounding agricultural pesticide applications are expected to also be protective of the relatively low inputs from biosolids.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.178954","usgsCitation":"Wheeler, J., Black, G.P., Hladik, M.L., Sanders, C., Teerlink, J., Wong, L., Zhang, X., Budd, R., and Young, T.M., 2025, Characterizing pyrethroid and fipronil concentrations in biosolids: Science of the Total Environment, v. 969, 178954, 11 p., https://doi.org/10.1016/j.scitotenv.2025.178954.","productDescription":"178954, 11 p.","ipdsId":"IP-171403","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":489969,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.178954","text":"Publisher Index Page"},{"id":482735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"969","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wheeler, John","contributorId":351645,"corporation":false,"usgs":false,"family":"Wheeler","given":"John","affiliations":[{"id":40320,"text":"California Department of Pesticide Regulation","active":true,"usgs":false}],"preferred":false,"id":929202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Black, Gabrielle Pecora 0000-0002-1578-742X","orcid":"https://orcid.org/0000-0002-1578-742X","contributorId":303108,"corporation":false,"usgs":true,"family":"Black","given":"Gabrielle","email":"","middleInitial":"Pecora","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929203,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanders, Corey 0000-0001-7743-6396","orcid":"https://orcid.org/0000-0001-7743-6396","contributorId":204711,"corporation":false,"usgs":true,"family":"Sanders","given":"Corey","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teerlink, Jennifer","contributorId":297910,"corporation":false,"usgs":false,"family":"Teerlink","given":"Jennifer","email":"","affiliations":[{"id":40320,"text":"California Department of Pesticide Regulation","active":true,"usgs":false}],"preferred":false,"id":929206,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wong, Luann","contributorId":313578,"corporation":false,"usgs":false,"family":"Wong","given":"Luann","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":929207,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zhang, Xuyang","contributorId":351647,"corporation":false,"usgs":false,"family":"Zhang","given":"Xuyang","affiliations":[{"id":40320,"text":"California Department of Pesticide Regulation","active":true,"usgs":false}],"preferred":false,"id":929208,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Budd, Robert","contributorId":351742,"corporation":false,"usgs":false,"family":"Budd","given":"Robert","affiliations":[],"preferred":false,"id":929209,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Young, Thomas M","contributorId":221103,"corporation":false,"usgs":false,"family":"Young","given":"Thomas","email":"","middleInitial":"M","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":929210,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70264011,"text":"70264011 - 2025 - The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought","interactions":[],"lastModifiedDate":"2025-03-04T15:45:33.410255","indexId":"70264011","displayToPublicDate":"2025-02-28T08:38:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought","docAbstract":"Quantitative understanding is lacking on how the depth of active groundwater circulation in\nbedrock affects mountain streamflow response to a multi‐year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage‐limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL112927","usgsCitation":"Carroll, R., Manning, A.H., and Williams, K., 2025, The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought: Geophysical Research Letters, v. 52, no. 4, e2024GL112927, 12 p., https://doi.org/10.1029/2024GL112927.","productDescription":"e2024GL112927, 12 p.","ipdsId":"IP-171471","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":487740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl112927","text":"Publisher Index Page"},{"id":482802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Copper Creek","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -106.1775889108842,\n              39.499761316913094\n            ],\n            [\n              -106.1775889108842,\n              39.45001492350738\n            ],\n            [\n              -106.1485831457982,\n              39.45001492350738\n            ],\n            [\n              -106.1485831457982,\n              39.499761316913094\n            ],\n            [\n              -106.1775889108842,\n              39.499761316913094\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"52","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Rosemary W.H.","contributorId":336921,"corporation":false,"usgs":false,"family":"Carroll","given":"Rosemary W.H.","affiliations":[{"id":55475,"text":"Desert Research Institute, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":929470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":929471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Kenneth H.","contributorId":336926,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth H.","affiliations":[{"id":80914,"text":"Rocky Mountain Biological Laboratory, Gothic, CO","active":true,"usgs":false}],"preferred":false,"id":929472,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70267890,"text":"70267890 - 2025 - Laboratory assessment for recovery of porcine circovirus 2 and porcine reproductive and respiratory syndrome virus using two types of commercially available hollow-fiber ultrafilters","interactions":[],"lastModifiedDate":"2025-06-06T14:41:49.988795","indexId":"70267890","displayToPublicDate":"2025-02-28T07:34:54","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2492,"text":"Journal of Veterinary Diagnostic Investigation","active":true,"publicationSubtype":{"id":10}},"title":"Laboratory assessment for recovery of porcine circovirus 2 and porcine reproductive and respiratory syndrome virus using two types of commercially available hollow-fiber ultrafilters","docAbstract":"<p><span>Groundwater near swine farms is an uninvestigated reservoir for porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circoviruses (PCVs). Enteric microorganisms are often collected from groundwater via dead-end ultrafiltration, but recovery of PRRSV and PCV with this method has not been assessed. We recovered PRRSV2 and PCV2 by dead-end ultrafiltration followed by polyethylene glycol (PEG) precipitation, nucleic acid extraction, and reverse-transcription quantitative real-time PCR. We also compared 2 commercial hemodialysis ultrafilters (Asahi Kasei Rexeed-25A, Nipro Elisio-25H) and compared PRRSV2 recovery in these filters to other waterborne microorganisms. On average, 8 ± 1% of PRRSV2 was recovered by dead-end ultrafiltration and PEG precipitation, compared to 25 ± 6% for adenovirus 41. Full-process recovery of bacteria in the same filters was 5–15%;&nbsp;</span><i>Cryptosporidium parvum</i><span>&nbsp;recovery was 42 ± 12%. PCV2 was detected in 4 of 12 replicate filters, but low stock concentrations precluded quantitative recovery estimates. Elisio-25H ultrafilters performed similarly to Rexeed-25A filters for all organisms tested and is an effective replacement for the Rexeed-25A, which is no longer available in the United States. Our recovery of PRRSV2 and PCV2 by dead-end ultrafiltration in the laboratory suggests that PRRSV2 detection limits are as low as 3–50 genomic copies/L in sample volumes of 100–1,500 L. Based on quantitative microbial risk assessment, these concentrations are relevant to PRRSV2 infection rates in the U.S. swine herd.</span></p>","language":"English","publisher":"Sage","doi":"10.1177/10406387251322506","usgsCitation":"Firnstahl, A.D., Doughan, G., Opelt, S., Cook, R.M., Heffron, J., Krueger, K., Borchardt, M.A., Karriker, L., Stokdyk, J.P., and Burch, T., 2025, Laboratory assessment for recovery of porcine circovirus 2 and porcine reproductive and respiratory syndrome virus using two types of commercially available hollow-fiber ultrafilters: Journal of Veterinary Diagnostic Investigation, v. 37, no. 3, p. 439-447, https://doi.org/10.1177/10406387251322506.","productDescription":"9 p.","startPage":"439","endPage":"447","ipdsId":"IP-166126","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":490661,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11871577/","text":"External Repository"},{"id":490196,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doughan, Gabi","contributorId":356357,"corporation":false,"usgs":false,"family":"Doughan","given":"Gabi","affiliations":[{"id":84970,"text":"Iowa State University, Swine Medicine Education Center, Ames, IA, United States; Iowa State University, College of Veterinary Medicine, Ames, IA, United States","active":true,"usgs":false}],"preferred":false,"id":939271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Opelt, Sarah A.","contributorId":300168,"corporation":false,"usgs":false,"family":"Opelt","given":"Sarah","middleInitial":"A.","affiliations":[],"preferred":false,"id":939272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Rachel M.","contributorId":300167,"corporation":false,"usgs":false,"family":"Cook","given":"Rachel","middleInitial":"M.","affiliations":[],"preferred":false,"id":939273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heffron, Joseph","contributorId":356380,"corporation":false,"usgs":false,"family":"Heffron","given":"Joseph","affiliations":[{"id":84980,"text":"U.S. Department of Agriculture-Agricultural Research Service, Environmentally Integrated Dairy Management Research Unit, Marshfield, WI, United States","active":true,"usgs":false}],"preferred":false,"id":939274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krueger, Karen","contributorId":356381,"corporation":false,"usgs":false,"family":"Krueger","given":"Karen","affiliations":[{"id":84983,"text":"Iowa State University, College of Veterinary Medicine, Ames, IA, United States","active":true,"usgs":false}],"preferred":false,"id":939275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":210973,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":939276,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Karriker, Locke","contributorId":356382,"corporation":false,"usgs":false,"family":"Karriker","given":"Locke","affiliations":[{"id":84984,"text":"Iowa State University, Swine Medicine Education Center, Ames, IA, United States","active":true,"usgs":false}],"preferred":false,"id":939277,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939278,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":939279,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70264313,"text":"70264313 - 2025 - Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model","interactions":[],"lastModifiedDate":"2025-03-11T14:33:16.317819","indexId":"70264313","displayToPublicDate":"2025-02-27T09:28:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5021,"text":"Environments","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model","docAbstract":"<p><span>Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/environments12030075","usgsCitation":"KarimiDermani, B., Green, C., Tick, G., Gholizadeh, H., Wei, W., and Zhang, Y., 2025, Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model: Environments, v. 12, no. 3, 75, 20 p., https://doi.org/10.3390/environments12030075.","productDescription":"75, 20 p.","ipdsId":"IP-169838","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487829,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/environments12030075","text":"Publisher Index Page"},{"id":483197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Nanjing Normal University, Nanjing 210023, China","active":true,"usgs":false}],"preferred":false,"id":930399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Yong","contributorId":352236,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930400,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70263949,"text":"70263949 - 2025 - Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","interactions":[],"lastModifiedDate":"2025-03-26T15:59:01.447164","indexId":"70263949","displayToPublicDate":"2025-02-27T09:07:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","docAbstract":"<p><span>Thermoelectric power generation accounts for over 41% of total U.S. freshwater withdrawals, making understanding the determinants of power plants’ water withdrawals (WW) and consumption (WC) critical for reducing the sector’s reliance on increasingly scarce water resources. However, reported data inconsistencies and incomplete analysis of potential determinants of thermoelectric water use hinder such understanding. We address these challenges by introducing a novel data filtering method and a more complete assessment of water use determinants. First, we applied a power-cooling ratio as an operations-based data filter that removed operationally implausible records while retaining more original data, outperforming previous statistical filtering methods. Second, we found that different water use reporting methods (WURMs) provided statistically significantly different WW and WC values, revealing the importance of this previously unrecognized feature in reported water use records. Third, our data-driven approach showed that traditionally emphasized features─such as cooling technology and gross generation─are of primary importance but can be surpassed by other, often overlooked, features when modeling WW or WC individually. The plant configuration, cooling technology, and gross generation were the most important features of WW, whereas WURM, cooling technology, and reporting month were the most important for WC. These findings can improve thermoelectric power plant management, water use reporting accuracy, and water use modeling.</span></p>","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c02024","usgsCitation":"Sjostedt, E., Rushforth, R., Tidwell, V., Harris, M.A., McManamay, R., and Marston, L., 2025, Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use: Environmental Science & Technology, v. 59, no. 9, p. 4482-4492, https://doi.org/10.1021/acs.est.4c02024.","productDescription":"11 p.","startPage":"4482","endPage":"4492","ipdsId":"IP-160568","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":488675,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.4c02024","text":"Publisher Index Page"},{"id":482736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Sjostedt, Eric 0000-0002-7755-8091","orcid":"https://orcid.org/0000-0002-7755-8091","contributorId":351702,"corporation":false,"usgs":false,"family":"Sjostedt","given":"Eric","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":929309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":929310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tidwell, Vincent 0000-0002-4954-897X","orcid":"https://orcid.org/0000-0002-4954-897X","contributorId":351704,"corporation":false,"usgs":false,"family":"Tidwell","given":"Vincent","affiliations":[{"id":84033,"text":"Pacific Northwest National Laboratories","active":true,"usgs":false}],"preferred":false,"id":929311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Melissa A. 0000-0003-2659-9763 mharris@usgs.gov","orcid":"https://orcid.org/0000-0003-2659-9763","contributorId":1903,"corporation":false,"usgs":true,"family":"Harris","given":"Melissa","email":"mharris@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McManamay, Ryan","contributorId":205277,"corporation":false,"usgs":false,"family":"McManamay","given":"Ryan","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":929313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marston, Landon 0000-0001-9116-1691","orcid":"https://orcid.org/0000-0001-9116-1691","contributorId":239626,"corporation":false,"usgs":false,"family":"Marston","given":"Landon","email":"","affiliations":[{"id":47941,"text":"Department of Civil Engineering, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":929314,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70264266,"text":"70264266 - 2025 - Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","interactions":[],"lastModifiedDate":"2025-03-10T13:53:35.39942","indexId":"70264266","displayToPublicDate":"2025-02-27T08:49:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","docAbstract":"<p><span>Greater understanding of the survival, travel time, and spatial distribution of juvenile salmonids among migration routes between their natal streams and the ocean is critical to the recovery of these threatened species. In the Sacramento–San Joaquin River Delta (Delta), a highly modified estuary in central California, USA, there is a critical need to evaluate how water management (e.g., water pumping) and environmental factors (e.g., water flow) impact these populations. While management actions can affect some environmental variables in the Delta, only recently have studies begun to uncover associations between these variables and key demographic parameters. In this study, we examine the effects of freshwater flows, water exports, tidal environment, and a temporary barrier on juvenile steelhead (</span><i>Oncorhynchus mykiss</i><span>) survival, travel times, and migration routing using a multiyear acoustic telemetry dataset and recent advancements in Bayesian multistate mark-recapture modeling. We found that no single covariate explained variation in juvenile steelhead population dynamics across the entire Delta, but that separate regions within the Delta showed association with specific environmental factors.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s12237-025-01493-5","usgsCitation":"Pope, A., Perry, R., Hance, D., and Buchanan, R., 2025, Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary: Estuaries and Coasts, v. 48, 75, 18 p., https://doi.org/10.1007/s12237-025-01493-5.","productDescription":"75, 18 p.","ipdsId":"IP-156547","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin River Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.03238643466244,\n              38.07227139327773\n            ],\n            [\n              -122.03238643466244,\n              37.61108647879182\n            ],\n            [\n              -121.16713739842452,\n              37.61108647879182\n            ],\n            [\n              -121.16713739842452,\n              38.07227139327773\n            ],\n            [\n              -122.03238643466244,\n              38.07227139327773\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buchanan, Rebecca A.","contributorId":300601,"corporation":false,"usgs":false,"family":"Buchanan","given":"Rebecca A.","affiliations":[{"id":65208,"text":"Columbia Basin Research, School of Aquatic and Fishery Sciences, University of Washington 1325 Fourth Avenue, Suite 1515, Seattle, Washington 98101-2540","active":true,"usgs":false}],"preferred":false,"id":930220,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70274270,"text":"70274270 - 2025 - Geochemical assessment of the suitability of converting a coal-fired power plant reservoir to a drinking-water reservoir","interactions":[],"lastModifiedDate":"2026-03-24T15:18:57.855959","indexId":"70274270","displayToPublicDate":"2025-02-26T10:14:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical assessment of the suitability of converting a coal-fired power plant reservoir to a drinking-water reservoir","docAbstract":"<p><span>There is an increasing need for additional water storage in the United States, especially in arid regions. Alternatives like decommissioned power plant raw-water reservoirs would be cheaper to use than creating new reservoirs; however, the biogeochemical pollution risk of these reservoirs is not well understood. The San Juan Generating Station power plant and the associated reservoir will be used as a sediment-settling basin to store drinking water. To evaluate whether the reservoir is appropriate for this use, inorganic and organic constituents of concern were measured in reservoir sediment cores, pore water, and reservoir water in 2020. Forty-six percent of sediment arsenic concentrations measured in core subsamples (8 to 12 mg/kg) were slightly above the New Mexico residential cancer threshold for soil of 7.07 mg/kg. One sediment sample contained elevated total barium concentrations (6020 mg/kg). The organic compounds analyzed were either below detection limits or below regulatory thresholds. Reservoir water had one sample with arsenic greater than the drinking water standard (10 µg/L). Overall, the reservoir sediment inorganic and organic analyte concentrations are within acceptable ranges. The few samples that have elevated concentrations are not of a sufficient magnitude that dilution and/or treatment processes would preclude the use of the reservoir for water storage. Our findings show potential for use of former coal power station raw-water reservoirs as drinking-water reservoirs after plant closure.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10402381.2025.2452543","usgsCitation":"Blake, J., Ferguson, C.L., Brown, J., and Mixon, R., 2025, Geochemical assessment of the suitability of converting a coal-fired power plant reservoir to a drinking-water reservoir: Lake and Reservoir Management, v. 41, no. 1, p. 41-58, https://doi.org/10.1080/10402381.2025.2452543.","productDescription":"18 p.","startPage":"41","endPage":"58","ipdsId":"IP-149906","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Juan River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -109,\n              37\n            ],\n            [\n              -109,\n              36.5\n            ],\n            [\n              -107.5,\n              36.5\n            ],\n            [\n              -107.5,\n              37\n            ],\n            [\n              -109,\n              37\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"41","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Blake, Johanna 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":217272,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferguson, Christina L. 0000-0003-3368-0770","orcid":"https://orcid.org/0000-0003-3368-0770","contributorId":225087,"corporation":false,"usgs":true,"family":"Ferguson","given":"Christina","email":"","middleInitial":"L.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jeb E. 0000-0001-7671-2379","orcid":"https://orcid.org/0000-0001-7671-2379","contributorId":225088,"corporation":false,"usgs":true,"family":"Brown","given":"Jeb E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mixon, Rachel Lynn 0000-0001-9863-6784","orcid":"https://orcid.org/0000-0001-9863-6784","contributorId":328595,"corporation":false,"usgs":true,"family":"Mixon","given":"Rachel Lynn","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957495,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70263858,"text":"70263858 - 2025 - Reproductive biology of invasive grass carp (Ctenopharyngodon idella) in two North American systems","interactions":[],"lastModifiedDate":"2025-08-19T15:25:34.216832","indexId":"70263858","displayToPublicDate":"2025-02-25T14:49:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reproductive biology of invasive grass carp (<i>Ctenopharyngodon idella</i>) in two North American systems","title":"Reproductive biology of invasive grass carp (Ctenopharyngodon idella) in two North American systems","docAbstract":"<p><span>Grass carp (</span><i>Ctenopharyngodon idella</i><span>) are nonnative, herbivorous freshwater fish that represent an ecological threat in North American waters. However, data are limited on reproductive biology specific to wild populations in midwestern North America, despite recent concern for grass carp establishment within the Great Lakes. Basic information on reproductive traits could be useful to determine reproductive potential, inform future population modelling and provide information to aid control efforts. Our objectives were to evaluate grass carp age-at-maturity, spawning season timing, fecundity type, developmental timing and spawning strategy. Additionally, we evaluated the reliability of the gonadosomatic index (GSI) as a proxy for identifying mature grass carp and compared body condition across study areas. We sampled grass carp in portions of the Mississippi River watershed within the state of Missouri and within the Lake Erie basin. Based on gonad histological samples (</span><i>n</i><span> = 274), grass carp are batch spawners with indeterminate fecundity and asynchronous ovarian developmental timing. This allows flexibility in their spawning, exemplified by a protracted spawning season in Lake Erie (April to November) in temperatures ranging from 12.3 to 27.3°C. Minimum observed age-at-maturity for females in Missouri and for both males and females in the Lake Erie population was age-3 and age-2 for males in Missouri. Accuracy of GSI as a measure of maturity during the spawning season was 89.7% and 87.5%, for females and males, respectively. Compared to Missouri, grass carp in Lake Erie had a significantly higher body condition, and females ready to spawn had significantly higher GSI values, suggesting that the grass carp in Lake Erie are healthier and more fecund relative to Missouri and, if left uncontrolled, potentially have a high probability of establishment within the Great Lakes. The results of this study will allow managers to identify the reproductive status of grass carp in the field and more accurately estimate populations and reproductive potential.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/jfb.70003","usgsCitation":"Wilson, T., Acre, M.R., Williams, F., Calfee, R.D., Mayer, C.M., Mapes, R., Kemp, C., Young, R., and Byrne, M.E., 2025, Reproductive biology of invasive grass carp (Ctenopharyngodon idella) in two North American systems: Journal of Fish Biology, v. 107, no. 1, p. 101-115, https://doi.org/10.1111/jfb.70003.","productDescription":"15 p.","startPage":"101","endPage":"115","ipdsId":"IP-168901","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":489960,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.70003","text":"Publisher Index Page"},{"id":482504,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Missouri, Ohio","otherGeospatial":"Lake Erie, Mississippi River, Missouri River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -90.09086863677885,\n              38.97424653619632\n            ],\n            [\n              -92.3648115327897,\n              38.97424653619632\n            ],\n            [\n              -92.3648115327897,\n              38.420958358947615\n            ],\n            [\n              -90.09086863677885,\n              38.420958358947615\n            ],\n            [\n              -90.09086863677885,\n              38.97424653619632\n            ]\n          ]\n        ],\n        \"type\": 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III","contributorId":351498,"corporation":false,"usgs":false,"family":"Williams","given":"Fred","suffix":"III","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":928720,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calfee, Robin D. 0000-0001-6056-7023 rcalfee@usgs.gov","orcid":"https://orcid.org/0000-0001-6056-7023","contributorId":1841,"corporation":false,"usgs":true,"family":"Calfee","given":"Robin","email":"rcalfee@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":928721,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mayer, Christine M.","contributorId":203271,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":928722,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mapes, Robert L.","contributorId":339890,"corporation":false,"usgs":false,"family":"Mapes","given":"Robert L.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":928723,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kemp, Chris M.","contributorId":351499,"corporation":false,"usgs":false,"family":"Kemp","given":"Chris M.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":928724,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Young, Ryan T.","contributorId":351500,"corporation":false,"usgs":false,"family":"Young","given":"Ryan T.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928725,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Byrne, Michael E. 0000-0001-9190-2728 mbyrne@usgs.gov","orcid":"https://orcid.org/0000-0001-9190-2728","contributorId":272077,"corporation":false,"usgs":false,"family":"Byrne","given":"Michael","email":"mbyrne@usgs.gov","middleInitial":"E.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":928726,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70267242,"text":"70267242 - 2025 - Carbon dioxide infusion reduces invasive mussel biofouling (quagga mussel; Dreissena rostriformis bugensis) in raw water systems","interactions":[],"lastModifiedDate":"2025-05-19T15:40:50.381767","indexId":"70267242","displayToPublicDate":"2025-02-25T08:32:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21637,"text":"Biofouling","active":true,"publicationSubtype":{"id":10}},"title":"Carbon dioxide infusion reduces invasive mussel biofouling (quagga mussel; Dreissena rostriformis bugensis) in raw water systems","docAbstract":"<p><span>The efficacy of carbon dioxide (CO</span><sub>2</sub><span>) to reduce biofouling by quagga mussels (</span><i>Dreissena rostriformis bugensis</i><span>) in raw water systems was investigated. Experiments were conducted in a mobile laboratory located at Bureau of Reclamation Davis Dam Hydropower Facility and supplied with raw water from Lake Mohave, a reservoir of the Colorado River, USA. Incoming water was split between five chambers, each infused with CO</span><sub>2</sub><span>&nbsp;at a different rate. Raw reservoir water containing quagga larvae (veligers) was mixed with CO</span><sub>2</sub><span>&nbsp;chamber outflows and delivered to tanks containing settlement plates. Two experiments were conducted. Experiment 1 tested continuous infusion at target concentrations of 30, 45, 60, 75, and 100 mg L</span><sup>-1</sup><span>&nbsp;dCO</span><sub>2</sub><span>&nbsp;(dissolved CO</span><sub>2</sub><span>). Experiment 2 evaluated intermittent infusion schedules: 24 h on/off with 50, 75, and 100 mg L</span><sup>-1</sup><span>&nbsp;dCO</span><sub>2</sub><span>&nbsp;and 24 h once/week with 100 mg L</span><sup>-1</sup><span>&nbsp;dCO</span><sub>2</sub><span>. In Experiment 1, the percent settlement decreased with mean CO</span><sub>2</sub><span>&nbsp;concentration, ranging from 5.0% to &lt; 0.1% in 28.7 and 92.2 mg L</span><sup>-1</sup><span>&nbsp;dCO</span><sub>2</sub><span>, respectively. In Experiment 2, the efficacy of 24 h on/off at dCO</span><sub>2</sub><span>&nbsp;&gt; 72.2 mg L</span><sup>-1</sup><span>&nbsp;was similar to continuous treatment. The least effective treatment was 24 h once weekly at 95 mg L</span><sup>-1</sup><span>&nbsp;dCO</span><sub>2</sub><span>. These results demonstrate that CO</span><sub>2</sub><span>&nbsp;treatment may reduce mussel biofouling in raw water systems.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/08927014.2025.2468282","usgsCitation":"Barbour, M., Severson, T.J., Wise, J.K., Meulemans, M.J., Kelly, K., Pucherelli, S., and Waller, D.L., 2025, Carbon dioxide infusion reduces invasive mussel biofouling (quagga mussel; Dreissena rostriformis bugensis) in raw water systems: Biofouling, v. 41, no. 3, p. 253-264, https://doi.org/10.1080/08927014.2025.2468282.","productDescription":"12 p.","startPage":"253","endPage":"264","ipdsId":"IP-166502","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":486161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada","otherGeospatial":"Lake Havasu, Lake Mead, Lake 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,{"id":70264699,"text":"70264699 - 2025 - Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems","interactions":[],"lastModifiedDate":"2025-03-20T14:50:08.933728","indexId":"70264699","displayToPublicDate":"2025-02-24T09:42:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems","docAbstract":"<p><span>The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.</span></p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-22-995-2025","usgsCitation":"Stegen, J., Burgin, A.J., Busch, M., Fisher, J.B., Ladau, J., Abrahamson, J., Kinsman-Costello, L., Li, L., Chen, X., Datry, T., McDowell, N., Tatariw, C., Braswell, A., Deines, J.M., Guimond, J., Regier, P., Rod, K., Bam, E., Fluet-Chouinard, E., Forbrich, I., Jaeger, K.L., O'Meara, T., Scheibe, T.D., Seybold, E., Sweetman, J.N., Zheng, J., Allen, D.C., Herndon, E., Middleton, B., Painter, S., Roche, K., Scamardo, J., Vander Vorste, R., Boye, K., Wohl, E., Zimmer, M., Hondula, K., Laan, M., Marshall, A., and Patel, K., 2025, Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems: Biogeosciences, v. 22, no. 4, p. 995-1034, https://doi.org/10.5194/bg-22-995-2025.","productDescription":"30 p.","startPage":"995","endPage":"1034","ipdsId":"IP-159303","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-22-995-2025","text":"Publisher Index Page"},{"id":483581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Stegen, James","contributorId":242792,"corporation":false,"usgs":false,"family":"Stegen","given":"James","affiliations":[{"id":48525,"text":"Earth and Biological Sciences Division, Pacific Northwest National 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,{"id":70269046,"text":"70269046 - 2025 - Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds.","interactions":[],"lastModifiedDate":"2025-07-15T15:10:57.061077","indexId":"70269046","displayToPublicDate":"2025-02-24T09:31:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds.","docAbstract":"<div id=\"abstracts\" data-extent=\"frontmatter\"><div class=\"core-container\"><div>Climate change is predicted to intensify lake algal blooms globally and result in regime shifts. However, observed increases in algal biomass do not consistently correlate with air temperature or precipitation, and evidence is lacking for a causal effect of climate or the nonlinear dynamics needed to demonstrate regime shifts. We modeled the causal effects of climate on annual lake chlorophyll (a measure of algal biomass) over 34 y for 24,452 lakes across broad ecoclimatic zones of the United States and evaluated the potential for regime shifts. We found that algal biomass was causally related to climate in 34% of lakes. In these cases, 71% exhibited abrupt but mostly temporary shifts as opposed to persistent changes, 13% had the potential for regime shifts. Climate was causally related to algal biomass in lakes experiencing all levels of human disturbance, but with different likelihood. Climate causality was most likely to be observed in lakes with minimal human disturbance and cooler summer temperatures that have increased over the 34 y studied. Climate causality was variable in lakes with low to moderate human disturbance, and least likely in lakes with high human disturbance, which may mask climate causality. 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,{"id":70263964,"text":"70263964 - 2025 - Hydroclimate projections and effects on runoff at National Wildlife Refuges in the semi-arid western U.S.","interactions":[],"lastModifiedDate":"2026-02-10T13:52:37.210978","indexId":"70263964","displayToPublicDate":"2025-02-24T09:03:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20192,"text":"JAWRA Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Hydroclimate projections and effects on runoff at National Wildlife Refuges in the semi-arid western U.S.","docAbstract":"<p><span>This study evaluated hydroclimate projections and effects on runoff at National Wildlife Refuges in a semiarid region of the western United States (U.S. Fish and Wildlife Service Region 6) using mean air temperature (TAVE) and precipitation (PPT) inputs and runoff (RO) output from a national application of a Monthly Water Balance Model (MWBM). An ensemble of statistically downscaled global circulation models for two future emissions scenarios from Coupled Model Intercomparison Project 3 and 5 (CMIP3 and 5) were assessed at the refuges for the years 1950–2099. TAVE, PPT, and RO and departures from mean baseline conditions were analyzed from MWBM hydrologic response units within refuge boundaries. Seasonal results were evaluated across four periods: historical (1951–1969), baseline (1981–1999), 2050 (2041–2059), and 2080 (2071–2089). Projected TAVE increases for all refuges and time periods, whereas PPT and RO are much more variable across ecoregions. Using the high emission scenario, summer mean monthly TAVE increases range from 4.8°C to 5.5°C by 2080. Summer mean monthly PPT departures vary from −5.7 to 3.9 mm (up to 14% decrease), with decreases at 41% of refuges. Summer RO departures range from −16.7 to 0.2 mm (up to 60% decrease), with decreases at 71% of refuges. Under the same emission scenario, winter PPT and RO increase at most refuges by 2080. These variable departures will create substantial challenges for future conservation management in the region.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13251","usgsCitation":"Caruso, B., Eng, L., Bock, A.R., and Hall, N.G., 2025, Hydroclimate projections and effects on runoff at National Wildlife Refuges in the semi-arid western U.S.: JAWRA Journal of the American Water Resources Association, v. 61, no. 1, e13251, https://doi.org/10.1111/1752-1688.13251.","productDescription":"e13251","ipdsId":"IP-159661","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":482792,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Caruso, Brian S. 0000-0002-2184-4961","orcid":"https://orcid.org/0000-0002-2184-4961","contributorId":257039,"corporation":false,"usgs":false,"family":"Caruso","given":"Brian S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eng, Lauren Ellissa 0009-0003-9808-4184","orcid":"https://orcid.org/0009-0003-9808-4184","contributorId":332901,"corporation":false,"usgs":true,"family":"Eng","given":"Lauren Ellissa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hall, Nicholas Graff 0000-0002-7331-8947","orcid":"https://orcid.org/0000-0002-7331-8947","contributorId":315497,"corporation":false,"usgs":true,"family":"Hall","given":"Nicholas","email":"","middleInitial":"Graff","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929373,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70264627,"text":"70264627 - 2025 - James Buttle review: The characteristics of baseflow resilience across diverse ecohydrological terrains","interactions":[],"lastModifiedDate":"2025-03-19T13:12:59.597752","indexId":"70264627","displayToPublicDate":"2025-02-23T08:21:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"James Buttle review: The characteristics of baseflow resilience across diverse ecohydrological terrains","docAbstract":"The dynamic storage of aquifers is the portion of groundwater that can potentially drain to any given point along a stream to create baseflow. Baseflow typically occurs year-round in perennial streams, though the characteristics and stability of dynamic storage are often most important to instream processes during extended dry periods (without precipitation and snowmelt) when runoff and quickflows are minimised. The term ‘baseflow resilience’ is defined for this review as the tendency of baseflow in streams to maintain a consistent volume and water quality year to year while under stress from climate variability and extremes, along with anthropogenic stressors such as water withdrawals, land use change, and water quality degradation. ‘Baseflow resilience’ has, in part, a user-defined meaning spanning water supply and water quality variables of primary interest. Watershed characteristics that directly impact resilience can often produce non-intuitive feedbacks that enhance some attributes of baseflow while simultaneously impairing others. For example, permeable stream corridor geology creates strong stream-groundwater hydrologic connectivity, yet fast groundwater drainage via preferential high-permeability flowpaths can lead to streamflow not being sustained during extended dry periods. Also, shallow groundwater sources are generally more immediately vulnerable to extreme events, warming, salinization, transpiration, and precipitation drought, compared to deeper groundwater. Yet baseflow drought in streams influenced by deeper groundwater can lag precipitation drought by years, and contaminant legacies may propagate through deep groundwater flowpaths to receiving waters for decades to centuries. Finally, irrigation withdrawals can intercept groundwater that would have drained to streams, and the application of irrigation may leach contaminants from the soil zone by unnaturally raising water tables, yet irrigation return flows can sustain baseflow and groundwater-dependent habitats in semiarid areas. This review covers the concept of hydrologic resilience in the context of stream baseflow processes and summarises the common hydrogeological controls on, and multiscale stressors of, dynamic groundwater storage. Further, we present several quantitative metrics to assess a range of water supply to water quality baseflow characteristics using both broadly available and boutique data types, a subset of which are demonstrated using data from the Delaware River Basin, USA.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70101","usgsCitation":"Briggs, M., Newman, C.P., Benton, J., Rey, D., Konrad, C., Ouellet, V., Torgersen, C.E., Gruhn, L.R., Fleming, B.J., Gazoorian, C.L., and Doctor, D.H., 2025, James Buttle review: The characteristics of baseflow resilience across diverse ecohydrological terrains: Hydrological Processes, v. 39, e70101, 21 p., https://doi.org/10.1002/hyp.70101.","productDescription":"e70101, 21 p.","ipdsId":"IP-172740","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":488334,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70101","text":"Publisher Index Page"},{"id":483482,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -75.72740040176602,\n              40.21569215354026\n            ],\n            [\n              -75.72740040176602,\n              39.17926830473752\n            ],\n            [\n              -74.59478267456883,\n              39.17926830473752\n            ],\n            [\n              -74.59478267456883,\n              40.21569215354026\n            ],\n            [\n              -75.72740040176602,\n              40.21569215354026\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2025-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":930998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benton, Joshua Robert 0000-0002-1698-6455","orcid":"https://orcid.org/0000-0002-1698-6455","contributorId":304604,"corporation":false,"usgs":true,"family":"Benton","given":"Joshua Robert","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":931001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Konrad, Christopher 0000-0002-7354-547X","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":220231,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931002,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ouellet, Valerie","contributorId":316799,"corporation":false,"usgs":false,"family":"Ouellet","given":"Valerie","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":931003,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":931004,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gruhn, Lance R. 0000-0002-7120-3003 lgruhn@usgs.gov","orcid":"https://orcid.org/0000-0002-7120-3003","contributorId":219710,"corporation":false,"usgs":true,"family":"Gruhn","given":"Lance","email":"lgruhn@usgs.gov","middleInitial":"R.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931005,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fleming, Brandon J. 0000-0001-9649-7485 bjflemin@usgs.gov","orcid":"https://orcid.org/0000-0001-9649-7485","contributorId":4115,"corporation":false,"usgs":true,"family":"Fleming","given":"Brandon","email":"bjflemin@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931006,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gazoorian, Christopher L. 0000-0002-5408-6212 cgazoori@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-6212","contributorId":2929,"corporation":false,"usgs":true,"family":"Gazoorian","given":"Christopher","email":"cgazoori@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931007,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":931008,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70263820,"text":"70263820 - 2025 - First evidence of lake trout Salvelinus namaycush spawning aggregation in Ohio waters of Lake Erie following reintroduction","interactions":[],"lastModifiedDate":"2025-02-25T14:59:52.64098","indexId":"70263820","displayToPublicDate":"2025-02-22T08:50:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"First evidence of lake trout <i>Salvelinus namaycush</i> spawning aggregation in Ohio waters of Lake Erie following reintroduction","title":"First evidence of lake trout Salvelinus namaycush spawning aggregation in Ohio waters of Lake Erie following reintroduction","docAbstract":"<p><span>Lake trout&nbsp;</span><i>Salvelinus namaycush,</i><span>&nbsp;an important apex predator native to Lake Erie, were extirpated by 1965 due to overexploitation, introduction of invasive species, and habitat degradation. Cooperative lake-wide lake trout stocking has been ongoing since 1982, with stocking strategies adapting as research identifies the age at stocking, locations, and strains that optimize the recovery of lake trout. Despite these efforts, limited evidence of lake trout spawning has been documented in the western half of Lake Erie. On 20 November 2023, n&nbsp;=&nbsp;99 lake trout were captured via gillnet in Fairport Harbor, Ohio. This sample consisted largely of ripe adults (79.4&nbsp;%) that were likely spawning within the vicinity of Fairport Harbor. Coded wire tags recovered from these fish revealed that most of these lake trout had been stocked in Fairport Harbor (99.0&nbsp;%), were of the Seneca Lake strain (92.7&nbsp;%), and were stocked as age-1 fish (93.7&nbsp;%). This study demonstrates the survival of fish from Fairport Harbor stockings, suggests evidence of stocking-site fidelity, supports the stocking of the Seneca Lake strain, and suggests that stocking age-1 lake trout may have advantages over younger life stages. Most importantly, this study demonstrates that lake trout are likely spawning near Fairport Harbor. These findings can guide future studies that identify lake trout spawning habitat, recruitment bottlenecks, movement, and stocking-site fidelity in Lake Erie and can be used to inform future recovery strategies.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2025.102540","usgsCitation":"Spitz, B., Montague, G., Schmitt, J., Guzzo, F., and Jenkins, P., 2025, First evidence of lake trout Salvelinus namaycush spawning aggregation in Ohio waters of Lake Erie following reintroduction: Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2025.102540.","ipdsId":"IP-162106","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":482437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -80.55472739657642,\n              41.9475113213193\n            ],\n            [\n              -80.65395989361728,\n              42.05062981852603\n            ],\n            [\n              -83.09908183181322,\n              41.59775843222013\n            ],\n            [\n              -82.87842021429307,\n              41.20061540985472\n            ],\n            [\n              -81.32584009993163,\n              41.56581314191055\n            ],\n            [\n              -80.55472739657642,\n              41.9475113213193\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Spitz, Benjamin J.","contributorId":351461,"corporation":false,"usgs":false,"family":"Spitz","given":"Benjamin J.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":928539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Montague, Graham F.","contributorId":351462,"corporation":false,"usgs":false,"family":"Montague","given":"Graham F.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":928540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":928541,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guzzo, Francesco 0009-0007-3207-6543","orcid":"https://orcid.org/0009-0007-3207-6543","contributorId":351463,"corporation":false,"usgs":true,"family":"Guzzo","given":"Francesco","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":928542,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenkins, Peter I.","contributorId":351464,"corporation":false,"usgs":false,"family":"Jenkins","given":"Peter I.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":928543,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70263944,"text":"70263944 - 2025 - Reservoir thermal energy storage pre-assessment for the United States","interactions":[],"lastModifiedDate":"2025-03-05T17:34:49.937548","indexId":"70263944","displayToPublicDate":"2025-02-22T08:33:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"title":"Reservoir thermal energy storage pre-assessment for the United States","docAbstract":"Storing thermal energy underground for later use in electricity production or direct-use heating/cooling is a promising, viable, and economical green energy option. Reservoir thermal energy storage (RTES) is one such option, which stores energy in underutilized permeable strata with low ambient groundwater flow rates and more geochemically evolved (e.g. brackish/saline) waters relative to overlying principal aquifer systems. The U.S. Geological Survey has begun assessing RTES potential nationally by focusing on five generalized geologic regions (Basin and Range, Coastal Plain, Illinois Basin, Michigan Basin, Pacific Northwest) across the United States. Hydrogeologic reservoir models are developed for the following eight metropolitan area cities within those regions to evaluate RTES performance across different climates and subsurface conditions: Albuquerque, New Mexico; Charleston, South Carolina; Chicago and Decatur, Illinois; Lansing, Michigan; Memphis, Tennessee; Phoenix, Arizona; and Portland, Oregon. Evaluated metrics include estimated required well spacing, thermal storage capacity, and thermal recovery efficiency through time. Also considered for each reservoir are potential complicating factors, including reservoir depth, thermally driven free convection, and groundwater salinity. This work focuses on direct-use cooling because the need for cooling modern office buildings greatly exceeds that for heating in most parts of the country (Falta and others, 2016); however, the evaluated metrics are also relevant to heating and electricity applications. Results indicate that favorable RTES conditions exist in each region, with the Coastal Plain and Basin and Range being especially favorable for thermal storage capacity, while the Pacific Northwest and Michigan Basin excel at energy recovery for the evaluated cooling application. The results underscore the utility of developing maps of thermal storage capacity, subsurface temperature models, and volumetric estimates of thermal storage capacity to serve as key RTES resource classification standards. Overall, this pre-assessment provides a basic understanding of RTES potential in several cities and geologic regions throughout the country and will aid ongoing thermal energy storage assessment efforts.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2025.103256","usgsCitation":"Pepin, J.D., Burns, E., Cahalan, R.C., Hayba, D.O., Dickinson, J.E., Duncan, L.L., and Kuniansky, E.L., 2025, Reservoir thermal energy storage pre-assessment for the United States: Geothermics, v. 129, 103256, 18 p., https://doi.org/10.1016/j.geothermics.2025.103256.","productDescription":"103256, 18 p.","ipdsId":"IP-160218","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":489977,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"www.osti.gov/servlets/purl/2522111","text":"Publisher Index Page"},{"id":482741,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Illinois, Michigan, New Mexico, Oregon, South Carolina, Tennessee","otherGeospatial":"Basin and Range, Coastal Plain, Illinois Basin, Michigan Basin, Pacific 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 \"}}]}","volume":"129","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":929212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahalan, Ryan Cain 0000-0002-3322-0654","orcid":"https://orcid.org/0000-0002-3322-0654","contributorId":302355,"corporation":false,"usgs":true,"family":"Cahalan","given":"Ryan","email":"","middleInitial":"Cain","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":929213,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayba, Daniel O. 0000-0003-4092-1894 dhayba@usgs.gov","orcid":"https://orcid.org/0000-0003-4092-1894","contributorId":396,"corporation":false,"usgs":true,"family":"Hayba","given":"Daniel","email":"dhayba@usgs.gov","middleInitial":"O.","affiliations":[],"preferred":true,"id":929214,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929215,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duncan, Leslie L. 0000-0002-5938-5721","orcid":"https://orcid.org/0000-0002-5938-5721","contributorId":204004,"corporation":false,"usgs":true,"family":"Duncan","given":"Leslie","email":"","middleInitial":"L.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929216,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kuniansky, Eve L. 0000-0002-5581-0225","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":214542,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"","middleInitial":"L.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":929217,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70263739,"text":"tm14A3 - 2025 - Grfin Tools—User guide and methods for modeling landslide runout and debris-flow growth and inundation","interactions":[],"lastModifiedDate":"2026-01-26T19:50:21.220267","indexId":"tm14A3","displayToPublicDate":"2025-02-21T10:59:08","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"14-A3","displayTitle":"Grfin Tools—User Guide and Methods for Modeling Landslide Runout and Debris-Flow Growth and Inundation","title":"Grfin Tools—User guide and methods for modeling landslide runout and debris-flow growth and inundation","docAbstract":"<p>The software package, Grfin Tools, can estimate potential runout from landslides or inundation from geophysical mass flows such as debris flows, lahars from volcanoes, and rock avalanches within a digital elevation model (DEM). Grfin is an acronym of <strong><i>gr</i></strong>owth + <strong><i>f</i></strong>low + <strong><i>in</i></strong>undation. The tools within this package apply simple, well-tested, empirical models of runout that are computationally efficient and require minimal parameters. These tools can be used individually (for example, to estimate debris-flow inundation) or in combination to represent a more complete series of linked processes, from landslide source areas, to unchannelized transport, to channelized flows. Grfin Tools can rapidly assess potential runout and inundation over large areas and the results are readily visualized in a geographic information system.</p><p>Tools for assessing areas affected by runout and flow inundation include a height-to-length (<i>H</i>/<i>L</i>) ratio, angle-of-reach approach for estimating open-slope, unchannelized landslide runout, and volume-area scaling relations for assessing flow inundation in channels. Potential landslide areas that constitute the sources of runout or inundation can be delineated with topographic features, such as slope and (or) curvature, derived by the software package, or by employing potential sources derived from other landslide susceptibility models. Grfin Tools also has the capability to assess inundation from flows that grow volumetrically downstream. This is a vital feature, as larger flows commonly result in longer runout and larger inundation. The software uses empirically derived growth factors applied over upslope contributing source areas or upstream channel lengths to integrate the effects of various growth processes, such as channel entrainment, streambank failures, adjacent landslides, and hillslope erosion. Inundation follows a drainage network defined with a separate tool that uses topographic curvature to identify channel initiation locations.</p><p>This document includes information on using Grfin Tools, the basis and methods underlying the tools and models, detailed descriptions of the software input and output files, and tips for handling special conditions such as roads and large water bodies. Multiple detailed examples illustrating different applications are also presented. Grfin Tools relies on the freely available TauDEM software package (Tarboton, 2005). The Grfin Tools software release is available from Cronkite-Ratcliff and others (2025).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm14A3","usgsCitation":"Reid, M.E., Brien, D.L., Cronkite-Ratcliff, C., and Perkins, J.P., 2025, Grfin Tools—User guide and methods for modeling landslide runout and debris-flow growth and inundation: U.S. Geological Survey Techniques and Methods, book 14, chap. A3, 105 p., https://doi.org/10.3133/tm14A3.","productDescription":"Report: xi, 105 p.; Data Release","numberOfPages":"105","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-159767","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":482304,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/14/a3/tm14a3.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":482303,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/14/a3/covrthb.jpg"},{"id":482305,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9NVKFE2","text":"USGS Software Release","description":"Cronkite-Ratcliff, C., Reid, M.E., Brien, D.L., Perkins, J.P., 2025, Grfin Tools—Software package and runtime documentation for users: U.S. Geological Survey software release, https://doi.org/10.5066/P9NVKFE2.","linkHelpText":"- Grfin Tools—Software package and runtime documentation for users"},{"id":499059,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118455.htm","text":"Naranjito, Puerto Rico","linkFileType":{"id":5,"text":"html"}},{"id":499058,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118454.htm","text":"Coast Range, Oregon","linkFileType":{"id":5,"text":"html"}},{"id":499057,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118453.htm","text":"Utuado, Puerto Rico","linkFileType":{"id":5,"text":"html"}},{"id":499056,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118452.htm","text":"Transverse Ranges, California","linkFileType":{"id":5,"text":"html"}},{"id":499055,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118451.htm","text":"Lassen Peak, California","linkFileType":{"id":5,"text":"html"}},{"id":499054,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118450.htm","text":"Kosrae","linkFileType":{"id":5,"text":"html"}}],"contact":"<p><a href=\"https://avo.alaska.edu/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://avo.alaska.edu/\">Alaska Volcano Observatory</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>4230 University Drive<br>Anchorage, AK 99508</p>","tableOfContents":"<div id=\":1k3\" class=\"Am aiL Al editable LW-avf tS-tW tS-tY\" role=\"textbox\" contenteditable=\"true\" spellcheck=\"false\" aria-label=\"Message Body\" aria-multiline=\"true\" aria-owns=\":1mi\" aria-controls=\":1mi\" aria-expanded=\"false\" data-mce-tabindex=\"1\"><ul><li>Abstract</li><li>1. Introduction</li><li>2. Using Grfin Tools</li><li>3. Methods Underlying Different Tools</li><li>4. Program Input and Output Files</li><li>5. Handling Special Conditions</li><li>6. Examples of Applications</li></ul></div>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2025-02-21","noUsgsAuthors":false,"publicationDate":"2025-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":928047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":928048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cronkite-Ratcliff, Collin 0000-0001-5485-3832 ccronkite-ratcliff@usgs.gov","orcid":"https://orcid.org/0000-0001-5485-3832","contributorId":203951,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":928049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Jonathan P. 0000-0002-6113-338X","orcid":"https://orcid.org/0000-0002-6113-338X","contributorId":237053,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":928050,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70263777,"text":"70263777 - 2025 - Cancer risk and estimated lithium exposure in drinking groundwater in the US","interactions":[],"lastModifiedDate":"2025-02-24T15:22:49.778702","indexId":"70263777","displayToPublicDate":"2025-02-20T09:17:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20081,"text":"JAMA Network Open","active":true,"publicationSubtype":{"id":10}},"title":"Cancer risk and estimated lithium exposure in drinking groundwater in the US","docAbstract":"<p><strong>Importance</strong>&nbsp;<span>&nbsp;</span><span>Lithium is a naturally occurring element in drinking water and is commonly used as a mood-stabilizing medication. Although clinical studies have reported associations between receiving lithium treatment and reduced cancer risk among patients with bipolar disorder, to our knowledge, the association between environmental lithium exposure and cancer risk has never been studied in the general population.</span></p><p><strong>Objectives</strong>&nbsp;<span>&nbsp;</span><span>To evaluate the association between exposure to lithium in drinking groundwater and cancer risk in the general population.</span></p><p><strong>Design, Setting, and Participants</strong>&nbsp;<span>&nbsp;</span><span>This cohort study included participants with electronic health record and residential address information but without cancer history at baseline from the All of Us Research Program between May 31, 2017, and June 30, 2022. Participants were followed up until February 15, 2023. Statistical analysis was performed from September 2023 through October 2024.</span></p><p><strong>Exposure</strong>&nbsp;<span>&nbsp;</span><span>Lithium concentration in groundwater, based on kriging interpolation of publicly available US Geological Survey data on lithium concentration for 4700 wells across the contiguous US between May 12, 1999, and November 6, 2018.</span></p><p><strong>Main Outcome and Measures</strong>&nbsp;<span>&nbsp;</span><span>The main outcome was cancer diagnosis or condition, obtained from electronic health records. Stratified Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% CIs for risk of cancer overall and individual cancer types for increasing quintiles of the estimated lithium exposure in drinking groundwater, adjusting for socioeconomic, behavioral, and neighborhood-level variables. The analysis was further conducted in the western and eastern halves of the US and restricted to long-term residents living at their current address for at least 3 years.</span></p><p><strong>Results</strong>&nbsp;<span>&nbsp;</span><span>A total of 252 178 participants were included (median age, 52 years [IQR, 36-64 years]; 60.1% female). The median follow-up time was 3.6 years (IQR, 3.0-4.3 years), and 7573 incident cancer cases were identified. Higher estimated lithium exposure was consistently associated with reduced cancer risk. Compared with the first (lowest) quintile of lithium exposure, the HR for all cancers was 0.49 (95% CI, 0.31-0.78) for the fourth quintile and 0.29 (95% CI, 0.15-0.55) for the fifth quintile. These associations were found for all cancer types investigated in both females and males, among long-term residents, and in both western and eastern states. For example, for the fifth vs first quintile of lithium exposure for all cancers, the HR was 0.17 (95% CI, 0.07-0.42) in females and 0.13 (95% CI, 0.04-0.38) in males; for long-term residents, the HR was 0.32 (95% CI, 0.15-0.66) in females and 0.24 (95% CI, 0.11-0.52) in males; and the HR was 0.01 (95% CI, 0.00-0.09) in western states and 0.34 (95% CI, 0.21-0.57) in eastern states.</span></p><p><strong>Conclusions and Relevance</strong>&nbsp;<span>&nbsp;</span><span>In this cohort study of 252 178 participants, estimated lithium exposure in drinking groundwater was associated with reduced cancer risk. Given the sparse evidence and unknown mechanisms of this association, follow-up investigation is warranted.</span></p>","language":"English","publisher":"American Medical Association","doi":"10.1001/jamanetworkopen.2024.60854","usgsCitation":"Luo, J., Zheng, L., Jin, Z., Yang, Y., Krakowka, W., Hong, E., Lombard, M.A., Ayotte, J.D., Ahsan, H., Pinto, J., and Aschebrook-Kilfoy, B., 2025, Cancer risk and estimated lithium exposure in drinking groundwater in the US: JAMA Network Open, v. 8, no. 2, e2460854, 15 p., https://doi.org/10.1001/jamanetworkopen.2024.60854.","productDescription":"e2460854, 15 p.","ipdsId":"IP-167705","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":487675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1001/jamanetworkopen.2024.60854","text":"Publisher Index 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,{"id":70263890,"text":"70263890 - 2025 - Community estimate of global glacier mass changes from 2000 to 2023","interactions":[],"lastModifiedDate":"2025-04-17T15:33:54.867443","indexId":"70263890","displayToPublicDate":"2025-02-19T09:03:16","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Community estimate of global glacier mass changes from 2000 to 2023","docAbstract":"<p><span>Glaciers are indicators of ongoing anthropogenic climate change</span><sup><a id=\"ref-link-section-d17083767e2015\" title=\"Bojinski, S. et al. The concept of essential climate variables in support of climate research, applications, and policy. Bull. Am. Meteorol. Soc. 95, 1431–1443 (2014).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR1\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 1\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR1\">1</a></sup><span>. Their melting leads to increased local geohazards</span><sup><a id=\"ref-link-section-d17083767e2019\" title=\"Haeberli, W. &amp; Whiteman, C. in Snow and Ice-Related Hazards, Risks, and Disasters (eds Shroder, J. F. et al.) 1–34 (Elsevier, 2015); \n                https://doi.org/10.1016/B978-0-12-394849-6.00001-9\n                \n              .\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR2\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 2\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR2\">2</a></sup><span>, and impacts marine</span><sup><a id=\"ref-link-section-d17083767e2023\" title=\"Hopwood, M. J. et al. How does glacier discharge affect marine biogeochemistry and primary production in the Arctic? Cryosphere 14, 1347–1383 (2020).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR3\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 3\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR3\">3</a></sup><span>&nbsp;and terrestrial</span><sup><a id=\"ref-link-section-d17083767e2027\" title=\"Ficetola, G. F. et al. The development of terrestrial ecosystems emerging after glacier retreat. Nature 632, 336–342 (2024).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR4\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 4\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR4\">4</a>,<a id=\"ref-link-section-d17083767e2030\" title=\"Bosson, J. B. et al. Future emergence of new ecosystems caused by glacial retreat. Nature 620, 562–569 (2023).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR5\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 5\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR5\">5</a></sup><span>&nbsp;ecosystems, regional freshwater resources</span><sup><a id=\"ref-link-section-d17083767e2034\" title=\"Huss, M. &amp; Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR6\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 6\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR6\">6</a></sup><span>, and both global water and energy cycles</span><sup><a id=\"ref-link-section-d17083767e2039\" title=\"Von Schuckmann, K. et al. Heat stored in the Earth system 1960–2020: where does the energy go? Earth Syst. Sci. Data 15, 1675–1709 (2023).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR7\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 7\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR7\">7</a>,<a id=\"ref-link-section-d17083767e2042\" title=\"Dorigo, W. et al. Closing the water cycle from observations across scales: where do we stand? Bull. Am. Meteorol. Soc. 102, E1897–E1935 (2021).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR8\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 8\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR8\">8</a></sup><span>. Together with the Greenland and Antarctic ice sheets, glaciers are essential drivers of present</span><sup><a id=\"ref-link-section-d17083767e2046\" title=\"Slater, T. et al. Earth’s ice imbalance.&nbsp;Cryosphere 15, 233–246 (2021).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR9\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 9\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR9\">9</a>,<a id=\"ref-link-section-d17083767e2049\" title=\"Bamber, J. L., Westaway, R. M., Marzeion, B. &amp; Wouters, B. The land ice contribution to sea level during the satellite era. Environ. Res. Lett. 13, 063008 (2018).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR10\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 10\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR10\">10</a></sup><span>&nbsp;and future</span><sup><a id=\"ref-link-section-d17083767e2053\" title=\"Rounce, D. R. et al. Global glacier change in the 21st century: every increase in temperature matters. Science 379, 78–83 (2023).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR11\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR11\">11</a>,<a id=\"ref-link-section-d17083767e2053_1\" title=\"Marzeion, B. et al. Partitioning the uncertainty of ensemble projections of global glacier mass change. Earths Future 8, e2019EF001470 (2020).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR12\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR12\">12</a>,<a id=\"ref-link-section-d17083767e2056\" title=\"Hock, R. et al. GlacierMIP—a model intercomparison of global-scale glacier mass-balance models and projections. J. Glaciol. 65, 453–467 (2019).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR13\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 13\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR13\">13</a></sup><span>&nbsp;sea-level rise. Previous assessments of global glacier mass changes have been hampered by spatial and temporal limitations and the heterogeneity of existing data series</span><sup><a id=\"ref-link-section-d17083767e2060\" title=\"Vaughan, D. G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 317–382 (IPCC, Cambridge Univ. Press, 2013).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR14\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR14\">14</a>,<a id=\"ref-link-section-d17083767e2060_1\" title=\"IPCC The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2019).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR15\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR15\">15</a>,<a id=\"ref-link-section-d17083767e2063\" title=\"IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR16\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 16\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR16\">16</a></sup><span>. Here we show in an intercomparison exercise that glaciers worldwide lost 273 ± 16 gigatonnes in mass annually from 2000 to 2023, with an increase of 36 ± 10% from the first (2000–2011) to the second (2012–2023) half of the period. Since 2000, glaciers have lost between 2% and 39% of their ice regionally and about 5% globally. Glacier mass loss is about 18% larger than the loss from the Greenland Ice Sheet and more than twice that from the Antarctic Ice Sheet</span><sup><a id=\"ref-link-section-d17083767e2067\" title=\"Otosaka, I. N. et al. Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020. Earth Syst. Sci. Data 15, 1597–1616 (2023).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR17\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 17\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR17\">17</a></sup><span>. Our results arise from a scientific community effort to collect, homogenize, combine and analyse glacier mass changes from in situ and remote-sensing observations. Although our estimates are in agreement with findings from previous assessments</span><sup><a id=\"ref-link-section-d17083767e2071\" title=\"Vaughan, D. G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 317–382 (IPCC, Cambridge Univ. Press, 2013).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR14\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR14\">14</a>,<a id=\"ref-link-section-d17083767e2071_1\" title=\"IPCC The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2019).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR15\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR15\">15</a>,<a id=\"ref-link-section-d17083767e2074\" title=\"IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR16\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 16\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR16\">16</a></sup><span>&nbsp;at a global scale, we found some large regional deviations owing to systematic differences among observation methods. Our results provide a refined baseline for better understanding observational differences and for calibrating model ensembles</span><sup><a id=\"ref-link-section-d17083767e2079\" title=\"Marzeion, B. et al. Partitioning the uncertainty of ensemble projections of global glacier mass change. Earths Future 8, e2019EF001470 (2020).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR12\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 12\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR12\">12</a>,<a id=\"ref-link-section-d17083767e2082\" title=\"IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR16\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 16\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR16\">16</a>,<a id=\"ref-link-section-d17083767e2085\" title=\"Zekollari, H. et al. Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations. Cryosphere 18, 5045–5066 (2024).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR18\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 18\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR18\">18</a></sup><span>, which will help to narrow projection uncertainty for the twenty-first century</span><sup><a id=\"ref-link-section-d17083767e2089\" title=\"Rounce, D. R. et al. Global glacier change in the 21st century: every increase in temperature matters. Science 379, 78–83 (2023).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR11\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 11\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR11\">11</a>,<a id=\"ref-link-section-d17083767e2092\" title=\"Marzeion, B. et al. Partitioning the uncertainty of ensemble projections of global glacier mass change. Earths Future 8, e2019EF001470 (2020).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR12\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 12\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR12\">12</a>,<a id=\"ref-link-section-d17083767e2095\" title=\"Zekollari, H. et al. Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations. Cryosphere 18, 5045–5066 (2024).\" href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR18\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 18\" data-mce-href=\"https://www.nature.com/articles/s41586-024-08545-z#ref-CR18\">18</a></sup><span>.</span></p>","language":"English","publisher":"Nature","doi":"10.1038/s41586-024-08545-z","usgsCitation":"GlaMBIE Team, Zemp, M., Jakob, L., Dussaillant, I., Nussbaumer, S., Gourmelen, N., Dubber, S., Geruo, A., Abdullahi, S., Andreassen, L.M., Berthier, E., Bhattacharya, A., Blazquez, A., Boehm Vock, L., Bolch, T., Box, J., Braun, M.H., Brun, F., Cicero, E., Colgan, W., Eckert, N., Farinotti, D., Florentine, C., Floricioiu, D., Gardner, A., Harig, C., Hassan, J., Hugonnet, R., Huss, M., Jóhannesson, T., Liang, C., Ke, C., Abbas, S., King, O., Kneib, M., Krieger, L., Maussion, F., Mattea, E., McNabb, R., Menounos, B., Miles, E., Moholdt, G., Nilsson, J., Palsson, F., Pfeffer, J., Piermattei, L., Plummer, S., Richter, A., Sasgen, I., Schuster, L., Seehaus, T., Shen, X., Sommer, C., Sutterley, T., Treichler, D., Velicogna, I., Wouters, B., Zekollari, H., and Zheng, W., 2025, Community estimate of global glacier mass changes from 2000 to 2023: Nature, v. 639, p. 382-388, https://doi.org/10.1038/s41586-024-08545-z.","productDescription":"7 p.","startPage":"382","endPage":"388","ipdsId":"IP-168262","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":487239,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41586-024-08545-z","text":"Publisher Index Page"},{"id":482568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"639","noUsgsAuthors":false,"publicationDate":"2025-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"GlaMBIE 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Tyler","contributorId":351602,"corporation":false,"usgs":false,"family":"Sutterley","given":"Tyler","affiliations":[],"preferred":false,"id":929012,"contributorType":{"id":1,"text":"Authors"},"rank":54},{"text":"Treichler, Desiree","contributorId":342095,"corporation":false,"usgs":false,"family":"Treichler","given":"Desiree","email":"","affiliations":[],"preferred":false,"id":929013,"contributorType":{"id":1,"text":"Authors"},"rank":55},{"text":"Velicogna, Isabella","contributorId":91727,"corporation":false,"usgs":true,"family":"Velicogna","given":"Isabella","email":"","affiliations":[],"preferred":false,"id":929014,"contributorType":{"id":1,"text":"Authors"},"rank":56},{"text":"Wouters, Bert","contributorId":221138,"corporation":false,"usgs":false,"family":"Wouters","given":"Bert","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":929015,"contributorType":{"id":1,"text":"Authors"},"rank":57},{"text":"Zekollari, Harry","contributorId":351603,"corporation":false,"usgs":false,"family":"Zekollari","given":"Harry","affiliations":[],"preferred":false,"id":929016,"contributorType":{"id":1,"text":"Authors"},"rank":58},{"text":"Zheng, Whyjay","contributorId":351604,"corporation":false,"usgs":false,"family":"Zheng","given":"Whyjay","affiliations":[],"preferred":false,"id":929017,"contributorType":{"id":1,"text":"Authors"},"rank":59}]}}
,{"id":70264360,"text":"70264360 - 2025 - Reduction of red bed sedimentary rocks in connection with energy metal ore formation: A case study from the Sinbad seep, Mesa County, Colorado","interactions":[],"lastModifiedDate":"2025-04-17T15:36:43.417614","indexId":"70264360","displayToPublicDate":"2025-02-18T08:43:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20209,"text":"Mining, Metallurgy and Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Reduction of red bed sedimentary rocks in connection with energy metal ore formation: A case study from the Sinbad seep, Mesa County, Colorado","docAbstract":"<p><span>The Paradox Basin’s Sinbad seep is a modern analog for ancient bleaching of red bed sediments by introduced alkaline, reducing brines. This bleaching, involving reductive alteration of former red beds, is essential ground preparation that enables the altered rocks to trap Cu, U, and V from later oxidized fluids, forming ore deposits. Study of Sinbad thus offers insights into these metallic mineralization processes in the Paradox and other sedimentary basins. The Sinbad seep occurs where shallow groundwater interacts with organic-rich petroleum source rocks, flows up a fault, and discharges into Salt Creek. Ratios of Na/Cl, Na/Br, and Cl/Br, plus high sulfate concentrations, indicate that the salinity of the seep water originated from dissolution of halite and gypsum during topographically driven flow of meteoric water across a diapir of the Pennsylvanian Paradox Formation, rather than from deep basinal brines. In contact with the organic-rich shales of the Paradox, these SO</span><sub>4</sub><span>-rich waters become reduced through bacterial reduction, producing H</span><sub>2</sub><span>S. The waters react with red beds of the Permian Cutler Formation, causing pervasive bleaching. The bleaching at Sinbad is characterized by iron-conservative reduction of ferric iron in diagenetic hematite and detrital ilmenite and magnetite to Fe sulfides. The ferrous iron is retained in these sulfides likely as sorbed Fe. Associated alteration includes precipitation of quartz and feldspar overgrowths, partial dissolution of detrital quartz and feldspar caused by pressure solution, formation of clay and authigenic rutile, and precipitation of carbonate and gypsum. The Fe sulfides rapidly degenerate to a mixture of jarosite and iron oxides on weathering at the surface. Among the major-oxide elements and most trace elements, there is no statistical difference between unbleached and bleached samples. The exceptions are uranium and sulfur, which are somewhat greater in bleached samples. Fission track radiography illuminates that uranium is preferentially concentrated in iron oxide cements, iron sorbed into detrital clasts, and finely disseminated iron oxide in illite cement in bleached samples. A leaching experiment suggests that uranium may be more easily available for mobilization from rocks that have undergone bleaching alteration, making them potential U sources. In addition, bleached rocks form effective traps for U, V, and some Cu mineralization.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s42461-025-01184-6","usgsCitation":"Barton, I., Thorson, J., Hall, S., Zielinski, R.A., McIntosh, J., and Kim, J., 2025, Reduction of red bed sedimentary rocks in connection with energy metal ore formation: A case study from the Sinbad seep, Mesa County, Colorado: Mining, Metallurgy and Exploration, v. 42, p. 1177-1197, https://doi.org/10.1007/s42461-025-01184-6.","productDescription":"21 p.","startPage":"1177","endPage":"1197","ipdsId":"IP-159441","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":483229,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Mesa County","otherGeospatial":"Sinbad seep","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-109.053,39.3667],[-108.4096,39.366],[-107.4309,39.3662],[-107.4288,39.3613],[-107.4306,39.3479],[-107.4509,39.33],[-107.463,39.3269],[-107.4661,39.3218],[-107.4653,39.314],[-107.4516,39.3045],[-107.4448,39.3087],[-107.4386,39.3091],[-107.4393,39.3022],[-107.4358,39.2989],[-107.4212,39.2969],[-107.4126,39.2919],[-107.397,39.295],[-107.3965,39.2837],[-107.3935,39.2787],[-107.3793,39.2756],[-107.3772,39.2719],[-107.3799,39.2686],[-107.3915,39.264],[-107.3944,39.2565],[-107.4004,39.253],[-107.4123,39.2542],[-107.42,39.255],[-107.423,39.255],[-107.4248,39.2545],[-107.4259,39.2531],[-107.4258,39.2477],[-107.4264,39.245],[-107.4275,39.2427],[-107.4298,39.2395],[-107.4339,39.2358],[-107.4363,39.2344],[-107.4445,39.2312],[-107.4551,39.2283],[-107.4617,39.2269],[-107.4652,39.2268],[-107.4682,39.2263],[-107.4706,39.2272],[-107.4742,39.229],[-107.476,39.2303],[-107.4784,39.2317],[-107.4808,39.2316],[-107.4825,39.2307],[-107.4837,39.2298],[-107.4854,39.2275],[-107.4884,39.2252],[-107.5001,39.2201],[-107.5095,39.2145],[-107.5177,39.2108],[-107.5236,39.2071],[-107.5242,39.2048],[-107.5235,39.2017],[-107.5192,39.1972],[-107.5198,39.1963],[-107.5216,39.1953],[-107.5275,39.1962],[-107.5305,39.1957],[-107.5346,39.1943],[-107.5369,39.1915],[-107.5398,39.1874],[-107.5409,39.1833],[-107.5414,39.1797],[-107.5431,39.176],[-107.5455,39.1737],[-107.5478,39.1724],[-107.5502,39.1714],[-107.5531,39.1723],[-107.5597,39.1745],[-107.5633,39.1758],[-107.5681,39.1762],[-107.5787,39.1742],[-107.5823,39.1737],[-107.5858,39.171],[-107.5886,39.1664],[-107.5891,39.1628],[-107.5903,39.1605],[-107.5926,39.1591],[-107.6032,39.1558],[-107.6109,39.153],[-107.625,39.146],[-107.6338,39.1404],[-107.6413,39.1349],[-107.6477,39.1294],[-107.653,39.1243],[-107.6564,39.1197],[-107.6587,39.1165],[-107.6592,39.1138],[-107.6604,39.1111],[-107.6627,39.1088],[-107.6644,39.1074],[-107.6691,39.105],[-107.6738,39.1032],[-107.6767,39.1],[-107.6772,39.0963],[-107.6753,39.0923],[-107.6759,39.0891],[-107.6776,39.0868],[-107.6805,39.0854],[-107.6858,39.084],[-107.6912,39.0844],[-107.6971,39.0843],[-107.7024,39.0828],[-107.7071,39.0805],[-107.7112,39.0782],[-107.7129,39.075],[-107.7147,39.0736],[-107.7245,39.0662],[-107.7368,39.0597],[-107.7573,39.0476],[-107.7655,39.043],[-107.7672,39.0425],[-107.7696,39.0434],[-107.7751,39.0501],[-107.7788,39.0537],[-107.7848,39.0577],[-107.7902,39.0603],[-107.7991,39.0629],[-107.8087,39.0655],[-107.823,39.0703],[-107.841,39.0768],[-107.8458,39.0785],[-107.8487,39.0789],[-107.8547,39.0784],[-107.8734,39.0709],[-107.8887,39.0665],[-107.8981,39.0632],[-107.91,39.0621],[-107.9271,39.0619],[-107.9319,39.0618],[-107.9555,39.0583],[-107.959,39.0578],[-107.9751,39.0589],[-107.9786,39.0588],[-107.981,39.0583],[-107.9839,39.0569],[-107.9891,39.0514],[-107.9943,39.0477],[-108.01,39.0379],[-108.0188,39.0332],[-108.031,39.0258],[-108.0333,39.023],[-108.0361,39.0166],[-108.0384,39.0143],[-108.0431,39.0111],[-108.0454,39.0101],[-108.0466,39.0106],[-108.0484,39.0119],[-108.0502,39.0123],[-108.052,39.0123],[-108.0537,39.0109],[-108.0577,39.0063],[-108.0658,38.9998],[-108.0669,38.9966],[-108.0704,38.9943],[-108.0803,38.9873],[-108.0861,38.9818],[-108.0896,38.9804],[-108.0925,38.9794],[-108.0936,38.9776],[-108.093,38.9749],[-108.0886,38.9668],[-108.0879,38.965],[-108.0879,38.9636],[-108.0907,38.9595],[-108.0993,38.9489],[-108.1016,38.9471],[-108.1044,38.942],[-108.1073,38.9384],[-108.1136,38.9328],[-108.1222,38.9236],[-108.1252,38.9222],[-108.1287,38.9217],[-108.1329,38.9225],[-108.1364,38.9229],[-108.1388,38.9229],[-108.1411,38.9206],[-108.1428,38.9183],[-108.1433,38.9155],[-108.1432,38.9123],[-108.1401,38.9074],[-108.1395,38.9052],[-108.1406,38.9047],[-108.1448,38.9051],[-108.1471,38.9041],[-108.1488,38.9014],[-108.1517,38.9004],[-108.1647,38.8997],[-108.1729,38.896],[-108.1763,38.8927],[-108.2267,38.876],[-108.2449,38.8707],[-108.3776,38.826],[-108.3772,38.6678],[-108.3771,38.6116],[-108.3791,38.4999],[-108.6635,38.4992],[-109.0602,38.4991],[-109.0604,38.5362],[-109.0604,38.5467],[-109.0602,38.5612],[-109.0597,38.6047],[-109.0597,38.6096],[-109.0596,38.6727],[-109.055,38.9283],[-109.0548,38.941],[-109.0547,38.9454],[-109.0546,38.9546],[-109.0543,38.9608],[-109.0544,38.9691],[-109.0538,39.0008],[-109.0538,39.0135],[-109.0537,39.028],[-109.0534,39.1011],[-109.0534,39.1151],[-109.0531,39.1813],[-109.0531,39.1881],[-109.0531,39.2026],[-109.053,39.2752],[-109.053,39.2897],[-109.053,39.3627],[-109.053,39.3667]]]},\"properties\":{\"name\":\"Mesa\",\"state\":\"CO\"}}]}","volume":"42","noUsgsAuthors":false,"publicationDate":"2025-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Barton, Isabel","contributorId":352254,"corporation":false,"usgs":false,"family":"Barton","given":"Isabel","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":930518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thorson, Jon P.","contributorId":352255,"corporation":false,"usgs":false,"family":"Thorson","given":"Jon P.","affiliations":[{"id":36466,"text":"Consulting Geologist","active":true,"usgs":false}],"preferred":false,"id":930519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, Susan 0000-0002-0931-8694","orcid":"https://orcid.org/0000-0002-0931-8694","contributorId":201829,"corporation":false,"usgs":true,"family":"Hall","given":"Susan","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":930520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zielinski, Robert A. 0000-0002-4047-5129 rzielinski@usgs.gov","orcid":"https://orcid.org/0000-0002-4047-5129","contributorId":1593,"corporation":false,"usgs":true,"family":"Zielinski","given":"Robert","email":"rzielinski@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":930521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McIntosh, Jennifer","contributorId":352256,"corporation":false,"usgs":false,"family":"McIntosh","given":"Jennifer","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":930522,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kim, Ji-Hyun","contributorId":352257,"corporation":false,"usgs":false,"family":"Kim","given":"Ji-Hyun","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":930523,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70266086,"text":"70266086 - 2025 - Application of transcriptomics concentration-response modeling for prioritization of contaminants detected in tributaries of the North American Great Lakes","interactions":[],"lastModifiedDate":"2025-05-12T15:47:36.606655","indexId":"70266086","displayToPublicDate":"2025-02-17T10:08:36","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":"Application of transcriptomics concentration-response modeling for prioritization of contaminants detected in tributaries of the North American Great Lakes","docAbstract":"<p><span>As part of the Great Lakes Restoration Initiative, chemical monitoring and surveillance efforts have detected approximately 330 chemicals in surface water of Great Lakes tributaries. There were 140 chemicals for which no empirical toxicity data were available. The aim of this study was to generate transcriptomic points of departure (tPODs) for 10 of these compounds and demonstrate how they could be applied in a screening-level prioritization. Organisms representing three trophic levels of the aquatic food web (</span><i>Pimephales promelas</i><span>,&nbsp;</span><i>Daphnia magna</i><span>, and&nbsp;</span><i>Raphidocelis subcapitata</i><span>) were exposed for 24 hr to a half-log dilution series of nominal exposure concentrations typically ranging from 66.7–0.021 µM of each chemical. In addition to observations of apical effects (e.g., survival and morphology), whole body transcriptomic responses (tPODs) to each chemical were evaluated with targeted analysis using TempO-seq for&nbsp;</span><i>P. promelas</i><span>&nbsp;and&nbsp;</span><i>D. magna</i><span>&nbsp;and nontargeted RNA-seq for&nbsp;</span><i>R. subcapitata</i><span>. The tPODs ranged from 0.18–10.8 µM for&nbsp;</span><i>P. promelas</i><span>&nbsp;and 0.32–29 µM for&nbsp;</span><i>D. magna</i><span>, with the most potent of the chemicals tested being fipronil carboxamide for both species. For&nbsp;</span><i>R. subcapitata</i><span>, the tPODs ranged from 0.04–1.77 µM, with gabapentin as the most potent chemical tested. Empirically derived tPODs from these data-poor chemicals were compared with concentrations detected in the Great Lakes basin. Environmental concentrations were less than the tPODs except for&nbsp;</span><i>R. subcapitata</i><span>&nbsp;and 3,4-dichlorophenyl isocyanate. Similarly, tPODs from previously tested data-rich chemicals were compared with environmental concentrations, in which case tPODs from several chemicals overlapped environmental concentrations. This work demonstrates the potential utility of emerging ecological high-throughput transcriptomics assays to support screening and prioritization of data-poor environmental contaminants.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/etojnl/vgaf050","usgsCitation":"Jenna Cavallin, Kendra Bush, Corsi, S., DeCicco, L., Kevin Flynn, Alex Kasparek, Monique Hazimi, Erin Maloney, Peter Schuman, and Daniel Villeneuve, 2025, Application of transcriptomics concentration-response modeling for prioritization of contaminants detected in tributaries of the North American Great Lakes: Environmental Toxicology and Chemistry, v. 44, no. 5, p. 1310-1321, https://doi.org/10.1093/etojnl/vgaf050.","productDescription":"12 p.","startPage":"1310","endPage":"1321","ipdsId":"IP-167135","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":484984,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenna Cavallin","contributorId":353840,"corporation":false,"usgs":false,"family":"Jenna Cavallin","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendra Bush","contributorId":353841,"corporation":false,"usgs":false,"family":"Kendra Bush","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeCicco, Laura 0000-0002-3915-9487 ldecicco@usgs.gov","orcid":"https://orcid.org/0000-0002-3915-9487","contributorId":215381,"corporation":false,"usgs":true,"family":"DeCicco","given":"Laura","email":"ldecicco@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kevin Flynn","contributorId":353846,"corporation":false,"usgs":false,"family":"Kevin Flynn","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alex Kasparek","contributorId":353847,"corporation":false,"usgs":false,"family":"Alex Kasparek","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":934554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monique Hazimi","contributorId":353850,"corporation":false,"usgs":false,"family":"Monique Hazimi","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erin Maloney","contributorId":353852,"corporation":false,"usgs":false,"family":"Erin Maloney","affiliations":[{"id":34699,"text":"University of Minnesota-Duluth","active":true,"usgs":false}],"preferred":false,"id":934556,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Peter Schuman","contributorId":353856,"corporation":false,"usgs":false,"family":"Peter Schuman","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934557,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Daniel Villeneuve","contributorId":353857,"corporation":false,"usgs":false,"family":"Daniel Villeneuve","affiliations":[],"preferred":false,"id":934558,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70263714,"text":"70263714 - 2025 - Contrasting magnitude and timing of pulsed aqueous methylmercury bioaccumulation across a reservoir food web","interactions":[],"lastModifiedDate":"2025-03-11T15:20:29.672209","indexId":"70263714","displayToPublicDate":"2025-02-17T08:29:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Contrasting magnitude and timing of pulsed aqueous methylmercury bioaccumulation across a reservoir food web","docAbstract":"<p><span>Water column hypoxia is a key process influencing methylmercury (MeHg) production and availability in waterbodies worldwide. During seasonal destratification, large, short-lived pulses of aqueous MeHg may be released into the subsequently mixed water column, but little is known about the fate of these pulses, particularly whether there are concomitant increases in MeHg uptake into aquatic food webs. We examined the magnitude and timing of MeHg uptake across several trophic guilds relative to the reservoir stratification status using biweekly mercury data from water, zooplankton, and fish (Bluegill,&nbsp;</span><i>Lepomis macrochirus</i><span>&nbsp;and Smallmouth Bass,&nbsp;</span><i>Micropterus dolomieu</i><span>). Zooplankton MeHg concentrations increased by up to 250% during destratification, concurrent with increases in aqueous MeHg concentrations. Zooplankton and filter-passing MeHg concentrations were positively correlated when the reservoir was mixed (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.95) and destratifying (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.57) but not while the reservoir was stratified (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.21). Mercury concentrations in adult bluegill and juveniles of both fish species increased 20–70% following destratification, with responses lagging 4–8 weeks behind those in water and zooplankton MeHg. Mercury concentrations in piscivorous adult bass varied little over the course of the study. Our findings demonstrate the responsiveness of reservoir food webs to pulses in MeHg availability, suggesting that these pulses could play an important role in biotic MeHg exposure within and downstream of reservoirs.</span></p>","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c10719","usgsCitation":"Willacker, J., Eagles-Smith, C., Baldwin, A.K., Tate, M., Poulin, B., Naymik, J., Krabbenhoft, D.P., Myers, R., and Chandler, J.A., 2025, Contrasting magnitude and timing of pulsed aqueous methylmercury bioaccumulation across a reservoir food web: Environmental Science & Technology, v. 59, no. 8, p. 38884-3894, https://doi.org/10.1021/acs.est.4c10719.","productDescription":"11 p.","startPage":"38884","endPage":"3894","ipdsId":"IP-171553","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":489859,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.4c10719","text":"Publisher Index Page"},{"id":482271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Brownlee Reservoir, Hells Canyon Complex, Snake River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -116.84602295639596,\n              45.65736840039159\n            ],\n            [\n              -116.99320578225903,\n              45.295454810899784\n            ],\n            [\n              -117.24983304017238,\n              44.80742011369799\n            ],\n            [\n              -117.30121339023736,\n              44.5717012606722\n            ],\n            [\n              -116.73893003569623,\n              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Center","active":false,"usgs":true}],"preferred":true,"id":927931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":927932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science 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Jesse","contributorId":229386,"corporation":false,"usgs":false,"family":"Naymik","given":"Jesse","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":927936,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":927937,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Myers, Ralph","contributorId":351132,"corporation":false,"usgs":false,"family":"Myers","given":"Ralph","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":927938,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chandler, James A.","contributorId":210045,"corporation":false,"usgs":false,"family":"Chandler","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":38056,"text":"Idaho Power Company 1221 West Idaho Street, Boise, ID 83702","active":true,"usgs":false}],"preferred":true,"id":927939,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70268813,"text":"70268813 - 2025 - Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops","interactions":[],"lastModifiedDate":"2025-07-08T16:10:56.678715","indexId":"70268813","displayToPublicDate":"2025-02-16T09:05:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":684,"text":"Agronomy Journal","active":true,"publicationSubtype":{"id":10}},"title":"Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops","docAbstract":"<p><span>Winter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha</span><sup>−1</sup><span>) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha</span><sup>−1</sup><span>) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha</span><sup>−1</sup><span>, while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj.&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;=&nbsp;0.089, root mean squared error [RMSE]&nbsp;=&nbsp;0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj.&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;=&nbsp;0.748, RMSE&nbsp;=&nbsp;13.10 kg ha</span><sup>−1</sup><span>&nbsp;from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within&nbsp;±4 days of destructive sampling (</span><i>n&nbsp;</i><span>=&nbsp;1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj.&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;=&nbsp;0.700–0.769, RMSE&nbsp;=&nbsp;10.70–15.40 kg ha</span><sup>−1</sup><span>). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj.&nbsp;</span><i>R</i><sup>2&nbsp;</sup><span>=&nbsp;0.765, RMSE&nbsp;=&nbsp;12.37 kg ha</span><sup>−1</sup><span>), management variables (species, season, adj.&nbsp;</span><i>R</i><sup>2&nbsp;</sup><span>=&nbsp;0.772, and RMSE&nbsp;=&nbsp;12.13 kg ha</span><sup>−1</sup><span>), and biophysical variables (height, fractional ground cover, adj.&nbsp;</span><i>R</i><sup>2&nbsp;</sup><span>=&nbsp;0.818, and RMSE&nbsp;=&nbsp;10.29 kg ha</span><sup>−1</sup><span>). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/agj2.70011","usgsCitation":"Thieme, A., Jennewein, J., Hively, W.D., Lamb, B.T., Whitcraft, A., Mirsky, S., Reberg-Horton, S., and Justice, C., 2025, Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops: Agronomy Journal, v. 117, no. 1, e70011, 22 p., https://doi.org/10.1002/agj2.70011.","productDescription":"e70011, 22 p.","ipdsId":"IP-166011","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":497994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/agj2.70011","text":"Publisher Index Page"},{"id":491812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Eastern Shore","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.72279086706524,\n              39.59745381238187\n            ],\n            [\n              -76.72279086706524,\n              38.03247531099828\n            ],\n            [\n              -75.71750552954822,\n              38.03247531099828\n            ],\n            [\n              -75.71750552954822,\n              39.59745381238187\n            ],\n            [\n              -76.72279086706524,\n              39.59745381238187\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"117","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":942060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":942061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942063,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitcraft, Alicia","contributorId":357632,"corporation":false,"usgs":false,"family":"Whitcraft","given":"Alicia","affiliations":[{"id":48035,"text":"Department of Geographical Sciences, University of Maryland","active":true,"usgs":false}],"preferred":false,"id":942064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mirsky, S.B.","contributorId":357633,"corporation":false,"usgs":false,"family":"Mirsky","given":"S.B.","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":942065,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reberg-Horton, S.C.","contributorId":357634,"corporation":false,"usgs":false,"family":"Reberg-Horton","given":"S.C.","affiliations":[{"id":85481,"text":"Department of Crop and Soil Sciences, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":942066,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Justice, C.","contributorId":146851,"corporation":false,"usgs":false,"family":"Justice","given":"C.","email":"","affiliations":[],"preferred":false,"id":942067,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70262010,"text":"70262010 - 2025 - Molluscan isotope sclerochronology in marine palaeoclimatology: Taxa, technique and timespan issues","interactions":[],"lastModifiedDate":"2025-01-10T15:47:05.625225","indexId":"70262010","displayToPublicDate":"2025-02-15T08:40:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Molluscan isotope sclerochronology in marine palaeoclimatology: Taxa, technique and timespan issues","docAbstract":"<p><span>Study of the accretionary biomineralised hardparts of organisms (sclerochronology) can make a useful contribution to palaeoclimatology. Ontogenetic sequences of isotopic data (δ</span><sup>18</sup><span>O and Δ</span><sub>47</sub><span>&nbsp;values) from the shells of marine molluscs are a source of information on seasonal sea-surface temperatures that can be used for detailed and rigorous evaluation of the outputs of numerical climate models. In situations where there is significant seasonality, and where shell preservation is adequate, accurate information about winter and summer surface temperature can be obtained from shallow-water benthic forms (bivalves and gastropods), in particular the early ontogeny of fast-growing species. Accurate information about winter surface temperature can also be obtained from individuals that lived at mid-shelf depths (20–40&nbsp;m), but summer seafloor values from these need upward adjustment to derive a plausible surface temperature if thermal stratification of the water column occurs in this season. Ontogenetic δ</span><sup>18</sup><span>O profiles from planktonic pteropod gastropods are a potential source of insight into seasonal surface temperatures in the ocean basins; these organisms merit investigation for provision of information to complement shelf data.</span></p><p><span>Temperature profiles constructed from shell δ<sup>18</sup>O require an estimate of the δ<sup>18</sup>O value of ambient seawater, which can be derived by back-calculation from the Δ<sub>47</sub>-temperature supplied by the same shell material. Alternatively, through appropriate sampling and data processing, seasonal temperatures can be obtained directly from Δ<sub>47</sub>&nbsp;profiles. Climate parameters are defined in terms of the mean state over a period of 30 consecutive years, a statistic (e.g., for seasonal temperatures) which can be derived from the long isotopic temperature records obtainable from bivalve species that live for many tens or hundreds of years. Efforts should be made to acquire such records, especially averaged data from crossdated shells, to specify climate parameters for precise times in the past. Information for precise times would be of particular value for icehouse intervals like the late Cenozoic, characterised by high frequency (high amplitude) climate fluctuation. Short records from non-crossdated shells can nevertheless provide useful insights into climate, particularly if a large dataset is obtained, supplying a reliable picture of the mean state and range of variation in climate parameters over the interval represented by the shells.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2024.109068","usgsCitation":"Johnson, A., Schone, B., Petersen, S.V., de Winter, N., Dowsett, H., Cudennec, J., Harper, E., and Winkelstern, I., 2025, Molluscan isotope sclerochronology in marine palaeoclimatology: Taxa, technique and timespan issues: Quaternary Science Reviews, v. 350, 109068, 21 p., https://doi.org/10.1016/j.quascirev.2024.109068.","productDescription":"109068, 21 p.","ipdsId":"IP-166743","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":487335,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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