Urban wildlife species have the potential to serve as links in disease transmission between wildlife, humans and domestic animals at the wildland–urban interface (WUI), contributing to both sustained cross-species transmission of pathogens and the emergence of diseases in susceptible populations. However, the relative roles of host and environmental factors in shaping the composition of pathogen communities in urban wildlife is understudied. In this study, we integrated DNA and RNA virome data with host genomic and GPS datasets to investigate factors shaping virome composition in bobcats (Lynx rufus ) at the WUI in the Tucson Mountains, Arizona, USA. Using a hybrid-capture approach for 31 scats and 17 buccal swabs, we identified multiple viruses that could affect carnivore health at the WUI, including canine parvovirus, feline astrovirus, Felis catus papillomaviruses 2 and 3 and Lyon-IARC polyomavirus. Models of virome composition and distribution of viral taxa indicated contributions of host genetic relatedness and factors relating to urbanisation (such as percentages of urban land cover, road and building densities and distances to roads). Genetic associations with virome compositions were particularly influenced by females. While females exhibit significant isolation by distance, partial Mantel tests revealed a significant correlation between beta diversity and host genetic distance in females only. To our knowledge, this study represents the first assessment of factors shaping virome composition in a wild felid. Our finding of known feline and canine pathogens in bobcats underscores the potential of the WUI to facilitate cross-species transmission between wild and domestic animals.
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Carter Johnson and Dennis H. Knight","interactions":[],"lastModifiedDate":"2025-04-18T13:21:43.301447","indexId":"70265832","displayToPublicDate":"2025-03-01T08:43:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3111,"text":"Prairie Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Ecology of Dakota landscapes: Past, present, and future, by W. Carter Johnson and Dennis H. Knight","docAbstract":"“Ecology of Dakota Landscapes” is an excellent example of fusing observation and scientific studies to describe the origins and development of the landscape of North and South Dakota, the impact of European settlement, the introduction of agriculture to the area, predictions for the future in the face of a changing climate, and a scientific framework for conservation and sustainability. 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\"}}]}","volume":"89","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Haserodt, Megan J. 0000-0002-8304-090X mhaserodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8304-090X","contributorId":174791,"corporation":false,"usgs":true,"family":"Haserodt","given":"Megan","email":"mhaserodt@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chase, Pete","contributorId":357288,"corporation":false,"usgs":false,"family":"Chase","given":"Pete","affiliations":[],"preferred":false,"id":941083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiersma, Amy","contributorId":357232,"corporation":false,"usgs":false,"family":"Wiersma","given":"Amy","affiliations":[{"id":27733,"text":"WGNHS","active":true,"usgs":false}],"preferred":false,"id":940830,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pruitt, Aaron","contributorId":357231,"corporation":false,"usgs":false,"family":"Pruitt","given":"Aaron","affiliations":[{"id":85353,"text":"WDNR","active":true,"usgs":false}],"preferred":false,"id":940829,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267776,"text":"70267776 - 2025 - Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids","interactions":[],"lastModifiedDate":"2025-05-30T15:24:10.593599","indexId":"70267776","displayToPublicDate":"2025-03-01T08:17:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1577,"text":"Environmetrics","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids","docAbstract":"Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer (Odocoileus virginianus) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.
","language":"English","publisher":"Wiley","doi":"10.1002/env.2901","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2025, Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids: Environmetrics, v. 36, no. 2, e2901, 14 p., https://doi.org/10.1002/env.2901.","productDescription":"e2901, 14 p.","ipdsId":"IP-166539","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490648,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/env.2901","text":"Publisher Index Page"},{"id":489259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.82700363741058,\n 43.768147806390715\n ],\n [\n -90.82700363741058,\n 42.50633570600206\n ],\n [\n -89.0890731993092,\n 42.50633570600206\n ],\n [\n -89.0890731993092,\n 43.768147806390715\n ],\n [\n -90.82700363741058,\n 43.768147806390715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"36","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":356170,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":356171,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":938823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":356173,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":356175,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":938825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":938826,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":356177,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938827,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266296,"text":"70266296 - 2025 - Development and evaluation of the remote passive integrated transponder tag reader for customizable monitoring of wildlife","interactions":[],"lastModifiedDate":"2025-05-02T18:09:54.530775","indexId":"70266296","displayToPublicDate":"2025-03-01T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Development and evaluation of the remote passive integrated transponder tag reader for customizable monitoring of wildlife","docAbstract":"Increasing the quantity and quality of mark-reencounter data can be important when individual identification data are needed to address research or management questions. Physical recapture methods tend to be labor-intensive and therefore expensive, while remote detection methods are not easily applied to all taxa or are proprietary and thus difficult to customize. However, partnerships between wildlife scientists and engineers have the potential to provide innovative solutions that improve data collection while reducing costs. We describe a collaborative effort to improve the collection of mark-reencounter data on an invasive reptile, the brown treesnake (Boiga irregularis). In laboratory trials conducted on Guåhan (CHamoru; Guam in English) in 2021, we assessed the feasibility of detecting snakes fitted with passive integrated transponder (PIT) tags using customized remote PIT-tag readers (RePTaR) paired with baited snake traps. We evaluated differences in scanning success as a function of snake traits, location of PIT tags within snakes, and the distance of the PIT tag from the reader. We successfully scanned all individuals, on average 529 times but ranging from 3 to 6,436 times, during 12-hour trials using a designed customizable RePTaR unit. Snake characteristics and marking location explained little of the variability in scanning success, with distance from the reader as the best-supported covariate. By building cross-disciplinary partnerships, we can develop customizable technological solutions and provide additional tools with which to address challenges in wildlife research and management.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1569","collaboration":"USGS, Department of Defense, US Department of Agriculture","usgsCitation":"Amburgey, S., Prakash, A., Yackel Adams, A.A., Siers, S., and Converse, S.J., 2025, Development and evaluation of the remote passive integrated transponder tag reader for customizable monitoring of wildlife: Wildlife Society Bulletin, v. 49, no. 1, e1569, 16 p., https://doi.org/10.1002/wsb.1569.","productDescription":"e1569, 16 p.","ipdsId":"IP-161941","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1569","text":"Publisher Index Page"},{"id":485358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.58089598320413,\n 13.72087583284113\n ],\n [\n 144.58089598320413,\n 13.236656345472099\n ],\n [\n 145.0547651347528,\n 13.236656345472099\n ],\n [\n 145.0547651347528,\n 13.72087583284113\n ],\n [\n 144.58089598320413,\n 13.72087583284113\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Amburgey, Staci M.","contributorId":354345,"corporation":false,"usgs":false,"family":"Amburgey","given":"Staci M.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":935425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prakash, Aadithya","contributorId":354346,"corporation":false,"usgs":false,"family":"Prakash","given":"Aadithya","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":935426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":935427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siers, Shane R.","contributorId":354347,"corporation":false,"usgs":false,"family":"Siers","given":"Shane R.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":935428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264059,"text":"70264059 - 2025 - Tracking diagenetic alteration of magnetic susceptibility in thrust ridge and slope basin sediments of the Cascadia margin (ODP Sites 1249 and 1252; IODP Site U1325)","interactions":[],"lastModifiedDate":"2025-03-05T16:50:20.008926","indexId":"70264059","displayToPublicDate":"2025-02-28T10:44:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Tracking diagenetic alteration of magnetic susceptibility in thrust ridge and slope basin sediments of the Cascadia margin (ODP Sites 1249 and 1252; IODP Site U1325)","docAbstract":"We investigated sediment core records from the Cascadia Margin (Ocean Drilling Program Sites 1249 and 1252 at Hydrate Ridge; Integrated Ocean Drilling Program Site U1325 offshore Vancouver Island) using a Zr/Rb heavy mineral proxy from X-ray fluorescence (XRF) core scanning to identify intervals of primary detrital magnetic susceptibility (κ) and predict intervals where diagenesis caused magnetite dissolution by hydrogen sulfide. We also measured total sulfur (TS) content, grain size distributions, total organic carbon (TOC) content, and the magnetic mineral assemblage to further constrain the role of diagenesis on κ. Understanding how κ can be used to better characterize the varied effects of detrital and diagenetic signals in marine settings is important for understanding biogeochemical cycling and records of paleoenvironmental change. The upper 100 m of slope basin Site 1252 contains multiple intervals (> 90 m total) of decreased κ correlated with elevated TS content, consistent with dissolution of magnetite and precipitation of pyrite, iron monosulfides, and/or elemental sulfur. Similarly at the other slope basin site, Site U1325, κ is lower and TS is elevated in the interval between 24 and 51 mbsf, due to sulfide formation. At both slope basin sites, these low κ intervals correspond with high TOC, suggesting the possibility that organoclastic sulfate reduction (OSR) is likely a major driver of diagenetic alteration of κ at these sites. High TS:TOC ratios at Site U1325 suggests anaerobic oxidation of methane (AOM) during sulfate-methane transition zone (SMTZ) migration may have contributed to alteration of κ. In contrast, within the upper 90 m of Site 1249, a methane seep site at the summit of Hydrate Ridge, κ is almost entirely altered by diagenetic processes, with much of the low κ explained by a high degree of iron sulfide formation, while some intervals are affected by precipitation of magnetic iron sulfides that maintain or even increase κ. The presence of abundant methane seepage and gas hydrate as well as chemosynthetic seafloor fauna at this site, suggests that sulfide is released to the water column and AOM, rather than OSR, drives diagenetic alteration of κ at this site. Overall, the slope basin sites show episodic variation of κ that is influenced by TOC content, likely driven by changes in marine primary productivity and sedimentation rate, while the seep site shows consistently altered κ with lower TS content and no correlation with TOC. Methane seep environments likely experience loss of hydrogen sulfide to the water column and oxidation of hydrogen sulfide by seafloor seep fauna, which limits the amount of solid phase sulfur (pyrite, iron monosulfides, elemental sulfur) that can be precipitated within the sediments. In contrast, the migration of a buried SMTZ at slope basin sites results in enhanced sulfur precipitation within the sediments. This integrated magnetic and geochemical approach reveals the diagenetic production pathway and residence time of sulfide with the sediment column ultimately controls the style and degree of diagenetic loss of κ in marine sediments. This approach works best in environments with unaltered reference intervals/sites, consistent magnetic mineralogy, and clay-to-silt grain sizes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2025.107519","usgsCitation":"Phillips, S.C., Johnson, J.E., Clyde, W., Hong, W., Setera, J., and Torres, M.E., 2025, Tracking diagenetic alteration of magnetic susceptibility in thrust ridge and slope basin sediments of the Cascadia margin (ODP Sites 1249 and 1252; IODP Site U1325): Marine Geology, v. 483, 107519, 25 p., https://doi.org/10.1016/j.margeo.2025.107519.","productDescription":"107519, 25 p.","ipdsId":"IP-145344","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":487428,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"www.osti.gov/servlets/purl/2524598","text":"Publisher Index Page"},{"id":482911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Cascadia margin, Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -129,\n 50\n ],\n [\n -129,\n 44\n ],\n [\n -123,\n 44\n ],\n [\n -123,\n 50\n ],\n [\n -129,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"483","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Stephen C. 0000-0003-0858-4701","orcid":"https://orcid.org/0000-0003-0858-4701","contributorId":268177,"corporation":false,"usgs":true,"family":"Phillips","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":929616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Joel E.","contributorId":346768,"corporation":false,"usgs":false,"family":"Johnson","given":"Joel","email":"","middleInitial":"E.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":929617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clyde, William","contributorId":268178,"corporation":false,"usgs":false,"family":"Clyde","given":"William","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":929618,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hong, Wei-Li","contributorId":351864,"corporation":false,"usgs":false,"family":"Hong","given":"Wei-Li","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":929619,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Setera, Jacob","contributorId":351865,"corporation":false,"usgs":false,"family":"Setera","given":"Jacob","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":929620,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Torres, Marta E.","contributorId":196035,"corporation":false,"usgs":false,"family":"Torres","given":"Marta","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":929621,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269955,"text":"70269955 - 2025 - Landscape transcriptomic analysis detects thermal stress responses and potential adaptive variation in wild brook trout (Salvelinus fontinalis) during successive heatwaves","interactions":[],"lastModifiedDate":"2025-08-07T17:03:16.958834","indexId":"70269955","displayToPublicDate":"2025-02-28T09:54:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5331,"text":"Science of Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Landscape transcriptomic analysis detects thermal stress responses and potential adaptive variation in wild brook trout (Salvelinus fontinalis) during successive heatwaves","docAbstract":"Extreme weather events, such as heatwaves, are becoming more frequent and intense as a result of climate change. Critically, such extreme weather events can be more important drivers of extirpation and selection than changes in annual or seasonal averages and they pose a particularly large threat to poikilothermic organisms. In this study, we evaluated the transcriptomic response of a coldwater adapted fish species, the eastern brook trout (Salvelinus fontinalis), to two successive heatwaves during July and August 2022. We sampled brook trout at eight time points from four streams (N = 116 fish), sequenced mRNA from gill samples using TagSeq, and quantified expression levels of 32,670 unique transcripts. Multivariate analyses found that overall expression patterns in response to water temperature change were similar among streams. These analyses further detected groups of genes involved in immune response and oxygen carrier activity that were upregulated and downregulated respectively at higher water temperatures. We also detected 43 genes that were differentially expressed at different time points and followed the same expression pattern during the two heatwaves. Of these genes, 42 covaried with water temperature and most (27, 62.8 %) exhibited responses that varied by stream. Some of the differentially expressed genes, including heat shock proteins and cold-inducible RNA binding proteins, have been widely linked to temperature responses in experimental studies, whereas other genes we identified have functions that have not been well-studied in relationship to temperature or have unknown functions. This study shows the utility of landscape transcriptomic approaches to identify important biological processes governing wild organismal responses to short-term stressors. The results of this study can guide future investigations to identify phenotypic and genetic diversity that contribute to adaptive responses to heatwaves and improve predictions of how populations will respond to future climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.178960","usgsCitation":"Waraniak, J., Batchelor, S., Wagner, T., and Keagy, J., 2025, Landscape transcriptomic analysis detects thermal stress responses and potential adaptive variation in wild brook trout (Salvelinus fontinalis) during successive heatwaves: Science of Total Environment, v. 969, 178960, 16 p., https://doi.org/10.1016/j.scitotenv.2025.178960.","productDescription":"178960, 16 p.","ipdsId":"IP-172846","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.178960","text":"Publisher Index Page"},{"id":493729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Centre County, Huntingdon County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.92993465175924,\n 40.881082085248664\n ],\n [\n -77.92993465175924,\n 40.57251392307114\n ],\n [\n -77.16376958399,\n 40.57251392307114\n ],\n [\n -77.16376958399,\n 40.881082085248664\n ],\n [\n -77.92993465175924,\n 40.881082085248664\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"969","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin","contributorId":359192,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":945038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Batchelor, Sarah","contributorId":359193,"corporation":false,"usgs":false,"family":"Batchelor","given":"Sarah","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":945039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":218091,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":945040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keagy, Jason","contributorId":359194,"corporation":false,"usgs":false,"family":"Keagy","given":"Jason","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":945041,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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":"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.
","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":70263901,"text":"ofr20251006 - 2025 - ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2024","interactions":[],"lastModifiedDate":"2025-02-28T16:23:31.312068","indexId":"ofr20251006","displayToPublicDate":"2025-02-28T09:39:35","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1006","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 3, 2024","title":"ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2024","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 3 (July–September) of 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website at https://earthexplorer.usgs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251006","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Levy, R., Miller, J., and Ding, L., 2025, ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2024: U.S. Geological Survey Open-File Report 2025–1006, 56 p., https://doi.org/10.3133/ofr20251006.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172164","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":482589,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251006/full"},{"id":482584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1006/coverthb.jpg"},{"id":482585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1006/ofr20251006.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025–1006"},{"id":482586,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1006/ofr20251006.XML"},{"id":482587,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1006/images/"},{"id":482588,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13474","usgsCitation":"Levy, Z., Glas, R.L., Stagnitta, T.J., and Terry, N., 2025, ARCHI: A new R package for automated imputation of regionally correlated hydrologic records: Groundwater, v. 62, no. 4, p. 595-610, https://doi.org/10.1111/gwat.13474.","productDescription":"16 p.","startPage":"595","endPage":"610","ipdsId":"IP-166664","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":492870,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13474","text":"Publisher Index Page"},{"id":492796,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P17NXGHV","text":"USGS data release","linkHelpText":"Example Groundwater-Level Datasets and Benchmarking Results for the Automated Regional Correlation Analysis for Hydrologic Record Imputation (ARCHI) Software Package"},{"id":492616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, New York","otherGeospatial":"Central Valley, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.13447900247726,\n 37.3805353859038\n ],\n [\n -121.13447900247726,\n 35.88363374499093\n ],\n [\n -118.93035501208706,\n 35.88363374499093\n ],\n [\n -118.93035501208706,\n 37.3805353859038\n ],\n [\n -121.13447900247726,\n 37.3805353859038\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.76788606860265,\n 41.23528810034301\n ],\n [\n -74.12298971233533,\n 41.23528810034301\n ],\n [\n -74.12298971233533,\n 40.43286561377994\n ],\n [\n -71.76788606860265,\n 40.43286561377994\n ],\n [\n -71.76788606860265,\n 41.23528810034301\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glas, Robin L. 0000-0002-7394-1667","orcid":"https://orcid.org/0000-0002-7394-1667","contributorId":300625,"corporation":false,"usgs":true,"family":"Glas","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stagnitta, Timothy J. 0000-0001-8903-428X","orcid":"https://orcid.org/0000-0001-8903-428X","contributorId":304230,"corporation":false,"usgs":true,"family":"Stagnitta","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":true,"id":943533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":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":"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 %. Fipronil sulfone, fipronil sulfide, and fipronil were the most frequently detected fiproles (DF = 100 %, 94 %, and 67 %, respectively); permethrin was the most frequently detected pyrethroid (DF = 100 %), followed by bifenthrin (DF = 94 %), cyhalothrin (DF = 89 %), and etofenprox (DF = 78 %). 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 (fOC) of each biosolids sample was measured, and a statistically significant negative correlation was observed between fOC 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.
","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":70264146,"text":"70264146 - 2025 - Avian influenza infection and antibodies not identified in eastern wild turkeys (Meleagris gallopavo silvestris) sampled on the Delmarva Peninsula","interactions":[],"lastModifiedDate":"2025-04-28T15:00:53.327484","indexId":"70264146","displayToPublicDate":"2025-02-28T08:56:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Avian influenza infection and antibodies not identified in eastern wild turkeys (Meleagris gallopavo silvestris) sampled on the Delmarva Peninsula","title":"Avian influenza infection and antibodies not identified in eastern wild turkeys (Meleagris gallopavo silvestris) sampled on the Delmarva Peninsula","docAbstract":"Although highly pathogenic avian influenza presents a notable threat to domestic turkeys (Meleagris gallopavo domesticus), there have been minimal infections identified in wild turkeys (Meleagris gallopavo) across the United States. Indeed, recent infections in wild turkeys have either been linked back to or suspected to be the result of spillback from nearby infected domestic turkeys. This study presents avian influenza sampling data for Eastern wild turkeys (Meleagris gallopavo silvestris) across the Maryland portion of the Delmarva Peninsula, in the winter of 2023–2024, with an objective of identifying any circulating influenza A viruses (IAVs), as well as determining the extent of previous exposure to IAVs in this population. We collected swab samples from 36 female wild turkeys. No IAV viral RNA was detected in any sample, based on real-time reverse transcriptase PCR. We collected sera from 31 of the 36 sampled individuals, all of which had biomimetic enzyme-linked immunosorbent assay (bELISA) S/N values >0.7 (range: 0.79–1.29) indicating a lack of antibodies to IAVs. Although our sample size is limited, these results indicate no evidence of current or recent previous IAV infection in sampled birds.
","language":"English","publisher":"Allen Press","doi":"10.1637/aviandiseases-D-24-00077","usgsCitation":"Sullivan, J.D., Harvey, J., Poulson, R., Stallknecht, D., Long, C., Driscoll, C.P., Prosser, D., and Mullinax, J.M., 2025, Avian influenza infection and antibodies not identified in eastern wild turkeys (Meleagris gallopavo silvestris) sampled on the Delmarva Peninsula: Avian Diseases, v. 69, no. 1, p. 2-5, https://doi.org/10.1637/aviandiseases-D-24-00077.","productDescription":"4 p.","startPage":"2","endPage":"5","ipdsId":"IP-171384","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Dorchester County, Wicomico County, Worcester 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Robert","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":929946,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Driscoll, Cindy P.","contributorId":190850,"corporation":false,"usgs":false,"family":"Driscoll","given":"Cindy","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":929947,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929948,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":929949,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"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":70263997,"text":"70263997 - 2025 - A practical decision tool for marine bird mortality assessments","interactions":[],"lastModifiedDate":"2025-03-04T14:44:08.99165","indexId":"70263997","displayToPublicDate":"2025-02-28T08:35:15","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18754,"text":"EcoEvoRxiv","active":true,"publicationSubtype":{"id":32}},"title":"A practical decision tool for marine bird mortality assessments","docAbstract":"Given the rise in anthropogenic, environmental, and disease events contributing to marine bird mortality, there is a critical need to improve the rigor of mortality assessments. Deficits in data collection and mortality estimation can hinder a manager’s ability to document event scales and inform population level impacts. Therefore, to inform decisions required during activities such as conservation status assessments or harvest management, organizations may choose to incorporate mortality assessments into response plans. Resources, capacity, and assets to assess mortality vary across jurisdictions (federal, state, Indigenous, local, etc.), and clear guidance to support mortality assessments is often unavailable or not clearly addressed. Here, we present a decision support tool to help managers identify and evaluate survey options to assess bird mortality in a diverse array of scenarios. The objective of the decision tool is to improve data collection and availability which will increase the ability to robustly estimate mortality, given situation-specific attributes and constraints. This decision tool is designed to guide the response when a mortality event is initially encountered and offers suggestions for assessment and reporting procedures in the absence of other guidance or to complement existing protocols. The decision tool is also meant to inform decision making for response determination and resource allocation. The tool facilitates examination of options for further assessment and monitoring which users determine by examining questions pertaining to species prioritization, mortality spatial extent, and the potential magnitude of impacts on affected species. Finally, identification of appropriate survey methods, that address imperfect detection when a complete census is not possible, are determined by exploring location, spatial and temporal extent, and the type of species affected. Ultimately, this tool aims to facilitate and improve the standardization of mortality assessments, equipping managers with a practical resource to navigate the decision-making process for marine bird mortality estimation.
","language":"English","publisher":"EcoEvoRxiv","doi":"10.32942/X23S6R","usgsCitation":"Harvey, J., Ramey, A.M., Avery-Gomm, S., Robertson, G., Romano, M., Mullinax, J.M., Boldenow, M., Atkinson, P., and Prosser, D.J., 2025, A practical decision tool for marine bird mortality assessments: EcoEvoRxiv, https://doi.org/10.32942/X23S6R.","productDescription":"48 p.","ipdsId":"IP-168193","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":487279,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.32942/x23s6r","text":"Publisher Index Page"},{"id":482790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Johanna Alexandra 0000-0003-4504-6777","orcid":"https://orcid.org/0000-0003-4504-6777","contributorId":351781,"corporation":false,"usgs":true,"family":"Harvey","given":"Johanna Alexandra","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":929443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":929444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avery-Gomm, Stephanie","contributorId":213093,"corporation":false,"usgs":false,"family":"Avery-Gomm","given":"Stephanie","email":"","affiliations":[{"id":12552,"text":"University of Queensland","active":true,"usgs":false}],"preferred":false,"id":929445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, Gregory","contributorId":331077,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory","affiliations":[],"preferred":false,"id":929446,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Romano, Marc","contributorId":200806,"corporation":false,"usgs":false,"family":"Romano","given":"Marc","affiliations":[],"preferred":false,"id":929447,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":929448,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boldenow, Megan","contributorId":169336,"corporation":false,"usgs":false,"family":"Boldenow","given":"Megan","affiliations":[],"preferred":false,"id":929449,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Atkinson, Philip W.","contributorId":295425,"corporation":false,"usgs":false,"family":"Atkinson","given":"Philip W.","affiliations":[{"id":38864,"text":"British Trust for Ornithology","active":true,"usgs":false}],"preferred":false,"id":929450,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929451,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":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":"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%; Cryptosporidium parvum 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.
","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":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":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":70263870,"text":"ofr20251003 - 2025 - Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska","interactions":[],"lastModifiedDate":"2025-07-21T18:27:29.016147","indexId":"ofr20251003","displayToPublicDate":"2025-02-27T11:50:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1003","displayTitle":"Phase 1 Technical Implementation Plan for the Expansion of the ShakeAlert Earthquake Early Warning System to Alaska","title":"Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska","docAbstract":"The conference report accompanying the fiscal year (FY) 2022 Consolidated Appropriations Act (Public Law 117–103) for the U.S. Department of the Interior and related agencies directed the U.S. Geological Survey (USGS) to “work with the State of Alaska to develop an implementation plan to be completed within two years in order to put ShakeAlert/Earthquake Early Warning in Alaska” (p. 29). Congress included $1 million in the FY 2022 appropriation to conduct this effort.
The USGS Earthquake Hazards Program, along with partner organizations, has developed the ShakeAlert earthquake early warning (EEW) system for the West Coast, which currently operates in California, Oregon, and Washington. The purpose of the system and its alert delivery partners is to reduce the impact of earthquakes and save lives and property by delivering ShakeAlert-powered alerts that are transmitted to the public via mass notification technologies, and by providing more detailed data streams to institutional users and commercial service providers to trigger automated, user-specific, protective actions.
ShakeAlert was designed in such a way that it could be expanded to other U.S. regions with high earthquake risk, after the build-out of seismic and geodetic networks to support ShakeAlert in a specified region is completed and the necessary funding is secured for long-term operation and maintenance.
When an earthquake occurs, seismic waves radiate from the rupturing fault like waves on a pond. It is these waves that people feel as earthquake shaking and that can cause damage to structures. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Early warnings of impending shaking are then sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking. There will almost always be a region near the earthquake epicenter where alerts arrive after damaging shaking has begun. The ShakeAlert system updates its ground-motion estimates as an earthquake grows larger.
In response to the FY 2022 congressional direction, the USGS worked with the State of Alaska to devise this implementation plan for ShakeAlert expansion to Alaska. The USGS engaged with the Alaska Division of Homeland Security and Emergency Management (DHS&EM) and the Alaska Division of Geological and Geophysical Surveys (DGGS). A cooperative agreement was awarded to the Alaska Earthquake Center (AEC) at the University of Alaska Fairbanks (UAF) for their contributions to the plan and their work coordinating with other networks in Alaska. The USGS engaged with the Alaska Seismic Hazards Safety Commission (ASHSC) throughout the process. The USGS also held a series of Alaska stakeholder engagements. The process of developing the implementation plan was facilitated by contracted staff from Corner Alliance, which is a government consulting firm.
This implementation plan describes the details and estimates the costs for a Phase 1 expansion of the ShakeAlert system to Alaska. A geographically limited Phase 1 goal was chosen that covers the highest risk and most populated areas of Alaska. The areas proposed encompass the State’s main population centers and 90 percent of the State’s population. This Phase 1 design is considered very challenging and ambitious from the viewpoint of network operators. The lessons learned if this plan is implemented could be used to consider subsequent phases to expand EEW beyond Phase 1 in Alaska in the future.
ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System (ANSS). This implementation plan calls for a total of 450 high-quality, real-time EEW-capable ANSS seismic stations in Alaska: 270 new stations, 160 upgraded stations, and 20 existing stations. These seismic station numbers are based on a station spacing of 10 kilometers (km) in urban areas, 20 km in seismic source areas that endanger population centers, and 40 km in other areas. The associated costs also include support for some EEW-capable global navigation satellite system (GNSS) stations, with a focus on improving warnings for large subduction zone earthquakes. For effective EEW, ShakeAlert requires low-latency, high-availability, robust telemetry links to deliver continuous, real-time data from field stations to the data centers.
The Alaska data processing hardware infrastructure would follow the general design for fail-safe operation that is used for the ShakeAlert system on the West Coast. The ShakeAlert architecture uses two independent layers: the production layer for earthquake processing and the alert layer to make alerting decisions and serve alerts to users. This implementation plan includes two geographically separated data centers in Alaska, each with two fully independent production and alert layers using the same system design developed for the West Coast. As of March 2024, the ShakeAlert system is at version 3.0.1, with more advanced versions in the development and testing pipeline. ShakeAlert originally used two algorithms to determine the location and magnitude of earthquakes using seismic data. A third algorithm that can calculate very large magnitudes of very large earthquakes with geodetic data was added in March 2024.
ShakeAlert publishes several data and alert products to meet the needs of different users. All messages include the location of the earthquake, either as a point or a line, and its magnitude. Ground-shaking estimates are published in two forms, as ground-motion contours and a map grid. Providing adequate warning time for strong shaking (the “target threshold”) requires sending alerts at a threshold lower than that strong shaking level (the “alert threshold”). The thresholds for public alerting in Alaska would be a joint USGS and State decision.
To have the greatest benefit, ShakeAlert-powered alerts would be delivered to institutional users and individuals by all practical pathways. The USGS alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission nor the infrastructure and expertise to perform mass notifications to the public or implement automatic actions for end users of the alerts. To meet this need, ShakeAlert recruits private sector “technology enablers” that have the necessary expertise to develop end-user implementations using EEW alerts with the goal of stimulating an EEW industry.
Earthquake early warning alerts are useless if people do not know how to respond to them. Although the alert messages include instructions about what to do (drop, cover, and hold on), alerts are more effective if people have been trained in advance. Messages about ShakeAlert’s capabilities, limitations, and benefits could be integrated with existing earthquake education programs, including State-run programs. Therefore, ShakeAlert would coordinate with both public and private partners and stakeholders through various partnerships and agreements to accomplish consistent and ongoing public earthquake hazard education.
The estimated capital cost of completing the computing infrastructure and sensor networks for the Phase 1 ShakeAlert expansion to Alaska is approximately $66 million in 2024 dollars. The annual operation and maintenance cost of the completed system is estimated to be $12 million per year in 2024 dollars when fully built out.
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Earthquake Hazards Program
U.S. Geological Survey
Mail Stop 905
12201 Sunrise Valley Drive
Reston, VA 20192
Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.
","language":"English","publisher":"MDPI","doi":"10.3390/environments12030075","usgsCitation":"KarimiDermani, B., Green, C., Tick, G., Gholizadeh, H., Wei, W., and Zhang, Y., 2025, Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model: Environments, v. 12, no. 3, 75, 20 p., https://doi.org/10.3390/environments12030075.","productDescription":"75, 20 p.","ipdsId":"IP-169838","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487829,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/environments12030075","text":"Publisher Index Page"},{"id":483197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"KarimiDermani, Bahareh","contributorId":352232,"corporation":false,"usgs":false,"family":"KarimiDermani","given":"Bahareh","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":930396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tick, Geoffrey","contributorId":352233,"corporation":false,"usgs":false,"family":"Tick","given":"Geoffrey","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gholizadeh, Hossein","contributorId":352234,"corporation":false,"usgs":false,"family":"Gholizadeh","given":"Hossein","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930398,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wei, Wei","contributorId":352235,"corporation":false,"usgs":false,"family":"Wei","given":"Wei","affiliations":[{"id":84137,"text":"School of Environment, Nanjing Normal University, Nanjing 210023, China","active":true,"usgs":false}],"preferred":false,"id":930399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Yong","contributorId":352236,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930400,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263959,"text":"70263959 - 2025 - Migration of seismicity from the mantle to the upper crust beneath Harrat Lunayyir volcanic field, Saudi Arabia","interactions":[],"lastModifiedDate":"2025-03-03T15:18:28.298036","indexId":"70263959","displayToPublicDate":"2025-02-27T09:10:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17454,"text":"Seismica","active":true,"publicationSubtype":{"id":10}},"title":"Migration of seismicity from the mantle to the upper crust beneath Harrat Lunayyir volcanic field, Saudi Arabia","docAbstract":"Harrat Lunayyir is a volcanic field in Saudi Arabia that experienced a Mw~5.4 earthquake driven by an upper-crustal dike intrusion in May 2009. This volcanic field has exhibited numerous forms of volcanic seismicity both prior to and since the 2009 dike intrusion. Significantly, earthquakes within the lithospheric mantle and, rarely, the lower crust are present in the two-decade long seismicity catalog of Harrat Lunayyir. Here we analyze 24 years of volcanic seismicity at Harrat Lunayyir from 1998 to 2022. We find that: 1) precursory seismicity began at least eight years prior to the 2009 event, with a particularly notable seismic episode one year prior; 2) lithospheric mantle seismicity is highly localized in space and in time, largely occurring in discrete sequences lasting on the order of a few hours to a few days; 3) one seismic sequence clearly migrates upward from the lithospheric mantle to the upper crust, including seismicity within the nominally ductile lower crust; 4) crustal seismicity has been slowly declining over time; and 5) lithospheric-mantle seismicity does not show any apparent decline with time. From these observations we infer that the seismicity is driven by magmatic fluids or volatiles, and seismic monitoring of this volcanic field should continue into the future.
","language":"English","publisher":"McGill Libraries","doi":"10.26443/seismica.v4i1.1148","usgsCitation":"Blanchette, A., Klemperer, S.L., Mooney, W.D., and Sehli, T., 2025, Migration of seismicity from the mantle to the upper crust beneath Harrat Lunayyir volcanic field, Saudi Arabia: Seismica, v. 4, no. 1, 1148, 17 p., https://doi.org/10.26443/seismica.v4i1.1148.","productDescription":"1148, 17 p.","ipdsId":"IP-159418","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487721,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v4i1.1148","text":"Publisher Index Page"},{"id":482737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Saudi Arabia","otherGeospatial":"Harrat Lunayyir volcanic field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 37,\n 25.6\n ],\n [\n 37,\n 24.7\n ],\n [\n 38.25,\n 24.7\n ],\n [\n 38.25,\n 25.6\n ],\n [\n 37,\n 25.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Blanchette, Alexander R.","contributorId":329986,"corporation":false,"usgs":false,"family":"Blanchette","given":"Alexander R.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":929350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klemperer, Simon L.","contributorId":106929,"corporation":false,"usgs":true,"family":"Klemperer","given":"Simon","email":"","middleInitial":"L.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":929351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mooney, Walter D. 0000-0002-5310-3631 mooney@usgs.gov","orcid":"https://orcid.org/0000-0002-5310-3631","contributorId":3194,"corporation":false,"usgs":true,"family":"Mooney","given":"Walter","email":"mooney@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sehli, Turki A.","contributorId":351738,"corporation":false,"usgs":false,"family":"Sehli","given":"Turki A.","affiliations":[{"id":36695,"text":"Saudi Geological Survey","active":true,"usgs":false}],"preferred":false,"id":929353,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263949,"text":"70263949 - 2025 - Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","interactions":[],"lastModifiedDate":"2025-03-26T15:59:01.447164","indexId":"70263949","displayToPublicDate":"2025-02-27T09:07:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","docAbstract":"Thermoelectric power generation accounts for over 41% of total U.S. freshwater withdrawals, making understanding the determinants of power plants’ water withdrawals (WW) and consumption (WC) critical for reducing the sector’s reliance on increasingly scarce water resources. However, reported data inconsistencies and incomplete analysis of potential determinants of thermoelectric water use hinder such understanding. We address these challenges by introducing a novel data filtering method and a more complete assessment of water use determinants. First, we applied a power-cooling ratio as an operations-based data filter that removed operationally implausible records while retaining more original data, outperforming previous statistical filtering methods. Second, we found that different water use reporting methods (WURMs) provided statistically significantly different WW and WC values, revealing the importance of this previously unrecognized feature in reported water use records. Third, our data-driven approach showed that traditionally emphasized features─such as cooling technology and gross generation─are of primary importance but can be surpassed by other, often overlooked, features when modeling WW or WC individually. The plant configuration, cooling technology, and gross generation were the most important features of WW, whereas WURM, cooling technology, and reporting month were the most important for WC. These findings can improve thermoelectric power plant management, water use reporting accuracy, and water use modeling.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c02024","usgsCitation":"Sjostedt, E., Rushforth, R., Tidwell, V., Harris, M.A., McManamay, R., and Marston, L., 2025, Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use: Environmental Science & Technology, v. 59, no. 9, p. 4482-4492, https://doi.org/10.1021/acs.est.4c02024.","productDescription":"11 p.","startPage":"4482","endPage":"4492","ipdsId":"IP-160568","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":488675,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.4c02024","text":"Publisher Index Page"},{"id":482736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Sjostedt, Eric 0000-0002-7755-8091","orcid":"https://orcid.org/0000-0002-7755-8091","contributorId":351702,"corporation":false,"usgs":false,"family":"Sjostedt","given":"Eric","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":929309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":929310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tidwell, Vincent 0000-0002-4954-897X","orcid":"https://orcid.org/0000-0002-4954-897X","contributorId":351704,"corporation":false,"usgs":false,"family":"Tidwell","given":"Vincent","affiliations":[{"id":84033,"text":"Pacific Northwest National Laboratories","active":true,"usgs":false}],"preferred":false,"id":929311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Melissa A. 0000-0003-2659-9763 mharris@usgs.gov","orcid":"https://orcid.org/0000-0003-2659-9763","contributorId":1903,"corporation":false,"usgs":true,"family":"Harris","given":"Melissa","email":"mharris@usgs.gov","middleInitial":"A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McManamay, Ryan","contributorId":205277,"corporation":false,"usgs":false,"family":"McManamay","given":"Ryan","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":929313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marston, Landon 0000-0001-9116-1691","orcid":"https://orcid.org/0000-0001-9116-1691","contributorId":239626,"corporation":false,"usgs":false,"family":"Marston","given":"Landon","email":"","affiliations":[{"id":47941,"text":"Department of Civil Engineering, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":929314,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264266,"text":"70264266 - 2025 - Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","interactions":[],"lastModifiedDate":"2025-03-10T13:53:35.39942","indexId":"70264266","displayToPublicDate":"2025-02-27T08:49:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","docAbstract":"Greater understanding of the survival, travel time, and spatial distribution of juvenile salmonids among migration routes between their natal streams and the ocean is critical to the recovery of these threatened species. In the Sacramento–San Joaquin River Delta (Delta), a highly modified estuary in central California, USA, there is a critical need to evaluate how water management (e.g., water pumping) and environmental factors (e.g., water flow) impact these populations. While management actions can affect some environmental variables in the Delta, only recently have studies begun to uncover associations between these variables and key demographic parameters. In this study, we examine the effects of freshwater flows, water exports, tidal environment, and a temporary barrier on juvenile steelhead (Oncorhynchus mykiss) survival, travel times, and migration routing using a multiyear acoustic telemetry dataset and recent advancements in Bayesian multistate mark-recapture modeling. We found that no single covariate explained variation in juvenile steelhead population dynamics across the entire Delta, but that separate regions within the Delta showed association with specific environmental factors.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-025-01493-5","usgsCitation":"Pope, A., Perry, R., Hance, D., and Buchanan, R., 2025, Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary: Estuaries and Coasts, v. 48, 75, 18 p., https://doi.org/10.1007/s12237-025-01493-5.","productDescription":"75, 18 p.","ipdsId":"IP-156547","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.03238643466244,\n 38.07227139327773\n ],\n [\n -122.03238643466244,\n 37.61108647879182\n ],\n [\n -121.16713739842452,\n 37.61108647879182\n ],\n [\n -121.16713739842452,\n 38.07227139327773\n ],\n [\n -122.03238643466244,\n 38.07227139327773\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buchanan, Rebecca A.","contributorId":300601,"corporation":false,"usgs":false,"family":"Buchanan","given":"Rebecca A.","affiliations":[{"id":65208,"text":"Columbia Basin Research, School of Aquatic and Fishery Sciences, University of Washington 1325 Fourth Avenue, Suite 1515, Seattle, Washington 98101-2540","active":true,"usgs":false}],"preferred":false,"id":930220,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267188,"text":"70267188 - 2025 - Practitioners’ perceived risks to biodiversity from renewable energy expansion through 2050","interactions":[],"lastModifiedDate":"2025-05-16T15:49:40.826847","indexId":"70267188","displayToPublicDate":"2025-02-27T08:40:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11448,"text":"Humanities and Social Sciences Communications","active":true,"publicationSubtype":{"id":10}},"title":"Practitioners’ perceived risks to biodiversity from renewable energy expansion through 2050","docAbstract":"Large-scale (e.g., > 1 megawatt capacity) wind and solar energy (hereafter, LSWS) developments are increasing to aid decarbonization. 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