Understanding salt marsh resilience under increasing sea levels can inform for management decisions. We compared temporal projections from various wetland process-based models and a geospatially derived metric (i.e., marsh lifespan) to understand key considerations and uncertainties about salt marsh resilience when using these products for decision-making. The influences of lidar topographic correction and marsh surface sediment accretion were explored across a suite of relative sea level rise (RSLR) projections to assess differences in the timing and amount of habitat change for each modeling approach. All models were run for a small coastal wetland site located in the Chesapeake Bay, Maryland, USA, to assess potential change in marsh habitat, and timing of marsh loss due to RSLR. All modeling results agreed that marsh longevity was threatened by RSLR but they varied in the time of predicted marsh submergence between the years 2070 and 2100 depending on the initial marsh surface elevation and accretion rates. Models with similar accretion rates predicted similar years until marsh submergence. Removing a positive elevation bias from lidar surveys in densely vegetated marsh areas for these models resulted in onset of submergence ~ 7 years earlier. Because there are many tradeoffs to each model type, end users need to evaluate management questions, overall goals, the amount of effort involved in model parameterization, and the amount of uncertainty in the model that they are willing to accept.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-025-01559-4","usgsCitation":"Martinez, M., Buffington, K., Ganju, N., Defne, Z., Ackerman, K., Thorne, K., Guntenspergen, G.R., and Carr, J., 2025, Multi-model comparison of salt marsh longevity under relative sea-level rise: Estuaries and Coasts, v. 48, 131, 17 p., https://doi.org/10.1007/s12237-025-01559-4.","productDescription":"131, 17 p.","ipdsId":"IP-175115","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":491006,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-025-01559-4","text":"Publisher Index Page"},{"id":490750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Kent County","otherGeospatial":"Eastern Neck Island National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.24030263498597,\n 39.05092670969253\n ],\n [\n -76.2464251064265,\n 39.02623477098268\n ],\n [\n -76.22111104181657,\n 39.0091277190638\n ],\n [\n -76.20939592819497,\n 39.005880153850384\n ],\n [\n -76.19532601786523,\n 39.010317858728\n ],\n [\n -76.20639356239224,\n 39.05275533676962\n ],\n [\n -76.22099330198093,\n 39.05568114506616\n ],\n [\n -76.24030263498597,\n 39.05092670969253\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Martinez, Melinda 0000-0001-6652-9220","orcid":"https://orcid.org/0000-0001-6652-9220","contributorId":290467,"corporation":false,"usgs":true,"family":"Martinez","given":"Melinda","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":940339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":940340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ackerman, Kate 0000-0003-3925-721X","orcid":"https://orcid.org/0000-0003-3925-721X","contributorId":293631,"corporation":false,"usgs":true,"family":"Ackerman","given":"Kate","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":940344,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940345,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940346,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268760,"text":"70268760 - 2025 - Rapid risk assessment framework to estimate potential for spillback at human-wildlife interfaces","interactions":[],"lastModifiedDate":"2025-07-10T14:57:17.754452","indexId":"70268760","displayToPublicDate":"2025-06-13T08:50:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Rapid risk assessment framework to estimate potential for spillback at human-wildlife interfaces","docAbstract":"More than 60% of emerging infectious diseases of humans have a wildlife origin, and when these diseases spread through human populations to new geographical areas, there is a considerable risk of spillback from humans to wildlife species. Spillback events can have severe consequences for wildlife populations, where the disease may cause morbidity and mortality, and human populations, where the establishment in wildlife may lead to prolonged transmission or new exposures in humans. Mitigating these consequences requires identifying the key risk factors that lead to human–wildlife transmission events and implementing risk-reducing actions, a challenge given that cross-species transmission events are rare and often data deficient. To identify potential species and locations that are most likely to lead to these rare events, we developed a spatially explicit, rapid risk assessment framework that incorporates three components of the spillback process: wildlife susceptibility, wildlife exposure, and pathogen introduction pressure. To demonstrate the broad applicability of our framework, we conducted a rapid risk assessment on two recent emerging zoonotic pathogens in humans, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and mpox, to determine the relative spillback risk to wild mammalian species in the continental United States. The rapid risk assessment identified both species and locations with higher than expected spillback risk, providing managers and researchers with valuable information to prioritize surveillance and risk-mitigation actions. Our framework represents a rapid and flexible approach to assess the risks of spillback to wildlife populations during rapidly evolving zoonotic disease outbreaks.
","language":"English","publisher":"PubMed Central","doi":"10.1155/tbed/4334954","usgsCitation":"Mcdevitt-Galles, T., Fry, T., Richgels, K., and Grear, D.A., 2025, Rapid risk assessment framework to estimate potential for spillback at human-wildlife interfaces: Transboundary and Emerging Diseases, v. 2025, 4334954, 15 p., https://doi.org/10.1155/tbed/4334954.","productDescription":"4334954, 15 p.","ipdsId":"IP-160096","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":492090,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/tbed/4334954","text":"Publisher Index Page"},{"id":491808,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.97084947037874,\n 48.633322740164715\n ],\n [\n -124.7392146445165,\n 37.00758771879266\n ],\n [\n -118.2521345151965,\n 32.82119172303341\n ],\n [\n -107.8473648454318,\n 30.862675765777055\n ],\n [\n -98.46601775789239,\n 26.182786928741095\n ],\n [\n -79.55043816735622,\n 24.502682268282584\n ],\n [\n -80.16713782720478,\n 31.37578863952467\n ],\n [\n -74.7558728135753,\n 35.46079116327094\n ],\n [\n -67.1408314266174,\n 43.72777509706573\n ],\n [\n -67.67002630536457,\n 47.164807560194056\n ],\n [\n -81.40402452012242,\n 42.203163887439615\n ],\n [\n -82.68676846828293,\n 45.67835235914693\n ],\n [\n -90.32310194724667,\n 48.78125858148974\n ],\n [\n -125.97084947037874,\n 48.633322740164715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2025","noUsgsAuthors":false,"publicationDate":"2025-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mcdevitt-Galles, Travis 0000-0002-4929-5431","orcid":"https://orcid.org/0000-0002-4929-5431","contributorId":315374,"corporation":false,"usgs":true,"family":"Mcdevitt-Galles","given":"Travis","email":"","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":941875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fry, Tricia L.","contributorId":357592,"corporation":false,"usgs":false,"family":"Fry","given":"Tricia L.","affiliations":[{"id":85464,"text":"Midwest Association for Fish and Wildlife Agencies","active":true,"usgs":false}],"preferred":false,"id":941876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richgels, Katherine 0000-0003-2834-9477 krichgels@usgs.gov","orcid":"https://orcid.org/0000-0003-2834-9477","contributorId":167016,"corporation":false,"usgs":true,"family":"Richgels","given":"Katherine","email":"krichgels@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":941877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":941878,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268148,"text":"70268148 - 2025 - Not all spatially structured populations are metapopulations: Re-examining paradigms for a threatened shorebird","interactions":[],"lastModifiedDate":"2025-06-16T13:43:43.023749","indexId":"70268148","displayToPublicDate":"2025-06-13T08:35:54","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Not all spatially structured populations are metapopulations: Re-examining paradigms for a threatened shorebird","docAbstract":"For at-risk species, understanding population vital rates is imperative for developing informed conservation strategies and population models. Managers often assume that species that are spatially distributed among patches of suitable habitat meet the criteria of a metapopulation. Metapopulation dynamics are determined not only by within-patch birth and death processes but also by between-patch dispersal movements of individuals that are infrequent but critical to maintaining population viability across space and time. To conserve and manage such species, an understanding of all these vital rates, including connectivity, is required. The degree to which the northern Great Plains piping plover (Charadrius melodus) breeding population functions as a metapopulation depends, in part, on the rate of movement among patchily distributed breeding areas. Here, we examined annual adult survival and breeding dispersal probabilities for 2582 individuals at two spatial scales within the northern Great Plains piping plover breeding population between 2014 and 2019. Inconsistent with a metapopulation structure, annual survival varied minimally among breeding regions but did vary across years. We also found that breeding dispersal probabilities were temporally variable, high, and unbalanced at both spatial scales examined, suggesting high connectivity in contrast to metapopulation dynamics. Further, we detected context-dependent effects of reproductive success on dispersal decisions. Individuals were more likely to disperse from the northern Missouri River to the US Alkali Wetlands following nest failure due to inundation or severe storms (including in the year prior to dispersal), whereas dispersal from the US Alkali Wetlands to the northern Missouri River decreased following successful nest attempts. Individuals also decreased dispersal from the US Alkali Wetlands to the northern Missouri River in response to renesting attempts in both the year of interest and the year prior to dispersal. Our results contradict the paradigm that northern Great Plains piping plovers are structured as a metapopulation and instead suggest a patchily distributed, likely panmictic, population. Our findings have implications for the conservation and management of this listed species and are also a general reminder that in the absence of robust knowledge of movement, spatial variation in birth and death processes across patches should not be conflated with a metapopulation structure.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70037","usgsCitation":"Swift, R.J., Anteau, M.J., Ellis, K.S., MacDonald, G.J., Ring, M., Sherfy, M.H., Toy, D.L., and Koons, D.N., 2025, Not all spatially structured populations are metapopulations: Re-examining paradigms for a threatened shorebird: Ecological Applications, v. 35, no. 4, e70037, 22 p., https://doi.org/10.1002/eap.70037.","productDescription":"e70037, 22 p.","ipdsId":"IP-159633","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":491308,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P145PZKB","text":"USGS data release","linkHelpText":"Piping plover adult breeding dispersal and annual survival in the northern Great Plains, USA, model code"},{"id":491004,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70037","text":"Publisher Index Page"},{"id":490748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.64188754784932,\n 48.98153164866767\n ],\n [\n -105.33724717279092,\n 48.994467973542584\n ],\n [\n -105.28467788209502,\n 48.50026956386816\n ],\n [\n -104.10186884143783,\n 48.307740036726486\n ],\n [\n -103.89159167865459,\n 47.93571533915883\n ],\n [\n -102.91905980078116,\n 47.82996004634143\n ],\n [\n -102.55107476591006,\n 47.435856274811385\n ],\n [\n -101.19084436915436,\n 46.827884767556554\n ],\n [\n -100.69800726888084,\n 45.38837693229408\n ],\n [\n -99.72547539100738,\n 45.508237243472905\n ],\n [\n -100.64543797818494,\n 47.626637063166925\n ],\n [\n -100.69143610754378,\n 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manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellis, Kristen S. 0000-0003-2759-3670","orcid":"https://orcid.org/0000-0003-2759-3670","contributorId":251877,"corporation":false,"usgs":true,"family":"Ellis","given":"Kristen","email":"","middleInitial":"S.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacDonald, Garrett J. 0000-0002-9487-7721","orcid":"https://orcid.org/0000-0002-9487-7721","contributorId":238820,"corporation":false,"usgs":true,"family":"MacDonald","given":"Garrett","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ring, Megan M. 0000-0001-8331-8492","orcid":"https://orcid.org/0000-0001-8331-8492","contributorId":225026,"corporation":false,"usgs":true,"family":"Ring","given":"Megan M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sherfy, Mark H. 0000-0003-3016-4105 msherfy@usgs.gov","orcid":"https://orcid.org/0000-0003-3016-4105","contributorId":125,"corporation":false,"usgs":true,"family":"Sherfy","given":"Mark","email":"msherfy@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940359,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Toy, Dustin L. 0000-0001-5390-5784 dtoy@usgs.gov","orcid":"https://orcid.org/0000-0001-5390-5784","contributorId":5150,"corporation":false,"usgs":true,"family":"Toy","given":"Dustin","email":"dtoy@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940360,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Koons, David N.","contributorId":28137,"corporation":false,"usgs":false,"family":"Koons","given":"David","email":"","middleInitial":"N.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":940361,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268057,"text":"ofr20251014 - 2025 - Black abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2022 annual report","interactions":[],"lastModifiedDate":"2025-07-01T16:55:19.83033","indexId":"ofr20251014","displayToPublicDate":"2025-06-13T07:39:45","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":"2025-1014","displayTitle":"Black Abalone Surveys at Naval Base Ventura County, San Nicolas Island, California—2022 Annual Report","title":"Black abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2022 annual report","docAbstract":"The U.S. Geological Survey monitors a suite of intertidal black abalone (Haliotis cracherodii) sites at San Nicolas Island, California, in cooperation with the U.S. Navy, which owns the island. The nine rocky intertidal sites were established in 1980 to study the potential effect of translocated southern sea otters (Enhydra lutris nereis) on the intertidal black abalone population at San Nicolas Island. The sites were monitored, typically annually or biennially, from 1981 to 1997. Monitoring resumed in 2001 and has been completed annually thereafter. Since 2018, the monitoring has been carried out by the U.S. Geological Survey Western Ecological Research Center. The study sites became particularly important from a management perspective after a virulent disease decimated black abalone populations throughout southern California beginning in the mid-1980s. The disease, withering syndrome, was first observed on San Nicolas Island in 1992, and during the next few years, withering syndrome reduced the black abalone population on San Nicolas Island by more than 99 percent. The black abalone was subsequently listed as endangered under the Endangered Species Act in 2009.
The subject of this report is the 2022 survey of the sites and the status of the measured population of black abalone in comparison to long-term patterns (based on data collected since 1981) at San Nicolas Island. Between the years 2000 and 2022, the total monitored black abalone population on the island has grown from roughly 200 to more than 2,000, approximately a ten-fold increase following the disease-related decline. Since it was first consistently measured in 2005, the distance between adjacent black abalone has decreased substantially from approximately 50 centimeters to less than 15 centimeters, indicating that black abalone are sufficiently close together at several of the sites to reproduce successfully. The total black abalone count in 2022 was 2,156, which was 7.9 percent lower than the total count in 2020 but 6.6 percent higher than in 2021. There were increases and decreases among the sites and transects within each site in 2022, but six of the nine sites had higher counts than in the previous year. The 2022 count is one of the highest since 1993, second only to the 2020 count. In 2022, the annual recruitment rate, defined as the percentage of measured black abalone with a shell length of 3 centimeters or less, was the second highest recorded, only slightly less than in 2017.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251014","collaboration":"Prepared in cooperation with the U.S. Navy","programNote":"Ecosystems Mission Area—Land Management Research Program and Species Management Research Program","usgsCitation":"Kenner, M.C., and Yee, J.L., 2025, Black abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2022 annual report: U.S. Geological Survey Open-File Report 2025–1014, 34 p., https://doi.org/10.3133/ofr20251014.","productDescription":"viii, 34 p.","onlineOnly":"Y","ipdsId":"IP-159889","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":490412,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1014/images"},{"id":491626,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20251015","text":"Open-File Report 2025-1015","description":"OFR 2025-1015","linkHelpText":"- Black abalone (Haliotis cracherodii) population density, recruitment, size structure, and population growth at Naval Base Ventura County, San Nicolas Island, California, 2013–22"},{"id":490413,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1014/ofr20251014.XML"},{"id":490410,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1014/ofr20251014.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1014"},{"id":490411,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251014/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1014"},{"id":490409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1014/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Naval Base, Ventura County, San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.41565036125932,\n 33.22855822061233\n ],\n [\n -119.4322978602211,\n 33.233849792002204\n ],\n [\n -119.46292925831023,\n 33.2600242059834\n ],\n [\n -119.53051810409356,\n 33.29036557133239\n ],\n [\n -119.58545485066676,\n 33.281459107969354\n ],\n [\n -119.57180390151814,\n 33.25000088849947\n ],\n [\n -119.54217135336674,\n 33.228836776039614\n ],\n [\n -119.47857790733391,\n 33.21379600415082\n ],\n [\n -119.45160895901617,\n 33.21379600415082\n ],\n [\n -119.41565036125932,\n 33.22855822061233\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This report details the application of the chains of consequences method within the postfire hazard context after the 2021 Cedar Creek and Muckamuck Fires around Okanogan County, Washington. The U.S. Geological Survey Social and Economic Analysis branch convened 20 stakeholders with content- and context-specific knowledge related to these fires and their postfire hazards in an April 2023 Postfire Hazards Chains of Consequences Workshop. Guided by U.S. Geological Survey facilitators, workshop participants identified the cascading consequences of a specific postfire hazard scenario before brainstorming interventions to reduce the likelihood and severity of those consequences. The participants worked across disciplinary boundaries to identify gaps in understanding around cascading postfire hazard consequences and to brainstorm multidisciplinary interventions.
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This “U.S. Geological Survey Pollinator Science Strategy, 2025–35—A Review and Look Forward” (“Pollinator Science Strategy”) describes the science vision of the U.S. Geological Survey (USGS) to support management, conservation, and policy decisions on animal pollinators and their habitats. As the science arm of the Department of the Interior, the USGS has a primary role in providing scientific information to natural resource managers and policymakers across the United States. This “Pollinator Science Strategy” was drafted by a team of USGS pollinator researchers and was further developed through feedback from Federal, State, Tribal, nongovernmental organizations, and industry partners. This “Pollinator Science Strategy” highlights the USGS’s role in the research to promote healthy pollinator populations and address partner information gaps so they can make more informed management decisions. By outlining the importance of USGS science in addressing the information needs of other agencies, organizations, and the public, the “Pollinator Science Strategy” reaffirms the USGS’s commitment to pollinator research and showcases our research priorities for 2025–35.
With more than 300 research centers nationally, the USGS is equipped to address pollinator science across scales of complexity and geographic range. USGS pollinator science is organized according to the following five thematic areas:
Our pollinator information products and associated data are made widely available to the public to ensure scientific transparency and accessibility. This “Pollinator Science Strategy” concludes with several research goals that the USGS will work towards from 2025 to 2035, representing our vision for USGS science. These research goals include synthesizing and modernizing the latest information on pollinator threats, developing research that assists managers with habitat design and restoration, providing training to partners on native bee identification and monitoring design, and developing new technologies for assessing the status and trends of our nation’s pollinators.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1556","usgsCitation":"Otto, C.R.V., Graves, T.A., Robertson-Thompson, D., Pearse, I., Thogmartin, W.E., Murphy, C., Webb, L., Droege, S., Steinkamp, M., and Grundel, R., 2025, U.S. Geological Survey Pollinator Science Strategy, 2025–35—A review and look forward (ver. 1.1, June 26, 2025): U.S. Geological Survey Circular 1556, 16 p., https://doi.org/10.3133/cir1556.","productDescription":"vi, 16 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172990","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":5057,"text":"NGTOC Reston","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true},{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":490448,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1556/images/"},{"id":490447,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1556/circ1556.XML"},{"id":490449,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1556/full"},{"id":490446,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1556/circ1556.pdf","text":"Report","size":"26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Cir 1556"},{"id":490445,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1556/coverthb4.jpg"},{"id":491238,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1556/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: June 12, 2025; Version 1.1: June 26, 2025","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Highly pathogenic avian influenza (HPAI) is an ecologically and economically important animal disease that can also directly affect humans (a “zoonotic” disease). HPAI was once limited almost exclusively to domestic poultry but has rapidly adapted to diverse animal hosts. Viruses causing HPAI now appear to be maintained and dispersed by wild birds largely independent of poultry, though HPAI continues to cause considerable economic losses and supply chain disruptions in the domestic poultry trade. Coincident with the adaptation of HPAI viruses to wild birds, particularly waterfowl and gulls, increasingly diverse wild bird hosts are becoming exposed to HPAI, often resulting in disease and death. More sporadically, HPAI has caused mass mortality events, particularly among seabirds. Furthermore, viral spillover to wild and domestic mammals has become more common. Spillover to wild mammals has resulted in mortality among diverse terrestrial and marine taxa, including episodic losses of such scale as to represent potential conservation challenges. Since approximately March 2024, HPAI has also affected dairy cows, which represents a new threat to the agricultural economy. Lastly, HPAI has increasingly affected humans through domestic animal exposures, exemplifying the considerable implications of this disease beyond animal health.
Rapid changes in the ecology of HPAI are currently outpacing research efforts. For example, it is not entirely clear which newly established hosts may become reservoirs for HPAI viruses (in other words, capable of maintaining HPAI viruses within a broad population indefinitely) and how this may influence viral evolution and dissemination. As a result, there are considerable information gaps regarding HPAI in wildlife that, if filled, would improve the ability of scientists, managers, agricultural industry representatives, and healthcare professionals to understand and to anticipate the effects of HPAI on wild animal, domestic animal, environmental, and human health (“One Health”).
The U.S. Geological Survey (USGS) is the lead Federal agency providing scientific research on avian influenza viruses (AIVs), including HPAI viruses, that affect wildlife for which the Department of the Interior (DOI) has management authority. States have jurisdiction over wildlife on Federal lands within their borders (43 CFR § 24.3), so the USGS Ecosystems Mission Area (EMA) coordinates with State natural resource management agencies. The EMA focuses its research on HPAI through priorities identified by the USGS Avian Influenza Science Team (app. 1). Priorities identified by the USGS Avian Influenza Science Team are based on Administration priorities, Congressional direction, and discussions with State, Federal, and Tribal natural resource management agencies that identify specific scientific gaps that need to be filled to inform sound wildlife management decisions. Notable non-DOI Federal partners include the U.S. Department of Agriculture, the lead for the HPAI regulatory response in poultry and livestock, and the Centers for Disease Control and Prevention (CDC), the lead agency for the HPAI response pertaining to human health.
The USGS offers unique expertise and capacity pertaining to research on diseases affecting free-ranging wildlife populations. This expertise has been critical to interjurisdictional surveillance and capacity-building efforts, including programs administered by the U.S. Department of Agriculture and the CDC. The USGS also provides resources, guidance, and tools to inform surveillance and interventions conducted by natural resource management agencies. More specifically, the USGS EMA provides objective and rigorous scientific data for inferring (1) the utility of new methods to detect and characterize AIVs, including those maintained in wildlife and the environment; (2) effects of HPAI on wildlife; (3) spatiotemporal patterns of wildlife host and AIV dispersal; (4) the presence and persistence of AIVs in the environment; (5) how HPAI in wildlife influences consumptive and nonconsumptive utilization of wildlife; (6) how new tools and scientific methods may promote sound management decisions for HPAI-affected wildlife, particularly species of conservation concern; and (7) the combined effects of HPAI and other stressors on ecosystem health and resiliency.
This science strategy builds upon research outlined in a previous USGS science strategy for HPAI (2016–20) by Harris and others (2016). This strategy also details research priorities identified by the Administration (for example, U.S. Department of Agriculture, 2025) and others based on USGS Avian Influenza Science Team discussions with natural resource management agencies to address HPAI and wildlife health over the next 5 years (2025–29). This strategy presents 7 goals and 26 objectives that focus USGS and partner efforts on priorities that will fill data gaps regarding the effects of HPAI on wildlife managed by or co-managed with the U.S. Department of the Interior such that agencies and partners might anticipate or limit adverse effects on public resources. This strategy also identifies research priorities intended to address HPAI in wildlife and wildlife habitat that are anticipated to support interjurisdictional One Health efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1558","usgsCitation":"Ramey, A.M., Prosser, D.J., Hubbard, L.E., Vazquez-Meves, G., George, A., and Hopkins, M.C., 2025, U.S. Geological Survey science strategy to address highly pathogenic avian influenza and its effects on wildlife health 2025–29:\nU.S. Geological Survey Circular 1558, 26 p., https://doi.org/10.3133/cir1558.","productDescription":"vi, 26 p.","onlineOnly":"Y","ipdsId":"IP-174779","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":490570,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161121","text":"Open-File Report 2016-1121","description":"OFR 2016-1121","linkHelpText":"- U.S. Geological Survey science strategy for highly pathogenic avian influenza in wildlife and the environment (2016–2020)"},{"id":490421,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1558/cir1558.XML"},{"id":490420,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1558/cir1558.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1558"},{"id":490419,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1558/coverthb.jpg"}],"contact":"Director, Alaska Science Center
4210 University Drive
Anchorage, Alaska 99508
Wildlife diseases can have substantial impacts on wildlife populations as well as on human and domestic animal health and well-being. Although many agencies and stakeholders share a goal of supporting wildlife health, reducing wildlife disease burden is complicated by a scarcity of effective interventions for wildlife, competition for funds, and conflicting priorities. As a result, agencies are unlikely to avoid the impacts of wildlife diseases in all contexts and need to evaluate where resisting disease is most feasible and beneficial. The resist–accept–direct (RAD) framework is a tool that assists natural resource managers in exploring and communicating about management interventions, including in situations where resisting ecological changes may not be possible. In the present article, we discuss how the RAD framework could be adapted to wildlife disease contexts to address several outstanding challenges in wildlife health management.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biaf061","usgsCitation":"Moss, W.E., Schuurman, G.W., Almberg, E.S., Buttke, D., Galloway, N.L., Gibbs, S., Hubbs, A., Richgels, K., White, C.L., and Cross, P., 2025, Applying the resist-accept-direct (RAD) framework to wildlife health management: BioScience, https://doi.org/10.1093/biosci/biaf061.","ipdsId":"IP-171833","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":490998,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biaf061","text":"Publisher Index Page"},{"id":490710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Moss, Wynne Emily 0000-0002-2813-1710","orcid":"https://orcid.org/0000-0002-2813-1710","contributorId":338331,"corporation":false,"usgs":true,"family":"Moss","given":"Wynne","email":"","middleInitial":"Emily","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":940275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schuurman, Gregor W. 0000-0002-9304-7742","orcid":"https://orcid.org/0000-0002-9304-7742","contributorId":147698,"corporation":false,"usgs":false,"family":"Schuurman","given":"Gregor","email":"","middleInitial":"W.","affiliations":[{"id":16909,"text":"U.S. National Park Service, Natural Resource Stewardship and Science, Fort Collins, CO, 80525, USA","active":true,"usgs":false}],"preferred":false,"id":940276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Almberg, Emily S.","contributorId":198304,"corporation":false,"usgs":false,"family":"Almberg","given":"Emily","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":940277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buttke, Danielle","contributorId":225082,"corporation":false,"usgs":false,"family":"Buttke","given":"Danielle","affiliations":[],"preferred":false,"id":940278,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galloway, Nathan L.","contributorId":276042,"corporation":false,"usgs":false,"family":"Galloway","given":"Nathan","email":"","middleInitial":"L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":940279,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibbs, Samantha E.J.","contributorId":127739,"corporation":false,"usgs":false,"family":"Gibbs","given":"Samantha E.J.","affiliations":[{"id":7128,"text":"Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 01536, USA.","active":true,"usgs":false}],"preferred":false,"id":940280,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hubbs, Anne","contributorId":356856,"corporation":false,"usgs":false,"family":"Hubbs","given":"Anne","affiliations":[{"id":85260,"text":"Alberta Environment and Protected Areas","active":true,"usgs":false}],"preferred":false,"id":940281,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Richgels, Katherine 0000-0003-2834-9477 krichgels@usgs.gov","orcid":"https://orcid.org/0000-0003-2834-9477","contributorId":167016,"corporation":false,"usgs":true,"family":"Richgels","given":"Katherine","email":"krichgels@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":940282,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"White, C. LeAnn 0000-0002-5004-5165 clwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-5004-5165","contributorId":4315,"corporation":false,"usgs":true,"family":"White","given":"C.","email":"clwhite@usgs.gov","middleInitial":"LeAnn","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":940283,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":940284,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70268224,"text":"70268224 - 2025 - Hydraulic connectivity and hydrochemistry influence microbial community structure in agriculturally-affected alluvial aquifers in the Midwestern United States","interactions":[],"lastModifiedDate":"2025-07-10T14:55:33.936187","indexId":"70268224","displayToPublicDate":"2025-06-12T09:53:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Hydraulic connectivity and hydrochemistry influence microbial community structure in agriculturally-affected alluvial aquifers in the Midwestern United States","docAbstract":"Alluvial aquifers can provide ecosystem services and drinking water, but much remains unknown about human effects on aquifer microbiomes. Therefore, we used amplicon sequencing and hydrochemical characterization to pair microbial communities with environmental conditions across 37 alluvial aquifer wells. The study region spanned eastern Iowa and southern Minnesota (USA) and contained a combination of drinking water and monitoring wells. In terms of microbial ecology, dominant phyla across the wells included Proteobacteria, Bacteroidota, Patescibacteria, Planctomycetota, and Nitrospirota. Tritium, an indicator of infiltration and surface water influence, was the highest correlated variable with the Shannon index (α-diversity) by the Spearman rank sum (ρ = 0.60) and one of only four significant environmental variables in the constrained correspondence analysis. We built random forest regression models to predict tritium concentrations from microbial family relative abundance (held-out testing coefficient of determination (R2) = 0.77 and mean absolute percentage error = 7%) and interpreted the models with Shapley additive explanation values. The most important families for predicting tritium concentrations were Nitrosopumilaceae and Methylomirabilaceae. Upwelling methane could contribute to the unusual coupling of ammonia oxidation by Nitrosopumilaceae with simultaneous nitrite-dependent methane oxidation by Methylomirabilaceae. Taken together, we illuminate the relationship among hydrochemistry, hydraulic connectivity, and alluvial aquifer microbiomes.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5c03155","usgsCitation":"Schroer, H., Markland, K.M., Ling, F., and Just, C.L., 2025, Hydraulic connectivity and hydrochemistry influence microbial community structure in agriculturally-affected alluvial aquifers in the Midwestern United States: Environmental Science and Technology, v. 59, no. 24, p. 12279-12291, https://doi.org/10.1021/acs.est.5c03155.","productDescription":"13 p.","startPage":"12279","endPage":"12291","ipdsId":"IP-169344","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":490912,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490985,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.5c03155","text":"Publisher Index Page"}],"country":"United States","state":"Iowa, Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.83164241356687,\n 40.76134763192243\n ],\n [\n -91.10093521849011,\n 40.76936587633509\n ],\n [\n -90.98444609847996,\n 41.11334070983898\n ],\n [\n -91.07975626082516,\n 41.37610567914743\n ],\n [\n -90.60321047132624,\n 41.542769354616865\n ],\n [\n -90.25374320174629,\n 41.8669244399662\n ],\n [\n -93.07065717548669,\n 43.93016784084011\n ],\n [\n -93.73782127267788,\n 43.983536010475774\n ],\n [\n -93.97079768432694,\n 42.314858946682534\n ],\n [\n -93.85431056836552,\n 41.92210428808144\n ],\n [\n -91.83164241356687,\n 40.76134763192243\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"24","noUsgsAuthors":false,"publicationDate":"2025-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Schroer, Hunter","contributorId":356950,"corporation":false,"usgs":false,"family":"Schroer","given":"Hunter","affiliations":[{"id":85293,"text":"Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":940520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Markland, Kendra M. 0000-0002-0276-8684 kmarkland@usgs.gov","orcid":"https://orcid.org/0000-0002-0276-8684","contributorId":306212,"corporation":false,"usgs":true,"family":"Markland","given":"Kendra","email":"kmarkland@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ling, Fangqiong","contributorId":356951,"corporation":false,"usgs":false,"family":"Ling","given":"Fangqiong","affiliations":[{"id":85296,"text":"Department of Energy, Environmental, & Chemical Engineering, Washington University in St. Louis","active":true,"usgs":false}],"preferred":false,"id":940522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Just, Craig L.","contributorId":178037,"corporation":false,"usgs":false,"family":"Just","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":940523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268380,"text":"70268380 - 2025 - Assimilation of reduced carbon triggers platinum alloy saturation in mafic and ultramafic magmas","interactions":[],"lastModifiedDate":"2025-08-04T15:53:27.169903","indexId":"70268380","displayToPublicDate":"2025-06-12T09:09:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Assimilation of reduced carbon triggers platinum alloy saturation in mafic and ultramafic magmas","docAbstract":"It is generally observed that magmatic sulfide ores have higher ratios of Pd/Pt than the mantle-like values of their parental magmas. This discrepancy has defied simple explanation because the partitioning behavior of both elements between sulfide and silicate liquids is very similar. Assimilation of sulfur- and carbon-rich country rocks by mafic and ultramafic magmas is considered a critical, if not essential, step in the formation of magmatic base metal sulfide deposits. Although there is general consensus that the assimilation of external sulfur and carbon promotes sulfide saturation, the effect of carbon assimilation on the solubilities of platinum-group elements in natural S-bearing silicate melt has been overlooked. In this study, we investigate the variations of platinum and palladium solubilities during assimilation of graphite and methane through thermodynamic modeling, in comparison with data from an array of highly distinctive magmatic sulfide ore systems representing ages from Archean to Paleozoic, melt compositions from komatiite to basalt, and magmatic settings including lavas, hypabyssal intrusions, plutonic continental arc roots, and plutonic layered intrusions, namely: Raglan, Norilsk-Talnakh, Lac des Iles, and the J-M Reef of the Stillwater Complex. We model assimilation-fractional crystallization processes to estimate the reduction of oxygen fugacity (fO2) of the melt due to incorporation of graphite and methane. The simulations show that although Pd remains highly soluble during the progressive assimilation of reduced carbon, Pt solubility decreases significantly as the silicate melt becomes increasingly reduced. With less than 8% of sediment assimilation, Pt alloy may saturate and then deviate from sulfide-undersaturated silicate melts, concomitantly increasing the Pd/Pt value of the remaining melts of the Raglan and Norilsk-Talnakh systems. For the Lac des Iles and Stillwater systems, a higher extent of assimilation is needed to reach Pt saturation because of the relatively carbon-poor nature of the lower crustal rocks. The assimilation of methane volatiles is shown to be more effective than graphite assimilation, and it provides a pathway to Pt alloy fractionation in the absence of detectable amounts of bulk host-rock assimilation. High Pd/Pt values have been documented in many world-class magmatic sulfide deposits whose parental magmas have demonstrably experienced crustal contamination. Our model suggests that although anomalous Pd/Pt values may be explained by other mechanisms such as incongruent melting of preexisting sulfide or differences in the diffusivities of the metals within achieving equilibration, the assimilation of graphite or methane may play an important role in the global occurrence of magmatic sulfide ores with elevated Pd/Pt values.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.5165","usgsCitation":"Li, Y., Smith, W.D., Jenkins, M., Yao, Z., and Mungall, J.E., 2025, Assimilation of reduced carbon triggers platinum alloy saturation in mafic and ultramafic magmas: Economic Geology, v. 120, no. 4, p. 1025-1036, https://doi.org/10.5382/econgeo.5165.","productDescription":"12 p.","startPage":"1025","endPage":"1036","ipdsId":"IP-151208","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":491179,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Ying Zhou","contributorId":357308,"corporation":false,"usgs":false,"family":"Li","given":"Ying Zhou","affiliations":[{"id":85402,"text":"Carleton University; Saskatchewan Geological Survey","active":true,"usgs":false}],"preferred":false,"id":941157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, William D.","contributorId":335361,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":941158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenkins, Michael 0000-0002-4261-409X mjenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-4261-409X","contributorId":172433,"corporation":false,"usgs":true,"family":"Jenkins","given":"Michael","email":"mjenkins@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":941159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yao, Zhuosen","contributorId":357309,"corporation":false,"usgs":false,"family":"Yao","given":"Zhuosen","affiliations":[{"id":12433,"text":"China University of Geosciences","active":true,"usgs":false}],"preferred":false,"id":941160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mungall, James E. 0000-0001-9726-8545","orcid":"https://orcid.org/0000-0001-9726-8545","contributorId":269537,"corporation":false,"usgs":false,"family":"Mungall","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":941161,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268974,"text":"70268974 - 2025 - A generalized deep learning model to detect and classify volcano seismicity","interactions":[],"lastModifiedDate":"2025-07-11T13:50:23.102749","indexId":"70268974","displayToPublicDate":"2025-06-12T08:44:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7593,"text":"Volcanica","active":true,"publicationSubtype":{"id":10}},"title":"A generalized deep learning model to detect and classify volcano seismicity","docAbstract":"Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] (10.1029/2024JB029194) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network (VOISS-Net) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings.
Salt marshes play a critical role in providing economic and ecological benefits but are susceptible to shoreline erosion. Natural and nature-based features (NNBF), such as breakwater reefs, are often used to reduce shoreline exposure to wave action and provide biogenic benefits. However, waves and water level are also responsible for the sediment supply necessary for marsh accretion, a critical component of marsh resilience to sea level rise. The goal of this study was to evaluate the effects of two subtidal breakwater reefs on wave energy, marsh shoreline erosion, and sediment deposition onto the marsh platform. As a restoration intervention, oyster shell and limestone gravel reefs were constructed within the nearshore zone of a high-energy shoreline where active shoreline erosion is causing marsh habitat loss. Although both sediment deposition and shoreline erosion were reduced after reef installation at all sites, the reefs demonstrated a statistically significant reduction in sediment deposition, whereas its effect on decreasing shoreline erosion was less pronounced. This variability in erosion reduction may be partly influenced by the physical dimensions of the reefs, affecting wave attenuation and leeward circulation. Wave measurements indicate that the reef reduced wave energy, particularly during south and southeast winds that could lead to the largest onshore waves. Given that these strong onshore winds are seasonal, extending the duration of data collection could provide deeper insights into the reef's influence on marsh shoreline erosion. This study is novel in that there are limited experimental or observational studies quantifying the wave reduction capacity and effects of subtidal reefs on marsh shoreline erosion and sediment dynamics. Studies such as these are critical to evaluate the capacity of subtidal reefs to protect marsh shorelines from erosion, but also to measure their impact on accretion processes necessary for the marsh to maintain elevation under future sea level rise.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-025-01564-7","usgsCitation":"Smith, K., Pitchford, J.L., Sparks, E., Archer, M., Virden, M., Terrano, J.F., and Smith, C., 2025, Evaluating the influence of constructed subtidal reefs on marsh shoreline erosion, sediment deposition, and wave energy: Estuaries and Coasts, v. 48, 128, 19 p., https://doi.org/10.1007/s12237-025-01564-7.","productDescription":"128, 19 p.","ipdsId":"IP-166569","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491001,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-025-01564-7","text":"Publisher Index Page"},{"id":490713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Grand Bay National Estuarine Research Reserve, Point Aux Chenes Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.45901043533199,\n 30.376549622657805\n ],\n [\n -88.45901043533199,\n 30.326593096623256\n ],\n [\n -88.40111942768058,\n 30.326593096623256\n ],\n [\n -88.40111942768058,\n 30.376549622657805\n ],\n [\n -88.45901043533199,\n 30.376549622657805\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Kathryn E.L. 0000-0002-7521-7875 kelsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-7521-7875","contributorId":173264,"corporation":false,"usgs":true,"family":"Smith","given":"Kathryn","email":"kelsmith@usgs.gov","middleInitial":"E.L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pitchford, Jonathan L.","contributorId":301251,"corporation":false,"usgs":false,"family":"Pitchford","given":"Jonathan","email":"","middleInitial":"L.","affiliations":[{"id":52643,"text":"Grand Bay National Estuarine Research Reserve","active":true,"usgs":false}],"preferred":false,"id":940245,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sparks, Eric L.","contributorId":356848,"corporation":false,"usgs":false,"family":"Sparks","given":"Eric L.","affiliations":[{"id":85257,"text":"Mississippi-Alabama Sea Grant","active":true,"usgs":false}],"preferred":false,"id":940246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Archer, Michael J.","contributorId":356849,"corporation":false,"usgs":false,"family":"Archer","given":"Michael J.","affiliations":[{"id":52643,"text":"Grand Bay National Estuarine Research Reserve","active":true,"usgs":false}],"preferred":false,"id":940247,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Virden, Matthew","contributorId":350892,"corporation":false,"usgs":false,"family":"Virden","given":"Matthew","affiliations":[{"id":83862,"text":"Mississippi State University, Mississippi","active":true,"usgs":false}],"preferred":false,"id":940248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Terrano, Joseph F. 0000-0003-3060-7682 jterrano@usgs.gov","orcid":"https://orcid.org/0000-0003-3060-7682","contributorId":173263,"corporation":false,"usgs":true,"family":"Terrano","given":"Joseph","email":"jterrano@usgs.gov","middleInitial":"F.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940249,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940250,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273217,"text":"70273217 - 2025 - Risk implications of Poisson assumptions and declustering inferred from a fully time-dependent earthquake forecast","interactions":[],"lastModifiedDate":"2025-12-22T15:27:13.439704","indexId":"70273217","displayToPublicDate":"2025-06-12T08:20:54","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Risk implications of Poisson assumptions and declustering inferred from a fully time-dependent earthquake forecast","docAbstract":"We use the Third Uniform California Earthquake Rupture Forecast Epidemic Type Aftershock Sequence model, which is fully time-dependent in terms of including spatiotemporal clustering, to evaluate the effects of the Poisson assumption and declustering algorithms on statewide loss exceedance curves. The model is simulation based, meaning it produces synthetic catalogs that exhibit realistic behavior with respect to aftershocks and multi-fault earthquakes. A Poisson version of the model was constructed by randomizing event times, and the influence of two declustering algorithms was examined as well. We demonstrate that the probability of one-or-more loss exceedances (occurrence exceedance probability) is greater for the Poisson model because it has fewer seismically quiet time windows. The discrepancy between dollar loss estimates with a given exceedance probability is up to a factor of 32% but varies depending on the loss threshold (the x-axis value) and the forecast duration (we examined a range between 24 h and 50 years, with the discrepancy for the latter being negligible). We discuss how the one-or-more loss exceedance metric is questionable because it ignores all but the maximum loss experienced in each timeframe. An alternative metric based on total aggregate loss in each time window (aggregate exceedance probability) was therefore also examined, for which the Poisson model again implies higher risk at intermediate losses but lower risk at higher losses (because large, triggered events now contribute to total aggregate losses for the fully time-dependent model). We also argue that declustering is not a scientifically justifiable way to deal with full time dependence, in agreement with a chorus from other recent studies. It is difficult to draw generally applicable conclusions from our study, in part because application specific details will likely be important, but our results highlight how full time dependence can be reckoned with once authoritative forecast models are made available.
","language":"English","publisher":"Earthquake Engineering Research Institute","doi":"10.1177/87552930251340677","usgsCitation":"Field, E.H., Milner, K., and Porter, K.A., 2025, Risk implications of Poisson assumptions and declustering inferred from a fully time-dependent earthquake forecast: Earthquake Spectra, v. 41, no. 3, p. 1977-1997, https://doi.org/10.1177/87552930251340677.","productDescription":"21 p.","startPage":"1977","endPage":"1997","ipdsId":"IP-175972","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":497866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253025","programNote":"National and Global Petroleum Assessment","usgsCitation":"Timm, K.K., Schenk, C.J., Hearon, J.S., Finn, T.M., Gelman, S.E., Woodall, C.A., Mercier, T.M., Cicero, A.D., Drake, R.M., II, Ellis, G.S., Gardner, M.H., Johnson, B.G., Lagesse, J.H., Le, P.A., Leathers-Miller, H.M., and Young, S.S., 2025, Assessment of continuous oil and gas resources in the Upper Cretaceous Niobrara Formation in the Southwestern Wyoming Province, Wyoming and Colorado, 2024: U.S. Geological Survey Fact Sheet 2025–3025, 4 p., https://doi.org/10.3133/fs20253025. ","productDescription":"Report: 2 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-157886","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":494139,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118649.htm","linkFileType":{"id":5,"text":"html"}},{"id":490212,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SO70CX","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - Southwestern Wyoming Province, Niobrara Formation Continuous Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":490211,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3025/fs20253025.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3025"},{"id":490210,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3025/coverthb.jpg"},{"id":490387,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3025/images"},{"id":490388,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3025/fs20253025.xml"},{"id":490436,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253025/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3025"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Niobrara Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.10003951443201,\n 43.951167856587375\n ],\n [\n -111.10003951443201,\n 39.53456629450929\n ],\n [\n -106,\n 39.53456629450929\n ],\n [\n -106,\n 43.951167856587375\n ],\n [\n -111.10003951443201,\n 43.951167856587375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Efficient learning about disease dynamics in free-ranging wildlife systems can benefit from active surveillance that is standardized across different ecological contexts. For example, active surveillance that targets specific individuals and populations with standardized sampling across ecological contexts (landscape-scale targeted surveillance) is important for developing a mechanistic understanding of disease emergence, which is the foundation for improving risk assessment of zoonotic or wildlife-livestock disease outbreaks and predicting hotspots of disease emergence. However, landscape-scale targeted surveillance systems are rare and challenging to implement. Increasing experience and infrastructure for landscape-scale targeted surveillance will improve readiness for rapid deployment of this type of surveillance in response to new disease emergence events. Here, we describe our experience developing and rapidly deploying a landscape-scale targeted surveillance system for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in two free-ranging deer species across their ranges in the United States. Our surveillance system was designed to collect data across individual, population, and landscape scales for future analyses aimed at understanding mechanisms and risk factors of SARS-CoV-2 transmission, evolution, and persistence. Our approach leveraged partnerships between state and federal public service sectors and academic researchers in a landscape-scale targeted surveillance research network. Methods describe our approach to developing the surveillance network and sampling design. Results report challenges with implementing our intended sampling design, specifically how the design was adapted as different challenges arose and summarize the sampling design that has been implemented thus far. In the discussion, we describe strategies that were important for the successful deployment of landscape-scale targeted surveillance, development and operation of the research network, construction of similar networks in the future, and analytical approaches for the data based on the sampling design.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71492","usgsCitation":"Pepin, K.M., Combs, M., Bastille-Rousseau, G., Craft, M., Cross, P., Diuk-Wasser, M., Gagne, R., Gallo, T., Garwood, T., Heale, J., Hewitt, J., Hoy-Petersen, J., Malmberg, J., Mullinax, J.M., Plimpton, L., Smith, L., VanAcker, M., Chandler, J., Walter, W., WIlson-Henjum, G., Wittemyer, G., and Manlove, K.R., 2025, Expanding national-scale wildlife disease surveillance systems with research networks: Ecology and Evolution, v. 15, no. 6, e71492, 21 p., https://doi.org/10.1002/ece3.71492.","productDescription":"e71492, 21 p.","ipdsId":"IP-167320","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":491396,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71492","text":"Publisher Index Page"},{"id":491393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Kim M.","contributorId":279406,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim","email":"","middleInitial":"M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":941379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Combs, Matthew A.","contributorId":357414,"corporation":false,"usgs":false,"family":"Combs","given":"Matthew A.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":941380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bastille-Rousseau, Guillaume 0000-0001-6799-639X","orcid":"https://orcid.org/0000-0001-6799-639X","contributorId":190877,"corporation":false,"usgs":false,"family":"Bastille-Rousseau","given":"Guillaume","email":"","affiliations":[{"id":40724,"text":"Cooperative Wildlife Research Laboratory and Department of Forestry, Southern Illinois University, 251 Life Science II, Mail Code 6504, Carbondale, Illinois 62901 USA","active":true,"usgs":false}],"preferred":false,"id":941381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craft, M.E.","contributorId":357418,"corporation":false,"usgs":false,"family":"Craft","given":"M.E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":941382,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":941383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diuk-Wasser, M.A.","contributorId":357421,"corporation":false,"usgs":false,"family":"Diuk-Wasser","given":"M.A.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":941384,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gagne, R.B.","contributorId":357423,"corporation":false,"usgs":false,"family":"Gagne","given":"R.B.","affiliations":[{"id":83379,"text":"University of Pennsylvania School of Veterinary Medicine","active":true,"usgs":false}],"preferred":false,"id":941385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gallo, Travis","contributorId":272224,"corporation":false,"usgs":false,"family":"Gallo","given":"Travis","email":"","affiliations":[],"preferred":false,"id":941386,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Garwood, Tyler","contributorId":225442,"corporation":false,"usgs":false,"family":"Garwood","given":"Tyler","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":941387,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heale, J.D.","contributorId":357426,"corporation":false,"usgs":false,"family":"Heale","given":"J.D.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":941388,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hewitt, J.","contributorId":357429,"corporation":false,"usgs":false,"family":"Hewitt","given":"J.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":941389,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hoy-Petersen, J.","contributorId":357432,"corporation":false,"usgs":false,"family":"Hoy-Petersen","given":"J.","affiliations":[{"id":83379,"text":"University of Pennsylvania School of Veterinary Medicine","active":true,"usgs":false}],"preferred":false,"id":941390,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Malmberg, Jennifer L.","contributorId":179193,"corporation":false,"usgs":false,"family":"Malmberg","given":"Jennifer L.","affiliations":[],"preferred":false,"id":941391,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":941392,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Plimpton, L.","contributorId":357435,"corporation":false,"usgs":false,"family":"Plimpton","given":"L.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":941393,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Smith, Lauren","contributorId":357446,"corporation":false,"usgs":false,"family":"Smith","given":"Lauren","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":941394,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"VanAcker, M.C.","contributorId":357438,"corporation":false,"usgs":false,"family":"VanAcker","given":"M.C.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":941395,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Chandler, J.C.","contributorId":357441,"corporation":false,"usgs":false,"family":"Chandler","given":"J.C.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":941397,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":941398,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"WIlson-Henjum, Grete","contributorId":302025,"corporation":false,"usgs":false,"family":"WIlson-Henjum","given":"Grete","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":941399,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Wittemyer, George","contributorId":198621,"corporation":false,"usgs":false,"family":"Wittemyer","given":"George","email":"","affiliations":[],"preferred":false,"id":941400,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Manlove, Kezia R.","contributorId":198305,"corporation":false,"usgs":false,"family":"Manlove","given":"Kezia","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":941401,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70268778,"text":"70268778 - 2025 - Targeted quantitation of 6ppd-quinone in fish tissue samples with liquid chromatography-tandem mass spectrometry","interactions":[],"lastModifiedDate":"2025-11-18T16:55:35.803204","indexId":"70268778","displayToPublicDate":"2025-06-11T10:44:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Targeted quantitation of 6ppd-quinone in fish tissue samples with liquid chromatography-tandem mass spectrometry","docAbstract":"The tire additive transformation product N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine-quinone (6PPD-quinone) has recently garnered global attention due to its acute toxicity to some salmonids, such as coho salmon (Oncorhynchus kisutch), and its ubiquitous presence in urban stormwater systems. In the present study, we developed and compared the extraction efficiency of two sample preparation methodologies for quantification of 6PPD-quinone among two fish tissue sample types that included fillet of smallmouth bass (Micropterus dolomieu) and whole-body samples of O. kisutch fry subjected to in vivo exposure tests with 6PPD-quinone. The two sample preparation methods tested included an accelerated solvent extraction (ASE) approach and a sonication extraction approach. Both sample preparation methods included identical purification steps for the crude sample extracts with enhanced matrix removal cartridges. The purified sample extracts were subjected to targeted analysis of 6PPD-quinone using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The results showed that extractions made with the reported ASE method demonstrated significantly higher absolute recovery (80–96%) of the extracted internal standard, [13C6]-6PPD-quinone, than sonication-based extractions (74–80%) in both fish tissue sample types. The proposed ASE method shows acceptable limits of quantification (0.37–0.67 ng g−1), linearity (R2 > 0.996), and repeatability (RSD ≤ 9%). This work advances research capabilities for investigations on the toxicokinetic processes of 6PPD-quinone in biological samples.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/etojnl/vgaf151","usgsCitation":"Moody, A.H., Soucek, D.J., and Alvarez, D.A., 2025, Targeted quantitation of 6ppd-quinone in fish tissue samples with liquid chromatography-tandem mass spectrometry: Environmental Toxicology and Chemistry, v. 44, no. 10, p. 2807-2817, https://doi.org/10.1093/etojnl/vgaf151.","productDescription":"11 p.","startPage":"2807","endPage":"2817","ipdsId":"IP-173953","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":492074,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/etojnl/vgaf151","text":"Publisher Index Page"},{"id":491845,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Moody, Adam H. 0000-0001-6160-7920","orcid":"https://orcid.org/0000-0001-6160-7920","contributorId":302592,"corporation":false,"usgs":true,"family":"Moody","given":"Adam","email":"","middleInitial":"H.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":941928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soucek, David J. 0000-0002-7741-0193 drieckssoucek@usgs.gov","orcid":"https://orcid.org/0000-0002-7741-0193","contributorId":295408,"corporation":false,"usgs":true,"family":"Soucek","given":"David","email":"drieckssoucek@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":941929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, David A. 0000-0002-6918-2709","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":220763,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":941930,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268302,"text":"70268302 - 2025 - Human perturbations to mercury in global rivers","interactions":[],"lastModifiedDate":"2025-06-20T15:03:57.600684","indexId":"70268302","displayToPublicDate":"2025-06-11T10:03:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Human perturbations to mercury in global rivers","docAbstract":"Mercury compounds are potent neurotoxins that pose threats to human health, primarily through fish consumption. Rivers, critical for drinking water and food supply, have seen rapid increases in mercury concentrations and export to coastal margins since the Industrial Revolution (~1850). However, patterns of these changes remain understudied, limiting assessments of environmental policies. Here, we develop a global model to simulate preindustrial riverine total mercury and assess human perturbations by comparing it to present-day conditions. We find that global rivers transported ~390 megagrams annually of mercury to the oceans in the preindustrial era, with spatial variability. Human activities have elevated riverine mercury budgets by two to three times in the present day. Establishing a baseline riverine mercury level, our findings reveal rapid responses of riverine mercury to human perturbations and could be used to inform targets for global riverine mercury restoration. Total riverine mercury concentrations could also be used as indicators to comprehensively understand the effectiveness of mercury pollution governance.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.adw0471","usgsCitation":"Peng, D., Tan, Z., Yuan, T., Wu, P., Song, Z., Zhang, P., Huang, S., Zhang, Y., Lei, T., Middleton, B., Sonke, J., Lei, G., and Gao, J., 2025, Human perturbations to mercury in global rivers: Science Advances, v. 11, no. 24, eadw0471, 13 p., https://doi.org/10.1126/sciadv.adw0471.","productDescription":"eadw0471, 13 p.","ipdsId":"IP-162091","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":491449,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adw0471","text":"Publisher Index Page"},{"id":491027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"24","noUsgsAuthors":false,"publicationDate":"2025-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Peng, Dong","contributorId":357103,"corporation":false,"usgs":false,"family":"Peng","given":"Dong","affiliations":[{"id":51913,"text":"Nanjing University","active":true,"usgs":false}],"preferred":false,"id":940738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tan, Zeli","contributorId":297281,"corporation":false,"usgs":false,"family":"Tan","given":"Zeli","email":"","affiliations":[{"id":28004,"text":"Pacific Northwest National Laboratory, Richland, WA, USA","active":true,"usgs":false}],"preferred":false,"id":940739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yuan, Tengfei","contributorId":357107,"corporation":false,"usgs":false,"family":"Yuan","given":"Tengfei","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":940740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Peipei","contributorId":357110,"corporation":false,"usgs":false,"family":"Wu","given":"Peipei","affiliations":[{"id":38724,"text":"Scripps Institution of Oceanography, University of California San Diego","active":true,"usgs":false}],"preferred":false,"id":940741,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Song, Zhengcheng","contributorId":357113,"corporation":false,"usgs":false,"family":"Song","given":"Zhengcheng","affiliations":[{"id":51913,"text":"Nanjing University","active":true,"usgs":false}],"preferred":false,"id":940742,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Peng","contributorId":357116,"corporation":false,"usgs":false,"family":"Zhang","given":"Peng","affiliations":[{"id":51913,"text":"Nanjing University","active":true,"usgs":false}],"preferred":false,"id":940743,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Shaojian","contributorId":357119,"corporation":false,"usgs":false,"family":"Huang","given":"Shaojian","affiliations":[{"id":51913,"text":"Nanjing University","active":true,"usgs":false}],"preferred":false,"id":940744,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhang, Yanxu","contributorId":357122,"corporation":false,"usgs":false,"family":"Zhang","given":"Yanxu","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":940745,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lei, Ting","contributorId":245022,"corporation":false,"usgs":false,"family":"Lei","given":"Ting","affiliations":[{"id":40912,"text":"Beijing Forestry","active":true,"usgs":false}],"preferred":false,"id":940746,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Middleton, Beth 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":206684,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":940747,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sonke, Jeroen E.","contributorId":357124,"corporation":false,"usgs":false,"family":"Sonke","given":"Jeroen E.","affiliations":[{"id":85336,"text":"Géosciences Environnement Toulouse, CNRS/IRD/Université de Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":940748,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lei, Guangchun","contributorId":259278,"corporation":false,"usgs":false,"family":"Lei","given":"Guangchun","email":"","affiliations":[],"preferred":false,"id":940749,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gao, Jianhua","contributorId":357125,"corporation":false,"usgs":false,"family":"Gao","given":"Jianhua","affiliations":[{"id":51913,"text":"Nanjing University","active":true,"usgs":false}],"preferred":false,"id":940750,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70268294,"text":"70268294 - 2025 - Population growth of threatened Gulf Sturgeon may be limited by the frequency of adult episodic mortality events","interactions":[],"lastModifiedDate":"2025-06-20T14:47:48.754686","indexId":"70268294","displayToPublicDate":"2025-06-11T09:42:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20748,"text":"Marine and Coastal Fisheries: Dynamics, Management and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Population growth of threatened Gulf Sturgeon may be limited by the frequency of adult episodic mortality events","docAbstract":"We identified spatial and temporal variation in population trends for Gulf Sturgeon Acipenser desotoi (previously known as Acipenser oxyrinchus desotoi) across the species’ range to inform recovery strategies. We also assessed whether adult survival or recruitment more strongly influences population change.
We analyzed adult Gulf Sturgeon capture–recapture data from 1990 to 2022 across seven Gulf of Mexico river systems. Using temporal symmetry models and fixed estimates of adult survival from a companion study, we estimated seniority probability (γi + 1), capture probability (p), recruitment (f), and population growth rate (λ) for adult fish. Models were compared using Akaike information criterion adjusted for small sample sizes, and parameter estimates were derived at both river-specific and rangewide scales.
Adult survival (φ) was the primary driver of λ across the species’ range, with γ consistently >0.5 in most rivers and time periods. While rangewide λ suggested stable or slightly increasing adult populations, river-level trends varied. Recent declines in λ and f were observed in the Escambia, Apalachicola, and Suwannee rivers—systems affected by red tide, hurricanes, or oil exposure following the Deepwater Horizon spill. Capture probabilities remained low across rivers and time periods.
Long-term recovery of Gulf Sturgeon is likely more sensitive to adult survival than recruitment to the adult population. Population trajectories differ across rivers and may reflect both demographic changes and inconsistencies in monitoring. Restoring consistent, standardized adult monitoring in select rivers will improve the ability to detect meaningful trends and guide conservation efforts focused on minimizing adult mortality.
Most previous research has used an individual water quality parameter, such as calcium, to predict likelihood of zebra mussel establishment in lakes; we employed two multiple-factor methods, our own susceptibility index for zebra mussels in lakes (SIZL) and aragonite saturation state, to evaluate the risk of mussel establishment. Thirty sites in Voyageurs National Park (VNP) were sampled in 2023 for water quality conditions, including those that play a key role in mussel survivability. These results were combined with existing data sets to determine which lakes, and which locations within the larger lakes, are at greatest risk for zebra mussel establishment. Results for VNP indicate that physical lake characteristics and water quality conditions (both single- and multiple-factor methods) put the large, interconnected lakes in VNP at greater risk of zebra mussel establishment than the smaller interior lakes. All sampled interior lakes had alkalinity and calcium concentrations below thresholds conducive to zebra mussel establishment, although Mukooda and O’Leary lakes were identified as the most at-risk interior lakes. The area in the large lakes most at risk was Sullivan Bay in Kabetogama Lake, where water quality conditions were found to be conducive to zebra mussel establishment. Results from this study could be used by resource managers to focus additional inspections, decontaminations, and regulations to protect the most at-risk lakes. These multiple-factor methods may be useful in determining the risk of zebra mussel infestation in other water bodies.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10402381.2025.2488829","usgsCitation":"Christensen, V., Katona, L.R., Trompeter, H., Maki, R., Smith, J., and Sandborn, D., 2025, Water quality-based risk assessment for zebra mussel establishment: A case study of single- and multiple-factor methods in northern temperate lakes: Lake and Reservoir Management, v. 41, no. 2, p. 124-142, https://doi.org/10.1080/10402381.2025.2488829.","productDescription":"19 p.","startPage":"124","endPage":"142","ipdsId":"IP-167134","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":492828,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Voyageurs National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.44469471404027,\n 48.25812750188285\n ],\n [\n -92.43814232688678,\n 48.30708718825505\n ],\n [\n -92.47445207574346,\n 48.377421556342625\n ],\n [\n -92.46304044038844,\n 48.41324094675181\n ],\n [\n -92.52009861716228,\n 48.4559157257907\n ],\n [\n -92.62902786373112,\n 48.4442181433015\n ],\n [\n -92.70890931121498,\n 48.4559157257907\n ],\n [\n -92.68504861910944,\n 48.49992951737704\n ],\n [\n -92.6321401279189,\n 48.50199172694181\n ],\n [\n -92.63628981350283,\n 48.538410262889045\n ],\n [\n -92.95374076064601,\n 48.61254059167456\n ],\n [\n -93.08134359234099,\n 48.62694220072322\n ],\n [\n -93.16952441099208,\n 48.62831356824623\n ],\n [\n -93.17367409657537,\n 48.578920857403915\n ],\n [\n -93.06267000721486,\n 48.45935567841727\n ],\n [\n -93.00872409462822,\n 48.42288032028864\n ],\n [\n -92.94544138947879,\n 48.40015601627843\n ],\n [\n -92.77841654473997,\n 48.40635456089478\n ],\n [\n -92.71409641819443,\n 48.387067719148206\n ],\n [\n -92.57300710835281,\n 48.324334982522885\n ],\n [\n -92.5055747176201,\n 48.284311171440095\n ],\n [\n -92.51076946771622,\n 48.257714102321444\n ],\n [\n -92.44469471404027,\n 48.25812750188285\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katona, Leon R. 0000-0001-5323-1871","orcid":"https://orcid.org/0000-0001-5323-1871","contributorId":331458,"corporation":false,"usgs":true,"family":"Katona","given":"Leon","email":"","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trompeter, Hailey Elizabeth 0009-0007-6855-5642","orcid":"https://orcid.org/0009-0007-6855-5642","contributorId":358493,"corporation":false,"usgs":true,"family":"Trompeter","given":"Hailey Elizabeth","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":943856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, James C.","contributorId":351486,"corporation":false,"usgs":false,"family":"Smith","given":"James C.","affiliations":[{"id":82351,"text":"U.S. National Park Service (NPS)","active":true,"usgs":false}],"preferred":false,"id":943857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sandborn, Daniel E.","contributorId":358495,"corporation":false,"usgs":false,"family":"Sandborn","given":"Daniel E.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":943858,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275055,"text":"70275055 - 2025 - Assessing the combined influence of biotic and anthropogenic stressors on polar bears to inform conservation planning","interactions":[],"lastModifiedDate":"2026-04-13T14:22:44.619452","indexId":"70275055","displayToPublicDate":"2025-06-11T09:14:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the combined influence of biotic and anthropogenic stressors on polar bears to inform conservation planning","docAbstract":"Developing conservation strategies for species vulnerable to the effects of climate change, like polar bears (Ursus maritimus), can be challenging given the uncertainty of future environmental conditions. Effective conservation planning requires identifying and ranking threats to the persistence of polar bears throughout their circumpolar range and then assessing the ability of mitigative actions to aid in meeting plan objectives. We used a Bayesian network model to (1) characterize the relative importance of multiple biotic and anthropogenic stressors on four ecoregional polar bear populations, at two future decadal time periods, and based on two Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios (Shared Socioeconomic Pathways [SSPs] 2.6 [low] and 8.5 [high]); and (2) identify achievable management actions that may enhance the prospects of long-term persistence. Normative model runs indicated that populations in all four ecoregions incurred increasing probabilities of being decreased or greatly decreased over time. The probabilities of polar bear populations being decreased or greatly decreased from mid- to end of the century ranged from ~55% to 87% for the SSP 2.6 emissions scenario, and 82% to 94% for the SSP 8.5 emissions scenario among ecoregions. Arctic sea ice conditions and marine prey availability had the greatest influences on future polar bear population outcomes and overrode any relative influence from all other stressors. Hunting mortality was the most influential individual anthropogenic stressor in the Archipelago and Seasonal Ice Ecoregions, whereas terrestrial refugia quality grouped with various anthropogenic activities or factors (e.g., resource extraction, oil spill) was most influential for the Polar Basin Divergent Ice and Polar Basin Convergent Ice ecoregions. Our findings indicate that near-term proactive management of multiple anthropogenic stressors could cumulatively reduce the decline in populations such that if future sea ice habitat loss is eventually curtailed, population abundance would be greater than it would have been otherwise. Additionally, our findings suggest that there is value in tailoring management actions to address ecoregion-specific threats, which may prove useful in informing the development of future circumpolar conservation plans.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70316","usgsCitation":"Atwood, T.C., Marcot, B.G., Douglas, D., Bromaghin, J.F., and Pagano, A.M., 2025, Assessing the combined influence of biotic and anthropogenic stressors on polar bears to inform conservation planning: Ecosphere, v. 16, no. 6, e70316, 18 p., https://doi.org/10.1002/ecs2.70316.","productDescription":"e70316, 18 p.","ipdsId":"IP-171776","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":502995,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70316","text":"Publisher Index Page"},{"id":502740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 85\n ],\n [\n -179.9,\n 49.40470578748864\n ],\n [\n 179.9,\n 49.40470578748864\n ],\n [\n 179.9,\n 85\n ],\n [\n -179.9,\n 85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":959320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marcot, Bruce G.","contributorId":140456,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":959321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":959322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":959323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":959324,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268116,"text":"70268116 - 2025 - High-pass corner frequency selection and review tool for use in ground-motion processing","interactions":[],"lastModifiedDate":"2025-09-09T14:38:21.181387","indexId":"70268116","displayToPublicDate":"2025-06-11T08:49:27","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"High-pass corner frequency selection and review tool for use in ground-motion processing","docAbstract":"Raw seismological waveform data contain noise from the instrument’s surroundings and the instrument itself that can dominate recordings at low and high frequencies. To use these data in ground‐motion modeling, the effects of noise on the signals must be reduced and the signals’ usable frequency range identified. We present automated procedures to efficiently reduce low‐frequency noise that are implemented in the software package gmprocess. These procedures check for, and as needed remove, low‐frequency artifacts in the displacement record using polynomial fits, which can be used in combination with existing signal‐to‐noise ratio (SNR)‐based corner‐frequency selection procedures. The automated selections are then efficiently verified and refined using a graphical user interface (GUI) that plots relevant ground‐motion time series and spectra and tracks modifications to signal processing parameters. We demonstrate these procedures using recordings from the 2020 M 5.1 Sparta, North Carolina, and the 2013 M 4.7 southern Ontario earthquakes. Data processed with the SNR‐only and polynomial criteria for these events contain displacement artifacts in 37% and 23% of processed traces, respectively. Records with remaining artifacts are corrected manually using the GUI. These processing steps illustrate the workflow for efficient data processing with quality control.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240265","usgsCitation":"Ramos-Sepulveda, M.E., Brandenberg, S.J., Buckreis, T.E., Parker, G.A., and Stewart, J., 2025, High-pass corner frequency selection and review tool for use in ground-motion processing: Seismological Research Letters, v. 96, no. 5, p. 3244-3252, https://doi.org/10.1785/0220240265.","productDescription":"9 p.","startPage":"3244","endPage":"3252","ipdsId":"IP-163865","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":490720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos-Sepulveda, Maria E.","contributorId":294748,"corporation":false,"usgs":false,"family":"Ramos-Sepulveda","given":"Maria","email":"","middleInitial":"E.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":940267,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandenberg, Scott J.","contributorId":303895,"corporation":false,"usgs":false,"family":"Brandenberg","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":940268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buckreis, Tristan E","contributorId":295733,"corporation":false,"usgs":false,"family":"Buckreis","given":"Tristan","email":"","middleInitial":"E","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":940269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":940270,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, Jonathan P.","contributorId":350854,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan P.","affiliations":[{"id":83855,"text":"University of California, Los Angeles, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":940271,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268102,"text":"70268102 - 2025 - Bioaccumulation and trophic transfer of selenium in a large oligotrophic river","interactions":[],"lastModifiedDate":"2025-11-19T14:19:13.03245","indexId":"70268102","displayToPublicDate":"2025-06-11T08:18:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Bioaccumulation and trophic transfer of selenium in a large oligotrophic river","docAbstract":"In flowing waters with elevated selenium concentrations, fish are often considered to be at risk from selenium toxicity owing to dietary exposure and accumulation in ovary tissues and subsequent deformities in developing larvae. We studied selenium throughout components of the aquatic food webs at geomorphically distinct locations along the oligotrophic Kootenai River (Montana and Idaho, USA), a river with moderately elevated dissolved selenium concentrations (~ 1 µg/L). Components included water, sediment, freshly accrued biofilms, in-situ periphyton, sestonic detritus, aquatic invertebrates, and fish, with spring and fall sampling. Selenium concentrations were similar among the sediment, biofilm, periphyton, and detritus samples with most concentrations ranging between 0.5 to 2.0 (mg/kg dry weight (dw)). Among the aquatic invertebrates, the highest selenium concentrations were observed in Paraleptophlebia sp. mayflies (>15 mg/kg dw) and oligochaetes (>30 mg/kg dw). Selenium in chironomids was higher in the spring than fall, but otherwise, no consistent concentration patterns with season or feeding traits were observed. Fish tissue selenium concentrations were highly variable among species and tissue type. Selenium in fish tissues tended to be highest in livers of rainbow trout and mountain whitefish relative to egg/ovary, muscle, and carcass tissue. With northern pikeminnow, redside shiner, and slimy sculpin, selenium concentrations tended to be highest in ovary tissues. For example, selenium in rainbow trout livers ranged from an average (range) of 37 (4.5 to 151) compared to 8.7 (2.7 to 12.3) in northern pikeminnow livers. Egg/ovary concentrations ranged from a high of 26 (10.7 to 64) in redside shiner in contrast to 12.2 (6.9 to 17) mg/kg dw in slimy sculpin. A drawback of the fish-tissue approach to monitoring and managing selenium risks in freshwaters is the need to kill multiple fish per site and event. Potential alternative monitoring approaches are illustrated using aquatic invertebrates or using the food web monitoring results to derive monitoring targets for selenium in water or invertebrate tissue that could avoid the need to kill fish to assess whether fish protection guidelines are met.","language":"English","publisher":"Oxford University Press","doi":"10.1093/etojnl/vgaf149","usgsCitation":"Mebane, C.A., Stewart, A.R., Murray, E., Short, T., Kocen, V., and Zinsser, L.M., 2025, Bioaccumulation and trophic transfer of selenium in a large oligotrophic river: Environmental Toxicology and Chemistry, v. 44, no. 10, p. 2864-2888, https://doi.org/10.1093/etojnl/vgaf149.","productDescription":"25 p.; Data Release","startPage":"2864","endPage":"2888","ipdsId":"IP-152806","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":490999,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/etojnl/vgaf149","text":"Publisher Index Page"},{"id":490930,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XUP6GT","text":"USGS data release","linkHelpText":"Selenium in water, sediment, periphyton, benthic invertebrate and fish tissues from the Kootenai River, Idaho and Montana"},{"id":490711,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana","otherGeospatial":"Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.60365905932548,\n 49.0066674443394\n ],\n [\n -116.60365905932548,\n 48.32358707536375\n ],\n [\n -115.03847921340105,\n 48.32358707536375\n ],\n [\n -115.03847921340105,\n 49.0066674443394\n ],\n [\n -116.60365905932548,\n 49.0066674443394\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, A. Robin 0000-0003-2918-546X arstewar@usgs.gov","orcid":"https://orcid.org/0000-0003-2918-546X","contributorId":1482,"corporation":false,"usgs":true,"family":"Stewart","given":"A.","email":"arstewar@usgs.gov","middleInitial":"Robin","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":40553,"text":"WMA - Office of the Chief Operating Officer","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":940217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murray, Erin 0000-0002-5007-3449","orcid":"https://orcid.org/0000-0002-5007-3449","contributorId":205705,"corporation":false,"usgs":true,"family":"Murray","given":"Erin","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940218,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Short, Terry M. 0000-0001-9941-4593","orcid":"https://orcid.org/0000-0001-9941-4593","contributorId":292135,"corporation":false,"usgs":false,"family":"Short","given":"Terry M.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":940219,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kocen, Veronika A. 0009-0006-9144-8549","orcid":"https://orcid.org/0009-0006-9144-8549","contributorId":336552,"corporation":false,"usgs":true,"family":"Kocen","given":"Veronika A.","affiliations":[{"id":40553,"text":"WMA - Office of the Chief Operating Officer","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940220,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zinsser, Lauren M. 0000-0002-8582-066X","orcid":"https://orcid.org/0000-0002-8582-066X","contributorId":205756,"corporation":false,"usgs":true,"family":"Zinsser","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":940221,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269937,"text":"70269937 - 2025 - Co-location of sheep grazing and solar energy production yields agrotechnological synergies","interactions":[],"lastModifiedDate":"2025-08-07T15:19:27.071437","indexId":"70269937","displayToPublicDate":"2025-06-11T08:10:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":679,"text":"Agricultural Systems","active":true,"publicationSubtype":{"id":10}},"title":"Co-location of sheep grazing and solar energy production yields agrotechnological synergies","docAbstract":"CONTEXT
Agrivoltaics—the co-location of solar energy and agricultural production—may reduce land-use competition and boost revenues for landowners. Sheep grazing in solar facilities (i.e., solar grazing/agrivoltaic grazing systems) is increasingly common in agricultural areas. Solar grazing can provide land access to flock owners and support agricultural viability via payments for vegetation management. However, there is a need for more data on how co-location of sheep grazing and solar energy production affects flock health, stocking rates, and feedback loops for maintenance of vegetation in solar facilities across regions.
OBJECTIVE
Our objective was to better understand synergies and tradeoffs associated with agrivoltaic grazing systems by investigating applied grazing management questions as well as questions regarding agrotechnological co-benefits related to simultaneous flock health and vegetation management in solar facilities.
METHODS
We tested effects of sheep stocking rates (0 to 10 sheep per ha–1), site preparation (fallow vs. legume seed-mix planting), and microclimate (panel-shaded areas vs. panel interspaces) on herbage yield and nutritional quality, flock health and condition, and a vegetation management success index. We collected these data across two grazing seasons in an operational, 21.85-ha photovoltaic solar facility (18 MW) established on a previous old field in New York State, USA.
RESULTS AND CONCLUSIONS
Shade from solar panels negatively affected herbage yield. We detected no significant differences in herbage yield or vegetation management outcomes between fallow and planted legume plots, suggesting that regrowth from native seed banks in solar facilities on previous old fields may be an economical alternative to seeding for sheep forage relative. Sheep stocking rates affected flock health and condition; we identified an optimal stocking rate of 8 sheep per ha–1 for achieving sufficient herbage yield and quality, maintaining flock health, and preventing vegetation overgrowth from shading solar panels at our study site. Solar grazing can yield an agrotechnological synergy supporting healthy forage, healthy sheep, and vegetation management in community-scale (i.e., <25 MW) solar facilities without mowing. In a well-managed solar grazing system, high herbage yield and quality promote high flock health and condition, and the healthy flock suppresses vegetation enough to prevent panel shading. SIGNIFICANCE Our study highlights the potential for sheep grazing in solar facilities to simultaneously benefit sheep and solar energy production systems. Solar grazing can present a “win-win” scenario for solar developers and sheep producers in the northeastern U.S.
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U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225