Between 23 and 27 September 2024, antecedent rain followed by Hurricane Helene produced one of the most damaging weather events in southern Appalachia history. The back-to-back storm events resulted in a maximum cumulative rainfall of 848 mm and hurricane-force wind gusts over 170 km/h in western North Carolina, eastern Tennessee, and southwestern Virginia. The resulting regional flooding, landslides, and tree blowdown caused over 100 fatalities, damaged or destroyed critical infrastructure and thousands of structures, and severed connectivity across the region. Over the next several weeks, a multi-agency landslide response produced a rapid hazard assessment and mapped 2217 landslides, 55% of which damaged infrastructure or property. Orographic uplift enhanced rainfall, resulting in concentrated landsliding along the ~250 km swath of the Blue Ridge escarpment in western North Carolina. Landslides initiated predominantly on windward-facing (southeast-facing) slopes, and localized clustering of initiation points indicated a strong influence of hillslope-scale meteorological and geomorphic factors. Many shallow landslides mobilized into larger, highly mobile, and damaging debris flows that graded into floods. Here, we put our preliminary observations in the context of historical storm-driven landslide events and open new avenues for investigating the nature and extent of landslides and their effects in southern Appalachia and similar environments.
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South Carolina, Tennessee, Virginia","otherGeospatial":"southern Appalachia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.0956246997159,\n 33.16712093076947\n ],\n [\n -80.70056220609001,\n 35.44500558858594\n ],\n [\n -78.92252283909625,\n 36.85612515045237\n ],\n [\n -78.86465883497438,\n 37.53726534359846\n ],\n [\n -81.27319094443648,\n 37.03692920257846\n ],\n [\n -83.98040433088295,\n 36.20191656996158\n ],\n [\n -85.11083948910141,\n 34.93803598167824\n ],\n [\n -84.63563450266271,\n 33.976759781860224\n ],\n [\n -84.49843468082626,\n 33.14701998591154\n ],\n [\n -84.0956246997159,\n 33.16712093076947\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"36","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Lauren N. 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The U.S. Gulf Coast and the Williston basins are the two major lignite-bearing basins within the country. Recent REE and CM studies of the lignite in these basins have indicated that the coals may be a viable source material for REE and CM extraction. To evaluate in-place coal as a potential source of REE and CM, the coal resources need to be quantified. This study presents the results of a regional analysis of the U.S. Gulf Coast lignite and bituminous coal resources that might be available as potential sources of REE and CM. The resource analysis used kriging methods to develop isopleth maps of cumulative coal thickness throughout the region using data from 31,181 drill holes and other data points. The estimated total coal resource in the Gulf Coast is about 83 billion metric tons in the upper 90 m (~ 300 ft) of the subsurface. Texas accounted for 40 percent (32 billion metric tons) of the total resource, followed by Mississippi (24 %, 20 billion metric tons), Louisiana (14 %, 12 billion metric tons), Tennessee (10 %, 8.5 billion metric tons), and Arkansas (6 %, 5.1 billion metric tons). The remaining states each accounted for less than 5 percent of the total resource. Georgia had the smallest resource estimated at 7 million metric tons. Here we report the first known state-wide lignite resource estimates for Georgia, Kentucky (820 million metric tons), and Missouri (1,800 million metric tons). A comparison of the results of this study with those of previous Gulf Coast and Williston Basin resource studies is difficult because each study used different data sources, assessment methodologies, overburden depths, and qualifying coal thicknesses. Coal-power electric generation has sharply decreased in past decades and mining of these coals for CM and REE could provide additional co-products such as activated carbon and other uses such as fertilizer (soil enhancer).
","language":"English","publisher":"EarthArXiv","doi":"10.31223/X53J17","usgsCitation":"Warwick, P., Reedy, R.C., and Scanlon, B.R., 2025, Regional characterization of coal resources in the U.S. Gulf Coast: EarthArXiv, preprint posted December 19, 2025, https://doi.org/10.31223/X53J17.","productDescription":"31 p.","ipdsId":"IP-179450","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":498314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":205928,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":953206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reedy, Robert C. 0009-0007-4810-7578","orcid":"https://orcid.org/0009-0007-4810-7578","contributorId":364779,"corporation":false,"usgs":false,"family":"Reedy","given":"Robert","middleInitial":"C.","affiliations":[{"id":86975,"text":"The Universality of Texas at Austin, Bureau of Economic Geology","active":true,"usgs":false}],"preferred":false,"id":953207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scanlon, Bridget R. 0000-0002-1234-4199","orcid":"https://orcid.org/0000-0002-1234-4199","contributorId":328586,"corporation":false,"usgs":false,"family":"Scanlon","given":"Bridget","email":"","middleInitial":"R.","affiliations":[{"id":78414,"text":"Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, J.J. 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We use a database of individual size measurements for US stream fishes to test for direct and indirect effects of traits, flow regimes, and land use on the interspecific ISD exponent. Path analysis indicates that traits have strong, direct effects on ISD. Flow and land use effects on the exponent are largely indirectly mediated by their influences on species traits. ISD exponents increase (abundances of larger-bodied individuals increase, relative to smaller-bodied) when environments favor higher trophic levels, warmer thermal tolerances, and periodic life histories. Alternatively, ISD exponents decrease in systems that favor opportunistic life histories. 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growth","interactions":[],"lastModifiedDate":"2026-01-30T16:50:39.742874","indexId":"70273795","displayToPublicDate":"2025-12-18T09:42:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Fungi, fire, and feedbacks: Grasses and wildfire interact to alter ectomycorrhizal fungal communities and decrease tree seedling growth","docAbstract":"Wildfire and its effects, including changes to soil biota and the introduction of invasive or seeded grasses, can cause long-term shifts in ecological communities. Post-wildfire establishment of long-lived trees and shrubs is a critical bottleneck to recovering native plant communities. Ectomycorrhizal fungi (EMF) can improve plant responses to stressors and influence seedling establishment following wildfire, but little is known about how introduced grasses alter plant-fungal relationships and influence woody plant recovery. We investigated how piñon pine (Pinus edulis) EMF colonization and growth responded to soil wildfire history and novel grasses. Piñon seedlings were grown in soils from areas that burned in a stand-replacing fire nearly two decades prior or in soils from unburned piñon-juniper woodlands. Each piñon was grown with an invasive grass (Bromus tectorum), a native rhizomatous grass (Pascopyrum smithii) or another piñon seedling. Even ∼20 years after fire, EMF community composition in burned areas differed from that of unburned woodlands. Fire history and plant neighbor identity interacted to affect EMF abundance. Piñon seedling biomass was positively associated with EMF abundance in unburned woodland soils, but not in post-burn soils, suggesting that the EMF community in unburned woodlands is more beneficial. Importantly, the presence of either an invasive or native grass had a negative effect on seedling growth and EMF abundance, resulting in an average 61.4 % drop in EMF abundance and altered EMF community composition. Our findings suggest that plant species interactions, long-term effects of fire on soil, and EMF may determine the trajectory of woodland recovery following wildfire.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2025.123197","usgsCitation":"Trimber, G., Reed, S.C., Bradford, J., Lauria, C.M., Spector, T., Rondeau, R., Phillips, M.L., and Gehring, C., 2025, Fungi, fire, and feedbacks: Grasses and wildfire interact to alter ectomycorrhizal fungal communities and decrease tree seedling growth: Forest Ecology and Management, v. 603, 123197, 11 p., https://doi.org/10.1016/j.foreco.2025.123197.","productDescription":"123197, 11 p.","ipdsId":"IP-179643","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Mesa Verde National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.5789541418332,\n 37.33103870231841\n ],\n [\n -108.5789541418332,\n 37.15677700278685\n ],\n [\n -108.27805784539187,\n 37.15677700278685\n ],\n [\n -108.27805784539187,\n 37.33103870231841\n ],\n [\n -108.5789541418332,\n 37.33103870231841\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"603","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Trimber, G.M.","contributorId":365810,"corporation":false,"usgs":false,"family":"Trimber","given":"G.M.","affiliations":[{"id":87225,"text":"Center for Adaptable Western Landscapes, Campus Box 6077, Northern Arizona University, Flagstaff, Arizona; 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We used camera traps and an occupancy modeling framework to identify factors influencing bear detection and space-use patterns. We used noninvasive genetic sampling techniques to evaluate genetic diversity, population structure, and bear abundance in the region. During the summers of 2022–2023, we deployed cameras at 160 sites across western Oklahoma (USA) and detected ≥1 bear at 20 sites. The most-supported model from our single-season single-species analysis indicated that bear detection was positively associated with temperature and precipitation, negatively associated with day of year, and differed between years. The most-supported model indicated that bear space use was negatively associated with elevation (β = −0.013, 85% CI = −0.025, 0.000), and positively associated with slope (β = 0.645, 85% CI = 0.305, 0.984) and coarse woody debris counts (β = 1.539, 85% CI = 0.314, 2.765). We deployed 41 hair snares in Oklahoma resulting in the collection of 153 hair samples and received 69 tissue samples from black bears harvested in northeastern New Mexico. Using 11 microsatellite markers, we identified 21 (12M:9F) bears in western Oklahoma, and 69 (40M:29F) in New Mexico. We found evidence that bears occurring in Oklahoma were an extension of a previously documented population that occurred in northcentral New Mexico. We detected significant population-level heterozygote deficiency (P = 0.013) compared to expectations under Hardy-Weinberg equilibrium. Using capture with replacement models, we estimated 26 (95% CI = 19–43) bears in western Oklahoma during 2022–2023. Our results provide baseline data on population distribution, abundance, and genetic health of bears in the region and identify factors that may drive human-bear conflicts as the bear population increases in western Oklahoma.
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and growing!","interactions":[],"lastModifiedDate":"2026-02-09T14:22:07.263598","indexId":"70273849","displayToPublicDate":"2025-12-18T08:34:11","publicationYear":"2025","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":18358,"text":"Flow Photo Explorer","active":true,"publicationSubtype":{"id":30}},"title":"USGS Flow Photo Explorer is still going and growing!","docAbstract":"The Flow Photo Explorer (FPE) platform continues to grow rapidly as a national resource for using imagery to monitor environmental conditions. As of early December 2025, FPE now supports more than 350 users, operating across more than 600 monitoring sites. The database has expanded to over 12 million images, 800,000 annotations, and approximately 160 trained models, reflecting accelerating engagement from federal, state, tribal, academic and nonprofit partners.
Please see two critical updates below. Thank you for your continued support and contributions, we’re looking forward to many exciting improvements in the year to come!
The Mississippi Sound, an estuarine environment located between the mainland and barrier islands bordering the northern Gulf of America (formerly the Gulf of Mexico), serves as a vital ecosystem for the States of Mississippi and Alabama. Spanning approximately 100 kilometers from east to west and covering 1,400 square kilometers, the sound is home to marine industry and ports, and its shallow and brackish waters sustain a diverse array of marine life. Barrier islands along the southern edge of the sound separate the microtidal estuary from the Gulf of America. This protection from gulf wave action mediates current flow within the sound, resulting in predominantly fine-grained sediment deposition along the seafloor. This study, conducted by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers, provides insight on fluvial and tidal processes spanning the past 5,000 years. The report synthesizes existing research to provide a comprehensive overview of the sound geology, from Pleistocene origins to present-day morphology, and utilizes high-resolution single channel seismic profiles and sediment data to identify and map sedimentary deposits and morphologic features at and below the seafloor. Despite its ecological significance, the Mississippi Sound faces environmental challenges, including water-quality issues, habitat degradation, storm-induced erosion, and the ongoing threats of sea-level rise and environmental changes. This study uses the present-day understanding of the sound's geology to inform coastal management decisions, hazard assessment, and potential mineral resources.
Contact Us- USGS Publications Warehouse
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
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St. Petersburg, FL 33701
Blue Mesa Reservoir, in the Curecanti National Recreation Area, is the largest storage reservoir in Colorado and consists of three distinct basins: Iola (the shallowest), Cebolla, and Sapinero. After algal toxins were first documented in Iola basin in 2018, the U.S. Geological Survey began a study in cooperation with the National Park Service, Colorado River Water Conservation District, Upper Gunnison River Water Conservancy District, Gunnison County, Project 7 Water Authority, and Uncompahgre Valley Water Users Association to better understand occurrence of toxic cyanobacteria harmful algal blooms (cyanoHABs) and identify possible causal mechanisms to potentially inform management strategies.
Toxic cyanoHABS occurred when the algal toxin microcystin exceeded a concentration of 8 micrograms per liter primarily in Iola basin in 2018 and 2020–22, years having some of the lowest reservoir water-level elevations (reservoir levels) since 1984. The toxic cyanoHABs started in mid-September and continued through the fall months. Algal abundance was greatest in Iola basin compared to Cebolla and Sapinero basins, with Aphanizomenon, a toxin-producing cyanobacterium, being the most abundant. During blooms, enhanced algal photosynthesis caused elevated pH and dissolved oxygen concentrations especially in Iola basin. Continuous monitor data in Iola basin indicated peaks in phycocyanin fluorescence, pH, and dissolved oxygen concentration that preceded the onset of toxic cyanoHABs by about 2 weeks potentially indicating a useful early warning monitoring strategy for future response to toxic cyanoHABs. Long-term trends showed increases in mean air and surface-water temperatures and chlorophyll-a concentrations in the reservoir but no change in nutrient inputs from major tributaries. In Iola basin, reservoir level was positively correlated with Secchi disk depth and inversely correlated with total phosphorus concentration. Because of its shallow depth, the effect of low reservoir levels may disproportionately affect Iola basin compared to other basins, resulting in algal blooms and toxin production especially at reservoir levels below about 7,470 feet above North American Vertical Datum of 1988. Elevated phosphorus at low reservoir level likely was primarily phosphorus contained in algal tissue.
This report indicates that the main driver for recent toxic cyanoHABs in Iola basin is low reservoir level that likely causes favorable conditions (shallow and warm) for algal growth and increased recruitment of algae from bottom sediments such as during wind-driven turbulence. Control of external nutrients to the reservoir is unlikely to help control algal blooms because Aphanizomenon fixes nitrogen from the atmosphere, and there is an abundant geogenic source of phosphorus. Maintenance of reservoir levels greater than about 7,470 feet might help minimize the occurrence of toxic cyanoHABs. Additional data could help better understand how the timing and duration of reservoir levels below 7,470 feet contribute to toxic cyanoHABs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255109","collaboration":"Prepared in cooperation with the National Park Service, Colorado River Water Conservation District, Upper Gunnison River Water Conservancy District, Gunnison County, Project 7 Water Authority, and Uncompahgre Valley Water Users Association","usgsCitation":"Walton-Day, K., Day, N.K., Mast, M.A., Gidley, R.G., Gohring, E.J., King, T.V., Day, W.C., Gibney, N.D., and Bauch, N.J., 2025, Environmental characterization of Blue Mesa Reservoir and potential causes of and management strategies for harmful algal blooms, 1970 through 2023, Curecanti National Recreation Area, Colorado: U.S. Geological Survey Scientific Investigations Report 2025–5109, 64 p., https://doi.org/10.3133/sir20255109.","productDescription":"Report: ix, 64 p.; 8 Linked Appendix Tables; Data Release; Dataset","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-175517","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":497594,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5109/sir20255109.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5109"},{"id":497593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5109/coverthb.jpg"},{"id":497595,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5109/sir20255109.XML"},{"id":497597,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5109/images"},{"id":497596,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255109/full"},{"id":497601,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14VZMGQ","text":"USGS data release","linkHelpText":"Phytoplankton, algal toxin, and water-quality data for Blue Mesa Reservoir, Colorado, 1970–2023"},{"id":497600,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":497599,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2025/5109/downloads/sir20255109_appendix2_tables.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Tables 2.1 to 2.3"},{"id":497598,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2025/5109/downloads/sir20255109_appendix1_tables.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Tables 1.1 to 1.5"}],"country":"United States","state":"Colorado","otherGeospatial":"Blue Mesa Reservoir, Curecanti National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.33792466715349,\n 38.52947110278791\n ],\n [\n -107.33792466715349,\n 38.44323521066457\n ],\n [\n -107.054272974311,\n 38.44323521066457\n ],\n [\n -107.054272974311,\n 38.52947110278791\n ],\n [\n -107.33792466715349,\n 38.52947110278791\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Blue Mesa Reservoir (Blue Mesa), in the Curecanti National Recreation Area, is the largest storage reservoir in Colorado and consists of three distinct basins: Iola (the shallowest), Cebolla, and Sapinero. After algal toxins were first documented in Iola basin in 2018, the U.S. Geological Survey began a study to better understand occurrence of toxic harmful algal blooms (HABs) and identify possible causal mechanisms to potentially inform management strategies. Harmful algal blooms occurred in Blue Mesa when concentration of a toxic substance produced by dying algae was greater than health advisory levels, prompting no contact warnings for humans and their pets in Blue Mesa. This condition occurred starting in September and lasted as late as early November in Iola basin in 2018 and 2020–22. These years had some of the lowest recorded reservoir water-level elevations since 1984. Iola basin had the greatest amount of algae compared to Cebolla and Sapinero Basins, and a type of algae that could produce toxins was the most abundant. Multiple climate and water-quality indicators were examined in the reservoir and its tributaries to determine the causes of toxic HABs in Blue Mesa. The results indicate that the main cause for recent toxic HABs in Iola basin may be low reservoir level that likely causes favorable conditions (shallow and warm) for algal growth and increased release of algae from bottom sediments, for example, during wind-driven turbulence. Control of external nutrients to the reservoir is unlikely to help control algal blooms because the toxin-producing algae can use nitrogen from the atmosphere, and there are abundant geologic sources of phosphorus providing that nutrient to Blue Mesa. Maintenance of reservoir water-level elevation greater than about 7,470 feet might help minimize the occurrence of toxic HABs in Blue Mesa.
","publicationDate":"2025-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Walton-Day, Katherine 0000-0002-9146-6193","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":336569,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":952453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mast, M. Alisa 0000-0001-6253-8162","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":211054,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952454,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gidley, Rachel G. 0000-0002-9840-8252","orcid":"https://orcid.org/0000-0002-9840-8252","contributorId":259315,"corporation":false,"usgs":true,"family":"Gidley","given":"Rachel","email":"","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952455,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gohring, Evan J. 0000-0002-2229-9512","orcid":"https://orcid.org/0000-0002-2229-9512","contributorId":315496,"corporation":false,"usgs":true,"family":"Gohring","given":"Evan","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952456,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952457,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Day, Warren C. 0000-0002-9278-2120 wday@usgs.gov","orcid":"https://orcid.org/0000-0002-9278-2120","contributorId":1308,"corporation":false,"usgs":true,"family":"Day","given":"Warren","email":"wday@usgs.gov","middleInitial":"C.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":952458,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gibney, Nicole D.","contributorId":364297,"corporation":false,"usgs":false,"family":"Gibney","given":"Nicole","middleInitial":"D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":952459,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bauch, Nancy J. 0000-0002-0302-2892","orcid":"https://orcid.org/0000-0002-0302-2892","contributorId":364298,"corporation":false,"usgs":false,"family":"Bauch","given":"Nancy","middleInitial":"J.","affiliations":[{"id":12443,"text":"U.S. Geological Survey (retired)","active":true,"usgs":false}],"preferred":false,"id":952460,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70273088,"text":"fs20253054 - 2025 - Assessment of undiscovered oil and gas resources in the Haynesville Formation within the onshore United States and State waters of the Gulf Coast Basin, 2024","interactions":[],"lastModifiedDate":"2026-02-03T16:57:11.180664","indexId":"fs20253054","displayToPublicDate":"2025-12-17T11:55:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3054","displayTitle":"Assessment of Undiscovered Oil and Gas Resources in the Haynesville Formation Within the Onshore United States and State Waters of the Gulf Coast Basin, 2024","title":"Assessment of undiscovered oil and gas resources in the Haynesville Formation within the onshore United States and State waters of the Gulf Coast Basin, 2024","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 152 million barrels of oil and 47.9 trillion cubic feet of gas in reservoirs of the Haynesville Formation within the onshore United States and State waters of the Gulf Coast Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253054","usgsCitation":"Gardner, R., Flaum, J.A., Birdwell, J.E., Kinney, S.A., Pitman, J.K., Paxton, S.T., French, K.L., Mercier, T.J., Leathers-Miller, H.M., and Schenk, C.J., 2025, Assessment of undiscovered oil and gas resources in the Haynesville Formation within the onshore United States and State waters of the Gulf Coast Basin, 2024: U.S. Geological Survey Fact Sheet 2025–3054, 4 p., https://doi.org/10.3133/fs20253054.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170936","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":497815,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119056.htm"},{"id":497508,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1AQ879T","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Gulf Coast Mesozoic Province, Haynesville Formation—Assessment Unit Boundaries, Assessment Input Tables, and Fact Sheet Data Tables"},{"id":497639,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3054/images/"},{"id":497638,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3054/fs20253054.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2025-3054 XML"},{"id":497637,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253054/full","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3054 HTML"},{"id":497506,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3054/coverthb.jpg"},{"id":497507,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3054/fs20253054.pdf","text":"Report","size":"2.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3054 PDF"}],"country":"United States","state":"Alabama, Arkansas, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102,\n 34\n ],\n [\n -102,\n 28\n ],\n [\n -85.5,\n 28\n ],\n [\n -85.5,\n 34\n ],\n [\n -102,\n 34\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
Urban runoff containing high amounts of nutrients like phosphorus (P) is a well-established driver of surface water eutrophication. In residential areas, a primary source of nutrients is derived from leaf litter. P contained in leaves is leached and transported by stormwater from source to stream. The majority of P leached from leaf litter is in the dissolved phase, which can be difficult to remove using conventional treatment practices, leaving source control as the most viable option. Additional tools are needed to help forecast how different tree species may improve or hinder contributions of nutrients to runoff. For this reason, ten street tree species that are common throughout the contiguous U.S. were chosen to evaluate the effect of species on leachable P from tree leaves using laboratory experiments. After 48 h of exposure to water, the amount of P released ranged from 2.16 mg P g−1 leaf for Silver Maple to 0.03 mg P g−1 leaf for Hackberry. More than half of the P was lost in the first 12 h for eight of the ten tree species, making guided source control important to reduce inputs to surface water from key locations. Results were used to identify ‘hotspots’ of P leaching in Madison, WI and can be used to assess current street tree inventories that can then guide management to areas with the highest nutrient reduction potential and inform urban foresters who may wish to tailor future planting scenarios that minimize nutrients in runoff.
","language":"English","publisher":"Springer","doi":"10.1007/s11270-025-08858-3","collaboration":"U.S. Forest Service, University of Wisconsin-Madison","usgsCitation":"Collin Klaubauf, Anita Thompson, Selbig, W.R., and Laxmir Prasad, 2025, Quantifying leachable phosphorus from the leaves of common midwest urban street trees and implications for stormwater management: Water, Air, & Soil Pollution, v. 237, 269, 19 p., https://doi.org/10.1007/s11270-025-08858-3.","productDescription":"269, 19 p.","ipdsId":"IP-174216","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11270-025-08858-3","text":"Publisher Index Page"},{"id":497680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Madison","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.18404405740438,\n 43.22776059386794\n ],\n [\n -89.64470062715654,\n 43.22776059386794\n ],\n [\n -89.64470062715654,\n 42.95864817099735\n ],\n [\n -89.18404405740438,\n 42.95864817099735\n ],\n [\n -89.18404405740438,\n 43.22776059386794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"237","noUsgsAuthors":false,"publicationDate":"2025-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Collin Klaubauf","contributorId":364361,"corporation":false,"usgs":false,"family":"Collin Klaubauf","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":952590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anita Thompson","contributorId":364362,"corporation":false,"usgs":false,"family":"Anita Thompson","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":952591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laxmir Prasad","contributorId":364364,"corporation":false,"usgs":false,"family":"Laxmir Prasad","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":952593,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273834,"text":"70273834 - 2025 - Virulence evolution of a salmonid virus following a host jump","interactions":[],"lastModifiedDate":"2026-02-05T15:19:25.582457","indexId":"70273834","displayToPublicDate":"2025-12-17T08:08:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2981,"text":"PLoS Pathogens","active":true,"publicationSubtype":{"id":10}},"title":"Virulence evolution of a salmonid virus following a host jump","docAbstract":"Emergent viral diseases remain a critical obstacle to welfare across landscapes and species, encompassing humans, wildlife, and agriculture. Following a jump to a novel host, the severity of disease resulting from infection is a critical determinant of the overall emergent pathogen threat. Conventional wisdom posits that virulence, defined here as host mortality, attenuates to intermediate levels as a pathogen adapts to a novel host, but this is largely based on data from just one system, myxoma virus, which was intentionally introduced as a biocontrol agent in rabbits (Oryctolagus cuniculus) in mid-1900s Australia. In this study, we demonstrate that infectious hematopoietic necrosis virus (IHNV), which made a host jump from sockeye salmon (Oncorhynchus nerka, ancestral host) to rainbow trout (O. mykiss, novel host), has not conformed to classical theory. We quantified virulence in the ancestral and novel hosts using common garden in vivo experiments with 16 archival IHNV isolates collected from 1972-2017, which span the period from shortly after the host jump and the subsequent 45 years of host adaptation. These virus isolates also represent two distinct phylogenetic genogroups, each associated with either the ancestral or novel host. The experiments were replicated across two research facilities, two challenges dosages, and two temperatures. While isolates from the ancestral genogroup showed no temporal change in virulence in either host, isolates from the novel viral genogroup displayed a significant increase in virulence over time in the novel host. Some possible indication of a virus temperature adaption after the host jump was also present. Potential drivers of virulence evolution are discussed. This represents one of only a handful of systems in which the evolution of increased virulence has been empirically characterized after a host jump and subsequent adaptation. It contributes to a growing body of evidence that contradicts the classical case study of myxoma virus attenuation after adaptation.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.ppat.1013806","usgsCitation":"Loeher, M.M., Kurath, G., Kennedy, D.A., Salzer, J.E., Batts, W.N., Breyta, R.B., and Wargo, A.R., 2025, Virulence evolution of a salmonid virus following a host jump: PLoS Pathogens, v. 21, no. 12, e1013806, 21 p., https://doi.org/10.1371/journal.ppat.1013806.","productDescription":"e1013806, 21 p.","ipdsId":"IP-176452","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.ppat.1013806","text":"Publisher Index Page"},{"id":499580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Loeher, Malina Mariko 0000-0001-9589-5641","orcid":"https://orcid.org/0000-0001-9589-5641","contributorId":365991,"corporation":false,"usgs":true,"family":"Loeher","given":"Malina","middleInitial":"Mariko","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":955121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurath, Gael 0000-0003-3294-560X","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":220175,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":955122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, David A.","contributorId":365992,"corporation":false,"usgs":false,"family":"Kennedy","given":"David","middleInitial":"A.","affiliations":[{"id":87303,"text":"The Pennsylvania State University, University Park, PA 16802, USA","active":true,"usgs":false}],"preferred":false,"id":955123,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Salzer, Joanne E. 0000-0002-6235-2779","orcid":"https://orcid.org/0000-0002-6235-2779","contributorId":345081,"corporation":false,"usgs":false,"family":"Salzer","given":"Joanne","middleInitial":"E.","affiliations":[{"id":82486,"text":"Formerly USGS, Western Fisheries Research Center","active":true,"usgs":false}],"preferred":false,"id":955124,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Batts, William N. 0000-0002-6469-9004 bbatts@usgs.gov","orcid":"https://orcid.org/0000-0002-6469-9004","contributorId":3815,"corporation":false,"usgs":true,"family":"Batts","given":"William","email":"bbatts@usgs.gov","middleInitial":"N.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":955125,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breyta, Rachel B. 0000-0002-9106-1014","orcid":"https://orcid.org/0000-0002-9106-1014","contributorId":365995,"corporation":false,"usgs":false,"family":"Breyta","given":"Rachel","middleInitial":"B.","affiliations":[{"id":87304,"text":"University of Washington, Seattle, WA 98195, USA","active":true,"usgs":false}],"preferred":false,"id":955126,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wargo, Andrew R.","contributorId":365996,"corporation":false,"usgs":false,"family":"Wargo","given":"Andrew","middleInitial":"R.","affiliations":[{"id":87305,"text":"Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA 23062, USA","active":true,"usgs":false}],"preferred":false,"id":955127,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273668,"text":"70273668 - 2025 - Dynamic risk from Mexican wolves and mountain lions influences elk foraging behavior","interactions":[],"lastModifiedDate":"2026-01-22T15:13:42.304546","indexId":"70273668","displayToPublicDate":"2025-12-17T08:08:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic risk from Mexican wolves and mountain lions influences elk foraging behavior","docAbstract":"Foraging time is a major component of ungulate activity budgets but can be limited by anti-predator behaviors (e.g., vigilance). Multitasking can reduce the nutritional costs of vigilance under heightened predation risk, but this may depend on the response of prey to risk from multiple predators across a complex spatiotemporal landscape. Mexican gray wolves (Canis lupus baileyi) and mountain lions (Puma concolor) are primary predators for elk (Cervus canadensis) in the Mexican wolf experimental population area in east-central Arizona and west-central New Mexico. We observed elk foraging across varying levels of wolf risk throughout all seasons and diel periods to quantify proportions of foraging, intense vigilance, and multitasking at the individual and herd levels. We quantified encounter and kill risk from Mexican wolves and mountain lions using habitat selection functions and utilization distributions. We modeled elk behaviors as functions of predicted risk for both predators in addition to temporal and environmental covariates and accounted for human presence. Our results indicate that individual elk reduced foraging in areas with higher predicted risk from Mexican wolves or mountain lions and increased intense vigilance and multitasking in areas with higher wolf risk. A reduction in the proportion of bedded elk in the herd during all diel periods under increased wolf risk supports previous findings. These results also suggest that elk compensate for higher intense vigilance and reduced foraging during foraging bouts by increasing cumulative foraging bouts per day at the cost of resting. Additionally, the probability of multitasking for individuals depended on an interaction between short- and long-term wolf risk, and the likelihood of intense vigilance was highest under the greatest combined spatial and temporal risk from wolves. This research provides insight into the fine-scale and complex behavioral responses of elk to their primary predators and implies a need for researchers to consider these non-consumptive effects in future studies of predator–prey dynamics.
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Land-use changes, such as urban development and increased activities in certain agricultural sectors, have degraded water quality and altered conditions in these streams, thereby affecting their health and function. The U.S. Geological Survey (USGS) is working with Federal, State, and local partners to develop modeled assessments of stream health in freshwater streams and rivers within the Chesapeake Bay watershed. The USGS compiled large datasets for multiple stream health indicators, including instream stressors (salinity, water temperature, physical habitat, and streambank erosion) and living resources (macroinvertebrates and fish communities; fig. 1). These datasets were used by USGS scientists to develop models to predict stream health conditions across the entire region, including areas with little or no monitoring data. 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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253056","issn":"2327-6932","isbn":"2327-6916","usgsCitation":"Maloney, K.O., Fanelli, R.M., Cashman, M.J., Boyle, L.J., Gordon, S.E., Gressler, B.P., Katoski, M.P., Kiser, A.H., Metes, M.J., Noe, G.B., Sekellick, A.J., Sussman, A., and Young, J.A., 2025, Assessing streams in the Chesapeake Bay Watershed to guide conservation and restoration activities: U.S. Geological Survey Fact Sheet 2025–3056, 4 p., https://doi.org/10.3133/fs20253056.","productDescription":"3 p.","onlineOnly":"N","ipdsId":"IP-180465","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":497539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3056/fs20253056.pdf","text":"Report","size":"39.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3056 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U.S. Geological Survey
12100 Beech Forest Rd., Ste 4039
Laurel, MD 20708
An evaluation of sediment deposition rates and volume of impounded sediments in Pahranagat Wash behind Arrow Canyon dam in southeastern Nevada was done between 2016 and 2022. Data were collected and interpreted to address concerns by the Moapa Band of Paiutes and local historical preservation groups regarding the burial of culturally important sites by the impounded sediment deposited behind the dam. Sediment cores from two wells, drilled to depths that reflect the original stream-channel profile, and a third site drilled and sampled at a finer resolution, were analyzed radiometrically for lead-210 (210Pb) and cesium-137 (137Cs) isotopes. The analysis of the 210Pb data yielded an overall estimated sediment deposition rate of 2.4 inches per year (in/yr). Using the 137Cs data, the sediment deposition rate declined from 9.4 in/yr from 1934 to 1951 to 4.2 in/yr from 1951 to 1964 to 1.0 in/yr between 1964 and 2019.
Sediment volume was determined by defining boundaries using a 1-foot contour map generated using Unmanned Aerial Survey datasets and field observations. The volume calculation involved segmenting the study area based on the availability of sediment thickness data. The average sediment thickness in each segment was multiplied by the surface area of each segment to obtain the total sediment volume of 4.3×107 cubic feet.
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U.S. Geological Survey
2730 N. Deer Run Road, Suite 3
Carson City, Nevada 89701
U.S. Geological Survey (USGS) Mission—The USGS national mission is to monitor, analyze, and predict the current and evolving dynamics of complex human and natural Earth-system interactions and to deliver actionable information at scales and timeframes relevant to decision makers. Consistent with the national mission, the USGS in Alaska provides timely and objective scientific information to help address issues and inform management decisions across five interconnected focus areas:
The USGS in Alaska consists of approximately 350 scientists and support staff working in 3 Alaska-based science centers. USGS science activities are also initiated by the Cooperative Research Unit and USGS centers outside Alaska. In the last 5 years, USGS research in Alaska has produced many scientific benefits resulting from more than 900 publications. Publications relevant to Alaska can be conveniently searched by keyword through the USGS Publications Warehouse at https://pubs.usgs.gov/.
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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Climate change is causing a range of changes that can affect the natural, cultural, and built resources of the Nation’s protected areas and affect opportunities to visit and recreate in these spaces. Changes in temperature and precipitation patterns also affect species and habitats, leading to ecological transformation. This report describes findings from pilot research conducted in Capitol Reef National Park, Utah (hereinafter referred to as “Capitol Reef” or “the Park”) as part of a larger interagency study of how National Park Service (NPS) staff are considering management of transforming ecosystems.
Semi-structured interviews were used to assess how Capitol Reef employees (n=9) understand the challenge of ecological transformation, including their perceptions of how climate change is affecting the Park’s natural and cultural resources, the multiple timeframes over which employees respond to climate change impacts, and their awareness and understanding of ecological transformation and the Resist-Accept-Direct (RAD) framework, which was developed to address ecological transformation, that is, ecosystems changing in response to changes in climate conditions (Schuurman and others, 2020, 2022). The interviews also solicited employee perceptions about constraints and enabling factors that allow Capitol Reef to effectively respond to ecological transformation. The report uses a conceptual framework that has been used by the National Park Service Climate Change Response Program (Clifford and others, 2022) to structure the reporting of the data about constraints and enabling factors, with sections describing the role of factors internal to an individual employee (culture, worldviews, and understanding of an ecological system) and contextual factors external to an individual (institutional context, social feasibility and scientific uncertainty as influenced by available scientific information).
Participating Capitol Reef staff perceived the most pressing climate impacts in the Park as increasing air temperatures, aridity, and flash floods, which are impacting natural and cultural resources, public safety, and infrastructure. Staff mostly agreed on what the future landscape (approximately 50 years into the future) may look like at Capitol Reef in terms of changes in vegetation and future temperature and precipitation conditions. However, staff had more divergent views or were uncertain about how specific species will adapt to future conditions (for example, how endemic plants might shift their ranges) and are grappling with which management strategies to take at which times. Staff also had differing opinions about how much data is needed to prompt action.
Interviewees agreed that leadership in the Park had made climate change a priority and created a climate-attuned culture among staff. Participating employees described the park culture at Capitol Reef as collaborative, with frequent communication and work across divisions, which, as an example, shapes responses to flash floods and other events (for example, working across divisions on search and rescue, or repairing fencing that is washed out by storms). This collaborative, climate-attuned culture may help Capitol Reef in future problem-solving as it grapples with how to respond to climate change and ecological transformation in the Park.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255103","programNote":"Prepared in cooperation with the National Park Service","usgsCitation":"Cravens, A.E., Hough Solomon, Z.B., Goolsby, J.B., Yocum, H.M., Tangen, S., and Carr, W., 2025, Responding to ecological transformation in Capitol Reef National Park, Utah—Employee perspectives from pilot interviews from\nthe Cross-Park Resist-Assist-Direct Project: U.S. Geological Survey Scientific Investigations Report 2025–5103, 23 p., https://doi.org/10.3133/sir20255103.","productDescription":"vi, 23 p.","onlineOnly":"Y","ipdsId":"IP-169526","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":497540,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5103/coverthb.jpg"},{"id":497541,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5103/sir20255103.pdf","text":"Report","description":"SIR 2025-5103"},{"id":497542,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255103/full","text":"Report","description":"SIR 2025-5103"},{"id":497544,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5103/sir20255103.XML"},{"id":497543,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5103/images"}],"country":"United States","state":"Utah","otherGeospatial":"Capitol Reef National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.4206935237935,\n 38.54694376656991\n ],\n [\n -111.41458665991418,\n 38.30470300316845\n ],\n [\n -111.23387900601416,\n 38.11831643948847\n ],\n [\n -111.12423304089816,\n 37.865524138177946\n ],\n [\n -110.92992373562915,\n 37.581668900982805\n ],\n [\n -110.83131704965363,\n 37.57462853840336\n ],\n [\n -111.1932875268971,\n 38.52436070427342\n ],\n [\n -111.4206935237935,\n 38.54694376656991\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
3200 SW Jefferson Way
Corvallis, OR 97331
Emplacement of the Siberian Traps large igneous province (LIP) around 252 Ma coincided with the most profound environmental disruption of the past 500 million years. The enormous volume of the Siberian Traps, its ability to generate greenhouse gases and other volatiles, and a temporal coincidence with extinction all suggest a causal link. Patterns of marine and terrestrial extinction/recovery are consistent with environmental stresses potentially triggered by the Siberian Traps. However, the nature of causal links between the LIP and mass extinction remains enigmatic. Understanding the origins, anatomy, and forcing potential of the Siberian Traps LIP and the spatiotemporal patterns of resulting stresses represents a critical counterpart to high-resolution fossil and proxy records of Permian–Triassic environmental and biotic shifts. This review provides a summary of recent advances and key questions regarding the Siberian Traps in an effort to illuminate what combination of factors made the Siberian Traps a uniquely deadly LIP.
Cyanobacterial toxicity, cyanotoxins, and their impact on aquatic ecosystems and human health are well documented. In comparison, less is known about bloom-associated bacterial communities. Co-occurring bacteria can influence bloom development, physiology and collapse, and may also provide a niche for pathogenic bacteria. Existing research focuses on the cyanosphere of Microcystis-dominated blooms, despite the increasing prevalence of filamentous genera (Aphanizomenon and Planktothrix). This pilot study aimed to broaden our understanding of the bacterial consortia attached to morphologically distinct cyanobacteria (coccoid and filamentous) dominating phytoplankton communities and to explore their potential roles in amplifying the impacts of cyanobacterial blooms. We investigated four shallow freshwater bodies across three continents and two climate zones: an urban pond in the USA, a dammed reservoir and a natural lake in Poland, and an urban water body in Singapore. Amplicon sequencing (16S rRNA gene) was used to characterize bacterial communities, while shotgun metagenomics identified nitrogen- and phosphorus-cycling genes to infer potential eco-physiological functions. Cyanobacteria dominated bacterioplankton assemblages at all sites (>35.6%), with bloom composition influencing toxigenic profiles. A mixed bloom of Microcystis, Snowella, and Aphanizomenon had the broadest range of cyanotoxin synthetase genes (mcyE, cyrJ, anaF and sxtA). Microcystis blooms correlated with increased Roseomonas, while Planktothrix co-occurred with Flavobacterium – both bacteria likely contribute to nutrient-cycling within blooms and represent potential opportunistic pathogens for aquatic organisms and humans. The Microcystis cyanosphere exhibited the highest number of significant positive correlations with bacteria (19 relations), compared to Planktothrix and Aphanizomenon (11 and 2 relations, respectively). Non-diazotrophic blooms of Microcystis and Planktothrix showed greater abundances of nitrogen – (ureB, glnA, narB, and narHZ) and phosphorus-cycling genes (phoBHPR and ppk1), indicating a strong dependence on associated bacteria for nutrient acquisition compared to diazotrophic Aphanizomenon. These findings suggest that Aphanizomenon-dominated blooms may be sustained by simpler microbiomes. Our results provide preliminary evidence of cyanosphere heterogeneity potentially shaped by the dominance or coexistence of three morphologically and eco-physiologically distinct genera of cyanobacteria. A comprehensive knowledge of the taxonomy and functional roles of bloom-associated microbiomes is therefore essential to understand bloom activity, evaluate the environmental threat, and develop effective strategies for prevention and mitigation.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2025.1655370","usgsCitation":"Mankiewicz-Boczek, J., Font Nájera, A., Gin, K.Y., Graham, J.L., Strapagiel, D., Gorney, R.M., Kok, J.W., Te, S.H., Kluska, M., Skóra, M., Seweryn, M., and Hun, F.J., 2025, Bacterial community diversity and potential eco-physiological roles in toxigenic blooms composed of Microcystis, Aphanizomenon or Planktothrix: Frontiers in Microbiology, v. 16, 1655370, 15 p., https://doi.org/10.3389/fmicb.2025.1655370.","productDescription":"1655370, 15 p.","ipdsId":"IP-179083","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497747,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2025.1655370","text":"Publisher Index 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1987 to 2017","interactions":[],"lastModifiedDate":"2026-01-07T17:45:08.410713","indexId":"70273185","displayToPublicDate":"2025-12-15T10:49:44","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":"The US EPA’s National Nutrient Inventory: Critical shifts in US nutrient pollution sources from 1987 to 2017","docAbstract":"Efforts to constrain the negative environmental impacts of excess nitrogen (N) and phosphorus (P) are costly and challenging, due in part to inconsistent reporting of nutrient sources at temporal and spatial scales relevant for local decision making. To meet this challenge, the U.S. Environmental Protection Agency’s National Nutrient Inventory provides estimates of major agricultural, urban, atmospheric, and natural nutrient fluxes for the contiguous United States at county and HUC12 scales annually from 1987 (from 1950 for agriculture) to 2017. Since the late 1980s, total N emissions and atmospheric N deposition have declined 22% and 15%, respectively, despite increased agricultural emissions. Over the same period, municipal wastewater N and P loads remained largely stable, despite population increases, through wastewater treatment upgrades and the phaseout of phosphorus-containing detergents. Improved agricultural efficiency allowed for dramatic increases in agricultural production and crop harvest since 1987 (∼25% for N and P), with little change in surplus nutrients left on fields. Overall, a combination of innovative technologies and management has stemmed or even decreased major sources of nutrient pollution to the environment over the last several decades, representing an important shift that, if continued, may contribute to improved air, land, and water quality and human health.
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0000-0001-8713-7699","orcid":"https://orcid.org/0000-0001-8713-7699","contributorId":178226,"corporation":false,"usgs":false,"family":"Sabo","given":"Robert","email":"","middleInitial":"D.","affiliations":[{"id":13479,"text":"University of Maryland Center for Environmental Science, Appalachian Laboratory, 301 Braddock Road, Frostburg, Maryland","active":true,"usgs":false}],"preferred":false,"id":952647,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70274638,"text":"70274638 - 2025 - Postglacial eruptive history of Laguna del Maule volcanic field and constraints on its magmatic system","interactions":[],"lastModifiedDate":"2026-04-02T17:42:17.826154","indexId":"70274638","displayToPublicDate":"2025-12-15T10:33:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2462,"text":"Journal of South American Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Postglacial eruptive history of Laguna del Maule volcanic field and constraints on its magmatic system","docAbstract":"The Laguna del Maule (LdM) volcanic field has produced >100 explosive and extrusive events over the past 17 ka. More than two-thirds of these have been silicic, with most being postglacial rhyolites (72–78 % SiO2) concentrated near the eponymous lake (LdM), an extraordinary anomaly in the Quaternary Andes and unprecedented in this 1.5–Ma-old volcanic field as a whole. The postglacial field includes >70 separate vents distributed over ∼360 km2 that together produced the many distinct eruptive events, of which 55 are rhyolitic (73 %−77 % SiO2), 18 are rhyodacitic (68 %−72 % SiO2), 4 are dacitic (63 %–66 % SiO2), 26 are intermediate (54–62 % SiO2), and 2 are true basalts (50 %–53 % SiO2). Of these, most originated from single-vent domes, cones, or craters that erupted effusive and/or explosive products, each with relatively short lifespans. Some originated from multi-vent centers, the largest one being the Barrancas complex southeast of the lake, which has as many as 18 vents that erupted over as much as 10 kyr. The LdM basin itself is ringed by 13 separate silicic centers, many of which are also multi-vent and built over time by multiple explosive and extrusive events. These surround the lake, near the middle of which is the vent for the high-silica rhyolite Plinian eruption that produced the “Rhyolite of Laguna del Maule”, which was the first and largest silicic event from the postglacial field. Explosive and effusive products from all these events have been put in a time-stratigraphic framework supported by radiocarbon dating and chemical analyses to reconstruct the postglacial eruptive history. Correlations of pyroclastics to eruptive vents have provided a spatial-temporal framework that helps characterize the magmatic system beneath the LdM field. Distribution of both silicic and mafic vents support the likelihood that two separate magmatic systems produced the postglacial eruptions in the volcanic field—one in the Laguna del Maule basin and the other at the Barrancas complex—with a cluster of silicic vents at each and mafic vents situated between the two. Vent distributions, compositions of eruptive products, and temporal and spatial trends of eruptive units suggest that the abundant rhyodacitic and mafic units in the LdM basin have no common magma reservoir, but instead each had its own evolutionary trend. In contrast, there is enough affinity among some of the rhyolitic units in the Basin to imply magmatic connections and/or continuity that span both time and space, although neither geographic proximity nor temporal similarity have singular control on LdM-basin rhyolite compositions. Compositional trends through time at the Barrancas center suggest the rhyolitic eruptions at West and East Barrancas were derived from separate, zoned reservoirs that were tapped in batches, not permitting development of a large high-silica reservoir such as that beneath the LdM basin.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jsames.2025.105813","usgsCitation":"Fierstein, J., Sruoga, P., Amigo, A., Elissondo, M., and Rosas, M., 2025, Postglacial eruptive history of Laguna del Maule volcanic field and constraints on its magmatic system: Journal of South American Earth Sciences, v. 168, 105813, 40 p., https://doi.org/10.1016/j.jsames.2025.105813.","productDescription":"105813, 40 p.","ipdsId":"IP-174310","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":502019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina, Chile","otherGeospatial":"Laguna del Maule lake basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.59545505016834,\n -35.97940016009683\n ],\n [\n -70.59545505016834,\n -36.12055654053491\n ],\n [\n -70.3551914157175,\n -36.12055654053491\n ],\n [\n -70.3551914157175,\n -35.97940016009683\n ],\n [\n -70.59545505016834,\n -35.97940016009683\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"168","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fierstein, Judith 0000-0001-8024-1426 jfierstn@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-1426","contributorId":147000,"corporation":false,"usgs":true,"family":"Fierstein","given":"Judith","email":"jfierstn@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":958509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sruoga, Patricia","contributorId":369133,"corporation":false,"usgs":false,"family":"Sruoga","given":"Patricia","affiliations":[{"id":87728,"text":"SEGEMAR, Argentina","active":true,"usgs":false}],"preferred":false,"id":958510,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amigo, Alvaro","contributorId":369134,"corporation":false,"usgs":false,"family":"Amigo","given":"Alvaro","affiliations":[{"id":82689,"text":"SERNAGEOMIN, Chile","active":true,"usgs":false}],"preferred":false,"id":958511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elissondo, Manuela","contributorId":345642,"corporation":false,"usgs":false,"family":"Elissondo","given":"Manuela","email":"","affiliations":[{"id":82670,"text":"Servicio Geológico Minero Argentino (SEGEMAR), Buenos Aires, Argentina","active":true,"usgs":false}],"preferred":false,"id":958512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosas, Mario","contributorId":369135,"corporation":false,"usgs":false,"family":"Rosas","given":"Mario","affiliations":[{"id":87728,"text":"SEGEMAR, Argentina","active":true,"usgs":false}],"preferred":false,"id":958513,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271247,"text":"70271247 - 2025 - Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning","interactions":[],"lastModifiedDate":"2026-01-16T16:01:42.820062","indexId":"70271247","displayToPublicDate":"2025-12-15T09:57:00","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning","docAbstract":"Monitoring the extent of surface water features (hydrography), accurately storing them in databases, and representing them on topographic maps are essential for various applications such as navigation and policy-making for legislative boundaries and permitting. In this context, hydrographic data includes features that generally have water present or image data showing signs that water is forming a terrain channel, and which would be included in 1:24,000 or larger scale topographic maps. In addition, reliable hydrographic data play a critical role to help manage environmental risks such as droughts, floods, fires, and landslides, as well as monitoring biological resources and pollutants. Inaccuracies in hydrography data can lead to modelling inaccuracies, resulting in economic, social, and environmental risks. However, generating sufficiently accurate high-resolution (HR) hydrography and terrain data for these purposes remains a substantial challenge primarily because of complex surface water dynamics and data handling limitations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Abstracts of the International Cartographic Association","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Cartographic Association","doi":"10.5194/ica-abs-10-275-2025","usgsCitation":"Stanislawski, L., Qin, R., Liu, J., Shavers, E.J., Wang, S., Jaroenchai, N., and Thiem, P.T., 2025, Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning, in Abstracts of the International Cartographic Association, v. 7, no. 10, 275, 3 p., https://doi.org/10.5194/ica-abs-10-275-2025.","productDescription":"275, 3 p.","ipdsId":"IP-179538","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":498917,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/ica-abs-10-275-2025","text":"Publisher Index Page"},{"id":498745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"7","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":947787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qin, Rongjun","contributorId":333939,"corporation":false,"usgs":false,"family":"Qin","given":"Rongjun","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":947788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, Jung-Kuan 0000-0001-8461-8200","orcid":"https://orcid.org/0000-0001-8461-8200","contributorId":333940,"corporation":false,"usgs":true,"family":"Liu","given":"Jung-Kuan","email":"","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":947789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":947790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Shaowen","contributorId":198966,"corporation":false,"usgs":false,"family":"Wang","given":"Shaowen","email":"","affiliations":[],"preferred":false,"id":947791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jaroenchai, Nattapon","contributorId":267318,"corporation":false,"usgs":false,"family":"Jaroenchai","given":"Nattapon","email":"","affiliations":[{"id":38021,"text":"University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":947792,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":947793,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273160,"text":"70273160 - 2025 - Mitigation of human cognitive bias in volcanic eruption forecasting","interactions":[],"lastModifiedDate":"2025-12-17T15:53:26.995551","indexId":"70273160","displayToPublicDate":"2025-12-15T09:51:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3841,"text":"Journal of Applied Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Mitigation of human cognitive bias in volcanic eruption forecasting","docAbstract":"Modern operational eruption forecasting methods rely heavily on human judgment in the face of uncertainty and are thus susceptible to myriad cognitive biases and errors by the scientist-forecasters. Recent developments in the behavioral sciences have elucidated cognitive biases across a wide spectrum of human behaviors and found ways to mitigate them. These insights have led to significant gains in human forecast accuracy across a range of disciplines. However, such gains have yet to widely penetrate volcanic eruption forecasting efforts. In this study, we review recent progress in these fields as relevant to improving current eruption forecasting methods as practiced by volcano observatories worldwide. We group cognitive biases into 1) information and selection biases, 2) group effects and social-emotional biases, and 3) framing and decision biases and highlight the numerous places in which these biases may permeate eruption forecasts. We present a framework for improving group discussion and forecasting processes at volcano observatories, considering recent gains from behavioral sciences along with a mitigation checklist to effectively reduce bias in operational eruption forecasts. Finally, we present an updated forecasting methodology for use in our own group, the US Geological Survey (USGS) Volcano Disaster Assistance Program, based on previous “multiple datasets” methods, that includes clear and deliberate efforts to minimize cognitive biases and thus improve eruption forecasting accuracy.
","language":"English","publisher":"Springer","doi":"10.1186/s13617-025-00156-1","usgsCitation":"Wright, H.M., Pesicek, J., and Spiller, S.A., 2025, Mitigation of human cognitive bias in volcanic eruption forecasting: Journal of Applied Volcanology, v. 14, 9, 18 p., https://doi.org/10.1186/s13617-025-00156-1.","productDescription":"9, 18 p.","ipdsId":"IP-163451","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":497740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13617-025-00156-1","text":"Publisher Index Page"},{"id":497641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2025-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Heather M. 0000-0001-9013-507X hwright@usgs.gov","orcid":"https://orcid.org/0000-0001-9013-507X","contributorId":3949,"corporation":false,"usgs":true,"family":"Wright","given":"Heather","email":"hwright@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":952538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesicek, J. D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":72604,"corporation":false,"usgs":true,"family":"Pesicek","given":"J. D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":952539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spiller, Stephen A. 0000-0001-6951-6046","orcid":"https://orcid.org/0000-0001-6951-6046","contributorId":364333,"corporation":false,"usgs":false,"family":"Spiller","given":"Stephen","middleInitial":"A.","affiliations":[{"id":86809,"text":"UCLA Anderson School of Management, Los Angeles, CA, USA","active":true,"usgs":false}],"preferred":false,"id":952540,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273796,"text":"70273796 - 2025 - An exploration of open-system uranium-series history of marine terrace corals, Perachora Peninsula, Greece and San Nicolas Island, California, USA","interactions":[],"lastModifiedDate":"2026-01-30T16:26:36.532206","indexId":"70273796","displayToPublicDate":"2025-12-15T09:19:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"An exploration of open-system uranium-series history of marine terrace corals, Perachora Peninsula, Greece and San Nicolas Island, California, USA","docAbstract":"Emergent marine terraces record past periods of relatively high sea level and are common on uplifting coasts worldwide. Such landforms are extensive around the Gulf of Corinth area of Greece and along the coast of California, USA. In the Gulf of Corinth region, marine terraces record Quaternary uplift due to ongoing basin extension on the northern side of the Africa-Eurasia-Aegean Sea plate boundary. Although dating of terrace fossils has been extensive in the Perachora Peninsula part of this region, some apparent ages are much older (∼190 ka to ∼150 ka) and some are much younger (∼91 ka to ∼69 ka) than expected for a marine terrace that has long been hypothesized to be of last-interglacial (∼120 ka) age. New uranium-series analyses of terrace corals from two previously studied localities are reported here. As with previous work, the new results show that these corals have behaved as open systems, with back-calculated, initial 234U/238U values higher than those found in modern seawater. New and previously published U-series analyses of last-interglacial corals from San Nicolas Island, California (USA) illustrate the possible mechanisms of open-system behavior, which include both “early” alpha-recoil-derived additions of 230Th and 234U, and “continuing” alpha-recoil-derived additions of these nuclides. Many corals from the Perachora Peninsula appear to have experienced the “continuing” alpha-recoil-derived open-system history, which explains the apparent older ages, and permits an interpretation that all analyzed corals likely date to the last interglacial period, ∼120 ka. If the estimated age of ∼120 ka reported here is correct, this age, along with considerations of glacial isostatic adjustment (GIA) modeling of past sea level, indicates that the southern side of the Perachora Peninsula has experienced uplift rates ranging from ∼0.23 m/kyr to ∼0.57 m/kyr, increasing to the southeast, in agreement with previous work. Interpretation of the geomorphology of the northern side of the peninsula has been controversial for more than four decades. The “stepped” topography has been interpreted as either a series of successively higher and older marine terraces, as on the southern side, or a marine terrace of one age that has been displaced by a series of shore-parallel, northward-dipping normal faults. In the second interpretation, topographic steps in the landscape are interpreted to be fault scarps, not ancient sea cliffs. Interpretation of the open-system U-series ages indicates that marine deposits at elevations of ∼22 m and ∼44 m are both likely ∼120 ka, supporting the scenario that steps in the landscape are likely fault scarps, not sea cliffs. Using the new U-series ages of ∼120 ka, along with GIA-modeled paleo-sea level confirms another previous study that uplift rates on the northern side of the peninsula are at least as high as 0.39–0.46 m/kyr.
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However, it is unclear whether wildfires are also becoming more destructive due to changes in wildfire behavior or in the development patterns of exposed communities. Here, we assessed trends in wildfire building exposure and destruction rates in the conterminous United States from 2002 to 2022. We mapped destroyed and surviving buildings within 100 m of all wildfires that destroyed 10 or more buildings (n = 362) and assessed trends relative to major ecoregions and vegetation types. We used logistic regression to assess relationships between destruction rates and landscape factors. We found that 10% of exposed buildings were destroyed in 2002–2012, but this percentage increased to 32% in 2013–2022. This increase was largely due to greater building exposure in evergreen forests in the northwestern United States, where exposed buildings were more than 3.4 times as likely to be destroyed as those in grass and shrublands. However, annual destruction rates also significantly increased in all other vegetation types and were correlated with development type, weather, and burn severity. These results indicate that increasing wildfire destruction in the United States has resulted not only from increased exposure but from rising rates of building destruction, potentially indicating more extreme wildfire behavior. This finding underscores the need to better understand how fuel management, community planning, and hardening buildings can reduce vulnerability.
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