We sought to assess bridle shiner (Notropis bifrenatus) habitat associations at local and regional scales across southern Maine and New Hampshire. We used local habitat data at 95 Maine sites to predict occupancy with classification and regression trees (CART). We then used ensemble species distribution models (SDMs) to model the historical (1898–2008) and current (2009–2022) ranges of the species. We used the BIOMOD platform to model the association between 35 environmental variables and bridle shiner presence during both time periods and at fine (pseudo-HUC14) and coarse (HUC12) spatial scales. We then calculated the change in predicted occupied drainages to estimate the change in the species' distribution at both scales. Within a site, bridle shiners were associated with submerged aquatic vegetation, organic substrate, and watermilfoil (Myriophyllum spp.). SDMs revealed an association with Appalachian (Hemlock-)Northern Hardwood Forest, sand substrate, and low-elevation terrain (at both spatial scales). Ensemble fine-scale SDMs suggest a substantial loss of historical bridle shiner habitat in both Maine (36% of drainages) and New Hampshire (16%), with comparable described losses (of 21% and 14%) at a coarse scale. Our local and regional models may be used to focus surveys on areas with high predicted habitat suitability or to inform habitat restoration efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72413","usgsCitation":"Katz, L.S., Coghlan, S.M., Carpenter, M.A., Kinnison, M.T., Zydlewski, J.D., 2026, Habitat-based predictions of bridle shiner (Notropis bifrenatus) in the northeastern U.S.: Ecology and Evolution, v. 16, no. 1, e72413, 19 p., https://doi.org/10.1002/ece3.72413.","productDescription":"e72413, 19 p.","ipdsId":"IP-158442","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":500597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72413","text":"Publisher Index Page"},{"id":500439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, New Hampshire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.25429976691849,\n 47.475434309112075\n ],\n [\n -71.47637622335257,\n 45.210844648573726\n ],\n [\n -72.2134392335759,\n 43.872712909182994\n ],\n [\n -72.54074189044374,\n 42.739662976646855\n ],\n [\n -71.15118191830072,\n 42.68091951244142\n ],\n [\n -69.70815322174685,\n 43.6220110458515\n ],\n [\n -66.83114905877525,\n 44.72524582330068\n ],\n [\n -67.67285260858687,\n 45.844248966240016\n ],\n [\n -67.78415740622219,\n 47.198837764366964\n ],\n [\n -68.11347172586534,\n 47.38520895041782\n ],\n [\n -69.25429976691849,\n 47.475434309112075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-01-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Katz, Lara S.","contributorId":366795,"corporation":false,"usgs":false,"family":"Katz","given":"Lara","middleInitial":"S.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":956239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coghlan, Stephen M. Jr.","contributorId":366796,"corporation":false,"usgs":false,"family":"Coghlan","given":"Stephen","suffix":"Jr.","middleInitial":"M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":956240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carpenter, Matthew A.","contributorId":366797,"corporation":false,"usgs":false,"family":"Carpenter","given":"Matthew","middleInitial":"A.","affiliations":[{"id":56597,"text":"New Hampshire Fish and Game Department","active":true,"usgs":false}],"preferred":false,"id":956241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinnison, Michael T.","contributorId":366798,"corporation":false,"usgs":false,"family":"Kinnison","given":"Michael","middleInitial":"T.","affiliations":[{"id":87507,"text":"Maine Center for Genetics in the Environment","active":true,"usgs":false}],"preferred":false,"id":956242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":956243,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273895,"text":"70273895 - 2026 - Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood","interactions":[],"lastModifiedDate":"2026-02-13T18:54:27.652376","indexId":"70273895","displayToPublicDate":"2026-02-12T09:51:46","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16689,"text":"Geoenergy Science and Engineering","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood","title":"Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood","docAbstract":"Groundwater quality in and around oil fields in the Southern San Joaquin Valley is of interest to many California residents that rely heavily on groundwater for domestic, commercial, and agricultural use. To help assess the effects of historical oil-field activities and natural geologic sources on groundwater near the southwest margins of the Kern County Groundwater Subbasin, a multiple-well monitoring site was installed near the administrative boundary between the Midway-Sunset and Buena Vista Oil Fields in Kern County, California. The installation of the Midway-Sunset Buena Vista multiple-well monitoring site (MSBV) supports regional analysis of the relations of oil and gas sources to groundwater quality by providing information about the geology, hydrology, geophysical properties, and water quality of the alluvial and upper Tulare aquifers in areas where groundwater data were limited. Data collected from the site included drill cuttings, whole core samples, sidewall core samples, mud-gas analysis, borehole geophysical logs, depth to water measurements, and water quality samples. Whole cores were scanned using dual energy computed tomography. Subsamples of selected cores were analyzed for density, porosity, specific retention, and bulk minerology. Thin sections of the subsamples were prepared, photographed, and examined. Two samples were analyzed using scanning electron microscope technology to examine the microporosity of diatomite laden sediment. Instrumentation installed in the wells collect hourly depth to water measurements.
Analysis of the data show there is 355 feet of alluvium overlying the Tulare Formation at the well site. The contact between the two formations is an aquitard resulting in a perched aquifer in the alluvium and unconfined aquifer in the Tulare Formation. The alluvium is more heterogenous and finer grained than the Tulare Formation resulting in markedly higher porosity in the alluvium compared to the Tulare Formation. Higher specific retention observed in the alluvium is attributed to the finer grained sediment and greater abundance of reworked diatomite (as represented by opal-CT [cristobalite-tridymite]) compared to the Tulare Formation. Total dissolved solids (TDS) approached or exceeded 10,000 milligrams per liter (mg/L) in the alluvium from approximately 176 to 242 feet below land surface and at the top of the Amnicola clay at approximately 670 feet below land surface within the Tulare Formation. Elevated TDS, chloride, and boron concentrations in the alluvium and on top of the Amnicola clay likely reflect groundwater that is mixed with oil-field water. Water chemistry and modern-aged groundwater in the alluvial monitoring well (MSBV #3) are consistent with the oil-field water in the alluvium being derived from documented historical surface disposal of oil-field water upslope (northwest) of the site. Water chemistry and pre-modern groundwater age in the deeper Tulare monitoring well (MSBV #1) on top of the Amnicola clay are consistent with oil-field fluids derived from upslope natural geologic sources or old oil wells that leak in the subsurface. Shallow groundwater in the Tulare (MSBV #2) is not affected by mixing with oil-field sources.
Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies.
","language":"English","publisher":"MDPI","doi":"10.3390/rs18040590","usgsCitation":"Shi, H., Barber, C.P., Sayler, K.L., Smith, K., and Hussain, R., 2026, Monitoring changes in Landsat thermal features in urban and non-urban interfaces from 1986 to 2023 in two international urban centers: Implications for climate and global issues: Remote Sensing, v. 18, no. 4, 590, 25 p., https://doi.org/10.3390/rs18040590.","productDescription":"590, 25 p.","ipdsId":"IP-184356","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":500820,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs18040590","text":"Publisher Index Page"},{"id":500672,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil, China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -48.23543379168905,\n -15.481214838053802\n ],\n [\n -48.23543379168905,\n -16.0688507831632\n ],\n [\n -47.30997066679669,\n -16.0688507831632\n ],\n [\n -47.30997066679669,\n -15.481214838053802\n ],\n [\n -48.23543379168905,\n -15.481214838053802\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 114.01001566832986,\n 30.818553311801224\n ],\n [\n 114.01001566832986,\n 30.334258663173557\n ],\n [\n 114.57676947004,\n 30.334258663173557\n ],\n [\n 114.57676947004,\n 30.818553311801224\n ],\n [\n 114.01001566832986,\n 30.818553311801224\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":956714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber, Christopher P. 0000-0003-0570-1140","orcid":"https://orcid.org/0000-0003-0570-1140","contributorId":223102,"corporation":false,"usgs":true,"family":"Barber","given":"Christopher","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":956715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sayler, Kristi L. 0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":2988,"corporation":false,"usgs":true,"family":"Sayler","given":"Kristi","email":"sayler@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":956716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Kelcy 0000-0001-6811-1485","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":272037,"corporation":false,"usgs":false,"family":"Smith","given":"Kelcy","affiliations":[{"id":56338,"text":"KBR, Inc., Contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":956717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hussain, Reza 0000-0002-5445-3027","orcid":"https://orcid.org/0000-0002-5445-3027","contributorId":301245,"corporation":false,"usgs":false,"family":"Hussain","given":"Reza","affiliations":[{"id":65343,"text":"KBR, Contractor to U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":956718,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274162,"text":"70274162 - 2026 - Tracking baseflow supply dynamics using SWOT data from small groundwater-dominated lakes","interactions":[],"lastModifiedDate":"2026-03-04T15:10:02.201402","indexId":"70274162","displayToPublicDate":"2026-02-12T07:43:39","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Tracking baseflow supply dynamics using SWOT data from small groundwater-dominated lakes","docAbstract":"In situ surface-water monitoring strategies are biased towards larger perennial streams and lakes and are generally not designed to track mechanisms of baseflow supply contributed by the dynamic storage of aquifers. Additionally, small (< 1 km2) groundwater-influenced lakes and wetlands globally have little in situ monitoring infrastructure. We explored the utility of remotely sensed Surface Water Ocean Topography Satellite (SWOT) data, collected from 2023 onward, to characterise the seasonal and multi-year water-level trends of groundwater flow-through kettle lakes distributed across the permeable sediments of eastern Massachusetts, USA. This analysis indicated that water levels for kettle lakes with areas down to approximately 0.05 km2 are resolvable in the study area. Our examination of 17 kettle lakes found that SWOT water-surface elevation data closely tracked groundwater levels in adjacent monitoring wells where available, including the timing of seasonal patterns (highest levels generally in late spring), although there was some variation between years and there was a substantial lag in the timing of high water levels for a lake located downgradient from a 30-m-thick vadose zone. Furthermore, SWOT-observed water-level increases in kettle lakes tracked with baseflow increases in two adjacent groundwater-dominated streams, as would be expected from increased hydraulic gradients. Unlike spectral remote sensing, SWOT data are generally not affected by cloud cover, resulting in a potential for groundwater-dominated lakes to be sentinels of dynamic storage patterns, including identification of baseflow drought lags, which are currently ill-defined hydrological processes. SWOT monitoring of groundwater-influenced surface waters shows potential for augmenting existing monitoring wells and streamgages as continuous monitors of groundwater levels and baseflow supply in permeable terrain.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70414","usgsCitation":"Briggs, M.A., Harlan, M.E., Rey, D., Hare, D.K., LeBlanc, D.R., Boutt, D.F., and Gooseff, M.N., 2026, Tracking baseflow supply dynamics using SWOT data from small groundwater-dominated lakes: Hydrological Processes, v. 40, no. 2, e70414, 12 p., https://doi.org/10.1002/hyp.70414.","productDescription":"e70414, 12 p.","ipdsId":"IP-178175","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":500848,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70414","text":"Publisher Index Page"},{"id":500722,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"coastal southeastern Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.67620889081189,\n 42.11446080523638\n ],\n [\n -70.67620889081189,\n 41.52445687414806\n ],\n [\n -69.90003630265832,\n 41.52445687414806\n ],\n [\n -69.90003630265832,\n 42.11446080523638\n ],\n [\n -70.67620889081189,\n 42.11446080523638\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":956729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harlan, Merritt Elizabeth 0000-0002-4019-4888","orcid":"https://orcid.org/0000-0002-4019-4888","contributorId":302672,"corporation":false,"usgs":true,"family":"Harlan","given":"Merritt","email":"","middleInitial":"Elizabeth","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":956730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":956731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hare, Danielle K.","contributorId":219738,"corporation":false,"usgs":false,"family":"Hare","given":"Danielle","middleInitial":"K.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":956732,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeBlanc, Denis R. 0000-0002-4646-2628","orcid":"https://orcid.org/0000-0002-4646-2628","contributorId":219907,"corporation":false,"usgs":true,"family":"LeBlanc","given":"Denis","email":"","middleInitial":"R.","affiliations":[{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956733,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boutt, David F.","contributorId":81095,"corporation":false,"usgs":false,"family":"Boutt","given":"David","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":956734,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gooseff, Michael N.","contributorId":367087,"corporation":false,"usgs":false,"family":"Gooseff","given":"Michael","middleInitial":"N.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":956735,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274027,"text":"70274027 - 2026 - Hierarchical mixture models and high-resolution monitoring data can inform siting and operational strategies to mitigate bat fatalities at wind turbines","interactions":[],"lastModifiedDate":"2026-02-20T14:43:05.194023","indexId":"70274027","displayToPublicDate":"2026-02-12T07:35:39","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"Hierarchical mixture models and high-resolution monitoring data can inform siting and operational strategies to mitigate bat fatalities at wind turbines","docAbstract":"Bats provide critical ecosystem services, but bat fatalities due to wind energy development may imperil some bat populations. Statistical models are used to estimate the total fatalities that occur based on carcasses observed during monitoring surveys. Current models often estimate fatalities aggregated across species, time, and/or turbines, but fall short of reliably informing siting and operational collision mitigation strategies that account for species-specific fatality patterns on a fine spatiotemporal scale. We developed a hierarchical mixture model for estimating species-specific covariate effects and total fatalities per species at each turbine on weekly intervals. We applied the model to a high-resolution dataset of bat carcasses found during turbine searches across nineteen wind facilities in Iowa over two years. Our model explains species-specific variation in bat fatalities at individual wind turbines according to turbine proximity to bat habitat, turbine design specifications, seasonal trends, and weather conditions such as nightly air temperature, air pressure, and wind speed. Turbines located on the edge of wind facilities had higher fatalities, and proximity to roosting and foraging habitat accounted for variation in species-specific fatality estimates. These insights into turbine placement effects can inform siting strategies. We also discovered species-specific relationships with average nightly wind speed and air temperature, among other weather conditions, that could inform operational mitigation strategies such as smart curtailment. Our model can transform observations of carcasses found during turbine searches across multiple facilities, years, and variable search efforts into estimates of total fatalities per species associated with species-specific spatial, temporal, and environmental covariate effects.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2026.103652","usgsCitation":"Labuzzetta, C.J., Johnsen, A.(., Andress, A., Bohner, T., Grajal-Puche, A., Seymour, M., Straw, B., Thogmartin, W.E., Udell, B.J., Wiens, A.M., Diffendorfer, J., 2026, Hierarchical mixture models and high-resolution monitoring data can inform siting and operational strategies to mitigate bat fatalities at wind turbines: Ecological Informatics, v. 94, 103652, 13 p., https://doi.org/10.1016/j.ecoinf.2026.103652.","productDescription":"103652, 13 p.","ipdsId":"IP-180409","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":500822,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoinf.2026.103652","text":"Publisher Index Page"},{"id":500334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Labuzzetta, Charles J. 0000-0002-6027-0120","orcid":"https://orcid.org/0000-0002-6027-0120","contributorId":332055,"corporation":false,"usgs":true,"family":"Labuzzetta","given":"Charles","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":956197,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnsen, Arnold (Contractor) 0009-0001-2442-249X","orcid":"https://orcid.org/0009-0001-2442-249X","contributorId":366769,"corporation":false,"usgs":true,"family":"Johnsen","given":"Arnold","middleInitial":"(Contractor)","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":956198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andress, Amber","contributorId":366770,"corporation":false,"usgs":false,"family":"Andress","given":"Amber","affiliations":[{"id":87506,"text":"U.S. Fish and Wildlife Service, Illinois-Iowa Ecological Services Field Office","active":true,"usgs":false}],"preferred":false,"id":956199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bohner, Teresa 0000-0003-2582-8771","orcid":"https://orcid.org/0000-0003-2582-8771","contributorId":366771,"corporation":false,"usgs":false,"family":"Bohner","given":"Teresa","affiliations":[{"id":85472,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":false}],"preferred":false,"id":956200,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grajal-Puche, Alejandro 0000-0003-1807-4799","orcid":"https://orcid.org/0000-0003-1807-4799","contributorId":265397,"corporation":false,"usgs":false,"family":"Grajal-Puche","given":"Alejandro","affiliations":[{"id":54677,"text":"Department of Biological Sciences, P.O. 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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20263062","programNote":"National and Global Petroleum Assessment","usgsCitation":"Schenk, C.J., Gelman, S.E., Hearon, J.S., Mercier, T.J., Le, P.A., Cicero, A.D., Johnson, B.G., Lagesse, J.H., and Leathers-Miller, H.M., 2026, Assessment of undiscovered conventional oil and gas resources in the Santos Basin, Campos Basin, and Espírito Santo Basin provinces of Brazil, 2024: U.S. Geological Survey Fact Sheet 2026–3062, 6 p., https://doi.org/10.3133/fs20263062.","productDescription":"Report: 6 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-170837","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":499768,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20263062/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2026-3062"},{"id":499760,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2026/3062/fs20263062.xml"},{"id":499759,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2026/3062/images"},{"id":499655,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2026/3062/coverthb.jpg"},{"id":500705,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119288.htm","linkFileType":{"id":5,"text":"html"}},{"id":499657,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13LEA4F","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Santos Basin, Campos Basin, and Espírito Santo Basin Provinces of Brazil—Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":499656,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2026/3062/fs20263062.pdf","text":"Report","size":"2.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2026-3062"}],"country":"Brazil","otherGeospatial":"Campos Basin, Espírito Santo Basin, Santos Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -35.75,\n -17.75\n ],\n [\n -48,\n -17.75\n ],\n [\n -48,\n -28\n ],\n [\n -35.75,\n -28\n ],\n [\n -35.75,\n -17.75\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
Purpose
Eutrophication and nuisance filamentous algal blooms (i.e. Cladophora) are increasingly common occurrences throughout much of the western United States. Wildfire may be contributing to the frequency and magnitude of algal blooms through excess sediment and nutrient loading to streams and rivers. Our objective was to evaluate the effects the 2021 Woods Creek Fire had on sediment yields and phosphorus (total and bioavailable) partitioning in Camas Creek, a major tributary to the Smith River in Montana where Cladophora are now consistently reaching nuisance levels.
Methods
We collected water quality samples during snowmelt pulsing events as well as fixed interval sampling using an established U.S. Geological Survey stream gage instrumented with a continuous water quality sonde and an automatic peristaltic pump sampler. Water samples were processed for total phosphorus (TP), sediment-bound bioavailable phosphorus (S-BioP), soluble reactive phosphorus (SRP), and suspended sediment concentrations and were evaluated using linear regression and other nonparametric statistical tests. Continuous turbidity and streamflow were evaluated using hysteresis analysis to determine sediment sourcing and connectivity.
Results
We found that the Woods Creek Fire did not significantly influence TP and S-BioP in Camas Creek. However, there was a significant increase in SRP and turbidity in both postfire years (2022 and 2023). Hysteresis analysis of 91 delineated events indicated positive (clockwise) hysteresis was the dominant event pattern during the snowmelt period. This may indicate a lower hillslope to channel connectivity, with the major sediment supply originating from the channel and/or riparian areas.
Conclusion
Results from this study demonstrate the benefits of combining discrete water quality samples with high-frequency turbidity sensors to characterize postfire sediment and phosphorus dynamics. While a lack of postfire response in TP and S-BioP is contrary to many other studies, our findings highlight the role climate and catchment morphology play in attenuating a disturbance effect.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11368-025-04192-7","usgsCitation":"Clark, G.D., Suplee, M.W., Brown, H.M., Moloney, M., and Caldwell, R.R., 2026, Suspended-sediment and phosphorus response in a fire-affected central Montana headwater catchment: Journal of Soils and Sediments, v. 26, 43, 14 p., https://doi.org/10.1007/s11368-025-04192-7.","productDescription":"43, 14 p.","ipdsId":"IP-179856","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":499950,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11368-025-04192-7","text":"Publisher Index Page"},{"id":499805,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Big Belt Mountains, Camas Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.54030626136881,\n 46.77963353252085\n ],\n [\n -112.54030626136881,\n 45.373934416602\n ],\n [\n -110.21790197229728,\n 45.373934416602\n ],\n [\n -110.21790197229728,\n 46.77963353252085\n ],\n [\n -112.54030626136881,\n 46.77963353252085\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2026-02-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark, Gregory D. 0000-0003-0066-8193 gmclark@usgs.gov","orcid":"https://orcid.org/0000-0003-0066-8193","contributorId":224364,"corporation":false,"usgs":true,"family":"Clark","given":"Gregory","email":"gmclark@usgs.gov","middleInitial":"D.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Suplee, Michael W.","contributorId":366218,"corporation":false,"usgs":false,"family":"Suplee","given":"Michael","middleInitial":"W.","affiliations":[{"id":87386,"text":"Montana Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":955452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Haylie M. 0009-0004-0278-1450","orcid":"https://orcid.org/0009-0004-0278-1450","contributorId":344815,"corporation":false,"usgs":true,"family":"Brown","given":"Haylie","middleInitial":"M.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moloney, Molly A. 0000-0001-8241-8467","orcid":"https://orcid.org/0000-0001-8241-8467","contributorId":329672,"corporation":false,"usgs":true,"family":"Moloney","given":"Molly A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":955454,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caldwell, Rodney R. 0000-0002-2588-715X","orcid":"https://orcid.org/0000-0002-2588-715X","contributorId":203416,"corporation":false,"usgs":true,"family":"Caldwell","given":"Rodney","middleInitial":"R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955455,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274138,"text":"70274138 - 2026 - Ecogeomorphic feedbacks influence elevation change across microtidal wetland settings of coastal Louisiana","interactions":[],"lastModifiedDate":"2026-02-27T15:15:55.683288","indexId":"70274138","displayToPublicDate":"2026-02-11T08:10:10","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Ecogeomorphic feedbacks influence elevation change across microtidal wetland settings of coastal Louisiana","docAbstract":"No abstract available.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41467-026-69091-y","usgsCitation":"Wilson, C., Quirk, T., Cahoon, D.R., Snedden, G.A., and Sharp, L.A., 2026, Ecogeomorphic feedbacks influence elevation change across microtidal wetland settings of coastal Louisiana: Nature Communications, v. 17, 1501, 3 p., https://doi.org/10.1038/s41467-026-69091-y.","productDescription":"1501, 3 p.","ipdsId":"IP-174230","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":500792,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-026-69091-y","text":"Publisher Index Page"},{"id":500646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"coastal Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.65933357953257,\n 30.084436357175647\n ],\n [\n -93.92324317789542,\n 29.876299552248952\n ],\n [\n -93.88264198340813,\n 29.62672593742836\n ],\n [\n -91.0760617337477,\n 29.073212627651046\n ],\n [\n -89.26421891953363,\n 28.98051418758179\n ],\n [\n -88.98000822369163,\n 29.089921190719664\n ],\n [\n -89.30482044751128,\n 30.119568355922397\n ],\n [\n -93.65933357953257,\n 30.084436357175647\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2026-02-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Carol","contributorId":302654,"corporation":false,"usgs":false,"family":"Wilson","given":"Carol","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":956661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quirk, Tracy","contributorId":208063,"corporation":false,"usgs":false,"family":"Quirk","given":"Tracy","email":"","affiliations":[{"id":37701,"text":"Academy of Natural Sciences of Drexel University, Philadelphia, Pa","active":true,"usgs":false}],"preferred":false,"id":956662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":219657,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":956663,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snedden, Gregg A. 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":212275,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":956664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharp, Leigh Anne","contributorId":367065,"corporation":false,"usgs":false,"family":"Sharp","given":"Leigh","middleInitial":"Anne","affiliations":[{"id":13608,"text":"Louisiana Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":956665,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274013,"text":"70274013 - 2026 - Using eye lens stable isotopes to identify the rearing origin of fall age-0 walleye (Sander vitreus)","interactions":[],"lastModifiedDate":"2026-02-23T15:13:46.445965","indexId":"70274013","displayToPublicDate":"2026-02-11T08:04:40","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using eye lens stable isotopes to identify the rearing origin of fall age-0 walleye (Sander vitreus)","docAbstract":"Isotope values in fish eye lenses may be useful in differentiating rearing origins. We compared eye lens isotopic values of fall fingerling age-0 walleye (Sander vitreus) reared in a hatchery pond, a recirculating aquaculture system (RAS), and a natural lake. Using 10 fish per rearing source, we delaminated layers from one eye lens per fish to assess temporal changes in carbon (δ13C) and nitrogen (δ15N) and pulverized the whole second eye lens for δ13C, δ15N, and sulfur (δ34S). RAS-reared walleye values exhibited high precision among individuals and were δ34S enriched. Pond-reared walleye had lower δ13C and δ15N core values compared to other rearing sources. For δ13C and δ15N, values remained consistent among layers for RAS-reared walleye, δ15N slightly increased for pond-reared and lake-reared walleye, and δ13C increased substantially among subsequent layers in pond-reared walleye. Bayesian 95% ellipses did not overlap among rearing sources. These results demonstrate that eye lens stable isotope analysis may be a useful tool for differentiating hatchery-reared and wild large fingerling walleye, specifically from RAS- and pond-reared sources.
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U.S. Geological Survey
University of Colorado - Boulder
Sustainability, Energy and Environment Community
4001 Discovery Dr., Suite 348
Boulder, CO 80303
Dams have dramatically altered rivers and are a major contributor to native fish population declines. However, many dams serve important ecological, social, and economic functions, such as flood control, invasive species control, and provision of recreational opportunities. Therefore, dam removal is often contentious among stakeholders and involves making tradeoffs among multiple competing objectives. This research uses structured decision making to evaluate the ecological, social, and economic consequences and tradeoffs of enhancing connectivity for migratory fishes in the Boardman River, Michigan. We describe efforts to engage a diverse group of stakeholders to elicit their objectives under various fish passage alternatives. We used multi-attribute tradeoff methods to help stakeholders weigh the costs and benefits of enhancing connectivity for several fish species with varying life history traits and initial distributions. We found that the optimal alternative was passage of native fishes only; however, the optimal alternative varied based on the weight stakeholders might place on each objective. We created four objective weighting scenarios to evaluate the sensitivity of the optimal alternative to changes in objective weights. This research will help inform decision-makers on fish passage alternatives that are preferred by stakeholders and that are likely to achieve their objectives.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.70234","usgsCitation":"Flinn, S., Muir, A.M., Robinson, K.F., 2026, Using structured decision making to evaluate the tradeoffs of selective fish passage: Conservation Science and Practice, e70234, 18 p., https://doi.org/10.1111/csp2.70234.","productDescription":"e70234, 18 p.","ipdsId":"IP-173409","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":500830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.70234","text":"Publisher Index Page"},{"id":500400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Boardman River, northwest lower peninsula of Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.14564739370071,\n 45.55888889464856\n ],\n [\n -86.14564739370071,\n 44.58153097113956\n ],\n [\n -84.73667772286038,\n 44.58153097113956\n ],\n [\n -84.73667772286038,\n 45.55888889464856\n ],\n [\n -86.14564739370071,\n 45.55888889464856\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2026-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Flinn, Shane","contributorId":353643,"corporation":false,"usgs":false,"family":"Flinn","given":"Shane","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":955968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muir, Andrew M.","contributorId":366474,"corporation":false,"usgs":false,"family":"Muir","given":"Andrew","middleInitial":"M.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":955969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robinson, Kelly Filer 0000-0001-8109-9492","orcid":"https://orcid.org/0000-0001-8109-9492","contributorId":340631,"corporation":false,"usgs":true,"family":"Robinson","given":"Kelly","email":"","middleInitial":"Filer","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":955970,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274086,"text":"70274086 - 2026 - Preface to the focus section on intraplate earthquakes","interactions":[],"lastModifiedDate":"2026-02-24T15:00:12.785791","indexId":"70274086","displayToPublicDate":"2026-02-10T07:54:52","publicationYear":"2026","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":"Preface to the focus section on intraplate earthquakes","docAbstract":"More than a half century after plate tectonics provided an overarching framework to explain earthquakes along active plate boundaries, numerous theories have been proposed to explain where, why, and how often earthquakes occur well away from active plate boundaries, but a paradigm remains elusive. Even the classification of earthquakes away from active plate boundaries as \"intraplate\" raises issues, with potentially important distinctions between Stable Continental Regions and more actively deforming regions including passive margins and failed rifts. Some of the largest known intraplate earthquakes themselves remain enigmatic, having occurred before the modern instrumental era. Hazard assessments are often data-limited: low fault-slip rates relative to landscape modification rates result in poor discoverability of fault sources, challenging the characterization of source zones and earthquake recurrence; the completeness and homogenization of instrumental earthquake catalogs using uncertain magnitude conversions can lead to uncertainties in earthquake recurrence; and, limited strong-motion observations for large-magnitude events at near-source distances leads to uncertainties in the selection and development of ground-motion models for seismic hazard studies. Data from recent intraplate earthquakes around the world—from the moment magnitude M 7.7 2001 Bhuj, India, earthquake 25 years ago to the 2024 M 4.8 Tewksbury, New Jersey earthquake—have yielded both new insights and new questions. The papers in this special focus discuss many of the long-standing challenges involved with intraplate earthquake investigations and provide a snapshot of the state of the art with current research to advance our understanding.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220260004","usgsCitation":"Allen, T.I., Hough, S.E., Boyd, O.S., Waldhauser, F., Assumpcao, M., 2026, Preface to the focus section on intraplate earthquakes: Seismological Research Letters, v. 97, no. 2A, p. 619-625, https://doi.org/10.1785/0220260004.","productDescription":"7 p.","startPage":"619","endPage":"625","ipdsId":"IP-184917","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":500602,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0220260004","text":"Publisher Index Page"},{"id":500476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"2A","noUsgsAuthors":false,"publicationDate":"2026-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Trevor I.","contributorId":138667,"corporation":false,"usgs":false,"family":"Allen","given":"Trevor","middleInitial":"I.","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. 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The western PNW faces similar challenges to other regions with shifting wildfire regimes and large population centers reliant on surface water from forested catchments. However, the uniquely wet and highly seasonal climate of the western PNW suggests that findings from other, more frequently burned regions may not be directly applicable. To identify science, monitoring, and management gaps and opportunities in the western PNW, this review was collaboratively undertaken by academics, non-government and industry representatives, and local, state, and federal government entities who have been working together since the 2020 Labor Day fires in Oregon. Focusing on Oregon and Washington, we found that monitoring networks for continuous water quantity and quality cover much of the state with greater representation in western U.S. ecoregions, but few studies have analyzed and published these data to capture and communicate the post-wildfire response. Approximately half of the streamgages in Oregon and Washington record major water quality parameters, and hundreds of sites in the area have discrete sampling for a wide range of water quality constituents. Still, numerous gaps exist in understanding the short- and long-term impacts of wildfire on hydrology, water chemistry, including pH and dissolved oxygen, mobilization of metals, aquatic ecosystems, and downstream drinking water treatment. Collective action to further collect, analyze, interpret, and publish the key data could help improve our understanding of post-wildfire water quality impacts in this and other increasingly wildfire-affected regions.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/3033-4942/ae36cb","usgsCitation":"Wall, S., Compton, J.E., Coble, A.A., Haley, B.M., Lin, J., Myers-Pigg, A., Reale, J.K., Wampler, K., Swartz, A., Moffett, K., Bladon, K.D., Carpenter, K., Chang, H., Chen, J., Donahue, D., Eckley, C.S., Hohner, A.K., Kiffney, P.M., Miralha, L., Regier, P., Seeds, J., and River, M., 2026, Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps: Environmental Research: Water, no. 2, 015004, 31 P., https://doi.org/10.1088/3033-4942/ae36cb.","productDescription":"015004, 31 P.","ipdsId":"IP-181756","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":500249,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/3033-4942/ae36cb","text":"Publisher Index 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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
In this work we revisit Þingmúli volcano (Þ = Th), a classic locality known as an example of a complete tholeiitic differentiation. Þingmúli is a ~ 9.5 Ma extinct central volcano located in the East Fjords of Iceland, in which the whole compositional spectrum from basalt to rhyolites have erupted. These volcanic products have been previously considered as petrogenetically related by an ideal fractionation trend, regardless any temporal relationship or volumetric considerations.
Here we report new whole-rock geochemistry, mineral chemistry, isotope analyses, estimation of residence times of the different eruptive deposits, and an update of the original petrogenetic model. Our results highlight that an enriched source, likely spinel lherzolites, generated transitional-alkaline basaltic melts after 15–20% of partial melting at depths of 40–45 km. Many of these basaltic melts erupted at various stages of the volcano's history, while others remained longer in the volcanic plumbing system. These evolved by fractional crystallisation into basaltic andesite magmas with a residence time of ~5 years based on the crystal size distribution of the plagioclase population. Isotopic differences between the basalts/basaltic andesites (87Sr/86Sr ~ 0.7034; 143Nd/144Nd ~ 0.51315) and the erupted rhyolites (87Sr/86Sr ~ 0.7037; 143Nd/144Nd ~ 0.51304) indicate that the latter are not petrogenetically related to the former. Therefore, instead of a fractional crystallisation mechanism to generate the rhyolites, we propose the partial melting of ignimbrite layers located beneath the volcano. The broad range of trace element concentrations in andesites and dacites and their different isotopic values compared to the basalts strongly suggest that these magmas have been generated by magma mixing between basaltic and rhyolitic melts, similar to modern day Icelandic volcanoes such as Hekla. These results highlight the need to revisit previously studied Icelandic classic localities and reassess their traditionally proposed petrogenetic models.
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Effective communication is critical for promoting coexistence, yet on-the-ground conservation messaging seldom links to behavior change theories. We conducted 23 semistructured interviews with practitioners to examine the approaches they used to communicate with private landowners about beaver coexistence in Oregon (USA). Although we did not set out to interview practitioners about their messages targeting capability, opportunity, and motivation (elements of the COM-B model of behavior), we used the COM-B model to synthesize the primary dimensions of practitioners’ complex, real-world communication about human–wildlife coexistence. We found that practitioners used multiple communication channels to listen for and respond to landowners’ capability, opportunity, and motivation. They tailored messages to affirm and enhance knowledge and skills, identify and address site-specific and social contexts, and align beaver impacts with landowner goals. Our findings suggest the COM-B model can go beyond guiding audience analysis and behavioral intervention design to help practitioners tailor real-time communication with landowners about coexistence behavior. The model, based on our use of COM-B to analyze existing communication, could be used to provide practitioners with techniques for making sense of their existing communication efforts, for identifying gaps, and for dynamically tailoring their communication.
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\"}}]}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Brian D.","contributorId":339269,"corporation":false,"usgs":false,"family":"Erickson","given":"Brian","middleInitial":"D.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":956184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Megan Siobhan 0000-0002-4284-3650","orcid":"https://orcid.org/0000-0002-4284-3650","contributorId":294651,"corporation":false,"usgs":true,"family":"Jones","given":"Megan","email":"","middleInitial":"Siobhan","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956185,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273904,"text":"70273904 - 2026 - Per- and polyfluoroalkyl substances in waters associated with oil and gas development in the Denver Basin","interactions":[],"lastModifiedDate":"2026-02-13T15:44:06.039327","indexId":"70273904","displayToPublicDate":"2026-02-07T08:37:30","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Per- and polyfluoroalkyl substances in waters associated with oil and gas development in the Denver Basin","docAbstract":"Use of per- and polyfluoroalkyl substances (PFAS) in the petroleum industry could be a cause for concern due to the large volumes of produced water (PW) generated during oil and gas extraction, the reuse of these wastes in water-stressed regions, and adverse health outcomes related to PFAS exposures. However, PW PFAS characterization is nearly absent in the literature, and hydraulic fracturing (HF) chemical disclosures often omit the identities of additives as proprietary. Here we evaluate PFAS in PW samples from three petroleum wells in the Denver Basin during their first year of production. Total concentrations of targeted PFAS (Σ40PFAS) were < 35 ng/L in PW samples, with short-chain PFAS like perfluorobutanoic acid persisting throughout the sampled duration. Analysis of freshwater inputs for hydraulic fracturing (Σ40PFAS ~ 113 ng/L) and mixed fracture fluid (Σ40PFAS ~ 69 ng/L) indicated much of the targeted PFAS content was derived from the input water, and not from HF additives, however samples subjected to oxidation indicated the presence of PFAS precursors that would not be detected by targeted analysis. This study highlights that while PFAS content is low in the studied PWs, the potential for redistribution of PFAS in the environment may be a consideration for reuse applications.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-33394-9","usgsCitation":"Varonka, M., Jubb, A., McDevitt, B., Shelton, J.L., Barnhart, E.P., Akob, D., and Cozzarelli, I.M., 2026, Per- and polyfluoroalkyl substances in waters associated with oil and gas development in the Denver Basin: Scientific Reports, v. 16, 5743, 8 p., https://doi.org/10.1038/s41598-025-33394-9.","productDescription":"5743, 8 p.","ipdsId":"IP-173284","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":500246,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-33394-9","text":"Publisher Index Page"},{"id":500087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Nebraska, Wyoming","otherGeospatial":"Denver Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.7394986500792,\n 41.98806529832831\n ],\n [\n -105.7394986500792,\n 39.252214457834185\n ],\n [\n -103.39682614563382,\n 39.252214457834185\n ],\n [\n -103.39682614563382,\n 41.98806529832831\n ],\n [\n -105.7394986500792,\n 41.98806529832831\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2026-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Varonka, Matthew S. 0000-0003-3620-5262","orcid":"https://orcid.org/0000-0003-3620-5262","contributorId":203231,"corporation":false,"usgs":true,"family":"Varonka","given":"Matthew S.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelton, Jenna L. 0000-0002-1377-0675","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":366369,"corporation":false,"usgs":false,"family":"Shelton","given":"Jenna","middleInitial":"L.","affiliations":[{"id":87467,"text":"Illinois State Water Survey, Prairie Research Institute, Champaign, IL, USA 61821","active":true,"usgs":false}],"preferred":false,"id":955717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Akob, Denise M. 0000-0003-1534-3025","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":204701,"corporation":false,"usgs":true,"family":"Akob","given":"Denise M.","affiliations":[{"id":436,"text":"National Research Program - 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The MCS is the earliest comprehensive source of 2025 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses.
Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the 2026 MCS are welcomed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2026","isbn":"978-1-4113-4643-7","usgsCitation":"U.S. Geological Survey, 2026, Mineral commodity summaries 2026 (ver. 1.1, March 2026): U.S. Geological Survey, 222 p., https://doi.org/10.3133/mcs2026.","productDescription":"Report: 222 p.; Data Release; Data Visualization","numberOfPages":"222","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-185894","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":500785,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/periodicals/mcs2026/versionHist.txt","size":"883 B","linkFileType":{"id":2,"text":"txt"}},{"id":499639,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://apps.usgs.gov/critical-minerals/mineral-commodities-2026.html","text":"Data visualization"},{"id":499537,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1WKQ63T","text":"USGS data release","linkHelpText":"Data release for mineral commodity summaries 2026"},{"id":498869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2026/coverthb2.jpg"},{"id":499534,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2026/mcs2026.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2026 PDF"},{"id":499535,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2026"},{"id":499536,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/commodity-statistics-and-information","text":"Commodity Statistics and Information"},{"id":499654,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119204.htm","linkFileType":{"id":5,"text":"html"}}],"edition":"Version 1.0: February 2026; Version 1.1: March 2026","contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
","tableOfContents":"A growing number of wind turbines (WTs) across the globe are now reaching or exceeding their expected service lifetime; WT decommissioning is on the rise. Accordingly, questions pertaining to WT end-of-life have risen in importance in policy and practice. Yet, research on the various factors relating to WT decommissioning is relatively sparse. Moreover, the key assumptions underpinning that prior research (e.g., the lifespan of WTs, characteristics of WTs being decommissioned, and whether the site is repowered with new WTs) have never been empirically tested across a large set of decommissioned WTs. Leveraging a uniquely comprehensive and spatially explicit dataset of decommissioned WTs in the United States, this research analyzes spatial, technological, and temporal trends in WT decommissioning and develops a novel predictive model for WT decommissioning. Our analysis pinpoints more than 12,400 WTs that have been fully decommissioned in the United States., the majority of which have been relatively old (> 30 years) and small (< 200 kW). While a WT's age alone is a good predictor of the likelihood of decommissioning, other factors such as the size of the WT and recent performance are also important and significant predictors. Most sites where decommissioning has occurred have seen subsequent repowering, with repowered plants featuring substantially fewer WTs (−86 on average) and higher rated plant capacity (+62 MW on average). Many existing WTs in the U.S. are approaching the end of their expected life with roughly 7500 being 20 or more years old. Findings can help policymakers and stakeholders begin preparing for this potential wave of future decommissioning and repowering.
","language":"English","publisher":"Wiley","doi":"10.1002/we.70099","usgsCitation":"Rand, J., Kramer, L., Hoen, B., Diffendorfer, J., and Garrity, C., 2026, Out with the old: Empirical trends in U.S. land-based wind turbine decommissioning and repowering: Wind Energy, v. 29, no. 3, e70099, 13 p., https://doi.org/10.1002/we.70099.","productDescription":"e70099, 13 p.","ipdsId":"IP-182443","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":499936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/we.70099","text":"Publisher Index Page"},{"id":499676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Rand, Joseph","contributorId":290223,"corporation":false,"usgs":false,"family":"Rand","given":"Joseph","affiliations":[{"id":41633,"text":"DOE Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":955277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Louisa 0000-0002-6776-9768","orcid":"https://orcid.org/0000-0002-6776-9768","contributorId":204878,"corporation":false,"usgs":true,"family":"Kramer","given":"Louisa","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoen, Ben 0000-0002-9512-5572","orcid":"https://orcid.org/0000-0002-9512-5572","contributorId":204879,"corporation":false,"usgs":false,"family":"Hoen","given":"Ben","email":"","affiliations":[{"id":37001,"text":"DOE Lawrence Berkeley National Labs","active":true,"usgs":false}],"preferred":false,"id":955279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diffendorfer, James 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":223504,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrity, Christopher 0000-0002-5565-1818 cgarrity@usgs.gov","orcid":"https://orcid.org/0000-0002-5565-1818","contributorId":220994,"corporation":false,"usgs":true,"family":"Garrity","given":"Christopher","email":"cgarrity@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955281,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273858,"text":"70273858 - 2026 - Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations","interactions":[],"lastModifiedDate":"2026-02-09T15:28:29.171335","indexId":"70273858","displayToPublicDate":"2026-02-06T08:20:00","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23297,"text":"Data Science in Science","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations","docAbstract":"Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/26941899.2026.2624195","usgsCitation":"Attanasi, E., McDevitt, B., Freeman, P., and Coburn, T., 2026, Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations: Data Science in Science, v. 5, no. 1, 2624195, 18 p., https://doi.org/10.1080/26941899.2026.2624195.","productDescription":"2624195, 18 p.","ipdsId":"IP-180475","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":499937,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/26941899.2026.2624195","text":"Publisher Index Page"},{"id":499678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Oklahoma, South Carolina, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.73317123367434,\n 35.41758190253161\n ],\n [\n -100.73317123367434,\n 28.707661068137995\n ],\n [\n -83.28804352213777,\n 28.707661068137995\n ],\n [\n -83.28804352213777,\n 35.41758190253161\n ],\n [\n -100.73317123367434,\n 35.41758190253161\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":347358,"corporation":false,"usgs":false,"family":"Freeman","given":"Philip A.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":955299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coburn, Timothy 0000-0002-7309-0962","orcid":"https://orcid.org/0000-0002-7309-0962","contributorId":366116,"corporation":false,"usgs":false,"family":"Coburn","given":"Timothy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":955300,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273893,"text":"70273893 - 2026 - Abiotic and biotic controls of non-native perennial plant success in drylands","interactions":[],"lastModifiedDate":"2026-02-12T16:42:55.789341","indexId":"70273893","displayToPublicDate":"2026-02-06T08:03:49","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6505,"text":"Nature Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Abiotic and biotic controls of non-native perennial plant success in drylands","docAbstract":"Drivers of non-native plant success in drylands are poorly understood. Here we identify functional differences between dryland native and non-native perennial plants and assess how biotic, abiotic and anthropogenic factors shape the success of the latter. On the basis of plant community and functional trait data from 98 sites across 25 countries, we report a total of 41 non-native plant species at 31 sites. Non-natives tend towards faster growth strategies than natives. Non-native plant richness is higher at sites with greater grazing pressure and under environmental conditions associated with higher soil fertility, decomposition and fungal richness—conditions that tend to occur in less arid regions—and lower where native plant and herbivore richness are greater. Non-native plant cover correlates positively with grazing pressure and negatively with native plant richness. Taken together, our results suggest that non-native plant success in drylands is facilitated when high grazing pressure coincides with elevated resource availability. Such context-dependence of non-native plant success and linkages with native plant and herbivore diversity highlight the need for managing grazing and conserving biodiversity across the world’s drylands.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41559-025-02971-6","usgsCitation":"Rahmanian, S., Eisenhauer, N., Huang, Y., Hejda, M., Pyšek, P., Feilhauer, H., Eldridge, D.J., Gross, N., Le Bagousse-Pinguet, Y., Saiz, H., Delgado-Baquerizo, M., Berdugo, M., Ochoa, V., Gozalo, B., Asensio, S., Guirado, E., Valencia, E., García-Gómez, M., Gaitán, J.J., Mendoza, B.J., Plaza, C., Díaz-Martínez, P., Martínez-Valderrama, J., Abedi, M., Ahmadian, N., Ahumada, R.J., Amghar, F., Araújo, T., Arroyo, A.I., Ben Salem, F., Blaum, N., Boldbat, E., Boldgiv, B., Bowker, M., van den Brink, L., Bu, C., Canessa, R., Castillo-Monroy, A.P., Castro, H., Castro-Quezada, P., Chaieb, G., Chibani, R., Conceição, A.A., Davila, Y.C., Deák, B., Donoso, D.A., Dougill, A., Espinosa, C.I., Fajardo, A., Farzam, M., Ferrante, D., Franzese, J., Fraser, L.H., Geiger, E.L., Gonzalez, S.L., Montalván, E.G., Hering, R., Marais, E., Hernández, R.M., Hernández-Valdez, S., Hölzel, N., Huber-Sannwald, E., Jadán, O., Jentsch, A., Kindermann, L., Köbel, M., le Roux, P.C., Leder, C.V., Li, X., Liancourt, P., Linstädter, A., Liu, J., Louw, M.A., Maggs-Kölling, G., Makhalanyane, T.P., Issa, O.M., Manzaneda, A.J., Margerie, P., Martin, R., McClaran, M.P., Messeder, J.V., Mora, J.P., Moreno, G., Munson, S.M., Nair, G.R., Nunes, A., Oliva, G., Palpurina, S., Peter, G., Pueyo, Y., Quiroga, E., Reed, S.C., Rey, P.J., Rodríguez, A., Rolo, V., Ruppert, J.C., Salah, A., Sarig, S., Singh, B.K., Swemmer, A.M., Teixido, A.L., Thomas, A.D., Tielbörger, K., Travers, S.K., Valkó, O., Wamiti, W., Wang, D., Wang, L., Wardle, G.M., Wolff, P., Yahdjian, L., Oñatibia, G.R., Yari, R., Zaady, E., Zhang, Y., Zhou, X., and Maestre, F.T., 2026, Abiotic and biotic controls of non-native perennial plant success in drylands: Nature Ecology and Evolution, https://doi.org/10.1038/s41559-025-02971-6.","ipdsId":"IP-183980","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Rahmanian, Soroor","contributorId":366231,"corporation":false,"usgs":false,"family":"Rahmanian","given":"Soroor","affiliations":[{"id":87393,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; 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The study area included 136 streamgages in urban areas in Tennessee, Mississippi, Alabama, Georgia, South Carolina, and North Carolina that had at least 10 percent developed imperviousness in their basins as indicated by data from the 2011 National Land Cover Database. Regression equations were developed to predict streamflows corresponding to the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEPs) and were incorporated into the StreamStats application. In generalized least-squares regression, the base-10 logarithm of drainage area, the percentages of the streamgage basins in developed land use, and the percentages of the streamgage basins in the Piedmont and Ridge and Valley Level 3 ecoregions were statistically significant in explaining the variability in annual peak streamflows in the study area. Drainage areas ranged from 0.164 to 93.4 square miles, the percentage of the streamgage basins in developed land use ranged from 26 to 100 percent, and the percentage of the streamgage basins in Piedmont and Ridge and Valley Level 3 ecoregions ranged from 0 to 100 percent. Pseudo R-squared values for the regression equations ranged from 0.86, or 86 percent, for the 50- and 20-percent AEPs (2- and 5-year floods) to 0.71, or 71 percent, for the 0.2-percent AEP (500-year flood). The average variance of prediction (in log base-10 units) ranged from 0.023 for the 20- and 10-percent AEPs to 0.05 for the 0.2-percent AEP. The average variance of prediction can be reported as a percentage of the predicted value, known as the standard error of prediction, which ranged from 35.8 percent for the 20-percent AEP (5-year flood) to 55.4 percent for the 0.2-percent AEP (500-year flood). Methods are presented for estimating annual peak streamflows for gaged locations, ungaged locations on gaged streams, and locations on ungaged streams.
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