{"pageNumber":"4","pageRowStart":"75","pageSize":"25","recordCount":46593,"records":[{"id":70274650,"text":"70274650 - 2026 - Intraspecific contact among white-tailed deer: A literature review and chronic wasting disease case study","interactions":[],"lastModifiedDate":"2026-04-02T16:47:01.123474","indexId":"70274650","displayToPublicDate":"2026-02-12T09:38:04","publicationYear":"2026","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":"Intraspecific contact among white-tailed deer: A literature review and chronic wasting disease case study","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>White-tailed deer (</span><i>Odocoileus virginianus</i><span>) are a valuable game mammal in the eastern United States necessitating detailed understanding of disease transmission. We conducted a literature review on intraspecific contact (i.e., interactions wherein disease transmission may occur) among deer. From 69 studies, we identified five themes underlying research on intraspecific deer contact: physical touch, social groups, spatial overlap, association rates, and social networks. Visual observations determined physical touch to be infrequent (&lt; 2 touches/h) and indicated deer social groups were dependent on spatial dynamics of parturition and dispersal; most females remained with matriarchal family groups while males dispersed and formed bachelor groups. Assessed using global positioning system (GPS) monitoring, spatial overlap and association rates (i.e., instances of deer in close spatial–temporal proximity) were higher in correspondence to within-group social dynamics, and between-group scores were correspondingly low. Social network analyses indicated between-group transmission may be driven by socially dominant males, often termed super-spreaders (i.e., hosts infecting disproportionately high numbers of healthy individuals). We investigated these themes via a case study of deer infected with chronic wasting disease (CWD) in southcentral Pennsylvania, United States. We assessed spatial overlap and association rates using GPS monitoring data from 180 deer. Our results supported findings in the literature, showing strong correlations among spatial overlap, association rates, and correlated movements. Further, CWD-infected deer exhibited similar association rates to deer in which CWD was not detected. Our literature review and case study indicate direct transmission of CWD and other diseases is likely greatest within social groups following seasonal behavioral dynamics and that between-group transmission is likely driven by males via dispersal and mating interactions. Our results may be used to inform population management models with future work focused on high resolution spatial assessments of transmission in localized areas.</span></span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.73040","usgsCitation":"Wehr, N.H., Bondo, K.J., Rosenberry, C.S., Stainbrook, D., Wallingford, B.D., and Walter, W., 2026, Intraspecific contact among white-tailed deer: A literature review and chronic wasting disease case study: Ecology and Evolution, v. 16, no. 2, e73040, 20 p., https://doi.org/10.1002/ece3.73040.","productDescription":"e73040, 20 p.","ipdsId":"IP-182245","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502090,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.73040","text":"Publisher Index Page"},{"id":502014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"southcentral Pennsylvania","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -78.32215898198577,\n              40.430818578170204\n            ],\n            [\n              -78.32215898198577,\n              39.73578816878745\n            ],\n            [\n              -77.0741919959731,\n              39.73578816878745\n            ],\n            [\n              -77.0741919959731,\n              40.430818578170204\n            ],\n            [\n              -78.32215898198577,\n              40.430818578170204\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wehr, Nathaniel H.","contributorId":369169,"corporation":false,"usgs":false,"family":"Wehr","given":"Nathaniel","middleInitial":"H.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":958559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bondo, Kristin J.","contributorId":369170,"corporation":false,"usgs":false,"family":"Bondo","given":"Kristin","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":958560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberry, Christopher S.","contributorId":369171,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher","middleInitial":"S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":958561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stainbrook, David","contributorId":272188,"corporation":false,"usgs":false,"family":"Stainbrook","given":"David","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":958562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wallingford, Bret D.","contributorId":369173,"corporation":false,"usgs":false,"family":"Wallingford","given":"Bret","middleInitial":"D.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":958563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":958564,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70274182,"text":"70274182 - 2026 - Multiple-well monitoring site adjacent to the Midway- Sunset and Buena Vista Oil Fields, Kern County, California","interactions":[],"lastModifiedDate":"2026-03-05T15:22:54.30645","indexId":"70274182","displayToPublicDate":"2026-02-12T09:15:08","publicationYear":"2026","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Multiple-well monitoring site adjacent to the Midway- Sunset and Buena Vista Oil Fields, Kern County, California","docAbstract":"<p><span>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.</span><br><span>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.</span></p>","language":"English","publisher":"EarthArXiv","doi":"10.31223/X5W48W","usgsCitation":"Everett, R.R., Gillespie, J.M., Gannon, R., Brown, A.A., and Morita, A., 2026, Multiple-well monitoring site adjacent to the Midway- Sunset and Buena Vista Oil Fields, Kern County, California: EarthArXiv, preprint posted February 12, 2026, https://doi.org/10.31223/X5W48W.","productDescription":"115 p.","ipdsId":"IP-183880","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":500778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2026-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Everett, Rhett R. 0000-0001-7983-6270","orcid":"https://orcid.org/0000-0001-7983-6270","contributorId":208212,"corporation":false,"usgs":true,"family":"Everett","given":"Rhett","email":"","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gillespie, Janice M. 0000-0003-1667-3472","orcid":"https://orcid.org/0000-0003-1667-3472","contributorId":219675,"corporation":false,"usgs":true,"family":"Gillespie","given":"Janice","email":"","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gannon, Riley 0000-0002-1239-1083","orcid":"https://orcid.org/0000-0002-1239-1083","contributorId":205967,"corporation":false,"usgs":true,"family":"Gannon","given":"Riley","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Anthony A. 0000-0001-9925-0197","orcid":"https://orcid.org/0000-0001-9925-0197","contributorId":219711,"corporation":false,"usgs":true,"family":"Brown","given":"Anthony","email":"","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956802,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morita, Andrew 0000-0002-8120-996X","orcid":"https://orcid.org/0000-0002-8120-996X","contributorId":221237,"corporation":false,"usgs":true,"family":"Morita","given":"Andrew","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956803,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70274155,"text":"70274155 - 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","interactions":[],"lastModifiedDate":"2026-03-03T14:25:13.74987","indexId":"70274155","displayToPublicDate":"2026-02-12T08:04:54","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"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","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>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.</span></span></p>","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":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>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 (&lt; 1 km</span><sup>2</sup><span>) groundwater-influenced lakes and wetlands globally have little in&nbsp;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 km</span><sup>2</sup><span>&nbsp;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.</span></span></p>","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":"<p><span>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.</span></p>","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. Box 5640, Northern Arizona University, Flagstaff, Arizona 86011, USA","active":true,"usgs":false}],"preferred":false,"id":956201,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Seymour, Megan","contributorId":271173,"corporation":false,"usgs":false,"family":"Seymour","given":"Megan","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":956202,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Straw, Bethany R. 0000-0001-9086-4600","orcid":"https://orcid.org/0000-0001-9086-4600","contributorId":271020,"corporation":false,"usgs":true,"family":"Straw","given":"Bethany","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":956203,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":956204,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Udell, Bradley James 0000-0001-5225-4959","orcid":"https://orcid.org/0000-0001-5225-4959","contributorId":271174,"corporation":false,"usgs":true,"family":"Udell","given":"Bradley","email":"","middleInitial":"James","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":956205,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wiens, Ashton M. 0000-0002-7030-0602","orcid":"https://orcid.org/0000-0002-7030-0602","contributorId":271176,"corporation":false,"usgs":true,"family":"Wiens","given":"Ashton","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956206,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Diffendorfer, James E. 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","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":956207,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"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. Current address:  TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":false,"id":956494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":956495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":956496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waldhauser, Felix","contributorId":344893,"corporation":false,"usgs":false,"family":"Waldhauser","given":"Felix","affiliations":[{"id":51448,"text":"Lamont Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":956497,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Assumpcao, Marcelo","contributorId":366977,"corporation":false,"usgs":false,"family":"Assumpcao","given":"Marcelo","affiliations":[{"id":48623,"text":"University of Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":956498,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70273941,"text":"70273941 - 2026 - Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps","interactions":[],"lastModifiedDate":"2026-02-18T14:40:29.316588","indexId":"70273941","displayToPublicDate":"2026-02-10T07:28:31","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23283,"text":"Environmental Research: Water","active":true,"publicationSubtype":{"id":10}},"title":"Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps","docAbstract":"<p><span>An increase in the occurrence of large, high severity wildfires in the western Pacific Northwest (PNW), USA, has created an urgent need for science to better inform forest management and policy decisions to maintain source water quality in the region. 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.</span></p>","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 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Ashley A.","contributorId":210129,"corporation":false,"usgs":false,"family":"Coble","given":"Ashley","email":"","middleInitial":"A.","affiliations":[{"id":38077,"text":"National Council for Air and Stream Improvement","active":true,"usgs":false}],"preferred":false,"id":955835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haley, Beth M. 0000-0003-0236-4687","orcid":"https://orcid.org/0000-0003-0236-4687","contributorId":366406,"corporation":false,"usgs":false,"family":"Haley","given":"Beth","middleInitial":"M.","affiliations":[{"id":87471,"text":"US Environmental Protection Agency, Pacific Ecological Systems Division","active":true,"usgs":false}],"preferred":false,"id":955836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lin, Jiajia","contributorId":211160,"corporation":false,"usgs":false,"family":"Lin","given":"Jiajia","email":"","affiliations":[{"id":38185,"text":"USEPA, Corvallis, Oregon","active":true,"usgs":false}],"preferred":false,"id":955837,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Myers-Pigg, Allison","contributorId":224762,"corporation":false,"usgs":false,"family":"Myers-Pigg","given":"Allison","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":955838,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reale, Justin Kevin 0000-0003-3523-4782","orcid":"https://orcid.org/0000-0003-3523-4782","contributorId":366407,"corporation":false,"usgs":true,"family":"Reale","given":"Justin","middleInitial":"Kevin","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955839,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wampler, Katie 0000-0002-5584-7554","orcid":"https://orcid.org/0000-0002-5584-7554","contributorId":366408,"corporation":false,"usgs":false,"family":"Wampler","given":"Katie","affiliations":[{"id":87472,"text":"Forest Ecosystems and Society, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":955840,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Swartz, Allison 0000-0002-2006-1455","orcid":"https://orcid.org/0000-0002-2006-1455","contributorId":298656,"corporation":false,"usgs":false,"family":"Swartz","given":"Allison","affiliations":[{"id":64645,"text":"Former USGSResearch Associate","active":true,"usgs":false}],"preferred":false,"id":955841,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Moffett, Kevan","contributorId":344523,"corporation":false,"usgs":false,"family":"Moffett","given":"Kevan","affiliations":[{"id":37380,"text":"Washington State 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0000-0002-5605-6500","orcid":"https://orcid.org/0000-0002-5605-6500","contributorId":366410,"corporation":false,"usgs":false,"family":"Chang","given":"Heejun","affiliations":[{"id":87474,"text":"School of Earth, Environment, and Society, Portland State University","active":true,"usgs":false}],"preferred":false,"id":955845,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Chen, Junjie 0000-0002-1396-1180","orcid":"https://orcid.org/0000-0002-1396-1180","contributorId":366411,"corporation":false,"usgs":false,"family":"Chen","given":"Junjie","affiliations":[{"id":87474,"text":"School of Earth, Environment, and Society, Portland State University","active":true,"usgs":false}],"preferred":false,"id":955846,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Donahue, David","contributorId":294722,"corporation":false,"usgs":false,"family":"Donahue","given":"David","email":"","affiliations":[{"id":12713,"text":"Eugene Water and Electric Board","active":true,"usgs":false}],"preferred":false,"id":955847,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Eckley, Chris S. 0000-0002-6986-4451","orcid":"https://orcid.org/0000-0002-6986-4451","contributorId":246031,"corporation":false,"usgs":false,"family":"Eckley","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":955848,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Hohner, Amanda K. 0000-0001-7704-4464","orcid":"https://orcid.org/0000-0001-7704-4464","contributorId":366412,"corporation":false,"usgs":false,"family":"Hohner","given":"Amanda","middleInitial":"K.","affiliations":[{"id":87475,"text":"Department of Civil Engineering, Montana State University","active":true,"usgs":false}],"preferred":false,"id":955849,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Kiffney, Peter M.","contributorId":338029,"corporation":false,"usgs":false,"family":"Kiffney","given":"Peter","middleInitial":"M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":955850,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Miralha, Lorrayne 0000-0003-1448-9321","orcid":"https://orcid.org/0000-0003-1448-9321","contributorId":366413,"corporation":false,"usgs":false,"family":"Miralha","given":"Lorrayne","affiliations":[{"id":87477,"text":"Department of Food, Agricultural and Biological Engineering, The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":955851,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Regier, Peter","contributorId":352457,"corporation":false,"usgs":false,"family":"Regier","given":"Peter","affiliations":[{"id":84230,"text":"Energy & Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA","active":true,"usgs":false}],"preferred":false,"id":955852,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Seeds, Joshua","contributorId":366414,"corporation":false,"usgs":false,"family":"Seeds","given":"Joshua","affiliations":[{"id":87478,"text":"Oregon Department of Environmental Quality, Water Quality Division","active":true,"usgs":false}],"preferred":false,"id":955853,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"River, Mark 0009-0005-4967-8087","orcid":"https://orcid.org/0009-0005-4967-8087","contributorId":366415,"corporation":false,"usgs":false,"family":"River","given":"Mark","affiliations":[{"id":56610,"text":"Weyerhaeuser Company","active":true,"usgs":false}],"preferred":false,"id":955854,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}}
,{"id":70273684,"text":"mcs2026 - 2026 - Mineral commodity summaries 2026","interactions":[],"lastModifiedDate":"2026-03-05T17:51:03.008909","indexId":"mcs2026","displayToPublicDate":"2026-02-06T11:55:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":323,"text":"Mineral Commodity Summaries","code":"MCS","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026","displayTitle":"Mineral Commodity Summaries 2026","title":"Mineral commodity summaries 2026","docAbstract":"<h1>Introduction&nbsp;</h1><p>Each mineral commodity chapter of the 2026 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production, reserves, and resources. 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.</p><p>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.</p><p>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.</p>","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":"<p>Director, <a href=\"https://www.usgs.gov/centers/national-minerals-information-center/connect\" data-mce-href=\"https://www.usgs.gov/centers/national-minerals-information-center/connect\">National Minerals Information Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>988 National Center<br>Reston, VA 20192</p><p>Email: <a href=\"mailto:nmicrecordsmgt@usgs.gov\" data-mce-href=\"mailto:nmicrecordsmgt@usgs.gov\">nmicrecordsmgt@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Figure 1—The Role of Nonfuel Mineral Commodities in the U.S. Economy</li><li>Significant Events, Trends, and Issues</li><li>Table 1—U.S. Mineral Industry Trends</li><li>Table 2—U.S. Mineral-Related Economic Trends</li><li>Figure 2—2025 U.S. Net Import Reliance</li><li>Figure 3—Import Sources (2021–24) of Critical Minerals</li><li>Table 3—Value of Nonfuel Mineral Production in the United States in 2025</li><li>Table 4—Export Control on Mineral Commodities, by Country</li><li>Table 5—Recent Mineral-Related Trade Agreements, by Country</li><li>Figures 4–8—Value of Nonfuel Minerals Produced in 2025</li><li>Table 6—The U.S. Final 2025 Critical Minerals List</li><li>U.S. Critical Minerals Update</li><li>Table 7—Salient Critical Minerals Statistics in 2025</li><li>Figure 9—20-Year Trend of U.S. Net Import Reliance for Critical Minerals</li><li>Figure 10—Estimated 1-Year Percent Change and 5-Year Compound Annual Growth Rate in Prices of&nbsp;Critical Minerals</li><li>Figures 11–12—Change in U.S. Consumption of Nonfuel Mineral Commodities</li><li>Figure 13—2025 Value of Old Scrap Domestically Recycled, Imported, and Exported</li><li>Appendix A—Abbreviations and Units of Measure</li><li>Appendix B—Definitions of Selected Terms Used in This Report</li><li>Appendix C—Reserves and Resources</li><li>Appendix D—Country Specialists Directory</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2026-02-06","revisedDate":"2026-03-05","noUsgsAuthors":false,"publicationDate":"2026-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":127955,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":955034,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"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":"<p><span>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.</span></p>","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":70273385,"text":"sir20255104 - 2026 - Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year","interactions":[],"lastModifiedDate":"2026-02-05T22:06:06.078649","indexId":"sir20255104","displayToPublicDate":"2026-02-05T08:41:23","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5104","displayTitle":"Estimating the Magnitude and Frequency of Floods at Ungaged Locations on Urban Streams in Tennessee and Parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, Using Data Through the 2022 Water Year","title":"Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year","docAbstract":"<p>In 2024, the U.S. Geological Survey, in cooperation with the Tennessee Department of Transportation, updated the methods for predicting the magnitude and frequency of floods at ungaged locations on streams in urban areas in Tennessee. 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.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255104","issn":"2328-​0328","collaboration":"Prepared in cooperation with the Tennessee Department of Transportation","usgsCitation":"Wagner, D.M., and Ladd, D.E., 2026, Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year: U.S. Geological Survey Scientific Investigations Report 2025–5104, 17 p., https://doi.org/10.3133/sir20255104.","productDescription":"Report: vi, 17 p.; 3 Data Releases","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-168835","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":498491,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1JTPBQY","text":"USGS Data Release","linkHelpText":"- Basin characteristics in support of generalized least-​squares (GLS) regression for 136 USGS streamgages in urban areas in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina (ver. 2.0, September 2025)"},{"id":498493,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1TE9KTH","text":"USGS Data Release","linkHelpText":"- Results of generalized least-squares (GLS) regression for 136 USGS streamgages in urban areas in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina (ver. 1.1, January 2026)"},{"id":498490,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255104/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5104 HTML"},{"id":498492,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VMP56R","text":"USGS Data Release","linkHelpText":"- At-​site flood frequency for 139 urban streamgages in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina using data through water year 2022"},{"id":498489,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5104/sir20255104.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5104 XML"},{"id":498488,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5104/sir20255104.pdf","size":"4.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5104 pdf"},{"id":498487,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5104/coverthb.jpg"},{"id":498486,"rank":1,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5104/images"},{"id":499610,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119203.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alabama, Georgia, Mississippi, North Carolina, South Carolina, Tennessee","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -77.72376308003552,\n              36.51223155288169\n            ],\n            [\n              -91.20372096539845,\n              36.51223155288169\n            ],\n            [\n              -91.20372096539845,\n              31.285937937939266\n            ],\n            [\n              -77.72376308003552,\n              31.285937937939266\n            ],\n            [\n              -77.72376308003552,\n            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PSC"},"publishedDate":"2026-02-05","noUsgsAuthors":false,"publicationDate":"2026-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Daniel M. 0000-0002-0432-450X dwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-0432-450X","contributorId":4531,"corporation":false,"usgs":true,"family":"Wagner","given":"Daniel","email":"dwagner@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":953529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ladd, David E. 0000-0002-9247-7839","orcid":"https://orcid.org/0000-0002-9247-7839","contributorId":216197,"corporation":false,"usgs":true,"family":"Ladd","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":953530,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70273860,"text":"70273860 - 2026 - James Buttle Review: A synthesis of riparian plant water use over two decades in North American drylands","interactions":[],"lastModifiedDate":"2026-02-09T15:06:28.28691","indexId":"70273860","displayToPublicDate":"2026-02-05T07:57:07","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":"James Buttle Review: A synthesis of riparian plant water use over two decades in North American drylands","docAbstract":"Assessing riparian ecosystem water use, particularly transpiration from vegetation and evaporation from soils (‘plant water use’, hereafter), is key to developing sound water management approaches. In western North America, a multidecadal drought is reducing water availability and increasing the use of detailed water budgets. Questions related to both removal of vegetation for water salvage and budgeting water to maintain valuable riparian areas have led to a wealth of studies on riparian plant water use across dryland river systems in North America. Towards evaluating broad patterns in riparian plant water use, we synthesise results from over two decades of research, with the goal of informing water management policies and planning. This study asks: (1) Do some riparian plant communities exhibit lower plant water use than others? (2) Do riparian plant communities have higher water use under hotter climates? (3) Can statistical models based on existing data, plant communities and climate data be used to predict water use for unmeasured locations? Using hierarchical Bayesian models to synthesise data on annual and daily-scale plant water use, we show that marshes, cottonwood-willow stands and tamarisk not impacted by biocontrol use larger amounts of water at the annual scale than other vegetation communities. All plant communities have higher annual water use in hotter climates, which is likely related to a longer growing season and higher evaporative demand. Statistical models based on existing water-use data, plant communities and climate provide bounds on plant water use that can be applied to unmeasured locations and used to evaluate the effects of plant community change on water use. This synthesis produces the most complete summary of riparian plant water use in North American drylands to date and provides water use predictions across different climate and community scenarios that can be used for current and future conditions.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70408","usgsCitation":"Palmquist, E.C., Nagler, P., Ogle, K., DiMartini, C., Kennedy, J.R., and Sankey, J., 2026, James Buttle Review: A synthesis of riparian plant water use over two decades in North American drylands: Hydrological Processes, v. 40, no. 2, e70408, 19 p., https://doi.org/10.1002/hyp.70408.","productDescription":"e70408, 19 p.","ipdsId":"IP-180955","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Idaho, Nevada, New Mexico, Oregon, Texas, Wyoming","otherGeospatial":"western Pacific Coastal Plain, western Sierra Madre Piedmont","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.30356692213257,\n              42.70636820692536\n            ],\n            [\n              -121.30356692213257,\n              26.163421921669297\n            ],\n            [\n              -103.74141493477907,\n              26.163421921669297\n            ],\n            [\n              -103.74141493477907,\n              42.70636820692536\n            ],\n            [\n              -121.30356692213257,\n              42.70636820692536\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":955301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":363777,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","middleInitial":"L.","affiliations":[],"preferred":true,"id":955302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ogle, Kiona","contributorId":360747,"corporation":false,"usgs":false,"family":"Ogle","given":"Kiona","affiliations":[{"id":86099,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86011, USA","active":true,"usgs":false}],"preferred":false,"id":955303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DiMartini, Claudia 0009-0001-2457-0513","orcid":"https://orcid.org/0009-0001-2457-0513","contributorId":356930,"corporation":false,"usgs":false,"family":"DiMartini","given":"Claudia","affiliations":[{"id":85285,"text":"formerly: US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":955304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":176478,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":955305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":955306,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70273788,"text":"sir20255112 - 2026 - Flood- inundation maps for Río Grande De Loíza in and near Caguas, Puerto Rico, 2026","interactions":[],"lastModifiedDate":"2026-02-05T22:03:23.350948","indexId":"sir20255112","displayToPublicDate":"2026-02-04T15:25:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5112","displayTitle":"Flood- Inundation Maps for Río Grande De Loíza In and Near Caguas, Puerto Rico, 2026","title":"Flood- inundation maps for Río Grande De Loíza in and near Caguas, Puerto Rico, 2026","docAbstract":"<p>Digital flood- inundation maps for a 2.7- mile reach of Río Grande De Loíza in Caguas, Puerto Rico, were created by the U.S. Geological Survey. Water- surface profiles were computed for the stream reach by using a one- dimensional, steady- state, step- backwater model. The model was calibrated to the current (2025) stage- streamflow relation (rating curve) for the U.S. Geological Survey streamgage 50055000, Río Grande De Loíza, Puerto Rico. The resulting hydraulic model was then used to compute 16 water- surface profiles for water levels (flood stages) ranging from 19.00 to 34.00 feet at the streamgage; these flood stages range from “moderate flood stage” to above “major flood stage” as defined by the National Weather Service. The 34.00- foot stage exceeds the historical maximum peak stage of 33.20 feet, recorded at the streamgage in 1945. The simulated water- surface profiles were used in combination with a digital elevation model derived from light detection and ranging (lidar) data to map the inundated areas associated with each flood profile.</p><p>The flood- inundation maps and the supporting hydraulic model produced by this study can be used by emergency managers and local officials to assess flood- mitigation strategies and to define flood- hazard areas to help protect life and property, to coordinate flood- response activities such as evacuations and road closures, and to aid post- flood recovery efforts.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255112","usgsCitation":"Ostheimer, C.J., Torres- Garcia, L.M., and Gomez- Fragoso, J.M., 2026, Flood- inundation maps for Río Grande De Loíza in and near Caguas, Puerto Rico, 2026: U.S. Geological Survey Scientific Investigations Report 2025–5112, 16 p., https://doi.org/10.3133/sir20255112.","productDescription":"Report: vii, 16 p.; 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,{"id":70273695,"text":"sir20255098 - 2026 - Water-budget simulations for selected watersheds in Cameron County, Texas, 2022–23","interactions":[],"lastModifiedDate":"2026-02-11T18:58:23.302299","indexId":"sir20255098","displayToPublicDate":"2026-02-04T09:07:41","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5098","displayTitle":"Water-Budget Simulations for Selected Watersheds in Cameron County, Texas, 2022–23","title":"Water-budget simulations for selected watersheds in Cameron County, Texas, 2022–23","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the City of Brownsville, Texas, configured and calibrated a set of hydrologic models for a 217-square-mile study area in Cameron County in south Texas during 2022–23. The models were used for estimating runoff and quantities of water diverted from the Rio Grande/Rio Bravo del Norte (hereinafter referred to as the “Rio Grande”) to maintain water-surface elevations in the canals and resacas (former distributary channels cut off from the main channel of the Rio Grande). Resacas provide habitat to aquatic species and help reduce the effects of flooding.</p><p>Because of the large size of the study area and diversity of hydrologic conditions, the study area was divided into 11 watersheds, and separate hydrologic models were developed for 9 of the watersheds. Six of the nine modeled watersheds are drained mostly by canals (canal watersheds), and three of the modeled watersheds drain to resacas (resaca watersheds). The Hydrological Simulation Program—FORTRAN was selected for modeling the study area watersheds because it is flexible in simulating a wide variety of watershed conditions.</p><p>The models were calibrated with streamflow data collected during 2022–23. The calibrated models were used to simulate water budgets (streamflow, evapotranspiration, water-storage volumes, and water diversions and withdrawals) during 2022–23. Model simulations showed that the resaca watersheds required more diversions from the Rio Grande and released less runoff than did the canal watersheds. Management practices maintaining resaca water levels constrained their runoff.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255098","issn":"2328-0328","collaboration":"Prepared in cooperation with the City of Brownsville, Texas","usgsCitation":"Ockerman, D.J., and Choi, N., 2026, Water-budget simulations for selected watersheds in Cameron County, Texas, 2022–23: U.S. Geological Survey Scientific Investigations Report 2025–5098, 37 p., https://doi.org/10.3133/sir20255098.","productDescription":"Report: viii, 37 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-167896","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":499763,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255098/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5098 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<a data-mce-href=\"https://www.usgs.gov/centers/ot-water\" href=\"https://www.usgs.gov/centers/ot-water\">Oklahoma-Texas Water Science Center</a><br>U.S. Geological Survey<br>1505 Ferguson Lane<br>Austin, <span data-olk-copy-source=\"MessageBody\">TX 78754–4501</span></p><p><span data-olk-copy-source=\"MessageBody\"><a id=\"LPlnkOWAb30f03cb-e6c0-c412-988f-235c353ce0b0\" class=\"OWAAutoLink\" href=\"../contact\" data-auth=\"NotApplicable\" data-mce-href=\"../contact\">Contact Us- USGS Publications Warehouse</a></span></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Water-Budget Simulations of Cameron County Study Area Watersheds</li><li>Model Development</li><li>Water-Budget Simulation Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2026-02-04","noUsgsAuthors":false,"publicationDate":"2026-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Ockerman, Darwin J. 0000-0003-1958-1688","orcid":"https://orcid.org/0000-0003-1958-1688","contributorId":222708,"corporation":false,"usgs":true,"family":"Ockerman","given":"Darwin","email":"","middleInitial":"J.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Choi, Namjeong 0000-0002-9526-0504","orcid":"https://orcid.org/0000-0002-9526-0504","contributorId":350953,"corporation":false,"usgs":true,"family":"Choi","given":"Namjeong","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954310,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70273882,"text":"70273882 - 2026 - Ensemble methods for history matching and uncertainty quantification with a watershed model","interactions":[],"lastModifiedDate":"2026-02-11T15:28:04.157861","indexId":"70273882","displayToPublicDate":"2026-02-04T08:23:01","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Ensemble methods for history matching and uncertainty quantification with a watershed model","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>History matching of large hydrologic models is challenging due to data sparsity and non-unique process combinations (and associated parameters) that can produce similar model predictions. We develop an ensemble-based history matching (and uncertainty quantification) approach using an iterative ensemble smoother (iES) method for three cutouts of the National Hydrologic Model (NHM) and qualitatively compare the results and performance to the stepwise history matching approach. In the latter approach, subsets of parameters and observations were sequentially calibrated to a diverse range of observations to mitigate non-uniqueness and local minima. In iES, localization simulates the same causal connections between parameters and observations without the need (and computational cost) of sequential history matching steps. iES uses a weighted sum-of-squared-errors objective function which allows differential weighting of multiple data sources. Formal adoption of range observation also pushes results to within ranges of observation values rather than discrete values. Overall, the ensemble approach performs similarly to the stepwise approach. Both approaches performed poorly for the cutout representing a snowmelt-dominated watershed, indicating a structural issue in the process representation of the model. The main advantage of iES is quantification of uncertainty in both the history matching and the predictions of interest.</span></span></p>","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70086","usgsCitation":"Fienen, M., Long, A.J., Markovich, K.H., Haj, A.E., and Barker, M., 2026, Ensemble methods for history matching and uncertainty quantification with a watershed model: Journal of the American Water Resources Association, v. 62, no. 1, e70086, 18 p., https://doi.org/10.1111/1752-1688.70086.","productDescription":"e70086, 18 p.","ipdsId":"IP-181945","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n   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Center","active":true,"usgs":true}],"preferred":true,"id":955412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markovich, Katherine H. 0000-0002-4455-8255","orcid":"https://orcid.org/0000-0002-4455-8255","contributorId":221065,"corporation":false,"usgs":false,"family":"Markovich","given":"Katherine","middleInitial":"H.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":955414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haj, Adel E. 0000-0002-3377-7161 ahaj@usgs.gov","orcid":"https://orcid.org/0000-0002-3377-7161","contributorId":147631,"corporation":false,"usgs":true,"family":"Haj","given":"Adel","email":"ahaj@usgs.gov","middleInitial":"E.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955415,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barker, Matthew Irwin 0000-0002-5286-4930","orcid":"https://orcid.org/0000-0002-5286-4930","contributorId":358465,"corporation":false,"usgs":true,"family":"Barker","given":"Matthew Irwin","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955416,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70273867,"text":"70273867 - 2026 - Cotton farming affects ileal virome in a sedentary wild passerine","interactions":[],"lastModifiedDate":"2026-02-10T15:01:13.562068","indexId":"70273867","displayToPublicDate":"2026-02-03T07:54:50","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23298,"text":"Animal Microbiome","active":true,"publicationSubtype":{"id":10}},"title":"Cotton farming affects ileal virome in a sedentary wild passerine","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Although a few studies have focused on avian gut virome variation in response to environmental stressors, none have assessed virome in relation to the production of chemically intensive crop-based agriculture that alters food resources and detrimentally affects various aspects of avian health and fitness. In this study, we used shotgun metatranscriptomics to assess whether exposure to cotton (</span><i>Gossypium</i><span>&nbsp;spp.) production had a deleterious effect on the ileal virome of sedentary northern mockingbirds (</span><i>Mimus polyglottos</i><span>) sampled from two cotton-producing areas (16 birds in total) and one uncultivated area (7 birds) in Texas, USA. We recovered 43 viruses representing 13 virus families, which included two viruses that appear to be potential vertebrate pathogens. Individual sample richness varied from 25 to 33 viruses. Both virome richness (Adj. r</span><sup>2</sup><span> = 0.247, F</span><sub>(2, 20)</sub><span> = 4.615,&nbsp;</span><i>P</i><span> = 0.022) and composition (r</span><sup>2</sup><span> = 0.370, F</span><sub>(2, 20)</sub><span> = 5.883,&nbsp;</span><i>P</i><span> = 0.001) differed among three sampling regions. Cotton production was associated with the increase of virome richness (Adj. r</span><sup>2</sup><span> = 0.283, df = 22,&nbsp;</span><i>P</i><span> = 0.005). Pesticide occurrence data collected using silicone bands at the three sites suggest that virome compositional changes are not only associated with total pesticide exposure but are also particularly sensitive to the pesticide combinations detected at each location.</span></span></p>","language":"English","publisher":"Elsevier","doi":"10.1186/s42523-026-00523-2","usgsCitation":"Drovetski, S.V., Bourke, B.P., Hladik, M.L., Ferreira, C.F., Ergunay, K., Linton, Y., Kolpin, D., and Voelker, G., 2026, Cotton farming affects ileal virome in a sedentary wild passerine: Animal Microbiome, v. 8, 8, 12 p., https://doi.org/10.1186/s42523-026-00523-2.","productDescription":"8, 12 p.","ipdsId":"IP-176492","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":499940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42523-026-00523-2","text":"Publisher Index Page"},{"id":499712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"8","noUsgsAuthors":false,"publicationDate":"2026-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":955328,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bourke, Brian P.","contributorId":335297,"corporation":false,"usgs":false,"family":"Bourke","given":"Brian","email":"","middleInitial":"P.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":955329,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferreira, Carolina F. 0000-0001-6303-5954","orcid":"https://orcid.org/0000-0001-6303-5954","contributorId":359793,"corporation":false,"usgs":false,"family":"Ferreira","given":"Carolina","middleInitial":"F.","affiliations":[{"id":85922,"text":"Department of Ecology and Conservation Biology, 2258 TAMU, Texas A&M University, College Station, TX 77843, USA","active":true,"usgs":false}],"preferred":false,"id":955331,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ergunay, Koray","contributorId":335300,"corporation":false,"usgs":false,"family":"Ergunay","given":"Koray","email":"","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":955332,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Linton, Yvonne-Marie","contributorId":335301,"corporation":false,"usgs":false,"family":"Linton","given":"Yvonne-Marie","email":"","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":955333,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":205652,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955334,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Voelker, Gary","contributorId":229521,"corporation":false,"usgs":false,"family":"Voelker","given":"Gary","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":955335,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70273806,"text":"70273806 - 2026 - Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations","interactions":[],"lastModifiedDate":"2026-02-03T14:43:09.95163","indexId":"70273806","displayToPublicDate":"2026-02-02T08:37:01","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations","docAbstract":"<p>In April 2020, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center introduced a Level 2 provisional Aquatic Reflectance (AR) product for the Landsat 8 Operational Land Imager (OLI), marking the initial phase in developing a standardized global product for Landsat-derived surface water measurements. The goal of USGS EROS aquatic product research and development is to prepare for an operational processing architecture for Landsat Collection 3 in the late 2020s that will enable use of quality-controlled data for emerging Landsat aquatic science applications. To achieve this, we released a subset of the Landsat 8/9 provisional AR products (Crawford et al., 2025, https://doi.org/10.5066/P14MBBRM) and examined its general performance through the Science Algorithms to Operations (SATO) framework alongside quantitative assessment using community made inland water data records (GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments, GLORIA) and radiometric coastal validation platforms (NASA’s Ocean Color component of the Aerosol Robotic Network, AERONET-OC). Variability within the validation datasets indicate that the performance of the Landsat 8/9 provisional AR retrieval is highly context-dependent; errors are minimal in optically simple waters (e.g., clear to moderately turbid coastal waters) but increase considerably in optically complex waters where factors such as elevated levels of turbidity, chlorophyll (Chl <i>a</i>) concentrations, or colored dissolved organic matter (CDOM) dominate the water column. Additionally, this paper examines key algorithmic considerations for atmospheric correction, highlighting factors that influence accuracy, scalability, and computational efficiency necessary for collection processing in the operational Landsat Product Generation System (LPGS). This paper is intended to communicate with aquatic scientists, satellite oceanographers, and the broader Earth observation community on the origins, requirements, challenges, successes, and future objectives for operationalizing global AR data products for Landsat satellite missions.</p>","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-2025-317","usgsCitation":"Benjamin Page, Crawford, C., Arab, S., Gail Schmidt, Barnes, C., and Wellington, D., 2026, Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations: Earth System Science Data, v. 18, no. 2, p. 779-800, https://doi.org/10.5194/essd-2025-317.","productDescription":"22 p.","startPage":"779","endPage":"800","ipdsId":"IP-170237","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin Page 0000-0002-9871-2406","orcid":"https://orcid.org/0000-0002-9871-2406","contributorId":359007,"corporation":false,"usgs":false,"family":"Benjamin Page","affiliations":[{"id":85733,"text":"Earth Space Technology Services (ESTS)","active":true,"usgs":false}],"preferred":false,"id":954888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":954889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":299964,"corporation":false,"usgs":false,"family":"Arab","given":"Saeed","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":954890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gail Schmidt 0000-0002-9684-8158","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":359008,"corporation":false,"usgs":false,"family":"Gail Schmidt","affiliations":[{"id":57411,"text":"KBR, Inc.","active":true,"usgs":false}],"preferred":false,"id":954891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnes, Christopher 0000-0002-4608-4364","orcid":"https://orcid.org/0000-0002-4608-4364","contributorId":359949,"corporation":false,"usgs":false,"family":"Barnes","given":"Christopher","affiliations":[{"id":68993,"text":"KBR Inc., Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":954892,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":954893,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70273899,"text":"70273899 - 2026 - New methods provide a 300–year perspective on modern area burned in two wilderness areas of the southwest United States","interactions":[],"lastModifiedDate":"2026-02-12T15:12:38.190359","indexId":"70273899","displayToPublicDate":"2026-02-02T08:05:57","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"New methods provide a 300–year perspective on modern area burned in two wilderness areas of the southwest United States","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Climate change, expanding human ignitions, and increased fuels from fire exclusion are driving increases in area burned and fire severity in dry conifer forests of the western United States. Increasing area burned is occurring against the backdrop of a large fire deficit caused by over a century of fire exclusion. A key land management question is whether historically frequent fire regimes can be restored. Accurate estimates of historical annual area burned (prior to circa 1900) are necessary to evaluate modern area burned (after circa 1900), but are difficult to derive, and have rarely been calibrated or validated against modern fires, leaving their accuracy uncertain. We developed new methods to use tree-ring fire scars to reconstruct historical annual area burned and compare it to modern annual area burned. We focused on two southwestern US wilderness areas—Saguaro National Park (SAGU) and the Gila Wilderness (GILA)—that have a long history of using prescribed and managed fires. The abundant modern low- and moderate-severity fires allowed us to (1) calibrate and validate the fire-scar models against mapped fires to derive the first uncertainty estimates of reconstructed annual area burned and (2) test whether active fire management can help restore annual area burned to historical levels. A multi-model ensemble consisting of 10 individual member models accurately estimated area burned of mapped modern fires with no consistent biases. Each member model had distinct strengths and assumptions that made them suitable for specific applications (e.g., the synchrony model is easily applied, and Thiessen polygons provide spatially explicit area burned estimates). The accurate reconstruction of modern area burned from relatively sparse fire-scar data at GILA suggests that dense grids may not be necessary for accurate reconstructions. Our findings reveal that despite the near absence of fire in the early 20th century, both annual and 20-year sums of area burned in recent decades are back within historical levels at GILA, and trending toward historical levels at SAGU. These results demonstrate that fire management can help restore the historically prevalent, ecologically important process of widespread, frequent, low-to-moderate-severity fire in dry conifer forests.</span></span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70471","usgsCitation":"Farris, C.A., Margolis, E.Q., Iniguez, J., Falk, D., Gerow, K., Baisan, C., Allen, C., and Swetnam, T., 2026, New methods provide a 300–year perspective on modern area burned in two wilderness areas of the southwest United States: Ecosphere, v. 17, no. 2, e70471, 29 p., https://doi.org/10.1002/ecs2.70471.","productDescription":"e70471, 29 p.","ipdsId":"IP-178352","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":499947,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70471","text":"Publisher Index Page"},{"id":499800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","otherGeospatial":"Gila Wilderness, Saguaro National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -108.86590653888511,\n              33.756145710724184\n            ],\n            [\n              -108.86590653888511,\n              32.727467884242714\n            ],\n            [\n              -107.59931693774328,\n              32.727467884242714\n            ],\n            [\n              -107.59931693774328,\n              33.756145710724184\n            ],\n            [\n              -108.86590653888511,\n              33.756145710724184\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -110.76309644861847,\n              32.38160500186068\n            ],\n            [\n              -110.76309644861847,\n              32.04323490904622\n            ],\n            [\n              -110.3491545222334,\n              32.04323490904622\n            ],\n            [\n              -110.3491545222334,\n              32.38160500186068\n            ],\n            [\n              -110.76309644861847,\n              32.38160500186068\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Farris, Calvin A.","contributorId":292802,"corporation":false,"usgs":false,"family":"Farris","given":"Calvin","email":"","middleInitial":"A.","affiliations":[{"id":63015,"text":"National Park Service, Division of Fire and Aviation Management, P.O. Box 1713, Klamath Falls, OR 97601, USA","active":true,"usgs":false}],"preferred":false,"id":955686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":955687,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iniguez, Jose","contributorId":298184,"corporation":false,"usgs":false,"family":"Iniguez","given":"Jose","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":955688,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falk, D.A.","contributorId":179335,"corporation":false,"usgs":false,"family":"Falk","given":"D.A.","affiliations":[],"preferred":false,"id":955689,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerow, K.","contributorId":171550,"corporation":false,"usgs":false,"family":"Gerow","given":"K.","email":"","affiliations":[],"preferred":false,"id":955690,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baisan, C.H.","contributorId":366357,"corporation":false,"usgs":false,"family":"Baisan","given":"C.H.","affiliations":[{"id":48442,"text":"Univ of AZ","active":true,"usgs":false}],"preferred":false,"id":955691,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Allen, C.D.","contributorId":366358,"corporation":false,"usgs":false,"family":"Allen","given":"C.D.","affiliations":[{"id":82169,"text":"Univ of NM","active":true,"usgs":false}],"preferred":false,"id":955692,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Swetnam, T.W.","contributorId":179331,"corporation":false,"usgs":false,"family":"Swetnam","given":"T.W.","affiliations":[],"preferred":false,"id":955693,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70273808,"text":"70273808 - 2026 - Simulated ground motion dataset in the Azores Plateau, Portugal, on rock and soil sites","interactions":[],"lastModifiedDate":"2026-02-03T15:02:37.82341","indexId":"70273808","displayToPublicDate":"2026-02-02T07:53:36","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23286,"text":"Journal of Earthquake Engineering (JEE)","active":true,"publicationSubtype":{"id":10}},"title":"Simulated ground motion dataset in the Azores Plateau, Portugal, on rock and soil sites","docAbstract":"<p><span>Building on a previously developed bedrock dataset, this study extends the Azores Plateau ground motion simulations to include soil-amplified records and introduces a comprehensive validation framework. Soil amplification is modeled using one-dimensional soil profiles. A stochastic source-based approach is employed to generate the dataset, incorporating randomization of input-model parameters to account for the aleatory uncertainty in seismic activity. The accuracy of the dataset is verified through a comprehensive validation framework, showing that the randomization effectively captures variance and inter-period correlation observed in records. This work provides a robust dataset for advancing seismic hazard and risk assessment in the Azores Plateau.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/13632469.2026.2619832","usgsCitation":"Karimzadeh, S., Hussaini, S., Caicedo, D., Alexandra Carvalho, Rezaeian, S., and Lourenco, P.B., 2026, Simulated ground motion dataset in the Azores Plateau, Portugal, on rock and soil sites: Journal of Earthquake Engineering (JEE), 29 p., https://doi.org/10.1080/13632469.2026.2619832.","productDescription":"29 p.","ipdsId":"IP-184242","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":499929,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/13632469.2026.2619832","text":"Publisher Index Page"},{"id":499438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Portugal","otherGeospatial":"central and eastern Azores islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -28.93285079773804,\n              39.26675694602011\n            ],\n            [\n              -28.93285079773804,\n              38.11219810967543\n            ],\n            [\n              -26.93875496541213,\n              38.11219810967543\n            ],\n            [\n              -26.93875496541213,\n              39.26675694602011\n            ],\n            [\n              -28.93285079773804,\n              39.26675694602011\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Karimzadeh, Shaghayegh","contributorId":365826,"corporation":false,"usgs":false,"family":"Karimzadeh","given":"Shaghayegh","affiliations":[{"id":87230,"text":"Department of Civil Engineering, University of Minho, Institute for Sustainability and Innovation in Structural Engineering, ARISE, Guimarães, Portugal.","active":true,"usgs":false}],"preferred":false,"id":954894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hussaini, S.M. Sajad","contributorId":365827,"corporation":false,"usgs":false,"family":"Hussaini","given":"S.M. Sajad","affiliations":[{"id":87230,"text":"Department of Civil Engineering, University of Minho, Institute for Sustainability and Innovation in Structural Engineering, ARISE, Guimarães, Portugal.","active":true,"usgs":false}],"preferred":false,"id":954895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caicedo, Daniel","contributorId":365828,"corporation":false,"usgs":false,"family":"Caicedo","given":"Daniel","affiliations":[{"id":87230,"text":"Department of Civil Engineering, University of Minho, Institute for Sustainability and Innovation in Structural Engineering, ARISE, Guimarães, Portugal.","active":true,"usgs":false}],"preferred":false,"id":954896,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexandra Carvalho","contributorId":365829,"corporation":false,"usgs":false,"family":"Alexandra Carvalho","affiliations":[{"id":87231,"text":"National Laboratory for Civil Engineering (LNEC), Lisbon, Portugal.","active":true,"usgs":false}],"preferred":false,"id":954897,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":954898,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lourenco, Paulo B.","contributorId":365830,"corporation":false,"usgs":false,"family":"Lourenco","given":"Paulo","middleInitial":"B.","affiliations":[{"id":87230,"text":"Department of Civil Engineering, University of Minho, Institute for Sustainability and Innovation in Structural Engineering, ARISE, Guimarães, Portugal.","active":true,"usgs":false}],"preferred":false,"id":954899,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70273888,"text":"70273888 - 2026 - Integrating climate and anthropogenic dynamics can inform multifaceted management for declining mule deer populations","interactions":[],"lastModifiedDate":"2026-02-12T16:04:53.061811","indexId":"70273888","displayToPublicDate":"2026-02-01T09:58:25","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Integrating climate and anthropogenic dynamics can inform multifaceted management for declining mule deer populations","docAbstract":"<p><span>Wildlife and their habitats face profound challenges from climate and landscape-scale changes that extend beyond the influence and time horizon of most biologists and land managers. In this changing environment, long-term datasets can enhance assessments of how demographic trends respond to interactions among local (e.g., habitat restoration decisions) and broad extent drivers, including energy development, to shape wildlife populations. Although many studies evaluate habitat selection or demographics for a single population, our multipopulation, multiscale study quantifies the influence of local management actions given broader environmental forces using both immediate and lagged effects. This approach may be particularly important for species with high site fidelity that may have less adaptive capacity, including mule deer (</span><i>Odocoileus hemionus</i><span>), which are experiencing widespread population declines. We analyzed a 40-year (1980–2019) dataset for 37 mule deer populations across Wyoming, USA, to test hypotheses about and quantify the relative influence of conditions within winter use areas on annual rates of juvenile recruitment. Recruitment has been strongly affected by multiple factors largely beyond the control of managers. Land cover (agriculture and shrubland) had the largest positive effects on recruitment, with estimates more than twice the magnitude of other variables, but also had limited presence in some winter use areas. The next strongest effect sizes were shared by energy developments (including oil/gas and wind energy) and climatic conditions, which, except for wind turbines, had broad distributions across winter use areas. Recruitment increased with higher mean winter temperatures and summer precipitation, but declined with wind, oil and gas developments, cumulative drought, and wildfire. Expected increases in drought and decreases in summer precipitation may constrain options to sustain mule deer populations. Although mule deer recruitment may sometimes be enhanced through habitat restoration, effects varied with treatment type, habitat type, and time since treatment. Given large constraining effects of temperature and drought, supporting drought resiliency for important habitat may be useful. Our results can be used to weigh the relative strength of threats and the value of restoration actions, interpret historic demographic change, prioritize populations for conservation, and optimize options for wildlife habitat management.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70107","usgsCitation":"Hayes, T.A., Johnston, A.N., Hall, L.E., Randall, J., Kauffman, M., Keefe, C., Monteith, K., and Graves, T., 2026, Integrating climate and anthropogenic dynamics can inform multifaceted management for declining mule deer populations: Ecological Applications, v. 36, no. 1, e70107, 21 p., https://doi.org/10.1002/eap.70107.","productDescription":"e70107, 21 p.","ipdsId":"IP-148937","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":499952,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70107","text":"Publisher Index Page"},{"id":499808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.06248692012866,\n              45.028990969926156\n            ],\n            [\n              -104.07597859026953,\n              45.028990969926156\n            ],\n            [\n              -104.07597859026953,\n              40.96979476463608\n            ],\n            [\n              -111.06248692012866,\n              40.96979476463608\n            ],\n            [\n              -111.06248692012866,\n              45.028990969926156\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"36","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hayes, Teagan A. 0000-0002-7299-8987","orcid":"https://orcid.org/0000-0002-7299-8987","contributorId":302262,"corporation":false,"usgs":true,"family":"Hayes","given":"Teagan","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":955432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnston, Aaron N. 0000-0003-4659-0504","orcid":"https://orcid.org/0000-0003-4659-0504","contributorId":201768,"corporation":false,"usgs":true,"family":"Johnston","given":"Aaron","email":"","middleInitial":"N.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":955433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, L. Embere","contributorId":362842,"corporation":false,"usgs":false,"family":"Hall","given":"L.","middleInitial":"Embere","affiliations":[],"preferred":false,"id":955434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Randall, Jill E.","contributorId":339122,"corporation":false,"usgs":false,"family":"Randall","given":"Jill","middleInitial":"E.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":955435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":210786,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":955436,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keefe, Christopher","contributorId":366211,"corporation":false,"usgs":false,"family":"Keefe","given":"Christopher","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":955437,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monteith, Kevin","contributorId":279766,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":955438,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":955439,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70274140,"text":"70274140 - 2026 - Living with wildfire in Santa Fe, New Mexico: 2024 data report","interactions":[],"lastModifiedDate":"2026-03-02T14:17:54.557296","indexId":"70274140","displayToPublicDate":"2026-02-01T08:48:59","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":72,"text":"Research Note","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"RMRS-RN-111","title":"Living with wildfire in Santa Fe, New Mexico: 2024 data report","docAbstract":"<p><span>Community wildfire readiness includes homeowner wildfire risk mitigation and wildfire evacuation preparedness. This report presents results from a household survey distributed to homeowners in the study area around Santa Fe, New Mexico in 2024 which was a follow-up to a previous survey of homeowners of the same residences, consisting of mostly identical questions, that was conducted in 2021. This follow-up survey was motivated by the nearby 2022 Hermit’s Peak/Calf Canyon Fire. Comparison of results indicates general stability in the aggregated responses to most survey questions. As found previously, 2024 results indicate that Santa Fe survey respondents are engaged in preparing for wildfire, yet more could be done to reduce risk in study communities. Despite decreases between 2021 and 2024 in the acceptability of fuels treatment methods that concern the use of fire, respondents generally support multiple types of programs intended to reduce wildfire risk to the city, including most but not all types of fuels treatments on public lands.</span></p>","language":"English","publisher":"USDA Forest Service","doi":"10.2737/RMRS-RN-111","usgsCitation":"Meldrum, J., Donovan, C., Wittenbrink, S., Chavarria, P., Champ, P.A., Brenkert-Smith, H., Barth, C.M., and Wagner, C., 2026, Living with wildfire in Santa Fe, New Mexico: 2024 data report: Research Note RMRS-RN-111, vi, 71 p., https://doi.org/10.2737/RMRS-RN-111.","productDescription":"vi, 71 p.","ipdsId":"IP-180270","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":500642,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Santa Fe","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -105.71813154034228,\n              35.93514709757443\n            ],\n            [\n              -106.10664457403807,\n              35.93514709757443\n            ],\n            [\n              -106.10664457403807,\n              35.53121746398324\n            ],\n            [\n              -105.71813154034228,\n              35.53121746398324\n            ],\n            [\n              -105.71813154034228,\n              35.93514709757443\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationDate":"2026-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":956666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donovan, Colleen","contributorId":240586,"corporation":false,"usgs":false,"family":"Donovan","given":"Colleen","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":956667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wittenbrink, Suzanne","contributorId":333353,"corporation":false,"usgs":false,"family":"Wittenbrink","given":"Suzanne","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":956668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chavarria, Porfirio","contributorId":336691,"corporation":false,"usgs":false,"family":"Chavarria","given":"Porfirio","email":"","affiliations":[{"id":80830,"text":"City of Santa Fe Fire Departments","active":true,"usgs":false}],"preferred":false,"id":956669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Champ, Patricia A. 0000-0003-1917-883X","orcid":"https://orcid.org/0000-0003-1917-883X","contributorId":360956,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","middleInitial":"A.","affiliations":[{"id":86128,"text":"U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":956670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":956671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barth, Christopher M.","contributorId":367066,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","middleInitial":"M.","affiliations":[{"id":86132,"text":"U.S. Department of Agriculture, Forest Service, Washington Office","active":true,"usgs":false}],"preferred":false,"id":956672,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wagner, Carolyn","contributorId":240587,"corporation":false,"usgs":false,"family":"Wagner","given":"Carolyn","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":956673,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70273809,"text":"70273809 - 2026 - Long- versus short-term changes in seafloor elevation and volume of the Upper Florida Keys Reef Tract: 1935–2002 and 2002–2016","interactions":[],"lastModifiedDate":"2026-02-03T14:56:43.906351","indexId":"70273809","displayToPublicDate":"2026-02-01T08:48:06","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Long- versus short-term changes in seafloor elevation and volume of the Upper Florida Keys Reef Tract: 1935–2002 and 2002–2016","docAbstract":"<p><span>Coral reefs provide immense ecosystem and economic value, supporting biodiversity, fisheries, tourism, and coastal protection worth billions annually. However, widespread degradation from thermal stress, storms, disease, and human impacts has caused significant coral cover and reef structure loss, increasing coastal vulnerability and economic risks. While coral loss is well-documented, degradation of underlying reef infrastructure and surrounding seafloor changes remain poorly understood. This study addresses this knowledge gap by quantifying seafloor elevation and volume changes across 234.2 km</span><sup>2</sup><span>&nbsp;of the Upper Florida Keys (UFK) reef tract using historical bathymetric and modern lidar (light detection and ranging) data collected from two periods with distinctly different disturbance regimes: 1935–2002 (frequent storms and major coral loss) and 2002–2016 (few storms and persistently low coral cover). Analysis of over 25,000 data points revealed substantial elevation and volume loss during 1935–2002 (−0.1 ± 0.8 m; 13.6 × 10</span><sup>6</sup><span>&nbsp;m</span><sup>3</sup><span>&nbsp;net loss), shifting to minimal gains by 2002–2016 (0.0 ± 0.3 m; 1.6 × 10</span><sup>6</sup><span>&nbsp;m</span><sup>3</sup><span>&nbsp;net gain). Despite this shift, benthic cover data showed continued declines in stony coral, with increases in macroalgae and octocorals, indicating that limited reef accretion persists even with reduced storm activity. Spatial analyses highlighted variable accretion and erosion patterns across habitats and subregions, underscoring the limitations of localized measurements for ecosystem-wide assessments. Our findings demonstrate the value of integrating historical and modern datasets for regional reef monitoring, establishing baselines for restoration planning, and emphasizing the need for continued high-resolution monitoring to guide adaptive management amid ongoing environmental change.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs18030463","usgsCitation":"Johnson, S.A., Zawada, D.G., Yates, K., and Jenkins, C., 2026, Long- versus short-term changes in seafloor elevation and volume of the Upper Florida Keys Reef Tract: 1935–2002 and 2002–2016: Remote Sensing, v. 18, no. 3, 463, 28 p., https://doi.org/10.3390/rs18030463.","productDescription":"463, 28 p.","ipdsId":"IP-112501","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499928,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs18030463","text":"Publisher Index Page"},{"id":499437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Upper Florida Keys Reef Tract","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -80.0833,\n              25.5\n            ],\n            [\n              -80.5,\n              25.5\n            ],\n            [\n              -80.5,\n              24.9\n            ],\n            [\n              -80.0833,\n              24.9\n            ],\n            [\n              -80.0833,\n              25.5\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":954900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":331852,"corporation":false,"usgs":true,"family":"Zawada","given":"David","email":"dzawada@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":true,"id":954901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yates, Kimberly 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":202055,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":954902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, Connor Monroe 0000-0003-1807-3665","orcid":"https://orcid.org/0000-0003-1807-3665","contributorId":357343,"corporation":false,"usgs":true,"family":"Jenkins","given":"Connor Monroe","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":954903,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70273909,"text":"70273909 - 2026 - Constraining the onset of carboniferous cyclicity in the Arkoma Basin of the Midcontinent, North America: Implications for calibrating a globally significant latest Bashkirian transgression","interactions":[],"lastModifiedDate":"2026-02-23T21:12:51.367042","indexId":"70273909","displayToPublicDate":"2026-01-31T08:03:19","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2996,"text":"Palaeogeography, Palaeoclimatology, Palaeoecology","printIssn":"0031-0182","active":true,"publicationSubtype":{"id":10}},"title":"Constraining the onset of carboniferous cyclicity in the Arkoma Basin of the Midcontinent, North America: Implications for calibrating a globally significant latest Bashkirian transgression","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Cyclothems are defined by the repeat juxtaposition of littoral and open marine successions over short stratigraphic distances (meters to 10's of meters) and are interpreted to be driven by glacioeustatic forcing of sea level during the late Paleozoic Ice Age. The concept of cyclothems was defined in the Midcontinent region of the United States. However, correlating the Midcontinent region to other cyclic successions is difficult, which is the result of no geochronologic control for the Midcontinent biostratigraphic framework. We present the first high-resolution U</span><img src=\"https://sdfestaticassets-us-east-1.sciencedirectassets.com/shared-assets/55/entities/sbnd.gif\" alt=\"single bond\" data-mce-src=\"https://sdfestaticassets-us-east-1.sciencedirectassets.com/shared-assets/55/entities/sbnd.gif\"><span>Pb zircon CA-ID-TIMS and feldspar&nbsp;</span><sup>40</sup><span>Ar/</span><sup>39</sup><span>Ar age control for the onset of Midcontinent cyclothem deposition in the Arkoma Basin, Arkansas USA. Geochronologic control is obtained from a volcaniclastic unit preserved in the newly recovered Dare Creek #1 core. We integrate these data with biostratigraphic, lithostratigraphic and trace element analyses to investigate the timing, stratigraphic and geochemical response to late Paleozoic climate forcing. The lowermost Atoka Formation is associated with the onset of five high frequency transgressive-regressive cycles, which are defined by nearshore sandstones juxtaposed on top of offshore marine mudstones and are associated with changes in salinity and redox conditions. The Trace Creek Member of the lower Atoka Formation hosts a thick, organic-rich black shale, which defines the last and maximum transgression of the lower Atoka Formation in the Arkoma Basin, in the latest Bashkirian. Base-level records from time equivalent stratigraphic successions from Arrow Canyon, Nevada, U.S.A. and the Donets Basin, Ukraine also record a maximum transgression in the latest Bashkirian. The synchroneity of maximum flooding events from multiple basins which span the low latitudes in the latest Bashkirian support that cyclothem deposition was controlled by allostratigraphic forcing mechanisms, likely glacioeustatic forcing resulting from dynamic glaciation in high-latitude Gondwana.</span></span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2026.113610","usgsCitation":"Griffis, N.P., Dechesne, M., Smith, T.M., Hudson, M., Henderson, C., Mundil, R., Shinn, M., Birdwell, J.E., Pianowski, L., Lutz, B.M., Mercer, C.M., Morgan, L.E., and Spangler, L.R., 2026, Constraining the onset of carboniferous cyclicity in the Arkoma Basin of the Midcontinent, North America: Implications for calibrating a globally significant latest Bashkirian transgression: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 687, 113610, 12 p., https://doi.org/10.1016/j.palaeo.2026.113610.","productDescription":"113610, 12 p.","ipdsId":"IP-183903","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":500468,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P145T6UW","text":"USGS Data Release","description":"USGS data release","linkHelpText":"Carboniferous cyclicity in the Arkoma Basin, Arkansas in the Midcontinent of North America"},{"id":500084,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":500244,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.palaeo.2026.113610","text":"Publisher Index Page"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Arkoma Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          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Center","active":true,"usgs":true}],"preferred":true,"id":955734,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dechesne, Marieke 0000-0002-4468-7495","orcid":"https://orcid.org/0000-0002-4468-7495","contributorId":213936,"corporation":false,"usgs":true,"family":"Dechesne","given":"Marieke","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955735,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Tyson Michael 0000-0003-2834-3526","orcid":"https://orcid.org/0000-0003-2834-3526","contributorId":330276,"corporation":false,"usgs":true,"family":"Smith","given":"Tyson","email":"","middleInitial":"Michael","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955736,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hudson, Mark R. 0000-0003-0338-6079 mhudson@usgs.gov","orcid":"https://orcid.org/0000-0003-0338-6079","contributorId":1236,"corporation":false,"usgs":true,"family":"Hudson","given":"Mark R.","email":"mhudson@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955737,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henderson, Charles M.","contributorId":177486,"corporation":false,"usgs":false,"family":"Henderson","given":"Charles M.","affiliations":[],"preferred":false,"id":955738,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mundil, Roland","contributorId":337129,"corporation":false,"usgs":false,"family":"Mundil","given":"Roland","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":955739,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shinn, Mikel","contributorId":366370,"corporation":false,"usgs":false,"family":"Shinn","given":"Mikel","affiliations":[{"id":36466,"text":"Consulting Geologist","active":true,"usgs":false}],"preferred":false,"id":955740,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":955741,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pianowski, Laura 0000-0002-5346-8251","orcid":"https://orcid.org/0000-0002-5346-8251","contributorId":218817,"corporation":false,"usgs":true,"family":"Pianowski","given":"Laura","email":"","affiliations":[],"preferred":true,"id":955742,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lutz, Brandon Michael 0000-0002-6580-9025","orcid":"https://orcid.org/0000-0002-6580-9025","contributorId":299272,"corporation":false,"usgs":true,"family":"Lutz","given":"Brandon","email":"","middleInitial":"Michael","affiliations":[{"id":64806,"text":"National Cooperative Geologic Mapping","active":true,"usgs":true}],"preferred":true,"id":955743,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mercer, Cameron Mark 0000-0003-0534-848X","orcid":"https://orcid.org/0000-0003-0534-848X","contributorId":301880,"corporation":false,"usgs":true,"family":"Mercer","given":"Cameron","email":"","middleInitial":"Mark","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":955744,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":955745,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Spangler, Leland R. 0000-0002-2223-7047","orcid":"https://orcid.org/0000-0002-2223-7047","contributorId":295310,"corporation":false,"usgs":true,"family":"Spangler","given":"Leland","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":955746,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70273826,"text":"70273826 - 2026 - Carbon sequestration along a gradient of tidal marsh degradation in response to sea level rise","interactions":[],"lastModifiedDate":"2026-02-05T16:13:45.501272","indexId":"70273826","displayToPublicDate":"2026-01-30T08:43:39","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Carbon sequestration along a gradient of tidal marsh degradation in response to sea level rise","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Tidal marshes are considered one of the world's most efficient ecosystems for belowground organic carbon sequestration and hence climate mitigation. Marsh systems are however also vulnerable to degradation due to climate-induced sea level rise, whereby marsh vegetation conversion to open water often follows distinct spatial patterns: levees (i.e. marsh zones&nbsp;</span><span class=\"inline-formula\"><i>&lt;</i></span><span> 10 m from tidal creeks) show lower vulnerability of vegetation conversion to open water than basins (i.e. interior marsh zones&nbsp;</span><span class=\"inline-formula\"><i>&gt;</i></span><span> 30 m from creeks). Here, we use sediment cores to investigate spatial variations in organic carbon accumulation rates (OCAR) in a microtidal system (Blackwater marshes, Maryland, USA): (1) across a gradient of marsh zones with increasing marsh degradation, assessed as increasing ratio of unvegetated versus vegetated marsh area and (2) by comparing levees versus basins. We show that OCAR is up to four times higher on marsh levees than in adjacent basins. The data suggest that this is caused by spatial variation in three processes: sediment accretion rate, vegetation productivity, and sediment compaction, which are all higher on levees. Additionally, OCAR was observed to increase with increasing degree of marsh degradation in response to sea level rise. We hypothesize this may be due to more soil waterlogging in more degraded marsh zones, which may decrease carbon decomposition. Our results highlight that tidal marsh levees, in a microtidal system, are among the fastest soil organic carbon sequestration systems on Earth, and that both levees and basins sustain their carbon accumulation rate along gradients of increasing marsh degradation in response to sea level rise.</span></span></p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-23-851-2026","usgsCitation":"Huyzentruyt, M., Wens, M., Fivash, G.S., Walters, D., Bouillon, S., Carr, J., Guntenspergen, G., Kirwan, M.L., and Temmerman, S., 2026, Carbon sequestration along a gradient of tidal marsh degradation in response to sea level rise: Biogeosciences, v. 23, no. 2, p. 851-865, https://doi.org/10.5194/bg-23-851-2026.","productDescription":"15 p.","startPage":"851","endPage":"865","ipdsId":"IP-179289","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":499932,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-23-851-2026","text":"Publisher Index Page"},{"id":499586,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Blackwater marshes, Chesapeake Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.18285542290785,\n              38.414369990989655\n            ],\n            [\n              -76.18285542290785,\n              38.19133724500452\n            ],\n            [\n              -75.92909587593012,\n              38.19133724500452\n            ],\n            [\n              -75.92909587593012,\n              38.414369990989655\n            ],\n            [\n              -76.18285542290785,\n              38.414369990989655\n  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Antwerp","active":true,"usgs":false}],"preferred":false,"id":955103,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, David 0000-0002-5836-681X waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-5836-681X","contributorId":270366,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":955104,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bouillon, Steven","contributorId":332316,"corporation":false,"usgs":false,"family":"Bouillon","given":"Steven","email":"","affiliations":[{"id":49038,"text":"KU Leuven","active":true,"usgs":false}],"preferred":false,"id":955105,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carr, Joel 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":220098,"corporation":false,"usgs":true,"family":"Carr","given":"Joel","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":955106,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Guntenspergen, Glenn 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":220096,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":955107,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kirwan, Matt L.","contributorId":189205,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matt","middleInitial":"L.","affiliations":[],"preferred":false,"id":955108,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Temmerman, Stijn","contributorId":189204,"corporation":false,"usgs":false,"family":"Temmerman","given":"Stijn","email":"","affiliations":[],"preferred":false,"id":955109,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70273778,"text":"sir20265115 - 2026 - Groundwater-level elevations in the bedrock aquifers of the Denver Basin aquifer system, Elbert County, Colorado, 2015–23","interactions":[],"lastModifiedDate":"2026-02-05T20:33:22.417407","indexId":"sir20265115","displayToPublicDate":"2026-01-29T16:30:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-5115","displayTitle":"Groundwater-Level Elevations in the Bedrock Aquifers of the Denver Basin Aquifer System, Elbert County, Colorado, 2015–23","title":"Groundwater-level elevations in the bedrock aquifers of the Denver Basin aquifer system, Elbert County, Colorado, 2015–23","docAbstract":"<p>Water users in Elbert County, Colorado, rely on groundwater from bedrock aquifers in the Denver Basin aquifer system (upper Dawson, lower Dawson, Denver, Arapahoe, and Laramie-Fox Hills aquifers) for approximately half of their water uses. Withdrawals from the bedrocks aquifers have increased to meet the water use needs of expanding regional population growth and development. The U.S. Geological Survey, in cooperation with the Elbert County Board of County Commissioners, began a study in 2015 to monitor groundwater levels within Elbert County. The primary purpose of this report is to present a summary of groundwater levels measured during the study period (2015–23) and present results from statistical analyses of changes in groundwater-level elevations through time.</p><p>Discrete groundwater levels were measured at 36 wells within Elbert County. Seven of those wells contained equipment to make and record continuous groundwater-level measurements at hourly intervals. All aquifers, except the lower Dawson aquifer, had only declining groundwater-level elevations in discrete measurements for wells with statistically significant trends. Of the eight statistically significant trends in the lower Dawson aquifer, two wells indicated increasing groundwater-level elevation from discrete measurements. The groundwater-level elevation trend medians in the upper Dawson, lower Dawson, Denver, Arapahoe, and Laramie-Fox Hills aquifers were −0.23, −0.66, −0.64, −0.39, and −0.63 feet per year, respectively, for discrete groundwater-level elevation measurements. Trends in continuous groundwater-level elevations were in agreement with statistically significant trends in discrete groundwater-level elevations for all wells. The groundwater-level elevation trend medians in this study, compared to the overall trends in a 2015−2018 study, both indicated declining groundwater-level elevations except in the upper Dawson aquifer, where the trend direction was opposite, a positive trend from 2015 to 2018 and a negative trend (declining groundwater elevations) from 2015 to 2023. The change in trends within the upper Dawson aquifer may be affected by differences in the study period and the trend analysis applied. Trends during the 2015–23 study period were compared to departures from the median 2015 groundwater-level elevation for each site in each aquifer. In general, the departures from the 2015 median supported trends observed at each site and correlated spatially with greater departures near the western border of Elbert County. Additionally, 30-year precipitation data showing wet and dry periods were overlaid with the departure from the 2015 median to assess groundwater-level patterns in wells in the five aquifers. Departures from the 2015 median groundwater-level elevations appeared greatest during the dry period between 2020 and 2023. Potentiometric-surface maps of the upper and lower Dawson aquifers created from static April 2023 groundwater elevations indicated groundwater-flow direction is generally from the south to the north. Results of this study could be used to guide additional groundwater monitoring in Elbert County and could aid in long-term planning of water resources.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20265115","collaboration":"Prepared in cooperation with the Elbert County Board of County Commissioners","usgsCitation":"Palko, K.M., Russell, C.A., and Pieseski, N.J., 2026, Groundwater-level elevations in the bedrock aquifers of the Denver Basin aquifer system, Elbert County, Colorado, 2015–23: U.S. Geological Survey Scientific Investigations Report 2026–5115, 41 p., https://doi.org/10.3133/sir20265115.","productDescription":"Report: vii, 41 p.; Database","onlineOnly":"Y","ipdsId":"IP-162981","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":499285,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5115/sir20265115.xml"},{"id":499284,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5115/images"},{"id":499210,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5115/coverthb.jpg"},{"id":499211,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5115/sir20265115.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5115"},{"id":499212,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"USGS Water Data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":499446,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265115/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5115"},{"id":499601,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119172.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","county":"Elbert County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-103.7126,39.5649],[-103.713,39.4761],[-103.7135,39.3876],[-103.7138,39.3011],[-103.7136,39.2136],[-103.7145,39.1265],[-103.7211,39.1266],[-103.722,39.0401],[-103.7201,38.9503],[-103.7186,38.8655],[-103.8315,38.867],[-103.9414,38.8666],[-104.0549,38.8666],[-104.0544,38.9528],[-104.0538,39.0407],[-104.0521,39.1264],[-104.166,39.1277],[-104.2733,39.1278],[-104.3854,39.1284],[-104.4958,39.1298],[-104.6072,39.1307],[-104.6642,39.1308],[-104.6638,39.2165],[-104.664,39.3026],[-104.663,39.3892],[-104.6626,39.4762],[-104.6627,39.5665],[-104.6054,39.5663],[-104.5374,39.5655],[-104.4927,39.5636],[-104.4891,39.5636],[-104.4742,39.5629],[-104.3841,39.5627],[-104.3763,39.5631],[-104.2695,39.5639],[-104.2647,39.5638],[-104.1602,39.5646],[-104.1543,39.565],[-104.0468,39.5652],[-104.0427,39.5651],[-103.9305,39.5646],[-103.9293,39.5646],[-103.8189,39.5646],[-103.8129,39.5649],[-103.7126,39.5649]]]},\"properties\":{\"name\":\"Elbert\",\"state\":\"CO\"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/colorado-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/colorado-water-science-center\">Colorado Water Science Center</a><br>U.S. Geological Survey<br>Box 25046, Mail Stop 415<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Study Methods</li><li>Groundwater-Level Elevations in the Denver Basin Bedrock Aquifers of Elbert County</li><li>Potential Additional Work</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Groundwater-Well Measurement Diagram</li><li>Appendix 2. Hydrographs Showing Groundwater-Level Elevation Through Time for Wells in Elbert County Groundwater-Level Monitoring Network</li><li>Appendix 3. Descriptions and Equations of Mann-Kendall Test, Seasonal Mann-Kendall Test, and Theil-Sen Slope Estimate</li></ul>","publishedDate":"2026-01-29","noUsgsAuthors":false,"publicationDate":"2026-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Palko, Kelli M. 0000-0001-8556-710X","orcid":"https://orcid.org/0000-0001-8556-710X","contributorId":343691,"corporation":false,"usgs":true,"family":"Palko","given":"Kelli","middleInitial":"M.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Cory A. 0000-0001-6358-1605","orcid":"https://orcid.org/0000-0001-6358-1605","contributorId":223018,"corporation":false,"usgs":true,"family":"Russell","given":"Cory","email":"","middleInitial":"A.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pieseski, Nicholas J. 0009-0008-5948-0922","orcid":"https://orcid.org/0009-0008-5948-0922","contributorId":356691,"corporation":false,"usgs":true,"family":"Pieseski","given":"Nicholas","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":954759,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70273752,"text":"ofr20251024 - 2026 - Vegetation cover and composition in environments surrounding uranium mines in the Grand Canyon ecosystem, Northern Arizona","interactions":[],"lastModifiedDate":"2026-02-05T21:59:59.663994","indexId":"ofr20251024","displayToPublicDate":"2026-01-27T11:56:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1024","displayTitle":"Vegetation Cover and Composition in Environments Surrounding Uranium Mines in the Grand Canyon Ecosystem, Northern Arizona","title":"Vegetation cover and composition in environments surrounding uranium mines in the Grand Canyon ecosystem, Northern Arizona","docAbstract":"<p>Mining uranium from breccia-pipe deposits in the greater Grand Canyon region has occurred since the mid-1900s. However, possible ecosystem contamination with harmful levels of radionuclides may have occurred due to mining activities in the 21st century. In response, a 20-year Federal moratorium on new mining claims in the Grand Canyon watershed was initiated in 2012, to allow time to evaluate the potential effects of uranium exploration and mining on human health, wildlife, and water resources. This moratorium, nor the 2023 designation of the “Baaj Nwaavjo I’tah Kukveni–Ancestral Footprints of the Grand Canyon National Monument,” precludes operation or development of mining claims predating 2012.</p><p>Vegetation is a core ecosystem component that may be affected by uranium mining (for instance, through uptake and storage of radionuclides from the air or soil) or may act as a vector of exposure to wildlife, livestock, and humans (for instance, via their consumption of contaminated plant tissues). To provide baseline information about the plant communities associated with uranium mines in the Grand Canyon region, the U.S. Geological Survey surveyed an approximately 200-meter-wide buffer surrounding four breccia-pipe deposits, each in a unique stage of mine development, and at one reference area (a livestock water tank) that underwent ground disturbance but contains no mineral deposits. We sectioned the buffer zones into 0.65–4.52 hectare plots, within which we (1) inventoried all plant species, (2) measured percent cover of plant species, plant functional groups, and ground surface types (dark cyanobacteria, lichen, moss, bedrock, rock, embedded litter, duff, plant bases, and bare soil) using line-point intercept, and (3) measured length and frequency of gaps between perennial plant canopies using canopy gap intercept. We found that plant composition at the mines and the reference area differed from one another but were all characteristic of expected regional vegetation patterns. We provide this data summary as potential baseline information for future research and management efforts.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251024","collaboration":"Prepared in cooperation with the Bureau of Land Management and U.S. Forest Service","usgsCitation":"Mann, R.K., Duniway, M.C., and Hinck, J.E., 2026, Vegetation cover and composition in environments surrounding uranium mines in the Grand Canyon ecosystem, Northern Arizona: U.S. Geological Survey Open-File Report 2025–1024, 44 p., https://doi.org/10.3133/ofr20251024.","productDescription":"Report: vii, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-100773","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499605,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119200.htm","linkFileType":{"id":5,"text":"html"}},{"id":499095,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P912U706","text":"USGS data release","description":"Mann, R.K., and Duniway, M.C., 2020, Vegetation cover and composition data in environments surrounding uranium mines in the Grand Canyon ecosystem, USA: U.S. Geological Survey data release, https://doi.org/10.5066/P912U706","linkHelpText":"Vegetation cover and composition data in environments surrounding uranium mines in the Grand Canyon ecosystem, USA"},{"id":499094,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1024/images"},{"id":499091,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1024/ofr20251024.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1024 PDF"},{"id":499090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1024/coverthb.jpg"},{"id":499092,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251024/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1024 HTML"},{"id":499093,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1024/ofr20251024.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1024 XML"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -113.1667,\n              36.667\n            ],\n            [\n              -113.1667,\n              35.667\n            ],\n            [\n              -111.667,\n              35.667\n            ],\n            [\n              -111.667,\n              36.667\n            ],\n            [\n              -113.1667,\n              36.667\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/sbsc\" data-mce-href=\"https://www.usgs.gov/centers/sbsc\">Southwest Biological Science Center</a><br><a href=\"https://www.usgs.gov/\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>2255 N. Gemini Drive<br>Flagstaff, AZ 86001</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Plant Species Occurrence and Cover Within Plots of All Study Sites, Grand Canyon Area, Northern Arizona</li><li>Appendix 2. Correlations Between Vegetation and Surface Metrics in Environments Surrounding Uranium Mines in the Grand Canyon Ecosystem, Northern Arizona</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2026-01-27","noUsgsAuthors":false,"publicationDate":"2026-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Mann, Rebecca K. 0000-0001-6006-2420","orcid":"https://orcid.org/0000-0001-6006-2420","contributorId":223957,"corporation":false,"usgs":true,"family":"Mann","given":"Rebecca","email":"","middleInitial":"K.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":954534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":954535,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hinck, Jo Ellen 0000-0002-4912-5766 jhinck@usgs.gov","orcid":"https://orcid.org/0000-0002-4912-5766","contributorId":2743,"corporation":false,"usgs":true,"family":"Hinck","given":"Jo","email":"jhinck@usgs.gov","middleInitial":"Ellen","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":954536,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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