The Clear Lake Hitch (Lavinia exilicauda chi) is a minnow endemic to Clear Lake, Lake County, California. This species is listed as a threatened species under the California Endangered Species Act and has been petitioned for listing under the United States Endangered Species Act. In 2017, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, initiated a Clear Lake Hitch monitoring program to generate information annually on relative abundance and size structure. The monitoring program was organized around a conceptual life cycle diagram, focused on life stages approximately ≥1 year of age, and incorporated a probabilistic study design involving approximately 10 days of short-duration (approximately 40 minutes) gillnet sampling undertaken during daytime. This report documents monitoring program activities from 2017 to 2023 and presents the results of an evaluation of the monitoring program. The evaluation was done after the 2023 sampling event, following 6 years of implementation, which is the approximate generation cycle of Clear Lake Hitch. The results of the evaluation indicated the following: (1) gillnets used in the monitoring program were effective at capturing Clear Lake Hitch aged 1 year or more; (2) the study design was effective at generating the information needed to characterize Clear Lake Hitch relative abundance and size structure, and meaningful operational efficiencies can be obtained by implementing simple changes; and (3) future sampling can be scaled to approximately 4–7 days of effort and maintain at least 80-percent confidence in detecting at least a 25-percent change in abundance, assuming past work productivity is maintained and future data are typical of previous data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251018","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","programNote":"Water Resources Mission Area","usgsCitation":"Feyrer, F., Young, M.J., Huntsman, B., Violette, V., Clause, J.K., Buxton, J., Palm, D., Wulff, M., Gronemyer, J., and Santana, L., 2025, Gillnet sampling methods for monitoring status and trends of Clear Lake Hitch in Clear Lake, Lake County, California: U.S. Geological Survey Open-File Report 2025–1018, 26 p., https://doi.org/10.3133/ofr20251018.","productDescription":"Report: viii, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-168545","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":484873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1018/coverthb.jpg"},{"id":484874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1018/ofr20251018.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1018"},{"id":484875,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251018/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1018"},{"id":484876,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KXAMKP","text":"USGS data release","description":"USGS data release","linkHelpText":"Abundance and distribution of fishes in Clear Lake, Lake County, California"},{"id":484877,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1018/images"},{"id":484878,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1018/ofr20251018.XML"}],"country":"United States","state":"California","county":"Lake County","otherGeospatial":"Clear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.65917771122702,\n 39.01735284231793\n ],\n [\n -122.72999697064151,\n 39.029142588016896\n ],\n [\n -122.751242748466,\n 39.044466317884144\n ],\n [\n -122.76945341517263,\n 39.038965874124074\n ],\n [\n -122.78108800779074,\n 39.076281906791024\n ],\n [\n -122.83015563752795,\n 39.12142755641764\n ],\n [\n -122.8807408228241,\n 39.123782188115115\n ],\n [\n -122.90046904508966,\n 39.101017451759986\n ],\n [\n -122.91311534141363,\n 39.076281906791024\n ],\n [\n -122.91665630438428,\n 39.031107354476376\n ],\n [\n -122.892881267295,\n 39.02285496831968\n ],\n [\n -122.83673171161647,\n 39.024033939613446\n ],\n [\n -122.80688645229174,\n 39.014994657245325\n ],\n [\n -122.79727526708535,\n 38.99887828791458\n ],\n [\n -122.77653534111394,\n 39.007133472381724\n ],\n [\n -122.7628773410841,\n 39.00123701034022\n ],\n [\n -122.74820763734814,\n 38.99298113784059\n ],\n [\n -122.73960815584789,\n 38.98275824692357\n ],\n [\n -122.74466667437748,\n 38.972533879655856\n ],\n [\n -122.72999697064151,\n 38.95955467222447\n ],\n [\n -122.72240919284712,\n 38.96152137166055\n ],\n [\n -122.71633897061167,\n 38.95916132578665\n ],\n [\n -122.71077460022903,\n 38.96584791826234\n ],\n [\n -122.70217511872879,\n 38.95444099821404\n ],\n [\n -122.69357563722826,\n 38.94499939981313\n ],\n [\n -122.66980060013927,\n 38.941065029307254\n ],\n [\n -122.65361334084434,\n 38.94735991731659\n ],\n [\n -122.65563674825637,\n 38.93949121997264\n ],\n [\n -122.6404611926676,\n 38.93437609838975\n ],\n [\n -122.63236756302015,\n 38.942638803709116\n ],\n [\n -122.64754311860891,\n 38.9627013651191\n ],\n [\n -122.65361334084434,\n 38.96899433164984\n ],\n [\n -122.66271867419766,\n 38.972533879655856\n ],\n [\n -122.69104637796346,\n 38.98236502932696\n ],\n [\n -122.69812830390507,\n 38.98039890858698\n ],\n [\n -122.69205808166961,\n 38.985903909070544\n ],\n [\n -122.7133038594938,\n 39.0043818512562\n ],\n [\n -122.68194104461014,\n 38.99455375924509\n ],\n [\n -122.66929474828619,\n 38.99848515986562\n ],\n [\n -122.66929474828619,\n 39.009098850512345\n ],\n [\n -122.65766015566808,\n 39.011457232184966\n ],\n [\n -122.65917771122702,\n 39.01735284231793\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Mass movements such as debris flows and landslides are some of the deadliest and most destructive natural hazards occurring mostly in alpine and volcanic settings. With ever-growing populations located downslope from known debris flow channels, early warning systems can help prevent loss of life. Geophysical and technological advances have improved monitoring and detection capabilities in recent years; however, they can often be cost prohibitive and resource intensive, making them less accessible to disadvantaged populations. We tested and validated a readily available and cost-effective two-dimensional swath laser rangefinder in a controlled experimental setting against two independent flow-depth lasers. The swath laser successfully recorded cross-sectional changes in flow depth from four debris flows and a water-only flood, in addition to geomorphic changes associated with landslide initiation. The results suggest that a swath laser could be integrated into systems for debris flow detection and characterization of mass movements in natural settings, thus improving the ability to monitor these hazards.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10346-025-02533-8","usgsCitation":"Obryk, M., Bedinger, E., Iezzi, A.M., Bryant, E.H., Allstadt, K.E., George, D.L., and Mirus, B., 2025, Utility of a swath laser rangefinder for characterizing mass movement flow depth and landslide initiation: Landslides, v. 22, p. 2693-2700, https://doi.org/10.1007/s10346-025-02533-8.","productDescription":"8 p.","startPage":"2693","endPage":"2700","ipdsId":"IP-171813","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":485443,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationDate":"2025-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Obryk, Maciej K. 0000-0002-8182-8656","orcid":"https://orcid.org/0000-0002-8182-8656","contributorId":203477,"corporation":false,"usgs":true,"family":"Obryk","given":"Maciej","middleInitial":"K.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":935931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedinger, Emily Christina 0000-0002-5717-6395","orcid":"https://orcid.org/0000-0002-5717-6395","contributorId":316272,"corporation":false,"usgs":true,"family":"Bedinger","given":"Emily Christina","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":935932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":304206,"corporation":false,"usgs":true,"family":"Iezzi","given":"Alexandra","email":"","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":935933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bryant, Emily H 0000-0003-4062-9711","orcid":"https://orcid.org/0000-0003-4062-9711","contributorId":344366,"corporation":false,"usgs":true,"family":"Bryant","given":"Emily","email":"","middleInitial":"H","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":935934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":935935,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":935936,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":935937,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266394,"text":"70266394 - 2025 - An early Holocene wet period in the southwestern United States","interactions":[],"lastModifiedDate":"2025-08-04T15:42:14.492448","indexId":"70266394","displayToPublicDate":"2025-05-02T08:37:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"An early Holocene wet period in the southwestern United States","docAbstract":"Multiple generations of spring-fed streams traversed ∼800 km2 of the Las Vegas Valley in southern Nevada between ca. 10.9 ka and 8.5 ka, depositing an extensive tufa network. The scale of this network and diversity of tufa morphologies is novel in North America and offers an opportunity to obtain quantitative paleoclimate data for the region during the early Holocene. We determined isotopic compositions and estimated past temperatures using clumped isotope data from early Holocene tufa on the valley floor (698 m) as well as tufa forming today at higher elevation in the nearby Spring Mountains at Cold Creek Spring (1856 m). Modern and fossil tufa yielded comparably low δ18O values, implying that source waters for both were derived from high-elevation winter precipitation. Clumped isotope temperatures of modern tufa average 15.8 ± 2.5 °C, aligning with mean summer temperatures of the emergent spring water, and indicate equilibrium conditions of tufa formation. The early Holocene tufa yielded similar clumped isotope temperatures, averaging 15.2 ± 3.9 °C, meaning it precipitated at temperatures that occur at much higher elevations today. The Las Vegas tufa record, combined with nearby and temporally correlative paleospring and lacustrine records, suggest that cool/wet conditions prevailed throughout the Mojave Desert during the early Holocene. These records also demonstrate that spring ecosystems responded to millennial-scale hydroclimate variations that supersede climate change driven solely by insolation. The previously unrecognized pattern of ecosystem response to hydroclimate documented here may assist in understanding climate drivers for the early Holocene and provide critical information for the fate of groundwater-dependent ecosystems in the southwestern United States.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G53174.1","usgsCitation":"Springer, K.B., Hudson, A.M., Pigati, J.S., Huntington, K.W., and Schauer, A.J., 2025, An early Holocene wet period in the southwestern United States: Geology, v. 53, no. 8, p. 631-635, https://doi.org/10.1130/G53174.1.","productDescription":"5 p.","startPage":"631","endPage":"635","ipdsId":"IP-147460","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":485440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Las Vegas Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.75,\n 36.75\n ],\n [\n -115.75,\n 36\n ],\n [\n -115,\n 36\n ],\n [\n -115,\n 36.75\n ],\n [\n -115.75,\n 36.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":935810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudson, Adam M. 0000-0002-3387-9838 ahudson@usgs.gov","orcid":"https://orcid.org/0000-0002-3387-9838","contributorId":195419,"corporation":false,"usgs":true,"family":"Hudson","given":"Adam","email":"ahudson@usgs.gov","middleInitial":"M.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":935811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":935812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huntington, Katharine W.","contributorId":195423,"corporation":false,"usgs":false,"family":"Huntington","given":"Katharine","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":935813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schauer, Andrew J.","contributorId":140713,"corporation":false,"usgs":false,"family":"Schauer","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":935814,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264061,"text":"70264061 - 2025 - Sources and risk factors for nitrate, pathogens, and fecal contamination of private wells in rural southwestern Wisconsin, USA","interactions":[],"lastModifiedDate":"2025-03-05T15:27:14.791487","indexId":"70264061","displayToPublicDate":"2025-05-02T08:20:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Sources and risk factors for nitrate, pathogens, and fecal contamination of private wells in rural southwestern Wisconsin, USA","docAbstract":"Household well water can be degraded by contaminants from the land's surface, but private well owners lack means to protect the source water from neighboring disturbances. Rural residents of southwestern Wisconsin, USA, rely on private well water, and the combination of land use and fractured carbonate bedrock makes groundwater vulnerable to contamination. To identify the extent, sources, and risk factors of private well contamination, randomly selected wells sampled during two-day periods in fall (n = 301) and spring (n = 529) were analyzed for nitrate and indicator bacteria, and a subset (n = 138) was sampled across four seasonal events for analysis of pathogens and microbial source tracking markers by quantitative polymerase chain reaction. Risk factors representing land use, hydrology, geology, and well construction were analyzed for associations with contamination in multivariable models. The importance of risk factors varied by contaminant, illustrating the multifaceted nature of rural groundwater quality. Nitrate contamination was associated with agricultural land use, and wells with casings that extended below a shale aquitard accessed less contaminated water than those drawing water from above it. Human fecal microbes were detected in 64 wells (46%), and rainfall was the key risk factor for contamination, indicating that wastewater from septic systems was available to contaminate wells when transport conditions were favorable. Manure microbes from cattle/ruminants and pigs were detected in 33 and 13 wells, respectively, and concentrations increased with the hectarage of cultivated land near wells. Pathogen genes for viruses, bacteria, and protozoa were detected in 66 wells (48%), including more detections of zoonotic than human-specific pathogens, and human Bacteroides, an indicator of wastewater, was an equivocal predictor of pathogen presence in private wells. Characterizing important elements of the setting, like geology, and identifying sources and risk factors for contaminants can inform landscape-level policies to protect groundwater quality.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2025.123202","usgsCitation":"Stokdyk, J.P., Firnstahl, A.D., Bradbury, K., Muldoon, M.A., Kieke Jr., B., and Borchardt, M.A., 2025, Sources and risk factors for nitrate, pathogens, and fecal contamination of private wells in rural southwestern Wisconsin, USA: Water Research, v. 275, 123202, 13 p., https://doi.org/10.1016/j.watres.2025.123202.","productDescription":"123202, 13 p.","ipdsId":"IP-171110","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":482899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Grant County, Iowa County, Lafayette County","otherGeospatial":"southwestern 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This study examines a nonstationary flood frequency analysis method that uses ordinary least squares linear regression to estimate flood magnitudes at U.S. Geological Survey streamgages that exhibit trends and change points in a nine-State region including Montana, North Dakota, South Dakota, Minnesota, Illinois, Iowa, Wisconsin, Missouri, and Michigan. Additionally, an extension of this method is introduced, which enables nonstationary flood frequency based on a statistical relation with a stochastic climate predictor.
Estimates of the 1-percent annual exceedance probability flood using regression equations to adjust for conditions in 2020 were computed at U.S. Geological Survey streamgages across the study area. Regression equations used either a time index or a climate variable as the explanatory variable for changes in peak streamflow. Of 153 candidate streamgages, the assumptions of time-adjusted analyses were met at 137 streamgages. Climate-adjusted flood frequency analyses were applicable at 98 streamgages based on annual precipitation, annual temperature, or annual snowfall. Time- and climate-adjusted methods produced similar estimates of the 1-percent annual exceedance probability flood magnitude at streamgages where both methods were applicable. Nonstationary estimates of the 1-percent annual exceedance probability flood were primarily greater than stationary estimates in eastern North and South Dakota, Minnesota, Iowa, Illinois, and parts of Missouri and less than stationary estimates in Montana, western North and South Dakota, and Wisconsin. The largest differences between stationary and nonstationary flood estimates were in North and South Dakota and Minnesota.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255034","collaboration":"Prepared in cooperation with the Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Levin, S.B., 2025, Nonstationary flood frequency analysis using regression in the north-central United States: U.S. Geological Survey Scientific Investigations Report 2025–5034, 33 p., https://doi.org/10.3133/sir20255034.","productDescription":"Report: viii, 33 p.; Dataset","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-168081","costCenters":[{"id":37947,"text":"Upper Midwest Water Science 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Ongoing, prolonged, and severe drought and water overuse during the first two decades of the 21st century have reduced water supplies of the Colorado River Basin, with effects cascading to ecosystems and human communities throughout the basin. In June and July 2021, the U.S. Geological Survey (USGS) Colorado River Basin Actionable and Strategic Integrated Science and Technology initiative team held a series of 12 collaboration meetings with USGS scientists and managers to discuss complicated, integrated science challenges and solutions related to drought in the Colorado River Basin. These Science and Technology collaboration meetings were structured to identify challenges experienced by meeting participants when working on complex problems, explore opportunities for coproducing scientific information, and envision future collaborative programs that leverage new technology. The 12 meetings were attended by 79 USGS staff representing 43 unique affiliations (for example, USGS science centers, mission areas, and regional offices). Meeting participants submitted 865 individual responses to six general discussion prompt topics (“Challenges,” “Knowledge Gaps,” “Existing Capabilities,” “Strategies and Actions,” “Example Applications,” and “Next Steps”) using a structured online collaboration tool. However, specific questions or tasks from each general discussion prompt varied by meeting topic. Terms from the USGS Thesaurus (https://apps.usgs.gov/thesaurus/) and USGS Data Lifecycle Model (https://www.usgs.gov/data-management/data-lifecycle) were used to identify and summarize participant responses relevant to science integration, stakeholder engagement, and information management technology. From these responses, opportunities for the Colorado River Basin Actionable and Strategic Integrated Science and Technology initiative to facilitate science integration in the Colorado River Basin are highlighted in this report, including (a) pursuing specific interdisciplinary research topics that require integrating knowledge across spatial and temporal scales, (b) connecting scientists across disciplines, (c) reducing barriers to stakeholder engagement, (d) identifying new technologies, and (e) facilitating data access. Multiple strategies for designing future Science and Technology collaboration meetings are also outlined in this circular to better collect and analyze participant responses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1551","usgsCitation":"Monroe, A.P., Alexander, J.S., Anderson, E.D., Anderson, P.J., Andrews, W.J., Driscoll, J.M., Frus, R.J., Hevesi, J.A., Jones, D.K., Thomas, K.A., Tillery, A.C., Torregrosa, A., and Dahm, K.G., 2025, U.S. Geological Survey Colorado River Basin science and technology collaboration meetings on drought (2021)—Synthesis of findings: U.S. Geological Survey Circular 1551, 17 p., https://doi.org/10.3133/cir1551.","productDescription":"Report: iv, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-159062","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":485359,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1551/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1551"},{"id":484771,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QIBOJY","text":"USGS data release","linkHelpText":"Summary of Responses at the 2021 Colorado River Basin Science and Technology Meetings"},{"id":484770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1551/cir1551.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1551"},{"id":484769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1551/coverthb.jpg"},{"id":485258,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1551/cir1551.xml"},{"id":485257,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1551/images"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.84288087645007,\n 39.71384827828035\n ],\n [\n -105.7152848621287,\n 40.31627361955492\n ],\n [\n -107.15413242417426,\n 42.58617572195294\n ],\n [\n -110.24707077090045,\n 42.71786640310043\n ],\n [\n -110.89757634299599,\n 41.77462246338027\n ],\n [\n -110.95331264260932,\n 40.79783839963443\n ],\n [\n -112.038576374738,\n 37.469561144392586\n ],\n [\n -115.98036476910397,\n 38.79317671202148\n ],\n [\n -115.44123475079675,\n 32.992451748753595\n ],\n [\n -114.82264190472011,\n 31.47874298522271\n ],\n [\n -112.27051510504592,\n 30.18537034627093\n ],\n [\n -108.23443902124117,\n 30.2194618603899\n ],\n [\n -106.42157190796223,\n 36.17948752808586\n ],\n [\n -105.84288087645007,\n 39.71384827828035\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Federally managed public lands and waters attract millions of visitors each year, generating significant economic benefits for surrounding communities. Accurate visitation data are crucial for guiding policy decisions and managing resources effectively. This report explores the methods employed by agencies to collect and use data on recreational visitation to Federal lands and waters. Visitation estimation practices across seven agencies are reviewed, revealing similarities such as the use of automated counters for on-site data collection, alongside differences in reporting frequencies, visit definitions, and public access to data. Emerging technologies, including social media, mobile device activity, and community science, are also evaluated for their potential to improve visitation estimation. Although these technologies offer promising opportunities, they come with challenges such as data biases, the need for calibration, costs, and privacy concerns. The report concludes with opportunities to enhance data collection, coordination, and accessibility, ensuring more efficient resource management and informed decision making.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255022","collaboration":"Prepared in cooperation with the U.S. Department of the Interior Office of Policy Analysis and University of Washington","programNote":"Land Management Research Program","usgsCitation":"Hanson, D., Wilkins, E.J., Wood, S.A., Crowley, C., Boone, W., and Schuster, R., 2025, Monitoring recreation on federally managed lands and waters—Visitation estimation: U.S. Geological Survey Scientific Investigations Report 2025–5022, 46 p., https://doi.org/10.3133/sir20255022.","productDescription":"vii, 46 p.","onlineOnly":"Y","ipdsId":"IP-172048","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":485325,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255022/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5022"},{"id":485230,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5022/sir20255022.xml"},{"id":485229,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5022/images"},{"id":485175,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5022/coverthb.jpg"},{"id":485176,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5022/sir20255022.pdf","text":"Report","size":"1.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5022"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
No abstract available.
","language":"English","publisher":"Ohio Department of Transportation","usgsCitation":"VonIns, B.L., and Crawford, C.J., 2025, Design, installation, and operation of a statewide crest-stage streamgage network in Ohio — Summary of methods and results 2021 to 2025: Final Report, 17 p.","productDescription":"17 p.","ipdsId":"IP-174102","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":498781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":498758,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.dot.state.oh.us/Divisions/Planning/SPR/Research/reportsandplans/Lists/Final%20Reports%20All/Item/displayifs.aspx?List=47f3581d%2Df21c%2D403b%2D9358%2Dfea0b008772b&ID=727&Web=3bc523de%2Dc756%2D4eeb%2D9b6c%2Df24c0435d45e"}],"country":"United 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"VonIns, Branden L. 0000-0001-6185-2854 blvonins@usgs.gov","orcid":"https://orcid.org/0000-0001-6185-2854","contributorId":191787,"corporation":false,"usgs":true,"family":"VonIns","given":"Branden","email":"blvonins@usgs.gov","middleInitial":"L.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":953915,"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":202517,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher","email":"cjcrawford@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":953916,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70268028,"text":"70268028 - 2025 - Alaskan hydrology in transition: Changing precipitation and evapotranspiration patterns are projected to reshape seasonal streamflow and water temperature by midcentury (2035-2064)","interactions":[],"lastModifiedDate":"2025-06-11T15:02:14.529259","indexId":"70268028","displayToPublicDate":"2025-05-01T09:43:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2344,"text":"Journal of Hydrometeorology","active":true,"publicationSubtype":{"id":10}},"title":"Alaskan hydrology in transition: Changing precipitation and evapotranspiration patterns are projected to reshape seasonal streamflow and water temperature by midcentury (2035-2064)","docAbstract":"High spatial and temporal resolution models are essential for understanding future climate impacts and developing effective climate resilience plans. However, existing regional and global river models often lack the resolution needed to accurately capture local conditions. This study uses a series of high-resolution models, including the Regional Arctic System Model, mizuRoute, and the river basin model, to analyze Arctic and sub-Arctic Alaskan hydrology. We compare a historical baseline (1991–2020) with six midcentury (2035–64) futures: two pseudo–global warming scenarios based on historical meteorology and four direct dynamically downscaled global climate models. The six futures reveal significant uncertainty in future annual discharge and peak flows, although a widespread increase in discharge during April (+63%) and October (+31%) is consistently shown across models. Projected increases in rain and shifting weather patterns lead to a transition from snow to rain in spring and autumn, reducing the fraction of snowmelt contributing to river discharge. Rising evapotranspiration moderates discharge changes, particularly in autumn, by offsetting precipitation increases. Average summer river temperatures are projected to increase by approximately 1.5°C, doubling the number of river segments that experience 18°C days, a critical threshold for salmon survival, and intensifying the heat flux to the ocean adding an average of 3.3 × 1012 MJ yr−1. These changes in the hydrologic cycle could profoundly impact riverine and oceanic ecosystems, posing substantial challenges to communities reliant on these environments.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-24-0121.1","usgsCitation":"Blaskey, D., Cheng, Y., Newman, A.C., Koch, J.C., Goseff, M., and Musselman, K., 2025, Alaskan hydrology in transition: Changing precipitation and evapotranspiration patterns are projected to reshape seasonal streamflow and water temperature by midcentury (2035-2064): Journal of Hydrometeorology, v. 26, no. 5, p. 613-626, https://doi.org/10.1175/JHM-D-24-0121.1.","productDescription":"14 p.","startPage":"613","endPage":"626","ipdsId":"IP-170645","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":490374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, 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C. 0000-0001-6621-2717","orcid":"https://orcid.org/0000-0001-6621-2717","contributorId":211589,"corporation":false,"usgs":false,"family":"Newman","given":"A.","email":"","middleInitial":"C.","affiliations":[{"id":38269,"text":"Aarhus, Denmark","active":true,"usgs":false}],"preferred":false,"id":940068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":940069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goseff, M","contributorId":356787,"corporation":false,"usgs":false,"family":"Goseff","given":"M","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":940070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Musselman, K","contributorId":302756,"corporation":false,"usgs":false,"family":"Musselman","given":"K","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":940071,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267209,"text":"70267209 - 2025 - Effects of climate change on midwestern ecosystems: Appalachian – Interior – Northeast Mesic Forest","interactions":[],"lastModifiedDate":"2026-03-17T14:22:08.438751","indexId":"70267209","displayToPublicDate":"2025-05-01T09:13:46","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Effects of climate change on midwestern ecosystems: Appalachian – Interior – Northeast Mesic Forest","docAbstract":"The Appalachian-Interior-Northeast Mesic Forest ecosystem, historically buffered by cool, moist conditions, may experience significant stress under future climate change, particularly due to intensifying droughts and milder winters in the midwestern United States. Droughts are expected to intensify in frequency and severity, depleting soil moisture, increasing tree mortality, and reshaping species composition. Increasing aridity and disrupted hydrologic cycles will likely accelerate soil erosion, deplete nutrients, and heighten wildfire risk. Meanwhile, milder winters may reduce snowpack insulation, increase freeze-thaw cycles, and alter growing seasons, potentially amplifying cold stress, disrupting phenology, and contributing to shifts in habitat structure and community composition. While easing winter severity may temporarily boost plant productivity and facilitate species migration into and throughout the Midwest, it can also increase the risk of frost damage for early-leafing trees and disrupt ecological relationships, such as plant-pollinator interactions.
Together, these stressors may drive fundamental shifts in habitat structure and community composition, favoring drought-, fire-, and cold-tolerant species, while historically dominant, moisture-dependent species decline. Species with limited drought resistance, such as those with shallow roots or low water-use efficiency, may be especially vulnerable, while drought-adapted taxa could gain a competitive advantage. This shift could trigger a departure from over a century of mesophication in the Appalachian-Interior-Northeast Mesic Forest, which has favored shade-loving, moisture-dependent species in fire-suppressed landscapes. As a result, these forests may be particularly ill-equipped to withstand the novel environmental conditions imposed by intensifying droughts and milder winters. The Appalachian-Northeast Mesic Forest habitat group, dominated by eastern hemlock (Tsuga canadensis) and eastern white pine (Pinus strobus), is likely particularly vulnerable, as both dominant species are projected to decline due to increasing drought stress and shifting competitive dynamics. In the North-Central Beech - Maple - Basswood Forest, the Driftless Area of Wisconsin, Minnesota, and Iowa may be more vulnerable than more eastern portions of the habitat due to its already drier conditions, with climate change expected to push these communities beyond favorable conditions.
Species interactions, including invasive species, pests, and herbivory, are also likely to be reshaped by climate change, compounding stress on habitat groups throughout the Appalachian-Interior-Northeast Mesic Forest. Warmer winters and increased disturbance may facilitate the expansion of invasive species, which outcompete native vegetation and alter ecosystem dynamics. At the same time, pests and pathogens are likely to become more destructive, as milder winters enhance their survival and spread and drought weakens tree defenses. Additionally, rising white-tailed deer (Odocoileus virginianus) populations, supported by warmer winters, may shift forest regeneration patterns by selectively browsing on sensitive seedlings and saplings, limiting the recruitment of historically dominant tree species while favoring browse-resistant plants. Collectively, these pressures can drive significant and ongoing ecological transformation in the Appalachian-Interior-Northeast Mesic Forest, highlighting the need for adaptive management strategies to sustain biodiversity and ecosystem function.
","language":"English","publisher":"Midwest Climate Adaptation Science Center","usgsCitation":"Ratcliffe, H., Charton, K., Siddons, T., Lyons, M.P., and LeDee, O.E., 2025, Effects of climate change on midwestern ecosystems: Appalachian – Interior – Northeast Mesic Forest, 97 p.","productDescription":"97 p.","ipdsId":"IP-177855","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":486042,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://mwcasc.umn.edu/research-publications"},{"id":501211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ratcliffe, Hugh","contributorId":352942,"corporation":false,"usgs":false,"family":"Ratcliffe","given":"Hugh","affiliations":[{"id":84312,"text":"Oak Ridge Institute for Higher Education","active":true,"usgs":false}],"preferred":false,"id":937283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Charton, Katherine","contributorId":352943,"corporation":false,"usgs":false,"family":"Charton","given":"Katherine","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":937284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siddons, Taylor","contributorId":343020,"corporation":false,"usgs":false,"family":"Siddons","given":"Taylor","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":937287,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, Marta P. 0000-0002-8117-8710 mlyons@usgs.gov","orcid":"https://orcid.org/0000-0002-8117-8710","contributorId":270223,"corporation":false,"usgs":true,"family":"Lyons","given":"Marta","email":"mlyons@usgs.gov","middleInitial":"P.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":937285,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeDee, Olivia E. 0000-0002-7791-5829 oledee@usgs.gov","orcid":"https://orcid.org/0000-0002-7791-5829","contributorId":242820,"corporation":false,"usgs":true,"family":"LeDee","given":"Olivia","email":"oledee@usgs.gov","middleInitial":"E.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":937286,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267993,"text":"70267993 - 2025 - Sampling dragonflies for mercury analysis in Grand Canyon National Park, 2018–2024: A contribution of the Dragonfly Mercury Project","interactions":[],"lastModifiedDate":"2025-06-10T13:45:18.487894","indexId":"70267993","displayToPublicDate":"2025-05-01T08:36:56","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18517,"text":"Science Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SR-2025/283","title":"Sampling dragonflies for mercury analysis in Grand Canyon National Park, 2018–2024: A contribution of the Dragonfly Mercury Project","docAbstract":"The Dragonfly Mercury Project is a collaborative initiative that utilizes dragonfly larvae as biosentinels to monitor mercury concentrations across 180 national parks and other protected lands, including Grand Canyon National Park (GRCA). These indicators serve as surrogates for environmental risk and can indicate where fish consumption could pose health risks through exposure to mercury. From 2018–2024, citizen scientists and staff from the National Park Service and U.S. Geological Survey helped collect close to 400 larvae from 25 GRCA sites across nearly 300 river miles, revealing mercury concentrations ranging 3.0–1337 ng/g (parts per billion) dry weight. Results were available for 20 sites. Upon comparing mercury concentrations to an impairment index, most sites were classified as low or no risk for ecological impairment, though 10% (2 sites) exceeded moderate or severe risk benchmarks (Pete’s Pocket and Buck Farm Canyon, respectively). Sources of mercury to GRCA likely stem from a combination of atmospheric deposition, upstream discharge from Lake Powell, and other watershed contributions. In addition, food web dynamics, underlying water chemistry, and environmental disturbances (e.g., floods) contribute to mercury mobilization, production, and bioaccumulation. Report findings provide a baseline for connecting ongoing science in the Colorado River watershed, informing management actions, and enhancing public engagement through citizen science.","language":"English","publisher":"National Park Service","doi":"10.36967/2310449","usgsCitation":"Flanagan Pritz, C., Emery, C., Johnson, B.L., Willacker, J., Kotalik, C.J., Ko, K., Bell, M.A., Walters, D., and Eagles-Smith, C., 2025, Sampling dragonflies for mercury analysis in Grand Canyon National Park, 2018–2024: A contribution of the Dragonfly Mercury Project: Science Report NPS/SR-2025/283, vi, 41 p., https://doi.org/10.36967/2310449.","productDescription":"vi, 41 p.","ipdsId":"IP-174801","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":490303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.04824667570179,\n 36.90791840821794\n ],\n [\n -114.04824667570179,\n 36.24396097153053\n ],\n [\n -111.66402641450159,\n 36.24396097153053\n ],\n [\n -111.66402641450159,\n 36.90791840821794\n ],\n [\n -114.04824667570179,\n 36.90791840821794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2025-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Flanagan Pritz, Colleen M.","contributorId":349812,"corporation":false,"usgs":false,"family":"Flanagan Pritz","given":"Colleen M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":939901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Emery, Colleen 0000-0002-1208-3224","orcid":"https://orcid.org/0000-0002-1208-3224","contributorId":215534,"corporation":false,"usgs":true,"family":"Emery","given":"Colleen","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":939902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Branden L. 0000-0002-8018-6452 branden_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-8018-6452","contributorId":257446,"corporation":false,"usgs":true,"family":"Johnson","given":"Branden","email":"branden_johnson@usgs.gov","middleInitial":"L.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":939903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":207883,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":939904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kotalik, Christopher James 0000-0001-6739-6036","orcid":"https://orcid.org/0000-0001-6739-6036","contributorId":301847,"corporation":false,"usgs":true,"family":"Kotalik","given":"Christopher","email":"","middleInitial":"James","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":939905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ko, Katherine","contributorId":356738,"corporation":false,"usgs":false,"family":"Ko","given":"Katherine","affiliations":[],"preferred":false,"id":939906,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bell, Michael A.","contributorId":173239,"corporation":false,"usgs":false,"family":"Bell","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":939907,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205921,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":939908,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":939909,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70267768,"text":"70267768 - 2025 - Feeding habits of sympatric aoudad (Ammotragus lervia) and desert bighorn sheep (Ovis canadensis mexicana) in West Texas","interactions":[],"lastModifiedDate":"2025-05-30T15:38:05.202282","indexId":"70267768","displayToPublicDate":"2025-05-01T08:31:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Feeding habits of sympatric aoudad (Ammotragus lervia) and desert bighorn sheep (Ovis canadensis mexicana) in West Texas","docAbstract":"Aoudad (Ammotragus lervia), native to northern Africa, were introduced as exotic game animals to the Chihuahuan Desert in West Texas, USA, and have become invasive. Aoudad and bighorn sheep (Ovis canadensis mexicana) are adapted to rugged terrain in arid climates, and both persist in desert regions with low primary productivity and limited perennial water availability, which suggests potential for competition for food and water resources. Aoudad are highly adaptable, which could make them more resilient to a changing environment with extreme conditions, providing a competitive edge over bighorn sheep. To evaluate the potential for exploitative competition between invasive aoudad and endemic desert bighorn sheep, we used genetic metabarcoding to assess diet composition using fecal samples collected from adults of each species in the Sierra Vieja Mountains in West Texas. We collected 32 composite samples from aoudad and 27 composite samples for bighorn sheep and identified 88 genera consumed. Bighorn sheep and aoudad diets (as inferred by genera) were most different during the warm-wet season (16 June–15 October; Kulczynski similarity index = 0.81) and most similar during the warm-dry season (16 February–15 June; Kulczynski similarity index = 1.05). During the warm-wet season, the 2 herbivores tended to consume different genera, suggesting the possibility of resource partitioning, with less likelihood of resource partitioning during the warm-dry season when forage was more limited and diets were similar. Diet diversity, measured by Shannon's diversity index, did not vary substantially between species, but for aoudad it was highest during the warm-wet season (1.1 ± 0.0.1 [SE]) and lowest during the cool-dry season (16 October–15 February, 0.9 ± 0.0.1). For bighorn sheep, diet richness was highest during the cool-dry season (1.1 ± 0.2) and lowest during the warm-dry season (0.8 ± 0.10). Bighorn sheep may specialize on high-quality forage species, particularly during the warm-wet season, while aoudad have a more generalist foraging strategy, although temporal windows for increased competition for food resources likely occur. Aoudad are well-adapted to low-quality forage and arid climates, which could increase their competitive ability and compromise bighorn sheep conservation efforts in areas of sympatry.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70008","usgsCitation":"Parikh, G., Etchart, J., O’Shaughnessy, R., Harveson, L., and Cain, J.W., 2025, Feeding habits of sympatric aoudad (Ammotragus lervia) and desert bighorn sheep (Ovis canadensis mexicana) in West Texas: Journal of Wildlife Management, v. 89, no. 4, e70008, 19 p., https://doi.org/10.1002/jwmg.70008.","productDescription":"e70008, 19 p.","ipdsId":"IP-146752","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":498241,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70008","text":"Publisher Index Page"},{"id":489265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Sierra Vieja Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.7583240959883,\n 30.51724717961632\n ],\n [\n -104.7583240959883,\n 29.83069639683191\n ],\n [\n -104.2980557227294,\n 29.83069639683191\n ],\n [\n -104.2980557227294,\n 30.51724717961632\n ],\n [\n -104.7583240959883,\n 30.51724717961632\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Parikh, Grace L.","contributorId":356127,"corporation":false,"usgs":false,"family":"Parikh","given":"Grace L.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":938784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Etchart, Jose L.","contributorId":356128,"corporation":false,"usgs":false,"family":"Etchart","given":"Jose L.","affiliations":[{"id":84917,"text":"Texas Parks & Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":938785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Shaughnessy, Ryan","contributorId":356129,"corporation":false,"usgs":false,"family":"O’Shaughnessy","given":"Ryan","affiliations":[{"id":84918,"text":"West Texas Quail Outfitters","active":true,"usgs":false}],"preferred":false,"id":938786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harveson, Louis A.","contributorId":356131,"corporation":false,"usgs":false,"family":"Harveson","given":"Louis A.","affiliations":[{"id":84919,"text":"Sul Ross State University","active":true,"usgs":false}],"preferred":false,"id":938787,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938788,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273792,"text":"70273792 - 2025 - Modeling wetland resources for spring migratory waterbirds under different agricultural management scenarios in the Iowa portion of the Prairie Pothole Region, USA","interactions":[],"lastModifiedDate":"2026-01-30T16:34:24.418814","indexId":"70273792","displayToPublicDate":"2025-04-30T10:30:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Modeling wetland resources for spring migratory waterbirds under different agricultural management scenarios in the Iowa portion of the Prairie Pothole Region, USA","docAbstract":"Constructed water quality wetlands, designed to accept tile drainage and surface runoff, are a promising solution for reducing surface water nutrient loading from agricultural systems. In addition to their water quality benefits, these systems may also offset losses of migratory waterbird stopover sites resulting from historical and future agricultural drainage modernization. To assess this possibility, we developed spatially explicit habitat models informed with expert opinion to explore the: 1) potential of water quality wetlands to provide suitable stopover resources for waterbirds during spring migration; and 2) the extent these wetlands can offset likely losses of stopover resources due to drainage modernization. We focused our modeling on the Iowa portion of the Prairie Pothole Region of North America as it was a historically important area within this vital region for waterbirds, but it has experienced widespread subsurface drainage. Model results indicate that unmitigated drainage modernization is likely to have a large negative effect on spring migratory resources for dabbling ducks and shorebirds and minimal effect on diving ducks. Water quality wetland installations are likely to provide habitat for dabbling and diving ducks, but wetland installation is unlikely to completely offset habitat losses for dabbling ducks and shorebirds. Drainage modernization aside, our results indicate that water quality wetlands can address several environmental issues associated with agricultural expansion and intensification by improving water quality and providing wetland resources for waterbirds and other organisms. Field-scale research is needed to validate these results.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-025-01930-y","usgsCitation":"Mitchell, M., Anteau, M.J., Pearse, A.T., Newcomer-Johnson, T., Christensen, J.R., Crumpton, W.R., Dyson, B., Canfield, T.J., Helmers, M., Green, D., and Forshay, K.J., 2025, Modeling wetland resources for spring migratory waterbirds under different agricultural management scenarios in the Iowa portion of the Prairie Pothole Region, USA: Wetlands, v. 45, 48, 19 p., https://doi.org/10.1007/s13157-025-01930-y.","productDescription":"48, 19 p.","ipdsId":"IP-167627","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":499637,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-025-01930-y","text":"Publisher Index Page"},{"id":499372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Agency, Ada, OK USA","active":true,"usgs":false}],"preferred":false,"id":954814,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Helmers, Matthew","contributorId":189905,"corporation":false,"usgs":false,"family":"Helmers","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":954815,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Green, David","contributorId":167789,"corporation":false,"usgs":false,"family":"Green","given":"David","affiliations":[],"preferred":false,"id":954816,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Forshay, Kenneth J.","contributorId":221060,"corporation":false,"usgs":false,"family":"Forshay","given":"Kenneth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":954817,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70266911,"text":"70266911 - 2025 - Reservoir and riverine sources of cyanotoxins in Oregon’s Cascade Range rivers tapped for municipal drinking water supply","interactions":[],"lastModifiedDate":"2025-05-15T14:40:53.503103","indexId":"70266911","displayToPublicDate":"2025-04-30T09:32:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21376,"text":"Phycology","active":true,"publicationSubtype":{"id":10}},"title":"Reservoir and riverine sources of cyanotoxins in Oregon’s Cascade Range rivers tapped for municipal drinking water supply","docAbstract":"Reservoirs and downstream rivers draining Oregon’s Cascade Range provide critical water supplies for over 1.5 million residents in dozens of communities. These waters also support planktonic and benthic cyanobacteria that produce cyanotoxins that may degrade water quality for drinking, recreation, aquatic life, and other beneficial uses. This 2016–2020 survey examined the sources and transport of four cyanotoxins—microcystins, cylindrospermopsins, anatoxins, and saxitoxins—in six river systems feeding 18 drinking water treatment plants (DWTPs) in northwestern Oregon. Benthic cyanobacteria, plankton net tows, and (or) Solid-Phase Adsorption Toxin Tracking (SPATT) samples were collected from 65 sites, including tributaries, reservoirs, main stems, and sites at or upstream from DWTPs. Concentrated extracts (320 samples) were analyzed with enzyme-linked immuno-sorbent assays (ELISA), resulting in >90% detection. Benthic cyanobacteria (n = 80) mostly Nostoc, Phormidium, Microcoleus, and Oscillatoria, yielded microcystins (76% detection), cylindrospermopsins (41%), anatoxins (45%), and saxitoxins (39%). Plankton net tow samples from tributaries and main stems (n = 94) contained saxitoxins (84%), microcystins (77%), anatoxins (25%), and cylindrospermopsins (22%), revealing their transport in seston. SPATT sampler extracts (n = 146) yielded anatoxins (81%), microcystins (66%), saxitoxins (37%), and cylindrospermopsins (32%), indicating their presence dissolved in the water. Reservoir plankton net tow samples (n = 15), most often containing Dolichospermum, yielded microcystins (87%), cylindrospermopsins (73%), and anatoxins (47%), but no saxitoxins. The high detection frequencies of cyanotoxins at sites upstream from DWTP intakes, and at sites popular for recreation, where salmon and steelhead continue to exist, highlight the need for additional study on these cyanobacteria and the factors that promote production of cyanotoxins to minimize effects on humans, aquatic ecosystems, and economies.
","language":"English","publisher":"MDPI","doi":"10.3390/phycology5020016","usgsCitation":"Carpenter, K.D., Rosen, B.H., Donahue, D., Duncan, K., Hilbrandt, B., Lewis, C., Swan, K., Triplett, T., and Welch, E., 2025, Reservoir and riverine sources of cyanotoxins in Oregon’s Cascade Range rivers tapped for municipal drinking water supply: Phycology, v. 5, no. 2, 16, 33 p., https://doi.org/10.3390/phycology5020016.","productDescription":"16, 33 p.","ipdsId":"IP-147561","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":488593,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/phycology5020016","text":"Publisher Index Page"},{"id":485991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Cascade Range rivers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.30282481409498,\n 45.719342390992665\n ],\n [\n -123.30282481409498,\n 43.15097016782153\n ],\n [\n -121.6655656767986,\n 43.15097016782153\n ],\n [\n -121.6655656767986,\n 45.719342390992665\n ],\n [\n -123.30282481409498,\n 45.719342390992665\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Carpenter, Kurt D. 0000-0002-6231-8335 kdcar@usgs.gov","orcid":"https://orcid.org/0000-0002-6231-8335","contributorId":127442,"corporation":false,"usgs":true,"family":"Carpenter","given":"Kurt","email":"kdcar@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosen, Barry H.","contributorId":317976,"corporation":false,"usgs":false,"family":"Rosen","given":"Barry","email":"","middleInitial":"H.","affiliations":[{"id":40458,"text":"Florida Gulf Coast University","active":true,"usgs":false}],"preferred":false,"id":937119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":937120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duncan, Kari","contributorId":355201,"corporation":false,"usgs":false,"family":"Duncan","given":"Kari","affiliations":[{"id":84729,"text":"City of Lake Oswego, OR (formerly)","active":true,"usgs":false}],"preferred":false,"id":937121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hilbrandt, Brandin","contributorId":355202,"corporation":false,"usgs":false,"family":"Hilbrandt","given":"Brandin","affiliations":[{"id":84731,"text":"North Santiam Watershed Council, Salem, OR","active":true,"usgs":false}],"preferred":false,"id":937122,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewis, Christopher","contributorId":190155,"corporation":false,"usgs":false,"family":"Lewis","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":937123,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Swan, Kim","contributorId":355203,"corporation":false,"usgs":false,"family":"Swan","given":"Kim","affiliations":[{"id":84732,"text":"Clackamas River Water Providers, Clackamas, OR","active":true,"usgs":false}],"preferred":false,"id":937124,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Triplett, Tracy","contributorId":355204,"corporation":false,"usgs":false,"family":"Triplett","given":"Tracy","affiliations":[{"id":84733,"text":"Clackamas River Water, Clackamas, OR","active":true,"usgs":false}],"preferred":false,"id":937125,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Welch, Elijah","contributorId":355205,"corporation":false,"usgs":false,"family":"Welch","given":"Elijah","affiliations":[{"id":84734,"text":"City of Salem, OR","active":true,"usgs":false}],"preferred":false,"id":937126,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70267841,"text":"70267841 - 2025 - Coastal fine-grained sediment plumes from beach nourishment near Santa Barbara, California","interactions":[],"lastModifiedDate":"2025-08-18T15:08:09.593383","indexId":"70267841","displayToPublicDate":"2025-04-30T09:07:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21808,"text":"Coastal Engineering Journal","active":true,"publicationSubtype":{"id":10}},"title":"Coastal fine-grained sediment plumes from beach nourishment near Santa Barbara, California","docAbstract":"Terrestrial sediments captured by flood control facilities such as dams, debris basins, and engineered stream channels can reduce sediment fluxes to littoral cells. The beneficial use of these sediments for beach nourishment may induce negative environmental effects from turbidity or sedimentation caused by the source material. Here, we examine the size and extent of turbid coastal plumes produced by beach nourishment with sediment containing significant fine-grained (silt and clay) fractions near Santa Barbara, California, to evaluate the potential effects of beneficial use of fluvial sediment captured in upstream facilities. Using remotely sensed imagery and hydrodynamic and sediment transport model simulations, we find that wave height, wind speed and direction, and sediment settling velocity have strong controls on the direction and extent of the turbid plume produced by beach nourishment. These results are consistent with monitored nourishment projects, suggesting generalizable patterns and processes across the studies. Additionally, we find that sediment placement method (hauling versus dredging) can influence the magnitude and duration of fine-sediment concentrations in the adjacent coastal waters. Combined, these results will inform sediment management strategies that intend to minimize the environmental effects of beach nourishment within the study area and across similar coastal sites.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/21664250.2025.2497705","usgsCitation":"Warrick, J.A., Stevens, A.W., and Tehranirad, B., 2025, Coastal fine-grained sediment plumes from beach nourishment near Santa Barbara, California: Coastal Engineering Journal, v. 67, no. 3, p. 558-582, https://doi.org/10.1080/21664250.2025.2497705.","productDescription":"25 p.","startPage":"558","endPage":"582","ipdsId":"IP-172366","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":489545,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490617,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/21664250.2025.2497705","text":"Publisher Index Page"}],"country":"United States","state":"California","city":"Santa Barbara","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.84691794723162,\n 34.42243236962841\n ],\n [\n -119.84691794723162,\n 34.399358663431116\n ],\n [\n -119.7692183271959,\n 34.399358663431116\n ],\n [\n -119.7692183271959,\n 34.42243236962841\n ],\n [\n -119.84691794723162,\n 34.42243236962841\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"67","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":939109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Andrew W. 0000-0003-2334-129X astevens@usgs.gov","orcid":"https://orcid.org/0000-0003-2334-129X","contributorId":139313,"corporation":false,"usgs":true,"family":"Stevens","given":"Andrew","email":"astevens@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":939110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tehranirad, Babak 0000-0002-1634-9165","orcid":"https://orcid.org/0000-0002-1634-9165","contributorId":299107,"corporation":false,"usgs":false,"family":"Tehranirad","given":"Babak","affiliations":[{"id":64774,"text":"contracted to USGS PCMSC","active":true,"usgs":false}],"preferred":false,"id":939111,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266480,"text":"70266480 - 2025 - Exposure to ultraviolet radiation induces escape hatching of Cisco (Coregonus artedi) embryos","interactions":[],"lastModifiedDate":"2025-05-08T14:59:37.345953","indexId":"70266480","displayToPublicDate":"2025-04-29T09:42:18","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exposure to ultraviolet radiation induces escape hatching of Cisco (Coregonus artedi) embryos","title":"Exposure to ultraviolet radiation induces escape hatching of Cisco (Coregonus artedi) embryos","docAbstract":"Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70243","usgsCitation":"Kleist, N.J., Domschke, C.T., Knight, A.C., Nauman, T.W., Duniway, M.C., and Carter, S.K., 2025, Mapping predicted ecological states at landscape scales using remote sensing data and machine learning: Ecosphere, v. 16, no. 4, e70243, 16 p., https://doi.org/10.1002/ecs2.70243.","productDescription":"e70243, 16 p.","ipdsId":"IP-157413","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":496158,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70243","text":"Publisher Index Page"},{"id":496018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.0509637142687,\n 42.64630716588371\n ],\n [\n -111.8266792759012,\n 39.96910670756075\n ],\n [\n -114.76249387460862,\n 35.930067724930424\n ],\n [\n -108.04708953433448,\n 35.58278337785393\n ],\n [\n -107.09257565522607,\n 37.663677272255455\n ],\n [\n -107.38346712125988,\n 40.56490356337224\n ],\n [\n -108.74500520215983,\n 41.22226787777939\n ],\n [\n -111.0509637142687,\n 42.64630716588371\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Kleist, Nathan J. 0000-0002-2468-4318","orcid":"https://orcid.org/0000-0002-2468-4318","contributorId":260598,"corporation":false,"usgs":true,"family":"Kleist","given":"Nathan","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":949335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Domschke, Christopher T.","contributorId":361734,"corporation":false,"usgs":false,"family":"Domschke","given":"Christopher","middleInitial":"T.","affiliations":[{"id":86338,"text":"Bureau of Land Management, Colorado State Office, 2850 Youngfield St., Lakewood, CO 80215","active":true,"usgs":false}],"preferred":false,"id":949336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W.","contributorId":360619,"corporation":false,"usgs":false,"family":"Nauman","given":"Travis","middleInitial":"W.","affiliations":[{"id":86060,"text":"USDA Natural Resources Conservation Service, Soil and Plant Science Division, Moab, UT, USA","active":true,"usgs":false}],"preferred":false,"id":949338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":949339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":949340,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269031,"text":"70269031 - 2025 - Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements","interactions":[],"lastModifiedDate":"2025-07-14T14:48:30.146948","indexId":"70269031","displayToPublicDate":"2025-04-28T07:43:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements","docAbstract":"Evapotranspiration (ET) describes the sum of water transfer from the ground surface through soil evaporation and water loss from leaf stomata into the atmosphere − critical factors linking the global water and carbon cycles. Myriad ET models based on remote sensing data provide spatially continuous estimates of ET; however, leaf photosynthetic information is critical to ensure accurate ET estimates, which are difficult to measure from space. Remotely sensed sun-induced chlorophyll fluorescence (SIF) provides a proxy of stomatal conductance activity with high performance in predicting plant transpiration, which can account for a large proportion of terrestrial and riverine ET. This study aims to improve estimates of tree water use in semi-arid to arid environments. In this study, a fixed stomatal conductance model and three SIF-driven canopy conductance (gsc) models were applied to model potential ET (PET). The models estimated PET using the Penman-Monteith equation with: (1) a constant leaf stomatal conductance; (2) a transpiration-driven gsc model; (3) a gsc model based on electron-transfer rate and vapor pressure deficit, and a (4) Ball-Berry stomatal conductance model. A machine learning model was then applied to scale PET to actual ET (AET) using remote sensing and climate data. Accordingly, four AET models were cross-validated with in-situ measured AET at 52 sites, including 21 eddy covariance flux tower sites, and 31 sap-flow measurement sites (semi-arid and plantation area), for various plant functional types in Australia. This study demonstrated that SIF effectively captured seasonal variations of gsc, finding that AET models with SIF-driven gsc models correlated well with in-situ measured AET (R2 = 0.64). Modelled AET with dynamic variations of gsc generated lower prediction error (0.85 mm day−1), while the AET model with fixed stomatal conductance tended to overestimate AET in floodplains and underestimate it in evergreen broadleaf forests, indicating using fixed stomatal conductance results in unstable performance when modelling AET. This study demonstrated that SIF-driven AET models improved broadscale estimation of ET. Our findings provide vital broadscale hydrological data to assist catchment and regional water management, particularly over unmonitored areas at risk of future climate-driven reductions in rainfall.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2025.133404","usgsCitation":"Gao, S., Nagler, P.L., Woodgate, W., Huete, A., and Doody, T.M., 2025, Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements: Journal of Hydrology, v. 660, no. Part B, 133404, 16 p., https://doi.org/10.1016/j.jhydrol.2025.133404.","productDescription":"133404, 16 p.","ipdsId":"IP-172126","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2025.133404","text":"Publisher Index Page"},{"id":492203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 123.28690979572599,\n -14.507783026538078\n ],\n [\n 113.28039887008524,\n -21.66065633263682\n ],\n [\n 112.17079996753354,\n -24.994521175001637\n ],\n [\n 115.10354300417447,\n -36.486501164702105\n ],\n [\n 122.24168422144697,\n -35.62034073401294\n ],\n [\n 131.93124828895753,\n -32.58906983701101\n ],\n [\n 141.10544225154464,\n -40.23302031896691\n ],\n [\n 152.81278701443853,\n -40.33448456027517\n ],\n [\n 155.92719705356842,\n -24.451431761649573\n ],\n [\n 144.11359477726128,\n -8.385497835754798\n ],\n [\n 130.88500806969773,\n -10.415819254694402\n ],\n [\n 123.28690979572599,\n -14.507783026538078\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"660","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Sicong","contributorId":303040,"corporation":false,"usgs":false,"family":"Gao","given":"Sicong","email":"","affiliations":[{"id":65623,"text":"CSIRO, Land and Water, Waite Campus, Adelaide, South Australia, Australia; University of Canberra, Canberra, Australian Capital Territory, Australia","active":true,"usgs":false}],"preferred":false,"id":942957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodgate, William","contributorId":357983,"corporation":false,"usgs":false,"family":"Woodgate","given":"William","affiliations":[{"id":85572,"text":"Earth Observation Research Centre, School of the Environment, The University of Queensland, Brisbane, QLD 4072, Australia; CSIRO, Space and Astronomy, Kensington, 6151, WA, Australia","active":true,"usgs":false}],"preferred":false,"id":942959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huete, Alfredo 0000-0003-2809-2376","orcid":"https://orcid.org/0000-0003-2809-2376","contributorId":208294,"corporation":false,"usgs":false,"family":"Huete","given":"Alfredo","email":"","affiliations":[],"preferred":false,"id":942960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doody, Tanya M.","contributorId":138691,"corporation":false,"usgs":false,"family":"Doody","given":"Tanya","email":"","middleInitial":"M.","affiliations":[{"id":12494,"text":"CSIRO Land and Water, Australia","active":true,"usgs":false}],"preferred":false,"id":942961,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266322,"text":"70266322 - 2025 - Bølling-Allerød productivity in the subarctic Pacific driven by seasonal upwelling","interactions":[],"lastModifiedDate":"2025-05-02T15:09:54.928503","indexId":"70266322","displayToPublicDate":"2025-04-26T10:03:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Bølling-Allerød productivity in the subarctic Pacific driven by seasonal upwelling","docAbstract":"The Bølling-Allerød deglacial event is marked by high diatom productivity and opal deposition throughout the subarctic Pacific. This opal could either constitute a strengthened biological pump and thus carbon sequestration, or a weakened biological pump and release of marine-sequestered CO2 to the atmosphere. We quantify silicic acid supply at IODP Site U1340 in the Bering Sea using biogenic opal and δ30Si of Coscinodiscus, a diatom genus. These records, along with diatom environmental indicators, suggest the Bølling-Allerød had high silicic acid availability related to a shift from stratification to seasonal upwelling dynamics. We thus propose the primary cause of the high productivity event was increased macronutrient supply from vertical exchange that injected old, nutrient-rich, CO2-rich waters into the surface. Enhanced CO2 release from the subarctic Pacific may help explain critical intervals of CO2 rise that occur at the onsets of the Bølling-Allerød and PreBoreal.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL109614","usgsCitation":"deLong, K., Blackburn, T., Caissie, B.E., Addison, J.A., Stroynowski, Z., Sipala, M., Marcantonio, F., and Ravelo, A., 2025, Bølling-Allerød productivity in the subarctic Pacific driven by seasonal upwelling: Geophysical Research Letters, v. 52, no. 4, e2024GL109614, 12 p., https://doi.org/10.1029/2024GL109614.","productDescription":"e2024GL109614, 12 p.","ipdsId":"IP-168382","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":487925,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl109614","text":"Publisher Index Page"},{"id":485329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"subarctic Pacific","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 75\n ],\n [\n -179.9,\n 40\n ],\n [\n -130,\n 40\n ],\n [\n -130,\n 75\n ],\n [\n -179.9,\n 75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.9,\n 74.9502433602874\n ],\n [\n 140,\n 74.9502433602874\n ],\n [\n 140,\n 40\n ],\n [\n 179.9,\n 40\n ],\n [\n 179.9,\n 74.9502433602874\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"deLong, Kimberly A. 0009-0002-2724-5297","orcid":"https://orcid.org/0009-0002-2724-5297","contributorId":354409,"corporation":false,"usgs":false,"family":"deLong","given":"Kimberly A.","affiliations":[{"id":84626,"text":"Ocean Sciences Department, University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":935592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blackburn, Terrence 0000-0003-0029-0709","orcid":"https://orcid.org/0000-0003-0029-0709","contributorId":259241,"corporation":false,"usgs":false,"family":"Blackburn","given":"Terrence","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":935593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caissie, Beth Elaine 0000-0001-9587-1842","orcid":"https://orcid.org/0000-0001-9587-1842","contributorId":292500,"corporation":false,"usgs":true,"family":"Caissie","given":"Beth","email":"","middleInitial":"Elaine","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":935594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Addison, Jason A. 0000-0003-2416-9743 jaddison@usgs.gov","orcid":"https://orcid.org/0000-0003-2416-9743","contributorId":4192,"corporation":false,"usgs":true,"family":"Addison","given":"Jason","email":"jaddison@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":935595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stroynowski, Zuzanna 0000-0003-3972-5757","orcid":"https://orcid.org/0000-0003-3972-5757","contributorId":354410,"corporation":false,"usgs":false,"family":"Stroynowski","given":"Zuzanna","affiliations":[{"id":84627,"text":"CCMAR","active":true,"usgs":false}],"preferred":false,"id":935596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sipala, Maria R. 0000-0002-3340-0743","orcid":"https://orcid.org/0000-0002-3340-0743","contributorId":354411,"corporation":false,"usgs":false,"family":"Sipala","given":"Maria R.","affiliations":[{"id":84626,"text":"Ocean Sciences Department, University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":935597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marcantonio, Franco 0000-0003-1414-426X","orcid":"https://orcid.org/0000-0003-1414-426X","contributorId":354412,"corporation":false,"usgs":false,"family":"Marcantonio","given":"Franco","affiliations":[{"id":62934,"text":"Department of Geology and Geophysics, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":935598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ravelo, Ana Christina","contributorId":349834,"corporation":false,"usgs":false,"family":"Ravelo","given":"Ana Christina","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":935599,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266320,"text":"70266320 - 2025 - Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole","interactions":[],"lastModifiedDate":"2025-05-02T15:25:36.189617","indexId":"70266320","displayToPublicDate":"2025-04-25T10:22:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17061,"text":"Planetary Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole","docAbstract":"We use a high-resolution digital elevation model and a numerical thermal model to produce a variety of inputs for a water-ice prospectivity model for the Volatiles Investigating Polar Exploration Rover (VIPER) landing site. These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.
","language":"English","publisher":"American Astronomical Society","doi":"10.3847/PSJ/adbc6c","usgsCitation":"Coyan, J.A., Siegler, M., Martinez-Comacho, J., Beyer, R.A., and Shirley, M., 2025, Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole: Planetary Science Journal, v. 6, no. 5, 105, 9 p., https://doi.org/10.3847/PSJ/adbc6c.","productDescription":"105, 9 p.","ipdsId":"IP-168617","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":487929,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/psj/adbc6c","text":"Publisher Index Page"},{"id":485333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mons Mouton, Moon","volume":"6","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Coyan, Joshua Aaron 0000-0002-8450-7364","orcid":"https://orcid.org/0000-0002-8450-7364","contributorId":247291,"corporation":false,"usgs":true,"family":"Coyan","given":"Joshua","email":"","middleInitial":"Aaron","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":935581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siegler, Matthew","contributorId":237898,"corporation":false,"usgs":false,"family":"Siegler","given":"Matthew","email":"","affiliations":[{"id":24584,"text":"PSI","active":true,"usgs":false}],"preferred":false,"id":935582,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martinez-Comacho, José 0000-0003-0542-7866","orcid":"https://orcid.org/0000-0003-0542-7866","contributorId":354404,"corporation":false,"usgs":false,"family":"Martinez-Comacho","given":"José","affiliations":[{"id":84624,"text":"University of Hawai’i at Manoa, Hawaii Institute for Geophysics and Planetology, 1680 East-West Road, POST Building, Honolulu, HI 96822","active":true,"usgs":false}],"preferred":false,"id":935583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beyer, Ross A.","contributorId":204235,"corporation":false,"usgs":false,"family":"Beyer","given":"Ross","email":"","middleInitial":"A.","affiliations":[{"id":36890,"text":"Sagan Center at the SETI Institute and NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":935584,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shirley, Mark 0000-0001-8767-1760","orcid":"https://orcid.org/0000-0001-8767-1760","contributorId":354405,"corporation":false,"usgs":false,"family":"Shirley","given":"Mark","affiliations":[{"id":84625,"text":"SETI Institute/NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":935585,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266101,"text":"fs20253008 - 2025 - U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing cutting-edge science to adapt to extreme weather events and stakeholder needs","interactions":[],"lastModifiedDate":"2025-04-29T18:25:49.532193","indexId":"fs20253008","displayToPublicDate":"2025-04-24T18:10:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3008","displayTitle":"U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing Cutting-Edge Science to Adapt to Extreme Weather Events and Stakeholder Needs","title":"U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing cutting-edge science to adapt to extreme weather events and stakeholder needs","docAbstract":"The Rocky Mountains and the Colorado River Basin in the Western United States are complex, interconnected systems that sustain a large variety of species, including tens of millions of humans. These regions face risks from drought, wildfires, invasive plant and animal species, and habitat reduction. Working with many stakeholders, scientists can help to characterize these risks by providing data and analytical tools to inform land and water resource management decisions.
The U.S. Geological Survey 2024 Rocky Mountain Region (Region 7) Science Exchange Workshop, held in April 2024, focused on cutting-edge science techniques, evaluating complex interconnected risks, and coproducing science with science partners and stakeholders. These science topics and communication strategies can be used for developing data, interpretations, and decision support tools needed to provide science that resource managers and other stakeholders can use to better understand complex, dynamic natural systems and develop management strategies to plan for and adapt to risks that threaten human communities and natural ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253008","usgsCitation":"Andrews, W.J., Titus, T.N., Eng, L.E., Zellman, K.L., Anderson, P.J., and Havens, J.C., 2025, U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing cutting-edge science to adapt to extreme weather events and stakeholder needs: U.S. Geological Survey Fact Sheet 2025-3008, 4 p., https://doi.org/10.3133/fs20253008.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-167985","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":485159,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3008"},{"id":484983,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3008/coverthb.jpg"},{"id":484985,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3008/fs20253008.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3008"},{"id":485041,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3008/images"},{"id":485042,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3008/fs20253008.xml"}],"contact":"Director, Rocky Mountain Region
U.S. Geological Survey
Box 25046, MS-911
Denver, CO 80225-0046
Atmospheric rivers (ARs) drive most riverine floods on the United States (U.S.) West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust non-linear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, two to four and a half times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-24-0078.1","collaboration":"Desert Research Institute, Reno, NV","usgsCitation":"Webb, M., Albano, C., Harpold, A., Wagner, D.M., and Wilson, A.M., 2025, Wet antecedent soil moisture increases atmospheric river streamflow magnitudes non-linearly: Journal of Hydrometeorology, v. 26, no. 6, p. 741-758, https://doi.org/10.1175/JHM-D-24-0078.1.","productDescription":"18 p.","startPage":"741","endPage":"758","ipdsId":"IP-166108","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":485998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.92121191786663,\n 33.407810452030205\n ],\n [\n -116.523784768193,\n 36.41502072713284\n ],\n [\n -119.64994516515054,\n 39.88994270690637\n ],\n [\n -120.51138519071847,\n 42.20041323206641\n ],\n [\n -119.3720758190301,\n 48.777534854733574\n ],\n [\n -122.9287960859929,\n 48.977595241145025\n ],\n [\n -123.36076938971661,\n 48.210450784064506\n ],\n [\n -125.09530509112804,\n 48.5114622057049\n ],\n [\n -124.0899142321606,\n 45.77238715755897\n ],\n [\n -124.63673786495912,\n 42.844084204189784\n ],\n [\n -124.31096484013645,\n 41.342509106103535\n ],\n [\n -124.81776995325123,\n 40.42865225014583\n ],\n [\n -123.57485679616491,\n 38.65999426544576\n ],\n [\n -122.45718830306802,\n 37.1653556881036\n ],\n [\n -121.77165200602349,\n 35.90307547409293\n ],\n [\n -120.64970139084915,\n 34.560810038256434\n ],\n [\n -117.92121191786663,\n 33.407810452030205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Webb, Mariana J. 0000-0003-0331-2635","orcid":"https://orcid.org/0000-0003-0331-2635","contributorId":353576,"corporation":false,"usgs":false,"family":"Webb","given":"Mariana J.","affiliations":[{"id":84438,"text":"Division of Hydrological Sciences, Desert Research Institute, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":933999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albano, Christine M.","contributorId":17681,"corporation":false,"usgs":true,"family":"Albano","given":"Christine M.","affiliations":[],"preferred":false,"id":934000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harpold, Adrian A. 0000-0002-2566-9574","orcid":"https://orcid.org/0000-0002-2566-9574","contributorId":353577,"corporation":false,"usgs":false,"family":"Harpold","given":"Adrian A.","affiliations":[{"id":84439,"text":"Dept. of Natural Resources and Environmental Science, Univ. of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":934001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":934002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Anna M.","contributorId":211536,"corporation":false,"usgs":false,"family":"Wilson","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":934003,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266282,"text":"70266282 - 2025 - Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","interactions":[],"lastModifiedDate":"2025-05-02T14:54:35.492386","indexId":"70266282","displayToPublicDate":"2025-04-24T09:53:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","docAbstract":"Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-19-1675-2025","usgsCitation":"Aberle, R., Enderlin, E., O'Neel, S., Florentine, C., Sass, L., Dickson, A., Marshall, H., and Flores, A., 2025, Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning: The Cryosphere, v. 19, p. 1675-1693, https://doi.org/10.5194/tc-19-1675-2025.","productDescription":"19 p.","startPage":"1675","endPage":"1693","ipdsId":"IP-161789","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":487924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-19-1675-2025","text":"Publisher Index Page"},{"id":485326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Unite States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.76360091465392,\n 47.06180837633883\n ],\n [\n -121.3176201884703,\n 48.805343460206615\n ],\n [\n -120.0015482436147,\n 50.335300241584264\n ],\n [\n -130.45623413084917,\n 62.431155673423405\n ],\n [\n -144.1767604606605,\n 64.77803371822662\n ],\n [\n -156.74065293077143,\n 62.94965967944643\n ],\n [\n -153.58716512013862,\n 58.34813914012679\n ],\n [\n -151.25294354192766,\n 56.670611414741046\n ],\n [\n -147.2303378681512,\n 59.785680900702204\n ],\n [\n -139.72127967128716,\n 59.315677918279675\n ],\n [\n -134.82069406634898,\n 55.48786621236388\n ],\n [\n -131.59648302534626,\n 51.752911462025\n ],\n [\n -123.76360091465392,\n 47.06180837633883\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Aberle, Rainey","contributorId":354302,"corporation":false,"usgs":false,"family":"Aberle","given":"Rainey","affiliations":[],"preferred":false,"id":935376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":935377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":935378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":935381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickson, Adam","contributorId":354305,"corporation":false,"usgs":false,"family":"Dickson","given":"Adam","affiliations":[],"preferred":false,"id":935383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marshall, Hans-Peter","contributorId":330964,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":935379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":935382,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273766,"text":"70273766 - 2025 - Movement patterns of invasive red swamp crayfish vary with sex and environmental factors","interactions":[],"lastModifiedDate":"2026-01-28T16:36:02.719179","indexId":"70273766","displayToPublicDate":"2025-04-24T09:27:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Movement patterns of invasive red swamp crayfish vary with sex and environmental factors","docAbstract":"Invasive species disproportionately invade freshwater ecosystems, threatening biodiversity. Defining when, where, and why aquatic invasive species move can help inform management strategies, yet the movement ecology of some of the most pervasive invasive species remains unknown. Red swamp crayfish (Procambarus clarkii; RSC) are the most widespread invasive crayfish and negatively affect ecosystems worldwide. We employed high-dimensional acoustic telemetry to investigate the movement patterns of 24 individual RSC across three months in an invaded water body. We assessed the effects of various extrinsic factors, such as time of day, temperature, precipitation, and proximity to the water’s edge, along with intrinsic factors, such as sex, reproductive form, and size, at different scales, including movement steps, range distribution, and behavioral states. We found that movement patterns across all scales were overwhelmingly driven by sex and reproductive form. Furthermore, RSC showed increased overall activity at night and near the water’s edge. By establishing baseline movement patterns and identifying key contributing factors, these findings provide a foundation for the development of adaptive management strategies for controlling invasive RSC populations.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-96379-8","usgsCitation":"Raboin, M.J., Roth, B.M., Sullivan, A., Allert, A.L., Stoeckel, J.A., Nathan, L.R., Quebedeaux, K.B., Sholtis, M.D., Smerud, J.R., Erickson, R.A., and Cupp, A.R., 2025, Movement patterns of invasive red swamp crayfish vary with sex and environmental factors: Scientific Reports, v. 15, 14312, 12 p., https://doi.org/10.1038/s41598-025-96379-8.","productDescription":"14312, 12 p.","ipdsId":"IP-172876","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-96379-8","text":"Publisher Index Page"},{"id":499178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","city":"Novi","geographicExtents":"{\n \"type\": 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