The Landsat Collection 2 Level-3 Fractional Snow Covered Area science product indicates the percentage of pixels covered by snow for Landsat 4–9 imagery. Landsat’s spatial resolution offers the capability to map snow cover patterns across topographically complex mountainous regions. Snow cover is spatially and temporally variable and is often concentrated in remote or inaccessible land regions, making spaceborne remote sensing the most feasible approach to measure and monitor snow cover change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223086","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Fractional Snow Covered Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3086, 2 p., https://doi.org/10.3133/fs20223086.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139626","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418251,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3086/fs20223086.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3086"},{"id":410799,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418250,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3086/coverthb2.jpg"},{"id":418252,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3086/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: December 20, 2022; Version 1.1: June 21, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
A significant number of the world’s approximately 1,400 subaerial volcanoes with Holocene eruptions are unmonitored by ground-based sensors yet constitute a potential hazard to nearby residents and infrastructure, as well as air travel and global commerce. Data from an international constellation of more than 60 current satellite instruments provide a cost-effective means of tracking activity and potentially forecasting hazards at volcanoes around the world. These data span the electromagnetic spectrum: ultraviolet, optical, infrared, and microwave (synthetic aperture radar). They can measure volcanic thermal and gas emissions, ground displacement, and surface and topographic change, providing information that addresses one of the grand challenges in volcanology—to overcome our incomplete understanding of the relation between volcanic unrest and eruption, which is currently based on only a few well-studied volcanoes.
Although the potential of volcano remote sensing has been recognized for decades, there are many hurdles to clear before remote sensing data can be used fully by all volcano observatories. These include: (1) the limited temporal and spatial coverage of active volcanoes by satellites and the delayed distribution of those data; (2) the lack of background data acquired at all volcanoes; and (3) limited access to, and utilization of, remote sensing data in some areas owing to a lack of expertise, licensing, user-friendly formats, data access portals, or computational infrastructure.
While remote sensing data will never replace ground-based monitoring, a joint observation strategy provides a powerful means of assessing volcanic activity before, during, and after hazardous eruptions, especially given the unique spatial, temporal, and spectral perspective provided by remote measurements. A coordinated international remote sensing observation strategy for volcanoes—similar to one used by the cryosphere community—along with a volcano space task group to maximize the utility of satellite data for volcano monitoring would be highly beneficial. Such a vision could facilitate (1) global coordination of satellite observations (as done for polar regions) for background monitoring and eruption response, (2) open data that can be rapidly distributed during crises, (3) communication tools and forums for discussion of satellite data, (4) integrated ground and satellite databases of unrest, and (5) global capacity building.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225116","usgsCitation":"Pritchard, M.E., Poland, M., Reath, K., Andrews, B., Bagnardi, M., Biggs, J., Carn, S., Coppola, D., Ebmeier, S.K., Furtney, M.A., Girona, T., Griswold, J., Lopez, T., Lundgren, P., Ogburn, S., Pavolonis, M., Rumpf, E., Vaughan, G., Wauthier, C., Wessels, R., Wright, R., Anderson, K.R., Bato, M.G., and Roman, A., 2022, Optimizing satellite resources for the global assessment and mitigation of volcanic hazards—Suggestions from the USGS Powell Center Volcano Remote Sensing Working Group: U.S. Geological Survey Scientific Investigations Report 2022–5116, 69 p., https://doi.org/10.3133/sir20225116.","productDescription":"Report: ix, 69 p.; 1 Table","onlineOnly":"Y","ipdsId":"IP-110516","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":410734,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5116/sir20225116_appendix1_table1.1.xlsx","text":"Table 1.1","size":"243 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5116 Table 1.1"},{"id":410733,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5116/sir20225116.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5116"},{"id":410732,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5116/coverthb2.jpg"}],"contact":"Director, Volcano Science Center
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA 38683
Jewel Cave National Monument is in the western Black Hills of South Dakota and contains an extensive cave network, including various subterranean water bodies (cave lakes) that are believed to represent the regionally important Madison aquifer. Recent investigations have sought to improve understanding of hydrogeologic characteristics of cave lakes in Jewel Cave. The U.S. Geological Survey, in cooperation with the National Park Service, collected water-level and water-chemistry data within and near Jewel Cave to better understand groundwater interactions in Jewel Cave and to evaluate recharge characteristics of cave lakes. Continuous water-level data were collected at two cave lakes (Hourglass and New Years Lakes) from 2018 to 2021, and discrete measurements were collected by National Park Service staff from 2015 to 2021. Water samples were collected from one stream, one rain collector, three springs, and two cave lakes. The approach for this study included comparing water-level data collected from two cave lakes to historical climate data and using multivariate statistical analyses to evaluate water samples collected during this study and from previous investigations. This study builds on interpretations from previous investigations that collected similar datasets and performed similar analyses.
Hydrographs of Hourglass and News Years Lakes from 2015 to 2021 demonstrated the variability of groundwater levels in Jewel Cave in response to dry and wet climate conditions. Hourglass Lake displayed small (up to 4.8 feet), gradual water-level changes, whereas New Years Lake displayed relatively large (up to at least 27.5 feet) and rapid water-level changes. Hourglass and New Years Lakes are about 0.4 mile apart at the land surface, and the water-level elevation between the lakes varied from 61 to 93.5 feet from 2016 to 2021. The proximity and relatively small elevation difference of Hourglass and New Years Lakes indicated different recharge sources and (or) mechanisms were responsible for hydrograph dissimilarities. Water-level changes at Hourglass Lake were similar to water-level changes at a well completed in the Madison aquifer about 9 miles south of Jewel Cave National Monument, which indicated Hourglass Lake may be recharged similar to the regional Madison aquifer along outcrops north of Jewel Cave. New Years Lake displayed almost no similarities to the well completed in the Madison aquifer—indicating a more direct connection to local recharge rather than solely from outcrops recharging the regional Madison aquifer.
Results from multivariate statistical analyses of water-chemistry data were used to evaluate recharge observations from water-level data. The water chemistry of Hourglass Lake indicated its water was chemically more similar to precipitation than other groundwater sites sampled. A conceptual karst recharge model indicated that the dominant recharge source to Hourglass Lake was diffuse allogenic recharge from vertical movement of infiltrated precipitation through vertical or near-vertical fractures that extend through the Minnelusa Formation and unsaturated zone of the Madison Limestone. The water chemistry of New Years Lake was chemically similar to Hell Canyon Creek about 0.2 mile from New Years Lake at the land surface. Streamflow loss zones (concentrated allogenic recharge) along Hell Canyon Creek have not been mapped, but their presence in the Jewel Cave area has been speculated by previous investigations. A fault observed in the cave ceiling above New Years Lake by National Park Service staff could provide a natural conduit for direct recharge from Hell Canyon Creek to New Years Lake if the fault is extensive. Additional water-chemistry and water-level data, as well as streamflow data upstream and downstream of the potential streamflow loss zone along Hell Canyon Creek, are needed to prove the presence of this loss zone and discern further correlations between streamflow and water levels in New Years Lake. Observations from previous investigations and this study indicated recharge to Jewel Cave is complex and occurs on various timescales that are affected temporally by precipitation patterns and spatially by hydrologic connection with the overlying Minnelusa aquifer of the Minnelusa Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225108","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., 2022, Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21: U.S. Geological Survey Scientific Investigations Report 2022–5108, 47 p., https://doi.org/10.3133/sir20225108.","productDescription":"Report: viii, 47 p.; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-137086","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":410716,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.XML"},{"id":410714,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5108/coverthb.jpg"},{"id":410715,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.pdf","text":"Report","size":"8.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5108"},{"id":410717,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5108/images"},{"id":410718,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410721,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225108/full","text":"Report"}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.2,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.738808274610875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Dry mixed-conifer forests of the southwestern United States are experiencing rapid, anthropogenically driven fire regime change. Prior to the Euro-American settlement, most of these forests experienced frequent surface fires but are now vulnerable to uncharacteristically large, high-severity fires. Fire directly influences the structure and composition of these forests and, in turn, the wildlife that inhabit them. Changing fire regimes result in a certain decline of some species and uncertain consequences for others. The Mexican spotted owl (Strix occidentalis lucida) is a federally listed threatened species of particular note in southwestern mixed-conifer forests. High-severity fire is cited as the owl’s primary threat in the revised species recovery plan, but uncertainties surround the impacts of high-severity fire on the habitat of the threatened owl, particularly across a timeframe longer than a few years. Our objective was to explore the long-term (100-year) effects of fire severity on elements of forest structure vital for Mexican spotted owl nesting. We quantified structural attributes for nest/roost habitat across mixed-conifer forests that burned at varying severity levels and time periods in the last century. We then examined the drivers of structural attributes by detecting statistical differences between severity classes and time periods through permutational multivariate analysis of variance.
High-severity fire has the strongest deleterious impact on elements of forest structure (total basal area, percent medium tree basal area, percent large tree basal area, large tree density, and canopy cover) vital to Mexican spotted owl nesting, and although the structural differences between severity classes diminish with time, it took ≥ 80–100 years to reach the structural conditions desired for Mexican spotted owl nesting after stand-replacing fires. The most important attribute measured, canopy cover, required 90–100 years after high-severity fires to reach levels most suitable for Mexican spotted owls in the Lincoln National Forest.
As fires increase in frequency, severity, and size compared to the last century, the Lincoln National Forest is projected to face an overall decrease in the structural conditions needed for Mexican spotted owl nesting habitat in this region. Short intervals between uncharacteristically high-severity fires in particular pose an imminent threat to nesting habitat.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-022-00158-z","usgsCitation":"Durboraw, T.D., Boal, C.W., Fleck, M.S., and Gill, N., 2022, Long-term recovery of Mexican spotted owl nesting habitat after fire in the Lincoln National Forest, New Mexico: Fire Ecology, v. 18, 31, 20 p., https://doi.org/10.1186/s42408-022-00158-z.","productDescription":"31, 20 p.","ipdsId":"IP-140844","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-022-00158-z","text":"Publisher Index Page"},{"id":433162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Lincoln National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106,\n 34\n ],\n [\n -106,\n 32.5\n ],\n [\n -105,\n 32.5\n ],\n [\n -105,\n 34\n ],\n [\n -106,\n 34\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2022-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Durboraw, Tara D.","contributorId":341366,"corporation":false,"usgs":false,"family":"Durboraw","given":"Tara","email":"","middleInitial":"D.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleck, Mary S.","contributorId":341367,"corporation":false,"usgs":false,"family":"Fleck","given":"Mary","email":"","middleInitial":"S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gill, Nathan S.","contributorId":341368,"corporation":false,"usgs":false,"family":"Gill","given":"Nathan S.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908312,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238955,"text":"70238955 - 2022 - Indigenous fire management and cross-scale fire-climate relationships in the Southwest United States from 1500 to 1900 CE","interactions":[],"lastModifiedDate":"2022-12-19T14:54:54.348462","indexId":"70238955","displayToPublicDate":"2022-12-19T08:38:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Indigenous fire management and cross-scale fire-climate relationships in the Southwest United States from 1500 to 1900 CE","docAbstract":"Prior research suggests that Indigenous fire management buffers climate influences on wildfires, but it is unclear whether these benefits accrue across geographic scales. We use a network of 4824 fire-scarred trees in Southwest United States dry forests to analyze up to 400 years of fire-climate relationships at local, landscape, and regional scales for traditional territories of three different Indigenous cultures. Comparison of fire-year and prior climate conditions for periods of intensive cultural use and less-intensive use indicates that Indigenous fire management weakened fire-climate relationships at local and landscape scales. This effect did not scale up across the entire region because land use was spatially and temporally heterogeneous at that scale. Restoring or emulating Indigenous fire practices could buffer climate impacts at local scales but would need to be repeatedly implemented at broad scales for broader regional benefits.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abq3221","usgsCitation":"Roos, C., Guiterman, C.H., Margolis, E.Q., Swetnam, T., Laluk, N.C., Thompson, K.F., Toya, C., Farris, C.A., Fule, P.Z., Iniguez, J.M., Kaib, J.M., O’Connor, C.D., and Whitehair, L., 2022, Indigenous fire management and cross-scale fire-climate relationships in the Southwest United States from 1500 to 1900 CE: Science Advances, v. 8, no. 49, eabq3221, 12 p., https://doi.org/10.1126/sciadv.abq3221.","productDescription":"eabq3221, 12 p.","ipdsId":"IP-140760","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":445649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.abq3221","text":"Publisher Index Page"},{"id":410707,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","otherGeospatial":"Chuska Mountains, Chiricahua Mountains, Defiance Plateau, Jemez Mountains, Lukachukai Mountains, Pinaleño Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.41484962647522,\n 36.560720206412725\n ],\n [\n -109.63457618897512,\n 36.144856877679004\n ],\n [\n -109.55767189210022,\n 35.44086420737669\n ],\n [\n -109.16216407960029,\n 35.22576055493633\n ],\n [\n -108.60186134522512,\n 35.904953643186545\n ],\n [\n -108.87651954835032,\n 36.560720206412725\n ],\n [\n -109.29400001710015,\n 36.640099917000086\n ],\n [\n -109.41484962647522,\n 36.560720206412725\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.99786729122823,\n 36.22998127242933\n ],\n [\n -107.02533311154072,\n 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]\n}","volume":"8","issue":"49","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roos, Chris I.","contributorId":300058,"corporation":false,"usgs":false,"family":"Roos","given":"Chris I.","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":859351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guiterman, Christopher H.","contributorId":190553,"corporation":false,"usgs":false,"family":"Guiterman","given":"Christopher","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":859352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":859353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swetnam, Thomas W.","contributorId":90455,"corporation":false,"usgs":false,"family":"Swetnam","given":"Thomas W.","affiliations":[],"preferred":false,"id":859354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laluk, Nicholas C.","contributorId":300059,"corporation":false,"usgs":false,"family":"Laluk","given":"Nicholas","email":"","middleInitial":"C.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":859355,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Kerry F.","contributorId":300060,"corporation":false,"usgs":false,"family":"Thompson","given":"Kerry","email":"","middleInitial":"F.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":859356,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Toya, Chris","contributorId":300061,"corporation":false,"usgs":false,"family":"Toya","given":"Chris","email":"","affiliations":[{"id":65008,"text":"Pueblo of Jemez","active":true,"usgs":false}],"preferred":false,"id":859357,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Farris, Calvin A.","contributorId":292802,"corporation":false,"usgs":false,"family":"Farris","given":"Calvin","email":"","middleInitial":"A.","affiliations":[{"id":63015,"text":"National Park Service, Division of Fire and Aviation Management, P.O. Box 1713, Klamath Falls, OR 97601, USA","active":true,"usgs":false}],"preferred":false,"id":859358,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fule, Peter Z.","contributorId":298160,"corporation":false,"usgs":false,"family":"Fule","given":"Peter","email":"","middleInitial":"Z.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":859359,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Iniguez, Jose M.","contributorId":300062,"corporation":false,"usgs":false,"family":"Iniguez","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":34678,"text":"US Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":859360,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kaib, J. Mark","contributorId":300063,"corporation":false,"usgs":false,"family":"Kaib","given":"J.","email":"","middleInitial":"Mark","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":859361,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"O’Connor, Christopher D.","contributorId":300064,"corporation":false,"usgs":false,"family":"O’Connor","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":34678,"text":"US Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":859362,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Whitehair, Lionel","contributorId":300065,"corporation":false,"usgs":false,"family":"Whitehair","given":"Lionel","email":"","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":859363,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70240649,"text":"70240649 - 2022 - Ecological Coastal Units – Standardized global shoreline characteristics","interactions":[],"lastModifiedDate":"2023-02-10T14:26:17.604346","indexId":"70240649","displayToPublicDate":"2022-12-19T08:19:59","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Ecological Coastal Units – Standardized global shoreline characteristics","docAbstract":"A new set of resources is now available that describe global shoreline characteristics. High resolution (30 m), globally comprehensive Coastal Segment Units (CSUs) and Ecological Coastal Units (ECUs) were developed in a collaboration between the U.S. Geological Survey (USGS), Esri, and the Marine Biodiversity Observation Network (MBON). The data were produced from a segmentation and characterization of a global shoreline vector extracted from year 2014 Landsat imagery. A total of 4 million 1 km shoreline segments were attributed with values from ten variables which describe the ecological settings in which the coastline occurs, including water-side properties, landside properties, and properties of the coastline itself. These data were developed as part of a Group on Earth Observations (GEO) global ecosystem mapping initiative called GEO Ecosystems (GEO ECO). The development of the resource and its intended utility are reviewed herein.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oceans 2022, Hampton Roads","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Oceans 2022, Hampton Roads","conferenceDate":"Oct 17-20, 2022","conferenceLocation":"Hampton Roads, VA","language":"English","publisher":"IEEE","doi":"10.1109/OCEANS47191.2022.9977390","usgsCitation":"Sayre, R., Butler, K., Van Graafeiland, K., Breyer, S., and Wright, D., 2022, Ecological Coastal Units – Standardized global shoreline characteristics, in Oceans 2022, Hampton Roads, Hampton Roads, VA, Oct 17-20, 2022, 4 p., https://doi.org/10.1109/OCEANS47191.2022.9977390.","productDescription":"4 p.","ipdsId":"IP-146446","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":412943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":302356,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":864110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butler, Kevin","contributorId":267714,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Graafeiland, Keith","contributorId":245012,"corporation":false,"usgs":false,"family":"Van Graafeiland","given":"Keith","affiliations":[],"preferred":false,"id":864112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breyer, Sean","contributorId":267716,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Dawn","contributorId":200268,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","affiliations":[],"preferred":false,"id":864114,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238970,"text":"70238970 - 2022 - Seismic multi-hazard and impact estimation via causal inference from satellite imagery","interactions":[],"lastModifiedDate":"2022-12-19T14:11:08.881758","indexId":"70238970","displayToPublicDate":"2022-12-19T08:07:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Seismic multi-hazard and impact estimation via causal inference from satellite imagery","docAbstract":"Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.
","language":"English","publisher":"Springer","doi":"10.1038/s41467-022-35418-8","usgsCitation":"Xu, S., Dimasaka, J., Wald, D.J., and Noh, H.Y., 2022, Seismic multi-hazard and impact estimation via causal inference from satellite imagery: Nature Communications, v. 13, 7793, 13 p., https://doi.org/10.1038/s41467-022-35418-8.","productDescription":"7793, 13 p.","ipdsId":"IP-131046","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":445655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-35418-8","text":"Publisher Index Page"},{"id":410700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2022-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":859455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dimasaka, Joshua","contributorId":300128,"corporation":false,"usgs":false,"family":"Dimasaka","given":"Joshua","email":"","affiliations":[{"id":65026,"text":"Stanford University, CA, USA","active":true,"usgs":false}],"preferred":false,"id":859456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":859457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":859458,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238906,"text":"sim3497 - 2022 - Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","interactions":[],"lastModifiedDate":"2026-04-01T15:30:56.71177","indexId":"sim3497","displayToPublicDate":"2022-12-19T07:51:11","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3497","displayTitle":"Delineating the Pierre Shale from Geophysical Surveys East and Southeast of Ellsworth Air Force Base, South Dakota, 2021","title":"Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineer Center, used surface-geophysical methods to delineate the top of Cretaceous Pierre Shale along survey transects in selected areas east and southeast of Ellsworth Air Force Base, South Dakota, from April to September 2021. Two complementary geophysical methods—electrical resistivity and passive seismic—were used along 21 colocated transect surveys east and southeast of Ellsworth Air Force Base for a total of 24.7 line-kilometers. Electrical resistivity results were analyzed using EarthImager2D electrical resistivity tomography processing and inversion software. Two-dimensional earth models showing the electrical properties of the subsurface were evaluated by directly comparing the high and low subsurface resistivity values to a surficial-geologic map and nearby wells with drillers logs. Passive seismic data were analyzed using the horizontal-to-vertical spectral ratio method to determine the depth to the Cretaceous Pierre Shale at each survey point. The depth to the Pierre Shale along the transects ranged from 0.0 to about 19.8 meters, and the mean and median depths were about 6.1 and 5.6 meters, respectively. The elevation of the Pierre Shale and thickness of unconsolidated deposits generally increased with land-surface elevation from south to north; however, some transects displayed topographically high and low areas that did not correlate with land-surface topography.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3497","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineer Center","usgsCitation":"Medler, C.J., 2022, Delineating the Pierre Shale from geophysical surveys east and southeast of Ellsworth Air Force Base, South Dakota, 2021: U.S. Geological Survey Scientific Investigations Map 3497, 3 sheets, 15-p. pamphlet, https://doi.org/10.3133/sim3497.","productDescription":"Report: vi, 15 p.; 3 Sheets: 64.00 × 53.33 inches or smaller; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-137098","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":501938,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113997.htm","linkFileType":{"id":5,"text":"html"}},{"id":410625,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet03.pdf","text":"Sheet 3","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 3","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 4A, 4B, 4D, 4E, 4FD3, 4FD4, 4FD5, 4G, 4H, and 5, Ellsworth Air Force Base, South Dakota"},{"id":410609,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet02.pdf","text":"Sheet 2","size":"14.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 2","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 2, 3A, 3B, 3D, 3E, and 3F, Ellsworth Air Force Base, South Dakota"},{"id":410608,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3497/sim3497_sheet01.pdf","text":"Sheet 1","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497, sheet 1","linkHelpText":"—Depth to Pierre Shale from Electrical Resistivity Tomography Inversion and Horizontal-to-Vertical Spectral Ratio Results for Transects 1A, 1C, 1D, 4F Alternate 1, and 4F Alternate 2, Ellsworth Air Force Base, South Dakota"},{"id":410607,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3497/images"},{"id":410606,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.XML"},{"id":410605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3497/sim3497.pdf","text":"Report","size":"8.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3497"},{"id":410604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3497/coverthb.jpg"},{"id":410698,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3497/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":410626,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X57BS0","text":"USGS data release","linkHelpText":"Electrical resistivity tomography (ERT) and horizontal-to-vertical spectral ratio (HVSR) data collected East and Southeast of Ellsworth Air Force Base, South Dakota, in 2021"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.08,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.06\n ],\n [\n -102.84286176348763,\n 44.2\n ],\n [\n -103.08,\n 44.2\n ],\n [\n -103.08,\n 44.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.
","language":"English","publisher":"SPIE Digital Library","doi":"10.1117/1.JRS.16.044522","usgsCitation":"King, T.V., Hundt, S., Hafen, K., Stengel, V.G., and Ducar, S.D., 2022, Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data: Journal of Applied Remote Sensing, v. 16, no. 4, 044522, 22 p., https://doi.org/10.1117/1.JRS.16.044522.","productDescription":"044522, 22 p.","ipdsId":"IP-127684","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":445656,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.16.044522","text":"Publisher Index Page"},{"id":435594,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GF0CBG","text":"USGS data release","linkHelpText":"Chlorophyll-a concentrations and algal bloom condition paired with Sentinel-2 aquatic reflectance values collected for Brownlee Reservoir, ID from 2015 through 2020"},{"id":410786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.6105415720396,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 43.793442297404255\n ],\n [\n -116.58806901557723,\n 45.15126198574853\n ],\n [\n -117.6105415720396,\n 45.15126198574853\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hundt, Stephen A. 0000-0002-6484-0637","orcid":"https://orcid.org/0000-0002-6484-0637","contributorId":204678,"corporation":false,"usgs":true,"family":"Hundt","given":"Stephen","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ducar, Scott D. 0000-0003-0781-5598","orcid":"https://orcid.org/0000-0003-0781-5598","contributorId":297547,"corporation":false,"usgs":true,"family":"Ducar","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238904,"text":"sir20225094 - 2022 - Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia","interactions":[],"lastModifiedDate":"2022-12-19T12:01:47.434879","indexId":"sir20225094","displayToPublicDate":"2022-12-16T19:15:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5094","displayTitle":"Groundwater Quality and Geochemistry of the Western Wet Gas Part of the Marcellus Shale Oil and Gas Play in West Virginia","title":"Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia","docAbstract":"Thirty rural residential water wells in the wet gas region of the Marcellus Shale oil and gas play in northwestern West Virginia were sampled by the U.S. Geological Survey (USGS) in 2018, in cooperation with West Virginia State agencies, to analyze for a range of water-quality constituents, including major ions, trace metals, radionuclides, bacteria, and methane and other dissolved hydrocarbon gases. The groundwater-quality data collected for this study were used to assess the overall quality of groundwater in the study area in relation to public drinking-water standards. The groundwater-quality data were also evaluated with respect to geology, well depth, topographic setting, and proximity to oil and gas wells to identify possible relations to these factors.
The presence of total coliform bacteria in groundwater is a potential indicator of surface contamination. The presence of Escherichia coli bacteria is indicative of fecal contamination of groundwater from either human or animal sources and may be considered an indicator of other related pathogens such as viruses. Total coliforms were detected in 26 of the 30 (87 percent) wells sampled. Eleven of the 30 (37 percent) wells sampled had detections of Escherichia coli bacteria.
Sodium concentrations in 24 of 30 (80 percent) samples exceeded the U.S. Environmental Protection Agency (EPA) 20-milligram per Liter (mg/L) health-based value (HBV). Manganese, aluminum, and iron concentrations exceeded the EPA 50, 2.0, and 300 micrograms per liter (μg/L) secondary maximum contaminant level (SMCL) drinking-water standards at 14 (47 percent), 7 (23 percent), and 5 (17 percent) of the 30 wells sampled. Two of the 30 (7 percent) wells sampled had concentrations of manganese that exceeded the 300-μg/L USGS health-based screening level (HBSL). Arsenic concentrations at 7 of 30 (23 percent) wells sampled exceeded the 10-μg/L EPA maximum contaminant level (MCL) health-based drinking water standard. The EPA maximum contaminant level goal (MCLG) for arsenic is 0 μg/L and 29 of 30 wells sampled contained detectable concentrations of arsenic.
None of the 30 wells sampled exceeded the U.S. Office of Surface Mining Reclamation and Enforcement (OSMRE) 28-mg/L immediate action level (IAL) for methane in groundwater and only 1 of 30 (3 percent) sites exceeded the 10-mg/L OSMRE level of concern (LOC) for methane in groundwater. Of the 28 wells sampled for radon-222 all 28 (100 percent) exceeded the EPA proposed 300-picocuries per liter (pCi/L) MCL for radon. None of the samples exceeded the 4,000-pCi/L alternate maximum contaminant level (AMCL) which is applicable to public drinking water systems that have adopted radon mitigation programs.
Wilcoxon Signed Rank Tests indicated statistically significant differences at a 95 percent confidence interval (p less than 0.05) in radium-226, barium, and ethane groundwater concentrations with respect to the density of oil and gas wells present within a 500-meter (m) radius around the rural residential wells sampled for the study. Samples from residential wells that had four or fewer oil and gas wells in the surrounding 500-m radius had statistically lower concentrations of radium-226, bromide, and ethane than samples from residential wells sampled that had five or more oil and gas wells in the surrounding 500-m radius. Given the available data, the relationship between concentrations of radium-226, bromide, and ethane for wells sampled in this study and oil and gas development or natural geochemical processes is not clear.
Groundwater-age tracers (chlorofluorocarbons, tritium, and sulfur hexafluoride) were sampled at 17 of the 30 wells. All 17 samples contained a fraction of young, post-1950s groundwater. Many of the groundwater samples collected for this study have high calcium to sodium ratios and low total dissolved solids concentrations, indicating they are dominated by recently recharged water. A subset of samples had chloride to bromide mass ratios between 70 and 200, indicating that deep Appalachian basin brines mixed with the shallow groundwater. For most of the samples in this study, the C1 through C6 hydrocarbons have characteristics that reflect a biogenic gas signature that has, to varying degrees, undergone oxidation processes during transport. None of the samples show a characteristic thermogenic cracking pattern among the hydrocarbon ratios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225094","isbn":"978-1-4113-4489-1","collaboration":"Prepared in cooperation with the West Virginia Department of Environmental Protection Division of Water and Waste Management and the West Virginia Department of Health and Human Resources Office of Environmental Health Services","usgsCitation":"Kozar, M.D., McAdoo, M.A., and Haase, K.B., 2022, Groundwater quality and geochemistry of the western wet gas part of the Marcellus Shale Oil and Gas Play in West Virginia: U.S. Geological Survey Scientific Investigations Report 2022–5094, 88 p., https://doi.org/10.3133/sir20225094.","productDescription":"Report: xiv, 88 p.; Data Release; Appendix","numberOfPages":"88","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-139572","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center 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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Streamflow in rivers can be separated into a relatively steady component, or baseflow, that represents reliably available surface water and more dynamic components of runoff that typically represent a large fraction of total streamflow. A spatially aggregated numerical time-series model was developed to separate the baseflow component of a streamflow time-series using a state-space framework in which baseflow is a non-linear function of upstream storage, an unmeasured state variable. The state-space framework allows forecasting of baseflow for periods with no rainfall or snowmelt and estimation of residence times in contrast to other hydrograph separation models. The use of a non-linear relation between baseflow and storage maintains model performance over a wide range of time scales but will only provide reliable predictions for periods when the rate of streamflow recession as a fraction of streamflow decreases over time.
The baseflow separation model, BFS, is implemented as set of functions in the statistical computing language R. BFS is run using the main function, bf_sep, which reads model input (a time series of streamflow), calculates the baseflow component of streamflow, writes model output to a file, and returns an error to the user to facilitate automated calibration. The function, bf_sep, has six arguments, which a user must enter: a numerical vector with the time series of measured streamflow volume for each time step; a character string, timestep, that has a value of either “daily” or “hourly” indicating the time step; a character string, error_basis, indicating which simulated streamflow components are used for error calculations; a six-element numeric vector, flow, with parameters characterizing streamflow; a six-element vector, basin_char, with parameters characterizing the geometry of stream basin and reservoirs; and a six-element vector, gw_hyd, with hydraulic parameters. The function bf_sep calls a series of other functions to calculate surface and base reservoir storage and fluxes.
Calibration of a non-linear model for baseflow recession must confront three issues. First, baseflow is a component of streamflow, so it is always less than or equal to streamflow but there is no independent standard for the baseflow component of streamflow. Second, optimization routines can converge on a set of model parameters that result in relatively steady but minimal baseflow that does not exceed streamflow, Q, but has a limited dynamic range. Third, the power function used to generate non-linear first-order baseflow recession (dQ/dt)/Q ≠ constant) may only be sensitive to parameters over a limited range of values, which may not be found by optimization routines.
To address these issues, BFS calculates error as the mean of weighted differences between measured streamflow and either simulated baseflow or the sum of simulated baseflow and surface flow as a fraction of measured streamflow. The difference for each time step is weighted by an exponential function of the length of recession for each time step ranging from 0 for periods when streamflow increases and approaching 1 for long recessional periods. The weight is set to 1 for any time step when simulated streamflow exceeds measured streamflow. Error calculation incorporates limited precision of streamflow measurements.
A four-step calibration process was developed to find a set of viable parameters that maximize the baseflow component within the constraints of the conceptual model (a first-order recession rate that decreases during dry periods). BFS was calibrated at 13,208 U.S. Geological Survey streamgages with available daily streamflow records for at least 300 days from water years 1981 to 2020. The total simulated baseflow component as a fraction of streamflow (BFF) was generally less than the baseflow index (BFI) for 8,368 streamgages where BFF and BFI were available. The median difference was BFF–BFI = 0.11. Large differences were most common in the Interior West where streamflow in many rivers is regulated and is generated predominantly by snowmelt. The baseflow separation model generally allocates less streamflow to baseflow than graphical hydrograph separation in snowmelt rivers.
BFS can be used to forecast streamflow during dry periods by using a time series of real-time streamflow with values of Not Available (NA), appended to the time-series to represent missing (future) streamflow values. The forecast skill of BFS was evaluated in terms of difference between simulated baseflow and measured streamflow as a fraction of measured streamflow on the days of the annual maximum recession period at 5,916 of the sites with at least 10 years of record. The median annual error was less than 50 percent at one-half of the sites and generally improved for drier years with longer recession periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225114","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the Washington State Department of Ecology","usgsCitation":"Konrad, C.P., 2022, BFS—A non-linear, state-space model for baseflow separation and prediction: U.S. Geological Survey Scientific Investigations Report 2022–5114, 24 p., https://doi.org/10.3133/sir20225114.","productDescription":"Report: vii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-122969","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":410595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5114/coverthb.jpg"},{"id":410596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5114"},{"id":410598,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIPHEP","text":"USGS data release","description":"USGS data release","linkHelpText":"Non-linear baseflow separation model with parameters and results (ver. 2.0, October 2022)"},{"id":410599,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5114/images"},{"id":410600,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.XML"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The US National Park Service mission includes conserving native species and historical landscapes ‘unimpaired for the enjoyment of future generations’. However, humans have increased the introduction of non-native species that can become invasive and which have harmful impacts on native species and landscapes. We revisit two previous papers, ‘Alien Species in National Parks: Drawing Lines in Space and Time’, published in 1995 by D.B. Houston and E.G. Schreiner, and ‘Climate Change and “Alien Species in National Parks”: Revisited’, published in 2014 by T.J. Stohlgren, J.R. Resnik and G.E. Plumb, to demonstrate the organizational progress that has been made in reducing impacts of invasive species despite the increasing pressure of increasing numbers of non-native species. The National Park Service has continued efforts on invasive plant management, established an Invasive Animal Program in 2018 and developed a Pest & Invasive Species Project Kit to compile information to inform management regardless of taxonomic group. Additionally, the Park Service has expanded their toolset to make decisions related to invasive species and climate change to focus on achievable goals. Since the 1995 publication, the scale of invasion has increased, and impacts of climate change are more noticeable since the 2014 publication, increasing the complexity in trying to achieve the National Park Service mission.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Invasive species and global climate change","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CAB International","usgsCitation":"Jarnevich, C.S., Hogan, T., Sieracki, J., Lipsky, C., and Wullschleger, J., 2022, Climate change and ‘alien species in National Parks’: Revisited, chap. of Invasive species and global climate change, p. 188-202.","productDescription":"15 p.","startPage":"188","endPage":"202","ipdsId":"IP-137011","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":420477,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cabidigitallibrary.org/doi/10.1079/9781800621459.0010","linkFileType":{"id":5,"text":"html"}},{"id":420479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Ziska, Lewis H.","contributorId":292083,"corporation":false,"usgs":false,"family":"Ziska","given":"Lewis","email":"","middleInitial":"H.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":882065,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogan, Terri","contributorId":240929,"corporation":false,"usgs":false,"family":"Hogan","given":"Terri","email":"","affiliations":[{"id":48162,"text":"National Park Service, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":881883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sieracki, Jennifer","contributorId":236914,"corporation":false,"usgs":false,"family":"Sieracki","given":"Jennifer","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":true,"id":881884,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lipsky, Christine","contributorId":328978,"corporation":false,"usgs":false,"family":"Lipsky","given":"Christine","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":881885,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wullschleger, John","contributorId":328980,"corporation":false,"usgs":false,"family":"Wullschleger","given":"John","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":881886,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239019,"text":"70239019 - 2022 - Note to Banders, December 2022","interactions":[],"lastModifiedDate":"2022-12-21T13:00:20.801021","indexId":"70239019","displayToPublicDate":"2022-12-16T06:58:35","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"title":"Note to Banders, December 2022","docAbstract":"Note to All Banders was a special extra communication with more urgent information relevant to banders. This Note to All Banders was sent to U.S. bird banders on December 20, 2022. This note includes holiday greetings and a review of the 2022 successes at the Bird Banding Laboratory. Throughout 2022, the BBL increased communication, engagement, and collaboration, within the Eastern Ecological Science Center, U.S. Geological Survey, and with organization partners and local communities. This Note to Banders highlights these efforts in more detail.
Ungulates (hooved mammals) have a broad distribution across the western United States and play an important role in maintaining predator-prey dynamics, affecting vegetation communities, and providing economic benefits to regional communities through tourism and hunting. Throughout the diverse landscapes they occupy, many ungulate populations undertake seasonal migrations to exploit spatially and temporally variable resources and to avoid predation or other threats. As the human footprint continues to expand across the western United States, ungulates increasingly face more obstacles on their migratory journeys. These obstacles threaten the long-term persistence of existing migrations. As a result, wildlife management agencies across the western United States have worked to identify and protect (or enhance) ungulate migration corridors and seasonal ranges identified from global positioning system (GPS) collar data. These efforts garnered additional support through the U.S. Department of the Interior Secretarial Order (SO) 3362, which was initiated in 2018 and provided Federal support for enhancing habitat quality of big-game winter ranges and migration corridors across the western states.
Further, SO 3362 prompted the U.S. Geological Survey (USGS) to establish the Corridor Mapping Team (CMT): a collaboration between USGS and participating State and Federal wildlife management agencies, as well as numerous Tribal Nations. The CMT works collaboratively to map ungulate migrations and seasonal ranges throughout the western United States within the Ungulate Migrations of the Western United States report series. Volume 1 of the series was published in 2020 and contained migrations and winter ranges from 42 herds across 5 states. Volume 2 was published in 2022 and contained migrations and seasonal ranges from an additional 65 herds. This report, Volume 3 in the series, details migrations and seasonal ranges from an additional 45 herds throughout most western states. In aggregate, the report series has detailed and mapped the migrations and seasonal ranges of 152 ungulate herds and serves as a map-based inventory of the documented ungulate migrations across the western United States. The data layers for most of the herds included in the report series are also available to the public by the USGS. In addition to the included herd maps, this volume provides an overview of the many ways the mapping efforts associated with the CMT are being integrated into local conservation, management, and policy throughout the western United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20225088","usgsCitation":"Kauffman, M., Lowrey, B., Berg, J., Bergen, S., Brimeyer, D., Burke, P., Cufaude, T., Cain, J.W., III, Cole, J., Courtemanch, A., Cowardin, M., Cunningham, J., DeVivo, M., Diamond, J., Duvuvuei, O., Fattebert, J., Ennis, J., Finley, D., Fort, J., Fralick, G., Freeman, E., Gagnon, J., Garcia, J., Gelzer, E., Graham, M., Gray, J., Greenspan, E., Hall, L.E., Hendricks, C., Holland, A., Holmes, B., Huggler, K., Hurley, M., Jeffreys, E., Johnson, A., Knox, L., Krasnow, K., Lockyer, Z., Manninen, H., McDonald, M., McKee, J.L., Meacham, J., Merkle, J., Moore, B., Mong, T.W., Nielsen, C., Oates, B., Olsen, K., Olson, D., Olson, L., Pieron, M., Powell, J., Prince, A., Proffitt, K., Reddell, C., Riginos, C., Ritson, R., Robatcek, S., Roberts, S., Sawyer, H., Schroeder, C., Shapiro, J., Simpson, N., Sprague, S., Steingisser, A., Tatman, N., Turnock, B., Wallace, C., and Wolf, L., 2022, Ungulate migrations of the western United States, Volume 3: U.S. Geological Survey Scientific Investigations Report 2022–5088, 114 p., https://doi.org/10.3133/sir20225088.","productDescription":"Report: xvi, 114 p.; Data Release","ipdsId":"IP-140325","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482396,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205101","text":"Ungulate Migrations of the Western United States, Volume 1"},{"id":410869,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225088/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5088"},{"id":410109,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LSKEZQ","text":"USGS data release","linkHelpText":"Ungulate migrations of the western United States—Volume 3"},{"id":410108,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5088/sir20225088.pdf","text":"Report","size":"46.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5088"},{"id":410590,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5088/sir20225088.xml"},{"id":482399,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245111","text":"Ungulate Migrations of the Western United States, Volume 5"},{"id":482398,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245006","text":"Ungulate Migrations of the Western United States, Volume 4"},{"id":482397,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225008","text":"Ungulate Migrations of the Western United States, Volume 2"},{"id":410589,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5088/images"},{"id":410107,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5088/coverthb.jpg"}],"country":"United States","state":"Arizona, California, Idaho, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.42035048637896,\n 31.96511270775069\n ],\n [\n -102.95879379353022,\n 32.09619220535299\n ],\n [\n -103.13494353105585,\n 37.18138856203808\n ],\n [\n -109.23595720566402,\n 36.94073809885069\n ],\n [\n -108.97760183013798,\n 41.07505691031014\n ],\n [\n -104.04885860727893,\n 41.037575887820424\n ],\n [\n -103.99337420317454,\n 45.12768947014476\n ],\n [\n -110.97964185421287,\n 44.941345247282584\n ],\n [\n -111.40161785171546,\n 44.544500805297844\n ],\n [\n -113.19445602509825,\n 44.46638294566654\n ],\n [\n -114.40947007708905,\n 45.686187837725555\n ],\n [\n -114.52887129856197,\n 46.79485489705277\n ],\n [\n -115.91818301512166,\n 47.62622801365191\n ],\n [\n -115.83639053795412,\n 49.05632926968573\n ],\n [\n -123.29267815383338,\n 48.983622439405565\n ],\n [\n -123.07800303736917,\n 47.953965988507235\n ],\n [\n -124.96057854880837,\n 48.33165246547705\n ],\n [\n -125.30525778678984,\n 41.07649272781754\n ],\n [\n -121.70178467265586,\n 34.62189088197306\n ],\n [\n -118.19264180429954,\n 32.88345216344581\n ],\n [\n -117.0106337464697,\n 32.50945638640991\n ],\n [\n -114.31442509866966,\n 32.635073285847525\n ],\n [\n -111.07671729769325,\n 31.381047387792464\n ],\n [\n -108.08998878966005,\n 31.351258024309203\n ],\n [\n -108.22418391489185,\n 31.845660224062968\n ],\n [\n -106.51293927385098,\n 31.96835429085307\n ],\n [\n -106.37491785144061,\n 31.922584970375112\n ],\n [\n -106.42035048637896,\n 31.96511270775069\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Associate Director, Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 300
Reston, VA 20192
Misinformation carries the potential for immense damage to public understanding of science and for evidence-based decision making at an individual and policy level. Our research explores the following questions within seismology: which claims can be considered misinformation, which are supported by a consensus, and which are still under scientific debate? Consensus and debate are important to quantify, because where levels of scientific consensus on an issue are high, communication of this fact may itself serve as a useful tool in combating misinformation. This is a challenge for earthquake science, where certain theories and facts in seismology are still being established. The present study collates a list of common public statements about earthquakes and provides–to the best of our knowledge–the first elicitation of the opinions of 164 earth scientists on the degree of verity of these statements. The results provide important insights for the state of knowledge in the field, helping identify those areas where consensus messaging may aid in the fight against earthquake related misinformation and areas where there is currently lack of consensus opinion. We highlight the necessity of using clear, accessible, jargon-free statements with specified parameters and precise wording when communicating with the public about earthquakes, as well as of transparency about the uncertainties around some issues in seismology.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.937055","usgsCitation":"Dryhurst, S., Mulder, F., Dallo, I., Kerr, J., McBride, S., Fallou, L., and Becker, J., 2022, Fighting misinformation in seismology: Expert opinion on earthquake facts vs fiction: Frontiers in Earth Science, v. 10, 937055, 9 p., https://doi.org/10.3389/feart.2022.937055.","productDescription":"937055, 9 p.","ipdsId":"IP-140840","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487710,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.937055","text":"Publisher Index Page"},{"id":482563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Dryhurst, Sarah","contributorId":351570,"corporation":false,"usgs":false,"family":"Dryhurst","given":"Sarah","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":928949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mulder, Femke","contributorId":351571,"corporation":false,"usgs":false,"family":"Mulder","given":"Femke","affiliations":[{"id":84010,"text":"Anglia Ruskin University, Chelmsford, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":928950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dallo, Irina","contributorId":351572,"corporation":false,"usgs":false,"family":"Dallo","given":"Irina","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":928951,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kerr, John","contributorId":351573,"corporation":false,"usgs":false,"family":"Kerr","given":"John","affiliations":[{"id":84011,"text":"Department of Psychology, University of Cambridge, Cambridge, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":928952,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928953,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fallou, Laure","contributorId":351574,"corporation":false,"usgs":false,"family":"Fallou","given":"Laure","affiliations":[{"id":35318,"text":"European-Mediterranean Seismological Centre","active":true,"usgs":false}],"preferred":false,"id":928954,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Becker, Julia S.","contributorId":217541,"corporation":false,"usgs":false,"family":"Becker","given":"Julia S.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":928955,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238949,"text":"70238949 - 2022 - Insights from the Alabama Hills into Mesozoic magmatism and tectonics in eastern California","interactions":[],"lastModifiedDate":"2022-12-19T15:24:47.46453","indexId":"70238949","displayToPublicDate":"2022-12-15T09:24:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2626,"text":"Lithosphere","active":true,"publicationSubtype":{"id":10}},"title":"Insights from the Alabama Hills into Mesozoic magmatism and tectonics in eastern California","docAbstract":"New zircon U-Pb ages for the Alabama Hills Granite in Owens Valley, eastern California, range from 103 to 102 Ma, nearly 20 Ma older than previously published zircon ages. The data preclude previously implied links between the pluton and the adjacent Late Cretaceous Mount Whitney Intrusive Suite. Geochronologic and isotopic data indicate a connection between the Alabama Hills Granite and leucogranites to the northwest on the Sierra Nevada crest, as well as a pluton to the southeast in the Coso Range. We refer to these units as the Kearsarge plutons. The suite was intruded from 103 to 100.5 Ma with
Green Lake, located in central Wisconsin USA within a watershed with land use dominated by agriculture, is listed as impaired under Sect. 303(d) of the Clean Water Act. The primary tributary, Silver Creek, is also impaired because of high total phosphorus (TP) concentrations. Silver Creek flows through a shallow marsh before reaching the lake. Prior to 2006, the marsh was turbid and free of macrophytes. Efforts to restrict carp (Cyprinus carpio) in the marsh and reduce the primary upstream phosphorus point source, resulted in the marsh becoming a clear-water, macrophyte-dominated system.
The point source reduction and marsh phytoplankton-to-macrophyte shift reduced the export of TP and suspended sediment (SS). These measured reductions at the marsh outlet exceeded the documented reductions in the upstream point source suggesting that the shift to a macrophyte-dominated system drove part of the TP reductions. TP loads at the marsh outlet significantly decreased in all seasons; however, SS loads significantly decreased in all seasons except winter, suggesting the vegetation shift was an important driver for these reductions. During 2012–2017, the marsh served as an overall sink for TP and SS, retaining on average 1.59 kg/day and 0.95 MT/day, respectively. Overall, this study documents benefits of a multi-stakeholder, collaborative ecological effort to restore a marsh from a turbid system to a macrophyte-dominated system, which resulted in significant reductions in downstream TP and SS loading to a major inland lake. This effort may serve as a model for similar restorations in other watersheds with land use dominated by agriculture.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01649-0","usgsCitation":"Fuller, S., Boswell, E.P., Thompson, A., and Robertson, D., 2022, Water-quality improvement of an agricultural watershed marsh after macrophyte establishment and point-source reduction: Wetlands, v. 42, 129, 13 p., https://doi.org/10.1007/s13157-022-01649-0.","productDescription":"129, 13 p.","ipdsId":"IP-139219","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":413530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.08144298728385,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.755719784168264\n ],\n [\n -88.8933819839438,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.85676732584028\n ],\n [\n -89.08144298728385,\n 43.755719784168264\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Sarah","contributorId":302730,"corporation":false,"usgs":false,"family":"Fuller","given":"Sarah","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boswell, Edward P 0000-0002-2644-4043","orcid":"https://orcid.org/0000-0002-2644-4043","contributorId":302732,"corporation":false,"usgs":false,"family":"Boswell","given":"Edward","email":"","middleInitial":"P","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":865264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Anita M.","contributorId":200233,"corporation":false,"usgs":false,"family":"Thompson","given":"Anita M.","affiliations":[{"id":16128,"text":"Department of Biological System Engineering, University of Wisconsin—Madison, Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":865265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865266,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270408,"text":"70270408 - 2022 - Examining landowners’ preferences for a chronic wasting disease management program","interactions":[],"lastModifiedDate":"2025-08-19T15:18:27.733358","indexId":"70270408","displayToPublicDate":"2022-12-15T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Examining landowners’ preferences for a chronic wasting disease management program","docAbstract":"Private landowners are key partners in chronic wasting disease (CWD) management, especially in landscapes where there is limited public ownership. In this study, we evaluated landowners' preferences for alternative hypothetical CWD management programs using a stated choice experiment. We were particularly interested in understanding preferences for the use of financial incentives to motivate white-tailed deer harvest and facilitate hunter access to private lands as potential CWD management tools. We used latent class analysis to characterize preference heterogeneity among landowners stemming from patterns of choice. We compared means and distributions of auxiliary variables related to landowners' perceived risks, trust, attitudes toward management, and sociodemographics across latent classes stemming from choice model results. The pooled model demonstrated that reducing deer population density, providing payments to landowners for CWD-positive deer taken from their property, the form of incentives for public access, and banning recreational deer feeding had a small positive effect on respondents' choice of CWD management program. However, providing financial payments to hunters for harvesting CWD-positive deer and the use of targeted culling had the opposite effect on choice. Latent class models revealed that a majority of respondents exhibited a pattern of preference where all forms of incentives exerted a negative effect on choice, but smaller subsets of landowners positively evaluate the use of some incentives. Post-hoc contrasts revealed relationships between patterns of preferences and trust, risk, and attitudes toward CWD management with small to medium effects. Results demonstrated limited support for the use of financial incentives as a tool to manage access and harvest in the southeast Minnesota CWD management zone
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1401","usgsCitation":"Landon, A., Smith, K., Cornicelli, L., Fulton, D.C., McInenly, L.E., and Schroeder, S.A., 2022, Examining landowners’ preferences for a chronic wasting disease management program: Wildlife Society Bulletin, v. 47, no. 1, e1401, 19 p., https://doi.org/10.1002/wsb.1401.","productDescription":"e1401, 19 p.","ipdsId":"IP-122867","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1401","text":"Publisher Index Page"},{"id":494314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Fillmore County, Houston County, Wisconsin County","otherGeospatial":"southeastern Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.13879824080021,\n 44.7131470360637\n ],\n [\n -93.13879824080021,\n 43.4796764343609\n ],\n [\n -91.23563813665416,\n 43.4796764343609\n ],\n [\n -91.23137485428805,\n 43.89405306400184\n ],\n [\n -92.58709230231764,\n 44.71116401915873\n ],\n [\n -93.13879824080021,\n 44.7131470360637\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Landon, Adam","contributorId":279439,"corporation":false,"usgs":false,"family":"Landon","given":"Adam","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kyle","contributorId":359833,"corporation":false,"usgs":false,"family":"Smith","given":"Kyle","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":946340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornicelli, Louis","contributorId":359827,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fulton, David C. 0000-0001-5763-7887","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":333043,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":79716,"text":"Minnesota Cooperative Unit","active":true,"usgs":false}],"preferred":true,"id":946335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McInenly, Leslie E.","contributorId":359829,"corporation":false,"usgs":false,"family":"McInenly","given":"Leslie","middleInitial":"E.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":946338,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schroeder, Susan A.","contributorId":359831,"corporation":false,"usgs":false,"family":"Schroeder","given":"Susan","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":946339,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238872,"text":"sir20225100 - 2022 - Analysis of groundwater and surface water in areas of isoxaflutole application, Tuscola and Kalamazoo Counties, Michigan","interactions":[],"lastModifiedDate":"2022-12-14T23:20:00.981699","indexId":"sir20225100","displayToPublicDate":"2022-12-14T17:45:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5100","displayTitle":"Analysis of Groundwater and Surface Water in Areas of Isoxaflutole Application, Tuscola and Kalamazoo Counties, Michigan","title":"Analysis of groundwater and surface water in areas of isoxaflutole application, Tuscola and Kalamazoo Counties, Michigan","docAbstract":"The herbicide 5-cyclopropyl-4-(2-methylsulfonyl-4-trifluoromethylbenzoyl) isoxazole, also known as isoxaflutole (IXF), was conditionally approved for use on corn in Michigan in 2015. The fate of IXF and its degradates in different environmental settings and the processes by which these compounds move to groundwater or to surface-water bodies have been previously studied, but little information about possible persistence and buildup of this herbicide and its degradates in Michigan’s groundwater and surface water is available. Therefore, from 2015 to 2020, the U.S. Geological Survey, in cooperation with the Michigan Department of Agriculture and Rural Development, studied IXF and two of its degradates in two locations where IXF was applied.
IXF and its degradates were rarely detected in shallow groundwater downgradient from IXF applications. In contrast, one or more of the three target IXF compounds were detected in 40 percent of surface-water samples. The degradates 1-(2-methylsulfonyl-4-trifluoromethylphenyl)-2-cyano-3-cyclopropyl propan-1-dione), also known as diketonitrile isoxaflutole (DKN), and 2-methylsulfonyl-4-(trifluoromethyl) benzoic acid (BAA), the benzoic acid analogue of IXF, were detected more frequently than the parent compound. At surface-water sites, DKN and BAA reached maximum concentrations within about the first 5 weeks after IXF application after rainfall-runoff events. Concentrations subsequent to post-application maxima decreased through time approximately following first-order, exponential loss kinetics. Carryover of DKN and BAA, from an application year to the following spring, occurred at several surface-water sites, and springtime concentrations were typically 1–5 percent of maximum, post-application concentrations. Virtually no detections were recorded later in the growing season during non-application years. Results indicate rapid loss of IXF and very few detections of the parent compound from the study areas. The degradates DKN and BAA were more frequently detected in surface runoff up to about 1 year after IXF application, but no evidence of longer-term accumulation was found.
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Box 25046, MS-980
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The United States is an Arctic nation because of Alaska and thus maintains tremendous interests and stewardship responsibilities in the region, especially as the region undergoes substantial environmental transformation. This Arctic Science Strategy is intended to support those interests and responsibilities by expressing the core values, mission, vision, and the broad research goals and priority objectives of the U.S. Geological Survey (USGS) for science coordination in Arctic Alaska. It synthesizes strategic planning activities across the USGS in the Arctic over the next 3-year planning horizon, identifies some major networks of collaboration, and aligns with current research priorities of the Department of the Interior Climate Action Plan, released October 7, 2021 (U.S. Department of the Interior, 2021). Also, in recognition of the rapid pace of Arctic environmental changes and the corresponding need to move with urgency beyond routine patterns of operation, this strategy extends an implementation challenge to staff and colleagues in hopes of reaching for breakthrough results in some target areas of performance.
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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The U.S. Geological Survey, in cooperation with the city of Harrisonville, Missouri, assessed flooding of Muddy Creek resulting from varying precipitation magnitudes and durations, antecedent runoff conditions, and channel modifications (cleaned culverts and added detention storage). The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.8-mile reach of Muddy Creek and tributaries within and adjacent to the city.
Hydrologic and hydraulic models of the upper Muddy Creek study basin were used to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of precipitation events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to route streamflows and map resulting areas of flood inundation.
The hydrologic and hydraulic models were calibrated to the September 28, 2019; May 27, 2021; and June 25, 2021, runoff events representing a range of antecedent runoff conditions and hydrologic responses. The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 30-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability. Flood-inundation maps were produced for reference stages of 1.0 foot (ft), or near bankfull, to 4.0 ft, or a stage exceeding the 0.1-percent annual exceedance probability interval precipitation, using the HEC–RAS model. The results of each precipitation duration-frequency value were represented by a 0.5-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the Muddy Creek reference location.
Seven scenarios were developed with the HEC–HMS hydrologic model with resulting streamflows routed in a HEC–RAS hydraulic model, and these scenarios varied by antecedent runoff condition and potential channel modifications. The same precipitation scenarios were used in each of the seven antecedent runoff and channel conditions, and the simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding stage at the Muddy Creek reference location.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225084","collaboration":"Prepared in cooperation with the city of Harrisonville, Missouri","usgsCitation":"Heimann, D.C., and Rydlund, P.H., 2022, Precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri: U.S. Geological Survey Scientific Investigations Report 2022–5084, 18 p., https://doi.org/10.3133/sir20225084.","productDescription":"Report: viii, 18 p.; Data Release; Dataset; Application Site","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-135285","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":503404,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113945.htm","linkFileType":{"id":5,"text":"html"}},{"id":410482,"rank":7,"type":{"id":4,"text":"Application Site"},"url":"https://ci.harrisonville.mo.us/1052/Stormwater-Management","text":"City of Harrisonville web page","linkHelpText":"—Flood-inundation mapping and model of Muddy Creek"},{"id":410391,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410390,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P969ZOLB","text":"USGS data release","linkHelpText":"Geospatial data and model archives associated with precipitation-driven flood-inundation mapping of Muddy Creek at Harrisonville, Missouri (ver. 2.0, December 2022)"},{"id":410389,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5084/images"},{"id":410388,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5084/sir20225084.XML"},{"id":410386,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5084/sir20225084.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5084"},{"id":410385,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5084/coverthb.jpg"}],"country":"United States","state":"Missouri","county":"Cass County","city":"Harrisonville","otherGeospatial":"Muddy Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.38510524959862,\n 38.66691333285476\n ],\n [\n -94.38510524959862,\n 38.60174153214416\n ],\n [\n -94.30827923799478,\n 38.60174153214416\n ],\n [\n -94.30827923799478,\n 38.66691333285476\n ],\n [\n -94.38510524959862,\n 38.66691333285476\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Field-based transplant gardens, including common and reciprocal garden experiments, are a powerful tool for studying genetic variation and gene-by-environment interactions. These experiments assume that individuals within the garden represent independent replicates growing in a homogenous environment. Plant neighborhood interactions are pervasive across plant populations and could violate assumptions of transplant garden experiments. We demonstrate how spatially explicit models for plant–plant interactions can provide novel insights on genotypes' performance in field-transplant garden designs. We used individual-based models, based on data from a sagebrush (Artemisia spp.) common garden, to simulate the impact of spatial plant–plant interactions on between-group differences in plant growth. We found that planting densities within the range of those used in many common gardens can bias experimental outcomes. Our results demonstrate that higher planting densities can lead to inflated group differences and may confound genotypes' competitive ability and genetically underpinned variation. Synthesis. We propose that spatially explicit models can help avoid biased results by informing the design and analysis of field-based transplant garden experiments. Alternately, including neighborhood effects in post hoc analyses of transplant garden experiments is likely to provide novel insights into the roles of biotic factors and density dependence in genetic differentiation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9630","usgsCitation":"Zaiats, A., Requena-Mullor, J.M., Germino, M., Forbey, J.S., Richardson, B.A., and Caughlin, T., 2022, Spatial models can improve the experimental design of field-based transplant gardens by preventing bias due to neighborhood crowding: Ecology and Evolution, v. 12, no. 12, e9630, 9 p., https://doi.org/10.1002/ece3.9630.","productDescription":"e9630, 9 p.","ipdsId":"IP-139927","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":445664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9630","text":"Publisher Index Page"},{"id":410630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii 0000-0001-8978-4152","orcid":"https://orcid.org/0000-0001-8978-4152","contributorId":257072,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Requena-Mullor, Juan M.","contributorId":218132,"corporation":false,"usgs":false,"family":"Requena-Mullor","given":"Juan","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":859168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forbey, Jennifer S.","contributorId":194442,"corporation":false,"usgs":false,"family":"Forbey","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":859169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, Bryce A.","contributorId":207820,"corporation":false,"usgs":false,"family":"Richardson","given":"Bryce","email":"","middleInitial":"A.","affiliations":[{"id":37640,"text":"U.S.D.A. Forest Service Rocky Mountain Research Station, Provo, UT, 84606 USA","active":true,"usgs":false}],"preferred":false,"id":859170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caughlin, T. Trevor","contributorId":257076,"corporation":false,"usgs":false,"family":"Caughlin","given":"T. Trevor","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":859171,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238856,"text":"70238856 - 2022 - Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","interactions":[],"lastModifiedDate":"2022-12-14T15:30:15.135816","indexId":"70238856","displayToPublicDate":"2022-12-14T09:10:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":850,"text":"Applied and Environmental Microbiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","title":"Acetylenotrophic and diazotrophic Bradyrhizobium sp. strain I71 from TCE-contaminated soils","docAbstract":"Rare plant species that are constrained by shading may be threatened by a lack of natural disturbance that removes overhanging vegetation. The original distribution of the study species Physostegia correllii (Lundell) Shinners included freshwater floodplains of large rivers in the southcentral U.S. (Colorado, Rio Grande, and Mississippi rivers). A second species, Trillium texanum Buckley was found in seep spring baygalls in east-central Texas and extreme northwestern Louisiana. Experiments to determine the effects of shading on P. correllii and T. texanum were conducted using short-term shade cloth treatments (full sunlight vs. 30% shading for 2–3 weeks), and a dryness treatment for T. texanum (moist vs. less moist). Mean height and cover responses of individuals for both species were determined in conservation gardens located in Lafayette, Louisiana. Physostegia correllii grown in shaded environments for 2.5 weeks had shorter mean height than if grown in full sunlight. Half of the shaded plants in shaded plots had died by the mid-summer. For T. texanum, shading reduced the mean height and cover of plants. Therefore, management to remove overhanging ground vegetation to mimic natural disturbance might revive P. correllii and/or T. texanum populations where overhanging vegetation is increasing due to lack of natural disturbance (e.g., flood pulsing, grazing, burning).
","language":"English","publisher":"Botanical Research Institute of Texas","doi":"10.17348/jbrit.v16.i2.1270","usgsCitation":"Middleton, B., Williams, C.R., Doffitt, C., and Johnson, D., 2022, Effects of shading on the rare plant species, Physostegia correllii (Lamiaceae) and Trillium texanum (Melanthiaceae): Journal of the Botanical Research Institute of Texas, v. 16, no. 2, p. 591-603, https://doi.org/10.17348/jbrit.v16.i2.1270.","productDescription":"13 p.; 2 Data Releases","startPage":"591","endPage":"603","ipdsId":"IP-133104","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":445669,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.17348/jbrit.v16.i2.1270","text":"Publisher Index Page"},{"id":410472,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414828,"rank":3,"type":{"id":30,"text":"Data 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