The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. We leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.abb0779","usgsCitation":"Ross, Z., Cochran, E.S., Trugman, D., and Smith, J., 2020, 3D fault architecture controls the dynamism of earthquake swarm: Science, v. 368, no. 6497, p. 1357-1361, https://doi.org/10.1126/science.abb0779.","productDescription":"5 p.","startPage":"1357","endPage":"1361","ipdsId":"IP-118187","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456344,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1739984","text":"External Repository"},{"id":378083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"368","issue":"6497","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ross, Z.","contributorId":215300,"corporation":false,"usgs":false,"family":"Ross","given":"Z.","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":797732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trugman, D.","contributorId":173991,"corporation":false,"usgs":false,"family":"Trugman","given":"D.","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":797734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Jonathan D.","contributorId":239737,"corporation":false,"usgs":false,"family":"Smith","given":"Jonathan D.","affiliations":[],"preferred":false,"id":797735,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228559,"text":"70228559 - 2020 - Using reproductive potential to assess oyster population sustainability","interactions":[],"lastModifiedDate":"2022-02-14T21:02:57.728758","indexId":"70228559","displayToPublicDate":"2020-06-19T16:02:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using reproductive potential to assess oyster population sustainability","docAbstract":"Ensuring that oysters remain sustainable in the face of significant coastal restoration activities, high local subsidence rates, and predicted sea-level rise requires a deeper understanding of basic population demographics, including reproductive potential. We quantified fecundity (eggs ind−1) of oysters at high- and low-salinity sites during a fall and spring spawn season. We assessed the relationships between oyster size, the relative proportion of females across size classes, and fecundity. Finally, we quantified reproductive potential (eggs m−2) of an engineered reef by connecting fecundity with annual oyster population demographic data as a means to assess population sustainability. The proportion of females generally increased with shell height, achieving a population with >50% females in Biloxi oysters >75 mm, and Grand Isle oysters >100 mm. Fecundity across both sites and seasons ranged from approximately 2,000 to >55 million eggs oyster−1. Mean fecundity generally increased with shell height, varying significantly by site, with Grand Isle (high salinity) oysters having greater fecundity than Biloxi (low salinity) oysters. Fecundity did not differ by season. Mean reproductive potential (eggs m−2) was driven by density and size distribution. Reefs with high densities and higher counts of market-sized oysters had reproductive potentials 5× greater than those with low densities and low counts of juvenile oysters. With increasing changes in water quality from coastal management and climate, impacts on oyster reproduction may critically impact population sustainability. Reproductive potential provides critical data to assess individual reef ecosystem services, and to assess the potential for maintenance of local metapopulations.
","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13225","usgsCitation":"Marshall, D., Moore, S., Sutor, M., La Peyre, J.F., and La Peyre, M., 2020, Using reproductive potential to assess oyster population sustainability: Restoration Ecology, v. 28, no. 6, p. 1621-1632, https://doi.org/10.1111/rec.13225.","productDescription":"12 p.","startPage":"1621","endPage":"1632","ipdsId":"IP-118044","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499854,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.lsu.edu/animalsciences_pubs/795","text":"External Repository"},{"id":395940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.2691650390625,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 29.563901551414418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Danielle A.","contributorId":239867,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle A.","affiliations":[{"id":48014,"text":"School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":834590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Samuel C.","contributorId":276133,"corporation":false,"usgs":false,"family":"Moore","given":"Samuel C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":834591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutor, Malinda","contributorId":276134,"corporation":false,"usgs":false,"family":"Sutor","given":"Malinda","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":834592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Jerome F.","contributorId":177346,"corporation":false,"usgs":false,"family":"La Peyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":834593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":834594,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210730,"text":"ofr20201056 - 2020 - Envisioning a multi-agency and multi-academic institution geomorphology data exchange portal","interactions":[],"lastModifiedDate":"2020-06-22T11:33:00.458228","indexId":"ofr20201056","displayToPublicDate":"2020-06-19T11:14:24","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1056","displayTitle":"Envisioning a Multi-Agency and Multi-Academic Institution Geomorphology Data Exchange Portal","title":"Envisioning a multi-agency and multi-academic institution geomorphology data exchange portal","docAbstract":"Access to bathymetry and geomorphology data for rivers and reservoirs is a critical need in multiple agencies and academia. These data are needed to make water-resource-management decisions regarding river restoration, resource protection, infrastructure design and sustainability, and flood-risk reduction, and during natural disasters. Sharing of data increases decision-making capacity by incorporating information from entire watersheds, provides knowledge from similar settings being managed or studied by other entities, and helps meet the goals of the Federal Open Water Data Initiative. Addressing these needs across broad spatial and temporal scales would be made more efficient if these data were available in consistent formats with standardized metadata and were either stored in a centralized database or integrated with existing geospatial datasets. Because of renewed interest and technological advances, representatives from multiple Federal agencies and academic institutions have created a new working group to scope the development of a Geomorphology Data Exchange Portal to increase access to needed data. The working group has developed a vision for the Portal and outlined possible approaches to achieve the vision. Short-term approaches may include leveraging existing data-access portals and data-processing tools and integrating geomorphology data with existing national geospatial datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201056","collaboration":"USGS Water Observing Systems ProgramOffice of Associate Director, Water
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Climate change is projected to alter the elevation and latitude of treeline globally. Seed germination and seedling survival are critical controls on treeline expansion. Neighbouring alpine plants, either through competition for resources or through altered microclimate, also affect seedlings emerging in the alpine zone. With warming, alpine plant species may interact with each other more or less strongly.
To determine whether establishing tree seedlings and an alpine herb are similarly sensitive to alpine plant neighbours under ambient and altered climate.
We imposed active heating, watering, and removed all plants adjacent to emerging conifer seedlings and an alpine herb.
Picea engelmannii seedlings showed lower survival compared with Pinus flexilis 3 weeks following neighbour removal, and after 1 year only survived in watered plots. Pinus seedlings responded to neighbour removal by lowering the quantum yield of photosynthesis (ϕPSII). Contrary to expectations from the stress gradient hypothesis, survival was reduced without neighbours near the low-elevation range limit of Chionophila jamesii.
Pinus flexilis has higher expansion potential into the alpine, while Picea engelmannii requires moist conditions that could be facilitated by neighbours to expand its range. This implies likely range expansion by P. flexilis with consequences for alpine plant diversity and ecosystem function.
Temperate savannas and grasslands are globally threatened. In the Midwest United States of America (USA), for example, oak savannas persist today at a small percentage of recent historic coverage. Therefore, restoration of habitats of low and intermediate canopy cover is a landscape conservation priority that often emphasizes returning tree density to a savanna-like target value. Understanding how animal species react to such changes in vegetation structure is important for assessing the value of these restoration plans. We examined how butterfly community attributes in northwest Indiana USA, including community composition, richness, and abundance responded to a grassland-to-forest gradient of canopy cover. Butterfly community composition under intermediate canopy cover differed significantly from community composition in the most open or closed-canopy habitats. Composition of the plant community in flower was a significant predictor of three assessed attributes of the butterfly community—composition, richness, and abundance. Phenology, expressed as day-of-the-year, was also a strong predictor of these butterfly community attributes. Few butterfly species were habitat specialists as adults although canopy cover was a more important predictor of adult community composition than of richness or abundance of butterflies. Therefore, adult butterfly community differences along the canopy cover gradient were less about butterfly communities filled with habitat specialists for different canopy-defined habitats and more about gradual changes in community composition along this gradient. Overall, butterfly community richness was predicted to peak at about 34% canopy cover, butterfly abundance at about 53% canopy cover, community conservation value at about 59% canopy cover, and a combination of desirable conservation attributes–high diversity, high abundance, and high conservation value–was predicted to reach a peak of co-occurrence at about 67% canopy cover suggesting that habitats of intermediate canopy cover might be particularly effective for butterfly conservation in this region.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0234139","usgsCitation":"Grundel, R., Dulin, G.S., and Pavlovic, N.B., 2020, Changes in conservation value from grasslands to savannas to forests: How a temperate canopy cover gradient affects butterfly community composition: PLoS ONE, v. 15, no. 6, e0234139, 22 p., https://doi.org/10.1371/journal.pone.0234139.","productDescription":"e0234139, 22 p.","ipdsId":"IP-102152","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":456354,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0234139","text":"Publisher Index Page"},{"id":436923,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TYX2AG","text":"USGS data release","linkHelpText":"Butterfly community abundance and distribution along a gradient of woody vegetation density at Indiana Dunes National Lakeshore, Hoosier Prairie Nature Preserve, and Tefft Savanna Nature Preserve, Indiana 1998-1999"},{"id":378395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Hoosier Prairie Nature Preserve, Indiana Dunes National Lakeshore (now National Park), Tefft Savanna Nature Preserve and Jasper-Pulaski Fish and Wildlife Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.593994140625,\n 40.64730356252251\n ],\n [\n -85.89111328125,\n 40.64730356252251\n ],\n [\n -85.89111328125,\n 41.72213058512578\n ],\n [\n -87.593994140625,\n 41.72213058512578\n ],\n [\n -87.593994140625,\n 40.64730356252251\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":798662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dulin, Gary S.","contributorId":240678,"corporation":false,"usgs":false,"family":"Dulin","given":"Gary","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":798704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavlovic, Noel B. 0000-0002-2335-2274 npavlovic@usgs.gov","orcid":"https://orcid.org/0000-0002-2335-2274","contributorId":1976,"corporation":false,"usgs":true,"family":"Pavlovic","given":"Noel","email":"npavlovic@usgs.gov","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":798663,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237278,"text":"70237278 - 2020 - Positional accuracy assessment of lidar point cloud from NAIP/3DEP pilot project","interactions":[],"lastModifiedDate":"2022-10-06T13:42:55.455503","indexId":"70237278","displayToPublicDate":"2020-06-19T08:38:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Positional accuracy assessment of lidar point cloud from NAIP/3DEP pilot project","docAbstract":"The Leica Geosystems CountryMapper hybrid system has the potential to collect data that satisfy the U.S. Geological Survey (USGS) National Geospatial Program (NGP) and 3D Elevation Program (3DEP) and the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) requirements in a single collection. This research will help 3DEP determine if this sensor has the potential to meet current and future 3DEP topographic lidar collection requirements. We performed an accuracy analysis and assessment on the lidar point cloud produced from CountryMapper. The boresighting calibration and co-registration by georeferencing correction based on ground control points are assumed to be performed by the data provider. The scope of the accuracy assessment is to apply the following variety of ways to measure the accuracy of the delivered point cloud to obtain the error statistics. Intraswath uncertainty from a flat surface was computed to evaluate the point cloud precision. Intraswath difference between opposite scan directions and the interswath overlap difference were evaluated to find boresighting or any systematic errors. Absolute vertical accuracy over vegetated and non-vegetated areas were also assessed. Both horizontal and vertical absolute errors were assessed using the 3D absolute error analysis methodology of comparing conjugate points derived from geometric features. A three-plane feature makes a single unique intersection point. Intersection points were computed from ground-based lidar and airborne lidar point clouds for comparison. The difference between two intersection points form one error vector. The geometric feature-based error analysis was applied to intraswath, interswath, and absolute error analysis. The CountryMapper pilot data appear to satisfy the accuracy requirements suggested by the USGS lidar specification, based upon the error analysis results. The focus of this research was to demonstrate various conventional accuracy measures and novel 3D accuracy techniques using two different error computation methods on the CountryMapper airborne lidar point cloud.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12121974","usgsCitation":"Kim, M., Park, S., Irwin, J., McCormick, C., Danielson, J.J., Stensaas, G.L., Sampath, A., Bauer, M.A., and Burgess, M.A., 2020, Positional accuracy assessment of lidar point cloud from NAIP/3DEP pilot project: Remote Sensing, v. 12, no. 12, 1974, 20 p., https://doi.org/10.3390/rs12121974.","productDescription":"1974, 20 p.","ipdsId":"IP-119473","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":456357,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12121974","text":"Publisher Index Page"},{"id":436924,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPDWUU","text":"USGS data release","linkHelpText":"Hybrid Lidar/Imagery Sensor Validation Survey Data"},{"id":408027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":853950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park, Seonkyung 0000-0003-3203-1998","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":223182,"corporation":false,"usgs":true,"family":"Park","given":"Seonkyung","email":"","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":853951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCormick, Collin","contributorId":270002,"corporation":false,"usgs":false,"family":"McCormick","given":"Collin","email":"","affiliations":[],"preferred":false,"id":853953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":853956,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bauer, Mark A. 0000-0002-4156-5759 mabauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4156-5759","contributorId":224288,"corporation":false,"usgs":true,"family":"Bauer","given":"Mark","email":"mabauer@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":853957,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burgess, Matthew Alexander 0000-0003-3487-4972 mburgess@usgs.gov","orcid":"https://orcid.org/0000-0003-3487-4972","contributorId":225090,"corporation":false,"usgs":true,"family":"Burgess","given":"Matthew","email":"mburgess@usgs.gov","middleInitial":"Alexander","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":853958,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70210696,"text":"pp1842HH - 2020 - The effects of management practices on grassland birds—Baird’s Sparrow (Centronyx bairdii)","interactions":[{"subject":{"id":70210696,"text":"pp1842HH - 2020 - The effects of management practices on grassland birds—Baird’s Sparrow (Centronyx bairdii)","indexId":"pp1842HH","publicationYear":"2020","noYear":false,"chapter":"HH","displayTitle":"The Effects of Management Practices on Grassland Birds—Baird’s Sparrow (Centronyx bairdii)","title":"The effects of management practices on grassland birds—Baird’s Sparrow (Centronyx bairdii)"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2023-12-20T21:07:44.827916","indexId":"pp1842HH","displayToPublicDate":"2020-06-18T16:00:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"HH","displayTitle":"The Effects of Management Practices on Grassland Birds—Baird’s Sparrow (Centronyx bairdii)","title":"The effects of management practices on grassland birds—Baird’s Sparrow (Centronyx bairdii)","docAbstract":"Keys to Baird’s Sparrow (Centronyx bairdii) management are providing native or tame grasslands with moderately deep litter, controlling excessive grazing, and curtailing shrub encroachment. Baird’s Sparrows have been reported to use habitats with less than or equal to (≤) 101 centimeters (cm) average vegetation height, 3–46 cm visual obstruction reading (VOR), 15–71 percent grass cover, 5–25 percent forb cover, ≤50 percent shrub cover, less than (<) 44 percent bare ground, 10–63 percent litter cover, and ≤21 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842HH","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Nenneman, M.P., and Euliss, B.R., 2020, The effects of management practices on grassland birds—Baird’s Sparrow (Centronyx bairdii), chap. HH of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 21 p., https://doi.org/10.3133/pp1842HH.","productDescription":"v, 21 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097125","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":375658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/hh/coverthb.jpg"},{"id":375659,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/hh/pp1842hh.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–HH"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Signs of disease, such as external lesions, have been prevalent in smallmouth bass throughout the Susquehanna River Basin, USA. Previous targeted chemical studies in this system have identified known persistent organic pollutants, but a common explanatory link across multiple affected sites remains undetermined. A fast and robust extraction method that can be applied to young-of-year fish is needed to effectively screen for target and non-target compounds that may be impacting organism health. The quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction methodology was optimized to perform both targeted and non-targeted chemical analyses from a single extraction of whole young-of-year fish. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) was used for extract analysis. Sample extraction was performed using the solvent ethyl acetate, followed by a two-step cleanup in which samples were frozen for lipid removal and subjected to dispersive solid phase extraction using Florisil. A sample of 21 young-of-year smallmouth bass collected from areas with disease and exhibiting different types of external lesions were evaluated for 233 target compounds. A total of 34 organic contaminants, including polychlorinated biphenyls, brominated diphenyl ethers, organochlorinated pesticides, and personal care products, were detected. Data from this sample set was then analyzed for non-targets. Using the Fisher ratio method and multivariate analysis, an additional 10 significant features were identified specific to either fish with visible lesions or with no visible disease characteristics.
Multiple restoration actions have been implemented in response to declining salmon populations. Among these is the addition of salmon carcasses or artificial nutrients to mimic marine-derived nutrients historically provided by large spawning runs of salmon. A key assumption in this approach is that increased nutrients will catalyze salmon population growth. Although effects on aquatic ecosystems have been observed during treatments, it is unclear whether permanent population increases for salmon will occur. To test this assumption and address associated uncertainties, we linked a food web model with a salmon life cycle model to examine whether carcass additions in a river reach would improve conditions for salmon in the long term. Model results confirmed immediate increases in the biomass of periphyton, macroinvertebrates, and fish during carcass additions. In turn, juvenile salmon grew larger and experienced improved freshwater and smolt survival, which translated to a greater number of adults returning to spawn. However, once additions ceased, salmon abundance returned to pretreatment levels, which, based on our model, is owing to a combination of instream and out-of-basin factors. Overall, results of this work suggest that benefits during carcass and nutrient additions may not translate into persistent productivity of salmon unless additions are sustained indefinitely or other limiting factors are addressed.
Ecological occupancy modeling has historically relied on high-quality, low-quantity designed-survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high-quantity, unknown-quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed-survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed-survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed-survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red-eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3165","usgsCitation":"Huberman, D.B., Reich, B.J., Pacifici, K., and Collazo, J.A., 2020, Estimating the drivers of species distributions with opportunistic data using mediation analysis: Ecosphere, v. 11, no. 6, e03165, 13 p., https://doi.org/10.1002/ecs2.3165.","productDescription":"e03165, 13 p.","ipdsId":"IP-113854","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3165","text":"Publisher Index Page"},{"id":396100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.03125,\n 24.84656534821976\n ],\n [\n -66.09375,\n 24.84656534821976\n ],\n [\n -66.09375,\n 49.26780455063753\n ],\n [\n -97.03125,\n 49.26780455063753\n ],\n [\n -97.03125,\n 24.84656534821976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Huberman, D. B.","contributorId":279615,"corporation":false,"usgs":false,"family":"Huberman","given":"D.","email":"","middleInitial":"B.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reich, B. J.","contributorId":279616,"corporation":false,"usgs":false,"family":"Reich","given":"B.","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210937,"text":"70210937 - 2020 - Geochemical characterization of groundwater evolution south of Grand Canyon, Arizona (USA)","interactions":[],"lastModifiedDate":"2020-12-10T13:16:29.306011","indexId":"70210937","displayToPublicDate":"2020-06-18T09:00:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical characterization of groundwater evolution south of Grand Canyon, Arizona (USA)","docAbstract":"Better characterization of the geochemical evolution of groundwater south of Grand Canyon, Arizona (USA), is needed to understand natural conditions and assess potential effects from breccia-pipe uranium mining in the region. Geochemical signatures of groundwater at 28 sampling locations were evaluated; baseline concentrations for select trace elements (As, B, Ba, Cr, Li, Mo, Rb, Se, Sr, Th, Tl, U, V) were established, and anomalous chemistry characteristics were identified. Concentrations at some groundwater sites exceeded the USEPA drinking water standard for As of 10 μg/L (Red Canyon, Miners, JT, Havasu, and Warm Springs) and U of 30 μg/L (Salt Creek Spring). Four springs from the study area (Blue, Havasu, Fern, and Warm Springs) had unique chemistry, which may indicate a deep flow path or potential contribution of fluids from lower in the crust. Other springs in the study area were distinguished by major anion water type: sulfate, bicarbonate, and a mixture of the two. Water type distinctions were somewhat spatially segregated, with sulfate type present on the western side of the study area, bicarbonate type on the eastern side, and a mixture of the two interspersed between the endmember sites. Sulfate-type water from this study area had low strontium isotopic ratio (87Sr/86Sr) values. The location of spring discharge within single drainages of the Grand Canyon may influence chemistry, as groundwater discharging from bedrock was altered after flowing through alluvial material. Geochemical analysis of groundwater in Grand Canyon indicates the importance of continued monitoring and better understanding of short-term chemical fluctuations.","language":"English","publisher":"Springer","doi":"10.1007/s10040-020-02192-0","usgsCitation":"Beisner, K.R., Solder, J.E., Tillman, F.D., Anderson, J.R., and Antweiler, R.C., 2020, Geochemical characterization of groundwater evolution south of Grand Canyon, Arizona (USA): Hydrogeology Journal, v. 28, p. 1615-1633, https://doi.org/10.1007/s10040-020-02192-0.","productDescription":"19 p.","startPage":"1615","endPage":"1633","ipdsId":"IP-109494","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":456364,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-020-02192-0","text":"Publisher Index Page"},{"id":376147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.44232177734374,\n 35.902399875143615\n ],\n [\n -111.697998046875,\n 35.902399875143615\n ],\n [\n -111.697998046875,\n 36.25977754677541\n ],\n [\n -112.44232177734374,\n 36.25977754677541\n ],\n [\n -112.44232177734374,\n 35.902399875143615\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Solder, John E. 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":201953,"corporation":false,"usgs":true,"family":"Solder","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792226,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Jessica R. 0000-0002-3286-7552 jranderson@usgs.gov","orcid":"https://orcid.org/0000-0002-3286-7552","contributorId":193158,"corporation":false,"usgs":true,"family":"Anderson","given":"Jessica","email":"jranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792227,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Antweiler, Ronald C. 0000-0001-5652-6034 antweil@usgs.gov","orcid":"https://orcid.org/0000-0001-5652-6034","contributorId":1481,"corporation":false,"usgs":true,"family":"Antweiler","given":"Ronald","email":"antweil@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":792228,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210783,"text":"70210783 - 2020 - Structural impacts, carbon losses, and regeneration in mangrove wetlands after two hurricanes on St. John, U.S. Virgin Islands","interactions":[],"lastModifiedDate":"2020-12-30T13:06:42.999769","indexId":"70210783","displayToPublicDate":"2020-06-18T08:59:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Structural impacts, carbon losses, and regeneration in mangrove wetlands after two hurricanes on St. John, U.S. Virgin Islands","docAbstract":"Hurricanes Irma and Maria ravaged the mangroves of St. John, U.S. Virgin Islands, in 2017. Basal area losses were large (63–100%) and storm losses of carbon associated with aboveground biomass amounted to 11.9–43.5 Mg C/ha. Carbon biomass of dead standing trees increased 8.1–18.3 Mg C/ha among sites, and carbon in coarse woody debris on the forest floor increased 1.9–18.2 Mg C/ha, with effects varying by mangrove typology. While St. John has only ~45 ha of mangroves, they exist as isolated basins, salt ponds, and fringe mangroves; the latter sometimes support diverse marine communities. Salt pond and fringe mangroves had proportionately more organic carbon (46.3 Mg C/ha) than inorganic carbon (1.1 Mg C/ha) in soils than isolated basins. Soil organic carbon was also appreciable in isolated basins (30.8 Mg C/ha) but was matched by inorganic C (36.7 Mg C/ha), possibly due to adjacent land use history (e.g., road construction), previous storm overwash, or geomorphology. Soil nitrogen stocks were low across all typologies. Mangroves had limited regeneration 26 months after the storms, and recovery on St. John may be hindered by pre-storm hydrologic change in some stands, and potential genetic bottlenecks and lack of propagule sources for expedient recovery in all stands.","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01313-5","usgsCitation":"Krauss, K., From, A., Rogers, C., Whelan, K.R., Grimes, K.W., Dobbs, R., and Kelley, T., 2020, Structural impacts, carbon losses, and regeneration in mangrove wetlands after two hurricanes on St. John, U.S. Virgin Islands: Wetlands, v. 40, p. 2397-2412, https://doi.org/10.1007/s13157-020-01313-5.","productDescription":"16 p.","startPage":"2397","endPage":"2412","ipdsId":"IP-116442","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436925,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3IYOT","text":"USGS data release","linkHelpText":"Forest structure, regeneration, and soil data to support mangrove forest damage assessment on St. John, U.S. Virgin Islands, from Hurricane Irma (2018-2019)"},{"id":375852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"U.S. Virgin Islands","otherGeospatial":"St Johns","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.64836120605469,\n 18.345075428248094\n ],\n [\n -64.71942901611328,\n 18.37505327646064\n ],\n [\n -64.75341796875,\n 18.377985612444007\n ],\n [\n -64.76268768310547,\n 18.370491765846573\n ],\n [\n -64.76062774658203,\n 18.359087461734603\n ],\n [\n -64.79290008544922,\n 18.358761613402578\n ],\n [\n -64.80560302734375,\n 18.33562481050443\n ],\n [\n -64.79393005371094,\n 18.309877404250322\n ],\n [\n -64.77161407470703,\n 18.306617965766893\n ],\n [\n -64.74723815917969,\n 18.31900350555467\n ],\n [\n -64.72835540771484,\n 18.308899579147358\n ],\n [\n -64.71427917480469,\n 18.30270655860875\n ],\n [\n -64.69814300537108,\n 18.2939055695918\n ],\n [\n -64.64836120605469,\n 18.345075428248094\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":791395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"From, Andrew 0000-0002-6543-2627","orcid":"https://orcid.org/0000-0002-6543-2627","contributorId":221941,"corporation":false,"usgs":true,"family":"From","given":"Andrew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":791396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rogers, Caroline 0000-0001-9056-6961","orcid":"https://orcid.org/0000-0001-9056-6961","contributorId":222443,"corporation":false,"usgs":true,"family":"Rogers","given":"Caroline","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":791397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whelan, Kevin R.T.","contributorId":225171,"corporation":false,"usgs":false,"family":"Whelan","given":"Kevin","email":"","middleInitial":"R.T.","affiliations":[{"id":41065,"text":"3U.S. National Park Service, Miami, FL 33157 USA","active":true,"usgs":false}],"preferred":false,"id":791398,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grimes, Kristen W.","contributorId":225506,"corporation":false,"usgs":false,"family":"Grimes","given":"Kristen","email":"","middleInitial":"W.","affiliations":[{"id":41149,"text":"University of the Virgin Islands","active":true,"usgs":false}],"preferred":false,"id":791399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dobbs, Robert C. 0000-0002-9079-7249 rdobbs@usgs.gov","orcid":"https://orcid.org/0000-0002-9079-7249","contributorId":200300,"corporation":false,"usgs":false,"family":"Dobbs","given":"Robert C.","email":"rdobbs@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":791400,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelley, Thomas","contributorId":225507,"corporation":false,"usgs":false,"family":"Kelley","given":"Thomas","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":791401,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210681,"text":"sir20205045 - 2020 - Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","interactions":[],"lastModifiedDate":"2020-06-18T14:21:59.738964","indexId":"sir20205045","displayToPublicDate":"2020-06-17T15:15:22","publicationYear":"2020","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":"2020-5045","displayTitle":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","title":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","docAbstract":"The city of Omaha, Nebraska, has a combined sewer system in some areas of the city. In Omaha, Nebr., a moderate amount of rainfall will lead to the combination of stormwater and untreated sewage or wastewater being discharged directly into the Missouri River and Papillion Creek and is called a combined sewer overflow (CSO) event. In 2009, the city of Omaha began the implementation of their Long Term Control Plan (LTCP) to mitigate the effects of CSOs on the Missouri River and Papillion Creek. As part of the LTCP, the city partnered with the U.S. Geological Survey (USGS) in 2012 to begin monitoring in the Missouri River. Since 2012, monthly discrete water-quality samples for many constituents have been collected from the Missouri River at four sites. At 3 of the 4 sites, water quality has been monitored continuously for selected constituents and physical properties. These discrete water-quality samples and continuous water-quality monitoring data (from July 2012 to 2020) have been collected to better understand the water quality of the Missouri River, how it is changing with time, how it changes upstream from the city of Omaha to downstream, and how it varies during base-flow conditions and during periods of runoff.
The purpose of this report is to document the development of Escherichia coli (E. coli) concentration models for these four Missouri River sites. Analysis was completed using the first 5 years of data (through 2016) to determine if the current approach is sufficient to meet future analysis goals and to understand if proposed models such as Load Estimator (LOADEST) models will be able to represent water-quality changes in the Missouri River.
Multiple linear regression models were developed to estimate E. coli concentration using LOADEST as implemented in the rloadest package in the R statistical software program. A set of explanatory variables, including streamflow and streamflow anomalies, precipitation, information about CSOs, and continuous water quality, were evaluated for potential inclusion in regression models. The best model at Missouri River at NP Dodge Park at Omaha, Nebr. (USGS station 412126095565201; hereafter “NP Dodge”) included basin explanatory variables of upstream antecedent precipitation index measured at Tekamah, Nebr.; decimal time; season; and turbidity. The best model at Missouri River at Freedom Park Omaha, Nebr. (USGS station 411636095535401; hereafter “Freedom Park”) included the same explanatory variables as the NP Dodge model with the addition of turbidity anomalies and flow anomalies. The best models at the two downstream sites (Missouri River near Council Bluffs, Iowa, USGS station 06610505 and Missouri River near La Platte, Nebr., USGS station 410333095530101) included the same explanatory variables as the Freedom Park model with the addition of local antecedent precipitation index as measured at Eppley Airport in Omaha, Nebr., and additional turbidity and flow anomalies. The final selected models were the best models given our modeling design constraint in which explanatory variables included in the model for the upstream site were included in the downstream models.
Explanatory variables currently (2020) being collected and included in the selected models through 2016 explained 64–75 percent of the variability of E. coli concentration in the Missouri River. Explaining 64–75 percent of the variability might be considered low when working with physical constituents (total nitrogen or sediment), but with the natural variability of biological constituents such as E. coli, the uncertainty of E. coli laboratory measurements, and the added complexity of modeling in a large drainage basin with multiple sources, these results are adequate and indicate that the explanatory variables being collected and models such as LOADEST can represent water-quality changes in the Missouri River for E. coli concentration from 2012 to 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205045","collaboration":"Prepared in cooperation with the city of Omaha, Nebraska","usgsCitation":"Densmore, B.K., Hall, B.M., and Moser, M.T., 2020, Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: U.S. Geological Survey Scientific Investigations Report 2020–5045, 24 p., https://doi.org/10.3133/sir20205045.","productDescription":"Report: vi, 24 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-098296","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":375621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5045/coverthb.jpg"},{"id":375622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5045/sir20205045.pdf","text":"Report","size":"9.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5045"},{"id":375623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S6WSV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: Model Inputs and Outputs"}],"country":"United States","state":"Nebraska","city":"Omaha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.23611450195312,\n 41.166249339092\n ],\n [\n -95.78155517578124,\n 41.15901221836655\n ],\n [\n -95.7843017578125,\n 41.37783904584602\n ],\n [\n -95.95321655273436,\n 41.37886950966323\n ],\n [\n -96.23611450195312,\n 41.37165592008984\n ],\n [\n -96.23611450195312,\n 41.166249339092\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Starting on 3 May 2018, a series of eruptive fissures opened in Kīlauea Volcano’s lower East Rift Zone (LERZ). Over the course of the next 3 months, intense degassing accompanied lava effusion from these fissures. Here, we report on ground-based observations of the gas emissions associated with Kīlauea’s 2018 eruption. Visual observations combined with radiative transfer modeling show that ultraviolet light could not efficiently penetrate the gas and aerosol plume in the LERZ, complicating SO2 measurements by differential optical absorption spectroscopy (DOAS). By applying a statistical method that integrates a radiative transfer model with the DOAS retrievals, we were able to calculate sulfur dioxide (SO2) emission rates along with estimates of their uncertainty. We find that sustained SO2 emissions were highest in June and early July, when approximately 200 kt SO2 were emitted daily. At the 68% confidence interval, we estimate that 7.1–13.6 Mt SO2 were released from the LERZ during the entire May to September eruptive episode. Scaling our results with in situ measurements of plume composition, we calculate that 11–21 Mt H2O and 1.5–2.8 Mt CO2 were also emitted. The gas and aerosol emissions caused hazardous conditions in areas proximal to the active vents, but plume dispersion modeling shows that the eruption also significantly impacted air quality hundreds of kilometers downwind. Combined with petrologic studies of the erupted lavas, our measurements indicate that 1.1–2.3 km3 dense-rock equivalent of lava were erupted from the LERZ, which is approximately twice the concomitant collapse volume of the volcano’s summit.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01390-8","usgsCitation":"Kern, C., Lerner, A., Elias, T., Nadeau, P.A., Holland, L., Kelly, P.J., Werner, C., Clor, L., and Cappos, M., 2020, Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements: Bulletin of Volcanology, v. 82, 55, 24 p., https://doi.org/10.1007/s00445-020-01390-8.","productDescription":"55, 24 p.","ipdsId":"IP-115197","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":436927,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LXBJF3","text":"USGS data release","linkHelpText":"Differential Optical Absorption Spectroscopy data acquired during the 2018 rift eruption of Kilauea Volcano"},{"id":376424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3082275390625,\n 19.379170499941292\n ],\n [\n -155.2333831787109,\n 19.379170499941292\n ],\n [\n -155.2333831787109,\n 19.449111649832837\n ],\n [\n -155.3082275390625,\n 19.449111649832837\n ],\n [\n -155.3082275390625,\n 19.379170499941292\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science 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0000-0002-6732-3686","orcid":"https://orcid.org/0000-0002-6732-3686","contributorId":215616,"corporation":false,"usgs":true,"family":"Nadeau","given":"Patricia","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792997,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holland, Lacey","contributorId":147879,"corporation":false,"usgs":false,"family":"Holland","given":"Lacey","email":"","affiliations":[{"id":16953,"text":"University of Utah, Atmospheric Sciences","active":true,"usgs":false}],"preferred":false,"id":792998,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792999,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werner, Cynthia 0000-0003-3311-6694","orcid":"https://orcid.org/0000-0003-3311-6694","contributorId":224428,"corporation":false,"usgs":false,"family":"Werner","given":"Cynthia","affiliations":[{"id":37768,"text":"USGS Contractor","active":true,"usgs":false}],"preferred":false,"id":793000,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Clor, Laura E. 0000-0003-2633-5100","orcid":"https://orcid.org/0000-0003-2633-5100","contributorId":209969,"corporation":false,"usgs":true,"family":"Clor","given":"Laura E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793001,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cappos, Michael 0000-0001-9883-1475","orcid":"https://orcid.org/0000-0001-9883-1475","contributorId":215607,"corporation":false,"usgs":true,"family":"Cappos","given":"Michael","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793002,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70212476,"text":"70212476 - 2020 - Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California","interactions":[],"lastModifiedDate":"2020-11-30T16:34:15.09351","indexId":"70212476","displayToPublicDate":"2020-06-17T09:05:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California","docAbstract":"To aid in setting scientific research priorities, we assess the potential value of removing each of the epistemic uncertainties currently represented in the US Geological Survey California seismic-hazard model, using average annual loss (AAL) as the risk metric of interest. Given all the uncertainties, represented with logic-tree branches, we find a mean AAL of $3.94 billion. The modal value is 17.5% lower than the mean, and there is a 78% chance that the true AAL value is more than 10% away from the mean, and a 5% chance that it is a factor 2.1 greater or lower than the mean. We quantify the extent to which resolving each uncertainty improves the AAL estimate. The most influential branch is one that adds additional epistemic uncertainty to ground motion models, but others are found to be influential as well, such as the rate of M ≥ 5 events throughout the region. We discuss the broader implications of our findings, and note that the time dependence caused by spatiotemporal clustering can be much more influential on AAL than the epistemic uncertainties explored here.
","language":"English","publisher":"Sage Journals","doi":"10.1177/8755293020926185","usgsCitation":"Field, E., Milner, K.R., and Porter, K., 2020, Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California: Earthquake Spectra, v. 36, no. 4, p. 1912-1929, https://doi.org/10.1177/8755293020926185.","productDescription":"18 p.","startPage":"1912","endPage":"1929","ipdsId":"IP-117774","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":796430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":796431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porter, Keith","contributorId":191074,"corporation":false,"usgs":false,"family":"Porter","given":"Keith","affiliations":[],"preferred":false,"id":796432,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213090,"text":"70213090 - 2020 - Resource segregation at fine spatial scales explains Karner blue butterfly (Lycaeides melissa samuelis) distribution","interactions":[],"lastModifiedDate":"2020-09-09T14:04:35.228478","indexId":"70213090","displayToPublicDate":"2020-06-17T09:03:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2356,"text":"Journal of Insect Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Resource segregation at fine spatial scales explains Karner blue butterfly (Lycaeides melissa samuelis) distribution","docAbstract":"The resource concentration hypothesis predicts that herbivorous insect density scales positively with plant density because insects are better able to identify, and remain longer in, patches with denser plant resources. While some studies support this hypothesis, others do not. Different explanations have been proposed for this discrepancy, including variation in insect dispersal ability and diet breadth. We test the resource concentration hypothesis using the Karner blue butterfly (Lycaeides melissa samuelis), a specialist that relies on wild blue lupine (Lupinus perennis) as its sole host plant. We extended this hypothesis to test whether Karner blue density also scales positively with nectar plant resources. Our findings did not support the resource concentration hypothesis and demonstrate that the spatial segregation of nectar and host plant resources relative to each other can influence the location and abundance of Karner blues on the landscape. This is because the location of resources relative to each other influences the energy and time butterflies expend for flight activity, and thereby influences resource acquisition. During early summer when first brood Karner blues emerge, nectar and host plants were spatially segregated, and Karner blue density peaked at intermediate densities of nectar and host plants occurring at ratios approximately equal to 1:1. During late summer, we found no significant relationships between second brood Karner blues and nectar plants or host plants when there was no correlation between nectar and host plants. Conservation practitioners of specialist insects with low vagility can strategically manage the distribution of plant resources to minimize insect time and energy expenditure and promote resource acquisition for all of an insect’s life stages.
","language":"English","publisher":"Springer","doi":"10.1007/s10841-020-00244-0","usgsCitation":"Chau, S.N., Bristow, L.V., Grundel, R., and Hellmann, J.J., 2020, Resource segregation at fine spatial scales explains Karner blue butterfly (Lycaeides melissa samuelis) distribution: Journal of Insect Conservation, v. 5, no. 24, p. 739-749, https://doi.org/10.1007/s10841-020-00244-0.","productDescription":"11 p.","startPage":"739","endPage":"749","ipdsId":"IP-095919","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":378261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"24","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Chau, Sophia N","contributorId":239960,"corporation":false,"usgs":false,"family":"Chau","given":"Sophia","email":"","middleInitial":"N","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":798230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bristow, Lainey V","contributorId":239961,"corporation":false,"usgs":false,"family":"Bristow","given":"Lainey","email":"","middleInitial":"V","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":798231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":798232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hellmann, Jessica J","contributorId":147694,"corporation":false,"usgs":false,"family":"Hellmann","given":"Jessica","email":"","middleInitial":"J","affiliations":[{"id":16905,"text":"University of Notre Dame, Dept. of Biological Sciences, Notre Dame, IN, 46556, USA","active":true,"usgs":false}],"preferred":false,"id":798233,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208133,"text":"sir20195144 - 2020 - Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016","interactions":[],"lastModifiedDate":"2020-06-17T14:21:21.015204","indexId":"sir20195144","displayToPublicDate":"2020-06-17T07:36:04","publicationYear":"2020","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":"2019-5144","displayTitle":"Small Basin Annual Yield and Percentage of Snowmelt Runoff in North Dakota, 1931–2016","title":"Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016","docAbstract":"The North Dakota hydrology manual prepared by the U.S. Department of Agriculture, Soil Conservation Service, presents methodologies primarily used for developing hydrology for onfarm conservation practices, watershed projects, Resource Conservation and Development project measures, and river basin studies. The manual includes data necessary for determining hydrologic factors and developing a design discharge for a given site and intended purpose. The U.S. Geological Survey, in cooperation with the North Dakota Natural Resources Conservation Service, developed methods to reproduce and update the annual yield maps for chapter 7 of the North Dakota hydrology manual. Annual yields, in acre-feet per square mile, for the 50- and 80-percent exceedance probabilities and expected percentage of snowmelt runoff isolines were estimated using U.S. Geological Survey streamflow data from 1931 to 2016 for 71 selected streamgages with drainage areas of 505 square miles or less. An application of a modified Maintenance of Variance Extension Type III was used to estimate missing annual streamflow volumes. An alternate expected percentage of snowmelt runoff isolines was estimated using High Plains Climatic Center precipitation and snowmelt data from 1931 to 2016 for 85 selected sites. The final expected percentage of snowmelt runoff isolines was estimated using streamflow data instead of precipitation and snowfall depth data. A snowmelt runoff seasonal period of March–May produced better isoline slopes than a November–May runoff seasonal period. Slopes of the expected percentage of snowmelt runoff isolines were sensitive to amounts of missing record. Suitable isoline slopes appeared when the missing record was set to 50 percent (43 years) and 66 percent (57 years) for the 86-year period of 1931–2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195144","collaboration":"Prepared in cooperation with the Natural Resources Conservation Service—North Dakota","usgsCitation":"Williams-Sether, T., and Wheeling, S.L., 2020, Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016: U.S. Geological Survey Scientific Investigations Report 2019–5144, 37 p., https://doi.org/10.3133/sir20195144.","productDescription":"Report: vii, 38 p.; Dataset; 2 Appendixes","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-104356","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":375620,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5144"},{"id":375416,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5144/coverthb.jpg"},{"id":375418,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_1.xlsx","text":"Appendix 1","size":"136 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5144 Appendix 1","linkHelpText":"—Table 1.1. Example data and computations for U.S. Geological Survey station 05056100"},{"id":375419,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2019–5144 Appendix 2","linkHelpText":"—R Code Script and Supporting Data for the Modified Maintenance of Variance Extension Type III, MOVE.3, Application"},{"id":375420,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","description":"USGS Data Release","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"North 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Dakota\",\"nation\":\"USA \"}}]}","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
The ascent and extrusion of crystal-rich magma is commonly facilitated by deformation partitioned within annular, conduit-parallel shear zones. The physical properties and textures of the shear zone materials, where exposed at surface, provide a record of ascent and eruption dynamics. We describe the shear zone developed in Dome C, part of Chaos Crags in the Lassen Volcanic Center (California, USA). The extruded shear zone comprises volcanic fault gouge and variably densified cataclasites. The competent cataclasites evidence deep-seated gouge production followed by gouge densification within the conduit on the timescale of lava dome ascent. Textural, geochemical and mineralogical data identify solid-state sintering as the densification mechanism. At the temperatures and pressures in the volcanic conduit, solid-state sintering causes rapid porosity and permeability loss within the gouge and concomitant material strengthening. Longer dwell times (i.e., slower ascent) allow for more sintering, producing stronger, denser and less permeable cataclasites. At Chaos Crags, we use the extent of sintering, quantified by residual porosity, to recover minimum in-conduit dwell times necessary to produce the observed cataclasites. Our analysis of the Dome C cataclasites suggests a maximum linear ascent rate of 10 m/day and a minimum ascent time of 100 days. We evaluate the consequences of shear zone lithification by solid-state sintering for the eruption of other crystal-rich, glass-poor magmas. Chaos Crags cataclasites preserve evidence of multiple cycles of fracturing, cataclasis and (re-)sintering suggesting a mechanism for transitions between effusive and explosive phases of dome-building eruptions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106935","usgsCitation":"Ryan, A., Heap, M.J., Russell, J.K., Kennedy, L.A., and Clynne, M.A., 2020, Cyclic shear zone cataclasis and sintering during lava dome extrusion: Insights from Chaos Crags, Lassen Volcanic Center (USA): Journal of Volcanology and Geothermal Research, v. 401, 106935, 14 p., https://doi.org/10.1016/j.jvolgeores.2020.106935.","productDescription":"106935, 14 p.","ipdsId":"IP-118124","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467288,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://insu.hal.science/insu-03093680","text":"External Repository"},{"id":462542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Chaos Crags, Lassen Volcanic Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.55292549839629,\n 40.56700203547902\n ],\n [\n -121.55292549839629,\n 40.49261388583824\n ],\n [\n -121.47706180792726,\n 40.49261388583824\n ],\n [\n -121.47706180792726,\n 40.56700203547902\n ],\n [\n -121.55292549839629,\n 40.56700203547902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"401","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ryan, Amy","contributorId":300368,"corporation":false,"usgs":false,"family":"Ryan","given":"Amy","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":914833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heap, Michael J. 0000-0002-4748-735X","orcid":"https://orcid.org/0000-0002-4748-735X","contributorId":297882,"corporation":false,"usgs":false,"family":"Heap","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":64429,"text":"Université de Strasbourg","active":true,"usgs":false}],"preferred":false,"id":914834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russell, James K. 0000-0002-2062-3155","orcid":"https://orcid.org/0000-0002-2062-3155","contributorId":344810,"corporation":false,"usgs":false,"family":"Russell","given":"James","email":"","middleInitial":"K.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":914835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Lori A. 0000-0001-5583-1264","orcid":"https://orcid.org/0000-0001-5583-1264","contributorId":344811,"corporation":false,"usgs":false,"family":"Kennedy","given":"Lori","email":"","middleInitial":"A.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":914836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":914837,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210673,"text":"sir20205040 - 2020 - Missouri StreamStats—St. Louis County and the City of St. Louis urban application","interactions":[],"lastModifiedDate":"2020-06-16T20:33:11.424554","indexId":"sir20205040","displayToPublicDate":"2020-06-16T09:37:03","publicationYear":"2020","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":"2020-5040","displayTitle":"Missouri StreamStats—St. Louis County and the City of St. Louis Urban Application","title":"Missouri StreamStats—St. Louis County and the City of St. Louis urban application","docAbstract":"To address a major limitation of the functionality of the Missouri statewide StreamStats application in the urban areas of St. Louis County and the City of St. Louis, Missouri, the U.S. Geological Survey, in cooperation with the Metropolitan St. Louis Sewer District, defined watershed boundaries and hydrography for the study area using high-resolution 3-meter digital elevation data derived from light detection and ranging sources, high-resolution 6-inch imagery, and storm sewer network geospatial data. The combined sanitary sewers, a part of the storm sewer network, were integrated into the open channel hydrography and elevation data using a new Arc Hydro stormwater tool developed to facilitate the incorporation of the combined sanitary sewer network into the StreamStats application.
The combined sanitary sewer network was edited for connectivity and flow direction before integration into the Missouri-St. Louis StreamStats application. Inlet structures in the geospatial data were defined as HydroJunction features that allow for stormwater runoff to enter the combined sanitary sewer network. An Arc Hydro stormwater processing workflow and a sewershed delineation tool were developed to integrate the combined sanitary sewer network with the hydrographic dataset and digital elevation model in the study area.
The StreamStats application developed for the study area provides various data exploration tools that can be used to examine the spatial data and to obtain general descriptive information and flow statistics at streamgages in the study area. Watersheds and sewersheds can be delineated and basin characteristics can be determined at any point on the open channel network or the combined sanitary sewer network in the study area. Peak-flow statistics can be computed at any point on the open channel network. A report summarizing the results is generated by the StreamStats application and can be downloaded and used in other software.
The Missouri-St. Louis StreamStats application is limited to the area inside St. Louis County and the City of St. Louis and excludes locations on the main stem of the Mississippi, Missouri, and Meramec Rivers. The limitations of the Missouri-St. Louis StreamStats application include possible inaccuracies using regression equations for peak-flow statistics developed assuming natural flow conditions and topographically derived watersheds determined from a coarser resolution of data than is used in this application. Additionally, published regression equations for peak-flow statistics did not incorporate any pipe flow or sewershed delineations when they were developed, which limits the applicability of peak-flow statistics to basins based on primarily topographic delineation. Inaccuracies in resolution, completeness, location, or attribution of geospatial elevation data, hydrographic data, derived stream lines, derived watershed boundaries, and combined sanitary sewer data can limit the accuracy and functionality of the Missouri-St. Louis StreamStats application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205040","collaboration":"Prepared in cooperation with the Metropolitan St. Louis Sewer District","usgsCitation":"Southard, R.E., Haluska, T., Richards, J.M., Ellis, J.T., Dartiguenave, C., and Djokic, D., 2020, Missouri StreamStats—St. Louis County and the City of St. Louis urban application: U.S. Geological Survey Scientific Investigations Report 2020–5040, 27 p., https://doi.org/10.3133/sir20205040.","productDescription":"Report: vii, 27 p.; Appendix; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098907","costCenters":[{"id":36532,"text":"Central Midwest Water Science 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This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.
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