{"pageNumber":"588","pageRowStart":"14675","pageSize":"25","recordCount":165309,"records":[{"id":70212520,"text":"70212520 - 2020 - Can nutrient additions facilitate recovery of Pacific salmon?","interactions":[],"lastModifiedDate":"2020-10-12T17:18:17.323586","indexId":"70212520","displayToPublicDate":"2020-06-18T09:41:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6455,"text":"Canadian Journal Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Can nutrient additions facilitate recovery of Pacific salmon?","docAbstract":"<div class=\"box-pad border-lightgray margin-bottom\"><div class=\"abstractSection\"><div class=\"abstractSection abstractInFull\"><p class=\"first last\"><span>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.</span></p></div></div></div>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0438","usgsCitation":"Benjamin, J.R., Bellmore, J.R., Whitney, E., and Dunham, J.B., 2020, Can nutrient additions facilitate recovery of Pacific salmon?: Canadian Journal Fisheries and Aquatic Sciences, v. 77, p. 1601-1611, https://doi.org/10.1139/cjfas-2019-0438.","productDescription":"11 p.","startPage":"1601","endPage":"1611","ipdsId":"IP-113407","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":500999,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/101959","text":"External 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0000-0002-5140-6460","orcid":"https://orcid.org/0000-0002-5140-6460","contributorId":195609,"corporation":false,"usgs":false,"family":"Bellmore","given":"James","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":796665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitney, Emily","contributorId":238834,"corporation":false,"usgs":false,"family":"Whitney","given":"Emily","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":796666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem 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,{"id":70228727,"text":"70228727 - 2020 - Estimating the drivers of species distributions with opportunistic data using mediation analysis","interactions":[],"lastModifiedDate":"2022-02-17T15:15:00.528851","indexId":"70228727","displayToPublicDate":"2020-06-18T09:07:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the drivers of species distributions with opportunistic data using mediation analysis","docAbstract":"<p><span>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.</span></p>","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":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico 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              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 }\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 <i>Escherichia coli</i> 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 <i>Escherichia coli</i> in the Missouri River near Omaha, Nebraska, 2012–16","title":"Modeling <i>Escherichia coli</i> in the Missouri River near Omaha, Nebraska, 2012–16","docAbstract":"<p>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.</p><p>The purpose of this report is to document the development of <i>Escherichia coli</i> (<i>E. coli</i>) 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.</p><p>Multiple linear regression models were developed to estimate <i>E. coli</i> 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.</p><p>Explanatory variables currently (2020) being collected and included in the selected models through 2016 explained 64–75 percent of the variability of <i>E. coli</i> 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 <i>E. coli</i>, the uncertainty of <i>E. coli</i> 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 <i>E. coli</i> concentration from 2012 to 2016.</p>","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 <i>Escherichia coli</i> 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":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/ne-water\" href=\"https://www.usgs.gov/centers/ne-water\">Nebraska Water Science Center</a> <br>U.S. Geological Survey<br>5231 South 19th Street <br>Lincoln, NE 68512</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods of Study</li><li>Missouri River <i>Escherichia Coli</i> Concentration Model Results</li><li>Model Capabilities and Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-17","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Densmore, Brenda K. 0000-0003-2429-638X bdensmore@usgs.gov","orcid":"https://orcid.org/0000-0003-2429-638X","contributorId":4896,"corporation":false,"usgs":true,"family":"Densmore","given":"Brenda","email":"bdensmore@usgs.gov","middleInitial":"K.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Brent M. 0000-0003-3815-5158 bhall@usgs.gov","orcid":"https://orcid.org/0000-0003-3815-5158","contributorId":4547,"corporation":false,"usgs":true,"family":"Hall","given":"Brent","email":"bhall@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moser, Matthew T. 0000-0002-4891-3381","orcid":"https://orcid.org/0000-0002-4891-3381","contributorId":94994,"corporation":false,"usgs":true,"family":"Moser","given":"Matthew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":790929,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70210708,"text":"70210708 - 2020 - Comparing trends in modeled and observed streamflows at minimally altered basins in the United States","interactions":[],"lastModifiedDate":"2020-06-19T15:59:45.860026","indexId":"70210708","displayToPublicDate":"2020-06-17T09:43:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Comparing trends in modeled and observed streamflows at minimally altered basins in the United States","docAbstract":"We compared modeled and observed streamflow trends from 1984–2016 using five statistical transfer models and one deterministic, distributed-parameter, process-based model, for 26 flow metrics at 502 basins in the United States that are minimally influenced by development. We also looked at a measure of overall model fit and average bias. A higher percentage of basins, for all models, had relatively low trend differences between modeled and observed mean/ median flows than for very high or low flows such as the annual 1-day high and 7-day low flows. Mean-flow metrics also had the largest percentage of basins with relatively good overall model fit and low bias. The five statistical transfer models performed better at more basins than the process-based model. The overall model fit for all models, for mean and/or high flows, was correlated with one or more measures of basin precipitation or aridity. Our study and previous studies generally observed good model performance for high flows up to 90th or 95th percentile flows. However, we found model performance was substantially worse for more extreme flows, including 99th percentile and annual 1-day high flows; this shows the importance of including more extreme high flows in analyses of model performance.","language":"English","publisher":"MDPI","doi":"10.3390/w12061728","usgsCitation":"Hodgkins, G.A., Dudley, R., Russell, A.M., and LaFontaine, J.H., 2020, Comparing trends in modeled and observed streamflows at minimally altered basins in the United States: Water, v. 12, 1728, 20 p., https://doi.org/10.3390/w12061728.","productDescription":"1728, 20 p.","ipdsId":"IP-117596","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":456370,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12061728","text":"Publisher Index Page"},{"id":436926,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XT4WSP","text":"USGS data release","linkHelpText":"Cross-validation results for five statistical methods of daily streamflow estimation at 1,385 reference streamgages in the conterminous United States, Water Years 1981-2017"},{"id":375682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              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\"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dudley, Robert W. 0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russell, Amy M. 0000-0003-0582-0094 arussell@usgs.gov","orcid":"https://orcid.org/0000-0003-0582-0094","contributorId":200011,"corporation":false,"usgs":true,"family":"Russell","given":"Amy","email":"arussell@usgs.gov","middleInitial":"M.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791053,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LaFontaine, Jacob H. 0000-0003-4923-2630 jlafonta@usgs.gov","orcid":"https://orcid.org/0000-0003-4923-2630","contributorId":2258,"corporation":false,"usgs":true,"family":"LaFontaine","given":"Jacob","email":"jlafonta@usgs.gov","middleInitial":"H.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791054,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70211183,"text":"70211183 - 2020 - Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements","interactions":[],"lastModifiedDate":"2020-07-16T17:45:55.970993","indexId":"70211183","displayToPublicDate":"2020-06-17T09:30:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements","docAbstract":"<p><span>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 SO</span><sub>2</sub><span>&nbsp;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 (SO</span><sub>2</sub><span>) emission rates along with estimates of their uncertainty. We find that sustained SO</span><sub>2</sub><span>&nbsp;emissions were highest in June and early July, when approximately 200 kt SO</span><sub>2</sub><span>&nbsp;were emitted daily. At&nbsp;the 68% confidence interval, we estimate that 7.1–13.6 Mt SO</span><sub>2</sub><span>&nbsp;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 H</span><sub>2</sub><span>O and 1.5–2.8 Mt CO</span><sub>2</sub><span>&nbsp;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 km</span><sup>3</sup><span>&nbsp;dense-rock equivalent of lava were erupted from the LERZ, which is approximately twice the concomitant collapse volume of the volcano’s summit.</span></p>","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 Center","active":true,"usgs":true}],"preferred":true,"id":792994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lerner, Allan 0000-0001-7208-1493","orcid":"https://orcid.org/0000-0001-7208-1493","contributorId":215623,"corporation":false,"usgs":false,"family":"Lerner","given":"Allan","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":792995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elias, Tamar 0000-0002-9592-4518 telias@usgs.gov","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":3916,"corporation":false,"usgs":true,"family":"Elias","given":"Tamar","email":"telias@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nadeau, Patricia A. 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":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska 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":"<p><span>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.</span></p>","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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,{"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":"<p><span>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 (</span><i>Lycaeides melissa samuelis</i><span>), a specialist that relies on wild blue lupine (</span><i>Lupinus perennis</i><span>) 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.</span></p>","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":"<p>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.</p>","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":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/dakota-water\" href=\"https://www.usgs.gov/centers/dakota-water\">Dakota Water Science Center</a> <br>U.S. Geological Survey<br>821 East Interstate Avenue<br>Bismarck, ND 58503–1608 <br>Mountain View Road<br>Rapid City, SD 57702 </p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction and Background</li><li>Purpose and Scope</li><li>Methods</li><li>Annual Yields and Percentage of Snowmelt Runoff</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Methods Used to Generate and Adjust Annual Streamflow Volumes Used in Move.3</li><li>Appendix 2. R Code Script and Supporting Data for the Modified Maintenance of Variance Extension Type III, MOVE.3, Application</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-17","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams-Sether, Tara 0000-0001-6515-9416","orcid":"https://orcid.org/0000-0001-6515-9416","contributorId":214143,"corporation":false,"usgs":true,"family":"Williams-Sether","given":"Tara","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wheeling, Spencer L. 0000-0003-4411-6526","orcid":"https://orcid.org/0000-0003-4411-6526","contributorId":221899,"corporation":false,"usgs":true,"family":"Wheeling","given":"Spencer","email":"","middleInitial":"L.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780664,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70227644,"text":"70227644 - 2020 - Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage","interactions":[],"lastModifiedDate":"2022-01-24T13:13:01.142534","indexId":"70227644","displayToPublicDate":"2020-06-17T07:09:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Geochemical modeling of precipitation reactions in the complex matrix of acid mine drainage is fundamental to understanding natural attenuation, lime treatment, and treatment procedures that separate constituents for potential reuse or recycling. The three main dissolved constituents in acid mine drainage are iron, aluminum, and sulfate. During the neutralization of acid mine drainage (AMD) by mixing with clean tributaries or by titration with a base such as sodium hydroxide or slaked lime, Ca(OH)<sub>2</sub>, iron precipitates at pH values of 2–3 if oxidized and aluminum precipitates at pH values of 4–5 and both processes buffer the pH during precipitation. Mixing processes were simulated using the ion-association model in the PHREEQC code. The results are sensitive to the solubility product constant (K<sub>sp</sub>) used for the precipitating phases. A field example with data on discharge and water composition of AMD before and after mixing along with massive precipitation of an aluminum phase is simulated and shows that there is an optimal K<sub>sp</sub><span>&nbsp;</span>to give the best fit to the measured data. Best fit is defined when the predicted water composition after mixing and precipitation matches most closely the measured water chemistry. Slight adjustment to the proportion of stream discharges does not give a better fit.</div>","language":"English","publisher":"MDPI","doi":"10.3390/min10060547","usgsCitation":"Nordstrom, D.K., 2020, Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage: Minerals, v. 10, no. 6, 547, 12 p., https://doi.org/10.3390/min10060547.","productDescription":"547, 12 p.","ipdsId":"IP-119702","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456374,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min10060547","text":"Publisher Index Page"},{"id":394754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Alpine County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-119.9025,38.9332],[-119.8471,38.8948],[-119.8261,38.8802],[-119.8223,38.8775],[-119.7899,38.8554],[-119.7873,38.8541],[-119.752,38.8297],[-119.743,38.8235],[-119.7285,38.8132],[-119.7033,38.7953],[-119.678,38.7781],[-119.6166,38.7357],[-119.6088,38.7303],[-119.5977,38.7226],[-119.5771,38.7084],[-119.5847,38.7029],[-119.5883,38.698],[-119.5912,38.6907],[-119.5965,38.6762],[-119.6024,38.6703],[-119.6118,38.668],[-119.6159,38.6649],[-119.6159,38.6585],[-119.6159,38.6458],[-119.6165,38.6345],[-119.6194,38.6254],[-119.6223,38.6159],[-119.6188,38.6059],[-119.6129,38.5991],[-119.5987,38.5924],[-119.597,38.5919],[-119.5958,38.5842],[-119.5928,38.5769],[-119.5893,38.5706],[-119.5869,38.5674],[-119.5857,38.5552],[-119.5781,38.5484],[-119.5734,38.5452],[-119.5687,38.5421],[-119.5657,38.5298],[-119.5592,38.5239],[-119.5551,38.5162],[-119.5592,38.5103],[-119.5586,38.5049],[-119.5557,38.5026],[-119.5498,38.5022],[-119.5427,38.4986],[-119.5451,38.4895],[-119.5427,38.4832],[-119.5463,38.4759],[-119.5527,38.4718],[-119.5574,38.4682],[-119.558,38.4591],[-119.5603,38.4446],[-119.5627,38.4378],[-119.5692,38.4356],[-119.5674,38.4283],[-119.5662,38.4247],[-119.5644,38.4215],[-119.5621,38.4165],[-119.5627,38.4093],[-119.5674,38.4056],[-119.5762,38.4034],[-119.582,38.4007],[-119.5856,38.397],[-119.5991,38.4047],[-119.6026,38.4038],[-119.6061,38.4011],[-119.6132,38.4015],[-119.6214,38.3961],[-119.6208,38.3911],[-119.6125,38.373],[-119.6078,38.3698],[-119.6084,38.3671],[-119.6149,38.3662],[-119.6248,38.3594],[-119.6342,38.3544],[-119.6301,38.3476],[-119.633,38.3421],[-119.6313,38.3372],[-119.6348,38.3308],[-119.6354,38.3294],[-119.6365,38.3286],[-119.6594,38.3461],[-119.6986,38.3652],[-119.6924,38.3791],[-119.6967,38.4089],[-119.708,38.4177],[-119.751,38.4168],[-119.7686,38.4067],[-119.7785,38.4078],[-119.7836,38.4051],[-119.8006,38.4015],[-119.8133,38.3877],[-119.8362,38.3826],[-119.8529,38.3728],[-119.8689,38.3678],[-119.8793,38.3597],[-120.0169,38.4363],[-120.0528,38.4534],[-120.0727,38.4478],[-120.0719,38.4936],[-120.072,38.5013],[-120.0727,38.5117],[-120.0719,38.5684],[-120.0729,38.611],[-120.0732,38.6554],[-120.0741,38.7016],[-119.9685,38.7746],[-119.9567,38.7796],[-119.9479,38.7842],[-119.9444,38.7887],[-119.942,38.7919],[-119.9391,38.7947],[-119.9439,38.8028],[-119.9368,38.8096],[-119.9292,38.8183],[-119.9215,38.8233],[-119.9239,38.8283],[-119.9216,38.8323],[-119.911,38.8342],[-119.908,38.8365],[-119.9074,38.8383],[-119.9075,38.8469],[-119.9052,38.8569],[-119.8969,38.8564],[-119.8904,38.8578],[-119.8833,38.8637],[-119.8792,38.866],[-119.8775,38.8692],[-119.8775,38.8724],[-119.8781,38.8746],[-119.887,38.8805],[-119.8799,38.8855],[-119.8841,38.8964],[-119.8806,38.9032],[-119.8854,38.9145],[-119.8985,38.9226],[-119.9003,38.9262],[-119.9025,38.9332]]]},\"properties\":{\"name\":\"Alpine\",\"state\":\"CA\"}}]}","volume":"10","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":831503,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70211077,"text":"70211077 - 2020 - Origin and geochemistry of formation waters from the lower Eagle Ford Group, Gulf Coast Basin, south central Texas","interactions":[],"lastModifiedDate":"2020-07-16T20:01:36.320547","indexId":"70211077","displayToPublicDate":"2020-06-16T10:46:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Origin and geochemistry of formation waters from the lower Eagle Ford Group, Gulf Coast Basin, south central Texas","docAbstract":"The lower Eagle Ford Group (LEFG) is one of the most productive continuous hydrocarbon plays in the United States but few associated produced waters data and minimal interpretation have been published. This effort focuses on results from compositional and isotopic data from 39 produced water samples collected from horizontal wells producing from the LEFG in south central Texas. The depth of the LEFG increases by approximately 1 km across the study area, from northwest (2.9 km) to southeast (3.9 km). Associated increases in calculated reservoir temperature (125-165 C), development of reservoir over-pressuring (400-800 bars total pressure), and increased thermal maturity (heavy oil to gas condensate) also occur along this trend. Produced water salinity starts at nearly 100 g/L in the shallowest samples and decreases linearly with depth to <35 g/L. Comparison of Br/Cl and ⁸⁷Sr/⁸⁶Sr data between LEFG produced waters and the Louann salt, suggests that halite recycling is the mechanism behind salinity greater than seawater. Decreasing salinity with depth and thermal maturity in the Gulf Coast Basin have previously been shown to be a result of release of inter-layer water during smectite to illite conversion. The produced water samples show increasing 18O and decreasing 2H with depth, which is attributed temperature-dependent isotope fractionation of O and H exchange between seawater and clays and calcite with increasing temperature. Multiple sources of data indicate that the waters in the LEFG are not connate, but rather entered the unit prior to smectite-illite conversion. Presence of allochthonous water in many major tight oil and shale gas plays in the U.S., including the LEFG, suggests there is unknown mechanism allowing for water advection into low permeability reservoirs.","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2020.119754","collaboration":"None","usgsCitation":"Engle, M.A., Doolan, C.A., Pitman, J., Varonka, M., Chenault, J., Orem, W.H., McMahon, P.B., and Jubb, A., 2020, Origin and geochemistry of formation waters from the lower Eagle Ford Group, Gulf Coast Basin, south central Texas: Chemical Geology, v. 550, 119754, 12 p., https://doi.org/10.1016/j.chemgeo.2020.119754.","productDescription":"119754, 12 p.","ipdsId":"IP-117920","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":456377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2020.119754","text":"Publisher Index Page"},{"id":376366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"San Marcos Arch","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -98.98681640625,\n              28.033197847676377\n            ],\n            [\n              -96.064453125,\n              28.033197847676377\n            ],\n            [\n              -96.064453125,\n              30.637912028341123\n            ],\n            [\n              -98.98681640625,\n              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cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pitman, Janet K. 0000-0002-0441-779X","orcid":"https://orcid.org/0000-0002-0441-779X","contributorId":228982,"corporation":false,"usgs":true,"family":"Pitman","given":"Janet K.","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varonka, Matthew S. 0000-0003-3620-5262","orcid":"https://orcid.org/0000-0003-3620-5262","contributorId":203231,"corporation":false,"usgs":true,"family":"Varonka","given":"Matthew S.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792699,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chenault, Jessica 0000-0002-5974-0762","orcid":"https://orcid.org/0000-0002-5974-0762","contributorId":222078,"corporation":false,"usgs":true,"family":"Chenault","given":"Jessica","email":"","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792750,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792701,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792751,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792703,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70259301,"text":"70259301 - 2020 - Cyclic shear zone cataclasis and sintering during lava dome extrusion: Insights from Chaos Crags, Lassen Volcanic Center (USA)","interactions":[],"lastModifiedDate":"2024-10-03T15:14:06.149272","indexId":"70259301","displayToPublicDate":"2020-06-16T10:06:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Cyclic shear zone cataclasis and sintering during lava dome extrusion: Insights from Chaos Crags, Lassen Volcanic Center (USA)","docAbstract":"<p><span>The ascent and extrusion of crystal-rich&nbsp;magma&nbsp;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&nbsp;fault gouge&nbsp;and variably densified&nbsp;cataclasites. The competent cataclasites evidence deep-seated gouge production followed by gouge&nbsp;densification&nbsp;within the conduit on the timescale of&nbsp;</span>lava dome<span>&nbsp;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&nbsp;m/day and a minimum ascent time of 100&nbsp;days. We evaluate the consequences of shear zone&nbsp;lithification&nbsp;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.</span></p>","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":"<p>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.</p><p>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.</p><p>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.</p><p>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.</p>","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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,{"id":70198567,"text":"sir20185104 - 2020 - Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","interactions":[],"lastModifiedDate":"2020-06-16T14:09:59.229883","indexId":"sir20185104","displayToPublicDate":"2020-06-16T09:20:00","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":"2018-5104","displayTitle":"Conceptual Framework and Approach for Conducting a Geoenvironmental Assessment of Undiscovered Uranium Resources","title":"Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","docAbstract":"<p>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.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185104","usgsCitation":"Gallegos, T.J., Walton-Day, K., and Seal, R.R., II, 2020, Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources: U.S. Geological Survey Scientific Investigations Report 2018–5104, 28 p., https://doi.org/10.3133/sir20185104.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070792","costCenters":[{"id":191,"text":"Colorado Water Science 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38.01551\n              ],\n              [\n                -75.94023,\n                37.21689\n              ],\n              [\n                -76.03127,\n                37.2566\n              ],\n              [\n                -75.72205,\n                37.93705\n              ],\n              [\n                -76.23287,\n                38.31921\n              ],\n              [\n                -76.35,\n                39.15\n              ],\n              [\n                -76.54272,\n                38.71762\n              ],\n              [\n                -76.32933,\n                38.08326\n              ],\n              [\n                -76.99,\n                38.23999\n              ],\n              [\n                -76.30162,\n                37.91794\n              ],\n              [\n                -76.25874,\n                36.9664\n              ],\n              [\n                -75.9718,\n                36.89726\n              ],\n      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    -81.49042,\n                30.72999\n              ],\n              [\n                -81.31371,\n                30.03552\n              ],\n              [\n                -80.98,\n                29.18\n              ],\n              [\n                -80.53558,\n                28.47213\n              ],\n              [\n                -80.53,\n                28.04\n              ],\n              [\n                -80.05654,\n                26.88\n              ],\n              [\n                -80.08801,\n                26.20576\n              ],\n              [\n                -80.13156,\n                25.81677\n              ],\n              [\n                -80.38103,\n                25.20616\n              ],\n              [\n                -80.68,\n                25.08\n              ],\n              [\n                -81.17213,\n                25.20126\n              ],\n              [\n                -81.33,\n                25.64\n              ],\n              [\n                -81.71,\n                25.87\n              ],\n              [\n                -82.24,\n                26.73\n              ],\n              [\n                -82.70515,\n                27.49504\n              ],\n              [\n                -82.85526,\n                27.88624\n              ],\n              [\n                -82.65,\n                28.55\n              ],\n              [\n                -82.93,\n                29.1\n              ],\n              [\n                -83.70959,\n                29.93656\n              ],\n              [\n                -84.1,\n                30.09\n              ],\n              [\n                -85.10882,\n                29.63615\n              ],\n              [\n                -85.28784,\n                29.68612\n              ],\n              [\n                -85.7731,\n                30.15261\n              ],\n              [\n                -86.4,\n                30.4\n              ],\n              [\n                -87.53036,\n                30.27433\n              ],\n              [\n                -88.41782,\n                30.3849\n              ],\n              [\n                -89.18049,\n                30.31598\n              ],\n              [\n                -89.59383,\n                30.15999\n              ],\n              [\n                -89.41373,\n                29.89419\n              ],\n              [\n                -89.43,\n                29.48864\n              ],\n              [\n                -89.21767,\n                29.29108\n              ],\n              [\n                -89.40823,\n                29.15961\n              ],\n              [\n                -89.77928,\n                29.30714\n              ],\n              [\n                -90.15463,\n                29.11743\n              ],\n              [\n                -90.88022,\n                29.14854\n              ],\n              [\n                -91.62678,\n                29.677\n              ],\n              [\n                -92.49906,\n                29.5523\n              ],\n              [\n                -93.22637,\n                29.78375\n              ],\n              [\n                -93.84842,\n                29.71363\n              ],\n              [\n                -94.69,\n                29.48\n              ],\n              [\n                -95.60026,\n                28.73863\n              ],\n              [\n                -96.59404,\n                28.30748\n              ],\n              [\n                -97.14,\n                27.83\n              ],\n              [\n                -97.37,\n                27.38\n              ],\n              [\n                -97.38,\n                26.69\n              ],\n              [\n                -97.33,\n                26.21\n              ],\n              [\n                -97.14,\n                25.87\n              ],\n              [\n                -97.53,\n                25.84\n              ],\n              [\n                -98.24,\n                26.06\n              ],\n              [\n                -99.02,\n                26.37\n              ],\n              [\n                -99.3,\n                26.84\n              ],\n              [\n                -99.52,\n                27.54\n              ],\n              [\n                -100.11,\n                28.11\n              ],\n              [\n                -100.45584,\n                28.69612\n              ],\n              [\n                -100.9576,\n                29.38071\n              ],\n              [\n                -101.6624,\n                29.7793\n              ],\n              [\n                -102.48,\n                29.76\n              ],\n              [\n                -103.11,\n                28.97\n              ],\n              [\n            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          32.03914\n              ],\n              [\n                -114.815,\n                32.52528\n              ],\n              [\n                -114.72139,\n                32.72083\n              ],\n              [\n                -115.99135,\n                32.61239\n              ],\n              [\n                -117.12776,\n                32.53534\n              ],\n              [\n                -117.29594,\n                33.04622\n              ],\n              [\n                -117.944,\n                33.62124\n              ],\n              [\n                -118.4106,\n                33.74091\n              ],\n              [\n                -118.51989,\n                34.02778\n              ],\n              [\n                -119.081,\n                34.078\n              ],\n              [\n                -119.43884,\n                34.34848\n              ],\n              [\n                -120.36778,\n                34.44711\n              ],\n              [\n                -120.62286,\n                34.60855\n              ],\n              [\n                -120.74433,\n                35.15686\n              ],\n              [\n                -121.71457,\n                36.16153\n              ],\n              [\n                -122.54747,\n                37.55176\n              ],\n              [\n                -122.51201,\n                37.78339\n              ],\n              [\n                -122.95319,\n                38.11371\n              ],\n              [\n                -123.7272,\n                38.95166\n              ],\n              [\n                -123.86517,\n                39.76699\n              ],\n              [\n                -124.39807,\n                40.3132\n              ],\n              [\n                -124.17886,\n                41.14202\n              ],\n              [\n                -124.2137,\n                41.99964\n              ],\n              [\n                -124.53284,\n                42.76599\n              ],\n              [\n                -124.14214,\n                43.70838\n              ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/eersc\" data-mce-href=\"https://www.usgs.gov/centers/eersc\">Eastern Energy Resources Science Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Background</li><li>The Proposed Geoenvironmental Assessment Framework for Undiscovered Uranium Resource</li><li>Establishing the Geoenvironmental Assessment Approach</li><li>Geoenvironmental Assessment Outcomes</li><li>Limitations and Science Needs</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Glossary</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-06-16","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":741953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-5908-2683 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-5908-2683","contributorId":206860,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":741954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":741955,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70245131,"text":"70245131 - 2020 - Newly documented population extends geographic range and genetic diversity for the Leaf-toed Gecko (Phyllodactylus nocticolus) into the Transverse Ranges of southern California","interactions":[],"lastModifiedDate":"2023-06-16T12:13:31.071544","indexId":"70245131","displayToPublicDate":"2020-06-16T07:12:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"title":"Newly documented population extends geographic range and genetic diversity for the Leaf-toed Gecko (Phyllodactylus nocticolus) into the Transverse Ranges of southern California","docAbstract":"Between 19 – 30 May 2018, one of us [AW] discovered a disjunct population of Peninsula leaf-toed geckos, Phyllodactylus nocticolus (Phyllodactylidae) on the northern edge of the Coachella Valley in the Little San Bernardino Mountains of the Transverse Ranges (Fig. 1a). The previously northernmost location for the species is Tahquitz Canyon, Riverside Co. (MVZ 212205) in the Peninsular Ranges 20 km to the south. Southern California has many herp enthusiasts and it is possible that this population is of anthropogenic origin through accidental or misguided purposeful introduction. The apparent barrier to dispersal suggests that, if of natural origin, the dispersal may have occurred at a time when the aeolian sand barrier was less severe. Thus, if this newly discovered population is of natural origin, we expect the genetic data of the disjunct Transverse Ranges population to differ from that of any population sampled in the Peninsular Ranges. Herein we describe this newly discovered population of P. nocticolus, we analyze genetic diversity from the new population,  and we compare it with genetic data gathered from populations of P. nocticolus throughout southern California to help determine if this isolated gecko population was of natural dispersal or the result of human intervention. This is particularly noteworthy given the apparent strength of the Coachella Valley’s sand fields as a barrier to dispersal of highly saxicolous lizard faunas.","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Wood, D.A., Worrel-Black, A., Black, R., Mitelberg, A., Fisher, M., Fisher, R., Vandergast, A.G., and Barrows, C., 2020, Newly documented population extends geographic range and genetic diversity for the Leaf-toed Gecko (Phyllodactylus nocticolus) into the Transverse Ranges of southern California: Herpetological Review, v. 51, p. 456-458.","productDescription":"2 p.","startPage":"456","endPage":"458","ipdsId":"IP-119922","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":418156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418146,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://ssarherps.org/herpetological-review-pdfs/"}],"country":"United States","state":"California","otherGeospatial":"Transverse Ranges","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.04865933568159,\n              34.067199754494396\n            ],\n            [\n              -117.04865933568159,\n              33.537937150590196\n            ],\n            [\n              -116.2579829525956,\n              33.537937150590196\n            ],\n            [\n              -116.2579829525956,\n              34.067199754494396\n            ],\n            [\n              -117.04865933568159,\n              34.067199754494396\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"51","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":875618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Worrel-Black, Alyssa","contributorId":310421,"corporation":false,"usgs":false,"family":"Worrel-Black","given":"Alyssa","email":"","affiliations":[{"id":67183,"text":"Morongo Valley, Riverside, CA","active":true,"usgs":false}],"preferred":false,"id":875619,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Black, Robert","contributorId":310422,"corporation":false,"usgs":false,"family":"Black","given":"Robert","affiliations":[{"id":67183,"text":"Morongo Valley, Riverside, CA","active":true,"usgs":false}],"preferred":false,"id":875620,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitelberg, Anna 0000-0002-3309-9946 amitelberg@usgs.gov","orcid":"https://orcid.org/0000-0002-3309-9946","contributorId":218945,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":875621,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fisher, Mark","contributorId":149936,"corporation":false,"usgs":false,"family":"Fisher","given":"Mark","affiliations":[{"id":17857,"text":"UC Natural Reserve System, Indian Wells, CA","active":true,"usgs":false}],"preferred":false,"id":875622,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":875623,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":875624,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barrows, Cameron W.","contributorId":236818,"corporation":false,"usgs":false,"family":"Barrows","given":"Cameron W.","affiliations":[],"preferred":false,"id":875625,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70236094,"text":"70236094 - 2020 - Kinematic rupture and 3D wave propagation simulations of the 2019 Mw 7.1 Ridgecrest, California, earthquake","interactions":[],"lastModifiedDate":"2022-08-29T11:59:56.732315","indexId":"70236094","displayToPublicDate":"2020-06-16T06:57:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Kinematic rupture and 3D wave propagation simulations of the 2019 Mw 7.1 Ridgecrest, California, earthquake","docAbstract":"<p><span>We model the kinematic rupture process of the 2019&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-3-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub xmlns=&quot;&quot;><mi>M</mi><mi mathvariant=&quot;normal&quot;>w</mi></msub></math>\"><span id=\"MathJax-Span-11\" class=\"math\"><span><span id=\"MathJax-Span-12\" class=\"mrow\"><span id=\"MathJax-Span-13\" class=\"msub\"><span id=\"MathJax-Span-14\" class=\"mi\">M</span><span id=\"MathJax-Span-15\" class=\"mi\">w</span></span></span></span></span><span class=\"MJX_Assistive_MathML\">Mw</span></span></span><span>&nbsp;7.1 Ridgecrest, California, earthquake using numerical simulations to reproduce the elastodynamic wave field observed by inertial seismometers, high‐rate Global Navigation Satellite System stations, and borehole strainmeters. This was the largest earthquake in Southern California in 20&nbsp;yr and was widely felt throughout the region. The&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-4-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub xmlns=&quot;&quot;><mi>M</mi><mi mathvariant=&quot;normal&quot;>w</mi></msub></math>\"><span id=\"MathJax-Span-16\" class=\"math\"><span><span id=\"MathJax-Span-17\" class=\"mrow\"><span id=\"MathJax-Span-18\" class=\"msub\"><span id=\"MathJax-Span-19\" class=\"mi\">M</span><span id=\"MathJax-Span-20\" class=\"mi\">w</span></span></span></span></span><span class=\"MJX_Assistive_MathML\">Mw</span></span></span><span>&nbsp;7.1 mainshock was part of a large sequence of&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-5-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mo xmlns=&quot;&quot; form=&quot;prefix&quot;>&amp;#x223C;</mo><mn xmlns=&quot;&quot;>30</mn><mo xmlns=&quot;&quot;>,</mo><mn xmlns=&quot;&quot;>000</mn></math>\"><span id=\"MathJax-Span-21\" class=\"math\"><span><span id=\"MathJax-Span-22\" class=\"mrow\"><span id=\"MathJax-Span-23\" class=\"mo\">∼</span><span id=\"MathJax-Span-24\" class=\"mn\">30</span><span id=\"MathJax-Span-25\" class=\"mo\">,</span><span id=\"MathJax-Span-26\" class=\"mn\">000</span></span></span></span><span class=\"MJX_Assistive_MathML\">∼30,000</span></span></span><span>&nbsp;aftershocks and was notably preceded by an&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-6-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub xmlns=&quot;&quot;><mi>M</mi><mi mathvariant=&quot;normal&quot;>w</mi></msub></math>\"><span id=\"MathJax-Span-27\" class=\"math\"><span><span id=\"MathJax-Span-28\" class=\"mrow\"><span id=\"MathJax-Span-29\" class=\"msub\"><span id=\"MathJax-Span-30\" class=\"mi\">M</span><span id=\"MathJax-Span-31\" class=\"mi\">w</span></span></span></span></span><span class=\"MJX_Assistive_MathML\">Mw</span></span></span><span>&nbsp;6.4 foreshock by 34&nbsp;hr on fault structures that were once poorly understood. A large number of seismic and geodetic instruments measured the rupture process for both events, with many stations located in the near field. Hence, this is a rare opportunity to better understand complex earthquake processes that arise in an immature fault zone using advanced computing. Of the kinematic rupture models that we tested, our preferred is the simplest one that reproduces signals recorded by the three different geophysical datasets; it is composed of four distinct ruptures that progressively migrate to the southeast with delayed initiation times, and typical rupture speeds. This type of model does a better job at matching the recorded ground motions and deformations than does one composed of a continuous rupture with very low‐rupture velocity, as proposed in other studies of this earthquake.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200031","usgsCitation":"Hirakawa, E.T., and Barbour, A.J., 2020, Kinematic rupture and 3D wave propagation simulations of the 2019 Mw 7.1 Ridgecrest, California, earthquake: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1644-1659, https://doi.org/10.1785/0120200031.","productDescription":"16 p.","startPage":"1644","endPage":"1659","ipdsId":"IP-115108","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":405783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.98217773437499,\n              35.38904996691167\n            ],\n            [\n              -117.3614501953125,\n              35.38904996691167\n            ],\n            [\n              -117.3614501953125,\n              35.79108281624994\n            ],\n            [\n              -117.98217773437499,\n              35.79108281624994\n            ],\n            [\n              -117.98217773437499,\n              35.38904996691167\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":849974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J. 0000-0002-6890-2452","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":215339,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":849975,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70228523,"text":"70228523 - 2020 - Keeping it classy: Classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach","interactions":[],"lastModifiedDate":"2022-02-11T12:48:31.240069","indexId":"70228523","displayToPublicDate":"2020-06-16T06:44:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Keeping it classy: Classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach","docAbstract":"<div>The ability of passive integrated transponder (PIT) tag data to improve demographic parameter estimates has led to the rapid advancement of PIT tag systems. However, ghost tags create uncertainty about detected tag status (i.e., live fish or ghost tag) when using mobile interrogation systems. We developed a method to differentiate between live fish and ghost tags using a random forest classification model with a novel data input structure based on known fate PIT tag detections in the San Juan River (New Mexico, Colorado, and Utah, USA). We used our model to classify detected tags with an overall error rate of 6.8% (1.6% ghost tags error rate and 21.8% live fish error rate). The important variables for classification were related to distance moved and response to monsoonal flood flows; however, habitat variables did not appear to influence model accuracy. Our results and approach allow the use of mobile detection data with confidence and allow for greater accuracy in movement, distribution, and habitat use studies, potentially helping identify influential management actions that would improve our ability to conserve and recover endangered fish.</div>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0403","usgsCitation":"Stout, J.B., Conner, M., Budy, P., Mackinnon, P., and McKinstry, M., 2020, Keeping it classy: Classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach: Canadian Journal of Fisheries and Aquatic Sciences, v. 7, no. 9, p. 1564-1573, https://doi.org/10.1139/cjfas-2019-0403.","productDescription":"10 p.","startPage":"1564","endPage":"1573","ipdsId":"IP-116366","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2019-0403","text":"Publisher Index Page"},{"id":395838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stout, J. Benjamin","contributorId":276038,"corporation":false,"usgs":false,"family":"Stout","given":"J.","email":"","middleInitial":"Benjamin","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":834499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conner, Mary","contributorId":276039,"corporation":false,"usgs":false,"family":"Conner","given":"Mary","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":834500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mackinnon, Peter","contributorId":276040,"corporation":false,"usgs":false,"family":"Mackinnon","given":"Peter","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":834501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKinstry, Mark","contributorId":276041,"corporation":false,"usgs":false,"family":"McKinstry","given":"Mark","email":"","affiliations":[{"id":12646,"text":"BOR","active":true,"usgs":false}],"preferred":false,"id":834502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70210631,"text":"sir20205036 - 2020 - Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts","interactions":[],"lastModifiedDate":"2020-06-15T15:30:35.833622","indexId":"sir20205036","displayToPublicDate":"2020-06-15T10:50:00","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":"2020-5036","displayTitle":"Updating Data Inputs, Assessing Trends, and Evaluating a Method To Estimate Probable High Groundwater Levels in Selected Areas of Massachusetts","title":"Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts","docAbstract":"<p>A method to estimate the probable high groundwater level in Massachusetts, excluding Cape Cod and the islands, was developed in 1981. The method uses a groundwater measurement from a test site, groundwater measurements from an index well, and a distribution of high groundwater levels from wells in similar geologic and topographic settings. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, conducted an update to the Frimpter method for estimating the probable high groundwater levels in Massachusetts. The study evaluated the potential changes to the method resulting from four decades of additional groundwater-level data and the expansion of the network of wells for monitoring groundwater levels. The differences and potential benefits of daily, as opposed to monthly, measurements in the application of the method were examined because of the increased availability of high-frequency (subdaily) groundwater-level data. The study also considered long-term trends in groundwater levels that may alter the accuracy of the method. Finally, the accuracy of the estimated high groundwater levels was evaluated, and improved implementation guidance was prepared.</p><p>For this study, groundwater levels in 153 wells in Massachusetts and surrounding States with records with lengths of 16 to 78 years were analyzed. The highest recorded groundwater levels ranged from 1.2 feet (ft) above land surface (flooded conditions) to 45.8 ft below land surface, with a median of 4.6 ft below land surface. The maximum annual groundwater-level range was 1.4 to 17.9 ft, with a median of 5.5 ft.</p><p>The within-month variation, maximum annual groundwater-level range, and highest recorded groundwater level were computed using daily mean groundwater-level values from 28 wells with continuous records. The use of daily data resulted in larger maximum annual groundwater-level ranges (0.02 to 2.94 ft larger, with a median of 0.58 ft larger) and shallower highest-recorded groundwater levels (0.0 to 1.60 ft shallower, with a median of 0.18 ft shallower) than computations based on monthly measurements in the same wells.</p><p>Statistical tests showed moderate to strong evidence of trends in measurements of both high and low groundwater levels within most of the periods during which water levels were analyzed. High groundwater levels rose beneath the land surface at most sites during four of the six periods used for analysis (1966–2015, 1986–2015, 1991–2010, and 1981–2010). Low groundwater levels also increased at many sites during most of the periods evaluated, but this trend was less widespread than the similar trends in high groundwater levels, and the trend was to deeper low groundwater levels at more sites than the trend to deeper high groundwater levels. There was no clear trend in annual groundwater-level ranges at most sites during the six periods analyzed.</p><p>In general, the Frimpter method predicted shallower (higher) high groundwater levels than were observed but correctly classified sites according to their suitabilities for unmounded septic systems. The mean error of the predictions (difference between the estimated and observed groundwater levels) ranged from −3.23 ft to −1.40 ft for various approaches to estimating the groundwater-level range and selecting an index well. The method correctly classified 83 to 86 percent of monitoring-well sites according to their suitability for an unmounded septic system for many approaches to estimating the annual groundwater-level range and selecting an index well. The approach selected for estimating the annual groundwater-level range and selecting an index well will depend upon the importance of an accurate estimate of the high groundwater level as compared to the importance of an estimated high groundwater level that is less likely to be exceeded.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205036","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2020, Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts: U.S. Geological Survey Scientific Investigations Report 2020–5036, 45 p., https://doi.org/10.3133/sir20205036.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"45","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103689","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":375551,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NM2PHP","text":"USGS data release","linkHelpText":"Data on well characteristics and well-pair characteristics for estimating high groundwater levels in selected areas of Massachusetts"},{"id":375553,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5036/sir20205036.pdf","text":"Report","size":"7.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5036"},{"id":375554,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5036/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, New Hampshire, Rhode Island, Vermont","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.8343505859375,\n              42.90011265525328\n            ],\n            [\n              -73.2952880859375,\n              42.9524020856897\n            ],\n            [\n              -73.27880859375,\n              42.65820178455667\n            ],\n            [\n              -73.5150146484375,\n              42.12267315117256\n            ],\n            [\n              -73.5479736328125,\n              41.393294288784865\n            ],\n            [\n              -73.54248046875,\n              41.29431726315258\n            ],\n            [\n              -73.487548828125,\n              41.20345619205131\n            ],\n            [\n              -73.7347412109375,\n              41.10005163093046\n            ],\n            [\n              -73.65234375,\n              41.000629848685385\n            ],\n            [\n              -72.9547119140625,\n              41.14143302653628\n            ],\n            [\n              -72.0538330078125,\n              41.17451935556443\n            ],\n            [\n              -71.43310546875,\n              41.29431726315258\n            ],\n            [\n              -70.6475830078125,\n              41.21585377825921\n            ],\n            [\n              -69.7686767578125,\n              41.16211393939692\n            ],\n            [\n              -69.8785400390625,\n              41.87774145109676\n            ],\n            [\n              -70.1806640625,\n              42.17968819665961\n            ],\n            [\n              -70.57617187499999,\n              42.718768102606326\n            ],\n            [\n              -70.8343505859375,\n              42.90011265525328\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Background</li><li>Well Network</li><li>Updated Water-Level Statistics</li><li>Daily Versus Monthly Data</li><li>Long-Term Trends in Water Levels</li><li>Evaluating and Implementing the Method</li><li>Summary</li><li>Selected References</li><li>Appendix 1. Groundwater Observation Wells in Massachusetts and Surrounding States</li><li>Appendix 2. Description of Data Used in the Predictive Equation To Select the Best Index Well</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-06-15","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullaney, John R. 0000-0003-4936-5046 jmullane@usgs.gov","orcid":"https://orcid.org/0000-0003-4936-5046","contributorId":1957,"corporation":false,"usgs":true,"family":"Mullaney","given":"John","email":"jmullane@usgs.gov","middleInitial":"R.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790895,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70211079,"text":"70211079 - 2020 - The prevalence and significance of offset magma reservoirs at arc volcanoes","interactions":[],"lastModifiedDate":"2020-07-15T13:18:27.240533","indexId":"70211079","displayToPublicDate":"2020-06-15T10:32:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The prevalence and significance of offset magma reservoirs at arc volcanoes","docAbstract":"Determining the spatial relations between volcanic edifices and their underlying magma storage zones is fundamental for characterizing long-term evolution and short-term unrest. We compile centroid locations of upper crustal magma reservoirs at 56 arc volcanoes inferred from seismic, magnetotelluric, and geodetic studies. We show that magma reservoirs are often horizontally offset from their associated volcanic edifices by multiple kilometers, and the degree of offset broadly scales with reservoir depth. Approximately 20% of inferred magma reservoir centroids occur outside of the overlying volcano’s mean radius. Furthermore, reservoir offset is inversely correlated with edifice size. Taking edifice volume as a proxy for long-term magmatic flux, we suggest that high flux or prolonged magmatism leads to more centralized magma storage beneath arc volcanoes by overprinting upper crustal heterogeneities that would otherwise affect magma ascent. Edifice volumes therefore reflect the spatial distribution of underlying magma storage, which could help guide monitoring strategies at volcanoes","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087856","usgsCitation":"Lerner, A.H., O'Hara, D., Karlstrom, L., Ebmeier, S.K., Anderson, K.R., and Hurwitz, S., 2020, The prevalence and significance of offset magma reservoirs at arc volcanoes: Geophysical Research Letters, v. 47, no. 14, e2020GL087856, 10 p., https://doi.org/10.1029/2020GL087856.","productDescription":"e2020GL087856, 10 p.","ipdsId":"IP-117935","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl087856","text":"Publisher Index Page"},{"id":376364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Lerner, Allan H.","contributorId":209926,"corporation":false,"usgs":false,"family":"Lerner","given":"Allan","email":"","middleInitial":"H.","affiliations":[{"id":38028,"text":"University of Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":792707,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Hara, Daniel","contributorId":228983,"corporation":false,"usgs":false,"family":"O'Hara","given":"Daniel","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":792708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karlstrom, Leif","contributorId":23048,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Leif","affiliations":[],"preferred":false,"id":792709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ebmeier, Susanna K.","contributorId":228984,"corporation":false,"usgs":false,"family":"Ebmeier","given":"Susanna","email":"","middleInitial":"K.","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":792710,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792711,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":792712,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70213327,"text":"70213327 - 2020 - The impact of lime additions on mercury dynamics in stream chemistry and macroinvertebrates: A comparison of watershed and direct stream addition management strategies","interactions":[],"lastModifiedDate":"2020-11-13T15:57:19.548891","indexId":"70213327","displayToPublicDate":"2020-06-15T10:14:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"The impact of lime additions on mercury dynamics in stream chemistry and macroinvertebrates: A comparison of watershed and direct stream addition management strategies","docAbstract":"<p><span>Acid deposition has declined across eastern North America and northern Europe due to reduced emissions of sulfur and nitrogen oxides. Ecosystem recovery has been slow with limited improvement in surface water chemistry. Delayed recovery has encouraged acid-neutralization strategies to accelerate recovery of impaired biological communities. Lime application has been shown to increase pH and dissolved organic carbon (DOC), which could also drive increased mobilization of mercury (Hg) to surface waters. A four-year study was conducted within Honnedaga Lake’s watershed in the Adirondack region of New York to compare the effects of watershed and direct channel lime additions on Hg in stream water and macroinvertebrates. All treatments sharply increased stream pH and DOC concentrations, but large differences in the duration of impacts were apparent. The watershed treatment resulted in multi-year increases in concentrations and loads of total Hg (150%; 390%), DOC (190%; 350%) and nutrients, whereas total Hg and DOC increased for short periods (72–96 h) after channel treatments. No response of Hg in macroinvertebrates was evident following the watershed treatment, but a potential short-term and spatially constrained increase occurred after the channel treatment. Our observations indicate that both treatment approaches mobilize Hg, but that direct channel liming mobilizes considerably less than watershed liming over any period longer than a few days. During the final study year, increased methyl Hg concentrations were observed across reference and treated streams, which may reflect an extended dry period, highlighting that climate variation may also affect Hg dynamics.</span></p>","language":"English","publisher":"Springer Nature","doi":"10.1007/s10646-020-02224-1","usgsCitation":"Millard, G., Riva-Murray, K., Burns, D., Montesdeoca, M.S., and Driscoll, C., 2020, The impact of lime additions on mercury dynamics in stream chemistry and macroinvertebrates: A comparison of watershed and direct stream addition management strategies: Ecotoxicology, v. 29, p. 1627-1643, https://doi.org/10.1007/s10646-020-02224-1.","productDescription":"17 p.","startPage":"1627","endPage":"1643","ipdsId":"IP-109979","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":436928,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C17PA0","text":"USGS data release","linkHelpText":"Methylmercury and associated data in macroinvertebrates from tributaries of Honnedaga Lake and from the Middle Branch Black River in New York."},{"id":378508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.89448547363281,\n              43.469864270218416\n            ],\n            [\n              -74.74754333496092,\n              43.469864270218416\n            ],\n            [\n              -74.74754333496092,\n              43.56496912804994\n            ],\n            [\n              -74.89448547363281,\n              43.56496912804994\n            ],\n            [\n              -74.89448547363281,\n              43.469864270218416\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Millard, Geoffrey D.","contributorId":240873,"corporation":false,"usgs":false,"family":"Millard","given":"Geoffrey D.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":168876,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":799041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montesdeoca, Mario S.","contributorId":240877,"corporation":false,"usgs":false,"family":"Montesdeoca","given":"Mario","email":"","middleInitial":"S.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Driscoll, Charles T.","contributorId":240874,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799040,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70210674,"text":"70210674 - 2020 - Dietary fat concentrations influence fatty acid assimilation patterns in Atlantic pollock (Pollachius virens)","interactions":[],"lastModifiedDate":"2020-06-16T14:58:09.969044","indexId":"70210674","displayToPublicDate":"2020-06-15T09:56:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Dietary fat concentrations influence fatty acid assimilation patterns in Atlantic pollock (Pollachius virens)","docAbstract":"A key aspect in the use of fatty acids (FA) to estimate predator diets using Quantitative FA Signature Analysis (QFASA) is the ability to account for FA assimilation through the use of calibration coefficients (CC). Here, we tested the assumption that CC are independent of dietary fat concentrations by feeding Atlantic pollock (Pollachius virens) three formulated diets with very similar FA proportions but different fat concentrations (5 – 9 % of diet) for 20 weeks. CC calculated using FA profiles of diet and triacylglycerols in pollock liver were significantly different for the three diets. To test the robustness of diet estimates to these differences, we used the CC set derived from feeding the diet with the lowest fat concentration, published prey FA profiles and realistic diet estimates of pollock to construct ‘pseudo-predators’. Application of QFASA to each pseudo-predator using the three sets of CC and the same prey FA profiles resulted in diet estimate biases of 2-fold for major prey items and ~ 5-fold for minor prey items. This work illustrates the importance of incorporating diets with fat concentrations that are similar to natural prey when conducting feeding experiments to calculate CC.","language":"English","publisher":"Royal Society Publishing","doi":"10.1098/rstb.2019.0649","usgsCitation":"Budge, S.M., Townsend, K., Lall, S.P., and Bromaghin, J.F., 2020, Dietary fat concentrations influence fatty acid assimilation patterns in Atlantic pollock (Pollachius virens): Philosophical Transactions of the Royal Society B: Biological Sciences, v. 375, no. 1804, 20190649, 9 p., https://doi.org/10.1098/rstb.2019.0649.","productDescription":"20190649, 9 p.","ipdsId":"IP-112457","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":456387,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2019.0649","text":"Publisher Index Page"},{"id":375619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"375","issue":"1804","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Budge, Suzanne M.","contributorId":92168,"corporation":false,"usgs":false,"family":"Budge","given":"Suzanne","email":"","middleInitial":"M.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":790903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Townsend, Katherine","contributorId":225363,"corporation":false,"usgs":false,"family":"Townsend","given":"Katherine","email":"","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":790904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lall, Santosh P","contributorId":225364,"corporation":false,"usgs":false,"family":"Lall","given":"Santosh","email":"","middleInitial":"P","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":790905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":790906,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70210700,"text":"70210700 - 2020 - Snow processes in mountain forests: Interception modeling for coarse-scale applications","interactions":[],"lastModifiedDate":"2020-06-18T14:54:10.16543","indexId":"70210700","displayToPublicDate":"2020-06-15T09:50:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Snow processes in mountain forests: Interception modeling for coarse-scale applications","docAbstract":"<p><span>Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of&nbsp;</span><span class=\"inline-formula\">≤10</span><span> % for both models and NRMSE of the standard deviation of&nbsp;</span><span class=\"inline-formula\">≤13</span><span> %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.</span></p>","language":"English","doi":"10.5194/hess-24-2545-2020","usgsCitation":"Helbig, N., Moeser, C.D., Teich, M., Vincent, L., Lejeune, Y., Sicart, J., and Monnet, J., 2020, Snow processes in mountain forests: Interception modeling for coarse-scale applications: Hydrology and Earth System Sciences, v. 24, p. 2545-2560, https://doi.org/10.5194/hess-24-2545-2020.","productDescription":"16 p.","startPage":"2545","endPage":"2560","ipdsId":"IP-111174","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":456397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-2545-2020","text":"Publisher Index Page"},{"id":375684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France, United States","state":"Utah","otherGeospatial":"French Alps, Rocky Mountains","volume":"24","noUsgsAuthors":false,"publicationDate":"2020-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Helbig, N. 0000-0002-8663-7306","orcid":"https://orcid.org/0000-0002-8663-7306","contributorId":225392,"corporation":false,"usgs":false,"family":"Helbig","given":"N.","email":"","affiliations":[{"id":41093,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland","active":true,"usgs":false}],"preferred":false,"id":791020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791021,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teich, M. 0000-0002-8850-9279","orcid":"https://orcid.org/0000-0002-8850-9279","contributorId":225393,"corporation":false,"usgs":false,"family":"Teich","given":"M.","email":"","affiliations":[{"id":41094,"text":"Austrian Research Centre for Forests (BFW), Innsbruck, Austria","active":true,"usgs":false}],"preferred":false,"id":791022,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vincent, L.","contributorId":225394,"corporation":false,"usgs":false,"family":"Vincent","given":"L.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791023,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lejeune, Y.","contributorId":225395,"corporation":false,"usgs":false,"family":"Lejeune","given":"Y.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791024,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sicart, J.-E.","contributorId":225396,"corporation":false,"usgs":false,"family":"Sicart","given":"J.-E.","email":"","affiliations":[{"id":41096,"text":"Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement (IGE) - UMR 5001,","active":true,"usgs":false}],"preferred":false,"id":791025,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monnet, J.-M.","contributorId":225397,"corporation":false,"usgs":false,"family":"Monnet","given":"J.-M.","email":"","affiliations":[{"id":41097,"text":"Univ. Grenoble Alpes, Irstea, LESSEM, 38000 Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791026,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70210859,"text":"70210859 - 2020 - Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","interactions":[],"lastModifiedDate":"2020-06-30T13:29:12.764279","indexId":"70210859","displayToPublicDate":"2020-06-15T08:24:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","docAbstract":"The National Park Service (NPS) manages hundreds of parks in the United States, and many contain important aquatic ecosystems and/or threatened and endangered aquatic species vulnerable to hydro-climatic change. Effective management of park resources under future hydro-climatic uncertainty requires information on both baseline conditions and the range of projected future conditions. A monthly water balance model was used to assess baseline (1981-1999) conditions and a range of projected future hydro-climatic conditions in 374 NPS parks. General circulation model outputs representing 214 future climate simulations were used to drive the model. Projected future changes in temperature (T), precipitation (P), and runoff (R) are expressed as departures from historical baselines. Climate simulations indicate increasing T in 2030 for all parks with 50th percentile simulations projecting increases of 1.67 oC or more in 50% of parks. Departures in 2030 P indicate a mix of mostly increases and some decreases, with 50th percentile simulations projecting increases in P in more than 70% of parks. Departures in R for 2030 are mostly decreases , with the 50th percentile simulations projecting decreases in R in more than 50% of parks in all seasons except winter. Hence in many parks, R is projected to decrease even when P is projected to increase because of increasing T in all NPS parks. Projected changes in future hydro-climatic conditions can also be assessed for individual parks, and Rocky Mountain National Park and Congaree National Park are used as examples.","language":"English","publisher":"MDPI","doi":"10.3390/w12061704","usgsCitation":"Battaglin, W., Hay, L., Lawrence, D.J., McCabe, G.J., and Norton, P.A., 2020, Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States: Water, v. 6, no. 12, 1704, 24 p., https://doi.org/10.3390/w12061704.","productDescription":"1704, 24 p.","ipdsId":"IP-117255","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":456399,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12061704","text":"Publisher Index Page"},{"id":376013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  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,{"id":70211015,"text":"70211015 - 2020 - Conceptualizing alternate regimes in a large floodplain-river ecosystem","interactions":[],"lastModifiedDate":"2020-07-10T13:20:01.000249","indexId":"70211015","displayToPublicDate":"2020-06-15T08:16:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Conceptualizing alternate regimes in a large floodplain-river ecosystem","docAbstract":"Regime shifts –persistent changes in the structure and function of an ecosystem - are well-documented in many ecosystems but remain poorly understood in floodplain-river ecosystems. We apply a resilience perspective to large floodplain-river ecosystems by presenting three examples of plausible sets of alternate regimes that are relevant to natural resource management interests within the Upper Mississippi River and Illinois River. These alternate regimes include: 1) a clear water and abundant vegetation regime vs. a turbid water and sparse vegetation regime in lentic, off-channel areas, 2) a diverse native fish community regime vs. an invasive-dominated fish community regime, and 3) a regime characterized by a diverse and dynamic mosaic of floodplain vegetation types vs. one characterized as a persistent invasive wet meadow monoculture. For each set of potential alternate regimes, we synthesize known or hypothesized feedback mechanisms that reinforce regimes, controlling variables that drive regime transitions, and restoration pathways. The conceptual models presented here provide a framework for synthesizing our understanding of the dynamics of this ecosystem and are relevant to other large floodplain-river ecosystems that face similar human pressures across the world. The models are currently being used to prioritize future research, test hypotheses, and inform restoration and management on the Upper Mississippi River and Illinois River. Through sharing our approach, we provide a case study in which we document an important step in operationalizing resilience concepts for the management of natural resources.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2020.110516","usgsCitation":"Bouska, K.L., Houser, J.N., De Jager, N.R., Drake, D.C., Collins, S.F., Gibson-Reniemer, C.K., and Thomsen, M.A., 2020, Conceptualizing alternate regimes in a large floodplain-river ecosystem: Journal of Environmental Management, v. 264, 110516, 15 p., https://doi.org/10.1016/j.jenvman.2020.110516.","productDescription":"110516, 15 p.","ipdsId":"IP-108847","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":376247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin, Iowa, Illinois, Missouri","otherGeospatial":"Upper Mississippi River, Illinois River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.58154296875,\n              37.020098201368114\n            ],\n            [\n              -88.22021484375,\n              37.020098201368114\n            ],\n            [\n              -88.22021484375,\n              45.27488643704891\n            ],\n            [\n              -93.58154296875,\n              45.27488643704891\n            ],\n            [\n              -93.58154296875,\n              37.020098201368114\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"264","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":792433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott F.","contributorId":172292,"corporation":false,"usgs":false,"family":"Collins","given":"Scott","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":792434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibson-Reniemer, Caniel K.","contributorId":228874,"corporation":false,"usgs":false,"family":"Gibson-Reniemer","given":"Caniel","email":"","middleInitial":"K.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":792435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thomsen, Meredith A.","contributorId":228875,"corporation":false,"usgs":false,"family":"Thomsen","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":792436,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
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