Sandbars along the Colorado River are used as campsites by river runners and hikers and are an important recreational resource within Grand Canyon National Park, Arizona. Regulation of the flow of river water through Glen Canyon Dam has reduced the amount of sediment available to be deposited as sandbars, has reduced the magnitude and frequency of flooding events, and has increased the magnitude of baseflows. This has caused widespread erosion of sandbars and has allowed native and non-native vegetation to expand on open sand. Previous studies show an overall decline in campsite area despite the use of controlled floods to rebuild sandbars. Monitoring of campsites since 1998 has shown changes in campsite area, but the factors that cause gains and losses in campsite area have not been quantified. These factors include, among others, changes in sandbar volume and slope under different dam flow regimes that include controlled floods, gullying caused by monsoonal rains, vegetation expansion, and reworking of sediment by aeolian processes.
Using 4-band aerial imagery and digital elevation models (DEMs) derived from total-station survey data, we analyzed topographic and vegetation change at 35 of 37 long-term monitoring sites (2 sites were excluded because topographic measurements do not overlap with measurements of campsite area) using data collected between 2002 and 2009 to quantify the factors affecting the size of campsite area. Over the course of the study period, there was a net loss in campsite area of 2,431 square meters (m2). We find that (1) 53 percent of the net loss was caused by topographic change associated with controlled floods and erosion of those flood deposits, (2) 47 percent of the net loss was caused by increases in vegetation cover, the majority of which occurred in high-elevation campsite area, and (3) gullying was significant at certain sites but overall was a minor factor.
Sites in critical reaches—sections of river where campsites are infrequent or where there is high demand by river runners—were subjected to more erosion and changes in sandbar slope than sites in noncritical reaches, suggesting that campsite area is less stable in those reaches. There was also a greater increase in vegetation cover at sites in noncritical reaches than at sites in critical reaches. Our results show a continuation of sandbar erosion and vegetation encroachment that has been occurring at campsites since construction of the dam.
A new campsite survey methodology using a tablet-based geographic information system (GIS) approach was also developed in an effort to map campsite area on digital orthophotographs. Using a series of repeat measurements, we evaluated the inherent uncertainty in mapping campsite area, the accuracy of the new tablet-based method, and if there is any bias between the tablet method and the total-station method that is currently used. We find that uncertainty associated with surveyor judgment while using the total-station method is about 15 percent, which is higher than a previously reported uncertainty of 10 percent. Use of the tablet method adds additional uncertainty; however, the benefits of being able to quantify factors that lead to campsite-area change in the field may outweigh the additional error. Future campsite monitoring may need to consist of a combination of total-station and orthophotograph techniques.
SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Land use changes often create in situ stress and eventual mortality in mangroves as unsuitable hydroperiods are created through tidal flow alterations. Here, we document mangrove forest and soil structural changes within transects established in tidally restricted areas on Marco Island (Collier County, Florida, USA), which has broad swaths of dead-standing or unhealthy mangroves (“ghost forests”). Transects (N = 4) were arranged to include full canopy, transitional, and open canopy (dead) forests, and compared to nearby reference forests. Aboveground and belowground carbon (C) stocks (Total C) ranged from 288 to 304 Mg C ha−1 on full canopy, transitional, and reference sites, which did not differ from each other. However, Total C was lower for dead sites (233 Mg C ha−1) dictated entirely by differences in aboveground C (live and dead trees, downed wood); no differences were found among forest condition in belowground C stocks. This belowground C has been persistent in the soil for 85 years since initial tidal restriction. Nevertheless, hydrologic rehabilitation has the potential to increase total C stocks on dead sites by 70–110 Mg C ha−1. Collapse of the soil surface by 6–8 cm just under the active root zone in chronically stressed mangroves was evident within the bulk density profiles from transitional versus dead sites, suggesting that surface elevation loss as root turnover ceases may work correlatively with chronic stressors (anoxia, P limitation) to affect rapid mortality of forests over short periods of time many years after stress initiation. Hydrologic rehabilitation of stressed or denuded mangroves must also include an understanding of how these soil processes might be re-established.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2018.06.026","usgsCitation":"Krauss, K.W., Demopoulos, A.W., Cormier, N., From, A., McClain-Counts, J.P., and Lewis, R.R., 2018, Ghost forests of Marco Island: Mangrove mortality driven by belowground soil structural shifts during tidal hydrologic alteration: Estuarine, Coastal and Shelf Science, v. 212, p. 51-62, https://doi.org/10.1016/j.ecss.2018.06.026.","productDescription":"12 p.","startPage":"51","endPage":"62","ipdsId":"IP-092474","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468560,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2018.06.026","text":"Publisher Index Page"},{"id":437817,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7736PSP","text":"USGS data release","linkHelpText":"Baseline data for a hydrological restoration of a mangrove forest near Goodland, Florida (2015 - 2017)"},{"id":437816,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7736PSP","text":"USGS data release","linkHelpText":"Baseline data for a hydrological restoration of a mangrove forest near Goodland, Florida (2015 - 2017)"},{"id":355999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Marco Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.70137405395508,\n 25.89968845861266\n ],\n [\n -81.64060592651367,\n 25.89968845861266\n ],\n [\n -81.64060592651367,\n 25.956346884929715\n ],\n [\n -81.70137405395508,\n 25.956346884929715\n ],\n [\n -81.70137405395508,\n 25.89968845861266\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"212","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc3f2e4b0f5d57878e957","contributors":{"authors":[{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":740995,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demopoulos, Amanda W.J. 0000-0003-2096-4694 ademopoulos@usgs.gov","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":196216,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","email":"ademopoulos@usgs.gov","middleInitial":"W.J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":740996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cormier, Nicole 0000-0003-2453-9900 cormiern@usgs.gov","orcid":"https://orcid.org/0000-0003-2453-9900","contributorId":4262,"corporation":false,"usgs":true,"family":"Cormier","given":"Nicole","email":"cormiern@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":740997,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"From, Andrew 0000-0002-6543-2627 froma@usgs.gov","orcid":"https://orcid.org/0000-0002-6543-2627","contributorId":169668,"corporation":false,"usgs":true,"family":"From","given":"Andrew","email":"froma@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":740998,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClain-Counts, Jennifer P. 0000-0002-3383-5472 jmcclaincounts@usgs.gov","orcid":"https://orcid.org/0000-0002-3383-5472","contributorId":200713,"corporation":false,"usgs":true,"family":"McClain-Counts","given":"Jennifer","email":"jmcclaincounts@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":740999,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewis, Roy R. III","contributorId":206546,"corporation":false,"usgs":false,"family":"Lewis","given":"Roy","suffix":"III","email":"","middleInitial":"R.","affiliations":[{"id":37338,"text":"Coastal Resources Group, Inc.","active":true,"usgs":false}],"preferred":false,"id":741000,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227672,"text":"70227672 - 2018 - Evaluation of vegetation-fire dynamics in the Okefenokee National Wildlife Refuge, Georgia, USA, with a Bayesian belief network","interactions":[],"lastModifiedDate":"2022-01-26T16:13:33.03348","indexId":"70227672","displayToPublicDate":"2018-07-28T10:09:09","publicationYear":"2018","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":"Evaluation of vegetation-fire dynamics in the Okefenokee National Wildlife Refuge, Georgia, USA, with a Bayesian belief network","docAbstract":"Vegetation response to wildfire has been studied extensively in upland ecosystems, but fire effects on temperate wetlands are less understood. We evaluated vegetation response to extensive wildfire in wetlands of Okefenokee National Wildlife Refuge (ONWR), USA, with a spatially explicit Bayesian belief network model informed with data recorded during 1990–2012. We assessed model accuracy and effects of fire frequency on vegetation composition with predictive scenarios of fire absence or a fire return interval (FRI) every 5 or 10 years during 2012–2032. In fire absence, shrubs increased 100%, primarily in the northern half of the Refuge, while the herbaceous class that was widespread in 2012 was eliminated. Areas dominated by forest during the past ~65 years were maintained with the 5- and 10-year FRI. Herbaceous-dominated areas maintained with the 5-year FRI decreased (90%) with the 10-year FRI. Shrub coverage increased with fire (17%, 5-year FRI; 20%, 10-year FRI), while scrub/shrub decreased (12%; 5-year FRI) or increased (6%; 10-year FRI). A 5-year FRI during conditions promoting severe fire may maintain the distribution of herbaceous and forested areas that followed an extensive drought and fires in 2011, and may limit scrub/shrub expansion that previously occurred with longer FRIs in the ONWR.
","language":"English","publisher":"Springer Link","doi":"10.1007/s13157-018-1033-6","usgsCitation":"Loftin, C., Guyette, M., and Wetzel, P., 2018, Evaluation of vegetation-fire dynamics in the Okefenokee National Wildlife Refuge, Georgia, USA, with a Bayesian belief network: Wetlands, v. 38, p. 819-834, https://doi.org/10.1007/s13157-018-1033-6.","productDescription":"16 p.","startPage":"819","endPage":"834","ipdsId":"IP-059938","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":394873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Okefenokee National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.5567626953125,\n 30.552800413453546\n ],\n [\n -82.08984375,\n 30.552800413453546\n ],\n [\n -82.08984375,\n 31.07\n ],\n [\n -82.5567626953125,\n 31.07\n ],\n [\n -82.5567626953125,\n 30.552800413453546\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationDate":"2018-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guyette, Margaret Q.","contributorId":272181,"corporation":false,"usgs":false,"family":"Guyette","given":"Margaret Q.","affiliations":[{"id":56366,"text":"St. Johns River Management District, Bureau of Water Resource Information, Palatka, FL","active":true,"usgs":false}],"preferred":false,"id":831680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wetzel, Paul R.","contributorId":272182,"corporation":false,"usgs":false,"family":"Wetzel","given":"Paul R.","affiliations":[{"id":56367,"text":"Center for the Environment, Ecological Design, and Sustainability, Smith College, Northampton, MA","active":true,"usgs":false}],"preferred":false,"id":831681,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198300,"text":"70198300 - 2018 - Host feeding ecology and trophic position significantly influence isotopic discrimination between a generalist ectoparasite and its hosts: Implications for parasite-host trophic studies","interactions":[],"lastModifiedDate":"2018-07-30T09:49:12","indexId":"70198300","displayToPublicDate":"2018-07-27T20:11:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5453,"text":"Food Webs","active":true,"publicationSubtype":{"id":10}},"title":"Host feeding ecology and trophic position significantly influence isotopic discrimination between a generalist ectoparasite and its hosts: Implications for parasite-host trophic studies","docAbstract":"Despite being one of the most prevalent forms of consumerism in ecological communities, parasitism has largely been excluded from food-web models. Stable isotope analysis of consumers and their diets has been widely used in the study of food webs for decades. However, the amount of information regarding parasite stable isotope ecology is limited, restricting the ability of ecologists to use stable isotope analysis to study parasites in food webs. This study took advantage of distinct differences in the feeding ecology and trophic position of different species of fish known to host the same common micropredatory gnathiid isopod to study the effects of host stable isotope ecology on that of the associated micropredator. Blood engorged juvenile gnathiids were in most cases indistinguishable from their hosts' blood, but significant isotope discrimination was observed for adults. Males were generally lower in δ13C and δ15N than host blood whereas host-specific isotopic discrimination for females varied among the different host species. Model predictions indicated that there is a significant effect of host blood isotope ratios on the rate of carbon and nitrogen isotopic discrimination between gnathiids and their host’s blood. As such, general differences in the feeding ecology and trophic positions of the different host species were reflected in their associated gnathiids, indicating that stable isotope analysis of gnathiids can provide significant details concerning previous hosts. The results presented herein have significant implications for how stable isotopes may be used as a tool to study the trophic dynamics and feeding ecology of gnathiids.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2018.e00092","usgsCitation":"Jenkins, W.G., Demopoulos, A.W., and Sikkel, P.C., 2018, Host feeding ecology and trophic position significantly influence isotopic discrimination between a generalist ectoparasite and its hosts: Implications for parasite-host trophic studies: Food Webs, v. 16, Article e00092, https://doi.org/10.1016/j.fooweb.2018.e00092.","productDescription":"Article e00092","ipdsId":"IP-095826","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468561,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fooweb.2018.e00092","text":"Publisher Index Page"},{"id":437818,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7416WBQ","text":"USGS data release","linkHelpText":"Host Feeding Ecology and Trophic Position Significantly influence Isotopic discrimination between a Generalist Ectoparasite and its hosts: Implications for parasite host trophic studies"},{"id":355993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc3f2e4b0f5d57878e95b","contributors":{"authors":[{"text":"Jenkins, William G. 0000-0001-5133-2628","orcid":"https://orcid.org/0000-0001-5133-2628","contributorId":200936,"corporation":false,"usgs":false,"family":"Jenkins","given":"William","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":740949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demopoulos, Amanda W.J. 0000-0003-2096-4694 ademopoulos@usgs.gov","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":196216,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","email":"ademopoulos@usgs.gov","middleInitial":"W.J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":740950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sikkel, Paul C.","contributorId":140403,"corporation":false,"usgs":false,"family":"Sikkel","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":13476,"text":"Arkansas State University, State University, AR","active":true,"usgs":false}],"preferred":false,"id":740951,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197360,"text":"ds1088 - 2018 - Detections of current-use pesticides at 12 surface water sites in California during a 2-year period beginning in 2015","interactions":[],"lastModifiedDate":"2018-07-30T10:37:03","indexId":"ds1088","displayToPublicDate":"2018-07-27T13:39:52","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1088","title":"Detections of current-use pesticides at 12 surface water sites in California during a 2-year period beginning in 2015","docAbstract":"Surface water samples were collected by the U.S. Geological Survey and multiple cooperators during base flow/irrigation runoff and storm runoff conditions from 12 sites throughout California, over 2 consecutive years beginning in April 2015, from both urban and agriculturally dominated watersheds. Water samples were analyzed by gas chromatography/mass spectrometry and liquid chromatrography/tandem mass spectrometry for a suite of 157 pesticides and degradates. Suspended sediments associated with these water samples were analyzed by gas chromatography/mass spectrometry for a suite of 131 pesticides and degradates. Overall, 85 pesticides and degradates were detected in the water: 32 fungicides, 25 herbicides, 27 insecticides, and 1 synergist. In the suspended sediment, 29 pesticides were detected: 9 fungicides, 10 herbicides, and 10 insecticides. Sixteen pesticides (bifenthrin, carbendazim, chlorpyrifos, clothianidin, diazinon, diuron, fenpyroximate, fipronil, fipronil sulfone, fluopicolide, imidacloprid, metolachlor, novaluron, oxyflurofen, permethrin, and simazine) were detected in the water at concentrations that were above at least one aquatic life benchmark value as defined by the U.S Environmental Protection Agency.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1088","collaboration":"Prepared in cooperation with the Central Valley Regional Water Quality Control Board","usgsCitation":"Sanders, C.J., Orlando, J.L., and Hladik, M.L., 2018, Detections of current-use pesticides at 12 surface water sites in California during a 2-year period beginning in 2015: U.S. Geological Survey Data Series 1088, 40 p., https://doi.org/10.3133/ds1088.","productDescription":"Report: viii; 40 p.; 2 Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086505","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":355893,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1088/ds1088.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 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\"}}]}","contact":"This 121-day experiment evaluated the rearing performance of juvenile brown trout (Salmo trutta; initial weight 56.1±1.6 g, length 167.2±1.4 mm, mean ±SE) fed one of four isonitrogenous and isocaloric diets (46% protein, 16% lipid). Fishmeal, the primary protein source for the reference diet, was compared to diets where bioprocessed soybean meal directly replaced approximately 60, 80, or 100% of the dietary fishmeal. At the end of the experiment there were no significant differences in gain, percent gain, food fed, feed conversion ratio, nor specific growth rate among any of the dietary treatments. Gains for the 0, 60, 80, and 100% treatments were 304.6 ± 40.3 g, 215.4±82.6 g, 199.0±39.4 g, and 218.1±37.0 g, respectively. There were also no significant differences observed in intestinal morphology, relative fin lengths, viscerosomatic index, hepatosomatic index, or splenosomatic index among the dietary treatments. Based on these results, bioprocessed soybean meal may be able to replace 100% of the dietary fishmeal in juvenile brown trout diets without any deleterious effects.
","language":"English","publisher":"Gavin Publishers","usgsCitation":"Voorhees, J.M., Barnes, M., Chipps, S.R., and Browne, M., 2018, Direct substitution of fishmeal with bioprocessed soybean meal in brown trout diets: Journal of Fisheries and Aquaculture Development, JFAD-143, 11 p.","productDescription":"JFAD-143, 11 p","ipdsId":"IP-097700","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395636,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.gavinpublishers.com/article/view/direct-substitution-of-fishmeal-with-bioprocessed-soybean-meal-in-brown-trout-diets"},{"id":395638,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Voorhees, Jill M.","contributorId":275085,"corporation":false,"usgs":false,"family":"Voorhees","given":"Jill","email":"","middleInitial":"M.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":833640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Michael","contributorId":275086,"corporation":false,"usgs":false,"family":"Barnes","given":"Michael","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":833641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Browne, Michael","contributorId":178752,"corporation":false,"usgs":false,"family":"Browne","given":"Michael","email":"","affiliations":[],"preferred":false,"id":833642,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198295,"text":"70198295 - 2018 - Using reverse-time egg transport analysis for predicting Asian Carp spawning grounds in the Illinois River","interactions":[],"lastModifiedDate":"2018-07-27T11:13:22","indexId":"70198295","displayToPublicDate":"2018-07-27T11:13:19","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Using reverse-time egg transport analysis for predicting Asian Carp spawning grounds in the Illinois River","docAbstract":"Identifying spawning grounds of Asian carp is important for determining the reproductive front of invasive populations. Ichthyoplankton monitoring along the Illinois Waterway (IWW) has provided information on abundances of Asian carp eggs in the IWW's navigation pools. Post-fertilization times derived from egg development stages and water temperatures can be used to estimate spawning times of Asian carp eggs, but estimating how far these eggs have drifted requires information on river hydraulics. A Fluvial Egg Drift Simulator (FluEgg) program was designed to predict the drift of Asian carp eggs in the riverine environment with egg growth considered. This paper presents a reverse-time particle tracking (RTPT) algorithm for back-casting the spawning location of eggs from their collection site. The RTPT algorithm was implemented as a module in FluEgg. The new version of FluEgg was coupled with an unsteady hydrodynamic model of the IWW to predict the spawning locations for 530 eggs that were collected in June 2015. The results indicate that tailwater sections below the Locks and Dams (L&Ds) in each navigation pool appear to be preferred spawning locations for Silver Carp. From the data analyzed, the most upstream spawning location for the June 2015 spawning period was in the upper Marseilles navigation pool, downstream of the Dresden Island L&D. The RTPT algorithm can efficiently estimate spawning locations for multiple egg samples.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.06.003","usgsCitation":"Zhu, Z., Soong, D., Garcia, T., Behrouz, M.S., Butler, S.E., Murphy, E.A., Diana, M.J., Duncker, J.J., and Wahl, D.H., 2018, Using reverse-time egg transport analysis for predicting Asian Carp spawning grounds in the Illinois River: Ecological Modelling, v. 384, p. 53-62, https://doi.org/10.1016/j.ecolmodel.2018.06.003.","productDescription":"10 p.","startPage":"53","endPage":"62","ipdsId":"IP-093404","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":355987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.7415771484375,\n 38.839707613545144\n ],\n [\n -87.506103515625,\n 38.839707613545144\n ],\n [\n -87.506103515625,\n 41.97174336327968\n ],\n [\n -90.7415771484375,\n 41.97174336327968\n ],\n [\n -90.7415771484375,\n 38.839707613545144\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"384","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc3f3e4b0f5d57878e95f","contributors":{"authors":[{"text":"Zhu, Zhenduo","contributorId":206524,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","email":"","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":740921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Tatiana","contributorId":206525,"corporation":false,"usgs":false,"family":"Garcia","given":"Tatiana","affiliations":[{"id":37335,"text":"Optimatics","active":true,"usgs":false}],"preferred":false,"id":740922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Behrouz, Mina Shahed","contributorId":206526,"corporation":false,"usgs":false,"family":"Behrouz","given":"Mina","email":"","middleInitial":"Shahed","affiliations":[],"preferred":false,"id":740923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Steven E.","contributorId":206527,"corporation":false,"usgs":false,"family":"Butler","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":740924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Elizabeth A. 0000-0002-8939-7678 emurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-8939-7678","contributorId":196368,"corporation":false,"usgs":true,"family":"Murphy","given":"Elizabeth","email":"emurphy@usgs.gov","middleInitial":"A.","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":740925,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Diana, Matthew J.","contributorId":206528,"corporation":false,"usgs":false,"family":"Diana","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":740927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"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":740926,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wahl, David H.","contributorId":206529,"corporation":false,"usgs":false,"family":"Wahl","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":740928,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70194500,"text":"sir20175137 - 2018 - Rating stability, and frequency and magnitude of shifts, for streamgages in Virginia through water year 2013","interactions":[],"lastModifiedDate":"2018-07-27T10:16:52","indexId":"sir20175137","displayToPublicDate":"2018-07-26T16:45:00","publicationYear":"2018","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":"2017-5137","title":"Rating stability, and frequency and magnitude of shifts, for streamgages in Virginia through water year 2013","docAbstract":"The U.S. Geological Survey, in cooperation with the Virginia Department of Environmental Quality, has quantified several measures of rating stability and the frequency and magnitude of changes to ratings through time for 174 real-time continuous streamgages active in Virginia as of September 30, 2013. Generalized additive models (GAMs) were fitted through all available flow measurements for all the streamgages in Virginia’s real-time network as of September 30, 2013, with at least 20 flow measurements with positive flow values. For each measurement with a positive flow value, residuals from the GAM curve were calculated. Time series of these residuals were used to identify major changes to the control (the stream feature or features which control the relation between stage and flow); the measurements in the periods of equilibrium between major changes were assigned to rating families. Of the 127 rating families that were identified as being distinct at sites, documented explanations were found for 67 of them. The most common reasons for the control to change enough to warrant a new rating family are moving the streamgage (28 times), floods (26 times), and construction activities (13 times). Provisional flow data from any streamgage that has recently experienced a major flood, regardless of historical stability, are more uncertain than usual until post-flood evidence emerges that the rating is stable, or if the rating has changed, until it is known to be well defined.
A direct comparison between provisional flow data (those data originally displayed on the web in near-real time) and flow data approved for publication following subsequent flow measurements and review could not be made because provisional flow data have not been archived. As a substitute, alternative flow (AltFlow) tables were constructed for periods with complete records of shifts and ratings. Alternate flows consist of $Q$same, the flow value from the shifted rating table used to compute the daily flow value at the time of the most recent flow measurement that corresponds to the gage height of each day’s daily flow value, and $Q$prev, the flow value from the shifted rating table in effect at the time of the previous flow measurement that corresponds to the gage height of each day’s daily flow value.
Several metrics that summarize AltFlow tables were computed and evaluated; particular importance was given to how well the metrics agreed with the descriptive stability class developed from interviews with hydrographers. Of these stability metrics, at least four were determined to be meaningful and to represent different aspects of control stability that might be relevant to water managers: total root mean square error between log-transformed $Q$prev and $Q$same, percentage of days when the difference between $Q$prev and $Q$same is greater than (>) $Q$same, the sum of absolute AltFlow error divided by total flow, and percentage of days with zero difference between $Q$prev and $Q$same.
Three other meaningful metrics of control stability or provisional flow-data quality were computed: R2 (coefficient of determination) of GAMs from the flow measurements, percentage of total estimated days, and percentage of estimated days in the winter. Correlations among metrics varied, indicating they responded to different aspects of control stability. Relations among the various stability metrics and quantitative basin and site characteristics were weak. Although quantitative relations between stability metrics and basin and site characteristics were all weak, some common patterns still emerged. Controls and ratings on large streams [>500-square mile (mi2) drainage area] and at high elevations (>1,000 feet) were more likely to be stable than controls and ratings on small streams (less than (<) 100-mi2 drainage area) and at low elevations (<500 feet). There were exceptions to both generalizations, and streamgages that were intermediate in both characteristics varied widely in stability.
Typical timing of record computation changed during water years 1991–2013. From 1991 through 2001, the median number of days between the start date of the shift and the date it was created fluctuated between about 240 and about 300 days and decreased by about 4 months from 2001 to 2002. Only in 2012 and 2013 did one-half of the shifts have a delay of about 60 days between start date and final modification.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175137","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"Messinger, T., and Burgholzer, R.W., 2018, Rating stability, and frequency and magnitude of shifts, for streamgages in Virginia through water year 2013: U.S. Geological Survey Scientific Investigations Report 2017–5137, 91 p., https://doi.org/10.3133/sir20175137. ","productDescription":"Report: viii, 91 p.; 5 Plates: 24.0 x 30.0 inches; 5 Tables; Appendixes; Data Release; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084661","costCenters":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":437819,"rank":19,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72R3PXB","text":"USGS data release","linkHelpText":"Ratings and estimated provisional streamflow for streamgages in Virginia, water years 1991 through 2013"},{"id":355791,"rank":11,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate05.pdf","text":"Plate 5","size":"25 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Scatterplot matrixes of stage-flow relation stability metrics in relation to selected basin characteristics for A, 64 plots and, B, 88 plots, for streamgages in Virginia, water years 1991–2013"},{"id":355792,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table08.xlsx","text":"Table 8","size":"57.6 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Hydrographer descriptions of controls for streamgages in Virginia at which real-time flow data were collected as of September 30, 2013, and descriptions of associated features of interest "},{"id":355783,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix01a.pdf","text":"Appendix 1a","size":"3.60 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Plots for real-time streamgages in Virginia (All available measurements)"},{"id":355788,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate02.pdf","text":"Plate 2","size":"212 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Patterns of variation in, A, long-term stage-flow relations at selected streamgages in Virginia, B, residual of flow and date with a LOESS smoother, and C, residual of flow and Julian date with a LOESS smoother"},{"id":355787,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate01.pdf","text":"Plate 1","size":"230 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- A, Long-term stage-flow relations at selected streamgages in Virginia with a generalized additive model smoother, B, seasonal variation of relations between residual of flow and date with a LOESS smoother, and C, seasonal variation of relations between residual of flow and Julian date with a LOESS smoother"},{"id":355789,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate03.pdf","text":"Plate 3","size":"360 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- AltFlow hydrographs for four selected streamgages in Virginia, water years 1991–2013"},{"id":355786,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix04.pdf","text":"Appendix 4","size":"24.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Plots showing daily time series of $Q$same and $Q$prev"},{"id":355790,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate04.pdf","text":"Plate 4","size":"20.6 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Matrix of proposed stability metrics developed using daily flow, shifted ratings, and flow measurements for streamgages in Virginia, water years 1991–2013"},{"id":355793,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table20.xlsx","text":"Table 20","size":"50.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary of selected rating stability metrics for streamgages in Virginia, water years 1991–2013"},{"id":355794,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table24.xlsx","text":"Table 24","size":"482 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Average of daily percent errors for flow in Virginia streams, by month and percentile range"},{"id":355796,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table26.xlsx","text":"Table 26","size":"228 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Proportion of days when the difference between Qprev and Qsame was zero at streamgages in Virginia, by month, 1990–2013"},{"id":355797,"rank":17,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F72R3PXB","text":"USGS data release","description":"USGS data release","linkHelpText":"Ratings and estimated provisional streamflow for streamgages in Virginia, water years 1991 through 2013"},{"id":355841,"rank":18,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_readme.txt","size":"8 MB","linkFileType":{"id":2,"text":"txt"}},{"id":355784,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix01b.pdf","text":"Appendix 1b","size":"5.11 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Plots for real-time streamgages in Virginia (Measurements grouped by rating families )"},{"id":355782,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137.pdf","text":"Report","size":"3.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5137"},{"id":355795,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table25.xlsx","text":"Table 25","size":"373 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Root-mean-square error of AltFlows and number of days of record for streamgages on Virginia streams by month and percentile of monthly flow, water years 1991–2013 "},{"id":355781,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5137/coverthb.jpg"},{"id":355785,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix03.xlsx","text":"Appendix 3","size":"640 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- UNIX shell scripts used to develop AltFlow record"}],"country":"United 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\"}}]}","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
A recent study posits that much of the 240-m-deep inner gorge of Grand Canyon was carved between 500 and 400 ka via passage of a migrating knickzone with incision rates of ~1600 m/Ma during that time period; this was based on dating of a ca. 500 ka travertine deposit perched on the rim of the inner gorge, near Hermit Rapid, and a ca. 400 ka travertine drape that extends to within 60 m of river level nearby. However, a new U/Th age of 517 ± 13 ka on the same travertine drape challenges this model of a migrating knickzone and punctuated incision. The presence of ca. 500 ka travertine just 95 m above river level requires that most of the inner gorge was carved before that time. The resulting maximum bedrock incision rate of 230 m/Ma is consistent with independent results from sites up and downstream and with models for semi-steady Quaternary bedrock incision and dispels problems with the transient incision model. Downstream from the Hermit Rapid area, dikes present on both sides of the canyon have been used to support the migrating knickzone model. We report a new 40Ar/39Ar age of 517 ± 16 ka on one of these dikes, but argue that they don’t necessarily gauge incision.
Field observations suggest that the discontinuous travertine deposits, near Hermit Rapid, were deposited by springs that emanated from the Redwall-Muav aquifer, mantled the Tonto Platform, and locally built downwards into the inner gorge and tributary canyons. The range of U/Th ages from ca. 10–600 ka suggests these were long-lived spring systems. The travertine cements predominantly angular to subrounded locally derived clasts consistent with deposition on hillslopes and by tributaries. Well-rounded gravels are exceedingly rare but have been used to suggest that the Colorado River was at the rim of the inner gorge at ca. 500 ka. No exotic Colorado River clasts, derived from the area outside of Grand Canyon, were observed by us. In-place gravel from the main stem or tributaries (e.g., from paleo–Hermit Creek) within the travertine deposits can be reconciled with existing data, if: (1) travertine was deposited at ca. 2 Ma, which is approximately when the steady incision model suggests the inner gorge began to incise; (2) a 500 ka lava dam in the Lava Falls Rapid area, 140 km downstream, backed water and sediment up to the rim of the inner gorge in the Hermit area; or (3) regional climate-driven aggradation took place at 500 ka.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01562.1","usgsCitation":"Crow, R.S., Karlstrom, K.E., Crossey, L.J., Polyak, V., Asmerom, Y., and McIntosh, W.C., 2018, Carving Grand Canyon’s inner gorge: A test of steady incision versus rapid knickzone migration: Geosphere, v. 14, no. 5, p. 1-17, https://doi.org/10.1130/GES01562.1.","productDescription":"17 p.","startPage":"1","endPage":"17","ipdsId":"IP-084123","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":468562,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01562.1","text":"Publisher Index Page"},{"id":356636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","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 -114.14794921875,\n 35.572448615622804\n ],\n [\n -111.67053222656249,\n 35.572448615622804\n ],\n [\n -111.67053222656249,\n 37.21283151445594\n ],\n [\n -114.14794921875,\n 37.21283151445594\n ],\n [\n -114.14794921875,\n 35.572448615622804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-26","publicationStatus":"PW","scienceBaseUri":"5b98a297e4b0702d0e842f85","contributors":{"authors":[{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":742965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlstrom, Karl E.","contributorId":75597,"corporation":false,"usgs":true,"family":"Karlstrom","given":"Karl","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":742966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crossey, Laura J.","contributorId":56265,"corporation":false,"usgs":true,"family":"Crossey","given":"Laura","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":742967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Polyak, Victor","contributorId":207162,"corporation":false,"usgs":false,"family":"Polyak","given":"Victor","email":"","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":742968,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asmerom, Yemane","contributorId":146278,"corporation":false,"usgs":false,"family":"Asmerom","given":"Yemane","email":"","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":742969,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McIntosh, William C.","contributorId":191163,"corporation":false,"usgs":false,"family":"McIntosh","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":742970,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202404,"text":"70202404 - 2018 - Stratifying ocean sampling globally and with depth to account for environmental variability","interactions":[],"lastModifiedDate":"2019-02-27T16:01:08","indexId":"70202404","displayToPublicDate":"2018-07-26T16:00:59","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Stratifying ocean sampling globally and with depth to account for environmental variability","docAbstract":"With increasing depth, the ocean is less sampled for physical, chemical and biological variables. Using the Global Marine Environmental Datasets (GMED) and Ecological Marine Units (EMUs), we show that spatial variation in environmental variables decreases with depth. This is also the case over temporal scales because seasonal change, surface weather conditions, and biological activity are highest in shallow depths. A stratified sampling approach to ocean sampling is therefore proposed whereby deeper environments, both pelagic and benthic, would be sampled with relatively lower spatial and temporal resolutions. Sampling should combine measurements of physical and chemical parameters with biological species distributions, even though species identification is difficult to automate. Species distribution data are essential to infer ecosystem structure and function from environmental data. We conclude that a globally comprehensive, stratification-based ocean sampling program would be both scientifically justifiable and cost-effective.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-018-29419-1","usgsCitation":"Costello, M.J., Basher, Z., Sayre, R., Breyer, S.P., and Wright, D.J., 2018, Stratifying ocean sampling globally and with depth to account for environmental variability: Scientific Reports, v. 8, p. 1-9, https://doi.org/10.1038/s41598-018-29419-1.","productDescription":"Article number: 11259; 9 p.","startPage":"1","endPage":"9","ipdsId":"IP-092411","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":468563,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-29419-1","text":"Publisher Index Page"},{"id":361597,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Costello, Mark John","contributorId":213684,"corporation":false,"usgs":false,"family":"Costello","given":"Mark","email":"","middleInitial":"John","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":758273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Basher, Zeenatul 0000-0002-6439-8324 zbasher@usgs.gov","orcid":"https://orcid.org/0000-0002-6439-8324","contributorId":208142,"corporation":false,"usgs":false,"family":"Basher","given":"Zeenatul","email":"zbasher@usgs.gov","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":758274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":213683,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":758272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breyer, Sean P.","contributorId":191634,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":758275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Dawn J.","contributorId":191639,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","email":"","middleInitial":"J.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":758276,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197846,"text":"ofr20181102 - 2018 - A method for determining avian influenza virus hemagglutinin and neuraminidase subtype association","interactions":[],"lastModifiedDate":"2024-03-04T19:09:11.886598","indexId":"ofr20181102","displayToPublicDate":"2018-07-26T14:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1102","title":"A method for determining avian influenza virus hemagglutinin and neuraminidase subtype association","docAbstract":"Methods for grouping specific avian influenza virus (AIV) hemagglutinin (HA) and neuraminidase (NA) subtype reverse-transcription polymerase chain reaction (RT-PCR) products into HA:NA subtypes when egg incubation is technically not feasible were evaluated. These approaches were adopted for use as post hoc methods after melt curve analysis. The methods are based on ratios obtained from amplicon copy count and amplicon molarity and were founded on the premise that infectious particles contain an equal copy count of single-stranded ribonucleic acid segments that encode HA or NA, and thus subtype-specific amplicons from a single AIV isolate should yield a theoretical HA:NA ratio of 1. Single and mixed HA:NA AIV subtype samples were evaluated to determine whether the calculated HA:NA ratios would approach the theoretical value. With these samples, preference was given to the molarity methods to better define and correct for the effects of multiple potential amplicons in the amplification mix. Further, the molarity method was used to evaluate pond sediment spiked with intact virus of known HA:NA subtype to determine whether the method is sufficiently robust to be used with complex samples, such as those acquired from waterfowl habitat. This was a proof-of-concept study intended to guide future methods development. The methods here are not meant to be applied in any other context.
From the analysis of fully characterized isolates of North American AIV, the HA:NA molarity-based ratios were found to be 1.63 ± 0.75 (mean ± standard deviation) when corrected for the difference in amplification strength and the production of multiple amplicons in some reactions using equations developed in this study. Copy count HA:NA ratios, obtained from HA and NA subtype (RT-qPCR), were 1.146 ± 0.124 (mean ± standard deviation) when corrected for amplification efficiency. Correct associations of HA:NA subtype sample composition were made with mixed samples containing 1 HA and 2 NA, and 2 HA and 2 NA. When spiked pond sediment was evaluated, the molar ratio obtained for the H4 and N6 identified in the sample was 1.28 with correction and 1.14 without correction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181102","usgsCitation":"Ottinger, C.A., Iwanowicz, D.D., Iwanowicz, L.R., Adams, C.R., Sanders, L.R., and Densmore, C.L., 2018, A method for determining avian influenza virus hemagglutinin and neuraminidase subtype association: U.S. Geological Survey Open-File Report 2018–1102, 15 p., https://doi.org/10.3133/ofr20181102.","productDescription":"v, 15 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-096308","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":355970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1102/ofr20181102.pdf","text":"Report","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1102"},{"id":355969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1102/coverthb.jpg"}],"contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Marbled murrelets (Brachyramphus marmoratus) have been listed as “Endangered” by the State of California and “Threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management and evaluation of conservation efficacy, and ultimately to meet Federal- and State-mandated recovery efforts for this species. During June–August 2017, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1999–2016), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (central California—San Francisco Bay to Monterey Bay). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 189 detections of 321 individuals observed on nine unique surveys. The abundance estimated for the entire study area using all surveys in 2017 was 530 birds (95-percent confidence interval, 384–732 birds). Estimated abundance from 2017 is comparable to most prior years of study, except for 2008 and 2015, which had anomalously low abundances. We estimated productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) in 2017 using three detections of three individuals observed in six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.022 ± 0.014 standard error. We created a synthesized database of all marbled murrelet survey data from 1999 to 2017 to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates. Future modifications of survey design could help reduce variance in abundance estimation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1093","usgsCitation":"Felis, J.J., Adams, J., and Kelsey, E.C.., 2018, Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2017 breeding season: U.S. Geological Survey Data Series 1093, 12 p., https://doi.org/10.3133/ds1093.","productDescription":"Report: iv, 12 p.; Data release","onlineOnly":"Y","ipdsId":"IP-098753","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":355984,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01RW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Annual marbled murrelet abundance and productivity surveys off central California (Zone 6), 1999-2017"},{"id":355983,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1093/ds1093.pdf","text":"Report","size":"749 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1093"},{"id":355982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1093/coverthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.53189086914061,\n 36.92464553397128\n ],\n [\n -121.93450927734375,\n 36.92464553397128\n ],\n [\n -121.93450927734375,\n 37.528242717975054\n ],\n [\n -122.53189086914061,\n 37.528242717975054\n ],\n [\n -122.53189086914061,\n 36.92464553397128\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive
Modoc Hall, Room 4004
Sacramento, California 95819
The Community for Data Integration (CDI) is a group that helps members grow their expertise on all aspects of working with scientific data. The CDI’s activities advance data and information integration capabilities in the U.S. Geological Survey and in the wider Earth and biological sciences. This annual report describes the presentations, activities, collaboration areas, workshop, and other CDI-sponsored events in fiscal year 2017. The report also describes the objectives of the 11 CDI-funded projects in fiscal year 2017. The report shows how the CDI activities fulfill the strategic objective of the U.S. Geological Survey’s Core Science Systems Mission Area to develop a workplace model for interdisciplinary science.
Core Science Analytics, Synthesis, and Library
U.S. Geological Survey
108 National Center
12201 Sunrise Valley Drive,
Reston, VA 20192
Phosphate mining in southeastern Idaho has been an important economic driver for the region and State for over 100 years, but weathering of mining waste rock has also released selenium into the Blackfoot River. This report analyzes and presents data from three separate but complementary studies monitoring selenium in streams in the region. The U.S. Geological Survey (USGS), in cooperation with the Bureau of Land Management, has been collecting streamflow and water-quality samples year-round on the Blackfoot River above reservoir near Henry, Idaho, (USGS streamgage 13063000) since 2001. Over the same period, the Idaho Department of Environmental Quality (IDEQ) has collected streamflow and water-quality samples from the Blackfoot River and tributaries during spring runoff. Data collected from 2001 to 2012 during these two studies were analyzed previously. This report extends the analysis using new data collected through 2016. This report also presents the results of a joint USGS and IDEQ seepage study conducted in June 2016 in the Blackfoot River near Dry Valley. Although limited in scope, this study explored the hypothesis that unaccounted selenium loading (loading in excess of tributary inputs) in this reach could be caused by groundwater inflow.
USGS dissolved selenium concentration data from streamgage 13063000 on the Blackfoot River and IDEQ data from the mainstem and mining-affected tributaries are highest shortly after peak runoff and correlate with streamflow magnitude. Although earlier analyses indicated increasing selenium concentrations from 2001 to 2012, this study shows that runoff and baseflow dissolved selenium concentrations increased and then decreased during 2001–16. High median runoff concentrations from 2005 through 2011 are associated with high snowpack and streamflow. This result suggests that more snowmelt moving through selenium-bearing waste rock leads to increased instream concentrations. The time lag between peak runoff and then peak selenium concentrations suggests that selenium mobilization may occur as snowmelt percolates through waste rock rather than by faster surface runoff. However, variability in local snow accumulation and snowmelt conditions likely affects interannual variability in selenium concentrations in the mainstem Blackfoot River and tributaries.
In contrast to runoff selenium concentrations, median baseflow (August to October) dissolved selenium concentrations were highest from 2009 to 2013. Aquatic plant senescence and release of selenium is an unlikely explanation for this trend because plants are still growing during this time of year. In addition, this trend is observed during and shortly after the observed period of high snowpack. Thus, increased baseflow selenium concentrations suggest that increased selenium loading to alluvial groundwater may occur during periods of high snowmelt and manifest in later years as higher instream concentrations during baseflows when the majority of streamflow is attributable to groundwater gains.
Runoff-period streamflow and selenium loads were calculated for the tributaries and mainstem Blackfoot River. Selenium loads vary from year to year with mainstem loads greater than the total tributary contributions in some years and less than tributary contributions in other years. In general, East Mill Creek usually accounted for the largest proportion of the total Blackfoot River load, and unaccounted loads (loads in excess of tributary inputs) often occurred in the vicinity of Spring Creek and Dry Valley. The latter observation led the USGS and IDEQ to conduct a seepage study to further investigate groundwater and selenium loading to the Blackfoot River near Dry Valley.
The seepage study results show consistent albeit small unaccounted increases in streamflow and dissolved selenium load in the Blackfoot River near Dry Valley. Field observation of a spring to the north of the river and independent groundwater monitoring data from Dry Valley to the south of the river suggest that alluvial groundwater may discharge to the river from both sides. However, the small unaccounted selenium load measured in the June 2016 study relative to loads measured during runoff suggest that groundwater loading in this reach may occur primarily during runoff. An improved understanding of alluvial groundwater extent, gradient, hydraulic conductivity, and quality would aid in interpreting unaccounted gains and losses in selenium loads in the Blackfoot River.
Finally, State of Idaho selenium water-quality criteria have recently shifted to a hierarchical fish tissue and water concentration scheme. This report summarizes existing fish tissue and water-quality data in the mainstem and offers considerations for future selenium monitoring in the Blackfoot River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185081","collaboration":"Prepared in cooperation with the Bureau of Land Management and the Idaho Department of Environmental Quality","usgsCitation":"Zinsser, L.M., Mebane, C.A., Mladenka, G.C., Van Every, L.R., and Williams, M.L., 2018, Spatial and temporal trends in selenium in the upper Blackfoot River watershed, southeastern Idaho, 2001–16: U.S. Geological Survey Scientific Investigations Report 2018-5081, 37 p., https://doi.org/10.3133/sir20185081.","productDescription":"vi, 37 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-090359","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":355978,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5081/coverthb.jpg"},{"id":355979,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5081/sir20185081.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 20185081"}],"country":"United States","state":"Idaho","otherGeospatial":"Upper Blackfoot River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63414001464844,\n 42.5\n ],\n [\n -111,\n 42.5\n ],\n [\n -111,\n 43\n ],\n [\n -111.63414001464844,\n 43\n ],\n [\n -111.63414001464844,\n 42.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
The UFINCH (Unit Flows In Networks of Channels) computer application can be used to simulate daily and unit flows in networks of streams based on geospatial data in the National Hydrography Dataset NHDPlus (with value added attributes), and U.S. Geoogical Survey daily streamflow data from a downstream (or base) streamgage. Among streamflow augmentation methods, UFINCH has the unique capability to estimate time series of flows from a single base (downstream) streamgage to many upstream reaches, while conserving flows within the basin. UFINCH also provides a simple statistical model to adjust simulated flows to better match continuous flows from data at an upstream streamgage. Parameters of the statistical model are estimated using overlapping periods of record at the two streamgages, but the adjustment can be applied to all years of record available at the base streamgage. This report describes the main features of UFINCH and presents results from a sample application. Interactive graphical user interfaces and automated geographical information processing facilitate flow-data retrievals provide an intuitive environment for efficient and effective generation of flow information in a network. UFINCH is coded in the Matlab programming language and can be run in the Matlab programming environment, with supporting statistical, optimization, and mapping toolboxes, or from compiled code on a Microsoft Windows computer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165074","collaboration":"National Water Quality Program","usgsCitation":"Holtschlag, D.J., 2018, UFINCH—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages: U.S. Geological Survey Scientific Investigations Report 2016-5074, 17 p., https://doi.org/10.3133/sir20165074.","productDescription":"Report: iv, 17 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073436","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":355884,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5074/sir20165074.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5074"},{"id":355883,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5074/coverthb.jpg"},{"id":355885,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319TC5","text":"USGS data release","description":"USGS data release","linkHelpText":"Code, data, executables, and other information used to run Unit Flows in Networks of Channels (UFINCH)—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages"}],"contact":"Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
Since the early 2000s, there has been a resurgence in shade coffee production on the island of Puerto Rico. The newly restored specialized shade canopy consists of four native tree species, three of which are nitrogen fixers, and is intended to provide 30% shade cover once the trees are matured. Though much is known about the benefits of rustic and traditional shade plantations to coffee production, there is little information available for specialized shade plantations. Here, we investigate the ways in which fruit set, seed predation, bean weight, proportion of peaberries (i.e., malformed fruits), and beverage quality differ between sun and specialized shade plantations of Coffea arabica and Coffea canephora. Additionally, to assess whether coffee plants were pollen limited, we conducted a pollen supplementation experiment in which we compared pollen deposition and berry production on hand-pollinated branches to those of naturally, open-pollinated ones. We found that there was no evidence of pollen limitation on fruit set between sun and specialized shade plantations of C. arabica or C. canephora. We found that specialized shade benefits the proportion of C. arabica fruit set, without compromising bean weight or the proportion of peaberries produced. In comparison, C. canephora showed no improvement in the proportion of fruits set with specialized shade; however, beans from open-pollinated flowers weighed more in specialized shade than sun. Beverage quality was marginally better under shade, and for hand-pollinated flowers. Our results add to the limited body of work investigating the benefits of specialized shade coffee plantations on ecosystem services.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agee.2018.07.002","usgsCitation":"Prado, S.G., Collazo, J.A., and Irwin, R.E., 2018, Resurgence of specialized shade coffee cultivation: Effects on pollination services and quality of coffee production: Agriculture, Ecosystems and Environment, v. 265, p. 567-575, https://doi.org/10.1016/j.agee.2018.07.002.","productDescription":"9 p.","startPage":"567","endPage":"575","ipdsId":"IP-097241","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468565,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agee.2018.07.002","text":"Publisher Index Page"},{"id":395342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.04681396484375,\n 18.049255668808147\n ],\n [\n -66.50299072265625,\n 18.049255668808147\n ],\n [\n -66.50299072265625,\n 18.304988223519228\n ],\n [\n -67.04681396484375,\n 18.304988223519228\n ],\n [\n -67.04681396484375,\n 18.049255668808147\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"265","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Prado, Sara Guiti","contributorId":274479,"corporation":false,"usgs":false,"family":"Prado","given":"Sara","email":"","middleInitial":"Guiti","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":833019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":833020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, R. E.","contributorId":274480,"corporation":false,"usgs":false,"family":"Irwin","given":"R.","email":"","middleInitial":"E.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":833021,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201150,"text":"70201150 - 2018 - Temporal evaluation of estrogenic endocrine disruption markers in smallmouth bass (Micropterus dolomieu) reveals seasonal variability in intersex","interactions":[],"lastModifiedDate":"2018-12-03T08:41:54","indexId":"70201150","displayToPublicDate":"2018-07-26T08:19:01","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Temporal evaluation of estrogenic endocrine disruption markers in smallmouth bass (Micropterus dolomieu) reveals seasonal variability in intersex","title":"Temporal evaluation of estrogenic endocrine disruption markers in smallmouth bass (Micropterus dolomieu) reveals seasonal variability in intersex","docAbstract":"A reconnaissance project completed in 2009 identified intersex and elevated plasma vitellogenin in male smallmouth bass inhabiting the Missisquoi River, VT. In an attempt to identify the presence and seasonality of putative endocrine disrupting chemicals or other factors associated with these observations, a comprehensive reevaluation was conducted between September 2012 and June 2014. Here, we collected smallmouth bass from three physically partitioned reaches along the river to measure biomarkers of estrogenic endocrine disruption in smallmouth bass. In addition, polar organic chemical integrative samples (POCIS) were deployed to identify specific chemicals associated with biological observations. We did not observe biological differences across reaches indicating the absence of clear point source contributions to the observation of intersex. Interestingly, intersex prevalence and severity decreased in a stepwise manner over the timespan of the project. Intersex decreased from 92.8% to 28.1%. The only significant predictor of intersex prevalence was year of capture, based on logistic regression analysis. The mixed model of fish length and year-of-capture best predicted intersex severity. Intersex severity was also significantly different across late summer and early spring collections indicating seasonal changes in this metric. Plasma vitellogenin and liver vitellogenin Aa transcript abundance in males did not indicate exposure to estrogenic endocrine disrupting chemicals at any of the four sample collections. Analysis of chemicals captured by the POCIS as well as results of screening discrete water samples or POCIS extracts did not indicate the contribution of appreciable estrogenic chemicals. It is possible that unreported changes in land-use activity have ameliorated the problem, and our observations indicate recovery. Regardless, this work clearly emphasizes that single, snap shot sampling for intersex may not yield representative data given that the manifestation of this condition within a population can change dramatically over time.
","language":"English","publisher":"Elsever","doi":"10.1016/j.scitotenv.2018.07.167","usgsCitation":"Iwanowicz, L.R., Pinkney, A., Guy, C., Major, A., Munney, K., Blazer, V., Alvarez, D., Walsh, H.L., Sperry, A.J., Sanders, L., and Smith, D., 2018, Temporal evaluation of estrogenic endocrine disruption markers in smallmouth bass (Micropterus dolomieu) reveals seasonal variability in intersex: Science of the Total Environment, v. 646, p. 245-256, https://doi.org/10.1016/j.scitotenv.2018.07.167.","productDescription":"12 p.","startPage":"245","endPage":"256","ipdsId":"IP-095285","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":359860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","otherGeospatial":"Missisquoi River","volume":"646","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c064ee3e4b0815414cecb10","contributors":{"authors":[{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178 liwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":190787,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke","email":"liwanowicz@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":752925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pinkney, A.E.","contributorId":150385,"corporation":false,"usgs":false,"family":"Pinkney","given":"A.E.","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":752926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, C.P.","contributorId":22983,"corporation":false,"usgs":true,"family":"Guy","given":"C.P.","email":"","affiliations":[],"preferred":false,"id":752927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Major, A.M.","contributorId":150387,"corporation":false,"usgs":false,"family":"Major","given":"A.M.","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":752928,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Munney, K.","contributorId":150388,"corporation":false,"usgs":false,"family":"Munney","given":"K.","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":752929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":752930,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Alvarez, David 0000-0002-6918-2709 dalvarez@usgs.gov","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":150499,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","email":"dalvarez@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":752931,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Walsh, Heather L. 0000-0001-6392-4604 hwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":4696,"corporation":false,"usgs":true,"family":"Walsh","given":"Heather","email":"hwalsh@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":752932,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sperry, Adam J. 0000-0002-4815-3730 asperry@usgs.gov","orcid":"https://orcid.org/0000-0002-4815-3730","contributorId":5872,"corporation":false,"usgs":true,"family":"Sperry","given":"Adam","email":"asperry@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":752933,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sanders, Lakyn R. lsanders@usgs.gov","contributorId":5714,"corporation":false,"usgs":true,"family":"Sanders","given":"Lakyn R.","email":"lsanders@usgs.gov","affiliations":[],"preferred":true,"id":752934,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Smith, D. R. 0000-0001-6074-9257","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":44108,"corporation":false,"usgs":true,"family":"Smith","given":"D. 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In addition to the traditional sources of errors that affect more permeable formations (e.g. sample selection, non-representative specimens, disturbance introduced during sample acquisition and preparation), tight rocks can be particularly prone to solid–fluid interactions and thus more sensitive to the methods, procedures and techniques used to measure permeability. To address this problem, it is desirable to collect, for a single material, measurements obtained by different methods and pore fluids. For that purpose, a benchmarking exercise involving 24 laboratories was organized for measuring and modelling the permeability of a single low-permeability material, the Grimsel granodiorite. The objectives of the benchmark were: (i) to compare the results for a given method, (ii) to compare the results between different methods, (iii) to analyse the accuracy of each method, (iv) to study the influence of experimental conditions (especially the nature of pore fluid), (v) to discuss the relevance of indirect methods and models and finally (vi) to suggest good practice for low-permeability measurements. To complement the data set of permeability measurements presented in a companion paper, we focus here on (i) quantitative analysis of microstructures and pore size distribution, (ii) permeability modelling and (iii) complementary measurements of permeability anisotropy and poroelastic parameters. Broad ion beam—scanning electron microscopy, micro-computerized tomography, mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) methods were used to characterize the microstructures and provided the input parameters for permeability modelling. Several models were used: (i) basic statistical models, (ii) 3-D pore network and effective medium models, (iii) percolation model using MICP data and (iv) free-fluid model using NMR data. The models were generally successful in predicting the actual range of measured permeability. Statistical models overestimate the permeability because they do not adequately account for the heterogeneity of the crack network. Pore network and effective medium models provide additional constraints on crack parameters such as aspect ratio, aperture, density and connectivity. MICP and advanced microscopy techniques are very useful tools providing important input data for permeability estimation. Permeability measured—orthogonal to foliation is lower that—parallel to foliation. Combining the experimental and modelling results provide a unique and rich data set.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggy305","usgsCitation":"David, C., Wassermann, J., Amann, F., Klaver, J., Davy, C., Sarout, J., Esteban, L., Rutter, E., Hu, Q., Louis, L., Delage, P., Lockner, D.A., Selvadurai, A., Vanorio, T., Amann Hildenbrand, A., Meredith, P., Browning, J., Mitchell, T., Madonna, C., Billiotte, J., Reuschle, T., Lasseux, D., Fortin, J., Lenormand, R., Loggia, D., Nono, F., Boitnott, G., Jahns, E., M., F., Berthe, G., Braun, P., Gregoire, D., Perrier, L., Polito, P., Jannot, Y., Sommier, A., Krooss, B., Fink, R., and Clark, A., 2018, KG²B, a collaborative benchmarking exercise for estimating the permeability of the Grimsel granodiorite - Part 2: modeling, microstructures and complementary data: Geophysical Journal International, v. 215, no. 2, p. 825-843, https://doi.org/10.1093/gji/ggy305.","productDescription":"29 p.","startPage":"825","endPage":"843","ipdsId":"IP-093307","costCenters":[{"id":237,"text":"Earthquake Science 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B.","contributorId":207636,"corporation":false,"usgs":false,"family":"Krooss","given":"B.","email":"","affiliations":[{"id":37574,"text":"EMR group, Aachen University, Germany","active":true,"usgs":false}],"preferred":false,"id":745310,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Fink, R.","contributorId":207637,"corporation":false,"usgs":false,"family":"Fink","given":"R.","email":"","affiliations":[{"id":37574,"text":"EMR group, Aachen University, Germany","active":true,"usgs":false}],"preferred":false,"id":745311,"contributorType":{"id":1,"text":"Authors"},"rank":38},{"text":"Clark, A.","contributorId":207640,"corporation":false,"usgs":false,"family":"Clark","given":"A.","affiliations":[{"id":37592,"text":"Stanford University, USA","active":true,"usgs":false}],"preferred":false,"id":745312,"contributorType":{"id":1,"text":"Authors"},"rank":39}]}} ,{"id":70208432,"text":"70208432 - 2018 - Lactation and resource limitation affect stress responses, thyroid hormones, immune function, and antioxidant capacity of sea otters (Enhydra lutris)","interactions":[],"lastModifiedDate":"2020-02-10T06:21:01","indexId":"70208432","displayToPublicDate":"2018-07-25T12:28:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Lactation and resource limitation affect stress responses, thyroid hormones, immune function, and antioxidant capacity of sea otters (Enhydra lutris)","docAbstract":"Lactation is the most energetically demanding stage of reproduction in female mammals. Increased energetic allocation toward current reproduction may result in fitness costs, although the mechanisms underlying these trade‐offs are not well understood. Trade‐offs during lactation may include reduced energetic allocation to cellular maintenance, immune response, and survival and may be influenced by resource limitation. As the smallest marine mammal, sea otters (Enhydra lutris) have the highest mass‐specific metabolic rate necessitating substantial energetic requirements for survival. To provide the increased energy needed for lactation, female sea otters significantly increase foraging effort, especially during late‐lactation. Caloric insufficiency during lactation is reflected in the high numbers of maternal deaths due to End‐Lactation Syndrome in the California subpopulation. We investigated the effects of lactation and resource limitation on maternal stress responses, metabolic regulation, immune function, and antioxidant capacity in two subspecies of wild sea otters (northern: E. l. nereis and southern: E. l. kenyoni) within the California, Washington, and Alaska subpopulations. Lactation and resource limitation were associated with reduced glucocorticoid responses to acute capture stress. Corticosterone release was lower in lactating otters. Cortisol release was lower under resource limitation and suppression during lactation was only evident under resource limitation. Lactation and resource limitation were associated with alterations in thyroid hormones. Immune responses and total antioxidant capacity were not reduced by lactation or resource limitation. Southern sea otters exhibited higher concentrations of antioxidants, immunoglobulins, and thyroid hormones than northern sea otters. These data provide evidence for allocation trade‐offs during reproduction and in response to nutrient limitation but suggest self‐maintenance of immune function and antioxidant defenses despite energetic constraints. Income‐breeding strategists may be especially vulnerable to the consequences of stress and modulation of thyroid function when food resources are insufficient to support successful reproduction and may come at a cost to survival, and thereby influence population trends.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4280","usgsCitation":"Chinn, S., Monson, D., Tinker, M., Staedler, M.M., and Crocker, D.E., 2018, Lactation and resource limitation affect stress responses, thyroid hormones, immune function, and antioxidant capacity of sea otters (Enhydra lutris): Ecology and Evolution, v. 8, no. 16, p. 8433-8447, https://doi.org/10.1002/ece3.4280.","productDescription":"15 p.","startPage":"8433","endPage":"8447","ipdsId":"IP-091093","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468567,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4280","text":"Publisher Index Page"},{"id":372171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California 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\"}}]}","volume":"8","issue":"16","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Chinn, Sarah M.","contributorId":222316,"corporation":false,"usgs":false,"family":"Chinn","given":"Sarah M.","affiliations":[{"id":40519,"text":"Department of Biology, Sonoma State University","active":true,"usgs":false}],"preferred":false,"id":781859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":781858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, M. Tim 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":214291,"corporation":false,"usgs":true,"family":"Tinker","given":"M. Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":781862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":222317,"corporation":false,"usgs":true,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":true,"id":781860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crocker, Daniel E.","contributorId":222318,"corporation":false,"usgs":false,"family":"Crocker","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":40519,"text":"Department of Biology, Sonoma State University","active":true,"usgs":false}],"preferred":false,"id":781861,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259590,"text":"70259590 - 2018 - Caldera life-cycles of the Yellowstone hotspot track: Death and rebirth of the Heise Caldera","interactions":[],"lastModifiedDate":"2024-10-16T12:13:23.196883","indexId":"70259590","displayToPublicDate":"2018-07-25T07:10:48","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Caldera life-cycles of the Yellowstone hotspot track: Death and rebirth of the Heise Caldera","docAbstract":"As one of the most geochemically unique drill cores recovered within the Yellowstone–Snake River Plain (YSRP) province, the Sugar City geothermal test well was drilled into intra-caldera rhyolite lavas and tuffs erupted during the middle to late Pliocene and the resurgent basaltic volcanism erupted during the Pleistocene. This sequence parallels the two main stages proposed for YSRP hotspot calderas: i.e. the eruption of several large-volume, ash-flow tuff sheets followed by caldera collapse, then cessation of major rhyolitic activity and gradual subsidence accompanied by filling and eventual burial of the caldera by basalt lava flows. We employ stratigraphic relationships, paleomagnetism, and major, trace element, and Sr–Nd isotope geochemistry to develop models for the origin of the basaltic and rhyolitic magmas within a geographical and temporal context. The basalts are characterized by distinct groupings based on depth and geochemistry and reflect the dominant compositions observed on the surface, e.g. Snake River olivine tholeiite (SROT) and evolved type (e.g. Craters of the Moon). We also observe contaminated basalts that interacted with rhyolite/granite. The basaltic magma formed by shallow partial melting in the plume channel carved into the lithosphere. The older rhyolites preserve the classical characteristics of A-type granites and display major element and trace element concentrations typical for Eastern SRP caldera centres and minimal stratigraphic variation. Multiple lines of evidence document extensive magmatic differentiation and coupled basalt–rhyolite interactions. We find that the most plausible origin for the rhyolites is via partial melting of a hybrid source, comprising Archean crustal components and younger juvenile mafic intrusions. Assimilation of hydrothermally altered material is also required for some eruptive units. The rhyolites did not evolve from residual magma left over from the climactic Kilgore eruption (4·0 Ma), but instead represent discrete magma generation events in the course of a few hundred thousand years between 4·0 to 3·8 Ma. Beginning at approximately 3.3 Ma, basalts were able to erupt through the solidified composite pluton that formed below the caldera. The transition from rhyolite to basalt is tied to the declining flux of basaltic magma as North America moved away from the Yellowstone hotspot core.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/petrology/egy074","usgsCitation":"Jean, M.M., Christiansen, E.H., Champion, D.E., Vetter, S.K., Phillips, W.M., Schuth, S., and Shervais, J., 2018, Caldera life-cycles of the Yellowstone hotspot track: Death and rebirth of the Heise Caldera: Journal of Petrology, v. 59, no. 8, p. 1643-1670, https://doi.org/10.1093/petrology/egy074.","productDescription":"28 p.","startPage":"1643","endPage":"1670","ipdsId":"IP-101148","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468568,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egy074","text":"Publisher Index Page"},{"id":462907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"8","noUsgsAuthors":false,"publicationDate":"2018-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Jean, Marlon M.","contributorId":296059,"corporation":false,"usgs":false,"family":"Jean","given":"Marlon","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":915835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christiansen, Eric H.","contributorId":206801,"corporation":false,"usgs":false,"family":"Christiansen","given":"Eric","email":"","middleInitial":"H.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":915836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Champion, Duane E. 0000-0001-7854-9034","orcid":"https://orcid.org/0000-0001-7854-9034","contributorId":345150,"corporation":false,"usgs":true,"family":"Champion","given":"Duane","email":"","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vetter, Scott K.","contributorId":345151,"corporation":false,"usgs":false,"family":"Vetter","given":"Scott","email":"","middleInitial":"K.","affiliations":[{"id":82501,"text":"Centenary College","active":true,"usgs":false}],"preferred":false,"id":915838,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Phillips, William M.","contributorId":9690,"corporation":false,"usgs":true,"family":"Phillips","given":"William","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":915839,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schuth, Stephan","contributorId":345153,"corporation":false,"usgs":false,"family":"Schuth","given":"Stephan","email":"","affiliations":[{"id":64319,"text":"Leibniz University","active":true,"usgs":false}],"preferred":false,"id":915840,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shervais, John W.","contributorId":237914,"corporation":false,"usgs":false,"family":"Shervais","given":"John W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":915841,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198172,"text":"sir20185073 - 2018 - Geochemistry and microbiology of groundwater and solids from extraction and monitoring wells and their relation to well efficiency at a Federally operated confined disposal facility, East Chicago, Indiana","interactions":[],"lastModifiedDate":"2018-07-24T12:44:39","indexId":"sir20185073","displayToPublicDate":"2018-07-24T11:00:00","publicationYear":"2018","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":"2018-5073","title":"Geochemistry and microbiology of groundwater and solids from extraction and monitoring wells and their relation to well efficiency at a Federally operated confined disposal facility, East Chicago, Indiana","docAbstract":"In cooperation with the U.S. Army Corps of Engineers, Chicago District, the U.S. Geological Survey investigated the processes affecting water quality, geochemistry, and microbiology in representative extraction and monitoring wells at a confined disposal facility (CDF) in East Chicago, Indiana. The CDF is a 140-acre Federally-managed facility that was the former location of an oil refinery and is now used for the long-term disposal and storage of dredge material from the Indiana Harbor and Indiana Harbor Canal. Residual petroleum hydrocarbons and leachate from the CDF are contained within the facility by use of a groundwater cutoff wall. The wall consists of a soil-bentonite slurry and a gradient control system made up of an automated network of 96 extraction wells, 42 monitoring wells, and 2 ultrasonic sensors that maintain an inward hydraulic gradient at the site. The pumps in the extraction wells require vigilant maintenance and must be replaced when unable to withdraw water at a rate sufficient to maintain the required inward gradient. The wells are screened in the Calumet aquifer, a coarse-grained sand and gravel unit that extends approximately 35 feet below the land surface and is not utilized for drinking-water supply at the CDF or in the surrounding area. This study was initiated to identify the cause of decreased pump discharges and to identify potential mitigation strategies.
For this study, the U.S. Geological Survey collected groundwater and solids from monitoring and extraction wells. Groundwater samples were collected during June 2014 for precautionary health screening and on four occasions during September 2014 through November 2014. Groundwater samples collected from two extraction wells during June 2014 were analyzed for concentrations of anthropogenic organic constituents. During September through November 2014, groundwater samples were collected from one additional extraction well, and samples from three monitoring wells were analyzed for concentrations of inorganic and organic constituents, dissolved gases, and bacterial abundance and diversity. Solid samples were collected during April 2014, during September 2014 through November 2014, and during November 2016. Solid samples were collected from the exterior of extraction-well pumps and as flocculent from water samples. Solid samples were collected from 10 wells, including 1 extraction well and 3 monitoring wells sampled for water quality. Solid samples were analyzed for mineralogy, solid-phase habit, geochemistry, and organic composition.
The following is a list of observations that were made during this study: (1) the water quality is substantially variable among the six well locations sampled as part of this study—lower (more negative) redox values and higher concentrations of many constituents (including calcium, magnesium, sodium, and sulfate) and properties (including dissolved solids, hardness, and turbidity) were detected in sampled wells located near the extraction wells with the highest frequency of failure; (2) water-level drawdown is variable between extraction wells—wells with the greatest drawdown may pull deeper groundwater into the borehole; (3) dissolved gas results indicate reducing oxidation-reduction processes in the aquifer material that can feasibly contribute iron, carbon dioxide, and other byproducts from hydrocarbon degradation to precipitates and solids that accumulate on and impair pump operation; (4) crystalline and amorphous solid-phase minerals are precipitating in the borehole; (5) several types of bacteria are present in water pumped from extraction wells and are likely responsible for bonding mineral and microbiologic matter to the pump (and other well components); and (6) bacteria may create microenvironments that facilitate precipitation of solids or inhibit dissolution of unstable minerals once the bacteria adhere to biofilm attached to the pump. Results of the study indicate that bacteria may be accumulating and entrapping solid material on the exterior of pumps. This accumulation reduces heat transfer and water discharge from the pump and may lead to decreased efficiency or mechanical failure. Observations could not be made on the well screen, gravel pack, or surrounding geologic formation; therefore, mitigating measures in the borehole may not solve well-productivity issues.
Remedies for the pump fouling problems were derived from the review and interpretation of data collected during this study and from information documented in other sources about groundwater well fouling. Potential remedies to problems associated with pump fouling at the CDF may include the following: (1) reducing attractiveness of the extraction wells for microbiological growth by modifying the chemical or physical environment of the well, (2) modifying the pump exterior to decrease microbiological adherence, (3) changing the pumping regime to control the chemistry of water entering the well from the surrounding aquifer material, (4) modifying the pumps to be less physically and thermally attractive, and (5) removing hydrocarbons from groundwater and the aquifer material surrounding the wells or adding surfactants to make them more mobile. Pilot scale testing may be necessary to identify the most effective treatment or combination of treatments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185073","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Bayless, E.R., Cole, T.R., Lampe, D.C., Travis, R.E., Schulz, M.S, and Buszka, P.M., 2018, Geochemistry and microbiology of groundwater and solids from extraction and monitoring wells and their relation to well efficiency at a Federally operated confined disposal facility, East Chicago, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5073, 134 p., https://doi.org/10.3133/sir20185073.","productDescription":"Report: xi, 134 p.","numberOfPages":"150","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077148","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":355818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5073/coverthb.jpg"},{"id":355819,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5073/sir20185073.pdf","text":"Report","size":"20.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5073"},{"id":355825,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5b1feed8e4b092d96525b0a2","text":"USGS data release","description":"USGS data release","linkHelpText":"X-ray diffraction trace analysis of solid phase samples collected from groundwater wells at a confined disposal facility in East Chicago, Indiana"}],"country":"United States","state":"Indiana","city":"East Chicago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.52395629882812,\n 41.59105362325491\n ],\n [\n -87.32585906982422,\n 41.59105362325491\n ],\n [\n -87.32585906982422,\n 41.71444263601197\n ],\n [\n -87.52395629882812,\n 41.71444263601197\n ],\n [\n -87.52395629882812,\n 41.59105362325491\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
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Ground‐motion prediction equations (GMPEs) are critical elements of probabilistic seismic hazard analysis (PSHA), as well as for other applications of ground motions. To isolate the path component for the purpose of building nonergodic GMPEs, we compute a regional GMPE using a large dataset of peak ground accelerations (PGAs) from small‐magnitude earthquakes (0.5 ≤ M ≤ 4.5 with > 10,000 events, yielding ∼120,000 recordings) that occurred in 2013 centered around the ANZA seismic network (hypocentral distances ≤180 km) in southern California. We examine two separate methods of obtaining residuals from the observed and predicted ground motions: a pooled ordinary least‐squares model and a mixed‐effects maximum‐likelihood model. Whereas the former is often used by the broader seismological community, the latter is widely used by the ground‐motion and engineering seismology community. We confirm that mixed‐effects models are the preferred and most statistically robust method to obtain event, path, and site residuals and discuss the reasoning behind this. Our results show that these methods yield different consequences for the uncertainty of the residuals, particularly for the event residuals. Finally, our results show no correlation (correlation coefficient [CC] <0.03) between site residuals and the classic site‐characterization term VS30, the time‐averaged shear‐wave velocity in the top 30 m at a site. We propose that this is due to the relative homogeneity of the site response in the region and perhaps due to shortcomings in the formulation of VS30 and suggest applying the provided PGA site correction terms to future ground‐motion studies for increased accuracy.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120170376","usgsCitation":"Sahakian, V.J., Baltay Sundstrom, A.S., Hanks, T.C., Buehler, J., Vernon, F.L., and Kilb, D.L., 2018, Decomposing leftovers: Event, path, and site residuals for a small magnitude ANZA region GMPE: Bulletin of the Seismological Society of America, v. 108, no. 5A, p. 2478-2492, https://doi.org/10.1785/0120170376.","startPage":"2478","endPage":"2492","ipdsId":"IP-092976","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118,\n 34.5\n ],\n [\n -118,\n 32.5\n ],\n [\n -115.45,\n 32.5\n ],\n [\n -115.45,\n 34.5\n ],\n [\n -118,\n 34.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"108","issue":"5A","noUsgsAuthors":false,"publicationDate":"2018-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sahakian, Valerie Jean 0000-0002-3278-4458","orcid":"https://orcid.org/0000-0002-3278-4458","contributorId":296738,"corporation":false,"usgs":true,"family":"Sahakian","given":"Valerie","email":"","middleInitial":"Jean","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buehler, Janine S.","contributorId":351082,"corporation":false,"usgs":false,"family":"Buehler","given":"Janine S.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":927867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vernon, Frank L. 0000-0002-9379-4000","orcid":"https://orcid.org/0000-0002-9379-4000","contributorId":333114,"corporation":false,"usgs":false,"family":"Vernon","given":"Frank","email":"","middleInitial":"L.","affiliations":[{"id":79734,"text":"Institute of Geophysics and Planetary Science, University of California at San Diego","active":true,"usgs":false}],"preferred":false,"id":927868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kilb, Deborah L.","contributorId":216380,"corporation":false,"usgs":false,"family":"Kilb","given":"Deborah","email":"","middleInitial":"L.","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":927869,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70246645,"text":"70246645 - 2018 - BioTIME: A database of biodiversity time series for the Anthropocene","interactions":[],"lastModifiedDate":"2023-07-25T20:22:32.607753","indexId":"70246645","displayToPublicDate":"2018-07-24T08:23:31","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"BioTIME: A database of biodiversity time series for the Anthropocene","docAbstract":"The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
.csv and .SQL.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.12729","usgsCitation":"Dornelas, M., Antao, L.H., Moyes, F., Bates, A.E., Magurran, A.E., Adam, D., Akhmetzhanova, A.A., Appeltans, W., Arcos, J., Arnold, H., Ayyappan, N., Badihi, G., Baird, A., Barbosa, M., Barreto, T.E., Bassler, C., Bellgrove, A., Belmaker, J., Benedetti-Cecchi, L., Bett, B.J., Bjorkman, A., Blazewicz, M., Blowes, S., Bloch, C.P., Bonebrake, T.C., Boyd, S., Bradford, M., Brooks, A.J., Brown, J., Bruelheide, H., Budy, P., Carvalho, F., Castaneda-Moya, E., Chen, C.A., Chamblee, J.F., Chase, T.J., Collier, L.S., Collinge, S.K., Condit, R., Cooper, E.J., Cornelissen, J.H., Cotano, U., Crow, S.K., Damasceno, G., Davies, C.H., Davis, R.A., Day, F.P., Degraer, S., Doherty, T.S., Dunn, T.E., Durigan, G., Duffy, J., Edelist, D., Edgar, G., Elahi, R., Elmendorf, S.C., Enemar, A., Ernest, S.K., Escribano, R., Estiarte, M., Evans, B., Fan, T., Farah, F.T., Fernandes, L.L., Farneda, F.Z., Fidelis, A., Fitt, R., Fosaa, A.M., Franco, G.A., Frank, G.E., Fraser, W.R., Garcia, H., Gatti, R.C., Givan, O., Gorgone-Barbosa, E., Gould, W.A., Gries, C., Grossman, G., Gutierrez, J.R., Hale, S., Harmon, M.E., Harte, J., Haskins, G., L., H.D., Hermanutz, L., Hidalgo, P., Higuchi, P., Hoey, A., Van Hoey, G., Hofgaard, A., Holeck, K.T., Hollister, R.D., Holmes, R., Hoogenboom, M., Hsieh, C., Hubbell, S.P., Huettmann, F., Huffard, C.L., Hurlbert, A.H., Ivanauskas, N.M., Janik, D., Jandt, U., Jazdzewska, A., Johannessen, T., Johnstone, J.F., Jones, J., Jones, F.A., Kang, J., Kartawijaya, T., Keeley, E.C., Kelt, D.A., Kinnear, R., Klanderud, K., Knutsen, H., Koenig, C.C., Kortz, A.R., Kral, K., Kuhnz, L.A., Kuo, C., Kushner, D.J., Laguionie-Marchais, C., Lancaster, L.T., Lee, C.M., Lefcheck, J., Levesque, E., Lightfoot, D.A., Lloret, F., Lloyd, J., Lopez-Baucells, A., Louzao, M., Madin, J.S., Magnusson, B., Malamud, S., Matthews, I., McFarland, K.P., McGill, B.J., McKnight, D.M., McLarney, W.O., Meador, J., 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The objectives of this study were to describe the distribution of groundwater near oil fields having total dissolved solids less than 10,000 milligrams per liter (mg/L) based on available data and to document where data gaps exist. Salinity was represented by the measured or calculated concentration of total dissolved solids (TDS) in samples of produced water obtained from petroleum wells and groundwater obtained from water wells. The water chemistry data were used to estimate the minimum depths of TDS greater than 3,000 mg/L and greater than 10,000 mg/L in areas near selected oil fields using historical water-chemistry data coupled with available well-location and construction information.
The 10,000 mg/L threshold, representing the highest level of TDS concentration of water that could be considered as a potential source of drinking water, was present in all but 4 (Jasmin, Kern Bluff, Kern Front, and Mount Poso) of the 31 individual oil fields. Among petroleum wells, the median TDS concentration of produced water ranged from 500 mg/L for the Jasmin field to 32,636 mg/L for the Elk Hills field. Among water wells, median TDS concentrations, either reported or calculated from specific conductance, ranged from 151 mg/L for wells within 2 miles of the Ten Section field to 9,750 mg/L for wells within 2 miles of the combined North and South Belridge fields.
In general, TDS across the eight geographic subregions increased with depth, but the relation of TDS with depth varied regionally. The most pronounced increases in TDS with depth were across the West Kern Valley Floor and West Kern Valley Margin subregions on the west side of the San Joaquin Valley, and in the vicinity of the Wilmington field in the Los Angeles Basin subregion; in these areas, relatively high TDS concentrations greater than 10,000 mg/L were present within the upper few hundred to several thousand feet of land surface. Total dissolved solids concentrations increased more gradually with depth in the Middle Kern Valley Floor subregion, in the South Kern Valley Margin subregion, in the vicinity of the Montebello and Santa Fe Springs fields in the Los Angeles Basin subregion, and in the Central Coast Basin subregion. The Kern Sierran Foothills and East Kern Valley Floor subregions, on the east side of the San Joaquin Valley, had the most gradual increases in TDS with depth. Fields in the East Kern Valley Floor subregion generally had groundwater and produced water with TDS less than 10,000 mg/L that extended to a large depth compared to most other subregions.
Overall, the west side of the San Joaquin Valley in Kern County and the Wilmington field in Los Angeles County generally have the highest TDS values and the shallowest depths to high TDS. High TDS at relatively shallow depths on the west side of the San Joaquin Valley may be because of a combination of natural conditions and anthropogenic factors. In the vicinity of the Wilmington field in the Los Angeles Basin subregion, high TDS at relatively shallow depths is attributable at least in part to seawater intrusion. Fields on the east side of the San Joaquin Valley in Kern County have the lowest TDS and greatest depths to TDS greater than 10,000 mg/L because of their geologic setting adjacent to Sierra Nevada recharge areas.
Reconnaissance salinity mapping was limited by several factors. The primary limitation was the lack of well-construction data for a significant number of water wells. Bottom perforation, well depth, or hole depth were not available for 35 percent of wells used for salinity mapping. A second limitation was variability in data quality. Total dissolved solids and specific conductance data were compiled from different data sources with varying degrees of documentation that ranged from comprehensive to very little or none. As a result, it was not always possible to assess the quality of the provided data with respect to either conditions at each well during sampling or the methodology used for sample collection and analysis. A third limitation was the lack of wells, either petroleum or water, and associated TDS data over large vertical intervals for some fields. As a result, the distribution of salinity and the depths at which TDS concentration exceeds the 3,000 and 10,000 mg/L thresholds could not always be precisely determined. This analysis highlights key gaps that need to be filled with additional analysis of other sources of information, such as borehole geophysical logs and new water sample or geophysical data collection.
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