The contribution of science to the development and implementation of policy is typically neither direct nor transparent. In 1995, the U.S. Fish and Wildlife Service (FWS) made a decision that was unprecedented in natural resource management, turning to an unused and unproven decision process to carry out trust responsibilities mandated by an international treaty. The decision process was adopted for the establishment of annual sport hunting regulations for the most economically important duck population in North America, the 6 to 11 million mallards Anas platyrhynchos breeding in the mid-continent region of north-central United States and central Canada. The key idea underlying the adopted decision process was to formally embed within it a scientific process designed to reduce uncertainty (learn) and thus make better decisions in the future. The scientific process entails use of models to develop predictions of competing hypotheses about system response to the selected action at each decision point. These prediction not only are used to select the optimal management action, but also are compared with the subsequent estimates of system state variables, providing evidence for modifying degrees of confidence in, and hence relative influence of, these models at the next decision point. Science and learning in one step are formally and directly incorporated into the next decision, contrasting with the usual ad hoc and indirect use of scientific results in policy development and decision-making. Application of this approach over the last 20 years has led to a substantial reduction in uncertainty, as well as to an increase in transparency and defensibility of annual decisions and a decrease in the contentiousness of the decision process. As resource managers are faced with increased uncertainty associated with various components of global change, this approach provides a roadmap for the future scientific management of natural resources.
","language":"English","publisher":"Wiley","doi":"10.1111/1365-2664.12406","usgsCitation":"Nichols, J., Johnson, F.A., Williams, B., and Boomer, G.S., 2015, On formally integrating science and policy: walking the walk: Journal of Applied Ecology, v. 52, no. 3, p. 539-543, https://doi.org/10.1111/1365-2664.12406.","productDescription":"5 p.","startPage":"539","endPage":"543","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056557","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":298301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.328125,\n 24.846565348219734\n ],\n [\n -141.328125,\n 69.7181066990676\n ],\n [\n -51.15234375,\n 69.7181066990676\n ],\n [\n -51.15234375,\n 24.846565348219734\n ],\n [\n -141.328125,\n 24.846565348219734\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-26","publicationStatus":"PW","scienceBaseUri":"54f97e2de4b02419550d9b5e","chorus":{"doi":"10.1111/1365-2664.12406","url":"http://dx.doi.org/10.1111/1365-2664.12406","publisher":"Wiley-Blackwell","authors":"Nichols James D., Johnson Fred A., Williams Byron K., Boomer G. Scott","journalName":"Journal of Applied Ecology","publicationDate":"2/26/2015"},"contributors":{"authors":[{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":541844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":541845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Byron K.","contributorId":139564,"corporation":false,"usgs":false,"family":"Williams","given":"Byron K.","affiliations":[{"id":12801,"text":"The Wildlife Society","active":true,"usgs":false}],"preferred":false,"id":541846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boomer, G. Scott","contributorId":139565,"corporation":false,"usgs":false,"family":"Boomer","given":"G.","email":"","middleInitial":"Scott","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":541847,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70137275,"text":"70137275 - 2015 - Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States","interactions":[],"lastModifiedDate":"2018-02-04T13:31:07","indexId":"70137275","displayToPublicDate":"2015-03-05T10:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States","docAbstract":"Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2014WR015924","usgsCitation":"Farmer, W.H., Over, T.M., and Vogel, R.M., 2015, Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States: Water Resources Research, v. 51, no. 3, p. 1775-1796, https://doi.org/10.1002/2014WR015924.","productDescription":"22 p.","startPage":"1775","endPage":"1796","numberOfPages":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057100","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":298299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.74609375,\n 25.24469595130604\n ],\n [\n -94.74609375,\n 37.71859032558816\n ],\n [\n -75.673828125,\n 37.71859032558816\n ],\n [\n -75.673828125,\n 25.24469595130604\n ],\n [\n -94.74609375,\n 25.24469595130604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-27","publicationStatus":"PW","scienceBaseUri":"54f97e2ce4b02419550d9b5a","chorus":{"doi":"10.1002/2014wr015924","url":"http://dx.doi.org/10.1002/2014wr015924","publisher":"Wiley-Blackwell","authors":"Farmer William H., Over Thomas M., Vogel Richard M.","journalName":"Water Resources Research","publicationDate":"3/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":537650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368 tmover@usgs.gov","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":1819,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"tmover@usgs.gov","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":537651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogel, Richard M.","contributorId":66811,"corporation":false,"usgs":true,"family":"Vogel","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":537652,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70141749,"text":"70141749 - 2015 - Geotechnical aspects in the epicentral region of the 2011, Mw5.8 Mineral, Virginia earthquake","interactions":[],"lastModifiedDate":"2017-04-14T10:22:17","indexId":"70141749","displayToPublicDate":"2015-03-04T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1727,"text":"GSA Special Papers","active":true,"publicationSubtype":{"id":10}},"title":"Geotechnical aspects in the epicentral region of the 2011, Mw5.8 Mineral, Virginia earthquake","docAbstract":"A reconnaissance team documented the geotechnical and geological aspects in the epicentral region of the Mw (moment magnitude) 5.8 Mineral, Virginia (USA), earthquake of 23 August 2011. Tectonically and seismically induced ground deformations, evidence of liquefaction, rock slides, river bank slumps, ground subsidence, performance of earthen dams, damage to public infrastructure and lifelines, and other effects of the earthquake were documented. This moderate earthquake provided the rare opportunity to collect data to help assess current geoengineering practices in the region, as well as to assess seismic performance of the aging infrastructure in the region. Ground failures included two marginal liquefaction sites, a river bank slump, four minor rockfalls, and a ~4-m-wide, ~12-m-long, ~0.3-m-deep subsidence on a residential property. Damage to lifelines included subsidence of the approaches for a bridge and a water main break to a heavily corroded, 5-cm-diameter valve in Mineral, Virginia. Observed damage to dams, landfills, and public-use properties included a small, shallow slide in the temporary (“working”) clay cap of the county landfill, damage to two earthen dams (one in the epicentral region and one further away near Bedford, Virginia), and substantial structural damage to two public school buildings.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2014.2509(09)","usgsCitation":"Green, R.A., Lasley, S., Carter, M.W., Munsey, J.W., Maurer, B.W., and Tuttle, M.P., 2015, Geotechnical aspects in the epicentral region of the 2011, Mw5.8 Mineral, Virginia earthquake: GSA Special Papers, v. 509, p. 151-172, https://doi.org/10.1130/2014.2509(09).","productDescription":"22 p.","startPage":"151","endPage":"172","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054097","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":298295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","city":"Mineral","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.495849609375,\n 36.10237644873644\n ],\n [\n -84.495849609375,\n 39.918162846609455\n ],\n [\n -74.77294921875,\n 39.918162846609455\n ],\n [\n -74.77294921875,\n 36.10237644873644\n ],\n [\n -84.495849609375,\n 36.10237644873644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"509","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54f82cafe4b02419550d99de","contributors":{"authors":[{"text":"Green, Russell A.","contributorId":94708,"corporation":false,"usgs":false,"family":"Green","given":"Russell","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":540989,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lasley, Samuel","contributorId":139385,"corporation":false,"usgs":false,"family":"Lasley","given":"Samuel","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":540990,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":540988,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munsey, Jeffrey W.","contributorId":139386,"corporation":false,"usgs":false,"family":"Munsey","given":"Jeffrey","email":"","middleInitial":"W.","affiliations":[{"id":12759,"text":"TVA","active":true,"usgs":false}],"preferred":false,"id":540991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maurer, Brett W.","contributorId":139387,"corporation":false,"usgs":false,"family":"Maurer","given":"Brett","email":"","middleInitial":"W.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":540992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tuttle, Martitia P.","contributorId":139388,"corporation":false,"usgs":false,"family":"Tuttle","given":"Martitia","email":"","middleInitial":"P.","affiliations":[{"id":12760,"text":"Tuttle and Associates","active":true,"usgs":false}],"preferred":false,"id":540993,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70140105,"text":"70140105 - 2015 - Rapid isolation of microsatellite DNAs and identification of polymorphic mitochondrial DNA regions in the fish rotan (Perccottus glenii) invading European Russia","interactions":[],"lastModifiedDate":"2017-06-29T12:13:27","indexId":"70140105","displayToPublicDate":"2015-03-04T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1325,"text":"Conservation Genetics Resources","active":true,"publicationSubtype":{"id":10}},"title":"Rapid isolation of microsatellite DNAs and identification of polymorphic mitochondrial DNA regions in the fish rotan (Perccottus glenii) invading European Russia","docAbstract":"Human-mediated translocations and subsequent large-scale colonization by the invasive fish rotan (Perccottus glenii Dybowski, 1877; Perciformes, Odontobutidae), also known as Amur or Chinese sleeper, has resulted in dramatic transformations of small lentic ecosystems. However, no detailed genetic information exists on population structure, levels of effective movement, or relatedness among geographic populations of P. glenii within the European part of the range. We used massively parallel genomic DNA shotgun sequencing on the semiconductor-based Ion Torrent Personal Genome Machine (PGM) sequencing platform to identify nuclear microsatellite and mitochondrial DNA sequences in P. glenii from European Russia. Here we describe the characterization of nine nuclear microsatellite loci, ascertain levels of allelic diversity, heterozygosity, and demographic status of P. glenii collected from Ilev, Russia, one of several initial introduction points in European Russia. In addition, we mapped sequence reads to the complete P. glenii mitochondrial DNA sequence to identify polymorphic regions. Nuclear microsatellite markers developed for P. glenii yielded sufficient genetic diversity to: (1) produce unique multilocus genotypes; (2) elucidate structure among geographic populations; and (3) provide unique perspectives for analysis of population sizes and historical demographics. Among 4.9 million filtered P. glenii Ion Torrent PGM sequence reads, 11,304 mapped to the mitochondrial genome (NC_020350). This resulted in 100 % coverage of this genome to a mean coverage depth of 102X. A total of 130 variable sites were observed between the publicly available genome from China and the studied composite mitochondrial genome. Among these, 82 were diagnostic and monomorphic between the mitochondrial genomes and distributed among 15 genome regions. The polymorphic sites (N = 48) were distributed among 11 mitochondrial genome regions. Our results also indicate that sequence reads generated from two three-hour runs on the Ion Torrent PGM can generate a sufficient number of nuclear and mitochondrial markers to improve understanding of the evolutionary and ecological dynamics of non-model and in particular, invasive species.
","language":"English","publisher":"Springer","publisherLocation":"Netherlands","doi":"10.1007/s12686-015-0430-x","usgsCitation":"King, T.L., Eackles, M.S., and Reshetnikov, A.N., 2015, Rapid isolation of microsatellite DNAs and identification of polymorphic mitochondrial DNA regions in the fish rotan (Perccottus glenii) invading European Russia: Conservation Genetics Resources, v. 7, no. 2, p. 363-368, https://doi.org/10.1007/s12686-015-0430-x.","productDescription":"6 p.","startPage":"363","endPage":"368","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060630","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":310265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"European Russia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 26.71875,\n 42.032974332441405\n ],\n [\n 26.71875,\n 76.84081641443098\n ],\n [\n 91.23046875,\n 76.84081641443098\n ],\n [\n 91.23046875,\n 42.032974332441405\n ],\n [\n 26.71875,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-04","publicationStatus":"PW","scienceBaseUri":"5628b73fe4b0d158f5926c49","contributors":{"authors":[{"text":"King, Tim L. tlking@usgs.gov","contributorId":3520,"corporation":false,"usgs":true,"family":"King","given":"Tim","email":"tlking@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":539793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eackles, Michael S. meackles@usgs.gov","contributorId":4371,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":539794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reshetnikov, Andrey N.","contributorId":149329,"corporation":false,"usgs":false,"family":"Reshetnikov","given":"Andrey","email":"","middleInitial":"N.","affiliations":[{"id":12617,"text":"A.N. Severtsov Ecology & Evolution Institute, Leninskiy 33, Moscow 119071, Russia","active":true,"usgs":false}],"preferred":false,"id":577989,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70142328,"text":"70142328 - 2015 - Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification","interactions":[],"lastModifiedDate":"2015-03-04T10:53:51","indexId":"70142328","displayToPublicDate":"2015-03-04T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1709,"text":"Fuel","active":true,"publicationSubtype":{"id":10}},"title":"Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification","docAbstract":"Coal seam degasification improves coal mine safety by reducing the gas content of coal seams and also by generating added value as an energy source. Coal seam reservoir simulation is one of the most effective ways to help with these two main objectives. As in all modeling and simulation studies, how the reservoir is defined and whether observed productions can be predicted are important considerations.
\nUsing geostatistical realizations as spatial maps of different coal reservoir properties is a more realistic approach than assuming uniform properties across the field. In fact, this approach can help with simultaneous history matching of multiple wellbores to enhance the confidence in spatial models of different coal properties that are pertinent to degasification. The problem that still remains is the uncertainty in geostatistical simulations originating from the partial sampling of the seam that does not properly reflect the stochastic nature of coal property realizations. Stochastic simulations and using individual realizations, rather than E-type, make evaluation of uncertainty possible.
\nThis work is an advancement over Karacan et al. (2014) in the sense of assessing uncertainty that stems from geostatistical maps. In this work, we batched 100 individual realizations of 10 coal properties that were randomly generated to create 100 bundles and used them in 100 separate coal seam reservoir simulations for simultaneous history matching. We then evaluated the history matching errors for each bundle and defined the single set of realizations that would minimize the error for all wells. We further compared the errors with those of E-type and the average realization of the best matches. Unlike in Karacan et al. (2014), which used E-type maps and average of quantile maps, using these 100 bundles created 100 different history match results from separate simulations, and distributions of results for in-place gas quantity, for example, from which uncertainty in coal property realizations could be evaluated.
\nThe study helped to determine the realization bundle that consisted of the spatial maps of coal properties, which resulted in minimum error. In addition, it was shown that both E-type and the average of realizations that gave the best match for invidual approximated the same properties resonably. Moreover, the determined realization bundle showed that the study field initially had 151.5 million m3 (cubic meter) of gas and 1.04 million m3 water in the coal, corresponding to Q90 of the entire range of probability for gas and close to Q75 for water. In 2013, in-place fluid amounts decreased to 138.9 million m3 and 0.997 million m3 for gas and water, respectively.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2015.01.046","usgsCitation":"Karacan, C., and Olea, R., 2015, Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification: Fuel, v. 148, p. 87-97, https://doi.org/10.1016/j.fuel.2015.01.046.","productDescription":"11 p.","startPage":"87","endPage":"97","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062278","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":472223,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://doi.org/10.1016/j.fuel.2015.01.046","text":"External Repository"},{"id":298276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.54592895507812,\n 39.00851330385611\n ],\n [\n -87.54592895507812,\n 39.089034905217474\n ],\n [\n -87.41134643554688,\n 39.089034905217474\n ],\n [\n -87.41134643554688,\n 39.00851330385611\n ],\n [\n -87.54592895507812,\n 39.00851330385611\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"148","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54f82cb1e4b02419550d99e2","contributors":{"authors":[{"text":"Karacan, C. Özgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":139554,"corporation":false,"usgs":true,"family":"Karacan","given":"C. Özgen","affiliations":[{"id":12800,"text":"National Institute for Occupational Safety and Health (NIOSH)","active":true,"usgs":false}],"preferred":false,"id":541822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olea, Ricardo A. 0000-0003-4308-0808 rolea@usgs.gov","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":1401,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","email":"rolea@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":541821,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173595,"text":"70173595 - 2015 - Occupancy modeling for improved accuracy and understanding of pathogen prevalence and dynamics","interactions":[],"lastModifiedDate":"2016-06-09T15:53:01","indexId":"70173595","displayToPublicDate":"2015-03-04T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Occupancy modeling for improved accuracy and understanding of pathogen prevalence and dynamics","docAbstract":"Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmonOncorhynchus tshawytscha and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon O. tshawytscha collected after spawning for common pathogens seen in this population:Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/metacercariae, and Renibacterium salmoninarum. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%.
","language":"English","publisher":"PLOS One","doi":"10.1371/journal.pone.0116605","usgsCitation":"Colvin, M., Peterson, J., Kent, M.L., and Schreck, C.B., 2015, Occupancy modeling for improved accuracy and understanding of pathogen prevalence and dynamics: PLoS ONE, v. 10, no. 3, https://doi.org/10.1371/journal.pone.0116605.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-056718","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":472225,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0116605","text":"Publisher Index Page"},{"id":323433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-04","publicationStatus":"PW","scienceBaseUri":"575a9334e4b04f417c27516c","contributors":{"authors":[{"text":"Colvin, Michael E.","contributorId":140975,"corporation":false,"usgs":false,"family":"Colvin","given":"Michael E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":638334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":637383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Michael L.","contributorId":16693,"corporation":false,"usgs":true,"family":"Kent","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":638335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schreck, Carl B. 0000-0001-8347-1139 carl.schreck@usgs.gov","orcid":"https://orcid.org/0000-0001-8347-1139","contributorId":878,"corporation":false,"usgs":true,"family":"Schreck","given":"Carl","email":"carl.schreck@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":638336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168685,"text":"70168685 - 2015 - Comparing models of Red Knot population dynamics","interactions":[],"lastModifiedDate":"2016-02-24T14:45:15","indexId":"70168685","displayToPublicDate":"2015-03-01T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Comparing models of Red Knot population dynamics","docAbstract":"Predictive population modeling contributes to our basic scientific understanding of population dynamics, but can also inform management decisions by evaluating alternative actions in virtual environments. Quantitative models mathematically reflect scientific hypotheses about how a system functions. In Delaware Bay, mid-Atlantic Coast, USA, to more effectively manage horseshoe crab (Limulus polyphemus) harvests and protect Red Knot (Calidris canutus rufa) populations, models are used to compare harvest actions and predict the impacts on crab and knot populations. Management has been chiefly driven by the core hypothesis that horseshoe crab egg abundance governs the survival and reproduction of migrating Red Knots that stopover in the Bay during spring migration. However, recently, hypotheses proposing that knot dynamics are governed by cyclical lemming dynamics garnered some support in data analyses. In this paper, I present alternative models of Red Knot population dynamics to reflect alternative hypotheses. Using 2 models with different lemming population cycle lengths and 2 models with different horseshoe crab effects, I project the knot population into the future under environmental stochasticity and parametric uncertainty with each model. I then compare each model's predictions to 10 yr of population monitoring from Delaware Bay. Using Bayes' theorem and model weight updating, models can accrue weight or support for one or another hypothesis of population dynamics. With 4 models of Red Knot population dynamics and only 10 yr of data, no hypothesis clearly predicted population count data better than another. The collapsed lemming cycle model performed best, accruing ~35% of the model weight, followed closely by the horseshoe crab egg abundance model, which accrued ~30% of the weight. The models that predicted no decline or stable populations (i.e. the 4-yr lemming cycle model and the weak horseshoe crab effect model) were the most weakly supported.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"The Condor","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Cooper Ornithological Club","publisherLocation":"Santa Clara","doi":"10.1650/CONDOR-15-9.1","usgsCitation":"McGowan, C.P., 2015, Comparing models of Red Knot population dynamics: The Condor, v. 117, no. 4, p. 494-502, https://doi.org/10.1650/CONDOR-15-9.1.","productDescription":"9 p.","startPage":"494","endPage":"502","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061278","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":472231,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-9.1","text":"Publisher Index Page"},{"id":318370,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56cee255e4b015c306ec5e96","contributors":{"authors":[{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":621321,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70162625,"text":"70162625 - 2015 - Risk assessment of brine contamination to aquatic resources from energy development in glacial drift deposits: Williston Basin, USA","interactions":[],"lastModifiedDate":"2016-01-27T13:38:31","indexId":"70162625","displayToPublicDate":"2015-03-01T14:45:00","publicationYear":"2015","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}},"title":"Risk assessment of brine contamination to aquatic resources from energy development in glacial drift deposits: Williston Basin, USA","docAbstract":"Contamination to aquatic resources from co-produced water (brine) associated with energy development has been documented in the northeastern portion of the Williston Basin; an area mantled by glacial drift. The presence and magnitude of brine contamination can be determined using the contamination index (CI) value from water samples. Recently, the U.S. Geological Survey published a section (~ 2.59 km2) level risk assessment of brine contamination to aquatic resources for Sheridan County, Montana, using oilfield and hydrogeological parameters.
\nOur goal was to improve the Sheridan County assessment (SCA) and evaluate the use of this new Williston Basin assessment (WBA) across 31 counties mantled by glacial drift in the Williston Basin. To determine if the WBA model improved the SCA model, results from both assessments were compared to CI values from 37 surface and groundwater samples collected to evaluate the SCA. The WBA (R2 = 0.65) outperformed the SCA (R2 = 0.52) indicating improved model performance. Applicability across the Williston Basin was evaluated by comparing WBA results to CI values from 123 surface water samples collected from 97 sections. Based on the WBA, the majority (83.5%) of sections lacked an oil well and had minimal risk. Sections with one or more oil wells comprised low (8.4%), moderate (6.5%), or high (1.7%) risk areas. The percentage of contaminated water samples, percentage of sections with at least one contaminated sample, and the average CI value of contaminated samples increased from low to high risk indicating applicability across the Williston Basin. Furthermore, the WBA performed better compared to only the contaminated samples (R2 = 0.62) versus all samples (R2 = 0.38). This demonstrates that the WBA was successful at identifying sections, but not individual aquatic resources, with an increased risk of contamination; therefore, WBA results can prioritize future sampling within areas of increased risk.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Science of the Total Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.scitotenv.2014.11.054","collaboration":"USFWS Region 6 Inventory and Monitoring Program","usgsCitation":"Preston, T.M., and Chesley-Preston, T.L., 2015, Risk assessment of brine contamination to aquatic resources from energy development in glacial drift deposits: Williston Basin, USA: Science of the Total Environment, v. 508, p. 534-545, https://doi.org/10.1016/j.scitotenv.2014.11.054.","productDescription":"12 p.","startPage":"534","endPage":"545","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059481","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":314928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.16259765625,\n 48.96579381461063\n ],\n [\n -109.40185546874999,\n 48.99463598353405\n ],\n [\n -109.57763671875,\n 47.82790816919327\n ],\n [\n -108.43505859374999,\n 47.635783590864854\n ],\n [\n -107.77587890625,\n 47.54687159892238\n ],\n [\n -106.8310546875,\n 47.7097615426664\n ],\n [\n -106.50146484374999,\n 48.019324184801185\n ],\n [\n -104.9853515625,\n 48.09275716032736\n ],\n [\n -104.765625,\n 48.04870994288686\n ],\n [\n -103.9306640625,\n 47.97521412341618\n ],\n [\n -103.71093749999999,\n 48.122101028190805\n ],\n [\n -103.447265625,\n 47.945786463687185\n ],\n [\n -103.1396484375,\n 48.1367666796927\n ],\n [\n -102.568359375,\n 48.03401915864286\n ],\n [\n -102.26074218749999,\n 47.66538735632654\n ],\n [\n -101.8212890625,\n 47.487513008956554\n ],\n [\n -101.337890625,\n 47.56170075451973\n ],\n [\n -101.337890625,\n 47.249406957888446\n ],\n [\n -100.986328125,\n 47.249406957888446\n ],\n [\n -100.8984375,\n 46.89023157359399\n ],\n [\n -100.5908203125,\n 46.51351558059737\n ],\n [\n -100.5029296875,\n 46.28622391806708\n ],\n [\n -100.6787109375,\n 46.10370875598026\n ],\n [\n -100.45898437499999,\n 45.96642454131025\n ],\n [\n -99.5361328125,\n 45.9511496866914\n ],\n [\n -98.228759765625,\n 45.96642454131025\n ],\n [\n -98.162841796875,\n 46.927758623434435\n ],\n [\n -97.987060546875,\n 47.71715357016648\n ],\n [\n -98.15185546874999,\n 48.29781249243716\n ],\n [\n -98.81103515625,\n 49.001843917978526\n ],\n [\n -99.16259765625,\n 48.96579381461063\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"508","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56a9f84be4b012c193aa3edc","contributors":{"authors":[{"text":"Preston, Todd M. 0000-0002-8812-9233 tmpreston@usgs.gov","orcid":"https://orcid.org/0000-0002-8812-9233","contributorId":1664,"corporation":false,"usgs":true,"family":"Preston","given":"Todd","email":"tmpreston@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":589941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chesley-Preston, Tara L. tchesley-preston@usgs.gov","contributorId":5557,"corporation":false,"usgs":true,"family":"Chesley-Preston","given":"Tara","email":"tchesley-preston@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":589942,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70143179,"text":"70143179 - 2015 - Distance measures and optimization spaces in quantitative fatty acid signature analysis","interactions":[],"lastModifiedDate":"2018-04-23T10:22:40","indexId":"70143179","displayToPublicDate":"2015-03-01T14:00:00","publicationYear":"2015","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":"Distance measures and optimization spaces in quantitative fatty acid signature analysis","docAbstract":"Quantitative fatty acid signature analysis has become an important method of diet estimation in ecology, especially marine ecology. Controlled feeding trials to validate the method and estimate the calibration coefficients necessary to account for differential metabolism of individual fatty acids have been conducted with several species from diverse taxa. However, research into potential refinements of the estimation method has been limited. We compared the performance of the original method of estimating diet composition with that of five variants based on different combinations of distance measures and calibration-coefficient transformations between prey and predator fatty acid signature spaces. Fatty acid signatures of pseudopredators were constructed using known diet mixtures of two prey data sets previously used to estimate the diets of polar bears Ursus maritimus and gray seals Halichoerus grypus, and their diets were then estimated using all six variants. In addition, previously published diets of Chukchi Sea polar bears were re-estimated using all six methods. Our findings reveal that the selection of an estimation method can meaningfully influence estimates of diet composition. Among the pseudopredator results, which allowed evaluation of bias and precision, differences in estimator performance were rarely large, and no one estimator was universally preferred, although estimators based on the Aitchison distance measure tended to have modestly superior properties compared to estimators based on the Kullback-Leibler distance measure. However, greater differences were observed among estimated polar bear diets, most likely due to differential estimator sensitivity to assumption violations. Our results, particularly the polar bear example, suggest that additional research into estimator performance and model diagnostics is warranted.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.1429","usgsCitation":"Bromaghin, J.F., Rode, K.D., Budge, S.M., and Thiemann, G.W., 2015, Distance measures and optimization spaces in quantitative fatty acid signature analysis: Ecology and Evolution, v. 6, no. 5, p. 1249-1262, https://doi.org/10.1002/ece3.1429.","productDescription":"14 p.","startPage":"1249","endPage":"1262","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059904","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":472234,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.1429","text":"Publisher Index Page"},{"id":298624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-24","publicationStatus":"PW","scienceBaseUri":"5509502ee4b02e76d757e614","contributors":{"authors":[{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":542494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":542495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budge, Suzanne M.","contributorId":92168,"corporation":false,"usgs":false,"family":"Budge","given":"Suzanne","email":"","middleInitial":"M.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":542496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thiemann, Gregory W.","contributorId":83023,"corporation":false,"usgs":false,"family":"Thiemann","given":"Gregory","email":"","middleInitial":"W.","affiliations":[{"id":27291,"text":"York University, Toronto, ON","active":true,"usgs":false}],"preferred":false,"id":542497,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70150428,"text":"70150428 - 2015 - Importance of reservoir tributaries to spawning of migratory fish in the upper Paraná River","interactions":[],"lastModifiedDate":"2015-06-26T12:01:15","indexId":"70150428","displayToPublicDate":"2015-03-01T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Importance of reservoir tributaries to spawning of migratory fish in the upper Paraná River","docAbstract":"Regulation of rivers by dams transforms previously lotic reaches above the dam into lentic ones and limits or prevents longitudinal connectivity, which impairs access to suitable habitats for the reproduction of many migratory fish species. Frequently, unregulated tributaries can provide important habitat heterogeneity to a regulated river and may mitigate the influence of impoundments on the mainstem river. We evaluated the importance of tributaries to spawning of migratory fish species over three spawning seasons, by comparing several abiotic conditions and larval fish distributions in four rivers that are tributaries to an impounded reach of the Upper Parana River, Brazil. Our study confirmed reproduction of at least 8 long-distance migrators, likely nine, out of a total of 19 occurring in the Upper Parana River. Total larval densities and percentage species composition differed among tributaries, but the differences were not consistent among spawning seasons and unexpectedly were not strongly related to annual differences in temperature and hydrology. We hypothesize that under present conditions, densities of larvae of migratory species may be better related to efficiency of fish passage facilities than to temperature and hydrology. Our study indicates that adult fish are finding suitable habitat for spawning in tributaries, fish eggs are developing into larvae, and larvae are finding suitable rearing space in lagoons adjacent to the tributaries. Our findings also suggest the need for establishment of protected areas in unregulated and lightly regulated tributaries to preserve essential spawning and nursery habitats.
","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Chichester, West Sussex, UK","doi":"10.1002/rra.2755","usgsCitation":"da Silva, P.S., Makrakis, M.C., Miranda, L.E., Makrakis, S., Assumpcao, L., Paula, S., Dias, J.H., and Marques, H., 2015, Importance of reservoir tributaries to spawning of migratory fish in the upper Paraná River: River Research and Applications, v. 31, no. 3, p. 313-322, https://doi.org/10.1002/rra.2755.","productDescription":"10 p.","startPage":"313","endPage":"322","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049149","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":302460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":302320,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/rra.2755/abstract"}],"volume":"31","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-04-02","publicationStatus":"PW","scienceBaseUri":"558e77b7e4b0b6d21dd6595d","contributors":{"authors":[{"text":"da Silva, P. S.","contributorId":143807,"corporation":false,"usgs":false,"family":"da Silva","given":"P.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":557147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Makrakis, Maristela Cavicchioli","contributorId":90208,"corporation":false,"usgs":true,"family":"Makrakis","given":"Maristela","email":"","middleInitial":"Cavicchioli","affiliations":[],"preferred":false,"id":557148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":556871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Makrakis, Sergio","contributorId":95349,"corporation":false,"usgs":true,"family":"Makrakis","given":"Sergio","email":"","affiliations":[],"preferred":false,"id":557149,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Assumpcao, L.","contributorId":143808,"corporation":false,"usgs":false,"family":"Assumpcao","given":"L.","email":"","affiliations":[],"preferred":false,"id":557150,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Paula, S.","contributorId":143809,"corporation":false,"usgs":false,"family":"Paula","given":"S.","affiliations":[],"preferred":false,"id":557151,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dias, Joao Henrique Pinheiro","contributorId":23843,"corporation":false,"usgs":true,"family":"Dias","given":"Joao","email":"","middleInitial":"Henrique Pinheiro","affiliations":[],"preferred":false,"id":557152,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Marques, H.","contributorId":143810,"corporation":false,"usgs":false,"family":"Marques","given":"H.","email":"","affiliations":[],"preferred":false,"id":557153,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70155022,"text":"70155022 - 2015 - The data quality analyzer: a quality control program for seismic data","interactions":[],"lastModifiedDate":"2018-02-07T19:04:03","indexId":"70155022","displayToPublicDate":"2015-03-01T12:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"The data quality analyzer: a quality control program for seismic data","docAbstract":"The U.S. Geological Survey's Albuquerque Seismological Laboratory (ASL) has several initiatives underway to enhance and track the quality of data produced from ASL seismic stations and to improve communication about data problems to the user community. The Data Quality Analyzer (DQA) is one such development and is designed to characterize seismic station data quality in a quantitative and automated manner.
\nThe DQA consists of a metric calculator, a PostgreSQL database, and a Web interface: The metric calculator, SEEDscan, is a Java application that reads and processes miniSEED data and generates metrics based on a configuration file. SEEDscan compares hashes of metadata and data to detect changes in either and performs subsequent recalculations as needed. This ensures that the metric values are up to date and accurate. SEEDscan can be run as a scheduled task or on demand. The PostgreSQL database acts as a central hub where metric values and limited station descriptions are stored at the channel level with one-day granularity. The Web interface dynamically loads station data from the database and allows the user to make requests for time periods of interest, review specific networks and stations, plot metrics as a function of time, and adjust the contribution of various metrics to the overall quality grade of the station.
\nThe quantification of data quality is based on the evaluation of various metrics (e.g., timing quality, daily noise levels relative to long-term noise models, and comparisons between broadband data and event synthetics). Users may select which metrics contribute to the assessment and those metrics are aggregated into a “grade” for each station. The DQA is being actively used for station diagnostics and evaluation based on the completed metrics (availability, gap count, timing quality, deviation from a global noise model, deviation from a station noise model, coherence between co-located sensors, and comparison between broadband data and synthetics for earthquakes) on stations in the Global Seismographic Network and Advanced National Seismic System.
","language":"English","publisher":"Computer Oriented Geological Society","publisherLocation":"Oxford","doi":"10.1016/j.cageo.2014.12.006","usgsCitation":"Ringler, A.T., Hagerty, M., Holland, J., Gonzales, A., Gee, L.S., Edwards, J., Wilson, D.C., and Baker, A., 2015, The data quality analyzer: a quality control program for seismic data: Computers & Geosciences, v. 76, p. 96-111, https://doi.org/10.1016/j.cageo.2014.12.006.","productDescription":"16 p.","startPage":"96","endPage":"111","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061275","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":305952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55b361b6e4b09a3b01b5dabb","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":564683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagerty, M.T.","contributorId":145577,"corporation":false,"usgs":false,"family":"Hagerty","given":"M.T.","email":"","affiliations":[{"id":13422,"text":"Boston College","active":true,"usgs":false}],"preferred":false,"id":564684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holland, James F. jholland@usgs.gov","contributorId":5334,"corporation":false,"usgs":true,"family":"Holland","given":"James F.","email":"jholland@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":564685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gonzales, A.","contributorId":145578,"corporation":false,"usgs":false,"family":"Gonzales","given":"A.","email":"","affiliations":[{"id":16157,"text":"Honeywell Technology Solutions Incoporation","active":true,"usgs":false}],"preferred":false,"id":564686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gee, Lind S. lgee@usgs.gov","contributorId":145579,"corporation":false,"usgs":true,"family":"Gee","given":"Lind","email":"lgee@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":564687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, J.D.","contributorId":69622,"corporation":false,"usgs":true,"family":"Edwards","given":"J.D.","email":"","affiliations":[],"preferred":false,"id":564688,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":564689,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baker, Adam ambaker@usgs.gov","contributorId":145581,"corporation":false,"usgs":true,"family":"Baker","given":"Adam","email":"ambaker@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":564690,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70143924,"text":"70143924 - 2015 - Soil disturbance as a driver of increased stream salinity in a semiarid watershed undergoing energy development","interactions":[],"lastModifiedDate":"2018-08-09T12:45:36","indexId":"70143924","displayToPublicDate":"2015-03-01T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Soil disturbance as a driver of increased stream salinity in a semiarid watershed undergoing energy development","docAbstract":"Salinization is a global threat to the quality of streams and rivers, but it can have many causes. Oil and gas development were investigated as one of several potential causes of changes in the salinity of Muddy Creek, which drains 2470 km2 of mostly public land in Wyoming, U.S.A. Stream discharge and salinity vary with seasonal snowmelt and define a primary salinity-discharge relationship. Salinity, measured by specific conductance, increased substantially in 2009 and was 53-71% higher at low discharge and 33-34% higher at high discharge for the years 2009-2012 compared to 2005-2008. Short-term processes (e.g., flushing of efflorescent salts) cause within-year deviations from the primary relation but do not obscure the overall increase in salinity. Dissolved elements associated with increased salinity include calcium, magnesium, and sulfate, a composition that points to native soil salts derived from marine shales as a likely source. Potential causes of the salinity increase were evaluated for consistency by using measured patterns in stream chemistry, slope of the salinity-discharge relationship, and inter-annual timing of the salinity increase. Potential causes that were inconsistent with one or more of those criteria included effects from precipitation, evapotranspiration, reservoirs, grazing, irrigation return flow, groundwater discharge, discharge of energy co-produced waters, and stream habitat restoration. In contrast, surface disturbance of naturally salt-rich soil by oil and gas development activities, such as pipeline, road, and well pad construction, is a reasonable candidate for explaining the salinity increase. As development continues to expand in semiarid lands worldwide, the potential for soil disturbance to increase stream salinity should be considered, particularly where soils host substantial quantities of native salts.
","language":"English","publisher":"European Geophysical Society","publisherLocation":"New York, NY","doi":"10.1016/j.jhydrol.2015.02.020","usgsCitation":"Bern, C., Clark, M.L., Schmidt, T., Holloway, J.M., and Mcdougal, R., 2015, Soil disturbance as a driver of increased stream salinity in a semiarid watershed undergoing energy development: Journal of Hydrology: Regional Studies, v. 524, p. 123-136, https://doi.org/10.1016/j.jhydrol.2015.02.020.","productDescription":"14 p.","startPage":"123","endPage":"136","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057757","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":298890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"524","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55128ab5e4b02e76d75bd621","contributors":{"authors":[{"text":"Bern, Carleton R. cbern@usgs.gov","contributorId":139818,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton R.","email":"cbern@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":543108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Melanie L. mlclark@usgs.gov","contributorId":1827,"corporation":false,"usgs":true,"family":"Clark","given":"Melanie","email":"mlclark@usgs.gov","middleInitial":"L.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":543110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holloway, JoAnn M. 0000-0003-3603-7668 jholloway@usgs.gov","orcid":"https://orcid.org/0000-0003-3603-7668","contributorId":918,"corporation":false,"usgs":true,"family":"Holloway","given":"JoAnn","email":"jholloway@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":543111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mcdougal, Robert rmcdouga@usgs.gov","contributorId":139819,"corporation":false,"usgs":true,"family":"Mcdougal","given":"Robert","email":"rmcdouga@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":543112,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70147094,"text":"70147094 - 2015 - Long‐term time‐dependent probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3)","interactions":[],"lastModifiedDate":"2015-04-28T09:15:02","indexId":"70147094","displayToPublicDate":"2015-03-01T10:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Long‐term time‐dependent probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3)","docAbstract":"The 2014 Working Group on California Earthquake Probabilities (WGCEP 2014) presents time-dependent earthquake probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3). Building on the UCERF3 time-independent model, published previously, renewal models are utilized to represent elastic-rebound-implied probabilities. A new methodology has been developed that solves applicability issues in the previous approach for un-segmented models. The new methodology also supports magnitude-dependent aperiodicity and accounts for the historic open interval on faults that lack a date-of-last-event constraint. Epistemic uncertainties are represented with a logic tree, producing 5,760 different forecasts. Results for a variety of evaluation metrics are presented, including logic-tree sensitivity analyses and comparisons to the previous model (UCERF2). For 30-year M≥6.7 probabilities, the most significant changes from UCERF2 are a threefold increase on the Calaveras fault and a threefold decrease on the San Jacinto fault. Such changes are due mostly to differences in the time-independent models (e.g., fault slip rates), with relaxation of segmentation and inclusion of multi-fault ruptures being particularly influential. In fact, some UCERF2 faults were simply too long to produce M 6.7 sized events given the segmentation assumptions in that study. Probability model differences are also influential, with the implied gains (relative to a Poisson model) being generally higher in UCERF3. Accounting for the historic open interval is one reason. Another is an effective 27% increase in the total elastic-rebound-model weight. The exact factors influencing differences between UCERF2 and UCERF3, as well as the relative importance of logic-tree branches, vary throughout the region, and depend on the evaluation metric of interest. For example, M≥6.7 probabilities may not be a good proxy for other hazard or loss measures. This sensitivity, coupled with the approximate nature of the model and known limitations, means the applicability of UCERF3 should be evaluated on a case-by-case basis.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"Stanford, CA","doi":"10.1785/0120140093","usgsCitation":"Field, E., Biasi, G.P., Bird, P., Dawson, T.E., Felzer, K.R., Jackson, D.A., Johnson, K.M., Jordan, T.H., Madden, C., Michael, A.J., Milner, K., Page, M.T., Parsons, T.E., Powers, P., Shaw, B., Thatcher, W.R., Weldon, R.J., and Zeng, Y., 2015, Long‐term time‐dependent probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3): Bulletin of the Seismological Society of America, v. 105, no. 2A, p. 511-543, https://doi.org/10.1785/0120140093.","productDescription":"33 p.","startPage":"511","endPage":"543","numberOfPages":"33","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061354","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":299910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"105","issue":"2A","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-10","publicationStatus":"PW","scienceBaseUri":"5540af2ce4b0a658d79392ad","contributors":{"authors":[{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":545633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biasi, Glenn P.","contributorId":20436,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":545634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bird, Peter","contributorId":78643,"corporation":false,"usgs":true,"family":"Bird","given":"Peter","affiliations":[],"preferred":false,"id":545635,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dawson, Timothy E.","contributorId":24429,"corporation":false,"usgs":false,"family":"Dawson","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":7099,"text":"Calif. Geol. Survey","active":true,"usgs":false}],"preferred":false,"id":545636,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Felzer, Karen R. kfelzer@usgs.gov","contributorId":2573,"corporation":false,"usgs":true,"family":"Felzer","given":"Karen","email":"kfelzer@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":545637,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jackson, David A.","contributorId":40906,"corporation":false,"usgs":true,"family":"Jackson","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":545638,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Kaj M.","contributorId":92526,"corporation":false,"usgs":true,"family":"Johnson","given":"Kaj","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":545639,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jordan, Thomas H.","contributorId":75055,"corporation":false,"usgs":true,"family":"Jordan","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":545640,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Madden, Christopher","contributorId":47280,"corporation":false,"usgs":true,"family":"Madden","given":"Christopher","affiliations":[],"preferred":false,"id":545641,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":545642,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Milner, Kevin","contributorId":28886,"corporation":false,"usgs":true,"family":"Milner","given":"Kevin","affiliations":[],"preferred":false,"id":545643,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":545674,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":545675,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Powers, Peter","contributorId":92596,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","affiliations":[],"preferred":false,"id":545676,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Shaw, Bruce E.","contributorId":93810,"corporation":false,"usgs":true,"family":"Shaw","given":"Bruce E.","affiliations":[],"preferred":false,"id":545677,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Thatcher, Wayne R. 0000-0001-6324-545X thatcher@usgs.gov","orcid":"https://orcid.org/0000-0001-6324-545X","contributorId":2599,"corporation":false,"usgs":true,"family":"Thatcher","given":"Wayne","email":"thatcher@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":545678,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Weldon, Ray J. II","contributorId":47859,"corporation":false,"usgs":true,"family":"Weldon","given":"Ray","suffix":"II","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":545679,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Zeng, Yuehua zeng@usgs.gov","contributorId":1623,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":545680,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70135275,"text":"70135275 - 2015 - Mineral resource of the month: silver","interactions":[],"lastModifiedDate":"2015-05-20T09:00:03","indexId":"70135275","displayToPublicDate":"2015-03-01T10:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1419,"text":"Earth","active":true,"publicationSubtype":{"id":10}},"title":"Mineral resource of the month: silver","docAbstract":"Silver, one of the eight precious or noble metals, has been used extensively throughout recorded history for various medical purposes, ornaments and utensils, and for its intrinsic value as the basis for trade and monetary systems. Silver has played a significant role in world history, financing a Greek victory over the Persians in 480 B.C., helping Spain become a world power in the 16th and 17th centuries, and helping fund the Union forces during the U.S. Civil War, to give a few examples.
\nSilver occurs as a native metal; in sulfide ores of copper, lead and zinc; and sometimes with bismuth and antimony. Silver is always present in ores containing gold. The Silver Institute estimated that, in 2013, about 29 percent of global mined silver came from silver ores, 38 percent came from lead-zinc ores, 20 percent came from copper ores and 13 percent came from gold ores.
\nSilver's properties include its ability to endure extreme temperatures, its high reflectance of light, its thermal and electrical conductivity (the highest of all metals), and its strength, malleability and ductility. Demand for silver arises from three areas: industrial applications (in electronics, brazing alloys and solders, photography and other uses), investment (including coins and bars), and silver jewelry and decor (including silverware).
\nSilver-halide X-rays were long the standard, but are now being replaced by digital imaging technology. Since 2000, demand for silver in photographic applications has also declined owing to the use of digital photography. In 2013, uses in electronics accounted for 42 percent of U.S. silver consumption; coins and metals for 35 percent; photography for 13 percent; jewelry and silverware for 7 percent; and other uses for 3 percent.
\nSilver is also used in solar power generation: 90 percent of crystalline silicon photovoltaic solar cells use silver paste. On windows, a transparent layer of silver reflects up to 95 percent of sunlight, saving energy. In water purification, use of silver eliminates the need for corrosive chlorine.
\nFor more information on silver and other mineral resources, visit: http://minerals.usgs.gov/minerals.
","language":"English","publisher":"American Geological Institute","publisherLocation":"Alexandria, VA","usgsCitation":"Katrivanos, F.C., 2015, Mineral resource of the month: silver: Earth, v. 60, no. 3, p. 53-53.","productDescription":"1 p.","startPage":"53","endPage":"53","numberOfPages":"1","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061694","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":300596,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.earthmagazine.org/article/mineral-resource-month-silver"},{"id":300597,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"555db054e4b0a92fa7eb831a","contributors":{"authors":[{"text":"Katrivanos, Florence C. fkatrivanos@usgs.gov","contributorId":2109,"corporation":false,"usgs":true,"family":"Katrivanos","given":"Florence","email":"fkatrivanos@usgs.gov","middleInitial":"C.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":527006,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70148597,"text":"70148597 - 2015 - The Red Atrapa Sismos (Quake Catcher Network in Mexico): assessing performance during large and damaging earthquakes.","interactions":[],"lastModifiedDate":"2015-06-26T12:39:02","indexId":"70148597","displayToPublicDate":"2015-03-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The Red Atrapa Sismos (Quake Catcher Network in Mexico): assessing performance during large and damaging earthquakes.","docAbstract":"The Quake‐Catcher Network (QCN) is an expanding seismic array made possible by thousands of participants who volunteered time and resources from their computers to record seismic data using low‐cost accelerometers (http://qcn.stanford.edu/; last accessed December 2014). Sensors based on Micro‐Electromechanical Systems (MEMS) technology have rapidly improved over the last few years due to the demand of the private sector (e.g., automobiles, cell phones, and laptops). For strong‐motion applications, low‐cost MEMS accelerometers have promising features due to an increasing resolution and near‐linear phase and amplitude response (Cochran, Lawrence, Christensen, and Jakka, 2009; Clayton et al., 2011; Evans et al., 2014).
\nEach volunteer computer monitors ground motion and communicates using the Berkeley Open Infrastructure for Network Computing (BOINC, Anderson, 2004). Using a standard short‐term average, long‐term average (STLA) algorithm (Earle and Shearer, 1994; Cochran, Lawrence, Christensen, Chung, 2009; Cochran, Lawrence, Christensen, and Jakka, 2009), volunteer computer and sensor systems detect abrupt changes in the acceleration recordings. Each time a possible trigger signal is declared, a small package of information containing sensor and ground‐motion information is streamed to one of the QCN servers (Chung et al., 2011). Trigger signals, correlated in space and time, are then processed by the QCN server to look for potential earthquakes.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220140171","usgsCitation":"Dominguez, L.A., Yildirim, B., Husker, A.L., Cochran, E.S., Christensen, C., and Cruz-Atienza, V., 2015, The Red Atrapa Sismos (Quake Catcher Network in Mexico): assessing performance during large and damaging earthquakes.: Seismological Research Letters, v. 86, no. 3, p. 848-855, https://doi.org/10.1785/0220140171.","productDescription":"8 p.","startPage":"848","endPage":"855","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059988","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":472248,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20210518-133755918","text":"External 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enhancement program","interactions":[],"lastModifiedDate":"2015-09-16T09:56:26","indexId":"70154890","displayToPublicDate":"2015-03-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3897,"text":"Freshwater Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using an experimental manipulation to determine the effectiveness of a stock enhancement program","docAbstract":"We used an experimental manipulation to determine the impact of stocking 178 mm channel catfish Ictalurus punctatus in six impoundments. The study design consisted of equal numbers (two) of control, ceased-stock, and stocked treatments that were sampled one year before and two years after stocking. Relative abundance, growth, size structure, and average weight significantly changed over time based on samples collected with hoop nets. Catch rates decreased at both ceased-stock lakes and increased for one stocked lake, while growth rates changed for at least one ceased-stock and stocked lake. The average weight of channel catfish in the ceased-stock treatment increased by 6% and 25%, whereas weight decreased by 28% and 78% in both stocked lakes. The variability in observed responses between lakes in both ceased-stock and stocked treatments indicates that a one-size-fits-all stocking agenda is impractical, suggesting lake specific and density-dependent mechanisms affect channel catfish population dynamics.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2015.1021715","usgsCitation":"Stewart, D., and Long, J.M., 2015, Using an experimental manipulation to determine the effectiveness of a stock enhancement program: Freshwater Ecology, https://doi.org/10.1080/02705060.2015.1021715.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056629","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":472256,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2015.1021715","text":"Publisher Index Page"},{"id":308165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-18","publicationStatus":"PW","scienceBaseUri":"55fa92d6e4b05d6c4e501ae6","contributors":{"authors":[{"text":"Stewart, David R.","contributorId":141323,"corporation":false,"usgs":false,"family":"Stewart","given":"David R.","affiliations":[],"preferred":false,"id":572466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564317,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154798,"text":"70154798 - 2015 - Monitoring and modeling wetland chloride concentrations in relationship to oil and gas development","interactions":[],"lastModifiedDate":"2018-01-05T10:03:54","indexId":"70154798","displayToPublicDate":"2015-03-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring and modeling wetland chloride concentrations in relationship to oil and gas development","docAbstract":"Extraction of oil and gas via unconventional methods is becoming an important aspect of energy production worldwide. Studying the effects of this development in countries where these technologies are being widely used may provide other countries, where development may be proposed, with some insight in terms of concerns associated with development. A fairly recent expansion of unconventional oil and gas development in North America provides such an opportunity. Rapid increases in energy development in North America have caught the attention of managers and scientists as a potential stressor for wildlife and their habitats. Of particular concern in the Northern Great Plains of the U.S. is the potential for chloride-rich produced water associated with unconventional oil and gas development to alter the water chemistry of wetlands. We describe a landscape scale modeling approach designed to examine the relationship between potential chloride contamination in wetlands and patterns of oil and gas development. We used a spatial Bayesian hierarchical modeling approach to assess multiple models explaining chloride concentrations in wetlands. These models included effects related to oil and gas wells (e.g. age of wells, number of wells) and surficial geology (e.g. glacial till, outwash). We found that the model containing the number of wells and the surficial geology surrounding a wetland best explained variation in chloride concentrations. Our spatial predictions showed regions of localized high chloride concentrations. Given the spatiotemporal variability of regional wetland water chemistry, we do not regard our results as predictions of contamination, but rather as a way to identify locations that may require more intensive sampling or further investigation. We suggest that an approach like the one outlined here could easily be extended to more of an adaptive monitoring approach to answer questions about chloride contamination risk that are of interest to managers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2014.10.028","usgsCitation":"Post van der Burg, M., and Tangen, B., 2015, Monitoring and modeling wetland chloride concentrations in relationship to oil and gas development: Journal of Environmental Management, v. 150, p. 120-127, https://doi.org/10.1016/j.jenvman.2014.10.028.","productDescription":"8 p.","startPage":"120","endPage":"127","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057019","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":306644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","county":"Bottineau County, Burke County, Daniels County, Divide County, McHenry County, Mountrail County, Renville County, Roosevelt County, Sheridan County (MO), Sheridan County (ND), Ward County, Williams County","otherGeospatial":"Bakken Formation, Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.00732421875,\n 48.06339653776211\n ],\n [\n -105.00732421875,\n 48.980216985374994\n ],\n [\n -100.94238281249999,\n 48.980216985374994\n ],\n [\n -100.94238281249999,\n 48.06339653776211\n ],\n [\n -105.00732421875,\n 48.06339653776211\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdbfb9e4b08400b1fe1419","chorus":{"doi":"10.1016/j.jenvman.2014.10.028","url":"http://dx.doi.org/10.1016/j.jenvman.2014.10.028","publisher":"Elsevier BV","authors":"Post van der Burg Max, Tangen Brian A.","journalName":"Journal of Environmental Management","publicationDate":"3/2015","auditedOn":"1/5/2015"},"contributors":{"authors":[{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":564194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tangen, Brian A. 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":467,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian A.","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":564195,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154774,"text":"70154774 - 2015 - Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon","interactions":[],"lastModifiedDate":"2022-11-14T17:37:39.358873","indexId":"70154774","displayToPublicDate":"2015-03-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon","docAbstract":"Dams and river regulation greatly alter the downstream environment for gross primary production (GPP) because of changes in water clarity, flow, and temperature regimes. We estimated reach-scale GPP in five locations of the regulated Colorado River in Grand Canyon using an open channel model of dissolved oxygen. Benthic GPP dominates in Grand Canyon due to fast transport times and low pelagic algal biomass. In one location, we used a 738 days time series of GPP to identify the relative contribution of different physical controls of GPP. We developed both linear and semimechanistic time series models that account for unmeasured temporal covariance due to factors such as algal biomass dynamics. GPP varied from 0 g O2 m−2 d−1 to 3.0 g O2 m−2 d−1 with a relatively low annual average of 0.8 g O2 m−2 d−1. Semimechanistic models fit the data better than linear models and demonstrated that variation in turbidity primarily controlled GPP. Lower solar insolation during winter and from cloud cover lowered GPP much further. Hydropeaking lowered GPP but only during turbid conditions. Using the best model and parameter values, the model accurately predicted seasonal estimates of GPP at 3 of 4 upriver sites and outperformed the linear model at all sites; discrepancies were likely from higher algal biomass at upstream sites. This modeling approach can predict how changes in physical controls will affect relative rates of GPP throughout the 385 km segment of the Colorado River in Grand Canyon and can be easily applied to other streams and rivers.
","language":"English","publisher":"Wiley","doi":"10.1002/lno.10031","usgsCitation":"Hall, R., Yackulic, C.B., Kennedy, T., Yard, M., Rosi-Marshall, E.J., Voichick, N., and Behn, K.E., 2015, Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon: Limnology and Oceanography, v. 60, no. 2, p. 512-516, https://doi.org/10.1002/lno.10031.","productDescription":"5 p.","startPage":"512","endPage":"516","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056074","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":472242,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10031","text":"Publisher Index Page"},{"id":306634,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.97690643788997,\n 35.96223553892966\n ],\n [\n -111.95607071326728,\n 36.15089215745617\n ],\n [\n -112.47488025637692,\n 36.439732993660684\n ],\n [\n -113.00202408933613,\n 36.35587791388548\n ],\n [\n -113.62917940048527,\n 35.88968479994075\n ],\n [\n -113.53125149475788,\n 35.705479139380046\n ],\n [\n -113.28747351666969,\n 35.724088071319485\n ],\n [\n -113.16870988631914,\n 35.9959573825395\n ],\n [\n -112.61031246642614,\n 36.256812611305506\n ],\n [\n -111.97690643788997,\n 35.96223553892966\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-01-30","publicationStatus":"PW","scienceBaseUri":"55cdbfc0e4b08400b1fe1456","chorus":{"doi":"10.1002/lno.10031","url":"http://dx.doi.org/10.1002/lno.10031","publisher":"Wiley-Blackwell","authors":"Hall Robert O., Yackulic Charles B., Kennedy Theodore A., Yard Michael D., Rosi-Marshall Emma J., Voichick Nicholas, Behn Kathrine E.","journalName":"Limnology and Oceanography","publicationDate":"1/30/2015","auditedOn":"1/29/2017","publiclyAccessibleDate":"1/30/2015"},"contributors":{"authors":[{"text":"Hall, Robert O. Jr.","contributorId":145459,"corporation":false,"usgs":false,"family":"Hall","given":"Robert O.","suffix":"Jr.","affiliations":[{"id":16121,"text":"Uni. of Wyoming, Department of Zoology and Physiology","active":true,"usgs":false}],"preferred":false,"id":564095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":564094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Theodore A. tkennedy@usgs.gov","contributorId":140027,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore A.","email":"tkennedy@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":564096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":2889,"corporation":false,"usgs":true,"family":"Yard","given":"Michael D.","email":"myard@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":564097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosi-Marshall, Emma J.","contributorId":17722,"corporation":false,"usgs":true,"family":"Rosi-Marshall","given":"Emma","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":564098,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Voichick, Nicholas nvoichick@usgs.gov","contributorId":5015,"corporation":false,"usgs":true,"family":"Voichick","given":"Nicholas","email":"nvoichick@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":564099,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Behn, Kathrine E.","contributorId":83839,"corporation":false,"usgs":true,"family":"Behn","given":"Kathrine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":564100,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70174829,"text":"70174829 - 2015 - Quantifying suspended sediment loads delivered to Cheney Reservoir, Kansas: Temporal patterns and management implications","interactions":[],"lastModifiedDate":"2016-07-18T11:37:50","indexId":"70174829","displayToPublicDate":"2015-03-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying suspended sediment loads delivered to Cheney Reservoir, Kansas: Temporal patterns and management implications","docAbstract":"Cheney Reservoir, constructed during 1962 to 1965, is the primary water supply for the city of Wichita, the largest city in Kansas. Sediment is an important concern for the reservoir as it degrades water quality and progressively decreases water storage capacity. Long-term data collection provided a unique opportunity to estimate the annual suspended sediment loads for the entire history of the reservoir. To quantify and characterize sediment loading to Cheney Reservoir, discrete suspended sediment samples and continuously measured streamflow data were collected from the North Fork Ninnescah River, the primary inflow to Cheney Reservoir, over a 48-year period. Continuous turbidity data also were collected over a 15-year period. These data were used together to develop simple linear regression models to compute continuous suspended sediment concentrations and loads from 1966 to 2013. The inclusion of turbidity as an additional explanatory variable with streamflow improved regression model diagnostics and increased the amount of variability in suspended sediment concentration explained by 14%. Using suspended sediment concentration from the streamflow-only model, the average annual suspended sediment load was 102,517 t (113,006 tn) and ranged from 4,826 t (5,320 tn) in 1966 to 967,569 t (1,066,562 tn) in 1979. The sediment load in 1979 accounted for about 20% of the total load over the 48-year history of the reservoir and 92% of the 1979 sediment load occurred in one 24-hour period during a 1% annual exceedance probability flow event (104-year flood). Nearly 60% of the reservoir sediment load during the 48-year study period occurred in 5 years with extreme flow events (9% to 1% annual exceedance probability, or 11- to 104-year flood events). A substantial portion (41%) of sediment was transported to the reservoir during five storm events spanning only eight 24-hour periods during 1966 to 2013. Annual suspended sediment load estimates based on streamflow were, on average, within ±20% of estimates based on streamflow and turbidity combined. Results demonstrate that large suspended sediment loads are delivered to Cheney Reservoir in very short time periods, indicating that sediment management plans eventually must address large, infrequent inflow events to be effective.
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.70.2.91","usgsCitation":"Stone, M.L., Juracek, K.E., Graham, J., and Foster, G.M., 2015, Quantifying suspended sediment loads delivered to Cheney Reservoir, Kansas: Temporal patterns and management implications: Journal of Soil and Water Conservation, v. 70, no. 2, p. 91-100, https://doi.org/10.2489/jswc.70.2.91.","productDescription":"10 p.","startPage":"91","endPage":"100","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058102","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":472250,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.70.2.91","text":"Publisher Index Page"},{"id":325358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.0692138671875,\n 37.67077737288316\n ],\n [\n -99.0692138671875,\n 38.01564013749379\n ],\n [\n -97.77145385742188,\n 38.01564013749379\n ],\n [\n -97.77145385742188,\n 37.67077737288316\n ],\n [\n -99.0692138671875,\n 37.67077737288316\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"70","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-12","publicationStatus":"PW","scienceBaseUri":"578dfdb8e4b0f1bea0e0f8e1","contributors":{"authors":[{"text":"Stone, Mandy L. 0000-0002-6711-1536 mstone@usgs.gov","orcid":"https://orcid.org/0000-0002-6711-1536","contributorId":4409,"corporation":false,"usgs":true,"family":"Stone","given":"Mandy","email":"mstone@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":642667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Juracek, Kyle E. 0000-0002-2102-8980 kjuracek@usgs.gov","orcid":"https://orcid.org/0000-0002-2102-8980","contributorId":2022,"corporation":false,"usgs":true,"family":"Juracek","given":"Kyle","email":"kjuracek@usgs.gov","middleInitial":"E.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":642668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":642669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, Guy M. 0000-0002-9581-057X gfoster@usgs.gov","orcid":"https://orcid.org/0000-0002-9581-057X","contributorId":149145,"corporation":false,"usgs":true,"family":"Foster","given":"Guy","email":"gfoster@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642670,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70175335,"text":"70175335 - 2015 - Landscape community genomics: understanding eco-evolutionary processes in complex environments","interactions":[],"lastModifiedDate":"2017-05-03T13:39:18","indexId":"70175335","displayToPublicDate":"2015-03-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Landscape community genomics: understanding eco-evolutionary processes in complex environments","docAbstract":"Extrinsic factors influencing evolutionary processes are often categorically lumped into interactions that are environmentally (e.g., climate, landscape) or community-driven, with little consideration of the overlap or influence of one on the other. However, genomic variation is strongly influenced by complex and dynamic interactions between environmental and community effects. Failure to consider both effects on evolutionary dynamics simultaneously can lead to incomplete, spurious, or erroneous conclusions about the mechanisms driving genomic variation. We highlight the need for a landscape community genomics (LCG) framework to help to motivate and challenge scientists in diverse fields to consider a more holistic, interdisciplinary perspective on the genomic evolution of multi-species communities in complex environments.
","language":"English","publisher":"Cell Press","doi":"10.1016/j.tree.2015.01.005","usgsCitation":"Hand, B.K., Lowe, W.H., Kovach, R.P., Muhlfeld, C.C., and Luikart, G., 2015, Landscape community genomics: understanding eco-evolutionary processes in complex environments: Trends in Ecology and Evolution, v. 30, no. 3, p. 161-168, https://doi.org/10.1016/j.tree.2015.01.005.","productDescription":"8 p.","startPage":"161","endPage":"168","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060258","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":326116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a5b8c7e4b0ebae89b7894c","contributors":{"authors":[{"text":"Hand, Brian K.","contributorId":145915,"corporation":false,"usgs":false,"family":"Hand","given":"Brian","email":"","middleInitial":"K.","affiliations":[{"id":16296,"text":"University of Montana, Polson Montana 59860 USA","active":true,"usgs":false}],"preferred":false,"id":644761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Winsor H.","contributorId":126722,"corporation":false,"usgs":false,"family":"Lowe","given":"Winsor","email":"","middleInitial":"H.","affiliations":[{"id":6577,"text":"University of Montana, Division of Biological Sciences, Missoula, MT, 59812, USA.","active":true,"usgs":false}],"preferred":false,"id":644762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kovach, Ryan P. rkovach@usgs.gov","contributorId":5772,"corporation":false,"usgs":true,"family":"Kovach","given":"Ryan","email":"rkovach@usgs.gov","middleInitial":"P.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":644763,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":644764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Luikart, Gordon","contributorId":145746,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","email":"","affiliations":[{"id":16220,"text":"Flathead Lake Biological Station, Div. Biological Science, UM","active":true,"usgs":false}],"preferred":false,"id":644765,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184222,"text":"70184222 - 2015 - Lapland longspur mortality at an oil well drilling rig site, Laramie County, Wyoming","interactions":[],"lastModifiedDate":"2018-09-04T15:50:20","indexId":"70184222","displayToPublicDate":"2015-03-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Lapland longspur mortality at an oil well drilling rig site, Laramie County, Wyoming","docAbstract":"Two hundred fifty-one Lapland longspur (Calcarius lapponicus) carcasses were recovered around an oil well drilling rig in Laramie County, Wyoming, USA, on December 13–14, 2010, apparent victims of a winter storm and “light entrapment” from the lights on the drilling rig during foggy conditions. We found Lapland longspur carcasses distributed around the drilling rig from 33 m to 171 m. Investigators did not find evidence of bird carcasses on the drilling rig deck or equipment immediately adjacent to the drilling rig. We ruled out chemical toxins and disease as a cause of mortality. Weather conditions, the circular depositional pattern of carcasses around the drilling rig, and bird necropsy results led investigators to conclude that the Lapland longspur mortality was the result of the migrating birds entering the area illuminated by the drilling rig lights in freezing fog and the birds repeatedly circling the drilling rig until they fell to the ground in exhaustion and dying from subsequent trauma. Further research is needed to understand how to most effectively adjust lighting of onshore drilling rigs to reduce the potential for avian light entrapment. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.495","usgsCitation":"Ramirez, P., Dickerson, K.K., Lindstrom, J., Meteyer, C.U., and Darrah, S., 2015, Lapland longspur mortality at an oil well drilling rig site, Laramie County, Wyoming: Wildlife Society Bulletin, v. 39, no. 1, p. 165-168, https://doi.org/10.1002/wsb.495.","productDescription":"4 p.","startPage":"165","endPage":"168","ipdsId":"IP-055019","costCenters":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":499884,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/071370fc3a124551bf09a5d0d8df6720","text":"External Repository"},{"id":336866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","county":"Laramie County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.6506,41.651],[-104.6491,41.5656],[-104.0521,41.5654],[-104.052,41.3949],[-104.0526,41.0236],[-104.0528,41.0017],[-104.1399,41.0019],[-104.4725,41.0027],[-104.4875,41.0027],[-104.5606,41.0028],[-104.5679,41.0028],[-104.6087,41.0046],[-104.6134,41.0048],[-104.6337,41.0056],[-104.6648,41.0047],[-104.6837,41.0041],[-104.7013,41.0035],[-104.83,40.9996],[-104.8341,40.9996],[-104.9385,40.9995],[-104.9425,40.9995],[-105.1109,40.9993],[-105.2763,40.9998],[-105.2774,41.6567],[-105.1706,41.6535],[-105.0575,41.6537],[-104.9419,41.6537],[-104.6506,41.651]]]},\"properties\":{\"name\":\"Laramie\",\"state\":\"WY\"}}]}","volume":"39","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2014-10-13","publicationStatus":"PW","scienceBaseUri":"58be833de4b014cc3a3a9a01","contributors":{"authors":[{"text":"Ramirez, Pedro Jr.","contributorId":99575,"corporation":false,"usgs":true,"family":"Ramirez","given":"Pedro","suffix":"Jr.","email":"","affiliations":[],"preferred":false,"id":680783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dickerson, Kimberly K.","contributorId":51824,"corporation":false,"usgs":true,"family":"Dickerson","given":"Kimberly","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":680784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindstrom, Jim","contributorId":187538,"corporation":false,"usgs":false,"family":"Lindstrom","given":"Jim","email":"","affiliations":[],"preferred":false,"id":680785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meteyer, Carol U. 0000-0002-4007-3410 cmeteyer@usgs.gov","orcid":"https://orcid.org/0000-0002-4007-3410","contributorId":111,"corporation":false,"usgs":true,"family":"Meteyer","given":"Carol","email":"cmeteyer@usgs.gov","middleInitial":"U.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":680609,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Darrah, Scott","contributorId":187539,"corporation":false,"usgs":false,"family":"Darrah","given":"Scott","email":"","affiliations":[],"preferred":false,"id":680786,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70142047,"text":"70142047 - 2015 - Model-based interpretation of sediment concentration and vertical flux measurements in a shallow estuarine environment","interactions":[],"lastModifiedDate":"2015-03-09T11:10:42","indexId":"70142047","displayToPublicDate":"2015-02-10T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Model-based interpretation of sediment concentration and vertical flux measurements in a shallow estuarine environment","docAbstract":"A one-dimensional numerical model describing tidally varying vertical mixing and settling was used to interpret sediment concentrations and vertical fluxes observed in the shoals of South San Francisco Bay by two acoustic Doppler velocimeters (ADVs) at elevations of 0.36 m and 0.72 m above bed. Measured sediment concentrations changed by up to 100 g m−3 over the semidiurnal tidal cycle. These dynamics were dominated by local resuspension and settling. Multiple particle class models suggested the existence of a class with fast settling velocities (ws of 9.0 × 10−4 m s−1 in spring and 5.8 × 10−4 m s−1 in fall) and a slowly settling particle fraction (ws of <1 × 10−7 m s−1 in spring and 1.4 × 10−5 m s−1 in fall). Modeled concentrations of slowly settling particles at 0.36 m were as high as 20 g m−3 during fall and varied with the spring-neap cycle while fine sediment concentrations in spring were constant around 5 g m−3. Analysis of in situ water column floc size distributions suggested that floc properties in the lower part of the water column were most likely governed by particle-size distribution on the bed and not by coagulation, validating our multiple particle size approach. A comparison of different sediment bed models with respect to model performance, sensitivity, and identifiability suggested that the use of a sediment erosion model linear in bottom shear stress τb (E = M (τb − τc)) was the most appropriate choice to describe the field observations when the critical shear stress τc and the proportionality factor M were kept constant.
","language":"English","publisher":"Wiley","doi":"10.1002/lno.10047","usgsCitation":"Brand, A., Lacy, J.R., Gladding, S., Holleman, R., and Stacey, M., 2015, Model-based interpretation of sediment concentration and vertical flux measurements in a shallow estuarine environment: Limnology and Oceanography, v. 60, no. 2, p. 463-481, https://doi.org/10.1002/lno.10047.","productDescription":"19 p.","startPage":"463","endPage":"481","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-030148","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":472281,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.dora.lib4ri.ch/eawag/islandora/object/eawag%3A8063","text":"External Repository"},{"id":298178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.40554809570311,\n 37.42307124980106\n ],\n [\n -122.40554809570311,\n 37.69849090879089\n ],\n [\n -121.92008972167969,\n 37.69849090879089\n ],\n [\n -121.92008972167969,\n 37.42307124980106\n ],\n [\n -122.40554809570311,\n 37.42307124980106\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"60","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-10","publicationStatus":"PW","scienceBaseUri":"54f19544e4b02419550ceae8","contributors":{"authors":[{"text":"Brand, Andreas","contributorId":32415,"corporation":false,"usgs":false,"family":"Brand","given":"Andreas","email":"","affiliations":[{"id":12775,"text":"Department of Surface Waters – Research and Management, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Kastanienbaum, Switzerland","active":true,"usgs":false}],"preferred":false,"id":541568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lacy, Jessica R. 0000-0002-2797-6172 jlacy@usgs.gov","orcid":"https://orcid.org/0000-0002-2797-6172","contributorId":3158,"corporation":false,"usgs":true,"family":"Lacy","given":"Jessica","email":"jlacy@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":541567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gladding, Steve","contributorId":54481,"corporation":false,"usgs":false,"family":"Gladding","given":"Steve","email":"","affiliations":[{"id":12776,"text":"Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA","active":true,"usgs":false}],"preferred":false,"id":541571,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holleman, Rusty","contributorId":139500,"corporation":false,"usgs":false,"family":"Holleman","given":"Rusty","affiliations":[{"id":12776,"text":"Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA","active":true,"usgs":false}],"preferred":false,"id":541570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stacey, Mark T.","contributorId":94531,"corporation":false,"usgs":false,"family":"Stacey","given":"Mark T.","affiliations":[{"id":12776,"text":"Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA","active":true,"usgs":false}],"preferred":false,"id":541569,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70141187,"text":"70141187 - 2015 - Medea genes, handedness and other traits","interactions":[],"lastModifiedDate":"2015-03-17T16:02:58","indexId":"70141187","displayToPublicDate":"2015-02-08T17:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3870,"text":"Journal of Sleep Disorders & Therapy","active":true,"publicationSubtype":{"id":10}},"title":"Medea genes, handedness and other traits","docAbstract":"Medea factors or genes are maternal-effects mechanisms, found in many species, in which the mother's body selectively kills embryos of a certain genotype.Humans have a similar genetic mechanism, the gene RHD which produces Rh-factor involved in blood type.Recently I proposed that RHD acts as a maternal-effects gene that determines handedness (i.e., right handed or non-right handed) in individuals of our species. Here, I argue that RHD functions as a Medea gene as well.The handedness gene (and also RHD itself in some cases) has been implicated in autism spectrum disorders (ASD), bipolar disorder, cerebral laterality (i.e., right-brained or left-brained speech laterality), hair-whorl rotation, schizophrenia, sexual orientation, and speech dyslexia.Identifying the gene or genes that determine handedness or cerebral laterality may help uncover the mechanisms underlying these behavioral phenotypes in our species.A relatively simple test of the handedness hypothesis has been proposed:In a sample of humans for whom handedness has been evaluated, we would need to genotype for RHD by determining whether Rh+ individuals have one or two copies of the dominant allele. If RHD and perhaps also an interaction with RHCE are involved in sexual orientation, it explains how selection could favor a gene or genes which cause some people to become non-heterosexual.The literature on Medea genes provides the explanation:A Medea allele must increase in frequency, sometimes to fixation (i.e., 100% frequency) even if it reduces fecundity (e.g., birth rate).In addition, treatment for RHD maternal-fetal genotype incompatibility, which allows more fetuses to survive to term now, may be one explanation for why ASD appears to be increasing in frequency in some populations, if RHD is indeed the handedness gene, although many other mechanisms have also been suggested. One wonders if bipolar disorder and the other alternative phenotypes are also increasing in frequency.
","language":"English","publisher":"OMICS Publishing Group","publisherLocation":"Los Angeles, CA","doi":"10.4172/2167-0277.1000188","usgsCitation":"Hatfield, J., 2015, Medea genes, handedness and other traits: Journal of Sleep Disorders & Therapy, v. 4, no. 1, 2 p., https://doi.org/10.4172/2167-0277.1000188.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062923","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":472282,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4172/2167-0277.1000188","text":"Publisher Index Page"},{"id":298652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55095031e4b02e76d757e628","contributors":{"authors":[{"text":"Hatfield, Jeffrey 0000-0002-6517-2925 jhatfield@usgs.gov","orcid":"https://orcid.org/0000-0002-6517-2925","contributorId":139261,"corporation":false,"usgs":true,"family":"Hatfield","given":"Jeffrey","email":"jhatfield@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":540545,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70140268,"text":"70140268 - 2015 - Long-term plant responses to climate are moderated by biophysical attributes in a North American desert","interactions":[],"lastModifiedDate":"2017-11-27T08:44:57","indexId":"70140268","displayToPublicDate":"2015-02-06T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2242,"text":"Journal of Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Long-term plant responses to climate are moderated by biophysical attributes in a North American desert","docAbstract":"Samples of soil, water, mine waste leachates, soil gas, and air were collected from areas mined for mercury (Hg) and baseline sites in the Big Bend area, Texas, to evaluate potential Hg contamination in the region. Soil samples collected within 300 m of an inactive Hg mine contained elevated Hg concentrations (3.8–11 µg/g), which were considerably higher than Hg in soil collected from baseline sites (0.03–0.05 µg/g) distal (as much as 24 km) from mines. Only three soil samples collected within 300 m of the mine exceeded the probable effect concentration for Hg of 1.06 µg/g, above which harmful effects are likely to be observed in sediment-dwelling organisms. Concentrations of Hg in mine water runoff (7.9–14 ng/L) were generally higher than those found in springs and wells (0.05–3.1 ng/L), baseline streams (1.1–9.7 ng/L), and sources of drinking water (0.63–9.1 ng/L) collected in the Big Bend region. Concentrations of Hg in all water samples collected in this study were considerably below the 2,000 ng/L drinking water Hg guideline and the 770 ng/L guideline recommended by the U.S. Environmental Protection Agency (USEPA) to protect aquatic wildlife from chronic effects of Hg. Concentrations of Hg in water leachates obtained from leaching of mine wastes varied widely from <0.001 to 760 µg of Hg in leachate/g of sample leached, but only one leachate exceeded the USEPA Hg industrial soil screening level of 31 µg/g. Concentrations of Hg in soil gas collected at mined sites (690–82,000 ng/m3) were highly elevated compared to soil gas collected from baseline sites (1.2–77 ng/m3). However, air collected from mined areas at a height of 2 m above the ground surface contained concentrations of Hg (4.9–64 ng/m3) that were considerably lower than Hg in soil gas from the mined areas. Although concentrations of Hg emitted from mine-contaminated soils and mine wastes were elevated, persistent wind in southwest Texas disperses Hg in the air within a few meters of the ground surface.
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