{"pageNumber":"533","pageRowStart":"13300","pageSize":"25","recordCount":46677,"records":[{"id":70048055,"text":"70048055 - 2014 - Antecedent flow conditions and nitrate concentrations in the Mississippi River basin","interactions":[],"lastModifiedDate":"2014-06-04T11:15:54","indexId":"70048055","displayToPublicDate":"2014-03-10T09:35:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Antecedent flow conditions and nitrate concentrations in the Mississippi River basin","docAbstract":"The relationship between antecedent flow conditions and nitrate concentrations was explored at eight sites in the 2.9 million square kilometers (km<sup>2</sup>) Mississippi River basin, USA. Antecedent flow conditions were quantified as the ratio between the mean daily flow of the previous year and the mean daily flow from the period of record (Qratio), and the Qratio was statistically related to nitrate anomalies (the unexplained variability in nitrate concentration after filtering out season, long-term trend, and contemporaneous flow effects) at each site. Nitrate anomaly and Qratio were negatively related at three of the four major tributary sites and upstream in the Mississippi River, indicating that when mean daily streamflow during the previous year was lower than average, nitrate concentrations were higher than expected. The strength of these relationships increased when data were subdivided by contemporaneous flow conditions. Five of the eight sites had significant negative relationships (<i>p</i> ≤ 0.05) at high or moderately high contemporaneous flows, suggesting nitrate that accumulates in these basins during a drought is flushed during subsequent high flows. At half of the sites, when mean daily flow during the previous year was 50 percent lower than average, nitrate concentration can be from 9 to 27 percent higher than nitrate concentrations that follow a year with average mean daily flow. Conversely, nitrate concentration can be from 8 to 21 percent lower than expected when flow during the previous year was 50 percent higher than average. Previously documented for small, relatively homogenous basins, our results suggest that relationships between antecedent flows and nitrate concentrations are also observable at a regional scale. Relationships were not observed (using all contemporaneous flow data together) for basins larger than 1 million km<sup>2</sup>, suggesting that above this limit the overall size and diversity within these basins may necessitate the use of more complicated statistical approaches or that there may be no discernible basin-wide relationship with antecedent flow. The relationships between nitrate concentration and Qratio identified in this study serve as the basis for future studies that can better define specific hydrologic processes occurring during and after a drought (or high flow period) which influence nitrate concentration, such as the duration or magnitude of low flows, and the timing of low and high flows.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Hydrology and Earth System Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Hydrology and Earth System Science","doi":"10.5194/hessd-10-11451-2013","usgsCitation":"Murphy, J.C., Hirsch, R.M., and Sprague, L.A., 2014, Antecedent flow conditions and nitrate concentrations in the Mississippi River basin: Hydrology and Earth System Sciences, p. 967-979, https://doi.org/10.5194/hessd-10-11451-2013.","productDescription":"13 p.","startPage":"967","endPage":"979","ipdsId":"IP-045515","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":473114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hessd-10-11451-2013","text":"Publisher Index Page"},{"id":288062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277439,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.5194/hessd-10-11451-2013"}],"country":"United States","otherGeospatial":"Mississippi River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383333 ], [ -66.95,49.383333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53903fe4e4b04eea98bf84ed","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":483677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":483676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sprague, Lori A. 0000-0003-2832-6662 lsprague@usgs.gov","orcid":"https://orcid.org/0000-0003-2832-6662","contributorId":726,"corporation":false,"usgs":true,"family":"Sprague","given":"Lori","email":"lsprague@usgs.gov","middleInitial":"A.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":483675,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70095212,"text":"sim3291 - 2014 - Geologic map of the Kechumstuk fault zone in the Mount Veta area, Fortymile mining district, east-central Alaska","interactions":[],"lastModifiedDate":"2020-06-16T14:36:03.296289","indexId":"sim3291","displayToPublicDate":"2014-03-10T06:42:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3291","title":"Geologic map of the Kechumstuk fault zone in the Mount Veta area, Fortymile mining district, east-central Alaska","docAbstract":"<p>This map was developed by the U.S. Geological Survey Mineral Resources Program to depict the fundamental geologic features for the western part of the Fortymile mining district of east-central Alaska, and to delineate the location of known bedrock mineral prospects and their relationship to rock types and structural features.</p><p>This geospatial map database presents a 1:63,360-scale geologic map for the Kechumstuk fault zone and surrounding area, which lies 55 km northwest of Chicken, Alaska. The Kechumstuk fault zone is a northeast-trending zone of faults that transects the crystalline basement rocks of the Yukon-Tanana Upland of the western part of the Fortymile mining district. The crystalline basement rocks include Paleozoic metasedimentary and metaigneous rocks as well as granitoid intrusions of Triassic, Jurassic, and Cretaceous age. The geologic units represented by polygons in this dataset are based on new geologic mapping and geochronological data coupled with an interpretation of regional and new geophysical data collected by the Alaska Department of Natural Resources, Division of Geological and Geophysical Surveys. The geochronological data are reported in the accompanying geologic map text and represent new U-Pb dates on zircons collected from the igneous and metaigneous units within the map area.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3291","usgsCitation":"Day, W.C., O’Neill, J., Dusel-Bacon, C., Aleinikoff, J.N., and Siron, C.R., 2014, Geologic map of the Kechumstuk fault zone in the Mount Veta area, Fortymile mining district, east-central Alaska (Version 1.1: March 12, 2014; Version 1.0: March 10, 201): U.S. Geological Survey Scientific Investigations Map 3291, 1 Plate: 45.00 x 36.00 inches; HTML Document, https://doi.org/10.3133/sim3291.","productDescription":"1 Plate: 45.00 x 36.00 inches; HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051711","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":375617,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3291/images/coverthb.jpg"},{"id":283773,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3291/downloads/"},{"id":283772,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3291/pdf/SIM3291.pdf"},{"id":283667,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3291/"}],"scale":"63360","projection":"Universal Transverse Mercator projection","datum":"1927 North American Datum","country":"United States","state":"Alaska","otherGeospatial":"Fortymile Mining District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -143.4,64.0 ], [ -143.4,64.25 ], [ -142.666667,64.25 ], [ -142.666667,64.0 ], [ -143.4,64.0 ] ] ] } } ] }","edition":"Version 1.1: March 12, 2014; Version 1.0: March 10, 201","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5c9de4b0b290850fa987","contributors":{"authors":[{"text":"Day, Warren C. 0000-0002-9278-2120 wday@usgs.gov","orcid":"https://orcid.org/0000-0002-9278-2120","contributorId":1308,"corporation":false,"usgs":true,"family":"Day","given":"Warren","email":"wday@usgs.gov","middleInitial":"C.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":491096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Neill, J. Michael","contributorId":98210,"corporation":false,"usgs":true,"family":"O’Neill","given":"J. Michael","affiliations":[],"preferred":false,"id":491099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dusel-Bacon, Cynthia 0000-0001-8481-739X cdusel@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-739X","contributorId":2797,"corporation":false,"usgs":true,"family":"Dusel-Bacon","given":"Cynthia","email":"cdusel@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":491098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aleinikoff, John N. 0000-0003-3494-6841 jaleinikoff@usgs.gov","orcid":"https://orcid.org/0000-0003-3494-6841","contributorId":1478,"corporation":false,"usgs":true,"family":"Aleinikoff","given":"John","email":"jaleinikoff@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":491097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siron, Christopher R.","contributorId":106410,"corporation":false,"usgs":true,"family":"Siron","given":"Christopher","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":491100,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70134834,"text":"70134834 - 2014 - An analysis of monthly home range size in the critically endangered California Condor <i>Gymnogyps californianus</i>","interactions":[],"lastModifiedDate":"2017-11-22T10:43:00","indexId":"70134834","displayToPublicDate":"2014-03-10T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1048,"text":"Bird Conservation International","active":true,"publicationSubtype":{"id":10}},"title":"An analysis of monthly home range size in the critically endangered California Condor <i>Gymnogyps californianus</i>","docAbstract":"<p>Condors and vultures comprise the only group of terrestrial vertebrates in the world that are obligate scavengers, and these species move widely to locate ephemeral, unpredictable, and patchily-distributed food resources. In this study, we used high-resolution GPS location data to quantify monthly home range size of the critically endangered California Condor Gymnogyps californianus throughout the annual cycle in California. We assessed whether individual-level characteristics (age, sex and breeding status) and factors related to endangered species recovery program efforts (rearing method, release site) were linked to variation in monthly home range size. We found that monthly home range size varied across the annual cycle, with the largest monthly home ranges observed during late summer and early fall (July&ndash;October), a pattern that may be linked to seasonal changes in thermals that facilitate movement. Monthly home ranges of adults were significantly larger than those of immatures, but males and females used monthly home ranges of similar size throughout the year and breeding adults did not differ from non-breeding adults in their average monthly home range size. Individuals from each of three release sites differed significantly in the size of their monthly home ranges, and no differences in monthly home range size were detected between condors reared under captive conditions relative to those reared in the wild. Our study provides an important foundation for understanding the movement ecology of the California Condor and it highlights the importance of seasonal variation in space use for effective conservation planning for this critically endangered species.</p>","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0959270913000592","usgsCitation":"Rivers, J.W., Johnson, M.J., Haig, S.M., Schwarz, C.J., Burnett, J., Brandt, J., George, D., and Grantham, J., 2014, An analysis of monthly home range size in the critically endangered California Condor <i>Gymnogyps californianus</i>: Bird Conservation International, v. 24, no. 4, p. 492-504, https://doi.org/10.1017/S0959270913000592.","productDescription":"13 p.","startPage":"492","endPage":"504","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051979","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":473116,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0959270913000592","text":"Publisher Index Page"},{"id":296464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"4","noUsgsAuthors":false,"publicationDate":"2014-03-10","publicationStatus":"PW","scienceBaseUri":"5482e53ee4b0aa6d77852ff6","contributors":{"authors":[{"text":"Rivers, James W.","contributorId":23072,"corporation":false,"usgs":false,"family":"Rivers","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":7005,"text":"Department of Forest Ecosystems and Society, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":526561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Matthew J. mjjohnson@usgs.gov","contributorId":3604,"corporation":false,"usgs":true,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"J.","affiliations":[{"id":27989,"text":"Colorado Plateau Research Station, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":526562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":526560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwarz, Carl J.","contributorId":42525,"corporation":false,"usgs":false,"family":"Schwarz","given":"Carl","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":526563,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burnett, Joseph","contributorId":127741,"corporation":false,"usgs":false,"family":"Burnett","given":"Joseph","email":"","affiliations":[{"id":7132,"text":"Ventana Wildlife Society, Salinas, CA","active":true,"usgs":false}],"preferred":false,"id":526564,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brandt, Joseph","contributorId":127742,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":526565,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"George, Daniel","contributorId":45221,"corporation":false,"usgs":false,"family":"George","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":526566,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grantham, Jesse","contributorId":89804,"corporation":false,"usgs":false,"family":"Grantham","given":"Jesse","email":"","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":526567,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70095615,"text":"sir20145026 - 2014 - Evaluation of the expected moments algorithm and a multiple low-outlier test for flood frequency analysis at streamgaging stations in Arizona","interactions":[],"lastModifiedDate":"2014-03-07T07:50:45","indexId":"sir20145026","displayToPublicDate":"2014-03-07T07:38:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5026","title":"Evaluation of the expected moments algorithm and a multiple low-outlier test for flood frequency analysis at streamgaging stations in Arizona","docAbstract":"<p>Flooding is among the costliest natural disasters in terms of loss of life and property in Arizona, which is why the accurate estimation of flood frequency and magnitude is crucial for proper structural design and accurate floodplain mapping. Current guidelines for flood frequency analysis in the United States are described in Bulletin 17B (B17B), yet since B17B’s publication in 1982 (Interagency Advisory Committee on Water Data, 1982), several improvements have been proposed as updates for future guidelines. Two proposed updates are the Expected Moments Algorithm (EMA) to accommodate historical and censored data, and a generalized multiple Grubbs-Beck (MGB) low-outlier test. The current guidelines use a standard Grubbs-Beck (GB) method to identify low outliers, changing the determination of the moment estimators because B17B uses a conditional probability adjustment to handle low outliers while EMA censors the low outliers. B17B and EMA estimates are identical if no historical information or censored or low outliers are present in the peak-flow data. EMA with MGB (EMA-MGB) test was compared to the standard B17B (B17B-GB) method for flood frequency analysis at 328 streamgaging stations in Arizona. The methods were compared using the relative percent difference (RPD) between annual exceedance probabilities (AEPs), goodness-of-fit assessments, random resampling procedures, and Monte Carlo simulations. The AEPs were calculated and compared using both station skew and weighted skew. Streamgaging stations were classified by U.S. Geological Survey (USGS) National Water Information System (NWIS) qualification codes, used to denote historical and censored peak-flow data, to better understand the effect that nonstandard flood information has on the flood frequency analysis for each method. Streamgaging stations were also grouped according to geographic flood regions and analyzed separately to better understand regional differences caused by physiography and climate.</p>\n<br/>\n<p>The B17B-GB and EMA-MGB RPD-boxplot results showed that the median RPDs across all streamgaging stations for the 10-, 1-, and 0.2-percent AEPs, computed using station skew, were approximately zero. As the AEP flow estimates decreased (that is, from 10 to 0.2 percent AEP) the variability in the RPDs increased, indicating that the AEP flow estimate was greater for EMA-MGB when compared to B17B-GB. There was only one RPD greater than 100 percent for the 10- and 1-percent AEP estimates, whereas 19 RPDs exceeded 100 percent for the 0.2-percent AEP. At streamgaging stations with low-outlier data, historical peak-flow data, or both, RPDs ranged from −84 to 262 percent for the 0.2-percent AEP flow estimate. When streamgaging stations were separated by the presence of historical peak-flow data (that is, no low outliers or censored peaks) or by low outlier peak-flow data (no historical data), the results showed that RPD variability was greatest for the 0.2-AEP flow estimates, indicating that the treatment of historical and (or) low-outlier data was different between methods and that method differences were most influential when estimating the less probable AEP flows (1, 0.5, and 0.2 percent). When regional skew information was weighted with the station skew, B17B-GB estimates were generally higher than the EMA-MGB estimates for any given AEP. This was related to the different regional skews and mean square error used in the weighting procedure for each flood frequency analysis. The B17B-GB weighted skew analysis used a more positive regional skew determined in USGS Water Supply Paper 2433 (Thomas and others, 1997), while the EMA-MGB analysis used a more negative regional skew with a lower mean square error determined from a Bayesian generalized least squares analysis.</p>\n<br/>\n<p>Regional groupings of streamgaging stations reflected differences in physiographic and climatic characteristics. Potentially influential low flows (PILFs) were more prevalent in arid regions of the State, and generally AEP flows were larger with EMA-MGB than with B17B-GB for gaging stations with PILFs. In most cases EMA-MGB curves would fit the largest floods more accurately than B17B-GB. In areas of the State with more baseflow, such as along the Mogollon Rim and the White Mountains, streamgaging stations generally had fewer PILFs and more positive skews, causing estimated AEP flows to be larger with B17B-GB than with EMA-MGB. The effect of including regional skew was similar for all regions, and the observed pattern was increasingly greater B17B-GB flows (more negative RPDs) with each decreasing AEP quantile.</p>\n<br/>\n<p>A variation on a goodness-of-fit test statistic was used to describe each method’s ability to fit the largest floods. The mean absolute percent difference between the measured peak flows and the log-Pearson Type 3 (LP3)-estimated flows, for each method, was averaged over the 90th, 75th, and 50th percentiles of peak-flow data at each site. In most percentile subsets, EMA-MGB on average had smaller differences (1 to 3 percent) between the observed and fitted value, suggesting that the EMA-MGB-LP3 distribution is fitting the observed peak-flow data more precisely than B17B-GB. The smallest EMA-MGB percent differences occurred for the greatest 10 percent (90th percentile) of the peak-flow data. When stations were analyzed by USGS NWIS peak flow qualification code groups, the stations with historical peak flows and no low outliers had average percent differences as high as 11 percent greater for B17B-GB, indicating that EMA-MGB utilized the historical information to fit the largest observed floods more accurately.</p>\n<br/>\n<p>A resampling procedure was used in which 1,000 random subsamples were drawn, each comprising one-half of the observed data. An LP3 distribution was fit to each subsample using B17B-GB and EMA-MGB methods, and the predicted 1-percent AEP flows were compared to those generated from distributions fit to the entire dataset. With station skew, the two methods were similar in the median percent difference, but with weighted skew EMA-MGB estimates were generally better. At two gages where B17B-GB appeared to perform better, a large number of peak flows were deemed to be PILFs by the MGB test, although they did not appear to depart significantly from the trend of the data (step or dogleg appearance). At two gages where EMA-MGB performed better, the MGB identified several PILFs that were affecting the fitted distribution of the B17B-GB method.</p>\n<br/>\n<p>Monte Carlo simulations were run for the LP3 distribution using different skews and with different assumptions about the expected number of historical peaks. The primary benefit of running Monte Carlo simulations is that the underlying distribution statistics are known, meaning that the true 1-percent AEP is known. The results showed that EMA-MGB performed as well or better in situations where the LP3 distribution had a zero or positive skew and historical information. When the skew for the LP3 distribution was negative, EMA-MGB performed significantly better than B17B-GB and EMA-MGB estimates were less biased by more closely estimating the true 1-percent AEP for 1, 2, and 10 historical flood scenarios.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145026","collaboration":"Prepared in cooperation with the Flood Control District of Maricopa County","usgsCitation":"Paretti, N., Kennedy, J.R., and Cohn, T., 2014, Evaluation of the expected moments algorithm and a multiple low-outlier test for flood frequency analysis at streamgaging stations in Arizona: U.S. Geological Survey Scientific Investigations Report 2014-5026, Report: viii, 61 p.; Appendixes, https://doi.org/10.3133/sir20145026.","productDescription":"Report: viii, 61 p.; Appendixes","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-040578","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":283442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145026.jpg"},{"id":283439,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5026/"},{"id":283440,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5026/pdf/sir2014-5026.pdf"},{"id":283441,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5026/downloads/sir2014-5026_Appendixes.xlsx"}],"projection":"Universal Transverse Mercator","datum":"North American datum 1983","country":"United States","state":"Arizona","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -114.82,31.33 ], [ -114.82,37.0 ], [ -109.05,37.0 ], [ -109.05,31.33 ], [ -114.82,31.33 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd587ce4b0b290850f8200","contributors":{"authors":[{"text":"Paretti, Nicholas V. nparetti@usgs.gov","contributorId":802,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas V.","email":"nparetti@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":491330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":2172,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":491331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cohn, Timothy A. tacohn@usgs.gov","contributorId":2927,"corporation":false,"usgs":true,"family":"Cohn","given":"Timothy A.","email":"tacohn@usgs.gov","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":491332,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70057372,"text":"ds805 - 2014 - Atrazine reduces reproduction in fathead minnow (<i>Pimephales promelas</i>): raw data report","interactions":[],"lastModifiedDate":"2016-10-20T14:59:30","indexId":"ds805","displayToPublicDate":"2014-03-06T11:41:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"805","title":"Atrazine reduces reproduction in fathead minnow (<i>Pimephales promelas</i>): raw data report","docAbstract":"The herbicide, atrazine, routinely is observed in surface and groundwaters, particularly in the “corn belt” region, a high-use area of the United States. Atrazine has demonstrated effects on reproduction in mammals and amphibians, but the characterization of endocrine-related effects in fish has received only limited attention. Peak concentrations of atrazine in surface water of streams from these agricultural areas coincide with annual spawning events of native fishes. Consequently, there was an unacceptable level of uncertainty in our understanding of the risks associated with the periods of greatest atrazine exposure and greatest vulnerability of certain species of fishes. For this reason, a study of the effects of atrazine on fathead minnow reproduction was undertaken (Tillitt and others, 2010). This report provides the raw data from that study.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds805","usgsCitation":"Tillitt, D.E., Papoulias, D.M., Whyte, J.J., and Richter, C.A., 2014, Atrazine reduces reproduction in fathead minnow (<i>Pimephales promelas</i>): raw data report: U.S. Geological Survey Data Series 805, iii, 136 p., https://doi.org/10.3133/ds805.","productDescription":"iii, 136 p.","numberOfPages":"144","onlineOnly":"Y","ipdsId":"IP-044472","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":283419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds805.jpg"},{"id":283417,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/805/"},{"id":283418,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/805/pdf/ds805.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd4e6ee4b0b290850f218a","contributors":{"authors":[{"text":"Tillitt, Donald E. 0000-0002-8278-3955 dtillitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":1875,"corporation":false,"usgs":true,"family":"Tillitt","given":"Donald","email":"dtillitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":486649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Papoulias, Diana M. 0000-0002-5106-2469 dpapoulias@usgs.gov","orcid":"https://orcid.org/0000-0002-5106-2469","contributorId":2726,"corporation":false,"usgs":true,"family":"Papoulias","given":"Diana","email":"dpapoulias@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":486651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whyte, Jeffrey J.","contributorId":100738,"corporation":false,"usgs":true,"family":"Whyte","given":"Jeffrey","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":486652,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":486650,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70068816,"text":"ds818 - 2014 - Quality of surface water in Missouri, water year 2012","interactions":[],"lastModifiedDate":"2016-08-10T11:14:27","indexId":"ds818","displayToPublicDate":"2014-03-05T11:17:06","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"818","title":"Quality of surface water in Missouri, water year 2012","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During the 2012 water year (October 1, 2011, through September 30, 2012), data were collected at 81 stations&mdash;73 Ambient Water-Quality Monitoring Network stations, 6 alternate Ambient Water-Quality Monitoring Network stations, and 2 U.S. Geological Survey National Stream Quality Accounting Network stations. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, fecal coliform bacteria, Escherichia coli bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 78 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak discharges, monthly mean discharges, and 7-day low flow is presented.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds818","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., 2014, Quality of surface water in Missouri, water year 2012: U.S. Geological Survey Data Series 818, iv, 24 p., https://doi.org/10.3133/ds818.","productDescription":"iv, 24 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-051073","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":283383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds818.jpg"},{"id":283381,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/818/"},{"id":283382,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/818/pdf/ds818.pdf"}],"country":"United States","state":"Missouri","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -95.77,36.0 ], [ -95.77,40.61 ], [ -89.1,40.61 ], [ -89.1,36.0 ], [ -95.77,36.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd6ea1e4b0b29085105e7d","contributors":{"authors":[{"text":"Barr, Miya N. 0000-0002-9961-9190 mnbarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9961-9190","contributorId":3686,"corporation":false,"usgs":true,"family":"Barr","given":"Miya","email":"mnbarr@usgs.gov","middleInitial":"N.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":488145,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70074340,"text":"ds823 - 2014 - Geophysical logging of bedrock wells for geothermal gradient characterization in New Hampshire, 2013","interactions":[],"lastModifiedDate":"2016-08-10T15:31:51","indexId":"ds823","displayToPublicDate":"2014-03-05T08:53:14","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"823","title":"Geophysical logging of bedrock wells for geothermal gradient characterization in New Hampshire, 2013","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the New Hampshire Geological Survey, measured the fluid temperature of groundwater and other geophysical properties in 10 bedrock wells in the State of New Hampshire in order to characterize geothermal gradients in bedrock. The wells selected for the study were deep (five ranging from 375 to 900 feet and five deeper than 900 feet) and 6 had low water yields, which correspond to low groundwater flow from fractures. This combination of depth and low water yield reduced the potential for flow-induced temperature changes that would mask the natural geothermal gradient in the bedrock. All the wells included in this study are privately owned, and permission to use the wells was obtained from landowners before geophysical logs were acquired for this study. National Institute of Standards and Technology thermistor readings were used to adjust the factory calibrated geophysical log data. A geometric correction to the gradient measurements was also necessary due to borehole deviation from vertical.</p>\n<p>Maximum groundwater temperatures at the bottom of the logs ranged from 11.2 to 15.4 degrees Celsius. Geothermal gradients were generally higher than those typically reported for other water wells in the United States. Some of the high gradients were associated with high natural gamma emissions. Groundwater flow was discernible in 4 of the 10 wells studied but only obscured the part of the geothermal gradient signal where groundwater actually flowed into, out of, or through the well. Temperature gradients varied by mapped bedrock type but can also vary by localized differences in mineralogy or rock type within the wells.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds823","collaboration":"Prepared in cooperation with the New Hampshire Geological Survey","usgsCitation":"Degnan, J.R., Barker, G., Olson, N., and Wilder, L., 2014, Geophysical logging of bedrock wells for geothermal gradient characterization in New Hampshire, 2013: U.S. Geological Survey Data Series 823, Report: vi, 19 p.; Log data, https://doi.org/10.3133/ds823.","productDescription":"Report: vi, 19 p.; Log data","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-052633","costCenters":[{"id":466,"text":"New England Water Science 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,{"id":70094668,"text":"fs20143018 - 2014 - The 1964 Great Alaska Earthquake and tsunamis: a modern perspective and enduring legacies","interactions":[],"lastModifiedDate":"2014-04-22T08:48:12","indexId":"fs20143018","displayToPublicDate":"2014-03-05T08:09:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3018","title":"The 1964 Great Alaska Earthquake and tsunamis: a modern perspective and enduring legacies","docAbstract":"The magnitude 9.2 Great Alaska Earthquake that struck south-central Alaska at 5:36 p.m. on Friday, March 27, 1964, is the largest recorded earthquake in U.S. history and the second-largest earthquake recorded with modern instruments. The earthquake was felt throughout most of mainland Alaska, as far west as Dutch Harbor in the Aleutian Islands some 480 miles away, and at Seattle, Washington, more than 1,200 miles to the southeast of the fault rupture, where the Space Needle swayed perceptibly. The earthquake caused rivers, lakes, and other waterways to slosh as far away as the coasts of Texas and Louisiana. Water-level recorders in 47 states—the entire Nation except for Connecticut, Delaware, and Rhode Island— registered the earthquake. It was so large that it caused the entire Earth to ring like a bell: vibrations that were among the first of their kind ever recorded by modern instruments. The Great Alaska Earthquake spawned thousands of lesser aftershocks and hundreds of damaging landslides, submarine slumps, and other ground failures. Alaska’s largest city, Anchorage, located west of the fault rupture, sustained heavy property damage. Tsunamis produced by the earthquake resulted in deaths and damage as far away as Oregon and California. Altogether the earthquake and subsequent tsunamis caused 129 fatalities and an estimated $2.3 billion in property losses (in 2013 dollars). Most of the population of Alaska and its major transportation routes, ports, and infrastructure lie near the eastern segment of the Aleutian Trench that ruptured in the 1964 earthquake. Although the Great Alaska Earthquake was tragic because of the loss of life and property, it provided a wealth of data about subductionzone earthquakes and the hazards they pose. The leap in scientific understanding that followed the 1964 earthquake has led to major breakthroughs in earth science research worldwide over the past half century. This fact sheet commemorates Great Alaska Earthquake and examines the advances in knowledge and technology that have helped to improve earthquake preparation and response both in Alaska and around the world.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143018","usgsCitation":"Brocher, T.M., Filson, J.R., Fuis, G.S., Haeussler, P.J., Holzer, T.L., Plafker, G., and Blair, J., 2014, The 1964 Great Alaska Earthquake and tsunamis: a modern perspective and enduring legacies: U.S. Geological Survey Fact Sheet 2014-3018, 6 p., https://doi.org/10.3133/fs20143018.","productDescription":"6 p.","ipdsId":"IP-053855","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":283366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20143018.jpg"},{"id":283364,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2014/3018/"},{"id":283365,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2014/3018/pdf/fs2014-3018.pdf"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -158.2,55.2 ], [ -158.2,64.1 ], [ -137.2,64.1 ], [ -137.2,55.2 ], [ -158.2,55.2 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517066e4b05569d805a3e1","contributors":{"authors":[{"text":"Brocher, Thomas M. 0000-0002-9740-839X brocher@usgs.gov","orcid":"https://orcid.org/0000-0002-9740-839X","contributorId":262,"corporation":false,"usgs":true,"family":"Brocher","given":"Thomas","email":"brocher@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":490788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Filson, John R. 0000-0001-8840-6301 jfilson@usgs.gov","orcid":"https://orcid.org/0000-0001-8840-6301","contributorId":5078,"corporation":false,"usgs":true,"family":"Filson","given":"John","email":"jfilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":490793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuis, Gary S. 0000-0002-3078-1544 fuis@usgs.gov","orcid":"https://orcid.org/0000-0002-3078-1544","contributorId":2639,"corporation":false,"usgs":true,"family":"Fuis","given":"Gary","email":"fuis@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":490790,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haeussler, Peter J. 0000-0002-1503-6247 pheuslr@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":503,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter","email":"pheuslr@usgs.gov","middleInitial":"J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":490789,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holzer, Thomas L. tholzer@usgs.gov","contributorId":2829,"corporation":false,"usgs":true,"family":"Holzer","given":"Thomas","email":"tholzer@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":490791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Plafker, George","contributorId":3920,"corporation":false,"usgs":false,"family":"Plafker","given":"George","email":"","affiliations":[],"preferred":false,"id":490792,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blair, J. Luke","contributorId":102573,"corporation":false,"usgs":true,"family":"Blair","given":"J. Luke","affiliations":[],"preferred":false,"id":490794,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70094909,"text":"ofr20141038 - 2014 - Passage and survival probabilities of juvenile Chinook salmon at Cougar Dam, Oregon, 2012","interactions":[],"lastModifiedDate":"2014-03-04T08:49:20","indexId":"ofr20141038","displayToPublicDate":"2014-03-03T16:02:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1038","title":"Passage and survival probabilities of juvenile Chinook salmon at Cougar Dam, Oregon, 2012","docAbstract":"<p>This report describes studies of juvenile-salmon dam passage and apparent survival at Cougar Dam, Oregon, during two operating conditions in 2012. Cougar Dam is a 158-meter tall rock-fill dam used primarily for flood control, and passes water through a temperature control tower to either a powerhouse penstock or to a regulating outlet (RO). The temperature control tower has moveable weir gates to enable water of different elevations and temperatures to be drawn through the dam to control water temperatures downstream. A series of studies of downstream dam passage of juvenile salmonids were begun after the National Oceanic and Atmospheric Administration determined that Cougar Dam was impacting the viability of anadromous fish stocks. The primary objectives of the studies described in this report were to estimate the route-specific fish passage probabilities at the dam and to estimate the survival probabilities of fish passing through the RO. The first set of dam operating conditions, studied in November, consisted of (1) a mean reservoir elevation of 1,589 feet, (2) water entering the temperature control tower through the weir gates, (3) most water routed through the turbines during the day and through the RO during the night, and (4) mean RO gate openings of 1.2 feet during the day and 3.2 feet during the night. The second set of dam operating conditions, studied in December, consisted of (1) a mean reservoir elevation of 1,507 ft, (2) water entering the temperature control tower through the RO bypass, (3) all water passing through the RO, and (4) mean RO gate openings of 7.3 feet during the day and 7.5 feet during the night. The studies were based on juvenile Chinook salmon (Oncorhynchus tshawytscha) surgically implanted with radio transmitters and passive integrated transponder (PIT) tags. Inferences about general dam passage percentage and timing of volitional migrants were based on surface-acclimated fish released in the reservoir. Dam passage and apparent survival probabilities were estimated using the Route-Specific-Survival Model with data from surface-acclimated fish released near the water surface directly upstream of the temperature control tower (treatment group) and slightly downstream of the dam (control group). In this study, apparent survival is the joint probability of surviving and migrating through the study area during the life of the transmitters.</p>\n<br/>\n<p>Two rearing groups were used to enable sufficient sample sizes for the studies. The groups differed in feed type, and for the December study only, the rearing location. Fish from each group were divided nearly equally among all combinations of release sites, release times, and surgeons. The sizes, travel times, and survivals of the two rearing groups were similar. There were statistical differences in fish lengths and travel times of the two groups, but they were small and likely were not biologically meaningful. There also was evidence of a difference in single-release estimates of survival between the rearing groups during the December study, but the differences had little effect on the relative survival estimates so the analyses of passage and survival were based on data from the rearing groups pooled.</p>\n<br/>\n<p>Conditions during the December study were more conducive to passing volitionally migrating fish than conditions during the November study. The passage percentage of the fish released in the reservoir was similar between studies (about 70 percent), but the passage occurred in a median of 1.0 day during the December study and a median of 9.3 days during the November study. More than 93 percent of the dam passage of volitionally migrating fish occurred at night during each study. This finding corroborates results of previous studies at Cougar Dam and suggests that the operating conditions at night are most important to volitionally migrating fish, given the current configuration of the dam.</p>\n<br/>\n<p>Most fish released near the temperature control tower passed through the RO. A total of 92.2 percent of the treatment group passed through the RO during the November study and the RO was the only route open during the December study.</p>\n<br/>\n<p>The assumptions of the survival model were either met or adjusted for during each study. There was little evidence that tagger skill or premature failure of radio transmitters had an effect on survival estimates. There were statistically significant differences in travel times between treatment and control groups through several of the river reaches they had in common, but the differences were typically only a few hours, and the two groups likely experienced the same in-river conditions. There was direct evidence of bias due to detection of euthanized fish with live transmitters released as part of the study design. The bias was ameliorated by adjusting the survival estimates for the probability of detecting dead fish with live transmitters, which reduced the estimated survival probabilities by about 0.02.</p>\n<br/>\n<p>The data and models indicated that the treatment effect was not fully expressed until the study reach terminating with Marshall Island Park on the Willamette River, a distance of 105.8 kilometers downstream of Cougar Dam. This was the first reach in which the 95-percent confidence interval of the estimated reach-specific relative survival overlapped 1.0, indicating similar survival of treatment and control groups. The median travel time of the treatment group from release to Marshall Island Park was 1.64 days during the November study and 1.36 days during the December study.</p>\n<br/>\n<p>The survival probability of fish that passed into the RO was greater during the December study than during the November study. The relative survival probability of fish passing through the RO was 0.4594 (standard error [SE] 0.0543) during the November study and 0.7389 (SE 0.1160) during the December study. These estimates represent relative survival probabilities from release near Cougar Dam to the Marshall Island site.</p>\n<br/>\n<p>The estimated survival probability of RO passage was lower than previous studies based on balloon and PIT tags, but higher than a similar study based on radio transmitters. We suggest that, apart from dam operations, the differences in survival primarily are due to the release location. We hypothesize that the balloon- and PIT-tagged fish released through a hose at a point near the RO gate opening experienced more benign conditions than the radio-tagged fish passing the RO volitionally. This hypothesis could be tested with further study. An alternative hypothesis is that some live fish remained within the study area beyond the life of their radio transmitter.</p>\n<br/>\n<p>The results from these and previous studies indicate that entrainment and survival of juvenile salmonids passing Cougar Dam varies with dam operating conditions. The condition most conducive to dam passage has been the discharge and low pool elevation condition tested during December 2012. That condition included large RO gate openings and was the condition with the highest dam passage survival.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141038","issn":"2331-1258","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Beeman, J.W., Evans, S.D., Haner, P.V., Hansel, H.C., Hansen, A.C., Smith, C., and Sprando, J.M., 2014, Passage and survival probabilities of juvenile Chinook salmon at Cougar Dam, Oregon, 2012: U.S. Geological Survey Open-File Report 2014-1038, vi, 64 p., https://doi.org/10.3133/ofr20141038.","productDescription":"vi, 64 p.","numberOfPages":"74","onlineOnly":"Y","temporalStart":"2012-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-049334","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":283195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141038.jpg"},{"id":283194,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1038/pdf/ofr2014-1038.pdf"},{"id":283193,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1038/"}],"country":"United States","state":"Oregon","otherGeospatial":"Cougar Dam","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.7449,43.356 ], [ -122.7449,44.9 ], [ -121.768,44.9 ], [ -121.768,43.356 ], [ -122.7449,43.356 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd6aa9e4b0b2908510367f","contributors":{"authors":[{"text":"Beeman, John W. jbeeman@usgs.gov","contributorId":2646,"corporation":false,"usgs":true,"family":"Beeman","given":"John","email":"jbeeman@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":490926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Scott D. 0000-0003-0452-7726 sdevans@usgs.gov","orcid":"https://orcid.org/0000-0003-0452-7726","contributorId":4408,"corporation":false,"usgs":true,"family":"Evans","given":"Scott","email":"sdevans@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":490930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haner, Philip V. 0000-0001-6940-487X phaner@usgs.gov","orcid":"https://orcid.org/0000-0001-6940-487X","contributorId":2364,"corporation":false,"usgs":true,"family":"Haner","given":"Philip","email":"phaner@usgs.gov","middleInitial":"V.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":490925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansel, Hal C. 0000-0002-3537-8244 hhansel@usgs.gov","orcid":"https://orcid.org/0000-0002-3537-8244","contributorId":2887,"corporation":false,"usgs":true,"family":"Hansel","given":"Hal","email":"hhansel@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":490927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Amy C. 0000-0002-0298-9137 achansen@usgs.gov","orcid":"https://orcid.org/0000-0002-0298-9137","contributorId":4350,"corporation":false,"usgs":true,"family":"Hansen","given":"Amy","email":"achansen@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":490929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Collin D. 0000-0003-4184-5686 cdsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-4184-5686","contributorId":7915,"corporation":false,"usgs":true,"family":"Smith","given":"Collin D.","email":"cdsmith@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":490931,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sprando, Jamie M. jsprando@usgs.gov","contributorId":4005,"corporation":false,"usgs":true,"family":"Sprando","given":"Jamie","email":"jsprando@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":490928,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70093762,"text":"ofr20141028 - 2014 - Contaminants of emerging concern in the lower Stillaguamish River Basin, Washington, 2008-11","interactions":[],"lastModifiedDate":"2016-06-06T09:02:27","indexId":"ofr20141028","displayToPublicDate":"2014-03-03T15:51:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1028","title":"Contaminants of emerging concern in the lower Stillaguamish River Basin, Washington, 2008-11","docAbstract":"<p>A series of discrete water-quality samples were collected in the lower Stillaguamish River Basin near the city of Arlington, Washington, through a partnership with the Stillaguamish Tribe of Indians. These samples included surface waters of the Stillaguamish River, adjacent tributary streams, and paired inflow and outflow sampling at three wastewater treatment plants in the lower river basin. Chemical analysis of these samples focused on chemicals of emerging concern, including wastewater compounds, human-health pharmaceuticals, steroidal hormones, and halogenated organic compounds on solids and sediment. This report presents the methods used and data results from the chemical analysis of these samples</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141028","issn":"2327-638X","collaboration":"Prepared in cooperation with the Stillaguamish Tribe of Indians","usgsCitation":"Wagner, R.J., Moran, P.W., Zaugg, S.D., Sevigny, J.M., and Pope, J.M., 2014, Contaminants of emerging concern in the lower Stillaguamish River Basin, Washington, 2008-11 (Version 1.0: Originally posted March 3, 2014; Version 2.0: June 3, 2016): U.S. Geological Survey Open-File Report 2014-1028, Report: vi, 14 p.; 20 Tables, https://doi.org/10.3133/ofr20141028.","productDescription":"Report: vi, 14 p.; 20 Tables","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2008-01-01","temporalEnd":"2011-12-31","ipdsId":"IP-040609","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":283191,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141028.PNG"},{"id":322167,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table04.xlsx","text":"Table 4"},{"id":322168,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table05.xlsx","text":"Table 5"},{"id":322169,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table06.xlsx","text":"Table 6"},{"id":322170,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table07.xlsx","text":"Table 7"},{"id":322171,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table08.xlsx","text":"Table 8"},{"id":322172,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table09.xlsx","text":"Table 9"},{"id":322173,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table10.xlsx","text":"Table 10"},{"id":322174,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableA1.xlsx","text":"Table A1"},{"id":322175,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableA2.xlsx","text":"Table A2"},{"id":322176,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableA3.xlsx","text":"Table A3"},{"id":322177,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableA4.xlsx","text":"Table A4"},{"id":322178,"rank":17,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableA5.xlsx","text":"Table A5"},{"id":322179,"rank":18,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableB1.xlsx","text":"Table B1"},{"id":322180,"rank":19,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableB2.xlsx","text":"Table B2"},{"id":322181,"rank":20,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableB3.xlsx","text":"Table B3"},{"id":322182,"rank":21,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableB4.xlsx","text":"Table B4"},{"id":322183,"rank":22,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableB5.xlsx","text":"Table B5"},{"id":322184,"rank":23,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_tableB6.xlsx","text":"Table B6"},{"id":322185,"rank":24,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2014/1028/versionHist.txt","text":"Revised June 3, 2016"},{"id":283186,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1028/"},{"id":283190,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1028/pdf/ofr2014-1028.pdf"},{"id":322165,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table02.xlsx","text":"Table 2"},{"id":322166,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2014/1028/downloads/ofr2014-1028_table03.xlsx","text":"Table 3"}],"projection":"Transverse Mercator projection","datum":"Northern American Datum of 1983","country":"United States","state":"Washington","otherGeospatial":"Stillaguasmish River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.333333,48.0 ], [ -122.333333,48.5 ], [ -121.5,48.5 ], [ -121.5,48.0 ], [ -122.333333,48.0 ] ] ] } } ] }","edition":"Version 1.0: Originally posted March 3, 2014; Version 2.0: June 3, 2016","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd52ace4b0b290850f4aba","contributors":{"authors":[{"text":"Wagner, Richard J. rjwagner@usgs.gov","contributorId":3122,"corporation":false,"usgs":true,"family":"Wagner","given":"Richard","email":"rjwagner@usgs.gov","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":490201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moran, Patrick W. 0000-0002-2002-3539 pwmoran@usgs.gov","orcid":"https://orcid.org/0000-0002-2002-3539","contributorId":489,"corporation":false,"usgs":true,"family":"Moran","given":"Patrick","email":"pwmoran@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":490199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zaugg, Steven D. sdzaugg@usgs.gov","contributorId":768,"corporation":false,"usgs":true,"family":"Zaugg","given":"Steven","email":"sdzaugg@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":true,"id":490200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sevigny, Jennifer M.","contributorId":36452,"corporation":false,"usgs":true,"family":"Sevigny","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":490202,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pope, Judy M.","contributorId":93377,"corporation":false,"usgs":true,"family":"Pope","given":"Judy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":490203,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70095372,"text":"ofr20141042 - 2014 - Evaluation of juvenile salmonid behavior near a prototype weir box at Cowlitz Falls Dam, Washington, 2013","interactions":[],"lastModifiedDate":"2014-03-04T08:47:56","indexId":"ofr20141042","displayToPublicDate":"2014-03-03T15:43:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-1042","title":"Evaluation of juvenile salmonid behavior near a prototype weir box at Cowlitz Falls Dam, Washington, 2013","docAbstract":"<p>Collection of juvenile salmonids at Cowlitz Falls Dam is a critical part of the effort to restore salmon in the upper Cowlitz River because the majority of fish that are not collected at the dam pass downstream and enter a large reservoir where they become landlocked and lost to the anadromous fish population. However, the juvenile fish collection system at Cowlitz Falls Dam has failed to achieve annual collection goals since it first began operating in 1996. Since that time, numerous modifications to the fish collection system have been made and several prototype collection structures have been developed and tested, but these efforts have not substantially increased juvenile fish collection. Studies have shown that juvenile steelhead (Oncorhynchus mykiss), coho salmon (Oncorhynchus kisutch), and Chinook salmon (Oncorhynchus tshawytscha) tend to locate the collection entrances effectively, but many of these fish are not collected and eventually pass the dam through turbines or spillways. Tacoma Power developed a prototype weir box in 2009 to increase capture rates of juvenile salmonids at the collection entrances, and this device proved to be successful at retaining those fish that entered the weir. However, because of safety concerns at the dam, the weir box could not be deployed near a spillway gate where the prototype was tested, so the device was altered and re-deployed at a different location, where it was evaluated during 2013. The U.S. Geological Survey conducted an evaluation using radiotelemetry to monitor fish behavior near the weir box and collection flumes.</p>\n<br/>\n<p>The evaluation was conducted during April–June 2013. Juvenile steelhead and coho salmon (45 per species) were tagged with a radio transmitter and passive integrated transponder (PIT) tag, and released upstream of the dam. All tagged fish moved downstream and entered the forebay of Cowlitz Falls Dam. Median travel times from the release site to the forebay were 0.8 d for steelhead and 1.2 d for coho salmon. Most fish spent several days in the dam forebay; median forebay residence times were 4.4 d for juvenile steelhead and 5.7 d for juvenile coho salmon. A new radio transmitter model was used during the study period. The transmitter had low detection probabilities on underwater antennas located within the collection system, which prevented us from reporting performance metrics (discovery efficiency, entrance efficiency, retention efficiency) that are traditionally used to evaluate fish collection systems.</p>\n<br/>\n<p>Most tagged steelhead (98 percent) and coho salmon (84 percent) were detected near the weir box or collection flume entrances during the study period; 39 percent of tagged steelhead and 55 percent of tagged coho salmon were detected at both entrances. Sixty-three percent of the tagged steelhead that were detected at both entrances were first detected at the weir box, compared to 52 percent of the coho salmon. Twelve steelhead and 15 coho salmon detected inside the weir box eventually left the device and were collected in collection flumes or passed the dam. Overall, collection rates were relatively high during the study period. Sixty-five percent of the steelhead and 80 percent of the coho salmon were collected during the study, and most of the remaining fish passed the dam and entered the tailrace (24 percent of steelhead; 13 percent of coho salmon). The remaining 11 percent of steelhead and 7 percent of coho salmon did not pass the dam while their transmitters were operating.</p>\n<br/>\n<p>We were able to confirm collection of tagged fish at the fish facility using three approaches: (1) detection of radio transmitters in study fish; (2) detection of PIT-tags in study fish; (3) observation of study fish by staff at the fish facility. Data from all three methods were used to develop a multistate mark-recapture model that estimated detection probabilities for the various monitoring methods. These estimates then were used to describe the percent of tagged fish that were collected through the weir box and collection flumes. Detection probabilities of PIT-tag antennas in the collection flumes were 0.895 for juvenile steelhead and 0.881 for juvenile coho salmon, although radiotelemetry detection probabilities were 0.654 and 0.646 for the two species, respectively. The multistate model estimates showed that all steelhead and most coho salmon (94.5 percent) that were collected at the dam entered the collection system through the flumes rather than through the weir box. None of the tagged steelhead and only 5.5 percent of the tagged coho salmon were collected through the weir box. These data show that juvenile steelhead and coho salmon collection rates were much higher through the collection flumes than through the weir box.</p>\n<br/>\n<p>Low detection probabilities of tagged fish in the fish collection system resulted in uncertainty for some aspects of our evaluation. Missing detection records within the collection system for fish that were known to have been collected resulted in four tagged steelhead and seven tagged coho salmon being removed from the dataset, which was used to assess discovery rates of the weir box and collection flumes. However, the multistate model allowed us to provide unbiased estimates of the percentage of tagged fish that were collected through each route, and these data showed that few fish were collected through the weir box.</p>\n<br/>\n<p>Overall, the fish collection system performed reasonably well in collecting juvenile steelhead and coho salmon during the 2013 collection season. Fish collection efficiency estimates from the Washington Department of Fish and Wildlife showed that steelhead collection efficiency was slightly higher than the 10-year average (46 percent compared to 42 percent), whereas coho salmon collection efficiency was more than twice as high as the 10-year average (63 percent compared to 30 percent). However, the performance of the weir box was poor because most fish were collected through the collection flumes.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141042","issn":"2331-1258","usgsCitation":"Kock, T.J., Liedtke, T.L., Ekstrom, B.K., Tomka, R.G., and Rondorf, D.W., 2014, Evaluation of juvenile salmonid behavior near a prototype weir box at Cowlitz Falls Dam, Washington, 2013: U.S. Geological Survey Open-File Report 2014-1042, iv, 24 p., https://doi.org/10.3133/ofr20141042.","productDescription":"iv, 24 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-052870","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":283189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141042.jpg"},{"id":283185,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1042/"},{"id":283188,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1042/pdf/ofr2014-1042.pdf"}],"country":"United States","state":"Washington","otherGeospatial":"Cowlitz Falls Dam","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -123.220474,45.85151 ], [ -123.220474,46.386227 ], [ -122.238731,46.386227 ], [ -122.238731,45.85151 ], [ -123.220474,45.85151 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5868e4b0b290850f8104","contributors":{"authors":[{"text":"Kock, Tobias J. 0000-0001-8976-0230 tkock@usgs.gov","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":3038,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","email":"tkock@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":491166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liedtke, Theresa L. 0000-0001-6063-9867 tliedtke@usgs.gov","orcid":"https://orcid.org/0000-0001-6063-9867","contributorId":2999,"corporation":false,"usgs":true,"family":"Liedtke","given":"Theresa","email":"tliedtke@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":491165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ekstrom, Brian K. 0000-0002-1162-1780 bekstrom@usgs.gov","orcid":"https://orcid.org/0000-0002-1162-1780","contributorId":3704,"corporation":false,"usgs":true,"family":"Ekstrom","given":"Brian","email":"bekstrom@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":491167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tomka, Ryan G. 0000-0003-1078-6089 rtomka@usgs.gov","orcid":"https://orcid.org/0000-0003-1078-6089","contributorId":3706,"corporation":false,"usgs":true,"family":"Tomka","given":"Ryan","email":"rtomka@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":491168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rondorf, Dennis W. drondorf@usgs.gov","contributorId":2970,"corporation":false,"usgs":true,"family":"Rondorf","given":"Dennis","email":"drondorf@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":491164,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70048964,"text":"sir20105070J - 2014 - A deposit model for carbonatite and peralkaline intrusion-related rare earth element deposits","interactions":[],"lastModifiedDate":"2022-12-09T23:54:22.187043","indexId":"sir20105070J","displayToPublicDate":"2014-03-03T14:19:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5070","chapter":"J","title":"A deposit model for carbonatite and peralkaline intrusion-related rare earth element deposits","docAbstract":"<p>Carbonatite and alkaline intrusive complexes, as well as their weathering products, are the primary sources of rare earth elements. A wide variety of other commodities have been exploited from carbonatites and alkaline igneous rocks including niobium, phosphate, titanium, vermiculite, barite, fluorite, copper, calcite, and zirconium. Other elements enriched in these deposits include manganese, strontium, tantalum, thorium, vanadium, and uranium. Carbonatite and peralkaline intrusion-related rare earth element deposits are presented together in this report because of the spatial, and potentially genetic, association between carbonatite and alkaline rocks. Although these rock types occur together at many locations, carbonatite and peralkaline intrusion-related rare earth element deposits are not generally found together.</p>\n<p>Carbonatite hosted rare earth element deposits are found throughout the world, but currently only five are being mined for rare earth elements: Bayan Obo, Daluxiang, Maoniuping, and Weishan deposits in China and the Mountain Pass deposit in California, United States. These deposits are enriched in light rare earth elements, including lanthanum, cerium, praseodynium, and neodynium. The principal rare earth element-minerals associated with carbonatites are fluocarbonates (bastn&auml;site, parisite, and synchysite), hydrated carbonates (ancylite), and phosphates (monazite) with bastn&auml;site being the primary ore mineral. Calcite and dolomite are the primary gangue minerals. At present, the only rare earth element production from a peralkaline intrusion-related deposit is as a byproduct commodity at the Lovozero deposit in Russia. Important rare earth element minerals found in various deposits include apatite, eudialyte, loparite, gittinsite, xenotime, gadolinite, monazite, bastn&auml;site, kainosite, mosandrite, britholite, allanite, fergusonite, and zircon, and these minerals tend to be enriched in heavy rare earth elements.</p>\n<p>Carbonatite and alkaline intrusive complexes are derived from partial melts of mantle material, and neodymium isotopic data are consistent with the rare earth elements being derived from the parental magma. Deposits and these associated rock types tend to occur within stable continental tectonic units, in areas defined as shields, cratons, and crystalline blocks; they are generally associated with intracontinental rift and fault systems. Protracted fractional crystallization of the magma leads to enrichment in rare earth elements and other incompatible elements. Rare earth element mineralization associated with carbonatites can occur as either primary mineral phases or as mineralization associated with late stage orthomagmatic fluids. Rare earth element mineralization associated with alkaline intrusive complexes may occur as primary phases in magmatic layered complexes or as late-stage dikes and veins.</p>\n<p>The greatest environmental challenges associated with carbonatite and peralkaline intrusion-related rare earth element deposits center on the associated uranium and thorium. Considerable uncertainty exists around the toxicity of rare earth elements and warrants further investigation. The acid-generating potential of carbonatites and peralkaline intrusion-related deposits is low due to the dominance of carbonate minerals in carbonatite deposits, the presence of feldspars and minor calcite within the alkaline intrusion deposits, and only minor quantities of potentially acid-generating sulfides. Therefore, acid-drainage issues are not likely to be a major concern associated with these deposits. Uranium has the potential to be recovered as a byproduct, which would mitigate some of its environmental effects. However, thorium will likely remain a waste-stream product that will require management since progress is not being made towards the development of thorium-based nuclear reactors in the United States or other large scale commercial uses. Because some deposits are rich in fluorine and beryllium, these elements may be of environmental concern in certain locations.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment (Scientific Investigations Report 2010-5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070J","usgsCitation":"Verplanck, P.L., Van Gosen, B.S., Seal, R., and McCafferty, A.E., 2014, A deposit model for carbonatite and peralkaline intrusion-related rare earth element deposits: U.S. Geological Survey Scientific Investigations Report 2010-5070, x, 58 p., https://doi.org/10.3133/sir20105070J.","productDescription":"x, 58 p.","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-039549","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":283180,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/j/pdf/sir2010-5070J.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":283179,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5070/j/"},{"id":283181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20105070j.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd49bae4b0b290850ef5c3","contributors":{"authors":[{"text":"Verplanck, Philip L. 0000-0002-3653-6419 plv@usgs.gov","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":728,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","email":"plv@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":485887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":485889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seal, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":397,"corporation":false,"usgs":true,"family":"Seal","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[],"preferred":false,"id":485886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":485888,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70093612,"text":"70093612 - 2014 - Estimating movement and survival rates of a small saltwater fish using autonomous antenna receiver arrays and passive integrated transponder tags","interactions":[],"lastModifiedDate":"2014-03-31T09:50:11","indexId":"70093612","displayToPublicDate":"2014-03-03T13:50:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Estimating movement and survival rates of a small saltwater fish using autonomous antenna receiver arrays and passive integrated transponder tags","docAbstract":"We evaluated the performance of small (12.5 mm long) passive integrated transponder (PIT) tags and custom detection antennas for obtaining fine-scale movement and demographic data of mummichog Fundulus heteroclitus in a salt marsh creek. Apparent survival and detection probability were estimated using a Cormack Jolly Seber (CJS) model fitted to detection data collected by an array of 3 vertical antennas from November 2010 to March 2011 and by a single horizontal antenna from April to August 2011. Movement of mummichogs was monitored during the period when the array of vertical antennas was used. Antenna performance was examined in situ using tags placed in wooden dowels (drones) and in live mummichogs. Of the 44 tagged fish, 42 were resighted over the 9 mo monitoring period. The in situ detection probabilities of the drone and live mummichogs were high (~80-100%) when the ambient water depth was less than ~0.8 m. Upstream and downstream movement of mummichogs was related to hourly water depth and direction of tidal current in a way that maximized time periods over which mummichogs utilized the intertidal vegetated marsh. Apparent survival was lower during periods of colder water temperatures in December 2010 and early January 2011 (median estimate of daily apparent survival = 0.979) than during other periods of the study (median estimate of daily apparent survival = 0.992). During late fall and winter, temperature had a positive effect on the CJS detection probability of a tagged mummichog, likely due to greater fish activity over warmer periods. During the spring and summer, this pattern reversed possibly due to mummichogs having reduced activity during the hottest periods. This study demonstrates the utility of PIT tags and continuously operating autonomous detection systems for tracking fish at fine temporal scales, and improving estimates of demographic parameters in salt marsh creeks that are difficult or impractical to sample with active fishing gear.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Ecology Progress Series","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Inter-Research","doi":"10.3354/meps10656","usgsCitation":"Rudershausen, P.J., Buckel, J.A., Dubreuil, T., O’Donnell, M.J., Hightower, J.E., Poland, S.J., and Letcher, B., 2014, Estimating movement and survival rates of a small saltwater fish using autonomous antenna receiver arrays and passive integrated transponder tags: Marine Ecology Progress Series, v. 499, p. 177-192, https://doi.org/10.3354/meps10656.","productDescription":"16 p.","startPage":"177","endPage":"192","numberOfPages":"16","temporalStart":"2010-11-01","temporalEnd":"2011-08-31","ipdsId":"IP-044977","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":473124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps10656","text":"Publisher Index Page"},{"id":285124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":282316,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3354/meps10656"}],"volume":"499","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517037e4b05569d805a1ea","contributors":{"authors":[{"text":"Rudershausen, Paul J.","contributorId":43669,"corporation":false,"usgs":true,"family":"Rudershausen","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":490084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buckel, Jeffery A.","contributorId":42872,"corporation":false,"usgs":true,"family":"Buckel","given":"Jeffery","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":490083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dubreuil, Todd","contributorId":36457,"corporation":false,"usgs":true,"family":"Dubreuil","given":"Todd","affiliations":[],"preferred":false,"id":490082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Matthew J. 0000-0002-9089-2377 modonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":2003,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"modonnell@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":490080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hightower, Joseph E. jhightower@usgs.gov","contributorId":835,"corporation":false,"usgs":true,"family":"Hightower","given":"Joseph","email":"jhightower@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":490079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Poland, Steven J.","contributorId":77455,"corporation":false,"usgs":true,"family":"Poland","given":"Steven","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":490085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":24774,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":490081,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70202702,"text":"70202702 - 2014 - The U. S. Geological Survey carbon dioxide storage efficiency value methodology: Results and observations","interactions":[],"lastModifiedDate":"2019-03-19T12:34:07","indexId":"70202702","displayToPublicDate":"2014-03-03T12:28:49","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5215,"text":"Energy Procedia","onlineIssn":"1876-6102","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The U. S. Geological Survey carbon dioxide storage efficiency value methodology: Results and observations","title":"The U. S. Geological Survey carbon dioxide storage efficiency value methodology: Results and observations","docAbstract":"<p><span>In order to complete the 2013 U.S. Geological Survey (USGS) assessment of carbon dioxide (CO</span><sub>2</sub><span>) storage resources</span><span>, a methodology was needed to determine the CO</span><sub>2</sub><span>storage efficiency of individual rock strata</span><span>. The method that was used involved a storage efficiency approximation by MacMinn et al.</span><span>, combined with a brine viscosity model by Mao and Duan,</span><span>&nbsp;and thermal and pressure data from petroleum fields across basins</span><span>. The resulting efficiencies indicated that both salinity of the pore fluid and the thermal gradient have a strong effect on the amount of CO</span><sub>2</sub><span>&nbsp;that strata could store.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.egypro.2014.11.542","issn":"1876-6102","usgsCitation":"Brennan, S.T., 2014, The U. S. Geological Survey carbon dioxide storage efficiency value methodology: Results and observations: Energy Procedia, v. 63, p. 5123-5129, https://doi.org/10.1016/j.egypro.2014.11.542.","productDescription":"7 p.","startPage":"5123","endPage":"5129","numberOfPages":"7","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":473125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.egypro.2014.11.542","text":"Publisher Index Page"},{"id":362178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brennan, Sean T. 0000-0002-7102-9359 sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":559,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":759543,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70202701,"text":"70202701 - 2014 - Significance of carbon dioxide density estimates for basin-scale storage resource assessments","interactions":[],"lastModifiedDate":"2019-03-19T12:34:41","indexId":"70202701","displayToPublicDate":"2014-03-03T12:21:19","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5215,"text":"Energy Procedia","onlineIssn":"1876-6102","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Significance of carbon dioxide density estimates for basin-scale storage resource assessments","title":"Significance of carbon dioxide density estimates for basin-scale storage resource assessments","docAbstract":"<p><span>The geologic carbon dioxide (CO</span><sub>2</sub><span>) storage resource size is a function of the density of CO</span><sub>2</sub><span>&nbsp;in the subsurface. The pressure and temperature of the storage reservoir at depth affect the CO</span><sub>2</sub><span>&nbsp;density. Therefore, knowing these subsurface conditions allows for improved resource estimates of potential geologic CO</span><sub>2</sub><span>&nbsp;storage capacity. In 2012, the U.S. Geological Survey (USGS) completed an assessment of geologic CO</span><sub>2</sub><span>&nbsp;storage resources for large sedimentary basins in onshore and State waters areas of the U.S. Evaluating the subsurface conditions and CO</span><sub>2</sub><span>&nbsp;density in these basins was integral to the assessment. To better understand these conditions, investigations of pressure and temperature gradients, typically derived from borehole data and analog studies, were assembled at the basin scale. Based on the USGS assessment results and findings here, changes in subsurface pressure and temperature may yield density changes up to 40 percent, which may translate into significant changes in storage resource estimates.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.egypro.2014.11.543","issn":"1876-6102","usgsCitation":"Buursink, M.L., 2014, Significance of carbon dioxide density estimates for basin-scale storage resource assessments: Energy Procedia, v. 63, p. 5130-5140, https://doi.org/10.1016/j.egypro.2014.11.543.","productDescription":"11 p.","startPage":"5130","endPage":"5140","numberOfPages":"11","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":473127,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.egypro.2014.11.543","text":"Publisher Index Page"},{"id":362179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buursink, Marc L. 0000-0001-6491-386X mbuursink@usgs.gov","orcid":"https://orcid.org/0000-0001-6491-386X","contributorId":3362,"corporation":false,"usgs":true,"family":"Buursink","given":"Marc","email":"mbuursink@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":759542,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70093882,"text":"70093882 - 2014 - ASPRS research on quantifying the geometric quality of lidar data","interactions":[],"lastModifiedDate":"2017-01-18T11:34:54","indexId":"70093882","displayToPublicDate":"2014-03-01T13:56:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"ASPRS research on quantifying the geometric quality of lidar data","docAbstract":"The ASPRS Lidar Cal/Val (calibration/validation) Working Group led by the US Geological Survey (USGS) to establish “Guidelines on Geometric Accuracy and Quality of Lidar Data” has made excellent progress via regular teleconferences and meetings. The group is focused on identifying data quality metrics and establishing a set of guidelines for quantifying the quality of lidar data. The working group has defined and agreed on lidar Data Quality Measures (DQMs) to be used for this purpose. The DQMs are envisaged as the first ever consistent way of checking lidar data. It is expected that these metrics will be used as standard methods for quantifying the geometric quality of lidar data. The goal of this article is to communicate these developments to the readers and the larger geospatial community and invite them to participate in the process.  ","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Photogrammetric Engineering and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","usgsCitation":"Sampath, A., Heidemann, H.K., Stensaas, G.L., and Christopherson, J., 2014, ASPRS research on quantifying the geometric quality of lidar data: Photogrammetric Engineering and Remote Sensing, p. 201-205.","productDescription":"5 p.","startPage":"201","endPage":"205","ipdsId":"IP-054569","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":295092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286298,"type":{"id":15,"text":"Index Page"},"url":"https://www.asprs.org/a/publications/pers/2014journals/March_2014_Flipping/HTML/files/assets/basic-html/toc.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54365215e4b0a4f4b46a31d2","contributors":{"authors":[{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":490239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heidemann, Hans K. 0000-0003-4306-359X","orcid":"https://orcid.org/0000-0003-4306-359X","contributorId":17171,"corporation":false,"usgs":true,"family":"Heidemann","given":"Hans","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":490240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":490237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christopherson, Jon 0000-0002-2472-0059 jonchris@usgs.gov","orcid":"https://orcid.org/0000-0002-2472-0059","contributorId":2552,"corporation":false,"usgs":true,"family":"Christopherson","given":"Jon","email":"jonchris@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":490238,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70095799,"text":"70095799 - 2014 - Getting the message across: using ecological integrity to communicate with resource managers","interactions":[],"lastModifiedDate":"2014-03-19T12:33:59","indexId":"70095799","displayToPublicDate":"2014-03-01T12:26:05","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Getting the message across: using ecological integrity to communicate with resource managers","docAbstract":"This chapter describes and illustrates how concepts of ecological integrity, thresholds, and reference conditions can be integrated into a research and monitoring framework for natural resource management. Ecological integrity has been defined as a measure of the composition, structure, and function of an ecosystem in relation to the system’s natural or historical range of variation, as well as perturbations caused by natural or anthropogenic agents of change. Using ecological integrity to communicate with managers requires five steps, often implemented iteratively: (1) document the scale of the project and the current conceptual understanding and reference conditions of the ecosystem, (2) select appropriate metrics representing integrity, (3) define externally verified assessment points (metric values that signify an ecological change or need for management action) for the metrics, (4) collect data and calculate metric scores, and (5) summarize the status of the ecosystem using a variety of reporting methods. While we present the steps linearly for conceptual clarity, actual implementation of this approach may require addressing the steps in a different order or revisiting steps (such as metric selection) multiple times as data are collected. Knowledge of relevant ecological thresholds is important when metrics are selected, because thresholds identify where small changes in an environmental driver produce large responses in the ecosystem. Metrics with thresholds at or just beyond the limits of a system’s range of natural variability can be excellent, since moving beyond the normal range produces a marked change in their values. Alternatively, metrics with thresholds within but near the edge of the range of natural variability can serve as harbingers of potential change. Identifying thresholds also contributes to decisions about selection of assessment points. In particular, if there is a significant resistance to perturbation in an ecosystem, with threshold behavior not occurring until well beyond the historical range of variation, this may provide a scientific basis for shifting an ecological assessment point beyond the historical range. We present two case studies using ongoing monitoring by the US National Park Service Vital Signs program that illustrate the use of an ecological integrity approach to communicate ecosystem status to resource managers. The Wetland Ecological Integrity in Rocky Mountain National Park case study uses an analytical approach that specifically incorporates threshold detection into the process of establishing assessment points. The Forest Ecological Integrity of Northeastern National Parks case study describes a method for reporting ecological integrity to resource managers and other decision makers. We believe our approach has the potential for wide applicability for natural resource management.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Application of threshold concepts in natural resource decision making","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Springer","doi":"10.1007/978-1-4899-8041-0_10","usgsCitation":"Mitchell, B.R., Tierney, G.L., Schweiger, E.W., Miller, K.M., Faber-Langendoen, D., and Grace, J.B., 2014, Getting the message across: using ecological integrity to communicate with resource managers, chap. <i>of</i> Application of threshold concepts in natural resource decision making, p. 199-230, https://doi.org/10.1007/978-1-4899-8041-0_10.","productDescription":"32 p.","startPage":"199","endPage":"230","ipdsId":"IP-028833","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":284216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":283826,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/978-1-4899-8041-0_10"},{"id":283827,"type":{"id":15,"text":"Index Page"},"url":"https://link.springer.com/chapter/10.1007/978-1-4899-8041-0_10"}],"country":"United States","noUsgsAuthors":false,"publicationDate":"2014-02-08","publicationStatus":"PW","scienceBaseUri":"53517043e4b05569d805a231","contributors":{"authors":[{"text":"Mitchell, Brian R.","contributorId":14683,"corporation":false,"usgs":true,"family":"Mitchell","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":491440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tierney, Geraldine L.","contributorId":26218,"corporation":false,"usgs":true,"family":"Tierney","given":"Geraldine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":491441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schweiger, E. William","contributorId":53635,"corporation":false,"usgs":true,"family":"Schweiger","given":"E.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":491442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Kathryn M.","contributorId":68582,"corporation":false,"usgs":true,"family":"Miller","given":"Kathryn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":491443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Faber-Langendoen, Don","contributorId":94396,"corporation":false,"usgs":true,"family":"Faber-Langendoen","given":"Don","affiliations":[],"preferred":false,"id":491444,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":491439,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70095738,"text":"70095738 - 2014 - Dynamic hyporheic exchange at intermediate timescales: testing the relative importance of evapotranspiration and flood pulses","interactions":[],"lastModifiedDate":"2014-03-11T12:11:59","indexId":"70095738","displayToPublicDate":"2014-03-01T11:54:21","publicationYear":"2014","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":"Dynamic hyporheic exchange at intermediate timescales: testing the relative importance of evapotranspiration and flood pulses","docAbstract":"Hyporheic fluxes influence ecological processes across a continuum of timescales. However, few studies have been able to characterize hyporheic fluxes and residence time distributions (RTDs) over timescales of days to years, during which evapotranspiration (ET) and seasonal flood pulses create unsteady forcing. Here we present a data-driven, particle-tracking piston model that characterizes hyporheic fluxes and RTDs based on measured vertical head differences. We used the model to test the relative influence of ET and seasonal flood pulses in the Everglades (FL, USA), in a manner applicable to other low-energy floodplains or broad, shallow streams. We found that over the multiyear timescale, flood pulses that drive relatively deep (∼1 m) flow paths had the dominant influence on hyporheic fluxes and residence times but that ET effects were discernible at shorter timescales (weeks to months) as a break in RTDs. Cumulative RTDs on either side of the break were generally well represented by lognormal functions, except for when ET was strong and none of the standard distributions applied to the shorter timescale. At the monthly timescale, ET increased hyporheic fluxes by 1–2 orders of magnitude; it also decreased 6 year mean residence times by 53–87%. Long, slow flow paths driven by flood pulses increased 6 year hyporheic fluxes by another 1–2 orders of magnitude, to a level comparable to that induced over the short term by shear flow in streams. Results suggest that models of intermediate-timescale processes should include at least two-storage zones with different RTDs, and that supporting field data collection occur over 3–4 years.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Water Resources Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/2013WR014195","usgsCitation":"Larsen, L., Harvey, J.W., and Maglio, M.M., 2014, Dynamic hyporheic exchange at intermediate timescales: testing the relative importance of evapotranspiration and flood pulses: Water Resources Research, v. 50, no. 1, p. 318-335, https://doi.org/10.1002/2013WR014195.","productDescription":"18 p.","startPage":"318","endPage":"335","ipdsId":"IP-052076","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":473134,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013wr014195","text":"Publisher Index Page"},{"id":283831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":283701,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/2013WR014195"},{"id":283702,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/2013WR014195/abstract"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81,5.555555555555556E-4 ], [ -81,5.555555555555556E-4 ], [ -80,5.555555555555556E-4 ], [ -80,5.555555555555556E-4 ], [ -81,5.555555555555556E-4 ] ] ] } } ] }","volume":"50","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-01-15","publicationStatus":"PW","scienceBaseUri":"53517034e4b05569d805a1cf","contributors":{"authors":[{"text":"Larsen, Laurel G.","contributorId":42111,"corporation":false,"usgs":true,"family":"Larsen","given":"Laurel G.","affiliations":[],"preferred":false,"id":491416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":491414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maglio, Morgan M. mmaglio@usgs.gov","contributorId":3991,"corporation":false,"usgs":true,"family":"Maglio","given":"Morgan","email":"mmaglio@usgs.gov","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":491415,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70095724,"text":"70095724 - 2014 - Characteristic length scales and time-averaged transport velocities of suspended sediment in the mid-Atlantic Region, USA","interactions":[],"lastModifiedDate":"2016-06-29T15:43:32","indexId":"70095724","displayToPublicDate":"2014-03-01T11:41:00","publicationYear":"2014","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":"Characteristic length scales and time-averaged transport velocities of suspended sediment in the mid-Atlantic Region, USA","docAbstract":"<p>Watershed Best Management Practices (BMPs) are often designed to reduce loading from particle-borne contaminants, but the temporal lag between BMP implementation and improvement in receiving water quality is difficult to assess because particles are only moved downstream episodically, resting for long periods in storage between transport events. A theory is developed that describes the downstream movement of suspended sediment particles accounting for the time particles spend in storage given sediment budget data (by grain size fraction) and information on particle transit times through storage reservoirs. The theory is used to define a suspended sediment transport length scale that describes how far particles are carried during transport events, and to estimate a downstream particle velocity that includes time spent in storage. At 5 upland watersheds of the mid-Atlantic region, transport length scales for silt-clay range from 4 to 60 km, while those for sand range from 0.4 to 113 km. Mean sediment velocities for silt-clay range from 0.0072 km/yr to 0.12 km/yr, while those for sand range from 0.0008 km/yr to 0.20 km/yr, 4&ndash;6 orders of magnitude slower than the velocity of water in the channel. These results suggest lag times of 100&ndash;1000 years between BMP implementation and effectiveness in receiving waters such as the Chesapeake Bay (where BMPs are located upstream of the characteristic transport length scale). Many particles likely travel much faster than these average values, so further research is needed to determine the complete distribution of suspended sediment velocities in real watersheds.</p>","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2013WR014485","usgsCitation":"Pizzuto, J., Schenk, E.R., Hupp, C.R., Gellis, A., Noe, G., Williamson, E., Karwan, D.L., O'Neal, M., Marquard, J., Aalto, R.E., and Newbold, D., 2014, Characteristic length scales and time-averaged transport velocities of suspended sediment in the mid-Atlantic Region, USA: Water Resources Research, v. 50, no. 2, p. 790-805, https://doi.org/10.1002/2013WR014485.","productDescription":"12 p.","startPage":"790","endPage":"805","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052956","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":473135,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013wr014485","text":"Publisher Index Page"},{"id":283829,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -78.365478515625,\n              38.77121637244273\n            ],\n            [\n              -78.365478515625,\n              40.713955826286046\n            ],\n            [\n              -75.2783203125,\n              40.713955826286046\n            ],\n            [\n              -76.3,\n              38.77121637244273\n            ],\n            [\n              -78.365478515625,\n              38.77121637244273\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"50","issue":"2","noUsgsAuthors":false,"publicationDate":"2014-02-03","publicationStatus":"PW","scienceBaseUri":"5351702ce4b05569d805a18e","contributors":{"authors":[{"text":"Pizzuto, James","contributorId":12366,"corporation":false,"usgs":true,"family":"Pizzuto","given":"James","affiliations":[],"preferred":false,"id":491393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schenk, Edward R. 0000-0001-6886-5754 eschenk@usgs.gov","orcid":"https://orcid.org/0000-0001-6886-5754","contributorId":2183,"corporation":false,"usgs":true,"family":"Schenk","given":"Edward","email":"eschenk@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":491391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hupp, Cliff R. 0000-0003-1853-9197 crhupp@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-9197","contributorId":2344,"corporation":false,"usgs":true,"family":"Hupp","given":"Cliff","email":"crhupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":491392,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gellis, Allen","contributorId":37051,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","affiliations":[],"preferred":false,"id":491396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noe, Greg","contributorId":18650,"corporation":false,"usgs":true,"family":"Noe","given":"Greg","email":"","affiliations":[],"preferred":false,"id":491395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williamson, Elyse","contributorId":66597,"corporation":false,"usgs":true,"family":"Williamson","given":"Elyse","email":"","affiliations":[],"preferred":false,"id":491398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karwan, Diana L.","contributorId":90211,"corporation":false,"usgs":true,"family":"Karwan","given":"Diana","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":491400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"O'Neal, Michael","contributorId":73499,"corporation":false,"usgs":true,"family":"O'Neal","given":"Michael","affiliations":[],"preferred":false,"id":491399,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Marquard, Julia","contributorId":98631,"corporation":false,"usgs":true,"family":"Marquard","given":"Julia","affiliations":[],"preferred":false,"id":491401,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Aalto, Rolf E.","contributorId":52486,"corporation":false,"usgs":false,"family":"Aalto","given":"Rolf","email":"","middleInitial":"E.","affiliations":[{"id":17840,"text":"University of Exeter","active":true,"usgs":false}],"preferred":false,"id":491397,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Newbold, Denis","contributorId":12367,"corporation":false,"usgs":true,"family":"Newbold","given":"Denis","email":"","affiliations":[],"preferred":false,"id":491394,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70150448,"text":"70150448 - 2014 - Retrospective analysis of associations between water quality and toxic blooms of golden alga (<i>Prymnesium parvum</i>) in Texas reservoirs: Implications for understanding dispersal mechanisms and impacts of climate change","interactions":[],"lastModifiedDate":"2015-06-26T10:34:05","indexId":"70150448","displayToPublicDate":"2014-03-01T11:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Retrospective analysis of associations between water quality and toxic blooms of golden alga (<i>Prymnesium parvum</i>) in Texas reservoirs: Implications for understanding dispersal mechanisms and impacts of climate change","docAbstract":"<p>Toxic blooms of golden alga (GA, <i>Prymnesium parvum</i>) in Texas typically occur in winter or early spring. In North America, they were first reported in Texas in the 1980s, and a marked range expansion occurred in 2001. Although there is concern about the influence of climate change on the future distribution of GA, factors responsible for past dispersals remain uncertain. To better understand the factors that influence toxic bloom dispersal in reservoirs, this study characterized reservoir water quality associated with toxic GA blooms since 2001, and examined trends in water quality during a 20-year period bracketing the 2001 expansion. Archived data were analyzed for six impacted and six nonimpacted reservoirs from two major Texas basins: Brazos River and Colorado River. Data were simplified for analysis by pooling spatially (across sampling stations) and temporally (winter, December-February) within reservoirs and generating depth-corrected (1 m) monthly values. Classification tree analysis [period of record (POR), 2001-2010] using salinity-associated variables (specific conductance, chloride, sulfate), dissolved oxygen (DO), pH, temperature, total hardness, potassium, nitrate+nitrite, and total phosphorus indicated that salinity best predicts the toxic bloom occurrence. Minimum estimated salinities for toxic bloom formation were 0.59 and 1.02 psu in Brazos and Colorado River reservoirs, respectively. Principal component analysis (POR, 2001-2010) indicated that GA habitat is best defined by higher salinity relative to nonimpacted reservoirs, with winter DO and pH also being slightly higher and winter temperature slightly lower in impacted reservoirs. Trend analysis, however, did not reveal monotonic changes in winter water quality of GA-impacted reservoirs during the 20-year period (1991-2010) bracketing the 2001 dispersal. Therefore, whereas minimum levels of salinity are required for GA establishment and toxic blooms in Texas reservoirs, the lack of trends in water quality suggests that conditions favorable for toxic blooms pre-date the 2001 expansion. These observations are consistent with a climate change-independent scenario of past GA dispersals in Texas reservoirs driven by novel introductions into pre-existing favorable habitat. Reports of latent GA populations in certain nonimpacted reservoirs, however, provide a plausible scenario of future dispersals characterized by prolonged periods between colonization and toxic bloom development and driven by changes in water quality, natural, or anthropogenic.</p>","language":"English","publisher":"Elsevier Science BV","publisherLocation":"Amsterdam","doi":"10.1016/j.hal.2013.12.006","usgsCitation":"Patino, R., Dawson, D., and VanLandeghem, M., 2014, Retrospective analysis of associations between water quality and toxic blooms of golden alga (<i>Prymnesium parvum</i>) in Texas reservoirs: Implications for understanding dispersal mechanisms and impacts of climate change: Harmful Algae, v. 33, p. 1-11, https://doi.org/10.1016/j.hal.2013.12.006.","productDescription":"11 p.","startPage":"1","endPage":"11","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049678","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":302372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"558e77b9e4b0b6d21dd65969","contributors":{"authors":[{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":556895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, D.","contributorId":72901,"corporation":false,"usgs":true,"family":"Dawson","given":"D.","email":"","affiliations":[],"preferred":false,"id":556953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"VanLandeghem, Matthew M.","contributorId":143728,"corporation":false,"usgs":false,"family":"VanLandeghem","given":"Matthew M.","affiliations":[],"preferred":false,"id":556954,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70093758,"text":"70093758 - 2014 - Using natural range of variation to set decision thresholds: a case study for great plains grasslands","interactions":[],"lastModifiedDate":"2018-08-15T11:55:46","indexId":"70093758","displayToPublicDate":"2014-03-01T11:16:54","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Using natural range of variation to set decision thresholds: a case study for great plains grasslands","docAbstract":"Natural range of variation (NRV) may be used to establish decision thresholds or action assessment points when ecological thresholds are either unknown or do not exist for attributes of interest in a managed ecosystem. The process for estimating NRV involves identifying spatial and temporal scales that adequately capture the heterogeneity of the ecosystem; compiling data for the attributes of interest via study of historic records, analysis and interpretation of proxy records, modeling, space-for-time substitutions, or analysis of long-term monitoring data; and quantifying the NRV from those data. At least 19 National Park Service (NPS) units in North America’s Great Plains are monitoring plant species richness and evenness as indicators of vegetation integrity in native grasslands, but little information on natural, temporal variability of these indicators is available. In this case study, we use six long-term vegetation monitoring datasets to quantify the temporal variability of these attributes in reference conditions for a variety of Great Plains grassland types, and then illustrate the implications of using different NRVs based on these quantities for setting management decision thresholds. Temporal variability of richness (as measured by the coefficient of variation, CV) is fairly consistent across the wide variety of conditions occurring in Colorado shortgrass prairie to Minnesota tallgrass sand savanna (CV 0.20–0.45) and generally less than that of production at the same sites. Temporal variability of evenness spans a greater range of CV than richness, and it is greater than that of production in some sites but less in other sites. This natural temporal variability may mask undesirable changes in Great Plains grasslands vegetation. Consequently, we suggest that managers consider using a relatively narrow NRV (interquartile range of all richness or evenness values observed in reference conditions) for designating a surveillance threshold, at which greater attention to the situation would be paid, and a broader NRV for designating management thresholds, at which action would be instigated.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Application of threshold concepts in natural resource decision making","language":"English","publisher":"Springer","doi":"10.1007/978-1-4899-8041-0_8","usgsCitation":"Symstad, A., and Jonas, J.L., 2014, Using natural range of variation to set decision thresholds: a case study for great plains grasslands, chap. <i>of</i> Application of threshold concepts in natural resource decision making, p. 131-156, https://doi.org/10.1007/978-1-4899-8041-0_8.","productDescription":"26 p.","startPage":"131","endPage":"156","ipdsId":"IP-034780","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":285884,"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              -113.115234375,\n              53.27835301753182\n            ],\n            [\n              -113.53271484375,\n              53.396432127095984\n            ],\n            [\n              -114.005126953125,\n              53.26521293124656\n            ],\n            [\n              -114.521484375,\n              51.80861475198521\n            ],\n            [\n              -115.04882812499999,\n              49.009050809382046\n            ],\n            [\n              -112.19238281249999,\n         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R.","contributorId":113070,"corporation":false,"usgs":false,"family":"Guntenspergen","given":"Glenn R.","affiliations":[],"preferred":false,"id":509795,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Symstad, Amy J.","contributorId":11721,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy J.","affiliations":[],"preferred":false,"id":490192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jonas, Jayne L.","contributorId":22680,"corporation":false,"usgs":true,"family":"Jonas","given":"Jayne","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":490193,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70115922,"text":"70115922 - 2014 - Distribution and transmission of the highly pathogenic parasite <i>Ichthyophonus</i> in marine fishes of Alaska","interactions":[],"lastModifiedDate":"2014-09-23T11:17:37","indexId":"70115922","displayToPublicDate":"2014-03-01T11:13:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Distribution and transmission of the highly pathogenic parasite <i>Ichthyophonus</i> in marine fishes of Alaska","docAbstract":"A combination of field surveys, molecular typing, and laboratory experiments were used to improve our understanding of the distribution and transmission mechanisms of fish parasites in the genus <i>Ichthyophonus</i>. <i>Ichthyophonus</i> spp. infections were detected from the Bering Sea to the coast of Oregon in 10 of 13 host species surveyed. Sequences of rDNA extracted from these isolates indicate that a ubiquitous <i>Ichthyophonus</i> type occurs in the NE Pacific Ocean and Bering Sea and accounts for nearly all the infections encountered. Among NE Pacific isolates, only parasites from yellowtail rockfish and Puget Sound rockfish varied at the DNA locus examined. These data suggest that a single source population of these parasites is available to fishes in diverse niches across a wide geographic range. A direct life cycle within a common forage species could account for the relatively low parasite diversity we encountered. In the laboratory we tested the hypothesis that waterborne transmission occurs among Pacific herring, a common NE Pacific forage species. No horizontal transmission occurred during a four-month cohabitation experiment involving infected herring and conspecific sentinels. The complete life cycle of <i>Ichthyophonus</i> spp. is not known, but these results suggest that system-wide processes maintain a relatively homogenous parasite population.","language":"English","publisher":"North Pacific Research Board","usgsCitation":"Gregg, J., Grady, C.A., Thompson, R.L., Purcell, M., Friedman, C., and Hershberger, P., 2014, Distribution and transmission of the highly pathogenic parasite <i>Ichthyophonus</i> in marine fishes of Alaska, 46 p.","productDescription":"46 p.","numberOfPages":"46","ipdsId":"IP-055829","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":294314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5422bb23e4b08312ac7cf008","contributors":{"authors":[{"text":"Gregg, Jacob L.","contributorId":30883,"corporation":false,"usgs":true,"family":"Gregg","given":"Jacob L.","affiliations":[],"preferred":false,"id":495692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grady, Courtney A.","contributorId":8352,"corporation":false,"usgs":true,"family":"Grady","given":"Courtney","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":495690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Rachel L. 0000-0001-6901-4361 rlthompson@usgs.gov","orcid":"https://orcid.org/0000-0001-6901-4361","contributorId":5707,"corporation":false,"usgs":true,"family":"Thompson","given":"Rachel","email":"rlthompson@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":495689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Purcell, Maureen K.","contributorId":104214,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen K.","affiliations":[],"preferred":false,"id":495693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friedman, Carolyn S.","contributorId":13890,"corporation":false,"usgs":true,"family":"Friedman","given":"Carolyn S.","affiliations":[],"preferred":false,"id":495691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hershberger, Paul K. phershberger@usgs.gov","contributorId":1945,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul K.","email":"phershberger@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":495688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70096237,"text":"70096237 - 2014 - A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (<i>Charadrius melodus</i>) using barrier island features","interactions":[],"lastModifiedDate":"2017-01-11T15:39:37","indexId":"70096237","displayToPublicDate":"2014-03-01T11:05:55","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (<i>Charadrius melodus</i>) using barrier island features","docAbstract":"Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat.  The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development.  The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development.  We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland.  Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset.  We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow.  We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence.  These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands.  We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions.  Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2014.01.005","usgsCitation":"Gieder, K.D., Karpanty, S.M., Fraser, J., Catlin, D.H., Gutierrez, B.T., Plant, N.G., Turecek, A.M., and Thieler, E.R., 2014, A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (<i>Charadrius melodus</i>) using barrier island features: Ecological Modelling, v. 276, p. 38-50, https://doi.org/10.1016/j.ecolmodel.2014.01.005.","productDescription":"13 p.","startPage":"38","endPage":"50","ipdsId":"IP-053272","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":473136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2014.01.005","text":"Publisher Index Page"},{"id":283876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":283872,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org//10.1016/j.ecolmodel.2014.01.005"}],"country":"United States","state":"Maryl","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -75.14,38.25 ], [ -75.14,38.34 ], [ -75.08,38.34 ], [ -75.08,38.25 ], [ -75.14,38.25 ] ] ] } } ] }","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53516eb2e4b05569d8059d1b","chorus":{"doi":"10.1016/j.ecolmodel.2014.01.005","url":"http://dx.doi.org/10.1016/j.ecolmodel.2014.01.005","publisher":"Elsevier BV","authors":"Gieder Katherina D., Karpanty Sarah M., Fraser James D., Catlin Daniel H., Gutierrez Benjamin T., Plant Nathaniel G., Turecek Aaron M., Robert Thieler E.","journalName":"Ecological Modelling","publicationDate":"3/2014","auditedOn":"3/22/2016","publiclyAccessibleDate":"1/24/2014"},"contributors":{"authors":[{"text":"Gieder, Katherina D.","contributorId":34426,"corporation":false,"usgs":true,"family":"Gieder","given":"Katherina","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":491488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karpanty, Sarah M.","contributorId":63307,"corporation":false,"usgs":false,"family":"Karpanty","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":33131,"text":"Dept of Fish and Wildlife Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":491490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fraser, James D.","contributorId":86686,"corporation":false,"usgs":false,"family":"Fraser","given":"James D.","affiliations":[{"id":33131,"text":"Dept of Fish and Wildlife Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":491491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Catlin, Daniel H.","contributorId":87859,"corporation":false,"usgs":false,"family":"Catlin","given":"Daniel","email":"","middleInitial":"H.","affiliations":[{"id":33131,"text":"Dept of Fish and Wildlife Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":491492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gutierrez, Benjamin T.","contributorId":58670,"corporation":false,"usgs":true,"family":"Gutierrez","given":"Benjamin","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":491489,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491486,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Turecek, Aaron M.","contributorId":22190,"corporation":false,"usgs":true,"family":"Turecek","given":"Aaron","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":491487,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491485,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70004470,"text":"70004470 - 2014 - Influence of landscape characteristics on retention of expandable radiocollars on young ungulates","interactions":[],"lastModifiedDate":"2016-06-07T11:38:56","indexId":"70004470","displayToPublicDate":"2014-03-01T11:05:00","publicationYear":"2014","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":"Influence of landscape characteristics on retention of expandable radiocollars on young ungulates","docAbstract":"<p>One tool used for wildlife management is the deployment of radiocollars to gain knowledge of animal populations. Understanding the influence of individual factors (e.g., species, collar characteristics) and landscape characteristics (e.g., forested cover, shrubs, and fencing) on retention of expandable radiocollars for ungulates is important for obtaining empirical data on factors influencing ecology of young-of-the-year ungulates. During 2001&ndash;2009, we captured and radiocollared 198 white-tailed deer (Odocoileus virginianus) fawns, 142 pronghorn (Antilocapra americana) fawns, and 73 mule deer (O. hemionus) fawns in South Dakota, Minnesota, and California, USA. We documented 72 (36.4%), 8 (5.6%), and 7 (9.6%) premature (2, SE&thinsp;=&thinsp;0.1, n&thinsp;=&thinsp;75) compared with areas where fawns shed collars (x&thinsp;=&thinsp;3.24&thinsp;km/km<sup>2</sup>, SE&thinsp;=&thinsp;0.1, n&thinsp;=&thinsp;56) prior to 270 days. Researchers of fawns should consider that radiocollars can be shed prematurely when estimating desired sample size to yield a suitable strength of inference about some natural process of interest.</p>","language":"English","publisher":"Wiley","doi":"10.1002/wsb.366","usgsCitation":"Grovenburg, T.W., Klaver, R.W., Jacques, C.N., Brinkman, T.J., Swanson, C., DePerno, C.S., Monteith, K.L., Sievers, J.D., Bleich, V.C., Kie, J.G., and Jenks, J., 2014, Influence of landscape characteristics on retention of expandable radiocollars on young ungulates: Wildlife Society Bulletin, v. 38, no. 1, p. 89-95, https://doi.org/10.1002/wsb.366.","productDescription":"7 p.","startPage":"89","endPage":"95","numberOfPages":"7","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-029654","costCenters":[],"links":[{"id":473138,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/232","text":"External Repository"},{"id":286324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286323,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/wsb.366"}],"country":"United States","state":"California;Minnesota;South Dakota","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.64,35.8 ], [ -122.64,45.92 ], [ -93.93,45.92 ], [ -93.93,35.8 ], [ -122.64,35.8 ] ] ] } } ] }","volume":"38","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-11-19","publicationStatus":"PW","scienceBaseUri":"53517050e4b05569d805a2ec","contributors":{"authors":[{"text":"Grovenburg, Troy W.","contributorId":57712,"corporation":false,"usgs":true,"family":"Grovenburg","given":"Troy","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":350477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":350470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacques, Christopher N.","contributorId":15521,"corporation":false,"usgs":true,"family":"Jacques","given":"Christopher","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":350474,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brinkman, Todd J.","contributorId":39696,"corporation":false,"usgs":true,"family":"Brinkman","given":"Todd","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":350475,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swanson, Christopher C.","contributorId":58505,"corporation":false,"usgs":true,"family":"Swanson","given":"Christopher C.","affiliations":[],"preferred":false,"id":350478,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DePerno, Christopher S.","contributorId":10327,"corporation":false,"usgs":true,"family":"DePerno","given":"Christopher","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":350472,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monteith, Kevin L.","contributorId":83400,"corporation":false,"usgs":true,"family":"Monteith","given":"Kevin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":350479,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sievers, Jaret D.","contributorId":10717,"corporation":false,"usgs":true,"family":"Sievers","given":"Jaret","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":350473,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bleich, Vernon C.","contributorId":10293,"corporation":false,"usgs":true,"family":"Bleich","given":"Vernon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":350471,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kie, John G.","contributorId":87274,"corporation":false,"usgs":true,"family":"Kie","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":350480,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jenks, Jonathan A.","contributorId":51591,"corporation":false,"usgs":true,"family":"Jenks","given":"Jonathan A.","affiliations":[],"preferred":false,"id":350476,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70047862,"text":"70047862 - 2014 - A conceptual framework for clutch size evolution in songbirds","interactions":[],"lastModifiedDate":"2014-03-27T14:23:29","indexId":"70047862","displayToPublicDate":"2014-03-01T10:51:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":740,"text":"American Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"A conceptual framework for clutch size evolution in songbirds","docAbstract":"Causes of evolved differences in clutch size among songbird species remain debated. I propose a new conceptual framework that integrates aspects of traditional life history theory, while including novel elements, to explain evolution of clutch size among songbirds. I review evidence that selection by nest predation on length of time that offspring develop in the nest creates a gradient in offspring characteristics at nest-leaving (fledging), including flight mobility, spatial dispersion, and self-feeding rate. I postulate that this gradient has consequences for offspring mortality rates and parental energy expenditure per offspring. These consequences then determine how reproductive effort is partitioned among offspring, while reproductive effort evolves from age-specific mortality effects. Using data from a long-term site in Arizona, as well as from the literature, I provide support for hypothesized relationships. Nestling development period consistently explains fledgling mortality, energy expenditure per offspring, and clutch size while accounting for reproductive effort (i.e., total energy expenditure) to thereby support the framework. Tests in this paper are not definitive, but they document previously unrecognized relationships and address diverse traits (developmental strategies, parental care strategies, energy requirements per offspring, evolution of reproductive effort, clutch size) that justify further investigations of hypotheses proposed here.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"American Naturalist","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1086/674966","usgsCitation":"Martin, T.E., 2014, A conceptual framework for clutch size evolution in songbirds: American Naturalist, v. 183, no. 3, p. 313-324, https://doi.org/10.1086/674966.","productDescription":"12 p.","startPage":"313","endPage":"324","ipdsId":"IP-044283","costCenters":[],"links":[{"id":285062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285060,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1086/674966"}],"country":"North America","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 177.1,5.6 ], [ 177.1,85.4 ], [ -4.0,85.4 ], [ -4.0,5.6 ], [ 177.1,5.6 ] ] ] } } ] }","volume":"183","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53516eb3e4b05569d8059d2a","contributors":{"authors":[{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":483173,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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