Process dynamics in fluvial-based dryland environments are highly complex with fluvial, aeolian, and alluvial processes all contributing to landscape change. When anthropogenic activities such as dam-building affect fluvial processes, the complexity in local response can be further increased by flood- and sediment-limiting flows. Understanding these complexities is key to predicting landscape behavior in drylands and has important scientific and management implications, including for studies related to paleoclimatology, landscape ecology evolution, and archaeological site context and preservation. Here we use multi-temporal LiDAR surveys, local weather data, and geomorphological observations to identify trends in site change throughout the 446-km-long semi-arid Colorado River corridor in Grand Canyon, Arizona, USA, where archaeological site degradation related to the effects of upstream dam operation is a concern. Using several site case studies, we show the range of landscape responses that might be expected from concomitant occurrence of dam-controlled fluvial sand bar deposition, aeolian sand transport, and rainfall-induced erosion. Empirical rainfall-erosion threshold analyses coupled with a numerical rainfall–runoff–soil erosion model indicate that infiltration-excess overland flow and gullying govern large-scale (centimeter- to decimeter-scale) landscape changes, but that aeolian deposition can in some cases mitigate gully erosion. Whereas threshold analyses identify the normalized rainfall intensity (defined as the ratio of rainfall intensity to hydraulic conductivity) as the primary factor governing hydrologic-driven erosion, assessment of false positives and false negatives in the dataset highlight topographic slope as the next most important parameter governing site response. Analysis of 4+ years of high resolution (four-minute) weather data and 75+ years of low resolution (daily) climate records indicates that dryland erosion is dependent on short-term, storm-driven rainfall intensity rather than cumulative rainfall, and that erosion can occur outside of wet seasons and even wet years. These results can apply to other similar semi-arid landscapes where process complexity may not be fully understood.
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Center","active":true,"usgs":true}],"preferred":false,"id":637683,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fairley, Helen C. 0000-0001-6151-4804 hfairley@usgs.gov","orcid":"https://orcid.org/0000-0001-6151-4804","contributorId":3040,"corporation":false,"usgs":true,"family":"Fairley","given":"Helen","email":"hfairley@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":637684,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cronkite-Ratcliff, Collin ccronkite-ratcliff@usgs.gov","contributorId":5478,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":637685,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204449,"text":"70204449 - 2016 - Integrative modelling reveals mechanisms linking productivity and plant species richness","interactions":[],"lastModifiedDate":"2019-07-24T13:43:37","indexId":"70204449","displayToPublicDate":"2016-01-13T13:03:52","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Integrative modelling reveals mechanisms linking productivity and plant species richness","docAbstract":"How ecosystem productivity and species richness are interrelated is one of the most debated subjects in the history of ecology. Decades of intensive study have yet to discern the actual mechanisms behind observed global patterns. Here, by integrating the predictions from multiple theories into a single model and using data from 1,126 grassland plots spanning five continents, we detect the clear signals of numerous underlying mechanisms linking productivity and richness. We find that an integrative model has substantially higher explanatory power than traditional bivariate analyses. In addition, the specific results unveil several surprising findings that conflict with classical models. These include the isolation of a strong and consistent enhancement of productivity by richness, an effect in striking contrast with superficial data patterns. Also revealed is a consistent importance of competition across the full range of productivity values, in direct conflict with some (but not all) proposed models. The promotion of local richness by macroecological gradients in climatic favourability, generally seen as a competing hypothesis, is also found to be important in our analysis. The results demonstrate that an integrative modelling approach leads to a major advance in our ability to discern the underlying processes operating in ecological systems.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/nature16524","usgsCitation":"Grace, J.B., Anderson, T.M., Seabloom, E.W., Borer, E.T., Adler, P.B., Harpole, W., Hautier, Y., Hillebrand, H., Lind, E.M., Partel, M., Bakker, J.D., Buckley, Y.M., Crawley, M.J., Damschen, E.I., Davies, K.F., Fay, P.A., Firn, J., Gruner, D.S., Hector, A., Knops, J.M., MacDougall, A.S., Melbourne, B.A., Morgan, J.W., Orrock, J., Prober, S.M., and Smith, M., 2016, Integrative modelling reveals mechanisms linking productivity and plant species richness: Nature, v. 529, p. 390-393, https://doi.org/10.1038/nature16524.","productDescription":"4 p.","startPage":"390","endPage":"393","ipdsId":"IP-051258","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471330,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://dspace.library.uu.nl/handle/1874/344413","text":"External Repository"},{"id":365909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"529","noUsgsAuthors":false,"publicationDate":"2016-01-13","publicationStatus":"PW","contributors":{"authors":[{"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":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":766959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, T. Michael","contributorId":203893,"corporation":false,"usgs":false,"family":"Anderson","given":"T.","email":"","middleInitial":"Michael","affiliations":[{"id":36744,"text":"Wake Forest University","active":true,"usgs":false}],"preferred":false,"id":766963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seabloom, Eric W.","contributorId":60762,"corporation":false,"usgs":false,"family":"Seabloom","given":"Eric","email":"","middleInitial":"W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":766964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Borer, Elizabeth T.","contributorId":45049,"corporation":false,"usgs":false,"family":"Borer","given":"Elizabeth","email":"","middleInitial":"T.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":766965,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adler, Peter B.","contributorId":64789,"corporation":false,"usgs":false,"family":"Adler","given":"Peter","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":766966,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harpole, W Stanley","contributorId":131028,"corporation":false,"usgs":false,"family":"Harpole","given":"W Stanley","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":766967,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hautier, Yann","contributorId":84065,"corporation":false,"usgs":true,"family":"Hautier","given":"Yann","email":"","affiliations":[],"preferred":false,"id":766968,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hillebrand, Helmut","contributorId":83655,"corporation":false,"usgs":true,"family":"Hillebrand","given":"Helmut","email":"","affiliations":[],"preferred":false,"id":766969,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lind, Eric M.","contributorId":87855,"corporation":false,"usgs":true,"family":"Lind","given":"Eric","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":766970,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Partel, Meelis","contributorId":217517,"corporation":false,"usgs":false,"family":"Partel","given":"Meelis","email":"","affiliations":[],"preferred":false,"id":766971,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bakker, Jonathan D.","contributorId":15754,"corporation":false,"usgs":true,"family":"Bakker","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":766972,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Buckley, Yvonne M.","contributorId":29945,"corporation":false,"usgs":true,"family":"Buckley","given":"Yvonne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":766973,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Crawley, Michael J.","contributorId":80810,"corporation":false,"usgs":true,"family":"Crawley","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":766974,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Damschen, Ellen Ingman","contributorId":6177,"corporation":false,"usgs":false,"family":"Damschen","given":"Ellen","email":"","middleInitial":"Ingman","affiliations":[{"id":16916,"text":"Dept. of Zoology, University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":766975,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Davies, Kendi F.","contributorId":30346,"corporation":false,"usgs":true,"family":"Davies","given":"Kendi","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":766976,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Fay, Philip A.","contributorId":51443,"corporation":false,"usgs":true,"family":"Fay","given":"Philip","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":766977,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Firn, Jennifer","contributorId":66405,"corporation":false,"usgs":false,"family":"Firn","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":766978,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Gruner, Daniel S.","contributorId":195507,"corporation":false,"usgs":false,"family":"Gruner","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":766979,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Hector, Andy","contributorId":102620,"corporation":false,"usgs":true,"family":"Hector","given":"Andy","email":"","affiliations":[],"preferred":false,"id":766980,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Knops, Johannes M.H.","contributorId":105843,"corporation":false,"usgs":true,"family":"Knops","given":"Johannes","email":"","middleInitial":"M.H.","affiliations":[],"preferred":false,"id":766981,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"MacDougall, Andrew S.","contributorId":39509,"corporation":false,"usgs":true,"family":"MacDougall","given":"Andrew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":766982,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Melbourne, Brett A.","contributorId":86473,"corporation":false,"usgs":true,"family":"Melbourne","given":"Brett","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":766983,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Morgan, John W.","contributorId":88077,"corporation":false,"usgs":true,"family":"Morgan","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":766984,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Orrock, John L.","contributorId":18101,"corporation":false,"usgs":true,"family":"Orrock","given":"John L.","affiliations":[],"preferred":false,"id":766985,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Prober, Suzanne M.","contributorId":74498,"corporation":false,"usgs":false,"family":"Prober","given":"Suzanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":766986,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Smith, Melinda D.","contributorId":94028,"corporation":false,"usgs":true,"family":"Smith","given":"Melinda D.","affiliations":[],"preferred":false,"id":766987,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70161859,"text":"sir20155133 - 2016 - Application of a Weighted Regression Model for Reporting Nutrient and Sediment Concentrations, Fluxes, and Trends in Concentration and Flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, Results Through Water Year 2012","interactions":[],"lastModifiedDate":"2021-07-02T13:50:02.84497","indexId":"sir20155133","displayToPublicDate":"2016-01-13T11:15:00","publicationYear":"2016","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":"2015-5133","title":"Application of a Weighted Regression Model for Reporting Nutrient and Sediment Concentrations, Fluxes, and Trends in Concentration and Flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, Results Through Water Year 2012","docAbstract":"In the Chesapeake Bay watershed, estimated fluxes of nutrients and sediment from the bay’s nontidal tributaries into the estuary are the foundation of decision making to meet reductions prescribed by the Chesapeake Bay Total Maximum Daily Load (TMDL) and are often the basis for refining scientific understanding of the watershed-scale processes that influence the delivery of these constituents to the bay. Two regression-based flux and trend estimation models, ESTIMATOR and Weighted Regressions on Time, Discharge, and Season (WRTDS), were compared using data from 80 watersheds in the Chesapeake Bay Nontidal Water-Quality Monitoring Network (CBNTN). The watersheds range in size from 62 to 70,189 square kilometers and record lengths range from 6 to 28 years. ESTIMATOR is a constant-parameter model that estimates trends only in concentration; WRTDS uses variable parameters estimated with weighted regression, and estimates trends in both concentration and flux. WRTDS had greater explanatory power than ESTIMATOR, with the greatest degree of improvement evident for records longer than 25 years (30 stations; improvement in median model R2= 0.06 for total nitrogen, 0.08 for total phosphorus, and 0.05 for sediment) and the least degree of improvement for records of less than 10 years, for which the two models performed nearly equally. Flux bias statistics were comparable or lower (more favorable) for WRTDS for any record length; for 30 stations with records longer than 25 years, the greatest degree of improvement was evident for sediment (decrease of 0.17 in median statistic) and total phosphorus (decrease of 0.05). The overall between-station pattern in concentration trend direction and magnitude for all constituents was roughly similar for both models. A detailed case study revealed that trends in concentration estimated by WRTDS can operationally be viewed as a less-constrained equivalent to trends in concentration estimated by ESTIMATOR. Estimates of annual mean flow-adjusted (ESTIMATOR) and flow-normalized (WRTDS) concentration for years initially constituting the end of a water-quality record showed a similar degree of variability as data for additional years were incrementally added and the initial estimates “aged.” On the basis of the results of this broad comparison of the two models, the U.S. Geological Survey is adopting WRTDS as the primary model for estimating constituent fluxes and trends throughout the CBNTN. Nutrient and sediment flux and trend estimates, based on WRTDS, are summarized narratively and tabulated in appendixes for all stations for which fluxes or trends were reported through water year 2012.
\nWRTDS also was used to explore the sensitivity of flux and trend estimates to three data-quality issues common in many large-scale monitoring networks and evident in some of the CBNTN records. The potential effects of inconsistency in annual sampling effort and inconsistency in storm sampling effort were explored by way of a subsampling experiment using eight of the most densely sampled long-term (1985–2012) stations in the CBNTN as baseline datasets. From each dataset, a set of 10 “design guideline” subsamples was selected, consisting of 12 monthly samples and 8 targeted storm samples per year. The selection was conducted in a manner that preserved the overall intensity of storm sampling in the baseline data. These 10 subsamples were further manipulated to create “heterogeneous” subsamples by removing storm samples prior to 2003. The maximum relative difference between flow-normalized flux estimated in a single year from any of the 10 design guideline subsamples and values estimated in the corresponding year from baseline data was smallest for dissolved inorganic nitrogen (median of 8 stations = 6 percent of baseline estimate), but more appreciable for total phosphorus and sediment (medians of 22 and 32 percent, respectively). The maximum relative difference between flow-normalized flux estimated from from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data was more pronounced, with medians for 8 stations of 15, 30, and 53 percent of the corresponding baseline estimates for dissolved inorganic nitrogen, total phosphorus, and sediment, respectively. The worst-case maximum relative differences between flow-normalize flux estimated in a single year from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data were 25 percent for dissolved inorganic nitrogen, 37 percent for total phosphorus, and 250 percent for sediment. The results for the heterogeneous subsamples indicate that changes in storm sampling frequency can result in appreciable distortion of estimated trends in flow-normalized flux, especially for total phosphorus and sediment. Trend lines estimated from heterogeneous subsamples tended to converge with the trend lines estimated from baseline data after 2003. In contrast, 2003–12 trends based on subsamples truncated by discarding all data prior to the induced heterogeneity in 2003 showed appreciable biases and differences in slope, relative to the corresponding 2003–12 segment of the trend computed from the design guideline subsamples. Overall, the results indicate that for particulate constituents, load and trend estimates computed using long-term records recently converted to CBNTN design guideline sampling protocols will be most reliable if the trend is computed using the entire record, but reported only for the period that design guideline sampling protocols were followed.
\nInconsistencies related to changing laboratory methods were also examined via two manipulative experiments. In the first experiment, increasing and decreasing “stair-step” patterns of changes in censoring level, overall representing a factor-of-five change in the laboratory reporting limit, were artificially imposed on a 27-year record with no censoring and a period-of-record concentration trend of –68.4 percent. Trends estimated on the basis of the manipulated records were broadly similar to the original trend (–63.6 percent for decreasing censoring levels and –70.3 percent for increasing censoring levels), lending a degree of confidence that the survival regression routines upon which WRTDS is based are generally robust to data censoring. The second experiment considered an abrupt disappearance of low-concentration observations of total phosphorus, associated with a laboratory method change and not reflected through censoring, near the middle of a 28-year record. By process of elimination, an upward shift in the estimated flow-normalize concentration trend line around the same time was identified as a likely artifact resulting from the laboratory method change, although a contemporaneous change in watershed processes cannot be ruled out. Decisions as to how to treat records with potential sampling protocol or laboratory methods-related artifacts should be made on a case-by-case basis, and trend results should be appropriately qualified.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155133","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Chanat, J.G., Moyer, D.L., Blomquist, J.D., Hyer, K.E., and Langland, M.J., 2016, Application of a weighted regression model for reporting nutrient and sediment concentrations, fluxes, and trends in concentration and flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, results through water year 2012: U.S. Geological Survey Scientific Investigations Report 2015–5133, 76 p., https://dx.doi.org/10.3133/sir20155133.","productDescription":"Report: viii, 74 p.; 5 Appendixes","numberOfPages":"88","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-063310","costCenters":[{"id":614,"text":"Virginia Water Science 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
http://va.water.usgs.gov/
Field-based studies are an essential component of research addressing the behavior of organic chemicals, and a unique line of evidence that can be used to assess bioaccumulation potential in chemical registration programs and aid in development of associated laboratory and modeling efforts. To aid scientific and regulatory discourse on the application of terrestrial field data in this manner, this article provides practical recommendations regarding the generation and interpretation of terrestrial field data. Currently, biota-to-soil-accumulation factors (BSAFs), biomagnification factors (BMFs), and bioaccumulation factors (BAFs) are the most suitable bioaccumulation metrics that are applicable to bioaccumulation assessment evaluations and able to be generated from terrestrial field studies with relatively low uncertainty. Biomagnification factors calculated from field-collected samples of terrestrial carnivores and their prey appear to be particularly robust indicators of bioaccumulation potential. The use of stable isotope ratios for quantification of trophic relationships in terrestrial ecosystems needs to be further developed to resolve uncertainties associated with the calculation of terrestrial trophic magnification factors (TMFs). Sampling efforts for terrestrial field studies should strive for efficiency, and advice on optimization of study sample sizes, practical considerations for obtaining samples, selection of tissues for analysis, and data interpretation is provided. Although there is still much to be learned regarding terrestrial bioaccumulation, these recommendations provide some initial guidance to the present application of terrestrial field data as a line of evidence in the assessment of chemical bioaccumulation potential and a resource to inform laboratory and modeling efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.1717","usgsCitation":"van den Brink, N.W., Arblaster, J.A., Bowman, S.R., Conder, J.M., Elliott, J., Johnson, M.S., Muir, D.C., Natal-da-Luz, T., Rattner, B.A., Sample, B.E., and Shore, R.F., 2016, Use of terrestrial field studies in the derivation of bioaccumulation potential of chemicals: Integrated Environmental Assessment and Management, v. 12, no. 1, p. 135-145, https://doi.org/10.1002/ieam.1717.","productDescription":"11 p.","startPage":"135","endPage":"145","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068305","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":471331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.1717","text":"Publisher Index Page"},{"id":314256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-01","publicationStatus":"PW","scienceBaseUri":"56977530e4b039675d00a6c2","contributors":{"authors":[{"text":"van den Brink, Nico W.","contributorId":39229,"corporation":false,"usgs":true,"family":"van den Brink","given":"Nico","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":588524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arblaster, Jennifer A.","contributorId":152218,"corporation":false,"usgs":false,"family":"Arblaster","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":588525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bowman, Sarah R.","contributorId":152219,"corporation":false,"usgs":false,"family":"Bowman","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":588526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conder, Jason M.","contributorId":81294,"corporation":false,"usgs":true,"family":"Conder","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":588527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elliott, John E.","contributorId":127368,"corporation":false,"usgs":false,"family":"Elliott","given":"John E.","affiliations":[{"id":6779,"text":"Environment Canada, Burlington, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":588528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Mark S.","contributorId":86058,"corporation":false,"usgs":true,"family":"Johnson","given":"Mark","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":588529,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Muir, Derek C.G.","contributorId":68679,"corporation":false,"usgs":true,"family":"Muir","given":"Derek","email":"","middleInitial":"C.G.","affiliations":[],"preferred":false,"id":588530,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Natal-da-Luz, Tiago","contributorId":152220,"corporation":false,"usgs":false,"family":"Natal-da-Luz","given":"Tiago","email":"","affiliations":[],"preferred":false,"id":588531,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":588516,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sample, Bradley E.","contributorId":61135,"corporation":false,"usgs":true,"family":"Sample","given":"Bradley","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":588532,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shore, Richard F.","contributorId":127369,"corporation":false,"usgs":false,"family":"Shore","given":"Richard","email":"","middleInitial":"F.","affiliations":[{"id":6919,"text":"Natural Environment Research Council, UK","active":true,"usgs":false}],"preferred":false,"id":588533,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70173995,"text":"70173995 - 2016 - Forcing and variability of nonstationary rip currents","interactions":[],"lastModifiedDate":"2018-03-26T13:51:49","indexId":"70173995","displayToPublicDate":"2016-01-13T04:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2315,"text":"Journal of Geophysical Research C: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Forcing and variability of nonstationary rip currents","docAbstract":"Surface wave transformation and the resulting nearshore circulation along a section of coast with strong alongshore bathymetric gradients outside the surf zone are modeled for a consecutive 4 week time period. The modeled hydrodynamics are compared to in situ measurements of waves and currents collected during the Nearshore Canyon Experiment and indicate that for the entire range of observed conditions, the model performance is similar to other studies along this stretch of coast. Strong alongshore wave height gradients generate rip currents that are observed by remote sensing data and predicted qualitatively well by the numerical model. Previous studies at this site have used idealized scenarios to link the rip current locations to undulations in the offshore bathymetry but do not explain the dichotomy between permanent offshore bathymetric features and intermittent rip current development. Model results from the month‐long simulation are used to track the formation and location of rip currents using hourly statistics, and results show that the direction of the incoming wave energy strongly controls whether rip currents form. In particular, most of the offshore wave spectra were bimodal and we find that the ratio of energy contained in each mode dictates rip current development, and the alongshore rip current position is controlled by the incident wave period. Additionally, model simulations performed with and without updating the nearshore morphology yield no significant change in the accuracy of the predicted surf zone hydrodyanmics indicating that the large‐scale offshore features (e.g., submarine canyon) predominately control the nearshore wave‐circulation system.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015JC010990","usgsCitation":"Long, J.W., and Ozkan-Haller, H., 2016, Forcing and variability of nonstationary rip currents: Journal of Geophysical Research C: Oceans, v. 121, no. 1, p. 520-539, https://doi.org/10.1002/2015JC010990.","productDescription":"20 p.","startPage":"520","endPage":"539","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2003-10-01","ipdsId":"IP-065750","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jc010990","text":"Publisher Index Page"},{"id":324145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-14","publicationStatus":"PW","scienceBaseUri":"576a653ae4b07657d1a11da3","contributors":{"authors":[{"text":"Long, Joseph W. 0000-0003-2912-1992 jwlong@usgs.gov","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":3303,"corporation":false,"usgs":true,"family":"Long","given":"Joseph","email":"jwlong@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":640099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ozkan-Haller, H.T.","contributorId":172266,"corporation":false,"usgs":false,"family":"Ozkan-Haller","given":"H.T.","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":640100,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168436,"text":"70168436 - 2016 - Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin","interactions":[],"lastModifiedDate":"2017-02-14T15:48:22","indexId":"70168436","displayToPublicDate":"2016-01-12T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin","docAbstract":"Evapotranspiration (ET) mapping at the Landsat spatial resolution (100 m) is essential to fully understand water use and water availability at the field scale. Water use estimates in the Colorado River Basin (CRB), which has diverse ecosystems and complex hydro-climatic regions, will be helpful to water planners and managers. Availability of Landsat 8 images, starting in 2013, provides the opportunity to map ET in the CRB to assess spatial distribution and patterns of water use. The Operational Simplified Surface Energy Balance (SSEBop) model was used with 528 Landsat 8 images to create seamless monthly and annual ET estimates at the inherent 100 m thermal band resolution. Annual ET values were summarized by land use/land cover classes. Croplands were the largest consumer of “blue” water while shrublands consumed the most “green” water. Validation using eddy covariance (EC) flux towers and water balance approaches showed good accuracy levels with R2 ranging from 0.74 to 0.95 and the Nash–Sutcliffe model efficiency coefficient ranging from 0.66 to 0.91. The root mean square error (and percent bias) ranged from 0.48 mm (13%) to 0.60 mm (22%) for daily (days of satellite overpass) ET and from 7.75 mm (2%) to 13.04 mm (35%) for monthly ET. The spatial and temporal distribution of ET indicates the utility of Landsat 8 for providing important information about ET dynamics across the landscape. Annual crop water use was estimated for five selected irrigation districts in the Lower CRB where annual ET per district ranged between 681 mm to 772 mm. Annual ET by crop type over the Maricopa Stanfield irrigation district ranged from a low of 384 mm for durum wheat to a high of 990 mm for alfalfa fields. A rainfall analysis over the five districts suggested that, on average, 69% of the annual ET was met by irrigation. Although the enhanced cloud-masking capability of Landsat 8 based on the cirrus band and utilization of the Fmask algorithm improved the removal of contaminated pixels, the ability to reliably estimate ET over clouded areas remains an important challenge. Overall, the performance of Landsat 8 based ET compared to available EC datasets and water balance estimates for a complex basin such as the CRB demonstrates the potential of using Landsat 8 for annual water use estimation at a national scale. Future efforts will focus on (a) use of consistent methodology across years, (b) integration of multiple sensors to maximize images used, and (c) employing cloud-computing platforms for large scale processing capabilities.
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Co.","publisherLocation":"New York, NY","doi":"10.1016/j.rse.2015.12.043","usgsCitation":"Senay, G., Friedrichs, M., Singh, R.K., and Velpuri, N.M., 2016, Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin: Remote Sensing of Environment, v. 185, p. 171-185, https://doi.org/10.1016/j.rse.2015.12.043.","productDescription":"15 p.","startPage":"171","endPage":"185","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069332","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471334,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2015.12.043","text":"Publisher Index Page"},{"id":318088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335400,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DF6PDR","text":"Satellite-based water use dynamics using historical Landsat data (1984-2014) in the southwestern United States"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.06152343749999,\n 43.54854811091286\n ],\n [\n -106.69921875,\n 42.65012181368025\n ],\n [\n -106.12792968749999,\n 41.44272637767212\n ],\n [\n -105.908203125,\n 40.613952441166596\n ],\n [\n -106.3037109375,\n 38.8225909761771\n ],\n [\n -107.490234375,\n 34.488447837809304\n ],\n [\n -108.19335937499999,\n 32.06395559466043\n ],\n [\n -109.599609375,\n 31.240985378021307\n ],\n [\n -111.22558593749999,\n 31.353636941500987\n ],\n [\n -114.82910156249999,\n 32.54681317351517\n ],\n [\n -115.00488281250001,\n 32.84267363195431\n ],\n [\n -116.76269531249999,\n 35.639441068973916\n ],\n [\n -116.01562499999999,\n 37.125286284966776\n ],\n [\n -114.3017578125,\n 37.96152331396616\n ],\n [\n -113.02734374999999,\n 38.376115424036016\n ],\n [\n -111.09374999999999,\n 41.918628865183045\n ],\n [\n -110.89599609375,\n 42.32606244456202\n ],\n [\n -110.4345703125,\n 42.956422511073335\n ],\n [\n -109.70947265625,\n 43.389081939117496\n ],\n [\n -109.0283203125,\n 43.56447158721811\n ],\n [\n -108.06152343749999,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"185","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c45640e4b0946c65218507","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":620124,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X mfriedrichs@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":5847,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","email":"mfriedrichs@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":620125,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singh, Ramesh K. 0000-0002-8164-3483 rsingh@usgs.gov","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":3895,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"rsingh@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":620126,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926 nvelpuri@usgs.gov","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":166813,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga","email":"nvelpuri@usgs.gov","middleInitial":"Manohar","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":620127,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154969,"text":"tm5B11 - 2016 - Determination of pesticides and pesticide degradates in filtered water by direct aqueous-injection liquid chromatography-tandem mass spectrometry","interactions":[],"lastModifiedDate":"2022-04-28T15:48:13.251832","indexId":"tm5B11","displayToPublicDate":"2016-01-11T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-B11","title":"Determination of pesticides and pesticide degradates in filtered water by direct aqueous-injection liquid chromatography-tandem mass spectrometry","docAbstract":"A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for determination of 229 pesticides compounds (113 pesticides and 116 pesticide degradates) in filtered water samples from stream and groundwater sites. The pesticides represent a broad range of chemical classes and were selected based on criteria such as current-use intensity, probability of occurrence in streams and groundwater, and toxicity to humans or aquatic organisms. More than half of the analytes are pesticide degradates. The method involves direct injection of a 100-microliter (μL) sample onto the LC-MS/MS without any sample preparation other than filtration. Samples are analyzed with two injections, one in electrospray ionization (ESI) positive mode and one in ESI negative mode, using dynamic multiple reaction monitoring (MRM) conditions, with two MRM transitions for each analyte. The LC-MS/MS instrument parameters were optimized for highest sensitivity for the most analytes. This report describes the analytical method and presents characteristics of the method validation including bias and variability, detection levels, and holding-time studies.
\nMean recoveries of most analytes (223 of 229) were within data-quality objectives of 100±30 percent at spike concentrations above method detection levels (MDLs) in all four matrices. The calculated MDLs ranged from 1 to 103 nanograms per liter (ng/L) for 182 analytes analyzed in the ESI positive mode, and from 2 to 106 ng/L for 42 analytes analyzed in the ESI negative mode. Five analytes had MDLs between 100 and 250 ng/L. The stability studies in reagent water demonstrated that the largest number of the pesticide compounds (227 of 229) were stable after 14 days of storage at 4 degrees Celsius, so these were selected as the practical holding time and storage temperature for routine sample processing. The use of antimicrobial reagent citric acid to adjust the sample pH to about 4 also resulted in lower recoveries of some analytes, so it should not be used as a routine sample preservative.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Methods of the National Water Quality Laboratory in Book 5 Laboratory Analysis","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5B11","usgsCitation":"Sandstrom, M.W., Kanagy, L.K., Anderson, C.A., and Kanagy, C.J., 2015, Determination of pesticides and pesticide\ndegradates in filtered water by direct aqueous-injection liquid chromatography-tandem mass spectrometry: U.S.\nGeological Survey Techniques and Methods, book 5, chap. B11, 54 p., https://dx.doi.org/10.3133/tm5B11.","productDescription":"Report: xv, 54 p.; Tables 1-62; 1 Figure; Appendix","numberOfPages":"73","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-054757","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"links":[{"id":323562,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t15_t20_mdl_study.xlsx","text":"Tables 15-20"},{"id":323561,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t1_t14_method_description.xlsx","text":"Tables 1-14"},{"id":313244,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/tm/05/b11/figure3.pdf","text":"Figure 3 - High-resolution"},{"id":399813,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/05/b11/appendix/pdf/","text":"Supporting Figures S1 - S14"},{"id":313230,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/05/b11/appendix/tm_supporting_tables.xlsx","text":"Supporting Tables S1 - S12"},{"id":313167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/b11/tm5b11.pdf","text":"Report","size":"7.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods 5–B11"},{"id":323567,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t62_s2437_tm_summary.xlsx","text":"Table 62"},{"id":323566,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t43_t61_stability_studies.xlsx","text":"Tables 43-61"},{"id":323565,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t28_t42_field_study.xlsx","text":"Tables 28-42"},{"id":323564,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t24_t27_lab_qc.xlsx","text":"Tables 24-27"},{"id":323563,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/tm/05/b11/tables/t21_t23_matrix_effects.xlsx","text":"Tables 21-23"},{"id":313166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/b11/coverthb.jpg"}],"publicComments":"This report is Chapter 11 of Section B: Methods of the National Water Quality Laboratory in Book 5 Laboratory Analysis","contact":"Chief, National Water Quality Laboratory
U.S. Geological Survey
Box 25585, Mail Stop 407
Denver, CO 80225-0585
http://nwql.usgs.gov/
\n
\n
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","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2016-01-11","noUsgsAuthors":false,"publicationDate":"2016-01-11","publicationStatus":"PW","scienceBaseUri":"5694d22ce4b039675d005dbc","contributors":{"authors":[{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":564420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kanagy, Leslie K. 0000-0001-5073-8538 lkkanagy@usgs.gov","orcid":"https://orcid.org/0000-0001-5073-8538","contributorId":4543,"corporation":false,"usgs":true,"family":"Kanagy","given":"Leslie","email":"lkkanagy@usgs.gov","middleInitial":"K.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":564421,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cyrissa A. cadamson@usgs.gov","contributorId":4379,"corporation":false,"usgs":true,"family":"Anderson","given":"Cyrissa","email":"cadamson@usgs.gov","middleInitial":"A.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":564422,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanagy, Christopher J. ckanagy@usgs.gov","contributorId":1201,"corporation":false,"usgs":true,"family":"Kanagy","given":"Christopher","email":"ckanagy@usgs.gov","middleInitial":"J.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":564423,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70161861,"text":"70161861 - 2016 - A thermodynamical model for the surface tension of silicate melts in contact with H2O gas","interactions":[],"lastModifiedDate":"2016-01-11T09:18:39","indexId":"70161861","displayToPublicDate":"2016-01-11T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"A thermodynamical model for the surface tension of silicate melts in contact with H2O gas","docAbstract":"
Surface tension plays an important role in the nucleation of H2O gas bubbles in magmatic melts and in the time-dependent rheology of bubble-bearing magmas. Despite several experimental studies, a physics based model of the surface tension of magmatic melts in contact with H2O is lacking. This paper employs gradient theory to develop a thermodynamical model of equilibrium surface tension of silicate melts in contact with H2O gas at low to moderate pressures. In the last decades, this approach has been successfully applied in studies of industrial mixtures but never to magmatic systems. We calibrate and verify the model against literature experimental data, obtained by the pendant drop method, and by inverting bubble nucleation experiments using the Classical Nucleation Theory (CNT). Our model reproduces the systematic decrease in surface tension with increased H2O pressure observed in the experiments. On the other hand, the effect of temperature is confirmed by the experiments only at high pressure. At atmospheric pressure, the model shows a decrease of surface tension with temperature. This is in contrast with a number of experimental observations and could be related to microstructural effects that cannot be reproduced by our model. Finally, our analysis indicates that the surface tension measured inverting the CNT may be lower than the value measured by the pendant drop method, most likely because of changes in surface tension controlled by the supersaturation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2015.10.037","usgsCitation":"Colucci, S., Battaglia, M., and Trigila, R., 2016, A thermodynamical model for the surface tension of silicate melts in contact with H2O gas: Geochimica et Cosmochimica Acta, v. 175, p. 113-127, https://doi.org/10.1016/j.gca.2015.10.037.","productDescription":"15 p.","startPage":"113","endPage":"127","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065296","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":314086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"175","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5694d22ae4b039675d005db6","contributors":{"authors":[{"text":"Colucci, Simone","contributorId":152109,"corporation":false,"usgs":false,"family":"Colucci","given":"Simone","affiliations":[{"id":18867,"text":"INGV-sezione di Pisa,Italy","active":true,"usgs":false}],"preferred":false,"id":587970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Battaglia, Maurizio mbattaglia@usgs.gov","contributorId":139631,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":587969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trigila, Raffaello","contributorId":152110,"corporation":false,"usgs":false,"family":"Trigila","given":"Raffaello","email":"","affiliations":[{"id":18868,"text":"Sapienza - University of Rome","active":true,"usgs":false}],"preferred":false,"id":587971,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173780,"text":"70173780 - 2016 - Using standardized fishery data to inform rehabilitation efforts","interactions":[],"lastModifiedDate":"2016-06-09T12:09:37","indexId":"70173780","displayToPublicDate":"2016-01-11T05:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Using standardized fishery data to inform rehabilitation efforts","docAbstract":"Lakes and reservoirs progress through an aging process often accelerated by human activities, resulting in degradation or loss of ecosystem services. Resource managers thus attempt to slow or reverse the negative effects of aging using a myriad of rehabilitation strategies. Sustained monitoring programs to assess the efficacy of rehabilitation strategies are often limited; however, long-term standardized fishery surveys may be a valuable data source from which to begin evaluation. We present 3 case studies using standardized fishery survey data to assess rehabilitation efforts stemming from the Nebraska Aquatic Habitat Plan, a large-scale program with the mission to rehabilitate waterbodies within the state. The case studies highlight that biotic responses to rehabilitation efforts can be assessed, to an extent, using standardized fishery data; however, there were specific areas where minor increases in effort would clarify the effectiveness of rehabilitation techniques. Management of lakes and reservoirs can be streamlined by maximizing the utility of such datasets to work smarter, not harder. To facilitate such efforts, we stress collecting both biotic (e.g., fish lengths and weight) and abiotic (e.g., dissolved oxygen, pH, and turbidity) data during standardized fishery surveys and designing rehabilitation actions with an appropriate experimental design.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10402381.2015.1118418","usgsCitation":"Spurgeon, J., Stewart, N.T., Pegg, M.A., Pope, K.L., and Porath, M.T., 2016, Using standardized fishery data to inform rehabilitation efforts: Lake and Reservoir Management, v. 32, no. 1, p. 41-50, https://doi.org/10.1080/10402381.2015.1118418.","productDescription":"10 p.","startPage":"41","endPage":"50","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066248","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471335,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/10402381.2015.1118418","text":"Publisher Index Page"},{"id":323373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.1064453125,\n 39.8928799002948\n ],\n [\n -104.1064453125,\n 43.02071359427862\n ],\n [\n -95.33935546875,\n 43.02071359427862\n ],\n [\n -95.33935546875,\n 39.8928799002948\n ],\n [\n -104.1064453125,\n 39.8928799002948\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-11","publicationStatus":"PW","scienceBaseUri":"575a9337e4b04f417c275190","contributors":{"authors":[{"text":"Spurgeon, Jonathan J.","contributorId":146395,"corporation":false,"usgs":false,"family":"Spurgeon","given":"Jonathan J.","affiliations":[],"preferred":false,"id":638167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, Nathaniel T.","contributorId":171639,"corporation":false,"usgs":false,"family":"Stewart","given":"Nathaniel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":638168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pegg, Mark A.","contributorId":45212,"corporation":false,"usgs":true,"family":"Pegg","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":638169,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":638165,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porath, Mark T.","contributorId":28846,"corporation":false,"usgs":true,"family":"Porath","given":"Mark","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":638170,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177887,"text":"70177887 - 2016 - Long-term changes in sediment and nutrient delivery from Conowingo Dam to Chesapeake Bay: Effects of reservoir sedimentation","interactions":[],"lastModifiedDate":"2017-07-19T15:46:21","indexId":"70177887","displayToPublicDate":"2016-01-08T06:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Long-term changes in sediment and nutrient delivery from Conowingo Dam to Chesapeake Bay: Effects of reservoir sedimentation","docAbstract":"Reduction of suspended sediment (SS), total phosphorus (TP), and total nitrogen is an important focus for Chesapeake Bay watershed management. The Susquehanna River, the bay’s largest tributary, has drawn attention because SS loads from behind Conowingo Dam (near the river’s mouth) have been rising dramatically. To better understand these changes, we evaluated histories of concentration and loading (1986–2013) using data from sites above and below Conowingo Reservoir. First, observed concentration-discharge relationships show that SS and TP concentrations at the reservoir inlet have declined under most discharges in recent decades, but without corresponding declines at the outlet, implying recently diminished reservoir trapping. Second, best estimates of mass balance suggest decreasing net deposition of SS and TP in recent decades over a wide range of discharges, with cumulative mass generally dominated by the 75∼99.5th percentile of daily Conowingo discharges. Finally, stationary models that better accommodate effects of riverflow variability also support the conclusion of diminished trapping of SS and TP under a range of discharges that includes those well below the literature-reported scour threshold. Overall, these findings suggest that decreased net deposition of SS and TP has occurred at subscour levels of discharge, which has significant implications for the Chesapeake Bay ecosystem.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.5b04073","usgsCitation":"Zhang, Q., Hirsch, R.M., and Ball, W.P., 2016, Long-term changes in sediment and nutrient delivery from Conowingo Dam to Chesapeake Bay: Effects of reservoir sedimentation: Environmental Science & Technology, v. 50, no. 4, p. 1877-1886, https://doi.org/10.1021/acs.est.5b04073.","productDescription":"10 p.","startPage":"1877","endPage":"1886","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072134","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":330431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Conowingo Dam, Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.7,\n 38.136716904135376\n ],\n [\n -76.7,\n 39.89\n ],\n [\n -75.87158203125,\n 39.89\n ],\n [\n -75.87158203125,\n 38.136716904135376\n ],\n [\n -76.7,\n 38.136716904135376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-01","publicationStatus":"PW","scienceBaseUri":"5811c0f4e4b0f497e79a5a89","contributors":{"authors":[{"text":"Zhang, Qian 0000-0003-0500-5655","orcid":"https://orcid.org/0000-0003-0500-5655","contributorId":174393,"corporation":false,"usgs":false,"family":"Zhang","given":"Qian","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":652028,"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":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":652027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ball, William P.","contributorId":174394,"corporation":false,"usgs":false,"family":"Ball","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":27446,"text":"Johns Hopkins University, Department of Geography and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":652029,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159625,"text":"sir20155161 - 2016 - Response of periphyton fatty acid composition to supplemental flows in the upper Esopus Creek, Catskill Mountains, New York","interactions":[],"lastModifiedDate":"2016-01-07T16:22:45","indexId":"sir20155161","displayToPublicDate":"2016-01-07T15:15:00","publicationYear":"2016","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":"2015-5161","title":"Response of periphyton fatty acid composition to supplemental flows in the upper Esopus Creek, Catskill Mountains, New York","docAbstract":"Fatty acid analysis of periphyton is an emerging tool for assessing the condition of a stream ecosystem on the basis of its water quality. The study presented in this report was designed to test the hypothesis that periphyton communities have a fatty acid profile that can detect excessive turbidity and suspended sediment. The fatty acid composition of periphyton was assessed during two seasons upstream and downstream from an underground aqueduct that provides supplemental flows, which are a potential source of turbidity and suspended sediment on the upper Esopus Creek, New York. These data were compared with measurements of periphyton standing crop, diatom community structure and integrity, and basic water-quality parameters. Periphyton standing crop and diatom community integrity indicated little evidence of impairment from the supplemental flows. The relative abundances of two physiologically important fatty acids, γ-linolenic acid (18:3ω6) and eicosapentaenoic acid (20:5ω3), were significantly lower downstream from the supplemental flows and multivariate analyses of fatty acid profiles identified significant differences between sites upstream and downstream from the supplemental flows. Individual fatty acids and summary metrics, however, were not significantly correlated with turbidity or suspended sediment. Together, these results indicate that the supplemental flows may cause some measurable effects but they do not constitute a major disturbance to the periphyton community on the upper Esopus Creek. Fatty acid analysis may have potential as a tool for monitoring changes in periphyton nutritional composition that may reflect water quality and ecosystem health but needs to be further evaluated around a more definitive source of water-quality impairment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155161","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"George, S.D., Ernst, A.G., Baldigo, B.P., and Honeyfield, D.C., 2016, Response of periphyton fatty acid composition to supplemental flows in the upper Esopus Creek, Catskill Mountains, New York: U.S. Geological Survey Scientific Investigations Report 2015–5161, 22 p., with appendixes, https://dx.doi.org/10.3133/sir20155161.","productDescription":"viii, 22 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-050857","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":313924,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5161/coverthb.jpg"},{"id":313925,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5161/sir20155161.pdf","text":"Report","size":"3.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5161"}],"country":"United States","state":"New York","otherGeospatial":"Esopus Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.6466064453125,\n 41.99522219923445\n ],\n [\n -74.6466064453125,\n 42.15118709351198\n ],\n [\n -74.1632080078125,\n 42.15118709351198\n ],\n [\n -74.1632080078125,\n 41.99522219923445\n ],\n [\n -74.6466064453125,\n 41.99522219923445\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
Information requests:
(518) 285-5602
or visit our Web site at:
http://ny.water.usgs.gov
The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium- to long-term restoration strategies to reconnect urban areas to national supply chain interdependent critical infrastructure systems (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publicly available data reveals a dearth of real-time data to assist modeling long-term recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publicly available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.
","language":"English","publisher":"Ubiquity Press","doi":"10.5334/dsj-2016-001","usgsCitation":"Ramachandran, V., Long, S.K., Shoberg, T.G., Corns, S., and Carlo, H.J., 2016, Post-disaster supply chain interdependent critical infrastructure system restoration: A review of data necessary and available for modeling: Data Science Journal, v. 15, no. 1, 13 p., https://doi.org/10.5334/dsj-2016-001.","productDescription":"13 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061444","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":471340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5334/dsj-2016-001","text":"Publisher Index Page"},{"id":314007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"568f8c3be4b0e7a44bc5ec8e","contributors":{"authors":[{"text":"Ramachandran, Varun","contributorId":146269,"corporation":false,"usgs":false,"family":"Ramachandran","given":"Varun","email":"","affiliations":[{"id":16655,"text":"Dept. of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":587965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Suzanna K.","contributorId":146270,"corporation":false,"usgs":false,"family":"Long","given":"Suzanna","email":"","middleInitial":"K.","affiliations":[{"id":16655,"text":"Dept. of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":587966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoberg, Thomas G. 0000-0003-0173-1246 tshoberg@usgs.gov","orcid":"https://orcid.org/0000-0003-0173-1246","contributorId":3764,"corporation":false,"usgs":true,"family":"Shoberg","given":"Thomas","email":"tshoberg@usgs.gov","middleInitial":"G.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":587964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corns, Steven","contributorId":146271,"corporation":false,"usgs":false,"family":"Corns","given":"Steven","affiliations":[{"id":16655,"text":"Dept. of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":587967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carlo, Hector J.","contributorId":95805,"corporation":false,"usgs":true,"family":"Carlo","given":"Hector","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":587968,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70169022,"text":"70169022 - 2016 - Assessing models of speciation under different biogeographic scenarios; An empirical study using multi-locus and RNA-seq analyses","interactions":[],"lastModifiedDate":"2020-12-17T20:29:25.108131","indexId":"70169022","displayToPublicDate":"2016-01-07T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Assessing models of speciation under different biogeographic scenarios; An empirical study using multi-locus and RNA-seq analyses","docAbstract":"Evolutionary biology often seeks to decipher the drivers of speciation, and much debate persists over the relative importance of isolation and gene flow in the formation of new species. Genetic studies of closely related species can assess if gene flow was present during speciation, because signatures of past introgression often persist in the genome. We test hypotheses on which mechanisms of speciation drove diversity among three distinct lineages of desert tortoise in the genus Gopherus. These lineages offer a powerful system to study speciation, because different biogeographic patterns (physical vs. ecological segregation) are observed at opposing ends of their distributions. We use 82 samples collected from 38 sites, representing the entire species' distribution and generate sequence data for mtDNA and four nuclear loci. A multilocus phylogenetic analysis in *BEAST estimates the species tree. RNA‐seq data yield 20,126 synonymous variants from 7665 contigs from two individuals of each of the three lineages. Analyses of these data using the demographic inference package ∂a∂i serve to test the null hypothesis of no gene flow during divergence. The best‐fit demographic model for the three taxa is concordant with the *BEAST species tree, and the ∂a∂i analysis does not indicate gene flow among any of the three lineages during their divergence. These analyses suggest that divergence among the lineages occurred in the absence of gene flow and in this scenario the genetic signature of ecological isolation (parapatric model) cannot be differentiated from geographic isolation (allopatric model).
","language":"English","publisher":"Wiley","publisherLocation":"Oxford","doi":"10.1002/ece3.1865","usgsCitation":"Edwards, T., Tollis, M., Hsieh, P., Gutenkunst, R.N., Liu, Z., Kusumi, K., Culver, M., and Murphy, R.W., 2016, Assessing models of speciation under different biogeographic scenarios; An empirical study using multi-locus and RNA-seq analyses: Ecology and Evolution, v. 6, no. 2, p. 379-396, https://doi.org/10.1002/ece3.1865.","productDescription":"18 p.","startPage":"379","endPage":"396","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070008","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471341,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.1865","text":"Publisher Index Page"},{"id":318812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United 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W.","contributorId":147498,"corporation":false,"usgs":false,"family":"Murphy","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":622606,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70159572,"text":"ofr20151210 - 2016 - Evaluation of the 8310-N-S manufactured by Sutron–Results of bench, temperature, and field deployment testing","interactions":[],"lastModifiedDate":"2016-01-07T09:37:27","indexId":"ofr20151210","displayToPublicDate":"2016-01-07T10:15:00","publicationYear":"2016","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":"2015-1210","title":"Evaluation of the 8310-N-S manufactured by Sutron–Results of bench, temperature, and field deployment testing","docAbstract":"The Sutron 8310-N-S (8310) data collection platform (DCP) manufactured by Sutron Corporation was evaluated by the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) for conformance to the manufacturer’s specifications for recording and transmitting data. The 8310-N-S is a National Electrical Manufacturers Association (NEMA)-enclosed DCP with a built-in Geostationary Operational Environmental Satellite transmitter that operates over a temperature range of −40 to 60 degrees Celsius (°C). The evaluation procedures followed and the results obtained are described in this report for bench, temperature chamber, and outdoor deployment testing. The three units tested met the manufacturer’s stated specifications for the tested conditions, but two of the units had transmission errors either during temperature chamber or deployment testing. During outdoor deployment testing, 6.72 percent of transmissions by serial number 1206109 contained errors, resulting in missing data. Transmission errors were also observed during temperature chamber testing with serial number 1208283, at an error rate of 3.22 percent. Overall, the 8310 has good logging capabilities, but the transmission errors are a concern for users who require reliable telemetered data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151210","usgsCitation":"Kunkle, G.A., 2016, Evaluation of the 8310 manufactured by Sutron—Results of bench, temperature, and field deployment testing: U.S. Geological Survey Open-File Report 2015–1210, 6 p., https://dx.doi.org/10.3133/ofr20151210.","productDescription":"iii, 6 p.","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-064816","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":313953,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1210/coverthb.jpg"},{"id":313954,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1210/ofr20151210.pdf","text":"Report","size":"410 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1210"}],"country":"United States","state":"Mississippi","otherGeospatial":"Stennis Space Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.66423034667969,\n 30.312431154103745\n ],\n [\n -89.66423034667969,\n 30.365173612441442\n ],\n [\n -89.58045959472656,\n 30.365173612441442\n ],\n [\n -89.58045959472656,\n 30.312431154103745\n ],\n [\n -89.66423034667969,\n 30.312431154103745\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
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http://water.usgs.gov/hif/
An urban pollutant loading model was used to demonstrate how incorrect assumptions on the particle size distribution (PSD) in urban runoff can alter the design characteristics of stormwater control measures (SCMs) used to remove solids in stormwater. Field-measured PSD, although highly variable, is generally coarser than the widely-accepted PSD characterized by the Nationwide Urban Runoff Program (NURP). PSDs can be predicted based on environmental surrogate data. There were no appreciable differences in predicted PSD when grouped by season. Model simulations of a wet detention pond and catch basin showed a much smaller surface area is needed to achieve the same level of solids removal using the median value of field-measured PSD as compared to NURP PSD. Therefore, SCMs that used the NURP PSD in the design process could be unnecessarily oversized. The median of measured PSDs, although more site-specific than NURP PSDs, could still misrepresent the efficiency of an SCM because it may not adequately capture the variability of individual runoff events. Future pollutant loading models may account for this variability through regression with environmental surrogates, but until then, without proper site characterization, the adoption of a single PSD to represent all runoff conditions may result in SCMs that are under- or over-sized, rendering them ineffective or unnecessarily costly.
","language":"English","publisher":"MDPI","doi":"10.3390/w8010017","collaboration":"Wisconsin Department of Natural Resources","usgsCitation":"Selbig, W.R., Fienen, M., Horwatich, J.A., and Bannerman, R.T., 2016, The effect of particle size distribution on the design of urban stormwater control measures: Water, v. 8, no. 1, w8010017: 17 p., https://doi.org/10.3390/w8010017.","productDescription":"w8010017: 17 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069813","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":471342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w8010017","text":"Publisher Index Page"},{"id":314000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-06","publicationStatus":"PW","scienceBaseUri":"568f8c3ce4b0e7a44bc5ec97","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":587952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":893,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":587953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horwatich, Judy A. 0000-0003-0582-0836 jahorwat@usgs.gov","orcid":"https://orcid.org/0000-0003-0582-0836","contributorId":1388,"corporation":false,"usgs":true,"family":"Horwatich","given":"Judy","email":"jahorwat@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":587954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bannerman, Roger T. 0000-0001-9221-2905 rbannerman@usgs.gov","orcid":"https://orcid.org/0000-0001-9221-2905","contributorId":5560,"corporation":false,"usgs":true,"family":"Bannerman","given":"Roger","email":"rbannerman@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":587955,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160939,"text":"70160939 - 2016 - Progress on water data integration and distribution: a summary of select U.S. Geological Survey data systems","interactions":[],"lastModifiedDate":"2016-03-31T13:03:15","indexId":"70160939","displayToPublicDate":"2016-01-05T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2340,"text":"Journal of Hydroinformatics","active":true,"publicationSubtype":{"id":10}},"title":"Progress on water data integration and distribution: a summary of select U.S. Geological Survey data systems","docAbstract":"Critical water-resources issues ranging from flood response to water scarcity make access to integrated water information, services, tools, and models essential. Since 1995 when the first water data web pages went online, the U.S. Geological Survey has been at the forefront of water data distribution and integration. Today, real-time and historical streamflow observations are available via web pages and a variety of web service interfaces. The Survey has built partnerships with Federal and State agencies to integrate hydrologic data providing continuous observations of surface and groundwater, temporally discrete water quality data, groundwater well logs, aquatic biology data, water availability and use information, and tools to help characterize the landscape for modeling. In this paper, we summarize the status and design patterns implemented for selected data systems. We describe how these systems contribute to a U.S. Federal Open Water Data Initiative and present some gaps and lessons learned that apply to global hydroinformatics data infrastructure.
","language":"English","publisher":"IWA Publishing","doi":"10.2166/hydro.2015.067","usgsCitation":"Blodgett, D.L., Lucido, J., and Kreft, J., 2016, Progress on water data integration and distribution: a summary of select U.S. Geological Survey data systems: Journal of Hydroinformatics, v. 18, no. 2, p. 226-237, https://doi.org/10.2166/hydro.2015.067.","productDescription":"12 p.","startPage":"226","endPage":"237","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064680","costCenters":[],"links":[{"id":471345,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2166/hydro.2015.067","text":"Publisher Index Page"},{"id":313323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-31","publicationStatus":"PW","scienceBaseUri":"568ce931e4b0e7a44bc0f111","contributors":{"authors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":584260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucido, Jessica M. jlucido@usgs.gov","contributorId":4695,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica M.","email":"jlucido@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":584261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kreft, James M. jkreft@usgs.gov","contributorId":250,"corporation":false,"usgs":true,"family":"Kreft","given":"James M.","email":"jkreft@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":false,"id":584262,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227260,"text":"70227260 - 2016 - Northern long-eared bat day-roosting and prescribed fire in the Central Appalachians, USA","interactions":[],"lastModifiedDate":"2022-01-05T13:03:27.471584","indexId":"70227260","displayToPublicDate":"2016-01-05T07:00:12","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Northern long-eared bat day-roosting and prescribed fire in the Central Appalachians, USA","docAbstract":"The northern long-eared bat (Myotis septentrionalis Trovessart) is a cavity-roosting species that forages in cluttered upland and riparian forests throughout the oak-dominated Appalachian and Central Hardwoods regions. Common prior to white-nose syndrome, the population of this bat species has declined to functional extirpation in some regions in the Northeast and Mid-Atlantic, including portions of the central Appalachians. Our long-term research in the central Appalachians has shown that maternity colonies of this species form non-random assorting networks in patches of suitable trees that result from long- and short-term forest disturbance processes, and that roost loss can occur with these disturbances. Following two consecutive prescribed burns on the Fernow Experimental Forest in the central Appalachians, West Virginia, USA, in 2007 to 2008, post-fire counts of suitable black locust (Robinia pseudoacacia L.; the most selected species for roosting) slightly decreased by 2012. Conversely, post-fire numbers of suitable maple (Acer spp. L.), primarily red maple (Acer rubrum L.), increased by a factor of three, thereby ameliorating black locust reduction. Maternity colony network metrics such as roost degree (use) and network density for two networks in the burned compartment were similar to the single network observed in unburned forest. However, roost clustering and degree of roost centralization was greater for the networks in the burned forest area. Accordingly, the short-term effects of prescribed fire are slightly or moderately positive in impact to day-roost habitat for the northern long-eared bat in the central Appalachians from a social dynamic perspective. Listing of northern long-eared bats as federally threatened will bring increased scrutiny of immediate fire impacts from direct take as well as indirect impacts from long-term changes to roosting and foraging habitat in stands being returned to historic fire-return conditions. Unfortunately, definitive impacts will remain speculative owing to the species’ current rarity and the paucity of forest stand data that considers tree condition or that adequately tracks snags spatially and temporally.
We present data on 136 high-frequency earthquakes and swarms, termed volcano-tectonic (VT) seismicity, which preceded 111 eruptions at 83 volcanoes, plus data on VT swarms that preceded intrusions at 21 other volcanoes. We find that VT seismicity is usually the earliest reported seismic precursor for eruptions at volcanoes that have been dormant for decades or more, and precedes eruptions of all magma types from basaltic to rhyolitic and all explosivities from VEI 0 to ultraplinian VEI 6 at such previously long-dormant volcanoes. Because large eruptions occur most commonly during resumption of activity at long-dormant volcanoes, VT seismicity is an important precursor for the Earth's most dangerous eruptions. VT seismicity precedes all explosive eruptions of VEI ≥ 5 and most if not all VEI 4 eruptions in our data set. Surprisingly we find that the VT seismicity originates at distal locations on tectonic fault structures at distances of one or two to tens of kilometers laterally from the site of the eventual eruption, and rarely if ever starts beneath the eruption site itself. The distal VT swarms generally occur at depths almost equal to the horizontal distance of the swarm from the summit out to about 15 km distance, beyond which hypocenter depths level out. We summarize several important characteristics of this distal VT seismicity including: swarm-like nature, onset days to years prior to the beginning of magmatic eruptions, peaking of activity at the time of the initial eruption whether phreatic or magmatic, and large non-double couple component to focal mechanisms. Most importantly we show that the intruded magma volume can be simply estimated from the cumulative seismic moment of the VT seismicity from:
\nLog10 V = 0.77 Log ΣMoment − 5.32, with volume, V, in cubic meters and seismic moment in Newton meters. Because the cumulative seismic moment can be approximated from the size of just the few largest events, and is quite insensitive to precise locations, the intruded magma volume can be quickly and easily estimated with few short-period seismic stations.
\nNotable cases in which distal VT events preceded eruptions at long-dormant volcanoes include: Nevado del Ruiz (1984–1985), Pinatubo (1991), Unzen (1989–1995), Soufriere Hills (1995), Shishaldin (1989–1999), Tacana' (1985–1986), Pacaya (1980–1984), Rabaul (1994), and Cotopaxi (2001). Additional cases are recognized at frequently active volcanoes including Popocateptl (2001–2003) and Mauna Loa (1984). We present four case studies (Pinatubo, Soufriere Hills, Unzen, and Tacana') in which we demonstrate the above mentioned VT characteristics prior to eruptions. Using regional data recorded by NEIC, we recognized in near-real time that a huge distal VT swarm was occurring, deduced that a proportionately huge magmatic intrusion was taking place beneath the long dormant Sulu Range, New Britain Island, Papua New Guinea, that it was likely to lead to eruptive activity, and warned Rabaul Volcano Observatory days before a phreatic eruption occurred. This confirms the value of this technique for eruption forecasting. We also present a counter-example where we deduced that a VT swarm at Volcan Cosiguina, Nicaragua, indicated a small intrusion, insufficient to reach the surface and erupt. Finally, we discuss limitations of the method and propose a mechanism by which this distal VT seismicity is triggered by magmatic intrusion.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.jvolgeores.2015.10.020","usgsCitation":"White, R.A., and McCausland, W., 2016, Volcano-tectonic earthquakes: A new tool for estimating intrusive volumes and forecasting eruptions: Journal of Volcanology and Geothermal Research, v. 309, p. 139-155, https://doi.org/10.1016/j.jvolgeores.2015.10.020.","startPage":"139","endPage":"155","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059009","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2015.10.020","text":"Publisher Index Page"},{"id":326598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"309","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b4395ce4b03bcb0103a01e","contributors":{"authors":[{"text":"White, Randall A. 0000-0003-4074-8577 rwhite@usgs.gov","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":1993,"corporation":false,"usgs":true,"family":"White","given":"Randall","email":"rwhite@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":645666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy wmccausland@usgs.gov","contributorId":5497,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"wmccausland@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":645667,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70164491,"text":"70164491 - 2016 - Scaling relationships among drivers of aquatic respiration from the smallest to the largest freshwater ecosystems","interactions":[],"lastModifiedDate":"2016-07-17T23:07:12","indexId":"70164491","displayToPublicDate":"2016-01-01T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"Scaling relationships among drivers of aquatic respiration from the smallest to the largest freshwater ecosystems","docAbstract":"To address how various environmental parameters control or constrain planktonic respiration (PR), we used geometric scaling relationships and established biological scaling laws to derive quantitative predictions for the relationships among key drivers of PR. We then used empirical measurements of PR and environmental (soluble reactive phosphate [SRP], carbon [DOC], chlorophyll a [Chl-a)], and temperature) and landscape parameters (lake area [LA] and watershed area [WA]) from a set of 44 lakes that varied in size and trophic status to test our hypotheses. We found that landscape-level processes affected PR through direct effects on DOC and temperature and indirectly via SRP. In accordance with predictions made from known relationships and scaling laws, scale coefficients (the parameter that describes the shape of a relationship between 2 variables) were found to be negative and have an absolute value 1, others <1). We also found evidence of a significant relationship between temperature and SRP. Because our dataset included measurements of respiration from small pond catchments to the largest body of freshwater on the planet, Lake Superior, these findings should be applicable to controls of PR for the great majority of temperate aquatic ecosystems.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Inland Waters","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Freshwater Biological Association","publisherLocation":"Ambleside, Cumbria, UK","doi":"10.5268/IW-6.1.839","usgsCitation":"Hall, E., Schoolmaster, D., Amado, A., Stets, E., Lennon, J., Domaine, L., and Cotner, J., 2016, Scaling relationships among drivers of aquatic respiration from the smallest to the largest freshwater ecosystems: Inland Waters, v. 6, no. 1, p. 1-10, https://doi.org/10.5268/IW-6.1.839.","productDescription":"10 p.","startPage":"1","endPage":"10","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063678","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":316697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56b9ca8de4b08d617f63a865","contributors":{"authors":[{"text":"Hall, Ed K","contributorId":156351,"corporation":false,"usgs":false,"family":"Hall","given":"Ed K","affiliations":[{"id":20320,"text":"University of Minnesota, Department of Ecology, Evolution and Behavior","active":true,"usgs":false}],"preferred":false,"id":597582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoolmaster, Donald 0000-0003-0910-4458 schoolmasterd@usgs.gov","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":156350,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","email":"schoolmasterd@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":597581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amado, A.M","contributorId":156352,"corporation":false,"usgs":false,"family":"Amado","given":"A.M","email":"","affiliations":[{"id":20321,"text":"Limnology Laboratory, Departamento de Oceanografia e Limnologia, Pós-graduação em Ecologia, Universidade Federal do Rio Grande do Norte, Natal","active":true,"usgs":false}],"preferred":false,"id":597583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stets, Edward G. estets@usgs.gov","contributorId":152533,"corporation":false,"usgs":true,"family":"Stets","given":"Edward G.","email":"estets@usgs.gov","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":597584,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lennon, J.T.","contributorId":156353,"corporation":false,"usgs":false,"family":"Lennon","given":"J.T.","email":"","affiliations":[{"id":20322,"text":"Department of Biology Indiana University, Bloomington, IN","active":true,"usgs":false}],"preferred":false,"id":597585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Domaine, L.","contributorId":156354,"corporation":false,"usgs":false,"family":"Domaine","given":"L.","email":"","affiliations":[{"id":20323,"text":"Department of Biology, University of St. Thomas, St. Paul, MN","active":true,"usgs":false}],"preferred":false,"id":597586,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cotner, J.B.","contributorId":95272,"corporation":false,"usgs":true,"family":"Cotner","given":"J.B.","affiliations":[],"preferred":false,"id":597587,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70200465,"text":"70200465 - 2016 - Demographic modeling for reintroduction decision-making","interactions":[],"lastModifiedDate":"2018-10-24T10:37:37","indexId":"70200465","displayToPublicDate":"2016-01-01T14:27:02","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Demographic modeling for reintroduction decision-making","docAbstract":"In this chapter we consider the construction and use of population models to support reintroduction decision making. We begin by reviewing the decision-analytic process, also known as structured decision making. The material on structured decision making builds on the chapter by Chauvenet et al. (This Volume) who focus their attention on the objective setting step of structured decision making. In a decision-analytic setting, modeling cannot commence before objective setting, because the purpose of models is to provide predictions about how different management alternatives will influence the attainment of management objectives. After the introductory section on structured decision making, we turn our attention to the construction and use of population models. We discuss the steps in modeling, including structuring and planning models, obtaining parameter estimates from data, filling information gaps using expert judgment, constructing models, and using models to evaluate action alternatives. We close the chapter with a discussion of research and development needs that we believe will advance the use of population models to support the effective management of reintroduced populations.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reintroduction of fish and wildlife populations","language":"English","publisher":"University of California Press","usgsCitation":"Converse, S.J., and Armstrong, D.P., 2016, Demographic modeling for reintroduction decision-making, chap. of Reintroduction of fish and wildlife populations, p. 123-146.","productDescription":"24 p.","startPage":"123","endPage":"146","ipdsId":"IP-069447","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":358689,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":358524,"type":{"id":15,"text":"Index Page"},"url":"https://www.ucpress.edu/book/9780520284616/reintroduction-of-fish-and-wildlife-populations"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10af84e4b034bf6a7e89da","contributors":{"authors":[{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":748992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Armstrong, Doug P.","contributorId":209868,"corporation":false,"usgs":false,"family":"Armstrong","given":"Doug","email":"","middleInitial":"P.","affiliations":[{"id":13571,"text":"Massey University","active":true,"usgs":false}],"preferred":false,"id":748993,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70169861,"text":"70169861 - 2016 - Combined effects of nitrogen to phosphorus and nitrate toammonia ratios on cyanobacterial metabolite concentrations ineutrophic Midwestern USA reservoirs","interactions":[],"lastModifiedDate":"2018-08-07T13:48:36","indexId":"70169861","displayToPublicDate":"2016-01-01T13:48:29","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"Combined effects of nitrogen to phosphorus and nitrate toammonia ratios on cyanobacterial metabolite concentrations ineutrophic Midwestern USA reservoirs","docAbstract":"Recent studies have shown that the total nitrogen to total phosphorus (TN:TP) ratio and nitrogen oxidation state may have substantial effects on secondary metabolite (e.g., microcystins) production in cyanobacteria. We investigated the relationship between the water column TN:TP ratio and the cyanobacterial secondary metabolites geosmin, 2-methylisoborneol (MIB), and microcystin using multiple years of data from 4 reservoirs located in the Midwestern United States. We also examined the relationship between water column concentrations of chemically oxidized (NO3) and reduced (NH3) nitrogen, the NO3:NH3 ratio, cyanobacterial biovolume, and associated secondary metabolites. We found that the cyanobacterial secondary metabolites geosmin, MIB, and microcystin primarily occurred when the TN:TP ratio was <30:1 (by mass), likely due to higher cyanobacterial biovolumes at lower TN:TP ratios. We also found that relative cyanobacterial biovolume was inversely related to the NO3:NH3 ratio. Both N2- and non-N2-fixing cyanobacteria seemed to produce secondary metabolites and had higher concentrations per unit biovolume when NO3:NH3 ratios were relatively low. Our data thus are consistent with the hypothesis that lower TN:TP ratios favor cyanobacterial dominance and also suggest that relatively low NO3:NH3 ratios provide conditions that may favor the production of cyanobacterial secondary metabolites. Our data further suggest that increases in the absolute concentrations of TP or NH3 (or both), causing decreases in TN:TP and NO3:NH3ratios, respectively, may stimulate cyanobacteria having the metabolic ability to produce geosmin, MIB, or microcystins. Future studies should address how the NO3:NH3 ratio affects phytoplankton community structure and occurrence and production of cyanobacterial secondary metabolites.
","language":"English","publisher":"Taylor & Francis","doi":"10.5268/IW-6.2.938","usgsCitation":"Harris, T.D., Smith, V.H., Graham, J., Van de Waal, D.B., Tedesco, L., and Clercin, N., 2016, Combined effects of nitrogen to phosphorus and nitrate toammonia ratios on cyanobacterial metabolite concentrations ineutrophic Midwestern USA reservoirs: Inland Waters, v. 6, no. 2, p. 199-210, https://doi.org/10.5268/IW-6.2.938.","productDescription":"12 p.","startPage":"199","endPage":"210","ipdsId":"IP-061939","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":471354,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5268/iw-6.2.938","text":"Publisher Index Page"},{"id":356288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fca0fe4b0f5d57878ec88","contributors":{"authors":[{"text":"Harris, Theodore D. 0000-0003-0944-8007 tdharris@usgs.gov","orcid":"https://orcid.org/0000-0003-0944-8007","contributorId":4040,"corporation":false,"usgs":true,"family":"Harris","given":"Theodore","email":"tdharris@usgs.gov","middleInitial":"D.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":625361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Val H.","contributorId":168292,"corporation":false,"usgs":false,"family":"Smith","given":"Val","email":"","middleInitial":"H.","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":625364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":625362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van de Waal, Dedmer B.","contributorId":168291,"corporation":false,"usgs":false,"family":"Van de Waal","given":"Dedmer","email":"","middleInitial":"B.","affiliations":[{"id":25240,"text":"NIOO-KNAW","active":true,"usgs":false}],"preferred":false,"id":625363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tedesco, Lenore","contributorId":168293,"corporation":false,"usgs":false,"family":"Tedesco","given":"Lenore","email":"","affiliations":[{"id":25241,"text":"Wetlands institute","active":true,"usgs":false}],"preferred":false,"id":625365,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clercin, Nicolas","contributorId":168294,"corporation":false,"usgs":false,"family":"Clercin","given":"Nicolas","email":"","affiliations":[{"id":17660,"text":"IUPUI (Indiana University-Purdue University at Indianapolis)","active":true,"usgs":false}],"preferred":false,"id":625366,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169240,"text":"70169240 - 2016 - Toward more realistic projections of soil carbon dynamics by Earth system models","interactions":[],"lastModifiedDate":"2016-05-17T16:11:58","indexId":"70169240","displayToPublicDate":"2016-01-01T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Toward more realistic projections of soil carbon dynamics by Earth system models","docAbstract":"Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015GB005239","usgsCitation":"Luo, Y., Ahlstrom, A., Allison, S.D., Batjes, N.H., Brovkin, V., Carvalhais, N., Chappell, A., Ciais, P., Davidson, E.A., Finzi, A., Georgiou, K., Guenet, B., Hararuk, O., Harden, J., He, Y., Hopkins, F., Jiang, L., Koven, C., Jackson, R.B., Jones, C.D., Lara, M., Liang, J., McGuire, A.D., Parton, W., Peng, C., Randerson, J., Salazar, A., Sierra, C., Smith, M.J., Tian, H., Todd-Brown, K.E., Torn, M.S., van Groenigen, K.J., Wang, Y., West, T.O., Wei, Y., Wieder, W.R., Xia, J., Xu, X., Xu, X., and Zhou, T., 2016, Toward more realistic projections of soil carbon dynamics by Earth system models: Global Biogeochemical Cycles, v. 30, no. 1, p. 40-56, https://doi.org/10.1002/2015GB005239.","productDescription":"17 p.","startPage":"40","endPage":"56","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067379","costCenters":[{"id":200,"text":"Coop Res Unit 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However, the spatial relationship between shallow earthquakes and LFEs remains unclear. Here, we present precise relocations of 34 earthquakes and 34 LFEs recorded during a temporary deployment of 13 broadband seismic stations from May 2010 to July 2011. We use the temporary array waveform data, along with data from permanent seismic stations and a new high‐resolution 3D velocity model, to illuminate the fine‐scale details of the seismicity distribution near Cholame and the relation to the distribution of LFEs. The depth of the boundary between earthquakes and LFE hypocenters changes along strike and roughly follows the 350°C isotherm, suggesting frictional behavior may be, in part, thermally controlled. We observe no overlap in the depth of earthquakes and LFEs, with an ∼5 km separation between the deepest earthquakes and shallowest LFEs. In addition, clustering in the relocated seismicity near the 2004 Mw 6.0 Parkfield earthquake hypocenter and near the northern boundary of the 1857 Mw 7.8 Fort Tejon rupture may highlight areas of frictional heterogeneities on the fault where earthquakes tend to nucleate.
","language":"English","publisher":"Seismological Society of Amercia","doi":"10.1785/0120150171","usgsCitation":"Harrington, R., Cochran, E.S., Griffiths, E.M., Zeng, X., and Thurber, C.H., 2016, Along-strike variations in fault frictional properties along the San Andreas Fault near Cholame, California from joint earthquake and low-frequency earthquake relocations: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 319-326, https://doi.org/10.1785/0120150171.","productDescription":"8 p.","startPage":"319","endPage":"326","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066396","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":316595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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M.","contributorId":156300,"corporation":false,"usgs":false,"family":"Griffiths","given":"Emily","email":"","middleInitial":"M.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":597374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zeng, Xiangfang","contributorId":177477,"corporation":false,"usgs":false,"family":"Zeng","given":"Xiangfang","email":"","affiliations":[],"preferred":false,"id":597375,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thurber, Clifford H. 0000-0002-4940-4618","orcid":"https://orcid.org/0000-0002-4940-4618","contributorId":73184,"corporation":false,"usgs":false,"family":"Thurber","given":"Clifford","email":"","middleInitial":"H.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":597376,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202285,"text":"70202285 - 2016 - Management-driven science synthesis: An evaluation of Everglades restoration trajectories","interactions":[],"lastModifiedDate":"2019-02-20T11:02:00","indexId":"70202285","displayToPublicDate":"2016-01-01T10:59:43","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Management-driven science synthesis: An evaluation of Everglades restoration trajectories","docAbstract":"The Synthesis of Everglades Restoration andEcosystem Services (SERES) Project was funded in 2010 by the U.S. Department of Interior (DOI) through the Critical Ecosystem Studies Initiative (CESI) and established to synthesize the ever-growing body of Everglades scientific information with the goal of addressing topics that have hampered restoration since the Comprehensive Everglades Restoration Plan (CERP) was passed in 2000. A distinguishing characteristic of this synthesis effort was that the target end-user was a management/\ndecision-maker audience. Specifically, the aim was to address the questions of the water managers and other decision leaders in a way that would illuminate and inform but not constrain or specify decisions. Since its inception, the SERES Project has been managed by the Everglades Foundation; however, a core group of scientifc experts from agencies, academic institutions, and the private sector have contributed to the project (see list on page 4). We\nbegan the project by interviewing key officials, including resource managers, decision-makers, and heads of agencies and environmental organizations. The objective of these interviews was to establish the Key Science Management Questions that needed to be addressed in order to advance restoration of the Everglades. The resulting questions led to the organization of project teams focused on Hydrology, Water Quality, Soils, Trophic Dynamics, and Landscape\nPattern. In order to establish the technical basis for the project, we conducted in depth reviews of the recent scientifc literature, evaluation tools and models, and available data in each of these core areas. Finally, we developed a suite of restoration options that would aid us in addressing the Key Questions and evaluated their relative performance from hydrological, ecological, and economic perspectives. General fndings of the SERES Project are described in subsequent sections, and technical reviews and results of analyses supporting this document are available in reports on the project website.","language":"English","publisher":"Everglades Foundation","usgsCitation":"Davis, S.E., Beerens, J.M., Borkhataria, R.R., Childers, D.L., Choi, J., Davis, S.M., Fitz, C., Gaiser, E., Henriquez, H., Lodge, T.E., Harvey, J., Marshall, F., McCormick, B., Naja, M., Osborne, T., Ross, M.S., Sah, J., Trexler, J.C., Van Lent, T., and Wetzel, P.R., 2016, Management-driven science synthesis: An evaluation of Everglades restoration trajectories, 60 p.","productDescription":"60 p.","ipdsId":"IP-071537","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":361381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361370,"type":{"id":15,"text":"Index Page"},"url":"https://www.evergladesfoundation.org/our-efforts/reports-papers/"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.8753662109375,\n 25.030861410390447\n ],\n [\n -79.98596191406249,\n 25.030861410390447\n ],\n [\n -79.98596191406249,\n 28.38173504322308\n ],\n [\n -82.8753662109375,\n 28.38173504322308\n ],\n [\n -82.8753662109375,\n 25.030861410390447\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Stephen E","contributorId":213386,"corporation":false,"usgs":false,"family":"Davis","given":"Stephen","email":"","middleInitial":"E","affiliations":[{"id":17761,"text":"Everglades Foundation","active":true,"usgs":false}],"preferred":false,"id":757631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beerens, James M. 0000-0001-8143-916X jbeerens@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-916X","contributorId":143722,"corporation":false,"usgs":true,"family":"Beerens","given":"James","email":"jbeerens@usgs.gov","middleInitial":"M.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":757630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borkhataria, Rena R.","contributorId":197425,"corporation":false,"usgs":false,"family":"Borkhataria","given":"Rena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":757632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Childers, Daniel L.","contributorId":201937,"corporation":false,"usgs":false,"family":"Childers","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":757633,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Choi, Jay","contributorId":213387,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":757634,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Steven M","contributorId":213398,"corporation":false,"usgs":false,"family":"Davis","given":"Steven","email":"","middleInitial":"M","affiliations":[{"id":38747,"text":"Ibis Ecosystems Associates, Inc","active":true,"usgs":false}],"preferred":false,"id":757649,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fitz, Carl","contributorId":213388,"corporation":false,"usgs":false,"family":"Fitz","given":"Carl","email":"","affiliations":[{"id":38744,"text":"EcoLandMod, Inc","active":true,"usgs":false}],"preferred":false,"id":757635,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gaiser, Evelyn","contributorId":213389,"corporation":false,"usgs":false,"family":"Gaiser","given":"Evelyn","email":"","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":757636,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Henriquez, Hiram","contributorId":213390,"corporation":false,"usgs":false,"family":"Henriquez","given":"Hiram","email":"","affiliations":[{"id":38745,"text":"H2H Graphics","active":true,"usgs":false}],"preferred":false,"id":757637,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lodge, Thomas E.","contributorId":202430,"corporation":false,"usgs":false,"family":"Lodge","given":"Thomas","email":"","middleInitial":"E.","affiliations":[{"id":36433,"text":"Thomas E. 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It has a relatively natural flow regime, which helps maintain diverse habitats and fish assemblages uncommon in large rivers elsewhere. The lower Yellowstone River was thought to support a diverse nongame fish assemblage including several species of special concern. However, comprehensive data on the small nongame fish assemblage of the lower Yellowstone River is lacking. Therefore, we sampled the Yellowstone River downstream of its confluence with the Clark’s Fork using fyke nets and otter trawls to assess distributions and abundances of small nongame fishes. We captured 42 species (24 native and 18 nonnative) in the lower Yellowstone River with fyke nets. Native species constituted over 99% of the catch. Emerald shiners Notropis atherinoides, western silvery minnows Hybognathus argyritis, flathead chubs Platygobio gracilis, sand shiners Notropis stramineus, and longnose dace Rhinichthys cataractae composed nearly 94% of fyke net catch and were caught in every segment of the study area. We captured 24 species by otter trawling downstream of the Tongue River. Sturgeon chubs Macrhybopsis gelida, channel catfish Ictalurus punctatus, flathead chubs, stonecats Noturus flavus, and sicklefin chubs Macrhybopsis meeki composed 89% of the otter trawl catch. The upstream distributional limit of sturgeon chubs in the Yellowstone River was the Tongue River; few sicklefin chubs were captured above Intake Diversion Dam. This study not only provides biologists with baseline data for future monitoring efforts on the Yellowstone River but serves as a benchmark for management and conservation efforts in large rivers elsewhere as the Yellowstone River represents one of the best references for a naturally functioning Great Plains river.
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