{"pageNumber":"442","pageRowStart":"11025","pageSize":"25","recordCount":46644,"records":[{"id":70158679,"text":"70158679 - 2016 - Environmental controls on spatial patterns in the long-term persistence of giant kelp in central California","interactions":[],"lastModifiedDate":"2016-03-17T13:43:39","indexId":"70158679","displayToPublicDate":"2015-10-06T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Environmental controls on spatial patterns in the long-term persistence of giant kelp in central California","docAbstract":"

As marine management is moving towards the practice of protecting static areas, it is 44 important to make sure protected areas capture and protect persistent populations. Rocky reefs in 45 many temperate areas worldwide serve as habitat for canopy forming macroalgae and these 46 structure forming species of kelps (order Laminariales) often serve as important habitat for a great 47 diversity of species. Macrocystis pyrifera is the most common canopy forming kelp species found 48 along the coast of California but the distribution and abundance of M. pyrifera varies in space and 49 time. The purpose of this study is to determine what environmental parameters are correlated with 50 the spatial and temporal persistence of M. pyrifera along the central coast of California and how 51 well those environmental parameters can be used to predict areas where M. pyrifera is more likely 52 to persist. Nine environmental variables considered in this study included depth of the seafloor, 53 structure of the rocky reef, proportion of rocky reef, size of kelp patch, biomass of kelp within a 54 patch, distance from the edge of a kelp patch, sea surface temperature, wave orbital velocities, and 55 population connectivity of individual kelp patches. Using a generalized linear mixed effects model 56 (GLMM), the persistence of M. pyrifera was significantly associated with seven of the nine 57 variables considered: depth, complexity of the rocky reef, proportion of rock, patch biomass, 58 distance from the edge of a patch, population connectivity, and wave-orbital velocities. These 59 seven environmental variables were then used to predict the persistence of kelp across the central 60 coast and these predictions were compared to a reserved dataset of M. pyrifera persistence, which 61 was not used in the creation of the GLMM. The environmental variables were shown to accurately 62 predict the persistence of M. pyrifera within the central coast of California (r = 0.71, P<0.001). 63 Because persistence of giant kelp is important to the community structure of kelp forests, 64 understanding those factors that support persistent populations of M. pyrifera will enable more 65 effective management of these ecosystems.

","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0267.1","collaboration":"Prepared in collaboration with University of California at Santa Cruz, University of California at Los Angeles","usgsCitation":"Young, M.A., Cavanaugh, K.C., Bell, T.W., Raimondi, P.T., Edwards, C.A., Drake, P.T., Erikson, L., and Storlazzi, C.D., 2016, Environmental controls on spatial patterns in the long-term persistence of giant kelp in central California: Ecology, v. 86, no. 1, p. 45-60, https://doi.org/10.1890/15-0267.1.","productDescription":"16 p.","startPage":"45","endPage":"60","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063935","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":502615,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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The subduction zone in northern Chile is a well-identified seismic gap that last ruptured in 1877. On 1 April 2014, this region was struck by a large earthquake following a two week long series of foreshocks. This study combines a wide range of observations, including geodetic, tsunami, and seismic data, to produce a reliable kinematic slip model of the Mw=8.1 main shock and a static slip model of the Mw=7.7 aftershock. We use a novel Bayesian modeling approach that accounts for uncertainty in the Green's functions, both static and dynamic, while avoiding nonphysical regularization. The results reveal a sharp slip zone, more compact than previously thought, located downdip of the foreshock sequence and updip of high-frequency sources inferred by back-projection analysis. Both the main shock and the Mw=7.7 aftershock did not rupture to the trench and left most of the seismic gap unbroken, leaving the possibility of a future large earthquake in the region.

","language":"English","publisher":"AGU","doi":"10.1002/2015GL065402","usgsCitation":"Duputel, Z., Jiang, J., Jolivet, R., Simons, M., Rivera, L., Ampuero, J., Riel, B., Owen, S.E., Moore, A.W., Samsonov, S.V., Ortega Culaciati, F., and Minson, S.E., 2016, The Iquique earthquake sequence of April 2014: Bayesian modeling accounting for prediction uncertainty: Geophysical Research Letters, v. 42, no. 19, p. 7949-7957, https://doi.org/10.1002/2015GL065402.","productDescription":"9 p.","startPage":"7949","endPage":"7957","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068742","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471446,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl065402","text":"Publisher Index Page"},{"id":325568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile, 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and laboratory data: a perspective in ecosystem restoration","interactions":[],"lastModifiedDate":"2016-01-18T09:22:20","indexId":"70157162","displayToPublicDate":"2015-10-01T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing accuracy and precision for field and laboratory data: a perspective in ecosystem restoration","docAbstract":"

Unlike most laboratory studies, rigorous quality assurance/quality control (QA/QC) procedures may be lacking in ecosystem restoration (“ecorestoration”) projects, despite legislative mandates in the United States. This is due, in part, to ecorestoration specialists making the false assumption that some types of data (e.g. discrete variables such as species identification and abundance classes) are not subject to evaluations of data quality. Moreover, emergent behavior manifested by complex, adapting, and nonlinear organizations responsible for monitoring the success of ecorestoration projects tend to unconsciously minimize disorder, QA/QC being an activity perceived as creating disorder. We discuss similarities and differences in assessing precision and accuracy for field and laboratory data. Although the concepts for assessing precision and accuracy of ecorestoration field data are conceptually the same as laboratory data, the manner in which these data quality attributes are assessed is different. From a sample analysis perspective, a field crew is comparable to a laboratory instrument that requires regular “recalibration,” with results obtained by experts at the same plot treated as laboratory calibration standards. Unlike laboratory standards and reference materials, the “true” value for many field variables is commonly unknown. In the laboratory, specific QA/QC samples assess error for each aspect of the measurement process, whereas field revisits assess precision and accuracy of the entire data collection process following initial calibration. Rigorous QA/QC data in an ecorestoration project are essential for evaluating the success of a project, and they provide the only objective “legacy” of the dataset for potential legal challenges and future uses.

","language":"English","publisher":"Wiley","doi":"10.1111/rec.12284","usgsCitation":"Stapanian, M.A., Lewis, T.E., Palmer, C.J., and Middlebrook Amos, M., 2016, Assessing accuracy and precision for field and laboratory data: a perspective in ecosystem restoration: Restoration Ecology, v. 24, no. 1, p. 18-26, https://doi.org/10.1111/rec.12284.","productDescription":"9 p.","startPage":"18","endPage":"26","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054244","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":309393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-23","publicationStatus":"PW","scienceBaseUri":"563486aae4b048076347fafb","contributors":{"authors":[{"text":"Stapanian, Martin A. 0000-0001-8173-4273 mstapanian@usgs.gov","orcid":"https://orcid.org/0000-0001-8173-4273","contributorId":3425,"corporation":false,"usgs":true,"family":"Stapanian","given":"Martin","email":"mstapanian@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":572040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewis, Timothy E","contributorId":147563,"corporation":false,"usgs":false,"family":"Lewis","given":"Timothy","email":"","middleInitial":"E","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":572041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmer, Craig J.","contributorId":36028,"corporation":false,"usgs":true,"family":"Palmer","given":"Craig","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":572042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middlebrook Amos, Molly","contributorId":147564,"corporation":false,"usgs":false,"family":"Middlebrook Amos","given":"Molly","email":"","affiliations":[{"id":6937,"text":"CSC – IT Centre for Science, P.O. Box 405, 02101, Espoo, Finland","active":true,"usgs":false}],"preferred":false,"id":572043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173663,"text":"70173663 - 2016 - Animal movement constraints improve resource selection inference in the presence of telemetry error","interactions":[],"lastModifiedDate":"2016-06-07T15:20:38","indexId":"70173663","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Animal movement constraints improve resource selection inference in the presence of telemetry error","docAbstract":"

Multiple factors complicate the analysis of animal telemetry location data. Recent advancements address issues such as temporal autocorrelation and telemetry measurement error, but additional challenges remain. Difficulties introduced by complicated error structures or barriers to animal movement can weaken inference. We propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. We specify a model for telemetry data observed with error conditional on unobserved true locations that reflects prior knowledge about constraints in the animal movement process. The observed telemetry data are modeled using a flexible distribution that accommodates extreme errors and complicated error structures. Although constraints to movement are often viewed as a nuisance, we use constraints to simultaneously estimate and account for telemetry error. We apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with measurement error and movement constraints. We then apply our framework to an Argos satellite telemetry data set on harbor seals (Phoca vitulina) in the Gulf of Alaska, a species that is constrained to move within the marine environment and adjacent coastlines.

","language":"English","publisher":"Ecological Society of America, Wiley","doi":"10.1890/15-0472.1","usgsCitation":"Brost, B.M., Hooten, M., Hanks, E., and Small, R.J., 2016, Animal movement constraints improve resource selection inference in the presence of telemetry error: Ecology, v. 96, no. 10, p. 2590-2597, https://doi.org/10.1890/15-0472.1.","productDescription":"8 p.","startPage":"2590","endPage":"2597","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060441","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471447,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/15-0472.1","text":"Publisher Index Page"},{"id":323199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5757f02ee4b04f417c24da1b","contributors":{"authors":[{"text":"Brost, Brian M.","contributorId":171484,"corporation":false,"usgs":false,"family":"Brost","given":"Brian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":637471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Ephraim M.","contributorId":104630,"corporation":false,"usgs":true,"family":"Hanks","given":"Ephraim M.","affiliations":[],"preferred":false,"id":637596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Small, Robert J.","contributorId":171486,"corporation":false,"usgs":false,"family":"Small","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":637597,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191236,"text":"70191236 - 2016 - An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States","interactions":[],"lastModifiedDate":"2017-10-02T16:19:59","indexId":"70191236","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1191,"text":"Cartography and Geographic Information Science","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States","docAbstract":"

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230406.2015.1067829","usgsCitation":"Wendel, J., Buttenfield, B., and Stanislawski, L.V., 2016, An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States: Cartography and Geographic Information Science, v. 43, no. 3, p. 233-249, https://doi.org/10.1080/15230406.2015.1067829.","productDescription":"17 p.","startPage":"233","endPage":"249","ipdsId":"IP-062949","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":346332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"43","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-15","publicationStatus":"PW","scienceBaseUri":"59d35029e4b05fe04cc34d65","contributors":{"authors":[{"text":"Wendel, Jochen","contributorId":196803,"corporation":false,"usgs":false,"family":"Wendel","given":"Jochen","affiliations":[],"preferred":false,"id":711651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P.","contributorId":145538,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":711652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":711650,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70157236,"text":"70157236 - 2016 - Blind identification of the Millikan Library from earthquake data considering soil–structure interaction","interactions":[],"lastModifiedDate":"2016-06-17T09:37:01","indexId":"70157236","displayToPublicDate":"2015-09-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5007,"text":"Structural Control and Health Monitoring","active":true,"publicationSubtype":{"id":10}},"title":"Blind identification of the Millikan Library from earthquake data considering soil–structure interaction","docAbstract":"

The Robert A. Millikan Library is a reinforced concrete building with a basement level and nine stories above the ground. Located on the campus of California Institute of Technology (Caltech) in Pasadena California, it is among the most densely instrumented buildings in the U.S. From the early dates of its construction, it has been the subject of many investigations, especially regarding soil–structure interaction effects. It is well accepted that the structure is significantly interacting with the surrounding soil, which implies that the true foundation input motions cannot be directly recorded during earthquakes because of inertial effects. Based on this limitation, input–output modal identification methods are not applicable to this soil–structure system. On the other hand, conventional output-only methods are typically based on the unknown input signals to be stationary whitenoise, which is not the case for earthquake excitations. Through the use of recently developed blind identification (i.e. output-only) methods, it has become possible to extract such information from only the response signals because of earthquake excitations. In the present study, we employ such a blind identification method to extract the modal properties of the Millikan Library. We present some modes that have not been identified from force vibration tests in several studies to date. Then, to quantify the contribution of soil–structure interaction effects, we first create a detailed Finite Element (FE) model using available information about the superstructure; and subsequently update the soil–foundation system's dynamic stiffnesses at each mode such that the modal properties of the entire soil–structure system agree well with those obtained via output-only modal identification.

","language":"English","publisher":"International Association for Structural Control and Monitoring","publisherLocation":"Chichester, UK","doi":"10.1002/stc.1803","usgsCitation":"Ghahari, S.F., Abazarsa, F., Avci, O., Çelebi, M., and Taciroglu, E., 2016, Blind identification of the Millikan Library from earthquake data considering soil–structure interaction: Structural Control and Health Monitoring, v. 23, no. 4, p. 684-706, https://doi.org/10.1002/stc.1803.","productDescription":"23 p.","startPage":"684","endPage":"706","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068999","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471450,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/stc.1803","text":"Publisher Index Page"},{"id":318538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Pasadena","otherGeospatial":"Robert A. Millikan Library, California Insttitute of Technology","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.12946319580077,\n 34.13136240467381\n ],\n [\n -118.12946319580077,\n 34.14203648796777\n ],\n [\n -118.12130928039551,\n 34.14203648796777\n ],\n [\n -118.12130928039551,\n 34.13136240467381\n ],\n [\n -118.12946319580077,\n 34.13136240467381\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-29","publicationStatus":"PW","scienceBaseUri":"56d96e3ce4b015c306f7644c","contributors":{"authors":[{"text":"Ghahari, S. F.","contributorId":147707,"corporation":false,"usgs":false,"family":"Ghahari","given":"S.","email":"","middleInitial":"F.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":572365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abazarsa, F.","contributorId":147708,"corporation":false,"usgs":false,"family":"Abazarsa","given":"F.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":572366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avci, O.","contributorId":147709,"corporation":false,"usgs":false,"family":"Avci","given":"O.","email":"","affiliations":[{"id":16914,"text":"University of Qatar","active":true,"usgs":false}],"preferred":false,"id":572367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Çelebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":3205,"corporation":false,"usgs":true,"family":"Çelebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":572364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taciroglu, E.","contributorId":147710,"corporation":false,"usgs":false,"family":"Taciroglu","given":"E.","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":572368,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211939,"text":"70211939 - 2016 - Seasonal temperature and precipitation regulate brook trout young-of-the-year abundance and population dynamics","interactions":[],"lastModifiedDate":"2021-04-27T18:50:22.024529","indexId":"70211939","displayToPublicDate":"2015-09-28T11:43:50","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal temperature and precipitation regulate brook trout young-of-the-year abundance and population dynamics","docAbstract":"
  1. Abundance of the young‐of‐the‐year (YOY) fish can vary greatly among years and it may be driven by several key biological processes (i.e. adult spawning, egg survival and fry survival) that span several months. However, the relative influence of seasonal weather patterns on YOY abundance is poorly understood.
  2. We assessed the importance of seasonal air temperature (a surrogate for stream temperature) and precipitation (a surrogate for stream flow) on brook trout (Salvelinus fontinalis) YOY summer abundance using a 29‐year data set from 115 sites in Shenandoah National Park, Virginia, U.S.A. We used a Bayesian hierarchical model that allowed the effect of seasonal weather covariates to vary among sites and accounted for imperfect detection of individuals.
  3. Summer YOY abundance was affected by preceding seasonal air temperature and precipitation, and these regional‐scale drivers led to spatial synchrony in YOY abundance dynamics across the 170‐km‐long study area. Mean winter precipitation had the greatest effect on YOY abundance and the relationship was negative. Mean autumn precipitation, and winter and spring temperature had significantly positive effects on YOY abundance, and mean autumn temperature had a significant negative effect. In addition, the effect of summer precipitation differed along a latitudinal gradient, with YOY abundance at more northern sites being more responsive to inter‐annual variation in summer precipitation.
  4. Strong YOY years resulted in high abundance of adults (>age 1 + fish) in the subsequent year at more than half of sites. However, higher adult abundance did not result in higher YOY abundance in the subsequent year at any of the study sites (i.e. no positive stock–recruitment relationship).
  5. Our results indicate that YOY abundance is a key driver of brook trout population dynamics that is mediated by seasonal weather patterns. A reliable assessment of climate change impacts on brook trout needs to account for how alternations in seasonal weather patterns impact YOY abundance and how such relationships may differ across the range of brook trout distribution.
","language":"English","publisher":"Wiley","doi":"10.1111/fwb.12682","usgsCitation":"Kanno, Y., Pregler, K.C., Hitt, N.P., Letcher, B., Hocking, D., and Wofford, J.E., 2016, Seasonal temperature and precipitation regulate brook trout young-of-the-year abundance and population dynamics: Freshwater Biology, v. 61, no. 1, p. 88-99, https://doi.org/10.1111/fwb.12682.","productDescription":"12 p.","startPage":"88","endPage":"99","ipdsId":"IP-063610","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.25561523437499,\n 39.04478604850143\n ],\n [\n -79.21142578125,\n 37.95286091815649\n ],\n [\n -79.6893310546875,\n 37.56199695314352\n ],\n [\n -79.486083984375,\n 37.42688834526727\n ],\n [\n -78.695068359375,\n 37.92686760148135\n ],\n [\n -77.904052734375,\n 38.6897975322717\n ],\n [\n -77.95898437499999,\n 39.0533181067413\n ],\n [\n -78.25561523437499,\n 39.04478604850143\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"61","issue":"1","noUsgsAuthors":false,"publicationDate":"2015-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":795888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pregler, Kasey C.","contributorId":149616,"corporation":false,"usgs":false,"family":"Pregler","given":"Kasey","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":795889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Letcher, Benjamin 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":169305,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hocking, Daniel 0000-0003-1889-9184 dhocking@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-9184","contributorId":149618,"corporation":false,"usgs":true,"family":"Hocking","given":"Daniel","email":"dhocking@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795892,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wofford, John E. B.","contributorId":38951,"corporation":false,"usgs":false,"family":"Wofford","given":"John","email":"","middleInitial":"E. B.","affiliations":[],"preferred":false,"id":795893,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70159174,"text":"70159174 - 2016 - Bayesian data analysis in population ecology: motivations, methods, and benefits","interactions":[],"lastModifiedDate":"2016-07-11T15:42:38","indexId":"70159174","displayToPublicDate":"2015-09-07T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian data analysis in population ecology: motivations, methods, and benefits","docAbstract":"

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

","language":"English","publisher":"Springer Japan","publisherLocation":"Tokyo, Japan","doi":"10.1007/s10144-015-0503-4","usgsCitation":"Dorazio, R., 2016, Bayesian data analysis in population ecology: motivations, methods, and benefits: Population Ecology, v. 58, no. 1, p. 31-44, https://doi.org/10.1007/s10144-015-0503-4.","productDescription":"14 p.","startPage":"31","endPage":"44","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060523","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":310033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"1","publicComments":"This manuscript was submitted for the special feature based on a symposium in Tsukuba, Japan, held on 11 October 2014.","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-07","publicationStatus":"PW","scienceBaseUri":"5626143be4b0fb9a11dd75f1","contributors":{"authors":[{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":149286,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":577750,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70187374,"text":"70187374 - 2016 - Multi‐season occupancy models identify biotic and abiotic factors influencing a recovering Arctic Peregrine Falcon Falco peregrinus tundrius population","interactions":[],"lastModifiedDate":"2019-12-30T09:19:56","indexId":"70187374","displayToPublicDate":"2015-09-07T13:12:14","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Multi‐season occupancy models identify biotic and abiotic factors influencing a recovering Arctic Peregrine Falcon Falco peregrinus tundrius population","title":"Multi‐season occupancy models identify biotic and abiotic factors influencing a recovering Arctic Peregrine Falcon Falco peregrinus tundrius population","docAbstract":"

Critical information for evaluating the effectiveness of management strategies for species of concern include distinguishing seldom occupied (or low‐quality) habitat from habitat that is frequently occupied and thus contributes substantially to population trends. Using multi‐season models that account for imperfect detection and a long‐term (1981–2002) dataset on migratory Arctic Peregrine Falcons Falco peregrinus tundrius nesting along the Colville River, Alaska, we quantified the effects of previous year's productivity (i.e. site quality), amount of prey habitat, topography, climate, competition and year on occupancy dynamics across two spatial scales (nest‐sites, cliffs) during recovery of the population. Initial occupancy probability was positively correlated with area of surrounding prey habitat and height of nest‐sites above the Colville River. Colonization probability was positively correlated with nest height and negatively correlated with date of snowmelt. Local extinction probability was negatively correlated with productivity, area of prey habitat and nest height. Colonization and local extinction probabilities were also positively and negatively correlated, respectively, with year. Our results suggest that nest‐sites (or cliffs) along the Colville River do not need equal protection measures. Nest‐sites and cliffs with historically higher productivity were occupied most frequently and had lower probability of local extinction. These sites were on cliffs high above the river drainage, surrounded by adequate prey habitat and with southerly aspects associated with early snowmelt and warmer microclimates in spring. Protecting these sites is likely to encourage continued occupancy by Arctic Peregrine Falcons along the Colville River and other similar areas. Our findings also illustrate the importance of evaluating fitness parameters along with climate and habitat features when analysing occupancy dynamics, particularly with a long‐term dataset spanning a range of annual climate variation.

","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12313","usgsCitation":"Bruggeman, J.E., Swem, T., Andersen, D., Kennedy, P.L., and Debora Nigro, 2016, Multi‐season occupancy models identify biotic and abiotic factors influencing a recovering Arctic Peregrine Falcon Falco peregrinus tundrius population: Ibis, v. 158, no. 1, p. 61-74, https://doi.org/10.1111/ibi.12313.","productDescription":"14 p.","startPage":"61","endPage":"74","ipdsId":"IP-050764","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":370113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Colville River Special Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.521728515625,\n 68.31002672261663\n ],\n [\n -151.11694335937497,\n 68.31002672261663\n ],\n [\n -151.11694335937497,\n 70.11422207508899\n ],\n [\n -158.521728515625,\n 70.11422207508899\n ],\n [\n -158.521728515625,\n 68.31002672261663\n ]\n ]\n ]\n }\n }\n ]\n}\n","volume":"158","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Bruggeman, Jason E.","contributorId":18983,"corporation":false,"usgs":false,"family":"Bruggeman","given":"Jason","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":776972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swem, Ted","contributorId":64463,"corporation":false,"usgs":true,"family":"Swem","given":"Ted","affiliations":[],"preferred":false,"id":776973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":2168,"corporation":false,"usgs":true,"family":"Andersen","given":"David E.","email":"dea@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":true,"id":693637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Patricia L.","contributorId":172826,"corporation":false,"usgs":false,"family":"Kennedy","given":"Patricia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":776974,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Debora Nigro","contributorId":217532,"corporation":false,"usgs":false,"family":"Debora Nigro","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":776975,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70162240,"text":"70162240 - 2016 - Assessing the robustness of quantitative fatty acid signature analysis to assumption violations","interactions":[],"lastModifiedDate":"2016-01-20T12:38:41","indexId":"70162240","displayToPublicDate":"2015-09-06T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the robustness of quantitative fatty acid signature analysis to assumption violations","docAbstract":"

 

\n
    \n
  1. Knowledge of animal diets can provide important insights into life history and ecology, relationships among species in a community and potential response to ecosystem change or perturbation. Quantitative fatty acid signature analysis (QFASA) is a method of estimating diets from data on the composition, or signature, of fatty acids stored in adipose tissue. Given data on signatures of potential prey, a predator diet is estimated by minimizing the distance between its signature and a mixture of prey signatures. Calibration coefficients, constants derived from feeding trials, are used to account for differential metabolism of individual fatty acids. QFASA has been widely applied since its introduction and several variants of the original estimator have appeared in the literature. However, work to compare the statistical properties of QFASA estimators has been limited.
    2. \n
  2. One important characteristic of an estimator is its robustness to violations of model assumptions. The primary assumptions of QFASA are that prey signature data contain representatives of all prey types consumed and the calibration coefficients are known without error. We investigated the robustness of two QFASA estimators to a range of violations of these assumptions using computer simulation and recorded the resulting bias in diet estimates.
    3. \n
  3. We found that the Aitchison distance measure was most robust to errors in the calibration coefficients. Conversely, the Kullback–Leibler distance measure was most robust to the consumption of prey without representation in the prey signature data.
    4. \n
  4. In most QFASA applications, investigators will generally have some knowledge of the prey available to predators and be able to assess the completeness of prey signature data and sample additional prey as necessary. Conversely, because calibration coefficients are derived from feeding trials with captive animals and their values may be sensitive to consumer physiology and nutritional status, their applicability to free-ranging animals is difficult to establish. We therefore recommend that investigators first make any improvements to the prey signature data that seem warranted and then base estimation on the Aitchison distance measure, as it appears to minimize risk from violations of the assumption that is most difficult to verify.
    5. \n
","language":"English","publisher":"John Wiley","publisherLocation":"Hoboken, NJ","doi":"10.1111/2041-210X.12456","usgsCitation":"Bromaghin, J.F., Budge, S.M., Thiemann, G.W., and Rode, K.D., 2016, Assessing the robustness of quantitative fatty acid signature analysis to assumption violations: Methods in Ecology and Evolution, v. 7, no. 1, p. 51-59, https://doi.org/10.1111/2041-210X.12456.","productDescription":"9 p.","startPage":"51","endPage":"59","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064116","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":471459,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12456","text":"Publisher Index Page"},{"id":438651,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PR7T2W","text":"USGS data release","linkHelpText":"Assessing the robustness of quantitative fatty acid signature analysis to assumption violations (Supplementary data)"},{"id":438650,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N877TK","text":"USGS data release","linkHelpText":"QFASA Robustness to Assumption Violations: Computer Code"},{"id":314526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-06","publicationStatus":"PW","scienceBaseUri":"56a0bdc6e4b0961cf280dc10","contributors":{"authors":[{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":588963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budge, Suzanne M.","contributorId":92168,"corporation":false,"usgs":false,"family":"Budge","given":"Suzanne","email":"","middleInitial":"M.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":589117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thiemann, Gregory W.","contributorId":83023,"corporation":false,"usgs":false,"family":"Thiemann","given":"Gregory","email":"","middleInitial":"W.","affiliations":[{"id":27291,"text":"York University, Toronto, ON","active":true,"usgs":false}],"preferred":false,"id":589118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":589119,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70156836,"text":"70156836 - 2016 - Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas","interactions":[],"lastModifiedDate":"2017-12-01T13:16:49","indexId":"70156836","displayToPublicDate":"2015-08-31T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas","docAbstract":"

A major challenge in the biological monitoring of stream ecosystems in protected wilderness areas is discerning whether temporal changes in community structure are significantly outside of a reference condition that represents natural or acceptable annual variation in population cycles. Otherwise sites could erroneously be classified as impaired. Long-term datasets are essential for understanding these trends, to ascertain whether any changes in community structure significantly beyond the reference condition are permanent shifts or with time move back to within previous limits. To this end, we searched for long-term (>8 years) quantitative data sets of macroinvertebrate communities in wadeable rivers collected by similar methods and time of year in protected wilderness areas with minimal anthropogenic disturbance. Four geographic areas with datasets that met these criteria in the USA were identified, namely: McLaughlin Nature Reserve in California (1 stream), Great Smoky Mountains National Park in Tennesse-North Carolina (14 streams), Wind River Wilderness Areas in Wyoming (3 streams) and Denali National Park and Preserve in Alaska (6 streams).

\n

Two statistical approaches were applied: Taxonomic Distinctness (TD) to describe changes in diversity over time and non-metric multidimensional scaling (MDS) to describe changes over time in community persistence (Jaccards Index) and community stability (Bray–Curtis Index). Control charts were used to determine if years in MDS plots were significantly outside a reference condition. For Hunting Creek, TD showed three years outside natural variation which could be attributed to severe hydrological events but years outside the natural-variation funnel at sites in other geographical areas were inconsistent and could not be explained by environmental variables. TD identified simulated severe pollutant events which caused the removal of entire invertebrate assemblages but not simulated water temperature shifts.

\n

Within a region, both MDS analyses typically identified similar years as exceeding reference condition variation, illustrating the utility of the approach for identifying wider spatial scale effects that influence more than one stream. MDS responded to both simulated water temperature stress and a pollutant event, and generally outlying years on MDS plots could be explained by environmental variables, particularly higher precipitation. Multivariate control charts successfully identified whether shifts in community structure identified by MDS were significant and whether the shift represented a press disturbance (long-term change) or a pulse disturbance. We consider a combination of TD and MDS with control charts to be a potentially powerful tool for determining years significantly outside of a reference condition variation.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2015.07.025","usgsCitation":"Milner, A.M., Woodward, A., Freilich, J.E., Black, R.W., and Resh, V.H., 2016, Detecting significant change in stream benthic macroinvertebrate communities in wilderness areas: Ecological Indicators, v. 60, p. 524-537, https://doi.org/10.1016/j.ecolind.2015.07.025.","productDescription":"14 p.","startPage":"524","endPage":"537","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052838","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":307725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, California, North Carolina, Tennesse, Wyoming","otherGeospatial":"Denali National Park and Preserve, Great Smoky Mountains National Park, McLaughlin Nature Reserve, Wind River Wilderness Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.40966796874999,\n 39.027718840211605\n ],\n [\n -122.40966796874999,\n 40.027614437486655\n ],\n [\n -121.26708984374999,\n 40.027614437486655\n ],\n [\n -121.26708984374999,\n 39.027718840211605\n ],\n [\n -122.40966796874999,\n 39.027718840211605\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.19287109375,\n 42.76314586689494\n ],\n [\n -110.19287109375,\n 43.8503744993026\n ],\n [\n -108.52294921875,\n 43.8503744993026\n ],\n [\n -108.52294921875,\n 42.76314586689494\n ],\n [\n -110.19287109375,\n 42.76314586689494\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.87451171875,\n 62.21163423606344\n ],\n [\n -153.87451171875,\n 64.52954819942195\n ],\n [\n -148.18359375,\n 64.52954819942195\n ],\n [\n -148.18359375,\n 62.21163423606344\n ],\n [\n -153.87451171875,\n 62.21163423606344\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.9794921875,\n 35.31736632923788\n ],\n [\n -83.9794921875,\n 36.13787471840729\n ],\n [\n -82.8369140625,\n 36.13787471840729\n ],\n [\n -82.8369140625,\n 35.31736632923788\n ],\n [\n -83.9794921875,\n 35.31736632923788\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"60","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55e56ca2e4b05561fa20866c","contributors":{"authors":[{"text":"Milner, Alexander M.","contributorId":90341,"corporation":false,"usgs":true,"family":"Milner","given":"Alexander","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":570771,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodward, Andrea 0000-0003-0604-9115 awoodward@usgs.gov","orcid":"https://orcid.org/0000-0003-0604-9115","contributorId":3028,"corporation":false,"usgs":true,"family":"Woodward","given":"Andrea","email":"awoodward@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":570770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freilich, Jerome E.","contributorId":147210,"corporation":false,"usgs":false,"family":"Freilich","given":"Jerome","email":"","middleInitial":"E.","affiliations":[{"id":12587,"text":"Olympic National Park, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":570772,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":570773,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Resh, Vincent H.","contributorId":12169,"corporation":false,"usgs":true,"family":"Resh","given":"Vincent","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":570774,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156834,"text":"70156834 - 2016 - Chemical considerations for an updated National assessment of brackish groundwater resources","interactions":[],"lastModifiedDate":"2016-07-28T11:01:26","indexId":"70156834","displayToPublicDate":"2015-08-27T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Chemical considerations for an updated National assessment of brackish groundwater resources","docAbstract":"

Brackish groundwater (BGW) is increasingly used for water supplies where fresh water is scarce, but the distribution and availability of such resources have not been characterized at the national scale in the United States since the 1960s. Apart from its distribution and accessibility, BGW usability is a function of the chemical requirements of the intended use, chemical characteristics of the resource, and treatment options to make the resource compatible with the use. Here, we discuss relations between these three chemical factors using national-scale examples and local case studies. In a preliminary compilation of BGW data in the United States, five water types accounted for the major-ion composition of 70% of samples. PHREEQC calculations indicate that 57–77% of samples were oversaturated with respect to barite, calcite, or chalcedony. In the study, 5–14% of samples had concentrations of arsenic, fluoride, nitrate, or uranium that exceeded drinking-water standards. In case studies of the potential use of BGW for drinking water, irrigation, and hydraulic fracturing, PHREEQC simulations of a hypothetical treatment process resembling reverse osmosis (RO) showed that BGW had the potential to form various assemblages of mineral deposits (scale) during treatment that could adversely affect RO membranes. Speciation calculations showed that most boron in the irrigation example occurred as boric acid, which has relatively low removal efficiency by RO. Results of this preliminary study indicate that effective national or regional assessments of BGW resources should include geochemical characterizations that are guided in part by specific use and treatment requirements.

","language":"English","publisher":"National Ground Water Association","publisherLocation":"Worthington, OH","doi":"10.1111/gwat.12367","usgsCitation":"McMahon, P.B., Bohlke, J.K., Dahm, K., Parkhurst, D.L., Anning, D.W., and Stanton, J.S., 2016, Chemical considerations for an updated National assessment of brackish groundwater resources: Groundwater, v. 54, no. 4, p. 464-475, https://doi.org/10.1111/gwat.12367.","productDescription":"12 p.","startPage":"464","endPage":"475","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065662","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":307711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-27","publicationStatus":"PW","scienceBaseUri":"55e57aace4b05561fa208685","chorus":{"doi":"10.1111/gwat.12367","url":"http://dx.doi.org/10.1111/gwat.12367","publisher":"Wiley-Blackwell","authors":"McMahon P.B., Böhlke J.K., Dahm K.G., Parkhurst D.L., Anning D.W., Stanton J.S.","journalName":"Groundwater","publicationDate":"8/2015"},"contributors":{"authors":[{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":570752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, John Karl 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":127841,"corporation":false,"usgs":true,"family":"Bohlke","given":"John","email":"jkbohlke@usgs.gov","middleInitial":"Karl","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":570753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahm, Katharine 0000-0002-4024-8110","orcid":"https://orcid.org/0000-0002-4024-8110","contributorId":147205,"corporation":false,"usgs":false,"family":"Dahm","given":"Katharine","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":570754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parkhurst, David L. 0000-0003-3348-1544 dlpark@usgs.gov","orcid":"https://orcid.org/0000-0003-3348-1544","contributorId":1088,"corporation":false,"usgs":true,"family":"Parkhurst","given":"David","email":"dlpark@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":570755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anning, David W. dwanning@usgs.gov","contributorId":432,"corporation":false,"usgs":true,"family":"Anning","given":"David","email":"dwanning@usgs.gov","middleInitial":"W.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":570756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanton, Jennifer S. 0000-0002-2520-753X jstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-2520-753X","contributorId":830,"corporation":false,"usgs":true,"family":"Stanton","given":"Jennifer","email":"jstanton@usgs.gov","middleInitial":"S.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":570757,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70158933,"text":"70158933 - 2016 - The Upper Mississippi River floodscape: spatial patterns of flood inundation and associated plant community distributions","interactions":[],"lastModifiedDate":"2015-12-21T13:32:03","indexId":"70158933","displayToPublicDate":"2015-08-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"The Upper Mississippi River floodscape: spatial patterns of flood inundation and associated plant community distributions","docAbstract":"

Questions How is the distribution of different plant communities associated with patterns of flood inundation across a large floodplain landscape? Location Thirty-eight thousand nine hundred and seventy hectare of floodplain, spanning 320 km of the Upper Mississippi River (UMR). Methods High-resolution elevation data (Lidar) and 30 yr of daily river stage data were integrated to produce a ‘floodscape’ map of growing season flood inundation duration. The distributions of 16 different remotely sensed plant communities were quantified along the gradient of flood duration. Results Models fitted to the cumulative frequency of occurrence of different vegetation types as a function of flood duration showed that most types exist along a continuum of flood-related occurrence. The diversity of community types was greatest at high elevations (0–10 d of flooding), where both upland and lowland community types were found, as well as at very low elevations (70–180 d of flooding), where a variety of lowland herbaceous communities were found. Intermediate elevations (20–60 d of flooding) tended to be dominated by floodplain forest and had the lowest diversity of community types. Conclusions Although variation in flood inundation is often considered to be the main driver of spatial patterns in floodplain plant communities, few studies have quantified flood–vegetation relationships at broad scales. Our results can be used to identify targets for restoration of historical hydrological regimes or better anticipate hydro-ecological effects of climate change at broad scales.

","language":"English","publisher":"International Association for Vegetation Science","doi":"10.1111/avsc.12189","usgsCitation":"De Jager, N.R., Rohweder, J.J., Yin, Y., and Hoy, E.E., 2016, The Upper Mississippi River floodscape: spatial patterns of flood inundation and associated plant community distributions: Applied Vegetation Science, v. 19, no. 1, p. 164-172, https://doi.org/10.1111/avsc.12189.","productDescription":"9 p.","startPage":"164","endPage":"172","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064126","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":309735,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1111/avsc.12189/abstract"},{"id":309799,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River","volume":"19","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-18","publicationStatus":"PW","scienceBaseUri":"5618e535e4b0cdb063e3fef0","contributors":{"authors":[{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rohweder, Jason J. jrohweder@usgs.gov","contributorId":460,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":576944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yin, Yao yyin@usgs.gov","contributorId":2170,"corporation":false,"usgs":true,"family":"Yin","given":"Yao","email":"yyin@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576945,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoy, Erin E. 0000-0002-2853-3242 ehoy@usgs.gov","orcid":"https://orcid.org/0000-0002-2853-3242","contributorId":4523,"corporation":false,"usgs":true,"family":"Hoy","given":"Erin","email":"ehoy@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576946,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70162643,"text":"70162643 - 2016 - Anticipating environmental and environmental-health implications of extreme storms: ARkStorm scenario ","interactions":[],"lastModifiedDate":"2018-09-25T11:11:36","indexId":"70162643","displayToPublicDate":"2015-07-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2823,"text":"Natural Hazards Review","active":true,"publicationSubtype":{"id":10}},"title":"Anticipating environmental and environmental-health implications of extreme storms: ARkStorm scenario ","docAbstract":"

The ARkStorm Scenario predicts that a prolonged winter storm event across California would cause extreme precipitation, flooding, winds, physical damages, and economic impacts. This study uses a literature review and geographic information system-based analysis of national and state databases to infer how and where ARkStorm could cause environmental damages, release contamination from diverse natural and anthropogenic sources, affect ecosystem and human health, and cause economic impacts from environmental-remediation, liability, and health-care costs. Examples of plausible ARkStorm environmental and health concerns include complex mixtures of contaminants such as petroleum, mercury, asbestos, persistent organic pollutants, molds, and pathogens; adverse physical and contamination impacts on riverine and coastal marine ecosystems; and increased incidences of mold-related health concerns, some vector-borne diseases, and valley fever. Coastal cities, the San Francisco Bay area, the Sacramento-San Joaquin River Delta, parts of the Central Valley, and some mountainous areas would likely be most affected. This type of screening analysis, coupled with follow-up local assessments, can help stakeholders in California and disaster-prone areas elsewhere better plan for, mitigate, and respond to future environmental disasters.



","language":"English","publisher":"American Soiey of Civil Engineers","doi":"10.1061/(ASCE)NH.1527-6996.0000188","usgsCitation":"Plumlee, G.S., Alpers, C.N., Morman, S.A., and San Juan, C.A., 2016, Anticipating environmental and environmental-health implications of extreme storms: ARkStorm scenario : Natural Hazards Review, v. 17, no. 4, A4015003; 11 p., https://doi.org/10.1061/(ASCE)NH.1527-6996.0000188.","productDescription":"A4015003; 11 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045785","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":471466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/(asce)nh.1527-6996.0000188","text":"Publisher Index Page"},{"id":314986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ab49c5e4b07ca61bfea539","contributors":{"authors":[{"text":"Plumlee, Geoffrey S. 0000-0002-9607-5626 gplumlee@usgs.gov","orcid":"https://orcid.org/0000-0002-9607-5626","contributorId":960,"corporation":false,"usgs":true,"family":"Plumlee","given":"Geoffrey","email":"gplumlee@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":590024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":590022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morman, Suzette A. 0000-0002-2532-1033 smorman@usgs.gov","orcid":"https://orcid.org/0000-0002-2532-1033","contributorId":996,"corporation":false,"usgs":true,"family":"Morman","given":"Suzette","email":"smorman@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":590025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"San Juan, Carma A. 0000-0002-9151-1919 csanjuan@usgs.gov","orcid":"https://orcid.org/0000-0002-9151-1919","contributorId":1146,"corporation":false,"usgs":true,"family":"San Juan","given":"Carma","email":"csanjuan@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":590023,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70161862,"text":"70161862 - 2016 - Imaging pathways in fractured rock using three-dimensional electrical resistivity tomography","interactions":[],"lastModifiedDate":"2018-08-07T12:30:11","indexId":"70161862","displayToPublicDate":"2015-07-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Imaging pathways in fractured rock using three-dimensional electrical resistivity tomography","docAbstract":"

Major challenges exist in delineating bedrock fracture zones because these cause abrupt changes in geological and hydrogeological properties over small distances. Borehole observations cannot sufficiently capture heterogeneity in these systems. Geophysical techniques offer the potential to image properties and processes in between boreholes. We used three-dimensional cross borehole electrical resistivity tomography (ERT) in a 9 m (diameter) × 15 m well field to capture high-resolution flow and transport processes in a fractured mudstone contaminated by chlorinated solvents, primarily trichloroethylene. Conductive (sodium bromide) and resistive (deionized water) injections were monitored in seven boreholes. Electrode arrays with isolation packers and fluid sampling ports were designed to enable acquisition of ERT measurements during pulsed tracer injections. Fracture zone locations and hydraulic pathways inferred from hydraulic head drawdown data were compared with electrical conductivity distributions from ERT measurements. Static ERT imaging has limited resolution to decipher individual fractures; however, these images showed alternating conductive and resistive zones, consistent with alternating laminated and massive mudstone units at the site. Tracer evolution and migration was clearly revealed in time-lapse ERT images and supported by in situ borehole vertical apparent conductivity profiles collected during the pulsed tracer test. While water samples provided important local information at the extraction borehole, ERT delineated tracer migration over spatial scales capturing the primary hydrogeological heterogeneity controlling flow and transport. The fate of these tracer injections at this scale could not have been quantified using borehole logging and/or borehole sampling methods alone.

","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12356","usgsCitation":"Robinson, J., Slater, L., Johnson, T.B., Shapiro, A.M., Tiedeman, C.R., Ntlargiannis, D., Johnson, C.D., Day-Lewis, F.D., Lacombe, P., Imbrigiotta, T.E., and Lane, J.W., 2016, Imaging pathways in fractured rock using three-dimensional electrical resistivity tomography: Groundwater, v. 54, no. 2, p. 186-201, https://doi.org/10.1111/gwat.12356.","productDescription":"16 p.","startPage":"186","endPage":"201","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065453","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":314125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","city":"West Trenton","otherGeospatial":"Naval Air Warfare Center (NAWC)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.9,\n 40.2\n ],\n [\n -74.9,\n 40.3\n ],\n [\n -74.8,\n 40.3\n ],\n [\n -74.8,\n 40.2\n ],\n [\n -74.9,\n 40.2\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-14","publicationStatus":"PW","scienceBaseUri":"5694e045e4b039675d005e27","contributors":{"authors":[{"text":"Robinson, Judith","contributorId":152111,"corporation":false,"usgs":false,"family":"Robinson","given":"Judith","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":587973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slater, Lee","contributorId":55707,"corporation":false,"usgs":false,"family":"Slater","given":"Lee","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":587974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Timothy B.","contributorId":49753,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":587975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - 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Previous work suggests domestic poultry are important contributors to the emergence and transmission of highly pathogenic avian influenza throughout Asia. In Poyang Lake, China, domestic duck production cycles are synchronized with arrival and departure of thousands of migratory wild birds in the area. During these periods, high densities of juvenile domestic ducks are in close proximity to migratory wild ducks, increasing the potential for the virus to be transmitted and subsequently disseminated via migration. In this paper, we use GPS dataloggers and dynamic Brownian bridge models to describe movements and habitat use of free-grazing domestic ducks in the Poyang Lake basin and identify specific areas that may have the highest risk of H5N1 transmission between domestic and wild birds. Specifically, we determine relative use by free-grazing domestic ducks of natural wetlands, which are the most heavily used areas by migratory wild ducks, and of rice paddies, which provide habitat for resident wild ducks and lower densities of migratory wild ducks. To our knowledge, this is the first movement study on domestic ducks, and our data show potential for free-grazing domestic ducks from farms located near natural wetlands to come in contact with wild waterfowl, thereby increasing the risk for disease transmission. This study provides an example of the importance of movement ecology studies in understanding dynamics such as disease transmission on a complicated landscape.

","language":"English","publisher":"Royal Institute of International Affairs","publisherLocation":"London","doi":"10.1080/13658816.2015.1065496","usgsCitation":"Prosser, D.J., Palm, E., Takekawa, J.Y., Zhao, D., Xiao, X., Li, P., Liu, Y., and Newman, S.H., 2016, Movement analysis of free-grazing domestic ducks in Poyang Lake, China: A disease connection: International Journal of Geographical Information Science, v. 30, no. 5, p. 869-880, https://doi.org/10.1080/13658816.2015.1065496.","productDescription":"12 p.","startPage":"869","endPage":"880","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066156","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471467,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5042146","text":"External 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H.","contributorId":101372,"corporation":false,"usgs":true,"family":"Newman","given":"Scott","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":625856,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70155166,"text":"70155166 - 2016 - Geostatistical borehole image-based mapping of karst-carbonate aquifer pores","interactions":[],"lastModifiedDate":"2016-03-17T13:38:40","indexId":"70155166","displayToPublicDate":"2015-07-01T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Geostatistical borehole image-based mapping of karst-carbonate aquifer pores","docAbstract":"

Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes.

","language":"English","publisher":"National Ground Water Association","publisherLocation":"Worthington, OH","doi":"10.1111/gwat.12354","usgsCitation":"Michael Sukop, and Cunningham, K.J., 2016, Geostatistical borehole image-based mapping of karst-carbonate aquifer pores: Groundwater, v. 54, no. 2, p. 202-213, https://doi.org/10.1111/gwat.12354.","productDescription":"12 p.","startPage":"202","endPage":"213","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044928","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":306283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-14","publicationStatus":"PW","scienceBaseUri":"55bc9c2ce4b033ef52100f26","contributors":{"authors":[{"text":"Michael Sukop","contributorId":145653,"corporation":false,"usgs":false,"family":"Michael Sukop","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":564917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cunningham, Kevin J. 0000-0002-2179-8686 kcunning@usgs.gov","orcid":"https://orcid.org/0000-0002-2179-8686","contributorId":1689,"corporation":false,"usgs":true,"family":"Cunningham","given":"Kevin","email":"kcunning@usgs.gov","middleInitial":"J.","affiliations":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":true,"id":564916,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148085,"text":"ds937 - 2016 - Marine geophysical data collected in a shallow back-barrier estuary, Barnegat Bay, New Jersey","interactions":[],"lastModifiedDate":"2016-09-08T16:14:11","indexId":"ds937","displayToPublicDate":"2015-06-26T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"937","title":"Marine geophysical data collected in a shallow back-barrier estuary, Barnegat Bay, New Jersey","docAbstract":"

In 2011, the U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, began a multidisciplinary research project to better understand the water quality in Barnegat Bay, New Jersey. This back-barrier estuary is experiencing degraded water quality, algal blooms, loss of seagrass, and increases in oxygen stress, macroalgae, stinging nettles, and brown tide. The spatial scale of the estuary and the scope of challenges within it necessitate a multidisciplinary approach that includes establishing the regional geology and the estuary’s physical characteristics and modeling how the estuary’s morphology interacts to affect its water quality. This report presents the data collected during this project for use in understanding the morphology and the distribution of sea-floor and sub-sea-floor sediments within Barnegat Bay, describes the methods used to collect and process those data, and includes links to the final processed datasets. These data can be used by scientists to understand the links between geomorphology, geologic framework, sediment transport, and estuarine water quality and circulation.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds937","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Andrews, B.D., Miselis, J.L., Danforth, W.W., Irwin, B.J., Worley, C.R., Bergeron, E.M., and Blackwood, D.S., 2016, Marine geophysical data collected in a shallow back-barrier estuary, Barnegat Bay, New Jersey (ver. 1.1, September 2016): U.S. Geological Survey Data Series 937, 15 p., https://dx.doi.org/10.3133/ds937.","productDescription":"Report: iv, 15 p.; Downloads Directory","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062258","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":302385,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/0937/downloads","text":"Downloads Directory","description":"DS 937","linkHelpText":"Contains: photographs, shapefiles, and raster files. Refer to the Readme.txt file for more information."},{"id":302386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0937/images/coverthbn.jpg"},{"id":327358,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/0937/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":302383,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0937/index.html"},{"id":302384,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0937/pdf/ds937.pdf","text":"Report","size":"3.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 937"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.49142456054688,\n 39.39375459224348\n ],\n [\n -74.410400390625,\n 39.364032338047984\n ],\n [\n -74.31015014648438,\n 39.47754556963625\n ],\n [\n -74.17007446289061,\n 39.65857056750545\n ],\n [\n -74.09591674804688,\n 39.7779358040303\n ],\n [\n -74.05471801757812,\n 40.0360265298117\n ],\n [\n -74.04510498046875,\n 40.07491896000657\n ],\n [\n -74.07394409179688,\n 40.06756263511062\n ],\n [\n -74.12338256835936,\n 40.065460682065535\n ],\n [\n -74.12750244140625,\n 40.0507451947963\n ],\n [\n -74.07669067382811,\n 40.042334918180536\n ],\n [\n -74.0863037109375,\n 40.02130468739707\n ],\n [\n -74.11651611328125,\n 40.03182061333687\n ],\n [\n -74.1357421875,\n 40.03392360399664\n ],\n [\n -74.11788940429688,\n 40.01078714046552\n ],\n [\n -74.12887573242188,\n 40.00447583427404\n ],\n [\n -74.15084838867188,\n 40.00447583427404\n ],\n [\n -74.12887573242188,\n 39.98027708862265\n ],\n [\n -74.11376953125,\n 39.9476478239225\n ],\n [\n -74.15084838867188,\n 39.950806175257355\n ],\n [\n -74.20852661132812,\n 39.95396438076642\n ],\n [\n -74.1851806640625,\n 39.93395994977672\n ],\n [\n -74.11102294921875,\n 39.925535281697286\n ],\n [\n -74.1302490234375,\n 39.90025505675715\n ],\n [\n -74.14672851562499,\n 39.871803651624425\n ],\n [\n -74.16732788085938,\n 39.82435840847207\n ],\n [\n -74.190673828125,\n 39.78321267821705\n ],\n [\n -74.20852661132812,\n 39.75576851405812\n ],\n [\n -74.17282104492188,\n 39.734650174341866\n ],\n [\n -74.17007446289061,\n 39.70824314819603\n ],\n [\n -74.2071533203125,\n 39.66808510414671\n ],\n [\n -74.234619140625,\n 39.64165260123416\n ],\n [\n -74.267578125,\n 39.61203619933693\n ],\n [\n -74.29229736328125,\n 39.6109782362526\n ],\n [\n -74.33761596679688,\n 39.57288079854952\n ],\n [\n -74.30740356445312,\n 39.54005788576377\n ],\n [\n -74.30328369140625,\n 39.51145753426751\n ],\n [\n -74.33486938476562,\n 39.51357648276841\n ],\n [\n -74.33074951171875,\n 39.536880650643056\n ],\n [\n -74.35821533203125,\n 39.547470868779406\n ],\n [\n -74.39804077148438,\n 39.54111693181784\n ],\n [\n -74.41452026367188,\n 39.562294459158174\n ],\n [\n -74.41864013671875,\n 39.54958871848275\n ],\n [\n -74.39666748046874,\n 39.487084981687495\n ],\n [\n -74.38568115234375,\n 39.47648555419741\n ],\n [\n -74.3939208984375,\n 39.4637641090409\n ],\n [\n -74.40765380859375,\n 39.42452501272267\n ],\n [\n -74.43099975585938,\n 39.459523110465156\n ],\n [\n -74.4268798828125,\n 39.47754556963625\n ],\n [\n -74.45297241210938,\n 39.46164364205549\n ],\n [\n -74.49142456054688,\n 39.39375459224348\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1: Originally posted June 26, 2015; Version 1.1: September 2016","contact":"

Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
http://woodshole.er.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2015-06-26","revisedDate":"2016-09-08","noUsgsAuthors":false,"publicationDate":"2015-06-26","publicationStatus":"PW","scienceBaseUri":"558e69aee4b0b6d21dd65900","contributors":{"authors":[{"text":"Andrews, Brian D. bandrews@usgs.gov","contributorId":138513,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian D.","email":"bandrews@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":547212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danforth, William W. 0000-0002-6382-9487 bdanforth@usgs.gov","orcid":"https://orcid.org/0000-0002-6382-9487","contributorId":3292,"corporation":false,"usgs":true,"family":"Danforth","given":"William","email":"bdanforth@usgs.gov","middleInitial":"W.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Barry J. birwin@usgs.gov","contributorId":3889,"corporation":false,"usgs":true,"family":"Irwin","given":"Barry","email":"birwin@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Worley, Charles R. cworley@usgs.gov","contributorId":3063,"corporation":false,"usgs":true,"family":"Worley","given":"Charles","email":"cworley@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547217,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bergeron, Emile M. ebergeron@usgs.gov","contributorId":3449,"corporation":false,"usgs":true,"family":"Bergeron","given":"Emile M.","email":"ebergeron@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":547218,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blackwood, Dann S. dblackwood@usgs.gov","contributorId":2457,"corporation":false,"usgs":true,"family":"Blackwood","given":"Dann","email":"dblackwood@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":547219,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70160802,"text":"70160802 - 2016 - Morphological identification and COI barcodes of adult flies help determine species identities of chironomid larvae (Diptera, Chironomidae)","interactions":[],"lastModifiedDate":"2016-06-17T11:26:12","indexId":"70160802","displayToPublicDate":"2015-06-15T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5028,"text":"Bulletin of Entomological Research","active":true,"publicationSubtype":{"id":10}},"title":"Morphological identification and COI barcodes of adult flies help determine species identities of chironomid larvae (Diptera, Chironomidae)","docAbstract":"

Establishing reliable methods for the identification of benthic chironomid communities is important due to their significant contribution to biomass, ecology and the aquatic food web. Immature larval specimens are more difficult to identify to species level by traditional morphological methods than their fully developed adult counterparts, and few keys are available to identify the larval species. In order to develop molecular criteria to identify species of chironomid larvae, larval and adult chironomids from Western Lake Erie were subjected to both molecular and morphological taxonomic analysis. Mitochondrial cytochrome c oxidase I (COI) barcode sequences of 33 adults that were identified to species level by morphological methods were grouped with COI sequences of 189 larvae in a neighbor-joining taxon-ID tree. Most of these larvae could be identified only to genus level by morphological taxonomy (only 22 of the 189 sequenced larvae could be identified to species level). The taxon-ID tree of larval sequences had 45 operational taxonomic units (OTUs, defined as clusters with >97% identity or individual sequences differing from nearest neighbors by >3%; supported by analysis of all larval pairwise differences), of which seven could be identified to species or ‘species group’ level by larval morphology. Reference sequences from the GenBank and BOLD databases assigned six larval OTUs with presumptive species level identifications and confirmed one previously assigned species level identification. Sequences from morphologically identified adults in the present study grouped with and further classified the identity of 13 larval OTUs. The use of morphological identification and subsequent DNA barcoding of adult chironomids proved to be beneficial in revealing possible species level identifications of larval specimens. Sequence data from this study also contribute to currently inadequate public databases relevant to the Great Lakes region, while the neighbor-joining analysis reported here describes the application and confirmation of a useful tool that can accelerate identification and bioassesment of chironomid communities.

","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0007485315000486","usgsCitation":"Failla, A.J., Vasquez, A.A., Hudson, P.L., Fujimoto, M., and Ram, J.L., 2016, Morphological identification and COI barcodes of adult flies help determine species identities of chironomid larvae (Diptera, Chironomidae): Bulletin of Entomological Research, v. 106, p. 34-46, https://doi.org/10.1017/S0007485315000486.","productDescription":"13 p.","startPage":"34","endPage":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061158","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":323878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-06-15","publicationStatus":"PW","scienceBaseUri":"57651f39e4b07657d19c7901","contributors":{"authors":[{"text":"Failla, Andrew Joseph","contributorId":151001,"corporation":false,"usgs":false,"family":"Failla","given":"Andrew","email":"","middleInitial":"Joseph","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":583953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vasquez, Adrian Amelio","contributorId":151002,"corporation":false,"usgs":false,"family":"Vasquez","given":"Adrian","email":"","middleInitial":"Amelio","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":583954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hudson, Patrick L. 0000-0002-7646-443X phudson@usgs.gov","orcid":"https://orcid.org/0000-0002-7646-443X","contributorId":5616,"corporation":false,"usgs":true,"family":"Hudson","given":"Patrick","email":"phudson@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":583952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fujimoto, Masanori","contributorId":151003,"corporation":false,"usgs":false,"family":"Fujimoto","given":"Masanori","email":"","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":583955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ram, Jeffrey L.","contributorId":33659,"corporation":false,"usgs":true,"family":"Ram","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":583956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156009,"text":"70156009 - 2016 - Modeling habitat connectivity to inform reintroductions: a case study with the Chiricahua Leopard Frog","interactions":[],"lastModifiedDate":"2016-03-10T10:56:49","indexId":"70156009","displayToPublicDate":"2015-06-01T01:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling habitat connectivity to inform reintroductions: a case study with the Chiricahua Leopard Frog","docAbstract":"

Managing species with intensive tools such as reintroduction may focus on single sites or entire landscapes. For vagile species, long-term persistence will require colonization and establishment in neighboring habitats. Therefore, both suitable colonization sites and suitable dispersal corridors between sites are required. Assessment of landscapes for both requirements can contribute to ranking and selection of reintroduction areas, thereby improving management success. Following eradication of invasive American Bullfrogs (Lithobates catesbeianus) from most of Buenos Aires National Wildlife Refuge (BANWR; Arizona, United States), larval Chiricahua Leopard Frogs (Lithobates chiricahuensis) from a private pond were reintroduced into three stock ponds. Populations became established at all three reintroduction sites followed by colonization of neighboring ponds in subsequent years. Our aim was to better understand colonization patterns by the federally threatened L. chiricahuensis which could help inform other reintroduction efforts. We assessed the influence of four landscape features on colonization. Using surveys from 2007 and information about the landscape, we developed a habitat connectivity model, based on electrical circuit theory, that identified potential dispersal corridors after explicitly accounting for imperfect detection of frogs. Landscape features provided little insight into why some sites were colonized and others were not, results that are likely because of the uniformity of the BANWR landscape. While corridor modeling may be effective in more-complex landscapes, our results suggest focusing on local habitat will be more useful at BANWR. We also illustrate that existing data, even when limited in spatial or temporal resolution, can provide information useful in formulating management actions.

","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/14-172","usgsCitation":"Jarchow, C.J., Hossack, B.R., Sigafus, B.H., Schwalbe, C.R., and Muths, E.L., 2016, Modeling habitat connectivity to inform reintroductions: a case study with the Chiricahua Leopard Frog: Journal of Herpetology, v. 50, no. 1, p. 63-69, https://doi.org/10.1670/14-172.","productDescription":"7 p.","startPage":"63","endPage":"69","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060747","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":306675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdbfb8e4b08400b1fe1416","contributors":{"authors":[{"text":"Jarchow, Christopher J. 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":5813,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":567644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":567645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":567646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwalbe, Cecil R. cschwalbe@usgs.gov","contributorId":3077,"corporation":false,"usgs":true,"family":"Schwalbe","given":"Cecil","email":"cschwalbe@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":567647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":567648,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70144279,"text":"ds931 - 2016 - Hurricane Sandy beach response and recovery at Fire Island, New York: Shoreline and beach profile data, October 2012 to October 2014","interactions":[],"lastModifiedDate":"2016-09-27T11:28:35","indexId":"ds931","displayToPublicDate":"2015-05-01T09:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"931","title":"Hurricane Sandy beach response and recovery at Fire Island, New York: Shoreline and beach profile data, October 2012 to October 2014","docAbstract":"

In response to the forecasted impact of Hurricane Sandy, which made landfall on October 29, 2012, the U.S. Geological Survey (USGS) began a substantial data-collection effort to assess the morphological impacts to the beach and dune system at Fire Island, New York. Global positioning system (GPS) field surveys of the beach and dunes were conducted just prior to and after landfall and these data were used to quantify change in several focus areas. In order to quantify morphologic change along the entire length of the island, pre-storm (May 2012) and post-storm (November 2012) lidar and aerial photography were used to assess changes to the shoreline and beach.

As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Sandy recovery. The alongshore state of the beach is recorded using a differential global positioning system (DGPS) to collect data around the mean high water (MHW; 0.46 meter North American Vertical Datum of 1988) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 10 profiles.

Overall, Hurricane Sandy substantially altered the morphology of Fire Island. However, the coastal system rapidly began to recover after the 2012­–13 winter storm season and continues to recover in the form of volume gains and shoreline adjustment.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds931","usgsCitation":"Henderson (Hehre), R.E., Hapke, C.J., Brenner, O.T., and Reynolds, B.J., 2016, Hurricane Sandy shoreline response and recovery at Fire Island, New York—Shoreline and beach profile data, October 2012 to October 2014 (ver. 1.1, September 2016): U.S. Geological Survey Data Series 931, https://dx.doi.org/10.3133/ds931.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2012-10-01","temporalEnd":"2014-10-31","ipdsId":"IP-060541","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":328391,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/0931/versionHist.txt","text":"Version 1.1","size":"1.85 MB","linkFileType":{"id":2,"text":"txt"},"description":"DS 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Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
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","tableOfContents":"","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"publishedDate":"2015-04-30","revisedDate":"2016-09-27","noUsgsAuthors":false,"publicationDate":"2015-04-30","publicationStatus":"PW","scienceBaseUri":"554495aae4b0a658d7947889","contributors":{"authors":[{"text":"Hehre Henderson, Rachel E.","contributorId":140513,"corporation":false,"usgs":false,"family":"Hehre Henderson","given":"Rachel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":545943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hapke, Cheryl J. 0000-0002-2753-4075 chapke@usgs.gov","orcid":"https://orcid.org/0000-0002-2753-4075","contributorId":2981,"corporation":false,"usgs":true,"family":"Hapke","given":"Cheryl","email":"chapke@usgs.gov","middleInitial":"J.","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":true,"id":545938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brenner, Owen T. 0000-0002-1588-721X obrenner@usgs.gov","orcid":"https://orcid.org/0000-0002-1588-721X","contributorId":4933,"corporation":false,"usgs":true,"family":"Brenner","given":"Owen","email":"obrenner@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":545940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reynolds, Billy J. 0000-0002-3232-8022 breynolds@usgs.gov","orcid":"https://orcid.org/0000-0002-3232-8022","contributorId":4272,"corporation":false,"usgs":true,"family":"Reynolds","given":"Billy","email":"breynolds@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":545941,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70146667,"text":"70146667 - 2016 - Variations in water temperature and implications for trout populations in the Upper Schoharie Creek and West Kill, New York, USA","interactions":[],"lastModifiedDate":"2016-01-18T08:54:13","indexId":"70146667","displayToPublicDate":"2015-04-18T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2299,"text":"Journal of Freshwater Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Variations in water temperature and implications for trout populations in the Upper Schoharie Creek and West Kill, New York, USA","docAbstract":"

Water temperature is a key component of aquatic ecosystems because it plays a pivotal role in determining the suitability of stream and river habitat to most freshwater fish species. Continuous temperature loggers and airborne thermal infrared (TIR) remote sensing were used to assess temporal and spatial temperature patterns on the Upper Schoharie Creek and West Kill in the Catskill Mountains, New York, USA. Specific objectives were to characterize (1) contemporary thermal conditions, (2) temporal and spatial variations in stressful water temperatures, and (3) the availability of thermal refuges. In-stream loggers collected data from October 2010 to October 2012 and showed summer water temperatures exceeded the 1-day and 7-day thermal tolerance limits for trout survival at five of the seven study sites during both summers. Results of the 7 August 2012 TIR indicated there was little thermal refuge at the time of the flight. About 690,170 m2 of water surface area were mapped on the Upper Schoharie, yet only 0.009% (59 m2) was more than 1.0 °C below the median water surface temperature (BMT) at the thalweg and no areas were more than 2.0 °C BMT. On the West Kill, 79,098 m2 were mapped and 0.085% (67 m2) and 0.018% (14 m2) were BMT by 1 and 2 °C, respectively. These results indicate that summer temperatures in the majority of the study area are stressful for trout and may adversely affect growth and survival. Validation studies are needed to confirm the expectation that resident trout are in poor condition or absent from the downstream portion of the study area during warm-water periods.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2015.1033769","usgsCitation":"George, S.D., Baldigo, B.P., Smith, M.J., Mckeown, D.M., and Faulringer, J., 2016, Variations in water temperature and implications for trout populations in the Upper Schoharie Creek and West Kill, New York, USA: Journal of Freshwater Ecology, v. 31, no. 1, p. 93-108, https://doi.org/10.1080/02705060.2015.1033769.","productDescription":"16 p.","startPage":"93","endPage":"108","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052348","costCenters":[],"links":[{"id":299796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Upper Schoharie Creek and West Kill","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.90753173828125,\n 41.94110578381595\n ],\n [\n -74.90753173828125,\n 42.771211138625894\n ],\n [\n -73.96270751953125,\n 42.771211138625894\n ],\n [\n -73.96270751953125,\n 41.94110578381595\n ],\n [\n -74.90753173828125,\n 41.94110578381595\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"1","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-16","publicationStatus":"PW","scienceBaseUri":"5536234ce4b0b22a15807ac7","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Martyn J. 0000-0002-1107-9653 marsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-1107-9653","contributorId":4474,"corporation":false,"usgs":true,"family":"Smith","given":"Martyn","email":"marsmith@usgs.gov","middleInitial":"J.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mckeown, Donald M","contributorId":140343,"corporation":false,"usgs":false,"family":"Mckeown","given":"Donald","email":"","middleInitial":"M","affiliations":[{"id":13462,"text":"Distinguished Researcher, Rochester Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":545341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Faulringer, Jason","contributorId":140344,"corporation":false,"usgs":false,"family":"Faulringer","given":"Jason","email":"","affiliations":[{"id":13463,"text":"Systems Integration Engineer, Rochester Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":545342,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70145835,"text":"70145835 - 2016 - Habitat suitability criteria via parametric distributions: estimation, model selection and uncertainty","interactions":[],"lastModifiedDate":"2016-06-15T15:58:27","indexId":"70145835","displayToPublicDate":"2015-04-09T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Habitat suitability criteria via parametric distributions: estimation, model selection and uncertainty","docAbstract":"

Previous methods for constructing univariate habitat suitability criteria (HSC) curves have ranged from professional judgement to kernel-smoothed density functions or combinations thereof. We present a new method of generating HSC curves that applies probability density functions as the mathematical representation of the curves. Compared with previous approaches, benefits of our method include (1) estimation of probability density function parameters directly from raw data, (2) quantitative methods for selecting among several candidate probability density functions, and (3) concise methods for expressing estimation uncertainty in the HSC curves. We demonstrate our method with a thorough example using data collected on the depth of water used by juvenile Chinook salmon (Oncorhynchus tschawytscha) in the Klamath River of northern California and southern Oregon. All R code needed to implement our example is provided in the appendix. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/rra.2900","usgsCitation":"Som, N.A., Goodman, D.H., Perry, R.W., and Hardy, T., 2016, Habitat suitability criteria via parametric distributions: estimation, model selection and uncertainty: River Research and Applications, v. 32, no. 5, p. 1128-1137, https://doi.org/10.1002/rra.2900.","productDescription":"10 p.","startPage":"1128","endPage":"1137","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062720","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":299536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.57421875,\n 41.77336007442078\n ],\n [\n -123.57421875,\n 42.259016415705766\n ],\n [\n -121.72302246093749,\n 42.259016415705766\n ],\n [\n -121.72302246093749,\n 41.77336007442078\n ],\n [\n -123.57421875,\n 41.77336007442078\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-27","publicationStatus":"PW","scienceBaseUri":"5527949ce4b026915857c83a","contributors":{"authors":[{"text":"Som, Nicholas A.","contributorId":36039,"corporation":false,"usgs":true,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":544452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goodman, Damon H.","contributorId":140150,"corporation":false,"usgs":false,"family":"Goodman","given":"Damon","email":"","middleInitial":"H.","affiliations":[{"id":13396,"text":"U.S. Fish and Wildlife Service, Arcata FWO, Arcata, CA 95521","active":true,"usgs":false}],"preferred":false,"id":544453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":544451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hardy, Thomas B.","contributorId":62936,"corporation":false,"usgs":true,"family":"Hardy","given":"Thomas B.","affiliations":[],"preferred":false,"id":544454,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173417,"text":"70173417 - 2016 - Synergistic and singular effects of river discharge and lunar illumination on dam passage of upstream migrant yellow-phase American eels","interactions":[],"lastModifiedDate":"2016-06-20T17:35:16","indexId":"70173417","displayToPublicDate":"2015-03-19T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1936,"text":"ICES Journal of Marine Science","active":true,"publicationSubtype":{"id":10}},"title":"Synergistic and singular effects of river discharge and lunar illumination on dam passage of upstream migrant yellow-phase American eels","docAbstract":"

Monitoring of dam passage can be useful for management and conservation assessments of American eel, particularly if passage counts can be examined over multiple years. During a 7-year study (2007–2013) of upstream migration of American eels within the lower Shenandoah River (Potomac River drainage), we counted and measured American eels at the Millville Dam eel pass, where annual study periods were determined by the timing of the eel pass installation during spring or summer and removal during fall. Daily American eel counts were analysed with negative binomial regression models, with and without a year (YR) effect, and with the following time-varying environmental covariates: river discharge of the Shenandoah River at Millville (RDM) and of the Potomac River at Point of Rocks, lunar illumination (LI), water temperature, and cloud cover. A total of 17 161 yellow-phase American eels used the pass during the seven annual periods, and length measurements were obtained from 9213 individuals (mean = 294 mm TL, s.e. = 0.49, range 183–594 mm). Data on passage counts of American eels supported an additive-effects model (YR + LI + RDM) where parameter estimates were positive for river discharge (β = 7.3, s.e. = 0.01) and negative for LI (β = −1.9, s.e. = 0.34). Interestingly, RDM and LI acted synergistically and singularly as correlates of upstream migration of American eels, but the highest daily counts and multiple-day passage events were associated with increased RDM. Annual installation of the eel pass during late spring or summer prevented an early spring assessment, a period with higher RDM relative to those values obtained during sampling periods. Because increases in river discharge are climatically controlled events, upstream migration events of American eels within the Potomac River drainage are likely linked to the influence of climate variability on flow regime.

","language":"English","publisher":"Academic Press","doi":"10.1093/icesjms/fsv052","usgsCitation":"Welsh, S., Aldinger, J.L., Braham, M., and Zimmerman, J.L., 2016, Synergistic and singular effects of river discharge and lunar illumination on dam passage of upstream migrant yellow-phase American eels: ICES Journal of Marine Science, v. 73, no. 1, p. 33-42, https://doi.org/10.1093/icesjms/fsv052.","productDescription":"10 p.","startPage":"33","endPage":"42","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057957","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":471473,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsv052","text":"Publisher Index Page"},{"id":324051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Potomac River drainage, Shenandoah River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.1787109375,\n 38.98076276501633\n ],\n [\n -78.1787109375,\n 39.51675478434244\n ],\n [\n -77.4261474609375,\n 39.51675478434244\n ],\n [\n -77.4261474609375,\n 38.98076276501633\n ],\n [\n -78.1787109375,\n 38.98076276501633\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"73","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-02","publicationStatus":"PW","scienceBaseUri":"576913ebe4b07657d19ff288","contributors":{"authors":[{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":637101,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldinger, Joni L.","contributorId":171886,"corporation":false,"usgs":false,"family":"Aldinger","given":"Joni","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":639935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Braham, Melissa A.","contributorId":140127,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":639936,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmerman, Jennifer L.","contributorId":171351,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":26870,"text":"West Virginia University, Mortgantown, WV","active":true,"usgs":false}],"preferred":false,"id":639937,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70135781,"text":"ofr20141153 - 2016 - Collaborative Studies for Mercury Characterization in Coal and Coal Combustion Products, Republic of South Africa","interactions":[],"lastModifiedDate":"2016-06-01T07:42:43","indexId":"ofr20141153","displayToPublicDate":"2014-12-17T08:15:00","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":"2014-1153","title":"Collaborative Studies for Mercury Characterization in Coal and Coal Combustion Products, Republic of South Africa","docAbstract":"

Mercury (Hg) analyses were obtained for 42 samples of feed coal provided by Eskom, the national electric utility of South Africa, representing all 13 coal-fired power stations operated by Eskom in South Africa. This sampling includes results for three older power stations returned to service starting in the late 2000s. These stations were not sampled in the most recent previous study. Mercury concentrations determined in the present study are similar to or slightly lower than those previously reported, and input Hg for the three stations returned to service is comparable to that for the other 10 power stations. Determination of halogen contents of the 42 feed coals confirms that chlorine contents are generally low, and as such, the extent of Hg self-capture by particulate control devices (PCDs) is rather limited. Eight density separates of a South African Highveld (#4) coal were also provided by Eskom, and these show a strong mineralogical association of Hg (and arsenic) with pyrite. The density separates were used to predict Hg and ash contents of coal products used in South Africa or exported. A suite of 48 paired samples of pulverization-mill feed coal and fly ash collected in a previous (2010) United Nations Environment Programme-sponsored study of emissions from the Duvha and Kendal power stations was obtained for further investigation in the present study. These samples show that in each station, Hg capture varies by boiler unit and confirms that units equipped with fabric filters for air pollution control are much more effective in capturing Hg than those equipped with electrostatic precipitators. Apart from tracking the performance of PCDs individually, changes resulting in improved mercury capture of the Eskom fleet are discussed. These include Hg reduction through coal selection and washing, as well as through optimization of equipment and operational parameters. Operational changes leading to increased mercury capture include increasing mercury adsorption on unburned carbon and minimizing the concentration of sulfuric acid vapor in the flue gas. Equipment options for improving Hg capture include addition of fabric filters, use of halogenated sorbents, and addition of flue gas desulfurization (FGD) scrubbers, listed in order of increasing cost. The capital cost of adding FGD scrubbers to existing plants is probably too high to be justified on the grounds of Hg removal alone. However, if future regulations require reductions in sulfur dioxide emissions, and FGDs are installed to meet these standards, further reduction in Hg emissions will be a co-benefit of this installation.

In this revised version, corrected results for the suite of 42 samples of feed coal and 8 density separates determined by inductively coupled plasma-mass spectrometry (ICP-MS) replace results originally reported in the 2014 version of this report. In many cases, especially for the transition metals, values reported here are lower than those originally reported, and in some cases, the corrected results are less than 50 percent of their original values. Note that results for mercury (Hg) and halogens contained in the original report are unaffected by revisions to ICP-MS data included here. This revised version also includes the following updates: (1) data for selenium, which were not available for inclusion in the original publication, are now provided; (2) results for ICP-MS trace element data are expressed here on a whole-coal dry basis to facilitate comparison with published results for coals elsewhere; and (3) the text has been updated to take into account the U.S. Supreme Court decision of June 29, 2015, which puts on hold implementation of U.S. Environmental Protection Agency Mercury and Air Toxics Standards in the United States.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141153","usgsCitation":"Kolker, Allan, Senior, C.L., and van Alphen, Chris, 2014, Collaborative studies for mercury characterization in coal and coal combustion products, Republic of South Africa (ver. 2.0, May 2016): U.S. Geological Survey Open-File Report 2014–1153, 47 p., https://dx.doi.org/10.3133/ofr20141153.","productDescription":"ix, 47 p.","numberOfPages":"55","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055869","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":296735,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1153/pdf/ofr20141153.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2014-1153"},{"id":296736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2014/1153/images/coverthb1.jpg"},{"id":296734,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1153/index.html","description":"OFR 2014-1153"},{"id":321863,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2014/1153/versionHist.txt","size":"1.60 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2014-1153"}],"country":"South Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 16.32568359375,\n -34.92197103616377\n ],\n [\n 16.32568359375,\n -22.12635475991967\n ],\n [\n 33.02490234375,\n -22.12635475991967\n ],\n [\n 33.02490234375,\n -34.92197103616377\n ],\n [\n 16.32568359375,\n -34.92197103616377\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1: Originally posted December 15, 2014; Version 2.0 May 31, 2016","contact":"

Director, Eastern Energy Resources Science Center
U.S. Geological Survey
956 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/

","tableOfContents":"","publishedDate":"2014-12-15","revisedDate":"2016-05-31","noUsgsAuthors":false,"publicationDate":"2014-12-15","publicationStatus":"PW","scienceBaseUri":"5492a92fe4b00eda8915acf5","contributors":{"authors":[{"text":"Kolker, Allan 0000-0002-5768-4533 akolker@usgs.gov","orcid":"https://orcid.org/0000-0002-5768-4533","contributorId":643,"corporation":false,"usgs":true,"family":"Kolker","given":"Allan","email":"akolker@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":536853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senior, Constance L.","contributorId":131002,"corporation":false,"usgs":false,"family":"Senior","given":"Constance","email":"","middleInitial":"L.","affiliations":[{"id":7205,"text":"ADA-ES, Inc. Littleton, CO","active":true,"usgs":false}],"preferred":false,"id":536854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Alphen, Chris","contributorId":131003,"corporation":false,"usgs":false,"family":"van Alphen","given":"Chris","email":"","affiliations":[{"id":7206,"text":"Eskom Holdings, Ltd. South Africa","active":true,"usgs":false}],"preferred":false,"id":536855,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}