Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters (Enhydra lutris) in Glacier Bay, in southeastern Alaska.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1643","usgsCitation":"Williams, P.J., Hooten, M., Womble, J.N., Esslinger, G.G., Bower, M., and Hefley, T.J., 2017, An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics: Ecology, v. 98, no. 2, p. 328-336, https://doi.org/10.1002/ecy.1643.","productDescription":"9 p.","startPage":"328","endPage":"336","ipdsId":"IP-076210","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":461769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.1643","text":"Publisher Index Page"},{"id":348565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-04","publicationStatus":"PW","scienceBaseUri":"5a06c8d2e4b09af898c8614e","contributors":{"authors":[{"text":"Williams, Perry J.","contributorId":169058,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":25400,"text":"U.S. Fish and Wildlife Service, Big Oaks National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":721553,"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":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":716573,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Womble, Jamie N.","contributorId":198631,"corporation":false,"usgs":false,"family":"Womble","given":"Jamie","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":721554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esslinger, George G. 0000-0002-3459-0083 gesslinger@usgs.gov","orcid":"https://orcid.org/0000-0002-3459-0083","contributorId":131009,"corporation":false,"usgs":true,"family":"Esslinger","given":"George","email":"gesslinger@usgs.gov","middleInitial":"G.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":721555,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bower, Michael R.","contributorId":44787,"corporation":false,"usgs":true,"family":"Bower","given":"Michael R.","affiliations":[],"preferred":false,"id":721556,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":721557,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192403,"text":"70192403 - 2017 - Population genetics and demography unite ecology and evolution","interactions":[],"lastModifiedDate":"2018-03-26T14:21:29","indexId":"70192403","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Population genetics and demography unite ecology and evolution","docAbstract":"The interplay of ecology and evolution has been a rich area of research for decades. A surge of interest in this area was catalyzed by the observation that evolution by natural selection can operate at the same contemporary timescales as ecological dynamics. Specifically, recent eco-evolutionary research focuses on how rapid adaptation influences ecology, and vice versa. Evolution by non-adaptive forces also occurs quickly, with ecological consequences, but understanding the full scope of ecology–evolution (eco–evo) interactions requires explicitly addressing population-level processes – genetic and demographic. We show the strong ecological effects of non-adaptive evolutionary forces and, more broadly, the value of population-level research for gaining a mechanistic understanding of eco–evo interactions. The breadth of eco-evolutionary research should expand to incorporate the breadth of evolution itself.
Conservation biology often requires the control of invasive species. One method is the development and use of biocides. Identifying new chemicals as part of the biocide registration approval process can require screening millions of compounds. Traditionally, screening new chemicals has been done in vivo using test organisms. Using in vitro (e.g., cell lines) and in silico (e.g., computer models) methods decrease test organism requirements and increase screening speed and efficiency. These methods, however, would be greatly improved by better understanding how individual fish species metabolize selected compounds.
We combined cell assays and metabolomics to create a powerful tool to facilitate the identification of new control chemicals. Specifically, we exposed cell lines established from bighead carp and silver carp larvae to thiram (7 concentrations) then completed metabolite profiling to assess the dose-response of the bighead carp and silver carp metabolome to thiram. Forty one of the 700 metabolomic markers identified in bighead carp exhibited a dose-response to thiram exposure compared to silver carp in which 205 of 1590 metabolomic markers exhibited a dose-response. Additionally, we identified 11 statistically significant metabolomic markers based upon volcano plot analysis common between both species. This smaller subset of metabolites formed a thiram-specific metabolomic fingerprint which allowed for the creation of a toxicant specific, rather than a species-specific, metabolomic fingerprint. Metabolomic fingerprints may be used in biocide development and improve our understanding of ecologically significant events, such as mass fish kills.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemosphere.2016.11.046","usgsCitation":"Putnam, J.G., Nelson, J., Leis, E.M., Erickson, R.A., Hubert, T.D., and Amberg, J.J., 2017, Using silver and bighead carp cell lines for the identification of a unique metabolite fingerprint from thiram-specific chemical exposure: Chemosphere, v. 168, p. 1477-1485, https://doi.org/10.1016/j.chemosphere.2016.11.046.","productDescription":"9 p.","startPage":"1477","endPage":"1485","ipdsId":"IP-078135","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":338818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"168","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58de194ee4b02ff32c699c9b","contributors":{"authors":[{"text":"Putnam, Joel G. 0000-0002-5464-4587 jgputnam@usgs.gov","orcid":"https://orcid.org/0000-0002-5464-4587","contributorId":5783,"corporation":false,"usgs":true,"family":"Putnam","given":"Joel","email":"jgputnam@usgs.gov","middleInitial":"G.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":687388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Justine 0000-0003-2530-5815 jenelson@usgs.gov","orcid":"https://orcid.org/0000-0003-2530-5815","contributorId":168767,"corporation":false,"usgs":true,"family":"Nelson","given":"Justine","email":"jenelson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":687389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leis, Eric M.","contributorId":187767,"corporation":false,"usgs":false,"family":"Leis","given":"Eric","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":687386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":687387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hubert, Terrance D. 0000-0001-9712-1738 thubert@usgs.gov","orcid":"https://orcid.org/0000-0001-9712-1738","contributorId":3036,"corporation":false,"usgs":true,"family":"Hubert","given":"Terrance","email":"thubert@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":687384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Amberg, Jon J. jamberg@usgs.gov","contributorId":147776,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":687385,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177786,"text":"70177786 - 2017 - Suppression of invasive lake trout in an isolated backcountry lake in Glacier National Park","interactions":[],"lastModifiedDate":"2018-03-26T14:16:36","indexId":"70177786","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Suppression of invasive lake trout in an isolated backcountry lake in Glacier National Park","docAbstract":"Fisheries managers have implemented suppression programmes to control non-native lake trout, Salvelinus namaycush (Walbaum), in several lakes throughout the western United States. This study determined the feasibility of experimentally suppressing lake trout using gillnets in an isolated backcountry lake in Glacier National Park, Montana, USA, for the conservation of threatened bull trout, Salvelinus confluentus (Suckley). The demographics of the lake trout population during suppression (2009–2013) were described, and those data were used to assess the effects of suppression scenarios on population growth rate (λ) using an age-structured population model. Model simulations indicated that the population was growing exponentially (λ = 1.23, 95% CI: 1.16–1.28) prior to suppression. However, suppression resulted in declining λ(0.61–0.79) for lake trout, which was concomitant with stable bull trout adult abundances. Continued suppression at or above observed exploitation levels is needed to ensure continued population declines.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12200","usgsCitation":"Fredenberg, C.R., Muhlfeld, C.C., Guy, C.S., D'Angelo, V., Downs, C.C., and Syslo, J.M., 2017, Suppression of invasive lake trout in an isolated backcountry lake in Glacier National Park: Fisheries Management and Ecology, v. 24, no. 1, p. 33-48, https://doi.org/10.1111/fme.12200.","productDescription":"16 p.","startPage":"33","endPage":"48","ipdsId":"IP-067234","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":330287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"5810c52ce4b0f497e7972c24","chorus":{"doi":"10.1111/fme.12200","url":"http://dx.doi.org/10.1111/fme.12200","publisher":"Wiley-Blackwell","authors":"Fredenberg C. R., Muhlfeld C. C., Guy C. S., D'Angelo V. S., Downs C. C., Syslo J. M.","journalName":"Fisheries Management and Ecology","publicationDate":"1/20/2017","auditedOn":"1/24/2017","publiclyAccessibleDate":"1/20/2017"},"contributors":{"authors":[{"text":"Fredenberg, C. R.","contributorId":187695,"corporation":false,"usgs":true,"family":"Fredenberg","given":"C.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":651809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":651797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":651796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"D'Angelo, Vincent S. vdangelo@usgs.gov","contributorId":4176,"corporation":false,"usgs":true,"family":"D'Angelo","given":"Vincent S.","email":"vdangelo@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":681371,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Downs, Christopher C.","contributorId":105067,"corporation":false,"usgs":true,"family":"Downs","given":"Christopher","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":651808,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Syslo, John M.","contributorId":171452,"corporation":false,"usgs":false,"family":"Syslo","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":681372,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182735,"text":"70182735 - 2017 - The importance of quality control in validating concentrationsof contaminants of emerging concern in source and treateddrinking water samples","interactions":[],"lastModifiedDate":"2017-02-28T11:35:18","indexId":"70182735","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"The importance of quality control in validating concentrationsof contaminants of emerging concern in source and treateddrinking water samples","docAbstract":"A national-scale survey of 247 contaminants of emerging concern (CECs), including organic and inorganic chemical\ncompounds, andmicrobial contaminants, was conducted in source and treated drinkingwater samples from\n25 treatment plants across the United States.Multiplemethodswere used to determine these CECs, including six\nanalytical methods tomeasure 174 pharmaceuticals, personal care products, and pesticides. A three-component\nquality assurance/quality control (QA/QC) programwas designed for the subset of 174 CECswhich allowed us to\nassess and compare performances of themethods used. The three components included: 1) a common field QA/\nQC protocol and sample design, 2) individual investigator-developed method-specific QA/QC protocols, and 3) a\nsuite of 46method comparison analytes thatwere determined in two or more analytical methods. Overallmethod\nperformance for the 174 organic chemical CECs was assessed by comparing spiked recoveries in reagent,\nsource, and treated water over a two-year period. In addition to the 247 CECs reported in the larger drinking\nwater study, another 48 pharmaceutical compoundsmeasured did not consistentlymeet predetermined quality\nstandards. Methodologies that did not seem suitable for these analytes are overviewed. The need to exclude\nanalytes based on method performance demonstrates the importance of additional QA/QC protocols.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.02.127","collaboration":"U.S. Environmental Protection Agency","usgsCitation":"Angela L. Batt, Furlong, E.T., Mash, H.E., Glassmeyer, S.T., and Kolpin, D.W., 2017, The importance of quality control in validating concentrationsof contaminants of emerging concern in source and treateddrinking water samples: Science of the Total Environment, v. 579, p. 1618-1628, https://doi.org/10.1016/j.scitotenv.2016.02.127.","productDescription":"11 p. ","startPage":"1618","endPage":"1628","ipdsId":"IP-061364","costCenters":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"links":[{"id":470083,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6145083","text":"Publisher Index Page"},{"id":336331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336290,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0048969716303369"}],"volume":"579","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b69a3fe4b01ccd54ff3f82","contributors":{"authors":[{"text":"Angela L. Batt","contributorId":184072,"corporation":false,"usgs":false,"family":"Angela L. Batt","affiliations":[],"preferred":false,"id":673494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":673493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mash, Heath E.","contributorId":184073,"corporation":false,"usgs":false,"family":"Mash","given":"Heath","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":673495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glassmeyer, Susan T.","contributorId":184074,"corporation":false,"usgs":false,"family":"Glassmeyer","given":"Susan","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":673496,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673497,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182798,"text":"70182798 - 2017 - Dynamic strains for earthquake source characterization","interactions":[],"lastModifiedDate":"2017-03-01T14:26:59","indexId":"70182798","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic strains for earthquake source characterization","docAbstract":"Strainmeters measure elastodynamic deformation associated with earthquakes over a broad frequency band, with detection characteristics that complement traditional instrumentation, but they are commonly used to study slow transient deformation along active faults and at subduction zones, for example. Here, we analyze dynamic strains at Plate Boundary Observatory (PBO) borehole strainmeters (BSM) associated with 146 local and regional earthquakes from 2004–2014, with magnitudes from M 4.5 to 7.2. We find that peak values in seismic strain can be predicted from a general regression against distance and magnitude, with improvements in accuracy gained by accounting for biases associated with site–station effects and source–path effects, the latter exhibiting the strongest influence on the regression coefficients. To account for the influence of these biases in a general way, we include crustal‐type classifications from the CRUST1.0 global velocity model, which demonstrates that high‐frequency strain data from the PBO BSM network carry information on crustal structure and fault mechanics: earthquakes nucleating offshore on the Blanco fracture zone, for example, generate consistently lower dynamic strains than earthquakes around the Sierra Nevada microplate and in the Salton trough. Finally, we test our dynamic strain prediction equations on the 2011 M 9 Tohoku‐Oki earthquake, specifically continuous strain records derived from triangulation of 137 high‐rate Global Navigation Satellite System Earth Observation Network stations in Japan. Moment magnitudes inferred from these data and the strain model are in agreement when Global Positioning System subnetworks are unaffected by spatial aliasing.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160155","usgsCitation":"Barbour, A., and Crowell, B.W., 2017, Dynamic strains for earthquake source characterization: Seismological Research Letters, v. 88, no. 2A, p. 354-370, https://doi.org/10.1785/0220160155.","productDescription":"17 p. ","startPage":"354","endPage":"370","ipdsId":"IP-076502","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":336775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":336351,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1785/0220160155"}],"volume":"88","issue":"2A","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-01","publicationStatus":"PW","scienceBaseUri":"58b7eba1e4b01ccd5500bad5","contributors":{"authors":[{"text":"Barbour, Andrew J. 0000-0002-6890-2452 abarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":140443,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew J.","email":"abarbour@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":673788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crowell, Brendan W.","contributorId":184207,"corporation":false,"usgs":false,"family":"Crowell","given":"Brendan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":673789,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193019,"text":"70193019 - 2017 - Fish and invertebrate flow-biology relationships to support the determination of ecological flows for North Carolina","interactions":[],"lastModifiedDate":"2017-11-21T13:54:05","indexId":"70193019","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Fish and invertebrate flow-biology relationships to support the determination of ecological flows for North Carolina","docAbstract":"A method was developed to characterize fish and invertebrate responses to flow alteration in the state of North Carolina. This method involved using 80th percentile linear quantile regressions to relate six flow metrics to the diversity of riffle-run fish and benthic Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness. All twelve flow-biology relationships were found to be significant, with both benthos and fish showing negative responses to ecodeficits and reductions in flow. The responses of benthic richness to reduced flows were consistent and generally greater than that of fish diversity. However, the riffle-run fish guild showed the greatest reductions in diversity in response to summer ecodeficits. The directional consistency and differential seasonal sensitivities of fish and invertebrates to reductions in flow highlight the need to consider seasonality when managing flows. In addition, all relationships were linear, and therefore do not provide clear thresholds to support ecological flow determinations and flow prescriptions to prevent the degradation of fish and invertebrate communities in North Carolina rivers and streams. A method of setting ecological flows based on the magnitude of change in biological condition that is acceptable to society is explored.
","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.12497","usgsCitation":"Phelan, J., Cuffney, T.F., Patterson, L.A., Eddy, M., Dykes, R., Pearsall, S., Goudreau, C., Mead, J., and Tarver, F., 2017, Fish and invertebrate flow-biology relationships to support the determination of ecological flows for North Carolina: Journal of the American Water Resources Association, v. 53, no. 1, p. 42-55, https://doi.org/10.1111/1752-1688.12497.","productDescription":"14 p.","startPage":"42","endPage":"55","ipdsId":"IP-077299","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":349217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North 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A.","contributorId":177289,"corporation":false,"usgs":false,"family":"Patterson","given":"Lauren","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":717668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eddy, Michele","contributorId":198941,"corporation":false,"usgs":false,"family":"Eddy","given":"Michele","email":"","affiliations":[],"preferred":false,"id":717669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dykes, Robert","contributorId":198942,"corporation":false,"usgs":false,"family":"Dykes","given":"Robert","email":"","affiliations":[],"preferred":false,"id":717670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearsall, Sam","contributorId":198943,"corporation":false,"usgs":false,"family":"Pearsall","given":"Sam","email":"","affiliations":[],"preferred":false,"id":717671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Goudreau, 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classifier","interactions":[],"lastModifiedDate":"2017-10-30T15:00:45","indexId":"70192944","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3251,"text":"Remote Sensing Letters","active":true,"publicationSubtype":{"id":10}},"title":"Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier","docAbstract":"Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/2150704X.2016.1271469","usgsCitation":"Friesz, A.M., Wylie, B., and Howard, D.M., 2017, Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier: Remote Sensing Letters, v. 8, no. 4, p. 389-398, https://doi.org/10.1080/2150704X.2016.1271469.","productDescription":"10 p.","startPage":"389","endPage":"398","ipdsId":"IP-075490","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-03","publicationStatus":"PW","scienceBaseUri":"59f83a38e4b063d5d30980ef","contributors":{"authors":[{"text":"Friesz, Aaron M. 0000-0003-4096-3824 afriesz@usgs.gov","orcid":"https://orcid.org/0000-0003-4096-3824","contributorId":5943,"corporation":false,"usgs":true,"family":"Friesz","given":"Aaron","email":"afriesz@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":197161,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce K.","email":"wylie@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":717394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Daniel M. 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":197063,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel","email":"danny.howard.ctr@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":717393,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193686,"text":"70193686 - 2017 - Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error","interactions":[],"lastModifiedDate":"2017-11-02T16:32:12","indexId":"70193686","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error","docAbstract":"We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR019141","usgsCitation":"Christensen, N.K., Minsley, B.J., and Christensen, S., 2017, Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error: Water Resources Research, v. 53, no. 2, p. 1019-1038, https://doi.org/10.1002/2016WR019141.","productDescription":"20 p.","startPage":"1019","endPage":"1038","ipdsId":"IP-081403","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488731,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/dcdb9b5e-bf3c-4826-83aa-0fb5cd606845","text":"External Repository"},{"id":348146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea5e4b0531197b27f85","contributors":{"authors":[{"text":"Christensen, Nikolaj K","contributorId":199736,"corporation":false,"usgs":false,"family":"Christensen","given":"Nikolaj","email":"","middleInitial":"K","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":719888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Steen","contributorId":199737,"corporation":false,"usgs":false,"family":"Christensen","given":"Steen","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":719890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195839,"text":"70195839 - 2017 - Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States","interactions":[],"lastModifiedDate":"2018-03-06T11:11:17","indexId":"70195839","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States","docAbstract":"A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere–ocean general circulation models (AOGCMs; CCSM4, HadGEM2-CC, MIROC5, and MRI-CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways (RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model PnET-BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water use efficiency (WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO2effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13444","usgsCitation":"Pourmokhtarian, A., Driscoll, C.T., Campbell, J.L., Hayhoe, K., Stoner, A., Adams, M.B., Burns, D., Fernandez, I., Mitchell, M.J., and Shanley, J.B., 2017, Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States: Global Change Biology, v. 23, no. 2, p. 840-856, https://doi.org/10.1111/gcb.13444.","productDescription":"17 p.","startPage":"840","endPage":"856","ipdsId":"IP-077080","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-25","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4ba","contributors":{"authors":[{"text":"Pourmokhtarian, Afshin","contributorId":202944,"corporation":false,"usgs":false,"family":"Pourmokhtarian","given":"Afshin","email":"","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Charles T.","contributorId":167460,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles","email":"","middleInitial":"T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, John L.","contributorId":178410,"corporation":false,"usgs":false,"family":"Campbell","given":"John","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":730245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayhoe, Katharine","contributorId":149192,"corporation":false,"usgs":false,"family":"Hayhoe","given":"Katharine","email":"","affiliations":[{"id":17667,"text":"Climate Science Center, Texas Tech University, Lubbock, Texas, United States","active":true,"usgs":false}],"preferred":false,"id":730246,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stoner, Anne M. 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Multi-component Reactive Transport Models (RTMs), initially developed more than three decades ago, have been used extensively to explore the interactions of geothermal, hydrologic, geochemical, and geobiological processes in subsurface systems. Driven by extensive data sets now available from intensive measurement efforts, there is a pressing need to couple RTMs with other community models to explore non-linear interactions among the atmosphere, hydrosphere, biosphere, and geosphere. Here we briefly review the history of RTM development, summarize the current state of RTM approaches, and identify new research directions, opportunities, and infrastructure needs to broaden the use of RTMs. In particular, we envision the expanded use of RTMs in advancing process understanding in the Critical Zone, the veneer of the Earth that extends from the top of vegetation to the bottom of groundwater. We argue that, although parsimonious models are essential at larger scales, process-based models offer tools to explore the highly nonlinear coupling that characterizes natural systems. We present seven testable hypotheses that emphasize the unique capabilities of process-based RTMs for (1) elucidating chemical weathering and its physical and biogeochemical drivers; (2) understanding the interactions among roots, micro-organisms, carbon, water, and minerals in the rhizosphere; (3) assessing the effects of heterogeneity across spatial and temporal scales; and (4) integrating the vast quantity of novel data, including “omics” data (genomics, transcriptomics, proteomics, metabolomics), elemental concentration and speciation data, and isotope data into our understanding of complex earth surface systems. With strong support from data-driven sciences, we are now in an exciting era where integration of RTM framework into other community models will facilitate process understanding across disciplines and across scales.
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","language":"English","publisher":"Wildlife Disease Association","usgsCitation":"Richards, B.J., Grear, D.A., Ballmann, A., Dusek, R.J., and Bodenstein, B., 2017, Quarterly wildlife mortality report January 2017: Wildlife Disease Association Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-082240","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":338351,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":338322,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildlifedisease.org/PersonifyEbusiness/Resources/Publications/Newsletter/Archive"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58da2519e4b0543bf7fda7f6","contributors":{"authors":[{"text":"Richards, Bryan J. 0000-0001-9955-2523 brichards@usgs.gov","orcid":"https://orcid.org/0000-0001-9955-2523","contributorId":3533,"corporation":false,"usgs":true,"family":"Richards","given":"Bryan","email":"brichards@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":686140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":686141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":686142,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":174374,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":686143,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":189820,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara","email":"bbodenstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":686144,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188352,"text":"70188352 - 2017 - Oxygen isotope geochemistry of mafic phenocrysts in primitive mafic lavas from the southernmost Cascade Range, California","interactions":[],"lastModifiedDate":"2018-03-16T11:29:21","indexId":"70188352","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":738,"text":"American Mineralogist","active":true,"publicationSubtype":{"id":10}},"title":"Oxygen isotope geochemistry of mafic phenocrysts in primitive mafic lavas from the southernmost Cascade Range, California","docAbstract":"Previously reported whole-rock δ18O values (5.6–7.8‰) for primitive quaternary mafic lavas from the southernmost Cascades (SMC) are often elevated (up to 1‰) relative to δ18O values expected for mafic magmas in equilibrium with mantle peridotite. Olivine, clinopyroxene, and plagioclase crystals were separated from 29 geochemically well-characterized mafic lavas for δ18O measurements by laser fluorination to assess modification of the mantle sources by ancient and modern subducted components. Oxygen isotope values of olivine phenocrysts in calc-alkaline lavas and contemporaneous high alumina olivine tholeiitic (HAOT) lavas generally exceed depleted mantle olivine values (~4.9–5.3‰). Modern addition of up to 6 wt% slab-derived fluid from Gorda serpentinized peridotite dehydration (~15‰) or chlorite dehydration (~10‰) within the serpentinized peridotite can provide the 18O enrichment detected in olivine phenocrysts (δ18Oolivine = 5.3–6.3‰) in calc-alkaline mafic lavas, and elevate 18O in overlying mantle lithosphere, as well. Specifically, although HAOT δ18Oolivine values (5.5–5.7‰) may reflect partial melting in heterogeneous 18O enriched mantle source domains that developed during multiple subduction events associated with terrane accretion (e.g., <1 wt% of ~15‰ materials), an additional 18O enrichment of up to 2 wt% of 10–15‰ slab-derived hydrous fluids might be accommodated. The calc-alkaline primitive magmas appear to have experienced a continuous range of open system processes, which operate in the mantle and during rapid magma ascent to eruption, and occasionally post quench. Textural relationships and geochemistry of these lava samples are consistent with blends of mafic phenocrysts and degassed melts in varying states of 18O disequilibrium. In lenses of accumulated melt within peridotite near the base of the crust, coexisting olivine and clinopyroxene δ18O values probably are not at isotopic equilibrium because fluids introduced into the system perturbed the δ18Omelt values. A “sudden” melt extraction event interrupts 18O equilibration in phenocrysts and poorly mixed melt(s). Rapid ascent of volatile oversaturated primitive mafic magma through the crust appears to be accompanied by devolatilization and crystallization of anorthite-rich plagioclase with elevated δ18Oplag values. The (Sr/P)N values for the whole rock geochemistry are consistent with a 87Sr/86Sr ~0.7027 slab-derived fluid addition into the infertile peridotite source of magmas, and melt devolatilization is recorded in the mixture of disequilibrium δ18O values for the constituent phases of lavas. Morbidity of the Gorda Plate as it undergoes intense deformation from the spreading ridge to the trench is likely a key factor to developing the carrying capacity of hydrous fluids and mineral phases in the slab subducting into the SMC mantle.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2138/am-2017-5588","usgsCitation":"Underwood, S.J., and Clynne, M.A., 2017, Oxygen isotope geochemistry of mafic phenocrysts in primitive mafic lavas from the southernmost Cascade Range, California: American Mineralogist, v. 102, no. 2, p. 251-261, https://doi.org/10.2138/am-2017-5588.","productDescription":"11 p.","startPage":"251","endPage":"261","ipdsId":"IP-075965","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":352601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Cascade Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.4920654296875,\n 39.9434364619742\n ],\n [\n -120.794677734375,\n 39.9434364619742\n ],\n [\n -120.794677734375,\n 40.990264773996884\n ],\n [\n -122.4920654296875,\n 40.990264773996884\n ],\n [\n -122.4920654296875,\n 39.9434364619742\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-01","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4be","contributors":{"authors":[{"text":"Underwood, Sandra J.","contributorId":192684,"corporation":false,"usgs":false,"family":"Underwood","given":"Sandra","email":"","middleInitial":"J.","affiliations":[{"id":13628,"text":"Department of Earth Sciences, P.O. Box 173480, Montana State University, Bozeman, MT, USA. 59717.","active":true,"usgs":false}],"preferred":false,"id":697361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":697360,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187165,"text":"70187165 - 2017 - Tracer-based characterization of hyporheic exchange and benthic biolayers in streams","interactions":[],"lastModifiedDate":"2017-04-25T15:20:57","indexId":"70187165","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Tracer-based characterization of hyporheic exchange and benthic biolayers in streams","docAbstract":"Shallow benthic biolayers at the top of the streambed are believed to be places of enhanced biogeochemical turnover within the hyporheic zone. They can be investigated by reactive stream tracer tests with tracer recordings in the streambed and in the stream channel. Common in-stream measurements of such reactive tracers cannot localize where the processing primarily takes place, whereas isolated vertical depth profiles of solutes within the hyporheic zone are usually not representative of the entire stream. We present results of a tracer test where we injected the conservative tracer bromide together with the reactive tracer resazurin into a third-order stream and combined the recording of in-stream breakthrough curves with multidepth sampling of the hyporheic zone at several locations. The transformation of resazurin was used as an indicator of metabolism, and high-reactivity zones were identified from depth profiles. The results from our subsurface analysis indicate that the potential for tracer transformation (i.e., the reaction rate constant) varied with depth in the hyporheic zone. This highlights the importance of the benthic biolayer, which we found to be on average 2 cm thick in this study, ranging from one third to one half of the full depth of the hyporheic zone. The reach-scale approach integrated the effects of processes along the reach length, isolating hyporheic processes relevant for whole-stream chemistry and estimating effective reaction rates.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016WR019393","usgsCitation":"Knapp, J., Gonzalez-Pinzon, R., Drummond, J.D., Larsen, L., Cirpka, O.A., and Harvey, J.W., 2017, Tracer-based characterization of hyporheic exchange and benthic biolayers in streams: Water Resources Research, v. 53, no. 2, p. 1575-1594, https://doi.org/10.1002/2016WR019393.","productDescription":"20 p.","startPage":"1575","endPage":"1594","ipdsId":"IP-080169","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":470095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr019393","text":"Publisher Index Page"},{"id":340374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-21","publicationStatus":"PW","scienceBaseUri":"59006062e4b0e85db3a5ddd1","contributors":{"authors":[{"text":"Knapp, Julia L.A.","contributorId":191389,"corporation":false,"usgs":false,"family":"Knapp","given":"Julia L.A.","affiliations":[],"preferred":false,"id":692887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gonzalez-Pinzon, Ricardo","contributorId":191362,"corporation":false,"usgs":false,"family":"Gonzalez-Pinzon","given":"Ricardo","email":"","affiliations":[],"preferred":false,"id":692888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drummond, Jennifer D.","contributorId":191390,"corporation":false,"usgs":false,"family":"Drummond","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":692889,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Laurel G.","contributorId":191391,"corporation":false,"usgs":false,"family":"Larsen","given":"Laurel G.","affiliations":[],"preferred":false,"id":692890,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cirpka, Olaf A.","contributorId":191392,"corporation":false,"usgs":false,"family":"Cirpka","given":"Olaf","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":692891,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":692886,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192093,"text":"70192093 - 2017 - Rainfall-runoff of anthropogenic waste indicators from agricultural fields applied with municipal biosolids","interactions":[],"lastModifiedDate":"2021-05-28T14:06:34.304778","indexId":"70192093","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Rainfall-runoff of anthropogenic waste indicators from agricultural fields applied with municipal biosolids","docAbstract":"The presence of anthropogenic contaminants such as antimicrobials, flame-retardants, and plasticizers in runoff from agricultural fields applied with municipal biosolids may pose a potential threat to the environment. This study assesses the potential for rainfall-induced runoff of 69 anthropogenic waste indicators (AWIs), widely found in household and industrial products, from biosolids amended field plots. The agricultural field containing the test plots was treated with biosolids for the first time immediately prior to this study. AWIs present in soil and biosolids were isolated by continuous liquid-liquid extraction and analyzed by full-scan gas chromatography/mass spectrometry. Results for 18 AWIs were not evaluated due to their presence in field blank QC samples, and another 34 did not have sufficient detection frequency in samples to analyze trends in data. A total of 17 AWIs, including 4-nonylphenol, triclosan, and tris(2-butoxyethyl)phosphate, were present in runoff with acceptable data quality and frequency for subsequent interpretation. Runoff samples were collected 5 days prior to and 1, 9, and 35 days after biosolids application. Of the 17 AWIs considered, 14 were not detected in pre-application samples, or their concentrations were much smaller than in the sample collected one day after application. A range of trends was observed for individual AWI concentrations (typically from 0.1 to 10 μg/L) over the course of the study, depending on the combination of partitioning and degradation mechanisms affecting each compound most strongly. Overall, these results indicate that rainfall can mobilize anthropogenic contaminants from biosolids-amended agricultural fields, directly to surface waters and redistribute them to terrestrial sites away from the point of application via runoff. For 14 of 17 compounds examined, the potential for runoff remobilization during rainstorms persists even after three 100-year rainstorm-equivalent simulations and the passage of a month.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.03.033","usgsCitation":"Gray, J.L., Borch, T., Furlong, E.T., Davis, J., Yager, T., Yang, Y., and Kolpin, D.W., 2017, Rainfall-runoff of anthropogenic waste indicators from agricultural fields applied with municipal biosolids: Science of the Total Environment, v. 580, p. 83-89, https://doi.org/10.1016/j.scitotenv.2016.03.033.","productDescription":"7 p.","startPage":"83","endPage":"89","ipdsId":"IP-073810","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"links":[{"id":470105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.03.033","text":"Publisher Index Page"},{"id":347164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"580","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59eeffaae4b0220bbd988fb9","contributors":{"authors":[{"text":"Gray, James L. 0000-0002-0807-5635 jlgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":1253,"corporation":false,"usgs":true,"family":"Gray","given":"James","email":"jlgray@usgs.gov","middleInitial":"L.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":true,"id":714191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borch, Thomas","contributorId":195631,"corporation":false,"usgs":false,"family":"Borch","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":714192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":714193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Jessica","contributorId":197745,"corporation":false,"usgs":false,"family":"Davis","given":"Jessica","affiliations":[],"preferred":false,"id":714194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yager, Tracy tjyager@usgs.gov","contributorId":1881,"corporation":false,"usgs":true,"family":"Yager","given":"Tracy","email":"tjyager@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714195,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Yun-Ya","contributorId":197746,"corporation":false,"usgs":false,"family":"Yang","given":"Yun-Ya","email":"","affiliations":[],"preferred":false,"id":714196,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714197,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193642,"text":"70193642 - 2017 - Nonbreeding isolation and population-specific migration patterns among three populations of Golden-winged Warblers","interactions":[],"lastModifiedDate":"2017-11-05T21:53:19","indexId":"70193642","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"Nonbreeding isolation and population-specific migration patterns among three populations of Golden-winged Warblers","docAbstract":"Golden-winged Warblers (Vermivora chrysoptera) are Nearctic–Neotropical migrants experiencing varied regional population trends not fully explained by breeding-grounds factors such as nest success. A lack of detailed information on the nonbreeding distributions, migration routes, or timing of migration among populations hampers our ability to identify population processes outside the breeding period. We used geolocators to track annual movements of 21 Golden-winged Warblers from 3 North American breeding locations experiencing varying population trends to investigate the potential for nonbreeding site factors to influence breeding populations. We used the template-fit method to estimate locations of individual warblers throughout the year. Geolocator-marked warblers exhibited significant isolation among populations during migration and the nonbreeding period. During the nonbreeding period, Golden-winged Warblers from Minnesota, USA (n = 12) occurred in Central America from southern Mexico to central Nicaragua; warblers from Tennessee, USA (n = 7) occurred along the border of northern Colombia and Venezuela; and warblers from Pennsylvania, USA (n = 2) occurred in north-central Venezuela. Warblers travelled at slower rates over more days in fall migration than spring migration. Fall migration routes at the Gulf of Mexico were population-specific, whereas spring routes were more varied and overlapped among populations. Golden-winged Warblers from Pennsylvania migrated 4,000 and 5,000 km yr−1 farther than Tennessee and Minnesota warblers, respectively, and spent almost twice as long migrating in the fall compared to Minnesota warblers. Our results reveal nearly complete temporal and geographic isolation among 3 populations of Golden-winged Warblers throughout the annual cycle, resulting in opportunities for population- and site-specific factors to differentially influence populations outside the breeding period. Our findings highlight the need for monitoring multiple populations of migratory species to understand and better inform conservation strategies.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-16-143.1","usgsCitation":"Kramer, G.R., Streby, H.M., Peterson, S.M., Lehman, J.A., Buehler, D.A., Wood, P.B., McNeil, D.J., Larkin, J.L., and Andersen, D., 2017, Nonbreeding isolation and population-specific migration patterns among three populations of Golden-winged Warblers: Condor, v. 119, no. 1, p. 108-121, https://doi.org/10.1650/CONDOR-16-143.1.","productDescription":"14 p.","startPage":"108","endPage":"121","ipdsId":"IP-077788","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-16-143.1","text":"Publisher Index Page"},{"id":348209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a003150e4b0531197b5a74c","contributors":{"authors":[{"text":"Kramer, Gunnar R.","contributorId":94184,"corporation":false,"usgs":false,"family":"Kramer","given":"Gunnar","email":"","middleInitial":"R.","affiliations":[{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":720404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Streby, Henry M.","contributorId":11024,"corporation":false,"usgs":false,"family":"Streby","given":"Henry","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":720405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Sean M.","contributorId":9354,"corporation":false,"usgs":false,"family":"Peterson","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":13013,"text":"Department of Environmental Science, Policy and Management, University of California, Berkeley","active":true,"usgs":false},{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":720406,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lehman, Justin A.","contributorId":166944,"corporation":false,"usgs":false,"family":"Lehman","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":720407,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buehler, David A.","contributorId":176238,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":720408,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wood, Petra B. 0000-0002-8575-1705 pbwood@usgs.gov","orcid":"https://orcid.org/0000-0002-8575-1705","contributorId":199090,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":720409,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McNeil, Darin J. Jr.","contributorId":37620,"corporation":false,"usgs":false,"family":"McNeil","given":"Darin","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":720410,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Larkin, Jeffrey L.","contributorId":169747,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false},{"id":34542,"text":"Department of Biology. Indiana University of Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":720411,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":720412,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70193475,"text":"70193475 - 2017 - Evolutionary and functional mitogenomics associated with the genetic restoration of the Florida panther","interactions":[],"lastModifiedDate":"2017-11-10T18:45:15","indexId":"70193475","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2333,"text":"Journal of Heredity","active":true,"publicationSubtype":{"id":10}},"title":"Evolutionary and functional mitogenomics associated with the genetic restoration of the Florida panther","docAbstract":"Florida panthers are endangered pumas that currently persist in reduced patches of habitat in South Florida, USA. We performed mitogenome reference-based assemblies for most parental lines of the admixed Florida panthers that resulted from the introduction of female Texas pumas into South Florida in 1995. With the addition of 2 puma mitogenomes, we characterized 174 single nucleotide polymorphisms (SNPs) across 12 individuals. We defined 5 haplotypes (Pco1–Pco5), one of which (Pco1) had a geographic origin exclusive to Costa Rica and Panama and was possibly introduced into the Everglades National Park, Florida, prior to 1995. Haplotype Pco2 was native to Florida. Haplotypes Pco3 and Pco4 were exclusive to Texas, whereas haplotype Pco5 had an undetermined geographic origin. Phylogenetic inference suggests that haplotypes Pco1–Pco4 diverged ~202000 (95% HPDI = 83000–345000) years ago and that haplotypes Pco2–Pco4 diverged ~61000 (95% HPDI = 9000–127000) years ago. These results are congruent with a south-to-north continental expansion and with a recent North American colonization by pumas. Furthermore, pumas may have migrated from Texas to Florida no earlier than ~44000 (95% HPDI = 2000–98000) years ago. Synonymous mutations presented a greater mean substitution rate than other mitochondrial functional regions: nonsynonymous mutations, tRNAs, rRNAs, and control region. Similarly, all protein-coding genes were under predominant negative selection constraints. We directly and indirectly assessed the presence of potential deleterious SNPs in the ND2 and ND5 genes in Florida panthers prior to and as a consequence of the introduction of Texas pumas. Screenings for such variants are recommended in extant Florida panthers.
","language":"English","publisher":"Oxford","doi":"10.1093/jhered/esx015","usgsCitation":"Ochoa, A., Onorato, D.P., Fitak, R.R., Roelke-Parker, M., and Culver, M., 2017, Evolutionary and functional mitogenomics associated with the genetic restoration of the Florida panther: Journal of Heredity, v. 108, no. 4, p. 449-455, https://doi.org/10.1093/jhered/esx015.","productDescription":"7 p.","startPage":"449","endPage":"455","ipdsId":"IP-084509","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":490049,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jhered/esx015","text":"Publisher Index Page"},{"id":348597,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"108","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-16","publicationStatus":"PW","scienceBaseUri":"5a06c8d1e4b09af898c86142","contributors":{"authors":[{"text":"Ochoa, Alexander","contributorId":169994,"corporation":false,"usgs":false,"family":"Ochoa","given":"Alexander","email":"","affiliations":[{"id":17653,"text":"School of Natural Resources & the Environment, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":721648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Onorato, David P.","contributorId":52704,"corporation":false,"usgs":true,"family":"Onorato","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":721649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitak, Robert R.","contributorId":169991,"corporation":false,"usgs":false,"family":"Fitak","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false},{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":721650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roelke-Parker, Melody","contributorId":72715,"corporation":false,"usgs":true,"family":"Roelke-Parker","given":"Melody","affiliations":[],"preferred":false,"id":721651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":197693,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719182,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188343,"text":"70188343 - 2017 - Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip","interactions":[],"lastModifiedDate":"2017-06-06T16:25:26","indexId":"70188343","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip","title":"Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip","docAbstract":"Strong‐motion recordings of the Mw 8.8 Maule earthquake were modeled using a compound rupture model consisting of (1) a background slip distribution with large correlation lengths, relatively low slip velocity, and long peak rise time of slip of about 10 s and (2) high stress‐drop subevents (asperities) on the deeper portion of the rupture with moment magnitudes 7.9–8.2, high slip velocity, and rise times of slip of about 2 s. In this model, the high‐frequency energy is not produced in the same location as the peak coseismic slip, but is generated in the deeper part of the rupture zone. Using synthetic seismograms generated for a plane‐layered velocity model, I find that the high stress‐drop subevents explain the observed Fourier spectral amplitude from about 0.1 to 1.0 Hz. Broadband synthetics (0–10 Hz) were calculated by combining deterministic synthetics derived from the background slip and asperities (≤1 Hz) with stochastic synthetics generated only at the asperities (≥1 Hz). The broadband synthetics produced response spectral accelerations with low bias compared to the data, for periods of 0.1–10 s. A subevent stress drop of 200–350 bars for the high‐frequency stochastic synthetics was found to bracket the observed spectral accelerations at frequencies greater than 1 Hz. For most of the stations, the synthetics had durations of the Arias intensity similar to the observed records.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160127","usgsCitation":"Frankel, A.D., 2017, Modeling strong‐motion recordings of the 2010 Mw 8.8 Maule, Chile, earthquake with high stress‐drop subevents and background slip: Bulletin of the Seismological Society of America, v. 107, no. 1, p. 372-386, https://doi.org/10.1785/0120160127.","productDescription":"15 p.","startPage":"372","endPage":"386","ipdsId":"IP-074295","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","city":"Maule","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74,\n -33\n ],\n [\n -70,\n -33\n ],\n [\n -70,\n -39\n ],\n [\n -74,\n -39\n ],\n [\n -74,\n -33\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"5937bf2ee4b0f6c2d0d9c760","contributors":{"authors":[{"text":"Frankel, Arthur D. 0000-0001-9119-6106 afrankel@usgs.gov","orcid":"https://orcid.org/0000-0001-9119-6106","contributorId":146285,"corporation":false,"usgs":true,"family":"Frankel","given":"Arthur","email":"afrankel@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697332,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70195760,"text":"70195760 - 2017 - Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results","interactions":[],"lastModifiedDate":"2018-02-28T14:03:02","indexId":"70195760","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results","docAbstract":"Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce the impacts of drought, it is important to improve monitoring tools that provide relevant and timely information in support of drought mitigation decisions. Given that drought is a complex natural hazard that manifests in different forms, monitoring can be improved by integrating various types of information (e.g., remote sensing and climate) that is timely and region specific to identify where and when droughts are occurring. The Vegetation Drought Response Index for Canada (VegDRI-Canada) is a recently developed drought monitoring tool for Canada. VegDRI-Canada extends the initial VegDRI concept developed for the conterminous United States to a broader transnational coverage across North America. VegDRI-Canada models are similar to those developed for the United States, integrating satellite observations of vegetation status, climate data, and biophysical information on land use and land cover, soil characteristics, and other environmental factors. Collectively, these different types of data are integrated into the hybrid VegDRI-Canada to isolate the effects of drought on vegetation. Twenty-three weekly VegDRI-Canada models were built for the growing season (April–September) through the weekly analysis of these data using a regression tree-based data mining approach. A 15-year time series of VegDRI-Canada results (s to 2014) was produced using these models and the output was validated by randomly selecting 20% of the historical data, as well as holdout year (15% unseen data) across the growing season that the Pearson’s correlation ranged from 0.6 to 0.77. A case study was also conducted to evaluate the VegDRI-Canada results over the prairie region of Canada for two drought years and one non-drought year for three weekly periods of the growing season (i.e., early-, mid-, and late season). The comparison of the VegDRI-Canada map with the Canadian Drought Monitor (CDM), an independent drought indicator, showed that the VegDRI-Canada maps depicted key spatial drought severity patterns during the two targeted drought years consistent with the CDM. In addition, VegDRI-Canada was compared with canola yields in the Prairie Provinces at the regional scale for a period from 2000 to 2014 to evaluate the indices’ applicability for monitoring drought impacts on crop production. The result showed that VegDRI-Canada values had a relatively higher correlation (i.e., r > 0.5) with canola yield for nonirrigated croplands in the Canadian Prairies region in areas where drought is typically a limiting factor on crop growth, but showed a negative relationship in the southeastern Prairie region, where water availability is less of a limiting factor and in some cases a hindrance to crop growth when waterlogging occurs. These initial results demonstrate VegDRI-Canada’s utility for monitoring drought-related vegetation conditions, particularly in drought prone areas. In general, the results indicated that the VegDRI-Canada models showed sensitivity to known agricultural drought events in Canada over the 15-year period mainly for nonirrigated areas.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2017.1286728","usgsCitation":"Tadesse, T., Champagne, C., Wardlow, B.D., Hadwen, T.A., Brown, J.F., Demisse, G.B., Bayissa, Y.A., and Davidson, A.M., 2017, Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results: GIScience and Remote Sensing, v. 54, no. 2, p. 230-257, https://doi.org/10.1080/15481603.2017.1286728.","productDescription":"28 p.","startPage":"230","endPage":"257","ipdsId":"IP-082660","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499999,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/99a8bce08c6143daaa4fc548ecdb117b","text":"External Repository"},{"id":352144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -138.779296875,\n 41.83682786072714\n ],\n [\n -51.67968749999999,\n 41.83682786072714\n ],\n [\n -51.67968749999999,\n 60\n ],\n [\n -138.779296875,\n 60\n ],\n [\n -138.779296875,\n 41.83682786072714\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-08","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4bc","contributors":{"authors":[{"text":"Tadesse, Tsegaye 0000-0002-4102-1137","orcid":"https://orcid.org/0000-0002-4102-1137","contributorId":147617,"corporation":false,"usgs":false,"family":"Tadesse","given":"Tsegaye","email":"","affiliations":[],"preferred":false,"id":729876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Champagne, Catherine","contributorId":202836,"corporation":false,"usgs":false,"family":"Champagne","given":"Catherine","email":"","affiliations":[{"id":27920,"text":"Agriculture and Agrifood Canada","active":true,"usgs":false}],"preferred":false,"id":729877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wardlow, Brian D. 0000-0002-4767-581X","orcid":"https://orcid.org/0000-0002-4767-581X","contributorId":191403,"corporation":false,"usgs":false,"family":"Wardlow","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":729878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hadwen, Trevor A.","contributorId":202837,"corporation":false,"usgs":false,"family":"Hadwen","given":"Trevor","email":"","middleInitial":"A.","affiliations":[{"id":27920,"text":"Agriculture and Agrifood Canada","active":true,"usgs":false}],"preferred":false,"id":729879,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":729875,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Demisse, Getachew B.","contributorId":202845,"corporation":false,"usgs":false,"family":"Demisse","given":"Getachew","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":729894,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bayissa, Yared A.","contributorId":202846,"corporation":false,"usgs":false,"family":"Bayissa","given":"Yared","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":729895,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davidson, Andrew M.","contributorId":202847,"corporation":false,"usgs":false,"family":"Davidson","given":"Andrew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":729896,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70195840,"text":"70195840 - 2017 - Using diurnal temperature signals to infer vertical groundwater-surface water exchange","interactions":[],"lastModifiedDate":"2018-03-06T11:07:46","indexId":"70195840","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Using diurnal temperature signals to infer vertical groundwater-surface water exchange","docAbstract":"Heat is a powerful tracer to quantify fluid exchange between surface water and groundwater. Temperature time series can be used to estimate pore water fluid flux, and techniques can be employed to extend these estimates to produce detailed plan-view flux maps. Key advantages of heat tracing include cost-effective sensors and ease of data collection and interpretation, without the need for expensive and time-consuming laboratory analyses or induced tracers. While the collection of temperature data in saturated sediments is relatively straightforward, several factors influence the reliability of flux estimates that are based on time series analysis (diurnal signals) of recorded temperatures. Sensor resolution and deployment are particularly important in obtaining robust flux estimates in upwelling conditions. Also, processing temperature time series data involves a sequence of complex steps, including filtering temperature signals, selection of appropriate thermal parameters, and selection of the optimal analytical solution for modeling. This review provides a synthesis of heat tracing using diurnal temperature oscillations, including details on optimal sensor selection and deployment, data processing, model parameterization, and an overview of computing tools available. Recent advances in diurnal temperature methods also provide the opportunity to determine local saturated thermal diffusivity, which can improve the accuracy of fluid flux modeling and sensor spacing, which is related to streambed scour and deposition. These parameters can also be used to determine the reliability of flux estimates from the use of heat as a tracer.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12459","usgsCitation":"Irvine, D.J., Briggs, M.A., Lautz, L.K., Gordon, R.P., McKenzie, J.M., and Cartwright, I., 2017, Using diurnal temperature signals to infer vertical groundwater-surface water exchange: Groundwater, v. 55, no. 1, p. 10-26, https://doi.org/10.1111/gwat.12459.","productDescription":"17 p.","startPage":"10","endPage":"26","ipdsId":"IP-077274","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":470089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.12459","text":"Publisher Index Page"},{"id":352253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-03","publicationStatus":"PW","scienceBaseUri":"5afee8d3e4b0da30c1bfc4b8","contributors":{"authors":[{"text":"Irvine, Dylan J.","contributorId":190404,"corporation":false,"usgs":false,"family":"Irvine","given":"Dylan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":730251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lautz, Laura K.","contributorId":124523,"corporation":false,"usgs":false,"family":"Lautz","given":"Laura","email":"","middleInitial":"K.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":730253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gordon, Ryan P.","contributorId":202947,"corporation":false,"usgs":false,"family":"Gordon","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":730254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":730255,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cartwright, Ian","contributorId":190405,"corporation":false,"usgs":false,"family":"Cartwright","given":"Ian","affiliations":[],"preferred":false,"id":730256,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189597,"text":"70189597 - 2017 - Four new species of Eimeria (Apicomplexa: Eimeriidae) from Emoia spp. Skinks (Sauria: Scincidae), from Papua New Guinea and the Insular Pacific","interactions":[],"lastModifiedDate":"2017-07-18T12:03:52","indexId":"70189597","displayToPublicDate":"2017-02-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2414,"text":"Journal of Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Four new species of Eimeria (Apicomplexa: Eimeriidae) from Emoia spp. Skinks (Sauria: Scincidae), from Papua New Guinea and the Insular Pacific","docAbstract":"Between September and November 1991, 54 adult skinks from 15 species were collected by hand or blowpipe from several localities on Rarotonga, Cook Islands, Ovalau Island, Fiji, and Papua New Guinea (PNG), and their feces were examined for coccidians. Species included 5 seaside skinks (Emoia atrocostata), 1 Pacific blue-tailed skink (Emoia caeroleocauda), 2 Fiji slender treeskinks (Emoia concolor), 15 white-bellied copper-striped skinks (Emoia cyanura), 1 Bulolo River forest skink (Emoia guttata), 6 dark-bellied copper-striped skinks (Emoia impar), 5 Papua five-striped skinks (Emoia jakati), 2 Papua slender treeskinks (Emoia kordoana), 3 Papua robust treeskinks (Emoia longicauda), 1 brown-backed forest skink (Emoia loveridgei), 3 Papua black-sided skinks (Emoia pallidiceps), 2 Papua white-spotted skinks (Emoia physicae), 2 Papua yellow-head skinks (Emoia popei), 1 Papua brown forest skink (Emoia submetallica), and 5 Fiji barred treeskinks (Emoia trossula) Species of Eimeria (Ei.) were detected from these Emoia (Em.) spp. and are described here as new. Oocysts of Eimeria iovai n. sp. from Em. pallidiceps from PNG were ellipsoidal with a bilayered wall (L × W) 26.5 × 18.1 μm, with a length/width ratio (L/W) of 1.1. Both micropyle and oocyst residuum were absent, but a fragmented polar granule was present. This eimerian also was found in Em. atrocostata from PNG. Oocysts of Eimeria kirkpatricki n. sp. from Em. atrocostata from PNG were ellipsoidal with a bilayered wall, 18.6 × 13.5 μm, L/W 1.4. A micropyle and oocyst residuum were absent, but a fragmented polar granule was present. This eimerian was also shared by Em. cyanura from the Cook Islands and Fiji, Em. imparfrom the Cook Islands, Em. loveridgei from PNG, Em. pallidicepsfrom PNG, Em. popei from PNG, and Em. submetallica from PNG. Oocysts of Eimeria stevejayuptoni n. sp. from Em. longicaudawere subspheroidal to ellipsoidal with a bilayered wall, 18.7 × 16.6 μm, L/W 1.1. A micropyle and oocyst residuum were absent, but a fragmented polar granule was present. Oocysts of Eimeria emoia n. sp. from Em. longicauda from PNG were cylindroidal with a bilayered wall, 29.2 × 15.7 μm, L/W 1.9. A micropyle and oocyst residuum were absent, but a polar granule was present. These are the first eimerians reported from Emoia spp. and they add to our growing knowledge of the coccidian fauna of scincid lizards of the South Pacific.
","language":"English","publisher":"American Society of Parasitologists","doi":"10.1645/16-67","usgsCitation":"McAllister, C.T., Duszynski, D.W., Austin, C., and Fisher, R.N., 2017, Four new species of Eimeria (Apicomplexa: Eimeriidae) from Emoia spp. Skinks (Sauria: Scincidae), from Papua New Guinea and the Insular Pacific: Journal of Parasitology, v. 103, no. 1, p. 103-110, https://doi.org/10.1645/16-67.","productDescription":"8 p.","startPage":"103","endPage":"110","ipdsId":"IP-078006","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":343987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Papua New Guinea","otherGeospatial":"Insular Pacific","volume":"103","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"596f1e25e4b0d1f9f0640756","contributors":{"authors":[{"text":"McAllister, Chris T.","contributorId":22704,"corporation":false,"usgs":true,"family":"McAllister","given":"Chris","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":705341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duszynski, Donald W.","contributorId":87869,"corporation":false,"usgs":true,"family":"Duszynski","given":"Donald","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":705342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Austin, Christopher C.","contributorId":8772,"corporation":false,"usgs":true,"family":"Austin","given":"Christopher C.","affiliations":[],"preferred":false,"id":705343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":705344,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70180197,"text":"70180197 - 2017 - Mineral commodity summaries 2017","interactions":[],"lastModifiedDate":"2017-02-14T14:07:52","indexId":"70180197","displayToPublicDate":"2017-01-31T15:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"seriesTitle":{"id":368,"text":"Mineral Commodity Summaries","active":false,"publicationSubtype":{"id":6}},"title":"Mineral commodity summaries 2017","docAbstract":"This report is the earliest Government publication to furnish estimates covering 2016 nonfuel mineral industry data. Data sheets contain information on the domestic industry structure, Government programs, tariffs, and 5-year salient statistics for more than 90 individual minerals and materials.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70180197","usgsCitation":"U.S. Geological Survey, 2017, Mineral commodity summaries 2017: U.S. Geological Survey, 202 p., https://doi.org/10.3133/70180197.","productDescription":"202 p.","numberOfPages":"202","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":334527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":335372,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/","text":"Mineral Commodity Summaries Index Page","linkFileType":{"id":5,"text":"html"},"description":"Link to page with all USGS Mineral Commodities Summaries"}],"publishedDate":"2017-01-31","noUsgsAuthors":false,"publicationDate":"2017-01-31","publicationStatus":"PW","scienceBaseUri":"589aeab1e4b0efcedb72d23d","contributors":{"authors":[{"text":"Ober, Joyce A. 0000-0003-1608-5611 jober@usgs.gov","orcid":"https://orcid.org/0000-0003-1608-5611","contributorId":394,"corporation":false,"usgs":true,"family":"Ober","given":"Joyce","email":"jober@usgs.gov","middleInitial":"A.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":662110,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70178092,"text":"ofr20161188 - 2017 - smwrGraphs—An R package for graphing hydrologic data, version 1.1.2","interactions":[{"subject":{"id":70159629,"text":"ofr20151202 - 2015 - smwrBase—An R package for managing hydrologic data, version 1.1.1","indexId":"ofr20151202","publicationYear":"2015","noYear":false,"title":"smwrBase—An R package for managing hydrologic data, version 1.1.1"},"predicate":"SUPERSEDED_BY","object":{"id":70178092,"text":"ofr20161188 - 2017 - smwrGraphs—An R package for graphing hydrologic data, version 1.1.2","indexId":"ofr20161188","publicationYear":"2017","noYear":false,"title":"smwrGraphs—An R package for graphing hydrologic data, version 1.1.2"},"id":1}],"lastModifiedDate":"2017-02-02T10:15:20","indexId":"ofr20161188","displayToPublicDate":"2017-01-31T00:00:00","publicationYear":"2017","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":"2016-1188","title":"smwrGraphs—An R package for graphing hydrologic data, version 1.1.2","docAbstract":"This report describes an R package called smwrGraphs, which consists of a collection of graphing functions for hydrologic data within R, a programming language and software environment for statistical computing. The functions in the package have been developed by the U.S. Geological Survey to create high-quality graphs for publication or presentation of hydrologic data that meet U.S. Geological Survey graphics guidelines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161188","usgsCitation":"Lorenz, D.L., and Diekoff, A.L., 2017, smwrGraphs—An R package for graphing hydrologic data, version 1.1.2: U.S. Geological Survey Open-File Report 2016–1188, 17 p., https://doi.org/10.3133/ofr20161188. [Supersedes USGS Open-File Report 2015–1202.]","productDescription":"Report: iii, 17 p.; Appendixes: 1–9","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-054442","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":334283,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1188/ofr20161188.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1188"},{"id":334282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1188/coverthb.jpg"},{"id":334284,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1188/downloads","text":"Appendixes 1–9","size":"3.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1188 Appendixes 1–9","linkHelpText":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
Paleomagnetic directions and 40Ar/39Ar ages have been determined for samples of lava flows from the same outcrops, where possible, for 84 eruptive units ranging in age from 3200 ka to 60 ka within the Boring Volcanic Field (BVF) of the Pacific Northwest, USA. This study expands upon our previous results for the BVF, and compares the combined results with the current geomagnetic polarity time scale (GPTS). Lava flows with transitional directions were found within the BVF at the Matuyama-Brunhes and Jaramillo-Matuyama polarity boundaries, and replicate ages corresponding to these and other boundaries have been newly ascertained. Although the BVF data generally agree with GPTS chronozone boundaries, they indicate that onset of the Gauss-Matuyama transition and Olduvai subchron occurred significantly earlier than given in the current time scale calibration. Additional comparisons show that the BVF results are consistent with recent statistical models of geomagnetic paleosecular variation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2016.07.008","usgsCitation":"Hagstrum, J.T., Fleck, R.J., Evarts, R., and Calvert, A.T., 2017, Paleomagnetism and 40Ar/39Ar geochronology of the Plio-Pleistocene Boring Volcanic Field: Implications for the geomagnetic polarity time scale and paleosecular variation: Physics of the Earth and Planetary Interiors, v. 262, p. 101-115, https://doi.org/10.1016/j.pepi.2016.07.008.","productDescription":"15 p.","startPage":"101","endPage":"115","ipdsId":"IP-076015","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.pepi.2016.07.008","text":"Publisher Index Page"},{"id":355622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"262","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e766e4b060350a15d2ad","contributors":{"authors":[{"text":"Hagstrum, Jonathan T. 0000-0002-0689-280X jhag@usgs.gov","orcid":"https://orcid.org/0000-0002-0689-280X","contributorId":3474,"corporation":false,"usgs":true,"family":"Hagstrum","given":"Jonathan","email":"jhag@usgs.gov","middleInitial":"T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleck, Robert J. 0000-0002-3149-8249 fleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3149-8249","contributorId":1048,"corporation":false,"usgs":true,"family":"Fleck","given":"Robert","email":"fleck@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evarts, Russell C.","contributorId":206202,"corporation":false,"usgs":false,"family":"Evarts","given":"Russell C.","affiliations":[],"preferred":false,"id":739781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calvert, Andrew T. 0000-0001-5237-2218 acalvert@usgs.gov","orcid":"https://orcid.org/0000-0001-5237-2218","contributorId":2694,"corporation":false,"usgs":true,"family":"Calvert","given":"Andrew","email":"acalvert@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739782,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}