Microbial abundance in surface ice on the Greenland Ice Sheet.

Marek Stibal, Erkin Gözdereliler, Karen A Cameron, Jason E Box, Ian T Stevens, Jarishma K Gokul, Morten Schostag, Jakub D Zarsky, Arwyn Edwards, Tristram D L Irvine-Fynn, Carsten S Jacobsen
Author Information
  1. Marek Stibal: Geological Survey of Denmark and Greenland Copenhagen, Denmark ; Center for Permafrost, University of Copenhagen Copenhagen, Denmark ; Department of Ecology, Charles University in Prague Prague, Czech Republic.
  2. Erkin Gözdereliler: Geological Survey of Denmark and Greenland Copenhagen, Denmark ; Center for Permafrost, University of Copenhagen Copenhagen, Denmark.
  3. Karen A Cameron: Geological Survey of Denmark and Greenland Copenhagen, Denmark ; Center for Permafrost, University of Copenhagen Copenhagen, Denmark.
  4. Jason E Box: Geological Survey of Denmark and Greenland Copenhagen, Denmark.
  5. Ian T Stevens: Centre for Glaciology, Aberystwyth University Aberystwyth, UK.
  6. Jarishma K Gokul: Centre for Glaciology, Aberystwyth University Aberystwyth, UK.
  7. Morten Schostag: Center for Permafrost, University of Copenhagen Copenhagen, Denmark.
  8. Jakub D Zarsky: Department of Ecology, Charles University in Prague Prague, Czech Republic ; Centre for Polar Ecology, University of South Bohemia České Budějovice, Czech Republic.
  9. Arwyn Edwards: Centre for Glaciology, Aberystwyth University Aberystwyth, UK.
  10. Tristram D L Irvine-Fynn: Centre for Glaciology, Aberystwyth University Aberystwyth, UK.
  11. Carsten S Jacobsen: Geological Survey of Denmark and Greenland Copenhagen, Denmark ; Center for Permafrost, University of Copenhagen Copenhagen, Denmark ; Department of Plant and Environmental Sciences, University of Copenhagen Copenhagen, Denmark.

Abstract

Measuring microbial abundance in glacier ice and identifying its controls is essential for a better understanding and quantification of biogeochemical processes in glacial ecosystems. However, cell enumeration of glacier ice samples is challenging due to typically low cell numbers and the presence of interfering mineral particles. We quantified for the first time the abundance of microbial cells in surface ice from geographically distinct sites on the Greenland Ice Sheet (GrIS), using three enumeration methods: epifluorescence microscopy (EFM), flow cytometry (FCM), and quantitative polymerase chain reaction (qPCR). In addition, we reviewed published data on microbial abundance in glacier ice and tested the three methods on artificial ice samples of realistic cell (10(2)-10(7) cells ml(-1)) and mineral particle (0.1-100 mg ml(-1)) concentrations, simulating a range of glacial ice types, from clean subsurface ice to surface ice to sediment-laden basal ice. We then used multivariate statistical analysis to identify factors responsible for the variation in microbial abundance on the ice sheet. EFM gave the most accurate and reproducible results of the tested methodologies, and was therefore selected as the most suitable technique for cell enumeration of ice containing dust. Cell numbers in surface ice samples, determined by EFM, ranged from ~ 2 × 10(3) to ~ 2 × 10(6) cells ml(-1) while dust concentrations ranged from 0.01 to 2 mg ml(-1). The lowest abundances were found in ice sampled from the accumulation area of the ice sheet and in samples affected by fresh snow; these samples may be considered as a reference point of the cell abundance of precipitants that are deposited on the ice sheet surface. Dust content was the most significant variable to explain the variation in the abundance data, which suggests a direct association between deposited dust particles and cells and/or by their provision of limited nutrients to microbial communities on the GrIS.

Keywords

References

  1. Microb Ecol. 2008 Apr;55(3):476-88 [PMID: 17876656]
  2. FEMS Microbiol Ecol. 2007 Feb;59(2):255-64 [PMID: 17328766]
  3. FEMS Microbiol Ecol. 2009 Nov;70(2):9-20 [PMID: 19796140]
  4. Appl Environ Microbiol. 2005 Nov;71(11):6986-97 [PMID: 16269734]
  5. Environ Microbiol Rep. 2015 Apr;7(2):293-300 [PMID: 25405749]
  6. Microbiol Res. 2012 Jun 20;167(6):372-80 [PMID: 22537873]
  7. Proc Natl Acad Sci U S A. 1998 Jun 9;95(12):6578-83 [PMID: 9618454]
  8. Science. 1999 Dec 10;286(5447):2144-7 [PMID: 10591643]
  9. Extremophiles. 2009 May;13(3):411-23 [PMID: 19159068]
  10. ISME J. 2013 Sep;7(9):1814-26 [PMID: 23552623]
  11. Microb Ecol. 2004 May;47(4):329-40 [PMID: 14994176]
  12. Environ Microbiol. 2009 Mar;11(3):640-56 [PMID: 19278450]
  13. Microb Ecol. 2012 Jan;63(1):74-84 [PMID: 21898102]
  14. Cytometry A. 2014 Jan;85(1):3-7 [PMID: 24273193]
  15. Environ Microbiol. 2015 Mar;17(3):594-609 [PMID: 24593847]
  16. Adv Space Res. 2001;28(4):701-6 [PMID: 11803975]
  17. Appl Environ Microbiol. 2003 Apr;69(4):2153-60 [PMID: 12676695]
  18. Environ Microbiol. 2008 Aug;10(8):2172-8 [PMID: 18430008]
  19. J Microbiol Methods. 2008 Oct;75(2):237-43 [PMID: 18602952]
  20. Proc Natl Acad Sci U S A. 2005 Dec 20;102(51):18292-6 [PMID: 16339015]
  21. Methods Cell Biol. 1994;42 Pt B:489-522 [PMID: 7533254]
  22. Trends Ecol Evol. 2012 Apr;27(4):219-25 [PMID: 22000675]
  23. ISME J. 2013 Jul;7(7):1402-12 [PMID: 23486249]
  24. Nucleic Acids Res. 2001 Jan 1;29(1):181-4 [PMID: 11125085]
  25. FEMS Microbiol Ecol. 2007 Feb;59(2):307-17 [PMID: 17313580]
  26. Appl Environ Microbiol. 2000 Oct;66(10):4514-7 [PMID: 11010907]
  27. ISME J. 2012 Dec;6(12):2302-13 [PMID: 23018772]
  28. Environ Microbiol. 2012 Nov;14(11):2998-3012 [PMID: 23016868]
  29. Arch Microbiol. 2013 May;195(5):313-22 [PMID: 23474777]
  30. J Microbiol Methods. 1999 Dec;39(1):1-16 [PMID: 10579502]
  31. Science. 2008 Feb 29;319(5867):1214 [PMID: 18309078]
  32. Science. 1999 Dec 10;286(5447):2141-4 [PMID: 10591642]

Word Cloud

Created with Highcharts 10.0.0iceabundancemicrobialcellsamplessurfaceglaciercells2ml-1enumerationGreenlandIceSheetEFM10sheetdustglacialnumbersmineralparticlesGrISthreeepifluorescencemicroscopyflowcytometryquantitativedatatested0mgconcentrationsmultivariateanalysisvariationranged~×depositedMeasuringidentifyingcontrolsessentialbetterunderstandingquantificationbiogeochemicalprocessesecosystemsHoweverchallengingduetypicallylowpresenceinterferingquantifiedfirsttimegeographicallydistinctsitesusingmethods:FCMpolymerasechainreactionqPCRadditionreviewedpublishedmethodsartificialrealistic-107particle1-100simulatingrangetypescleansubsurfacesediment-ladenbasalusedstatisticalidentifyfactorsresponsiblegaveaccuratereproducibleresultsmethodologiesthereforeselectedsuitabletechniquecontainingCelldetermined3601lowestabundancesfoundsampledaccumulationareaaffectedfreshsnowmayconsideredreferencepointprecipitantsDustcontentsignificantvariableexplainsuggestsdirectassociationand/orprovisionlimitednutrientscommunitiesMicrobialPCR

Similar Articles

Cited By