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Journal of Paleolimnology

, Volume 9, Issue 2, pp 147–153 | Cite as

Scaled chrysophytes and pH inference models: the effects of converting scale counts to cell counts and other species data transformations

  • Brian F. Cumming
  • John P. Smol
Notes

Abstract

Predictive pH models developed using scaled chrysophytes (Synurophyceae, Chrysophyceae) have thus far been based on the relative abundance of scales and not whole cells. This paper examines the effects of transforming scale to cell numbers on the predictive abilities of pH inference models, and the effects of logarithmic and square-root transformations of the species data on the predictive abilities of pH inference models.

Very similar pH inference models were developed based on either the relative abundance of scales or cells. Thus, in this data-set, there appears to be no statistical advantage in transforming raw scale counts to cell counts prior to calculating the relative abundances. However, if one wishes to compare paleochrysophyte populations to actual long-term limnological chrysophyte collections, a scale-to-cell transformation would be desirable. Logarithmic and square-root transformations of the species data improve the pH inference models. These transformations increase the effective number of occurrences of chrysophyte taxa when compared to the untransformed scale and cell pH models. The logarithmic and square-root transformations improve the pH inference models because the dominant taxa, which are often pH generalists, are down-weighted in comparison to the more pH specialist, sub-dominant taxa. We suggest researchers use either a logarithmic or square-root transformation on chrysophyte scale data to improve quantitative reconstructions of lakewater pH and possibly other variables.

Key words

scaled chrysophytes Synurophyceae pH weighted-averaging Adirondack Park (New York) paleolimnology 

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References

  1. Andersen, R. A., 1987, Synurophyceae classis nov., a new class of algae. Am. J. Bot. 74: 337–353.Google Scholar
  2. Asmund, B. & J. Kristiansen, 1986. The genusMallomonas (Chrysophyceae). A taxonomic survey based on the ultrastructure of silica scales and bristles. Op. Bot. 85: 1–128.Google Scholar
  3. Baker, J. P., D. P. Bernard, S. W. Christensen, M. J. Sale, J. Freda, K. Heltcher, D. Marmorek, L. Rowe, P. Scanlon, G. Suter, W. Warren-Hicks & P. Welbourn. 1990. Biological effects of changes in surface water acid-base chemistry. NAPAP Report 13. In Irving (ed.), National Precipitation Assessment Program, Acid Deposition: State of Science and Technology. Volume II (Aquatic processes and effects), Government Printing Office, Washington, D.C.: 1–381.Google Scholar
  4. Battarbee, R. W. & I. Renberg, 1990. The Surface Water Acidification Project (SWAP) Palaeolimnology Programme. Phil. Trans. r. Soc., Lond. B 327: 1–6.Google Scholar
  5. Battarbee, R. W., G. Cronberg & S. Lowry, 1980. Observations on the occurrence of scales and bristles ofMallomonas spp. (Chrysophyceae) in the micro-laminated sediments of a small lake in Finnish North Karelia. Hydrobiologia 71: 225–232.Google Scholar
  6. Birks, H. J. B. & A. D. Gordon, 1985. Numerical Methods in Quaternary Pollen Analysis. Academic Press, London, 317 pp.Google Scholar
  7. Charles, D. F., R. W. Battarbee, I. Renberg, H. van Dam & J. P. Smol, 1989. Paleoecological analysis of lake acidification trends in North America and Europe using diatoms and chrysophytes. In S. A. Norton, S. E. Lindberg & A. L. Page (eds.), Soils, Aquatic Processes, and Lake Acidification. Springer-Verlag, New York: 207–276.Google Scholar
  8. Cronberg, G., 1990. Recent acidification and changes in the subfossil chrysophyte flora of lakes in Sweden, Norway, and Scotland. Phil. Trans. r. Soc., Lond. B. 327: 289–293.Google Scholar
  9. Cumming, B. F., J. P. Smol & H. J. B. Birks, 1992b. Scaled Chrysophytes (Chrysophyceae and Synurophyceae) from Adirondack drainage lakes and their relationship to environmental variables. J. Phycol. 28: 162–178.Google Scholar
  10. Cumming, B. F., S. E. Wilson, J. P. Smol., 1993. Paleolimnological potential of chrysophyte cysts and scales, and sponge spicules as indicators of lakewater salinity. Int. J. Salt Lake Res. (in press).Google Scholar
  11. Cumming, B. F., J. P. Smol, J. C. Kingston, D. F. Charles, H. J. B. Birks, K. E. Camburn, S. S. Dixit, A. J. Uutala & A. R. Selle, 1992a. How much acidification has occurred in Adirondack lakes (New York, USA) since pre-industrial times? Can. J. Fish. aquat. Sci. 49: 128–141.Google Scholar
  12. Davis, R. B. & J. P. Smol, 1986. The use of sedimentary remains of siliceous algae for inferring past chemistry of lake water — problems, potential and research needs. In J. P. Smol, R. W. Battarbee, R. B. Davis & J. Meriläinen (eds.), Diatoms and Lake Acidity. Dr W. Junk, Dordrecht, The Netherlands: 291–300.Google Scholar
  13. Hill, M. O., 1973. Diversity and evenness: a unifying notation and it consequences. Ecology 54: 427–432.Google Scholar
  14. Kristiansen, J., 1986. Silica-scale bearing chrysophytes as environmental indicators. Br. J. Phycol. 21: 425–436.Google Scholar
  15. Line, J. M., C. J. ter Braak & H. J. B. Birks, 1993. WACALIB 3.2 — An extended computer program to reconstruct environmental variables from fossil assemblages by weighted averaging and to derive sample-specific bootstrap errors of prediction.Google Scholar
  16. Munch, C. S., 1985. Chrysophycean scales as paleoindicators in the sediments of Hall Lake, Washington, U.S.A. Nord. J. Bot. 5: 505–510.Google Scholar
  17. Pienitz, R., I. R. Walker, B. A. Zeeb, J. P. Smol & P. R. Leavitt, 1992. Biomonitoring past salinity changes in an athalassic subarctic lakes. Int. J. Salt Lake Res. 1: 91–123.Google Scholar
  18. Siver, P. A., 1991a. The biology ofMallomonas: Morphology, Taxonomy and Ecology. In Developments in Hydrobiology series, Kluwer Academic Publishers, Dordrecht, The Netherlands, 230 pp.Google Scholar
  19. Siver, P. A., 1991b. Implications for improving paleolimnological inference models utilizing scalebearing siliceous algae: transforming scale counts to cell counts. J. Paleolim. 5: 219–225.Google Scholar
  20. Smol, J. P., 1980. Fossil synuracean (Chrysophyceae) scales in lake sediments: A new group of paleoindicators. Can. J. Bot. 58: 458–465.Google Scholar
  21. Smol, J. P., 1981. Problems associated with the use of ‘species diversity’ in paleolimnological studies. Quat. Res. 15: 209–212.Google Scholar
  22. Smol, J. P., 1986. Chrysophycean microfossils as indicators of lakewater pH. In Smol J. P., Battarbee, R. W., Davis, R. B. & J. Meriläinen (eds.), Diatoms and Lake Acidity. Dr W. Junk, Dordrecht, The Netherlands: 275–287.Google Scholar
  23. Smol, J. P., 1988. Diatoms and Chrysophytes — A useful combination in palaeolimnological studies: Report of a workshop and working bibliography. In H. Simola (ed.), Proceedings of the Tenth International Diatom Symposium. Koeltz Scientific Books, Koenigstein: 585–592.Google Scholar
  24. Sullivan, T. J., R. S. Turner, D. F. Charles, B. F. Cumming, J. P. Smol, C. L. Schofield, C. T. Driscoll, H. J. B. Birks, A. J. Uutala, J. C. Kingston, S. S. Dixit, J. A. Bernert & P. F. Ryan, 1992. Use of historical assessment for evaluation of process-based model projections of future environmental change: lake acidification in the Adirondack mountains New York, U.S.A. Envir. Pollut. 77: 253–262.Google Scholar
  25. Takahashi, E., 1978. Electron Microscopical studies of the Synuraceae (Chrysophyceae) in Japan. Tokai University Press, Tokyo, 194 pp.Google Scholar
  26. Wee, J. L., 1982. Studies on the Synuraceae (Chrysophyceae) of Iowa. Bibl. Phycol. 62: 1–183.Google Scholar
  27. Zeeb, B. & J. P. Smol, 1991. Paleolimnological investigation of the effects of road salt seepage on scaled chrysophytes in Fonda Lake, Michigan. J. Paleolim. 5: 263–266.Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Brian F. Cumming
    • 1
  • John P. Smol
    • 1
  1. 1.Paleoecological Environmental Assessment and Research Laboratory (PEARL), Department of BiologyQueen's UniversityKingstonCanada

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