Journal of Paleolimnology

, Volume 26, Issue 3, pp 343–350 | Cite as

Effect of low count sums on quantitative environmental reconstructions: an example using subfossil chironomids

  • Oliver Heiri
  • André F. Lotter


The concentrations of chironomid remains in lake sediments are very variable and, therefore, chironomid stratigraphies often include samples with a low number of counts. Thus, the effect of low count sums on reconstructed temperatures is an important issue when applying chironomid‐temperature inference models. Using an existing data set, we simulated low count sums by randomly picking subsets of head capsules from surface‐sediment samples with a high number of specimens. Subsequently, a chironomid‐temperature inference model was used to assess how the inferred temperatures are affected by low counts. The simulations indicate that the variability of inferred temperatures increases progressively with decreasing count sums. At counts below 50 specimens, a further reduction in count sum can cause a disproportionate increase in the variation of inferred temperatures, whereas at higher count sums the inferences are more stable. Furthermore, low count samples may consistently infer too low or too high temperatures and, therefore, produce a systematic error in a reconstruction. Smoothing reconstructed temperatures downcore is proposed as a possible way to compensate for the high variability due to low count sums. By combining adjacent samples in a stratigraphy, to produce samples of a more reliable size, it is possible to assess if low counts cause a systematic error in inferred temperatures.

quantitative temperature reconstruction Chironomidae sample size error estimation palaeolimnology subfossils 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Birks, H. J. B., 1995. Quantitative palaeoenvironmental reconstructions. In Maddy, D. & Brew, J. S. (eds), Statistical Modelling of Quaternary Science Data. Technical Guide 5. Quaternary Research Association, Cambridge, 161–254.Google Scholar
  2. Birks, H. J. B., 1998. Numerical tools in palaeolimnology – progress, potentialities, and problems. J. Paleolim. 20: 307–332.Google Scholar
  3. Birks, H. J. B. & J. M. Line, 1992. The use of rarefaction analysis for estimating palynological richness from Quaternary pollenanalytical data. Holocene 2: 1–10.Google Scholar
  4. Birks, H. J. B., J. M. Line, S. Juggins, A. C. Stevenson & C. J. F. ter Braak, 1990. Diatoms and pH reconstruction. Phil. Trans. R. Soc. Lond. B 327: 263–278.Google Scholar
  5. Brooks, S. J. & H. J. B. Birks, 2000. Chironomid–inferred late–glacial and early–Holocene mean July air temperatures for Krakenes Lake, western Norway. J. Paleolim. 23: 77–89.Google Scholar
  6. Cwynar, L. C. & A. J. Levesque, 1995. Chironomid evidence for lateglacial climatic reversals in Maine. Quat. Res. 43: 405–413.Google Scholar
  7. Hill, M. O., 1973. Diversity and evenness: a unifying notation and its consequences. Ecology 54: 427–432.Google Scholar
  8. Hofmann, W., 1986. Chironomid analysis. In Berglund, B. E. (ed.), Handbook of Palaeoecology and Palaeohydrology. John Wiley and Sons, Chichester, 715–727.Google Scholar
  9. Legendre, P. & L. Legendre, 1998. Numerical Ecology. Developments in Environmental Modelling 20. Elsevier, Amsterdam, 853 pp.Google Scholar
  10. Levesque, A. J., F. E. Mayle, I. R. Walker & L. C. Cwynar, 1993. A previously unrecognized late–glacial cold event in eastern North America. Nature 361: 623–626.Google Scholar
  11. Line, J. M., C. J. F. ter Braak & H. J. B. Birks, 1994. WACALIB version 3.3 – a computer program to reconstruct environmental variables from fossil assemblages by weighted averaging and to derive sample–specific errors of prediction. J. Paleolim. 10: 147–152.Google Scholar
  12. Lotter, A. F., H. J. B. Birks, W. Hofmann & A. Marchetto, 1997. Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators for the reconstruction of past environmental conditions in the Alps. I. Climate. J. Paleolim. 18: 395–420.Google Scholar
  13. Lotter, A. F., H. J. B. Birks, W. Hofmann & A. Marchetto, 1998. Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators for the reconstruction of past environmental conditions in the Alps. II. Nutrients. J. Paleolim. 19: 443–463.Google Scholar
  14. Maher, L. J., Jr., 1972. Nomograms for computing 0.95 confidence limits of pollen data. Rev. Palaeobot. Palynol. 13: 85–93.Google Scholar
  15. Mosimann, J. E., 1965. Statistical methods for the pollen analyst: multinomial and negative nomial techniques. In Kummel, B. & Raup, D. (eds), Handbook of Paleontological Techniques. Freeman, San Francisco, 636–673.Google Scholar
  16. Müller, B., A. F. Lotter, M. Sturm & A. Ammann, 1998. Influence of catchment quality and altitude on the water and sediment composition of 68 small lakes in Central Europe. Aquat. Sci. 60: 316–337.Google Scholar
  17. Olander, H., H. J. B. Birks, A. Korhola & T. Blom, 1999. An expanded calibration model for inferring lakewater and air temperatures from fossil chironomid assemblages in northern Fennoscandia. Holocene 9: 279–294.Google Scholar
  18. Palmer, S. L., 1998. Subfossil chironomids (Insecta: Diptera) and climatic change at high elevation lakes in the Engelmann sprucesubalpine fir zone in Southwestern British Columbia. M.Sc. Thesis, University of British Columbia, Vancouver, 105 pp.Google Scholar
  19. Rull, V., 1987. A note on pollen counting in palaeoecology. Pollen et Spores 29: 471–480.Google Scholar
  20. ter Braak, C. J. F. & S. Juggins, 1993. Weighted averaging partial least squares regression (WA–PLS): an improved method for reconstructing environmental variables from species assemblages. Hydrobiologia 269/270: 485–502.Google Scholar
  21. ter Braak, C. J. F., S. Juggins, H. J. B. Birks & H. van der Voet, 1993. Weighted averaging partial least squares regression (WA–PLS): definition and comparison with other methods for species–environment calibration. In Patil, G. P. & Rao, C. R. (eds), Multivariate Environmental Statistics. Elsevier Science Publishers, Amsterdam, 525–560.Google Scholar
  22. Walker, I. R., 1987. Chironomidae (Diptera) in paleoecology. Quat. Sci. Rev. 6: 29–40.Google Scholar
  23. Walker, I. R., 1993. Paleolimnological biomonitoring using freshwater benthic macroinvertebrates. In Rosenberg, D. M. & Resh, V. H. (eds), Freshwater Biomonitoring and Benthic Macroinvertebrates. Chapman & Hall, New York, 306–343.Google Scholar
  24. Walker, I. R., R. J. Mott & J. P. Smol, 1991b. Allerød–Younger Dryas lake temperatures from midge fossils in Atlantic Canada. Science 253: 1010–1012.Google Scholar
  25. Walker, I. R., S. E. Wilson & J. P. Smol, 1995. Chironomidae (Diptera): quantitative paleosalinity indicators for lakes of western Canada. Can. J. Fish. Aquat. Sci. 52: 950–960.Google Scholar
  26. Walker, I. R., A. J. Levesque, L. C. Cwynar & A. F. Lotter, 1998. An expanded surface–water palaeotemperature inference model for use with fossil midges from eastern Canada. J. Paleolim. 18: 165–178.Google Scholar
  27. Walker, I. R., J. P. Smol, D. R. Engstrom & H. J. B. Birks, 1991a. An assessment of Chironomidae as quantitative indicators of past climatic change. Can. J. Fish. Aquat. Sci. 48: 975–987.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Oliver Heiri
    • 1
  • André F. Lotter
    • 2
  1. 1.Institute of Plant SciencesUniversity of BernBernSwitzerland
  2. 2.Laboratorium voor Palaeobotanie en PalynologieUniversiteit UtrechtUtrechtNetherlands

Personalised recommendations