, Volume 328, Issue 1, pp 57–74

Quantitative relationships of invertebrates to pH in Norwegian river systems

  • Jorunn Larsen
  • H. J. B. Birksl
  • Gunnar G. Raddum
  • Arne Fjellheim


The invertebrate fauna has been surveyed for twenty one unlimed generally acidic river systems in Norway. The data consist of 180 samples and 127 invertebrate taxa and associated water chemistry data (pH, calcium, acid neutralizing capacity, total aluminium, and conductivity). Multivariate numerical methods are used to quantify the relationships between aquatic invertebrates and water chemistry. Detrended canonical correspondence analysis (DCCA) shows one dominant axis of variation with high correlations for pH and aluminium. DCCA axis 2 is significantly correlated with calcium. The predictive abilities of invertebrates to pH are explored by means of weighted averaging (WA) regression and calibration and weighted averaging partial-least-squares regression (WA-PLS). The performance of the methods is reported in terms of the root mean square error of prediction (RMSEP) of (observed pH-inferred pH). Bootstrapping and leave-one-out jackknifing are used as cross-validation procedures. The predictive abilities of invertebrates are good (RMSEPboot for WA = 0.309 pH units). Comparison of the invertebrates with diatom studies shows that invertebrates are as good predictors of modern pH as diatoms are. RMSEPjack shows that WA-PLS improves the predictive abilities. Indicator taxa for pH are found by Gaussian regression. Anisoptera, Agrypnia obsoleta, Leptophlebia marginata, Sialis lutaria, and Zygoptera have significant sigmoidal curves where abundances increase with decreasing pH. Cyrnus flavidus shows a significant unimodal response and has an estimated optimum in the acid part of the gradient. Isoperla spp. and Ostracoda show significant sigmoidal responses where abundances increase with increasing pH. Amphinemura borealis, Diura nanseni, Isoperla grammatica, I. obscura, and Siphonoperla burmeisteri show significant unimodal responses and have high pH optima. Many taxa do not have statistically significant unimodal or sigmoidal curves, but are found by WA to be characteristic of either high pH or low pH. These results suggest that a combined use of Gaussian regression and direct gradient analysis is needed to get a full overview of potential indicator taxa.

Key words

invertebrates gradient analysis canonical correspondence analysis weighted averaging weighted averaging partial-least-squares regression indicator taxa for pH 


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  1. Batterbee, R. W., 1984. Diatom analysis and the acidification of lakes. Phil. Trans. r. Soc. Lond. B 305: 451–477.Google Scholar
  2. Battarbee, R. W. & D. F. Charles, 1987. The use of diatom assemblages in lake sediments as a means of assessing the timing, trends, and causes of lake acidification. Progr. Phys. Geogr. 11: 552–580.Google Scholar
  3. Battarbe, R. W., N. J. Anderson, P. G. Appleby, R. J. Flower, S. C. Fritz, E. Y Haworth, S. Higgit, V. J. Jones, A. Kreiser, M. A. R. Munro, J. Nathanski, F. Oldfield, S. T. Patrick, N. G. Richardson, B. Rippey & A. C. Stevenson, 1988. Lake acidification in the United Kingdom 1800–1986. Palaeoecology Research Unit, Department of Geography, University College London, 68 pp.Google Scholar
  4. Birks, H. J. B., 1994. The importance of pollen and diatom taxonomic precision in quantitative palaeoenvironmental reconstructions. Rev. Palaeobot. Palynol. 83: 107–117.CrossRefGoogle Scholar
  5. Birks, H. J. B., S. Juggins & J. M. Line, 1990a. Lake surface-water chemistry reconstructions from palaeolimnological data. In The Surface Water Acidification Programme (ed. by B. J. Mason), Cambridge University Press: 301–313.Google Scholar
  6. Birks, H. J. B., S. M. Peglar & H. A. Austin, 1994. An annotated bibliography of canonical correspondence analysis and related constrained ordination methods 1986–1993. Botanical Institute, University of BergenAllégaten 41, N-5007 Bergen, Norway, 58 pp.Google Scholar
  7. Birks, H. J. B., J. M. Line, S. Juggins, A. C. Stevenson & C. J. F. ter Braak, 1990b. Diatoms and pH reconstructions. Phil. Trans. r. Soc., Lond. B 327: 263–278.Google Scholar
  8. Charles, D. F. & S. A. Norton, 1986. Paleolimnological evidence for trends in atmospheric deposition of acids and metals. In Acid Deposition: Long Term Trends. National Research Council, Committee on Monitoring and assessment of Trends in Acid Deposition. National Academy Press, Washington D.C.: 335–506.Google Scholar
  9. Cumming, B. F., J. P. Smol & H. J. B. Birks, 1991. The relationship between sedimentary chrysophyte scales (Chrysophyceae and Synurophyceae) and limnological characteristics in 25 Norwegian lakes. Nord. J. Bot. 11: 231–241.Google Scholar
  10. Dixit, S. S., A. S. Dixit & J. P. Smol, 1991. Multivariable environmental inferences based on diatom assemblages from Sudbury (Canada) lakes. Freshwat. Biol. 26: 251–266.Google Scholar
  11. Dixit, A. S., S. S. Dixit & J. P. Stool, 1992. Algal microfossils provide high temporal resolution of environmental trends. Wat. Air Soil Pollut. 62: 75–87.Google Scholar
  12. Fjellheim, A. & G. G. Raddum, 1990. Acid precipitation: Biological monitoring of streams and lakes. The Science of Total Environment 96: 57–66.CrossRefGoogle Scholar
  13. Hämäläinen, H. & P. Huttunen, 1990. Estimation of acidity in streams by means of benthic invertebrates: Evaluation of two methods. In (eds), Acidification in Finland. Kauppi P., P. Anttila & Kenttämies Springer-Verlag Berlin: 1051–1070.Google Scholar
  14. Henriksen, A., L. Lien, T. S. Traaen, I. S. Sevaldrud & D. F. Brakke, 1988. Lake acidification in Norway — present and predicted chemical status. Ambio 17: 259–266.Google Scholar
  15. Hill, M. O., 1973. Diversity and eveness: a unifying notation and its consequences. Ecology 54: 427–432.Google Scholar
  16. Hill, M. O. & H. G. Gauch, 1980. Detrended correspondence analysis: an improved ordination technique. Vegetatio 42: 47–58.Google Scholar
  17. Jongman, R. H. G., C. J. F. ter Braak & O. F. R. van Tongeren, 1987. Data Analysis in Community and Landscape Ecology. Pudoc, Wageningen, 299 pp.Google Scholar
  18. Juggins, S. & C. J. F. ter Braak, 1993. CALIBRATE—a program for species-environment calibration by [weighted averaging] partialleast-squares regression. Unpublished computer program, Environmental Change Research Centre, University College London, 20 pp.Google Scholar
  19. Lien, L., G. G. Raddum & A. Fjellheim, 1992. Critical loads of acidity to freshwater fish and invertebrates. NIVA report 0/89185, Oslo, 36 pp.Google Scholar
  20. 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. Paleolimnol. 10: 147–152.Google Scholar
  21. Marchetto, A., 1994. Rescaling species optima estimated by weighted averaging. J. Paleolimnol. 12: 155–162.Google Scholar
  22. Otto, C. & B. Svensson, 1983. Properties of acid brown water streams in South Sweden. Arch. Hydrobiol. 99: 15–36.Google Scholar
  23. Palmer, M. W., 1983. Putting things in even better order: the advantages of canonical correspondence analyses. Ecology 74: 2215–2230.Google Scholar
  24. Payne, C. D. (ed) 1986. The GLIM System Release 3.77. Oxford: Numerical Algorithms Group.Google Scholar
  25. Raddum, G. G. & A. Fjellheim, 1984. Acidification and early warning organisms in freshwater in western Norway. Verb. Int. Ver. Limnoi. 22: 1973–1980.Google Scholar
  26. Raddum, G. G., A. Fjellheim & T. Hesthagen, 1988. Monitoring of acidification through the use of aquatic organisms. Verb. Int. Ver. Limnol. 23: 2291–2297.Google Scholar
  27. Stenson, J. A. E., 1985. Biothic structure and relations in the acidified Lake Gårdsjøen system; A synthesis. Ecol. Bull. 37: 319–326.Google Scholar
  28. Stevenson, A. C., H. J. B. Birks, R. J. Flower & R. W. Battarbee, 1989. Diatom-based pH reconstruction of lake acidification using canonical correspondence analysis. Ambio 18: 228–233.Google Scholar
  29. Stevenson, A. C., S. Juggins, H. J. B. Birks, D. S. Anderson, N. J. Anderson, R. W. Battarbee, F. Berge, R. B. Davis, R. J. Flower, E. Y. Haworth, V. J. Jones, J. C. Kingston, A. M. Kreiser, J. M. Line, M. A. R. Munro & I. Renberg, 1991. The Surface Water Acidification Project Palaeolimnology Programme: Modern diatom/lake water chemistry data set. Ensis Publishing London, 86 pp.Google Scholar
  30. ter Braak, C. J. F., 1986. Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology 67: 1167–1178.Google Scholar
  31. ter Braak, C. J. F., 1987. The analysis of vegetaion- environment relationships by canonical correspondence analysis. Vegetatio 69: 69–77.Google Scholar
  32. ter Braak, C. J. F, 1988a. CANOCO — a FORTRAN program for canonical community ordination by [partial] [detrended] [canonical] correspondence analysis, principal components analysis and redundancy analysis (version 2.1). Technical report: LWA-88–02. Agricultural Mathematics Group Box 100, 6700 AC Wageningen, The Netherlands, 95 pp.Google Scholar
  33. ter Braak, C. J. F., 1988b. Partial canonical correspondence analysis. In: Classification and Related Methods of Data Analysis (ed. H. H. Bock), pp. 551–558. Elsevier Science Publishers, Amsterdam.Google Scholar
  34. ter Braak, C. J. F., 1990a. CANOCO — a FORTRAN program for canonical community ordination by [partial] [detrended] [canonical] correspondence analysis, principal components analysis and redundancy analysis, version 3.10. Microcomputer Power, Ithaca, NY.Google Scholar
  35. ter Braak, C. J. F., 1990b. Updates notes: CANOCO version 3.10. Agricultural Mathematical Group, Wageningen, 35 pp.Google Scholar
  36. 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 (Dev. Hydrobiol. 90): 485–502.Google Scholar
  37. ter Braak, C. J. F. & C. W. N. Looman, 1986. Weighted averaging, logistic regression and the Gaussian response model. Vegetatio 65: 3–11.Google Scholar
  38. ter Braak, C. J. F. & I. C. Prentice, 1988. A theory of gradient analysis. Advances in ecological research. 18, 271–317.Google Scholar
  39. ter Braak, C. J. F. & H. van Dam, 1989. Inferring pH from diatoms: a comparison of old and new methods. Hydrobiologia 178: 209–223.Google Scholar
  40. ter Braak, C. J. F. & P. F. M. Verdonschot, 1995. Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquat. Sci. 37: 255–289.Google Scholar
  41. ter Braak, C. J. F., S. Juggins, H. J. B. Birks & H. van der Voet, 1993. Weighted averaging partial-least- squares regression (WAPLS): Definition and comparison with other methods for species-environment calibration. In Multivariate Enviromental Statistics. G. P. Patil & C. R. Rao (eds.) Elsevier Science Publishers BV., Amsterdam: 525–560.Google Scholar
  42. Walker, I. R., J. P. Smol, D. R. Engstrom & H. J. B. Birks, 1991. 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 1996

Authors and Affiliations

  • Jorunn Larsen
    • 1
  • H. J. B. Birksl
    • 2
  • Gunnar G. Raddum
    • 3
  • Arne Fjellheim
    • 3
  1. 1.Botanical InstituteUniversity of BergenBergenNorway
  2. 2.Environmental Change Research Centre, Department of GeographyUniversity College LondonLondonU.K.
  3. 3.Zoological InstituteUniversity of BergenBergenNorway

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