Aquatic Ecology

, Volume 41, Issue 3, pp 387–398 | Cite as

Application of clustering techniques for the characterization of macroinvertebrate communities to support river restoration management

  • V. Adriaenssens
  • P. F. M. Verdonschot
  • P. L. M. Goethals
  • N. De Pauw


The European Water Framework Directive prescribes that the development of a river assessment system should be based on an ecological typology taking the biological reference conditions of each river type as a starting point. Aside from this assessment, water managers responsible for river restoration actions also need to know the steering environmental factors to meet these reference conditions for biological communities in each ecological river type. As such, an ecological typology based on biological communities is a necessity for efficient river management. In this study, different clustering techniques including the Sørensen similarity ratio, ordination analysis and self-organizing maps were applied to come to an ecological classification of a river. For this purpose, a series of sites within the Zwalm river basin (Flanders, Belgium) were monitored. These river sites were then characterized in terms of biotic (macroinvertebrates), physical–chemical and habitat variables. The cluster analysis resulted in a series of characteristic biotic communities that are found under certain environmental conditions, natural as well as human-influenced. The use of multiple clustering techniques can be of advantage to draw more straightforward and robust conclusions with regard to the ecological classification of river sites. The application of the clustering techniques on the Zwalm river basin, allowed for distinguishing five mutually isolated clusters, characterized by their natural typology and their pollution status. On the basis of this study, one may conclude that river management could benefit from the use of clustering methods for the interpretation of large quantities of data. Furthermore, the clustering results might enable the development of a cenotypology useful for efficiently steering river restoration and enabling river managers to meet a good ecological status in most of the rivers as set by the European Water Framework Directive.

Key words

European Water Framework Directive Multivariate analysis Ordination River typology Self-organizing maps Similarity ratio 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adriaenssens V., Simons F., Goddeeris B., NguyenThi Hong L. and Goethals P.L.M. (2004). Potential of bio-indication of chironomid communities for assessment of running water quality (Flanders, Belgium). Belg. J. Zool. 134(1): 31–40Google Scholar
  2. Austin M.P. (1985). Continuum conceptordination methods and niche theory. Annu. Rev. Ecol. Syst. 16: 39–61CrossRefGoogle Scholar
  3. Austin M.P. and Smith T.M. (1989). A new model for the continuum concept. Vegetatio 83: 35–47CrossRefGoogle Scholar
  4. Barbour M.T., Gerritsen J., Snyder B.D. and Stribling J.B. 1999. Rapid Bioassessment Protocols for use in Wadable Streams and Rivers: Periphyton, Benthic Macroinvertebrates and Fish, 2nd Ed. EPA 841-B-99-002. USEPAOffice of Water, Washington, DC.Google Scholar
  5. Brosse S., Giraudel J.L. and Lek S. (2001). Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages. Ecol. Model. 146: 159–166CrossRefGoogle Scholar
  6. Cao Y., Bark A.W. and Williams W.P. (1997). A comparison of clustering methods for river benthic community analysis. Hydrobiologia 347: 25–40CrossRefGoogle Scholar
  7. Chave P. (2001). The EU Water Framework Directivean introduction. IWA Publishing, 208Google Scholar
  8. Collins S.L., Glenn S.M. and Roberts D.W. (1993). The hierarchical continuum concept. J. Veg. Sci. 4: 391–413CrossRefGoogle Scholar
  9. Cooley W.W. and Lohnes P.R. (1971). Multivariate Data Analysis. John Wiley and Sons, Inc.Google Scholar
  10. Vanhooren G. (1983). Method for biological quality assessment of watercourses in Belgium. Hydrobiologia 100: 153–183CrossRefGoogle Scholar
  11. Vannevel R. (1991). Macroinvertebrates and Water Quality. Stichting Leefmilieu, Antwerp, Belgium, 316Google Scholar
  12. Heylen S. (2001). Biotic index for sediment quality assessment of watercourses in Flanders, Belgium. Aquat. Ecol. 35(2): 121–133CrossRefGoogle Scholar
  13. EU 2000. Directive of the European Parliament and of the Council 2000/60/EC Establishing a Framework for Community Action in the Field of Water Policy. European Union, The European ParliamentThe Council, PE-CONS 3639/1/00 REV 1 EN62p. + annexes.Google Scholar
  14. Foody G.M. (1999). Applications of the self-organising feature map neural network in community data analysis. Ecol. Model. 120: 97–107CrossRefGoogle Scholar
  15. Furse M.T., Moss D., Wright J.F. and Armitage P.D. (1984). The influence of seasonal and taxonomic factors on the ordination and classification of running-water sites in Great Britain on the prediction of macroinvertebrate communities. Freshwater Biol. 14: 257–280CrossRefGoogle Scholar
  16. Giraudel J.L. and Lek S. (2003). Ecological applications of unsupervised neural networks. In: Recknagel, F. (eds) Understanding Ecology by Biologically Inspired Computation, pp 15–33. Springer-Verlag, Berlin HeidelbergGoogle Scholar
  17. Goethals P.L.M. and Pauw N. (2001). Development of a concept for integrated ecological river assessment in Flanders (Belgium). J. Limnol. 60: 7–16Google Scholar
  18. Halkidi M., Batistakis Y. and Vazirgiannis M. (2001). On clustering validation techniques. J. Intell. Inf. Syst. 17(2–3): 107–145CrossRefGoogle Scholar
  19. Hawkes H.A. (1979). Invertebrates as indicators of river water quality. In: James, A. and Evison, L. (eds) Biological Indicators of Water Quality, pp. John Wiley, Chichester, UKGoogle Scholar
  20. Hawkins C.P., Norris R.H., Gerritsen J., Hughes R.M., Jackson S.K., Johnson R.K. and Stevenson R.J. (2000). Evaluation of the use of landscape classifications for the prediction of freshwater biota: synthesis and recommendations. J. N. Am. Benthol. Soc. 19: 541–556CrossRefGoogle Scholar
  21. Hildrew A.G. (1992). Food webs and species interactions. In: Calow, P. and Petts, G.E. (eds) The Rivers Handbook: Hydrological and Ecological Principles, Vol. I, pp 309–329.Google Scholar
  22. Hill M.O. (1973). Reciprocal averaging: an eigenvector method of ordination. J. Ecol. 61: 237–249CrossRefGoogle Scholar
  23. Hill M.O. (1979). DECORANA – a FORTRAN Program for Detrended Correspondence Analysis and Reciprocal Averaging. – Ecology and Systematics. Cornell University, Ithaca, New York, USA, 52Google Scholar
  24. (1984). Norme Belge T 92-402. Biological Water Quality. Determination of a Biotic Index Based on Aquatic Macroinvertebrates. Institut Belge de Normalisation, Brussels, Belgium, 11Google Scholar
  25. Jackson D.A. (1993). Multivariate analysis of benthic invertebrate communities: the implication of choosing particular data standardizations, measures of association and ordination methods. Hydrobiologia 268: 9–26Google Scholar
  26. Kohonen T. (1982). Self-organization and associative memory. Springer-Verlag, Berlin, Germany, 312Google Scholar
  27. McIntosh R.P. (1967). The continuum concept of vegetation. Bot. Rev. 33: 130–187Google Scholar
  28. Palmer A. and White P.S. (1994). On the existence of ecological communities. J. Veg. Sci. 5: 279–282CrossRefGoogle Scholar
  29. Palmer A., Ambrose R.F. and LeRoy Poff N. (1997). Ecological theory and community restoration ecology. Restor. Ecol. 5(4): 291–300CrossRefGoogle Scholar
  30. Pardo I. and Armitage P.D. (1997). Species assemblages as descriptors of mesohabitats. Hydrobiologia 344: 111–128CrossRefGoogle Scholar
  31. Parsons M., Thoms M.C. and Norris R.H. (2003). Scales of macroinvertebrate distribution in relation to the hierarchical organization of river systems. J. N. Am. Benth. Soc. 22(1): 105–122CrossRefGoogle Scholar
  32. Prati L., Pavanello R. and Pesarin F. (1971). Assessment of surface water quality by a single index of pollution. Water Res. 5: 741–751CrossRefGoogle Scholar
  33. Rosenberg D.M. and Resh V.H. (1993). Introduction to freshwater biomonitoring and benthic macroinvertebrates. In: Rosenberg, D.M. and Resh, V.H. (eds) Freshwater Biomonitoring and Benthic Macroinvertbrates, pp. Chapman and Hall, New York, USAGoogle Scholar
  34. Ruse L.P. (1996). Multivariate techniques relating macroinvertebrate and environmental data from a river catchment. Water Res. 30(12): 3017–3024CrossRefGoogle Scholar
  35. Schneiders A. and Verheyen R. (1998). A concept of integrated water management illustrated for Flanders (Belgium). Ecosyst. Health 4(4): 256–263CrossRefGoogle Scholar
  36. Schneiders A., Wils C. and Verheyen R. (1999). The use of ecological information in the selection of quality objectives for river conservation and restoration in Flanders (Belgium). Aquat. Ecosyst. Health Manage. 2: 137–154CrossRefGoogle Scholar
  37. Sørensen T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content. Det Kong Danske Vidensk Selsk Biol Slr (Copenhagen) 5(4): 1–34Google Scholar
  38. Ter Braak C.J.F. and Smilauer P. (1998). Reference Manual and User's Guide to Canoco for Windows: Software for Canonical Community Ordination (version 4). Microcomputer Power, Ithaca, NY, USA, 352Google Scholar
  39. Ter Braak C.J.F. and Verdonschot P.F.M. (1995). Canonical correspondence analysis and related multivariate analysis in aquatic ecology. Aquat. Sci. 57(3): 255–289CrossRefGoogle Scholar
  40. Townsend C.R. (1989). The patch dynamic concept of stream community ecology. J. N. Am. Benthol. Soc. 8: 36–50CrossRefGoogle Scholar
  41. Ultsch A. and Siemon H.P. (1990). Kohonen's self organizing feature maps for exploratory data analysis. Kluwer, Dordrecht, The Netherlands, 305–308Google Scholar
  42. Vandenberghe V., Goethals P.L.M., Van Griensven A., Meirlan J., De Pauw N., Vanrolleghem P. and Bauwens W. 2004. Application of automated measurement stations for continuous water quality monitoring of the Dender river in Flanders, Belgium. Environ. Monit. Assess., in press.Google Scholar
  43. Vannote R.M., Minshall G.W., Cummins K.W., Sedell J.R. and Cushing E. (1980). The river continuum concept. Can. J. Fish Aquat. Sci. 37: 130–137CrossRefGoogle Scholar
  44. (1986). Flexclus, an interactive program for classification and tabulation of ecological data. Acta Bot. Neerl. 35(3): 137–142Google Scholar
  45. Verdonschot P.F.M. (1990). Ecological Characterization of Surface Waters in the Province of Overijssel (the Netherlands). PhD thesis, Wageningen, 255Google Scholar
  46. Verdonschot P.F.M. (1995). Typology of macrofaunal assemblages: a tool for the management of running waters in the Netherlands. Hydrobiologia 297: 99–122CrossRefGoogle Scholar
  47. Verdonschot P.F.M. (2000). Integrated ecological assessment methods as a basis for sustainable catchment management. Hydrobiologia 422(423): 389–412CrossRefGoogle Scholar
  48. Verdonschot P.F.M. and Nijboer R.C. (2000). Typology of macrofaunal assemblages applied to water and nature management: a Dutch approach. In: Wright, J.F., Sutcliffe, D.W. and Furse, M.T. (eds) Assessing the Biological Quality of Fresh Water: RIVPACS and Other Techniques, pp 241–262. Ambleside, UK, FBAGoogle Scholar
  49. Verdonschot P.F.M., Nijboer R.C. and Janssen S.N. (2000). Ecological typology limburg. Alterra, Wageningen, The Netherlands, 78Google Scholar
  50. Verdonschot P.F.M. and Nijboer R.C. (2002). Towards a decision support system for stream restoration in the Netherlands: an overview of restoration projects and future needs. Hydrobiologia 478(1–3): 131–148CrossRefGoogle Scholar
  51. Vesanto J., Himber J., Alhoniemi E. and Parhankangas J. (2000). SOM toolbox for MATLAB 5. Helsinki University of Technology, Publications in Computer and Information Science, Helsinki, Finland, 59Google Scholar
  52. (1997). Water quality 1996. Report Surface Water Monitoring. Aalst, Belgium (in Dutch)Google Scholar
  53. Walley W.J., Martin R.W. and O’Connor M.A. (2000). Self-organising maps for classification of river quality from biological and environmental data. In: Denzer, R., Swayne, D.A., Purvis, M., and Schimak, G. (eds) Environmental Software Systems: Environmental Information and Decision Support. IFIP Conference Series, pp 27–41. Kluwer Academic PublishersGoogle Scholar
  54. Walley W.J. and O’Connor M.A. (2001). Unsupervised pattern recognition for the interpretation of ecological data. Ecol. Model. 146: 219–230CrossRefGoogle Scholar
  55. Whittaker R.H. (1967). Gradient analysis of vegetation. Biol. Rev. 49: 207–264Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • V. Adriaenssens
    • 1
  • P. F. M. Verdonschot
    • 2
  • P. L. M. Goethals
    • 1
  • N. De Pauw
    • 1
  1. 1.Laboratory of Environmental Toxicology and Aquatic EcologyGhent UniversityGentBelgium
  2. 2.Alterra Green World ResearchWageningenThe Netherlands

Personalised recommendations