, Volume 566, Issue 1, pp 505–521

Assessing the impact of errors in sorting and identifying macroinvertebrate samples

  • Peter Haase
  • John Murray-Bligh
  • Susanne Lohse
  • Steffen Pauls
  • Andrea Sundermann
  • Rick Gunn
  • Ralph Clarke


This study assesses the impact of errors in sorting and identifying macroinvertebrate samples collected and analysed using different protocols (e.g. STAR-AQEM, RIVPACS). The study is based on the auditing scheme implemented in the EU-funded project STAR and presents the first attempt at analysing the audit data. Data from 10 participating countries are analysed with regard to the impact of sorting and identification errors. These differences are measured in the form of gains and losses at each level of audit for 120 samples. Based on gains and losses to the primary results, qualitative binary taxa lists were deducted for each level of audit for a subset of 72 data sets. Between these taxa lists the taxonomic similarity and the impact of differences on selected metrics common to stream assessment were analysed. The results of our study indicate that in all methods used, a considerable amount of sorting and identification error could be detected. This total impact is reflected in most functional metrics. In some metrics indicative of taxonomic richness, the total impact of differences is not directly reflected in differences in metric scores. The results stress the importance of implementing quality control mechanisms in macroinvertebrate assessment schemes.


stream assessment error estimation sample sorting macroinvertebrate identification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. AQEM consortium, 2004. AQEMdip: AQEM data input program. Downloadable from http://www.eu-star.at
  2. Armitage, P. D., Moss, D., Wright, J. F., Furse, M. T. 1983The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sitesWater Research17333347CrossRefGoogle Scholar
  3. Biss, R., P. Kübler, I. Pinter & U. Braukmann, 2002. Leitbildbezogenes biozönotisches Bewertungsverfahren für Fließgewässer (aquatischer Bereich) in der Bundesrepublik Deutschland. Ein erster Beitrag zur integrierten ökologischen Fließgewässerbewertung – Final report on CD-ROM. UBA Texts 62/02, BerlinGoogle Scholar
  4. Böhmer, J., Rawer-Jost, C., Zenker, A., Meier, C., Feld, C., Biss, R., Hering, D. 2004Development of a multimetric invertebrate based assessment system for German riversLimnologica34416432Google Scholar
  5. Boulton, A. J., Lake, P. S. 1992The ecology of two streams in Victoria, Australia. III. Temporal changes in species compositionFreshwater Biology27123138CrossRefGoogle Scholar
  6. Cao, Y., Hawkins, C. P., Vinson, M. R. 2003Measuring and controlling data quality in biological assemblage surveys with special reference to stream benthic macroinvertebratesFreshwater Biology4818981911CrossRefGoogle Scholar
  7. Carter, J. L., Resh, V. H. 2001After site selection and before data analysis: sampling, sorting, and laboratory procedures used in stream benthic macroinvertebrate monitoring programs by USA state agenciesJournal of the North American Benthological Society20658682CrossRefGoogle Scholar
  8. Clarke, R. T. 2000

    Uncertainty in estimates of river quality based on RIVPACS

    Wright, J. F.,Sutcliffe, D. W.Furse, M. T. eds. Assessing the Biological Quality of Freshwaters: RIVPACS and Similar TechniquesFreshwater Biological AssociationAmbleside3954
    Google Scholar
  9. Clarke, R. T., Furse, M. T., Gunn, R. J. M., Winder, J. M., Wright, J. F. 2002Sampling variation in macroinvertebrate data and implications for river quality indicesFreshwater Biology4717351751CrossRefGoogle Scholar
  10. Clarke, R. T., Davy-Bowker, J., Sandin, L., Friberg, N., Johnson, R. K., Bis, B 2006aEstimates and comparisons of the effects of sampling variation using ‘national’ macroinvertebrate sampling protocols on the precision of metrics used to assess ecological statusHydrobiologia566477503Google Scholar
  11. Clarke, R. T., Lorenz, A., Sandin, L., Schmidt-Kloiber, A., Strackbein, J., Kneebone, N. T., Haase, P. 2006bEffects of sampling and sub-sampling variation using the STAR-AQEM sampling protocol on the precision of macroinvertebrate metricsHydrobiologia566441459Google Scholar
  12. Doberstein, C., Karr, J., Conquest, L. 2000The effect of fixed-count subsampling on macroinvertebrate biomonitoring in small streamsFreshwater Biology44355371CrossRefGoogle Scholar
  13. European Union, 2000. Directive 2000/60/EC. Establishing a framework for community action in the field of water policy. European Commission PE-CONS 3639/1/100 Rev 1, LuxemburgGoogle Scholar
  14. Furse, M., Hering, D., Moog, O., Verdonschot, P., Johnson, R. K., Brabec, K., Gritzalis, K., Buffagni, A., Pinto, P., Friberg, N., Murray-Bligh, J., Kokes, J., Alber, R., Usseglio-Polatera, P., Haase, P., Sweeting, R., Bis, B., Szoszkiewicz, K., Soszka, H., Springe, G., Sporka, F., Krno, I. 2006The STAR project: context, objectives and approachesHydrobiologia566329Google Scholar
  15. Ganasan, V., Hughes, R. M. 1998Application of an index of biological integrity (IBI) to fish assemblages of the rivers Khan and Kshipra (Madhya Pradesh), IndiaFreshwater Biology40367383CrossRefGoogle Scholar
  16. Haase, P., Lohse, S., Pauls, S., Schindehütte, K., Sundermann, A., Rolauffs, P., Hering, D. 2004aAssessing streams in Germany with benthic invertebrates: development of a practical standardised protocol for macroinvertebrate sampling and sortingLimnologica34349365Google Scholar
  17. Haase, P., Pauls, S., Sundermann, A., Zenker, A. 2004bTesting different sorting techniques in macroinvertebrate samples from running watersLimnologica34366378Google Scholar
  18. Hering, D., Meier, C., Rawer-Jost, C., Biss, R., Feld, C., Zenker, A., Sundermann, A., Lohse, S., Böhmer, J. 2004aAssessing streams in Germany with benthic invertebrates: selection of candidate metricsLimnologica34398415Google Scholar
  19. Hering, D., Moog, O., Sandin, L., Verdonschot, P. F. M. 2004bOverview and application of the AQEM assessment systemHydrobiologia516120CrossRefGoogle Scholar
  20. Jaccard, P. 1901Étude comparative de la distribution florale dans une portion des Alpes et des JuraBulletin de la Société Vaudoise des Sciences Naturelles37547579Google Scholar
  21. Lorenz, A., Kirchner, L., Hering, D. 2004‘Electronic subsampling’ of macrobenthic samples: how many individuals are needed for a valid assessment result?Hydrobiologia516299312CrossRefGoogle Scholar
  22. Mann, H. B., Whitney, D. R. 1947On a test of whether one of two random variables is stochastically larger than the otherAnnals of Mathematical Statistics185060Google Scholar
  23. McCune, B., Mefford, M. J. 1999PC-ORD. Multivariate Analysis of Ecological Data. Version 4.25MjM SoftwareGleneden Beach, Oregon, USAGoogle Scholar
  24. McElravy, E. P., Lamberti, G. A., Resh, V. H. 1989Year-to-year variation in the aquatic macroinvertebrate fauna of a northern Californian StreamJournal of the North American Benthological Society85163CrossRefGoogle Scholar
  25. Murray-Bligh, J. A. D., M. T. Furse, F. H. Jones, R. J. M. Gunn, R. A. Dines & J. F. Wright, 1997. Procedure for collecting and analysing macroinvertebrate samples for RIVPACS. Joint publication by the Institute of Freshwater Ecology and the Environment Agency, 162 ppGoogle Scholar
  26. Murray-Bligh, J., J. van der Molen & P. Verdonschot, 2006. STAR deliverable No. 7: Audit of Performance incorporating Results of the La Bresse sampling and analysis workshop. Unpublished report. www.eu-star.atGoogle Scholar
  27. National Water Council1981River Quality: The 1980 Survey and Future OutlookNational Water CouncilUKGoogle Scholar
  28. Ofenböck, T., Moog, O., Gerritsen, J., Barbour, M. 2004A stressor specific multimetric approach for monitoring running waters in Austria using benthic macro-invertebratesHydrobiologia516251268CrossRefGoogle Scholar
  29. Ostermiller, J. D., Hawkins, C. P. 2004Effects of sampling error on bioassessments of stream ecosystems: application to RIVPACS-type modelsJournal of the North American Benthological Society23363382CrossRefGoogle Scholar
  30. Shannon, C. E., Weaver, W. 1949Mathematical Theory of CommunicationThe University of Illinois PressUrbana, ILGoogle Scholar
  31. Schweder, H. 1992Neue Indices für die Bewertung des ökologischen Zustandes von Fließgewässern, abgeleitet aus der Makroinvertebraten-ErnährungstypologieLimnologie Aktuell3353377Google Scholar
  32. Šporka, F., Vlek, H. E., Bulánková, E., Krno, I. 2006Influence of seasonal variation on bioassessment of streams using macroinvertebratesHydrobiologia566543555Google Scholar
  33. StatSoft, Inc., 2002. STATISTICA for Windows (Software-System for Data Analysis) Version 6.1. www.statsoft.comGoogle Scholar
  34. Weatherby, N. S., Ormerod, S. J. 1990The constancy of univoltine assemblages in soft water streams: implications for the publication and detection of environmental changeJournal of Applied Ecology27952964CrossRefGoogle Scholar
  35. Wiberg-Larsen, P., Brodersen, K. P., Birkholm, S., Grøn, P. N., Skriver, J. 2000Species richness and assemblage structure of Trichoptera in Danish streamsFreshwater Biology43633647CrossRefGoogle Scholar
  36. Wilcoxon, F. 1945Individual Comparisons by Ranking MethodsBiometrics18083CrossRefGoogle Scholar
  37. Vlek, H. E., Šporka, F., Krno, I. 2006Influence of macroinvertebrate sample size on bioassessment of streamsHydrobiologia566523542Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Peter Haase
    • 1
  • John Murray-Bligh
    • 2
  • Susanne Lohse
    • 1
  • Steffen Pauls
    • 1
  • Andrea Sundermann
    • 1
  • Rick Gunn
    • 3
  • Ralph Clarke
    • 3
  1. 1.Department of Limnology and Conservation ResearchSenckenberg – Research Institute and Natural History MuseumGelnhausenGermany
  2. 2.Environment AgencyExeterUK
  3. 3.CEH Dorset, Winfrith Technology CentreDorchesterUK

Personalised recommendations