Journal of Paleolimnology

, Volume 44, Issue 1, pp 253–264 | Cite as

Palaeolimnological validation of estimated reference values for a lake profundal macroinvertebrate metric (Benthic Quality Index)

  • Jussi Jyväsjärvi
  • Jukka Nyblom
  • Heikki Hämäläinen
Original paper
First Online:
Received:
Accepted:

Abstract

Modern assessment and monitoring of aquatic ecosystems is increasingly based on biota and the “reference condition” approach, in which the observed values (O) of biological variables are compared to those expected in the absence of human disturbance (E). To use this approach, correct estimation and validation of reference conditions are critical. Because appropriate modern or historical data are never available for this approach, palaeolimnological data offer an alternative. We used a calibration data set from 73 profundal sites in semi-pristine Finnish lakes to construct a regression model for estimating expected values for the chironomid Benthic Quality Index (BQI)—a macroinvertebrate metric widely used in bioassessment—from environmental variables that are insensitive to human disturbance. For comparison, reference values were estimated using the European legislative rationale based on a priori lake typology. Performance of the alternative approaches was assessed by internal ‘leave-one-out’ cross-validation using the calibration set and by external cross-validation using independent palaeolimnological data on BQI values representing the historical pristine status of 24 lake basins. Additionally, for 19 of these sites, which vary in their degree of human impact, the ratio of present BQI to that in pristine condition, which shows the degree of actual change, if any, was calculated from palaeolimnological data and compared with the O/E ratios based on the present chironomid data and estimated E. A linear regression model with mean depth and mean/maximum depth ratio as independent variables estimated the reference values of BQI much closer to the observed ones (r 2 = 0.58, RMSEP = 0.65 and r 2 = 0.71 RMSEP = 0.55; for internal and external cross-validation, respectively) than did the typology approach (r 2 = 0.28, RMSEP = 0.86; r 2 = 0.10, RMSEP = 0.97). The regression approach also yielded O/E ratios more similar to the actual ones (r 2 = 0.79, RMSEP = 0.09) than did the typology approach (r 2 = 0.62, RMSEP = 0.23). Our results strongly support the use of lake morphometric variables and modelling instead of categorical lake typology for the establishment of reference conditions for profundal macroinvertebrate communities and demonstrate the utility of palaeolimnological data in the validation of reference values and assessment methods.

Keywords

Bioassessment Predictive modelling Reference condition approach Chironomid communities Lake morphometry 
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Notes

Acknowledgments

The preparation of this manuscript was financially supported by the Maj and Tor Nessling Foundation and the Ella and Georg Ehrnrooth Foundation. Professor Roger Jones kindly provided helpful comments for the manuscript.

References

  1. Andersen JH, Conley DJ, Hedal S (2004) Palaeoecology, reference conditions and classification of ecological status: the EU water framework directive in practice. Mar Pollut Bull 49:283–290CrossRefGoogle Scholar
  2. Anderson NH, Battarbee RW (1994) Aquatic community persistence and variability: a palaeolimnological perspective. In: Giller PS, Hildrew AG, Raffaelli D (eds) Aquatic ecology: scale pattern and process. Blackwell Scientific, Oxford, pp 233–259Google Scholar
  3. Aroviita J, Koskenniemi E, Kotanen J, Hämäläinen H (2008) A priori typology-based prediction of benthic macroinvertebrate fauna for ecological classification of rivers. Environ Manage 42:894–906CrossRefGoogle Scholar
  4. Aroviita J, Mykrä H, Muotka T, Hämäläinen H (2009) Influence of geographical extent on typology- and model-based assessments of taxonomic completeness of river macroinvertebrates. Freshw Biol 54:1774–1787CrossRefGoogle Scholar
  5. Bailey R, Norris RH, Reynoldson TB (2004) Bioassessment of freshwater ecosystems: using the reference condition approach. Springer, New YorkGoogle Scholar
  6. Bennion H, Fluin J, Simpson GL (2004) Assessing eutrophication and reference conditions for Scottish freshwater lochs using subfossil diatoms. J Appl Ecol 41:124–138CrossRefGoogle Scholar
  7. Brinkhurst RO (1974) The benthos of lakes. Blackburn Press, CaldwellGoogle Scholar
  8. Davies SP, Jackson SK (2006) The biological condition gradient: a descriptive model for interpreting change in aquatic ecosystems. Ecol Appl 16:1251–1266CrossRefGoogle Scholar
  9. Davy-Bowker J, Clarke RT, Johnson RK, Kokes J, Murphy JF, Zahradkova S (2006) A comparison of the European water framework directive physical typology and RIVPACS-type models as alternative methods of establishing reference conditions for benthic macroinvertebrates. Hydrobiologia 566:91–105CrossRefGoogle Scholar
  10. Efron B, Gong G (1983) A leisure look at the bootstrap, the jackknife, and cross-validation. Am Stat 37:36–48CrossRefGoogle Scholar
  11. European Commission (2000) Directive 2000/60/EC 2000. Establishing a framework for Community action in the field of water policy. Off J Eur Communities L327(1):1–72Google Scholar
  12. European Commission (2003) Common implementation strategy for the water framework directive (2000/60/EC). Guidance document no. 10 river and lakes—Typology, reference conditions and classification systems. Office for Official Publications of the European Communities, LuxembourgGoogle Scholar
  13. Håkanson L (1981) A manual of lake morphometry. Springer, BerlinGoogle Scholar
  14. Hawkins CP (2006) Quantifying biological integrity by taxonomic completeness: its utility in regional and global assessments. Ecol Appl 16:1277–1294CrossRefGoogle Scholar
  15. Heiri O (2004) Within-lake variability of subfossil chironomid assemblages in shallow Norwegian lakes. J Paleolimnol 32:67–84CrossRefGoogle Scholar
  16. Honkanen V, Laitinen K, Meriläinen JJ (2004) Saarijärven mahtava Pyhäjärvi. City of Saarijärvi, SaarijärviGoogle Scholar
  17. Hughes RM (1995) Defining Acceptable Biological Status by Comparing with Reference Conditions. In: Davis WS, Simon TP (eds) Biological assessment and criteria tools for water resource planning and decision making. Lewis Publishers, Boca Raton, pp 31–47Google Scholar
  18. Hynynen J, Palomäki A, Meriläinen JJ, Witick A, Mäntykoski K (2004) Pollution history and recovery of a boreal lake exposed to a heavy bleached pulping effluent load. J Paleolimnol 32:351–374CrossRefGoogle Scholar
  19. Ilyashuk B, Ilyashuk E, Dauvalter V (2003) Chironomid responses to long-term metal contamination: a palaeolimnological study in two bays of Lake Imandra, Kola Peninsula, Northern Russia. J Paleolimnol 30:217–230CrossRefGoogle Scholar
  20. Itkonen A, Marttila V, Meriläinen JJ, Salonen V (1999) 8000-year history of palaeoproductivity in a large boreal lake. J Paleolimnol 21:271–294CrossRefGoogle Scholar
  21. Johnson RK (1996) The indicator concept in freshwater biomonitoring. In: Cranston P (ed) Chironomids: from genes to ecosystems. CSIRO, Canberra, Australia, pp 11–27Google Scholar
  22. Jyväsjärvi J, Tolonen KT, Hämäläinen H (2009) Natural variation of profundal macroinvertebrate communities in boreal lakes is related to lake morphometry: implications for bioassessment. Can J Fish Aquat Sci 66:589–601CrossRefGoogle Scholar
  23. Kansanen PH, Paasivirta L, Väyrynen T (1990) Ordination analysis and bioindices based on zoobenthos communities used to assess pollution of a lake in southern Finland. Hydrobiologia 202:153–170CrossRefGoogle Scholar
  24. Kauppila T, Moisio T, Salonen V-P (2002) A diatom-based inference model for autumn epilimnetic total phosphorus concentration and its application to a presently eutrophic boreal lake. J Paleolimnol 27:261–273CrossRefGoogle Scholar
  25. Kurek J, Cwynar LC (2009) Effects of within-lake gradient on the distribution of fossil chironomids from maar lakes in western Alaska: implications for environmental reconstructions. Hydrobiologia 623:37–52CrossRefGoogle Scholar
  26. Leira M, Jordan P, Taylor D, Dalton C, Bennion H, Rose N, Irvine K (2006) Assessing the ecological status of candidate reference lakes in Ireland using palaeolimnology. J Appl Ecol 43:816–827CrossRefGoogle Scholar
  27. Mazor RD, Reynoldson TB, Rosenberg DM, Resh VH (2006) Effects of biotic assemblage, classification, and assessment method on bioassessment performance. Can J Fish Aquat Sci 63:394–411CrossRefGoogle Scholar
  28. Meriläinen JJ, Hamina V (1993) Recent environmental history of a large, originally oligotrophic lake in Finland: a palaeolimnological study of chironomid remains. J Paleolimnol 9:129–140CrossRefGoogle Scholar
  29. Meriläinen JJ, Hynynen J, Palomäki A, Reinikainen P, Teppo A, Granberg K (2000) Importance of diffuse nutrient loading and lake level changes to the eutrophication of an originally oligotrophic boreal lake: a palaeolimnological diatom and chironomid analysis. J Paleolimnol 24:251–270CrossRefGoogle Scholar
  30. Meriläinen JJ, Hynynen J, Palomäki A, Veijola H, Witick A, Mäntykoski K, Granberg K, Lehtinen K (2001) Pulp and paper mill pollution and subsequent ecosystem recovery of a large boreal lake in Finland: a paleolimnological analysis. J Paleolimnol 26:11–35CrossRefGoogle Scholar
  31. Meriläinen JJ, Hynynen J, Palomäki A, Mäntykoski K, Witick A (2003) Environmental history of an urban lake: a palaeolimnological study of Lake Jyväsjärvi, Finland. J Paleolimnol 30:387–406CrossRefGoogle Scholar
  32. Miettinen JO, Hämäläinen H, Simola H (2002) Iisalmen reitin Onkiveden luonnontilan arviointi paleolimnologisilla analyyseillä–vesidirektiivin toteuttamisen pilottitutkimus. In: Grönlund E, Viljanen M, Juvonen P, Holopainen IJ (eds) Suurjärviseminaari 2001–Ympäristö ja Yhteiskunta. University of Joensuu, Joensuu, pp 345–351Google Scholar
  33. Miettinen JO, Kukkonen M, Simola H (2005) Hindcasting baseline values for water colour and total phosphorus concentration in lakes using sedimentary diatoms–implications for lake typology in Finland. Boreal Environ Res 10:31–43Google Scholar
  34. Neale MW, Rippey B (2008) A comparison of environmental and biological site classifications for the prediction of macroinvertebrate communities of lakes in Northern Ireland. Aquat Conserv Mar Freshw Ecosyst 18:729–741CrossRefGoogle Scholar
  35. Paasivirta L (1989) Pohjaeläimistötutkimuksen liittäminen järvisyvännealueiden seurantaan. Report no. 164, Vesi- ja ympäristöhallitusGoogle Scholar
  36. Reynoldson TB, Bailey RC, Day KE, Norris RH (1995) Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state. Aust J Ecol 20:198–219CrossRefGoogle Scholar
  37. Reynoldson TB, Norris RH, Resh VH, Day KE, Rosenberg DM (1997) The reference condition: a comparison of multimetric and multivariate approaches to assess water-quality impairment using benthic macroinvertebrates. J North Am Benthol Soc 16:833–852CrossRefGoogle Scholar
  38. Saether OA (1979) Chironomid communities as water quality indicators. Holarctic Ecol 2:65–74Google Scholar
  39. Sandman O, Meriläinen JJ, Simola H, Hynynen J, Lahtinen J, Marttila V, Reinikainen P (2000) Short-code paleolimnological investigation of Lake Pihlajavesi in the Saimaa Lake complex, eastern Finland: assessment of habitat quality of an endemic and endangered seal population. J Paleolimnol 24:317–329CrossRefGoogle Scholar
  40. Simola H, Meriläinen JJ, Sandman O, Marttila V, Karjalainen H, Kukkonen M, Julkunen-Tiitto R, Hakulinen J (1996) Palaeolimnological analyses as information source for large lake biomonitoring. Hydrobiologia 322:283–292CrossRefGoogle Scholar
  41. Smol JP (1992) Paleolimnology: an important tool for effective ecosystem management. J Aquat Ecosyst Health 1:49–58CrossRefGoogle Scholar
  42. Stoddard JL, Larsen DP, Hawkins CP, Johnson RK, Norris RH (2006) Setting expectations for the ecological condition of streams: the concept of reference condition. Ecol Appl 16:1267–1276CrossRefGoogle Scholar
  43. Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J Roy Stat Soc 36:111–147Google Scholar
  44. Veijola H, Meriläinen JJ, Marttila V (1996) Sample size in the monitoring of benthic macrofauna in the profundal of lakes: evaluation of the precision of estimates. Hydrobiologia 322:301–315CrossRefGoogle Scholar
  45. Vuori KM, Bäck S, Hellsten S, Karjalainen SM, Kauppila P, Lax HG, Lepistö L, Londesborough S, Mitikka S, Niemelä P, Niemi J, Perus J, Pietiläinen OP, Pilke A, Riihimäki J, Rissanen J, Tammi J, Tolonen K, Vehanen T, Vuoristo H, Westberg V (2006) The basis for typology and ecological classification of water bodies in Finland. Report no. 807, Finnish Environment Institute, Yliopistopaino, Helsinki (in Finnish with English abstract)Google Scholar
  46. Wallach D, Goffinet B (1989) Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecol Modell 44:299–306CrossRefGoogle Scholar
  47. Wiederholm T (1980) Use of benthos in lake monitoring. J Water Pollut Control Fed 52:537–547Google Scholar
  48. Wiederholm T, Eriksson L (1979) Subfossil chironomids as evidence of eutrophication in Ekoln Bay, central Sweden. Hydrobiologia 62:195–208Google Scholar
  49. Wright JF, Sutcliffe DW, Furse MT (2000) Assessing the biological quality of fresh waters RIVPACS and other techniques. Freshwater Biological Association, AmblesideGoogle Scholar
  50. Yuan LL, Hawkins CP, Van Sickle J (2008) Effects of regionalization decisions on an O/E index for the US national assessment. J North Am Benthol Soc 27:892–905CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Jussi Jyväsjärvi
    • 1
  • Jukka Nyblom
    • 2
  • Heikki Hämäläinen
    • 1
  1. 1.Department of Biological and Environmental ScienceUniversity of JyväskyläJyväskyläFinland
  2. 2.Department of Mathematics and StatisticsUniversity of JyväskyläJyväskyläFinland

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