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Chemometric discrimination between streams based on chemical, limnological and biological data taken from freshwater fishes and their interrelationships

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Journal of Aquatic Ecosystem Stress and Recovery

Abstract

The VALIMAR project aims at identifyingbiomarkers in fish that are suitable to detectand predict environmental stress from chemicalpollution or from limnological parameters inthe field. For two small streams in SouthernGermany, concentration values of 31contaminants in water and sediment and 12 limnological parameters as well as 27 biomarkersmeasured in brown trout and stone loach werecollected. All these physicochemical andbiological parameters have been analysed forpatterns that discriminate between the streams,using discriminant analysis (DA), analysis ofvariance (ANOVA) and of covariance (ANCOVA), and principal component analysis (PCA) asmultivariate statistical techniques. Moreover,the biological data were analyzed with respectto species-specific patterns, and the partialleast-squares regression method (PLS) was usedto study the impact of chemical and limnological data on the health status of the targetspecies as characterized by the biomarker data.Abiotic as well as biotic data yielded goodseparations between the streams, with theultrastructure of gill (US-gill) being thestrongest discriminator variable among all 27biomarkers tested. With regard to the two fishspecies, the biomarker data from brown troutshow significantly greater differences betweenthe two streams than the biological responsesin stone loach. Application of PLS yieldssignificant regression models for only fewbiomarkers including US-Gill, which can bepartly traced back to significant noise levelsin the data set as quantified by permutationtests.

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References

  • Adam, S., M. Pawert, R. Lehmann, B. Roth, E. Müller & R. Triebskorn, 2001. Physicochemical and morphological characterization of two small polluted streams in southwest Germany. J. Aquat. Ecosyst. Stress. Recov. 8: 179-194.

    Google Scholar 

  • Adams, S. M., K. D. Ham & J. J. Beauchamp, 1994. Application of canonical variate analysis in the evaluation and presentation of multivariate biological response data. Environ. Toxicol. Chem. 13: 1673-1683.

    Google Scholar 

  • Aries, R. E., Lidiard, R. A. & R. A. Spragg, 1991. Principal component analysis. Chem. Br.: 821-824.

  • Behrens, A. & H. Segner, 2001. Hepatic biotransformation enzymes of fish exposed to non-point source pollution in small streams. J. Aquat. Ecosyst. Stress. Recov. 8: 281-297.

    Google Scholar 

  • Einax, J. W., A. Aulinger, W. V. Tümpling & A. Prange, 1999. Quantitative description of element concentrations in longitudinal river profiles by multiway PLS models. Fresenius J. Anal. Chem. 363: 655-661.

    Google Scholar 

  • Einax, J. W., O. Kampe & D. Truckenbrodt, 1998a. Assessing the deposition and remobilisation behaviour of metals between river water and river sediment using partial least squares regression. Fresenius J. Anal. Chem. 361: 149-154.

    Google Scholar 

  • Einax, J. W., D. Truckenbrodt & O. Kampe, 1998b. River pollution data interpreted by means of chemometrical methods. Microchem. J. 58: 315-324.

    Google Scholar 

  • Eriksson, L. & J. L. M. Hermens, 1995. A multivariate approach to quantify structure-activity and structure-property relationships. In: Einax, J. (ed.), Chemometrics in Environmental Chemistry-Applications. The Handbook of Environmental Chemistry (ed. Hutzinger, O.), Vol. 2, Part H, pp. 135-168.

  • Geladi, P. & B. R. Kowalski, 1986. Partial least squares regression: A Tutorial. Analytica Chimica Acta 185: 1-17.

    Google Scholar 

  • Gernhöfer, M., M. Pawert, M. Schramm, E. Müller & R. Triebskorn, 2001. Ultrastructural biomarkers as tools to characterize the health status of fish in contaminated streams. J. Aquat. Ecosyst. Stress. Recov. 8: 241-260.

    Google Scholar 

  • Hollander, M. & D. A. Wolfe, 1973. Nonparametric Statistical Methods. John Wiley & Sons, New York.

    Google Scholar 

  • Honnen, W., K. Rath, T. Schlegel, A. Schwinger & D. Frahne, 2001. Chemical analyses of water, sediment and biota in two small streams in southwest Germany. J. Aquat. Ecosyst. Stress. Recov., 8: 195-213.

    Google Scholar 

  • Klecka, W. R., 1980. Discriminant Analysis. Quantitative Applications in the Social Sciences Series, No. 19. Sage Publications, Thousand Oaks, CA.

    Google Scholar 

  • Köhler, H.-R., C. Bartussek, H. Eckwert, K. Farian, S. Gränzer, T. Knigge & N. Kunz, 2001. The hepatic stress protein (hsp70) response to interacting abiotic parameters in fish exposed to various levels of pollution. J. Aquat. Ecosyst. Stress Recov. 8: 261-279.

    Google Scholar 

  • Konradt, J. & T. Braunbeck, 2001. Alterations of selected metabolic enzymes in fish following long-term exposure to contaminated streams. J. Aquat. Ecosyst. Stress Recov. 8: 299-318.

    Google Scholar 

  • Schmitt, H., R. Altenburger, B. Jastorff & G. Schüürmann, 2000. Quantitative structure-activity analysis of the algae toxicity of nitroaromatic compounds. Chem. Res. Toxicol. 13: 441-450.

    Google Scholar 

  • Schüürmann, G., 1998. Ecotoxic modes of action of chemical substances. In: Schüürmann, G. & Markert, B. (eds), Ecotoxicology, pp. 665-749. John Wiley and Spektrum Akademischer Verlag, New York.

    Google Scholar 

  • Schüürmann, G., H. Segner & K. Jung, 1997. Multivariate mode-ofaction analysis of acute toxicity of phenols. Aquat. Toxicol. 38: 277-296.

    Google Scholar 

  • Schwaiger, J., 2001. Histopathological alterations and parasite infection in fish: indicators of multiple stress factors. J. Aquat. Ecosyst. Stress. Recov. 8: 231-240.

    Google Scholar 

  • StatSoft, Inc., 1999. STATISTICA for Windows '99 Edition. Stat-Soft, Inc., 2300 East 14th Street, Tulsa, OK 74104.

    Google Scholar 

  • Triebskorn, R., J. Böhmer, T. Braunbeck, W. Honnen, H.-R. Köhler, R. Lehmann, A. Oberemm, J. Schwaiger, H. Segner, G. Schüürmann & W. Traunspurger, 2001. The project VALIMAR (VALIdation of bioMARkers for the assessment of small stream pollution): objectives, experimental design, summary of results, and recommendations for the application of biomarkers in risk assessment. J. Aquat. Ecosyst. Stress recov. 8: 161-178.

    Google Scholar 

  • Verhaar, H. J. M., L. Eriksson, M. Sjöström, G. Schüürmann, W. Seinen & J. Hermens, 1994. Modelling the toxicity of organophosphorus compounds: A comparison of multiple linear regression and PLS regression methods. Quant. Struct.-Act. Relat. 13: 133-143.

    Google Scholar 

  • Wold, S., 1995. PLS for multivariate modeling. In: van de Waterbeemd (ed.), Chemometric Methods in Molecular Design, pp. 195-218. VCH Weinheim (Germany).

    Google Scholar 

  • Zupan, M., J. W. Einax, J. Kraft, F. Lobnik & V. Hudnik, 2000. Chemometric characterization of soil and plant pollution. Part 1: Multivariate data analysis and geostatistical determination of relationship and spatial structure of inorganic contaminants in soil. ESPR-Environ. Sci. Pollut. Res. 7: 89-96.

    Google Scholar 

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Correspondence to Gerrit Schüürmann.

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Dietze, U., Braunbeck, T., Honnen, W. et al. Chemometric discrimination between streams based on chemical, limnological and biological data taken from freshwater fishes and their interrelationships. Journal of Aquatic Ecosystem Stress and Recovery 8, 319–336 (2001). https://doi.org/10.1023/A:1012979502278

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