Modelling Community Response for Ecological Significance Based on Laboratory Simulations of Variable Copper Exposure

  • Khun C. Tan
  • Carolyn Oldham
Conference paper


Communities in aquatic ecosystems receiving runoff contaminants from urban catchments are often preconditioned to a background contamination level during dry periods. This pre-exposure may affect the community response to subsequent storm runoff contaminants. This work presents a model to describe the response of an assemblage of estuarine periphyton to such variable copper exposure, i.e. long term at a baseline concentration, followed by short-term at higher runoff concentration. The model allows for differentiation of effects between the long-term and short-term exposures, with quantification of the enhancing and suppressing effects of copper on the community. The model was evaluated based on the response curve of a periphyton community under laboratory-simulated exposure to variable copper concentrations, with PSII quantum yield as the response measure. Model predictions are close to observed values. The model shows improved goodness of fit for positive response compared to the traditional logistic model. Diagnosis of the model identified new effect concentration points which are of ecological relevance. They include the Pivotal-Effect Concentration (PC) at maximum yield (Y max) and the effect concentration at 50 % yield (E 0.5Y). Therefore, the model described can be a useful tool for better understanding and managing ecological impacts of runoff on receiving aquatic ecosystems.


Copper Concentration Effect Concentration Community Response Akaike Information Criterion Ecological Relevance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Thanks to Louis Evans, Curtin University of Technology, and Carolyn Oldham, the University of Western Australia, for organizing the student scholarship and project funding, and sincere thanks to Brian Jones, Department of Fisheries, Western Australia, Australia, and Peter Chapman, EVS Environmental Consultants, Canada, for their valuable discussion.


  1. 1.
    Tsihrintzis VA, Hamid R (1997) Modelling and management of urban stormwater runoff quality: a review. Water Resour Manag 11:137–164Google Scholar
  2. 2.
    Scherman PA, Muller WJ, Palmer CG (2003) Links between ecotoxicology, biomonitoring and water chemistry in the integration of water quality into environmental flow assessments. River Res Appl 19:483–493CrossRefGoogle Scholar
  3. 3.
    Linhurst RA, Bourdeau P, Tardiff RG (1995) A conceptual approach for ecological research, monitoring, and assessment. In: Linhurst RA, Bourdeau P, Tardiff RG (eds) Methods to assess the effects of chemicals on ecosystems. Wiley, ChichesterGoogle Scholar
  4. 4.
    Koepp H (1997) NOEC or what? SETAC Eur News 8:3–4Google Scholar
  5. 5.
    ANZECC (2000) The national water quality management strategy Paper No. 4: Australian and New Zealand guidelines for fresh and marine water quality. Australian and New Zealand Environment and Conservation Council, CanberraGoogle Scholar
  6. 6.
    Newman MC, Ownby DR, Mezin LCA, Powell DC, Christensen TL, Lerberg SB, Anderson BA (2000) Applying species -sensitivity distributions in ecological risk assessment: assumptions of distribution type and sufficient numbers of species. Environ Toxicol Chem 19:508–515Google Scholar
  7. 7.
    Chapman PM (2002) Ecological risk assessment (ERA) and hormesis. Sci Total Environ 288:131–140CrossRefGoogle Scholar
  8. 8.
    Brain P, Cousens R (1989) An equation to describe dose responses where there is simulation of growth at low doses. Weed Res 29:93–96CrossRefGoogle Scholar
  9. 9.
    Calow P, Sibly RM (1990) A physiological basis of population processes: ecotoxicological implications. Funct Ecol 4:283–288CrossRefGoogle Scholar
  10. 10.
    Tan CK (2006) An ecotoxicological study of urban runoff, Doctoral Thesis. The University of Western AustraliaGoogle Scholar
  11. 11.
    Stauber JL, Florence TM (1987) Mechanism of toxicity of ionic copper and copper complexes to algae. Mar Biol 94:511–519CrossRefGoogle Scholar
  12. 12.
    Sakamoto Y, Ishiguro M, Kitagawa G (1986) Akaike information criterion statistics. D Reidel Publishing Company, DordrechtMATHGoogle Scholar
  13. 13.
    Davison AC, Hinkley DV (1999) Bootstrap methods and their application. Cambridge University Press, CambridgeGoogle Scholar
  14. 14.
    Efron B, Tibshirani R (1993) An Introduction to the Bootstrap. Chapman and Hall, New YorkCrossRefMATHGoogle Scholar
  15. 15.
    Grist EPM, Leung KMY, Wheeler JR, Crane M (2002) Better bootstrap estimation of hazardous concentration thresholds for aquatic assemblages. Environ Toxicol Chem 21:1515–1524CrossRefGoogle Scholar
  16. 16.
    Chernick MR (1999) Bootstrap methods. Wiley, New YorkMATHGoogle Scholar
  17. 17.
    van Ewijk PH, Hoekstra JA (1993) Calculation of the EC50 and its confidence interval when subtoxic stimulus is present. Ecotoxicol Environ Saf 25:25–32CrossRefGoogle Scholar
  18. 18.
    Scholze M, Boedeker W, Faust M, Backhaus T, Altenburger R, Grimme LH (2001) A general best-fit method for concentration-response curves and the estimation of low-effect concentrations. Environ Toxicol Chem 20:448–457CrossRefGoogle Scholar
  19. 19.
    Schindler DW (1996) Ecosystems and ecotoxicology: a personal perspective. In: Newman MC, Jagoe CH (eds) Ecotoxicology: a hierarchical treatment. Lewis Publishers, Boca RatonGoogle Scholar
  20. 20.
    Chapman PM, Wand F (2000) Issues in ecological risk assessment of metals and metalloids. Hum Ecol Risk Assess 6:965–988CrossRefGoogle Scholar
  21. 21.
    Lassiter RR (1986) Design criteria for a predictive ecological effects modelling system. Aquatic Toxicol Environ 921:42–54Google Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  1. 1.Universiti Tunku Abdul Rahman, Jalan UniversitiKamparMalaysia
  2. 2.The University of Western AustraliaCrawleyAustralia

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