Statistical analysis of regulatory ecotoxicity tests

ANOVA-type data analysis, i.e., determination of lowest-observed-effect concentrations (LOECs), and no-observed-effect concentrations (NOECs), has been widely used for statistical analysis of chronic ecotoxicity data. However, it is more and more criticised for several reasons, among which the most...

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Bibliographic Details
Published in:	Chemosphere (Oxford) Vol. 45; no. 4; pp. 659 - 669
Main Authors:	Isnard, P., Flammarion, P., Roman, G., Babut, M., Bastien, Ph, Bintein, S., Esserméant, L., Férard, J.F., Gallotti-Schmitt, S., Saouter, E., Saroli, M., Thiébaud, H., Tomassone, R., Vindimian, E.
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01-11-2001 Elsevier
Subjects:	Animal, plant and microbial ecology Animals ANOVA Applied ecology Biological and medical sciences Daphnia EC 50 EC x Ecosystem Ecotoxicology, biological effects of pollution Environmental Sciences Eukaryota Fishes Fundamental and applied biological sciences. Psychology Hypothesis testing Models, Statistical No-Observed-Adverse-Effect Level NOEC Pisces Regression Regression Analysis Risk Assessment Techniques Toxicity Tests - statistics & numerical data Hypothesis testing Regression NOEC ANOVA EC 50 EC x Hypothesis test Statistical analysis Ecotoxicology Concentration effect Regression analysis Effective dose Toxicity test No observed effect level Variance analysis REGRESSION EC50 ECX HYPOTHESIS TESTING
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Summary:	ANOVA-type data analysis, i.e., determination of lowest-observed-effect concentrations (LOECs), and no-observed-effect concentrations (NOECs), has been widely used for statistical analysis of chronic ecotoxicity data. However, it is more and more criticised for several reasons, among which the most important is probably the fact that the NOEC depends on the choice of test concentrations and number of replications and rewards poor experiments, i.e., high variability, with high NOEC values. Thus, a recent OECD workshop concluded that the use of the NOEC should be phased out and that a regression-based estimation procedure should be used. Following this workshop, a working group was established at the French level between government, academia and industry representatives. Twenty-seven sets of chronic data (algae, daphnia, fish) were collected and analysed by ANOVA and regression procedures. Several regression models were compared and relations between NOECs and EC x , for different values of x, were established in order to find an alternative summary parameter to the NOEC. Biological arguments are scarce to help in defining a negligible level of effect x for the EC x . With regard to their use in the risk assessment procedures, a convenient methodology would be to choose x so that EC x are on average similar to the present NOEC. This would lead to no major change in the risk assessment procedure. However, experimental data show that the EC x depend on the regression models and that their accuracy decreases in the low effect zone. This disadvantage could probably be reduced by adapting existing experimental protocols but it could mean more experimental effort and higher cost. EC x (derived with existing test guidelines, e.g., regarding the number of replicates) whose lowest bounds of the confidence interval are on average similar to present NOEC would improve this approach by a priori encouraging more precise experiments. However, narrow confidence intervals are not only linked to good experimental practices, but also depend on the distance between the best model fit and experimental data. At least, these approaches still use the NOEC as a reference although this reference is statistically not correct. On the contrary, EC 50 are the most precise values to estimate on a concentration–response curve, but they are clearly different from the NOEC and their use would require a modification of existing assessment factors.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0045-6535 1879-1298
DOI:	10.1016/S0045-6535(00)00600-7