Cost-effective experimental design to support modeling of concentration-response functions.
Modeling concentration-response function became extremely popular in ecotoxicology during the last decade. Indeed, modeling allows determining the total response pattern of a given substance. However, reliable modeling is consuming in term of data, which is in contradiction with the current trend in ecotoxicology, which aims to reduce, for cost and ethical reasons, the number of data produced during an experiment. It is therefore crucial to determine experimental design in a cost-effective manner. In this paper, we propose to use the theory of locally D-optimal designs to determine the set of concentrations to be tested so that the parameters of the concentration-response function can be estimated with high precision. We illustrated this approach by determining the locally D-optimal designs to estimate the toxicity of the herbicide dinoseb on daphnids and algae. The results show that the number of concentrations to be tested is often equal to the number of parameters and often related to the their meaning, i.e. they are located close to the parameters. Furthermore, the results show that the locally D-optimal design often has the minimal number of support points and is not much sensitive to small changes in nominal values of the parameters. In order to reduce the experimental cost and the use of test organisms, especially in case of long-term studies, reliable nominal values may therefore be fixed based on prior knowledge and literature research instead of on preliminary experiments.
Chèvre
Nathalie
N
Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland. nathalie.chevre@unil.ch
Brazzale
Alessandra R
AR
D019297:2,4-Dinitrophenol / Q000032:analysis / Q000633:toxicity; D000465:Algorithms; D000818:Animals; D003362:Cost-Benefit Analysis; D003621:Daphnia; D004785:Environmental Pollutants / Q000032:analysis / Q000633:toxicity*; D056890:Eukaryota / Q000187:drug effects; D006128:Growth / Q000187:drug effects; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010575:Pesticides / Q000032:analysis / Q000633:toxicity; D012044:Regression Analysis; D012098:Reproduction / Q000187:drug effects; D012107:Research Design / Q000706:statistics & numerical data*; D013534:Survival
D004785:Environmental Pollutants; D010575:Pesticides; D019297:2,4-Dinitrophenol