Orateur
Prof.
Gilles Durrieu
(Universite Bordeaux 1)
Description
The high-frequency measurements of valve activity in bivalves (e.g. valvometry) over a long period of time and in various environmental conditions allows a very
accurate study of the animal’s behaviors as well as a global analysis of possible perturbations due to the environment. Valvometry uses the bivalve’s ability to close its
shell when exposed to a contaminant or other abnormal environmental conditions as an alarm that indicates some possible perturbations of the environment. The modeling
of such high-frequency serial valvometry data is statistically challenging and here we propose a nonparametric approach based on kernel estimation. This method has the advantage to summarize the complex data into a simple density profile obtained for every animal and at every 24 hours period and then to make inference about the effect of time and external conditions on each animal’s profile. The statistical properties of the estimator are presented. Through an application to a sample of 16 oysters living in the Bay of Arcachon (France), we demonstrate how this method can be used, first to estimate the normal biological rhythms of permanently immersed oysters and second, to detect perturbations of these rhythms due to changes in their environment. We anticipate that this approach could have an important contribution to the survey of aquatic systems.
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G. Durrieu