Stickleback mathematical models to improve ecotoxicological risk assessments : from toxicokinetic to population dynamics

The integration of stickleback mechanistic models in environmental risk assessment can describe the complex processes linking exposure or environmental concentrations (external dose) to internal dose, up to toxicity in a given population, community or the whole ecosystem. Two phases can be distinguished in the dose-response relationship of a chemical in an organism, or at higher level of organisation: the toxicokinetics (TK), that is the fate of the compound in the organism including absorption, distribution, metabolism and excretion (ADME), and the toxicodynamics (TD), that is the expression of the toxicity/effect of the substance. The stickleback offers the opportunity in a same biological model to integrate all these processes, and then, to describe for a given substance the TK, TD at the organism and population levels. A Physiological-based TK model (PBTK) has been developed for sticklebacks. This model integrates the main physiological characteristics to model ADME processes, including the stickleback growth and the impacts of the water temperature on these processes. This model was evaluated using data obtained on chlorpyrifos (water exposure), and provided accurate predictions. To extrapolate toxic effects from the individuals to the population level, a convenient approach is to use Individual-Based Models (IBMs) coupled to a bioenergetic dynamic model describing the physiological processes of the individuals (Dynamic Energy Budget theory). Accordingly, a DEB-IBM model was developed for describing the population dynamics of sticklebacks in artificial streams (mesocosms). The model predictions were compared to the data observed in a mesocosm experiment to assess the impacts of an endrocrine disruptor, the bisphenol A. The DEB-IBM provided great insight of the effects of Bisphenol A on the population dynamics. The mathematical models developed for sticklebacks coupled to the use of this fish in active biomonitoring strategy which integrate a multiFbiomarker approach highlight the strengths of the stickleback in environmental risk assessments