Bayesian estimation of weather-driven processes to model ixodes ricinus population dynamics

International audience; The vector tick I. ricinus population dynamic is driven by weather conditions, mainly temperature and relative humidity. Both variables are of major concern in the context of climate warming. Providing relevant predictions for present and future tick activity time patterns requires integrating the joint effect of temperature and relative humidity on the life cycle biological processes in population dynamic models (PDM). A PDM of I. ricinus was established in which the biological processes varied with temperature and relative humidity time series. The PDM focused on host-questing and survival. Both were a priori modelled from expert knowledge and literature data respectively. The parameters associated with each process were then posteriori estimated based on observed field and laboratory data, using a Bayesian inference method. The posteriori parameters can be integrated in a PDM for simulations so as to assess their ability to fit to observed field time series