Hierarchical Bayesian Modeling of Brown Trout Population Dynamics

Managing brown trout (Salmo trutta fario) populations is of high concerns for many stakeholders (e.g. angling associations or hydropower companies). However, building trout life cycle models remains challenging. Here, we describe a general hierarchical Bayesian model of brown trout population dynamics that combines global (e.g. an average value of age-class annual survival) and local parameters (e.g. shelter availability influencing local carrying capacity). To fit the model, we used annual fish samplings (i.e., two-pass electrofishing followed by density estimation) conducted at 40 reaches. Reaches were located in natural (n=21) or bypassed (i.e. downstream dam; n=19) stream sections across France, thus offering different habitat characteristics. Habitat characteristics were measured at each reach, including daily flow, daily hydraulics conditions, daily temperature and shelter availability. This study (1) allowed us to understand between-reach differences in survival processes, (2) was consistent with previous studies and explained spatial variations in resident brown trout population dynamics and (3) offers a promising framework for future developments.