Résumé
The growing interest for studying questions in the wild requires
acknowledging that eco-evolutionary processes are complex, hierarchically
structured and often partially observed or with measurement error. These
issues have long been ignored in evolutionary biology, which might have
led to flawed inference when addressing evolutionary questions.
Hierarchical modelling (HM) has been proposed as a generic statistical
framework to deal with complexity in ecological data and account for
uncertainty. However, to date, HM has seldom been used to investigate
evolutionary mechanisms possibly underlying observed patterns. Here, we
contend the HM approach offers a relevant approach for the study of
eco-evolutionary processes in the wild by confronting formal theories to
empirical data through proper statistical inference. Studying
eco-evolutionary processes requires considering the complete and often
complex life histories of organisms. We show how this can be achieved by
combining sequentially all life histories components and all available
sources of information through HM. We demonstrate how eco-evolutionary
processes may be poorly inferred or even missed without using the full
potential of HM. As a case study, we use the Atlantic salmon and data on
wild marked juveniles. We assess a reaction norm for migration and two
potential trade-offs for survival. Overall, HM has a great potential to
address evolutionary questions and investigate important processes that
could not previously be assessed in laboratory or short time-scale
studies.