Because of its complexity, dispersal has often been simplified when implemented in models aiming at understanding and predicting population dynamics and persistence in a context of environmental change. In particular, informed dispersal, that is the use of personal and social information to decide whether to leave a natal or current breeding site and where to settle, has seldom been considered. Informed dispersal could nevertheless be critical for predicting population dynamics, structure and persistence, as it could help populations track environmental change. Here, we develop a simulation model to examine the consequences of four dispersal strategies (informed, semi‐informed, fixed random dispersal and philopatry) on the dynamics, structure and persistence of a spatially structured population under different environmental scenarios.
We built and parameterized a metapopulation dynamic model using a long‐lived colonial seabird species as an example, the black‐legged kittiwake Rissa tridactyla, breeding on a set of distinct patches. Various scenarios of environmental variability and multiple factors potentially driving natal and breeding dispersal decisions (local habitat quality, individual and conspecific breeding success, personal and social information use) were considered to explore their respective effects.
Environmental change and dispersal strategies strongly influenced metapopulation dynamics and structure. In spatially variable environments, informed and semi‐informed dispersal maintained populations in the long term, whereas philopatry and random dispersal led to extinction. Contrasted dynamics also arose: philopatry led to ecological traps, random and semi‐informed dispersal led to source‐sink dynamics and informed dispersal drove extinction–recolonization dynamics.
This study demonstrates the importance of including informed dispersal in models aiming at predicting the dynamics of spatially structured populations. It also serves to highlight the urgent need to collect more empirical data on dispersal processes to properly parameterize such models.