A modeling exercise to show why population models should incorporate distinct life histories of dispersers

Dispersal is an important form of movement influencing population dynamics, species distribution and gene flow between populations. In population models, dispersal is often included in a simplified manner by removing a random proportion of the population. Many ecologists now argue that models should be formulated at the level of individuals instead of the population level. To fully understand the effects of dispersal on natural systems, it is therefore necessary to incorporate individual‐level differences in dispersal behavior in population models. Here, we parameterized an integral projection model, which allows for studying how individual life histories determine population‐level processes, using bulb mites, Rhizoglyphus robini, to assess to what extent dispersal expression (frequency of individuals in the dispersal stage) and dispersal probability affect the proportion of successful dispersers and natal population growth rate. We find that allowing for life‐history differences between resident phenotypes and disperser phenotypes shows that multiple combinations of dispersal probability and dispersal expression can produce the same proportion of leaving individuals. Additionally, a given proportion of successful dispersing individuals result in different natal population growth rates. The results highlight that dispersal life histories, and the frequency with which disperser phenotypes occur in the natal population, significantly affect population‐level processes. Thus, biological realism of dispersal population models can be increased by incorporating the typically observed life‐history differences between resident phenotypes and disperser phenotypes, and we here present a methodology to do so.