Environmental uncertainty and the evolution of population’s space of information

Pedro B. Mendes, Sabrina B.L. Araújo &

Walter A. P. Boeger

The role of environmental fluctuations is a core element in the theory of eco-evolutionary dynamics of populations. Fluctuations continuously change the fitness landscapes, generating uncertainty in reproductive investments and survival strategies. Under such circumstances, current theory predicts that advantageous types (or lineages) are those with greater geometric mean fitness, a measure of long-term persistence. However, the geometric fitness does not account for cases in which the selection is frequency-dependent. In these cases, frequency dependence may give advantage for specific population distributions, rather than for specific types. When associated with inheritable components, these distributions represent the population's space of information. Here, our goal is to evaluate how environmental uncertainty shapes the information space of natural populations. We modelled an eco-evolutionary competition game in which individual's traits affect both its reproductive effort and competitive ability through ecological pleiotropy. Then, we evaluated how the structure of the competition network interplays with environmental entropy to shape the population's distribution of inheritable components. We found that specific population's distribution emerges as stationary states that are consistent with the entropy and shape of the probability distribution of environmental states. These stationary states allow type coexistence and may serve as a mechanism of polymorphism maintenance. Our results also suggests that processes of population convergence and divergence depends on properties of the trade-off generated by the ecological pleiotropy and on the boundaries of the information space. Moreover, the structure of the competition network leaves signatures in the space of information. These signatures seems to be identifiable for a wide range of parameter values. Finally, we discuss how these findings can be incorporated in epidemiological models to study parasite evolution.