  Laboratory experiments with bacterial colonies, under well-controlled
conditions often lead to evolutionary diversification, where at least two
ecotypes emerge from an initially monomorphic population. Empirical evidence
suggests that such "evolutionary branching" occurs stochastically, even under
fixed and stable conditions. This stochastic nature is characterized by: (i)
occurrence in a significant fraction, but not all, of experimental settings,
(ii) emergence at widely varying times, and (iii) variable relative abundances
of the resulting subpopulations across experiments. Theoretical approaches to
understanding evolutionary branching under these conditions have been
previously developed within the (deterministic) framework of "adaptive
dynamics." Here, we advance the understanding of the stochastic nature of
evolutionary outcomes by introducing the concept of "stochastic trade-offs" as
opposed to "hard" ones. The key idea is that the stochasticity of mutations
occurs in a high-dimensional trait space and this translates into variability
that is constrained to a flexible tradeoff curve. By incorporating this
additional source of stochasticity, we are able to account for the observed
empirical variability and make predictions regarding the likelihood of
evolutionary branching under different conditions. This approach effectively
bridges the gap between theoretical predictions and experimental observations,
providing insights into when and how evolutionary branching is more likely to
occur in laboratory experiments.
