Evolvability and Divergence
Abstract
Recent empirical findings suggest that long-term evolutionary divergence—over millions of years—can be predicted from the standing genetic variation present in extant populations. This challenges traditional evolutionary theory, which holds that population-level processes are unlikely to influence divergence across such vast timescales. Evo-devo models have been proposed to reconcile this apparent contradiction, but the mechanisms remain unclear. Here, we propose to investigate this question using a mechanistic, experimentally validated model of tooth development embedded within large-scale evolutionary simulations. Our central hypothesis is that conserved features of developmental processes shape the space of phenotypic variation (i.e., evolvability), and that these features persist long enough to influence evolutionary trajectories across thousands of generations. We will simulate evolutionary dynamics under a range of selective regimes—including directional, stabilizing, fluctuating, neutral, and pleiotropically constrained selection—and assess whether short-term evolvability predicts long-term divergence. These simulations require substantial computational resources due to the complexity of modeling development within evolutionary timeframes. This work aims to provide a mechanistic foundation for observed statistical patterns in macroevolution and contribute to a deeper understanding of how development biases evolutionary outcomes over deep time.
