Improve the predictability of evolution with the moving optima model

The striking mismatch between evolution at short (microevolution) and long (macroevolution) time scales is a major remaining problem in evolutionary biology.

With this proposal, I test a hypothesis that the moving optima model – which enables quantification of the dynamics of adaptive optima – can unify microevolution and macroevolution using wing morphology of an insect order Odonata (damselflies and dragonflies). Odonata wing morphology represents a remarkable mosaic of macroevolutionary divergence and stasis.

This allows us to study phenotypic evolution on time scales that range from individual generations to 350 million years.

I will use a new phenotyping platform to produce a unique dataset of wing morphological trait that includes observations from field studies, manipulative field experiments, and museum specimens.

By applying quantitative genetics and phylogenetic comparative methods for this dataset, I will (1) uncover the link between moving optima, microevolution, and macroevolution, and (2) experimentally verify the link between moving optima and evolution. if my hypothesis is correct, predictability of evolution will be significantly improved with knowledge of ecological and environmental causes of fluctuation in adaptive optima.

Outcomes of my research will pave the way for unifying causal factors external to the organisms (environment and ecology) with internal causes (genome and development) to better cope with the ongoing global environmental changes.