Organisms are functionally constrained by physical and chemical laws, yet natural selection has produced seemingly endless forms within these fundamental limits. The outcome of natural selection is adaptation, which all biologists recognize as the exquisite fit between organisms and their environment. Yet fields within biology study adaptation quite differently ( Mayr 1985). Evolutionary biologists typically address why a trait is adaptive by measuring how selection and other forces act on it; physiologists and other functional biologists typically address how a trait is adaptive by disrupting its normal function and measuring how the organism responds. Both approaches are necessary, but neither is sufficient. A sufficient account of adaptation should explain how organisms work and why they evolved to work that way. It is only through conceptual unification of biology that we can explain adaptive variation and constraint on phenotypic evolution.
My lab specifically studies the evolutionary physiology of plants. Physiologists have uncovered numerous tradeoffs that organisms must confront in order to survive and reproduce, and such fitness tradeoffs are the theoretical foundation for many models of adaptive evolution. Despite this seemingly natural integration between disciplines, an evo-phys synthesis has largely eluded us. Plants systems are especially useful because i) there is a rich body of physical and chemical theory that explains how plants work and ii) field experiments that measure the relationship between physiological variation and fitness are tractable.
To study adaptation, my lab uses the framework of Olson and Arroyo (2015), which integrates optimality modeling, comparative methods, and field experiments:
The major projects my lab works on will build up piece-by-piece all of the theoretical tools, experimental methods, and collaborators needed to study adaptive variation using a synthetic evo-phys approach. Over the coming years my lab will work on:
In the long term, these projects will coalesce when we can use optimality models to predict how physiological traits should vary across environmental gradients, identify natural genetic variation in those traits, and measure the fitness consequences of that variation under historic and novel climates.