OOS 17-7
Predicting evolutionary trajectories of life history traits in wild populations

Tuesday, August 11, 2015: 10:10 AM
317, Baltimore Convention Center
Richard P. Shefferson, General Systems Studies, University of Tokyo, Meguro-ku, Japan
Deborah A. Roach, Biology, University of Virginia, Charlottesville, VA
Michael J. Hutchings, University of Sussex, United Kingdom

Population dynamics are driven by extrinsic and intrinsic factors affecting demographic rates. Climate change can be expected to alter demographic rates, but evolutionary change in response to climate change may exaggerate or counteract such changes. In this presentation, we show how adaptive dynamics models may be used to predict evolution, particularly of life history traits, in response to climate change. In addition to showcasing the underlying methods, we use long-term demographic data for populations of three species, Ophrys sphegodes, Cypripedium parviflorum, and Plantago lanceolata, to predict 1) how demographic rates will respond to climate change, and 2) how the probabilities of sprouting and flowering will evolve in response.


Using adaptive dynamics models that yielded optimal sprouting levels at observed frequencies, and predicted climatic patterns using the Sousei program (JAMSTEC, Japan), we predicted the evolution of sprouting frequency in Ophrys and Cypripedium, which are both capable of vegetative dormancy. For Ophrys in particular, we found both sprouting and flowering evolving to greater levels than currently seen. Cypripedium showed contrasting patterns, within increased dormancy (i.e., lower sprouting) and lower flowering. Plantago is not dormancy-prone, but evolved a lower tendency to flower as well. These evolutionary patterns contrast with purely ecological predictions, which generally predict increased sprouting and flowering when evolution is not considered. We make the case that such predictions can be increasingly made across all demographic studies, given the reliability of current climatic modeling techniques and evolutionary optimization protocols.