Two major goals in plant evolutionary biology are to understand and to predict how plants respond and adapt to environmental stress.  In this 4-year, NSF-funded project, graduate students Devin Gamble, Helen Payne, Lisa Kim and I are measuring the process of adaptive evolutionary change in real time across a naturally occurring gradient in climate in order to test long-standing predictions concerning how wild rapidly populations of the widespread wildflower species, Nemophila menziesii (Baby Blue Eyes: Boraginaceae) evolve from generation to generation. This research integrates:

(1) the study of geographic variation in traits that affect plant fitness among populations of a widespread annual herb with estimates of the strength of natural selection;

(2) inter-generational change in genetic variation in survival and reproduction; and

(3) the effects of natural selection on population performance (i.e., mean fitness). 

We are using the powerful quantitative genetic Aster models to estimate genetic variance in individual lifetime fitness in pedigreed, experimental populations under field conditions as well as to estimate the strength and direction of selection on phenological, morphological, and physiological traits.

A novel component of this research is that it is designed to evaluate, in multiple field populations, the accuracy of Fisher’s Fundamental Theorem of Natural Selection, which predicts that the rate of adaptive change in natural populations can be accurately forecasted from the ratio of genetic variance in fitness to mean fitness. This ratio represents a population’s capacity to adapt to current conditions, or its “adaptive capacity”.