R.A. Fisher, foundational 20th-century figure in statistics and in evolutionary biology, famously (famously among evolutionary biologists, anyway) developed an idea he called the Fundamental Theorem of Natural Selection: "The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time." To grasp this idea requires understanding evolutionary fitness, the nature of a biological population (Fisher didn't mean an individual organism, of course, because individuals don't evolve.), variance as a statistical concept, and what genetic variance is. Even scientists who understand those things (or think they do) have argued about exactly what Fisher meant in the 85 years since he proposed it. A simple take on the idea is that it explains, in mathematical terms, why natural selection is responsible for adaptation (Grafen 2003), something Darwin (1959) knew, though he couldn't do the math. A more nuanced (and sassier) take on the Theorem goes like this: "Sure. Genetic variance in fitness limits how fast adaptation by natural selection occurs, but, given all the things Fisher neglected (such as the facts that environments don't stay constant from generation to generation, and that chance events matter), how fast does adaptation really happen?"
The answer to this sassy question isn't just "academic." It's essential to understanding how wild organisms might respond to changing climates and other circumstances.
With apologies to Laura Ingalls Wilder, that's where Grinnell comes in. At Grinnell's Conard Environmental Research Area, my MAP students (Sam Sokolsky and Greg Margida, below, left and right, respectively) and I are carrying out an experiment to answer that question. Working with researchers from Professor Ruth Shaw's lab at the University of Minnesota, we're going to compare "Fisher-predicted" increases in evolutionary fitness to the actual changes that occur in complex natural environments, using the native prairie plant Chamaecrista fasciculata ("partridge pea") as a model.
Stay-tuned. We're just starting.
