Biologists can become distinctly cranky when we hear evolution described as “random.” This is because evolution isn’t random — it’s undirected. Although it acts on mutations that turn up randomly, natural selection is highly nonrandom, in that (all else being equal) traits that help their owners make more babies are always the ones that spread through a population.
However, even if natural selection predictably aims for the same target, that target is not necessarily fixed. The most obvious case of this is in the coevolution of interacting species, where adaptation by one forces adaptation in the other. This is a field of study in its own right; but one recent innovation is a theory paper by Gandon and Day, which tracks changes in the “fitness landscape” resulting from adaptations and counter-adaptations [$-a]. (For more detail on the paper, see Coevolvers.)
Ground finches (Geospiza fortis) with big beaks might be favored this year, but what about next? Photo by kookr.
Empirical studies have shown that selection’s target can move in unexpected ways, too. One of the best examples of this turned up in the course of the ongoing, decades-long study of finches on the Galapagos Island Daphne Major. As rainfall on the island fluctuated from year to year, the mix of available seeds changed as well, and the finches’ beaks — the size of which determines what seeds are easily cracked and eaten — evolved to keep up [$-a]. The resulting evolutionary path looks like a drunkard’s walk, and the study’s authors, Peter and Rosemary Grant, put the word unpredictable right in the title.
Making things still more complicated, there is actually a random component to the effects of natural selection. That is, in the real world, advantageous traits may not automatically result in greater fitness — they result in greater expected fitness. Last year, Sean Rice published a mathematical model of evolution in which fitness is a random variable. He found that greater variation around the expected fitness can increase the strength of natural selection; that is, more uncertainty about the relationship between fitness and a given trait may actually make that trait adapt more rapidly. In a just-published extension of this work, Rice and Anthony Papadopolous examined the effect of random migration among different populations on adaptive evolution in each population, and found that greater variation in migration rates can reduce the effect of migration on local evolution.
Introducing all this randomness into our view of evolution doesn’t necessarily make evolution unpredictable. As an excellent recent Radiolab episode discusses, there are patterns to be extracted from randomness. It takes more work — larger sample sizes, longer-term studies — for these patterns to become apparent. Yet it’s clear that this is work we’ll have to do in order to understand biological systems.
Gandon, S., & Day, T. (2009). Evolutionary epidemiology and the dynamics of adaptation Evolution, 63 (4), 826-38 DOI: 10.1111/j.1558-5646.2009.00609.x
Grant, P., & Grant, R. (2002). Unpredictable evolution in a 30-year study of Darwin’s finches Science, 296 (5568), 707-11 DOI: 10.1126/science.1070315
Rice, S. (2008). A stochastic version of the Price equation reveals the interplay of deterministic and stochastic processes in evolution BMC Evolutionary Biology, 8 (1) DOI: 10.1186/1471-2148-8-262
Rice, S., & Papadopoulos, A. (2009). Evolution with stochastic fitness and stochastic migration PLoS ONE, 4 (10) DOI: 10.1371/journal.pone.0007130