After the big gay post came out Tuesday, there was really only one shirt I could wear to the Twin Cities Pride parade. You, too, can have the thrill of explaining the Wright-Fisher model of drift and mutation in front of a gay bar—this design is available for purchase, with your choice of American Apparel shirt colors.
Science is often said to work in three easy steps: (1) observe something interesting, (2) formulate a hypothesis for why that something is interesting in the way it is, and (3) collect more observations to see if they also support that hypothesis. Wash, rinse, repeat, and you eventually get from Newton to Einstein, from Aristotle to Darwin.
Except, of course, it’s never that straightforward. Sometimes scientists come up with a hypothesis without a clear-cut example to support it, and then go looking that example. Sometimes observations that support a hypothesis turn out not to, if you look closer. And—here’s the funny thing—this can even happen with hypotheses that are, in the end, pretty much correct.
In the spirit of this month’s Giants Shoulders blog carnival, which focuses on “fools, failures, and frauds” in the history of science, I’m going to recount a case in which one of the greatest biologists of the Twentieth Century was fooled by a small desert flower. Sewall Wright was no fool or failure, and he certainly didn’t commit fraud, but he does seem to have been totally wrong about his favorite example of a particular population genetic process, one he discovered. That process, isolation by distance, is widely documented in natural populations today—but it also doesn’t seem to have worked the way Wright thought it did for Linanthus parryae.
If your coin is fair, I can guess that it’s come up heads and have a fifty percent chance, or probability equal to 0.5, that I’ve guessed correctly. Now, flip the coin ten times in a row. How many times did heads come up? Again, the best guess is that it came up five times—but it’s not all that unlikely that it came up six times, or four, or even as many as eight.
Now, if you flipped the coin an infinite number of times, then exactly fifty percent of the total flips would be heads. But who has time for that? Similarly, populations of living organisms are not infinite—often far from it—and this means that the frequency of genes in those finite populations can change as a result of the same phenomenon at work on your coin. Biologists call this genetic drift.
Evolution at random
The basic principle behind genetic drift is that each generation is a process of sampling from the parental population to create a population of offspring. As most folks know from discussions of opinion polls, smaller samples tend to be less representative of the pool from which they are drawn. Say a population of annual plants begins with equal numbers of plants bearing blue flowers or white flowers. If only ten seeds survive from that population to form the next generation, you would expect them to be five blue-flowered and five white-flowered seeds. However, it’s just like flipping a coin ten times: the probability of drawing six blue seeds is actually a little less than 21%, or one in five. The probability of drawing nine blue seeds is almost one in one hundred—small, but hardly impossible.
Consider, too, that once you draw six blue seeds, it becomes slightly more likely that you’ll draw seven in the next generation, which makes it slightly more likely you’ll draw eight in the next. Repeated selection of small samples means that traits can drift to fixation (or loss, depending on your perspective), so that everyone in the population has the same trait. Rare traits are more likely to be lost to drift, and large populations are less prone to its effects. This is nicely illustrated in this online simulation from the University of Connecticut—over time, a focal gene fixes or disappears from the population as a function of the population size and the initial frequency of the gene.
In general, drift interferes with the efficient operation of natural selection. Even in relatively large populations, the probability that a new beneficial mutation will become fixed is approximately twice the selective benefit of that mutation—typically very small. (This is from a 1927 paper by J.B.S. Haldane that doesn’t seem to be online in any form, but which is discussed by Otto and Whitlock in a 1997 paper extending the classic result.) In a small enough population, a trait can become fixed even if it reduces its carriers’ fitness [PDF].
Evolving differences without selection
As I’ve discussed above, the effect of drift in a single population is to reduce variation as rare traits are lost to chance. This means that, when more than one independently-evolving population is considered, drift actually increases variation among them [$a], as different traits fix or are lost in each. That is, drift can make isolated populations evolve into different species even if they experience identical regimes of natural selection.
A flagship example of this sort of non-adaptive diversification are the woodland salamanders of eastern North America, genus Plethodon. Woodland salamanders are quite diverse, having accumulated more than 40 species in the last 27 million years, but all of these species live in more or less the same habitat, under the leaf litter in moist Appalachian forests, and many are “cryptic” species distinguishable only by DNA analysis. How, then, did Plethodon become so diverse?
The answer is simply that woodland salamanders don’t travel very well. Salamanders need moist environments–they breathe through their skin, which doesn’t work well if it dries out—and so have difficulty moving from one stream drainage to another. This means that it doesn’t take much distance to isolate one Plethodon population from another, allowing drift to take them in different directions. Salamanders form new species, in other words, by staying at home.
This effect of drift means that biologists must adjust their “null” expectation when they observe differences in natural populations—the mere fact that some Joshua trees look different from other Joshua trees does not necessarily mean that natural selection has created those differences. Furthermore, the degree to which drift or selection can generate differences among populations depends strongly on the fourth force in the Big Four, which I’ll discuss next week: migration.
Godsoe, W., Yoder, J., Smith, C., & Pellmyr, O. (2008). Coevolution and divergence in the Joshua tree/yucca moth mutualism. The American Naturalist, 171 (6), 816-23 DOI: 10.1086/587757
Kozak, K., Weisrock, D., & Larson, A. (2006). Rapid lineage accumulation in a non-adaptive radiation: phylogenetic analysis of diversification rates in eastern North American woodland salamanders (Plethodontidae: Plethodon) Proc. Royal Soc. B, 273 (1586), 539-46 DOI: 10.1098/rspb.2005.3326
Lande, R. (1992). Neutral theory of quantitative genetic variance in an island model with local extinction and colonization. Evolution, 46 (2), 381-9 DOI: 10.2307/2409859
Otto S.P., & Whitlock M.C. (1997). The probability of fixation in populations of changing size. Genetics, 146 (2), 723-33 PMID: 9178020
Wright S (1931). Evolution in Mendelian populations. Genetics, 16 (2), 97-159 PMID: 17246615
The nice thing about a field season away from all regular internet access is that it gives you a real sabbatical of a sort—a chance to reassess plans and set new goals. One of the new goals I set myself this last field season was to introduce a new kind of topic here at Denim and Tweed.
Most of my writing about science at D&T focuses on recently published discoveries in evolution and ecology. It’s fun writing, and it coincides neatly with my regular journal reading, and I intend to keep doing it. But I’ve discovered that when I want to put new work in context, I often need to discuss fundamental concepts of evolutionary biology that aren’t necessarily common knowledge, such as genetic drift or sexual selection. However, I rarely have room to explain these concepts in depth within a blog post devoted to something else.
So maybe the solution is to devote some posts to explaining these “basics.” I’m going to start with a series of posts on the “Big Four” processes of population genetics. These are the four processes that account, in one way or another, for every change in the frequency of genes within natural populations. In other words, the Big Four account for much of evolution itself. They are:
- Natural selection, changes in gene frequencies due to fitness advantages, or disadvantages, associated with different genes.
- Mutation, the source of new forms of genes;
- Genetic drift, or changes in gene frequencies that arise from the way probability works in finite populations; and
- Migration, or changes in gene frequencies due to the movement of organisms from site to site.
Lay readers may be surprised both by what we know, and what we don’t, about how these four processes operate in nature. Natural selection is relatively easy to measure, and apparently ubiquitous [PDF] in natural populations—but we don’t know how often the resulting short-term changes impact evolution over millions of years. Mutation, the source of variation on which natural selection acts, seems to vary widely among living things. Genetic drift means that a trait can come to dominate a population even if it has no fitness effect—or sometimes a deleterious one. Finally, migration across variable landscapes can interact with selection, drift, and mutation [$a] to completely alter their effects.
I’ll devote one post each to selection, mutation, drift, and migration, discussing classic findings as well as more recent scientific discoveries about each. They’ll arrive as my usual mid-week science posts for the next four weeks, and I’ll update this post with links to the others as they go online—so if this looks worth following, you can either bookmark this post, or subscribe to D&T’s RSS Feed.
Drake JW, Charlesworth B, Charlesworth D, & Crow JF (1998). Rates of spontaneous mutation. Genetics, 148 (4), 1667-86 PMID: 9560386
Kingsolver, J., Hoekstra, H., Hoekstra, J., Berrigan, D., Vignieri, S., Hill, C., Hoang, A., Gibert, P., & Beerli, P. (2001). The strength of phenotypic selection in natural populations. The American Naturalist, 157 (3), 245-61 DOI: 10.1086/319193
Slatkin, M. (1987). Gene flow and the geographic structure of natural populations. Science, 236 (4803), 787-92 DOI: 10.1126/science.3576198
Wright S (1931). Evolution in Mendelian populations. Genetics, 16 (2), 97-159 PMID: 17246615
Being a poisonous animal isn’t much help if your predators don’t know about it. That’s why lots of poison-defended critters – monarch butterflies or poison dart frogs, for instance – advertise with bright “warning” colors. This is called aposematism. The idea is that predators will learn (or even evolve) to avoid bad-tasting, poisonous prey if they’re well-marked for future reference.
The trouble with aposematism, though, is that it requires giving up another, more common defensive color scheme: camouflage. If you’re a poisonous critter, and you evolve bright coloration for the first time, predators don’t yet know that you’re poisonous – but you’re really brightly colored and easy to see. How, then, does aposematism evolve from non-aposematic ancestors?
Photo by dbarronoss.
A new study on early release from Biology Letters suggests that it isn’t easy. The authors, Noonan and Comeault, set out to determine whether brightly-colored poison dart frogs are more likely to be attacked when they evolve new color patterns [$-a]. It’s possible that the frogs’ predators avoid all brightly-colored prey regardless of pattern, in which case new frog patterns would be just as good for predator deterrence as the old ones. But it’s also possible that predators only avoid patterns they’ve run across (and spat out) before – so that new, rare patterns would have all the disadvantages of giving up camouflage with none of the benefits of aposematism.
Noonan and Comeault performed an elegant behavioral experiment, setting out clay model frogs in an area where frogs of one color pattern predominate. One set of models matched the local color pattern, another was brightly colored but different from the local pattern, and a third was drab and camouflaged. Birds were much more likely to attack the “new” color pattern than either the “local” version or the drab one. This result is hard to understand at the first pass – if new color patterns are vulnerable to attack, how can aposematism evolve in the first place? The answer is, not by natural selection, but by genetic drift.
Genetic drift is a natural, mathematical consequence of finite populations: imagine a bag full of marbles, half of them black and half white. If you pull a sample of marbles from the bag, you expect them to be half black and half white on average (i.e., over many samples) – but any individual sample might have a very different frequency of white and black marbles, especially if it’s small. If the probability of picking a white marble from the bag is 0.5 (because half the marbles are white), then the probability of picking a sample of four white marbles is 0.5 × 0.5 × 0.5 × 0.5 = 0.0625. That’s a small probability, but not zero. Drift is a very real effect in the natural world, especially during the establishment of new local populations, when the population size is initially quite small.
The key to understanding Noonan and Comeault’s result is that aposematism is frequency dependent – it favors not the old pattern as such, but whatever bright color pattern is most common in the frog population. Birds attacked the “local” color pattern at a low rate, which suggests that they’re always re-learning which pattern to avoid. A new color pattern might be hard to establish within a population of frogs that look very different from it, but if a new pattern pops up in the course of establishing a new population, then – thanks to genetic drift – it may be common enough for predators to learn to avoid it.
B.P. Noonan, A.A. Comeault (2008). The role of predator selection on polymorphic aposematic poison frogs. Biology Letters DOI: 10.1098/rsbl.2008.0586