One (interesting) way to study evolution is to ‘make it happen’. In other words, start with an experimental population, add (an) experimenter(s) who imposes certain conditions and watch what happens. With the recent advances in genetics, genomics and molecular biology, it has become more and more possible to see evolution happen (yes, my dear evolution deniers, you read that right).
A recent review in the journal Trends in Ecology and Evolution discusses this experimental evolution approach and provides and great overview of this burgeoning research field. Although it does have a bit of a history, and, of course, one could argue that domestication of crops and animals is a way of (perhaps unintended) experimental evolution.
Anyway, onwards. Why do it? What can we learn from experimental evolution studies? The review list some answers:
- Adaptation to specific environments: see what happens when you change just one environmental factor.
- Trade-offs and constraints: what’s good in one environment isn’t necessarily so in another one.
- Estimating genetic population parameters: for example, figuring out how often different types of mutation take place.
- Testing the theory: one can test predictions from evolutionary theory.
- Long-term potential: it’s not unusual that, once an experimental evolution trial is underway, new questions arise, which can lead to letting the experiment go on, leading to a host of new interesting inquiries and answers. (A wonderful example of this is the E. coli experiment, 55 000 generations and counting).
- Medicine and technology: the approach is used in the development of vaccines and identification of mutations that can confer drug resistance before they happen ‘in the wild’. Further, experimental evolution is being explored in fields such as biofuel development, carbon sequestration, and even artificial systems such as software improvement and robotics.
Pretty cool, huh? The authors continue with some thoughts on the design of experimental evolution… well… experiments:
- Study system: most studies use a model organism with a relatively short generation time, for reasons of convenience (an experimental evolution study of, say, elephants, would take a while). Of course, there are things to keep in mind, such as the potential difference between the model organisms and other species, and the dissimilarity between the lab and the world outside. So, careful with inferences and extrapolations.
- Regime and control: often, the experimental population is subjected to different regimes, and is then compared to the ancestral population, from which the experimental individuals were sampled. The best way to assess the changes in the experimental population is to compare the different regimes with the ancestral population, which should, if possible be preserved (freezing, seeds, resting stages).
- Replicates: in science, you often have to do everything several times. Same here. Subject several experimental populations to the same regime, in order to assess whether the divergence between regimes is greater than the divergence between replicate populations subjected to the same regime (due to, for example, random genetic drift).
- One or many?: one starting ancestral population is good for the statistical power (different populations may respond differently), but more starting populations means more general conclusions.
- Population size and number of generations: again, often a matter of convenience. The more, the merrier, but practical limitations are a severe constraint.
- Maternal effects: conditions experienced by the parents often influence the offspring. So, rearing samples of the populations in a common environment may prove useful.
- Inbreeding: strong imposed selection pressures may result in a smaller population, meaning more inbreeding. One way to deal with this, is crossing replicate populations within the same regime. (This, of course, only matters in sexual organisms.)
So, a lot of things to take into account. No wonder then, that, currently, there are some difficulties to consider in experimental evolution approaches:
- Timescale and serendipity: Even with organisms reproducing very quickly, some evolutionary processes (think of the rise of new species) take time. Also, some adaptations may take a rare sequence of mutations, which may or may not happen in an experimental set-up.
- Difficulties and artifacts: many experimental evolution studies take place in the lab, meaning that things such as inadvertent contamination can happen. Further, laboratory environments fail to capture all relevant aspects that may have an influence in the wild.
- Genetics of lab evolution: similar to the point above, lab populations often differ from their wild counterparts in, for example, populations size, the genetic variation present, and so on, which have an effect on the evolutionary path.
One way the authors suggest to deal with some of these things, is experimental evolution in the field, involving moving populations, altering natural environments, and so forth. Islands and mesocosm (such as, for example, artificial ponds) seem to be prime candidates for this.
Finally, it’s proposed that experimental evolution could be a valuable educational tool. Even though there is an incredible amount of evidence for evolution, actually seeing it happen can be very stimulating.
Overall, experimental evolution is a valuable research tool that has lots of potential to make important contributions to evolutionary biology. As the authors write:
The past decade has seen increasing application of experimental evolution to an expanding range of questions, while advances in genomic technology are beginning to provide unprecedented insights into the genetic and molecular bases of evolutionary change. These and other technological advances open new avenues, while discoveries in fields including genetics, developmental biology, and global change pose new questions that can be tackled with experimental evolution.
Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, & Whitlock MC (2012). Experimental evolution. Trends in Ecology & Evolution, 27 (10), 547-560 PMID: 22819306