A mountain vista in Colorado, with trees killed by pine beetles in the foreground. (Flickr: John B. Kalla)
Over at Nothing in Biology Makes Sense, I discuss a big new review article on all the ways understanding evolutionary biology will be critical for human health and development in the next hundred years:
The long list of authors, led by Scott P. Carroll and including Ford Denison, whose lab is just down the hall from my office at the University of Minnesota, explicitly connect evolutionary principles to global goals for sustainable development. These include the reduction of both “chronic lifestyle” diseases and infectious diseases, establishment of food and water security, clean energy, and maintenance of healthy ecosystems. Carroll and his coauthors divide the applications of evolution to these problems into cases where evolution is the problem, and those where evolution may offer the solution.
Over at The Molecular Ecologist I’m discussing a new paper in the journal Genetics, which demonstrates that selection acts more strongly on genes that affect multiple traits:
Genes that have roles in multiple traits—pleiotropic genes—have long been thought to be under stronger selection as a result of those multiple functions. The basic logic is that, when a gene produces a protein that has a lot of different functional roles, there are more functions that will be disrupted by changes to that protein. Which would be more inconvenient: if your smartphone suddenly needed a new type of power connector, or if every electrical outlet in your house suddenly accepted only plugs with four prongs?
A team at the University of Queensland tested this idea using a lot of fruit flies and some cleverly applied gene expression resources. To find out how it all worked, go read the whole post, and check out the original paper.◼
The collection locations for plant lines sampled in my analysis. Figure 1 from Yoder et al. (2014).
This week at The Molecular Ecologist, I’ve just posted a new discussion of the latest publication to come out of my postdoctoral research with the Medicago HapMap Project. It’s an attempt to find genome regions that might be important for adaptation to climate, by scanning through a whole lot of genetic data from plants collected in different climates.
This is what’s known as a “reverse ecology” approach—it skips over the process of identifying specific traits that are important for surviving changing climates, and instead uses population genetic patterns to infer what’s going on. One approach for such a scan is presented in my latest paper, which is in this month’s issue of Genetics. Essentially I think of this as what you can do, given a lot of genetic data for a geographically distributed sample—in this case for barrel medick, or Medicago truncatula. Medicago truncatula is a model legume species, which has been used in a great deal of laboratory and greenhouse experimentation—but in this project, I tried to treat M. truncatula as a “field model” organism.
At The Molecular Ecologist today, I highlight a couple of recent literature reviews that seek to understand how natural populations are structured by the limitations of distance, and by local adaptation:
Taken together, these two papers are a nice compilation of a very large literature. If nothing else, they demonstrate that we’re well past the point of asking whether environmental isolation happens at all—in fact, it looks to be quite common—and we’re ready to start digging into the details of when and why it develops.
To see what broad patterns two different groups of authors were able to extract from their surveys of many population genetic studies, go read the whole thing.◼
In the course of adaptive evolution — evolutionary change via natural selection — gene variants that increase the odds of survival and reproduction become more common in a population as a whole. When we’re only talking about a single gene variant with a strong beneficial effect, that makes for a pretty simple picture: the beneficial variant becomes more and more common with each generation, until everyone in the population carries it, and it’s “fixed.” But when many genes are involved in adaptation, the picture isn’t so simple.
This is because the more genes there are contributing to a trait, the more the trait behaves like a quantitative, not a Mendelian, feature. That is, instead of being a simple question of whether or not an individual has the more useful variant, or allele, at a single gene — like a light switch turned on or off — it becomes possible to add up to the same trait value with different combinations of variants at completely different genes. As a result, advantageous alleles may never become completely fixed in the course of an adaptive evolutionary response to, say, changing environmental conditions.
That principle is uniquely well illustrated by a paper published in the most recent issue of Molecular Ecology, which pairs classic experimental evolution of the fruitfly Drosophila melanogaster with modern high-throughput sequencing to directly observe changes in gene variant frequencies during the course of adaptive evolution. It clearly demonstrates that when many genes contribute to adaptation, fixation is no longer inevitable, or even necessary.
Turning up the heat, homogenizing flies
The authors of the new study, a team from the Institut für Populationsgenetik led by Pablo Orozco-terWengel, conducted what would otherwise be a rather simple experiment in evolutionary change in the laboratory. Starting with fruitflies collected from a wild population in Portugal (yes, Virginia, Drosophila melanogaster has wild populations!) they established three replicate populations of about 1,000 flies, which they put in temperature-controlled conditions somewhat warmer than the original collection location, and allowed them to propagate for 37 generations. Exensive previous work with Drosophila has established that simply moving the flies into a laboratory setting — where they live in bottles, and eat prepared food — exerts natural selection on them, and the increased temperature added a little bit more novelty to the lab environment to make it more likely adaptation would occur.
This experiment is different from all that previous experimental evolution of Drosophila, though, is that the coauthors tracked allele frequencies at thousands of markers during the course of those 37 generations of adaptation to the lab. To do this efficiently, they used an approach called “pooled sequencing.”
The principle behind pooled sequencing is that, if all you care about is the relative frequency of a gene variant in a whole population, you don’t need to know the genotype of any specific individual in that population. So to track changes in allele frequency, the team sampled hundreds of flies from the experimental population, and ground them all up together. (The polite, technical term used here is “homogenized.”) They then extracted DNA from this “pooled” sample, and used a high-throughput sequencer to collect millions of reads — short snippets of DNA sequence — out of the pool as a whole.
To extract allele frequencies from all of those sequence reads, the team identified where each read matched the Drosophila melanogaster reference genome. When multiple reads matched to the same location, but differed in one or more DNA nucleotide bases, they identified those bases as variable markers — single-nucleotide polymorphisms, or SNPs. Because the original DNA sample was pooled from many mashed-together flies, the relative frequency of each different variant of a SNP in the Illumina output should reflect the relative frequency of that SNP variant in the population as a whole.
Using this approach, Orozco-terWengel et al. could track allele frequency changes across more than a million SNP markers by taking these pooled samples from the intial population of flies, then at multiple points during the 37-generation evolutionary experiment. By comparing the allele frequencies in samples taken during the course of adaptation to the allele frequencies in the sample from the starting population, they could identify SNPs that became more common as the population adapted — and, because they had a big sample from across the genome, they could identify those SNPs whose allele frequencies had changed more than would be expected due to genetic drift. They examined samples taken after 15 and 27 generations of evolution, and at the end of the 37-generation experiment.
Two paths to adaptation
Allele frequency changes (AFC) in SNPs showing significant change by generation 15 (a) and by generation 37 (b). Image from Orozco-terWengel et al. (2012), figure 3.
What they found was largely in line with the verbal model I outlined at the beginning of this post. Over the course of experimental evolution, significant increases in allele frequency occurred at thousands of SNPs — suggesting that a great many genes are involved in the process of adaptation to life in the lab. Accordingly, very few of those allele frequency changes (in about 0.5% of the 2,000 SNPs that showed the greatest change from start to finish) represented complete or near-complete fixation.
More interestingly, comparison of allele frequency changes at the 15th generation and at the end of the experiment revealed two major “paths” taken by alleles. In the first case, the SNPs with strongest allele frequency changes by generation 15 all hit a “plateau” in subsequent generations — they didn’t see any significant increase in frequency between generations 15 and 37. In the second case, SNPs with the strongest allele frequency changes by generation 37, the end of the experiment, had increased steadily from the beginning population through the samples taken at the 15th and 27th generation. The SNPs in this second set had not shown significant allele frequency increases by generation 15 — which means the SNPs underlying most of the adaptive change in the first half of the experiment were a completely different set than the SNPs underlying adaptive change in subsequent generations.
If it’s already adapted, don’t fix it.
On the one hand, that suggests that Orozco-terWengel et al. managed to capture SNPs with a range of different contributions to the adaptation the observed by the end of the experiment. The SNPs with the biggest contribution showed rapid initial increases in allele frequency, then leveled off; SNPs with weaker effects showed slower, steady increases that continued for the entire experiment. But if it’s that simple, why didn’t the large-effect SNPs show continuing allele frequency change after the midpoint of the experiment?
It may be, as the coauthors speculate, that the two classes of SNPs identified in their experiment are separated by more than just the size of their respective contributions to adaptive change. There could be interactions among the alleles at these SNPs, such as overdominance, in which an individual is most fit when he or she carries two different alleles at a locus, rather than two copies of either allele. Overdominance would explain why most of the SNPs showing rapid initial increases in allele frequency then leveled out at intermediate frequencies.
So this combination of experimental evolution and modern sequencing technology raises some interesting questions even as it supports a lot of previous thinking about how natural selection acts on traits that are created by the collective action of many genes. It’s an exciting result, and, I hope, inspiration for much more work digging into the details of such “polygenic” adaptation.◼
Burke, M. and A. Long. 2012. What paths do advantageous alleles take during short-term evolutionary change? Molecular Ecology 4913–4916. DOI: 10.1111/j.1365-294X.2012.05745.x.
Orozco-Terwengel, P., M. Kapun, V. Nolte, R. Kofler, T. Flatt and C. Schlötterer. 2012. Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles. Molecular Ecology 4931–4941. 10.1111/j.1365-294X.2012.05673.x.
Pavlidis, P., D. Metzler and W. Stephan. 2012. Selective sweeps in multi-locus models of quantitative traits. Genetics 192:225–239. DOI: 10.1534/genetics.112.142547.
It is a widespread misconception that, as we developed the technology to reshape our environment to our preferences, human beings neutralized the power of natural selection. Quite the opposite is true: some of the best-known examples of recent evolutionary change in humans are attributable to technology. People who colonized high-altitude environments were selected for tolerance of low-oxygen conditions in the high Himalayas and Andes; populations that have historically raised cattle for milk evolved the ability to digest milk sugars as adults.
A recent study of population genetics in Native American groups suggests that another example is ripening in the experimental fields just a few blocks away from my office at the University of Minnesota: Corn, or maize, may have exerted natural selection on the human populations that first cultivated it.
One of the biggest dietary changes in the history of Native American humans was the domestication of corn, which provided a staple crop to support settlements across North and South America long before Europeans arrived. However, a staple crop is something of a double-edged sword: it can provide a more predictable food source than hunting and gathering—but if the crop fails, it means famine. It’s been proposed that the 230Cys variant makes people who carry it better at storing food as fat, which might come in handy for ancient farmers who had to weather bad harvests every few years.
Corn on display at the 2011 Minnesota State Fair. (Flickr: jby)
So the new study looks for an association between frequencies of 230Cys and corn-based agriculture in Native populations from Central and South America. The study’s authors—a big international team from universities in Brazil, Argentina, Mexico, Chile, Costa Rica, France, and Great Britain—first show that there’s a strong correlation between the frequency of Cys230 in Native populations and the length of time that domestic corn has been grown by those populations, as determined by the radiocarbon date of maize pollen found in archeological sites. That is, 230Cys is more common in Native populations that have a longer history of growing corn.
The team also used genetic data from the vicinity of Cys230 to estimate the age of the allele, and found that it probably originated between 19,000 and 7,000 years ago—which is to say, all the copies of Cys230 in the population genetic sample are descended from a single mutation that occurred after humans colonized the Americas. The lower age estimate is also pretty close to how long ago native populations are thought to have first begun farming maize.
That data makes a pretty good case for 230Cys having arisen as an adaptation to the diet created by Native American corn-based agriculture. But it’s not the whole story, by a long shot. Although 230Cys is strongly associated with metabolic disease in today’s modern, mostly famine-free, lifestyle, it only explains about four percent of variation in blood cholesterol levels. Moreover, it’s not clear to me that agriculture based on maize should be more prone to famine than agriculture based on wheat or rice—so why didn’t European and Asian populations evolve their own versions of 230Cys? It seems much more probable that there are a lot of other genes involved in determining how human bodies respond to modern-day feasting or prehistoric famine.
And, in fact, a 2010 study of world-wide human population genetics found evidence of selection associated with both climate and with diet type across the genome. That study found genetic markers with strong associations to climate and diet in close proximity to genes connected to blood glucose levels, diabetes risk, cancer risk, and, yes, blood cholesterol levels. The climate and dietary categories examined in that study are very broad, however, so it’s hard to know what, specifically, helped create the natural selection suggested by the observed associations between gene variants and evironments.
Corn and 230Cys may be the most recently described specific case of recent human evolution in response to agricultural technology—but we can expect to find a lot more stories like this one as we dig deeper into human population genetics.◼
Acuña-Alonzo, V., T. Flores-Dorantes, J. K. Kruit, T. Villarreal-Molina, O. Arellano-Campos, T. Hünemeier, A. Moreno-Estrada, M. G. Ortiz-López, H. Villamil-Ramírez, P. León-Mimila, & et al. (2010). A functional ABCA1 gene variant is associated with low HDL-cholesterol levels and shows evidence of positive selection in Native Americans. Human Molecular Genetics, 19, 2877-85 : 10.1093/hmg/ddq173
Hancock, A. M., D. B. Witonsky, E. Ehler, G. Alkorta-Aranburu, C. Beall, A. Gebremedhin, R. Sukernik, G. Utermann, J. Pritchard, & G. Coop (2010). Human adaptations to diet, subsistence, and ecoregion are due to subtle shifts in allele frequency. Proc. Nat. Acad. Sci. USA., 107, 8924-8930 : 10.1073/pnas.0914625107
Hünemeier, T., C. E. G. Amorim, S. Azevedo, V. Contini, V. Acuña-Alonzo, F. Rothhammer, J.-M. Dugoujon, S. Mazières, R. Barrantes, M. T. Villarreal-Molina, & et al. (2012). Evolutionary responses to a constructed niche: Ancient Mesoamericans as a model of gene-culture coevolution. PLoS ONE, 7 : 10.1371/journal.pone.0038862
Ever since Charles Darwin and Alfred Russell Wallace first described the workings of natural selection, one popular way to summarize about selective change has gone something like this: A population of critters is well-adapted to its environment until that environment changes—maybe the critters move to a new climate, maybe the climate changes on them, maybe some new competitors or predators move in. Life gets harder for our critters, until one of them is born … different. That lucky mutant has a never-before-seen trait that lets it cope in the new conditions, and in a few generations, every critter in the population is a descendent of that original mutant.
That narrative isn’t wrong. But it does miss one of the key insights that led to the discovery of natural selection—natural populations are variable.
That population of critters encountering new conditions of life may very well not need to wait around for the lucky mutant before it can begin adapting to new conditions. Mutations happen at random, and continuously—and, particularly if they don’t leave the mutant much less fit, can hang around in a population for generations. And this “standing” variation is raw material waiting for natural selection to act.
High-octane fuel for adaptation
There’s good reason to think that natural selection is more efficient when it has standing variation to work with. Joachim Hermisson and Pleuni Pennings demonstrated this principle rather neatly in a 2005 theory paper, in which they modeled the fate of new genetic mutations that had a weak negative effect when they first appeared in a population, but then became beneficial after the population’s environment changed.
Normally, when a new mutation appears in a population, it’s almost immediately lost to the random effects of genetic drift, even if it confers a benefit. This means that a new mutation needs to be quite strongly favored by selection to have a high probability of “fixing,” or spreading through an entire population.
However, under Hermisson and Pennings’s model, the mutations considered are only those that survive the initial effects of drift. The flip-side of the randomness that can make a weakly beneficial mutation disappear can also help a weakly deleterious mutation spread, achieving an equilibrium between drift, selection, and new mutation events that create new copies of the same variant to replace the ones lost to selection or drift. So, when conditions changed, and the mutation became even weakly beneficial, it was ready to start spreading.
This graph, the key figure from Hermisson and Pennings’s paper, shows the probability that a mutation will “fix,” or spread to dominate the population over the course of several generations, given the power of natural selection (alpha, the term on the horizontal axis). The dotted line tracks the probability of fixation for a brand-new mutation; the solid line tracks probability of fixation for a mutation that existed before selection began to act, and had achieved mutation-selection-drift equilibrium. No matter how strong selection is, the pre-existing mutation is more likely to “fix” than the new mutation—and that difference is most pronounced when selection favoring the mutation is weakest.
In other words, if mutations provide the variation that fuels evolution by natural selection, standing variation is fuel with a substantially higher octane rating.
Harder to spot
But the same features that make adaptation from standing variation so much more efficient also act as a sort of population genetic stealthing. This is because adaptation from standing variation has very different effects on the genetics of an adapting population than the spread of a single new mutation.
The key to this difference is that gene variants, or alleles, aren’t transmitted from one generation to another one at a time. Instead, they come as part of chromosome regions, physically linked to genetic code that may have nothing to do with the function of the focal gene. And population geneticists use that fact to zero in on genetic regions that might have been recently affected by selection.
It’s a little bit like buying LEGO bricks—or, at least, how it used to be when I was still buying a lot of LEGOs, back before you could custom-build your own sets online. Say you want a hundred copies of a particularly special type of LEGO brick, one that’s only available in a single kit. To get those hundred bricks, you need to buy a hundred copies of that one kit. So you end up with a selection of bricks—the ones you wanted, and the ones that came with the ones you wanted—that probably doesn’t have a very wide diversity of brick types.
But suppose you want a hundred copies of a more common LEGO brick, one that’s included in dozens of different kits—kits for pirate ships and castles, race cars and railroads. You might still need to buy a hundred kits, but you can buy many different kinds of kits, and so in addition to the hundred copies of the brick you want, you also have bricks to build anything from a starship to a dragon.
Selection on a single beneficial mutation is like that first LEGO shopping case, where there’s only one kit containing the brick you want. The one lucky mutation exists with only one “genetic background” of other, associated genetic code, and so when the mutation spreads through the population, a chunk of that background code spreads with it. (At least, until recombination can separate the favored mutation from its background; that takes time, sometimes a lot of time.)
Just as purchasing a hundred copies of the same LEGO kit would leave an obvious mark on the makeup of your brick collection, a selective sweep that starts with a single mutation—what’s called a “hard sweep”—results in a region of genetic code with noticeably lower variation across the population, because everyone is carrying the original lucky mutation plus its associated background.
Figure 4 from Linnen et al. (2009), demonstrates the reduced diversity in a gene region associated with fur color in deer mice. Image from Linnen et al. (2009).
In practice, biologists use this principle in two major ways. First, if a biologist has a particular gene in mind that might have recently experienced selection, she can collect DNA sequence data in the vicinity of that gene for many individuals in a popualtion, and see whether it’s less diverse than it ought to be. This is how Catherine Linnen and her collaborators demonstrated that a population of deer mice living on light-colored soils in the Sand Hills of Nebraska had experienced natural selection for lighter color. In a study [PDF] I’ve discussed previously, the team identified a genetic region that was associated with coat color in the mice, then collected sequence data from that region in mice collected from the light-soil population. Compared to the same genetic region in mice from nearby sites with dark soil, the light-soil mice had markedly less variation in the coat-color region.
Alternatively, biologists who don’t know which genes might have been targeted by natural selection can collect sequence data from a whole lot of gene regions—or even “scan” the whole genome—and compare the diversity at each region. Any region that has lower diversity than most of the other sampled regions may have experienced selection recently, and is probably a good candidate for follow-up study.
But selection froms standing variation doesn’t leave such a clear mark on the genome. It’s more like that second LEGO shopping spree, for a brick found in many different kits. If a useful variant is located on many different genetic backgrounds, than selection can make the variant more common in the population without necessarily reducing the diversity of gene regions near the focal variant. This is called a “soft sweep.” Soft sweeps present a problem for those of us who want to find genes that have recently been affected by natural selection—without the loss of diversity, genetic regions that have undergone soft sweeps may not stand out in the genome as a whole.
But the absence of hard sweeps doesn’t mean that soft sweeps are going on all over the place instead. For instance, in an (ongoing) analysis I presented [PDF] at the recent Evolution meetings in Ottawa, I examined patterns of diversity in genetic regions close to genetic markers that are very strongly associated with differing climate conditions in the small but awesome wildflower Medicago truncatula—and I found little evidence of recent hard sweeps. Does that mean all those strongly associated gene variants are strongly associated as a result of adaptation from standing variation? Maybe; but some portion of the associations could also be due to population genetic processes like drift and isolation-by-distance—I’m still thinking about ways to kill the soft sweep hypothesis.
Pennings and Hermisson followed up their original theory paper with a study comparing the power of several different statistical tests to detect soft sweeps, and they found some promising results with an approach based on linkage between genetic variants in the vicinity of a favored variant. More recently, Pennings has approached the question of adaptation from standing variation from a somewhat different angle, by studying selective sweeps in human immunodeficiency virus, HIV. The evolution of HIV after it infects a patient, and as it adapts to antiviral drugs, is quite well understood—to the point that virologists know to expect particular mutations to sweep the viral population within a patient who starts taking a particular drug.
In an analysis recently published in PLoS Computational Biology, Pennings found that the virus’s evolution of drug resistance could be based on standing variation in about 6% of patients on a standard anti-viral drug cocktail—which is to say, about 6% of all patients carry viral populations that are primed to evolve drug resistance the moment therapy begins. (Pennings’s lab website has a good explanation of the clinical implications of this result, with video, even.)
Then, at the Ottawa Evolution meetings, Pennings presented [PDF] an examination of HIV genetic samples taken from multiple patients undergoing antiviral treatment. She identified cases when the virus’s adaptation to the drugs was fueled by standing variation or based on a mutation that occurred after the drug treatment started; one resistance mutation evolved to fixation via a soft sweep in eight out of 23 patients. [Correction, 6 Aug 2012: See Pennings’s comment below for a correction on this point; it’s not known whether this particular soft sweep started from standing variation, or whether it’s simply the case that two different mutations with the same effect managed to sweep the population together.]
If evolutionary biologists want to understand how natural selection helped make the living world we see around us today, it looks like we’re going to have to learn to love soft sweeps. We’re still learning how to differentiate the aftermath of soft sweeps from the results of other, non-selective processes. But fortunately, we live in an era when the genome-scale data that may let us untangle this question are increasingly easy to collect.◼
I started working on this post quite a while before the Ottawa Evolution meetings, when I was pleased to meet Pleuni Pennings for the first time. If there are mistakes in what I’ve written above, they’re my own; but I hope she’ll let me know if I’ve made any!
Flintoft, L. (2011). Human evolution: Sweep model is swept away. Nature Reviews Genetics, 12, 228-9 DOI: 10.1038/nrg2978
Hermisson, J., & Pennings, P.S. (2005). Soft sweeps: Molecular population genetics of adaptation from standing genetic variation. Genetics, 169 (4), 2335-52 DOI: 10.1534/genetics.104.036947
Hernandez, R. D., J. L. Kelley, E. Elyashiv, S. Melton, A. Auton, G. McVean, G. Sella, & M. Przeworski (2011). Classic selective sweeps were rare in recent human evolution. Science, 331, 920-4 DOI: 10.1126/science.1198878
Linnen, C. R., E. P. Kingsley, J. D. Jensen, & H. E. Hoekstra (2009). On the origin and spread of an adaptive allele in deer mice. Science, 325, 1095-8 DOI: 10.1126/science.1175826
Oleksyk, T. K., M. W. Smith, & S. J. O’Brien (2010). Genome-wide scans for footprints of natural selection. Phil. Trans. Royal Soc. B, 365, 185-205 DOI: 10.1098/rstb.2009.0219
Pennings, P.S. (2012). Standing genetic variation and the evolution of drug resistance in HIV. PLoS Computational Biology, 8 : 10.1371/journal.pcbi.1002527
Pennings, P.S., & J. Hermisson (2006). Soft sweeps III: The signature of positive selection from recurrent mutation. PLoS Genetics, 2 DOI: 10.1371/journal.pgen.0020186.eor
Pritchard, J. K., & A. Di Rienzo (2010). Adaptation—not by sweeps alone Nature Reviews Genetics, 11, 665-7 DOI: 10.1038/nrg2880
Does evolutionary change happen in big jumps, or a series of small steps? The question may seem a little esoteric to non-scientists—how many mutations can dance on the head of a pin?—but it has direct implications for how we identify the genetic basis of human diseases, or desirable traits in domestic plants and animals.
That’s because the evolutionary path by which a particular phenotype, or visible trait, first evolved in a population is closely related to the genetics that underlie the trait in the present. Phenotypes that arose in a single mutational jump will probably remain connected to one or a few genes with large effects; phenotypes that evolved more gradually do so because they are created by the collective action of many genes. So what kind of evolutionary change is most common will determine which kind of gene-to-phenotype relationships we should expect to find.
In an excellent recent review article for the journal Evolution, Matthew Rockman, a biologist with the Department of Biology and Center for Genomics and Systems Biology at New York University, makes the case that the era of genomics has, so far, been much too focused on finding genes of large effect. Fortunately, Rockman also sees the beginnings of a new movement towards acknowledging the importance of small-effect genes—one which may ultimately make genomic association studies more useful.
High-elevation populations of deer mice have evolved “stickier” hemoglobin to cope with the thin atmosphere. (Animal Diversity Web)
It’s easy to walk through the woods and fields of North America and never spot Peromyscus maniculatus, the deer mouse, but you’ve probably heard them scampering off through the leaf litter or under cover of tall grass. They’re exceptionally widespread little rodents, found in forest undergrowth and fields from central Mexico all the way north to the Arctic treeline. In all this range, they look about the same: small and brown, with white underparts and big, sensitive ears.
That apparent sameness is deceptive, however.
A big, varied range presents lots of different environmental conditions to which a widespread species must adapt. And when that big, varied range includes the Rocky Mountains, one of those environmental conditions is as basic as the air itself. At high altitudes, atmospheric pressure is lower, which means lower partial pressure of oxygen, the gas that makes life as we know it work.
The fundamental problem at high altitude is to pull more oxygen from thinner air. Natural selection is good at solving problems, and it has multiple options for adapting a mammal to thinner air at high altitudes, to the extent that these traits are heritable. Selection could favor individuals who more readily respond to thin air by breathing faster and deeper, pulling in more air to make up for its lower oxygen content. Or selection could favor individuals who produce more red blood cells, so that a given volume of blood pumped through their lungs picks up more oxygen. Or, at the most basic level, selection could favor individuals whose individual red blood cells are better at picking up oxygen, via a new form of hemoglobin, the oxygen-binding molecule that packs every red blood cell.
Who needs pollinators? Not monkeyflowers—at least not after a few generations of evolution. Photo by Brewbooks.
The loss of animal pollinators poses a potentially big problem for plants. However, many plant species that rely on animals to move pollen from anther to stigma have the capacity to make due if that service goes undone—and, as a new study released online early by the journal Evolution demonstrates, such plants can rapidly evolve to do without pollinators [$a] if they must.
The paper’s authors, Sarah Bodbyl Roels and John Kelly, demonstrate this using a simple greenhouse experiment with the monkeyflower Mimulus guttatus, a wildflower native to western North America, and a member of a genus rapidly developing into a major model system for studying the evolution of ecological isolation and floral evolution.
Mimulus species vary in their reliance on animal pollinators—some grow minimalistic flowers, with the anther so close to the stigma that pollen transfers without any assistance. In natural populations, M. guttatus is usually pollinated by bees, but individual plants vary in the distance between anther and stigma, and this variation has a genetic basis. So a population of M. guttatus deprived of pollinators would have the raw material to evolve a solution—natural selection would favor plants that are better able to self-pollinate. As the population evolved to be more self-fertilizing, it might also evolve to look more like self-pollinating Mimulus species, losing the bright petals that attract pollinators.
To see whether this could actually happen, Bobdyl Roels and Kelly challenged an experimental population of Mimulus guttatus to do without pollinators, and tracked its response.
The authors raised seeds derived from a natural wild population of Mimulus guttatus in greenhouses under two trial conditions: control populations were provided with hives of bumblebees to pollinate them when their flowers were ready for servicing; and experimental populations were left to produce what seed they could without pollinators. The authors collected the seeds produced by each population, and planted them to form the next generation.
A bumblebee digs for nectar in flowers of Mimulus moschatus. Photo by Mollivan Jon.
Early on in the experiment, the experimental populations deprived of pollinators fared badly. Without pollinators, the average plant produced two seeds or fewer by the end of the generation, compared to eight or ten seeds per plant in the population provided with bees. By the fifth generation, however, this was starting to improve—plants in both populations without pollinators were producing more seeds, and one of the two experimental populations produced nearly as many seeds as the control plants.
Examining the traits of plants produced by this final generation (actually, the grand-offspring of the fifth generation, to control for effects of inbreeding), the authors found that the average distance between the pollen-producing anther and the pollen-receiving stigma had shrunk significantly in plants from the experimental population. Across all the treatments, plants with a shorter distance between stigma and anther produced more self-pollinated seeds. There was no evolved change in other floral measurements, however—plants in the no-pollinators treatment had petals as big and showy as plants evolved with bumble bees.
In a natural population of Mimulus guttatus, the drop-off in seed production created by loss of pollinators should have much the same effect as in this experiment, creating a strong selective advantage for individual plants that can make more seeds on their own. The fact that the experimental plants did not evolve reduced petals could mean that in the cushy conditions of a greenhouse, there wasn’t much need to stop spending resources making showy flowers. Or maybe, when the major source of natural selection is the need to make any seeds at all, selection to save resources on flower production is relatively weak and correspondingly slow-acting.
As the authors point out, one of many changes humans are making to natural communities around the world is to disrupt pollination relationships. In a sense, experiments like theirs are being carried out worldwide, on hundreds of plant species—and each species will adapt, or fail to adapt, in its own way.