We are searching data for your request:
Upon completion, a link will appear to access the found materials.
I remember reading about a concept-in evolutionary biology or natural selection, I think-whereby a particular trait is advantageous to the population or species but only so long as that trait is only exhibited by a minority of the population. That is, the population is more likely to survive if the trait's existence and expression is maintained, but only if that expression is limited to a small percentage of the population. If the trait becomes too common, the selection advantage of having it will decrease, and the fact that most of the population has it may even negatively impact the survivability of that population.
I can't recall where I encountered it. It may have been in the context of reading about ADD, Aspberger's/Autism, neurotypicality, introversion, or mania/OCD. I want to revisit the topic and learn some more about it, but Google is failing me.
Can anyone tell me the name of or term for the concept I'm thinking of?
Frequency-dependent selection is the term you are looking for, I believe. Positive frequency-dependent selection encompasses traits that become more advantageous as they become more common. Negative frequency-dependent selection encompasses traits that become more advantageous as they become rarer.
Mating advantage for rare males in wild guppy populations
To understand the processes that maintain genetic diversity is a long-standing challenge in evolutionary biology, with implications for predicting disease resistance, response to environmental change, and population persistence 1,2,3 . Simple population genetic models are not sufficient to explain the high levels of genetic diversity sometimes observed in ecologically important traits 2 . In guppies (Poecilia reticulata), male colour pattern is both diverse and heritable, and is arguably one of the most extreme examples of morphological polymorphism known 4,5 . Negative frequency-dependent selection (NFDS), a form of selection in which genotypes are favoured when they are rare 6 , can potentially maintain such extensive polymorphism, but few experimental studies have confirmed its operation in nature 7,8 . Here we use highly replicated experimental manipulations of natural populations to show that males with rare colour patterns have higher reproductive fitness, demonstrating NFDS mediated by sexual selection. Rare males acquired more mates and sired more offspring compared to common males and, as previously reported, had higher rates of survival 8 . Orange colour, implicated in other studies of sexual selection in guppies, did predict male reproductive success, but only in one of three populations. These data support the hypothesis that NFDS maintains diversity in the colour patterns of male guppies through two selective agents, mates and predators. Similar field-based manipulations of genotype frequencies could provide a powerful approach to reveal the underlying ecological and behavioural mechanisms that maintain genetic and phenotypic diversity.
Cystic Fibrosis: An Example of the Heterozygote Advantage
Cystic Fibrosis is a recessive genetic disease caused by a mutation in the CFTR gene. This gene is responsible for the transport of chloride ions into and out of cells. If an individual carries two mutations of the gene, Cystic Fibrosis will develop: the disease alters the production of sweat, digestive juices, and mucus. Eventually, pancreatic obstruction leads to difficulty with food digestion, and thickened pulmonary secretions lead to life-threatening lung infections.
Cystic Fibrosis is a fairly common genetic trait among Europeans. The highest prevalence is in Ireland, whereas many as 1 in 19 people carry the disorder. With a high concentration of carriers in a specific geographical area, scientists wondered if there could be a benefit to carrying the Cystic Fibrosis gene.
As it turns out, carriers of the CFTR genetic mutation do have an advantage over those who do not carry the gene. Individuals infected with certain diseases, like cholera or typhus, often succumb to an electrolyte imbalance and dehydration caused by severe, acute diarrhea. At the University of Chapel Hill in North Carolina, mice with the Cystic Fibrosis genetic mutation were infected with cholera. None of the mice succumbed to dehydration, as their intestines did not have the chloride channels to secrete fluid. Of course, these mice did have the actual disease of Cystic Fibrosis, which is not an advantage to survival in the long run.
For a heterozygote, however, a balance is achieved. The carrier of the CFTR mutation will not have Cystic Fibrosis, but will have only half as many chloride channels as a non-carrier. This limits the amount of fluid lost to diarrhea in the event a carrier gets infected with a disease like Typhoid or Cholera.
The question remains, why would the advantage be limited to the European population? Typhoid Fever and Cholera exist throughout the globe, but the increase in Cystic Fibrosis carriers is only seen in the norther climate of Europe. Another theory accounts for this finding: carriers of the CFTR mutation will lose more salt in their sweat. This is a relative disadvantage in hot climates - dehydration would occur more quickly for a carrier in a hot environment. For those in hot climates, carrying the CFTR mutation would not be a benefit. In cold climates, however, carriers are unlikely to dehydrate due to sweating - carrying the CFTR mutation offers protection from diarrhea-causing illness without the worry of heat-induced dehydration.
Genetic architecture rare variants of large effect, or common variants of small effect?
The motivation to conduct GWAS can be either to identify causative/predictive factors for a given trait, or to determine aspects of the genetic architecture of the trait (i.e. the number of loci that contribute and their respective contribution to the phenotype). Some traits are underpinned by a small number of loci with large effect sizes (a simple genetic architecture) and are highly amenable to GWAS. This scenario might be common for traits under biotic selection . Other traits may possess more complex architectures that present difficulties for GWAS. Two possibilities are either that a trait is controlled by many rare variants, each having a large effect on the phenotype, or in contrast, many common variants of only a small phenotypic effect. In both cases the causative variants may be clustered in one or a small number of genes, or across many genes (polygenetic).
The power of GWAS to identify a true association between a SNP and trait is dependent on the phenotypic variance within the population explained by the SNP (Figure 1a). The phenotypic variance is determined by how strongly the two allelic variants differ in their phenotypic effect (the effect size), and their frequency in the sample. Because of this both rare variants and small effect size present problems for GWAS [29, 30].
Sample size and effect size. a) Power and FDR for an idealized phenotype. Simulations in which a single random SNP explaining 5%, 10% or 20% of the phenotypic variance (with heritability
0.75) were performed in either 200, 400 or 800 individuals . Simulations are based on the available SNP data for Arabidopsis, with structure added by giving 10,000 random SNPs a tiny effects size. The star indicates power (the ability to find true positives) and FDR (false positives) at the 5% bonferroni-corrected threshold for 220,000 markers. b) An example of one particular simulation in which the causative SNP (red diamond) is not the most significant SNP in the local window. Remaining SNPs are colored according to their linkage to the causative SNP. Dashed line denotes the 5% bonferroni-corrected threshold for 220,000 markers.
Additionally, rare variants suffer from being in strong or complete association with many other non-causative rare variants within the genome, regardless of the LD decay, and thus a single causative locus may drag with it many synthetic associations . This point is illustrated clearly if one considers multiple private SNPs within an individual: they are completely linked regardless of their genomic locations.
How does one increase the power to detect meaningful association when variants are either at low frequency or have a small effect size? Several important considerations including sample size, incomplete genotyping, genetic heterogeneity and accounting for confounding genetic background are discussed below. We note however, that the importance of rare variants for a particular trait may also be disentangled using QTL analysis as rare variants are elevated to intermediate frequency by the crossing scheme.
How Natural Selection Works
Several hundred million years ago, there were no vertebrate animals on land. The only vertebrate species in the world were fish, all of which lived underwater. Competition for food was intense. Some species of fish that lived near the coast developed a strange mutation: the ability to push themselves along in the mud and sand on the shore with their fins. This gave them access to food sources that no other fish could reach. The advantage gave them greater reproductive success, so the mutation was passed along. This is what we call natural selection.
Natural selection is the engine that drives evolution. The organisms best suited to survive in their particular circumstances have a greater chance of passing their traits on to the next generation. But plants and animals interact in very complex ways with other organisms and their environment. These factors work together to produce the amazingly diverse range of life forms present on Earth.
By understanding natural selection, we can learn why some plants produce cyanide, why rabbits produce so many offspring, how animals first emerged from the ocean to live on land, and how some mammals eventually went back again. We can even learn about microscopic life, such as bacteria and viruses, or figure out how humans became humans.
Charles Darwin coined the term "natural selection." You'll typically hear it alongside the often misunderstood evolutionary catchphrase "survival of the fittest." But survival of the fittest isn't necessarily the bloody, tooth-and-claw battle for survival we tend to make it out to be (although sometimes it is). Rather, it is a measure of how efficient a tree is at dispersing seeds a fish's ability to find a safe spawning ground before laying her eggs the skill with which a bird retrieves seeds from the deep, fragrant cup of a flower a bacterium's resistance to antibiotics.
With a little help from Darwin himself, we're going to learn about natural selection and how it created the astonishing complexity and diversity of life on planet Earth.
Evolution is the result of the tendency for some organisms to have better reproductive success than others -- natural selection.
It's important to remember that differences between individuals, even individuals from different generations, don't constitute evolution. Those are just variations of traits. Traits are characteristics that are inheritable -- they can be passed down from one generation to the next. Not all traits are physical -- the ability to tolerate close contact with humans is a trait that evolved in dogs. Here's an example that helps explain these concepts:
Now imagine that some conditions arise that make it more likely for jockeys to reproduce successfully than basketball players. Jockeys have children more frequently, and these children tend to be short. Basketball players have fewer children, so there are fewer tall people. After a few generations, the average height of humans decreases. Humans have evolved to be shorter.
Evolution is all about change, but what is the mechanism that causes these changes? Every living thing has everything about its construction encoded in a special chemical structure called DNA. Within the DNA are chemical sequences that define a certain trait or set of traits. These sequences are known as genes. The part of each gene that results in the varying expression of traits is called an allele. Because a trait is an expression of an allele, the tendency of a certain trait to show up in a population is referred to as allele frequency. In essence, evolution is a change in allele frequencies over the course of several generations.
Different alleles (and thus different traits) are created in three ways:
- Mutations are random changes that occur in genes. They're relatively rare, but over thousands of generations, they can add up to very profound changes. Mutations can introduce traits that are completely new and have never appeared in that species before.
- Sexual reproduction mixes the genes of each parent by splitting, breaking and blending chromosomes (the strands that contain DNA) during the creation of each sperm and egg. When the sperm and the egg combine, some genes from the male parent and some genes from the female parent are blended randomly, creating a unique mix of alleles in their offspring.
- Bacteria, which don't reproduce sexually, can absorb bits of DNA they encounter and incorporate it into their own genetic code through various methods of genetic recombination [source: Winning].
Sexual reproduction itself is a product of natural selection -- organisms that blend genes in this way gain access to a greater variety of traits, making them more likely to find the right traits for survival. For more detailed information on evolution, head over to How Evolution Works.
A population is a defined group of organisms. In terms of evolutionary science, a population usually refers to a group of organisms that have reproductive access to each other. For example, zebras that live on the plains of Africa are a population. If other zebras lived in South America (none do, but let's pretend they do for the sake of the example), they would represent a different population because they're too far away to mate with the African zebras. Lions that live on the plains of Africa are a different population as well, because lions and zebras are biologically unable to mate with each other.
Fitness is the key to natural selection. We're not talking about how many reps a sea otter can burn through at the gym -- biological fitness is an organism's ability to successfully survive long enough to reproduce. Beyond that, it also reflects an organism's ability to reproduce well. It isn't enough for a tree to create a bunch of seeds. Those seeds need the ability to end up in fertile soil with enough resources to sprout and grow.
Fitness and natural selection were first explained in detail by Charles Darwin, who observed wildlife around the world, took copious notes, then sought to understand what he had seen. Natural selection is probably best explained in his words, taken from his landmark work "On the Origin of Species."
Organisms show variation of traits. "The many slight differences which appear in the offspring of the same parents may be called individual differences. No one supposes that all the individuals of the same species are cast in the same actual mould."
More organisms are born than could ever possibly be supported by the planet's resources. "Every being … must suffer destruction at some period of its life, otherwise, on the principle of geometrical increase, its numbers would quickly become so … great that no country could support the product."
Therefore, all organisms must struggle to live. "As more individuals are produced than can possibly survive, there must in every case be a struggle for existence, either one individual with another of the same species, or with the individuals of distinct species, or with the physical conditions of life."
Some traits offer advantages in the struggle. "Can we doubt … that individuals having any advantage, however slight, over others, would have the best chance of surviving and procreating?"
Organisms that have those traits are more likely to successfully reproduce and pass the traits on to the next generation. "The slightest differences may turn the nicely balanced scale in the struggle for life, and so be preserved."
Successful variations accumulate over the generations as the organisms are exposed to population pressure. "Natural Selection acts exclusively by the preservation and accumulation of variations which are beneficial under the conditions to which each creature is exposed. The ultimate result is that each creature tends to become more and more improved in relation to its conditions."
Let's delve deeper into the concept of population pressure.
The process of natural selection can be sped up immensely by strong population pressures. Population pressure is a circumstance that makes it harder for organisms to survive. There's always some kind of population pressure, but events like floods, droughts or new predators can increase it. Under high pressure, more members of a population will die before reproducing. This means that only those individuals with traits that allow them to deal with the new pressure will survive and pass along their alleles to the next generation. This can result in drastic changes to allele frequencies within one or two generations.
Here's an example -- imagine a giraffe population with individuals that range in height from 10 feet to 20 feet tall. One day, a brush fire sweeps through and destroys all the vegetation below 15 feet. Only the giraffes taller than 15 feet can reach the higher leaves to eat. Giraffes below that height are unable to find any food at all. Most of them starve before they can reproduce. In the next generation, very few short giraffes are born. The population's average height has gone up by several feet.
There are other ways to quickly and drastically affect allele frequency. One way is a population bottleneck. In a large population, alleles are evenly distributed across the population. If some event, such as a disease or a drought, wipes out a large percentage of the population, the remaining individuals may have an allele frequency very different from the larger population. By pure chance, they may have a high concentration of alleles that were relatively rare before. As these individuals reproduce, the formerly rare traits become the average for the population.
The founder effect can also bring about rapid evolution. This occurs when a small number of individuals migrate to a new location, "founding" a new population that no longer mates with the old population. Just as with a population bottleneck, these individuals may have unusual allele frequencies, leading subsequent generations to have very different traits from the original population that the founders migrated from.
The difference between slow, gradual changes over many generations (gradualism) and rapid changes under high population pressure interspersed with long periods of evolutionary stability (punctuated equilibrium) is an ongoing debate in evolutionary science.
So far, we've looked at natural selection as an agent of change. When we look around the world, however, we see many animals that have remained relatively unchanged for tens of thousands of years -- in some cases, even millions of years. Sharks are one example. It turns out that natural selection is also an agent of stability.
Sometimes an organism reaches a state of evolution in which its traits are very well-suited to its environment. When nothing happens to exert strong population pressure on that population, natural selection favors the allele frequency already present. When mutations cause new traits, natural selection weeds these traits out because they're not as efficient as the others.
The Superorganism vs. the Selfish Gene
Evolutionary biologist Richard Dawkins wrote a book called "The Selfish Gene" in the 1970s. Dawkins' book reframed evolution by pointing out that natural selection favors the passing on of genes, not the organism itself. Once an organism has successfully reproduced, natural selection doesn't care what happens after. This explains why certain strange traits continue to exist -- traits that seem to cause harm to the organism but benefit the genes. In some spider species, the female eats the male after mating. As far as natural selection is concerned, a male spider that dies 30 seconds after mating is just as successful as one that lives a full, rich life.
Since the publication of "The Selfish Gene," most biologists agree that Dawkins' ideas explain a great deal about natural selection, but they don't answer everything. One of the main sticking points is altruism. Why do people (and many animal species) do good things for others, even when it offers no direct benefit to themselves? Research has shown that this behavior is instinctive and appears without cultural training in human infants [source: CBC]. It also appears in some primate species. Why would natural selection favor an instinct to help others?
One theory revolves around kinship. People who are related to you share many of your genes. Helping them could help ensure that some of your genes are passed down. Imagine two families of early humans, both competing for the same food sources. One family has alleles for altruism -- they help each other hunt and share food. The other family doesn't -- they hunt separately, and each human only eats whatever he can catch. The cooperative group is more likely to achieve reproductive success, passing along the alleles for altruism.
Biologists are also exploring a concept known as the superorganism. It's basically an organism made out of many smaller organisms. The model superorganism is the insect colony. In an ant colony, only the queen and a few males will ever pass their genes to the next generation. Thousands of other ants spend their entire lives as workers or drones with absolutely no chance of passing on their genes directly. Yet they work to contribute to the success of the colony. In terms of the "selfish gene," this doesn't make a whole lot of sense. But if you look at an insect colony as a single organism made up of many small parts (the ants), it does. Each ant works to ensure the reproductive success of the colony as a whole. Some scientists think the superorganism concept can be used to explain some aspects of human evolution [source: Wired Science].
All organisms carry traits that no longer confer any real benefit to them in terms of natural selection. If the trait doesn't harm the organism, then natural selection won't weed it out, so these traits stick around for generations. The result: organs and behaviors that no longer serve their original purpose. These traits are called vestigial.
There are many examples in the human body alone. The tailbone is the remnant of an ancestor's tail, and the ability to wiggle your ears is left over from an earlier primate that was able to move his ears around to pinpoint sounds. Plants have vestigial traits as well. Many plants that once reproduced sexually (requiring pollination by insects) evolved the ability to reproduce asexually. They no longer need insects to pollinate them, but they still produce flowers, which were originally needed to entice insects to visit the plant.
Sometimes, a mutation causes a vestigial trait to express itself more fully. This is known as an atavism. Humans are sometimes born with small tails. It's fairly common to find whales with hind legs. Sometimes snakes have the equivalent of toenails, even though they don't have toes. Or feet.
Case Studies in Natural Selection
We usually think of evolution as something we don't see happening right before our eyes, instead looking at fossils to find evidence of it happening in the past. In fact, evolution under intense population pressure happens so fast that we've seen it occur within the span of a human lifetime.
African elephants typically have large tusks. The ivory in the tusks is highly valued by some people, so hunters have hunted and killed elephants to tear out their tusks and sell them (usually illegally) for decades. Some African elephants have a rare trait -- they never develop tusks at all. In 1930, about 1 percent of all elephants had no tusks. The ivory hunters didn't bother killing them because there was no ivory to recover. Meanwhile, elephants with tusks were killed off by the hundreds, many of them before they ever had a chance to reproduce.
The alleles for "no tusks" were passed along over just a few generations. The result: As many as 38 percent of the elephants in some modern populations have no tusks [source: BBC News]. Unfortunately, this isn't really a happy ending for the elephants, since their tusks are used for digging and defense.
The bollworm, a pest that eats and damages cotton crops, has shown that natural selection can act even faster than scientists can genetically engineer something. Some cotton crops have been genetically modified to produce a toxin that's harmful to most bollworms. A small number of bollworms had a mutation that gave them immunity to the toxin. They ate the cotton and lived, while all non-immune bollworms died. The intense population pressure has produced broad immunity to the toxin in the entire species within the span of just a few years [source: EurekAlert].
Some species of clover developed a mutation that caused the poison cyanide to form in the plant's cells. This gave the clover a bitter taste, making it less likely to be eaten. However, when the temperature drops below freezing, some cells rupture, releasing the cyanide into the plant's tissues and killing the plant. In warm climates, natural selection acted in favor of the cyanide-producing clover, but where the winters are cold, non-cyanide clover was favored. Each kind exists almost exclusively in each climate area [source: Purves].
What about humans? Are we subject to natural selection as well? It's certain that we were -- humans only became humans because an assortment of traits (larger brains, walking upright) conferred advantages to those primates that developed them. But we're capable of influencing the distribution of our genes directly. We can use birth control, so that those of who are "fittest" in terms of natural selection might not pass on our genes at all. We use medicine and science to allow many people to live (and reproduce) who otherwise wouldn't likely survive past childhood. Much like domesticated animals, which we breed to specifically favor certain traits, humans are influenced by a sort of unnatural selection.
However, we're still evolving. Some humans have more reproductive success than others, and the factors that affect that equation have added a layer of human complexity on top of the already complicated interactions of the animal world. In other words, we don't really know what we're going to evolve into. Change is inevitable, but remember that natural selection doesn't care about making "better" humans, just more of us.
Genetic vs. heritable trait
When someone tells you that height is 80% heritable , does that mean: a) 80% of the reason you are the height you are is due to genes b) 80% of the variation within the population on the trait of height is due to variation of the genes The answer is of course b . Unfortunately in the 5 years I’ve been blogging the conception of heritability has been rather difficult to get across, and I regularly have to browbeat readers who conflate the term with a . That is, they assume that if I say that a trait is mostly heritable I mean that its development is mostly a function of genes. In reality not only is that false, it’s incoherent. Heritability is addressing the population level correlation between phenotypic variation and genotypic variation. In other words, how well can genetic variation work as a proxy for phenotypic variation? What proportion of the phenotypic variation can be accounted for by genotypic variation? The key terms here are population level and variation (or technically, variance ). We are not usually talking about individuals and we are restricting our discussion to traits which vary within the population.
Evolution Is Just a Theory
Critics of the theory of evolution dismiss its importance by purposefully confounding the everyday usage of the word &ldquotheory&rdquo with the way scientists use the word. In science, a &ldquotheory&rdquo is understood to be a body of thoroughly tested and verified explanations for a set of observations of the natural world. Scientists have a theory of the atom, a theory of gravity, and the theory of relativity, each of which describes understood facts about the world. In the same way, the theory of evolution describes facts about the living world. As such, a theory in science has survived significant efforts to discredit it by scientists. In contrast, a &ldquotheory&rdquo in common vernacular is a word meaning a guess or suggested explanation this meaning is more akin to the scientific concept of &ldquohypothesis.&rdquo When critics of evolution say evolution is &ldquojust a theory,&rdquo they are implying that there is little evidence supporting it and that it is still in the process of being rigorously tested. This is a mischaracterization.
Trait No. 5: Luck
You may have rolled your eyes when we mentioned luck, but it's completely true. Just like any other trophy-sized animal, there are seemingly countless factors that must go right in order for a bass to reach its full potential. When you really think about it, the cards are stacked against them.
"To reach double-digit status, a bass definitely needs Lady Luck on its side," Bardin said. "It has to be born a female, which immediately eliminates 50 percent of the population. It has to survive as a fry (less than 1 percent reach adulthood), live in an environment flush with forage and very consistent water quality, avoid fishing pressure and have limited exposure to predatory threats throughout its entire life."
It might seem like a lot has to go right in order for a bass to grow into a trophy, but that's exactly why they're so rare. If they were easy to find and catch, we'd probably find another species to pursue. After all, that's what keeps us coming back for more—the challenge and the improbable odds.
Species Accumulation Rate
The Bayesian probability of recording a ‘new’ species in the suction-trap network was about 80% for the next year (P = 0·793) (Appendix S3a). As an estimate, the average trap produces 0·529 new species per annum ± SE 0·172, but there are clearly biases (Appendix S3b). As expected, new species to the network are considerably more difficult to find as time passes (Fig. 3). Whilst Rothamsted and Broom's Barn were the first traps to begin providing long-term data in 1965, Rothamsted has produced twice as many species. Silwood Park appears to produce many more species, particularly given that it has a fragmented time series that began in 1968, stopped in 1988 and began once more in 2000 at a time when new species to the network were considerably harder to find. Of the species that remain to be discovered, three subspecies are of economic importance: Myzus persicae nicotianae, Aphis fabae cirsiiacanthoidis and Aphis fabae mordvilkoi (Appendix S2).
Phenology and Climate
Phenology of aphids and effect of climate over five decades
Averaging out over all species, the LME RoC models showed that first flights were getting significantly earlier at a rate of −0·611 ± SE 0·015 days year −1 r 2 = 0·465 (t = −40·716 P < 0·0001) but last flights appeared relatively stationary (−0·010 ± SE 0·022 days year −1 r 2 = 0·558 t = −0·445 P = 0·656). The average flight season was getting significantly longer (0·3357 ± SE 0·02614 days year −1 r 2 = 0·339 t = 12·842 P ≤ 0·0001) even though the log annual count was only moderately increasing (0·002 ± SE 0·0008 log annual count year −1 r 2 = 0·539 t = 2·58404 P = 0·0098). As a spatial covariate in the additive climate models, year also acted on the aphid responses in a nonlinear way. All the LME estimations are correct in linear terms, but first flight analysis revealed a nonlinear component, being ‘humped’ around 1980 and thereafter showing a dramatic advancement (F = 165·2 P < 0·001 Fig. 4a). Although stationary over the time period, last flights appeared to oscillate over time with roughly 20-year period length and with greater uncertainty during 1965–1970 (F = 15·30 P < 0·001 Appendix S4b). The average flight season did get longer over the whole time series, but there was a period between 1975 and 1995 when the response was relatively flat (F = 57·62 P < 0·001 Appendix S4b). Lastly, log annual counts showed a moderate increase, but a post-millennial trough suggests that these are dynamic (F = 9·24 P < 0·001 Appendix S4b).
Both the accumulated degree days above 16 °C (ADD16) during April and May and winter climate were linked to the advancement of aphid first flights (Fig. 4). Per unit change in degree days yielded an advance of 0·379 days ± SE 0·025 (t = −14·74 P < 0·001), but the previous winter had a much stronger effect leading to 2·93 days ± SE 0·089 earlier (t = −32·89 P < 0·001) per unit change of the NAO. The combined effects of these climate variables can be seen in Fig. 4d: warm wet winters, signalled by positive NAO values, combined with very high ADD16 values led to very early first flights in spring (i.e. area of deep red interpolation). As the aphid flight season accumulated degree days above 16 °C, the duration of the flight season contracted (0·078 days ± SE 0·005 t = −13·59 P < 0·001) and the effect of cold winter appeared to extend the duration of the flight season (2·261 days ± SE 0·163 t = 13·86 P < 0·001). The effect of winter was not significant by the time that the last flight is realized (t = −1·441 P > 0·05). The temperature effect through the year accumulated but brought forward, rather than pushed back, the last flight (0·043 days ± SE 0·004 t = −8·814 P < 0·001), similar to the effect on the duration of the flight season that measures the 5th–95th percentiles. The effect of climate on log annual counts produces an unconvincing gamm model perhaps because the response itself oscillates considerably between years and across species (Appendix S4b). Looking in more detail at the intercept of species random effect component (intercept = 0·991 standard deviation = 0·996), it is clear that there is very high variation in the species responses when the fixed effects are zero. The fixed effect of the NAO is close to the 0·05 significance boundary (t = −2·546 P < 0·05) and should be treated with caution (Zuur et al. 2009 ). Second, whilst ADD16 produces a highly significant result, the per unit change in ADD16 produces very small increments to the log count (0·001 log aphids ± SE <0·001 t = 6·921 P < 0·001). We cautiously accept that increasing the accumulated degree days will produce small, positive changes in annual counts recognizing that this model also struggles to explain the wide species variation (Fig. 5).
The spatiotemporal covariates in all the above gamm models suggest that aphid phenology was significantly determined by where and when the event takes place. Latitudinal effects were clear and close to linear for first flight (Fig. 4b F = 101·80 P < 0·001). As first flights progressed northwards, they took place later in the year. The further south, the longer the duration of the flight season was (F = 43·90 P < 0·001) although aphid populations become distinctly nonlinear in terms of last flight (F = 26·57 P < 0·001) and log annual count (F = 26·57 P < 0·001, Appendix S4b). In both cases a noisier, nonlinear response occurred northwards from 54°, approximately where the Preston trap is located (Fig. 2 Appendix S4b). First and last flights also appeared to have an opposing pattern in terms of longitude: in the west, first flights were earlier, but last flights were earlier in the east (first: F = 17·7 P < 0·001 last: F = 10·19 P < 0·001 – Fig. 4c). Longitudinal effects were close to the boundary for log annual counts and appear to have wide confidence intervals (F = 3·03 P < 0·01) and although this spatial influence is stronger for the duration of flight season (F = 9·31 P < 0·01), longitude had a nonlinear effect.
The log annual count data for many species suggest that they oscillate around a baseline linear trend that indicates either no long-term change is apparent or a very marginal upward trend is detectable, but both with substantial year-to-year variation (Appendix S4a). This is particularly true of pest species such as Brachycaudus helichrysi, Brevicoryne brassicae, Metopolophium dirhodum and Sitobion avenae whose annual counts are determined by many within-year processes that are not necessarily carried over to the following year. There is a small minority, such as Anoecia corni and Tetraneura ulmi, that are less volatile in terms of the long-term trend in log annual counts, but this is not a common response amongst the group (Appendix S4a).
Utamphorophora humboldti showed the most dramatic advancement in first flights (−2·709 days year −1 ) and also the most dramatic shift to later in the year of all last flights (2·713 days year −1 ). Consequently it also had the largest increase in the duration of flight season (2·531 days year −1 ) and largest population increase. Its outlier status is captured in the PCA and neighbourhood-joining dendrogram (Appendix S5). Two other species highlighted as outliers in the dendrogram are Myzus ascalonicus and Periphyllus testudinaceus. Myzus ascalonicus is advancing its first flight very slowly (−0·087 days year −1 ), has an earlier last flight (−1·900 days year −1 ) and, like P. testudinaceus (−0·901 days year −1 ), is contracting its flight season length (−0·896 days year −1 ). Curiously, even though P. testudinaceus has a shorter flight season, it has later last flights (0·529 days year −1 ), suggesting that there is a longer period at the end of the year when a small number of aphids remain in flight.
Considering all the phenological responses, there were seven groups of aphids defined by neighbour joining (Fig. 5). Overall, there was not one group in which all are pests (see Appendix S2), or all have a common host or trait profile, or are equally rare or common. In short, the groups are well mixed although the highest density of pests is grouped as ‘ELPS’ (i.e. those that have earlier first flights, later last flights, protracted flight seasons and smaller log annual counts) bar Cavariella archangelicae in the ELPS group that feeds on umbellifers and does not have pest status. By far the largest group, with 20 species, is the ‘ELPB’ group [i.e. those that have earlier first flights, later last flights, protracted flight seasons and bigger annual counts (Fig. 5)]. Six of these species are pests (Acyrthosiphon pisum, Cavariella aegopodii, Hyperomyzus lactucae, Myzus cerasi, Rhopalosiphum insertum (Rhopalosiphum oxycanthae), Sitobion fragariae). The common trait profile amongst 10 of the 20 species is for obligate heteroecious holocycly. The second largest group (EEPS) comprises 16 species that are either obligate heteroecious and holocyclic (six species) or obligate monoecious and holocyclic (six species). Even though their last flights are getting earlier and populations smaller, three pests can be found in this group Aulacorthum solani and Hyalopterus pruni are pests of potatoes and plums, respectively, and M. dirhodum can cause damage to wheat. Of the remaining four smaller groups, the EECB group with three species that include two major pests, B. helichrysi and B. brassicae, and the EEPB group that comprises five species, two of which are major pests (Elatobium abietinum, M. persicae) are notable because both groups show earlier last flights.
The trait ecology was found to explain some of the variability in observed patterns in first and last flight, duration of flight season and the log annual count of aphids (Table 1). A formal single traits mixed model analysis showed that generalizing across all species, heteroecious species tend to have a significantly longer duration to their flight season, with later last flights and larger log annual counts, although their first flights also tend to be later than for monoecious species (Table 1). Anholocycly promotes significantly earlier first and last flights, shorter flight seasons and smaller log annual counts than holocycly. A lack of life cycle plasticity incurring a compulsory single reproductive type (i.e. obligate) produces a first flight that is significantly later and a last flight significantly earlier with no significant increase in the duration of the flight season. The log annual count for a compulsory single reproductive type aphid was also significantly smaller.
|LME response||LME fixed effects|
|Host plant alternation. Reference level: Holocyclic||Reproductive strategy. Reference level: Monoecious||Life cycle plasticity. Reference level: Obligate|
|1. First flight||2·316 ± SE 0·560***||−8·274 ± SE 0·437***||15·182 ± SE 0·614***|
|2. Last flight||6·400 ± SE 1·034***||−24·028 ± SE 0·809***||−5·262 ± SE 1·135***|
|3. Duration of flight season||5·171 ± SE 0·983***||−16·319 ± SE 0·768***||2·085 ± SE 1·079|
|4. Log annual count||0·551 ± SE 0·055***||−0·243 ± SE 0·028***||−1·026 ± SE 0·039***|
- The table includes the marginal significance (P < 0·001***) derived from conditional t tests for each of the three fixed coefficients. For each fixed effect, the trait of interest has a reference level which is compared against the remaining ‘free level’. In each case, these were (i) a host-alternating potential trait: heteroecious, the ‘free level’, was compared with a monoecious reference level (ii) predominant mode of reproduction trait: anholocyclic, the ‘free level’, was compared with a holocyclic reference level (iii) life cycle plasticity trait: facultative, the ‘free level’, was compared with an obligate reference level. If coefficient values were negative, the reference level in question is lower than the ‘free level’ and vice versa. Thus, holocyclic aphids have significantly later first flights (2·316 on the log10 scale) than anholocyclic aphids but monoecious aphids have significantly earlier first flights (i.e. −8·274 on the log10 scale) compared with heteroecious aphids. In all cases, bar life cycle plasticity in explaining duration of flight season, traits were highly significant in explaining observed responses in aphid migration characteristics.
Crow, J. F. How much do we know about spontaneous human mutation rates? Environ. Mol. Mutagen. 21, 122–129 (1993).
Kondrashov, A. S. & Crow, J. F. A molecular approach to estimating the human deleterious mutation rate. Hum. Mutat. 2, 229–234 (1993).
Ohta, T. The nearly neutral theory of molecular evolution. Annu. Rev. Ecol. Syst. 23, 263–286 (1992).
Loewe, L. Quantifying the genomic decay paradox due to Muller's ratchet in human mitochondrial DNA. Genet. Res. 87, 133–159 (2006).
Charlesworth, D., Charlesworth, B. & Morgan, M. T. The pattern of neutral molecular variation under the background selection model. Genetics 141, 1619–1632 (1995).
Peck, J. R., Barreau, G. & Heath, S. C. Imperfect genes, Fisherian mutation and the evolution of sex. Genetics 145, 1171–1199 (1997).
Caballero, A. & Keightley, P. D. A pleiotropic nonadditive model of variation in quantitative traits. Genetics 138, 883–900 (1994).
Zhang, X. S. & Hill, W. G. Genetic variability under mutation selection balance. Trends Ecol. Evol. 20, 468–470 (2005).
Eyre-Walker, A., Woolfit, M. & Phelps, T. The distribution of fitness effects of new deleterious amino acid mutations in humans. Genetics 173, 891–900 (2006).
Schultz, S. T. & Lynch, M. Mutation and extinction: the role of variable mutational effects, synergistic epistasis, beneficial mutations and degree of outcrossing. Evolution 51, 1363–1371 (1997).
Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502–510 (2001).
Johnson, T. & Barton, N. H. Theoretical models of selection and mutation on quantitative traits. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 1411–1425 (2005).
Burch, C. L., Guyader, C., Samarov, D. & Shen, H. Experimental estimate of the abundance and effects of nearly neutral mutations in the RNA virus φ6. Genetics 176, 467–476 (2007).
Elena, S. F., Ekunwe, L., Hajela, N., Oden, S. A. & Lenski, R. E. Distribution of fitness effects caused by random insertion mutations in Escherichia coli. Genetica 102–103, 349–358 (1998).
Sanjuan, R., Moya, A. & Elena, S. F. The distribution of fitness effects caused by single-nucleotide substitutions in an RNA virus. Proc. Natl Acad. Sci. USA 101, 8396–8401 (2004). Estimates the DFE for an RNA virus by measuring the fitness consequences of single mutations.
Thatcher, J. W., Shaw, J. M. & Dickinson, W. J. Marginal fitness contributions of nonessential genes in yeast. Proc. Natl Acad. Sci. USA 95, 253–257 (1998).
Wloch, D. M., Szafraniec, K., Borts, R. H. & Korona, R. Direct estimate of the mutation rate and the distribution of fitness effects in the yeast Saccharomyces cerevisiae. Genetics 159, 441–452 (2001).
Mukai, T. The genetic structure of natural populations of Drosophila melanogaster. I. Spontaneous mutation rate of polygenes controlling viability. Genetics 50, 1–19 (1964). The first mutation accumulation experiment.
Ohnishi, O. Spontaneous and ethyl methanesulfate-induced mutations controlling viability in Drosophila melanogaster. II. Homozygous effect of polygenic mutations. Genetics 87, 529–545 (1977).
Bataillon, T. Estimation of spontaneous genome-wide mutation rate parameters: whither beneficial mutations? Heredity 84, 497–501 (2000).
Joseph, S. B. & Hall, D. W. Spontaneous mutations in diploid Saccharomyces cerevisiae: more beneficial than expected. Genetics 168, 1817–1825 (2004).
Shaw, F. H., Geyer, C. J. & Shaw, R. G. A comprehensive model of mutations affecting fitness and inferences for Arabidopsis thaliana. Evolution 56, 453–463 (2002).
Bateman, A. J. The viability of near-normal irradiated chromosomes. Int. J. Radiat. Biol. 1, 170–180 (1959).
Garcia-Dorado, A. The rate and effects distribution of viable mutation in Drosophila: minimum distance estimation. Evolution 51, 1130–1139 (1997).
Keightley, P. D. The distribution of mutation effects on viability in Drosophila melanogaster. Genetics 138, 1315–1322 (1994).
Keightley, P. D. Inference of genome wide mutation rates and distributions of mutations effects for fitness traits: a simulation study. Genetics 150, 1283–1293 (1998).
Davies, E. K., Peters, A. D. & Keightley, P. D. High frequency of cryptic deleterious mutations in Caenorhabditis elegans. Science 285, 1748–1751 (1999). Shows that most mutations are undectable in a mutation accumulation study and that the DFE of deleterious mutations must be complex and multi-modal.
Denver, D. R., Morris, K., Lynch, M. & Thomas, W. K. High mutation rate and predominance of insertions in the Caenorhabditis elegans nuclear genome. Nature 430, 679–682 (2004).
Eyre-Walker, A., Keightley, P. D., Smith, N. G. C. & Gaffney, D. Quantifying the slightly deleterious model of molecular evolution. Mol. Biol. Evol. 19, 2142–2149 (2002).
Loewe, L. & Charlesworth, B. Inferring the distribution of mutational effects on fitness in Drosophila. Biol. Lett. 2, 426–430 (2006).
Loewe, L., Charlesworth, B., Bartolome, C. & Noel, V. Estimating selection on nonsynonymous mutations. Genetics 172, 1079–1092 (2006).
Nielsen, R. & Yang, Z. Estimating the distribution of selection coefficients from phylogenetic data with applications to mitochondrial and viral DNA. Mol. Biol. Evol. 20, 1231–1239 (2003).
Piganeau, G. & Eyre-Walker, A. Estimating the distribution of fitness effects from DNA sequence data: implications for the molecular clock. Proc. Natl Acad. Sci. USA 100, 10335–10340 (2003).
Sawyer, S., Kulathinal, R. J., Bustamante, C. D. & Hartl, D. L. Bayesian analysis suggests that most amino acid replacements in Drosophila are driven by positive selection. J. Mol. Evol. 57, S154–S164 (2003).
Bustamante, C. D., Wakeley, J., Sawyer, S. & Hartl, D. L. Directional selection and the site-frequency spectrum. Genetics 159, 1779–1788 (2001).
Bubb, K. L. et al. Scan of human genome reveals no new loci under ancient balancing selection. Genetics 173, 2165–2177 (2006).
Kimura, M. Genetic variability maintained in a finite population due to the mutational production of neutral and nearly neutral isoalleles. Genet. Res. 11, 247–269 (1968).
Johnson, K. P. & Seger, J. Elevated rates of nonsynonymous substitution in island birds. Mol. Biol. Evol. 18, 874–881 (2001).
Woolfit, M. & Bromham, L. Population size and molecular evolution on islands. Proc. Biol. Sci. 272, 2277–2282 (2005).
Silander, O. K., Tenaillon, O. & Chao, L. Understanding the evolutionary fate of finite populations: the dynamics of mutational effects. PLoS Biol. 5, e94 (2007). Shows that the DFE is highly dependent on the fitness of the population that is being considered.
Lynch, M. & Conery, J. S. The origins of genome complexity. Science 302, 1401–1404 (2003).
Subramanian, S. & Kumar, S. Higher intensity of purifying selection on &gt90% of the human genes revealed by the intrinsic replacement mutation rates. Mol. Biol. Evol. 23, 2283–2287 (2006).
Eyre-Walker, A. Changing effective population size and the McDonald–Kreitman test. Genetics 162, 2017–2024 (2002).
Charlesworth, J. & Eyre-Walker, A. The rate of adaptive evolution in enteric bacteria. Mol. Biol. Evol. 23, 1348–1356 (2006).
Orgel, L. E. & Crick, F. H. C. Selfish DNA: the ultimate parasite. Nature 284, 604–607 (1980).
Cliften, P. et al. Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science 301, 71–76 (2003).
Shabalina, S. A. & Kondrashov, A. S. Pattern of selective constraint in C. elegans and C. briggsae genomes. Genet. Res. 74, 23–30 (1999).
Webb, C. T., Shabalina, S. A., Ogurtsov, A. Y. & Kondrashov, A. S. Analysis of similarity within 142 pairs of orthologous intergenic regions of Caenorhabditis elegans and Caenorhabditis briggsae. Nucleic. Acids Res. 30, 1233–1239 (2002).
Andolfatto, P. Adaptive evolution of non-coding DNA in Drosophila. Nature 437, 1149–1152 (2005). Provided the first evidence that adaptive evolution is widespread in Drosophila non-coding DNA.
Bergman, C. M. & Kreitman, M. Analysis of conserved noncoding DNA in Drosophila reveals similar constraints in intergenic and intronic sequences. Genome Res. 11, 1335–1345 (2001). Provided the first indication that extensive amounts of Drosophila non-coding DNA is subject to selection.
Halligan, D. L. & Keightley, P. D. Ubiquitous selective constraints in the Drosophila genome revealed by a genome-wide interspecies comparison. Genome Res. 16, 875–884 (2006).
Dermitzakis, E. T. et al. Numerous potentially functional but non-genic conserved sequences on human chromosome 21. Nature 420, 578–582 (2002). Provided the clearest evidence that substantial amounts of mammalian non-coding DNA is subject to selective constraint.
Koop, B. F. Human and rodent DNA sequence comparisons: a mosaic model of genomic evolution. Trends Genet. 11, 367–371 (1995).
Shabalina, S. A., Ogurtsov, A. Y., Kondrashov, V. A. & Kondrashov, A. S. Selective constraint in intergenic regions of human and mouse genomes. Trends Genet. 17, 373–376 (2001).
Mouse Genome Sequencing Consortium. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).
Dermitzakis, E. T., Reymond, A. & Antonarakis, S. E. Conserved non-genic sequences — an unexpected feature of mammalian genomes. Nature Rev. Genet. 6, 151–157 (2005).
Gaffney, D. & Keightley, P. D. Genomic selective constraints in murid noncoding DNA. PLoS Genet. 2, e204 (2006).
Eyre-Walker, A. The genomic rate of adaptive evolution. Trends Ecol. Evol. 21, 569–575 (2006).
Chimpanzee Sequencing and Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437, 69–87 (2005).
Zhang, L. & Li, W.-H. Human SNPs reveal no evidence of frequent positive selection. Mol. Biol. Evol. 22, 2504–2507 (2005).
Bierne, N. & Eyre-Walker, A. Genomic rate of adaptive amino acid substitution in Drosophila. Mol. Biol. Evol. 21, 1350–1360 (2004).
Smith, N. G. C. & Eyre-Walker, A. Adaptive protein evolution in Drosophila. Nature 415, 1022–1024 (2002).
Welch, J. J. Estimating the genome-wide rate of adaptive protein evolution in Drosophila. Genetics 173, 821–837 (2006).
Bachtrog, D. & Andolfatto, P. Selection, recombination and demographic history in Drosophila miranda. Genetics 174, 2045–2059 (2006).
Williamson, S. H. Adaptation in the env gene of HIV-1 and evolutionary theories of disease progression. Mol. Biol. Evol. 20, 1318–1325 (2003).
Keightley, P. D., Lercher, M. J. & Eyre-Walker, A. Evidence for widespread degradation of gene control regions in hominid genomes. PLoS Biol. 3, e42 (2005).
Gillespie, J. H. Molecular evolution over the mutational landscape. Evolution 38, 1116–1129 (1984).
Orr, H. A. The distribution of fitness effects among beneficial mutations. Genetics 163, 1519–1526 (2003). An extension of the work of Gillespie showing that the DFE of advantageous mutations should be an exponential distribution under certain simplifying assumptions.
Imhof, M. & Schlotterer, C. Fitness effects of advantageous mutations in evolving Escherichia coli populations. Proc. Natl Acad. Sci. USA 98, 1113–1117 (2001).
Kassen, R. & Bataillon, T. Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria. Nature Genet. 38, 484–488 (2006).
Rokyta, D. R., Joyce, P., Caudle, S. B. & Wichman, H. A. An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus. Nature Genet. 37, 441–444 (2005).
Cowperthwaite, M. C., Bull, J. J. & Meyers, L. A. Distributions of beneficial fitness effects in RNA. Genetics 170, 1449–1457 (2005). Provided evidence that the DFE of advantageous mutations is not an exponential distribution.
Bratteler, M., Lexer, C. & Widmer, A. Genetic architecture of traits associated with serpentine adaptation of Silene vulgaris. J. Evol. Biol. 19, 1149–1156 (2006).
Lexer, C., Rosenthal, D. M., Raymond, O., Donovan, L. A. & Rieseberg, L. H. Genetics of species differences in the wild annual sunflowers, Helianthus annuus and H. petiolaris. Genetics 169, 2225–2239 (2005).
Schemske, D. W. & Bradshaw, H. D. Jr. Pollinator preference and the evolution of floral traits in monkeyflowers (Mimulus). Proc. Natl Acad. Sci. USA 96, 11910–11915 (1999).
Mukai, T., Chigusa, S. I., Mettler, L. E. & Crow, J. F. Mutation rate and dominance of genes affecting viability in Drosophila melanogaster. Genetics 2, 333–355 (1972).
Vassilieva, L., Hook, A. M. & Lynch, M. The fitness effects of spontaneous mutations in Caenorhabditis elegans. Evolution 54, 1234–1246 (2000).
Elena, S. F. & Moya, A. Rate of deleterious mutation and the distribution of its effects on fitness in vesicular stomatitis virus. J. Evol. Biol. 12, 1078–1088 (1999).
Zeyl, C. & DeVisser, J. A. Estimates of the rate and distribution of fitness effects of spontaneous mutation in Saccharomyces cerevisiae. Genetics 157, 53–61 (2001).
Avila, V. et al. Increase of the spontaneous mutation rate in a long-term experiment with Drosophila melanogaster. Genetics 173, 267–277 (2006).
Garcia-Dorado, A., Monedero, J. L. & Lopez-Fanjul, C. The mutation rate and the distribution of mutational effects of viability and fitness in Drosophila melanogaster. Genetica 102–103, 255–256 (1998).
Keightley, P. D. Nature of deleterious mutation load in Drosophila. Genetics 144, 1993–1999 (1996).
Schoen, D. J. Deleterious mutation in related species of the plant genus Amsinckia with contrasting mating systems. Evolution 59, 2370–2377 (2005).
Garcia-Dorado, A. & Caballero, A. On the average coefficient of dominance of deleterious spontaneous mutations. Genetics 155, 1991–2001 (2000).
Peters, A. D., Halligan, D. L., Whitlock, M. C. & Keightley, P. D. Dominance and overdominance of mildly deleterious induced mutations for fitness traits in Caenorhabditis elegans. Genetics 165, 589–599 (2003).
Shaw, R. G. & Chang, S. M. Gene action of new mutations in Arabidopsis thaliana. Genetics 172, 1855–1865 (2006).
Williamson, S. H. et al. Simultaneous inference of selection and population growth from patterns of variation in the human genome. Proc. Natl Acad. Sci. USA 102, 7882–7887 (2005).
Li, W.-H., Tanimura, M. & Sharp, P. M. An evaluation of the molecular clock hypothesis using mammalian DNA sequences. J. Mol. Evol. 25, 330–342 (1987).
Ohta, T. Synonymous and nonsynonymous substitutions in mammalian genes and the nearly neutral theory. J. Mol. Evol. 40, 56–63 (1995).
Bush, E. C. & Lahn, B. T. Selective constraint on noncoding regions of hominid genomes. PLoS Comput. Biol. 1, e73 (2005).
Keightley, P. D., Kryukov, G. V., Sunyaev, S., Halligan, D. L. & Gaffney, D. J. Evolutionary constraints in conserved nongenic sequences of mammals. Genome Res. 15, 1373–1378 (2005).
Kryukov, G. V., Schmidt, S. & Sunyaev, S. Small fitness effect of mutations in highly conserved non-coding regions. Hum. Mol. Genet. 14, 2221–2229 (2005).
Lynch, M. et al. Spontaneous deleterious mutation. Evolution 53, 645–663 (1999).