Prof: The lecture today
is about neutral evolution. So let’s get going on that.
I want to remind you,
when people think about evolution they often think that
it’s only natural selection. But it’s not.
It is both micro and macro.
So macro gives us history and
constraint, and micro consists basically of natural selection
and drift; and developmental biology is
involved in both. So what we’re going to talk
about today is basically neutral evolution.
What happens to genes or traits
that are not experiencing natural selection because
they’re not making any difference to reproductive
success? There’s actually a lot of that
that goes on, and it’s very useful that it
does. It gives us a baseline,
it gives us a method of measuring things,
and it gives us a lot of information about history.
So there are going to be three
messages I want you to remember today.
One is going to be how meiosis
is like a fair coin. The probability that a gene
will get into a specific gamete in meiosis is 50%.
The second point is how the
fixation of a neutral allele in a population is like radioactive
decay; and it’s like it in this sense:
in neither the case of the fixation of neutral alleles,
nor in the case of say looking at a gram of uranium-238,
do you know which mutation will be fixed or which atom will
decay. But, because there’s so many of
them, in both cases you know very precisely how many events
will happen in a certain period of time.
This is a kind of law of large
numbers for random events. If a lot of random events go
on, the average is a very predictable thing.
But if you just examine one
nucleotide in a genome, or one atom in a gram of
uranium, you can’t predict when it will mutate,
when it might be fixed, when it will decay.
The third thing that I want you
to remember is this regular fixation of neutral alleles,
this steady process whereby if you look at an entire genome,
over a given period of time–10,000 years,
100,000 years–a certain very predictable,
average number of mutations will be fixed if they’re
neutral. So if you can locate the
neutral ones in the genome, you can use them to estimate
relationships and the times to the last common ancestors.
So there’s actually some
interesting, rather abstract and rather big ideas in this
lecture. Randomness is not something
that everyone finds intuitive. Our brains are apparently not
designed by natural selection to deal extremely well with Las
Vegas, or the stock market. Okay?
So we need to hone your
intuition a bit about how random processes work.
By the way, people who do
really well in calculus and analysis often find their
introduction to probability and statistics a little confusing.
The thing that’s going on here
is that you have to learn to think about entire populations
of things and about distributions and frequencies of
things, rather than about billiard
balls hitting each other on a table or planets being attracted
to the sun, by gravity.
It’s a different kind of
thinking. It’s population thinking.
So the outline of the lecture
is a bit about how neutrality arises.
I want you to know
mechanistically why it is that some genes are neutral;
the reasons why genetic variation might not produce any
variation in fitness– that’s what we mean by neutral,
there’s variation at one level but it doesn’t make any
difference to reproductive success;
the mechanisms that cause random change;
and then the significance of neutral for molecular evolution.
And now I’m briefly going to
mention maladaptive evolution so that you can see how it is that
an evolutionary process can actually result in a situation
where organisms are not well adapted to their habitats.
And with that we will have
covered the major possible outcomes of evolution:
adaptation, neutrality and maladaptation.
Okay, here’s a nice abstract
diagram to explain why neutrality arises.
What I want you to imagine is a
genotype space in which all possible genotypes for that
organism might occur. Just think about that as being
all the different ways that you might have been constructed if
all of the possible recombination events in your
father and mother had produced all the possible gametes and all
the possible zygotes. There’s a genotype space for
you. Many of those genotypes will
produce the same phenotype, and that’s because many of the
genes and many of the nucleotides in the genome,
many of the DNA sequences in the genome,
are not making any difference to the proteins that are being
produced. There are other things going on
and we’ll run through them. Many phenotypes have the same
fitness. How many of you come from
one-child families? Okay, all of your parents have
the same fitness. How many from two-child
families? All of your parents have the
same fitness. Okay?
This happens a lot.
Basically when we say that many
phenotypes have the same fitness,
we just mean that in any population there will be a lot
of organisms that all have two offspring or all have three
offspring or something like that.
The two offspring class all
have the same fitness. Then when we look at the whole
halfway here, we can see that G1,
G2 and G3 are neutral with respect to each other,
when measured in a certain environment,
but they differ from G4. So here we have a lot of
genetic variation that’s neutral, and it’s neutral for
various reasons. We’re going to run through some
of those reasons. First, some of the mutations in
DNA sequences are synonymous. That means they don’t produce
any change in the amino acids that are coded in the proteins.
Secondly, there are pseudogenes
and other kinds of non-transcribed DNA in the
genome. A pseudogene is a gene that
resulted from a gene duplication event sometime in the past and
never got used to make anything. And if you go through an entire
genome, which you now can do for many
organisms, looking for these things,
you will find that they’re all over the place.
There have been many gene
duplications in the past, and some of them resulted in
genes that were then acquired by selection and used
developmentally for some function.
Others were not.
The pseudogenes are the ones
that weren’t used. Their usual fate is to be
eroded by mutation. So gradually the useful
information that was once in them gets destroyed by mutation,
and if they sit around long enough they are no longer
detectible; you can’t tell anymore that
they were once really a functional gene,
before they got duplicated. There’s neutral amino acid
variation, for a variety of reasons.
Some amino acids have very
similar molecular size and charge properties,
so that if you substitute them in a protein they don’t really
make much difference to the shape or the charge distribution
on the protein. And if you look at a whole
protein, which is usually a pretty big
thing– say if it’s an enzyme–normally
it will have an active site that is in a very small spatial
portion of it, so that the amino acid
substations that are occurring right at the active site are
making a big difference to its function,
and then potentially down the line to fitness,
and the amino acid substitutions that are occurring
a long way from that active site are having little impact on the
function of the protein, even if they have a different
size or a different charge structure.
So there’s neutral amino acid
variation, and finally there’s something
which is a little bit more abstract,
and basically it’s abstract because we don’t understand it
very well– it’s a real phenomenon but we
don’t always know what the mechanisms are–
and that is the canalization of development.
So I’ll run through these and
then try to explain canalization a little bit in a few slides.
Here is, uh,
the genetic code, and basically you can see here
the nucleotide triplets that are translated into the various
amino acids. And the take-home point,
the first take-home point from this,
is that for any particular amino acid–
phenylalanine, for example,
here there are two codes for phenylalanine,
and look there are six codes for leucine.
So any changes within this set
of nucleotide sequences produce no change at all in the amino
acid that goes into the protein. They are neutral with respect
to each other, because they’re synonymous.
And you can get some hint of
another level of synonymity by looking at the classes of
positively-negatively charged amino acids,
aromatic amino acids and so forth.
Substitutions between aspartic
acid and glutamic acid, that are both negatively
charged, are less likely to make a fitness difference than a
substitution say of lysine, for glutamic acid.
So there is a level in the
protein as well. The pseudogenes I’ve talked
about a little bit. They are not transcribed and
all of their nucleotides are free to diverge at random.
That means that there is no
real editing process going on–natural selection isn’t
preferring one mutation to another.
It’s not any more likely to
turn up in children or grandchildren than another.
This gene has been turned off,
and it will inevitably get eroded because all DNA sequences
are subject to mutation and if a mutation occurs in a pseudogene,
there isn’t any particular reason for repair mechanisms to
pay any more attention to it than they do to anything else.
So these things are not
especially repaired by repair mechanisms and they’re not at
all repaired by natural selection.
So this comment will apply to a
lot of the DNA that’s not transcribed.
twenty years ago, when this class of DNA was
discovered, people labeled it ‘junk DNA’
because they didn’t think it did anything,
and of course it’s been then the pleasure of younger
scientists to show the older ones that this stuff actually
often does have a function– usually it’s a regulatory
function. Some of it makes small RNA
molecules that are used in regulation,
but some of it is also being used as,
uh, sites and signaling pathways and helping to regulate
development. However, some of it truly is
junk. For example,
there is a steady process by which viruses of various sorts
splice themselves into the genomes of their hosts,
and this is part of the adaptive strategy of viruses
that they are able to hedge their bets by sticking
themselves into a genome and hanging around for awhile and
then popping out, at a point which might be
advantageous to them but inconvenient for their host.
However, it’s a dangerous
strategy because sometimes they stick themselves into parts of
genomes that never get transcribed, and they never get
out. So in fact the genomes of most
of the organisms on earth are littered with the fossil
skeletons of viruses. I read an estimate once that
the human genome had a substantial percentage of fossil
viruses in it. I have forgotten the exact
figure at the time. This kind of thing was popular
when DNA sequences were first starting to come out in large
numbers. But just know that.
So there is junk DNA,
and some of it’s there because either fossil viruses or
transposons, jumping genes,
got into positions where they could no longer be transcribed,
and they then become a graveyard.
Kind of an uncomfortable
thought isn’t it, that you’re just carrying
around a viral graveyard? But you are.
Okay, neutral amino acid
variation. I’ve talked about this a bit
when I introduced the genetic code.
So these are amino acid
substitutions that aren’t producing any change in geometry
or any charge change in the geometry and electrochemistry of
a functional site within a protein.
And I’d like to talk a little
bit about a very early case of molecular evolution;
that’s the case of alpha-globin. So your hemoglobin has two
alpha and two non-alpha chains. It has a beta chain if you’re
an adult and it has a gamma chain if you’re an embryo.
The reason it changes from a
gamma to a beta is to change the oxygen binding properties,
because embryos have to suck oxygen out of their mother’s
If we look at these
alpha-globin sequences, across a pretty broad range of
vertebrates, and we take samples in such a
way that we can look fairly far back in time,
we can date these branch points approximately from the fossil
So dogs and humans shared an
ancestor probably somewhere late in the Cretaceous,
mid–late mid-Cretaceous. Our last common ancestor with
the kangaroo was at about 140 million years perhaps.
The mammals were there while
the dinosaurs were there. They were just small little
guys, but there were mammals there.
Our last common ancestor with
the shark is back at about 440 million years.
So take the sequences for all
the alpha hemoglobins that you pull out of these things–
it’s a convenient molecule, you just need a blood sample–
and plot them on a graph. So you estimate the time from
the fossils and you estimate the average differences.
is a measure of amino acid differences in a protein,
and the straight line is what you would expect to get if the
rate of amino acid substitution is random,
just uniform, just steady. Okay?
It’s pretty close to the line.
There are some deviations.
But this is some of the
earliest evidence– this was before DNA sequencing
became easy, this was when protein
sequencing was easier than DNA sequencing–
this was some of the earliest evidence that there’s something
like a molecular clock. In other words,
if we got a vertebrate that we’d never seen before,
living in some forgotten jungle, and it had a weird
morphology and we didn’t know who its relatives were,
and we wanted to find out when it might have shared an ancestor
with something that we had, and it plotted right here–its
difference with something that we were comparing it with right
now, plotted right here–then we
would have a good estimate of time to last common ancestor,
for that new, undiscovered species,
based on the assumption that it was experiencing evolution like
all these other guys. Okay, the fourth reason why
genetic variation might be neutral is canalization.
Now canalization in general
means that there are developmental mechanisms that
are limiting the range of phenotypic variation,
so that even though there is a mutation in the genome,
or there is a disturbing environmental effect on a
genetically controlled pathway, that you’re still going to get
the same phenotype. Some things about your
phenotype are extremely stable. They do not respond to mutation
much at all. The fact that you have four
limbs, the fact that you’ve got five fingers;
things like that are ancient and stable and there are
developmental buffering mechanisms that keep them that
way. So these things,
these canalizing mechanisms, resist the tendency of
variation in either genetic or environmental factors to perturb
the phenotype; they keep it in a stable state.
So what happens to the genes
that are forming this phenotype but they’re being buffered by
these developmental mechanisms? Well they are then freer to
accumulate neutral variation, because basically the fitness
consequences of a mutation in those genes have been removed,
they’ve been buffered out. Now there’s been a lot of
speculation about why canalization might evolve,
or whether it might just be a byproduct.
And frankly in most cases we
have no idea. This is an open research
question. So one of the reasons people
think that say whole organism traits,
like say five fingers or four limbs,
might be buffered is not because of selection to buffer
those traits but because there are very,
very strong selection forces operating at the micro level
within cells on gene signaling pathways.
So you buffer those,
and then as a byproduct of that you get buffering at a higher
level. We don’t know what’s the case,
but we do know that canalization exists and we do
know it has a consequence; it allows hidden genetic
variation to accumulate. So that’s the fourth major
reason why there can be neutral genes.
Now, what causes random or
genetic drift? That will generate neutrality,
but then what happens to the genes that are neutral?
Well these are the mechanisms
that can introduce randomness into evolution;
most of them, there probably are a few
others. The first is mutation.
The second is the Mendelian
lottery, which is the idea that meiosis is like a fair coin.
Then we have some population
level effects. So mutation you can think of as
a molecular event. The Mendelian lottery is a
cellular event. Founder effects and genetic
bottlenecks are population effects.
And then we have a demographic
effect, which is variation in reproductive success in a
population of any size. All of these things contribute
to random change. And now I want to step through
them and give you a more concrete feel for how they work.
There are some senses in which
mutation is not random. Okay?
Mutations occur at some sites
more frequently than others. In a pathogenic bacterium that
is encountering a challenging environment, it will up its
entire mutation rate by down-regulating its DNA repair.
It’s a fairly simple thing to
increase the mutation rate on a whole genome.
You just neglect to repair it
and it will mutate faster. Okay?
So if bacteria are moved into a
new environment or, for example,
if a pathogenic bacterium is put into a vertebrate with a
very active and threatening immune system,
it increases its mutation rate. The transitions between the
nucleotide classes–so purine to purine, pyrimidine to
pyrimidine–are more frequent than transversions.
So purines will mutate to
purines more frequently than purines will mutate to
pyrmidines. And mutations do not produce
random changes in phenotype space.
This one again is a little bit
But a mutation can only cause a
change in the inherited set of possibilities.
There is very,
very little mutational variance in the human population for a
sixth set of appendages, growing in the middle of our
backs, that could be turned into the
wings of angels; very little.
There is very little mutational
variance in a clam for any organ that could be involved in air
breathing. So mutations do not cover all
of conceivable phenotypic space. Mutations are only causing
perturbations in the inherited set of possibilities that a
given evolutionary lineage has produced.
So they’re not making random
changes in phenotype space. But they are random in an
extremely important sense. There is no systematic
relationship between the phenotypic effect of a mutation
and the need of the organism in which it occurs.
They’re random with respect to
fitness. So when those bacteria are
going into the vertebrate immune systems and it would be
extremely convenient for them to have a mutation that was just
exactly the right thing that they needed to avoid that
particular defensive maneuver on the part of their host,
they don’t get it. Okay?
All nature will give them is
random mutations with respect to that particular function,
and then if they have a lot of progeny,
one of them may have the right one by luck.
Similarly, in your case,
it might be extremely convenient for you to have an
adaptation which allowed you to look at a computer screen for 48
hours without getting a headache and without having to get up to
go to the bathroom. Okay?
That mutation is not going to
happen, because you need that function.
Your genome is going to be
covered by random mutations, and it may very well be that
one of your children is able to look at that screen a little bit
longer than you are. But that will be because it
happened at random, not because somehow development
or evolution could anticipate that that function was going to
be useful. So the process of mutation
produces a lot of variation, and then natural selection
edits it, it sorts it, it screens it.
And at the point at which that
variation is produced, the potential function of the
variation is not a question, it’s not an issue;
it’s just making variations. Okay, second,
meiosis is like a fair coin. So this is something that you
may find boring. You’ve all heard about meiosis.
You’ve all heard about Mendel’s
Laws. You know that the probability
that a gamete will get into a particular–that a gene will get
into a particular gamete is 50%. And you’re all familiar with
this because you know that the probability that a child will be
a boy or a girl is 50%, and that’s because at the sex
chromosomes, and at all the other
chromosomes that we have, the probability that the
chromosome will go one way or the other is 50%.
That is absolutely amazing.
Why is it that my Y chromosomes
don’t get 80% of the action? Why is it 50%?
There’s actually something very
deep here. If you construct a system in
which every one of the potentially competing elements
has been forced to have the same chance,
those elements must then cooperate,
because the only way they can increase their own chances is by
increasing everybody else’s as well.
And that is why this particular
effect is called the parliament of the genes.
It is a discovery that Nature,
about probably two billion years ago,
hit upon a principle that human political science didn’t
discover until the Enlightenment,
which is that democracies are stable.
Meiosis is a democracy.
In meiosis each gene has a fair
chance, and that means that in a sense you’ve got a one-gene,
one-vote situation. So I’ll come back–I’ll come
back to this fairness of meiotic segregation, but there’s a
general idea behind that. I’ve just given you a little
scenario that would suggest why it was selected;
it was selected to repress conflict.
Every other aspect of genetics
has evolved. So when you take genetics,
or you take cell biology, or you take developmental
biology, there were– there were selective processes
that produce what you study, and there were alternatives
that were rejected, and you’re looking only at a
sample of what nature can produce.
And that in itself becomes an
interesting research program. Okay, back to the parliament of
the genes. I referred to conflict.
Here’s the conflict.
There are things called meiotic
drivers. So there are genes which
actually change Mendel’s Laws; they change the probability
that they will get into the next generation.
Anybody already heard how a
meiotic driver works? It’s kind of a cool system.
They use a long-range poison
and a short-range antidote. So a meiotic driver usually
operates by killing off any cell that does not have a copy of
itself, of its gene, and giving an antidote to its
own cell. So as the cells sit there,
in the ovary or in the testes or in whatever organ that
particular organism has, the biotic drivers are
basically wiping out the competition and promoting their
own interests. These things are all over the
place. They are common in drosophila,
and there is evidence that there have been meiotic drivers
in the human genome. Okay?
Once the diploid state evolved,
there was a long history of invasion by meiotic drivers,
and the response to that is that all the other genes wanted
to cause these meiotic drivers to go away.
They were distorting their own
interests. You’re sitting there on a
chromosome, you’re innocent. Some wild bandit comes along
and highjacks your interests, and now your probability of
getting into the next generation is only 20% rather than 50%.
Who wants that, you know?
That’s not a good deal.
So throughout the genome
various mechanisms arose to repress meiotic drive;
and the result was a very complicated mechanism and we
call it meiosis. So that’s not the only possible
reason for the complexity in fairness of meiosis.
It is a plausible one.
I invite you to consider the
cultural evolution of democracy and decide whether it too might
have been driven by a history of cheating,
particularly the defection of leaders who no longer
represented the interests of their people.
I think there’s a similarity,
and I think you’ll find it articulated in the Declaration
of Independence. Okay, mechanisms that cause
random change also occur at the population level.
One of them is the founder
effect. Let’s suppose that I were to
found a new population with only you;
it would have a high probability of blue eyes.
And with you it would have a
high probability of brown eyes. And in order to choose you I
flipped a coin. Okay?
At the founding of that
population there was a random event, which was just sampling;
just sampling a couple of individuals out of a big
population. And the result of this is that
there are certain diseases, human genetic diseases,
that are rare in the human population in general,
but are common in populations that were founded by just a few
people, including Tay-Sachs disease in
Quebec, porphyria in the Afrikaners of
the Cape, and diabetes in Pitcairn Island.
So you just take a little
sample out of a big population and you get something that’s not
representative, and sometimes that contains a
genetic disease. Another population level
phenomenon that yields randomness is a bottleneck.
So that will happen when a
population crashes to a very, very small size,
and then only a few alleles make it through.
So you might have a lot of
versions of a gene in a big population,
but if you’re only founding a new population with two or three
individuals, they’re–and they’re diploid,
well two individuals only carry four copies of the gene.
So if there had been twenty
alleles in the original population, the maximum possible
number that could get through that bottleneck is only four;
you’ve left behind sixteen. It appears that this is what
happened with the cheetahs. And they are apparently almost
completely homozygous, particularly with respect to
their immune genes. It is a weird biological fact
that you can take a skin graft off of one cheetah and graft it
to a cheetah, any other cheetah in the world,
and the graft will take. In other words,
their immune system finds a sample of skin from any other
cheetah in the world to be their own skin.
They don’t detect a difference.
And that probably is a signal
that cheetahs went through a very small population bottleneck
within the last few thousand years.
Genetic drift is then a
consequence of neutrality. It’s the random wandering of
the frequencies of neutral genes.
If you look through a
microscope, Brownian motion is the jiggling of little dust
particles that you see in the microscope,
and it is actually the result of the random impacts of water
molecules hitting that dust particle.
Well the population level
analog of heat in water is variation in population
size–uh, excuse me, variation in family size.
A gene which has gone through
the Mendelian lottery of meiosis lands in a zygote.
It got into the zygote.
The zygote grows up.
This particular gene is neutral.
It’s not making any difference
to reproductive success. But that particular individual
that it landed in could have a small family or a big family,
for reasons that have nothing to do with the function of the
gene. It’s just a flip of the coin
that determines whether it will be in a family that produces two
children, zero children, or a lot of children.
So that’s what I mean by
combining the lottery of meiosis with variation in reproductive
success. And this is a process that goes
on in all populations. When people are first learning
about genetic drift, they think oh,
that’s something that happens in small populations,
because small populations don’t have all the smoothing effects
of the Law of Large Numbers. But this will happen in a
population of any size. Okay?
And basically what I mean by
that is this interesting consequence of variation in
reproductive success. If it’s correlated with a trait
or with a gene, strongly, it produces natural
selection. If it’s not correlated it
produces drift. So one of the real puzzles of
evolution has to do with what causes a gene to end up at
random in an individual making one,
two or three, or zero recruits per lifetime;
what makes the difference between an adaptive and a
neutral gene. I’ve sketched four possible
answers to that question. In any particular case we
normally do not know exactly which one is contributing the
most to that. So, what is it that happens to
neutral alleles?>If we draw time on the X-axis,
and we draw frequency on the Y-axis,
and a mutation occurs, the usual thing that will
happen to a mutation is it will increase a little bit and
disappear. Then we wait for awhile,
another mutation occurs. We’re looking,
by the way, across many different genes in the
population. We wait awhile,
another mutation occurs. It comes into the population.
The probability that it will
ever get fixed is pretty low because the probability is
proportional to the frequency; excuse me, is proportional to
1/N, frequency equal to 1/N. When it’s rare,
its frequency is very low and so its probability of being
fixed is low. But once in awhile a mutation
comes along that manages to go through all of this drift,
and making it through organisms that had,
on average, more than two progeny per lifetime,
and it gets fixed. And if you just look at this
class of mutations, the time that it takes them to
fix is proportional to the population size.
So things will get fixed faster
in small populations than they will in big ones.
There will be more of them,
more mutations will occur in a big population,
but it will take them longer to get fixed.
Now because the bigger
populations have more mutations, it turns out that their size
exactly compensates for the longer fixation times.
So if you’re just counting how
many get fixed– it doesn’t matter whether
you’re in a small population or a big one–
the same number of mutations are getting fixed in both cases.
That means that over the course
of evolutionary history populations could’ve gone
through crashes and explosions, and at the end of it,
if you’re a geneticist studying the DNA,
looking back, it doesn’t make any difference
that the populations had crashes and explosions,
in terms of how many neutral alleles got fixed.
They were just getting steadily
fixed, with no effect of population size.
So we don’t know which one will
be fixed. We do know how many will be
fixed. So this is why the molecular
clock is like an atomic clock; it’s driven by radioactive
decay. We don’t know how many
atoms–we don’t know which atom will decay, but in a second we
know how many will, for a given radioactive
substance. The reason for this is that
there’s regularity in large numbers.
It emerges because there are a
large number of independent events.
Our haploid genome has about
three billion base pairs. One mole of uranium has about 6
times 10^(23) atoms– actually if it’s a mole it has
exactly that many atoms– and these large numbers give
the regularity to the process. Okay, so this is what connects
microevolution to macroevolution.
It creates uniform substitution
rates in neutral portions of the genome.
And this is the assumption that
molecular evolution makes when it reconstructs the Tree of
Life. It allows us to estimate branch
lengths and branch points to last common ancestors.
It allows us to make
comparative inferences on phylogenetic trees.
And therefore neutral evolution
is a actually a central tool in the construction of the
evolutionary framework. It’s not something to be
neglected; it’s something to be
understood, because it gives us a source of regularity that can
take us back into deep time. As an example,
here are nucleotide substitutions occurring in flu.
These are isolates that are
still in the freezer. Okay?
And they run here from about
1925 up to 1990. We don’t have any,
any error of estimate in age; we know when they were isolated.
The population sizes have
fluctuated dramatically. At some point,
some of these flu strains were sitting in a few ducks or pigs
in southeastern China. At other points,
they were inhabiting a billion people around the world.
They went through huge
fluctuations, and a nice steady rate of
All the mechanisms of genetic
drift are in play here, except meiosis,
because flu is a virus, doesn’t go through meiosis.
The effect of variation in
population size was exactly compensated by the much slower
rate of fixation of neutral mutations in larger populations.
So even in an epidemic disease,
like flu, the molecular clock is nice and steady.
A few caveats about that.
Different proteins and
different parts of proteins evolve at different rates.
They only use non-transcribed
DNA sequences. There are some differences
among lineages because of different generation times.
And I’m not going to talk about
maladaptation because I took too long to talk about neutrality.
So you can read about
maladaptation, and I’ll just give you the
basic idea. Here’s the basic idea of
maladaptation. If natural selection is strong
in one place and organisms get really well adapted to it,
but they move to another place, where they don’t do well,
for whatever reason, we call the place that is
producing an excess of organisms the source,
and the place which is not good for the organisms a sink.
The genes in the sink represent
organisms usually that were adapted to the source.
So if organisms get well
adapted in one place and moved to another that’s quite
different, and they never get an
opportunity to come into evolutionary equilibrium with
that new place, which we call the sink,
then they are maladapted to the sink.
That is the basic idea behind
how maladaptation can occur. Okay?
So, let me jump ahead.
I will just run quickly through
these examples and get to the end, just to let you know what
happens next time. These are the keys I want you
to remember. I want you to remember how it
is that meiosis is like a fair coin.
I want you to remember how the
fixation of a neutral allele is like radioactive decay.
And I want you to remember that
the regular fixation of neutral alleles generates a molecular
clock that allows us to connect micro to macroevolution.
Okay, that’s it.