11. Life History Evolution
today we’re going to talk about life history evolution,
and life history evolution deals with some big questions.
It’s explained why organisms
are small or large, why they mature early or late,
why they have few or many offspring,
and why they have a short or a long life.
Basically what life history
evolution does is it analyzes the evolution of all of the
components of fitness, all the different things that
combine to result in lifetime reproductive success,
and in so doing it visualizes the design of the organism as an
evolutionary solution to an ecological problem.
So it’s fundamentally about the
interface between evolution and ecology,
and it is one of the places where scientists confronted the
problem of how do we explain phenotypic evolution rather than
genetic evolution? So this is really about the
design of the large-scale features of organisms,
and it brings us to ask questions about ourselves as
well, of course. Why is it that we have a
lifespan of about eighty years? Why is it that we’re about
three kilos when we’re born, etcetera?
Okay, so we fit into this
matrix of questions. Now here are a few world
records. Biggest baby is a blue whale,
twelve tons. And the interesting thing about
it is that it will grow to be sixty tons in the next six
months. So it’s really pumping it in.
And by the way,
a mother blue whale, and most whale mothers,
actually have muscles in their breasts so that they actively
pump the milk into their offspring.
Baby isn’t just sucking.
Baby is attached to a fire hose.
And look at what happens to the
mom. She goes to warm tropical
waters to give birth, has her baby,
and then she nourishes that child until it is independent,
without eating herself. Imagine how big she is,
because he turns into something sixty tons.
And you can imagine how cranky
she is before she swims back to Antarctica to get lunch.
Then if you ask yourself,
for a given body weight what is the biggest thing?
It’s not the blue whale baby,
it’s the babies– the twin babies of a bat are
the largest weight of any offspring in mammals,
and she actually flies with them.
And in the kiwi,
it has a 400 gram egg. If you take a radiograph,
if you put a kiwi into an x-ray machine,
take a radiograph of it, you are to imagine an egg
that’s occupying about two-thirds of the body cavity of
the kiwi, it’s got a giant egg.
The fewest offspring per
lifetime of anything that’s out there bearing a significant
risk– and this is actually less than
humans– is the Mexican dung beetle,
that only has four to five babies per lifetime,
which is pretty remarkable when you think about how risky you
would think life would be for a Mexican dung beetle.
How can it get away with only
having four or five babies, if some of them are likely to
die? But, in fact,
it has such good parental care that it’s around,
and it’s doing just fine, thank you, with only four to
five babies. And the most offspring per
reproductive event is orchids. Orchids produce typically
billions of seeds and they are extremely tiny and the only
reason that they can hatch is that they have a fungal midwife
that helps them. Orchid hatching is dependent
upon fungi. So the mother doesn’t have to
put the nutrients into the seed. So she makes billions of tiny
seeds. And in bivalves and codfish,
they can get up to hundreds of millions of eggs per
reproductive attempt. So you can see that just by
comparing some numbers and looking broadly–
and this is a typical thing that happens in comparative
biology, it’s one of the neat things
about it– if you look across the Tree of
Life and you see how different things live their life
histories, you’ll immediately start to ask
questions. You guys have all been
generating wonderful questions this week.
You look at that stuff and you
say, “Well why are things big and small?
Why do they have few babies or
many babies? What has caused the evolution
of all of this diversity?” So here is the largest whale
and the smallest dolphin. So this is the whale radiation.
You can see that since the
ancestor, there’s been considerable change in body
size. Here’s Pipistrellus,
flying with babies. Here’s a dung beetle,
and it’s going to lay its egg into that pile of dung.
That’s why it’s going to have
an extremely well protected baby.
Not too many things are going
to come along and eat baby.>
And here’s a kiwi with its egg.
So in the history of ideas,
life history theory and the rest of evolutionary and
behavioral ecology fit about here.
Darwin showed us that natural
selection and descent with modification from ancestors can
explain a lot, but then genetics remained a
problem until 1900. Then we had the genetical
reaction to that issue, which is the neo-Darwinian
synthesis that basically says Darwin works with genetics.
And this concentration on
genetics then, in its own turn,
elicited a reaction. So this is a reaction to that.
And what’s the role of
phenotypes in evolution is the reaction to the neo-Darwinian
synthesis. So the phenotypic reaction,
it’s been going on for about forty years.
It has a selectionist
part–that is, how are phenotypes designed for
reproductive success– and it has a developmental
part: what are the restrictions on the expression of genetic
variation? So the phenotypes are actually
both being designed by natural selection for reproductive
success and, in the process of their
production, they are themselves editing
genetic variation. So life history evolution is
the part that explains the design of phenotypes for
reproductive success, and it concentrates on size at
birth, how fast things grow,
age and size at maturity, reproductive investment,
and mortality rates and lifespan.
So part of life history
evolution is why do we grow old and die?
And after a lot of discussion,
it was possible– this is after about twenty
years of discussion– to make this simple statement:
What causes life histories to evolve?
They result from the
interaction of extrinsic and intrinsic factors.
So the extrinsic factors are
things that are influencing the age-specific rates of mortality
and reproduction, and that’s where ecology comes
in. It’s not just ecology,
there’s a lot of phylogenetic effects on this stuff,
but the point is that if you look at whatever is affecting
changes in mortality and reproduction,
in age and size of the organism, you will be able to
explain a great deal of what you see in the life history.
But that’s not enough.
There’s interaction between
that and factors that are intrinsic to the organism,
and the intrinsic factors are conceptualized as tradeoffs
among traits. The idea here is there’s no
free lunch. If you change one thing in
evolution, a byproduct of that change will be a change in
another trait. So even though you are gaining
fitness through changes in one trait,
almost inevitably, whatever you change is going to
cause a decrease in fitness in some other trait,
and this forces compromises. So the intrinsic factors then
can be looked into, and we find phylogenetic
effects, developmental effects, genetic effects,
physiological effects; all sorts of things.
Tradeoffs in a evolutionary
situation are often conceptualized as being strictly
energetic. If I take calories away from my
growth in order to reproduce and make more babies,
then I won’t be so big next year and I can’t have so many
babies next year. That would be kind of a
standard physiological story about a tradeoff.
But they can also occur in many
other ways. So that would be a
physiological story. But certainly there are
developmental and genetic influences on tradeoffs as well.
So there’s a lot of biology
that’s hiding behind these simple summary statements,
on this slide. In the rest of the lecture I’m
just going to show you how to explain age and size at
maturity, reproductive investment, and aging and death.
So, not too much.
This is kind of a standard
statement out of life history theory,
and this generic statement could be applied to clutch size
and lifespan and a lot of other things.
But let’s just look at age and
size at maturity. They will be optimal when the
positive difference between the benefits and the costs–
so the difference between the benefits and the costs–
is maximized. And we can conceive of that as
either being maximized just at a stable equilibrium point–
that’s kind of a simple statement, that’s a theoretical
statement; so that would be,
okay, everybody in this species, they ought to mature at
just one age and size, which is a little unrealistic.
Or we can use that kind of
analysis to predict a stable equilibrium reaction norm.
So here we’re beginning to use
this idea that we got of a reaction norm.
And that one summarizes pretty
easily. You’re going to–whatever
problem you’re faced with, you’re going to mature at the
age and size where the payoff in fitness is going to be greatest.
The problem analytically is to
decide what you have to bring in to the mix in order to
successfully make that prediction.
You want to keep it as simple
as possible, because it can get very complex,
but you want to keep it realistic enough to actually be
successful. So it’s a balancing act.
Now with–I’m going to show you
one way to do this. If we make four general
assumptions, we can predict age and size at maturity.
Here they are.
The first one is that if you’re
older when you first reproduce, your offspring are going to
have better survival rates, they will be of higher quality;
so one reason to wait is that you get higher quality
offspring. Another reason to wait is that
because you’ve been growing for longer,
you’ve taken longer to grow before you start to reproduce,
you can have more of them, because you’re bigger;
especially important in plants and in fish.
However, these advantages of
delaying maturity are counter-balanced by the
advantages of having a shorter generation time,
and you can only get a shorter generation time if you mature
earlier. Let me just illustrate the
advantage of a shorter generation time.
I give you a hundred bucks and
I tell you you can invest it in a bank that’s going to give you
compound interest once a day, on the one hand,
or once a year, on the other hand.
You all know the advantages of
compound interest; you get interest on your
A shorter generation time is
the bank that gives you interest earlier;
you get grandchildren quicker. Okay?
So that is basically the
elements that you need to put into a quantitative tradeoff.
Delaying, you can get higher
quality, or more offspring; doing it quicker,
you’re going to get a shorter generation time and a quicker
payoff. Now in a population that’s at
evolutionary equilibrium, these advantages and
disadvantages should have come into balance.
So let’s see how that might
work. Here’s a simple example.
This is using data from the
Western Fence Lizard, and what you’re looking at
here, this plot here, where you see these curves
going up and down, that’s a fitness profile.
So we have some kind of trait
along the y-axis; in this case it’s age of
maturity–along the x-axis. Along the y-axis we have
relative fitness; so this is the rate at which a
population of organisms with that age at maturity would grow,
given what we know about the physiology and mortality rates
of fence lizards. And if we just put in one of
those assumptions, which is that the bigger they
are the more babies they have — so their fecundity grows
linearly with size — their optimal age at maturity
is just about twelve months. If we put in that if they get
higher quality offspring as they delay maturity,
given the assumptions in the model,
we predict actually that they ought to be maturing at about
six months. Their observed age at maturity
is ten months. That indicates that this effect
is probably important and perhaps accurately modeled.
This number tells us that well
perhaps we don’t really understand what makes for a good
baby lizard. Okay?
And you can see that
interestingly the age at maturity is pretty strongly
peaked; the fitness profile has a peak
that’s pretty close to one value.
That means there’s pretty
strong selection operating on this.
It’s not flat.
Now if you repeat that kind of
thing–and by the way, there’s a bunch of math behind
that; I’m just waving my hands and
covering up that black box. If you repeat that for a bunch
of fish species that are growing in different kinds of
conditions–these are haplochromine cichlids in Lake
Victoria; the painted greenling lives in
Seattle; these roaches are living in
Greece–and these are all cases in which very good population
biology has been done for long periods of time in the field.
So we know growth rates and
mortality rates, and we have some estimate of
tradeoffs. Then that kind of thinking says
this is the predicted age at maturity and this is the
observed age of maturity, and the correlation is .93.
So it looks like that way of
thinking is capturing something that is not a bad reflection of
what’s going on in Nature. This sort of result doesn’t
mean you’ve got the right answer.
You can have the right answer
for the wrong reason, because this is just
descriptive work, this is not a manipulative
experimental study. We’ll see such an experimental
study later on. However, that’s not the whole
story. I now want to extend that to
the case when growth rates vary, and I want to introduce you to
the idea that age and size at maturity can have a reaction
norm. And the way I want to do that
is by dealing with some incredibly blockheaded
So here we have rapid growth.
So this is an organism that is
born down here, and it’s well fed and it grows
rapidly. So it gains weight well,
reaches a large size. And this is an organism that
grows slowly; it’s under food restriction,
down here. Now let’s take the blue
strategy–this is a very, very simple one–and what it
says is I’m always going to mature at the same weight.
If that organism is growing
rapidly, it matures at a pretty early
age, but if it’s growing slowly and it adheres to this rule,
it has to wait a long time until it matures,
and its problem here is that it might die before it matures.
So that strategy has the cost
of mortality. On the other hand,
if it’s always the same age when it matures,
under good circumstances, it’s doing okay,
but under poor circumstances it’s much smaller,
and therefore it can have fewer babies.
And so the problem here is
fecundity; it’s going to not have as many
babies if it does that. And so just intuitively you
might think that there is some kind of intermediate compromise
so that when it is not being fed as much,
it changes both its age at maturity and its size at
maturity. And, in fact,
this kind of thing can be calculated.
This is an optimal reaction
norm for age and size at maturity.
They don’t all look like this.
This is a common one,
but there are conditions under which you can make this thing
bend. You can actually sometimes get
them so that they go up like this, under very special
circumstances. It depends on a bunch of stuff.
I don’t want to trouble you
with the complexities. I just want you to take home
the message that you can predict what the plastic flexible
response should be if evolution has come to equilibrium.
And for this one,
basically what this graph is telling you is this–this is the
reaction norm here, these are growth curves here;
so this is good conditions, this is poor conditions–
and what this picture is telling is that when life is
good, you should mature when you are
young and big, and when life is bad,
you should mature when you’re old and small.
That’s the English take-home
message, out of that picture. Well when Nile perch were
introduced to Lake Victoria, there hadn’t been any Nile
perch in there before, and they went bananas and ate
their way around the lake– and in the process,
by the way, they probably drove about 200 haplochromine species
to extinction– but while they going through
their initial population burst and they had a lot of food,
they were about six feet long. This is the business end of a
Nile perch. You can see it’s a big fish.
After they had expanded in the
lake, which occurred between 1976 and
1979, they ate down the population of
their prey, there wasn’t as much food and
they didn’t grow as well, and they slid down this
reaction norm, and now instead of being six
feet long, the Nile perch in Lake Victoria
are about that big. They still form a fishery and
people are still making money on selling Nile perch fillets,
but they’re much smaller. And that was a predictable
And this will happen whenever
population densities change. Back in the 1930s and 1940s,
there was a huge sardine fishery off the coast of
California. John Steinbeck wrote novels
about it, short stories. There’s a book called
Cannery Row that talks about the Monterey Bay sardine
canneries. In the 1950s that fishery
collapsed, not because of over-fishing,
but because of changes in the oceanic conditions where the
baby fish were growing up. At the time that it collapsed,
there were sardines that had been born under better
conditions and started to grow, and then all the competition
went away; nobody else came along because
all the baby sardines were getting killed by bad conditions
out in the ocean. Just before the fishery folded
and there were no longer enough sardines to catch,
the fishermen in Monterey were catching female sardines that
were one meter long. So they had gone in the other
direction, they’d gone up the reaction norm.
These things are predictable as
population density changes. I’d like to give you one more
example, and it has to do with the issue
of whether or not the mammals died out because of bad weather,
or over-hunting. Dan Fisher, who’s a
paleontologist at the University of Michigan,
has recovered a lot of mammoth bones from a Native American
mammoth slaughterhouse that was outside of Ann Arbor.
They used to kill the mammoths
and then store them under ice, in a lake, over the winter,
so that the other predators wouldn’t get the meat,
and there are a lot of mammoth bones very close to Ann Arbor.
And you can ask yourself–when
you look at a mammoth bone, you can tell how big the
mammoth was and whether or not it is mature,
because the bones of all mammals undergo a change when
they reach maturity. Now if it was bad weather,
then they would’ve been growing slowly,
and they should’ve been small and older when they matured,
according to the reaction norm. If it was hunting,
then just like the California sardine,
when the population density drops, each individual has more
to eat, and they should have been big
and young when they matured. Do you think they were old and
small, or big and young? How many for old and small;
bad weather? A few.
How many for young and large;
hunting? Most people believe the
over-kill hypothesis. Yes, they were young and large,
and some of them had arrow points embedded in their ribs.
So you can use that for various
things. This is what that model tells
us about human females. These are some pretty
theoretical growth curves for human females under poor
conditions and under good conditions.
We actually have data on how
female age and size at maturity has changed.
There are measurements on women
working in industrial squalor in North England,
in the nineteenth century, and there are good records
measured on Hutterite colonies in North America in the
twentieth century. The nineteenth century women
were poorly nourished. The twentieth century women
were well nourished. They moved right up a reaction
norm. They got younger and bigger
when they matured; and it was about four year’s
difference. So they went–there are various
measures of when a woman is–physiological measures–but
they kind of all move together. So it’s about a four-year
advance, earlier maturity in the twentieth century.
And this other line here
illustrates another point that I want you to take away from this.
If modern medicine were to keep
juvenile mortality rates as low as it currently does,
then it would cause a further shift in age at maturity in
humans, and that shift is represented
here. This probably would take
somewhere around 5 or 10,000 years to occur.
This is the evolutionary
genetic response; this is the immediate
developmental response to better nutrition;
and this is the evolutionary genetic response to a drop in
juvenile mortality rates. The whole reaction norm evolves;
it will move up and down. It’s embodying an evolutionary
set of rules of thumb, contingent decisions–if I’m
well nourished, do this;
if I’m poorly nourished do that–and those things evolve.
Okay, now the second major life
history trait is once you’ve matured how many babies should
you have, and how big should they be?
You want to be an orchid with
billions of tiny ones, or you want to be a kiwi with
one big one? Well the ideas on this go back
to David Lack. David Lack was the man who more
or less created the idea of Darwin’s finches in the
Galapagos. Darwin’s finches,
as a concept, emerged in the middle twentieth
century. They were never called Darwin’s
finches before David Lack went to the Galapagos,
studied them, came back and wrote a book
called Darwin’s Finches. It was 120 years after Darwin
had been there. And he went on to become head
of the Edward Gray Institute at Oxford,
which is an ornithological institute and one of the best
places in the world to go if you’re interested in bird
biology and you’re not working with Rick Prum at Yale.
So what David said basically
was this. If nestling survival decreases
as clutch size increases, then an intermediate number of
eggs produces the most fledglings.
The idea behind that was this.
If you make too many babies,
you won’t be able to feed them. There are only so many hours in
the day. You might be able to work as
hard as possible and not bring off a clutch of say ten babies,
but you could do quite well with five.
Now I’m going to show you that
he was wrong on the details, but he got the main point,
which is that fitness is often maximized at intermediate
reproductive investments, particularly in organisms that
reproduce more than once per lifetime.
You don’t do it all now,
you hold some back, and you actually do better if
you spread it out. So if we then take Lack’s idea,
and we make a simple model out of it–basically what he was
saying is this. As clutch size goes up,
well that just means that eggs go up, but if survival goes
down, as eggs go up–this is the per egg survival probability;
basically this is saying that if you only laid one egg,
you’d have very good survival, and if you lay ten eggs they
all die. You can turn that into an
equation for how many fledglings do you get for a given number of
eggs? Well it’s going to be 1 minus a
constant, times the number of eggs you
lay, which means, if you multiply that out,
that you’ve got a quadratic term here in eggs;
and that is what leads to the parabola,
it’s this quadratic term that means that as clutch size goes
up, the number of babies that you
get out of it has a parabolic form with an intermediate
optimum. And you then just do the
standard basic calculus thing of taking the first derivative,
setting it equal to zero. It tells you that this point
right here is going to be at 1/2C,
in this equation, and if C is 0.1,
this optimal number of eggs will be 5,
and the number of fledglings that you get out of it will be
2.5. Of course, you never get 2.5,
but that’s just because the model’s continuous and the eggs
are discontinuous. Well if this is the case,
if birds are laying the optimal clutch, then a larger or a
smaller clutch should have lower fitness.
Basically all we’re saying is
that if we were able to take a bird,
and she wants to do this, but we give her either fewer
eggs or we give her more eggs, then she should have lower
fitness. This should be the best,
which she naturally does, and we perturb that,
she should have less fitness. This was done on kestrels in
the Netherlands by Dutch ecologists, in a rather
remarkable study. A kestrel is a sparrow hawk,
and these animals live, these birds live for several
years, and the Dutch ecologists
actually followed them long enough to count the
grandchildren; they went three generations.
So, this is the setup.
They reduced the size of 28
clutches, enlarged the size of 20 clutches, and in 54 clutches
they took the eggs out and put them back again;
those were the controls. And if you just look at this,
it looks like these birds should be laying more eggs,
because if you look over at the enlarged clutches,
they’ve been able to change the brood size up by 2.5.
They’ve been able to get more
fledglings out– they’ve gotten nearly two more
fledglings out of the enlarged clutches–
and the reproductive value of that clutch,
which is how many grandchildren do I get out of that clutch,
is higher. So it looks like these birds
are blockheaded, they should be laying more
eggs. But that’s only looking at what
happens that season. While they were examining these
birds, one of them, Serge Daan is a good
physiologist, and so he did the experiment
with doubly labeled water. He wanted to find out how hard
the birds would work, and they were coming into nest
boxes. So mommy and daddy kestrel fly
into a nest box with food for baby;
evil Dutch ecologist, sitting in back of the nest
box, takes food away from baby. Baby cries.
Baby gets hungry,
mommy and daddy work harder. Evil Dutch ecologist takes away
food. Mommy and daddy work even
harder. How hard do mommy and daddy
work? Mommy and daddy work about
eight hours that day– daylight’s about sixteen hours
a day in the summer in North Holland–
and they hit a rate of physiological output which is
nearly four times basal metabolic rate,
which is what Lance Armstrong puts out on the Alpe d’Huez in
the middle of the Tour de France.
So the Dutch ecologists
basically forced these birds to work as hard as a peak human
athlete would, and then they quit after eight
hours, because they didn’t want to die.
And then the Dutch ecologists
gave the babies their food. Just so you don’t have
nightmares about that. Okay?
So that introduces parental
survival. If you increase the clutch
size, the parents died the next winter at a higher rate,
because they worked harder. Okay?
And if you add all of that up,
the residual reproductive value of the rest of their lifetime;
the number of grandchildren they would get out of the rest
of their lifetime was highest for the reduced broods,
intermediate for the control broods,
and strikingly lower for the enlarged broods,
because of this effect. If you die before the next
year, you get zero babies next year.
So if you look at their total
reproductive value, which is the value they got
this year, plus the value they got in the
rest of their life, it’s highest for the control
group, and if their clutches were
enlarged, they had one grandchild less,
and if their clutches were reduced,
they had a half a grandchild less.
Which has an interesting
take-home message. These Dutch kestrels know
what’s best for them. They lay the right number of
eggs. That’s the control group.
So the take-home points
basically are that what’s going on here is that clutch size is
trading off with an important fitness component,
but it’s not fledgling survival, it’s parental
survival. In this case–it’s different in
other species– but in this case the reason
that they don’t lay more eggs is that they themselves are more
likely to die; not that their offspring are
more likely to die. And these kestrels are
optimizing their reproductive investment with a clutch that’s
of intermediate size. They could lay more eggs but
they don’t. They know how many to lay.
Okay, so that’s just one
example of clutch size analysis. It’s a big literature,
there’s a lot of experiments on this.
Now let’s go to lifespan.
So I’m taking you through the
major life history traits from birth to reproduction to death.
In Fragment of an Agon,
T.S. Eliot wrote, “Birth,
reproduction and death. That’s all the facts,
when you come to brass tacks, birth and reproduction and
death.” He wrote that in the 1930s I
think. I didn’t realize that
T.S. Eliot was a behavioral ecologist;
evidently he was. So reproductive lifespan,
under this kind of analysis, is a balance between selection
that increases the number of reproductive events per life–
you live longer, you can reproduce more–
and effects that increase the intrinsic sources of mortality
with age. And it’s this idea that there’s
an evolution of aging or of senescence;
there’s an evolution of the body falling apart,
as a byproduct of something, which is the key feature of
this part of life history theory.
So the first kinds of selection
pressures are going to lengthen life to give you more
reproductive opportunities, but if there are byproducts
that are causing intrinsic increases in mortality rate,
those will shorten your lifespan.
So these things then come into
some kind of balance. Any increase in intrinsic
mortality rates, or decrease in reproductive
rates with age, is called aging or senescence.
So now we’re talking about why
people fall apart when they get old, and why organisms age and
die. To do that I need to introduce
you first to the way selection operates at different ages.
Selection is quite age specific
in its impact. Any selection pressure that
lengthens life is going to be one that decreases the relative
contribution to fitness of offspring,
and increases that of adults. So if an adult has survived to
some intermediate age, and juvenile mortality in that
species is pretty high, then the adult represents a
relatively improbable event that’s quite valuable,
and if it’s making babies in that environment,
each of them has a relatively low chance of surviving to be
that big and that old, and therefore there is a
certain fitness advantage in investing in the preservation of
that adult, because it’s unlikely that
you’ll get another one up to that state.
The things that will do this
are lower adult mortality rates and higher juvenile mortality
rates. So if life is relatively good
for adults and pretty risky for juveniles, and infants,
then you’re going to get the evolution of a longer lifespan.
But in contrast,
if adult mortality rates increase,
then organisms should evolve more rapid aging,
basically because there really isn’t much point in maintaining
a body that’s going to be dead anyway for other reasons.
Why should I take away from my
reproduction and invest it in say disease resistance,
or running away from predators, if I’m not going to be able to
avoid them anyway? Then I should make more babies.
So those are the basic ideas.
And I’d like to illustrate a
little bit of the math behind this, with a pictorial model.
So this is why senescence
evolves. I’m going to use the fruit fly
Drosophila as the model organism.
We’re going to start this thing
off, not when it’s an egg,
but when it ecloses and is an adult,
and we’re going to say that our model has no intrinsic mortality
at all. So this one doesn’t age;
this is our baseline, this is what happens if an
organism doesn’t age. Its risk of dying is 20% per
day, and every day it lays ten eggs.
So on the first day it gets ten
eggs. On the second day 80% of them
are still around, and each of them lays ten eggs,
and on the third day 64% of the original are still around (.8
times .8), ten eggs, ta-da ta-da.
And this thing is potentially
So it can just go on pumping
out the eggs, if it survives for as long as
ever; and its probability of survival
isn’t changing with age, it’s 80% each time.
This one gets 50 progeny.
We do that just by using an
infinite series. Okay?
And the numbers were set up to
give you a nice simple output. Okay?
The numbers are cooked.
So this one gets 50.
Now, what happens if everybody
dies between the nineteenth and the twentieth day?
That one gets 49.3.
That’s all the difference that
death at old age makes. And this is in a case where
there’s no senescence. Right?
This is kind of like light
bulbs failing or something like that.
However, now let’s throw in a
little life history tradeoff, and it’s a really small one.
This genotype here,
because it can lay eleven instead of ten eggs,
on the first day of life, dies, between the nineteenth
and the twentieth day, it leaves 50.3 progeny.
It has a .6% fitness advantage.
If we introduce this genotype
into the populations of the ones that live forever,
it will take over. There won’t be any immortal
flies anymore. There will be flies that have
evolved a shorter lifespan because they had a reproductive
advantage early in life and it didn’t take much of one to do
it. As you contemplate your own
mortality, I hope you realize that the Drosophila example in
fact is non-trivial; it’s giving you an important
message. This is the strength of
selection on further survival in human males in the United States
in the year 1960, calculated from real
demographic data from the U.S. Census.
This is the partial derivative
of fitness with respect to further survival.
And it’s a very interesting
picture. What it shows you is that as
soon as you become a teenager and you have some probability of
surviving in that human population,
your fitness starts to drop, because as soon as you’ve had a
baby, you have some probability of
grandchildren. And it shows you that after the
age of 46, evolution doesn’t care if you’re there anymore,
from the point of view of getting grandchildren.
As someone who is out here,
I would like to congratulate all of you.
There’s a reason I look
different from you. Now this way of looking at
aging basically says that aging is a byproduct of selection for
reproductive performance, and the reason that it occurs
is that there’s an accumulation of a lot of genes,
and they have positive or neutral effects on fitness
components early in life, and they have negative effects
on fitness components late in life.
The positive effect is called
the antagonistic pleiotropy hypothesis.
The idea is that the gene has
two effects: good early and bad late.
It’s like that one that gave
the fly one more baby, on the first day of life,
but killed it off at the nineteenth day of life.
And neutral effects early and
negative effects late is called the mutation accumulation
hypothesis. And these two hypotheses formed
sort of the intellectual basis of research on the evolution of
aging for quite awhile, and they turn out to be not too
productive. It looks like–in fact,
most of the cases that have been well investigated,
suggest that it’s positive effects early and negative
effects late; not neutral early and negative
But it’s hard to distinguish
between these sometimes. A general take-home point is
this: that organisms age is actually the best evidence we
have that it’s the replication of genes,
not the survival of organisms, that is the object of
evolution. So that gives you strong
empirical evidence that a gene-centered view of evolution
is in fact empirically correct. This is extremely discouraging
for the organisms that have consciousness and the ability to
analyze a situation.>
So, a bit of experimental
evidence. By the way, there have been
five or six experiments like this.
I’m just showing you because
this is the one I did. We had two treatments.
We had high and low adult
mortality. And if you followed the logic
so far, then you already know that if
you apply high adult mortality, then the organism should age
rapidly, and if you apply low adult
mortality, they should evolve to age more
slowly. So if you make the environment
risky, why try to invest in surviving, because somebody’s
going to kill you anyway? And in this case it was a Swiss
laboratory technician that was doing the killing,
but one can imagine that it might have been a lion or
something like that. The result is that after five
years, which is about 70 to 110 generations, in these flies,
aging evolved as expected. The higher extrinsic mortality
rates have produced shorter intrinsic life spans,
and the change was about five days.
It’s convenient that a day in
the life of a Drosophila is about a year in the life of a
human. So that gives you some feel,
some kind of intuitive feel for what this means.
Basically what that means is
that if we had started applying this strength of selection at
the time of the Trojan War, we would have produced a
response in the human population of about five years by now.
Just to put it back into the
human time scale. There’s a paper here.
You can go read about that if
you want. It gives you an entry into that
literature. To summarize today’s lecture,
all the major life history traits–age and size at
maturity; number and size of offspring;
lifespan; reproductive investment–are
involved in tradeoffs, and that causes them to come to
evolutionary equilibrium at intermediate,
not at extreme values. They are all under stabilizing
selection caused by tradeoffs. Age and size at maturity,
number of offspring per birth and per lifetime,
and lifespan and aging have all evolved.
I’ll just riff on this for a
moment, to tell you how you’ve changed, compared to chimpanzees
and bonobos. Humans live a bit longer,
about oh twenty years longer. The unique human life history
traits that appear to have evolved since we shared
ancestors with chimpanzees and bonobos are menopause,
which does occur, but rarely, in zoo chimps,
and is almost never observed in the wild.
The most striking thing though
is that we can have babies twice as fast as they can.
The average time in a Neolithic
or hunter-gatherer society, between births is two years,
in humans, and in chimps it’s five to six.
That, despite the fact that
human babies are much more helpless and need a lot more
parental care when they’re born. So, in fact,
humans have somehow managed to almost double the reproductive
output of chimpanzees, and it appears that they’ve
done it through social interaction.
So family members help raise
the kids. Sometimes even partners help
raise the kids. Grandmothers help raise the
kids. But there’s a lot of help.
And so the reason that the
inter-birth interval in humans has been shortened dramatically
in the last five or six million years is because we have become
a much more highly integrated– we have a much better
integrated family life. The evolution of all of these
traits can be understood, in general, as an interaction
between extrinsic ecological conditions,
that determine mortality rates, and conditions inside organisms
to cause tradeoffs. So if you’re looking for a
general explanatory structure, it is that the environment
poses problems, and when you answer that
problem with a solution, you are forced to make
compromises; and we know usually which kind
of compromises, and we are now in a position to
say if you’re looking in the environment you should look for
these kinds of factors. Okay, next time we’re going to
extend this framework into a particular part of life history
evolution called sex allocation, and how investment is divided
between male and female function,
and when it pays to switch sex and to be born as one sex and
turn into the other.