WEBVTT

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Prof: Okay,
let's get going.

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We're into the last segment of
the course.

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We did evolution,
and then we did ecology,

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and now we're going to do
behavior.

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I think the sequence does make
sense,

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because evolution helps to
explain how the things we deal

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with in ecology evolved,
and it also explains how much

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of what we see in behavior
evolved.

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But I want to say at the outset
that the behavioral ecology view

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of behavior--which is basically
expressed on this slide;

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so behaviors evolved--the
evolved patterns that we see in

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behavior should reflect things
that happen frequently to the

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organisms in their environment,
and the way animals behave

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should reflect the consequences
of behavior for lifetime

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reproductive success.

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That is really only part of the
biology of behavior.

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If you really want to
understand it at all levels,

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you have to understand how
behavior evolved

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phylogentically;
so you need a comparative view

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of behavior.

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You need to understand this
issue, which is how is it that

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behavior is adaptive;
is it, or is it a maladaptation?

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But then you also need to
understand how behavior

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develops;
that is, if we follow the

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organism from zygote to
death--you'll see some patterns

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of that today--how is it that
organisms learn?

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How is behavior acquired?

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That's a whole field in and of
itself.

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And then finally we need to
understand the mechanistic

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underpinnings of behavior.

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So in that respect there are a
lot of different ways you can go

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at it.

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You can go at it through
neurophysiology;

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you can go at it through
endocrinology.

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There are many different kinds
of mechanisms that are involved

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in triggering behavior patterns.

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So what we're going to
concentrate on in this course is

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primarily the behavioral ecology
approach to it,

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which is well exemplified in
the book that you've got by

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Krebs and Davies.

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But these other issues are also
very interesting biology,

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and I'm just indicating that if
you get interested in behavior,

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there are lots of ways you can
go at it,

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and there are entirely
different paradigms you can use

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to analyze it.

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So the five themes that we'll
approach--and these are the next

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five lectures;
so this is a sketch of how the

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course finishes.

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Today we'll talk about foraging
and hunting.

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Then next time we'll talk about
evolutionary game theory,

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which is one of the major
analytical frameworks within

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which people approach behavior.

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We'll have a look at mating
systems and parental care,

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and they are connected in
interesting ways.

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We'll take a look at
alternative breeding strategies,

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which are frequency dependent
breeding strategies,

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often best analyzed with
evolutionary game theory.

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And then we'll close with the
evolutionary and ecological

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analysis of selfishness,
altruism and cooperation,

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in animals and in humans.

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So those are the five themes
that I have selected out of

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behavioral ecology to emphasize
in this course.

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It's an introductory course
and, you know,

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frankly it would be great to
give you an entire semester just

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on behavior,
because it's such an

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interesting topic.

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But I will signal that we do
have other courses in the

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department on it,
and if you get interested in

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them, they might be fun to take.

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Okay?

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So I'm going to start with
foraging by bringing back in

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something that you guys
presented on that Friday,

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which is the marginal value
theorem.

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And this time I'm going to
apply it not to whether you

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should fill your plate up,
your tray up,

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in the dining hall full,
if you're going to the far end,

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or just with a little bit if
you're going to be close to the

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counter,
but to the issue of how long

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you should guard your mate,
and indicate that,

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in fact, the same intellectual
framework applies in both cases.

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Then I'll give you an example
where we can actually do a

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clever experiment to get the
foraging organism to tell us

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what fitness measure it is
using;

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and that's often a very
satisfying kind of experiment to

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do, if you can get the animal,
which cannot talk,

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to tell you what it thinks it's
doing.

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Then I'll illustrate how two
different birds deal with risk.

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Because a small bird,
at the end of a cold winter

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day, is exposed to extreme risk
of dying overnight--and I can

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tell you this is quite real.

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Over the course of a normal
Connecticut winter,

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I am often picking up the
occasional house sparrow,

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or robin, or whatever,
which has died next to my house

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because we've had a cold night;
so that risk is real.

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Then I'll discuss a little bit
how predators shape crypsis and

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conspicuousness.

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But then the sort of--the thing
that you'll probably remember a

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week from now is the part of the
lecture that deals with why hunt

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in a group?

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And at that point I'll show
some chimpanzees hunting.

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And I want to warn you that is
not something where you want to

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be bringing- eating the food
that you've brought into the

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room,
or anticipating lunch,

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okay, because this is pretty
gory stuff.

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Okay, marginal value theorem.

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The important thing about the
marginal value theorem first is

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that it's dealing with foraging
in space.

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And it's assuming you're
starting in one point,

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which would normally be your
home, your nest,

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your refuge,
your den, and you are going out

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to another point where you are
looking for food.

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And you have options.

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You could either go to this
place or you might go to some

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other place, when you go out to
get food.

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So you have to travel to get to
that patch of food,

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and then you have to search in
the patch,

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and then once you start getting
food in the patch,

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you accumulate it--and this is
a cumulative curve--

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in a way that expresses
diminishing marginal returns.

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So the harder- the longer
you're in the patch,

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the harder you have to look,
basically because you've

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already eaten some of the stuff
in the patch.

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Okay?

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So there are a number of things
to remember about this kind of

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diagram.

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One is the X axis is time.

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Two is it's split up into
travel time and search time,

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and at the point that you start
searching is where you draw your

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payoff curve here,
because that's the point at

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which you're going to draw this
cumulative payoff curve.

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The vertical axis is some kind
of payoff, and it's assumed to

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have a relationship to fitness.

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Okay?

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So it's going to be food,
or it could be mates.

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And probably the clever thing,
the most clever thing about

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this--and I well remember when
Rick Charnov first drew this

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thing;
he and I were grad students

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together, and he was in his
office and he was drawing this

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thing on a piece of paper.

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So I saw it before it was
published.

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The clever thing is the nice
geometrical solution to the

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optimality problem.

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And the way to think of that is
this: here is the measurement of

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time,
and the question is at what

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point should you stop searching
in this patch and go on to

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another one?

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And if you imagine all the
possible lines that you could

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draw,
that fan out from this axis,

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it turns out that the one which
is tangent to that curve has the

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highest slope.

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Okay?

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So the slope--I mean,
you can see that,

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just geometrically from looking
at it.

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Okay, this is the line which is
going to have the highest slope

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of all of the possible lines
that you could draw that are

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within this envelope.

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And you can't go above this
line.

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The reason you can't go above
this line basically is that

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you're not getting any food
above that line.

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This line is defining the rate
at which you can conceivably

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accumulate food,
just by the ecological

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constraints of that patch.

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And so this is the maximal
point for the slope.

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And then you ask yourself,
what is the slope?

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Well the slope is ∆y/∆x.

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Right?

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∆y is the change that you
get, or the amount of payoff you

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get per amount of time you spend
searching;

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so it's the payoff per unit
time.

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So drawing a line that way,
as a tangent,

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maximizes the payoff per unit
time.

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You don't have to write down
any equation;

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it's just geometry.

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Okay?

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And that actually is the cool
thing about the marginal value

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theorem.

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Now I want you to imagine you
are walking through a field in

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the Alps,
and there are some cows grazing

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in the field,
and you look off to the side

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and you see two biologists down
on the ground,

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looking at a cow-pie,
and you wonder what the hell is

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going on;
why do they have their noses

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down in a pile of cow dung?

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And the answer is they are
looking at this guy.

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Okay?

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And one of the biologists is
Geoff Parker and the other one

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is me;
because I had this happen to me

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with Geoff in the Alps.

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Okay?

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So a cow--let me go back--a cow
has come along and it has

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dropped a pile of dung here,
and it has been rapidly

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colonized by a bunch of dung
flies,

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and because of that,
within about a minute,

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a male will find a female and
they'll go into copulation.

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And then the issue is how long
should the male stay on that

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female before he jumps off and
goes to find another one?

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You can measure how many of the
offspring of that female will

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get fertilized if he stays on.

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And this is the proportion of
eggs that he will fertilize,

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if he stays on.

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Okay?

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So it's starting to look pretty
much like a marginal value

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theorem problem.

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You can set up the analysis as
search and guard time,

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plus time spent in copula.

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So this is his- how long it
takes him to search and guard a

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female, and this is how long he
spends in copula on her,

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and this is his payoff.

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And now look at what the Y axis
is.

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It's directly fitness;
that's a direct fitness payoff.

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So actually this is the purest
form of the biological

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implementation of marginal value
theorem;

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finding a mate and fertilizing
some eggs.

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Now the interesting thing is
that he jumps off about ten

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minutes earlier than predicted.

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Any ideas on why he might jump
off ten minutes earlier than

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predicted?

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It's actually a method of risk
minimizing or spreading of risk.

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He cannot predict when the next
fresh cow-pie is going to hit

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the ground,
and that's going to be the next

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open resource which gets
colonized by females,

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and he needs some time to find
it, because he wants to be the

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first one there.

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If he can be the first one
there, he'll get the best

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female.

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So he has to jump off this
female a little bit earlier than

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this simple analysis would
predict,

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just to hedge against the
problem of trying to be first to

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the next cow-pie.

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This problem is actually almost
quantifiable.

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Of course you are measuring
some rather strange things.

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You're sitting there with your
stopwatch watching as the

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cow-pies arrive on the surface
of the pasture,

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right?

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But it's a completely
analyzable problem.

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Now, so that's the marginal
value theorem applied to mate

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guarding.

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Now let's look at an experiment
that you can do with honeybees.

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And this was designed by Paul
Schmid-Hempel,

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who is a very clever Swiss
biologist.

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And what Paul did was he built
a model in which he could

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predict how long bees would
spend flying between flowers,

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and how many flowers they would
visit before they came back to

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the hive.

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Okay?

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And he had two different
measures that they might be

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using.

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So this is the optimal
relationship between how long it

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is flying between flowers and
how many flowers you visit

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before you fly back to the hive.

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In one model he had calories
per minute, which is the usual

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rate.

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Okay?

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In the marginal value theorem,
when you maximize that slope,

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if you're measuring that payoff
curve in calories what you're

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measuring then is maximal
calories per minute,

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in the marginal value theorem.

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Paul thought well maybe they
have another fitness measure.

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They might be using calories
gained per calories spent.

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So he built the two models,
and he had quite different

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predictions,
and then he manipulated them by

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gluing a little wire onto their
back and adding a tiny little

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weight.

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So he made them--you know,
he had a series of treatments

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where they weren't weighted and
then they had a little weight

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and then they had a lot of
weight.

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And this is what they did.

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This is the data, okay?

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So it really looks like he was
able to get them to tell him

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which fitness measure they were
using while they were foraging.

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That's a very,
very clever experiment.

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And this is the kind of thing
that you can do in behavioral

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ecology.

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It's possible to construct
situations in which the

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decisions that the animals are
making are so precisely

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constrained that you can get
them to give you an answer.

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I am waving over all the
details in the mathematical

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models.

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Okay?

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That's graduate student stuff.

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But I think the essential point
is that you can do an experiment

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that will get you to tell you
what an animal is actually using

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as a fitness measure.

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Okay, now two comments on the
problem of how to deal with

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risk;
and this is the small bird in

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winter problem.

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And this was a nice experiment.

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This is a Great Tit,
which is a European form of

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chickadee, and this is an
experiment which is done in an

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aviary, and this is a variable
environment.

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Okay?

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So when the experiment starts,
the food supply starts becoming

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unpredictable in time;
and that's just a manipulation

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that the experimenter is
imposing on the animal.

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And as the food starts getting
unpredictable,

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the bird starts getting fat.

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That tells you a couple of
interesting things right there.

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It says normally the bird
doesn't like to be that fat,

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but it's going to get fat
because it sees that its food

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supply is getting very
unpredictable.

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And then the way that you build
one control into this experiment

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is then to switch it at this
point into a constant food

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supply so that the environment
just becomes nice and

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predictable and the bird
relaxes,

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shrugs its shoulders and says,
"Oh,

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I can get away with getting
back to a normal weight,"

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and drops its weight.

15:36.019 --> 15:38.279
So that's one way of dealing
with it.

15:38.279 --> 15:41.939
But there's another way of
dealing with it,

15:41.937 --> 15:47.417
and that is that if you look at
this other relative of the Great

15:47.422 --> 15:53.072
Tit--okay, this is a Marsh Tit;
it also looks a bit like a Coal

15:53.070 --> 15:55.590
Tit;
it's a little bit smaller,

15:55.586 --> 15:58.716
has a black head--
and you put it in a high

15:58.717 --> 16:02.247
variance environment or a low
variance environment,

16:02.250 --> 16:05.500
this one, it doesn't change its
weight at all.

16:05.500 --> 16:09.840
It just packs on as much weight
as it can, by evening.

16:09.840 --> 16:12.330
And by the way,
at that latitude it's getting

16:12.332 --> 16:14.942
dark at 4:00 in the afternoon in
the winter.

16:14.940 --> 16:17.370
So it's going up to just about
peak weight.

16:17.370 --> 16:22.980
And you can see how much it's
losing by the next morning.

16:22.980 --> 16:25.450
What it does though is it
stores seeds,

16:25.452 --> 16:28.582
and if you put it in a high
variance environment,

16:28.575 --> 16:32.475
it greatly increases the number
of seeds that it stores.

16:32.480 --> 16:34.880
So one of these
species--they're very closely

16:34.875 --> 16:37.235
related by the way;
it's interesting that there

16:37.243 --> 16:39.783
doesn't seem to be much
phylogenetic component to this;

16:39.779 --> 16:43.059
one of them decided to pack it
on as body mass that it carries

16:43.057 --> 16:45.687
around with itself,
and the other one decided that

16:45.690 --> 16:47.410
it was going to store seeds.

16:47.408 --> 16:49.688
It may have something to do
with the risks of predation.

16:49.690 --> 16:52.470
Big, fat birds don't get away
from predators quite as easily

16:52.469 --> 16:53.929
as nice slender little birds.

16:53.929 --> 16:57.809


16:57.808 --> 17:02.328
Now what are some of the
consequences of predatory

17:02.326 --> 17:04.536
behavior for the prey?

17:04.538 --> 17:09.528
Well one of them is so-called
aposematic coloration;

17:09.528 --> 17:14.078
and that is that if you're
carrying around something that

17:14.075 --> 17:18.535
is going to poison your
predator, you want your predator

17:18.539 --> 17:19.919
to know that.

17:19.920 --> 17:23.660
You don't want--you know,
if you put a bunch of ham

17:23.663 --> 17:26.513
sandwiches out in Saybrook
Commons,

17:26.509 --> 17:29.419
and you've got cyanide in five
of them,

17:29.420 --> 17:32.400
and it's in the interest of
those five cyanide-laced ham

17:32.395 --> 17:36.055
sandwiches not to be eaten,
then you want a big warning

17:36.063 --> 17:38.723
label on it that says,
"Do not eat."

17:38.720 --> 17:39.460
Right?

17:39.460 --> 17:42.670
Well that's what these are,
these warning colorations.

17:42.670 --> 17:47.560
If you go out and you pick up a
warningly colored millipede,

17:47.555 --> 17:52.105
and you shake it in your hand,
it will smell like bitter

17:52.108 --> 17:53.018
almond.

17:53.019 --> 17:55.169
And it's perfectly safe,
by the way.

17:55.170 --> 17:57.750
There's not enough cyanide in
it to get hurt by sniffing it.

17:57.750 --> 18:00.280
So you're perfectly safe
picking up and shaking a

18:00.275 --> 18:02.515
millipede,
except you may feel a little

18:02.518 --> 18:05.908
moral compromise at the act of
shaking an invertebrate nervous

18:05.913 --> 18:06.473
system.

18:06.470 --> 18:07.460
Okay?

18:07.460 --> 18:09.580
But they will emit cyanide all
over your hand,

18:09.580 --> 18:11.420
and it does smell like bitter
almond.

18:11.420 --> 18:15.740
And of course the monarch
butterfly caterpillar,

18:15.740 --> 18:20.060
which gets its cardiac
glycosides from milkweed,

18:20.059 --> 18:24.839
will cause tachycardia in the
birds that eat it.

18:24.838 --> 18:29.098
Tachycardia is a condition
where your heart jumps say from

18:29.104 --> 18:32.184
a pulse rate of 80,
in a human--let's say if you've

18:32.181 --> 18:35.041
been exercising a little bit you
might have your pulse at 80 or

18:35.044 --> 18:37.884
100 or something like that;
with tachycardia you go up to

18:37.882 --> 18:40.542
250 or 300 and you pass out,
because your heart starts

18:40.536 --> 18:42.536
fluttering and it can't pump
anymore.

18:42.538 --> 18:46.088
So that's what eating that will
do to a Blue Jay;

18:46.089 --> 18:49.199
it will have cardiac arrest.

18:49.200 --> 18:52.770
So it would take a few of them
to do that to you.

18:52.769 --> 18:54.629
So I suggest that if you want
to try this one,

18:54.626 --> 18:55.696
don't eat more than one.

18:55.700 --> 18:56.940
Okay?

18:56.940 --> 18:59.060
If you really want to get into
it, you're probably getting

18:59.060 --> 19:00.960
threatened if you eat maybe five
or six of them,

19:00.960 --> 19:02.820
or make a milkshake out of them
or something like that.

19:02.819 --> 19:06.539


19:06.538 --> 19:09.358
One can do experiments with
this kind of thing as well.

19:09.358 --> 19:14.518
This is a situation in which
chicks, just regular domestic

19:14.517 --> 19:18.587
chicks, were given different
colored baits.

19:18.589 --> 19:19.399
Okay?

19:19.400 --> 19:23.480
And in both of these situations
they were given seeds that had

19:23.481 --> 19:27.101
been stained green or blue,
and they had been soaked in

19:27.096 --> 19:29.586
quinine;
and chicks do not like seeds

19:29.585 --> 19:31.265
that are soaked in quinine.

19:31.269 --> 19:34.059
So they were both distasteful.

19:34.058 --> 19:37.568
The only difference here is
that in one case the green and

19:37.567 --> 19:41.077
blue seeds are on a green
background, and here they are on

19:41.076 --> 19:42.426
a blue background.

19:42.430 --> 19:46.140
And what you can see is that
the ones that match the

19:46.140 --> 19:48.760
background continue to get
eaten,

19:48.759 --> 19:51.449
and the ones that stand out
from the background start

19:51.449 --> 19:52.379
getting avoided.

19:52.380 --> 19:53.450
Okay?

19:53.450 --> 19:56.200
So there's a bit of learning;
oh, I don't like to taste these

19:56.201 --> 19:56.641
things.

19:56.640 --> 19:59.800
But then they avoid the ones
that they see most easily.

19:59.798 --> 20:01.798
And that's what's going on over
here.

20:01.798 --> 20:03.948
It's this process that has
produced these colors.

20:03.950 --> 20:08.000


20:08.000 --> 20:09.490
Of course it can go in the
other direction.

20:09.490 --> 20:13.400
If you, in fact,
are not distasteful,

20:13.400 --> 20:17.670
and you want to avoid being
eaten, then often natural

20:17.667 --> 20:22.177
selection will change your
morphology in such a way that

20:22.182 --> 20:25.962
it's rather difficult to see
what you are.

20:25.960 --> 20:27.150
Okay?

20:27.150 --> 20:30.160
The head is here;
that's the end of the right

20:30.161 --> 20:32.491
wing, that's the end of the left
wing;

20:32.490 --> 20:35.250
there's a nice wing vein
running down the middle.

20:35.250 --> 20:39.720
Some of these things are just
remarkably precise in resembling

20:39.721 --> 20:41.921
a dead leaf or other things.

20:41.920 --> 20:44.690
I think some of the ones that I
like the most are the praying

20:44.693 --> 20:46.593
mantises that look like flower
petals,

20:46.588 --> 20:49.388
and sit on flowers,
and grab things when they come

20:49.393 --> 20:53.483
in;
they're very nasty.

20:53.480 --> 20:54.970
Here's another one that's very
nasty;

20:54.970 --> 20:56.230
same kind of thing, okay.

20:56.230 --> 20:57.740
So this is aggressive mimicry.

20:57.740 --> 21:03.160
These are the light signals
which are given out by different

21:03.163 --> 21:09.603
species of so-called fireflies--
in fact, these are beetles--and

21:09.596 --> 21:15.556
they have a light organ,
and you can see that there's a

21:15.555 --> 21:19.055
species specific signal pattern.

21:19.058 --> 21:21.878
Now normally you would think,
okay fine, they're just

21:21.875 --> 21:23.495
dividing up the frequencies.

21:23.500 --> 21:27.600
They don't need to have an FCC
to regulate which frequency they

21:27.602 --> 21:28.002
use.

21:28.000 --> 21:29.770
They're flying through the
night.

21:29.769 --> 21:31.929
They see a signal over there
and they can say,

21:31.930 --> 21:33.840
"Oh, that is another one
of my species,

21:33.838 --> 21:38.188
I'll go check it out and
potentially mate with it."

21:38.190 --> 21:42.120
So you might think,
oh, that's all just normal mate

21:42.115 --> 21:42.975
behavior.

21:42.980 --> 21:47.530
However, these are males that
are flickering,

21:47.532 --> 21:52.192
and then they get a response
from a female.

21:52.190 --> 21:52.870
Okay?

21:52.868 --> 21:55.868
So this would be male-male,
female, male-male,

21:55.874 --> 21:57.414
female kind of thing.

21:57.410 --> 22:01.200
And so there are some that
mimic the light signals of

22:01.204 --> 22:03.894
another species;
some females that are sitting

22:03.890 --> 22:05.450
there saying--they're faking it.

22:05.450 --> 22:06.340
Okay?

22:06.338 --> 22:08.638
A male blinks,
the female can see,

22:08.636 --> 22:12.666
oh, that's not a male of my
species, therefore I can safely

22:12.673 --> 22:13.513
eat him.

22:13.509 --> 22:15.929
So she goes blink,
with the signal of the other

22:15.925 --> 22:18.285
species, he flies in,
and she chews him up.

22:18.289 --> 22:21.519
In so doing she gets two things.

22:21.519 --> 22:27.179
She gets calories out of him,
but in some cases she also is

22:27.181 --> 22:32.651
absorbing a defensive chemical
that will protect her from

22:32.646 --> 22:35.376
birds, bats and spiders.

22:35.380 --> 22:37.400
So she gets a double dose.

22:37.400 --> 22:41.320
She gets both calories and she
gets defense from doing this.

22:41.318 --> 22:45.038
This kind of aggressive mimicry
is reasonably widespread,

22:45.036 --> 22:48.816
and it has been evolved
convergently a number of times.

22:48.818 --> 22:52.298
The one that always got me as a
kid is the saber-tooth blenny.

22:52.298 --> 22:55.658
You know about cleaning wrasses
that come into the mouths of big

22:55.660 --> 22:58.700
fish and clean the parasites off
of them and so forth,

22:58.700 --> 23:02.360
and the big fish have evolved,
because this is a beneficial

23:02.361 --> 23:03.981
thing,
to kind of relax.

23:03.980 --> 23:06.540
And so you will see giant
groupers and barracudas opening

23:06.539 --> 23:09.239
their mouths and letting these
little fish swim through them

23:09.238 --> 23:10.608
and clean off their teeth.

23:10.608 --> 23:14.188
Well, there is another fish
called the saber-tooth blenny

23:14.194 --> 23:17.914
that mimics both the color and
the approach behavior of the

23:17.909 --> 23:20.399
cleaning wrasse;
which is, by the way,

23:20.396 --> 23:21.486
a sigmoidal dance.

23:21.490 --> 23:22.670
It goes through the water like
this.

23:22.670 --> 23:25.260
And so you see one of these
things coming up and you kind of

23:25.261 --> 23:27.041
relax and say,
"Oh, it's just a cleaning

23:27.039 --> 23:28.219
wrasse,
it's going to be fine."

23:28.220 --> 23:29.880
And it comes up,
and if it's a human,

23:29.880 --> 23:31.510
it takes a chunk out of your
thigh,

23:31.509 --> 23:35.129
and if it's a fish that has its
mouth open,

23:35.130 --> 23:37.530
it rips out a chunk of gill and
goes running off.

23:37.529 --> 23:41.699
So this is the sort of thing
that led Darwin to think that

23:41.703 --> 23:46.103
evolution just fills the world
up with things that are taking

23:46.096 --> 23:49.316
advantage of every possible
opportunity;

23:49.318 --> 23:54.098
and aggressive mimicry is a
good example of this.

23:54.098 --> 24:01.758
One of the interesting issues
with predation and parasitism

24:01.758 --> 24:07.038
and mimicry has to do with the
cuckoo.

24:07.038 --> 24:10.098
And I want to mention it
because it really is an

24:10.103 --> 24:12.193
interesting series of puzzles.

24:12.190 --> 24:13.300
Okay?

24:13.299 --> 24:14.759
So here's a cuckoo.

24:14.759 --> 24:17.449
Cuckoos, by the way,
feed on caterpillar larvae,

24:17.445 --> 24:20.525
and so they tend to disappear
from places where lots of

24:20.532 --> 24:22.022
insecticides are used.

24:22.019 --> 24:24.049
So they're kind of a canary in
a coalmine.

24:24.048 --> 24:27.228
If there have been cuckoos on
the landscape and you can't hear

24:27.233 --> 24:29.393
them anymore,
it means that intensive

24:29.392 --> 24:32.502
agricultural practice has
probably wiped out the entire

24:32.497 --> 24:35.877
large fauna of caterpillars;
so they like to eat

24:35.880 --> 24:37.060
caterpillars.

24:37.058 --> 24:40.398
And what they do is they go
around and they find a nest,

24:40.398 --> 24:43.798
like a robin's nest here,
and they lay their own egg into

24:43.799 --> 24:44.589
the nest.

24:44.588 --> 24:48.458
And they have their whole
developmental program set up in

24:48.458 --> 24:52.808
such a way that their baby will
hatch earlier than the babies of

24:52.808 --> 24:54.258
the host species.

24:54.259 --> 24:57.459
And its behavior is set up in
such a way that the first thing

24:57.464 --> 24:59.814
that it does is--
you know, it's just a tiny

24:59.807 --> 25:02.237
little baby bird,
and it's just hatched out,

25:02.244 --> 25:05.014
but it has enough muscular
coordination and enough

25:05.008 --> 25:08.278
behavioral complexity to take
the other eggs and shove them

25:08.277 --> 25:11.707
out of the nest onto the ground,
so it's the only one left.

25:11.710 --> 25:13.790
And then it sits there.

25:13.788 --> 25:17.078
And it's got a very effective
feeding behavior.

25:17.078 --> 25:19.158
It opens its mouth,
it gives all of the

25:19.163 --> 25:21.473
morphological and behavioral
cues that say,

25:21.465 --> 25:23.765
"Feed me,
feed me, feed me."

25:23.769 --> 25:26.859
And the parents work really
hard, the parents of the other

25:26.856 --> 25:29.616
species work really hard to come
in and feed it,

25:29.618 --> 25:31.368
and you get a baby cuckoo out
of the nest,

25:31.368 --> 25:33.428
instead of a warbler or
whatever.

25:33.430 --> 25:35.110
Okay?

25:35.108 --> 25:39.618
Well why don't the hosts throw
out the cuckoo egg?

25:39.619 --> 25:40.579
There they are.

25:40.579 --> 25:41.379
They're good parents.

25:41.380 --> 25:43.220
They come back to the nest.

25:43.220 --> 25:46.190
There's an egg in it that they
haven't laid.

25:46.190 --> 25:48.800
Sometimes it looks quite a bit
like their egg,

25:48.797 --> 25:51.377
sometimes it doesn't;
it all depends on which

25:51.380 --> 25:54.530
particular host species it is
and how close that particular

25:54.534 --> 25:57.584
race of cuckoo is to matching
the host species color.

25:57.578 --> 26:00.858
Well there are really,
I think, two reasons,

26:00.862 --> 26:04.452
but they may not be
quantitatively sufficient to

26:04.450 --> 26:06.740
explain everything we see.

26:06.740 --> 26:09.660
One is the source-sink
distinction.

26:09.660 --> 26:13.080
The hosts can't adapt as fast
as the cuckoo because the

26:13.075 --> 26:16.735
parasitized nests are sinks for
the host, and unparasitized

26:16.742 --> 26:19.022
nests are sources for the hosts.

26:19.019 --> 26:22.299
Most of the sources are coming
out of nests that have not had

26:22.304 --> 26:23.294
cuckoos in them.

26:23.288 --> 26:25.618
If a cuckoo has gotten into the
nest, it's wiped out that

26:25.615 --> 26:26.235
reproduction.

26:26.240 --> 26:28.660
Okay?

26:28.660 --> 26:31.700
So the adaptation is to the
source, which is to the

26:31.700 --> 26:34.680
condition without cuckoos,
and not to the sink.

26:34.680 --> 26:37.740
But there's another issue,
and that is if you're just

26:37.743 --> 26:41.223
starting to evolve the behavior
of throwing eggs out of your

26:41.220 --> 26:43.160
nest,
and you're not very good at it

26:43.160 --> 26:45.180
yet,
you can make a serious mistake

26:45.176 --> 26:47.096
by killing one of your own kids.

26:47.098 --> 26:51.268
So there's kind of a threshold
there that you have to get over.

26:51.269 --> 26:54.429
You have to actually--this is a
behavior where you actually have

26:54.430 --> 26:57.390
to be accurate and pretty good
at it, before it pays off.

26:57.390 --> 27:00.980
Before you get good at it,
you are indulging in some very

27:00.977 --> 27:02.127
costly behavior.

27:02.130 --> 27:02.880
Okay?

27:02.880 --> 27:04.570
So that's another reason.

27:04.568 --> 27:08.478
Another reason that we don't
see the hosts throwing the

27:08.482 --> 27:12.622
cuckoo egg out is that the
cuckoos may be moving on to new

27:12.615 --> 27:13.335
hosts.

27:13.338 --> 27:16.488
So it may be that the cuckoos
for say a hundred years

27:16.494 --> 27:18.774
parasitized robins,
and the robins may

27:18.770 --> 27:21.020
slowly--slowly,
because of these reasons--

27:21.019 --> 27:23.719
start to evolve a response to
cuckoos,

27:23.720 --> 27:26.380
at which point the cuckoos just
switch over and start

27:26.381 --> 27:27.611
parasitizing warblers.

27:27.608 --> 27:30.028
And they do that for awhile,
and they just keep moving

27:30.028 --> 27:32.628
around among the different
species in their landscape,

27:32.630 --> 27:35.630
so that they're always able to
stay ahead,

27:35.630 --> 27:41.240
because their evolution is a
little bit faster than that of

27:41.239 --> 27:42.399
the hosts.

27:42.400 --> 27:45.040
So that process is hard to
observe.

27:45.038 --> 27:48.098
And the egg mimicry isn't very
precise.

27:48.098 --> 27:51.728
You can see here,
this isn't a very good match.

27:51.730 --> 27:53.420
This is one kind of cuckoo egg.

27:53.420 --> 27:55.070
There are some that are a bit
closer to robins.

27:55.068 --> 27:57.078
This might be appropriate for
another kind of species.

27:57.078 --> 28:01.608
The egg mimicry isn't very
precise, and it is still kind of

28:01.605 --> 28:06.125
puzzling why the hosts don't
throw out more cuckoo eggs.

28:06.130 --> 28:08.270
It's an interesting problem.

28:08.269 --> 28:13.269
Now I'd like to talk about
hunting in a group.

28:13.269 --> 28:19.329
And this is a situation that is
interesting,

28:19.328 --> 28:25.508
both because we can quantify
the benefits of foraging styles,

28:25.509 --> 28:29.489
and we can see whether or not
animals are actually doing what

28:29.486 --> 28:31.736
is quantitatively best for them.

28:31.740 --> 28:35.500
But it also addresses the whole
issue of why animals should

28:35.501 --> 28:36.671
exist in groups.

28:36.670 --> 28:40.340
And since we are a group living
and hunting primate,

28:40.338 --> 28:44.438
this is a very interesting
thing for us to contemplate.

28:44.440 --> 28:49.450
Now when an individual joins a
group, it's making a pretty

28:49.448 --> 28:51.468
fundamental decision.

28:51.470 --> 28:55.760
It's basically deciding that
the payoff it's going to get,

28:55.759 --> 28:57.639
from the coordination of group
hunting,

28:57.640 --> 29:00.790
is going to more than
compensate for the fact it's

29:00.791 --> 29:04.381
going to have to share the food;
unless it's an extremely

29:04.375 --> 29:07.565
confident dominant type,
it's going to have to share the

29:07.574 --> 29:08.044
food.

29:08.039 --> 29:09.349
Okay?

29:09.348 --> 29:13.118
So what you see in wolves,
coyotes,

29:13.118 --> 29:16.518
African hunting dogs and hyenas
is that all of these things will

29:16.517 --> 29:19.807
hunt alone for little things and
they'll hunt together for big

29:19.806 --> 29:20.396
things.

29:20.400 --> 29:24.340
So go up to Ellesmere Island in
the Arctic, pop yourself down

29:24.342 --> 29:27.562
onto a wolf study site,
and go out and look at the

29:27.560 --> 29:28.810
wolves hunting.

29:28.808 --> 29:32.068
And if they are hunting for
voles and mice,

29:32.068 --> 29:34.858
which are about this big,
you'll see individual wolves

29:34.859 --> 29:38.499
moving about the landscape,
trapping them with their paws

29:38.503 --> 29:40.073
and munching them up.

29:40.068 --> 29:43.688
However, if they decide to
tackle something like a muskox,

29:43.690 --> 29:47.110
they will form up into a pack
and go into coordinated

29:47.109 --> 29:49.349
behavior,
with a division of labor and

29:49.348 --> 29:52.488
assigned roles,
to bring down the muskox.

29:52.490 --> 29:54.200
And the muskox,
of course, have a

29:54.195 --> 29:56.745
counter-adaptation,
which is a group adaptation,

29:56.748 --> 29:58.518
and they form a protective
circle,

29:58.519 --> 30:00.369
and they all face outward
towards the wolves,

30:00.368 --> 30:02.698
and they defend themselves with
their horns.

30:02.700 --> 30:05.820
Similarly for coyotes,
African hunting dogs and

30:05.818 --> 30:06.428
hyenas.

30:06.430 --> 30:08.880
With African hunting dogs,
they will be going after

30:08.876 --> 30:11.516
usually small rodents or birds
or things like that,

30:11.519 --> 30:14.589
on the ground,
but they're actually capable of

30:14.585 --> 30:16.215
bringing down a zebra.

30:16.220 --> 30:17.940
This is a dog which is this big.

30:17.940 --> 30:18.400
You know?

30:18.400 --> 30:21.300
A zebra is a horse,
which is this big.

30:21.298 --> 30:26.458
And five or six African hunting
dogs, each of which weighs say

30:26.459 --> 30:30.689
about 75 pounds maybe,
at max--50 to 75 pounds--can

30:30.688 --> 30:33.478
bring down a 500 pound zebra.

30:33.480 --> 30:38.180
So the interesting thing is
that they switch facultatively

30:38.182 --> 30:43.132
from solitary hunting to group
hunting, as group size- as the

30:43.132 --> 30:45.362
size of prey increases.

30:45.358 --> 30:50.098
Now in chimpanzees--I'm going
to show you a bit of the gory

30:50.097 --> 30:53.037
detail of chimpanzees in a
minute.

30:53.038 --> 30:55.258
But before I do that I just
want to show you some of the

30:55.262 --> 30:56.762
kinds of things that these
capture.

30:56.759 --> 31:01.229
So we have wolves and coyotes
and hyenas and African hunting

31:01.232 --> 31:04.302
dogs,
and these are pictures where

31:04.299 --> 31:09.219
they have done a sophisticated,
coordinated group hunt to bring

31:09.217 --> 31:10.907
down a big piece of meat.

31:10.910 --> 31:22.560


31:22.558 --> 31:24.378
Now let's see what chimpanzees
do.

31:24.380 --> 31:31.880


31:31.880 --> 32:14.840
<<Professor Stearns plays
a video>>

32:14.839 --> 32:15.959
Prof: That's Brutus.

32:15.960 --> 32:20.380


32:20.380 --> 32:24.490
Brutus was born in 1952, August.

32:24.490 --> 36:02.730
<<Professor Stearns plays
the video>>

36:02.730 --> 36:04.730
Prof: That's an
important point.

36:04.730 --> 36:13.440


36:13.440 --> 36:17.920
They use seasoning,
a little spice from the weeds.

36:17.920 --> 37:11.050
<<Professor Stearns plays
the video>>

37:11.050 --> 37:15.010
Prof: So that's in
Christophe Boesch's study site,

37:15.005 --> 37:18.605
which is in Tai National Park
in the Ivory Coast.

37:18.610 --> 37:27.700
Those pictures were taken in
about 1990,1991.

37:27.699 --> 37:31.439
I had been there in February of
1989, and I had been out on a

37:31.436 --> 37:33.176
hunt with that same group.

37:33.179 --> 37:36.569
I had had lunch,
which was by the way fruit,

37:36.572 --> 37:37.522
with them.

37:37.518 --> 37:40.148
I ate the same stuff they did,
but I was eating fruit,

37:40.148 --> 37:41.238
not colobus monkeys.

37:41.239 --> 37:47.959
Those guys are all dead now,
the chimps.

37:47.960 --> 37:50.530
They died of Ebola and they
died of poaching.

37:50.530 --> 37:54.750
However, a smaller group has
replaced them.

37:54.750 --> 37:58.460
Of all of the chimps that were
in that group in 1990,

37:58.463 --> 38:01.753
there is only one female who is
still alive.

38:01.750 --> 38:04.600
But the group is back to about
oh fifteen or twenty;

38:04.599 --> 38:07.319
at that point there were sixty
in it.

38:07.320 --> 38:13.130
So let's look at an analysis of
why they choose to hunt in a

38:13.130 --> 38:13.920
group.

38:13.920 --> 38:15.600
Okay?

38:15.599 --> 38:18.769
So the increase must more than
balance the cost:

38:18.766 --> 38:19.436
sharing.

38:19.440 --> 38:24.020
They do it during the rainy
season.

38:24.018 --> 38:26.208
During the dry season they
crack nuts.

38:26.210 --> 38:29.800
So these chimps actually have a
culture where they teach their

38:29.802 --> 38:33.162
offspring how to crack nuts with
a hammer and an anvil.

38:33.159 --> 38:36.539
This is true west of a certain
river in the Ivory Coast and

38:36.536 --> 38:38.746
it's not true in the rest of
Africa.

38:38.750 --> 38:43.320
And you can see that they hunt
a lot more frequently in

38:43.318 --> 38:45.348
September and October.

38:45.349 --> 38:48.929
And I was actually there and
saw the hunt in February;

38:48.929 --> 38:51.429
so they weren't so frequent in
February, I was lucky to see

38:51.431 --> 38:51.691
one.

38:51.690 --> 38:53.970
But these chimps hunt a lot.

38:53.969 --> 38:57.879
And if you look at the hunting
success as a function of the

38:57.880 --> 39:02.620
numbers in the hunting party--
you can see on the top here a

39:02.617 --> 39:07.857
comparison of solitary and group
hunts in the Ivory Coast in

39:07.864 --> 39:09.914
Gombe and at Mahale.

39:09.909 --> 39:14.379
So Gombe is Jane Goodall's
study site in Tanzania,

39:14.380 --> 39:19.400
Tai is in the Ivory Coast,
and Mahaleis a Japanese study

39:19.398 --> 39:20.218
site.

39:20.219 --> 39:23.279
And you can see that they hunt
quite a bit in Tai,

39:23.278 --> 39:26.088
and they do a lot more group
hunting there.

39:26.090 --> 39:29.390
And if you look at the impact
of group size on capture

39:29.393 --> 39:31.053
success,
you can see that the more

39:31.047 --> 39:32.557
chimps that are hunting in the
group,

39:32.559 --> 39:36.349
the more likely it is they are
to make a kill,

39:36.349 --> 39:41.409
the longer the hunt lasts,
and the greater the degree of

39:41.407 --> 39:44.347
collaboration during the hunt.

39:44.349 --> 39:48.839
So this is team behavior,
where individuals have roles,

39:48.840 --> 39:51.340
and they learn to play a team
role,

39:51.340 --> 39:54.600
and they learn to do what is
good for the team so that the

39:54.601 --> 39:56.491
team will have greater success.

39:56.489 --> 40:01.249
If we look at success as a
function of group size,

40:01.250 --> 40:02.630
you can see that the net--and
by the way,

40:02.630 --> 40:06.880
this is now measured in net
benefit in calories,

40:06.880 --> 40:11.100
and in order to calculate that,
you have to be able to estimate

40:11.097 --> 40:14.427
how many calories a chimpanzee
is putting out,

40:14.429 --> 40:16.949
if it's running along the
ground or climbing a tree or

40:16.952 --> 40:18.002
something like that.

40:18.000 --> 40:20.370
And so this is taken from
exercise physiology,

40:20.371 --> 40:23.061
the estimates are taken from
exercise physiology.

40:23.059 --> 40:26.469
And then you can figure out how
many calories are there in a

40:26.471 --> 40:28.381
colobus monkey of a given size.

40:28.380 --> 40:32.970
And it turns out that the right
number to have--if you are a

40:32.965 --> 40:37.935
hunter you do better up until
you get to a group size of five;

40:37.940 --> 40:40.940
after that you have to share
with too many others and the

40:40.943 --> 40:42.233
payoff isn't so great.

40:42.230 --> 40:45.560
If you are a bystander,
it's pretty much the same,

40:45.559 --> 40:47.189
the curve mimics that.

40:47.190 --> 40:49.790
And if you are a latecomer
who's coming in,

40:49.789 --> 40:53.319
then normally you don't get too
much of what is caught.

40:53.320 --> 40:58.600
So in this particular hunt,
Brutus led the hunt and he made

40:58.603 --> 40:59.883
the capture.

40:59.880 --> 41:03.030
So that was the chimp that you
saw going up as the blocker;

41:03.030 --> 41:04.330
that was Brutus.

41:04.329 --> 41:06.579
Brutus, by the way,
was the oldest male in the

41:06.583 --> 41:09.543
group, and he also had a couple
of strategic innovations.

41:09.539 --> 41:12.579
Brutus had figured out that in
competition with neighboring

41:12.576 --> 41:14.326
groups--
and chimps do have wars with

41:14.327 --> 41:17.067
neighboring groups--
if you were in a state of being

41:17.070 --> 41:20.710
the weaker group and you were
being confronted by a big one,

41:20.710 --> 41:23.380
what you would do is you would
go over and you would display at

41:23.380 --> 41:23.940
the border.

41:23.940 --> 41:26.190
They would come in,
and then you would quiet down--

41:26.190 --> 41:28.530
and he got them all to quiet
down--and the pack of males

41:28.527 --> 41:31.077
would run around the back and
steal a female from the back of

41:31.079 --> 41:31.929
the other group.

41:31.929 --> 41:34.759
And this caused such chaos and
disarray that they would

41:34.755 --> 41:37.785
normally be able to overpower a
larger neighboring group by

41:37.791 --> 41:39.521
using this sneaking behavior.

41:39.518 --> 41:42.228
Just think of how much
strategic thinking it requires

41:42.226 --> 41:44.096
to figure something like that
out.

41:44.099 --> 41:46.309
And that was Brutus;
he was a smart guy.

41:46.309 --> 41:47.779
And there he is right there.

41:47.780 --> 41:53.280
So Macho was contending with
Brutus to be an alpha male--

41:53.280 --> 41:56.280
there are two or three
different ways you can define

41:56.275 --> 41:59.985
group hierarchy in chimps--
and Macho was trying to be

41:59.987 --> 42:01.067
group leader.

42:01.070 --> 42:03.540
And he was actually a very
collaborative hunter.

42:03.539 --> 42:06.749
It's one way to sort of weasel
your way into power is to

42:06.751 --> 42:07.921
collaborate a lot.

42:07.920 --> 42:09.990
And Rousseau never made it.

42:09.989 --> 42:13.259
And Rousseau actually got
attacked by a leopard once,

42:13.255 --> 42:16.705
who managed to rip off half of
his scrotum, and Rousseau

42:16.708 --> 42:22.128
survived that;
pretty amazing.

42:22.130 --> 42:25.730
So this is the function--it's
sort of a hill-shaped or

42:25.728 --> 42:29.058
hump-shaped function of success
versus group size,

42:29.056 --> 42:32.246
with the best group size being
around five.

42:32.250 --> 42:37.140
And then if you look at who
shares in the capture,

42:37.135 --> 42:42.515
what you see is a breakdown
where there is considerable

42:42.518 --> 42:45.508
sharing which is going on.

42:45.510 --> 42:48.710
And you can see that
bystanders, who are often

42:48.708 --> 42:51.758
females,
are eating meat often after a

42:51.759 --> 42:56.789
capture for nearly half an hour,
and the captor usually gets

42:56.793 --> 42:57.853
most of it.

42:57.849 --> 43:00.919
But it's very interesting how
much gets shared.

43:00.920 --> 43:05.880
And if you look at the kind of
hunt that was involved,

43:05.880 --> 43:08.660
you can see interestingly,
if you classify the hunts by

43:08.657 --> 43:11.127
kind of a primitive,
not terribly well-coordinated

43:11.128 --> 43:13.118
hunt,
which is a half-anticipation,

43:13.121 --> 43:15.271
through a single-anticipation
hunt,

43:15.268 --> 43:18.278
where one of the chimps is
actually successfully

43:18.278 --> 43:21.988
anticipating where the colobus
are going and manages to get

43:21.992 --> 43:25.162
over and block them off,
to a doubled-anticipation hunt,

43:25.159 --> 43:26.639
which is even more
sophisticated,

43:26.639 --> 43:30.899
you can see that there is a
reward in terms of amount of

43:30.900 --> 43:35.550
meat eaten for participating in
a more sophisticated hunt.

43:35.550 --> 43:36.720
Okay?

43:36.719 --> 43:41.099


43:41.099 --> 43:43.379
They pay a tax,
and they are willing to pay a

43:43.378 --> 43:44.878
tax to belong to the group.

43:44.880 --> 43:47.760
The tax that they pay is by
sharing what they capture.

43:47.760 --> 43:50.770


43:50.768 --> 43:55.588
So there is an anticipation of
being willing to give up

43:55.592 --> 43:58.632
personal gain for group benefit.

43:58.630 --> 44:03.060
And there's another thing going
on here, and that is that quite

44:03.056 --> 44:06.766
a bit of the reward of
bystanders is trading food for

44:06.768 --> 44:07.338
sex.

44:07.340 --> 44:12.890
So both taxes and prostitution
appear to be present in

44:12.885 --> 44:14.345
chimpanzees.

44:14.349 --> 44:15.929
Now what about learning?

44:15.929 --> 44:19.549
Well this is the frequency of
ambushes that are used by

44:19.550 --> 44:21.430
hunters of different ages.

44:21.429 --> 44:24.439
Ambushing is,
you know, a moderately

44:24.440 --> 44:26.420
sophisticated tactic.

44:26.420 --> 44:29.000
It's not quite as sophisticated
as a big group hunt,

44:29.001 --> 44:31.731
but it's an indication that,
you know, young chimps are

44:31.733 --> 44:33.003
learning how to hunt.

44:33.000 --> 44:34.810
And this is how old they are.

44:34.809 --> 44:38.109
And the interesting thing about
this is--well of course they're

44:38.106 --> 44:40.276
weaned at five;
so they aren't really going to

44:40.284 --> 44:42.924
start participating until they
get there, and they don't do too

44:42.918 --> 44:44.828
much hunting before they become
teenagers.

44:44.829 --> 44:47.199
But then let's suppose they
start playing here,

44:47.204 --> 44:49.274
they start going out and
hunting here.

44:49.268 --> 44:52.498
It takes them until they're
thirty-five-years-old before

44:52.496 --> 44:54.196
they really hit their peak.

44:54.199 --> 44:57.339
It takes them about twenty
years to learn how to be an

44:57.342 --> 44:58.472
effective hunter.

44:58.469 --> 45:00.289
That's pretty amazing.

45:00.289 --> 45:03.389


45:03.389 --> 45:05.909
And if you look,
if you break this down by

45:05.911 --> 45:08.681
half-anticipations and
full-anticipations,

45:08.679 --> 45:12.889
what you see is the frequency
with which full-anticipations

45:12.894 --> 45:17.694
are practiced in hunts is lower,
and it takes a long time before

45:17.692 --> 45:21.182
they get up to the level of
half-anticipations.

45:21.179 --> 45:24.549
So these are categories of
sophistication in hunting,

45:24.552 --> 45:27.992
and it takes a long time to get
more sophisticated.

45:27.989 --> 45:34.049
So to sum up on the behaviors
that are involved in foraging

45:34.052 --> 45:40.432
and hunting, I've really given
you today two paradigms for how

45:40.429 --> 45:42.729
to think about it.

45:42.730 --> 45:45.100
One of them is the marginal
value theorem,

45:45.099 --> 45:48.669
which tells you how you should
decide when to stop hunting in a

45:48.670 --> 45:51.380
certain place,
or when to stop copulating in a

45:51.378 --> 45:53.848
certain place,
and go off to find either food

45:53.847 --> 45:56.707
or mates in another place;
and that works in a spatial

45:56.710 --> 46:00.070
situation where you have to move
some distance before you get to

46:00.074 --> 46:00.774
the patch.

46:00.768 --> 46:04.368
And the other paradigm that
I've given you is simply the

46:04.367 --> 46:08.287
cost-benefit analysis of how
much caloric reward do I get out

46:08.293 --> 46:11.633
of hunting in a group versus
hunting by myself?

46:11.630 --> 46:15.600
And you can see that if you do
that cost-benefit analysis,

46:15.599 --> 46:20.969
it turns out that organisms
don't do it perfectly,

46:20.969 --> 46:24.959
but they do get better and
better at approximating the

46:24.960 --> 46:26.240
optimal payoff.

46:26.239 --> 46:30.799
So that tells us that hunting
is basically microeconomics.

46:30.800 --> 46:33.400
It's a very short-term,
kind of selfish behavior,

46:33.400 --> 46:37.140
where the group hunting
behavior, which looks like it

46:37.137 --> 46:42.217
might not be so selfish,
in terms of caloric reward is

46:42.224 --> 46:45.664
quite selfish,
and it looks like it's

46:45.659 --> 46:47.129
long-term selfish.

46:47.130 --> 46:52.670
So the sharing is stabilizing
relationships that are paying

46:52.666 --> 46:58.676
off in terms of future hunts and
future sexual opportunities.

46:58.679 --> 47:02.219
We've seen that predation is
shaping both the behavior and

47:02.224 --> 47:03.784
the morphology of prey.

47:03.780 --> 47:06.270
We saw that with both
conspicuous coloration,

47:06.268 --> 47:08.868
aposematic coloration,
which is advertising the

47:08.871 --> 47:12.291
presence of poisons;
and we saw it with crypsis,

47:12.291 --> 47:16.491
which is cryptic coloration,
which is hiding and looking

47:16.492 --> 47:18.252
like something else.

47:18.250 --> 47:21.810
We saw that aggressive mimicry
illustrates an important

47:21.809 --> 47:25.429
evolutionary trend,
which is that every available

47:25.431 --> 47:30.001
opportunity will eventually be
seized by some species evolving

47:29.996 --> 47:31.416
into that niche.

47:31.420 --> 47:34.990
And we saw it with fireflies,
and then I told you about

47:34.994 --> 47:37.844
cleaning wrasses in saber-tooth
blennies.

47:37.840 --> 47:41.600
And then finally I showed you
the cooperative hunting,

47:41.599 --> 47:43.939
complex example in chimpanzees.

47:43.940 --> 47:48.430
It requires strategic thinking;
it requires a kind of teamwork

47:48.434 --> 47:53.184
that really is only attained in
fairly complex organisms.

47:53.179 --> 47:55.639
I'll mention one other,
which is deeply cool,

47:55.635 --> 47:58.365
and that is the bubble-nets of
humpback whales.

47:58.369 --> 48:01.579
Who has heard of a humpback
whale bubble-net?

48:01.579 --> 48:03.099
A few.

48:03.099 --> 48:06.999
Humpback whales hunt in family
groups in Alaska and down near

48:07.001 --> 48:10.711
Antarctica, and they hunt for
fish that are in schools.

48:10.710 --> 48:11.890
They hunt herring.

48:11.889 --> 48:15.569
And a mama humpback will go
down below a group of herring,

48:15.570 --> 48:19.890
and she'll start up in a
spiral, just at the right rate,

48:19.889 --> 48:22.909
so that the bubbles that she's
blowing out of her nose will all

48:22.913 --> 48:25.593
rise up in a curtain to the
surface around the school of

48:25.594 --> 48:28.464
herring at the same time;
so a curtain of bubbles,

48:28.463 --> 48:31.553
that looks like a net,
is going up around the school

48:31.552 --> 48:32.402
of herring.

48:32.400 --> 48:36.150
And at the same time she gives
a few little signals with her

48:36.150 --> 48:39.380
complex calling behavior,
and the whole family of whales

48:39.378 --> 48:41.408
will dive down,
and they'll come up as a group

48:41.414 --> 48:43.564
and they'll open their mouths
underneath the school of

48:43.563 --> 48:45.333
herring,
and all of the mouths will

48:45.333 --> 48:47.313
emerge from the water at the
same time.

48:47.309 --> 48:50.509
They can take out an entire
school of herring with one bite

48:50.512 --> 48:51.012
apiece.

48:51.010 --> 48:55.350
They're taking in maybe two or
three tons of fish when they do

48:55.351 --> 48:56.841
this;
like five or six whales doing

48:56.840 --> 48:58.240
it all at once,
they're right next to each

48:58.242 --> 48:58.552
other.

48:58.550 --> 49:05.010
So there are very interesting
examples where other species,

49:05.010 --> 49:09.040
that appear to have complex
cognition and good signaling

49:09.039 --> 49:11.239
capacity,
learn really rather

49:11.244 --> 49:12.924
sophisticated behavior.

49:12.920 --> 49:17.530
And I think the chimpanzee hunt
is one of the most interesting.

49:17.530 --> 49:19.790
I'm sorry it's so bloody,
and I do hope you enjoy your

49:19.789 --> 49:20.129
lunch.

49:20.130 --> 49:43.000