1
00:00:00,000 --> 00:00:04,000
OK, so finishing up predation.
We ran out of time last time.  We

2
00:00:04,000 --> 00:00:08,000
talked about predation,
and let me give you the big

3
00:00:08,000 --> 00:00:13,000
overarching context here.
We've been talking about

4
00:00:13,000 --> 00:00:17,000
interactions between organisms.
We talked about competition between

5
00:00:17,000 --> 00:00:22,000
organisms and how this shapes
community structure and drives

6
00:00:22,000 --> 00:00:26,000
evolution.  And then we moved onto
predation, the interaction where one

7
00:00:26,000 --> 00:00:30,000
of the organism's fitness is
increased and the other one's

8
00:00:30,000 --> 00:00:35,000
fitness has decreased.
And that this,

9
00:00:35,000 --> 00:00:39,000
too, shapes community structure,
and in many different ways.  One way

10
00:00:39,000 --> 00:00:43,000
we talked about was keystone
predators and how when you remove a

11
00:00:43,000 --> 00:00:47,000
predator from the system it can
completely change the competitive

12
00:00:47,000 --> 00:00:51,000
interactions of the organisms in
that ecosystem.

13
00:00:51,000 --> 00:00:55,000
That was the starfish example.
And we also started talking about

14
00:00:55,000 --> 00:01:00,000
predation as an evolutionary agent,
as a selective force.

15
00:01:00,000 --> 00:01:04,000
And I started showing you just some
pictures.  Some of the best evidence

16
00:01:04,000 --> 00:01:09,000
for that is what we see in the
characteristics of prey populations.

17
00:01:09,000 --> 00:01:14,000
So just very quickly going back
here.  The evolution of defense

18
00:01:14,000 --> 00:01:19,000
mechanisms like these spines on the
cactus that wards of predation,

19
00:01:19,000 --> 00:01:24,000
spines on a porcupine that ward of
predation.  Here's another defense

20
00:01:24,000 --> 00:01:29,000
mechanism of an octopus,
when disturbed puts out that big ink

21
00:01:29,000 --> 00:01:34,000
cloud.
And here are some caterpillars that

22
00:01:34,000 --> 00:01:39,000
look very much like snakes that are
more threatening to a predator than

23
00:01:39,000 --> 00:01:44,000
a caterpillar but they are still
caterpillars.  An alarm.

24
00:01:44,000 --> 00:01:49,000
Many snakes have coloration on
their tails to attract the predator

25
00:01:49,000 --> 00:01:54,000
to their tail rather than their head
if they're going to be attacked.

26
00:01:54,000 --> 00:02:00,000
And this is one of my favorite bugs.
This is its tail versus its head.

27
00:02:00,000 --> 00:02:05,000
And then we talked about alarm,
you like that one?  So do I.  I

28
00:02:05,000 --> 00:02:11,000
don't know why.
It's so silly.  And then we talked

29
00:02:11,000 --> 00:02:16,000
about eyes or things that look like
eyes, that we assume are also what

30
00:02:16,000 --> 00:02:22,000
attract a predator to the wings here
rather than the head.

31
00:02:22,000 --> 00:02:27,000
And then this system,
which can develop into having these

32
00:02:27,000 --> 00:02:33,000
eyes or not, depending
on the substrate.

33
00:02:33,000 --> 00:02:37,000
So here we are.
This is some form of caterpillar.

34
00:02:37,000 --> 00:02:42,000
This is what's called cryptic
coloration.  In other words,

35
00:02:42,000 --> 00:02:47,000
it blends into the leaf of the plant
that it lives on.

36
00:02:47,000 --> 00:02:52,000
And if it develops during this time
of year it looks like a leaf.

37
00:02:52,000 --> 00:02:56,000
If it develops during the time of
year when the plant is making these

38
00:02:56,000 --> 00:03:01,000
catkins, these flowers,
it actually looks like the

39
00:03:01,000 --> 00:03:06,000
reproductive part of the plant.
So it has developmental mechanisms,

40
00:03:06,000 --> 00:03:10,000
a developmental switch in the same
species that can make it look one

41
00:03:10,000 --> 00:03:14,000
way or another,
depending on what the plant looks

42
00:03:14,000 --> 00:03:18,000
like that it is living on.
And, again, as we talk about these

43
00:03:18,000 --> 00:03:22,000
I hope you will constantly remember
all of the molecular biology that

44
00:03:22,000 --> 00:03:34,000
you learned from Professor Walker.

45
00:03:34,000 --> 00:03:38,000
Now that's a very good question.
The question was does it actually

46
00:03:38,000 --> 00:03:43,000
change the phenotype while it's
developing or do both phenotypes

47
00:03:43,000 --> 00:03:48,000
develop and one gets selected out
depending on what the background is?

48
00:03:48,000 --> 00:03:52,000
And I don't know the answer but
that's an excellent question.

49
00:03:52,000 --> 00:03:57,000
And I will see if I can find out
the answer, but a very

50
00:03:57,000 --> 00:04:02,000
good question.
And it would be interesting either

51
00:04:02,000 --> 00:04:07,000
way.  Well, I think it would be more
interesting if it actually changed

52
00:04:07,000 --> 00:04:13,000
the development,
because figuring out what the cues

53
00:04:13,000 --> 00:04:18,000
were at the molecular level would be
interesting.  Here's one,

54
00:04:18,000 --> 00:04:23,000
a leafhopper that resembles the leaf
that it lived on.

55
00:04:23,000 --> 00:04:28,000
A cryptic coloration.
.  This is an insect looking very

56
00:04:28,000 --> 00:04:34,000
much like the leaf
that lives there.

57
00:04:34,000 --> 00:04:38,000
I think we'll see one of those in
the clip.  Here is another.

58
00:04:38,000 --> 00:04:42,000
This is a praying matis.  There is
his head.  There are his legs

59
00:04:42,000 --> 00:04:47,000
sitting in with the flower pedal.
Butterflies.  Here's a slug on a

60
00:04:47,000 --> 00:04:51,000
leaf.  Here's a flounder blending
into the sand on the bottom.

61
00:04:51,000 --> 00:04:55,000
And there's another one which you
can barely see.  There

62
00:04:55,000 --> 00:05:00,000
are his eyes.
And in this way avoiding predators.

63
00:05:00,000 --> 00:05:04,000
And the amazing thing about these
flounders is they can change their

64
00:05:04,000 --> 00:05:09,000
color very rapidly.
And they can match the substrate.

65
00:05:09,000 --> 00:05:13,000
And this one is trying to look like
checkerboard which it would never

66
00:05:13,000 --> 00:05:18,000
encounter in nature,
but you can see these checkerboard

67
00:05:18,000 --> 00:05:22,000
patterns on it.
So how they do this,

68
00:05:22,000 --> 00:05:27,000
you know, the actual mechanisms for
this are probably not that well

69
00:05:27,000 --> 00:05:32,000
understood.  But it's
pretty convincing.

70
00:05:32,000 --> 00:05:37,000
Here is a toad blending into its
substrate.  Another toad.

71
00:05:37,000 --> 00:05:43,000
Look, you can barely see it on this
rock.  Some more insects that are

72
00:05:43,000 --> 00:05:48,000
looking like spines on a leaf.
And here's a moth that looks very

73
00:05:48,000 --> 00:05:54,000
much like a twig that it's on.
And then there are these alarms.

74
00:05:54,000 --> 00:05:59,000
This guy is trying to say, you know,
don't come near me.

75
00:05:59,000 --> 00:06:05,000
I look bigger than I really am by
putting out these.

76
00:06:05,000 --> 00:06:09,000
And then there are frogs that use a
different strategy that draw

77
00:06:09,000 --> 00:06:13,000
attention to themselves,
but they have a good reason to do

78
00:06:13,000 --> 00:06:17,000
that because they have a poison.
So they're saying to the predator,

79
00:06:17,000 --> 00:06:22,000
you know, move onto some other frog
because if you attack me I'm going

80
00:06:22,000 --> 00:06:26,000
to taste bad.  And then there are
chemical defenses.

81
00:06:26,000 --> 00:06:31,000
This is a bombardier beetle being
pinched by somebody's forceps here.

82
00:06:31,000 --> 00:06:35,000
And it's spraying this very nasty
substance on the predator.

83
00:06:35,000 --> 00:06:40,000
And you can see it can direct it in
any direction.

84
00:06:40,000 --> 00:06:45,000
And there is a wonderful story
about this beetle that was studied

85
00:06:45,000 --> 00:06:50,000
extensively by scientists at Cornell
University.  There's a whole field

86
00:06:50,000 --> 00:06:55,000
called Chemical Ecology where people
actually study the chemistry.

87
00:06:55,000 --> 00:07:00,000
This would be a great MIT problem
because there's a really interesting

88
00:07:00,000 --> 00:07:05,000
chemical reaction in the system here
that creates this explosive force.

89
00:07:05,000 --> 00:07:08,000
They put two things together and
create this force here.

90
00:07:08,000 --> 00:07:12,000
And this is a very acidic solution.
And these scientists were out in

91
00:07:12,000 --> 00:07:16,000
the field in the middle of the
desert studying these guys.

92
00:07:16,000 --> 00:07:20,000
And they needed an acid to develop
some chromatography reaction so they

93
00:07:20,000 --> 00:07:24,000
had the beetles spray on it.
And they made the beetle a

94
00:07:24,000 --> 00:07:28,000
co-author on the paper and it got by
the editors.  That's an

95
00:07:28,000 --> 00:07:32,000
interesting story.
So there's a paper out there with

96
00:07:32,000 --> 00:07:36,000
the beetle's Latin name as a
co-author on the paper.

97
00:07:36,000 --> 00:07:41,000
Just a little story.  And here's
another beetle that doesn't have

98
00:07:41,000 --> 00:07:45,000
that defense, but it mimics the
posture.  So a predator comes up,

99
00:07:45,000 --> 00:07:49,000
and this gets into this whole area
of mimicry that we're going to talk

100
00:07:49,000 --> 00:07:54,000
about, where there are a lot of
systems where you have a defense

101
00:07:54,000 --> 00:07:58,000
mechanism and then another organism
evolves to mimic that defense even

102
00:07:58,000 --> 00:08:03,000
though it doesn't have
the real thing.

103
00:08:03,000 --> 00:08:08,000
So this beetle just assumes the
posture and some of the predators

104
00:08:08,000 --> 00:08:13,000
think, oh, I better avoid that one.
Fireflies have a particular flash

105
00:08:13,000 --> 00:08:18,000
that attracts.
The females have a flash that

106
00:08:18,000 --> 00:08:23,000
attracts the mates of their
particular species.

107
00:08:23,000 --> 00:08:28,000
Some of them have learned to mimic
the flash of other species.

108
00:08:28,000 --> 00:08:34,000
And when the male is drawn into
that flash they actually eat them.

109
00:08:34,000 --> 00:08:40,000
Another evolutionary function of the
predatory force.

110
00:08:40,000 --> 00:08:47,000
And then there are many examples of
this mimicry.  These are two

111
00:08:47,000 --> 00:08:54,000
butterflies of different species.
One of which is toxic or very

112
00:08:54,000 --> 00:09:00,000
unpalatable to prey species.
And the other is not unpalatable but

113
00:09:00,000 --> 00:09:06,000
because it looks like that one is
less likely to be preyed upon.

114
00:09:06,000 --> 00:09:12,000
And people have done experiments to
test hypotheses about these and

115
00:09:12,000 --> 00:09:18,000
shown how that works.
And there are lots of also fly

116
00:09:18,000 --> 00:09:24,000
species that have evolved to look
like these species for the same

117
00:09:24,000 --> 00:09:30,000
reason.  So there are several of
these mimetic systems.

118
00:09:30,000 --> 00:09:34,000
So we're going to move on them to
mutualism.  So we've talked about

119
00:09:34,000 --> 00:09:39,000
minus-minus interactions,
competition, minus-plus interactions,

120
00:09:39,000 --> 00:09:44,000
which was predation,
and mutualism is plus-plus,

121
00:09:44,000 --> 00:09:49,000
win-win.  Both organisms benefit
from being in contact with the other

122
00:09:49,000 --> 00:09:54,000
organisms.  And several of the clips
I'm going to show you have to do

123
00:09:54,000 --> 00:09:59,000
with mutualism.
So I'm going to go through this

124
00:09:59,000 --> 00:10:04,000
rather quickly.
And these examples,

125
00:10:04,000 --> 00:10:09,000
for the most part, are from your
book.  I'm showing this example

126
00:10:09,000 --> 00:10:14,000
because it also has an experiment
that goes with it in your book.

127
00:10:14,000 --> 00:10:19,000
These are treehoppers, like insects
that suck on the sugar solutions

128
00:10:19,000 --> 00:10:24,000
from the tree.
And they squirt out the sugar for

129
00:10:24,000 --> 00:10:29,000
the ants that they're symbiotic with.
They feed the ants because the ants,

130
00:10:29,000 --> 00:10:34,000
in turn, protect them from
spider predators.

131
00:10:34,000 --> 00:10:38,000
And there are many systems like this.
And I show you this one because

132
00:10:38,000 --> 00:10:42,000
they did experiments.
And you can do them easily.

133
00:10:42,000 --> 00:10:46,000
Here's a study plot.  These are the
plants that have the ants on them.

134
00:10:46,000 --> 00:10:51,000
And these are plants.  You can
remove the plants and then measure

135
00:10:51,000 --> 00:10:55,000
whether the plants have an effect on
treehopper survival.

136
00:10:55,000 --> 00:10:59,000
And so this is the average number
of young treehoppers per plant with

137
00:10:59,000 --> 00:11:04,000
the ants, without the ants.
So with the ants there are more.

138
00:11:04,000 --> 00:11:10,000
And they also showed in this study,
though, that if you, in years where

139
00:11:10,000 --> 00:11:15,000
the predators of the treehoppers,
the spiders were less abundant, the

140
00:11:15,000 --> 00:11:21,000
symbiosis loosens up.
There's no point in feeding the

141
00:11:21,000 --> 00:11:26,000
ants if you don't need them to
protect you.  So these are dynamic

142
00:11:26,000 --> 00:11:32,000
systems.  So treehopper survivorship
increased by the present of ants.

143
00:11:32,000 --> 00:11:37,000
Another system just like this is
where ants live in these thorns in

144
00:11:37,000 --> 00:11:42,000
acacia plants.
These are hollowed out thorns that

145
00:11:42,000 --> 00:11:48,000
provide a habitat for the ants,
but when the plant is vulnerable to

146
00:11:48,000 --> 00:11:53,000
herbivore grazers the ants all come
out in force and scare off the

147
00:11:53,000 --> 00:11:59,000
herbivore.  This is my absolute
favorite, one of my favorite

148
00:11:59,000 --> 00:12:05,000
biological systems of all times.
And we're going to show a clip of

149
00:12:05,000 --> 00:12:11,000
this.  These are tree cutter ants
that go out and get pieces of leafs

150
00:12:11,000 --> 00:12:17,000
and bring them back to the nest,
chew them up, and they farm this

151
00:12:17,000 --> 00:12:23,000
fungus on the leaf chewate or
whatever you want to call them.

152
00:12:23,000 --> 00:12:30,000
They pulverize the leaf.  And they
actually farm a fungus.

153
00:12:30,000 --> 00:12:35,000
And then they eat the fungus.
That's their food.  So there's this

154
00:12:35,000 --> 00:12:40,000
mutualistic interaction between the
ants and the fungus.

155
00:12:40,000 --> 00:12:45,000
And there's even a really
interesting complexity here that

156
00:12:45,000 --> 00:12:50,000
you'll see in the clip where the
ants actually also carry a bacterium

157
00:12:50,000 --> 00:12:55,000
on their chest that makes an
antibiotic that keeps the fungus

158
00:12:55,000 --> 00:13:00,000
farm free of infection by other
fungi that might take it over.

159
00:13:00,000 --> 00:13:05,000
So it's this beautiful,
beautiful co-evolutionary system

160
00:13:05,000 --> 00:13:10,000
that predates human beings many,
many, many millions and millions of

161
00:13:10,000 --> 00:13:15,000
years of agriculture.
I mean the ants are literally

162
00:13:15,000 --> 00:13:21,000
farming and of natural antibiotic.
So I'll stop talking so we can get

163
00:13:21,000 --> 00:13:26,000
to the clip.  Another example of
mutualism that is one of my

164
00:13:26,000 --> 00:13:31,000
favorites are cleaner fish.
In coral reef ecosystems there are

165
00:13:31,000 --> 00:13:36,000
cleaning stations where these little
cleaner fish, here is one,

166
00:13:36,000 --> 00:13:41,000
which hang out in these giant fish.
And turtles and other organisms in

167
00:13:41,000 --> 00:13:47,000
the ecosystem come to these stations,
and these cleaner fish pick of

168
00:13:47,000 --> 00:13:52,000
little ectoparasites from these
giant fish.  So the fish get cleaned

169
00:13:52,000 --> 00:13:57,000
and these guys get fed.
There's a moray eel being cleaned

170
00:13:57,000 --> 00:14:02,000
by one of these fish.
I mean that could wipe that guy out

171
00:14:02,000 --> 00:14:06,000
in two seconds if it wanted to make
a meal out of it,

172
00:14:06,000 --> 00:14:10,000
but it's not worth it to get a meal
because it's better to get

173
00:14:10,000 --> 00:14:14,000
parasite-free.
However, whenever you have

174
00:14:14,000 --> 00:14:18,000
mutualism you also have cheaters.
And so this is an example of, and I

175
00:14:18,000 --> 00:14:22,000
love this picture because it kind of
has the cheater smiling,

176
00:14:22,000 --> 00:14:26,000
you see?  And this is a fish that
has evolved to look just

177
00:14:26,000 --> 00:14:31,000
like this cleaner fish.
But it's not a cleaner.

178
00:14:31,000 --> 00:14:35,000
And it comes into these cleaning
stations, and it dashes in and just

179
00:14:35,000 --> 00:14:39,000
takes a chunk out of the skin of one
of these fish because they're off

180
00:14:39,000 --> 00:14:43,000
guard, you know,
they're not on guard for fish that

181
00:14:43,000 --> 00:14:47,000
look like this.
And you can get into beautiful,

182
00:14:47,000 --> 00:14:51,000
beautiful population analyses and
game theory analyses of these

183
00:14:51,000 --> 00:14:55,000
systems, because the presence of
these cheaters actually strengthens

184
00:14:55,000 --> 00:15:00,000
the mutualistic bond between the
cleaner fish and the host.

185
00:15:00,000 --> 00:15:05,000
Because if the fish that's being
cleaned is more fit,

186
00:15:05,000 --> 00:15:10,000
the better it can recognize,
tell the difference between these

187
00:15:10,000 --> 00:15:16,000
two, right?  And the better it is at
recognizing the real cleaner fish

188
00:15:16,000 --> 00:15:21,000
the more fit it's going to be,
and that tightens that relationship.

189
00:15:21,000 --> 00:15:27,000
So it's a really interesting dance.
And you can do game theory analyses.

190
00:15:27,000 --> 00:15:33,000
OK, more mutualisms.
These are all from your text.

191
00:15:33,000 --> 00:15:41,000
Those are my cats cleaning each
other.  I thought you should meet my

192
00:15:41,000 --> 00:15:49,000
cats.  They clean each other and
then they fight.

193
00:15:49,000 --> 00:15:57,000
OK, so enough from me.
Let me show you some clips,

194
00:15:57,000 --> 00:16:10,000
if I can make this thing work right.

195
00:16:10,000 --> 00:16:14,000
OK, so the first one,
well, I'll just start playing it.

196
00:16:14,000 --> 00:16:17,000
So your challenge is to figure out
what these are about.