WEBVTT

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Prof: Today we're going
to talk about these phylogenetic

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trees that we've been discussing
for the last couple of sessions;

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and this completes the three
introductory lectures on methods

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that are used,
or basic concepts that are used

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in macroevolution.

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So we began that with
speciation, so that you can

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understand where the branches on
the Tree of Life came from.

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And then we had a quick
overview of how to construct a

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phylogenetic tree,
so that you could see how the

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whole tree was put together.

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And now we're going to see what
happens when you either lay

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these trees onto maps,
or you put traits onto the

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trees, or you do both at the
same time.

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This is basically comparative
biology, in its modern sense.

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The outline is first a bit
about looking into time;

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using phylogenetic trees and
looking into geographic history.

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Then we'll look at how we can
map traits onto trees and draw

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some surprising conclusions.

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And then we will put trees
together with traits and put

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them on maps and see what that
tells us actually about

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evolutionary ecology of lizards
on Caribbean islands.

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And then we'll end with a
take-home message from

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comparative biology,
which is that species are not

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independent samples,
and that because they're not

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independent samples,
we need special methods for

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trying to assess how frequently
things evolve in the Tree of

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Life.

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And that will lead us into both
looking at how seeds do in sun

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and shade,
in related species,

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and the issue of whether you
should be more faithful to your

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mate if you're going to be
married to her for a long time.

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Okay, well let's start with
some of Godfrey Hewitt's work.

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And this has to do with what
happened in Europe after the

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glaciers melted.

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Now just to remind you about
what Europe looked like at the

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peak of the last Ice Age.

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The glacier came down out of
Scandinavia and got down about

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into Northern Germany and
Poland.

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The English Channel was dry
because there was so much water

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that had been locked up in the
continental ice sheath that the

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level of the earth's oceans
dropped about 100 meters,

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and at that point there was a
sub-glacial tundra that

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stretched essentially from
Ireland all the way across

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France and through Russia,
out into Siberia.

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So there was pretty much--it
was called the Mammoth Step.

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And at that point many of the
animals that you now find in

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Northern Europe had retreated
south, into glacial refugia.

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There was one in Spain,
there was one in Italy,

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there was one in Greece and the
Balkans, and in Asia Minor.

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And you can now take the
mitochondrial DNA out of these

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various organisms that are
plotted here,

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and you can reconstruct where
they spent the last Ice Age,

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and how they got back into
Northern Europe.

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So, for example,
most of the grasshoppers of

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Northern Europe came out of the
Balkans,

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and the ones that spent that
time in Spain never got over the

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Pyrenees.

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The hedgehogs managed to get
over the Pyrenees and spread

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through France and Belgium and
the Netherlands.

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However, about at that point
they met the hedgehogs that were

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moving north from Italy,
and there were a bunch of other

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hedgehogs that were coming up
from the Balkans.

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The bears, interestingly,
managed to do just fine,

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getting out over the Pyrenees,
and they moved right up into

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Scandinavia.

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And up in Northern Sweden they
have met some bears probably

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that had been in the Ukraine and
had come out from north of the

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Black Sea;
and so forth.

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So it's possible,
using mitochondrial DNA,

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molecular phylogenies,
to reconstruct the recent

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history of movements of animals
across the planet,

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and trees, across the planet,
and to understand why it is

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that there are certain places
where we see hybrid zones.

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And in fact here are some
hybrid zones,

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in Europe.

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These are places where you will
frequently run into hybrids,

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and they are there because
populations are coming back

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together that had been isolated
in the Ice Ages and breeding

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with each other.

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So, for example,
this is one I know pretty well,

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because I went there for
thirteen years.

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There's a spot in the Swiss
Alps, in the eastern part of the

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Swiss Alps,
just north of the Italian

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border, or just south of the
Austrian border,

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right about here,
where almost every flowering

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plant that you see is a hybrid.

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And the guidebooks are just
lousy, they just are horrible.

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It's extremely difficult to
identify what you're looking at.

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But when you see the big
picture, you can understand it.

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So if you're interested in this
kind of thing,

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this is a good paper to read,
by Godfrey Hewitt,

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and the papers that have cited,
that have cited that one;

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those are good sources.

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Now what about humans?

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Well I'm going to show you
first what's happened in about

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the last 10,000 years,
and we're going to see that in

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Europe the agriculturists spread
out from the Middle East and

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squeezed the Celts into the
northwest.

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In Africa we're going to see
the Bantu migration out of

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Cameroon, and how the Hottentots
were squeezed into the southwest

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of Africa.

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And in Asia we'll see that
agriculturists spread from both

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the Middle East and from China,
and squeezed Siberians into the

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north.

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So these things are laid out in
a beautiful book by Cavalli

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Sforza, Paolo Menozzi,
and another author,

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Alberto Piazza,
I believe.

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And basically what they did was
they tried to come up with a

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method of compressing a huge
amount of genetic information

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onto a map,
and they did it by taking gene

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frequencies,
at hundreds of genes,

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and then compressing them,
using statistical analysis,

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into a few factors,
and then plotting those factors

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onto the map.

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So what you can see here is
basically the population

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differentiation of humans,
in Europe, and you can see that

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there is kind of a wave that
comes out of the Fertile

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Crescent and moves up to the
north and to the west.

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And this tracks the
agricultural expansion,

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which started about five or six
thousand years ago,

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out of the Middle East.

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And you can see that the Celtic
genes did get squeezed up into

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Ireland and England,
and out into Brittany,

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and so forth.

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So there are lots of neat
details in this book.

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If you focus in on particular
areas,

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you can see that there's a
hotspot,

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right in Palermo,
of Viking genes from the Viking

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occupation of Sicily,
etcetera.

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Interesting stuff.

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If you look at Africa,
what you can see is that there

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has been an invasion of Africa
by Caucasoid Northern Africans,

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and by sort of an Arab Nilotic
expansion,

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coming down this way.

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The Bantu expansion out of
Cameroon is what is coloring

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part of the continent pretty
red.

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And, by the way,
this migration got through here

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about a thousand years ago.

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So the Bantu migration down
into East Africa is something

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that is relatively recent.

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And if you've been reading
about the war in the Congo,

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and the conflicts between the
Hutus and the Tutsis,

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the Hutus are Bantu,
and the Tutsis are Nilotic,

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and you can see where the
Nilotic and the Bantu mix comes

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together here in the Great Lakes
region of Central Africa.

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So this kind of map gives you
some feel for the way things

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have moved.

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By the way, there was a kingdom
in Mali, centered around

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Timbuktu--or an empire--and this
orange spot is a relic of that.

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So the history of human
movement on the face of the

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globe is written in the genes
and can, to a certain extent,

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still be recovered.

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This kind of study will no
longer be possible after another

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few hundred years of jet travel
and crossbreeding.

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My son, for example,
is in a relationship with a

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woman from here.

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Another few generations of that
and this map will not be

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reconstructable.

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If we look at Asia,
what we see is that there were

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nomads and agriculturists coming
out of the area around the

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Middle East,
and around the Black Sea and

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the Caspian Sea,
that have pushed into Central

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Asia,
and the Chinese

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agriculturalists have spread
into Southern Asia.

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But there's a very interesting
hotspot of human biodiversity in

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southeastern Asia,
going from India over about to

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Taiwan;
and actually this is where the

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Polynesians came out of.

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The Polynesians left from
Taiwan we think about 5000 years

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ago, and that's confirmed both
in the language reconstruction

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and in the mitochondrial DNA.

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That just came out a few weeks
ago.

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So you can see that one of the
themes of recent human history

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has basically been of the
expansion of some groups at the

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expense of others,
and that that was often a

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technology-driven thing,
and often involved agriculture.

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Now, I've shown you this
before, and I just want to bring

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this back in again,
at this point,

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to indicate what you can do
with phylogenetic trees,

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and just to remind you.

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It is now possible to get
information on single nucleotide

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polymorphisms at 650,000
different sites in the human

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genome,
and this is a paper that did

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that for 928 unrelated
individuals from 51 populations.

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So these are the 928
individuals out here.

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These names down here are the
51 populations,

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and the 650,000 different
positions in the human genome

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are on the Y axis,
all compressed together;

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so it's very hard to see any
differentiation there.

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And you can see that there is
certainly a genetic signature

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across this;
certain kinds of genomes in

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certain geographical areas.

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And if you then do the
molecular phylogenetics on it,

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and construct the phylogenetic
tree,

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you see that the oldest part of
the modern human tree is

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centered in Africa.

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This, by the way,
is the Classical view.

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This was a picture that could
be drawn in 1995--and this is

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2008;
so this thirteen years

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later--and this tree largely
confirms this picture.

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Okay?

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So you could lay this tree onto
this map and come up with

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something that looks pretty much
like this.

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What you see here basically is
we came out of Africa,

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we paused in the Middle East.

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Then various groups moved out
of the Middle East.

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One group went into Europe,
thought to be about 40,000

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years ago;
that's these guys.

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Other groups set out into Asia
and spread out through Asia.

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And then out of the group that
had settled basically in Eastern

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China and Japan and Korea,
one group up here split off.

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Part of them--actually an early
part of this branch--

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went down here,
through Papua,

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New Guinea,
into Australia,

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and another part went out into
the New World.

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So phylogenetic methods can now
be used to give us quite a bit

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of insight into our own history,
as well as into the history of

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other plants and animals.

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Now the Hawaiian Islands are an
interesting test case.

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When we look at something like
the human expansion across the

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globe,
it's actually difficult to get

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precise markers for the times
when they arrived in certain

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places.

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Archeology gives us some;
sometimes we can recover fossil

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DNA from bones.

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But in the case of the Hawaiian
Islands, at least on the scale

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of the last 5,000,000 years,
we have very precise geological

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dates.

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For example,
we know that Kohala Mountain,

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the oldest rock on Kohala
Mountain is 430,000 years old,

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and that the oldest rock on
Kauai is 5.1 million years old;

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and that's because the islands
are made over a hotspot here,

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and carried on a plate up in
that direction.

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And you can actually lay down,
on this plate,

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how long ago it was that that
island was actually sitting down

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here.

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And that's nice,
because when we then do

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phylogenetics and we start
putting phylogenetic trees onto

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this map,
it gives us some feel for when

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in time those different branch
points might have been.

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And that has been done for a
number of groups.

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I'm sure there's now more
information.

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This is about five years old
here.

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And these are three different
ways that spiders and some other

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arthropods and some insects
speciated over the last

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5,000,000 years in Hawaii.

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Interestingly,
you can see that they all moved

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down from Kauai onto the younger
islands.

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So they were going from older
islands onto younger islands,

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and they just kept hopping.

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And, by the way,
if you just continue this

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island chain up to where it
dives into Siberia,

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there were islands there
350,000,000 years ago that are

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now getting subducted under
Kamchatka,

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and there are things that are
one or two-thousand miles up to

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the northwest that were once
high islands above water.

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So this hopping movement could
have been going on for quite

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awhile.

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And, in fact,
we think that some of the

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things that we now see in Hawaii
got there about 20 or 30,000,000

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years ago, before Kauai came
above water.

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So it's an old process.

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At any rate,
in some cases things simply

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moved from one island to the
other, and then every time they

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got onto a new island they
speciated.

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In other cases there were four
or five species from Kauai that

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moved down to Oahu,
and then several of them sorted

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out on Oahu and speciated on
Oahu,

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and four or five of them moved
down to Maui Nui,

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and so forth,
and then a lot of them

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speciated on Maui Nui and moved
over to the Big Island;

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and that process went on.

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Another process is one where
you have a lot of species on

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Kauai.

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One of them moves to Oahu.

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You get a lot on Oahu,
but only one then is the

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ancestor that goes on to Maui
Nui,

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and you get a lot there,
and then one goes onto the Big

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Island,
and then you get a species

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complex on each of the five
volcanoes on the Big Island,

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that are coming from that one
ancestor.

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So all of these processes can
actually be seen written in the

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genes,
and this is all resulting

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basically from sequencing,
either nuclear or mitochondrial

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DNA.

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There are other kinds of
questions that you can answer.

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And this is one that was
answered by Anne Yoder,

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who was on the faculty here.

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She now is the head of the Duke
Primate Center.

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And Anne has specialized in the
mammals that live on Madagascar.

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And on Madagascar you find a
local radiation of things that

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look kind of like civet cats or
mongooses.

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And the question was,
did they come over from Africa

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separately, or did they all
speciate on Madagascar?

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And Anne was able to
reconstruct the phylogeny of

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this group of animals well
enough so she could lay this

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tree onto this map and determine
that in fact these guys are

17:09.631 --> 17:12.581
actually all relatives of
mongooses,

17:12.578 --> 17:15.958
and mongooses are close
relatives of hyenas,

17:15.960 --> 17:19.000
and those things have a sister
group of civets,

17:19.000 --> 17:21.810
and those things have a sister
group of cats.

17:21.809 --> 17:25.159
And they got across right here.

17:25.160 --> 17:28.730
Now Madagascar split off from
Africa about 65,000,000 years

17:28.730 --> 17:29.100
ago.

17:29.098 --> 17:31.718
It's part of the Tectonic
breakup that led to India

17:31.719 --> 17:33.809
splitting off,
skitting across the Indian

17:33.814 --> 17:36.804
Ocean, crashing into Asia,
and raising the Himalayas.

17:36.798 --> 17:39.468
And so you might wonder,
well how the heck does

17:39.468 --> 17:42.718
something like this cross a
strait which is now more than

17:42.717 --> 17:43.817
200 miles wide?

17:43.818 --> 17:48.318
Well have any of you read
Rudyard Kipling's story,

17:48.323 --> 17:52.003
How the Elephant Got His
Trunk?

17:52.000 --> 17:55.260


17:55.259 --> 17:58.109
"Down by the grey-green,
greasy Limpopo River,

17:58.113 --> 18:00.573
all set about with fever
trees"--

18:00.568 --> 18:03.018
the elephant child looked into
the water,

18:03.019 --> 18:05.889
and a crocodile grabbed his
nose and pulled it out,

18:05.890 --> 18:07.930
and that was how the elephant
got his trunk.

18:07.930 --> 18:12.140
Well the grey-green,
greasy Limpopo River is right

18:12.137 --> 18:12.737
here.

18:12.740 --> 18:15.370
And in flood stage,
if you go out on the Limpopo

18:15.365 --> 18:17.755
River,
there are large rafts of trees

18:17.756 --> 18:20.206
and vegetation being carried
down it,

18:20.210 --> 18:24.060
and in a good storm you can put
a mongoose on a raft of

18:24.058 --> 18:27.408
vegetation and get it out across
that strait;

18:27.410 --> 18:31.050
so it rafted across,
and it probably came out of the

18:31.050 --> 18:33.550
grey-green, greasy Limpopo
River.

18:33.549 --> 18:36.479
Okay? So that's right here.

18:36.480 --> 18:41.200
Now what about issues--I'm
running through a series of

18:41.195 --> 18:46.085
issues that can be resolved
using comparative methods in

18:46.092 --> 18:48.052
phylogentic trees.

18:48.048 --> 18:53.968
And this represents one of the
phylogenetic surprises.

18:53.970 --> 18:57.470
There are parasitoid wasps
which can be either

18:57.471 --> 19:00.041
ectoparasites or endoparasites.

19:00.038 --> 19:03.088
The ectoparasites lay their
eggs on the outsides of

19:03.093 --> 19:05.393
caterpillars,
and the eggs hatch and make

19:05.385 --> 19:07.965
little baby wasps that crawl
around the outside of the

19:07.970 --> 19:11.000
caterpillar,
and they eat it from the

19:11.003 --> 19:11.853
outside.

19:11.848 --> 19:15.418
And they have relatives who are
endoparasites.

19:15.420 --> 19:17.500
The Ichneumonids are like that.

19:17.500 --> 19:20.030
Here's a nice Ichneumonid-like
wasp.

19:20.029 --> 19:22.099
You can see her long ovipositor.

19:22.098 --> 19:27.758
And here she is on an insect
larva, and she is injecting an

19:27.755 --> 19:29.115
egg into it.

19:29.118 --> 19:38.448
And the ancestors turn out to
have been wasps that did this.

19:38.450 --> 19:41.510
It had been thought that if you
just look at it mechanically,

19:41.509 --> 19:44.179
it would be easier to start the
evolutionary process off with

19:44.179 --> 19:46.359
the wasp just flying around and
laying its egg.

19:46.359 --> 19:48.589
Right?

19:48.588 --> 19:52.108
But in this particular
radiation--that may have been

19:52.114 --> 19:56.044
the case far in the past;
somewhere off the slide out

19:56.035 --> 20:00.375
here there may have been an
ectoparasitic wasp that was an

20:00.375 --> 20:01.285
ancestor.

20:01.288 --> 20:06.618
But it turns out that all of
these things are endoparasites,

20:06.618 --> 20:10.588
and the ectoparasites evolved
within that,

20:10.588 --> 20:14.538
and then within the
ectoparasites you had a reversal

20:14.541 --> 20:18.031
again and got some endoparasites
out of it.

20:18.028 --> 20:22.218
So if you lay the traits onto
the tree, and you are confident

20:22.224 --> 20:25.304
of your tree,
you can reconstruct the history

20:25.301 --> 20:29.601
of an important trait like this;
and that's a nice kind of

20:29.597 --> 20:31.587
insight to be able to have.

20:31.588 --> 20:36.768
Okay, now I would like to
discuss the Anolis lizards.

20:36.769 --> 20:41.899
And this is something that a
large group of scientists,

20:41.900 --> 20:47.690
currently centered at Harvard
but with branches in Seattle and

20:47.693 --> 20:48.273
in St.

20:48.265 --> 20:53.295
Louis and other places,
have been working on for about

20:53.298 --> 20:55.958
the last fifty years.

20:55.960 --> 21:03.320
And they study Anolis lizards
because these are prominent,

21:03.324 --> 21:07.004
easily observed;
you can get a pretty good

21:07.002 --> 21:08.522
sample size fairly quickly.

21:08.519 --> 21:10.339
And they've done fascinating
things.

21:10.338 --> 21:15.328
So the Anolis lizards have had
a big radiation on islands in

21:15.333 --> 21:18.803
the Caribbean,
and they have made what are

21:18.801 --> 21:20.581
called ecomorphs.

21:20.578 --> 21:25.098
Now these ecomorphs can be
grouped by appearance.

21:25.098 --> 21:31.608
So if you look at them and you
see where they're living and

21:31.612 --> 21:36.892
what kinds of grasping
appendages they have,

21:36.890 --> 21:41.550
basically what the phenotype
looks like and how they behave,

21:41.548 --> 21:44.558
you can come up with things
that live on the crowns of

21:44.556 --> 21:46.646
trees;
out on twigs;

21:46.650 --> 21:49.660
down in the ground in grass and
in bushes;

21:49.660 --> 21:53.400
between the trunk and the crown;
only on the trunk;

21:53.400 --> 21:56.120
or down on the ground and going
up on the trunk.

21:56.118 --> 21:56.518
Okay?

21:56.515 --> 21:58.725
So there are six ecomorphs.

21:58.730 --> 22:00.080
And you find them again and
again.

22:00.078 --> 22:02.918
You find them on many islands
in the Caribbean.

22:02.920 --> 22:04.840
And it's really tragic that in
the middle of winter,

22:04.838 --> 22:07.118
up here in New England,
you have to be continually

22:07.115 --> 22:09.945
flying down to the Caribbean to
increase your sample size,

22:09.950 --> 22:10.370
okay?

22:10.368 --> 22:13.808
And doing that for twenty or
thirty years,

22:13.808 --> 22:17.918
through many generations of
graduate students.

22:17.920 --> 22:23.580
Well, after DNA sequencing came
along, you could do their

22:23.577 --> 22:24.787
phylogeny.

22:24.788 --> 22:27.958
And look just--it's hard to see
because the reproduction isn't

22:27.961 --> 22:30.981
very good on the slides--but
just look at the difference in

22:30.976 --> 22:32.116
the color pattern.

22:32.118 --> 22:32.508
Okay?

22:32.506 --> 22:35.666
So, for example,
this is the trunk crown's

22:35.673 --> 22:36.373
space.

22:36.368 --> 22:42.108
Well, it turns out to pop up
here, and here,

22:42.106 --> 22:46.776
and here, and here:
one, two, three,

22:46.777 --> 22:50.777
four times, independently.

22:50.779 --> 22:56.509
Or you could take something
like the twig form.

22:56.509 --> 22:59.619
Here's a twig form,
here's a twig form,

22:59.622 --> 23:02.492
and here's a bunch of twig
forms.

23:02.490 --> 23:06.110
Now what's going on here
basically is you're having the

23:06.114 --> 23:10.214
independent evolution and the
convergence on different islands

23:10.208 --> 23:13.928
of these different ecomorphs,
among these lizards.

23:13.930 --> 23:18.930
And if you take that down and
you break it down by island,

23:18.928 --> 23:23.048
what you can see is that on
Cuba the trunk form,

23:23.048 --> 23:26.468
the crown trunk form was
ancestral.

23:26.470 --> 23:31.380
On Hispaniola,
the twig crown form--or no,

23:31.380 --> 23:36.890
this is trunk crown and this is
crown giant;

23:36.890 --> 23:39.460
this is a crown giant form,
was the ancestral form.

23:39.460 --> 23:43.150
So these are inferences about
what first got onto that island

23:43.151 --> 23:45.921
and what first got out to the
other island.

23:45.920 --> 23:49.330
And then these other things all
evolved from that.

23:49.328 --> 23:52.238
And what you see from that
basically is that it doesn't

23:52.236 --> 23:55.516
matter which form of lizard you
first throw onto an island;

23:55.519 --> 23:58.239
all of the other ones are going
to evolve from it.

23:58.240 --> 24:00.330
And these are all different
species.

24:00.328 --> 24:04.868
And we're talking about things
that range in size from about

24:04.865 --> 24:09.555
that big, up to about that big,
and that are differentiated at

24:09.555 --> 24:11.395
the level of genera.

24:11.400 --> 24:13.330
So this is really major
evolution,

24:13.328 --> 24:15.368
which is being repeated again
and again,

24:15.368 --> 24:19.628
across the Caribbean,
and generating essentially--

24:19.630 --> 24:22.990
what's going on here is that
essentially the same ecological

24:22.992 --> 24:25.732
community is being generated
again and again,

24:25.730 --> 24:31.250
independent of which kind of
species founded that group.

24:31.250 --> 24:31.510
Okay?

24:31.512 --> 24:34.922
That was highly unexpected;
people didn't think this would

24:34.923 --> 24:35.443
happen.

24:35.440 --> 24:38.170
So this means that you're
getting the same ecomorphs from

24:38.172 --> 24:40.712
different ancestral states,
and that means you've got

24:40.709 --> 24:41.489
convergence.

24:41.490 --> 24:44.790


24:44.788 --> 24:50.528
Okay, now for an insight from a
guy named Joe Felsenstein.

24:50.529 --> 24:54.309
And if you want to go back to
the original paper,

24:54.308 --> 24:55.488
it's in 1985.

24:55.490 --> 24:59.670
So this is one of Joe's many
contributions to phylogenetics.

24:59.670 --> 25:04.040
If you just look at this
picture that I showed you the

25:04.040 --> 25:08.580
other day, you can see that the
red trait evolved in the

25:08.577 --> 25:10.967
ancestor of both B and C.

25:10.970 --> 25:11.410
Okay?

25:11.413 --> 25:15.143
So that means that B and C
share a trait.

25:15.140 --> 25:19.670
However, in evolutionary terms,
it only evolved once.

25:19.670 --> 25:23.160
Now at this point,
the reason that red increased

25:23.159 --> 25:27.019
in frequency might have been
that it was adaptive.

25:27.019 --> 25:30.599
Things that had the red state
had a fitness advantage.

25:30.598 --> 25:33.398
So microevolution was driving
it at that point.

25:33.400 --> 25:37.810
But then everybody inherited
it, and it is not an adaptation

25:37.807 --> 25:41.837
to the difference between
whatever environments B and C

25:41.844 --> 25:43.044
now live in.

25:43.038 --> 25:48.328
So if you were to look at B and
C now,

25:48.328 --> 25:51.118
and you saw that they shared
some trait,

25:51.118 --> 25:55.498
you wouldn't really know why
that was there,

25:55.500 --> 25:58.400
until you could get a much,
much larger sample size;

25:58.400 --> 26:01.900
because you just have a sample
size of one, when you're looking

26:01.896 --> 26:02.796
at that trait.

26:02.799 --> 26:04.819
Okay?

26:04.818 --> 26:07.838
So how do you deal with that
problem?

26:07.838 --> 26:11.898
Well Joe came up with what he
called the method of independent

26:11.897 --> 26:12.627
contrast.

26:12.630 --> 26:17.210
And in this context a contrast
is the difference of the value,

26:17.207 --> 26:21.407
the mean value of a trait in
one species and its value in

26:21.409 --> 26:22.909
another species.

26:22.910 --> 26:27.060
And if you look up just at the
tips of this phylogenetic tree

26:27.064 --> 26:30.804
and you take the differences
across the closest related

26:30.803 --> 26:33.603
sister pairs,
at the tops of the tree,

26:33.601 --> 26:35.541
you generate these contrasts.

26:35.539 --> 26:39.529
You get X2 minus X1;
X4 minus X3;

26:39.529 --> 26:41.069
and so forth.

26:41.068 --> 26:44.798
Well the important thing about
the contrasts is this.

26:44.798 --> 26:49.918
The difference that evolved,
after this point on the tree,

26:49.916 --> 26:54.406
is independent of the
difference that evolved after

26:54.405 --> 26:56.825
this point on the tree.

26:56.828 --> 27:00.658
You've taken out whatever was
there because of the common

27:00.657 --> 27:01.407
ancestor.

27:01.410 --> 27:05.950
So whatever was going on over
in this part of the tree is

27:05.945 --> 27:10.885
biologically separated and now
statistically separated by this

27:10.886 --> 27:13.736
method,
from whatever was going on in

27:13.741 --> 27:15.561
any other part of the tree.

27:15.558 --> 27:19.118
So this actually is a method of
getting the correct sample size,

27:19.119 --> 27:20.589
off a phylogenetic tree.

27:20.588 --> 27:24.178
And that's very important in
statistics, because if you have

27:24.181 --> 27:27.411
the wrong sample size,
all your statistical tests will

27:27.407 --> 27:28.197
be wrong.

27:28.200 --> 27:34.100
So this was important for the
mental health of people who were

27:34.102 --> 27:38.072
doing statistics on phylogenetic
trees.

27:38.068 --> 27:44.868
So let's take a look at some
approaches that are kind of like

27:44.866 --> 27:45.656
that.

27:45.660 --> 27:48.180
So this is from a guy named
Peter Grubb.

27:48.180 --> 27:52.620
Peter was the president of the
Ecological Society in the UK.

27:52.619 --> 27:54.589
He's a botanist at Cambridge.

27:54.588 --> 27:59.598
And what he's done here is plot
the log of seed mass of

27:59.599 --> 28:04.519
light-demanding seeds,
against the log of seed mass of

28:04.517 --> 28:06.927
shade- tolerant seeds.

28:06.930 --> 28:10.580
And his question was this:
Do plants living in shade

28:10.580 --> 28:14.090
produce larger seeds than plants
living in sun?

28:14.088 --> 28:17.088
And he wanted to do a
phylogenetically controlled

28:17.087 --> 28:17.897
comparison.

28:17.900 --> 28:23.520
So what he's doing here is he's
taking essentially the value

28:23.516 --> 28:27.016
within a genus,
or within a family--the mean

28:27.019 --> 28:30.729
value of species within a genus
or the mean value of genera

28:30.730 --> 28:33.680
within families--
for related trees,

28:33.675 --> 28:39.045
some of which live in open
areas and demand light for their

28:39.048 --> 28:42.538
germination,
and others of which have seeds

28:42.540 --> 28:45.560
that can germinate and survive
in the shade.

28:45.558 --> 28:48.818
So the open circles are
comparing genera within

28:48.823 --> 28:53.083
families, and the closed circles
are comparing species within

28:53.078 --> 28:53.858
genera.

28:53.858 --> 28:59.578
And what you see here basically
is this.

28:59.578 --> 29:03.258
Plants living in shade do
produce larger seeds than plants

29:03.259 --> 29:05.579
living in sun,
but you only see it in

29:05.583 --> 29:08.233
comparison of genera within
families;

29:08.230 --> 29:13.570
you don't see it in comparison
of species within genera.

29:13.568 --> 29:13.888
Okay?

29:13.888 --> 29:17.718
So the ones that need light and
the ones that need shade have

29:17.719 --> 29:21.489
just about exactly the same seed
size, if you are looking at

29:21.486 --> 29:23.206
species within genera.

29:23.210 --> 29:25.630
But if you then give them
longer to evolve,

29:25.630 --> 29:28.870
you go further out on the
phylogenetic tree,

29:28.868 --> 29:32.468
you compare things between
families,

29:32.470 --> 29:35.900
where that contrast is
possible, then you start to see

29:35.903 --> 29:38.433
them moving off the one-to-one
line,

29:38.430 --> 29:41.850
and the ones that are
shade-tolerant have seeds which

29:41.851 --> 29:44.221
are falling quite a bit-- not
all;

29:44.220 --> 29:46.960
this is an exception--but quite
a bit above the line.

29:46.960 --> 29:51.430
So this is a way not only to
answer that kind of question,

29:51.429 --> 29:56.059
using the comparative method,
but also to get an estimate on

29:56.055 --> 29:57.775
how long it takes.

29:57.779 --> 30:01.249
It takes a long time to
generate that difference,

30:01.246 --> 30:05.286
because you only see it at a
higher level on the tree.

30:05.288 --> 30:10.188
Now what about the albatrosses
and their relatives?

30:10.190 --> 30:12.480
These are the
Procellariiformes,

30:12.484 --> 30:13.674
the Tubenoses.

30:13.670 --> 30:16.840
You can see the Tubenose right
here--and that's a Wandering

30:16.843 --> 30:19.423
Albatross;
and you can see the Tubenose

30:19.419 --> 30:21.269
right here on this Petrel.

30:21.269 --> 30:23.759
And these things have totally
different life histories.

30:23.759 --> 30:25.679
Okay?

30:25.680 --> 30:30.520
The Wandering Albatross,
which has a wingspread of

30:30.516 --> 30:34.066
between twelve and thirteen
feet,

30:34.068 --> 30:37.178
and is probably the heaviest
flying bird,

30:37.180 --> 30:41.370
it lives on islands in the
Southern Ocean.

30:41.368 --> 30:46.628
And it mates,
usually starting at about the

30:46.631 --> 30:53.181
age of twelve or fifteen or so,
and it usually will produce an

30:53.182 --> 30:55.862
offspring every other year or
so,

30:55.859 --> 30:58.979
for about thirty or forty years.

30:58.980 --> 31:02.990
They mate for life;
they're monogamous.

31:02.990 --> 31:07.330
And they have very precise
homing behavior to their chicks.

31:07.328 --> 31:11.478
They lay their eggs on places
like South Georgia,

31:11.476 --> 31:15.706
Macquarie Island,
places in the Southern Ocean.

31:15.710 --> 31:20.100
And some French biologists put
a radio collar on one of these

31:20.103 --> 31:24.133
Wandering Albatrosses and
tracked the mother as she took

31:24.130 --> 31:26.620
off to go get lunch,
for baby.

31:26.618 --> 31:29.478
And she flew north,
from South Georgia,

31:29.480 --> 31:32.320
and she peeled off towards
Australia and flew up the West

31:32.323 --> 31:34.863
Coast of Australia,
and came back across the Indian

31:34.862 --> 31:36.792
Ocean and down the East Coast of
Africa,

31:36.788 --> 31:40.328
and back down to South Georgia
a month later;

31:40.328 --> 31:42.488
at which point baby,
by that point,

31:42.491 --> 31:45.801
was extremely hungry,
got a lunch of rotten squid.

31:45.798 --> 31:50.728
And this wide foraging means
that they're only going to be

31:50.728 --> 31:54.358
raising one child every two
years or so;

31:54.358 --> 31:57.588
and there are all kinds of
adaptations in the infant's

31:57.586 --> 32:00.566
physiology to deal with this
irregular feeding.

32:00.569 --> 32:03.789
A Storm-Petrel is a lot smaller.

32:03.789 --> 32:05.959
It forages much closer to shore.

32:05.960 --> 32:09.030
It is not so faithful to its
mate.

32:09.029 --> 32:10.539
These things are related.

32:10.538 --> 32:10.928
Okay?

32:10.932 --> 32:15.502
So if you look within this
family, can you ask the question

32:15.502 --> 32:20.072
about whether or not a long life
really is a situation that

32:20.073 --> 32:22.283
promotes mate fidelity?

32:22.278 --> 32:28.548
The argument there is basically
that if you're going to have a

32:28.549 --> 32:31.509
short life,
there isn't going to be enough

32:31.508 --> 32:34.598
time to pick up the advantages
that you would get from knowing

32:34.596 --> 32:37.646
a particular mate,
and adapting behavior to that

32:37.653 --> 32:40.213
particular mate,
and learning exactly how to be

32:40.213 --> 32:42.113
a good parent with that
particular mate,

32:42.109 --> 32:44.299
rather than with some other.

32:44.298 --> 32:48.378
And if you look across the
Procellariiformes;

32:48.380 --> 32:55.010
so this would be the Wandering
Albatross up here,

32:55.009 --> 32:57.069
and this would be a Petrel down
here,

32:57.068 --> 32:58.828
and you've got some other
things in the middle--

32:58.828 --> 33:00.798
these are all separate species
now;

33:00.799 --> 33:03.139
the dots here are all species.

33:03.140 --> 33:07.170
And we have here a study in
which--you are looking at

33:07.172 --> 33:10.892
independent contrasts now,
and we're plotting the

33:10.893 --> 33:12.913
independent contrasts.

33:12.910 --> 33:17.960
So this is the deviation in
adult life expectancy from an

33:17.964 --> 33:20.644
overall mean,
for the whole group,

33:20.637 --> 33:24.137
and this is the deviation in
mate fidelity from an overall

33:24.137 --> 33:24.627
mean.

33:24.630 --> 33:27.500
So the ones that tend to live a
long time are more faithful to

33:27.502 --> 33:29.872
their mates,
and the ones that tend to live,

33:29.868 --> 33:32.738
have a short life,
are much less faithful to their

33:32.738 --> 33:34.128
mates and switch mates.

33:34.130 --> 33:36.420
And that's interesting because
in fact these guys,

33:36.424 --> 33:38.724
the ones that reproduce many
times, have much more

33:38.720 --> 33:39.470
opportunity.

33:39.470 --> 33:41.400
They have thirty or forty years
of reproduction.

33:41.400 --> 33:44.720
They could go out and they
could get divorced and pair

33:44.721 --> 33:46.101
again several times.

33:46.098 --> 33:48.868
But they don't,
they stick together.

33:48.868 --> 33:52.838
And the functional reasons for
that are things that are not

33:52.835 --> 33:55.155
really terribly well understood.

33:55.160 --> 33:58.970
Student:  What's
negative mate fidelity?

33:58.970 --> 33:59.810
Prof: What?

33:59.808 --> 34:00.738
Student:  Negative mate
fidelity.

34:00.740 --> 34:02.820
Prof: Negative mate
fidelity is just a statistical

34:02.821 --> 34:03.121
thing.

34:03.118 --> 34:06.028
They are taking the
overall--they've measured mate

34:06.032 --> 34:09.002
fidelity on some scale,
they've taken an average for

34:08.998 --> 34:11.288
the whole group,
and then they are asking,

34:11.286 --> 34:14.146
how far does this species
deviate from the average?

34:14.150 --> 34:16.020
So if it's below average,
it gets a negative number,

34:16.018 --> 34:17.998
and if it's above average it
gets a positive number.

34:18.000 --> 34:21.970
Okay?

34:21.969 --> 34:28.579
Okay, so now I want to ask you
a question that comes right out

34:28.579 --> 34:33.619
of molecular phylogenetics,
and I want to see whether or

34:33.619 --> 34:37.069
not you can actually put
together some information that

34:37.072 --> 34:39.952
you've now gotten from different
lectures.

34:39.949 --> 34:46.579
So this is going to require you
to piece together phylogenetics

34:46.579 --> 34:48.719
and genetic drift.

34:48.719 --> 34:55.519
So this is the figure from the
Becky Cann/Allan Wilson paper

34:55.523 --> 34:59.793
that came out now twenty years
ago.

34:59.789 --> 35:03.969
It's basically on human
mitochondrial evolution.

35:03.969 --> 35:08.999
And what it showed was that all
human mitochondria appear to be

35:08.998 --> 35:14.138
derived from an ancestor,
all of whose closest relatives

35:14.143 --> 35:16.703
now--
the close relatives now are all

35:16.697 --> 35:19.967
out at the tip of the tree--
lived in Africa.

35:19.969 --> 35:23.129
You don't start picking up
non-African members of this tree

35:23.130 --> 35:26.840
until you get out to this point,
and then you can see that by

35:26.844 --> 35:29.364
the time you get way out on the
tree,

35:29.360 --> 35:33.220
that most of these now are
non-Africans.

35:33.219 --> 35:36.249
So assertion number one out of
this is, hey,

35:36.251 --> 35:39.991
human mitochondria show that we
came out of Africa.

35:39.989 --> 35:41.829
Well we now know from that
paper I showed you more

35:41.829 --> 35:44.069
recently,
from SNP polymorphisms,

35:44.072 --> 35:47.892
that this is an extremely well
supported thing,

35:47.889 --> 35:51.029
and that we see it in the
nuclear genes as well.

35:51.030 --> 35:55.150
But the claim here was an
interesting one.

35:55.150 --> 36:00.380
It said there was one woman,
living in Africa,

36:00.380 --> 36:03.890
about 220,000 years ago,
from whom all other

36:03.891 --> 36:08.631
mitochondria in all humans on
the planet are descended,

36:08.630 --> 36:12.900
and so they gave her the name
Mitochondrial Eve;

36:12.900 --> 36:15.320
that's observation number one.

36:15.320 --> 36:21.420
Observation number two--and
this now comes from an

36:21.418 --> 36:29.118
immunobiology group in Germany--
and basically it has to do with

36:29.117 --> 36:34.487
how old are the polymorphisms in
our MHC genes?

36:34.489 --> 36:37.469
And these are things that have
been selected,

36:37.469 --> 36:40.589
probably through frequency
dependent selection,

36:40.585 --> 36:42.885
in co-evolution with diseases.

36:42.889 --> 36:44.949
And the observation is this.

36:44.949 --> 36:50.239
Say we have two MHC genes that
have resulted from a gene

36:50.244 --> 36:53.184
duplication--
and that would be this one here

36:53.184 --> 36:56.614
and this one here--
and at each of those genes

36:56.614 --> 37:00.694
there is a polymorphism,
so that we have different

37:00.692 --> 37:04.142
alleles at that locus,
for each of those two genes.

37:04.139 --> 37:09.849
Who are those alleles most
closely related to?

37:09.849 --> 37:14.069
Well it turns out that Allele 1
in humans is most closely

37:14.065 --> 37:19.205
related to Allele 1 in chimps,
and Allele 2 in humans is most

37:19.210 --> 37:22.670
closely related to Allele 2 in
chimps.

37:22.670 --> 37:24.900
In other words,
the closest relatives of the

37:24.898 --> 37:28.288
alleles are not in this species;
they're in another species.

37:28.289 --> 37:34.369
The only possible way that that
could have occurred is if the

37:34.367 --> 37:39.937
polymorphism originated before
the speciation event--

37:39.940 --> 37:43.420
okay?--so that you had
ancestral species here,

37:43.420 --> 37:48.780
and this polymorphism
originated, and you got this one

37:48.777 --> 37:54.537
coming down from the ancestral
species into the chimp,

37:54.539 --> 37:58.229
and into the human;
and this one coming down from

37:58.226 --> 38:01.536
the ancestral species into the
chimp and into the human.

38:01.539 --> 38:06.439
Now, on the one hand we have
the claim all the mitochondria

38:06.438 --> 38:08.548
came out of one person.

38:08.550 --> 38:11.150
On the other hand we have
evidence that there are

38:11.150 --> 38:12.830
trans-specific polymorphisms.

38:12.829 --> 38:16.289
And if you take this point in
time and you put it on this

38:16.288 --> 38:17.768
tree, it's about here.

38:17.769 --> 38:21.679


38:21.679 --> 38:24.789
I now want you to talk to each
other for a minute,

38:24.789 --> 38:27.459
and then we'll see if you can
come up with the explanation for

38:27.460 --> 38:30.090
how those two observations are
consistent with each other.

38:30.090 --> 38:35.500


38:35.500 --> 38:38.220
So just turn to your partner
and figure it out.

38:38.219 --> 40:47.179
<<Students confer with
one another>>

40:47.179 --> 40:50.159
Prof: Silence descends.

40:50.159 --> 40:52.479
Enlightenment has been reached.

40:52.480 --> 40:56.170
Okay, can anybody tell me why
they are not surprised that all

40:56.166 --> 40:58.806
of the mitochondria came from
one female?

40:58.809 --> 41:03.889


41:03.889 --> 41:05.649
Why are you not surprised?

41:05.650 --> 41:07.360
Student:  Because
they're going to come down

41:07.355 --> 41:07.965
through meiosis.

41:07.969 --> 41:12.009
It comes down…
Prof: It's asexually

41:12.007 --> 41:12.767
inherited.

41:12.768 --> 41:16.468
How many females do you think
there were in the African

41:16.467 --> 41:18.587
population 220,000 years ago?

41:18.590 --> 41:23.190


41:23.190 --> 41:27.180
Do you think the second
observation tells us anything

41:27.184 --> 41:30.724
about that, the trans-specific
polymorphism?

41:30.719 --> 41:36.049


41:36.050 --> 41:39.420
What happens when a population
is really small?

41:39.420 --> 41:49.480


41:49.480 --> 41:53.860
You get genetic drift.

41:53.860 --> 41:57.180
If a population were as small
as one female,

41:57.175 --> 42:01.645
it would've been impossible to
maintain this trans-specific

42:01.648 --> 42:02.958
polymorphism.

42:02.960 --> 42:09.240


42:09.239 --> 42:13.579
People have done simulations to
find out what is the average

42:13.577 --> 42:18.077
size of a population that would,
over a period of 5,000,000

42:18.079 --> 42:22.609
years, maintain the amount of
trans-specific polymorphism that

42:22.606 --> 42:25.436
we see in our MHC complex;
in other words,

42:25.443 --> 42:27.863
the amount of genes that we
share with chimpanzees,

42:27.860 --> 42:30.920
where our alleles are more
closely related to the chimp's

42:30.920 --> 42:33.710
alleles than they are to the
other human alleles?

42:33.710 --> 42:37.780
And the answer is the minimum
size is about 10,000.

42:37.780 --> 42:40.350
In other words,
we have good genetic

42:40.351 --> 42:44.171
information that tells us that
the smallest the human

42:44.173 --> 42:47.363
population ever has been,
over the last several

42:47.360 --> 42:49.410
hundred-thousand years,
actually over the last

42:49.409 --> 42:51.599
5,000,000 years,
since we shared ancestors with

42:51.599 --> 42:55.079
chimps,
is about 10,000.

42:55.079 --> 42:58.209
Okay?

42:58.210 --> 43:02.900
Given that, now tell me,
are you surprised that we can

43:02.896 --> 43:06.966
trace all the mitochondria,
in all the females on the

43:06.965 --> 43:11.165
planet, back to one woman,
living in East Africa about

43:11.166 --> 43:13.006
220,000 years ago?

43:13.010 --> 43:16.640
And if you're not surprised,
I want to know why.

43:16.639 --> 43:18.029
Yes?

43:18.030 --> 43:20.390
Student:  Well you were
talking before about how

43:20.385 --> 43:22.605
recessive genes spread out in a
small population.

43:22.610 --> 43:28.110
So if she had enough children
over enough generations,

43:28.105 --> 43:31.415
you'd have enough of a
population

43:31.422 --> 43:34.432
>

43:34.429 --> 43:36.539
Prof: Okay,
it's possible that in fact she

43:36.539 --> 43:39.209
had a particularly advantageous
mitochondrion,

43:39.210 --> 43:42.610
and that it then got selected
and it fixed,

43:42.610 --> 43:45.590
and then everything would go
back to her.

43:45.590 --> 43:46.380
That's correct.

43:46.380 --> 43:48.500
And that could've been done in
a larger population.

43:48.500 --> 43:51.620
However, it could also have
been done with drift,

43:51.619 --> 43:54.349
and it happened so long ago
that we can't really tell

43:54.346 --> 43:57.336
whether it was selection or
drift that gave that one woman

43:57.335 --> 43:58.275
the advantage.

43:58.280 --> 44:01.090
By the way, the same thing has
been done for the Y chromosome.

44:01.090 --> 44:05.070
Okay, the Y chromosome is also
asexually inherited,

44:05.070 --> 44:09.610
and the estimate on the Y
chromosome is roughly also about

44:09.606 --> 44:13.026
200,000 years ago,
also in East Africa.

44:13.030 --> 44:15.270
And the fact that the
mitochondrion and the Y

44:15.271 --> 44:17.821
chromosome both converged to a
common ancestor,

44:17.820 --> 44:22.110
at about the same age,
might suggest that drift is

44:22.112 --> 44:25.882
more likely than selection to
explain it.

44:25.880 --> 44:28.390
But basically what's going on,
if you think about it,

44:28.389 --> 44:32.379
is that in any process like
that, if you go back far enough

44:32.382 --> 44:34.922
in time,
it will always converge on a

44:34.922 --> 44:36.452
single common ancestor.

44:36.449 --> 44:38.379
Okay?

44:38.380 --> 44:42.680
Now the next thing I'd like to
tell you is that there was a

44:42.684 --> 44:45.584
controversy about when that
happened;

44:45.579 --> 44:51.159
and the controversy on dating
that point of convergence is all

44:51.163 --> 44:56.293
about how long has it been since
we split with chimps?

44:56.289 --> 45:01.719
Because that turns out to be
the baseline that gives us an

45:01.715 --> 45:06.375
estimate of how rapidly
evolution is going on,

45:06.380 --> 45:09.890
molecular evolution is going
on, in the human clade.

45:09.889 --> 45:12.889
Well when you apply that
criterion to what the confidence

45:12.885 --> 45:15.815
limits on the estimate are,
hey, it was anywhere from last

45:15.822 --> 45:17.382
year to about a million years
ago.

45:17.380 --> 45:18.930
>

45:18.929 --> 45:21.459
So the confidence limits are
lousy.

45:21.460 --> 45:25.010
The observation,
however, that that person was

45:25.007 --> 45:31.447
in Africa is pretty solid and,
as I say, is now confirmed by

45:31.445 --> 45:35.705
the nuclear genes,
the SNPs that I showed you

45:35.706 --> 45:37.196
earlier in the lecture.

45:37.199 --> 45:41.209
So the point of this exercise
is that when you see results

45:41.213 --> 45:43.843
like this,
when you see some claim of

45:43.835 --> 45:47.605
Mitochondrial Eve,
or Y Chromosome Adam--which are

45:47.610 --> 45:50.310
both out there in the
literature;

45:50.309 --> 45:52.549
just go on Web of Science,
type Mitochondrial Eve,

45:52.545 --> 45:54.775
you'll pick up a lot of
controversy about this.

45:54.780 --> 45:59.220
I want you to realize that (a)
we should never be surprised if

45:59.222 --> 46:03.372
particular mitochondria or
particular chromosomes converge

46:03.373 --> 46:07.693
at some point in time--
it looks like they were just in

46:07.688 --> 46:08.898
one individual.

46:08.900 --> 46:12.580
That's just because of the way
that branching processes work,

46:12.579 --> 46:16.319
and that will be going on all
the time, and it's happened over

46:16.322 --> 46:18.042
and over and over again.

46:18.039 --> 46:21.369
So it's no surprise that it
goes back to one person.

46:21.369 --> 46:25.009


46:25.010 --> 46:28.550
The other point that I would
like to bring out is that if you

46:28.550 --> 46:31.560
contrast different kinds of
historical evidence,

46:31.559 --> 46:34.309
you can often gain
enlightenment by seeing that

46:34.309 --> 46:36.939
there is a puzzle that needs to
be solved.

46:36.940 --> 46:40.820
And in this case the puzzle
basically is that this tells us

46:40.820 --> 46:44.900
something about population size,
and this tells us very little

46:44.900 --> 46:46.640
about population size.

46:46.639 --> 46:49.199
This process,
convergence back to a single

46:49.197 --> 46:52.757
ancestor from the mitochondria,
it's not entirely independent

46:52.764 --> 46:55.044
of population size--
it'll take longer in a big

46:55.039 --> 46:57.359
population than it would in a
small population--

46:57.360 --> 46:59.770
but it doesn't give us an
estimate of how big the

46:59.773 --> 47:00.933
population had to be.

47:00.929 --> 47:04.199
Whereas the trans-specific
polymorphism could only have

47:04.199 --> 47:07.529
been maintained,
even with strong selection,

47:07.525 --> 47:12.595
in a population that was larger
than about 10,000 individuals;

47:12.599 --> 47:15.889
and that can be done with
computer simulations.

47:15.889 --> 47:21.759
So these are different forms of
enlightenment into the history

47:21.755 --> 47:26.725
of genes in a particular clade;
a history that happens to

47:26.728 --> 47:28.598
matter to us quite a bit.

47:28.599 --> 47:34.369
And we can gain that by looking
carefully at phylogenetic trees.

47:34.369 --> 47:36.349
These are actually both
phylogenetic trees.

47:36.349 --> 47:39.769
This tree here is just laid on
its side, and you'll find that

47:39.766 --> 47:42.726
this is commoner and commoner
practice these days.

47:42.730 --> 47:46.500
If you look at the cover of
Nature,

47:46.500 --> 47:49.100
or Science,
or the--actually the one I

47:49.101 --> 47:52.591
remember is in the New York
Times Science Section for

47:52.590 --> 47:55.940
Darwin's birthday,
on February 12^(th).

47:55.940 --> 48:00.730
That week the Science Section
had a phylogenetic tree covering

48:00.733 --> 48:04.093
Darwin's face,
and it had thousands of species

48:04.088 --> 48:05.908
on it,
and they're laid out this way,

48:05.909 --> 48:08.509
just so that you can fit the
species onto a piece of paper.

48:08.510 --> 48:14.840
So time kind of wraps around,
in the way it's presented.

48:14.840 --> 48:20.370
Okay, so to summarize this part
of our exploration of

48:20.371 --> 48:22.181
macroevolution.

48:22.179 --> 48:25.669
These molecular methods allow
us to reconstruct geographic

48:25.666 --> 48:27.866
movements, as well as
phylogenies.

48:27.869 --> 48:30.979
And we saw that in the
hedgehogs going north from Spain

48:30.976 --> 48:33.326
and the Balkans,
and we saw it in the humans

48:33.327 --> 48:37.967
moving out of Africa,
and we saw it in lots of things.

48:37.969 --> 48:41.519
We see that our own migrations
have left genetic traces on all

48:41.519 --> 48:42.509
the continents.

48:42.510 --> 48:46.320
And there's an imprecise map
that's suggestive between the

48:46.322 --> 48:49.602
genetic geography and the
linguistic geography.

48:49.599 --> 48:52.609
Greek genes go with Greek
family names,

48:52.608 --> 48:55.778
from the boot of Italy,
up to about Rome,

48:55.775 --> 48:58.615
and then stop;
that kind of thing.

48:58.619 --> 49:02.249
So even in the last two or
three-thousand years,

49:02.248 --> 49:06.958
you can see that family names
and genes have been inherited in

49:06.956 --> 49:08.266
similar ways.

49:08.268 --> 49:10.948
We can use these methods to
determine which trait states

49:10.954 --> 49:12.864
were ancestral and which are
derived.

49:12.860 --> 49:15.220
And that was particularly
interesting in the case of the

49:15.217 --> 49:17.427
parasitic wasp,
whether it was an ectoparasite

49:17.425 --> 49:20.785
or an endoparasite,
because it changed received

49:20.793 --> 49:23.863
opinion about fundamental
biology.

49:23.860 --> 49:26.670
And this method that Joe
Felsenstein worked out for

49:26.672 --> 49:29.712
independent contrast is
something that will control for

49:29.708 --> 49:32.218
common ancestry,
and it can reveal the

49:32.224 --> 49:35.724
correlated changes in two or
more traits that have taken

49:35.721 --> 49:38.011
place since branches in the
tree.

49:38.010 --> 49:43.960
So it can be used as a fairly
powerful method to explore

49:43.958 --> 49:49.798
hypotheses in behavioral
ecology, evolutionary ecology,

49:49.800 --> 49:51.640
and ethology.

49:51.639 --> 49:54.339
So next time we're going to
start talking about key events

49:54.344 --> 49:57.254
in the history of life;
it's the first of three ways to

49:57.250 --> 49:58.860
look at the history of life.

49:58.860 --> 50:04.000