WEBVTT

e73a1e39-535c-4bce-a0e0-626818df4c5d-0
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All right, everyone,
it gives me great pleasure to hand the

e73a1e39-535c-4bce-a0e0-626818df4c5d-1
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microphone over to Professor Jim Cullen.

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I was going to start by saying who I am,
but I guess I don't have to.

864ee10c-6330-460c-8719-39ad4d654103-0
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The Department of Geological Sciences is
very pleased to have be able to welcome

864ee10c-6330-460c-8719-39ad4d654103-1
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Doctor Dana Reuter to our department as a
visiting professor.

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As a native New Englander,
she received her undergraduate degree at

e3813697-385e-4e2f-a086-62dc46c3cdcd-1
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Mount Koyo College,
which many of you are probably familiar

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with,
and then she headed West where she

e3813697-385e-4e2f-a086-62dc46c3cdcd-3
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received her PhD at the University of
Oregon.

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And then subsequent to her finishing her
doctorate,

971d747d-687f-495b-94a0-670b58377895-1
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she got awarded A prestigious NSF
postdoctoral fellowship that she

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completed at Texas A&
M and the Florida Museum of Natural

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History.

33d702b2-5369-48d7-91af-6b753543be9b-0
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Dana is a paleoecologist whose research
focuses on ecological links among extinct

33d702b2-5369-48d7-91af-6b753543be9b-1
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mammalium taxa and the influence of past
climate change has on ecological

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relationships.

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Her special focus is on diet.

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She looks to the fossil record to
understand the processes that Dr.

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ecological diversity and community
changes.

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Her talk today is entitled.

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Is it up there?

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Good.

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I don't have to say it.

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And please welcome her with a round of
applause.

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Thank you, Jim.

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So we'll see if I want a wander.

f3c141e2-df7b-4c55-8946-38413e5da541-0
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I also have a laser pointer,
so that'll help today.

3096e80d-a536-40be-b9c1-737cc9a56b07-0
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So thank you for having me.

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I'm very excited to be here,
and I'm very excited to talk in honor of

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Darwin's birthday.

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This is quite exciting for me and we're
going to talk a little bit about

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something that I think Darwin would be
highly interested in,

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basically diet and how it interacts with
other traits in mammals and how that

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influences how they go extinct and the
communities that they live in.

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So if you don't know me,
this is something you should know about

dc197f61-32b1-4806-9e7c-67a623609dd2-1
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me.

ce5e8c54-87c4-46ac-b545-7bc34c086f34-0
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I really love food.

68350a88-9bea-4cad-8f1b-414aaf24d4f9-0
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I love thinking about food.

eb2eef9b-1698-47d9-a7d5-df6457ed713d-0
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I love cooking food.

15d8fe93-0466-45ad-82b1-145040e9c831-0
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I actually gave my family a homemade
cookbook this year for Christmas,

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and this is a picture of me eating one of
my favorite foods,

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which is a cardamom bun.

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I still have not perfected baking them,
but maybe someday.

095876ec-0b28-4727-9a5d-1be2b0127886-0
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But I'm also very interested in animals
and why they eat certain foods and how

095876ec-0b28-4727-9a5d-1be2b0127886-1
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they eat those foods and why we have so
many different kinds of animals that

095876ec-0b28-4727-9a5d-1be2b0127886-2
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consume such different varieties of
things from their environment.

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And mammals are a great group to study
this in because they have evolved such

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different shapes and sizes,
and they have a lot of traits that

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actually align with their foods that
they're eating.

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So if you think about the teeth in your
head,

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I think about them a lot because as a
paleontologist,

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we use those as clues to see what they
have been consuming.

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And the Today,
what we're going to talk about is how I

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think about this as a paleoecologist.

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So how I can think about diet in the past,
how I can kind of reconstruct it.

d7c04b12-196f-40e8-bdf3-de4a23b78c09-0
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And then I'm going to talk about how
lovely the Oregon fossil record is and

d7c04b12-196f-40e8-bdf3-de4a23b78c09-1
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the projects that I I did there when I
was living out in Oregon and I'm still

d7c04b12-196f-40e8-bdf3-de4a23b78c09-2
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working on to this day.

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So we're going to talk about first the
this project I have on herbivore

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community structure.

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So we're going to really focus in on the
herbivores in the community.

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And I'm going to show you some isotopic
evidence for the foods that they were

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eating.

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And then I'm going to talk about a
project that I'm still picking at,

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and it's still ongoing,
where I'm reconstructing the entire

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community.

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I'm making food webs for these ancient
communities.

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So we need a little primer first before
we can really dig into what I do as a

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paleoecologist.

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We need to think about traits and how
they're linked to feeding strategies and

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mammals.

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And as a paleo ecologist,
I do something and you use what is called

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diet proxies.

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And this is a stand in for actual
observations of of the diets of organisms.

6dd3a5d6-2f60-494a-8f40-d7ce1389620b-0
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So I can't go out in the field and watch
my extinct Organism eat something.

e7d00310-d465-48bf-b30a-6e9e1e849043-0
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So I need to take a measurement or have
something stand in for that data.

68d2c8c5-f345-4099-bf61-1d799b47dc0c-0
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And the ones that I use the most are
tooth shape and size.

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So the two examples I have here are of a
browser and a grazer.

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And so if a browser is picking up nice
soft leaves,

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its teeth will usually be lower.

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It also allows them to maybe crack things
open in a little if they're eating nuts

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and stuff.

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So a lower tooth is better for that job.

b850b7bc-8ed7-4fc6-99e8-7a156484e20a-0
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But if you're a grazer,
you're eating a lot of grit.

b82ffdf3-cd12-49c6-a3c2-25834a0731bb-0
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So both grass itself has abrasive objects
in it and it's also dirty if you're

b82ffdf3-cd12-49c6-a3c2-25834a0731bb-1
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thinking about it's really close to the
ground.

793cb5f0-443e-45f8-b190-7c08109dc459-0
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So you need a taller tooth to deal with
all that grit that you're eating.

fb459707-f582-446b-8186-40639ff7db03-0
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So we can use tooth shape as a stand in
for the diets of these organisms.

fe73f479-36c1-4379-b089-877c61a3af67-0
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Snout shape and jaw shape are also useful.

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I've done some work looking at the jaw
shape of animals and how thick and how

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long the jaws are,
and that can indicate the diet of the

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organisms as well.

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Another really good trait that I use a
lot as a paleoecologist is actually body

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mass,
and this is a really nice figure that I

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like to show.

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So we have body size at the bottom here,
and it's actually getting bigger this way,

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and it's also getting larger in the other
direction as well.

cc200c8d-9865-4b12-977b-d8427f9e1bca-0
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And this is showing how changes occur in
diet related to body mass in mammals.

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And so if you're an herbivore,
you might be eating pollen and nectar

8c9da501-97f2-4424-a5aa-54c90269ab94-1
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when you're smaller.

81fd57d8-c900-4577-bb18-67914877c989-0
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But as you get larger and larger,
you actually need to start eating plants

81fd57d8-c900-4577-bb18-67914877c989-1
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that are more abundant on the landscape.

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So you might be relying more on leaves
and grasses that you can find in many

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places.

46cca940-2c46-4695-8abd-5d9c534ae794-0
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The same thing kind of happens in
carnivores where you're eating small

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things like insects, eggs,
and then as you get larger and larger,

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you need to take down larger prey in
order to meet your energetic needs.

681b67ce-a8a0-4c86-b82c-d9e1a2c693b9-0
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You can't run around and catch a ton of
spiders or ants.

f1f00213-2dac-4852-9366-81957d6f421b-0
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You need to actually eat larger organisms
once you get to a certain size.

bc64c6a3-99af-4103-9951-e72ec8439bf4-0
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And one of the first projects I kind of
really got into was trying to figure out

bc64c6a3-99af-4103-9951-e72ec8439bf4-1
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what's going on with the omnivores.

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Because if you imagine, if you mix foods,
you can kind of break some rules.

7321b843-4f99-41db-a791-a4439f943ddb-0
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And if we're trying to reconstruct what's
going on in the past,

7321b843-4f99-41db-a791-a4439f943ddb-1
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we need to know a little bit more about
what omnivores are doing as well.

da287a79-35d3-45e4-8a29-cd43a225cef4-0
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So I spent a lot of time actually working
on that project,

da287a79-35d3-45e4-8a29-cd43a225cef4-1
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collected data from modern animals and
actually plotted their body mass versus

da287a79-35d3-45e4-8a29-cd43a225cef4-2
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the different dietary types that they
have.

a3eb293e-b485-4b15-9d59-bb1d659e7510-0
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And one of the first findings that we
found was that most omnivores don't eat

a3eb293e-b485-4b15-9d59-bb1d659e7510-1
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all types of food.

3a7ea11c-7b31-48ae-ad93-ff9ffabf6850-0
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There's kind of subsets within omnivore
that seem pretty consistent.

b1c56a88-66f9-415c-899c-3b2729f69e4b-0
00:06:56.160 --> 00:06:59.320
It's actually really rare to eat every
single type of food.

ce8e80b4-7b56-4884-a2f2-b57b56f4b82c-0
00:06:59.840 --> 00:07:04.840
It's hard to combine them all down here
just to kind of clue you in.

00b04cff-bac7-4e08-a8fe-d27bcaffba6e-0
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A lot of the scale that I'm going to be
talking about today is actually in log

00b04cff-bac7-4e08-a8fe-d27bcaffba6e-1
00:07:09.146 --> 00:07:11.762
scale,
because if you look at body mass between

00b04cff-bac7-4e08-a8fe-d27bcaffba6e-2
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a really small thing and a really big
thing on the same scale,

00b04cff-bac7-4e08-a8fe-d27bcaffba6e-3
00:07:15.197 --> 00:07:16.560
it's really hard to read.

b5296aca-279b-423c-9309-3064ad9dc791-0
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So just to clue you in on the numbers
here.

4efa9316-3633-4b54-85fa-acf95bbe1239-0
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So the first thing I really want to point
out is that what we found is that prey

4efa9316-3633-4b54-85fa-acf95bbe1239-1
00:07:25.250 --> 00:07:29.943
type is more important than plant type
for omnivore and how it relates to body

4efa9316-3633-4b54-85fa-acf95bbe1239-2
00:07:29.943 --> 00:07:30.240
mass.

41775968-c0df-4771-bb96-e60f951e050e-0
00:07:30.640 --> 00:07:36.213
So here we have Invertebrore omnivores,
so they're eating invertebrate prey and

41775968-c0df-4771-bb96-e60f951e050e-1
00:07:36.213 --> 00:07:41.577
they are on the average smaller than
omnivores that are combining foods with

41775968-c0df-4771-bb96-e60f951e050e-2
00:07:41.577 --> 00:07:43.039
say, vertebrate prey.

f4f82a79-2e10-42f1-bf19-ea27636ec824-0
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We don't see a similar pattern in plants.

c19af9d3-6902-4c82-bebd-d16bd02de0b3-0
00:07:45.680 --> 00:07:50.905
So eating fibrous foods versus non
fibrous foods doesn't really affect the

c19af9d3-6902-4c82-bebd-d16bd02de0b3-1
00:07:50.905 --> 00:07:52.160
body mass as much.

5b2268c3-3b8b-4a24-9dfe-2ee30d2ffaa9-0
00:07:52.400 --> 00:07:57.479
So fibrous foods we typically think of as
leaves and grasses versus things like

5b2268c3-3b8b-4a24-9dfe-2ee30d2ffaa9-1
00:07:57.479 --> 00:08:00.400
fruit nectar,
pollen on the non fibrous side.

5f79e58c-3332-40c2-a90e-1f4676f3e2d2-0
00:08:01.000 --> 00:08:04.538
So we found this really interesting and
intriguing and it helps us think about

5f79e58c-3332-40c2-a90e-1f4676f3e2d2-1
00:08:04.538 --> 00:08:06.599
what's going on in the fossil record as
well,

5f79e58c-3332-40c2-a90e-1f4676f3e2d2-2
00:08:06.599 --> 00:08:10.227
because now we can look at the body mass
of omnivores and make a little bit of a

5f79e58c-3332-40c2-a90e-1f4676f3e2d2-3
00:08:10.227 --> 00:08:12.960
prediction about what they were eating in
the fossil record.

5556b549-20ee-4b6f-a6d0-bba0f746d42c-0
00:08:14.920 --> 00:08:18.655
And so just to kind of put together this
whole picture of using proxies and

5556b549-20ee-4b6f-a6d0-bba0f746d42c-1
00:08:18.655 --> 00:08:20.817
thinking about the the diet of an
Organism,

5556b549-20ee-4b6f-a6d0-bba0f746d42c-2
00:08:20.817 --> 00:08:24.847
we're going to use the panda as a lovely
example because I love talking about the

5556b549-20ee-4b6f-a6d0-bba0f746d42c-3
00:08:24.847 --> 00:08:27.600
panda's diet because it's really
interesting and weird.

a13ea987-22ea-483b-9f1c-3a5dd0a5ec54-0
00:08:27.800 --> 00:08:29.600
So it eats bamboo even though it's a bear.

5a312b09-b9f6-4b00-b3b0-fe89fe522eed-0
00:08:30.160 --> 00:08:34.296
And so in order to understand the panda,
we need to think about its evolutionary

5a312b09-b9f6-4b00-b3b0-fe89fe522eed-1
00:08:34.296 --> 00:08:35.880
history, so where it came from.

70f99e7e-4760-462a-bb3f-d584a7f3c46a-0
00:08:35.880 --> 00:08:40.087
So for instance, it's a bear,
so it has different tools and its body is

70f99e7e-4760-462a-bb3f-d584a7f3c46a-1
00:08:40.087 --> 00:08:41.840
already in a particular shape.

9fcd1b1c-1c6d-4105-9909-d7b028cf9a42-0
00:08:42.600 --> 00:08:45.699
And the lineage it came from wasn't
eating bamboo before,

9fcd1b1c-1c6d-4105-9909-d7b028cf9a42-1
00:08:45.699 --> 00:08:49.760
but it had to change and evolve in a way
that would allow it to eat bamboo.

3be8296c-5e7d-4969-b747-011f6f71ddc7-0
00:08:50.520 --> 00:08:54.166
Its environment is causing a lot of this
influence because it's living in an

3be8296c-5e7d-4969-b747-011f6f71ddc7-1
00:08:54.166 --> 00:08:56.440
environment that has a lot of bamboo
around it.

5b1ea5b4-57fd-4a90-a606-0c0641705a2b-0
00:08:56.440 --> 00:09:00.883
So it's there's natural selection there,
pressure to maybe evolve to eat this

5b1ea5b4-57fd-4a90-a606-0c0641705a2b-1
00:09:00.883 --> 00:09:02.080
particular food item.

f28d7ab8-d601-4f7c-bad7-f32a0626c752-0
00:09:02.440 --> 00:09:05.600
But in order for that to happen,
we need to think about morphology.

6ccebf88-a614-4a84-8b51-b7a56005b022-0
00:09:05.760 --> 00:09:10.465
So what changes need to happen and what
tools does it have that can indicate that

6ccebf88-a614-4a84-8b51-b7a56005b022-1
00:09:10.465 --> 00:09:14.080
it's eating that particular diet and then
also it's body mass?

6bd5c8b5-6113-416e-85de-d28ea615f06e-0
00:09:14.080 --> 00:09:18.398
Is there energetic constraints from that
food that is causing it to have a

6bd5c8b5-6113-416e-85de-d28ea615f06e-1
00:09:18.398 --> 00:09:19.320
particular size?

e3a501af-865f-484d-8fbe-cf25c85c3ffa-0
00:09:19.760 --> 00:09:25.226
And so I kind of think about all of these
things together when I'm thinking about

e3a501af-865f-484d-8fbe-cf25c85c3ffa-1
00:09:25.226 --> 00:09:26.360
diet in the past.

454eb3ad-7b90-424a-8dd6-d1ef421efbe2-0
00:09:26.840 --> 00:09:29.440
And I'm combining a lot of different data
sets.

9153827b-0648-47c9-b37c-51ead33ce540-0
00:09:29.480 --> 00:09:33.306
So I'm combining field data if I'm going
out and collecting fossils,

9153827b-0648-47c9-b37c-51ead33ce540-1
00:09:33.306 --> 00:09:36.467
museum data if I'm going to a museum and
taking samples,

9153827b-0648-47c9-b37c-51ead33ce540-2
00:09:36.467 --> 00:09:39.240
and also previously published data and
databases.

d10fe35f-43b6-49bd-b65d-80bba6fe80ae-0
00:09:39.600 --> 00:09:44.229
And I'm combining that to form this
picture of what the Organism was eating

d10fe35f-43b6-49bd-b65d-80bba6fe80ae-1
00:09:44.229 --> 00:09:44.960
in the past.

922a9a5e-c505-45c3-b8db-144b448248f8-0
00:09:44.960 --> 00:09:50.154
And then I'm using that information to
kind of make predictive rules about what

922a9a5e-c505-45c3-b8db-144b448248f8-1
00:09:50.154 --> 00:09:54.440
might happen in the future and how
ecosystems function over time.

cef7abc1-84f4-44eb-aa03-604bfc15e898-0
00:09:56.640 --> 00:10:01.108
So today we're going to spend a little
bit of time mostly in the environmental

cef7abc1-84f4-44eb-aa03-604bfc15e898-1
00:10:01.108 --> 00:10:02.240
part of my research.

d546027a-ffe3-473e-b1e1-db6693dea247-0
00:10:02.480 --> 00:10:05.861
Kind of clued you in a little bit to what
I've done before with morphology and body

d546027a-ffe3-473e-b1e1-db6693dea247-1
00:10:05.861 --> 00:10:07.754
mass,
but today we're going to talk a lot more

d546027a-ffe3-473e-b1e1-db6693dea247-2
00:10:07.754 --> 00:10:08.640
about the environment.

00ec6839-881b-4496-a2da-0d99ea11ade2-0
00:10:09.600 --> 00:10:14.142
And I think this would have been a topic
that Darwin would have been really close

00ec6839-881b-4496-a2da-0d99ea11ade2-1
00:10:14.142 --> 00:10:18.629
to Darwin's heart because he really knew
that the environment plays a large role

00ec6839-881b-4496-a2da-0d99ea11ade2-2
00:10:18.629 --> 00:10:22.507
in the selection on organisms,
and the environment determines what is

00ec6839-881b-4496-a2da-0d99ea11ade2-3
00:10:22.507 --> 00:10:24.279
available to an Organism to eat.

864407a6-ce19-4ce3-9c74-926f0ac4ca51-0
00:10:24.280 --> 00:10:29.235
So the environment is where we're finding
all these foods that the organisms can

864407a6-ce19-4ce3-9c74-926f0ac4ca51-1
00:10:29.235 --> 00:10:29.480
eat.

a0500ef6-797b-4441-add5-15d1e1bb5831-0
00:10:30.240 --> 00:10:33.996
And so for a lot of different studies
have been done in the past,

a0500ef6-797b-4441-add5-15d1e1bb5831-1
00:10:33.996 --> 00:10:38.095
We've been learning more about how
ecosystem type can affect diversity,

a0500ef6-797b-4441-add5-15d1e1bb5831-2
00:10:38.095 --> 00:10:42.080
community composition, food web structure,
and also animal abundance,

a0500ef6-797b-4441-add5-15d1e1bb5831-3
00:10:42.080 --> 00:10:45.040
even how many animals we can have on the
landscape.

1279ac90-0798-40d0-8e74-56377feedede-0
00:10:46.320 --> 00:10:50.044
And so as a paleontologist,
I'm kind of looking at the same thing in

1279ac90-0798-40d0-8e74-56377feedede-1
00:10:50.044 --> 00:10:52.420
the past,
but I'm asking slightly different

1279ac90-0798-40d0-8e74-56377feedede-2
00:10:52.420 --> 00:10:52.960
questions.

f275373a-bcf7-4370-8958-4941ac6517f4-0
00:10:52.960 --> 00:10:56.853
I'm asking questions like,
did extinct communities behave the same

f275373a-bcf7-4370-8958-4941ac6517f4-1
00:10:56.853 --> 00:10:58.480
way as modern ones do today?

a2b2900b-d287-44d9-8168-32920fc465cf-0
00:10:58.480 --> 00:11:00.280
Do we see a pattern through time?

84e9d584-d59a-4283-be95-2c9f27fa3d2e-0
00:11:01.160 --> 00:11:03.640
How has past climate change influence
communities?

16766a33-536b-41e4-9987-1678167132f3-0
00:11:03.640 --> 00:11:06.640
So if we do see differences,
is this because of climate change?

2b6543c7-6dee-4ef3-9bfa-4e97f418c3e4-0
00:11:06.640 --> 00:11:10.149
Is it because there's a different climate
system that was going on at that time

2b6543c7-6dee-4ef3-9bfa-4e97f418c3e4-1
00:11:10.149 --> 00:11:10.720
versus today?

194d5fad-8994-499a-8736-214f81dab8e6-0
00:11:11.800 --> 00:11:15.560
And does past community change say
something about our future again?

0e15a4bd-762e-45cc-8907-a921c4030044-0
00:11:15.560 --> 00:11:19.040
Can we make predictions to how we as
humans are maybe changing the planet?

c4abbfad-16a7-4000-a485-36ef23c62456-0
00:11:19.040 --> 00:11:21.520
What might happen to those ecosystems in
the future?

b7f3f010-15b7-406b-9961-d046fda2bca5-0
00:11:23.000 --> 00:11:28.440
And the really good example I'm going to
walk us through is about grasslands.

08853cde-c6b1-432f-b45c-1c4688eac266-0
00:11:28.640 --> 00:11:32.865
So if you didn't know,
grasses are actually geologically young

08853cde-c6b1-432f-b45c-1c4688eac266-1
00:11:32.865 --> 00:11:37.360
and grasslands are geologically young
compared to Earth's history.

faf884c4-c162-460c-9b94-9a8642bdd03d-0
00:11:37.920 --> 00:11:40.306
And we kind of take them for granted
because as humans,

faf884c4-c162-460c-9b94-9a8642bdd03d-1
00:11:40.306 --> 00:11:43.886
we actually evolved in this landscape and
we've lived in them and we spend a lot of

faf884c4-c162-460c-9b94-9a8642bdd03d-2
00:11:43.886 --> 00:11:44.439
time in them.

e4b1ffb8-b259-44ba-8220-66377249683c-0
00:11:44.440 --> 00:11:47.821
And as we drive across North America,
we see these lovely fields,

e4b1ffb8-b259-44ba-8220-66377249683c-1
00:11:47.821 --> 00:11:51.663
but this is actually quite a unique
environment that wasn't around all the

e4b1ffb8-b259-44ba-8220-66377249683c-2
00:11:51.663 --> 00:11:51.920
time.

c11350ab-504a-43ae-8605-5c02f8097253-0
00:11:51.920 --> 00:11:55.760
So maybe around 28,000,000 years ago,
that's when grasslands start appearing.

a7511874-c176-4b48-8ea8-f5627c89df95-0
00:11:56.040 --> 00:11:59.528
And then really 3,000,
000 years ago is when we start seeing

a7511874-c176-4b48-8ea8-f5627c89df95-1
00:11:59.528 --> 00:12:03.360
really expansive grasslands across North
America and in the globe,

a7511874-c176-4b48-8ea8-f5627c89df95-2
00:12:03.360 --> 00:12:07.478
and we can think about how unique this
environment is compared to, say,

a7511874-c176-4b48-8ea8-f5627c89df95-3
00:12:07.478 --> 00:12:09.880
forests that would have been there before.

6c3e22d6-3719-4757-9664-dbff97fedcfb-0
00:12:10.120 --> 00:12:14.976
So this is a really open landscape that
requires different traits and new

6c3e22d6-3719-4757-9664-dbff97fedcfb-1
00:12:14.976 --> 00:12:19.176
adaptations and new ways of being than
when you're in a forest,

6c3e22d6-3719-4757-9664-dbff97fedcfb-2
00:12:19.176 --> 00:12:21.079
there's fewer places to hide.

6a3ee6fd-18ed-4f03-998b-13150d7069b9-0
00:12:21.080 --> 00:12:23.920
You have to maybe change your body mass
to avoid predators.

32c86b32-9a04-4daa-9945-2cc55862d7d6-0
00:12:23.920 --> 00:12:26.040
You have to maybe Burrow underground.

7881b3d2-207d-405f-99bb-73c164c533ba-0
00:12:26.360 --> 00:12:29.989
I gave the example of your teeth have to
change to deal with all the grit that

7881b3d2-207d-405f-99bb-73c164c533ba-1
00:12:29.989 --> 00:12:31.000
might be on your food.

2e216766-7a8c-4142-a8ad-88d97a4f3009-0
00:12:31.600 --> 00:12:37.273
And so we can see these changes through
time and past research has really shown

2e216766-7a8c-4142-a8ad-88d97a4f3009-1
00:12:37.273 --> 00:12:41.600
how if we take all of the different
groups in North America.

8f346b4b-830b-40de-ac51-c11235fe18a3-0
00:12:41.600 --> 00:12:45.360
So these are this is evidence in North
America for both rodents.

3390bfc7-040a-46b7-a60c-d356a92f27c8-0
00:12:45.360 --> 00:12:49.920
So this is smaller organisms and
ungulates which are hoofed mammals.

2012e190-8f2c-430a-a9b6-496022ccc98d-0
00:12:49.920 --> 00:12:54.811
So think horses, cows, etcetera,
deer and we can see a change in their

2012e190-8f2c-430a-a9b6-496022ccc98d-1
00:12:54.811 --> 00:12:58.600
morphology through time as we're
progressing to today.

95310100-e6dc-4d3b-8d49-329acf9349a7-0
00:12:58.600 --> 00:13:03.689
So this is today and grasses would have
started appearing around maybe 30 million

95310100-e6dc-4d3b-8d49-329acf9349a7-1
00:13:03.689 --> 00:13:08.405
years ago, maybe a little earlier,
but they really start getting going like

95310100-e6dc-4d3b-8d49-329acf9349a7-2
00:13:08.405 --> 00:13:10.640
I said kind of towards the end here.

b51e7c3f-a668-458f-a972-cc9a8b2f0746-0
00:13:11.040 --> 00:13:14.000
And in rodents what we're looking at is
again that tooth height.

f0bd3c00-7327-4be1-aa9d-6d0f8435d6fc-0
00:13:14.000 --> 00:13:16.680
So this is smaller teeth, shorter teeth.

19b38475-27e7-4e4a-ac36-4b8c04b8bc2a-0
00:13:16.680 --> 00:13:18.520
Brachyidone teeth are low crowned.

5b9125d1-668d-4367-9093-c9e38eb5b3e1-0
00:13:19.000 --> 00:13:22.774
We have mid height teeth,
taller teeth and these are those ever

5b9125d1-668d-4367-9093-c9e38eb5b3e1-1
00:13:22.774 --> 00:13:23.600
growing teeth.

6da5307e-b436-4630-9393-06962cdcd0d3-0
00:13:23.600 --> 00:13:28.353
So if you think about rodents having
those ever growing teeth and we can see

6da5307e-b436-4630-9393-06962cdcd0d3-1
00:13:28.353 --> 00:13:31.748
that there's an increase in taller teeth
through time,

6da5307e-b436-4630-9393-06962cdcd0d3-2
00:13:31.748 --> 00:13:36.440
especially in rodents and they react
quite quickly to this new environment.

45af81d0-8ad5-4cd4-950d-fbb5d0db3c3d-0
00:13:36.440 --> 00:13:40.880
So rodents have a shorter lifespan,
they can reproduce faster so they can

45af81d0-8ad5-4cd4-950d-fbb5d0db3c3d-1
00:13:40.880 --> 00:13:43.280
adapt and evolve quicker to new changes.

5ab9350c-dce2-4364-8807-0d874acf62be-0
00:13:43.520 --> 00:13:48.853
So this is this is expected is that we'd
see the signal of grassland environments

5ab9350c-dce2-4364-8807-0d874acf62be-1
00:13:48.853 --> 00:13:51.520
in them first compared to hooved mammals.

ce372e2c-c177-445f-a76a-8a326cf7f554-0
00:13:51.760 --> 00:13:53.400
And same thing here that we're seeing.

0e7c7f93-748c-4dda-9086-3315e364b74e-0
00:13:53.400 --> 00:13:57.520
We have low crown teeth, brachyidont,
mesodont, hipscelodont.

0cadcea8-0da7-4992-8ca5-01f975267af2-0
00:13:57.960 --> 00:14:02.414
And what we're seeing is we have an
increase in tall toothed ungulates and

0cadcea8-0da7-4992-8ca5-01f975267af2-1
00:14:02.414 --> 00:14:05.800
then we actually have a a decrease in
overall diversity.

ce02f0e8-1fa1-4e2a-9b06-930719987aab-0
00:14:05.800 --> 00:14:10.080
And this is driven by the brachyidont
browsing organisms going extinct.

21e815e6-578a-4ed9-833c-b20f04ced0a4-0
00:14:13.800 --> 00:14:17.443
We are learning, though,
that this change in North America of

21e815e6-578a-4ed9-833c-b20f04ced0a4-1
00:14:17.443 --> 00:14:22.320
grasslands expanding is kind of variable
and is patchy throughout the rock record.

9c6def0d-ed33-4eb4-9b5b-4ca11ca507a0-0
00:14:23.040 --> 00:14:25.400
This is an image of what are known as
phytoliths.

03552a87-dc8f-4387-a37d-20693e2ebfa1-0
00:14:25.400 --> 00:14:27.720
So these are inside all plants.

c1b3cb89-e0aa-4e2b-bfdd-4c99dee221d5-0
00:14:27.720 --> 00:14:31.040
They're little silica bodies that help
the plants stay upright.

3de67b3d-b66b-469d-8757-138ee299f323-0
00:14:31.320 --> 00:14:36.560
They help at all along with the lignin
and cell walls that a lot of plants have.

bf3aa3d4-f07e-4cfa-a2e6-307adc22facf-0
00:14:37.000 --> 00:14:40.200
And they have unique shapes depending on
which plant they're actually coming from.

c57e6d20-acbe-4e2b-8a19-ac086530962a-0
00:14:40.200 --> 00:14:42.480
So we can identify them based on
morphology.

ba5a71ea-1c3a-4ede-9727-de39df5f6e86-0
00:14:42.760 --> 00:14:45.753
And as we study these and also the pollen
record,

ba5a71ea-1c3a-4ede-9727-de39df5f6e86-1
00:14:45.753 --> 00:14:50.303
we're we're learning how the spread of
grasslands in North America was also

ba5a71ea-1c3a-4ede-9727-de39df5f6e86-2
00:14:50.303 --> 00:14:51.800
different at local areas.

eab199e3-495c-4498-8a57-15dd481ce0bf-0
00:14:51.800 --> 00:14:53.520
It wasn't just all at the same time.

05acf079-9521-4af5-8315-4a88fc4feb77-0
00:14:53.520 --> 00:14:56.316
Maybe a mountain was in the way causing a
rain shadow,

05acf079-9521-4af5-8315-4a88fc4feb77-1
00:14:56.316 --> 00:15:00.080
and that prevented the grasses from quite
getting there for a little bit.

b133aaf3-2bd6-4a39-b296-0d3a76eef5b3-0
00:15:01.920 --> 00:15:05.151
And so this is the motivation for looking
at this in Oregon,

b133aaf3-2bd6-4a39-b296-0d3a76eef5b3-1
00:15:05.151 --> 00:15:07.960
because Oregon actually has a wonderful
rock record.

dd11b81a-3372-4f36-b7a8-09e75b8eecd0-0
00:15:08.000 --> 00:15:11.152
There's a lot of volcanoes in Oregon,
if you didn't know,

dd11b81a-3372-4f36-b7a8-09e75b8eecd0-1
00:15:11.152 --> 00:15:15.445
and they make a lot of volcanic ash and
that kills and also preserves a lot of

dd11b81a-3372-4f36-b7a8-09e75b8eecd0-2
00:15:15.445 --> 00:15:15.880
fossils.

fe46ae31-8714-4ce9-87c1-4f5f48ce1588-0
00:15:16.120 --> 00:15:20.813
And so there's almost a continuous rock
record in Oregon from around 40 million

fe46ae31-8714-4ce9-87c1-4f5f48ce1588-1
00:15:20.813 --> 00:15:23.160
years ago to around 7,000,000 years ago.

8a7c2f25-c35d-489f-8729-319cbdbb2556-0
00:15:23.520 --> 00:15:27.320
And there's many fossiliferous deposits
throughout that record.

e57b5da8-bea8-41ac-9cad-d9b199f0966a-0
00:15:28.240 --> 00:15:31.960
And so here's just a example of some of
the fossil localities that I've studied.

5f98fa40-53dd-41f6-96b6-7dc876a30bf8-0
00:15:32.360 --> 00:15:36.182
And these are murals at the John Day
Fossil Beds Park,

5f98fa40-53dd-41f6-96b6-7dc876a30bf8-1
00:15:36.182 --> 00:15:41.604
the National Monument representing this
change that we think occurred also in

5f98fa40-53dd-41f6-96b6-7dc876a30bf8-2
00:15:41.604 --> 00:15:44.870
Oregon,
going from more forested ecosystems to

5f98fa40-53dd-41f6-96b6-7dc876a30bf8-3
00:15:44.870 --> 00:15:48.485
maybe something that looked sort of like
a Saviana,

5f98fa40-53dd-41f6-96b6-7dc876a30bf8-4
00:15:48.485 --> 00:15:52.516
where you have patchy forested areas and
more open areas,

5f98fa40-53dd-41f6-96b6-7dc876a30bf8-5
00:15:52.516 --> 00:15:56.199
and then finally to this open grassland
environment.

413d6795-5a3b-415c-8b65-8da2c40040c8-0
00:15:59.040 --> 00:16:03.767
Here's some examples of the really lovely
fossil bearing rocks that I got to work

413d6795-5a3b-415c-8b65-8da2c40040c8-1
00:16:03.767 --> 00:16:08.032
in out in Oregon just to show you the
scale of the rock record that we're

413d6795-5a3b-415c-8b65-8da2c40040c8-2
00:16:08.032 --> 00:16:08.840
talking about.

6c0e2b4f-b0e2-405b-91e4-190d23bfe492-0
00:16:09.120 --> 00:16:13.876
And I also want to point this out because
all of these little ledges here are

6c0e2b4f-b0e2-405b-91e4-190d23bfe492-1
00:16:13.876 --> 00:16:16.560
actually ash deposits and we can date
them.

63fe14ac-5c0b-4d80-9688-3281133f3084-0
00:16:16.880 --> 00:16:20.447
And this is important because it allows
us to really precisely know the ages of

63fe14ac-5c0b-4d80-9688-3281133f3084-1
00:16:20.447 --> 00:16:23.168
some of these fossils,
which is why I can tell you the story

63fe14ac-5c0b-4d80-9688-3281133f3084-2
00:16:23.168 --> 00:16:24.640
that I'm going to tell you today.

0d3986ef-5ade-4aa3-897f-9205ab76af03-0
00:16:27.360 --> 00:16:34.202
So another important reason why I wanted
to study this phenomenon in Oregon is

0d3986ef-5ade-4aa3-897f-9205ab76af03-1
00:16:34.202 --> 00:16:37.320
that it's very geologically dynamic.

4b31a4b2-b952-42c7-b18e-ca8b7a5cf579-0
00:16:37.320 --> 00:16:41.087
And there's been a lot of events that
have occurred in Oregon throughout this

4b31a4b2-b952-42c7-b18e-ca8b7a5cf579-1
00:16:41.087 --> 00:16:42.440
time that has been recorded.

289a827b-b31b-4350-b377-0a6e7ee68af5-0
00:16:42.880 --> 00:16:45.680
So we have a lot of volcanoes in Oregon.

a3c22806-0bf8-4d35-8bb1-d1c2b9278364-0
00:16:45.840 --> 00:16:49.353
There's a lot of mountain building events,
there's faulting,

a3c22806-0bf8-4d35-8bb1-d1c2b9278364-1
00:16:49.353 --> 00:16:53.731
there's the the Columbia Flood basalts
that kind of coded the in almost the

a3c22806-0bf8-4d35-8bb1-d1c2b9278364-2
00:16:53.731 --> 00:16:54.480
entire state.

b2d8f32d-fed7-44d7-a5b3-9b8210fedece-0
00:16:55.080 --> 00:16:58.063
And so these,
these geologic events make it very

b2d8f32d-fed7-44d7-a5b3-9b8210fedece-1
00:16:58.063 --> 00:17:01.412
different and unique compared to say the
Great Plains,

b2d8f32d-fed7-44d7-a5b3-9b8210fedece-2
00:17:01.412 --> 00:17:06.222
the interior of North America where we
have a lot of the evidence of grassland

b2d8f32d-fed7-44d7-a5b3-9b8210fedece-3
00:17:06.222 --> 00:17:07.440
expansion happening.

3884229f-5177-4a72-915f-4bf85a4361db-0
00:17:07.440 --> 00:17:11.794
So a lot of the studies that have been
done before have mostly focused on the

3884229f-5177-4a72-915f-4bf85a4361db-1
00:17:11.794 --> 00:17:12.520
Great Plains.

b23d5fd9-6c44-4174-90ea-4b39523ef700-0
00:17:12.640 --> 00:17:15.811
And instead,
we want to see if the same pattern is

b23d5fd9-6c44-4174-90ea-4b39523ef700-1
00:17:15.811 --> 00:17:18.920
occurring here in this geologically
complex area.

b284ca41-4b79-44da-863e-479b0b46aad9-0
00:17:20.160 --> 00:17:24.646
So basically the question is,
is the same thing happening in Oregon

b284ca41-4b79-44da-863e-479b0b46aad9-1
00:17:24.646 --> 00:17:26.560
that we are seeing elsewhere?

495a44b0-e406-46f0-9586-77b9c186cf4a-0
00:17:28.560 --> 00:17:32.116
So the specific questions that I ended up
asking are,

495a44b0-e406-46f0-9586-77b9c186cf4a-1
00:17:32.116 --> 00:17:37.320
how has the Oregon mammalian community
changed over the last 32,000,000 years?

21d51b1f-e1c8-477a-b873-4382b424dfc5-0
00:17:37.440 --> 00:17:40.120
Did dietary diversity change in this time?

b6e528f7-c983-4c5f-844b-26004e40836c-0
00:17:40.120 --> 00:17:44.664
So the Lego Miocene is this time period
that I'm talking about around 32,000,

b6e528f7-c983-4c5f-844b-26004e40836c-1
00:17:44.664 --> 00:17:45.480
000 years ago.

ed26a952-35f3-4c57-9f9e-3c1f4dc60a97-0
00:17:46.040 --> 00:17:50.123
And do these changes in functional
diversity and trophic structure align

ed26a952-35f3-4c57-9f9e-3c1f4dc60a97-1
00:17:50.123 --> 00:17:53.480
with our current knowledge of what was
happening in Oregon?

defe8e46-4887-42a1-a343-27ff285bb8c2-0
00:17:53.720 --> 00:17:59.534
A lot of the evidence that we have for
this change from this forested ecosystem

defe8e46-4887-42a1-a343-27ff285bb8c2-1
00:17:59.534 --> 00:18:05.640
to a grassland ecosystem is based on very
little of our plant fossils that we have.

62af4c72-7421-4734-a1da-1b9e37cb7cec-0
00:18:07.240 --> 00:18:08.200
And also paleosols.

8579507b-155d-408b-a396-4d68457039fc-0
00:18:08.200 --> 00:18:11.480
There was some paleosol which are ancient
soil data.

37d3109f-82d7-4c87-99d4-8e80290f903f-0
00:18:12.800 --> 00:18:17.247
So the proxies that I'm going to use
instead are tooth morphology that I

37d3109f-82d7-4c87-99d4-8e80290f903f-1
00:18:17.247 --> 00:18:18.040
talked about.

5fb5ec18-f8ac-4e99-b6ec-f60ad2a72760-0
00:18:18.040 --> 00:18:20.794
So we're going to look at tooth height,
body size again,

5fb5ec18-f8ac-4e99-b6ec-f60ad2a72760-1
00:18:20.794 --> 00:18:24.080
so because that tracks with a lot of
different dietary differences.

c837d307-1fbc-44de-b487-097d17030721-0
00:18:24.320 --> 00:18:27.760
And then we're also going to use isotopic
evidence because you are what you eat.

447ae593-30df-41b3-b4ea-2a30cc29376c-0
00:18:27.760 --> 00:18:31.973
So if you eat a particular kind of plant
that is going to be preserved in your

447ae593-30df-41b3-b4ea-2a30cc29376c-1
00:18:31.973 --> 00:18:32.240
body.

4d261c02-82d3-4570-8b9b-ef8036743502-0
00:18:32.240 --> 00:18:36.150
And so this is another line of evidence
that we can use to kind of balance

4d261c02-82d3-4570-8b9b-ef8036743502-1
00:18:36.150 --> 00:18:38.080
against those other two measurements.

ea615031-3684-49f3-8b52-5aabb9b57b23-0
00:18:39.200 --> 00:18:42.282
So again,
the tooth height that I mentioned, Oh,

ea615031-3684-49f3-8b52-5aabb9b57b23-1
00:18:42.282 --> 00:18:45.680
and this is the example of the isotopic
measurements.

a30b7b66-3fd7-41a6-be8e-f2d2d8d241e2-0
00:18:45.680 --> 00:18:50.054
So if you're a grazer,
you're going to have a different value of

a30b7b66-3fd7-41a6-be8e-f2d2d8d241e2-1
00:18:50.054 --> 00:18:55.504
both carbon and oxygen in your body then
compared if you eat ate plants that are

a30b7b66-3fd7-41a6-be8e-f2d2d8d241e2-2
00:18:55.504 --> 00:18:57.119
in a closed environment.

8fa4d68f-0b68-4def-a919-2d3440f747c9-0
00:18:57.160 --> 00:18:59.120
I'm going to break that down for you even
more.

73b2c05c-081f-430c-8287-85ede0aa8d71-0
00:18:59.840 --> 00:19:03.840
So in order for us to talk about isotopes,
we actually need to talk about plants.

3d86cfc8-2355-41a8-8301-a68126da6900-0
00:19:03.960 --> 00:19:06.640
So you thought maybe it was just about
mammals today.

8df97c4e-2cb1-40d6-8274-8e172a7b1af1-0
00:19:06.640 --> 00:19:07.160
JK.

cc64d744-597e-47b5-996b-227a00cb86d2-0
00:19:07.160 --> 00:19:08.280
We got to go back to plants.

39fce96b-a21a-4eef-9ae1-9d06c1a91716-0
00:19:08.720 --> 00:19:12.532
So if you haven't thought about them in a
while,

39fce96b-a21a-4eef-9ae1-9d06c1a91716-1
00:19:12.532 --> 00:19:15.800
there are two pathways for photosynthesis.

1a14d3eb-d937-44b2-ba10-9cf9d230912a-0
00:19:15.800 --> 00:19:17.720
There's the C3 pathway.

57afa7c9-2e10-4b1a-867c-9cde6f103d3a-0
00:19:17.720 --> 00:19:21.560
So we have C3 plants and then we have C4
plants.

e3b15376-0ffb-4bc2-b0cf-be84397ec7f4-0
00:19:21.840 --> 00:19:26.984
And the main difference that's important
for isotopic work is that C3 plants do

e3b15376-0ffb-4bc2-b0cf-be84397ec7f4-1
00:19:26.984 --> 00:19:32.192
all of their photosynthesis in the same
cell that they actually take CO2 in from

e3b15376-0ffb-4bc2-b0cf-be84397ec7f4-2
00:19:32.192 --> 00:19:35.922
the atmosphere,
but C4 plants actually pass that CO2 down

e3b15376-0ffb-4bc2-b0cf-be84397ec7f4-3
00:19:35.922 --> 00:19:37.080
into another cell.

e6ee7534-b278-413b-9902-cd8f9f3225fe-0
00:19:37.440 --> 00:19:41.354
And this allows them to do better in
drier environments,

e6ee7534-b278-413b-9902-cd8f9f3225fe-1
00:19:41.354 --> 00:19:44.720
but it also influences their isotopic
signature.

4818e246-f17d-45fd-a387-c34aaf5dd3c3-0
00:19:45.840 --> 00:19:49.880
And Oregon is actually mostly C3 plants.

51ca0910-725a-403e-89a3-f5906a01df03-0
00:19:49.880 --> 00:19:54.533
There's very little C4 plants in Oregon,
and this is because of the amount of rain

51ca0910-725a-403e-89a3-f5906a01df03-1
00:19:54.533 --> 00:19:56.160
and the climate in that area.

9c0cc54c-0072-4377-84c7-6f918d844ac6-0
00:19:56.160 --> 00:20:00.261
So C4 plants do better in drier,
sort of like the interior of North

9c0cc54c-0072-4377-84c7-6f918d844ac6-1
00:20:00.261 --> 00:20:03.760
America and Oregon gets too much rain for
them basically.

aef979d3-a505-4e7b-bf37-9b46f612bc0e-0
00:20:04.720 --> 00:20:05.640
And this is actually.

0cf5b306-35eb-4010-9171-9983ee026ae5-0
00:20:06.160 --> 00:20:11.400
Great for us and for my project because
C3 plants are very sensitive to the

0cf5b306-35eb-4010-9171-9983ee026ae5-1
00:20:11.400 --> 00:20:16.709
environmental conditions and with which
they're growing and they'll actually

0cf5b306-35eb-4010-9171-9983ee026ae5-2
00:20:16.709 --> 00:20:18.640
record that in their bodies.

2d42cdd1-0c7d-460a-89cb-d92a148f101f-0
00:20:19.160 --> 00:20:23.939
And so this is a plot here done from a
whole bunch of measurements from around

2d42cdd1-0c7d-460a-89cb-d92a148f101f-1
00:20:23.939 --> 00:20:28.840
the globe of C3 plants and their isotopic
values specifically related to carbon.

bf88ad09-8663-4eb9-8ee5-0bcecaa60adb-0
00:20:29.160 --> 00:20:33.250
And down here is the precipitation,
so the mean precipitation that they're

bf88ad09-8663-4eb9-8ee5-0bcecaa60adb-1
00:20:33.250 --> 00:20:33.960
experiencing.

6a260d58-4c28-4eb2-a875-efe24d92c756-0
00:20:34.320 --> 00:20:39.694
And so if we have a very closed forested
environment that's very humid and damp

6a260d58-4c28-4eb2-a875-efe24d92c756-1
00:20:39.694 --> 00:20:42.918
and dark,
those will have a different value and

6a260d58-4c28-4eb2-a875-efe24d92c756-2
00:20:42.918 --> 00:20:48.360
will be they'll have a different value
than those that live in dry environments.

0b8aa0a1-89a3-4262-adad-d3f09df0abb9-0
00:20:48.360 --> 00:20:51.234
So as the plants become more and more
water stressed,

0b8aa0a1-89a3-4262-adad-d3f09df0abb9-1
00:20:51.234 --> 00:20:54.960
they'll actually become heavier and
heavier in their isotopic values.

da51135e-5442-4944-8f4a-27dd2153c06f-0
00:20:56.400 --> 00:20:59.480
It is important to note a few things.

e8642b5f-be03-4785-8d26-fbc466eaa8ba-0
00:20:59.480 --> 00:21:03.160
So these differences,
we can we can track them in animals too.

7deb1901-6f49-4db5-ac7a-461a208b18c6-0
00:21:03.160 --> 00:21:07.306
So if an animal is eating these plants
that are experiencing water stress,

7deb1901-6f49-4db5-ac7a-461a208b18c6-1
00:21:07.306 --> 00:21:09.960
we can actually see that in their their
bodies.

bd00db82-58b0-4458-9240-dfe9a80f6d6c-0
00:21:10.160 --> 00:21:13.505
And this is a measurement of tooth enamel,
which is what I'm going to talk about

bd00db82-58b0-4458-9240-dfe9a80f6d6c-1
00:21:13.505 --> 00:21:15.240
because it's preserved in the rock record.

462afed8-5a1c-47b8-87df-31572d30f6b5-0
00:21:16.120 --> 00:21:20.913
And this is a bison versus some of the
other deer that live in Yellowstone,

462afed8-5a1c-47b8-87df-31572d30f6b5-1
00:21:20.913 --> 00:21:25.896
showing that if you're a grazing bison
grazing a lot of grass and driers parts

462afed8-5a1c-47b8-87df-31572d30f6b5-2
00:21:25.896 --> 00:21:29.555
of the environment,
then you'll have a different isotopic

462afed8-5a1c-47b8-87df-31572d30f6b5-3
00:21:29.555 --> 00:21:34.160
signature than if you're more of a
browser eating things in the forests.

92117b56-5a84-4a43-b1c1-2edcbecce383-0
00:21:35.400 --> 00:21:39.972
One thing to keep in mind though,
that'll come up later when I talk about

92117b56-5a84-4a43-b1c1-2edcbecce383-1
00:21:39.972 --> 00:21:43.557
the actual values,
is that a lot of the time we make this

92117b56-5a84-4a43-b1c1-2edcbecce383-2
00:21:43.557 --> 00:21:48.253
distinction between grass and trees,
and this is pretty well documented and

92117b56-5a84-4a43-b1c1-2edcbecce383-3
00:21:48.253 --> 00:21:51.899
it's kind of true,
but more evidence is showing that there

92117b56-5a84-4a43-b1c1-2edcbecce383-4
00:21:51.899 --> 00:21:56.720
are many different kinds of plants that
can have the same isotopic signature.

e103d64b-3837-4fa1-a90d-ae6316a718a1-0
00:21:56.720 --> 00:22:00.360
And it's really going back to that amount
of evaporation that's happening.

abda6631-4fdb-4486-b096-8c4480fead04-0
00:22:00.480 --> 00:22:02.972
So if they're all hanging out in a dry
environment,

abda6631-4fdb-4486-b096-8c4480fead04-1
00:22:02.972 --> 00:22:06.760
even if there is a tree and a grass,
a lot of the times they'll look the same.

14801a54-cd0f-41ba-bca6-de253c0428fd-0
00:22:07.000 --> 00:22:10.200
And even parts of the plant can be
different from one another.

2317f951-ac1f-4ddf-9290-753eed543e97-0
00:22:10.440 --> 00:22:15.393
So if you have the top of a plant that's
experiencing more evaporation,

2317f951-ac1f-4ddf-9290-753eed543e97-1
00:22:15.393 --> 00:22:18.696
then the top of the plant will be not as
it'll,

2317f951-ac1f-4ddf-9290-753eed543e97-2
00:22:18.696 --> 00:22:22.480
it'll be a little bit more heavy than the
other parts.

310b68bb-6029-46aa-b3af-01e5d7cc4431-0
00:22:26.440 --> 00:22:30.422
OK, so data collection,
we'll come back to the isotopes and I'll

310b68bb-6029-46aa-b3af-01e5d7cc4431-1
00:22:30.422 --> 00:22:34.404
explain it again and it'll,
it'll make sense once we have actual

310b68bb-6029-46aa-b3af-01e5d7cc4431-2
00:22:34.404 --> 00:22:36.120
values from these organisms.

253d10e8-708d-4a73-8bc0-d4ae22070f13-0
00:22:36.440 --> 00:22:39.794
So data collection,
I spent a long time in the field and also

253d10e8-708d-4a73-8bc0-d4ae22070f13-1
00:22:39.794 --> 00:22:43.960
visiting a number of museums to find the
right specimens to do this project.

4480944e-4902-4753-9a54-0f39c7605959-0
00:22:44.320 --> 00:22:46.755
And because I was looking at many fossil
sites,

4480944e-4902-4753-9a54-0f39c7605959-1
00:22:46.755 --> 00:22:50.966
we actually needed quite a few specimens,
partially because we're going to compare

4480944e-4902-4753-9a54-0f39c7605959-2
00:22:50.966 --> 00:22:54.568
many different organisms and we wanted a
sample size greater than one,

4480944e-4902-4753-9a54-0f39c7605959-3
00:22:54.568 --> 00:22:58.120
which is actually harder to do in the
fossil record than you'd think.

97bbae69-3560-4ebc-84d4-536e0075af67-0
00:22:58.920 --> 00:23:01.800
And then we're also comparing different
formations.

63e5879f-b924-49cc-9331-d69136748fc5-0
00:23:01.800 --> 00:23:04.880
So we wanted to make sure that we had
enough of a sample size in each one.

18614c2d-12b0-47e3-a79c-ff3538a1642c-0
00:23:05.920 --> 00:23:09.537
And I'm highly grateful to a lot of the
people that allowed me to go to museums

18614c2d-12b0-47e3-a79c-ff3538a1642c-1
00:23:09.537 --> 00:23:11.120
and look through their collections.

d671d03e-533b-4d2e-8baf-1bf3ee86d4a1-0
00:23:11.120 --> 00:23:15.120
And isotopic work can be kind of
challenging because it is destructive.

1f7e9487-3de6-46b1-9989-9676bc51be3a-0
00:23:15.120 --> 00:23:18.120
So you actually need to take a tiny
sample of the tooth.

2c6ca7d0-7ffd-4ec4-b41f-e99f1afa95e2-0
00:23:18.160 --> 00:23:20.000
You need to take a drill and drill it off.

297d24df-9ed7-4f4a-baea-50e07c6a262f-0
00:23:20.640 --> 00:23:25.297
And so I spent a long time trying to sift
through what was in the collections and

297d24df-9ed7-4f4a-baea-50e07c6a262f-1
00:23:25.297 --> 00:23:28.592
finding the worst teeth but that were
still identifiable,

297d24df-9ed7-4f4a-baea-50e07c6a262f-2
00:23:28.592 --> 00:23:30.240
which is also very difficult.

c3a9b524-370b-44aa-bf5e-cd291ebd1000-0
00:23:30.720 --> 00:23:33.202
You want the tooth that you know what the
animal is,

c3a9b524-370b-44aa-bf5e-cd291ebd1000-1
00:23:33.202 --> 00:23:36.200
but no one's going to mind if you just
take a little bit of it.

007b3d17-0337-49b3-8657-ac565260707d-0
00:23:36.800 --> 00:23:38.920
And we do only take a really tiny bit.

f74718e4-5076-4d35-a74b-62408874ce9c-0
00:23:38.920 --> 00:23:42.960
And I wish I had a vial to show you how
small of the amount of powder.

a4a382d9-341d-4b0a-9e64-c06d4a4bdbf8-0
00:23:43.080 --> 00:23:46.938
We're getting better and better at this
technique as as time progresses,

a4a382d9-341d-4b0a-9e64-c06d4a4bdbf8-1
00:23:46.938 --> 00:23:49.000
we need fewer and fewer bits of enamel.

6a8f4172-fc82-4aa1-8f4f-8ecf0edead0c-0
00:23:49.520 --> 00:23:51.988
But still,
it can be somewhat difficult to convince

6a8f4172-fc82-4aa1-8f4f-8ecf0edead0c-1
00:23:51.988 --> 00:23:54.600
someone to let you drill such a nice,
beautiful tooth.

9cc7d26c-d999-457f-8dd5-2e25f937f196-0
00:23:56.200 --> 00:23:59.021
So once we got all these samples,
we had to clean them,

9cc7d26c-d999-457f-8dd5-2e25f937f196-1
00:23:59.021 --> 00:24:00.080
we had to drill them.

8719e4f9-89da-4c4b-9350-2f965900aabc-0
00:24:00.200 --> 00:24:03.800
We took them back to the Oregon Stable
Isotope lab.

5da92714-dfc9-4790-bc73-6dd3b3affcf2-0
00:24:04.120 --> 00:24:08.548
And as you can see,
I spent a lot of my COVID time alone in

5da92714-dfc9-4790-bc73-6dd3b3affcf2-1
00:24:08.548 --> 00:24:12.238
the lab,
measuring out tiny little bits of enamel

5da92714-dfc9-4790-bc73-6dd3b3affcf2-2
00:24:12.238 --> 00:24:15.928
powder,
washing them to get out any contaminants,

5da92714-dfc9-4790-bc73-6dd3b3affcf2-3
00:24:15.928 --> 00:24:19.839
drying them,
and then running them on the mass spec.

a0823a3f-ab1f-414d-92f1-a9afa448a63d-0
00:24:20.200 --> 00:24:24.000
So many days were spent looking and
lording over my samples.

cca6454b-2a01-4aa4-846c-de8aeeddba51-0
00:24:26.400 --> 00:24:28.872
So the first thing we're going to talk
about,

cca6454b-2a01-4aa4-846c-de8aeeddba51-1
00:24:28.872 --> 00:24:33.120
the results that I would like to talk
about is actually not the isotopic work.

f5565d27-9063-4af9-a52e-0fac6d286035-0
00:24:33.120 --> 00:24:36.481
So we're combining different types of
data again, because again,

f5565d27-9063-4af9-a52e-0fac6d286035-1
00:24:36.481 --> 00:24:38.240
we can't see these animals eating.

73ed20d4-42b5-4773-b184-e6135bd4dea2-0
00:24:38.240 --> 00:24:40.880
So we have to use many lines of evidence
to figure it out.

427e2b5c-9805-49ca-8700-9b3870f362df-0
00:24:41.360 --> 00:24:44.480
So the first thing we're going to look at
is actually the morphology again.

40e1bc0a-a674-4c96-934f-e316e1ec52a3-0
00:24:44.720 --> 00:24:48.840
So we're going to look at tooth height,
oops, tooth height.

757e76c9-f510-4a20-8573-a45058786946-0
00:24:49.880 --> 00:24:54.400
And for this plot, again,
we've got body mass heavier on this side.

67621ced-8d52-4656-824a-e343deebd014-0
00:24:54.400 --> 00:24:59.177
This is log scale again and we're
actually got three different types of

67621ced-8d52-4656-824a-e343deebd014-1
00:24:59.177 --> 00:25:00.040
tooth height.

fc5d4ab1-aa3d-4976-a4d1-46649349b442-0
00:25:00.040 --> 00:25:04.120
We've got that low crown, brachydont,
mesodont, hipsodont.

0f27a623-0446-4fe3-92ed-efdb1ef5a225-0
00:25:04.920 --> 00:25:09.120
And so hipsodont's the taller tooth,
Brachyodont's the lower tooth.

b9b27499-2613-4ac6-b700-e62df4bb19ee-0
00:25:09.200 --> 00:25:13.027
And the first thing to notice is that in
the Turtle Cove Member,

b9b27499-2613-4ac6-b700-e62df4bb19ee-1
00:25:13.027 --> 00:25:16.972
which is our oldest fossil site,
and this is during the Oligocene,

b9b27499-2613-4ac6-b700-e62df4bb19ee-2
00:25:16.972 --> 00:25:20.682
during that time that we think there
wasn't any grasses there,

b9b27499-2613-4ac6-b700-e62df4bb19ee-3
00:25:20.682 --> 00:25:22.920
we have no high crown tooth organisms.

0211f8cc-66e3-47bc-bb6a-d3af9bf94f81-0
00:25:22.920 --> 00:25:27.440
So these are all herbivores, ungulates,
but none of them have tall teeth.

9479094a-5b3b-4d43-9a0e-3ad0d1bdca5f-0
00:25:27.520 --> 00:25:29.000
They're all very short.

bd14e144-715b-435d-9df9-be14298b58d3-0
00:25:29.280 --> 00:25:31.720
And they also don't have really large
body sizes.

49655ead-5c17-46a2-81c3-885c56edfe25-0
00:25:31.720 --> 00:25:35.530
There's some that do get kind of large,
but a lot of the organisms are kind of in

49655ead-5c17-46a2-81c3-885c56edfe25-1
00:25:35.530 --> 00:25:36.320
this lower range.

ebcc430b-ea88-448f-8027-b19e4a06d17c-0
00:25:38.040 --> 00:25:42.840
Once we get into the Maskel formation,
which is during this time which is called

ebcc430b-ea88-448f-8027-b19e4a06d17c-1
00:25:42.840 --> 00:25:47.284
the Mid Myosin climatic optimum,
which is when we have a warming event and

ebcc430b-ea88-448f-8027-b19e4a06d17c-2
00:25:47.284 --> 00:25:49.240
it becomes a little bit more wet.

47c064c8-9a70-4c35-87fe-ba646a4ca16d-0
00:25:50.720 --> 00:25:56.320
And this is when we think there was maybe
a mixture of a mixture of environments.

4163fe27-b669-4058-a8ca-48d02931665a-0
00:25:56.320 --> 00:25:59.652
And so we maybe have some grasses,
some patchiness,

4163fe27-b669-4058-a8ca-48d02931665a-1
00:25:59.652 --> 00:26:01.960
some heterogeneity on the landscape.

327cf401-7715-49f6-8972-9c5c76d81303-0
00:26:02.240 --> 00:26:04.320
We have a lot of different organisms.

3b75257d-e424-4c30-88d1-bd801c55f1d0-0
00:26:04.320 --> 00:26:08.767
We have some that are smaller and have
low crown teeth and then we have some mid

3b75257d-e424-4c30-88d1-bd801c55f1d0-1
00:26:08.767 --> 00:26:13.160
crown teeth and then we have those high
crown tooth organisms living there too.

16e1759e-f7a4-44d1-aac1-087aab982405-0
00:26:13.840 --> 00:26:16.537
And then once we get into the rattlesnake
formation,

16e1759e-f7a4-44d1-aac1-087aab982405-1
00:26:16.537 --> 00:26:20.557
which is when we think grasslands had
really established themselves in Oregon,

16e1759e-f7a4-44d1-aac1-087aab982405-2
00:26:20.557 --> 00:26:24.119
we can see a big shift in the community
and we have larger organisms.

e44f8187-2381-44bc-89d0-85852d319dc9-0
00:26:24.360 --> 00:26:26.381
Again,
it's kind of hard to be small in a

e44f8187-2381-44bc-89d0-85852d319dc9-1
00:26:26.381 --> 00:26:27.440
grassland environment.

56f1cec8-3994-49f3-bdd7-3f821fc5cf71-0
00:26:27.760 --> 00:26:31.992
And then we also have a lot more
organisms that have taller teeth and we

56f1cec8-3994-49f3-bdd7-3f821fc5cf71-1
00:26:31.992 --> 00:26:36.920
only have one individual species that has
a low crown tooth and this is the peccary.

c5ab0037-630c-48ed-93e4-279d81706a25-0
00:26:36.920 --> 00:26:39.040
So this is more of a a pig like animal.

10776612-f2b6-4fd3-aaa1-67c63e0036e7-0
00:26:41.880 --> 00:26:43.080
So that's the morphology.

b9da2bbe-5ed0-47f0-a648-f9301fa50ee1-0
00:26:43.080 --> 00:26:44.600
That's what the morphology is telling us.

406ef9de-7386-40a2-8ebe-efd8a7cfe7d6-0
00:26:44.600 --> 00:26:46.360
But let's let's think about the isotopes.

0a4977e5-a02e-42db-a3c8-12bab841f9d7-0
00:26:46.880 --> 00:26:51.333
And I'm going to like explain the isotope
chart real quick because it can be kind

0a4977e5-a02e-42db-a3c8-12bab841f9d7-1
00:26:51.333 --> 00:26:52.040
of confusing.

a8eaba13-2b33-4fa3-a3af-c9a960a1c57b-0
00:26:52.360 --> 00:26:55.459
Basically,
we have lower values which are indicating

a8eaba13-2b33-4fa3-a3af-c9a960a1c57b-1
00:26:55.459 --> 00:27:00.080
more closed environment and we're going
to plot the tooth values on this plot.

28372c8d-feb4-495f-8fc8-572ed9c90d81-0
00:27:00.400 --> 00:27:05.595
And these lines are indicating the
different sort of levels for those global

28372c8d-feb4-495f-8fc8-572ed9c90d81-1
00:27:05.595 --> 00:27:08.160
plant values that I mentioned earlier.

54c73cf1-5518-4b02-97b0-cea171bc86ba-0
00:27:08.160 --> 00:27:12.932
So beyond this line,
we're in the water stressed C3 plant

54c73cf1-5518-4b02-97b0-cea171bc86ba-1
00:27:12.932 --> 00:27:13.920
environment.

f45ed9cc-dfe6-4bdd-bd42-b691f2b8efc8-0
00:27:13.920 --> 00:27:16.917
So if an animal has a tooth value in this
area,

f45ed9cc-dfe6-4bdd-bd42-b691f2b8efc8-1
00:27:16.917 --> 00:27:21.600
we're thinking that it lived and ate food
in a water stressed environment.

226b605b-9542-470e-a962-c14fff163cfb-0
00:27:22.000 --> 00:27:26.360
Beyond the line into the white would
actually indicate C4 plants.

efa8117d-55f3-475f-bc13-a812994d3a16-0
00:27:26.440 --> 00:27:30.997
So that would indicate that we have C4
plants and it was eating something even

efa8117d-55f3-475f-bc13-a812994d3a16-1
00:27:30.997 --> 00:27:31.920
higher in value.

6443df1a-6611-47f7-b9f5-8efd66e355ee-0
00:27:33.880 --> 00:27:35.970
Again,
I'm going to remind you that these cut

6443df1a-6611-47f7-b9f5-8efd66e355ee-1
00:27:35.970 --> 00:27:39.607
offs for thinking about the environment,
they also can indicate different plant

6443df1a-6611-47f7-b9f5-8efd66e355ee-2
00:27:39.607 --> 00:27:39.880
parts.

a8c81933-88c3-4a10-9f22-2adc39c4cbbb-0
00:27:39.880 --> 00:27:44.280
So think tops of trees and bushes that
are experiencing a lot of evaporation.

bd93a87d-3cf3-4f81-ab6a-b1ebfd5003b6-0
00:27:45.480 --> 00:27:45.880
OK.

dbe8f614-cb5d-4185-b8b2-6bdffeaf411b-0
00:27:45.960 --> 00:27:50.142
So this is all of our data put together,
which is again,

dbe8f614-cb5d-4185-b8b2-6bdffeaf411b-1
00:27:50.142 --> 00:27:55.720
we've got the Turtle Cove member here,
Masco formation and the rattlesnake.

eb71cfe9-6a07-40ed-8aa8-a17a0101d092-0
00:27:55.720 --> 00:27:59.680
So this is that closed environment that
we think is happening in the Oligocene.

859ea7a9-a2c6-4f02-9632-d741648350c4-0
00:27:59.920 --> 00:28:04.190
We've got this warm environment where we
think we have a mixture of environments

859ea7a9-a2c6-4f02-9632-d741648350c4-1
00:28:04.190 --> 00:28:06.510
and then we have the rattlesnake
formation,

859ea7a9-a2c6-4f02-9632-d741648350c4-2
00:28:06.510 --> 00:28:09.200
which we think is more of a grassland
environment.

3eedecf4-3246-49b6-82f9-0908c1092762-0
00:28:09.720 --> 00:28:13.173
So the first thing to note is that we
don't actually have any evidence of

3eedecf4-3246-49b6-82f9-0908c1092762-1
00:28:13.173 --> 00:28:14.200
closed canopy feeding.

797aa410-7419-4ed9-8b61-d701f95459a2-0
00:28:14.200 --> 00:28:18.686
So the forests that were there,
we're not dense enough to have the same

797aa410-7419-4ed9-8b61-d701f95459a2-1
00:28:18.686 --> 00:28:22.800
signal that we get from a very densely
closed canopy environment.

1f5a4b6c-55f5-4116-a34c-6f3830d9a01f-0
00:28:24.440 --> 00:28:28.453
The other thing to know is that a lot of
the organisms in the rattlesnake

1f5a4b6c-55f5-4116-a34c-6f3830d9a01f-1
00:28:28.453 --> 00:28:30.080
formation have similar values.

3728fb2c-aa28-4e03-9a90-8b9d1a7ff2b9-0
00:28:30.320 --> 00:28:35.435
So the there's kind of a shrinking of the
values and expansion of space that they

3728fb2c-aa28-4e03-9a90-8b9d1a7ff2b9-1
00:28:35.435 --> 00:28:37.120
were occupying at the time.

58c6d47b-b6ed-4f60-bff8-5e25f195563f-0
00:28:37.120 --> 00:28:40.560
So maybe the landscape was more
homogeneous at that late stage.

ebfe3758-2437-43de-8280-6a04d6b88133-0
00:28:40.560 --> 00:28:42.795
So if we're thinking it's a continuous
grassland,

ebfe3758-2437-43de-8280-6a04d6b88133-1
00:28:42.795 --> 00:28:44.360
this would be consistent with that.

55fad513-6b87-473e-90cf-66923a7d51ef-0
00:28:45.560 --> 00:28:48.232
So again,
narrow values for the rattlesnake

55fad513-6b87-473e-90cf-66923a7d51ef-1
00:28:48.232 --> 00:28:48.840
formation.

cd3a98b1-f55d-4f28-8647-68e9ebdbb125-0
00:28:50.240 --> 00:28:54.349
I kind of want to get into the specific
animals because something that we can do

cd3a98b1-f55d-4f28-8647-68e9ebdbb125-1
00:28:54.349 --> 00:28:58.306
now that we have this information is we
can talk about specific organisms and

cd3a98b1-f55d-4f28-8647-68e9ebdbb125-2
00:28:58.306 --> 00:29:00.640
what they were eating related to one
another.

ce29260f-08e8-4ac7-9c2b-f4efb45eac98-0
00:29:01.040 --> 00:29:04.064
And I'd like to tell you a little bit
about oriadons because I find them

ce29260f-08e8-4ac7-9c2b-f4efb45eac98-1
00:29:04.064 --> 00:29:04.520
intriguing.

6d29590b-5a1e-4bd0-bd57-fbc682b24e36-0
00:29:04.760 --> 00:29:09.314
This is an extinct group of ungulates
that we don't have any analogues for

6d29590b-5a1e-4bd0-bd57-fbc682b24e36-1
00:29:09.314 --> 00:29:11.440
today, so they're not around today.

17081398-3852-42da-b84e-7e7c87eb2241-0
00:29:11.440 --> 00:29:13.280
We don't really know how they functioned.

687c4846-9679-43be-8028-152ce9221794-0
00:29:13.280 --> 00:29:15.960
We have fairly good idea from their
morphology.

64138bcf-e3a5-402f-9610-669080ebc0e1-0
00:29:16.600 --> 00:29:19.320
They're kind of like sheep, pig,
camel things.

99a4850d-ee23-4817-8dea-8fbbd8c46272-0
00:29:19.320 --> 00:29:20.720
They're very, they're very cute.

6b678fcd-e5c7-46be-8c00-af008b70e8e7-0
00:29:21.920 --> 00:29:27.600
And so they start off actually eating
plants more in the closed environment.

4c455466-835b-4c68-aa84-b25d39894257-0
00:29:27.600 --> 00:29:32.151
So this isn't completely closed canopy,
but they're kind of more hanging out in

4c455466-835b-4c68-aa84-b25d39894257-1
00:29:32.151 --> 00:29:35.280
the forests,
browsing a lot of the foods in that area.

03eb576d-a8a8-4281-8b63-a33fd0c2cb60-0
00:29:35.560 --> 00:29:41.380
And then actually as time goes on in the
Maskel, we only have one or a dot left,

03eb576d-a8a8-4281-8b63-a33fd0c2cb60-1
00:29:41.380 --> 00:29:47.200
that one species of oreont and it's kind
of expanded the foods that it's eating.

a45184d5-cc66-4ea2-bf17-bc0bdd4c220c-0
00:29:47.320 --> 00:29:50.480
So Ticoleptus has a little bit taller
teeth.

9c252dd3-7a60-44df-b301-f9dff29afea9-0
00:29:50.480 --> 00:29:54.840
It's getting into that mesodont,
that mid level high.

289fa4ff-904c-4320-b67e-c184e65bc175-0
00:29:55.000 --> 00:29:57.880
So only middle,
it's kind of hard to describe.

716b6ea0-3c23-492d-95a8-49ca4b864ecc-0
00:29:58.800 --> 00:30:01.280
Not high crowned,
it's middle crowned teeth.

a4068347-8935-4365-b9f0-030c4b26cd81-0
00:30:01.360 --> 00:30:07.400
So mesodont is what we say it is,
and so it we think it expanded its diet.

ea26f011-638c-4830-989b-4228a00a4383-0
00:30:07.400 --> 00:30:11.252
And this is maybe in response to this
changing landscape,

ea26f011-638c-4830-989b-4228a00a4383-1
00:30:11.252 --> 00:30:14.840
it's trying to kind of adapt to this new
food source.

8c5c9fce-8695-49b5-955d-3ae4443934f1-0
00:30:15.040 --> 00:30:18.240
And then actually when we get to the
rattlesnake, there's no more oridans.

3b766a5c-c0a8-4ab5-b17b-9be92aa97ab3-0
00:30:18.240 --> 00:30:20.000
They've actually gone extinct at this
point.

e5e031eb-b170-4245-acf6-fa63e83658e8-0
00:30:20.000 --> 00:30:23.480
So we don't see them on the landscape and
we have no evidence of them around.

8deac4c0-f4d4-433e-8044-bbdff0426eff-0
00:30:23.800 --> 00:30:29.248
So we actually might have this story of
them trying to adapt to this new

8deac4c0-f4d4-433e-8044-bbdff0426eff-1
00:30:29.248 --> 00:30:32.160
environment that's coming on the scene.

2d47cc50-66f7-4db1-bc37-21fcb97b6e8a-0
00:30:33.440 --> 00:30:39.137
Another example that was very intriguing
and very interesting to my colleagues and

2d47cc50-66f7-4db1-bc37-21fcb97b6e8a-1
00:30:39.137 --> 00:30:43.600
I from this data is there's a small horse
called Archaeo Hippus.

0e0acb26-cf1d-4be8-8ddd-31fee94cd105-0
00:30:43.600 --> 00:30:46.400
And that's what these dots are here.

75a503cb-111b-4dd7-af0d-26b3b65e48d4-0
00:30:46.600 --> 00:30:48.040
And it's actually quite small.

5c5cdee4-05de-4636-9f7e-ef5166086b72-0
00:30:48.200 --> 00:30:50.800
It's it's very, very small for the time.

4e8b1a59-9f70-4e85-a57a-5c92511ac91a-0
00:30:51.440 --> 00:30:54.618
And it's very interesting because its
values were quite high,

4e8b1a59-9f70-4e85-a57a-5c92511ac91a-1
00:30:54.618 --> 00:30:57.080
suggesting it was eating water stressed
plants.

e118a6a4-a782-4364-9518-09eddd8b8bac-0
00:30:57.280 --> 00:31:01.989
So we were trying to think about what
this would look like and we actually read

e118a6a4-a782-4364-9518-09eddd8b8bac-1
00:31:01.989 --> 00:31:03.520
up a lot about the poodoo.

8b2bd507-c822-447c-9329-4c99a7384867-0
00:31:03.960 --> 00:31:08.480
And the poodoo lives a life where it goes
out into these open these open areas.

620c6cc8-202c-4840-85a7-e676a37e6083-0
00:31:08.760 --> 00:31:11.800
It eats a lot of the the plants in these
open patches.

d12f2fe4-ba11-4a55-bcf2-deddb791f02a-0
00:31:11.800 --> 00:31:15.120
And then when it's threatened by a
predator, it runs back into the woods.

641762d3-8beb-4526-b501-81d320b3dfc3-0
00:31:15.600 --> 00:31:21.097
So it actually eats eats a lot of the
things that we would think would be

641762d3-8beb-4526-b501-81d320b3dfc3-1
00:31:21.097 --> 00:31:26.520
consistent with this value of having a
more high or enriched value here.

657ab617-05d4-4909-9c49-c72a3aba25c8-0
00:31:27.000 --> 00:31:30.388
And but it's body size and other parts of
it,

657ab617-05d4-4909-9c49-c72a3aba25c8-1
00:31:30.388 --> 00:31:36.429
it's morphology suggests that it probably
was relying on these forested areas for

657ab617-05d4-4909-9c49-c72a3aba25c8-2
00:31:36.429 --> 00:31:37.240
protection.

8ef0c596-af17-401e-94c4-362d940d3373-0
00:31:41.240 --> 00:31:44.080
OK, let's see how I'm doing on time.

a5123209-7374-4d03-a371-2d53c3e3b52d-0
00:31:44.440 --> 00:31:45.240
I'm doing all right.

e923d4c1-6805-49db-9594-fc30192d2986-0
00:31:45.920 --> 00:31:50.581
So conclusions from the isotopic study so
far that we have and actually this work

e923d4c1-6805-49db-9594-fc30192d2986-1
00:31:50.581 --> 00:31:51.320
is published.

dbfeb234-df9a-401a-b724-af0bbecfe70b-0
00:31:51.720 --> 00:31:55.506
So there's a lot more conclusions that I
didn't quite pick apart for you today,

dbfeb234-df9a-401a-b724-af0bbecfe70b-1
00:31:55.506 --> 00:31:57.400
but there's a, there's a bunch in there.

f7dc1e1c-7dd9-410b-a939-bd5abdd83ffe-0
00:31:57.960 --> 00:32:02.118
We have a more homogeneous ungulate
community both morphologically and

f7dc1e1c-7dd9-410b-a939-bd5abdd83ffe-1
00:32:02.118 --> 00:32:02.880
isotopically.

49b8d264-e382-4be8-8e3a-43759cdf5982-0
00:32:02.880 --> 00:32:08.286
So both lines of evidence kind of support
this as these global temperatures change

49b8d264-e382-4be8-8e3a-43759cdf5982-1
00:32:08.286 --> 00:32:09.720
and grasslands expand.

b971851d-d6be-4b4d-8995-0dbb295b2a1e-0
00:32:09.720 --> 00:32:14.128
So this story of going from a more
forested ecosystem to a grassland

b971851d-d6be-4b4d-8995-0dbb295b2a1e-1
00:32:14.128 --> 00:32:18.600
ecosystem is fairly consistent both
isotopically and morphologically.

4cf53681-1c15-4a92-af49-e7bf28829477-0
00:32:18.880 --> 00:32:23.208
And the things that are really changing
is we have those small browsers with the

4cf53681-1c15-4a92-af49-e7bf28829477-1
00:32:23.208 --> 00:32:25.720
low crown teeth being lost from the
landscape.

7aef4a6e-ce2e-4231-9b5a-c1d159ccc11d-0
00:32:28.560 --> 00:32:30.880
So the poor Oridan's no longer there.

520adf8b-8e56-4b80-8920-aa601093f1ff-0
00:32:33.880 --> 00:32:35.982
OK,
so that was the kind of the first project

520adf8b-8e56-4b80-8920-aa601093f1ff-1
00:32:35.982 --> 00:32:36.760
and that's great.

4818c46b-c357-402f-b682-6b74164777c9-0
00:32:36.760 --> 00:32:38.320
But what about the rest of the community?

02e19c77-e3f5-4209-a5f6-da5bbafa405a-0
00:32:38.320 --> 00:32:39.720
So we're seeing this change.

59e18d6c-e93b-4790-8db1-2e51fdcd087b-0
00:32:39.840 --> 00:32:43.481
So if we think about these communities
and these ecosystems,

59e18d6c-e93b-4790-8db1-2e51fdcd087b-1
00:32:43.481 --> 00:32:47.600
starting with the change in vegetation,
maybe grasses are coming in.

79ae2bda-e469-4344-8ea7-00bc8eab3b32-0
00:32:47.760 --> 00:32:51.930
We see this change in the ungulates,
the herbivores on the landscape that are

79ae2bda-e469-4344-8ea7-00bc8eab3b32-1
00:32:51.930 --> 00:32:53.000
eating those plants.

fef0808e-9de1-4be4-ada0-d170bbfc57fc-0
00:32:53.200 --> 00:32:58.539
Maybe we can layer in another layer here
and put a lot of the other organisms into

fef0808e-9de1-4be4-ada0-d170bbfc57fc-1
00:32:58.539 --> 00:32:59.440
the community.

cf6028b1-5eea-49f0-ac02-8274aca56704-0
00:32:59.440 --> 00:33:01.621
And that's,
that's the next project that my

cf6028b1-5eea-49f0-ac02-8274aca56704-1
00:33:01.621 --> 00:33:02.960
colleagues and I worked on.

7e418ee8-ff5b-40d4-8a7c-63908da8ba61-0
00:33:03.640 --> 00:33:07.089
So we actually,
the previous isotopic work was just on

7e418ee8-ff5b-40d4-8a7c-63908da8ba61-1
00:33:07.089 --> 00:33:10.476
the John Day formation,
the Maskel formation and the,

7e418ee8-ff5b-40d4-8a7c-63908da8ba61-2
00:33:10.476 --> 00:33:11.480
the rattlesnake.

1f57bfb3-5d17-41e2-b3d3-66a5fe7bf4da-0
00:33:11.480 --> 00:33:15.336
But now we're actually going to put in
the rest of these fossil localities that

1f57bfb3-5d17-41e2-b3d3-66a5fe7bf4da-1
00:33:15.336 --> 00:33:18.760
kind of track this global temperature
change that we're interested in.

5a83043b-37fc-4c8a-a868-978dcc272045-0
00:33:19.520 --> 00:33:22.920
And we're going to look at 2 aspects from
these fossil sites.

8a973bd5-9cc2-479a-9f7f-9e4c79fa641a-0
00:33:23.280 --> 00:33:25.080
We're going to look at functional
diversity.

6bcd6cf6-b3a2-4114-b1c3-1889d2bd113a-0
00:33:25.080 --> 00:33:30.640
And this is sort of the the amount of
each Organism and each kind of bin.

c541e7ea-6aa3-4733-b7ac-21e8c6254700-0
00:33:30.800 --> 00:33:35.390
If we're going to classify them as extra
large herbivores, small carnivores,

c541e7ea-6aa3-4733-b7ac-21e8c6254700-1
00:33:35.390 --> 00:33:39.504
small omnivores, etcetera,
we can actually look at the the shape and

c541e7ea-6aa3-4733-b7ac-21e8c6254700-2
00:33:39.504 --> 00:33:43.320
changes in those bins and see how they've
changed through time.

b2692ce6-046e-46cc-8fa6-2c871ed3ced6-0
00:33:43.320 --> 00:33:45.280
And this has been done for the Great
Plains.

04c36abc-f66b-448d-bec0-bbbe8b03f268-0
00:33:45.600 --> 00:33:49.332
So this paper looked at changes in
ecosystems in the Great Plains from

04c36abc-f66b-448d-bec0-bbbe8b03f268-1
00:33:49.332 --> 00:33:52.328
around the same time periods that we were
interested in,

04c36abc-f66b-448d-bec0-bbbe8b03f268-2
00:33:52.328 --> 00:33:53.800
and we do see changes there.

1bcfafc6-7d8a-4e4f-bda1-ff8e52032b9f-0
00:33:53.800 --> 00:33:55.720
So do we see the same in Oregon?

6cda369e-21e5-4262-aee8-7434adbd8dde-0
00:33:56.360 --> 00:34:00.182
And then we're also going to reconstruct
food webs because I am very obsessed with

6cda369e-21e5-4262-aee8-7434adbd8dde-1
00:34:00.182 --> 00:34:03.360
trying to figure out how to do this
correctly for the fossil record.

bfddef83-df10-481c-b27c-6f4c5813519d-0
00:34:04.200 --> 00:34:08.455
And there are some really great people
that have worked on this for marine

bfddef83-df10-481c-b27c-6f4c5813519d-1
00:34:08.455 --> 00:34:09.080
ecosystems.

4798b4d1-1c0e-4a0a-acb1-ebb79b7fc3b9-0
00:34:09.080 --> 00:34:12.752
And so trying to translate them to
terrestrial ecosystems in the past is

4798b4d1-1c0e-4a0a-acb1-ebb79b7fc3b9-1
00:34:12.752 --> 00:34:15.520
something I'm very passionate about and
interested in.

39a472e5-35a8-4017-b69b-56877d4402ec-0
00:34:16.000 --> 00:34:20.050
And so something that this can give us
that's different than functional

39a472e5-35a8-4017-b69b-56877d4402ec-1
00:34:20.050 --> 00:34:24.269
diversity is we can actually think about
how energy is flowing through the

39a472e5-35a8-4017-b69b-56877d4402ec-2
00:34:24.269 --> 00:34:28.320
community and how the relationships
between the organisms are changing.

9c4f3b24-4366-4188-8496-fbe6a1f1ab9a-0
00:34:28.320 --> 00:34:35.600
So the basically the question is here are
the players on the landscape changing?

9438368f-a6b7-4011-83df-b03ec4a00bb8-0
00:34:35.600 --> 00:34:38.880
And here the question is, are the,
is the game kind of changing?

4549139a-3d87-4777-a136-3d6d9aa5180e-0
00:34:39.160 --> 00:34:43.941
So if we have this shift in the amount of
organisms we have in each bin,

4549139a-3d87-4777-a136-3d6d9aa5180e-1
00:34:43.941 --> 00:34:49.181
will we also see a shift in the basic
structure of the ecosystem or does it not

4549139a-3d87-4777-a136-3d6d9aa5180e-2
00:34:49.181 --> 00:34:49.640
matter?

878a179e-2211-4dba-96c9-cdf74004d4f1-0
00:34:50.440 --> 00:34:51.200
That's the question.

7cae5864-0e20-4967-bbae-31d3af54f576-0
00:34:52.280 --> 00:34:58.107
So in order to do this reconstruction,
we collected data from modern papers on

7cae5864-0e20-4967-bbae-31d3af54f576-1
00:34:58.107 --> 00:35:02.606
predator prey interactions that occur in
today's ecosystems,

7cae5864-0e20-4967-bbae-31d3af54f576-2
00:35:02.606 --> 00:35:08.360
and we kind of made-up a set of rules
that will help us outline what we think

7cae5864-0e20-4967-bbae-31d3af54f576-3
00:35:08.360 --> 00:35:11.680
the link should be for these past
organisms.

e5cf86f1-6aaa-4c81-b6b7-8144156505b1-0
00:35:12.480 --> 00:35:15.321
Some details, though,
that we had to leave out is we had to

e5cf86f1-6aaa-4c81-b6b7-8144156505b1-1
00:35:15.321 --> 00:35:19.015
leave out things like pack hunting,
because that can be really hard to figure

e5cf86f1-6aaa-4c81-b6b7-8144156505b1-2
00:35:19.015 --> 00:35:20.200
out in the fossil record.

485e9026-2a3b-4f67-a4ef-780dd1b92a3f-0
00:35:20.440 --> 00:35:25.509
It's very hard to know if a community of
ancient carnivores hung out together or

485e9026-2a3b-4f67-a4ef-780dd1b92a3f-1
00:35:25.509 --> 00:35:25.760
not.

b49cb5b7-68ca-46db-aaf7-aa4dbec6f469-0
00:35:26.920 --> 00:35:27.560
Same thing.

d4606ce0-aa21-4827-8d62-9d55b7b0539a-0
00:35:27.560 --> 00:35:30.107
Also,
we had to make some rules about omnivores

d4606ce0-aa21-4827-8d62-9d55b7b0539a-1
00:35:30.107 --> 00:35:33.080
and carnivores were treated as both prey
and predators.

8814e9d9-ec94-44bb-a255-4051c4b2f725-0
00:35:33.320 --> 00:35:39.131
But we did build in a rule that if the
prey is bigger than you and is also a

8814e9d9-ec94-44bb-a255-4051c4b2f725-1
00:35:39.131 --> 00:35:42.000
carnivore, you did not eat that thing.

cc1daa8d-b96d-41d4-93d4-f27a33a04fa4-0
00:35:42.000 --> 00:35:45.200
So we had to kind of cut out the
impossible links.

51b7844d-a4c5-4732-9ea6-e61de190245c-0
00:35:45.720 --> 00:35:47.840
And so these are the rules that we ended
up falling on.

16320a71-141b-442c-86d2-aa55f01f22b5-0
00:35:48.080 --> 00:35:52.888
And so we have our predator size classes
bend and then we have our prey size

16320a71-141b-442c-86d2-aa55f01f22b5-1
00:35:52.888 --> 00:35:54.200
classes here as well.

801b0c9a-7dc0-4cef-90ca-416e05313914-0
00:35:56.520 --> 00:36:03.191
And then after we kind of reconstructed
these links just to kind of hone in on

801b0c9a-7dc0-4cef-90ca-416e05313914-1
00:36:03.191 --> 00:36:06.400
like why we made the decisions we did.

db26cee7-b647-4077-b77a-74c0279914db-0
00:36:07.240 --> 00:36:09.960
These are supposed to be potential
interactions.

777ba844-6eff-4088-a327-72028f5f3d0b-0
00:36:09.960 --> 00:36:14.640
These aren't necessarily maybe the actual
interactions on the landscape.

adfa4a7d-10f5-4881-9a45-304938331336-0
00:36:14.640 --> 00:36:19.156
So we're trying to narrow in on the most
likely scenario for that fossil locality,

adfa4a7d-10f5-4881-9a45-304938331336-1
00:36:19.156 --> 00:36:20.680
for that group of organisms.

31508c06-b93a-4d0f-9b4a-e0439068b3fe-0
00:36:21.960 --> 00:36:25.410
And so in order to do that,
one of the things that we did also to

31508c06-b93a-4d0f-9b4a-e0439068b3fe-1
00:36:25.410 --> 00:36:27.920
control for uncertainties in the fossil
record.

905f5dc9-0f9b-4093-bb8c-0e123cb9667f-0
00:36:27.920 --> 00:36:30.616
So if you imagine the fossil record is
not always complete,

905f5dc9-0f9b-4093-bb8c-0e123cb9667f-1
00:36:30.616 --> 00:36:34.032
so we don't always have all the organisms
that maybe would have been on the

905f5dc9-0f9b-4093-bb8c-0e123cb9667f-2
00:36:34.032 --> 00:36:35.560
landscape at that particular time.

1dff16b8-ef2b-4d8a-bc54-fc912f641f2f-0
00:36:36.400 --> 00:36:41.440
We're going to actually lump the nodes
into what is called a trophic species.

7ea46ca4-4d29-4fe1-9a23-960092a2ce8e-0
00:36:41.600 --> 00:36:46.651
So if by reconstructing these food webs,
they have the same exact interactions

7ea46ca4-4d29-4fe1-9a23-960092a2ce8e-1
00:36:46.651 --> 00:36:50.680
that we're reconstructing in say a giant
web for each species.

ddef2fb4-371a-46ab-ba4f-1ea8fcd3a866-0
00:36:50.920 --> 00:36:55.600
So this would be maybe the web with each
individual species plotted.

c060a399-69c7-41d0-8ef2-aeacdaae2266-0
00:36:55.920 --> 00:37:00.932
And after we simplify it down it,
it breaks down those nodes into the

c060a399-69c7-41d0-8ef2-aeacdaae2266-1
00:37:00.932 --> 00:37:03.080
unique nodes in the landscape.

46aae36d-0199-4110-8b7a-ac7f5fa60cc1-0
00:37:05.840 --> 00:37:09.118
And we,
we did this process and we also wrapped

46aae36d-0199-4110-8b7a-ac7f5fa60cc1-1
00:37:09.118 --> 00:37:13.284
in body mass variability because again,
we have these rules,

46aae36d-0199-4110-8b7a-ac7f5fa60cc1-2
00:37:13.284 --> 00:37:17.860
but that doesn't necessarily mean that,
say, if you're a predator,

46aae36d-0199-4110-8b7a-ac7f5fa60cc1-3
00:37:17.860 --> 00:37:21.480
you're not always going to be 4.
5 kilograms, right?

da544f4b-c1b7-443c-b60a-2d407505e98f-0
00:37:21.480 --> 00:37:23.920
You're not completely set in that box.

9aa6bb0e-67ec-449c-909a-62f9a185a170-0
00:37:24.560 --> 00:37:29.400
So we had to build in a sort of
variability in body mass for that as well.

0012b511-ff29-401f-9344-27d59b267539-0
00:37:29.720 --> 00:37:35.816
And so then we we made 1000 simulations
for each fossil food,

0012b511-ff29-401f-9344-27d59b267539-1
00:37:35.816 --> 00:37:39.160
fossil locality for each food web.

0f247856-3cf2-47e1-b869-63447977fa90-0
00:37:39.160 --> 00:37:41.880
So we made 1000 food webs for each
locality.

0b65bc38-2db2-4b0f-b9ff-cec655c0e3f9-0
00:37:44.960 --> 00:37:49.176
OK, so the first results,
I'll talk about the functional diversity

0b65bc38-2db2-4b0f-b9ff-cec655c0e3f9-1
00:37:49.176 --> 00:37:49.680
results.

ef05fdb8-40f0-4ac8-b074-69b453ab7b58-0
00:37:52.400 --> 00:37:55.597
So this is not the food web results and
we've got our,

ef05fdb8-40f0-4ac8-b074-69b453ab7b58-1
00:37:55.597 --> 00:37:57.400
so this is a contribution plot.

1d99d0e7-2310-423f-98e8-b8ecc5004646-0
00:37:57.400 --> 00:38:03.103
So it's showing how much of each category
of Organism is kind of contributing to

1d99d0e7-2310-423f-98e8-b8ecc5004646-1
00:38:03.103 --> 00:38:04.160
that community.

aa8717a2-6540-44e7-950f-fa01c5052d33-0
00:38:04.520 --> 00:38:06.720
So these are the fossil localities here.

293ac714-d7ae-4149-af72-377cc940f901-0
00:38:06.720 --> 00:38:11.200
So we have Rattlesnake, Genturra, McKay,
these are all sort of the younger.

2437ae37-55ef-401e-aada-7357cbb7a9bb-0
00:38:11.200 --> 00:38:16.880
So Maskel is 15,000,000 years ago,
McKay is about 5 million years ago.

d654f71f-7b87-4b48-826c-8d0a64b19cfb-0
00:38:17.600 --> 00:38:20.800
Our oldest localities are actually
plotting way down here.

cbcefe3f-0823-42a6-9b0a-a24a361204ca-0
00:38:20.800 --> 00:38:22.640
So these are from the Oligocene.

a8e6c466-3696-434a-acca-7bb3969b71ec-0
00:38:23.240 --> 00:38:26.169
Again,
that's those communities that we thought

a8e6c466-3696-434a-acca-7bb3969b71ec-1
00:38:26.169 --> 00:38:30.320
had those really low crowned teeth
browsers in our herbivore study.

240de22a-237c-4434-8890-aff2a462b26e-0
00:38:30.920 --> 00:38:33.400
And then this is actually the modern John
Day community.

cee47c7e-5bab-4f01-a8f3-17ac51e092e4-0
00:38:33.400 --> 00:38:35.840
So these are the modern animals that are
living there today.

e7002d7c-0ab1-43bc-b890-011c13d01570-0
00:38:36.840 --> 00:38:41.793
And one of the things that we found was
that the communities differ with respect

e7002d7c-0ab1-43bc-b890-011c13d01570-1
00:38:41.793 --> 00:38:44.240
to large herbivores and large omnivores.

5925b176-3b62-4b58-98a1-dff794806aa3-0
00:38:44.240 --> 00:38:49.678
So the more large omnivores you have in
your community are kind of plotting down

5925b176-3b62-4b58-98a1-dff794806aa3-1
00:38:49.678 --> 00:38:51.760
here, the two oldest plot here.

854d9007-f6c0-44ac-bd47-60f4699f2f3c-0
00:38:52.200 --> 00:38:56.477
And if you have a large herbivores,
and especially if you have extra large

854d9007-f6c0-44ac-bd47-60f4699f2f3c-1
00:38:56.477 --> 00:38:59.101
herbivores,
and what I mean by an extra large

854d9007-f6c0-44ac-bd47-60f4699f2f3c-2
00:38:59.101 --> 00:39:01.440
herbivore is a proboscidian specifically.

45e43f49-6f24-4ed6-a758-6d7c52c6f1f8-0
00:39:01.480 --> 00:39:04.120
They're the only ones that really make it
into that category.

12e90188-579d-4420-a1e7-ccb8cef73deb-0
00:39:04.320 --> 00:39:06.240
There's a few others,
but this is the main 1.

9f5b137a-4df3-4eec-a50a-b1c93131d34c-0
00:39:06.560 --> 00:39:09.748
So if you have proboscidians in your
community,

9f5b137a-4df3-4eec-a50a-b1c93131d34c-1
00:39:09.748 --> 00:39:14.000
that changes kind of the way that you you
look as an ecosystem.

e3301ad7-c5a7-4822-abaa-675e6890e049-0
00:39:18.040 --> 00:39:22.280
So we learned a lot by doing this and
looking at the functional diversity.

0da12d91-9c51-4332-82b3-56811f8deb70-0
00:39:22.280 --> 00:39:25.871
But the food webs are telling us a
slightly more detailed story,

0da12d91-9c51-4332-82b3-56811f8deb70-1
00:39:25.871 --> 00:39:27.640
which is I'm still figuring out.

16f22137-8cb8-4641-a7ae-a2b6f91fc825-0
00:39:27.680 --> 00:39:30.800
To be completely honest,
this is fresh hot off the press.

08d2d5aa-89dd-47ad-ab56-6a653b8af41b-0
00:39:30.800 --> 00:39:34.993
I made this last week,
so pardon that it's a little messy,

08d2d5aa-89dd-47ad-ab56-6a653b8af41b-1
00:39:34.993 --> 00:39:38.760
but basically these are all our simulated
food webs.

6e01eb07-16ea-4c5d-b4c0-1937f06ffb36-0
00:39:38.760 --> 00:39:43.808
So each one of these little dots here is
one of the food webs with that body mass

6e01eb07-16ea-4c5d-b4c0-1937f06ffb36-1
00:39:43.808 --> 00:39:44.240
change.

aac9d4f2-0b4e-4b40-8173-152d451ae74f-0
00:39:44.240 --> 00:39:47.146
So again,
we're shifting the body masses and

aac9d4f2-0b4e-4b40-8173-152d451ae74f-1
00:39:47.146 --> 00:39:51.666
sampling from a distribution of body
masses for each species and then

aac9d4f2-0b4e-4b40-8173-152d451ae74f-2
00:39:51.666 --> 00:39:55.800
reconstructing a food web for that
distribution of body masses.

b4cac56e-0caf-44b8-9d0e-7eada964e959-0
00:39:56.320 --> 00:39:58.400
And this is for each fossil locality.

61f784eb-2c98-4a52-bd15-b712b0250a8c-0
00:39:58.400 --> 00:40:00.640
So we have here we have the turtle, the.

9fa9b6eb-0ad9-4e47-ba42-d50209dd1be8-0
00:40:01.360 --> 00:40:02.360
Lower turtle coves.

2678ac3a-b5bf-4536-9073-610f7d848368-0
00:40:02.360 --> 00:40:03.720
This is our oldest one.

03928026-d1f8-4a51-8c91-fafe43b14b98-0
00:40:04.080 --> 00:40:09.298
The interesting thing to me is that on
the last plot, the last plot,

03928026-d1f8-4a51-8c91-fafe43b14b98-1
00:40:09.298 --> 00:40:14.971
the upper and lower Turtle Cove members,
these are our Ligacine sites plot

03928026-d1f8-4a51-8c91-fafe43b14b98-2
00:40:14.971 --> 00:40:17.240
together pretty nicely, right?

accd135a-f191-47f3-bac2-6e34cd268001-0
00:40:17.440 --> 00:40:20.889
They look great,
but then once we start putting the links,

accd135a-f191-47f3-bac2-6e34cd268001-1
00:40:20.889 --> 00:40:22.760
we're seeing slight differences.

ab5d4795-c371-4039-921a-4a7f546c216b-0
00:40:22.760 --> 00:40:28.005
So this is the lower Turtle Cove here,
but the upper Turtle Cove is actually

ab5d4795-c371-4039-921a-4a7f546c216b-1
00:40:28.005 --> 00:40:33.320
plotting closer to the modern John Day
and the younger Rattlesnake formation.

90a16b34-e842-4e90-abc4-4ca3f6aafce1-0
00:40:33.600 --> 00:40:38.232
So the way that the links are actually
linking up the organisms for these two

90a16b34-e842-4e90-abc4-4ca3f6aafce1-1
00:40:38.232 --> 00:40:41.024
differ,
these older sites actually is making a

90a16b34-e842-4e90-abc4-4ca3f6aafce1-2
00:40:41.024 --> 00:40:43.400
difference in their community structure.

307e517e-d4cd-4c50-b746-c009bfc46f3b-0
00:40:45.000 --> 00:40:49.298
And one of the reasons why I think this
is happening is because of the amount of

307e517e-d4cd-4c50-b746-c009bfc46f3b-1
00:40:49.298 --> 00:40:52.960
or amount of omnivores they're actually
having in those communities.

2c96ee13-c047-41ab-aafd-b77dc05f4b96-0
00:40:53.240 --> 00:40:58.392
So the Lower Turtle Cove actually has a
wide variety of omnivorous canids and

2c96ee13-c047-41ab-aafd-b77dc05f4b96-1
00:40:58.392 --> 00:41:03.280
this is influencing the number of links
they are having in this food web.

15f4f71a-804b-4956-942b-5b5c43893947-0
00:41:03.680 --> 00:41:08.480
And these are all the metrics we're using
to describe the food webs.

2a8976ca-b0c4-4402-9810-63911974f45a-0
00:41:08.480 --> 00:41:12.409
So compartmentalisation is basically,
do you have a subset within the food web

2a8976ca-b0c4-4402-9810-63911974f45a-1
00:41:12.409 --> 00:41:16.189
where all the animals are eating each
other and they're one compartment and

2a8976ca-b0c4-4402-9810-63911974f45a-2
00:41:16.189 --> 00:41:19.969
there's another compartment over here
that are mostly eating each other and

2a8976ca-b0c4-4402-9810-63911974f45a-3
00:41:19.969 --> 00:41:21.760
there's very few links between them.

d2f19aa1-b526-43d0-8936-8c67b9e418bb-0
00:41:23.000 --> 00:41:27.889
And then a lot of these other ones are
describing number of links and degree of

d2f19aa1-b526-43d0-8936-8c67b9e418bb-1
00:41:27.889 --> 00:41:28.440
omnivore.

508664e6-1595-43dd-b4e5-ef66608edcec-0
00:41:28.920 --> 00:41:33.567
There's also one called Connectants and
that's telling us how connected,

508664e6-1595-43dd-b4e5-ef66608edcec-1
00:41:33.567 --> 00:41:36.560
how densely connected those the food webs
are.

94ad1d58-7732-4a73-93b0-d06d8ffe82f1-0
00:41:36.880 --> 00:41:40.460
And so again,
this one seems to be really driven by

94ad1d58-7732-4a73-93b0-d06d8ffe82f1-1
00:41:40.460 --> 00:41:41.080
omnivore.

ed03d574-8ad7-4466-bc37-a7ec223b7530-0
00:41:41.480 --> 00:41:45.720
The Maskell here seems to have a lot more
compartmentalisation going on.

6d0a4bbc-49d7-4f4d-9113-69e1584b779d-0
00:41:45.720 --> 00:41:48.480
So we think it's a heterogeneous
landscape.

861a27f7-cfb1-4f4b-998b-0e0c7ccc1c1c-0
00:41:48.640 --> 00:41:52.772
Maybe that's what we're seeing here where
we have compartments within our food web

861a27f7-cfb1-4f4b-998b-0e0c7ccc1c1c-1
00:41:52.772 --> 00:41:55.760
representing those different landscapes
preserved together.

21f5961a-37b5-49cc-8754-a8c04ff46894-0
00:41:57.600 --> 00:42:02.620
So this is still something I'm working
out to compare the functional diversity

21f5961a-37b5-49cc-8754-a8c04ff46894-1
00:42:02.620 --> 00:42:04.400
data with the food web data.

0478dd79-8a1e-49a3-910b-9631cf697c86-0
00:42:05.320 --> 00:42:08.909
Another insight that we got from
reconstructing these food webs,

0478dd79-8a1e-49a3-910b-9631cf697c86-1
00:42:08.909 --> 00:42:13.272
which I found really interesting was that
we can actually see the place to see

0478dd79-8a1e-49a3-910b-9631cf697c86-2
00:42:13.272 --> 00:42:15.039
megafauna extinction of impacts.

c402b1b6-4c61-463e-8254-d4f5d23b08bc-0
00:42:15.280 --> 00:42:18.720
This isn't something I really set out to
study on this project.

b62b493e-5871-4197-9324-bb55c0db245e-0
00:42:19.040 --> 00:42:21.280
I was more interested in the spread of
grasslands.

9ef8fc08-f1cd-4783-8aed-089b30449ac8-0
00:42:21.520 --> 00:42:26.421
But because I also reconstructed the
modern John day food web as kind of a

9ef8fc08-f1cd-4783-8aed-089b30449ac8-1
00:42:26.421 --> 00:42:30.408
comparison to see if my food web
reconstructing process was,

9ef8fc08-f1cd-4783-8aed-089b30449ac8-2
00:42:30.408 --> 00:42:31.520
was a a good one.

b87d5e59-2524-4bdc-9ba4-7fb3bdf2de46-0
00:42:32.240 --> 00:42:32.560
Whoops.

0026dc7a-003f-4d23-bee6-a6aea3fea10c-0
00:42:33.800 --> 00:42:39.482
We we found that a lot of our food webs
that had Taiya sewids and proboscidians,

0026dc7a-003f-4d23-bee6-a6aea3fea10c-1
00:42:39.482 --> 00:42:42.920
they have really unique places in the
food webs.

9b309d35-6920-483a-9316-530d0fe4d55b-0
00:42:42.920 --> 00:42:46.560
And once they go extinct,
it really shifts around the structure.

446cd20f-1817-4983-b5a9-7110657a19f0-0
00:42:46.840 --> 00:42:48.920
And this is again, body mass.

95c447f4-c2f0-4667-afde-029bb5bd3de3-0
00:42:48.920 --> 00:42:53.640
And you can see where proboscidians sit
is quite on the larger end of things.

a350cb58-35f2-460c-9799-18246c3fd636-0
00:42:53.640 --> 00:42:57.717
And when they're gone,
there's a whole section of the food web

a350cb58-35f2-460c-9799-18246c3fd636-1
00:42:57.717 --> 00:42:59.400
that is not there anymore.

33a73021-0d65-4fc8-9931-287a35dac7bd-0
00:42:59.880 --> 00:43:04.451
And same thing with Taiya sewids,
they're quite a large omnivore and they

33a73021-0d65-4fc8-9931-287a35dac7bd-1
00:43:04.451 --> 00:43:09.516
exist on the landscape for a long time in
a unique position here in the centre of

33a73021-0d65-4fc8-9931-287a35dac7bd-2
00:43:09.516 --> 00:43:10.319
the food web.

2b267715-8612-4f62-adb6-12e99b7c05b6-0
00:43:10.320 --> 00:43:12.806
And again,
they disappear and you're really just

2b267715-8612-4f62-adb6-12e99b7c05b6-1
00:43:12.806 --> 00:43:15.040
left with small omnivores on the
landscape.

54db63e3-170b-4a5d-9226-ee0207d02c22-0
00:43:16.320 --> 00:43:18.320
That's where they are in the food webs
there.

7fc9874c-5d19-4f7f-9d5b-4cbb31ca814d-0
00:43:20.720 --> 00:43:25.827
So if we want to kind of put together
everything that isotopic work that I've

7fc9874c-5d19-4f7f-9d5b-4cbb31ca814d-1
00:43:25.827 --> 00:43:31.196
done and then also now this new food web
material that I'm trying to reconstruct,

7fc9874c-5d19-4f7f-9d5b-4cbb31ca814d-2
00:43:31.196 --> 00:43:36.173
we can start to put together a more
complicated story than just saying, OK,

7fc9874c-5d19-4f7f-9d5b-4cbb31ca814d-3
00:43:36.173 --> 00:43:39.120
grasslands came in and the ecosystem
change.

06ccfc13-eb05-414d-8c20-a72bf726c1a3-0
00:43:39.120 --> 00:43:41.000
We can start to really get into the weeds
here.

c639c326-9a54-4bb4-830e-bae43b78e102-0
00:43:41.320 --> 00:43:46.144
So we can start to say things like, OK,
so we start off with a well connected

c639c326-9a54-4bb4-830e-bae43b78e102-1
00:43:46.144 --> 00:43:49.360
omnivore and browser rich community in
this forest.

d1dbd98a-de11-4c75-8cdc-a8be4c4a5659-0
00:43:49.360 --> 00:43:53.422
So we have a lot of different kinds of
omnivores and there's a lot of browsers

d1dbd98a-de11-4c75-8cdc-a8be4c4a5659-1
00:43:53.422 --> 00:43:54.040
hanging out.

4034edc1-918e-47e9-8e04-c3529e9599d1-0
00:43:55.960 --> 00:43:58.815
And then as warm and wet conditions
arrive,

4034edc1-918e-47e9-8e04-c3529e9599d1-1
00:43:58.815 --> 00:44:04.136
because the masculine formation is during
this mid Miocene climatic optimum where

4034edc1-918e-47e9-8e04-c3529e9599d1-2
00:44:04.136 --> 00:44:08.418
we have it being a little warmer,
we're starting to get that more

4034edc1-918e-47e9-8e04-c3529e9599d1-3
00:44:08.418 --> 00:44:13.480
heterogeneic genetic landscape and we end
up with a less connected landscape,

4034edc1-918e-47e9-8e04-c3529e9599d1-4
00:44:13.480 --> 00:44:16.400
food webs, sorry,
a less connected food web.

39a76a28-52bc-467c-adfb-9baf08326408-0
00:44:16.800 --> 00:44:20.320
And we have a lot more diversity of
herbivores.

bc6aa894-cdf6-439c-a457-760fce7de85c-0
00:44:20.320 --> 00:44:22.946
So there's a lot of herbivores on the
landscape,

bc6aa894-cdf6-439c-a457-760fce7de85c-1
00:44:22.946 --> 00:44:25.359
but they're not as well connected with,
say,

bc6aa894-cdf6-439c-a457-760fce7de85c-2
00:44:25.359 --> 00:44:28.040
the the predators and omnivores on the
landscape.

bac22ce3-311a-4038-8fdb-df703d71b6af-0
00:44:29.120 --> 00:44:35.215
And we also have the expansion of that
large herbivore space in in both

bac22ce3-311a-4038-8fdb-df703d71b6af-1
00:44:35.215 --> 00:44:37.840
morphology and in the food web.

e3b655e2-fba7-4573-bbaf-8c0e0366885b-0
00:44:37.840 --> 00:44:41.859
So again,
those proboscidians show up from Asia and

e3b655e2-fba7-4573-bbaf-8c0e0366885b-1
00:44:41.859 --> 00:44:44.720
then things start cooling down again.

7d1b4a54-250c-42be-a692-f3a220717523-0
00:44:44.720 --> 00:44:48.280
And this is when we think grasslands
really started to pick up in Oregon.

19e05314-5c88-49a4-8d97-fb213f2cab3f-0
00:44:48.360 --> 00:44:53.924
We start seeing them and we have our kind
of our last transition where we're we

19e05314-5c88-49a4-8d97-fb213f2cab3f-1
00:44:53.924 --> 00:44:58.306
have fewer browsers,
not as many omnivores and the communities

19e05314-5c88-49a4-8d97-fb213f2cab3f-2
00:44:58.306 --> 00:45:02.480
are shifting to having more small and mid
sized carnivores.

0018ff52-d42a-4893-90c1-97df2efb99c1-0
00:45:05.760 --> 00:45:09.064
And so overall,
our conclusions are saying that these

0018ff52-d42a-4893-90c1-97df2efb99c1-1
00:45:09.064 --> 00:45:13.837
landscape changes do 'cause certain
mammalian functional groups to be more at

0018ff52-d42a-4893-90c1-97df2efb99c1-2
00:45:13.837 --> 00:45:15.000
risk of extinction.

a488f326-9112-4fb9-8341-4e2262a39eaa-0
00:45:16.080 --> 00:45:20.294
Kind of knew this a little bit that
browsers disappear from North America as

a488f326-9112-4fb9-8341-4e2262a39eaa-1
00:45:20.294 --> 00:45:21.280
grasslands expand.

4c21ad1c-016a-4331-91c7-358a9a51047d-0
00:45:21.520 --> 00:45:23.360
But we're seeing this in Oregon as well.

2ad0aeaf-7ff1-423e-92c3-299de282ef5c-0
00:45:23.640 --> 00:45:27.600
And we can specifically point to certain
species that go extinct at certain times.

a25d2167-2d4e-4988-b97c-15c17e29a71a-0
00:45:28.600 --> 00:45:31.920
And this is also having community
structure consequences.

7ae76f77-cf2c-49df-a1ef-ec95ff1f1173-0
00:45:32.120 --> 00:45:35.954
So the food webs are changing through
time because of these disappearing

7ae76f77-cf2c-49df-a1ef-ec95ff1f1173-1
00:45:35.954 --> 00:45:36.480
organisms.

98001ff6-96f7-4a43-b361-60c8b3002288-0
00:45:37.160 --> 00:45:41.330
Other things that I know that's different
than just looking at say we,

98001ff6-96f7-4a43-b361-60c8b3002288-1
00:45:41.330 --> 00:45:44.385
we knew that browsers were taking hit at
this time,

98001ff6-96f7-4a43-b361-60c8b3002288-2
00:45:44.385 --> 00:45:47.440
but we also see the decrease in mid size
omnivores.

da75771a-9ccf-4c44-86bc-e70300119d82-0
00:45:47.440 --> 00:45:50.000
So there's not as many of those on the
landscape as well.

28d32d92-c619-4665-92af-9b1ce8663e4c-0
00:45:51.000 --> 00:45:54.000
Also fruit and plant dependent omnivores
disappear.

03b1364b-bf87-4d41-9d5f-f851cd03496a-0
00:45:54.440 --> 00:45:58.400
This is a lovely reconstruction of an
ancient primate that lived in Oregon.

f15f3eda-365b-41e2-8c06-3e33cff2a56d-0
00:45:58.560 --> 00:45:59.800
It's no longer with us.

6eb5ef29-01d6-42e7-9fb5-1646cbff05fb-0
00:46:00.240 --> 00:46:04.160
And then also the shift in extra large
herbivores I found was really interesting.

8fbe12e0-e4d5-4081-b026-7e98f3d89842-0
00:46:04.200 --> 00:46:08.684
The kind of arrival of probositians and
then subsequent disappearance also shifts

8fbe12e0-e4d5-4081-b026-7e98f3d89842-1
00:46:08.684 --> 00:46:09.560
the communities.

87d9e617-3ce6-4bad-8b1e-8d36f82b4d19-0
00:46:16.120 --> 00:46:18.040
OK, so I'm almost done.

ea85005a-59eb-44ee-97da-bcfb329dc3d1-0
00:46:18.040 --> 00:46:20.061
We'll,
we'll have questions here in a second,

ea85005a-59eb-44ee-97da-bcfb329dc3d1-1
00:46:20.061 --> 00:46:21.160
but I do want to plug in.

6999daa6-9e0e-4dfa-a501-07dbd30edec9-0
00:46:21.160 --> 00:46:25.462
I just want to give you a little bit of a
taste of something else I'm working on

6999daa6-9e0e-4dfa-a501-07dbd30edec9-1
00:46:25.462 --> 00:46:29.764
because I just talked a lot about fossil
sites and fossil localities and ancient

6999daa6-9e0e-4dfa-a501-07dbd30edec9-2
00:46:29.764 --> 00:46:30.880
communities and such.

26bacb83-da91-4d41-93c3-b0283d146103-0
00:46:31.080 --> 00:46:35.576
And I actually have a hard time talking
about this sometimes and having the

26bacb83-da91-4d41-93c3-b0283d146103-1
00:46:35.576 --> 00:46:40.072
appropriate language to talk to other
biologists and other geologists about

26bacb83-da91-4d41-93c3-b0283d146103-2
00:46:40.072 --> 00:46:40.959
these concepts.

02ffb507-b666-4977-b616-5982c0258180-0
00:46:41.360 --> 00:46:45.819
And one of the things that I got into
when I was working on my post doc is

02ffb507-b666-4977-b616-5982c0258180-1
00:46:45.819 --> 00:46:50.755
we're actually working on defining terms
to make it a little easier for someone to

02ffb507-b666-4977-b616-5982c0258180-2
00:46:50.755 --> 00:46:55.333
talk about what's going on with, say,
a modern ecosystem and what's going on

02ffb507-b666-4977-b616-5982c0258180-3
00:46:55.333 --> 00:46:56.879
with an extinct ecosystem.

b1ad2221-5620-4e1f-b26a-4c41d5978327-0
00:46:56.880 --> 00:47:01.960
And specifically, we want to,
we want to define the dietary niche.

cb423444-3e87-4b24-bd97-331906875562-0
00:47:02.600 --> 00:47:06.360
So a niche is a concept that is used
often in biology.

4f547ae6-2439-491a-ae9d-bac9433ece07-0
00:47:06.360 --> 00:47:11.081
And we want to define the terms that are
related to the dietary niche specifically

4f547ae6-2439-491a-ae9d-bac9433ece07-1
00:47:11.081 --> 00:47:14.778
so that when I say that I'm talking about
something in the past,

4f547ae6-2439-491a-ae9d-bac9433ece07-2
00:47:14.778 --> 00:47:19.215
I can say something like I'm talking
about the potential dietary niche versus

4f547ae6-2439-491a-ae9d-bac9433ece07-3
00:47:19.215 --> 00:47:22.400
if I'm looking at real dietary data from
the landscape,

4f547ae6-2439-491a-ae9d-bac9433ece07-4
00:47:22.400 --> 00:47:24.959
I can talk about the realised dietary
niche.

57e10ee3-8bcb-4220-a3bb-493fe5a73b51-0
00:47:25.200 --> 00:47:28.383
So this is something that I have been
thinking a lot about and it's been kind

57e10ee3-8bcb-4220-a3bb-493fe5a73b51-1
00:47:28.383 --> 00:47:29.240
of breaking my brain.

ca63c7c2-e075-4665-8466-78244dfde1c2-0
00:47:29.480 --> 00:47:32.438
But it's been,
it's been a lovely conversation with

ca63c7c2-e075-4665-8466-78244dfde1c2-1
00:47:32.438 --> 00:47:37.161
other palaeontologists, other biologists,
and we've put together quite a big group

ca63c7c2-e075-4665-8466-78244dfde1c2-2
00:47:37.161 --> 00:47:41.600
of people and we're actually writing a
theory and review paper at the moment.

d1eaddb5-3e1a-4939-8351-4616acc5545f-0
00:47:42.240 --> 00:47:43.640
So keep an eye out for that too.

bf12f9fe-387b-4b94-85aa-90fc6d5d5e88-0
00:47:45.280 --> 00:47:47.880
With that,
I have a lot of people to thank.

13a6554f-854a-467f-adf4-3c2f377b7cb7-0
00:47:48.200 --> 00:47:49.560
I did a lot of field work.

deaf7d30-c942-4e9c-9058-fe64bbebecba-0
00:47:49.560 --> 00:47:51.080
I went to a lot of museums.

d48b1242-9178-4ab6-87b8-0f8096bca0a7-0
00:47:51.080 --> 00:47:56.097
I touched a lot of amazing fossils,
and a lot of people helped me out with

d48b1242-9178-4ab6-87b8-0f8096bca0a7-1
00:47:56.097 --> 00:47:59.910
with all of that, identifying things,
having lab access,

d48b1242-9178-4ab6-87b8-0f8096bca0a7-2
00:47:59.910 --> 00:48:02.720
just even living outside for weeks on end.

858aa4a6-3d10-48e8-9575-2715eeb90075-0
00:48:02.720 --> 00:48:08.120
So I have lots of lab groups to think and
a lot of students actually too.

ed1e2215-5f79-4766-a324-7d4e8523d65a-0
00:48:08.120 --> 00:48:10.200
A lot of students always go out with us
in the field.

de1b7eb5-1aa9-4c8b-b676-6126c861c0fc-0
00:48:10.200 --> 00:48:12.960
So I love their contributions to
everything.

4bb2fced-e2a6-4fb5-a394-0241bafc44e4-0
00:48:13.400 --> 00:48:15.080
And with that, I'll take questions.

385a321f-5e70-45e6-93ca-a5d7428e51e5-0
00:48:22.520 --> 00:48:23.920
So it's actually really interesting.

e736b757-9d2d-4f77-88b1-52eae517ad2d-0
00:48:23.920 --> 00:48:28.968
There's a very good paper talking about
how much time you have in a day to eat

e736b757-9d2d-4f77-88b1-52eae517ad2d-1
00:48:28.968 --> 00:48:34.080
and how much you can actually how many
insects you can suck up in a given time.

91f539ed-7648-4bd3-b91c-4b05768aae64-0
00:48:34.080 --> 00:48:38.284
And then calorie count of how many you
need to eat in order to meet your like

91f539ed-7648-4bd3-b91c-4b05768aae64-1
00:48:38.284 --> 00:48:41.680
amount of calories you need to eat for
your certain body size.

12653a62-1e67-4d29-9eb8-e794b89e8362-0
00:48:41.680 --> 00:48:46.480
And there's actually a nice cut off,
it's around 21 kilograms that once you

12653a62-1e67-4d29-9eb8-e794b89e8362-1
00:48:46.480 --> 00:48:50.713
get bigger than 21 kilograms,
you run out of time basically to eat

12653a62-1e67-4d29-9eb8-e794b89e8362-2
00:48:50.713 --> 00:48:53.240
enough insects to feed yourself to live.

eafc47ba-58f2-49d3-85f9-719f140dd37f-0
00:48:54.120 --> 00:48:57.120
So, and it's it's kind,
it's not a completely hard cut off.

50d2f835-4526-4fe4-af3d-5ac42930144b-0
00:48:57.120 --> 00:48:59.936
They're always,
there's always things that break the

50d2f835-4526-4fe4-af3d-5ac42930144b-1
00:48:59.936 --> 00:49:00.840
rules in biology.

895c6552-5cad-4a6b-85ae-875242695eb3-0
00:49:00.840 --> 00:49:04.652
I feel like as a biologist,
you just start realizing how many wacky

895c6552-5cad-4a6b-85ae-875242695eb3-1
00:49:04.652 --> 00:49:07.680
animals out there that you thought that
didn't exist.

b7e0c145-5520-487a-9052-5fb174408d75-0
00:49:07.680 --> 00:49:09.280
And they're just like, Nope,
I can do that.

2320b96d-09a9-40b9-a732-bf757fd38f2a-0
00:49:09.280 --> 00:49:13.193
So with some, you know,
adaptations for let's, you know,

2320b96d-09a9-40b9-a732-bf757fd38f2a-1
00:49:13.193 --> 00:49:16.969
become quicker at sucking up ants,
you can get bigger,

2320b96d-09a9-40b9-a732-bf757fd38f2a-2
00:49:16.969 --> 00:49:18.960
but it's around 21 kilograms.

f35e3f15-5b75-4589-8902-17c697848f63-0
00:49:18.960 --> 00:49:23.600
It's harder to sustain anything larger on
just insects alone.

a84aa77e-1d57-4353-a6ed-f3a50f02dcbd-0
00:49:24.520 --> 00:49:24.800
Yeah.

4f81f223-12d6-40a5-8672-158e00f67003-0
00:49:25.640 --> 00:49:26.200
I want a question.

fd7dd5c8-bd0b-4269-9965-18a7107ab12c-0
00:49:26.480 --> 00:49:29.760
And you said that it was hard to not be
big in the grasslands.

04711605-8ea7-4ff9-bc5a-69d9c476e511-0
00:49:30.200 --> 00:49:32.400
Is there like a reason why?

f3fcd7b8-5a58-4924-9067-dc5306957c87-0
00:49:33.080 --> 00:49:33.440
Yeah.

e185c3f0-97a5-4322-a861-98b1887b80b6-0
00:49:33.520 --> 00:49:34.400
So you can be really.

19557643-eead-4150-a34d-57a440322eea-0
00:49:34.400 --> 00:49:41.875
So in grasslands you can be small and
hide maybe underground or not be seen by

19557643-eead-4150-a34d-57a440322eea-1
00:49:41.875 --> 00:49:43.200
larger things.

92b6a1c0-c7bd-45a4-aab8-10f08f5abae3-0
00:49:43.520 --> 00:49:48.394
But that like mid size is kind of hard
because predators will try to eat you

92b6a1c0-c7bd-45a4-aab8-10f08f5abae3-1
00:49:48.394 --> 00:49:53.711
basically because you don't have cover as
much as in a forest or you can't run up a

92b6a1c0-c7bd-45a4-aab8-10f08f5abae3-2
00:49:53.711 --> 00:49:55.800
tree to get away from a predator.

313c7f6b-f683-4306-a31e-169cdb2c89ef-0
00:49:56.200 --> 00:49:58.456
And so if you have a predator on the
landscape,

313c7f6b-f683-4306-a31e-169cdb2c89ef-1
00:49:58.456 --> 00:49:59.960
you need to be faster than them.

d8bd8fab-1efa-43c5-8737-c41ac4ad2d90-0
00:49:59.960 --> 00:50:03.829
And that's why, for instance,
horses developed like a singular hoof to

d8bd8fab-1efa-43c5-8737-c41ac4ad2d90-1
00:50:03.829 --> 00:50:08.080
run faster and faster or you need to be
big enough to defend yourself, right?

3d9d9453-a323-40eb-b2f8-15e4f6bba93a-0
00:50:08.080 --> 00:50:10.360
You think of like a rhino is just like
really big.

2e0b4335-c85e-47df-8dcd-cec321c3cb79-0
00:50:11.080 --> 00:50:14.280
And lions don't really mess with rhinos
as much because they're dangerous.

23b4c344-a612-43f3-a41a-723f9ba84426-0
00:50:18.000 --> 00:50:19.800
Other questions, yes.

e590b937-9d8b-4de0-be86-60eea0a6ea13-0
00:50:20.440 --> 00:50:24.762
So as grasslands expanded and tooth
morphology changed,

e590b937-9d8b-4de0-be86-60eea0a6ea13-1
00:50:24.762 --> 00:50:30.320
did you also notice like a difference in
the wear pattern on the teeth?

0321e843-670e-4ac7-85a6-e5aadbc88d99-0
00:50:30.760 --> 00:50:33.784
And if so,
does that act as a function of the

0321e843-670e-4ac7-85a6-e5aadbc88d99-1
00:50:33.784 --> 00:50:35.560
expansion of the grassland?

ded56cd2-7c6b-410b-b452-d572144a9f5d-0
00:50:36.520 --> 00:50:39.030
So I didn't,
I didn't myself look at wear patterns,

ded56cd2-7c6b-410b-b452-d572144a9f5d-1
00:50:39.030 --> 00:50:42.652
but there is a lot of studying of this
and people that have looked in this

ded56cd2-7c6b-410b-b452-d572144a9f5d-2
00:50:42.652 --> 00:50:43.280
specifically.

f571088c-5d5b-45f3-a8e3-f45bf323ffef-0
00:50:43.280 --> 00:50:45.280
There's two different kinds of wear
patterns.

5ec64180-a552-4213-acfa-af11af95d484-0
00:50:45.280 --> 00:50:49.162
You can look at miso wear,
which is kind of the overall wearing down

5ec64180-a552-4213-acfa-af11af95d484-1
00:50:49.162 --> 00:50:52.313
of your tooth,
and you can look at the shape your tooth

5ec64180-a552-4213-acfa-af11af95d484-2
00:50:52.313 --> 00:50:55.240
takes with that,
like overall profile of the tooth.

fffee402-675b-443e-a166-6f16dbc513ef-0
00:50:55.720 --> 00:50:59.071
And in grasslands,
you'd tend to get more of like a flat

fffee402-675b-443e-a166-6f16dbc513ef-1
00:50:59.071 --> 00:51:00.600
wearing down of the tooth.

3b270a6e-f8b3-4416-8402-37134565b5d5-0
00:51:00.880 --> 00:51:02.280
And then there's micro wear.

97069a20-d3f0-4492-9d47-fe89d642df3a-0
00:51:02.280 --> 00:51:04.607
And micro wear is actually going back to
those,

97069a20-d3f0-4492-9d47-fe89d642df3a-1
00:51:04.607 --> 00:51:08.438
those phytoliths in the plants where if
you imagine everything you eat kind of

97069a20-d3f0-4492-9d47-fe89d642df3a-2
00:51:08.438 --> 00:51:10.960
leaves a little bit of an impression on
your teeth.

35899291-a8dd-4916-ba46-550e6f4d9df4-0
00:51:11.160 --> 00:51:13.600
It's very small and we have to use
microscopes to see them.

a4090401-d75c-440a-b829-2d194c25d19f-0
00:51:13.920 --> 00:51:16.071
But if you're eating a lot of grit and
sand,

a4090401-d75c-440a-b829-2d194c25d19f-1
00:51:16.071 --> 00:51:18.080
you'll actually get pitting in your teeth.

264d8ec1-581d-42c7-af8c-c40f7f172096-0
00:51:18.160 --> 00:51:19.960
And we can see that in the fossil record
too.

f1a1d165-0e24-43ea-be45-c5641c1d2e4e-0
00:51:20.360 --> 00:51:24.264
I didn't look at that because it took,
I actually did want to look at microware

f1a1d165-0e24-43ea-be45-c5641c1d2e4e-1
00:51:24.264 --> 00:51:26.705
as well,
including with the isotopic data because

f1a1d165-0e24-43ea-be45-c5641c1d2e4e-2
00:51:26.705 --> 00:51:28.560
these are really good dietary proxies.

46312fea-976d-4378-b5ad-c863c678df4a-0
00:51:28.800 --> 00:51:33.389
If you imagine an isotopic signal is all
the food that you've eaten that you've

46312fea-976d-4378-b5ad-c863c678df4a-1
00:51:33.389 --> 00:51:36.200
used to develop your teeth kind of
averaged out.

b842f812-e954-4ff2-a34e-c3f698eeb918-0
00:51:36.400 --> 00:51:40.401
Microware is a little bit more of a finer
signal, a shorter amount of time,

b842f812-e954-4ff2-a34e-c3f698eeb918-1
00:51:40.401 --> 00:51:43.560
kind of like maybe your last meal before
you're fossilized.

1b1c7a2e-88f7-43a8-ab97-d73ce576a16b-0
00:51:44.320 --> 00:51:46.360
But it just takes a long time to collect
that data.

df5caab5-4939-4ac7-aaff-d627476c9a64-0
00:51:47.280 --> 00:51:49.160
So there are really good groups that are
working on that.

f57f9edc-c0aa-4c08-9a7c-81b0ce9e782c-0
00:51:49.160 --> 00:51:52.600
And there is a group at Oregon State that
they do microware there.

c1d7d0da-ea2f-49ae-80c5-0c5825311bc9-0
00:51:54.680 --> 00:51:55.400
It's a great question.

a574032f-e7dd-456c-add4-afa0904f47d6-0
00:51:56.440 --> 00:51:57.040
Other questions?

f28a5ca3-da52-4b77-bd5d-80569ab23374-0
00:51:58.000 --> 00:51:59.200
Wait, yeah.

31a46ff9-8ffd-44e1-bc6b-7d3518e2dfae-0
00:52:01.400 --> 00:52:04.986
Did you set out to specifically study
this in grasslands or did you end up on

31a46ff9-8ffd-44e1-bc6b-7d3518e2dfae-1
00:52:04.986 --> 00:52:08.572
grasslands because they had the most type
of diversification and like changes

31a46ff9-8ffd-44e1-bc6b-7d3518e2dfae-2
00:52:08.572 --> 00:52:09.400
occurring in them?

5088ca49-0217-4347-88e0-730d3a606a79-0
00:52:10.640 --> 00:52:14.438
So this kind of goes to like when you go
to grad school and you have to design

5088ca49-0217-4347-88e0-730d3a606a79-1
00:52:14.438 --> 00:52:15.400
your project, right.

60ed1ed6-2d5b-45e1-9892-39dd176aeb20-0
00:52:15.440 --> 00:52:18.960
So when you get into grad school,
you maybe have interests.

1824def4-acf9-4fa6-a358-db2338185af3-0
00:52:18.960 --> 00:52:21.320
You're like,
I'm interested in these broad themes.

4441fd6a-c230-4cc0-9477-614447c82528-0
00:52:21.320 --> 00:52:25.236
And then you and your advisor work really
hard to narrow in on a specific question

4441fd6a-c230-4cc0-9477-614447c82528-1
00:52:25.236 --> 00:52:27.360
that you can kind of spend a lot of time
on.

f0e5fcae-8d0a-4a8e-8c0d-32fa280568c1-0
00:52:27.880 --> 00:52:30.128
And when I went to grad school,
I actually,

f0e5fcae-8d0a-4a8e-8c0d-32fa280568c1-1
00:52:30.128 --> 00:52:33.960
I knew a couple of things because I had
an internship at the Field Museum.

3c948ec4-7972-4e03-b01d-f550df54e6c0-0
00:52:34.120 --> 00:52:36.440
I liked looking at teeth,
thought they were really cool.

079dc9ac-2dde-49ff-93c9-0630254f370e-0
00:52:36.880 --> 00:52:40.084
I liked thinking about predator prey
interactions,

079dc9ac-2dde-49ff-93c9-0630254f370e-1
00:52:40.084 --> 00:52:43.539
and I liked thinking about the foods that
animals ate,

079dc9ac-2dde-49ff-93c9-0630254f370e-2
00:52:43.539 --> 00:52:48.000
and then from there narrowed in on
specific questions related to that.

2b49bdd6-3a16-410c-909a-139a60f965aa-0
00:52:48.240 --> 00:52:51.103
And my advisor,
she was the one that worked on that

2b49bdd6-3a16-410c-909a-139a60f965aa-1
00:52:51.103 --> 00:52:55.178
rodent plot where we had the rodents
changing through time in response to

2b49bdd6-3a16-410c-909a-139a60f965aa-2
00:52:55.178 --> 00:52:56.280
grassland expansion.

f7eab898-cb33-41f7-885a-a12320d07278-0
00:52:56.560 --> 00:52:59.000
And so she had already thought about this
for a long time.

11789187-c712-4c13-95f5-7778c0bcc564-0
00:52:59.000 --> 00:53:02.200
So then I kind of got into it after that.

f519c6a9-f281-404e-85d0-66b484781344-0
00:53:02.600 --> 00:53:05.120
Yeah, it's a good question though.

17f8a9e3-ffce-42eb-8fe2-ff748b524397-0
00:53:07.720 --> 00:53:09.200
Zachary,
did you have how long to speak out?

5b7e00c5-977b-4bc2-9be4-c8125f16157b-0
00:53:09.320 --> 00:53:09.720
Yeah, I did.

565551b1-c8a9-4284-a3d4-075a50686473-0
00:53:09.720 --> 00:53:11.520
I was going to ask how can he apply?

b834d091-92ae-4e28-a7fd-0a6c1d823368-0
00:53:11.760 --> 00:53:14.040
Is there a way to be applied to like
other ecosystems?

9097b71c-6469-4ca2-8e34-4638b7294ff3-0
00:53:14.520 --> 00:53:17.488
Like for example,
I was watching a video about Diana Pyrus

9097b71c-6469-4ca2-8e34-4638b7294ff3-1
00:53:17.488 --> 00:53:21.665
and basically suggested that it leave in
a more aquatic ecosystem in front of more

9097b71c-6469-4ca2-8e34-4638b7294ff3-2
00:53:21.665 --> 00:53:23.979
aquatic rotation compared to the
Hydrosaurus,

9097b71c-6469-4ca2-8e34-4638b7294ff3-3
00:53:23.979 --> 00:53:27.200
which is which it lives alongside,
can be applied to like them.

69044f96-fe3f-4cbd-9eba-652ab6c7b5bb-0
00:53:27.320 --> 00:53:29.854
Even though Diapiris doesn't really have
much teeth,

69044f96-fe3f-4cbd-9eba-652ab6c7b5bb-1
00:53:29.854 --> 00:53:31.720
it's that had a peak for some rotation.

9d887e20-42a6-4574-b2f4-370165c55c40-0
00:53:32.200 --> 00:53:34.718
Yeah,
it can be a little tricky if you so I'm,

9d887e20-42a6-4574-b2f4-370165c55c40-1
00:53:34.718 --> 00:53:37.880
I'm using enamel to kind of look at the
diet of organisms.

b7273f12-b3c5-457c-93bf-74812823e944-0
00:53:38.120 --> 00:53:41.880
It can be tricky to use other parts of
the body because they they change a lot

b7273f12-b3c5-457c-93bf-74812823e944-1
00:53:41.880 --> 00:53:42.880
in the fossil record.

c59d3f0d-99d2-461b-ae31-e3ebeb12bb75-0
00:53:42.880 --> 00:53:45.784
So if you're trying to take a sample,
say of bone,

c59d3f0d-99d2-461b-ae31-e3ebeb12bb75-1
00:53:45.784 --> 00:53:50.397
bone will re crystallize a lot more and
get a lot of other minerals inside of it

c59d3f0d-99d2-461b-ae31-e3ebeb12bb75-2
00:53:50.397 --> 00:53:52.960
versus your teeth are almost already a
rock.

00c5a0a6-cb39-48f4-8a84-35e563bc51ca-0
00:53:53.240 --> 00:53:56.650
So when they fossilize,
they don't change as much,

00c5a0a6-cb39-48f4-8a84-35e563bc51ca-1
00:53:56.650 --> 00:54:01.600
but you can use tooth enamel to look at
like aquatic habits in organisms.

27cbcfb4-900a-44c0-856f-09488d868ab0-0
00:54:01.920 --> 00:54:06.612
And I know that for like the lineage of
hippopotamus and like looking at them

27cbcfb4-900a-44c0-856f-09488d868ab0-1
00:54:06.612 --> 00:54:09.320
being in the water,
that's been done before.

0b341b4e-5479-4ecb-af65-d69e494f8b8e-0
00:54:10.360 --> 00:54:14.803
But the the methods that I used in these
projects can definitely be applied to

0b341b4e-5479-4ecb-af65-d69e494f8b8e-1
00:54:14.803 --> 00:54:15.760
other ecosystems.

4b015719-2da1-4806-baa8-7088b5843ee9-0
00:54:15.760 --> 00:54:19.557
And I just specifically didn't look at
aquatic animals as much,

4b015719-2da1-4806-baa8-7088b5843ee9-1
00:54:19.557 --> 00:54:23.948
although we do have a student that's
looking at ancient Beavers in Oregon

4b015719-2da1-4806-baa8-7088b5843ee9-2
00:54:23.948 --> 00:54:26.440
because we found a really, really large 1.

d0e0f1ae-f21a-4b6b-990a-fced772d0386-0
00:54:26.640 --> 00:54:30.440
And so it's very exciting to think about
how we can maybe sample those teeth as

d0e0f1ae-f21a-4b6b-990a-fced772d0386-1
00:54:30.440 --> 00:54:32.625
well,
to think about if it was also living an

d0e0f1ae-f21a-4b6b-990a-fced772d0386-2
00:54:32.625 --> 00:54:33.480
aquatic lifestyle.

18d4fa60-d703-41ec-a55a-4c930d3eadfb-0
00:54:34.960 --> 00:54:36.120
Yeah, yes.

e73ee1e5-2830-4a5e-8f8b-9d93a389fe76-0
00:54:36.640 --> 00:54:40.987
Would you expect to find similar changes
occurring in Africa at the same time as

e73ee1e5-2830-4a5e-8f8b-9d93a389fe76-1
00:54:40.987 --> 00:54:43.080
it went from forest to more grasslands?

83dcab2e-50c7-4e53-bc3b-89e4eb6d190a-0
00:54:43.520 --> 00:54:44.520
Yeah, I think so.

814d045f-9350-45e0-b2b4-2793dc6d09c0-0
00:54:44.520 --> 00:54:47.826
And a lot of the papers that I actually
relied on there,

814d045f-9350-45e0-b2b4-2793dc6d09c0-1
00:54:47.826 --> 00:54:51.422
they look at differences also in modern
ecosystems in Africa,

814d045f-9350-45e0-b2b4-2793dc6d09c0-2
00:54:51.422 --> 00:54:56.295
because one of the things about Africa is
it still has a lot of the megafauna and a

814d045f-9350-45e0-b2b4-2793dc6d09c0-3
00:54:56.295 --> 00:54:59.080
lot of the ecosystems that we think we're
here.

011d0b9b-0fd6-41b5-aac6-7d0192abc2da-0
00:54:59.080 --> 00:55:02.558
And we're here in North America,
we just don't have as analogues anymore,

011d0b9b-0fd6-41b5-aac6-7d0192abc2da-1
00:55:02.558 --> 00:55:02.840
right?

a6da0c67-ad48-4b92-9c1e-51f8b7da6b24-0
00:55:03.600 --> 00:55:05.933
So, yeah,
there are differences between the

a6da0c67-ad48-4b92-9c1e-51f8b7da6b24-1
00:55:05.933 --> 00:55:10.176
ecosystems in Africa even today that we
can see between grasslands and forested

a6da0c67-ad48-4b92-9c1e-51f8b7da6b24-2
00:55:10.176 --> 00:55:10.760
ecosystems.

9fc2cdc6-233f-40aa-bc11-71902a47a9ab-0
00:55:10.760 --> 00:55:10.880
Yeah.

55d48ebd-fe2e-4a21-a53d-d0c076d3fa09-0
00:55:11.360 --> 00:55:14.280
Any other questions?