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Speaker 1: This is me eat your podcast coming at you shirtless, severely,

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Speaker 1: bug bitten and in my case, underwear listening podcast. You

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Speaker 1: can't predict anything. Okay, we're recording in the you see

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Speaker 1: Santa Cruz, which is which I found I've been. I've

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Speaker 1: been in a lot of campuses in my life. I

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Speaker 1: guess I was gonna say I haven't been on many,

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Speaker 1: but I've been on a ton of them. This is

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Speaker 1: the most gorgeous campus I've ever been to. It's pretty amazing.

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Speaker 1: Did you happen to see any e walks on your

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Speaker 1: way out? No, but I was. It was like, It's

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Speaker 1: funny because driving in here, I was thinking I was

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Speaker 1: about to form the sentence to Janice that this is

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Speaker 1: like a Star Wars set, and then yeah, like interrupted

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Speaker 1: my thought to comment about a Star Wars set quality.

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Speaker 1: My youngest child calls it the e walk for us.

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Speaker 1: We come up and he goes, it's the book forest. Yeah,

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Speaker 1: it's like it's got to be good for your brain

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Speaker 1: to be around these trees. I feel like I could

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Speaker 1: be hopeful about that. Yeah, all right, No, I think

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Speaker 1: it is. Next time students come up here saying if

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Speaker 1: this is a good place for them to come to school,

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Speaker 1: and I'll try to give him that line. These trees

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Speaker 1: are good for your brain. And you're hearing the voice

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Speaker 1: of Dr Beth Shapiro. Who is who deal? Okay, I

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Speaker 1: don't let her tell you what she is who. I

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Speaker 1: know her from the fact that she deals with what's

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Speaker 1: called ancient d n A. And if you follow wildlife

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Speaker 1: conservation and wildlife politics, I think that you will in

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Speaker 1: your lifetime here a lot you'll hear that term ancient

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Speaker 1: d n A, And in surrounding conversations around it, you'll

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Speaker 1: hear that progressively more and more, to the point where

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Speaker 1: when you grow old and die, it might just be

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Speaker 1: like a fact of life that it may be transformed

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Speaker 1: our understanding of That's really great. I'm really intrigued to

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Speaker 1: hear you say that you know ancient DNA isn't isn't

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Speaker 1: something that most people have heard of. And while my

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Speaker 1: motivation for doing this is to be able to learn

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Speaker 1: something that's useful for conservation by a diversity conservation, I

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Speaker 1: think a lot of times people think of it as

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Speaker 1: a way to learn about history, particularly human history. I'm

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Speaker 1: most interested in animals, but I'm pleased to hear you

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Speaker 1: say that you think there's a place for this, and yeah,

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Speaker 1: well maybe let me let me let me all to that.

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Speaker 1: Because let's say that that you have a technology, and

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Speaker 1: you have a technology like the internal combustion engine. Now

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Speaker 1: no one talks about the internal combustion engine, but they

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Speaker 1: most definitely talk about the creations built around that, right, Okay,

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Speaker 1: So um, instead of building it all up and talking

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Speaker 1: about how important it's going to wind up being, we

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Speaker 1: should talk about what it is and can you you're

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Speaker 1: probably good at this by now, can you like sketch

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Speaker 1: out what ancient DNA is the fields where it's being applied, okay,

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Speaker 1: particular to wildlife, and what are some things that's taught us,

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Speaker 1: What are some things, what are some areas where the

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Speaker 1: work done around ancient DNA has has challenged or added

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Speaker 1: to our assumptions about our world? All right, there's a

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Speaker 1: there's a lot there, and even me a lot of room.

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Speaker 1: But I think what I'll start with is probably something

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Speaker 1: that we can come back to later on. So I'm

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Speaker 1: going to start with a teaser about what I hope

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Speaker 1: ancient DNA can do for wildlife conservations, and then we

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Speaker 1: can turn back around and go to the beginning and

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Speaker 1: talk more about ancient DNA and the origins of the

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Speaker 1: field and how it came about and what else has

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Speaker 1: been applied to. So imagine that you are trying to

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Speaker 1: protect the last of a particular species um and this

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Speaker 1: species is not doing well because the habitat that it

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Speaker 1: lives in is disappearing. The climate is changing. Maybe it's

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Speaker 1: a bit too warm for it, or maybe it's getting

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Speaker 1: a bit cold. Maybe there's just something different about today's

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Speaker 1: climate that is affecting this animal and the animals in

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Speaker 1: particular trouble because its population has been small for a

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Speaker 1: long time, and because it's been small for a long time,

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Speaker 1: it's lost a lot of genetic diversity. A good example

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Speaker 1: of this right now is the black footed ferret. So,

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Speaker 1: the black footed ferret is a species that we thought

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Speaker 1: was extinct, but then a population, the surviving population was discovered.

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Speaker 1: It turned up on a rancher's doorstep and Matit Wyoming, Wyoming. Good.

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Speaker 1: You know more about this than I do I know

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Speaker 1: about the DNA. A guy, Yeah, guy in in Martz.

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Speaker 1: A guy in Matitz, Wyoming. Um, As I understand it.

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Speaker 1: One day, his dog is standing there with a black

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Speaker 1: footed ferret that's amazing, and he went out to try

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Speaker 1: to find what the animal was and it turned out

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Speaker 1: that um, they were not in fact gone, They were

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Speaker 1: not gone, so but they are in trouble. And this

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Speaker 1: is a problem with black footed farets. So they were

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Speaker 1: very small population for quite a long time, and they

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Speaker 1: have almost no genetic diversity, and they're threatened in the

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Speaker 1: wild today because while they can breathe them, there are

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Speaker 1: nice captive breeding facilities that can produce lots of black

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Speaker 1: footed farets. As soon as they've released them into the wild,

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Speaker 1: they get sick and they die. Can can you just

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Speaker 1: a whole because when you hear I think people often say,

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Speaker 1: like no genetic diversity, But is there a way to

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Speaker 1: put it? Is there a way to put it in

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Speaker 1: human terms where you imagine is it as bad as

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Speaker 1: if you only had one family? Yes, like you had

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Speaker 1: a mother, father, two daughters, two sons, and they needed

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Speaker 1: to recolonize and that was it. And then the daughters

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Speaker 1: had to breed with their brothers or with their fathers

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Speaker 1: literally like literally literally siblings breeding with siblings or extreme inbreeding,

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Speaker 1: extreme inbreeding, and it's bad for the population. And and

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Speaker 1: you know this population is doing okay, and that it

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Speaker 1: can survive in captive environments where it's not exposed to

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Speaker 1: any sort of allunges. But as soon as they're released

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Speaker 1: into the wild, this inbreeding shows up as something that

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Speaker 1: causes them to not be able to survive. It shows

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Speaker 1: up behaviorally, it shows up behaviorally probably, but in the

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Speaker 1: case of the blackfooted ferrets, it shows up because they

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Speaker 1: have no resistance to the diseases that are actually circulating

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Speaker 1: in a wild, so they're they're not able to survive

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Speaker 1: as they get infected. So one of the most diverse

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Speaker 1: parts of our genome of any species genome, is what's

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Speaker 1: called the major histamine complex NHC. It creates the proteins

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Speaker 1: and enzymes in our body that allow us to fight diseases.

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Speaker 1: And we're very different. Everybody is very different. We have

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Speaker 1: lots of different circulating alleles or variants in our population,

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Speaker 1: and this is so that you know, the more diversity

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Speaker 1: we have, the more um diversity of diseases that we

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Speaker 1: are potentially able to fight off. And so it's good

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Speaker 1: for a population and for an individual to have lots

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Speaker 1: of diversity at these alleles, and these black footed ferrets

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Speaker 1: and other species that go through these what's called population

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Speaker 1: bottle X, where you have a very small population size

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Speaker 1: for a long time and you lose a lot of

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Speaker 1: your diversity, become um completely lacking in diversity at this

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Speaker 1: really important part of the genome. So here's where I'm

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Speaker 1: going with ancient DNA. Imagine that you could find a

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Speaker 1: bone or some tissue from blackfooted ferrets that lived before

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Speaker 1: they went through this population bottleneck. So these would have

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Speaker 1: been individuals that are no longer alive. Maybe they lived

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Speaker 1: a hundred years ago, maybe they lived thousands of years ago,

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Speaker 1: but they have diversity in their genome that used to

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Speaker 1: be there, that used to be able to help this

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Speaker 1: population to fight disease. If we could get that information

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Speaker 1: sequence their DNA, grind up a bit of that bone

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Speaker 1: and extract the DNA sequences that are preserved in that bone,

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Speaker 1: or in the case of blackfooted ferrets, that are actually

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Speaker 1: preserved tissue specimens from individuals that lived a couple of

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Speaker 1: decades ago that are in what's called the frozen Zoo

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Speaker 1: in San Diego. So we can a couple of decades.

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Speaker 1: A couple of decades ago in this case is enough.

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Speaker 1: In other species, it depend some when your bottleneck was

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Speaker 1: So for blackfooted ferrets, the bottleneck was relatively recently. For

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Speaker 1: American buffalo the bottleneck was thirteen thousand years ago. So

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Speaker 1: you would need older individuals to be able to see

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Speaker 1: what the diversity in the past look like, because that's

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Speaker 1: that's surprising to hear. But I know you're interested in bison.

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Speaker 1: But so we can go back and we can sequence

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Speaker 1: the DNA from these older individuals that have this diversity,

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Speaker 1: learn what that diversity used to look like, and then

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Speaker 1: use genome engineering technologies to cut and paste the no

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Speaker 1: diversity region of individuals genomes today living individuals and paste

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Speaker 1: in its place, uh synthetic version of the sequences that

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Speaker 1: used to exist in that population. And in doing so,

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Speaker 1: you have modified the genomes of a living organism in

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Speaker 1: a way that gives them a fighting chance to survive. Today.

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Speaker 1: You haven't brought back the extinct thing. You have used

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Speaker 1: gene sequences that are extinct from the same species to

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Speaker 1: bolster the immunity or potentially help this species to survive.

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Speaker 1: And this this is one example, but this is where

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Speaker 1: I really see the power and the potential of this

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Speaker 1: sort of technology. The idea that we can look at

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Speaker 1: DNA sequences in the past. Let's say we want to

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Speaker 1: create an animal that's more able to survive somewhere cold

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Speaker 1: or somewhere hot if we can identify an extinct species

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Speaker 1: or a close relative that used to be alive, that

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Speaker 1: has a gene sequence that might be able to cause,

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Speaker 1: for example, the idea of bringing mammoths back to life.

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Speaker 1: And you and you, I'll point out, you have a

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Speaker 1: book how to Clone a Mammoth. Yes, so this is

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Speaker 1: something that I've been thinking about a lot, and a

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Speaker 1: sub doesn't A subtitle could be how to clone a mammoth?

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Speaker 1: Um all the reasons why you might want to and

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Speaker 1: might might not want to. I think the subtitle is

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Speaker 1: actually this science of de extinction or something. But I

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Speaker 1: have been suggested several several better subtitles. I think my

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Speaker 1: favorite was, um, why cloning a mammoth? Or why how

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Speaker 1: cloning a mammoth? Might be? No, I'm trying to think

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Speaker 1: of what the best one was that was suggested with

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Speaker 1: my friend of mine. It was like, if you have

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Speaker 1: limited ethics, a billion dollars and a mammoth, that that

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Speaker 1: opens it up. That always what I was trying to

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Speaker 1: what I was trying to suggest about it. But but anyway,

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Speaker 1: they I mean, we can talk about this later, but

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Speaker 1: I'm going to use it as an example because I mean,

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Speaker 1: it's very easy to think through. So if you have

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Speaker 1: an elephant, this is something that's adapted to living in

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Speaker 1: the tropics. Um. The tropics is not a place that's

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Speaker 1: really conducive to elephants living right now. Let's say we

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Speaker 1: wanted to create an elephant that is able to survive

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Speaker 1: somewhere colder, not conducive for human reasons, for human reasons,

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Speaker 1: because of because of rampant poaching development, yeah, influx of

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Speaker 1: a like pastoral agriculturalists. Right. And and I should say

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Speaker 1: right up front that I am not advocating creating mammoths

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Speaker 1: that can live in the cold as an alternative to

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Speaker 1: trying to fix the terrible situation that Asian elephants and

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Speaker 1: African elephants are in right now. I think that this

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Speaker 1: is just an alternate pathway that potentially should be followed

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Speaker 1: at the same time as existing conservation efforts. UM, But

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Speaker 1: it is something that I think we should consider. It's

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Speaker 1: not possible to do it yet, but there's no reason

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Speaker 1: to turn to say no to a technology before we

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Speaker 1: know what's actually feasible because we're a little bit scared

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Speaker 1: about the ecological and ethical consequences of of doing it.

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Speaker 1: These are things that we need to think through very clearly. However,

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Speaker 1: stepping away from the ethics and of and morality of

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Speaker 1: this right now, and we will come back to this,

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Speaker 1: but I'm just trying to explain the technology here. Let's

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Speaker 1: say we can go and sequence the genome of a mammoth.

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Speaker 1: Mammoths and Asian elephants are very closely related to each other.

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Speaker 1: They shared a common ancestors sometime in the last five

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Speaker 1: million years, so that really closely related to each other.

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Speaker 1: You pointed out that there's, uh, there's a the difference

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Speaker 1: between an Asian elephant in a wooly man ammoth is

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Speaker 1: about are similar to the difference between humans and chimps.

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Speaker 1: That's right, About one percent of the genome sequence is different.

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Speaker 1: I told that to your friend and he said, uh, yeah,

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Speaker 1: but that's the that's the percent that gave us Mozart.

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Speaker 1: It's probably true, and that's the one percent that we're

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Speaker 1: thinking about the difference between mammoths and elephants. If what

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Speaker 1: we want to do is figure out what it is

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Speaker 1: that made mammoths which is an elephant able to survive

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Speaker 1: in the cold, and we want to be able to

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Speaker 1: create an elephant that that is able to survive in

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Speaker 1: a colder environment. If we could identify those important parts

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Speaker 1: of mammoth that are different and then cut and paste

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Speaker 1: those into an elephant genome, could we create not a mammoth,

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Speaker 1: but an elephant that can live somewhere that's colder, that

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Speaker 1: can eat this same diet, that can somehow protect itself

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Speaker 1: from these cold winters, so that we can potentially find

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Speaker 1: a place to put elephants while we are trying to

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Speaker 1: solve the problems that are that are ongoing in there

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Speaker 1: exists environment. Can I pause you there to have you

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Speaker 1: explained a couple of things real quick. Um, you're saying, though,

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Speaker 1: like just based on even with some future projecting, you're saying,

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Speaker 1: we will not no matter how journalist frame news that

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Speaker 1: comes from your world, we will not make a mammoth.

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Speaker 1: We will not bring back a living, breathing mammoth. Is

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Speaker 1: just a continuation of the mammoths that we're there. I

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Speaker 1: think that this is something that is really important for

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Speaker 1: people to understand, is that once a species is extinct,

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Speaker 1: it is gone. This is not a solution to the

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Speaker 1: extinction crisis. De extinction this is the term that people

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Speaker 1: are using too to refer to bringing something that's extinct

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Speaker 1: back to life. Is it's an idea that is shaped

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Speaker 1: more by imagination than by reality. It's very romantic to

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Speaker 1: think of the I think that you might be able

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Speaker 1: to bring something back that's been gone for a long time.

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Speaker 1: But there are people who the scientific reality is there

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Speaker 1: are ones that try to do it, who kick around

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Speaker 1: ideas it's gone, the ideas. If you dig into these

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Speaker 1: ideas though, it's it's not. It's not that. It's not

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Speaker 1: that you're going to bring something back that is identical

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Speaker 1: to something that's gone. It's that you're going to be

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Speaker 1: able to recreate components of those organisms. You could bring

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Speaker 1: back traits. You could move genes from a mammoth into

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Speaker 1: an elephant. Potentially, we don't know how to do that yet.

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Speaker 1: We can move genes from mammoth sequences that we generate

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Speaker 1: from bones into cells of elephants that are growing in

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Speaker 1: petrie dishes and labs, but we can't then turn those

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Speaker 1: cells into some hybrid between a mammoth and an elephant.

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Speaker 1: So what would you need if you were going to

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Speaker 1: create an exact replica of a species that's extinct, you

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Speaker 1: would need it's a DNA sequence. We can do that.

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Speaker 1: For for a mammoth, there are incredibly well preserved bones.

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Speaker 1: You mean that map its entire genome. Yeah. So the

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Speaker 1: way we do that in angel DNA kind of getting

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Speaker 1: back to this, is we collect these bones. The best

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Speaker 1: preserved bones are frozen in the Arctic soil called parmafrost,

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Speaker 1: and mostly they've been de fleshed, probably by something like

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Speaker 1: a lion or a big bear, and so the bone

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Speaker 1: doesn't have any tissue on it. It It gets buried in

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Speaker 1: the soil and rapidly frozen. And you spend time including

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Speaker 1: this that you spend some you spend field seasons up

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Speaker 1: actually like physically look at like actually looking for bones

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Speaker 1: sticking out of the ground. Yep, I do. It's good

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Speaker 1: fun too. I recommend it. Yeah. Um. But you can

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Speaker 1: take these bones and you can take a chunk out

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Speaker 1: of them with a drum will drill or something like that,

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Speaker 1: and you grind it up into a fine powder and

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Speaker 1: then you can dissolve away all the components that aren't

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Speaker 1: the DNA, so the tissue and the actual bone. You

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Speaker 1: dissolve it away, and then you can chemically and somatically

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Speaker 1: pull out the DNA. So the DNA that we get

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Speaker 1: out of those bones is not in good shape. This

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Speaker 1: is one of the important things to remember. If I

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Speaker 1: were to take a swab Q tip in the inside

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Speaker 1: of my cheek or spit in the tube like you

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Speaker 1: do when you send something off to one of these

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Speaker 1: companies that sends you your DNA sequence, you can get

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Speaker 1: really long fragments of DNA. Our genomes have about three

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Speaker 1: billion nucleotides bases, these A, C, S, G S, and

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Speaker 1: T s that make up the the sequence that has

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Speaker 1: the genes that make the proteins that make us look

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Speaker 1: and act the way we do. And we can get

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Speaker 1: millions of them, strings of millions in a row from

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Speaker 1: a living person, of living piece of tissue. But the

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Speaker 1: bones that we get out of the Arctic, the DNA

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Speaker 1: in them is chopped up into tiny fragments. And this

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Speaker 1: happens first because once an organism dies, there are enzymes

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Speaker 1: in their own body that chop up DNA. These exist

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Speaker 1: because if you eat a piece of meat, where you

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Speaker 1: eat a leaf of a plant, you don't want that

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Speaker 1: DNA to stay really big and long and powerful. In

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Speaker 1: your body, you've get enzymes that chew that up and

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Speaker 1: make it go away, and that happens to your own

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Speaker 1: tissue when cells burst, When cells die, and life chops

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Speaker 1: up your DNA so that you can get rid of it.

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Speaker 1: That happens post mortem as well. And then there are

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Speaker 1: things like bacteria and fung gui that will get into

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Speaker 1: these bones and the tissue remains when they're decaying, and

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Speaker 1: they will also chop up that DNA. They consume all

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Speaker 1: this kind of carbon material for food. And then the sun.

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Speaker 1: You know, you go outside and you're supposed to wear

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Speaker 1: sun block, and that's because the ultra violet radiation will

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Speaker 1: hit your cells and break your DNA. Now, when you're alive,

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Speaker 1: you have proof reading enzymes that will go along and

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Speaker 1: fix those those bits of damage that the UVY radiation

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Speaker 1: causes so that you don't get skin cancer every time

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Speaker 1: you walk outside. But once you're dead, those proofitting enzymes

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Speaker 1: are not doing their job anymore, and the UV radiation

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Speaker 1: and other sorts of radiation will continue to hit the

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Speaker 1: cells and break down the DNA, so that the end

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Speaker 1: result there is that pretty soon after death, the DNA

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Speaker 1: is no longer in long strands. It's in really short,

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Speaker 1: chopped up strands, and after time they just getting get

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Speaker 1: smaller and smaller and smaller and small. And you say

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Speaker 1: pretty soon after death, the first pretty soon means like

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Speaker 1: within years. First pretty soon means within minutes, and the

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Speaker 1: second pretty soon we're talking tens of thousands of years there.

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Speaker 1: It depends on environment. So if something were to die

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Speaker 1: and sit in the sun in Arizona today it's supposed

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Speaker 1: to be hunter twenty degrees in Arizona, probably we would

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Speaker 1: get no good recoverable DNA tomorrow, right, Um, but I

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Speaker 1: don't all that because stuff decase. Also, you'll have really

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Speaker 1: rapid microbial activity when it's things are rotting in the

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Speaker 1: sun like that. So maybe you could get DNA tomorrow

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Speaker 1: is probably an exaggeration, but certainly within a couple of days.

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Speaker 1: It would be very hard to get recover good quality

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Speaker 1: DNA from these things. If something dies, it's that volatile. Yeah, Well,

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Speaker 1: it depends on microbial activity, so and also the sun

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Speaker 1: and temperature. So things decay faster when it's hot, and

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Speaker 1: when the temperature fluctuates, a lot of things decay. If

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Speaker 1: you think about ideal temperatures for stuff to break stuff down.

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Speaker 1: I mean when you want to when you're cooking something,

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Speaker 1: you want to get it above a particular temperature, and

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Speaker 1: that's not the temperature of like your normal ambient temperature

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Speaker 1: and Phoenix, Arizona today, right, it's so you don't the

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Speaker 1: all the microbes will just multiply at some point, can

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Speaker 1: cause a lot of microbial life forms, and you get

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Speaker 1: sick when you eat stuff. So you eat I wanted

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Speaker 1: to stay cold and frozen, or you wanted to be

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Speaker 1: really hot, like cooked. And if it's really hot and cooked,

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Speaker 1: you're destroying all the DNA, all the living material. But

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Speaker 1: if it's cold and frozen, then you're slowing down the decay,

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Speaker 1: just like sticking your steak in the freezer so that

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Speaker 1: it lasts for an extra couple of In those cases,

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Speaker 1: like when you when you find a well preserved mammoth

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Speaker 1: coming out of the permafrost, it probably so that thing

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Speaker 1: probably died in sub freezing conditions. Yes, yes, uh maybe,

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Speaker 1: I mean when when these animals died during the summer.

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Speaker 1: In the summer in the Arctic, it can be you know,

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Speaker 1: sixty seventy degrees during the day, but those ones and

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Speaker 1: those ones could still potentially be preserved. Could be because

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Speaker 1: the the sediment, the dirt in the ground is very cold,

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Speaker 1: and if it gets buried right away in volcanic dust

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Speaker 1: or whatever, then these the remains of these animals will

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Speaker 1: will preserve for a long time. The oldest DNA that

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Speaker 1: we've ever recovered was from a bone that we found

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Speaker 1: in permafrost in the Yukon territory, and it was associated

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Speaker 1: with a volcanic ash layer that we think is around

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Speaker 1: seven hundred thousand years old. So we're estimating that that

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Speaker 1: is the age of this horse bone. It's also the

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Speaker 1: oldest frozen dirt that anyone has ever known. So that

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Speaker 1: horse bone, that horse lived around seven hundred thousand years ago.

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Speaker 1: It died, its bones were immediately buried and frozen, and

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Speaker 1: we're kept in that freezer, that dirt freezer, for the

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Speaker 1: last seven hundred thousand years, and that's the only reason

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Speaker 1: we were able to recover DNA from that bone. And

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Speaker 1: the DNA was in terrible condition. The longest fragments were

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Speaker 1: thirty or forty letters long. Remember I said, so that's

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Speaker 1: where you're going saying, we have them that are a million, millions,

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Speaker 1: millions or millions or million long millions. Yes, we can

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Speaker 1: do millions, but because you said three billion total. Yeah,

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Speaker 1: so it depends you can get very large fragments of DNA.

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Speaker 1: How large this depends on how good you are at

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Speaker 1: extracting what's called high molecular weight DNA, and there are

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Speaker 1: hits that you can purchase in different approaches you can

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Speaker 1: use to get larger and larger fragments. Were limited by

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Speaker 1: technology in living things rather than by the actual size

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Speaker 1: of the DNA, whereas an ancient DNA you're limited by

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Speaker 1: the actual size of the surviving fragments of DNA. Our

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Speaker 1: technology would allow us to get larger fragments if they existed,

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Speaker 1: they just don't. And why is this important? Why are

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Speaker 1: we having such a long conversation about this. It's important

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Speaker 1: because an elephant genome, a mammoth genome is about four

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Speaker 1: billion letters long, and if we have thirty letter fragments,

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Speaker 1: we it's kind of like having a trillion zillion piece

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Speaker 1: puzzle and we don't know what a mammoth genome actually

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Speaker 1: looks like. So we're taking these tiny little puzzle pieces

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Speaker 1: and we're trying to figure out where in the elephant

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Speaker 1: genome they go. So you've got your massive trillion piece puzzle,

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00:21:51,160 --> 00:21:55,199
Speaker 1: and the box top is actually not the picture of

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Speaker 1: the puzzle that you're trying to put together. Some close

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Speaker 1: but not exactly the right picture. And there's another problem,

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Speaker 1: and that is that, remember I said that there were

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Speaker 1: all these bacteria and fungui and things that were eating

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Speaker 1: up the DNA. Their DNA is also in that bone.

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Speaker 1: So when you extract DNA from these mammoth bones, you

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Speaker 1: get loads of tiny pieces of mammoth DNA. Maybe about

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Speaker 1: one to four percent of what you get is tiny

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Speaker 1: pieces of mammoth DNA. The rest of it is tiny

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Speaker 1: pieces of other types of DNA, and you don't know

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Speaker 1: which is which. So you've got chillion piece puzzle that

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Speaker 1: actually includes the pieces for about a hundred different puzzles,

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Speaker 1: and you've got the wrong box top, right. Can you

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Speaker 1: can you? Can you? When you talk about contaminants, can

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Speaker 1: you include the anecdote about sheep contaminants in MOA bones?

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Speaker 1: So yeah, so this wasn't MOA bones I think that

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Speaker 1: you're talking about. This was from we were trying to

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Speaker 1: get DNA directly from dirt in New Zealand. And so

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Speaker 1: it is true that DNA is preserved in sentiment columns.

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Speaker 1: This is really cool and it's something that people are

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Speaker 1: just starting to focus on. I think that it's going

427
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Speaker 1: to be really neat way of trying to figure out

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Speaker 1: where stuff lives. You know, we there are species that

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Speaker 1: are rare or whose ranges we don't know. It turns

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Speaker 1: out you can just go out and you can get

431
00:23:09,400 --> 00:23:11,640
Speaker 1: a bit of soil and you can extract DNA from

432
00:23:11,640 --> 00:23:13,840
Speaker 1: that soil and you can ask, is this incredibly rare

433
00:23:13,880 --> 00:23:16,040
Speaker 1: small mammal ever found in this location? And if their

434
00:23:16,119 --> 00:23:18,480
Speaker 1: DNA is there? The answer is yes. So we wanted

435
00:23:18,520 --> 00:23:20,840
Speaker 1: to know how far back in time we could do this,

436
00:23:20,880 --> 00:23:23,160
Speaker 1: So we went to different caves in New Zealand where

437
00:23:23,200 --> 00:23:26,480
Speaker 1: there are sandy environments and DNA will actually percolate through

438
00:23:26,560 --> 00:23:29,240
Speaker 1: sands depending on what the sources of DNA is. And

439
00:23:29,320 --> 00:23:34,160
Speaker 1: we knew that there should not be moa and sheep together,

440
00:23:34,680 --> 00:23:38,280
Speaker 1: right because the moa went extinct before sheep were introduced,

441
00:23:38,320 --> 00:23:40,359
Speaker 1: And means it like four pound birds that used to

442
00:23:40,359 --> 00:23:43,959
Speaker 1: live in New Zealand and we're extrapated by humans, right

443
00:23:44,200 --> 00:23:47,640
Speaker 1: or not not extra but driven to extinction by human Yes,

444
00:23:47,800 --> 00:23:55,320
Speaker 1: like big enormous kisy. Yes, they were impressive birds, and

445
00:23:55,960 --> 00:23:59,120
Speaker 1: they were preyed upon by an even more impressive bird.

446
00:23:58,840 --> 00:24:01,439
Speaker 1: I digress here, but because this is an opportunity to

447
00:24:01,440 --> 00:24:04,360
Speaker 1: talk about one of my favorite extinct species, Harper gourns

448
00:24:04,400 --> 00:24:07,600
Speaker 1: the hosts eagle, the giant eagle that would swoop down

449
00:24:07,640 --> 00:24:11,199
Speaker 1: and pick up these massive moa. So how big was

450
00:24:11,200 --> 00:24:13,920
Speaker 1: the eagle? I can't say. I was top of my head.

451
00:24:13,960 --> 00:24:15,680
Speaker 1: But somebody who has a computer in front of them

452
00:24:15,680 --> 00:24:18,200
Speaker 1: can look this up on and figure it out right now,

453
00:24:18,240 --> 00:24:21,320
Speaker 1: because it's you have to give me the here. I'll

454
00:24:21,359 --> 00:24:23,520
Speaker 1: turn my phone here, I'll turn my phone A A

455
00:24:23,960 --> 00:24:28,159
Speaker 1: st you need a connection, I can do it. H

456
00:24:28,240 --> 00:24:31,520
Speaker 1: A s T alright, so continue, we'll get out of that.

457
00:24:32,720 --> 00:24:35,320
Speaker 1: We'll find it. Yeah, anyway, so this is an amazing,

458
00:24:35,440 --> 00:24:38,320
Speaker 1: amazing animal. Anyway, both of these things went extinct because

459
00:24:38,400 --> 00:24:41,080
Speaker 1: you know, if you're a giant eagle and you thrive

460
00:24:41,480 --> 00:24:44,080
Speaker 1: on eating these giant birds, and the the giant birds go extinct,

461
00:24:44,080 --> 00:24:45,879
Speaker 1: and you're probably going to go extinct too. They went

462
00:24:45,960 --> 00:24:48,640
Speaker 1: extinct several hundred years past, and sheep were introduced into

463
00:24:48,680 --> 00:24:51,479
Speaker 1: New Zealand. So our idea was, if DNA is not

464
00:24:51,680 --> 00:24:54,360
Speaker 1: moving up and down in these caves, we should find

465
00:24:54,480 --> 00:24:57,399
Speaker 1: MOA DNA and then a layer where there's nothing, and

466
00:24:57,440 --> 00:25:00,200
Speaker 1: then a layer where there's sheep dna. But in act,

467
00:25:00,240 --> 00:25:03,399
Speaker 1: what we found was that there was sheep DNA intermingled

468
00:25:03,440 --> 00:25:06,120
Speaker 1: with the MOA DNA. And this is probably because there

469
00:25:06,119 --> 00:25:10,200
Speaker 1: were so many sheep that were wandering around and urinating,

470
00:25:10,280 --> 00:25:12,359
Speaker 1: and of course the urine is a nice source of

471
00:25:12,480 --> 00:25:15,959
Speaker 1: DNA and this was percolating through this sandy soil that

472
00:25:16,040 --> 00:25:18,200
Speaker 1: the sheep DNA was getting down into the MOA DNA.

473
00:25:18,240 --> 00:25:20,840
Speaker 1: And all this tells us is that in some soil

474
00:25:20,920 --> 00:25:24,679
Speaker 1: environments you don't have this nice layering effect, and you

475
00:25:24,720 --> 00:25:29,040
Speaker 1: have to really archaeologists always talk about and it exists

476
00:25:29,040 --> 00:25:32,879
Speaker 1: in some places, for example in and the Arctic, where

477
00:25:33,200 --> 00:25:34,720
Speaker 1: I said, you know, we found this horse bone and

478
00:25:34,720 --> 00:25:38,000
Speaker 1: it associated with this volcanic eruption. You can see these

479
00:25:38,080 --> 00:25:40,479
Speaker 1: volcanic ash layers, they call them tephra, and they go

480
00:25:40,840 --> 00:25:43,800
Speaker 1: cleanly across this permafrost dirt. And you might not know

481
00:25:43,920 --> 00:25:46,399
Speaker 1: anything about whether the layering is good below it or

482
00:25:46,440 --> 00:25:48,040
Speaker 1: the layering is good above it, but if you can

483
00:25:48,040 --> 00:25:51,840
Speaker 1: see this nice clean layer of thick ash, you know

484
00:25:51,920 --> 00:25:54,639
Speaker 1: that there's not stuff moving above and below that ash

485
00:25:54,640 --> 00:25:56,800
Speaker 1: and if we find a bone below it, we know

486
00:25:56,880 --> 00:25:59,439
Speaker 1: it must be older. The bone must be older than

487
00:25:59,480 --> 00:26:01,439
Speaker 1: that eruption. And if we find a bone above it,

488
00:26:01,560 --> 00:26:04,440
Speaker 1: we know that it must be younger. So the bone

489
00:26:04,480 --> 00:26:07,520
Speaker 1: doesn't migrate through the line. The bone won't move. You're

490
00:26:07,520 --> 00:26:09,639
Speaker 1: saying that that's all stuff that ash was coming in from,

491
00:26:09,680 --> 00:26:12,240
Speaker 1: like eruptions in the illusions. Uh, there are a couple

492
00:26:12,280 --> 00:26:15,400
Speaker 1: of different volcanic um mountain chains that are up there

493
00:26:15,400 --> 00:26:17,560
Speaker 1: that cause like the logo, there are a couple of

494
00:26:17,600 --> 00:26:19,440
Speaker 1: different mountain chains up there that will erupt at different

495
00:26:19,440 --> 00:26:22,320
Speaker 1: time points. And you can actually tell by the chemical

496
00:26:22,359 --> 00:26:25,480
Speaker 1: composition of the ash which mountain it came from, and

497
00:26:25,600 --> 00:26:28,160
Speaker 1: you can link eruptions together that you see the ash

498
00:26:28,160 --> 00:26:30,040
Speaker 1: from in different places, and you can kind of learn

499
00:26:30,119 --> 00:26:32,800
Speaker 1: something about the geologic history by studying these ash. So

500
00:26:33,040 --> 00:26:35,400
Speaker 1: that's other cool thing that you can do when you're

501
00:26:35,440 --> 00:26:39,560
Speaker 1: out there working in the tundra. Yeah, it's fascinating, like

502
00:26:39,560 --> 00:26:43,320
Speaker 1: like little time stamps. Right, So where were we? We

503
00:26:43,320 --> 00:26:47,680
Speaker 1: were talking about piecing together the mammoth genome real quick though,

504
00:26:47,800 --> 00:26:50,399
Speaker 1: just everybody knows that how do you pronounce it? Host?

505
00:26:51,280 --> 00:26:53,720
Speaker 1: Twenty six pounds. That's an average between the male and

506
00:26:53,760 --> 00:26:57,920
Speaker 1: the female and uh ten ft to twelve foot wingspan.

507
00:26:58,080 --> 00:27:05,080
Speaker 1: That's a big eagle. Yeah, twelve fan and its closest

508
00:27:05,160 --> 00:27:07,920
Speaker 1: living relative, I believe, at least it was a while

509
00:27:07,920 --> 00:27:10,119
Speaker 1: ago when we studied this UM when I was a

510
00:27:10,160 --> 00:27:13,320
Speaker 1: grad student. Is something called the booted eagle from Australia,

511
00:27:13,359 --> 00:27:15,720
Speaker 1: which is a tiny little thing. I think whenever we

512
00:27:15,800 --> 00:27:19,280
Speaker 1: figure out that there these these enormous phenotypic differences between

513
00:27:19,320 --> 00:27:21,280
Speaker 1: things that are really closely related to each other, it

514
00:27:21,400 --> 00:27:24,800
Speaker 1: just astounds me. The power of evolution and genetic variation,

515
00:27:24,880 --> 00:27:27,359
Speaker 1: and it has a lot to do right with um.

516
00:27:27,680 --> 00:27:32,000
Speaker 1: Certain groups get to islands and they seem to get

517
00:27:33,080 --> 00:27:36,800
Speaker 1: huge islands, they seem to get teeny And there's another

518
00:27:36,920 --> 00:27:39,520
Speaker 1: And I like to talk about bison. So do you

519
00:27:39,560 --> 00:27:44,120
Speaker 1: know about bison ladder frons um. This is an enormous bison,

520
00:27:44,520 --> 00:27:47,440
Speaker 1: much bigger than the other bison that lived in North

521
00:27:47,480 --> 00:27:50,320
Speaker 1: America at the time. We just were able to get

522
00:27:50,440 --> 00:27:53,199
Speaker 1: DNA from a bison ladder frons that was found in

523
00:27:53,280 --> 00:27:57,080
Speaker 1: snowmass Colorado, at this site that was found recently and

524
00:27:57,119 --> 00:27:59,560
Speaker 1: it's about a hundred and twenty thousand years old. This

525
00:27:59,640 --> 00:28:03,560
Speaker 1: particular remain based on the geological setting, and we were

526
00:28:03,560 --> 00:28:06,480
Speaker 1: also able to get DNA from a step bison. This

527
00:28:06,560 --> 00:28:08,879
Speaker 1: is the bison that lived at the same time in

528
00:28:08,880 --> 00:28:11,440
Speaker 1: Alaska that was much smaller, about half the size, if

529
00:28:11,440 --> 00:28:14,879
Speaker 1: not smaller, from bison ladder fronds, and they are the

530
00:28:14,920 --> 00:28:21,600
Speaker 1: same yoetically are you familiar with Do you ever read

531
00:28:21,880 --> 00:28:25,679
Speaker 1: the work of Valarious Geistes? Are you with his idea

532
00:28:25,680 --> 00:28:27,639
Speaker 1: about it? Was that him that came up with the

533
00:28:27,640 --> 00:28:32,800
Speaker 1: founding effect. The founder effect, we're like one of species

534
00:28:32,840 --> 00:28:36,720
Speaker 1: colonized as a new era area, okay, and they have

535
00:28:36,960 --> 00:28:41,760
Speaker 1: like unfettered like like they're in a non competitive environment

536
00:28:42,840 --> 00:28:45,560
Speaker 1: that they will invest for a while. They invest a

537
00:28:45,560 --> 00:28:51,360
Speaker 1: lot of energy into elaborate sexual display and have an

538
00:28:51,440 --> 00:28:54,000
Speaker 1: experience like periods of very high fecundity and kind of

539
00:28:54,000 --> 00:28:57,200
Speaker 1: have a good old days, and then things kind of

540
00:28:57,240 --> 00:28:59,840
Speaker 1: catch up with themselves and they shrink. I think that

541
00:29:00,040 --> 00:29:03,160
Speaker 1: he wrote about ladder Franz as being that it was

542
00:29:03,240 --> 00:29:08,840
Speaker 1: colonizing areas in the wake of glaciers. Yeah, and got huge.

543
00:29:09,120 --> 00:29:11,360
Speaker 1: That idea. Is that idea sound? You know? With bison,

544
00:29:11,400 --> 00:29:15,880
Speaker 1: there's so many competing ideas about the history of these guys.

545
00:29:15,880 --> 00:29:17,600
Speaker 1: You know, at one point there were more than fifty

546
00:29:17,640 --> 00:29:21,080
Speaker 1: different name species of bison that supposedly lived in North

547
00:29:21,120 --> 00:29:23,800
Speaker 1: America during the late licens scene, and I think probably

548
00:29:23,840 --> 00:29:26,560
Speaker 1: there there was actually only one. It was just changing

549
00:29:26,600 --> 00:29:28,280
Speaker 1: all the time depending on where it was, and there

550
00:29:28,320 --> 00:29:31,560
Speaker 1: was a competition between paleontologists to name new species. This

551
00:29:31,640 --> 00:29:34,400
Speaker 1: is a time where people would find these partial horn

552
00:29:34,400 --> 00:29:36,280
Speaker 1: cores and they would turn them in different directions and

553
00:29:36,280 --> 00:29:38,640
Speaker 1: they would measure the width and the length of the

554
00:29:38,680 --> 00:29:42,800
Speaker 1: horn cores, which is a terrible marker because these things

555
00:29:42,800 --> 00:29:45,680
Speaker 1: are they're manipulated by depending on what fights you get into,

556
00:29:45,760 --> 00:29:47,560
Speaker 1: or how much you eat when you're growing up, et cetera.

557
00:29:47,640 --> 00:29:51,200
Speaker 1: This is not a paleontologically equivocal trade. This is not

558
00:29:51,280 --> 00:29:53,560
Speaker 1: something that you can say, ah, that definitely means this

559
00:29:53,640 --> 00:29:56,280
Speaker 1: species or that species. And so people were naming new

560
00:29:56,320 --> 00:29:58,840
Speaker 1: species right and left based on not very much information,

561
00:29:58,880 --> 00:30:01,200
Speaker 1: but the genetic data that we've started to get from

562
00:30:01,240 --> 00:30:04,160
Speaker 1: these bison bones. Um. We think the oldest bison in

563
00:30:04,200 --> 00:30:08,280
Speaker 1: North America are around hundred and sixty years old. And

564
00:30:08,360 --> 00:30:10,200
Speaker 1: that's when they showed up. That's when they showed up,

565
00:30:10,200 --> 00:30:12,640
Speaker 1: came across the bearing strait um. And this is a

566
00:30:12,720 --> 00:30:14,920
Speaker 1: paper that was published really recently that I worked on

567
00:30:14,960 --> 00:30:17,840
Speaker 1: with a colleague collaborative of mine from University of Alberda,

568
00:30:17,960 --> 00:30:19,560
Speaker 1: and it was kicked up in the New York Times. Yeah,

569
00:30:19,680 --> 00:30:24,239
Speaker 1: and I wrote you an email, so I want to like,

570
00:30:24,560 --> 00:30:27,120
Speaker 1: see we got We're actually having two discussions right now both.

571
00:30:27,640 --> 00:30:29,760
Speaker 1: I want to come back to these animals. Okay, I

572
00:30:29,760 --> 00:30:33,560
Speaker 1: want to come back to bison because just remember this

573
00:30:34,480 --> 00:30:37,200
Speaker 1: because you hear people talk about and this, because we're

574
00:30:37,200 --> 00:30:39,280
Speaker 1: gonna talk about extinction. You hear people say, like, an

575
00:30:39,280 --> 00:30:43,480
Speaker 1: extinct form, Okay, the bison ladd of France, which had

576
00:30:43,800 --> 00:30:49,480
Speaker 1: a six ft horn tips tip, huge horns, so six

577
00:30:49,520 --> 00:30:52,280
Speaker 1: ft from tip to tip. People be like, it's an

578
00:30:52,280 --> 00:30:56,600
Speaker 1: extinct one, but it's not. Nope, there's still bison around.

579
00:30:56,600 --> 00:30:58,640
Speaker 1: It's just like it just as different than what we

580
00:30:58,720 --> 00:31:01,040
Speaker 1: have now. But people used to dig this stuff up.

581
00:31:01,080 --> 00:31:03,280
Speaker 1: And there's a there's one on display in in North

582
00:31:03,360 --> 00:31:05,440
Speaker 1: Dakota that came out of the Missouri River that wants

583
00:31:05,440 --> 00:31:09,400
Speaker 1: to look at a really nice skull and um, people

584
00:31:09,400 --> 00:31:10,960
Speaker 1: would dig it up and they'd be like, well that's

585
00:31:11,000 --> 00:31:15,600
Speaker 1: all kind that's not here anymore. Yeah, you know it's

586
00:31:16,680 --> 00:31:18,360
Speaker 1: you know, this is a this is a tough thing. Right,

587
00:31:18,400 --> 00:31:20,239
Speaker 1: I mean, and this is a I think it's an

588
00:31:20,240 --> 00:31:22,680
Speaker 1: important question to people who care about wildlife. It's an

589
00:31:22,680 --> 00:31:25,680
Speaker 1: important questions people who care about conservation is how do

590
00:31:25,800 --> 00:31:29,720
Speaker 1: we define the thing that is worth protecting, the thing

591
00:31:29,760 --> 00:31:31,960
Speaker 1: that we don't want to go extinct? Do we define

592
00:31:31,960 --> 00:31:34,800
Speaker 1: it as a species, do we define it as a population?

593
00:31:34,840 --> 00:31:37,320
Speaker 1: Do we find it as something that looks different from

594
00:31:37,320 --> 00:31:40,560
Speaker 1: other things? Because a lot of fronts looked decidedly different

595
00:31:40,600 --> 00:31:43,160
Speaker 1: from the step bison that lived in Alaska, which also

596
00:31:43,280 --> 00:31:48,640
Speaker 1: looked different than the bison bison. The forms that exist today, Um,

597
00:31:48,680 --> 00:31:52,160
Speaker 1: they're a continuum. They certainly are closely related to each other,

598
00:31:52,880 --> 00:31:56,280
Speaker 1: but they aren't. They don't exist anymore. But the same

599
00:31:56,360 --> 00:31:58,000
Speaker 1: thing could be said for you know, you look at

600
00:31:58,320 --> 00:32:01,480
Speaker 1: wolf populations that are alive to day, and they are

601
00:32:01,960 --> 00:32:05,280
Speaker 1: sometimes phenotypically or behaviorally different from each other, but they're

602
00:32:05,320 --> 00:32:08,680
Speaker 1: all wolves. So where do you draw the line? Where

603
00:32:08,680 --> 00:32:12,160
Speaker 1: do you decide what is the thing that you want

604
00:32:12,160 --> 00:32:15,520
Speaker 1: to protect? And and traditionally people think about species, but

605
00:32:15,600 --> 00:32:19,200
Speaker 1: the name species is something that is kind of arbitrary.

606
00:32:19,280 --> 00:32:22,440
Speaker 1: Is something that we decided on and who is well,

607
00:32:22,440 --> 00:32:24,680
Speaker 1: it depends on who's thinking about it and who's asking

608
00:32:24,680 --> 00:32:27,800
Speaker 1: the question, and what the point of asking the question is. Um.

609
00:32:27,840 --> 00:32:31,520
Speaker 1: There's a concept called the biological species concept, which says

610
00:32:31,560 --> 00:32:36,160
Speaker 1: that species are defined as reproductively isolated units. So two

611
00:32:36,200 --> 00:32:39,640
Speaker 1: things that can't make or if they do, they don't

612
00:32:39,640 --> 00:32:42,400
Speaker 1: produce offspring, or if they do produce offspring, those offspring

613
00:32:42,440 --> 00:32:45,640
Speaker 1: are not viable or also can't produce offering themselves. So

614
00:32:45,920 --> 00:32:48,760
Speaker 1: donkeys and mules are distinct species. They can produce. Sorry, sorry,

615
00:32:48,800 --> 00:32:51,440
Speaker 1: donkeys and horses are distinct species. They can produce offspring,

616
00:32:51,520 --> 00:32:54,800
Speaker 1: but that offspring can't reproduce, So the biological species concept

617
00:32:54,840 --> 00:32:58,440
Speaker 1: says they're different. But meanwhile, that definition would mean that

618
00:32:59,400 --> 00:33:04,120
Speaker 1: all of are like mountain cariboo, woodland caribou, bar and

619
00:33:04,200 --> 00:33:11,040
Speaker 1: ground caribou reindeer from Eurasia are just one species, and

620
00:33:11,080 --> 00:33:13,560
Speaker 1: it would get rid of our discussions about the Mexican

621
00:33:13,600 --> 00:33:17,880
Speaker 1: gray wolf and the gray wolf proper. But something I

622
00:33:17,880 --> 00:33:20,360
Speaker 1: think a little bit closer to heart, right, it would

623
00:33:20,360 --> 00:33:24,520
Speaker 1: say that humans and Neanderthals are the same species, because

624
00:33:24,640 --> 00:33:28,520
Speaker 1: we were clearly different, behaviorally, physically different from each other.

625
00:33:28,840 --> 00:33:32,560
Speaker 1: But after our ancestors moved out of Africa, they met

626
00:33:32,600 --> 00:33:35,320
Speaker 1: with Neanderthals and they hybridized with them. And because of that,

627
00:33:35,720 --> 00:33:39,240
Speaker 1: most of us have some component of Neanderthal DNA in

628
00:33:39,280 --> 00:33:42,800
Speaker 1: our genomes. So the biological species concept would call humans

629
00:33:42,800 --> 00:33:45,720
Speaker 1: and Neanderthals the same species. They would also call brown

630
00:33:45,800 --> 00:33:49,160
Speaker 1: bears and polar bears the same species, despite that these

631
00:33:49,200 --> 00:33:55,160
Speaker 1: two animals are behaviorally and physically and ecologically quite different

632
00:33:55,200 --> 00:33:57,320
Speaker 1: from each other. Yeah, because you can see people would

633
00:33:57,320 --> 00:33:59,960
Speaker 1: be like very resistant to the idea because of like,

634
00:34:00,280 --> 00:34:06,680
Speaker 1: hold on right, and it only eats seals, and it swims,

635
00:34:06,720 --> 00:34:09,360
Speaker 1: and it has different dentition and it doesn't hibernate, whereas

636
00:34:09,400 --> 00:34:11,640
Speaker 1: the other one is incredibly different. But they mate and

637
00:34:11,680 --> 00:34:13,640
Speaker 1: they produce offspring, and they do so in zoos, and

638
00:34:13,640 --> 00:34:17,160
Speaker 1: they do so in nature viable viable offspring. All the

639
00:34:17,239 --> 00:34:21,239
Speaker 1: bears on Alaska's Abc Islands are hybrids, all of them.

640
00:34:21,320 --> 00:34:24,319
Speaker 1: They have up to eight percent polar bear ancestry. And

641
00:34:24,360 --> 00:34:27,440
Speaker 1: that's because after the last ice Age, we believe that

642
00:34:27,480 --> 00:34:30,719
Speaker 1: the ABC Islands were actually colonized, were actually just a

643
00:34:30,800 --> 00:34:33,560
Speaker 1: home for polar bears, and then as the climate warmed up,

644
00:34:33,560 --> 00:34:36,959
Speaker 1: brown bear boys because boys leave, and brown bears moved

645
00:34:36,960 --> 00:34:39,719
Speaker 1: from the Alaska mainland onto the Abc Islands, where they

646
00:34:39,760 --> 00:34:42,839
Speaker 1: met this population of polar bears and hybridized with them,

647
00:34:43,120 --> 00:34:47,280
Speaker 1: and gradually this population was converted back to being brown

648
00:34:47,360 --> 00:34:49,879
Speaker 1: bear like, more brown bear like, because brown bears kept

649
00:34:49,920 --> 00:34:53,080
Speaker 1: coming over and mating with these these bears that lived there,

650
00:34:53,480 --> 00:34:57,080
Speaker 1: but mitochondrially, which is only inherited from your mom. This

651
00:34:57,160 --> 00:34:59,200
Speaker 1: is part of DNA and everyone of yourselves that only

652
00:34:59,200 --> 00:35:01,839
Speaker 1: comes from your mom. They are polar bears. They are

653
00:35:01,880 --> 00:35:05,600
Speaker 1: all polar bears with their mitochondrial DNA really and their

654
00:35:05,880 --> 00:35:09,160
Speaker 1: X chromosomes, which come more from mom because Dad only

655
00:35:09,160 --> 00:35:11,520
Speaker 1: has one copy of the X. The X chromosome has

656
00:35:11,760 --> 00:35:14,160
Speaker 1: more polar bear DNA on it than the rest of

657
00:35:14,200 --> 00:35:18,200
Speaker 1: their genome, which again is evidence that their mom's mom's

658
00:35:18,200 --> 00:35:20,680
Speaker 1: mom's mom's mom, at some point in the past, probably

659
00:35:20,800 --> 00:35:23,319
Speaker 1: twelve to fifteen thousand years ago, was a polar bear.

660
00:35:24,040 --> 00:35:26,800
Speaker 1: And and that then jives what you said earlier that

661
00:35:26,840 --> 00:35:30,279
Speaker 1: it was like colonizing males, which kind of fits in

662
00:35:30,400 --> 00:35:33,680
Speaker 1: with just general brown bear behavior like well, like a

663
00:35:33,680 --> 00:35:36,040
Speaker 1: lot of those, like a lot of big predators where

664
00:35:36,080 --> 00:35:38,680
Speaker 1: when they turn up in weird places, not always, but

665
00:35:38,800 --> 00:35:43,839
Speaker 1: so often it's a male turning up in a weird place. Yeah, well,

666
00:35:44,239 --> 00:35:47,080
Speaker 1: many in many animals, like I mean mountain lions which

667
00:35:47,080 --> 00:35:48,560
Speaker 1: we have out here, do this to The males are

668
00:35:48,600 --> 00:35:50,239
Speaker 1: the ones that disperse. They're the ones that go out

669
00:35:50,239 --> 00:35:52,120
Speaker 1: to try to find new territory. And so that that's

670
00:35:52,120 --> 00:35:55,080
Speaker 1: what's going on in brown bears, is that juvenile, juvenile

671
00:35:55,120 --> 00:35:58,040
Speaker 1: males move outside, whereas the females tend to stay with

672
00:35:58,040 --> 00:36:00,520
Speaker 1: their mom. Is called maternal philopatrie. But you know that.

673
00:36:00,640 --> 00:36:03,760
Speaker 1: But uh um, that's common in a lot of especially

674
00:36:03,840 --> 00:36:06,439
Speaker 1: I think large predatory species. But okay, now I'm gonna

675
00:36:06,440 --> 00:36:08,439
Speaker 1: back you way up to where you are. I don't

676
00:36:08,480 --> 00:36:15,200
Speaker 1: even remember that you're finding little instead of the millions

677
00:36:15,800 --> 00:36:20,719
Speaker 1: long DNA strands, you're finding little thirties and forties. And

678
00:36:20,840 --> 00:36:24,120
Speaker 1: a problem with those little thirties and forties is that

679
00:36:24,640 --> 00:36:30,800
Speaker 1: there in shitty condition, and their and finding them, figuring

680
00:36:30,840 --> 00:36:32,640
Speaker 1: out where they go, how to line them, and they're

681
00:36:32,640 --> 00:36:36,319
Speaker 1: corrupted because of part of the trickery of finding them

682
00:36:36,400 --> 00:36:38,719
Speaker 1: is that they're corrupted with so much other stuff, right,

683
00:36:38,800 --> 00:36:41,399
Speaker 1: And also they're corrupted themselves because all of these things

684
00:36:41,440 --> 00:36:43,959
Speaker 1: like UVY radiation beating down on the DNA will actually

685
00:36:44,000 --> 00:36:46,759
Speaker 1: cause the molecules to change, to become damaged in their

686
00:36:46,800 --> 00:36:49,520
Speaker 1: own way. So ancient DNA has its own kind of damage.

687
00:36:49,520 --> 00:36:51,799
Speaker 1: It's broken into tiny fragments, and it's mixed up with

688
00:36:51,840 --> 00:36:54,880
Speaker 1: all sorts of other contaminants. And your job as an

689
00:36:54,920 --> 00:36:58,080
Speaker 1: ancient DNA scientist is to take that little thirty and

690
00:36:58,160 --> 00:37:00,640
Speaker 1: figure out where on that big four bill in genome

691
00:37:00,719 --> 00:37:03,319
Speaker 1: that isn't actually the same genome it goes, and then

692
00:37:03,360 --> 00:37:05,799
Speaker 1: to gradually piece this puzzle together. And we do that

693
00:37:06,040 --> 00:37:09,400
Speaker 1: using computers. Um, you know, lots of DNA sequencing and

694
00:37:09,440 --> 00:37:12,359
Speaker 1: lots of computers. Gradually pieces together and come up with

695
00:37:12,400 --> 00:37:16,160
Speaker 1: what we believe the mammoth genome looked like. And you

696
00:37:16,160 --> 00:37:20,040
Speaker 1: can only move it relative to other pieces you found. Yeah,

697
00:37:20,080 --> 00:37:23,640
Speaker 1: so if you find one isolated piece, if you picture

698
00:37:23,640 --> 00:37:26,200
Speaker 1: on a number line like one to a hundred, you

699
00:37:26,320 --> 00:37:28,400
Speaker 1: have no idea where to place it until you find

700
00:37:28,440 --> 00:37:30,600
Speaker 1: some other thing. Right, Well, what you have is a

701
00:37:30,680 --> 00:37:33,239
Speaker 1: number line. Let's say you have a number line that's

702
00:37:33,239 --> 00:37:35,520
Speaker 1: one to one, and you have a little thirty piece,

703
00:37:35,560 --> 00:37:40,120
Speaker 1: and your thirty piece says so you can kind of

704
00:37:40,160 --> 00:37:42,239
Speaker 1: scan along that one to one hundred to figure out

705
00:37:42,239 --> 00:37:44,759
Speaker 1: where it goes, and you'll find the matching sequence. So

706
00:37:44,840 --> 00:37:47,400
Speaker 1: let's say that number line is your elephant genome, and

707
00:37:47,440 --> 00:37:49,799
Speaker 1: then you've got your little thirty to four, your little

708
00:37:49,840 --> 00:37:52,600
Speaker 1: thirty base pair piece. You can figure out where it goes. Now,

709
00:37:52,640 --> 00:37:54,680
Speaker 1: mammoths and elephants are kind of different from each other,

710
00:37:54,760 --> 00:37:57,200
Speaker 1: so there'll be some places where it doesn't match up exactly.

711
00:37:57,600 --> 00:37:59,520
Speaker 1: But if it's long enough, you can figure out the

712
00:37:59,560 --> 00:38:02,480
Speaker 1: best ice in that number line where your tiny little

713
00:38:02,480 --> 00:38:04,719
Speaker 1: thing goes, because there'll be some common ground. So there

714
00:38:04,719 --> 00:38:07,040
Speaker 1: are lots of computer algorithms that people used to do that,

715
00:38:07,560 --> 00:38:09,920
Speaker 1: and heuristic searching approaches that people used to do that,

716
00:38:10,000 --> 00:38:12,240
Speaker 1: and and this is this is possible? And how apparent

717
00:38:12,440 --> 00:38:16,960
Speaker 1: is it? What that piece? What function that things served

718
00:38:16,960 --> 00:38:20,000
Speaker 1: for the organism function? Now this is something you're getting

719
00:38:20,000 --> 00:38:23,120
Speaker 1: into another whole realm of issue here. We hadn't quite

720
00:38:23,120 --> 00:38:27,200
Speaker 1: gotten there yet, but we can totally get there. So

721
00:38:27,280 --> 00:38:30,640
Speaker 1: this is this is a great question. Um, we have

722
00:38:31,360 --> 00:38:35,399
Speaker 1: very little idea what parts of genomes do. We have

723
00:38:35,520 --> 00:38:39,319
Speaker 1: algorithms that help us to find genes. Genes are not

724
00:38:39,360 --> 00:38:41,399
Speaker 1: the only thing that are in our genomes. There's also

725
00:38:41,520 --> 00:38:45,360
Speaker 1: lots of noncoding stuff. There's positional stuff. There's lots of

726
00:38:45,440 --> 00:38:47,360
Speaker 1: viruses that have gotten in there and made copies of

727
00:38:47,440 --> 00:38:50,600
Speaker 1: themselves and moved around. There are repeat elements. There are

728
00:38:50,680 --> 00:38:52,759
Speaker 1: all these kind of things called like allue elements and

729
00:38:52,800 --> 00:38:55,200
Speaker 1: stuff like that. There's just our genomes are chock full

730
00:38:55,239 --> 00:38:59,400
Speaker 1: of other stuff that's not genes, and that other stuff

731
00:38:59,520 --> 00:39:02,560
Speaker 1: might be portant and it might not. Write. This is

732
00:39:02,600 --> 00:39:06,359
Speaker 1: true for every animal, every organism that's out there. So

733
00:39:08,080 --> 00:39:11,640
Speaker 1: today we have people are saying we have complete genome sequences,

734
00:39:11,760 --> 00:39:14,919
Speaker 1: we have genome sequences available for lots of different species. Um,

735
00:39:14,920 --> 00:39:17,239
Speaker 1: it's true, we have genome sequences available for lots of

736
00:39:17,239 --> 00:39:19,600
Speaker 1: different species, but there are very few species that we

737
00:39:19,680 --> 00:39:21,560
Speaker 1: know very much about, and those that we do know

738
00:39:21,600 --> 00:39:23,520
Speaker 1: a lot about tend to be the ones that we

739
00:39:23,600 --> 00:39:27,239
Speaker 1: study a lot. So things like humans because we care

740
00:39:27,280 --> 00:39:31,080
Speaker 1: a lot about humans, and lab organisms like mice and

741
00:39:31,239 --> 00:39:35,040
Speaker 1: rats and Drosophila fruit flies, things that people use to

742
00:39:35,320 --> 00:39:39,680
Speaker 1: manipulate experimentally in the lab, other things. Any wildlife. You

743
00:39:39,719 --> 00:39:43,440
Speaker 1: pick a wildlife that isn't a domestic, agriculturally important species

744
00:39:43,719 --> 00:39:47,279
Speaker 1: we know very little about, and we guess, we guess

745
00:39:47,320 --> 00:39:50,000
Speaker 1: the function. So we find a gene that we believe

746
00:39:50,120 --> 00:39:53,399
Speaker 1: is the same gene as something that we know that

747
00:39:53,560 --> 00:39:56,080
Speaker 1: if you turn off in a mouse changes the color

748
00:39:56,080 --> 00:39:58,040
Speaker 1: of their eyes. I'm just making that up right, And

749
00:39:58,080 --> 00:40:00,319
Speaker 1: then we can say, ah ha, that gene in the

750
00:40:00,360 --> 00:40:03,719
Speaker 1: mammoth was probably associated with something like that. We have

751
00:40:03,760 --> 00:40:07,080
Speaker 1: no idea, right really, but we have some idea. That's

752
00:40:07,160 --> 00:40:10,680
Speaker 1: kind of unfair. We have some we've educated guesses about

753
00:40:10,719 --> 00:40:14,560
Speaker 1: the functions of genes based on learning something about functions

754
00:40:14,560 --> 00:40:17,120
Speaker 1: of genes in a very different animal that was living

755
00:40:17,160 --> 00:40:21,280
Speaker 1: in a lab. That makes sense, okay. So for example,

756
00:40:21,320 --> 00:40:23,759
Speaker 1: if we want to know what genes are associated with

757
00:40:23,840 --> 00:40:28,239
Speaker 1: cold tolerance in an elephant, we might look at what

758
00:40:28,280 --> 00:40:32,520
Speaker 1: people have written published about um cold tolerance or subcutaneous

759
00:40:32,520 --> 00:40:35,200
Speaker 1: fat or hair development or things like that in mice

760
00:40:35,800 --> 00:40:38,719
Speaker 1: or in humans, and then say, hmm, what's the same

761
00:40:38,800 --> 00:40:42,280
Speaker 1: gene that we found in this mammoth genomesequence. That's probably

762
00:40:42,320 --> 00:40:45,160
Speaker 1: the function of that gene. So we have an educated guess,

763
00:40:45,160 --> 00:40:50,320
Speaker 1: but we don't know for sure. Did when when did elephants?

764
00:40:50,360 --> 00:40:53,759
Speaker 1: Were they like they were in equatorial areas like pre

765
00:40:53,920 --> 00:40:57,600
Speaker 1: mammoth and the mammoth was like a north word. Yes,

766
00:40:57,719 --> 00:41:00,120
Speaker 1: it wasn't the other way around, right, So another the

767
00:41:00,160 --> 00:41:02,080
Speaker 1: thing that we can do to figure out so he

768
00:41:02,160 --> 00:41:06,799
Speaker 1: was like, they were figuring, like Asian elephants existed, No, No,

769
00:41:07,000 --> 00:41:10,040
Speaker 1: as mammoths for figuring out how to deal with the cold. Yes,

770
00:41:10,120 --> 00:41:13,839
Speaker 1: so they had a common ancestor that's probably tropically adapted, right,

771
00:41:14,160 --> 00:41:17,400
Speaker 1: and then they dive. That common ancestor diverged into elephants

772
00:41:17,640 --> 00:41:21,680
Speaker 1: and mammoths Asian elephants in mammos. Yeah, it's kind of

773
00:41:21,719 --> 00:41:24,480
Speaker 1: like you know, um, we didn't evolve from chimpanzees, and

774
00:41:24,560 --> 00:41:27,799
Speaker 1: chimpanzees didn't evolve from us. The two species evolved from

775
00:41:27,800 --> 00:41:31,160
Speaker 1: a common ancestor that was neither a chimpanzee nor a human. Right.

776
00:41:31,440 --> 00:41:33,520
Speaker 1: The same thing is true for Asian elephants in mammoth

777
00:41:33,640 --> 00:41:35,560
Speaker 1: when people say we had come from monkeys, and I

778
00:41:35,600 --> 00:41:36,880
Speaker 1: was like, I don't know if anybody is saying you

779
00:41:36,880 --> 00:41:39,600
Speaker 1: came from a monkey, well, great ape, some sort of

780
00:41:39,600 --> 00:41:42,759
Speaker 1: great ape. Yes, and prior to that monkeys or maybe

781
00:41:42,840 --> 00:41:47,279
Speaker 1: they diverged. Anyway, I digress into parts of human evolutionary

782
00:41:47,280 --> 00:41:50,319
Speaker 1: history that I'm not confident. Yeah, well, yeah, don't do that.

783
00:41:50,760 --> 00:41:52,520
Speaker 1: There's enough you are confident with. We don't need to

784
00:41:52,520 --> 00:41:54,879
Speaker 1: do what you're not confident with. I'm not saying I'm

785
00:41:54,880 --> 00:41:56,960
Speaker 1: not confident that we came from great apes. We certainly

786
00:41:57,040 --> 00:42:05,000
Speaker 1: evolved from great apes. Anyway, Um, where was I function mammoths? Yeah,

787
00:42:05,040 --> 00:42:10,280
Speaker 1: you were talking about finding things that would um allow

788
00:42:10,400 --> 00:42:13,719
Speaker 1: cold tolerance and understanding where those things are, and I

789
00:42:13,760 --> 00:42:19,520
Speaker 1: interrupt you to make sure that that, uh, mammoths moved northwards.

790
00:42:20,320 --> 00:42:22,080
Speaker 1: That's right. So another way that we can try to

791
00:42:22,120 --> 00:42:25,080
Speaker 1: identify things that are potentially important to making a mammoth

792
00:42:25,440 --> 00:42:27,720
Speaker 1: looking at like a mammoth instead of like the common

793
00:42:27,760 --> 00:42:31,600
Speaker 1: ancestor of the Asian elephant is to use evolution to

794
00:42:31,600 --> 00:42:33,120
Speaker 1: know what to learn to to use what we know

795
00:42:33,160 --> 00:42:36,680
Speaker 1: about evolution. So we have um African elephant genome sequence,

796
00:42:36,680 --> 00:42:39,640
Speaker 1: which we know diverged prior to the divergence between Asian

797
00:42:39,640 --> 00:42:42,359
Speaker 1: elephants and mammoths, and so we kind of know what

798
00:42:42,400 --> 00:42:45,880
Speaker 1: that ancestor of Asian elephants and mammoths looked like. And

799
00:42:45,920 --> 00:42:48,440
Speaker 1: then we can use the genome sequences and what we

800
00:42:48,480 --> 00:42:51,319
Speaker 1: know about how evolution works to identify the mutations that

801
00:42:51,360 --> 00:42:54,239
Speaker 1: happened just along the mammoth lineage, and we can think,

802
00:42:54,320 --> 00:42:56,279
Speaker 1: maybe those are some of the things that are really

803
00:42:56,280 --> 00:42:58,480
Speaker 1: important to making a mammoth looking act like a mammoth.

804
00:42:59,640 --> 00:43:05,080
Speaker 1: You'ring to the finding the cold tolerance stuff, right, Well,

805
00:43:05,080 --> 00:43:08,480
Speaker 1: how we how we find it? Yeah? I mean you

806
00:43:08,560 --> 00:43:12,759
Speaker 1: just think about the way you can look along these lineages,

807
00:43:12,800 --> 00:43:15,680
Speaker 1: these evolutionary lineages, and ask what things are fixed, what

808
00:43:15,760 --> 00:43:17,560
Speaker 1: things are all the same in mammoth. So we know

809
00:43:17,640 --> 00:43:20,160
Speaker 1: that there are a lot of places in our genomes

810
00:43:20,160 --> 00:43:24,120
Speaker 1: where you and I will differ, and those are probably

811
00:43:24,160 --> 00:43:27,680
Speaker 1: not fundamentally important to making us human. If they were,

812
00:43:27,840 --> 00:43:30,160
Speaker 1: we wouldn't differ. We would be the same as each other,

813
00:43:30,360 --> 00:43:33,839
Speaker 1: but different from our closest living relative, chimpanzee. So that's

814
00:43:33,880 --> 00:43:36,000
Speaker 1: similar to what we're doing with mammoths. If we sequence

815
00:43:36,040 --> 00:43:37,879
Speaker 1: a whole bunch of mammoths, we can look and see

816
00:43:37,880 --> 00:43:40,799
Speaker 1: where there's variation in mammoths and say that's probably not

817
00:43:40,840 --> 00:43:42,719
Speaker 1: that important to making a mammoth look and act like

818
00:43:42,760 --> 00:43:45,360
Speaker 1: a mammoth. But we can also find places where mammoths

819
00:43:45,360 --> 00:43:47,600
Speaker 1: are all the same as each other, but also all

820
00:43:47,719 --> 00:43:50,759
Speaker 1: different from all elephants, and we can say, ah ha,

821
00:43:51,040 --> 00:43:54,560
Speaker 1: there is likely to be some evolutionary difference, some change

822
00:43:54,600 --> 00:43:57,480
Speaker 1: that happened along that lineage to making mammoths look and

823
00:43:57,520 --> 00:44:00,400
Speaker 1: act like mammoths rather than like the ancestral elephant that

824
00:44:00,440 --> 00:44:03,760
Speaker 1: they were, and we can then target those as something

825
00:44:03,800 --> 00:44:05,239
Speaker 1: that we might need to change if we were going

826
00:44:05,280 --> 00:44:10,319
Speaker 1: to turn an elephant into a mammoth. That section of

827
00:44:10,360 --> 00:44:15,319
Speaker 1: the that one one letter. Oh, that's a one letter part,

828
00:44:15,400 --> 00:44:18,200
Speaker 1: one letter, one letter. Yeah, so that's the thing you know,

829
00:44:18,480 --> 00:44:20,840
Speaker 1: or you're talking about. You know, you have four billion

830
00:44:20,920 --> 00:44:24,759
Speaker 1: bases that are different between asian elephant and a wooly

831
00:44:24,840 --> 00:44:28,040
Speaker 1: mammoth and they're four it, no, sorry, four billion basis

832
00:44:28,080 --> 00:44:30,920
Speaker 1: total in a wooly mammoth genome and about one and

833
00:44:30,960 --> 00:44:34,640
Speaker 1: a half million differences, right, and they're going to be

834
00:44:34,680 --> 00:44:38,840
Speaker 1: spread randomly throughout the genome because mutations happen randomly, and

835
00:44:38,880 --> 00:44:40,960
Speaker 1: only some of them are going to be really important

836
00:44:41,040 --> 00:44:44,520
Speaker 1: to making a mammoth mammoth and an elephant an elephant. Right,

837
00:44:44,600 --> 00:44:47,520
Speaker 1: So the goal is to use what we know about

838
00:44:47,760 --> 00:44:50,439
Speaker 1: where genes are and the way evolution works to try

839
00:44:50,440 --> 00:44:52,680
Speaker 1: to figure out which of those million and a half

840
00:44:52,719 --> 00:44:56,840
Speaker 1: differences really are fundamentally important. And if we're only interested

841
00:44:56,880 --> 00:45:01,080
Speaker 1: in in creating specific traits or moving specific mammoth like

842
00:45:01,280 --> 00:45:04,200
Speaker 1: traits into elephants, we got to figure out somehow which

843
00:45:04,239 --> 00:45:08,000
Speaker 1: of those differences that we've decided are important differences making

844
00:45:08,000 --> 00:45:11,040
Speaker 1: mammoths different are actually important differences in making them different

845
00:45:11,080 --> 00:45:13,680
Speaker 1: in that very specific way that we're interested in them

846
00:45:13,719 --> 00:45:16,000
Speaker 1: being different, you know, in the case of cold tolerance,

847
00:45:16,160 --> 00:45:18,560
Speaker 1: which would further limit the number of changes that you

848
00:45:18,560 --> 00:45:20,600
Speaker 1: would have to make if you were going to make

849
00:45:20,680 --> 00:45:23,560
Speaker 1: an elephant that had that particular trait. But this is hard.

850
00:45:23,640 --> 00:45:25,759
Speaker 1: This is something that you know, we we we kind

851
00:45:25,760 --> 00:45:27,640
Speaker 1: of have some idea about how to do, but we

852
00:45:27,640 --> 00:45:31,000
Speaker 1: don't know enough about the way genomes function, or the

853
00:45:31,040 --> 00:45:34,759
Speaker 1: way mammoth genome in particular functions, to to know exactly

854
00:45:34,800 --> 00:45:37,160
Speaker 1: what the right what the right decision would be. So

855
00:45:37,200 --> 00:45:40,880
Speaker 1: what what year was it when there was the announcement

856
00:45:41,120 --> 00:45:44,439
Speaker 1: that they had mapped the human genome? That was two

857
00:45:44,440 --> 00:45:47,279
Speaker 1: thousand and one, And the person who led that the

858
00:45:47,920 --> 00:45:50,200
Speaker 1: public effort for the Human Genome Consortium is in that

859
00:45:50,239 --> 00:45:55,279
Speaker 1: building right there behind you through the treats. What what

860
00:45:55,440 --> 00:46:00,200
Speaker 1: percent of the mamoth genome is complete? Uh? Well, can

861
00:46:00,239 --> 00:46:03,920
Speaker 1: I answer the question about the human genome? First? Two

862
00:46:03,920 --> 00:46:06,640
Speaker 1: thousand and one we said we had mapped the human genome?

863
00:46:07,000 --> 00:46:12,640
Speaker 1: About the human genome is known? Now? Oh yeah, Now

864
00:46:13,320 --> 00:46:15,920
Speaker 1: sixteen years later, we still don't have the whole thing.

865
00:46:16,400 --> 00:46:23,080
Speaker 1: Oh well, why was it? Well, you know, to be fair, well, no, there,

866
00:46:23,120 --> 00:46:25,560
Speaker 1: I mean there are The genome is a big and

867
00:46:25,680 --> 00:46:29,480
Speaker 1: complicated place, right and there are parts of our genome

868
00:46:30,000 --> 00:46:32,200
Speaker 1: towards the centromeres the middle of the genome, and toward

869
00:46:32,239 --> 00:46:34,719
Speaker 1: the end of the telomeres that are just made up

870
00:46:34,719 --> 00:46:40,839
Speaker 1: of these really um tightly wrapped repeat sequences that there

871
00:46:40,920 --> 00:46:44,160
Speaker 1: is no existing sequencing technology that we can get through. Um.

872
00:46:44,160 --> 00:46:46,600
Speaker 1: There's no way to sequence through these things right now.

873
00:46:46,680 --> 00:46:48,279
Speaker 1: There's no way to do it. And in fact, a

874
00:46:48,280 --> 00:46:51,160
Speaker 1: big challenge that genome scientists are often thinking about it's

875
00:46:51,160 --> 00:46:53,480
Speaker 1: who is going to actually finish the complete human genome?

876
00:46:53,480 --> 00:46:55,279
Speaker 1: This would be a really cool thing to be able

877
00:46:55,320 --> 00:46:57,520
Speaker 1: to do. To be fair, we know most of the

878
00:46:57,560 --> 00:47:00,000
Speaker 1: genome that actually has genes in it that's doing stuff,

879
00:47:00,280 --> 00:47:03,120
Speaker 1: and the parts that we don't know are is very

880
00:47:03,160 --> 00:47:05,880
Speaker 1: small compared to that. But we don't know all of

881
00:47:05,920 --> 00:47:08,520
Speaker 1: it yet, and we certainly don't know the entire genome

882
00:47:08,560 --> 00:47:11,800
Speaker 1: sequence for something that is not human, where we haven't

883
00:47:11,840 --> 00:47:15,480
Speaker 1: spent billions and billions and billions to tell me that

884
00:47:15,520 --> 00:47:18,200
Speaker 1: you're almost there on the mammoth. No, and a harder

885
00:47:18,239 --> 00:47:20,919
Speaker 1: thing about something like a mammoth. There's something that's something

886
00:47:20,920 --> 00:47:24,080
Speaker 1: that's extinct. Is that, Remember I said, we don't we

887
00:47:24,120 --> 00:47:27,080
Speaker 1: don't have long sequences. So the only way to get

888
00:47:27,080 --> 00:47:29,920
Speaker 1: through these repeat fragments for these regions of the genome

889
00:47:29,920 --> 00:47:31,719
Speaker 1: that are just the same thing, repeated over and over

890
00:47:31,760 --> 00:47:33,320
Speaker 1: and over and over again is to be able to

891
00:47:33,360 --> 00:47:35,800
Speaker 1: sequence these long strands of DNA. We're never going to

892
00:47:35,920 --> 00:47:39,720
Speaker 1: have that for something that's extinct, and so we're always

893
00:47:39,760 --> 00:47:42,399
Speaker 1: going to have to take these broken fragments and map

894
00:47:42,520 --> 00:47:45,399
Speaker 1: them to an existing genome sequence. We can't do what's

895
00:47:45,400 --> 00:47:48,360
Speaker 1: called a DiNovo genome assembly, where you don't have anything,

896
00:47:48,400 --> 00:47:51,239
Speaker 1: which is what impressively these teams managed to do for

897
00:47:51,280 --> 00:47:53,960
Speaker 1: the human genome. We had no map, we had no puzzle,

898
00:47:54,000 --> 00:47:56,279
Speaker 1: top right. They just did it. They took these long

899
00:47:56,320 --> 00:48:00,719
Speaker 1: fragments and used sophisticated computer algorithms to piece these these

900
00:48:00,840 --> 00:48:03,560
Speaker 1: long fragments together. And the more data they get, so

901
00:48:03,760 --> 00:48:05,879
Speaker 1: the more they have to realize they got some parts wrong.

902
00:48:05,960 --> 00:48:08,439
Speaker 1: They can rearrange it and and and try to figure

903
00:48:08,480 --> 00:48:10,640
Speaker 1: out what the real sequence is. It's very hard to

904
00:48:10,719 --> 00:48:13,440
Speaker 1: put together these denovo genomes where all you have is

905
00:48:13,480 --> 00:48:15,920
Speaker 1: just good quality tissue and you don't want to use

906
00:48:15,920 --> 00:48:17,839
Speaker 1: any map. The reason you don't want to use any

907
00:48:17,840 --> 00:48:20,680
Speaker 1: map is that the map might be wrong, and this

908
00:48:20,760 --> 00:48:24,560
Speaker 1: is particularly important when something is extinct and doesn't have

909
00:48:24,600 --> 00:48:29,840
Speaker 1: any close relatives. Um think, for example, of the moa,

910
00:48:30,080 --> 00:48:34,000
Speaker 1: where the closest living relative is the tinamou, and they diverged.

911
00:48:34,320 --> 00:48:36,720
Speaker 1: I can't remember exactly how many, but more than thirty

912
00:48:36,760 --> 00:48:41,600
Speaker 1: million years of between these two lineages, so there's a

913
00:48:41,640 --> 00:48:44,880
Speaker 1: lot of opportunity for parts of the genome to move around,

914
00:48:45,360 --> 00:48:48,479
Speaker 1: for chromosomes to break and move around. Probably doesn't happen

915
00:48:48,480 --> 00:48:51,000
Speaker 1: so much in birds, but in in mammals we know

916
00:48:51,040 --> 00:48:54,680
Speaker 1: that chromosomes rearrange all the time. And if your map

917
00:48:54,800 --> 00:48:57,880
Speaker 1: your living thing, the tinamou is really different from the

918
00:48:58,000 --> 00:49:00,279
Speaker 1: ancient sequence ancient genome you're trying to map, where you

919
00:49:00,280 --> 00:49:03,000
Speaker 1: only have your thirties and forties, there might be big

920
00:49:03,080 --> 00:49:06,960
Speaker 1: chunks the genome that you just never get. They'll never recover,

921
00:49:07,040 --> 00:49:09,560
Speaker 1: no matter how many bones are because you don't have

922
00:49:09,640 --> 00:49:11,520
Speaker 1: those long fragments, which is what you would need to

923
00:49:11,560 --> 00:49:14,320
Speaker 1: be able to extend off the ends of these sequences.

924
00:49:14,320 --> 00:49:17,680
Speaker 1: So this is a hard thing for ancient genomics and

925
00:49:17,760 --> 00:49:21,600
Speaker 1: for many species. We might be forever restricted to just

926
00:49:21,800 --> 00:49:24,480
Speaker 1: being able to use this stuff that doesn't change so quickly,

927
00:49:24,719 --> 00:49:27,160
Speaker 1: and maybe this is a bad thing, right right, And

928
00:49:27,200 --> 00:49:30,000
Speaker 1: this goes back to um whether you can bring back

929
00:49:30,040 --> 00:49:34,520
Speaker 1: a species that's extinct If the most important parts are

930
00:49:34,520 --> 00:49:37,440
Speaker 1: the most divergent parts, and therefore the parts that you

931
00:49:37,480 --> 00:49:40,520
Speaker 1: actually can't sequence or put together, how are you ever

932
00:49:40,560 --> 00:49:47,480
Speaker 1: going to know what they are? So if if a

933
00:49:47,560 --> 00:49:50,960
Speaker 1: fellow wanted to go make a mammoth, right, okay, all right,

934
00:49:53,640 --> 00:49:57,479
Speaker 1: like and there are some of those fellas, right, Well,

935
00:49:57,920 --> 00:50:01,240
Speaker 1: that's the thing you talked about is there was someone

936
00:50:01,280 --> 00:50:04,120
Speaker 1: who was hopeful. I don't want to dwell on things

937
00:50:04,160 --> 00:50:06,160
Speaker 1: that just aren't going to happen, But just as an example,

938
00:50:06,200 --> 00:50:09,319
Speaker 1: there was someone who is hopeful, um that you talked about,

939
00:50:09,400 --> 00:50:13,760
Speaker 1: who would find semen. Right, So, there are two teams

940
00:50:13,760 --> 00:50:16,040
Speaker 1: that are out there that are looking either for semen

941
00:50:16,239 --> 00:50:19,400
Speaker 1: or for cells, just frozen cells that are in good condition,

942
00:50:19,640 --> 00:50:22,840
Speaker 1: and they want to clone a mammoth. This is most

943
00:50:22,920 --> 00:50:25,840
Speaker 1: common word that you hear when you think about Jurassic

944
00:50:25,920 --> 00:50:29,879
Speaker 1: Park type clone of mammoth. Yeah. Clone. Well, you see,

945
00:50:29,920 --> 00:50:32,120
Speaker 1: even bringing up Jurassic Park kind of calls all this

946
00:50:32,160 --> 00:50:33,759
Speaker 1: into questions because then you got to talk about how

947
00:50:33,760 --> 00:50:38,600
Speaker 1: many journalists have asked you to explain why amber it's

948
00:50:38,640 --> 00:50:41,279
Speaker 1: not actually good for DNA. How many journalists are there.

949
00:50:44,760 --> 00:50:46,959
Speaker 1: It's a good question, though, I mean to be fair.

950
00:50:47,120 --> 00:50:49,600
Speaker 1: This is this is what people think about ancient DNA

951
00:50:49,719 --> 00:50:52,479
Speaker 1: is is, Oh, look we can find things preserved and amber.

952
00:50:52,480 --> 00:50:55,360
Speaker 1: We're gonna able to bring dinosaurs back to life. Medium

953
00:50:55,719 --> 00:50:57,839
Speaker 1: It makes sense. It does. It does. When you see

954
00:50:57,840 --> 00:50:59,319
Speaker 1: a piece of amber, you see a fly in it,

955
00:50:59,360 --> 00:51:04,200
Speaker 1: you're like, well of And it was inspired by reality.

956
00:51:04,200 --> 00:51:06,720
Speaker 1: So Michael Crichton, when he wrote his book actually wrote

957
00:51:06,800 --> 00:51:09,400
Speaker 1: in the acknowledgments UM that he was grateful to the

958
00:51:09,440 --> 00:51:11,880
Speaker 1: Extinct Species Working Group at you see Berkeley Allen Will

959
00:51:13,360 --> 00:51:15,600
Speaker 1: because they were talking about ancient DNA and that was

960
00:51:15,640 --> 00:51:19,560
Speaker 1: what inspired him. And then his movie book inspired people

961
00:51:19,680 --> 00:51:22,120
Speaker 1: to see if they could actually recover DNA from insights

962
00:51:22,120 --> 00:51:25,080
Speaker 1: in amber, and people published papers saying that they had

963
00:51:25,480 --> 00:51:29,520
Speaker 1: um Fortunately or unfortunately, depending on who you are and

964
00:51:29,520 --> 00:51:34,680
Speaker 1: how you feel about these things. There is this ubiquitous,

965
00:51:35,440 --> 00:51:39,480
Speaker 1: uh source of DNA that's everywhere, that gets into everything,

966
00:51:39,560 --> 00:51:43,320
Speaker 1: and I could extract DNA from anything and get some DNA.

967
00:51:43,440 --> 00:51:46,400
Speaker 1: That doesn't mean that it's DNA from that thing. Amber

968
00:51:46,680 --> 00:51:48,799
Speaker 1: is very porous, it's formed a very hot environment. It

969
00:51:48,840 --> 00:51:52,440
Speaker 1: turns out that it is a terrible, terrible preserver for DNA,

970
00:51:52,480 --> 00:51:55,680
Speaker 1: which is very sad that there's these beautiful skeletons or

971
00:51:55,719 --> 00:51:57,880
Speaker 1: exo skeletons of things that you see in amber, but

972
00:51:57,960 --> 00:52:01,200
Speaker 1: there isn't any DNA that is from those animals that's

973
00:52:01,200 --> 00:52:04,400
Speaker 1: in there. There was a a group of scientists in

974
00:52:04,400 --> 00:52:06,680
Speaker 1: in London at the Natural Museum in London in the

975
00:52:06,760 --> 00:52:09,560
Speaker 1: late nineties who tried to replicate some of these experiments

976
00:52:09,920 --> 00:52:12,560
Speaker 1: by going into their collection, and they were They recovered

977
00:52:12,560 --> 00:52:16,239
Speaker 1: pieces of amber and copal. Copal is the recent precursor

978
00:52:16,280 --> 00:52:18,800
Speaker 1: to amble. Amber. First it's copeal and then it hardens

979
00:52:18,800 --> 00:52:20,759
Speaker 1: and becomes amber. And these were only decades old. We

980
00:52:20,800 --> 00:52:23,279
Speaker 1: know we can recover decade old DNA. And some of

981
00:52:23,280 --> 00:52:25,120
Speaker 1: these things had bugs in them and some of them didn't.

982
00:52:25,239 --> 00:52:28,439
Speaker 1: They extracted DNA from all of these different pieces of amber, saying,

983
00:52:28,440 --> 00:52:30,359
Speaker 1: if it doesn't have an insect, we shouldn't be able

984
00:52:30,400 --> 00:52:32,040
Speaker 1: to get DNA. If it does, we should be able

985
00:52:32,080 --> 00:52:33,960
Speaker 1: to get DNA. And therefore this is some sort of

986
00:52:34,040 --> 00:52:37,640
Speaker 1: test of the hypothesis of whether amber preserves DNA, and

987
00:52:37,680 --> 00:52:40,840
Speaker 1: they were able to recover DNA from their pieces of

988
00:52:40,880 --> 00:52:44,200
Speaker 1: copal and amber, but there was no correlation between their

989
00:52:44,200 --> 00:52:47,239
Speaker 1: ability to recover DNA and whether there were insects there.

990
00:52:47,480 --> 00:52:50,120
Speaker 1: And it turns out they were just recovering insect DNA

991
00:52:50,520 --> 00:52:54,319
Speaker 1: because there's insect DNA everywhere. I mean, I could take

992
00:52:54,360 --> 00:52:57,360
Speaker 1: a swab off this tabletop here and get insect DNA

993
00:52:57,440 --> 00:52:59,920
Speaker 1: off of it, and probably your DNA as well, because

994
00:53:00,000 --> 00:53:01,600
Speaker 1: you've been sitting here and breathing on the table for

995
00:53:01,640 --> 00:53:07,279
Speaker 1: a while. That doesn't mean it's already there. Yes, yes,

996
00:53:07,400 --> 00:53:09,440
Speaker 1: So I could go to the toy store and get

997
00:53:09,480 --> 00:53:12,600
Speaker 1: a dinosaur and extract DNA and show that I have

998
00:53:12,680 --> 00:53:14,880
Speaker 1: recovered dinosaur DNA, but really it's just going to be

999
00:53:14,920 --> 00:53:17,880
Speaker 1: chopped up pieces of human and cockroach DNA, right, you know.

1000
00:53:17,960 --> 00:53:22,040
Speaker 1: So the early days of ancient DNA were filled with

1001
00:53:22,040 --> 00:53:24,239
Speaker 1: some of these spectacular claims, none of which have been

1002
00:53:24,280 --> 00:53:26,600
Speaker 1: able to be shown to be true. The oldest DNA

1003
00:53:26,719 --> 00:53:29,479
Speaker 1: that we've recovered as reliable is that seven hundred thousand

1004
00:53:29,560 --> 00:53:33,040
Speaker 1: year old horse bone from the Arctic because it was frozen, right,

1005
00:53:33,400 --> 00:53:36,120
Speaker 1: and that's why it was recovered. Um Dinosaurs went extinct

1006
00:53:36,200 --> 00:53:39,200
Speaker 1: sixty five million years ago. There is no frozen dirt

1007
00:53:39,239 --> 00:53:42,120
Speaker 1: that's sixty five million years old. There is no DNA

1008
00:53:43,040 --> 00:53:47,080
Speaker 1: and dinosaurs and talk about the um. You don't need

1009
00:53:47,080 --> 00:53:49,239
Speaker 1: to dwell on it. But the sperm path to a

1010
00:53:49,280 --> 00:53:52,520
Speaker 1: mammoth cloning. Uh, let's do cells and then sperm. Right,

1011
00:53:52,560 --> 00:53:54,480
Speaker 1: So the idea with sperm, I guess I'll start with sperm,

1012
00:53:54,560 --> 00:53:56,600
Speaker 1: would be that you could find frozen sperm and then

1013
00:53:56,640 --> 00:53:59,799
Speaker 1: you could you could get an elephant egg cell and

1014
00:53:59,840 --> 00:54:01,920
Speaker 1: you could use it to fertilize the elephant egg cell,

1015
00:54:02,000 --> 00:54:04,320
Speaker 1: so you would have something that's half mammoth half elephant.

1016
00:54:04,760 --> 00:54:09,680
Speaker 1: Um like you surrogate, surrogate, you'd like impregnate a female

1017
00:54:09,920 --> 00:54:13,520
Speaker 1: Asian elephant with this frozen sperm and get a half mammoth,

1018
00:54:14,040 --> 00:54:15,879
Speaker 1: and then do it again and get a three quarter

1019
00:54:15,960 --> 00:54:18,839
Speaker 1: mammoth's And they're fired up about that because I think

1020
00:54:18,840 --> 00:54:20,800
Speaker 1: you should explained that. And you get in your book

1021
00:54:20,880 --> 00:54:25,200
Speaker 1: that they found that old frozen sperm is still viable.

1022
00:54:26,320 --> 00:54:29,879
Speaker 1: That gives him whole We're not old old right, right,

1023
00:54:30,040 --> 00:54:34,680
Speaker 1: but just the term you guys use old old, it's

1024
00:54:34,719 --> 00:54:43,040
Speaker 1: like alternative old. Yeah. Uh no, So when an animal

1025
00:54:43,080 --> 00:54:45,080
Speaker 1: dies and I think I've already said this. The DNA

1026
00:54:45,200 --> 00:54:48,080
Speaker 1: and IT cell starts to degrade immediately, and the cells

1027
00:54:48,080 --> 00:54:50,560
Speaker 1: started to grade immediately. So this requires that you were

1028
00:54:50,640 --> 00:54:52,400
Speaker 1: able to be fine, you would able you would be

1029
00:54:52,440 --> 00:54:56,799
Speaker 1: able to find frozen viable cells or frozen viables. Oh,

1030
00:54:56,880 --> 00:54:59,120
Speaker 1: I got you, So the same problem. I hadn't really

1031
00:54:59,160 --> 00:55:02,400
Speaker 1: put that together, right, Yeah, the sperm has Yeah, I

1032
00:55:02,440 --> 00:55:04,560
Speaker 1: got you. It's destroyed everywhere, and so it had to

1033
00:55:04,560 --> 00:55:07,760
Speaker 1: be that sperm was It's like sperms that some special holders,

1034
00:55:08,200 --> 00:55:10,960
Speaker 1: so that makes it a special holder. Actually probably would

1035
00:55:11,000 --> 00:55:13,919
Speaker 1: be the testicles, right, And this is what I read

1036
00:55:13,920 --> 00:55:15,319
Speaker 1: when I was doing my research for this, is that

1037
00:55:15,560 --> 00:55:17,839
Speaker 1: because the testicles were outside of the body, they would

1038
00:55:17,880 --> 00:55:20,200
Speaker 1: get frozen faster and that would protect the sperm. It

1039
00:55:20,239 --> 00:55:22,680
Speaker 1: turns out they're not outside of the body in a mammoth,

1040
00:55:22,719 --> 00:55:25,759
Speaker 1: which is probably you know, for good reason, right if

1041
00:55:25,760 --> 00:55:28,520
Speaker 1: you think about the environment where they lived, So yeah,

1042
00:55:28,600 --> 00:55:30,960
Speaker 1: they're not then no, it's not a viable pathway. That

1043
00:55:31,040 --> 00:55:33,560
Speaker 1: was the interesting thing I heard about mammoth um. You

1044
00:55:33,680 --> 00:55:36,440
Speaker 1: talk about the cold tolerance for but a thing that

1045
00:55:37,440 --> 00:55:42,680
Speaker 1: Asian and African elephants have big ears, and mammoths had

1046
00:55:42,760 --> 00:55:48,439
Speaker 1: small ears because imagine that thin flat Yeah, what would

1047
00:55:48,520 --> 00:55:51,360
Speaker 1: happen to it in cold temperatures? Right? And the elephants

1048
00:55:51,360 --> 00:55:54,520
Speaker 1: have big ears for heat this patient, right, so um, yeah,

1049
00:55:54,760 --> 00:55:56,640
Speaker 1: you don't want to dissipate your heat if you're living

1050
00:55:56,640 --> 00:55:58,880
Speaker 1: at forty below. Okay, So now that I understand the

1051
00:55:58,880 --> 00:56:00,880
Speaker 1: sperm thing that it is, it's it's just like the

1052
00:56:00,920 --> 00:56:04,640
Speaker 1: cell thing. Yeah. And and so when when people say

1053
00:56:04,640 --> 00:56:08,319
Speaker 1: cloning cloning, what what you really mean? And we say

1054
00:56:08,320 --> 00:56:11,359
Speaker 1: this cloning dinosaurs and dinosaur bar you say cloning mammoths,

1055
00:56:11,400 --> 00:56:13,200
Speaker 1: What you really mean when you say cloning is an

1056
00:56:13,200 --> 00:56:18,080
Speaker 1: actual scientific process where you take a cell and that's

1057
00:56:18,160 --> 00:56:20,799
Speaker 1: already a particular type of cell, like a skin cell

1058
00:56:20,880 --> 00:56:24,760
Speaker 1: or Okay, so here we go. Who's the most famous clone? Dolly,

1059
00:56:25,200 --> 00:56:28,839
Speaker 1: Dolly the sheep. That's so, Dolly was a clone and

1060
00:56:29,040 --> 00:56:32,520
Speaker 1: she was a clone of a mammary cell from another

1061
00:56:32,640 --> 00:56:36,040
Speaker 1: female sheep. Right, So what you do in cloning is

1062
00:56:36,080 --> 00:56:39,719
Speaker 1: you take an egg cell that is viable, ripe egg cell,

1063
00:56:40,080 --> 00:56:42,520
Speaker 1: and you suck out the nucleus, the stuff that has

1064
00:56:42,560 --> 00:56:45,440
Speaker 1: the nuclear DNA, the all the stuff that is going

1065
00:56:45,480 --> 00:56:47,440
Speaker 1: to code for the genes that make the animal look

1066
00:56:47,480 --> 00:56:49,759
Speaker 1: and act like it does that normally, in an egg

1067
00:56:49,760 --> 00:56:52,280
Speaker 1: cell would be fertilized by sperm that would make everything

1068
00:56:52,400 --> 00:56:55,200
Speaker 1: diploid ut of Mom's DNA and Dad's DNA, and then

1069
00:56:55,239 --> 00:56:59,320
Speaker 1: that would um cause this process of differentiation. Because that

1070
00:57:00,200 --> 00:57:04,759
Speaker 1: fertilized cell is is a stem cell. It has the

1071
00:57:05,000 --> 00:57:07,520
Speaker 1: it's called tote potent. It has the capacity to become

1072
00:57:07,600 --> 00:57:11,400
Speaker 1: every type of cell that's necessary to create an organism.

1073
00:57:11,800 --> 00:57:14,239
Speaker 1: Um it doesn't yet have any instructions that say, be

1074
00:57:14,360 --> 00:57:16,280
Speaker 1: heart cell, be a memory cell, be a lung cell,

1075
00:57:16,440 --> 00:57:18,760
Speaker 1: but it will begin to divide and differentiate, and as

1076
00:57:18,760 --> 00:57:21,360
Speaker 1: it does, those cells will gradually get the instructions that

1077
00:57:21,400 --> 00:57:23,520
Speaker 1: are necessary to be different types of cells. You don't

1078
00:57:23,520 --> 00:57:25,560
Speaker 1: need the same genes turned on to be a heart

1079
00:57:25,600 --> 00:57:27,920
Speaker 1: cell as you do to be a liver cell, for example.

1080
00:57:28,000 --> 00:57:31,200
Speaker 1: So this process of differentiation just turns genes on and

1081
00:57:31,240 --> 00:57:34,880
Speaker 1: off as necessary to create different functions. So the idea

1082
00:57:34,920 --> 00:57:37,520
Speaker 1: of cloning is that you have a cell that's already

1083
00:57:37,640 --> 00:57:40,920
Speaker 1: way down that path. It already has exactly the genes

1084
00:57:40,960 --> 00:57:43,240
Speaker 1: turned on and off to be that particular type of cell.

1085
00:57:43,280 --> 00:57:45,480
Speaker 1: In Golly's case, it was a mamory cell. And you

1086
00:57:45,560 --> 00:57:49,400
Speaker 1: have to somehow trick it into forgetting those instructions and

1087
00:57:49,520 --> 00:57:53,240
Speaker 1: resetting itself into one of those types of cells that

1088
00:57:53,320 --> 00:57:57,680
Speaker 1: can begin this process of dividing and differentiation. This reprogramming

1089
00:57:57,800 --> 00:58:01,120
Speaker 1: is really important in cloning. So you take that egg

1090
00:58:01,160 --> 00:58:03,960
Speaker 1: cell and there's some magic in that egg cell, and

1091
00:58:04,000 --> 00:58:06,200
Speaker 1: that is that the proteins that are in that egg

1092
00:58:06,280 --> 00:58:10,480
Speaker 1: cell can cause that reprogramming to happen. So you take

1093
00:58:10,480 --> 00:58:12,680
Speaker 1: the excel, suck out of the nucleus, and then you

1094
00:58:12,680 --> 00:58:15,640
Speaker 1: you take this tissue cell that you want to clone,

1095
00:58:15,720 --> 00:58:17,760
Speaker 1: and you stress it out. You starve it if nutrients

1096
00:58:17,800 --> 00:58:19,760
Speaker 1: and put it in a state where super stressed, right,

1097
00:58:20,040 --> 00:58:22,360
Speaker 1: and then you can suck the nucleus out of that cell,

1098
00:58:22,400 --> 00:58:24,680
Speaker 1: injected into the egg cell, zap it with a bit

1099
00:58:24,680 --> 00:58:27,960
Speaker 1: of electricity. Some magic happens that causes the proteins in

1100
00:58:27,960 --> 00:58:31,080
Speaker 1: that egg cell to reset that cell, causing it to

1101
00:58:31,120 --> 00:58:33,560
Speaker 1: forget all the instructions to be a memory cell and

1102
00:58:33,960 --> 00:58:37,160
Speaker 1: start that process of dividing and differentiating. That, it turns out,

1103
00:58:37,280 --> 00:58:40,800
Speaker 1: is really hard and still is really inefficient. This if

1104
00:58:40,840 --> 00:58:45,320
Speaker 1: the cell is not entirely reprogrammed, reset it completely to scratch,

1105
00:58:45,360 --> 00:58:48,120
Speaker 1: then it won't work. It won't divide correctly. It'll go

1106
00:58:48,160 --> 00:58:51,400
Speaker 1: wrong at some point, and that's why cloning of animals

1107
00:58:51,480 --> 00:58:55,240
Speaker 1: remains um really inefficient. It's gotten better than than it

1108
00:58:55,320 --> 00:58:57,200
Speaker 1: was in Dolly's time, but it still isn't you know.

1109
00:58:57,200 --> 00:58:59,240
Speaker 1: It's not like every time you do it it works.

1110
00:59:00,120 --> 00:59:03,160
Speaker 1: You need, though, is for that cell, that tissue cell

1111
00:59:03,480 --> 00:59:06,800
Speaker 1: to be alive. There can't be anything wrong with it.

1112
00:59:07,040 --> 00:59:10,760
Speaker 1: If there's anything wrong with it, miracle of life alive,

1113
00:59:11,000 --> 00:59:13,280
Speaker 1: like is able to divide in a in a dish,

1114
00:59:13,440 --> 00:59:15,760
Speaker 1: it has to be, you know. It can't be broken,

1115
00:59:15,920 --> 00:59:18,000
Speaker 1: It can't be turned off. The DNA can't be chopped up.

1116
00:59:18,200 --> 00:59:21,400
Speaker 1: It has to be capable of resetting itself. And as

1117
00:59:21,400 --> 00:59:24,840
Speaker 1: we've already established, once an animal dies, all of its

1118
00:59:24,840 --> 00:59:29,040
Speaker 1: cells start to break up and die, the enzymes chop

1119
00:59:29,080 --> 00:59:32,720
Speaker 1: up the DNA, it can't replicate itself anymore. And because

1120
00:59:32,760 --> 00:59:36,760
Speaker 1: that is true, one will never find a living mammoth cell.

1121
00:59:36,960 --> 00:59:40,840
Speaker 1: The most recently live mammoths were live years ago. They

1122
00:59:40,840 --> 00:59:45,919
Speaker 1: have no living cells remaining, and end of story, one

1123
00:59:45,960 --> 00:59:52,880
Speaker 1: will never be able to clone a mammoth, really sorry,

1124
00:59:53,120 --> 00:59:57,800
Speaker 1: or dinosaur. And that's that's a particular particularly bold statement

1125
00:59:57,840 --> 01:00:01,000
Speaker 1: comes from someone in your position. It it's a statement

1126
01:00:01,000 --> 01:00:04,840
Speaker 1: I've been making for very but just but okay, I've

1127
01:00:04,880 --> 01:00:08,320
Speaker 1: seen the deal, the operating You live and operate in

1128
01:00:08,400 --> 01:00:12,640
Speaker 1: the world of the impossible, do I? Yes? Because things

1129
01:00:12,680 --> 01:00:15,160
Speaker 1: that things that would have been regarded things that a

1130
01:00:15,240 --> 01:00:18,160
Speaker 1: decade ago or two decades ago would have been regarded

1131
01:00:18,200 --> 01:00:22,520
Speaker 1: as No, that won't happen, right, Okay, how do you

1132
01:00:22,560 --> 01:00:25,280
Speaker 1: know that you're not? But I don't. I don't doubt

1133
01:00:25,320 --> 01:00:28,680
Speaker 1: that you are. But you're not worried about becoming the

1134
01:00:28,760 --> 01:00:32,080
Speaker 1: laughing stock. You know what, if somebody finds a living

1135
01:00:32,200 --> 01:00:35,200
Speaker 1: mammoth cell, it will be so freaking exciting that I

1136
01:00:35,280 --> 01:00:39,320
Speaker 1: won't mind being a laughing stock. The chances that'll outweigh

1137
01:00:39,400 --> 01:00:44,640
Speaker 1: your embarrassment. Exactly is that the likelihood of this happening

1138
01:00:44,840 --> 01:00:48,080
Speaker 1: is very, very very close to zero, so close to

1139
01:00:48,160 --> 01:00:49,960
Speaker 1: zero that I'm willing to say it's never going to happen.

1140
01:00:50,600 --> 01:00:53,520
Speaker 1: And is that is that? Um? I don't want dwell,

1141
01:00:53,720 --> 01:00:55,880
Speaker 1: But is that like sort of like the consensus among

1142
01:00:55,920 --> 01:00:59,360
Speaker 1: your peers. Yes, So if that's the case, let's move

1143
01:00:59,400 --> 01:01:02,960
Speaker 1: on to what might work. Well, this is why we

1144
01:01:03,000 --> 01:01:05,800
Speaker 1: get to moving genes, so we know that we can

1145
01:01:05,840 --> 01:01:08,280
Speaker 1: come up with these DNA sequences if we can identify

1146
01:01:08,440 --> 01:01:11,560
Speaker 1: using a computer which parts of those genomes are important

1147
01:01:11,600 --> 01:01:14,520
Speaker 1: to making something look an act like mammoth. Then we

1148
01:01:14,560 --> 01:01:17,880
Speaker 1: can take an elephant cell that is alive, right, that's

1149
01:01:17,920 --> 01:01:20,600
Speaker 1: living in a dish, that's able to replicate itself and

1150
01:01:20,640 --> 01:01:24,840
Speaker 1: turn into two cells whatever from an Asian elephant, and

1151
01:01:25,280 --> 01:01:28,720
Speaker 1: we can then cut and paste using geno mediting technologies,

1152
01:01:29,040 --> 01:01:32,520
Speaker 1: the elephant DNA sequences can be cut out and paste

1153
01:01:32,600 --> 01:01:35,240
Speaker 1: into their place the parts of the mammoth genome sequence

1154
01:01:35,280 --> 01:01:36,920
Speaker 1: that are there. So then you have a living cell

1155
01:01:37,400 --> 01:01:41,520
Speaker 1: as an elephant cell that has some mammoth DNA sequences

1156
01:01:41,520 --> 01:01:44,400
Speaker 1: in it, right, Yeah, but that is not the same

1157
01:01:44,440 --> 01:01:47,800
Speaker 1: thing as having a mammoth cell. No, yeah, I'm with you, right,

1158
01:01:48,560 --> 01:01:55,680
Speaker 1: And what are the things that you would be Let's

1159
01:01:55,720 --> 01:01:57,640
Speaker 1: just make let's assume for a menthy that this is

1160
01:01:57,640 --> 01:01:59,880
Speaker 1: already and you could do it. Can we can we

1161
01:02:00,040 --> 01:02:02,680
Speaker 1: finish why that cell is not ever going to be

1162
01:02:02,880 --> 01:02:06,440
Speaker 1: the exact same thing as a mamothy, because this was

1163
01:02:06,480 --> 01:02:08,000
Speaker 1: the question you asked me at the very beginning, and

1164
01:02:08,040 --> 01:02:10,120
Speaker 1: we kind of gone down a lot of different rabbit

1165
01:02:10,120 --> 01:02:14,800
Speaker 1: holes here. But let's see. So let's say you somehow

1166
01:02:15,200 --> 01:02:18,600
Speaker 1: managed to identify all the places where mamoths and elephants

1167
01:02:18,600 --> 01:02:21,600
Speaker 1: are different. And you managed to make all of those changes,

1168
01:02:21,760 --> 01:02:24,680
Speaker 1: cut and paste one and a half million different letters

1169
01:02:24,720 --> 01:02:27,360
Speaker 1: in that cell that's growing in additional lab So now

1170
01:02:27,440 --> 01:02:29,840
Speaker 1: you have a genome sequence that looks, as far as

1171
01:02:29,840 --> 01:02:33,000
Speaker 1: you can tell, like a mammoth genome sequence. Right, Why

1172
01:02:33,000 --> 01:02:37,360
Speaker 1: wouldn't that turn into a mammoth? Well, the main reason

1173
01:02:37,720 --> 01:02:41,960
Speaker 1: is that we are more. Every organism is more than

1174
01:02:42,040 --> 01:02:44,240
Speaker 1: the sequence of the A, C, S, G S, and

1175
01:02:44,280 --> 01:02:47,640
Speaker 1: T s that make up our d N A. That um.

1176
01:02:47,680 --> 01:02:50,480
Speaker 1: There are things that happened during development that change the

1177
01:02:50,520 --> 01:02:54,479
Speaker 1: way our genes expressed. Our mom's diet, whether she gets sick,

1178
01:02:54,600 --> 01:02:57,120
Speaker 1: what she's exposed to, how stressed she is, et cetera.

1179
01:02:57,200 --> 01:03:00,560
Speaker 1: All those things will change the way our genes are expressing.

1180
01:03:00,880 --> 01:03:04,400
Speaker 1: Some of the developmental things that happen in utero are

1181
01:03:04,440 --> 01:03:08,080
Speaker 1: caused by hormonal changes in mom, which are coded for

1182
01:03:08,200 --> 01:03:11,360
Speaker 1: by her genome, which is an elephant at this point, right.

1183
01:03:11,800 --> 01:03:14,840
Speaker 1: And then the animal is born and it consumes an

1184
01:03:14,840 --> 01:03:17,800
Speaker 1: elephants diet, and it's taught how to behave like an elephant,

1185
01:03:17,880 --> 01:03:22,320
Speaker 1: and it has a gut microbes that are like an elephant,

1186
01:03:22,400 --> 01:03:25,400
Speaker 1: and we're just beginning to learn how important the things

1187
01:03:25,440 --> 01:03:28,320
Speaker 1: that live in our gut are to a lot of

1188
01:03:28,360 --> 01:03:31,080
Speaker 1: animals I know do this, but they'll eat like fegal

1189
01:03:31,160 --> 01:03:34,400
Speaker 1: matter of the mother to colonize their guts what it needs.

1190
01:03:34,960 --> 01:03:38,840
Speaker 1: That's right. And so those organisms living in its gut

1191
01:03:38,880 --> 01:03:41,760
Speaker 1: are going to be expressing different chemicals and etcetera, and

1192
01:03:41,760 --> 01:03:44,000
Speaker 1: those are going to affect the way the genes are expressed.

1193
01:03:44,040 --> 01:03:47,000
Speaker 1: And so this thing that is born might have mammoth DNA,

1194
01:03:47,480 --> 01:03:49,920
Speaker 1: but it's not going to be a identical to a

1195
01:03:49,920 --> 01:03:52,240
Speaker 1: mammoth that used to be alive, and that's because mammoths

1196
01:03:52,240 --> 01:03:55,080
Speaker 1: aren't here anymore. You would need a family of mammoths

1197
01:03:55,080 --> 01:03:58,200
Speaker 1: and a mammoth habitat and and mammoth gut microbes and

1198
01:03:58,280 --> 01:04:01,280
Speaker 1: etcetera if you were going to make something that's identical

1199
01:04:01,320 --> 01:04:04,520
Speaker 1: to a mammoth, which is why it can't happen. But

1200
01:04:04,840 --> 01:04:07,480
Speaker 1: I think the people who are proponents of using this

1201
01:04:07,520 --> 01:04:10,680
Speaker 1: sort of technology as a way of preserving by a

1202
01:04:10,680 --> 01:04:15,480
Speaker 1: diversity or or replacing parts of ecosystems that are missing

1203
01:04:15,520 --> 01:04:18,160
Speaker 1: because of an extinction don't really care that you're not

1204
01:04:18,200 --> 01:04:21,920
Speaker 1: creating something that's identical to something that's there What they

1205
01:04:21,960 --> 01:04:25,160
Speaker 1: really want is to create an ecological proxy, to create

1206
01:04:25,240 --> 01:04:28,120
Speaker 1: something that can fill the components of that niche that

1207
01:04:28,480 --> 01:04:32,400
Speaker 1: are missing and therefore somehow threatening either the stability of

1208
01:04:32,400 --> 01:04:36,080
Speaker 1: the ecosystem in the given in the existing climate or

1209
01:04:36,400 --> 01:04:40,240
Speaker 1: or phenomena or threatening other species from going extinct. Now,

1210
01:04:40,400 --> 01:04:43,640
Speaker 1: I'm not sure that this is necessarily true for mammoths.

1211
01:04:43,680 --> 01:04:45,240
Speaker 1: I think that there are people who are interested in

1212
01:04:45,320 --> 01:04:49,680
Speaker 1: bringing mammoths back because it's phenomenal, Like, how cool would

1213
01:04:49,720 --> 01:04:52,440
Speaker 1: it be to have a mammoth that's back? Can you

1214
01:04:52,480 --> 01:04:57,760
Speaker 1: can you hold that thought like touch on why why mammoth?

1215
01:04:57,920 --> 01:05:01,720
Speaker 1: Is it because the person they're crazy enough, but RecA

1216
01:05:01,920 --> 01:05:04,800
Speaker 1: they're crazy enough to have attention, but recent enough to

1217
01:05:04,840 --> 01:05:09,080
Speaker 1: be in the realm of supposed possibility. I think kind

1218
01:05:09,080 --> 01:05:11,880
Speaker 1: of boils down to that. My personal opinion about why

1219
01:05:12,120 --> 01:05:15,160
Speaker 1: people have focused on mammoth's and my book is about

1220
01:05:15,200 --> 01:05:18,360
Speaker 1: mammoth's as well, even though I don't personally work on

1221
01:05:18,640 --> 01:05:20,480
Speaker 1: mammoths in my lab, but it is the thing that

1222
01:05:20,520 --> 01:05:24,440
Speaker 1: people talk about. I think people think of mammoths as

1223
01:05:24,480 --> 01:05:26,680
Speaker 1: soon as they realize that they can bring back dinosaurs.

1224
01:05:28,120 --> 01:05:32,680
Speaker 1: I just think it's like the second most spectacular things

1225
01:05:32,720 --> 01:05:37,880
Speaker 1: t rex is out but right right, it's also but

1226
01:05:38,960 --> 01:05:41,440
Speaker 1: it is because it's like saber tooth cats, let's bring

1227
01:05:41,480 --> 01:05:44,680
Speaker 1: them back. Or arc Todus giant short faced bear that

1228
01:05:45,120 --> 01:05:47,840
Speaker 1: we made extinct because it would stand up and would

1229
01:05:47,880 --> 01:05:50,000
Speaker 1: be fourteen feet tall and we didn't like that when

1230
01:05:50,040 --> 01:05:51,840
Speaker 1: we were trying to let our kids run around outside.

1231
01:05:51,880 --> 01:05:54,760
Speaker 1: You know, that was um. So mammoth they seem well,

1232
01:05:54,760 --> 01:05:58,440
Speaker 1: they're huge, they're spectacular, they're definitely gone. Um, but they

1233
01:05:58,480 --> 01:06:01,120
Speaker 1: probably wouldn't kill us. You know, there's some like kind

1234
01:06:01,120 --> 01:06:03,840
Speaker 1: of snuggly about them. But it's nothing else in that.

1235
01:06:03,880 --> 01:06:07,280
Speaker 1: It's nothing, it's it's nothing other than just those like

1236
01:06:07,560 --> 01:06:13,680
Speaker 1: sort of issues of charisma. And maybe it's in the

1237
01:06:13,680 --> 01:06:16,000
Speaker 1: realm of possibility because they're coming up out of the ice.

1238
01:06:16,120 --> 01:06:18,080
Speaker 1: And I think this is the reason that we see

1239
01:06:18,080 --> 01:06:19,760
Speaker 1: a lot of popular attention to it. Now. There are

1240
01:06:19,840 --> 01:06:24,960
Speaker 1: people who make ecological arguments for bringing mammoths back to life. Um.

1241
01:06:25,000 --> 01:06:28,880
Speaker 1: There's a father son team that live in northeastern Siberia,

1242
01:06:29,440 --> 01:06:31,960
Speaker 1: the Zimov Sergey Zimov and his son Nikita. They have

1243
01:06:32,040 --> 01:06:36,280
Speaker 1: this this place called Pleistocene park Um where they're trying

1244
01:06:36,320 --> 01:06:40,160
Speaker 1: to bring enough big herbivores back that they can re

1245
01:06:40,360 --> 01:06:43,160
Speaker 1: establish this rich grassland that used to be in the

1246
01:06:43,200 --> 01:06:47,040
Speaker 1: Siberian tundra during the ice age. And they have um

1247
01:06:47,160 --> 01:06:49,800
Speaker 1: imported bison from Canada, and they have a couple different

1248
01:06:49,840 --> 01:06:52,640
Speaker 1: species of deer, and they have horses, etcetera. And they

1249
01:06:52,680 --> 01:06:54,960
Speaker 1: have been able to show that having these animals on

1250
01:06:55,000 --> 01:06:58,640
Speaker 1: the landscape sort of increases the production of this grassland.

1251
01:06:58,680 --> 01:07:01,560
Speaker 1: So they move things around, the recycling nutrients, they're chewing

1252
01:07:01,600 --> 01:07:05,160
Speaker 1: stuff up. And they've even made the argument um that

1253
01:07:06,400 --> 01:07:09,680
Speaker 1: because these animals are there and they're feeding during the winter,

1254
01:07:10,040 --> 01:07:14,680
Speaker 1: they're pulling away the snow and creating these exposed bits

1255
01:07:14,680 --> 01:07:17,680
Speaker 1: of soil. And this would have happened during the ice age,

1256
01:07:17,680 --> 01:07:20,280
Speaker 1: where the snow would have been removed and the soil

1257
01:07:20,360 --> 01:07:24,360
Speaker 1: was exposed. And in doing so, they're actually causing the

1258
01:07:24,440 --> 01:07:28,360
Speaker 1: sediment that is in the area to warm up less

1259
01:07:28,440 --> 01:07:30,680
Speaker 1: quickly than it does when the snow is on top

1260
01:07:30,680 --> 01:07:32,440
Speaker 1: of this is a little bit counterintuitive. So if you

1261
01:07:32,440 --> 01:07:36,360
Speaker 1: think about it, if the average temperature of the soil,

1262
01:07:36,840 --> 01:07:38,960
Speaker 1: if the sorry, if the soil temperature is really the

1263
01:07:39,000 --> 01:07:43,560
Speaker 1: average annual ambient temperature, right then um, during the summer,

1264
01:07:43,640 --> 01:07:46,400
Speaker 1: it's you know, up there during the winter it's forty

1265
01:07:46,440 --> 01:07:49,160
Speaker 1: below So the soil temperature can be very cold as

1266
01:07:49,200 --> 01:07:51,720
Speaker 1: long as there's not snow sitting on top of it,

1267
01:07:51,760 --> 01:07:55,240
Speaker 1: because snow is a really efficient insulator. And what the

1268
01:07:55,280 --> 01:07:57,480
Speaker 1: snow sitting on top of this the soil does is

1269
01:07:57,520 --> 01:08:00,760
Speaker 1: it keeps that summer heat in the soil and actually

1270
01:08:00,800 --> 01:08:03,520
Speaker 1: causes the soil to warm up faster. Whereas if you

1271
01:08:03,560 --> 01:08:06,720
Speaker 1: can pull that snow away, the bare earth is exposed

1272
01:08:06,720 --> 01:08:10,640
Speaker 1: to the really cold Siberian winter and cools down that

1273
01:08:11,040 --> 01:08:13,520
Speaker 1: that sediment. And so they have made the argument that

1274
01:08:13,560 --> 01:08:15,440
Speaker 1: if we could get rid of a lot of the snow,

1275
01:08:15,560 --> 01:08:18,360
Speaker 1: which we could do by having really big herbivores like

1276
01:08:18,479 --> 01:08:21,760
Speaker 1: mammoth's wandering around, we could slow the rate of permafrost

1277
01:08:21,840 --> 01:08:24,960
Speaker 1: warming and slow the relate rate of release of carbon

1278
01:08:25,040 --> 01:08:27,880
Speaker 1: into the atmosphere that's coming from parmafrost warming. So they

1279
01:08:27,880 --> 01:08:31,160
Speaker 1: are making an ecological argument for why we should have

1280
01:08:31,160 --> 01:08:34,200
Speaker 1: these animals back on the landscape. That's something I hadn't

1281
01:08:34,240 --> 01:08:38,200
Speaker 1: heard of, because I know that um the the area

1282
01:08:38,920 --> 01:08:41,879
Speaker 1: like the Arctic and what was the Bearing land Bridge

1283
01:08:42,360 --> 01:08:44,439
Speaker 1: at the time when people talk about with their horses

1284
01:08:44,520 --> 01:08:48,240
Speaker 1: up there, there was like an American lion. Everything lots

1285
01:08:48,240 --> 01:08:50,400
Speaker 1: of cool things. There was a grassland, there was like

1286
01:08:50,560 --> 01:08:56,559
Speaker 1: step grasslands, and now it's tassi, it's tundra. I'd never

1287
01:08:56,600 --> 01:08:59,559
Speaker 1: heard the idea that that train. I had always heard

1288
01:08:59,640 --> 01:09:03,840
Speaker 1: that transition explained as a climate issue. I never heard

1289
01:09:03,840 --> 01:09:08,559
Speaker 1: it explained as perhaps related to grazing habits. Yeah, um,

1290
01:09:09,600 --> 01:09:12,920
Speaker 1: I do you know. Things don't have been in isolation. Obviously,

1291
01:09:12,960 --> 01:09:16,559
Speaker 1: ecosystems change. Ecosystems are dynamic. But if you remove grazing

1292
01:09:16,560 --> 01:09:19,160
Speaker 1: herbivores from a landscape, the landscape changes. You can see

1293
01:09:19,200 --> 01:09:21,280
Speaker 1: that in the desert southwest. There's this little thing called

1294
01:09:21,320 --> 01:09:24,040
Speaker 1: the kangaroo rat and it's kind of makes these little tons.

1295
01:09:25,479 --> 01:09:28,360
Speaker 1: They're pretty cool, huh. And but once they disappear, and

1296
01:09:28,400 --> 01:09:31,639
Speaker 1: they are disappearing, it takes you half a year, and

1297
01:09:31,800 --> 01:09:35,479
Speaker 1: the entire landscape has changed because that animal was doing

1298
01:09:35,520 --> 01:09:39,240
Speaker 1: a lot to maintain this different type of habitat. It changes,

1299
01:09:39,800 --> 01:09:42,880
Speaker 1: other species move in, some other species will disappear. But

1300
01:09:43,000 --> 01:09:46,000
Speaker 1: having that little guy there really maintained that habitat. And

1301
01:09:46,120 --> 01:09:49,960
Speaker 1: there's little doubt to my mind that having these herbivores

1302
01:09:50,000 --> 01:09:52,960
Speaker 1: on the landscape in the High Arctic will have had

1303
01:09:53,000 --> 01:09:56,679
Speaker 1: an impact on the the grasslands. I mean they were

1304
01:09:56,720 --> 01:10:00,320
Speaker 1: consuming things. They were favoring some plants over others. They

1305
01:10:00,360 --> 01:10:03,040
Speaker 1: were moving nutrients around all over the place. They were

1306
01:10:03,120 --> 01:10:06,080
Speaker 1: churning the soil by walking over things. Um, they were

1307
01:10:06,439 --> 01:10:10,520
Speaker 1: We know that when mammoths and other large mammals disappeared

1308
01:10:10,560 --> 01:10:14,080
Speaker 1: from the southern part of North America and California, for example,

1309
01:10:14,200 --> 01:10:16,799
Speaker 1: they would have actually kept the trees at bay, these mammoths,

1310
01:10:17,000 --> 01:10:18,960
Speaker 1: and so there would have been an enormous change to

1311
01:10:19,000 --> 01:10:21,439
Speaker 1: the ecosystem that happened with the extinction of mammoths. And

1312
01:10:21,479 --> 01:10:25,639
Speaker 1: it's probably the change that caused Native Americans who lived

1313
01:10:25,640 --> 01:10:28,400
Speaker 1: there to start using fire instead of these large animals

1314
01:10:28,400 --> 01:10:29,880
Speaker 1: to try to keep the trees at bay so that

1315
01:10:29,920 --> 01:10:33,040
Speaker 1: they other things would grow there. So yeah, I mean,

1316
01:10:33,240 --> 01:10:36,519
Speaker 1: the animals that live in a habitat definitely have some

1317
01:10:36,640 --> 01:10:40,280
Speaker 1: feedback into the what habitat is there? Now you know

1318
01:10:40,320 --> 01:10:42,759
Speaker 1: that you have the chicken and egg problem. What happened first?

1319
01:10:42,800 --> 01:10:44,920
Speaker 1: Did the landscape change so much that it couldn't support

1320
01:10:44,920 --> 01:10:47,640
Speaker 1: the animals, or did the animals disappears that the landscape disappeared.

1321
01:10:47,760 --> 01:10:51,040
Speaker 1: Probably these things happened together. So the a biotic changes,

1322
01:10:51,080 --> 01:10:55,360
Speaker 1: the climate changes associated with warming probably fed into the

1323
01:10:55,479 --> 01:10:58,960
Speaker 1: disappearances some of these animals that then fed into more

1324
01:10:59,120 --> 01:11:02,120
Speaker 1: changes that were being to the landscape. So remember that,

1325
01:11:02,240 --> 01:11:05,080
Speaker 1: you know, when you think about the ecology of a system,

1326
01:11:05,120 --> 01:11:08,280
Speaker 1: you're not thinking about one animal or just the vegetation.

1327
01:11:08,400 --> 01:11:10,640
Speaker 1: You really have to think about how everything interacts with

1328
01:11:10,640 --> 01:11:14,559
Speaker 1: each other, which is one of the arguments for UM

1329
01:11:14,600 --> 01:11:18,639
Speaker 1: potentially thinking about using this genome engineering technology to try

1330
01:11:18,680 --> 01:11:23,719
Speaker 1: to preserve some components of ecosystems, because as components disappear,

1331
01:11:23,880 --> 01:11:30,679
Speaker 1: ecosystems change. However, proximate, however proximate. Are you ready now?

1332
01:11:30,920 --> 01:11:35,160
Speaker 1: Can I now prompt you along to what the uh

1333
01:11:35,400 --> 01:11:39,800
Speaker 1: what might the mammoth? In quotes, I'm making quotes what

1334
01:11:39,960 --> 01:11:43,559
Speaker 1: might the mammoth be? And look like I have no idea.

1335
01:11:43,640 --> 01:11:46,280
Speaker 1: It would depend on what genes were changed, you know,

1336
01:11:46,360 --> 01:11:49,160
Speaker 1: it would really depend on what what sciencests were interested

1337
01:11:49,200 --> 01:11:51,200
Speaker 1: in doing this. We're trying to select. Probably if it

1338
01:11:51,240 --> 01:11:53,080
Speaker 1: was something that wanted to live in the high Arctic,

1339
01:11:53,120 --> 01:11:55,240
Speaker 1: it would be something that was harriier than an elephant,

1340
01:11:55,280 --> 01:11:57,080
Speaker 1: because it needs to be able to protect itself from

1341
01:11:57,080 --> 01:12:03,639
Speaker 1: the cold. Um, so our ears probably smaller ears, but

1342
01:12:03,720 --> 01:12:07,240
Speaker 1: you know it's a these are it's fluid. You know,

1343
01:12:07,320 --> 01:12:08,720
Speaker 1: there has to be so that you thought about a

1344
01:12:08,720 --> 01:12:13,880
Speaker 1: great deal. I haven't. I you know, I if I

1345
01:12:13,920 --> 01:12:16,599
Speaker 1: had to pick species that I think we should use

1346
01:12:16,600 --> 01:12:19,200
Speaker 1: this technology on, I don't think the mammoth would be

1347
01:12:19,320 --> 01:12:22,200
Speaker 1: high up on my list. Yeah, is it your Your

1348
01:12:22,280 --> 01:12:25,759
Speaker 1: lab has the greatest connection of collection of passenger pigeon

1349
01:12:26,240 --> 01:12:28,400
Speaker 1: also not high up on my list for a species

1350
01:12:28,960 --> 01:12:31,280
Speaker 1: we should bring back to life, but a species that

1351
01:12:31,360 --> 01:12:34,439
Speaker 1: I think is fascinating, which is why we have this collection.

1352
01:12:34,760 --> 01:12:38,280
Speaker 1: I am so you're you're not gonning for to bring

1353
01:12:38,280 --> 01:12:40,840
Speaker 1: back a billion passenger pigeons. I don't think that's a

1354
01:12:40,880 --> 01:12:42,840
Speaker 1: good idea. I think that. You know, when you think

1355
01:12:42,880 --> 01:12:45,479
Speaker 1: about bringing a species back to life, there are there

1356
01:12:45,479 --> 01:12:48,599
Speaker 1: are technical hurdles, there are ethical hurdles, and there are

1357
01:12:48,680 --> 01:12:52,519
Speaker 1: ecological hurdles to doing this. In this case for passenger pigeon,

1358
01:12:52,560 --> 01:12:55,800
Speaker 1: there are technical hurdles. One can't clone birds. So the

1359
01:12:55,840 --> 01:12:58,439
Speaker 1: point where you have a living cell that you edited

1360
01:12:58,600 --> 01:13:01,439
Speaker 1: that you then clone using regular cloning technologies. We can

1361
01:13:01,520 --> 01:13:03,240
Speaker 1: do that with birds because we can't get to the

1362
01:13:03,280 --> 01:13:06,000
Speaker 1: egg cells at the time in their reproductive cycle where

1363
01:13:06,120 --> 01:13:09,599
Speaker 1: they're actually um ripe. They're ready to ready to have

1364
01:13:09,640 --> 01:13:12,200
Speaker 1: that little magical thing that happens that reprograms the cells.

1365
01:13:12,280 --> 01:13:14,880
Speaker 1: We can't do that. So in order to clone or

1366
01:13:14,880 --> 01:13:18,360
Speaker 1: genetically modify birds, we need entirely new technology. And there

1367
01:13:18,400 --> 01:13:20,559
Speaker 1: are some technologies that are under development, but they're really

1368
01:13:20,600 --> 01:13:24,880
Speaker 1: not as far advanced as I think. So there's technical hurdle. Um. Ethically, uh,

1369
01:13:24,960 --> 01:13:28,000
Speaker 1: now with mammoths, there are many ethical hurdles. I mean,

1370
01:13:28,280 --> 01:13:31,439
Speaker 1: I elephants in captivity don't do well. We need to

1371
01:13:31,520 --> 01:13:34,880
Speaker 1: know a lot more about how to you know, keep

1372
01:13:34,920 --> 01:13:37,320
Speaker 1: them psychologically and physically healthy if they're going to be

1373
01:13:37,320 --> 01:13:40,320
Speaker 1: in captivity. Obviously this would be a captive breading experiment. Um.

1374
01:13:40,360 --> 01:13:42,639
Speaker 1: I think elephants should be allowed to make more elephants

1375
01:13:42,840 --> 01:13:44,920
Speaker 1: rather than to be used in experiments to do this.

1376
01:13:45,000 --> 01:13:46,920
Speaker 1: I think there are a lot of sort of moral

1377
01:13:46,960 --> 01:13:50,320
Speaker 1: ethical questions involved with and also they're very highly social creatures.

1378
01:13:50,560 --> 01:13:52,920
Speaker 1: Why would you bring one back? You'd need to do

1379
01:13:52,960 --> 01:13:55,960
Speaker 1: this over the course of you know, many many generations.

1380
01:13:56,040 --> 01:13:59,920
Speaker 1: Elephants have fourteen to eighteen year um eighteen years generate

1381
01:14:00,200 --> 01:14:02,479
Speaker 1: times in the wild. Generation times that's how old they

1382
01:14:02,479 --> 01:14:04,600
Speaker 1: are when they first have their first babies. This is

1383
01:14:04,640 --> 01:14:06,600
Speaker 1: a long and I have a two year gestation, And

1384
01:14:06,640 --> 01:14:09,559
Speaker 1: to your gestation, yeah, so there there are technical and

1385
01:14:09,840 --> 01:14:12,439
Speaker 1: to my mind a lot of ethical problems with mammoths.

1386
01:14:12,720 --> 01:14:17,120
Speaker 1: So has your feeling about this matured over time? I

1387
01:14:17,160 --> 01:14:20,479
Speaker 1: think as I've learned more about the technical hurdles, I

1388
01:14:20,479 --> 01:14:25,560
Speaker 1: think I've thought more about Um. I don't know. I

1389
01:14:25,960 --> 01:14:28,400
Speaker 1: guess obviously your feelings about anything that you're learning a

1390
01:14:28,400 --> 01:14:31,000
Speaker 1: lot about mature as you learn more about them. But

1391
01:14:31,120 --> 01:14:33,120
Speaker 1: I don't think I've ever really been in favor of

1392
01:14:33,840 --> 01:14:37,679
Speaker 1: mammoths for for these ethical reasons. UM. What I try

1393
01:14:37,720 --> 01:14:40,240
Speaker 1: to do when I think about what species might be

1394
01:14:40,280 --> 01:14:43,880
Speaker 1: good for this is I try to think through these questions. First,

1395
01:14:43,920 --> 01:14:46,479
Speaker 1: what are the technical hurdles, what are the ethical hurdles,

1396
01:14:46,479 --> 01:14:48,840
Speaker 1: what are the ecological implications? And if we get to

1397
01:14:49,080 --> 01:14:53,160
Speaker 1: passenger pigeons, I mean, where would they live? But this

1398
01:14:53,240 --> 01:14:57,479
Speaker 1: is a species that flocked in the billions, one big

1399
01:14:57,520 --> 01:15:02,000
Speaker 1: flock of billions of individuals that would move through forests,

1400
01:15:02,080 --> 01:15:04,400
Speaker 1: just destroying forests in their way. We don't even have

1401
01:15:04,520 --> 01:15:07,400
Speaker 1: those forests anymore, so where would they go? Maybe they

1402
01:15:07,400 --> 01:15:09,320
Speaker 1: didn't need to live in such big flocks. We have

1403
01:15:09,360 --> 01:15:11,960
Speaker 1: some genomic evidence now that suggests that they might have

1404
01:15:12,080 --> 01:15:14,559
Speaker 1: been genetically adapted to living in large flock, So maybe

1405
01:15:14,560 --> 01:15:17,240
Speaker 1: they did that. Yeah, that's that's been explained to me

1406
01:15:17,320 --> 01:15:20,200
Speaker 1: that with some things like passenger pigeons, it would be

1407
01:15:20,240 --> 01:15:26,320
Speaker 1: that you might have to have many to have any

1408
01:15:26,640 --> 01:15:31,160
Speaker 1: because those mass groupings of birds trigger Yeah. Well, this

1409
01:15:31,200 --> 01:15:34,720
Speaker 1: is actually fascination with passenger pigeons and why we've been

1410
01:15:34,720 --> 01:15:36,840
Speaker 1: interested in studying their their d n A. It is

1411
01:15:36,880 --> 01:15:39,480
Speaker 1: amazing to me that a bird could be that abundant,

1412
01:15:40,000 --> 01:15:42,720
Speaker 1: and even with the amount of hunting and you know,

1413
01:15:42,840 --> 01:15:45,360
Speaker 1: human use of these birds that went on, how did

1414
01:15:45,360 --> 01:15:48,360
Speaker 1: they actually disappear? How is it that no tiny little

1415
01:15:48,360 --> 01:15:51,960
Speaker 1: pockets of these birds survived. There's no long autumn, right.

1416
01:15:52,080 --> 01:15:54,679
Speaker 1: There must have been something about them that made them

1417
01:15:54,720 --> 01:15:57,360
Speaker 1: adapted to living in these large flocks, and that's why

1418
01:15:57,360 --> 01:16:00,519
Speaker 1: we've been studying them. I'm I'm fascinated to one standard,

1419
01:16:00,520 --> 01:16:03,519
Speaker 1: why how something could evolve to be adapted to living

1420
01:16:03,520 --> 01:16:06,360
Speaker 1: in such big populations, and why that extinction would have happened.

1421
01:16:06,360 --> 01:16:09,439
Speaker 1: And you say you have not found small pockets. No,

1422
01:16:09,439 --> 01:16:12,120
Speaker 1: no one ever found small pockets of passenger pigeons surviving.

1423
01:16:12,400 --> 01:16:16,479
Speaker 1: They in forty years, they went from millions to billions

1424
01:16:16,479 --> 01:16:21,040
Speaker 1: of individuals to extinct, So what are what are if

1425
01:16:21,080 --> 01:16:24,080
Speaker 1: those two are out? Like, what is a good candidate? Spees?

1426
01:16:24,120 --> 01:16:27,360
Speaker 1: I mean, I know that you like you professionally, like

1427
01:16:27,520 --> 01:16:32,840
Speaker 1: I I you don't separate plausibility with the ethics, right

1428
01:16:32,880 --> 01:16:34,880
Speaker 1: like you have the conversations at the same time, and

1429
01:16:35,000 --> 01:16:37,439
Speaker 1: there's no sense in doing this big ethical exploration of

1430
01:16:37,560 --> 01:16:40,080
Speaker 1: something that just isn't going to happen. So you're doing

1431
01:16:40,080 --> 01:16:44,120
Speaker 1: these in tandem. As you do them in tandem, considering

1432
01:16:44,280 --> 01:16:48,040
Speaker 1: the technology and the ethics, where would be a place

1433
01:16:48,080 --> 01:16:50,559
Speaker 1: that maybe not even in your generation, but in the

1434
01:16:50,600 --> 01:16:53,400
Speaker 1: next generation of people in your field, where would be

1435
01:16:53,400 --> 01:16:56,120
Speaker 1: a place where you might picture if you were able

1436
01:16:56,160 --> 01:16:57,800
Speaker 1: to make an edict? Now, I think this is going

1437
01:16:57,840 --> 01:17:00,760
Speaker 1: to disappoint you, but I think that this technology has

1438
01:17:00,760 --> 01:17:03,240
Speaker 1: its most I'm already disappointed because you're not You're not

1439
01:17:03,280 --> 01:17:06,760
Speaker 1: shooting for You're not shooting for the stars here. I

1440
01:17:06,760 --> 01:17:09,880
Speaker 1: think this technology has the most potential as a tool

1441
01:17:09,960 --> 01:17:14,000
Speaker 1: for conserving species, preserving species that are still alive today.

1442
01:17:14,439 --> 01:17:17,160
Speaker 1: I think that we should think about this technology, and

1443
01:17:17,160 --> 01:17:20,080
Speaker 1: obviously people like this sort of spectacular nature of thinking

1444
01:17:20,080 --> 01:17:22,200
Speaker 1: about bringing things that are extinct back to life. But

1445
01:17:22,280 --> 01:17:25,680
Speaker 1: we should think about how we might use DNA sequences

1446
01:17:25,760 --> 01:17:28,920
Speaker 1: from individuals from the same or related species that used

1447
01:17:28,920 --> 01:17:33,800
Speaker 1: to be alive to increase the diversity decrease vulnerability of

1448
01:17:33,840 --> 01:17:35,960
Speaker 1: species that are in danger of going extinct today. And

1449
01:17:35,960 --> 01:17:40,360
Speaker 1: whether that means wooly rhinos or kangaroo rats or blackfooted ferrets,

1450
01:17:40,520 --> 01:17:43,240
Speaker 1: I don't care, right but I I what I worry

1451
01:17:43,320 --> 01:17:47,679
Speaker 1: about is that the kind of spectacular nature of thinking

1452
01:17:47,680 --> 01:17:53,840
Speaker 1: about bringing extinct species back might make people less likely

1453
01:17:53,920 --> 01:17:56,520
Speaker 1: to think about some of the real benefits of this technology.

1454
01:17:56,560 --> 01:17:58,920
Speaker 1: Could have two species that are still alive. There's you

1455
01:17:58,960 --> 01:18:03,519
Speaker 1: know this, this thoughts among conservation groups that um, this

1456
01:18:04,120 --> 01:18:07,880
Speaker 1: excitement about the extinction is taking away resources that would

1457
01:18:07,880 --> 01:18:10,320
Speaker 1: otherwise go to protecting species that are alive. And and

1458
01:18:10,479 --> 01:18:13,960
Speaker 1: I don't think that's true. Um. I don't think that

1459
01:18:14,000 --> 01:18:18,040
Speaker 1: people who care about preserving polar bears or care about

1460
01:18:18,080 --> 01:18:21,400
Speaker 1: preserving woodpeckers are all of a sudden going to stop

1461
01:18:21,439 --> 01:18:24,120
Speaker 1: caring about that because some far off possibility of bringing

1462
01:18:24,160 --> 01:18:26,439
Speaker 1: mammoths back to life might be there. The money thing

1463
01:18:26,520 --> 01:18:29,040
Speaker 1: seems real unless you feel that no money would really

1464
01:18:29,160 --> 01:18:31,800
Speaker 1: was headed in one direction and goes off in a

1465
01:18:31,840 --> 01:18:35,000
Speaker 1: different direction. I think that where the extinction is right now,

1466
01:18:35,120 --> 01:18:39,240
Speaker 1: which is in this let's see how the mammoth genome looks,

1467
01:18:39,320 --> 01:18:41,720
Speaker 1: or let's see that Any money that goes into that

1468
01:18:41,840 --> 01:18:43,439
Speaker 1: is going to be new money. It's gonna be it.

1469
01:18:44,240 --> 01:18:47,160
Speaker 1: There wasn't going to the Eastern Bluebird Society now, but

1470
01:18:47,439 --> 01:18:50,920
Speaker 1: you know later if if you actually have an animal,

1471
01:18:51,240 --> 01:18:54,439
Speaker 1: you would need to figure out how to regulate it,

1472
01:18:54,479 --> 01:18:56,720
Speaker 1: how to rear it, where it goes, and that I

1473
01:18:56,720 --> 01:18:59,519
Speaker 1: think would come into conflict with some of the money

1474
01:18:59,560 --> 01:19:02,479
Speaker 1: that's going into conservation UM, which is why I think

1475
01:19:02,479 --> 01:19:07,280
Speaker 1: that we need to have more realistic conversations about where

1476
01:19:07,320 --> 01:19:09,960
Speaker 1: this technology can go and bring people together to think

1477
01:19:09,960 --> 01:19:13,479
Speaker 1: about how we might develop this technology as a new

1478
01:19:14,479 --> 01:19:17,120
Speaker 1: weapon in what I really feel should be a growing

1479
01:19:17,240 --> 01:19:21,360
Speaker 1: arsenal in ways that we are thinking about combating UM

1480
01:19:21,400 --> 01:19:25,040
Speaker 1: the extinctions that are happening today, the crises that of

1481
01:19:25,080 --> 01:19:29,480
Speaker 1: biodiversity loss that are real UM where wildlife is disappearing,

1482
01:19:29,760 --> 01:19:32,320
Speaker 1: and what can we do what what? How can we

1483
01:19:32,920 --> 01:19:35,919
Speaker 1: think about modern technologies in a way that is conducive

1484
01:19:35,960 --> 01:19:38,800
Speaker 1: to collaboration with people who are interested in conservation rather

1485
01:19:38,840 --> 01:19:42,639
Speaker 1: than conflict. I think some of the more spectacle also

1486
01:19:42,680 --> 01:19:44,320
Speaker 1: there's this this fear that there's a lot of money

1487
01:19:44,360 --> 01:19:48,920
Speaker 1: going to the extinction, which is not true. I know

1488
01:19:49,000 --> 01:19:52,639
Speaker 1: it's not true. I know that there are some people

1489
01:19:52,680 --> 01:19:55,680
Speaker 1: who care very much about particular species who have been

1490
01:19:55,720 --> 01:19:57,559
Speaker 1: who have been generous in thinking about so there are

1491
01:19:57,560 --> 01:20:00,479
Speaker 1: people who care about prairie chickens, for example, and are

1492
01:20:00,560 --> 01:20:03,960
Speaker 1: very interested in helping to UM to think about ways

1493
01:20:03,960 --> 01:20:06,160
Speaker 1: that we can use this technology to increase diversity and

1494
01:20:06,200 --> 01:20:09,880
Speaker 1: the robustness of prairie chickens. UM including maybe thinking about

1495
01:20:10,120 --> 01:20:14,040
Speaker 1: what is the heathen, which is a prairie chicken that

1496
01:20:14,120 --> 01:20:16,400
Speaker 1: used to live on Martha's vineyard, and can we find

1497
01:20:16,439 --> 01:20:18,880
Speaker 1: out the differences between heath hens and other species and

1498
01:20:19,160 --> 01:20:21,240
Speaker 1: maybe think about using this as a technology to bring

1499
01:20:21,280 --> 01:20:22,720
Speaker 1: heathens back. And there have been some people who have

1500
01:20:22,760 --> 01:20:25,080
Speaker 1: been generous and donating small amounts of money to do

1501
01:20:25,120 --> 01:20:28,320
Speaker 1: sequencing of heathen remains and then some analyzes to figure

1502
01:20:28,320 --> 01:20:32,839
Speaker 1: out what we might do there UM the mammoth funding stuff. UM.

1503
01:20:32,880 --> 01:20:34,840
Speaker 1: You know, George Church is doing a lot of that

1504
01:20:34,920 --> 01:20:37,599
Speaker 1: work at his lab and Harvard. He might have some

1505
01:20:38,080 --> 01:20:40,320
Speaker 1: UM specific donors who have been given him money to

1506
01:20:40,360 --> 01:20:43,439
Speaker 1: do that. I'm not sure there's zero public funding going

1507
01:20:43,479 --> 01:20:51,440
Speaker 1: to this zero. So that's a checkable big number. Yes. Um.

1508
01:20:51,479 --> 01:20:52,960
Speaker 1: In fact, I think when I assume my book, I

1509
01:20:52,960 --> 01:20:56,280
Speaker 1: actually looked at places like World Wildlife Fund and conservation

1510
01:20:56,400 --> 01:20:58,680
Speaker 1: organizations and to figure out exactly how much money had

1511
01:20:58,720 --> 01:21:01,599
Speaker 1: gone into de extinction projects, and the number when I

1512
01:21:01,600 --> 01:21:05,439
Speaker 1: was writing this book was it was zero. So so

1513
01:21:05,520 --> 01:21:07,720
Speaker 1: let me throw two hypotheticals that you if you don't,

1514
01:21:07,720 --> 01:21:09,960
Speaker 1: and you can pick which one you like, but I'm

1515
01:21:10,000 --> 01:21:12,599
Speaker 1: talk in what you're talking about with that you would

1516
01:21:12,640 --> 01:21:17,960
Speaker 1: prevent the technology would be applicable in preventing extinctions, what

1517
01:21:18,080 --> 01:21:21,400
Speaker 1: might be imminent extinctions. And I'll throw two cases at you,

1518
01:21:21,479 --> 01:21:24,760
Speaker 1: so one you have. We spent a long time having

1519
01:21:24,800 --> 01:21:27,840
Speaker 1: a conversation with someone about Mexican the Mexican gray wolf.

1520
01:21:28,080 --> 01:21:31,599
Speaker 1: Now they were down to seven all in captivity. They've

1521
01:21:31,600 --> 01:21:34,920
Speaker 1: got them up to around a hundred living in the wild.

1522
01:21:36,080 --> 01:21:42,080
Speaker 1: They're the barrier to recovery is that they're inconvenient to

1523
01:21:42,120 --> 01:21:45,519
Speaker 1: have around. Okay, that's it's like, it's not a habitat issue,

1524
01:21:46,080 --> 01:21:49,040
Speaker 1: it's not an animal issue. It's just people don't like

1525
01:21:49,160 --> 01:21:53,600
Speaker 1: predators that they're inconvenient. Right, I don't know how to

1526
01:21:53,680 --> 01:21:57,200
Speaker 1: quite break it. Out, But fifty of that fifty of

1527
01:21:57,240 --> 01:22:01,800
Speaker 1: the inconvenience argument comes from hunters who want more dear

1528
01:22:01,840 --> 01:22:04,800
Speaker 1: and out, particularly out on the ground that they can

1529
01:22:04,880 --> 01:22:09,400
Speaker 1: hunt and eat and enjoy. And again I'm not sure

1530
01:22:09,400 --> 01:22:12,920
Speaker 1: on the percentage is livestock producers who don't these wolves

1531
01:22:12,960 --> 01:22:18,040
Speaker 1: are affecting their ability to make a living. Let's say, like,

1532
01:22:18,200 --> 01:22:20,280
Speaker 1: would it be the kind of thing you're talking about,

1533
01:22:20,479 --> 01:22:24,360
Speaker 1: could you ever imagine that you would make a gray

1534
01:22:24,400 --> 01:22:29,160
Speaker 1: wolf that doesn't eat? No, no, let's rule that out

1535
01:22:29,280 --> 01:22:32,680
Speaker 1: manipulated gray wolf that you would find in them? Like

1536
01:22:32,880 --> 01:22:37,880
Speaker 1: what is it about lives cattle? That's you can pick

1537
01:22:37,920 --> 01:22:39,880
Speaker 1: from that one or you can pick from this one.

1538
01:22:40,360 --> 01:22:46,000
Speaker 1: Why are why is the greater sage grouse? So per

1539
01:22:46,080 --> 01:22:50,400
Speaker 1: snickitty about where it lives? Which of those is better?

1540
01:22:50,720 --> 01:22:53,040
Speaker 1: If you're gonna look at some way to explore like

1541
01:22:53,080 --> 01:22:55,360
Speaker 1: what you're talking about with helping species, because we have

1542
01:22:55,400 --> 01:23:00,840
Speaker 1: two species sage grouse and the reason, well, just because behavior.

1543
01:23:01,160 --> 01:23:04,479
Speaker 1: Trying to to understand the behavior of a predator that's

1544
01:23:04,479 --> 01:23:06,880
Speaker 1: not going to be one gene or ten genes or

1545
01:23:07,000 --> 01:23:09,800
Speaker 1: hunter genes. This is going to be a gene environment,

1546
01:23:09,880 --> 01:23:13,599
Speaker 1: heredity interaction thing that's going to be extremely difficult to understand,

1547
01:23:13,960 --> 01:23:15,640
Speaker 1: so you're never going to suss out like why do

1548
01:23:15,720 --> 01:23:19,200
Speaker 1: these things? But you might be able to do experiments

1549
01:23:19,439 --> 01:23:23,320
Speaker 1: with sage grouse that we're able to identify individuals that

1550
01:23:23,320 --> 01:23:26,479
Speaker 1: were capable of living in different habitats um, and and

1551
01:23:26,520 --> 01:23:29,280
Speaker 1: then you could hone in on whatever genes are associated

1552
01:23:29,320 --> 01:23:31,599
Speaker 1: with the why is this? Why is this one? Okay?

1553
01:23:31,640 --> 01:23:36,000
Speaker 1: With being with breeding, with nesting next to an oil rig, Yeah,

1554
01:23:36,240 --> 01:23:39,160
Speaker 1: and just as happy and productive. So that would be

1555
01:23:39,600 --> 01:23:42,360
Speaker 1: It's not easy, right because you're still talking about behavior

1556
01:23:42,439 --> 01:23:44,800
Speaker 1: and you're still talking, but there are other things about

1557
01:23:44,800 --> 01:23:47,559
Speaker 1: stage grouse. They have shorter generation times, it's an easier

1558
01:23:47,560 --> 01:23:51,160
Speaker 1: thing to think about. Um, you're talking about nesting habitat preference,

1559
01:23:51,160 --> 01:23:53,000
Speaker 1: which is something that you could select for. You could

1560
01:23:53,000 --> 01:23:55,840
Speaker 1: do artificial selection for individuals that want to nest in

1561
01:23:55,880 --> 01:23:59,720
Speaker 1: particular places. Whereas trying to teach a wolf not to

1562
01:23:59,760 --> 01:24:03,439
Speaker 1: be a wolf. That's a tough one, right. So is

1563
01:24:03,479 --> 01:24:05,599
Speaker 1: there a one like I gave you two? Is there

1564
01:24:05,640 --> 01:24:08,120
Speaker 1: one that you really love, like a scenario that you

1565
01:24:08,160 --> 01:24:11,679
Speaker 1: think is like right for exploration? I you know, I

1566
01:24:11,720 --> 01:24:16,400
Speaker 1: would like a low hanging fruit um, like the black

1567
01:24:16,400 --> 01:24:20,840
Speaker 1: footed ferret, so is there Yeah, so something where there's

1568
01:24:20,840 --> 01:24:23,040
Speaker 1: a particular trait that you can hone in on that's

1569
01:24:23,080 --> 01:24:26,120
Speaker 1: not caused by too many different genes that is missing

1570
01:24:26,120 --> 01:24:28,880
Speaker 1: in a population, or that one population has but another

1571
01:24:28,880 --> 01:24:30,479
Speaker 1: one doesn't, and that would be like that would be

1572
01:24:30,520 --> 01:24:33,519
Speaker 1: the disease resistance. Yeah. So, well this isn't a wildlife question,

1573
01:24:33,520 --> 01:24:35,800
Speaker 1: but it's kind of easier to get your get your

1574
01:24:36,000 --> 01:24:39,360
Speaker 1: wrap your head around. Um, there are we know that

1575
01:24:39,479 --> 01:24:43,519
Speaker 1: oceans are becoming more cidic, and if you could identify

1576
01:24:43,560 --> 01:24:45,759
Speaker 1: populations of fish and there there was a paper recently

1577
01:24:45,760 --> 01:24:48,559
Speaker 1: where they identified a picular population of particular species of

1578
01:24:48,600 --> 01:24:51,559
Speaker 1: fish that was capable of surviving and producing more offspring

1579
01:24:51,640 --> 01:24:54,960
Speaker 1: in an environment of higher acidity than other populations. If

1580
01:24:55,000 --> 01:24:57,040
Speaker 1: you could figure out what genes cause that, you could

1581
01:24:57,040 --> 01:25:00,240
Speaker 1: move those genes into other fish, then maybe we would

1582
01:25:00,240 --> 01:25:02,960
Speaker 1: have a way of of safeguarding fish against some of

1583
01:25:02,960 --> 01:25:05,479
Speaker 1: the acidity increases happening in the oceans while we try

1584
01:25:05,479 --> 01:25:07,320
Speaker 1: to figure out a way to stop that as well.

1585
01:25:07,360 --> 01:25:09,639
Speaker 1: I'm not saying we should do this instead. This is important,

1586
01:25:10,000 --> 01:25:12,599
Speaker 1: but you know these changes, some of these anthrogenic changes

1587
01:25:12,640 --> 01:25:15,439
Speaker 1: to our our climate are happening too quickly for evolution

1588
01:25:15,479 --> 01:25:17,840
Speaker 1: to sort it out on its own, and if there

1589
01:25:17,840 --> 01:25:20,160
Speaker 1: are these scenarios where we could find genes and move

1590
01:25:20,160 --> 01:25:23,080
Speaker 1: them around. Another thing is heat tolerance and corals. So

1591
01:25:23,120 --> 01:25:25,200
Speaker 1: if you could find corals that are able to survive

1592
01:25:25,240 --> 01:25:27,720
Speaker 1: and higher temperature environments, and you could figure out what

1593
01:25:27,760 --> 01:25:29,960
Speaker 1: genes are associated with that, could you then move those

1594
01:25:30,000 --> 01:25:32,240
Speaker 1: genes into different species of corals so we could stop

1595
01:25:32,280 --> 01:25:36,560
Speaker 1: all the corals from dying. These are hard, like probably

1596
01:25:36,960 --> 01:25:40,120
Speaker 1: really hard, maybe impossible questions to answer, but there are

1597
01:25:40,120 --> 01:25:43,600
Speaker 1: things where you can imagine targeting coming up with a

1598
01:25:43,640 --> 01:25:46,519
Speaker 1: way of figuring it out. Now, you know, there's, as

1599
01:25:46,600 --> 01:25:49,280
Speaker 1: I said, there's little, very little money going into this

1600
01:25:49,360 --> 01:25:51,960
Speaker 1: because you know, public funding these days, we only like

1601
01:25:52,080 --> 01:25:53,639
Speaker 1: to fund things that we know are going to work,

1602
01:25:53,680 --> 01:25:55,680
Speaker 1: which mostly means you have to already have done the

1603
01:25:55,720 --> 01:25:59,160
Speaker 1: experiments using your own money in order to do it,

1604
01:25:59,280 --> 01:26:01,599
Speaker 1: or it has to have immediate impact on human health.

1605
01:26:01,880 --> 01:26:04,960
Speaker 1: And there has as yet to be a recognition enough

1606
01:26:05,000 --> 01:26:09,360
Speaker 1: of a recognition of how important healthy, diverse habitats are

1607
01:26:09,439 --> 01:26:12,960
Speaker 1: to maintaining healthy humans. Um but this is something that

1608
01:26:13,000 --> 01:26:15,840
Speaker 1: I think is going to become more and more apparent. Hopefully,

1609
01:26:15,960 --> 01:26:17,840
Speaker 1: hold on, you're saying that we're like connected to the

1610
01:26:17,920 --> 01:26:24,439
Speaker 1: natural world. Um. Yeah, don't don't tell Congress, but tell them.

1611
01:26:24,479 --> 01:26:32,400
Speaker 1: Actually A questions. Two questions. Question number one, Um, you're

1612
01:26:32,400 --> 01:26:38,880
Speaker 1: sensitive about Uh, you're sensitive about the idea that people

1613
01:26:38,920 --> 01:26:42,840
Speaker 1: would accuse people in your field of promoting this idea

1614
01:26:42,880 --> 01:26:46,680
Speaker 1: that we could just say screw it, will fix it later. Yeah,

1615
01:26:46,800 --> 01:26:51,960
Speaker 1: because we can't. We cannot fix it. Once something is gone,

1616
01:26:52,280 --> 01:26:55,120
Speaker 1: it is gone. Even if we create proxies of that

1617
01:26:55,200 --> 01:26:57,760
Speaker 1: thing so that we can try to have other things

1618
01:26:57,840 --> 01:27:00,240
Speaker 1: not disappear, it's not the same thing as say, eavning

1619
01:27:00,240 --> 01:27:04,519
Speaker 1: it in the first place. And you know that I

1620
01:27:04,560 --> 01:27:08,599
Speaker 1: don't think that you know, people have made that argument

1621
01:27:08,600 --> 01:27:10,160
Speaker 1: to me before. I tend to be more of an

1622
01:27:10,160 --> 01:27:13,839
Speaker 1: optimist than that. I think that it thinks that assumes

1623
01:27:13,880 --> 01:27:18,360
Speaker 1: two things about people that are both kind of awful. Um.

1624
01:27:18,400 --> 01:27:21,519
Speaker 1: Actually one of them maybe I'm not being too optimistic

1625
01:27:21,600 --> 01:27:23,320
Speaker 1: about I think the first thing is it assumes is

1626
01:27:23,360 --> 01:27:29,240
Speaker 1: that people in general care about extinction. Um. And I

1627
01:27:29,240 --> 01:27:32,400
Speaker 1: think maybe they don't. I think maybe most people, inasmuch

1628
01:27:32,439 --> 01:27:35,719
Speaker 1: as it doesn't actually affect them personally, don't care. And

1629
01:27:36,400 --> 01:27:39,880
Speaker 1: maybe by talking about things that are extinct and what

1630
01:27:40,000 --> 01:27:42,000
Speaker 1: we're missing we can get more people to actually care

1631
01:27:42,040 --> 01:27:45,600
Speaker 1: about things going extinct in the first place. Will it

1632
01:27:45,680 --> 01:27:49,280
Speaker 1: make these people feel more comfortable about stuff going extinct? Maybe,

1633
01:27:49,280 --> 01:27:51,360
Speaker 1: And that is something we have to work against by

1634
01:27:51,720 --> 01:27:54,960
Speaker 1: not letting this report that mommoths are going to be

1635
01:27:55,000 --> 01:27:57,559
Speaker 1: cloned in two years continue to go through the news

1636
01:27:57,560 --> 01:28:00,599
Speaker 1: cycle because they're not. We can never bring a mammoth back,

1637
01:28:00,640 --> 01:28:04,800
Speaker 1: and it's really important that we don't falsely say that

1638
01:28:04,840 --> 01:28:08,320
Speaker 1: we can, because this or what I could see happening,

1639
01:28:08,439 --> 01:28:12,439
Speaker 1: is that someone creates a hairy elephant and he's in

1640
01:28:12,479 --> 01:28:14,280
Speaker 1: a part and then all of a sudden there's a

1641
01:28:14,320 --> 01:28:16,760
Speaker 1: story about how you know, however they want to pitch

1642
01:28:16,800 --> 01:28:19,880
Speaker 1: at that time, and we'll go, oh, yes, that might

1643
01:28:19,920 --> 01:28:22,000
Speaker 1: be the thing that happens. First, we're a very far

1644
01:28:22,040 --> 01:28:25,240
Speaker 1: away away from from creating any sort of manipulated elephants.

1645
01:28:25,640 --> 01:28:27,800
Speaker 1: We can't we can't actually do any of that reproductive

1646
01:28:27,800 --> 01:28:30,360
Speaker 1: technology for elephants yet. So there's you know, there's a

1647
01:28:30,360 --> 01:28:32,720
Speaker 1: lot of technical stuff that we didn't talk about that's

1648
01:28:32,720 --> 01:28:35,719
Speaker 1: in the in between you know, today and having edited

1649
01:28:35,760 --> 01:28:39,400
Speaker 1: Mammoth's Mammoth's Back. But the other thing that it assumes

1650
01:28:39,439 --> 01:28:41,760
Speaker 1: is that people who do care about extinction, people like

1651
01:28:42,000 --> 01:28:44,840
Speaker 1: me and hopefully people like you and people listening to

1652
01:28:44,840 --> 01:28:47,720
Speaker 1: this podcast, are all of a sudden not going to

1653
01:28:47,840 --> 01:28:51,320
Speaker 1: do so because some far off crazy thing happens and

1654
01:28:51,439 --> 01:28:54,040
Speaker 1: a mammoth like thing comes back. I think people are

1655
01:28:54,040 --> 01:28:56,280
Speaker 1: still going to care about losing the animals that are

1656
01:28:56,280 --> 01:28:59,559
Speaker 1: in their backyard that they care about having there, and

1657
01:28:59,600 --> 01:29:02,439
Speaker 1: that like some idea that maybe someday in the future

1658
01:29:02,479 --> 01:29:04,240
Speaker 1: someone might be able to bring them back, won't stop

1659
01:29:04,240 --> 01:29:05,840
Speaker 1: them from worrying that they're not going to be there

1660
01:29:06,000 --> 01:29:09,160
Speaker 1: next week or in ten years or when their kids

1661
01:29:09,200 --> 01:29:11,519
Speaker 1: want to go out and and hunt or play with

1662
01:29:11,520 --> 01:29:13,599
Speaker 1: these animals that are in the backyard. I think people

1663
01:29:13,600 --> 01:29:17,120
Speaker 1: who care will continue to care. I hope that people

1664
01:29:17,120 --> 01:29:20,439
Speaker 1: who don't care will care even less. And that's that's

1665
01:29:20,439 --> 01:29:22,560
Speaker 1: the fear well sort of the argument. Like someone with

1666
01:29:22,600 --> 01:29:26,000
Speaker 1: a big trust fund right doesn't develop a sort of

1667
01:29:26,040 --> 01:29:30,280
Speaker 1: aggressiveness in an opportunistic sense because they always know that

1668
01:29:30,320 --> 01:29:32,600
Speaker 1: no matter what they do, they're going to be okay

1669
01:29:32,960 --> 01:29:35,759
Speaker 1: down the road. But here's my here's my second last question.

1670
01:29:36,200 --> 01:29:40,400
Speaker 1: Do you feel like the that the people in I

1671
01:29:40,400 --> 01:29:42,120
Speaker 1: don't know how to put it, like your peers, what

1672
01:29:42,120 --> 01:29:44,120
Speaker 1: do you call your community. That's like, it's not your community.

1673
01:29:44,120 --> 01:29:47,200
Speaker 1: Who are the you know, my peers, my colleague, Yeah,

1674
01:29:47,280 --> 01:29:50,519
Speaker 1: your colleagues who who deal in this world? How much

1675
01:29:50,560 --> 01:29:54,040
Speaker 1: are you guys? Sort of um, like a jockey looking

1676
01:29:54,080 --> 01:29:59,400
Speaker 1: for a horse. Okay. So obviously it was like a

1677
01:29:59,479 --> 01:30:03,439
Speaker 1: love of the technology that drew many of many of

1678
01:30:03,479 --> 01:30:06,439
Speaker 1: your peers into this field. Have you had to try

1679
01:30:06,479 --> 01:30:10,439
Speaker 1: to become a little bit elastic in how you apply

1680
01:30:10,600 --> 01:30:13,559
Speaker 1: it or talk about applying it in order to make

1681
01:30:13,600 --> 01:30:17,639
Speaker 1: it palatable like this to sort of steer the conversation about,

1682
01:30:18,320 --> 01:30:21,440
Speaker 1: you know, playing god. As you pointed out a criticism

1683
01:30:21,439 --> 01:30:24,880
Speaker 1: in your book that that you found that it's advantageous

1684
01:30:24,920 --> 01:30:30,120
Speaker 1: to like turn the technology towards a discussion about de

1685
01:30:30,320 --> 01:30:34,600
Speaker 1: extinction or saving nearly extinct species, because it just is

1686
01:30:34,640 --> 01:30:37,479
Speaker 1: a good way to sell it. I think that, um,

1687
01:30:37,479 --> 01:30:39,120
Speaker 1: it's a it's a big group of people and were

1688
01:30:39,120 --> 01:30:42,479
Speaker 1: actually a UM, we have a big community and a

1689
01:30:42,520 --> 01:30:44,639
Speaker 1: list serve and it's very active and people are talking

1690
01:30:44,640 --> 01:30:47,240
Speaker 1: about people have different motivations for being interested in this,

1691
01:30:47,280 --> 01:30:49,280
Speaker 1: and there are some people who really want to bring

1692
01:30:49,280 --> 01:30:51,760
Speaker 1: a particular species back, Like there's the group in the

1693
01:30:51,800 --> 01:30:54,400
Speaker 1: Netherlands that want the ROCs, which is the ancestor of

1694
01:30:54,560 --> 01:30:56,840
Speaker 1: domestic cattle. They want to bring this back and are

1695
01:30:56,880 --> 01:30:59,479
Speaker 1: trying to do this by breeding together different breeds of

1696
01:31:00,040 --> 01:31:03,439
Speaker 1: all that have different characteristics of the ancestor to eventually

1697
01:31:03,439 --> 01:31:05,840
Speaker 1: come up with some new breed that has, you know,

1698
01:31:05,920 --> 01:31:10,479
Speaker 1: a cluster of characteristics. Their work was initiated by the

1699
01:31:10,600 --> 01:31:15,400
Speaker 1: desire to see like by the desire to make the orcs,

1700
01:31:15,439 --> 01:31:17,879
Speaker 1: because they want to be able to have this ORACX

1701
01:31:18,040 --> 01:31:21,960
Speaker 1: in these habitats that they're trying to rewild. And so

1702
01:31:22,160 --> 01:31:25,519
Speaker 1: they think that in order to bring um wildlife back

1703
01:31:25,560 --> 01:31:27,640
Speaker 1: to these parts of Europe that where all all the

1704
01:31:27,680 --> 01:31:29,400
Speaker 1: trees were cut down and went to pasture, it said,

1705
01:31:29,520 --> 01:31:31,120
Speaker 1: they need to have some of these animals back because

1706
01:31:31,120 --> 01:31:33,599
Speaker 1: they want to re establish it. And so their desire

1707
01:31:33,720 --> 01:31:36,920
Speaker 1: is to see wildlife in its natural state and they

1708
01:31:36,960 --> 01:31:38,960
Speaker 1: think in order to do that, they need to bring

1709
01:31:39,000 --> 01:31:42,160
Speaker 1: back something that is like an orox. And so that's

1710
01:31:42,160 --> 01:31:45,200
Speaker 1: what's motivating that there's a group in Australia that there's

1711
01:31:45,240 --> 01:31:48,920
Speaker 1: there's like the wildlife to biochem path Yeah, yeah, yeah,

1712
01:31:49,040 --> 01:31:50,360
Speaker 1: there's you know, there are people who are interested in

1713
01:31:50,520 --> 01:31:52,960
Speaker 1: gastric brooding frogs that people who are interested in MOA's

1714
01:31:53,000 --> 01:31:55,439
Speaker 1: for the sake of MOA's there are you know, George

1715
01:31:55,479 --> 01:31:57,479
Speaker 1: is interested in using this technology to come up with

1716
01:31:57,479 --> 01:32:00,600
Speaker 1: ways to cure um. I think it's her peace in

1717
01:32:00,760 --> 01:32:04,200
Speaker 1: elephants and and you know other things. And also then

1718
01:32:04,200 --> 01:32:07,040
Speaker 1: there's the Zimovs who really want to to re establish

1719
01:32:07,120 --> 01:32:10,440
Speaker 1: tundra in Siberia. So I would say that the motivations

1720
01:32:10,520 --> 01:32:15,040
Speaker 1: for this range from conservation to ecological to just really

1721
01:32:15,040 --> 01:32:18,040
Speaker 1: being astounded and impressed by the technology, to really wanting

1722
01:32:18,040 --> 01:32:21,240
Speaker 1: to bring a particular species back. And obviously people are

1723
01:32:21,840 --> 01:32:23,840
Speaker 1: flexible in the way that they talk about this, and

1724
01:32:23,880 --> 01:32:27,120
Speaker 1: as people learn more about different motivations and different opportunities

1725
01:32:27,479 --> 01:32:31,760
Speaker 1: and different technical and ethical ecological challenges, we change the

1726
01:32:31,800 --> 01:32:33,880
Speaker 1: way we are thinking about these things. We grow, we

1727
01:32:33,960 --> 01:32:37,680
Speaker 1: learn and adapt, and that's that's not a bad thing. No,

1728
01:32:37,880 --> 01:32:39,800
Speaker 1: And it's not like you're and as you pointed out,

1729
01:32:39,880 --> 01:32:41,840
Speaker 1: it's not like you're like chasing the money because right

1730
01:32:41,840 --> 01:32:44,639
Speaker 1: now there isn't any money change. It's not like you're

1731
01:32:44,640 --> 01:32:47,600
Speaker 1: trying to like like human longevity. I imagine there's a

1732
01:32:47,640 --> 01:32:54,000
Speaker 1: budget there. In fact, if anyone would like to donate, Yeah, no,

1733
01:32:54,120 --> 01:32:58,799
Speaker 1: if we were studying aging or you know, human diseases.

1734
01:32:58,840 --> 01:33:00,720
Speaker 1: Then there's there's pockets and money out there for that.

1735
01:33:00,800 --> 01:33:03,240
Speaker 1: But as people who are involved with conservation, no, there's

1736
01:33:03,600 --> 01:33:06,559
Speaker 1: there's not enough money going around in conservation. I don't

1737
01:33:06,680 --> 01:33:09,799
Speaker 1: want to compete with people who are trying to conserve

1738
01:33:09,840 --> 01:33:12,519
Speaker 1: species um that are alive today. What I'd like to

1739
01:33:12,560 --> 01:33:16,560
Speaker 1: do is collaborate with them. I'd like to create opportunities

1740
01:33:16,600 --> 01:33:20,519
Speaker 1: for us to work together so that our motivations, my

1741
01:33:20,600 --> 01:33:23,200
Speaker 1: desire to see this technology developed so that it can

1742
01:33:23,240 --> 01:33:27,000
Speaker 1: be a useful tool for conservation, happens along with someone

1743
01:33:27,000 --> 01:33:29,519
Speaker 1: who's really trying to conserve a particular species. And in

1744
01:33:29,520 --> 01:33:32,000
Speaker 1: that way, I guess I am kind of a jockey

1745
01:33:32,040 --> 01:33:35,360
Speaker 1: looking for a horse. I want to find people who

1746
01:33:35,400 --> 01:33:38,280
Speaker 1: have a question, a problem that they're trying to solve

1747
01:33:38,560 --> 01:33:42,000
Speaker 1: that this technology might help to solve, and I want

1748
01:33:42,080 --> 01:33:45,320
Speaker 1: to work with them, not against them, because I do

1749
01:33:45,479 --> 01:33:48,720
Speaker 1: see that there's tremendous potential in this technology as long

1750
01:33:48,800 --> 01:33:51,040
Speaker 1: as we're not too scared of it to try it

1751
01:33:52,560 --> 01:33:56,559
Speaker 1: real quick. Um. So your own book, uh, Beth Shapiro,

1752
01:33:56,960 --> 01:34:00,720
Speaker 1: How to Clone a Mammoth The Science of de Extinction. Um?

1753
01:34:00,760 --> 01:34:03,080
Speaker 1: Where else might people go if they want to if

1754
01:34:03,080 --> 01:34:06,040
Speaker 1: they're curious about this, are there's some good There's lots

1755
01:34:06,040 --> 01:34:08,240
Speaker 1: of videos on YouTube. There is UM. There was an

1756
01:34:08,240 --> 01:34:11,479
Speaker 1: event that's our Community de Extinction community held at National

1757
01:34:11,560 --> 01:34:13,960
Speaker 1: Geographic several years ago that you can find a ted

1758
01:34:14,160 --> 01:34:16,840
Speaker 1: x D extinction. So there's lots of different talks from

1759
01:34:16,920 --> 01:34:20,600
Speaker 1: ethicists and conservation biologists, both for and against UM some

1760
01:34:20,640 --> 01:34:22,320
Speaker 1: of the technology, and there is a bit out at

1761
01:34:22,320 --> 01:34:24,120
Speaker 1: a date now, but it's a nice place to start,

1762
01:34:24,120 --> 01:34:26,439
Speaker 1: a good resource for finding out about the way that

1763
01:34:26,439 --> 01:34:31,000
Speaker 1: people are thinking about this. Now is your book current? Yeah, yep.

1764
01:34:31,439 --> 01:34:35,400
Speaker 1: The technology moves slowly. All right, Well, thanks so much

1765
01:34:35,439 --> 01:34:39,000
Speaker 1: for talking to this man. This is great stuff. And yeah,

1766
01:34:39,080 --> 01:34:41,800
Speaker 1: I want I want to schedule another talk for one

1767
01:34:41,880 --> 01:34:45,840
Speaker 1: decade from now. Okay, I'll put it on my calendar. Yeah,

1768
01:34:46,160 --> 01:34:48,120
Speaker 1: and then and we'll we'll send up come up with

1769
01:34:48,120 --> 01:34:50,040
Speaker 1: the funding to have you for a whole day. I

1770
01:34:50,080 --> 01:34:53,400
Speaker 1: feel like we could have talked a lot longer. Thank you,

1771
01:34:53,680 --> 01:35:13,280
Speaker 1: Thank you very much. Thank you asssssssssssssssssssssss