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Speaker 1: This is the Meat Eater Podcast coming at you shirtless, severely,

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

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Speaker 2: Listening to podcast, you can't predict anything.

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Speaker 1: The Meat Eater Podcast is brought to you by First Light.

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Speaker 1: Whether you're checking trail cams, hanging deer stands, or scouting

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Speaker 1: for ELK. First Light has performance apparel to support every

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Speaker 1: hunter in every environment. Check it out at first light

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Speaker 1: dot com. F I R S T L I T

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Speaker 1: E dot com. All right, everybody hot, damn.

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Speaker 3: It's first episode is doctor Renela.

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Speaker 4: Oh you stole the okay, Phil, cue up the music.

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Speaker 4: I was you weren't supposed to introduce yourself that way.

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Speaker 2: It was gonna be a surprise.

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Speaker 3: My daughter teases my ears. With that hat on? Do

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Speaker 3: you know what you can tell by the hat? What

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Speaker 3: you got going on?

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Speaker 1: Yeah? Like lesser people with the square hat, they don't.

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Speaker 1: They're not they don't have that hat. Notice that you've

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Speaker 1: got an octagon going You get a different You get a.

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Speaker 4: Taking photos of Steve with his diploma.

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Speaker 1: Hey man, you guys, just is there a tier above

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Speaker 1: that of other hands?

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Speaker 5: No?

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Speaker 1: I think that's the top half.

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Speaker 2: Okay, it's like Pope after that.

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Speaker 1: I don't know that they make a better.

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Speaker 2: Hat than that.

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Speaker 5: Where'd you put that diploma?

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Speaker 1: Well, that's just the you know, a little inside.

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Speaker 3: Baseball here, it's just a mod that's a prop.

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Speaker 1: My actual diploma, my actual diploma is a big framed thing.

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Speaker 3: And you can imagine that.

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Speaker 1: It'll be right over your head probably right here is

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Speaker 1: the time of my life. Down there is the tassel.

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Speaker 2: Go on a certain side.

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Speaker 1: It's pinned. Oh, it's pinned in place. M a couple

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Speaker 1: of things. You guys think it's all like. You guys

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Speaker 1: think it's all grave. You haven't that happen to you. Well,

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Speaker 1: I'll tell you something, something you might not realize. So

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Speaker 1: you go up.

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Speaker 3: You go up.

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Speaker 1: It's in the it's in the arena, you know, like

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Speaker 1: one hundreds, like six hundred and fifty students, all their parents.

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Speaker 1: You march up and the seat they give you is

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Speaker 1: right by the right in the line of action.

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Speaker 2: Mm hmmm.

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Speaker 3: And it's televised.

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Speaker 1: So you stand up and you get what they call hooded,

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Speaker 1: which you can't really see in that picture because it's

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Speaker 1: all going on behind your hood's all going on behind you.

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Speaker 1: They give you that. You give up, and you give

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Speaker 1: your speech, and I gave a speech called There's No

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Speaker 1: Plan B. Then you sit down and then they have

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Speaker 1: to hand six hundred and fifty diplomas. But you're on

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Speaker 1: the camera is aimed in such a way that you're there.

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Speaker 5: Not doing anything.

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Speaker 3: They've warned you, don't fiddle around, don't look at your phone.

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Speaker 4: I fell asleep during my graduation. There's a picture of

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Speaker 4: me sleeping.

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Speaker 1: I was wide away because you had to sit there

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Speaker 1: for all those people.

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Speaker 6: How long did that take?

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Speaker 5: A couple hours?

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Speaker 1: Yeah?

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Speaker 2: Yeah, So there was a morning and an afternoon ceremony.

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Speaker 1: They split the schools.

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Speaker 2: Which one were you?

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Speaker 3: I was in the morning.

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Speaker 2: Sce Is that one more prime time?

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Speaker 1: I don't think that anybody looks at it that way.

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Speaker 1: They split the schools. So my honorary doctor was from

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Speaker 1: the School of Forestry and Conservation. So that school all

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Speaker 1: the foresters were coming up. Which is like a stressful

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Speaker 1: time to be coming out of a forestry program right now,

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Speaker 1: because that was like, you know, so many of those

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Speaker 1: kids that come out of that forestry program, I'm going

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Speaker 1: to land management agencies. It's stressful.

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Speaker 6: A doctorate in an area outside of your your area

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Speaker 6: of expertise.

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Speaker 1: Yeah, well, like technically, like I think the forestry part,

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Speaker 1: but the School of Forestry and Conservation covers a lot

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Speaker 1: of areas that are of relevance. The fact I went

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Speaker 1: and had I went and did like a Q and

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Speaker 1: A with the forestry students who have a lot of

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Speaker 1: considerations around conservation funding and other issues. So I don't

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Speaker 1: think they mean that I'm like as good as Seth

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Speaker 1: as telling what trees are what right.

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Speaker 3: Jean Seth was in forestry school.

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Speaker 1: There's a test where you have to identify a hundred

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Speaker 1: and some trees by the bud, not the leaf a stick.

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Speaker 5: Bet you Doug Dern could do that.

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Speaker 1: Yeah, he's pretty good. So they were doing a podcast

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Speaker 1: on something hardly anyone know about except for people who

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Speaker 1: listen to this show. This is old subject for people

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Speaker 1: who listen to the show, but other than that, everybody

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Speaker 1: found out about them from Game of Thrones.

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Speaker 3: Dire Wolves.

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Speaker 1: This is our current estimate. Is this to be our

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Speaker 1: second and a half episode on this subject.

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Speaker 4: Yeah, because we had a first one.

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Speaker 1: We had a first one which was episode four six six,

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Speaker 1: which was called dire Wolves and Ancient Hunting Dogs, which

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Speaker 1: was with Doctor Angela Perry.

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Speaker 4: That was number one, and then our half one half one,

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Speaker 4: touching on it with today's guest.

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Speaker 1: With today's guest Matt James from Colossal Bioscience, as we

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Speaker 1: touched on dire wolves, so there's one point five. At

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Speaker 1: the conclusion of today's episode, the count will be up

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Speaker 1: to two point five. If you if you did learn

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Speaker 1: about that there was such a thing once upon a

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Speaker 1: time called a dire wolf from Game of Thrones, which

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Speaker 1: was a misleading portrayal, then you might have caught headlines

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Speaker 1: such as everywhere, a headline everywhere on the planet that you

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Speaker 1: could possibly have a headline. Scientists say they have resurrected

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Speaker 1: the dire wolf, the return of the dire Wolf, the

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Speaker 1: dire Wolf is back, Scientists revived the dire wolf or

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Speaker 1: something else. Massive amounts of coverage everywhere about Colossal Bioscience's

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Speaker 1: dire Wolf project. So today we're going to dig into

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Speaker 1: all kinds of questions around that, including the big one,

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Speaker 1: why why would you do that? What exactly are they

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Speaker 1: and uh is it in fact? Like how do you

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Speaker 1: make it relevant to conservation? But first the news here's

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Speaker 1: my news item. We already covered my my heightened level

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Speaker 1: of everything, which is my ascendency.

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Speaker 3: Certainly a framing coming home from my ascendency.

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Speaker 1: We drive all the way home and I'm pulling into

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Speaker 1: my you know, like not my driveway, but our circle

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Speaker 1: where our house is off, and I see a crow

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Speaker 1: like we pull in and up ahead of me, I

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Speaker 1: see a crow like regurgitate what I take to be

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Speaker 1: a synthetic item, baby blue like a baby. And I'm like,

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Speaker 1: and I already tell my kids, like, what is that

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Speaker 1: thing doing? And all of a sudden, he's he like

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Speaker 1: deposits out of his mouth two robin eggs he carried

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Speaker 1: at the same time, mm just standing in the road,

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Speaker 1: and he like spits two robin eggs out. He starts

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Speaker 1: pecking at one and pulls out a little robin undeveloped robin.

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Speaker 1: At this point, my younger boy and my daughter out

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Speaker 1: of the truck, heading toward the crow, which leaves its

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Speaker 1: whole project in the road and flies off. My older

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Speaker 1: boy is now heading to he's like making plans to

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Speaker 1: kill the crow in retaliation. But my daughter gets there

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Speaker 1: and she comes back to the truck and in her

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Speaker 1: hand is a little writhing, featherless pink robin, and on

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Speaker 1: the other hand is an egg with a little crack.

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Speaker 4: I know where this is going.

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Speaker 1: Well, I make my older boy dispatch the I make

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Speaker 1: him kill the one that isn't going to survive. He

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Speaker 1: use his boots, but his fingers. Then my daughter goes

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Speaker 1: around everywhere trying to find a robin ness to put

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Speaker 1: the egg in.

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Speaker 3: And I'm telling you it what works. It's cracked.

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Speaker 1: And then she's getting upset and more upset and can't

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Speaker 1: find a nest. I said, but I want to return

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Speaker 1: to what I telling you. It'll never live. It's cracked,

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Speaker 1: so now is living in it's buried, no garden. I

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Speaker 1: thought it was gonna's nutrients will return.

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Speaker 4: I thought this was like, you're gonna put it under

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Speaker 4: like a pen picture.

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Speaker 1: She was, she was, She's like, I know, it's expensive,

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Speaker 1: we need to get in. I'm like, sweetheart, it's cracked.

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Speaker 1: Like there's nothing you're gonna do. You're gonna put it

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Speaker 1: so it's returning to the earth. In fact, it's gonna

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Speaker 1: turn into raspberries. No, it's it's actually where it's positioned,

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Speaker 1: it will be an acorn squash.

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Speaker 5: We've got.

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Speaker 6: You've got to rob a nest up under our portrait

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Speaker 6: now and I'm just waiting. You know how those babies

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Speaker 6: they can't. They'll come out of the nest, but they

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Speaker 6: can't really fly real well, when that happens, the dog's

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Speaker 6: going to eat them.

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Speaker 1: This is just gonna happen.

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Speaker 5: Well yeah, I mean, what am I gonna do?

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Speaker 1: Mo the whole nest? No? I would be smart. You

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Speaker 1: know what was one of the more interesting things we've

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Speaker 1: had on the show over the years is, you know

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Speaker 1: the myth that if you like go near the egg

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Speaker 1: as abandoned or something. You remember when we had you know,

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Speaker 1: the whole thing if you touch a fawn, remember we

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Speaker 1: had Randall Kaufman and Matt from Keith or montef He's like, well,

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Speaker 1: that's true. None of our colloring projects would ever work out.

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Speaker 3: Where we like.

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Speaker 1: Net them, handle them and then they stand up and

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Speaker 1: go back with their mom.

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Speaker 3: You know, uh, probably I was raised to believe it's.

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Speaker 2: A good assumption to operate under.

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Speaker 1: That's what I was telling my daughter to She says, well,

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Speaker 1: she won't take care of it anyway, And I'm like,

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Speaker 1: that's a myth.

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Speaker 3: But they tell you that so you.

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Speaker 1: Don't mess with stuff, but it's not true. It's just

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Speaker 1: the manipulating you into not they're manipulating you into not

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Speaker 1: messing with things you shouldn't mess with. Two book thing

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Speaker 1: News is one forever for those of you awaiting it.

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Speaker 1: Our guide to wilderness skills and survivals out in Japanese.

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Speaker 4: How many other languages don't know?

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Speaker 1: I'm not sure of any of the life.

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Speaker 5: I don't think any other.

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Speaker 1: So they usually buy. When they buy, it'll get it'll

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Speaker 1: go to like Australia, New Zealand, Canada, America. This is

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Speaker 1: the only translation I'm aware of.

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Speaker 3: But you, being you know, a.

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Speaker 2: Little adjacent to.

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Speaker 1: You know, here we go significantly Chinese, perhaps you can

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Speaker 1: help me with something.

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Speaker 4: Yep, there are there are some kanji. Uh, there's there's Yes,

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Speaker 4: there are some script characters that are similar. But I

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Speaker 4: long long forgot how.

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Speaker 3: In China do they read books backwards or forwards?

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Speaker 4: So traditionally it's compared to us backwards, and it's not backwards, right,

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Speaker 4: it's not backwards to them, and let's be friends.

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Speaker 1: I'm looking at this from American right to left.

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Speaker 4: If I'm not mistaken, right, I think that's what it

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Speaker 4: is traditionally.

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Speaker 1: Okay, that's interesting. You already kind of answered my question.

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Speaker 1: Traditionally because I have a Japanese fish Cleaning Book, which

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Speaker 1: is my favorite book of all the books I've ever owned.

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Speaker 1: It's like process descriptions on how to clean all kinds

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Speaker 1: of fish. But if you read it the right way,

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Speaker 1: the American way, the fish get put back together. So

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Speaker 1: you're like, this is a book about resurrecting fish. But

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Speaker 1: you're supposed to go back to what would be for us,

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Speaker 1: back to front. So when I picked this up, I

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Speaker 1: expected it to be that they would have reversed.

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Speaker 4: It, but they because it's more modern now.

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Speaker 1: So you might if you lived in Japan, you might

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Speaker 1: grab a book and you might encounter a book that

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Speaker 1: has read from from our perspective, back to front, or

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Speaker 1: you might pick up a book that's read front to

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Speaker 1: back and it would just be like whatever, You're adaptable

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Speaker 1: either way.

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Speaker 4: I think I think more. I think books that are

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Speaker 4: printed uh these days are just done in the I

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Speaker 4: don't know if I mean Western orientation.

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Speaker 1: Me and doctor Randall were trying to find the translator online.

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Speaker 1: We found her LinkedIn. I couldn't read it being in Japanese,

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Speaker 1: just in Japanese. I just wanted to get her impressions.

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Speaker 1: I wanted to get her impressions.

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Speaker 2: I thought that was a bootleg copy.

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Speaker 1: I saw it.

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Speaker 6: It's interesting how they put a dust jacket on a paperback.

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Speaker 2: I thought that too. I also thought this version was

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Speaker 2: prettier than that's a gorgeous cover.

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Speaker 4: Yeah what what didn't the American one?

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Speaker 7: Americans aren't that.

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Speaker 3: Americans aren't that discrete.

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Speaker 4: As well, like the the Japanese have this wonderful art

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Speaker 4: at packaging things up. So if you if you go

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Speaker 4: there and you buy I don't know, cookies, there's a

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Speaker 4: problem because there's a ton of waste, but individual cookies

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Speaker 4: have their own wrapping and then these wrapped individual cookies

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Speaker 4: are in a wrap box maybe with wrapping paper around it,

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Speaker 4: as you know, presentation as a reflection of respect and

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Speaker 4: of mil and so.

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Speaker 1: It's not ell that. It's like it's three times the

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Speaker 1: pack international business.

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Speaker 4: Yeah, three that's right, that's right. I mean they have

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Speaker 4: a huge, like plastic waste problem in Japan, but I'm

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Speaker 4: not surprised that there is a cover on a soft bound.

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Speaker 1: Book another paper But oh, you know, I had a

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Speaker 1: joke I liked a lot and when I put that

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Speaker 1: Japanese book on Instagram, and I said, if this book

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Speaker 1: would have come out in nineteen forty we'd still be

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Speaker 1: fighting in the Pacific.

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Speaker 4: That was pretty funny, get it, That's pretty funny.

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Speaker 1: This catch our Kids book, Catch Crayfish, Count Stars is

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Speaker 1: out in paperback.

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Speaker 5: Yep.

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Speaker 1: So I was saying, if you had a kid that

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Speaker 1: you don't like enough for a hardcover, or who's out

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Speaker 1: well behaved enough to deserve a hardcover.

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Speaker 5: Or if you have a kid that trashed the hardcover.

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Speaker 1: Now there's every kid in America should deserve a soft

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Speaker 1: cover too.

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Speaker 3: Let there's no kids that are that bad.

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Speaker 1: Number one New York Times bestseller. This book, Catch Crayfish,

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Speaker 1: Count Stars fun projects, skills and adventures for outdoor kids.

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Speaker 1: So if you want to raise outdoor competent children who understand,

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Speaker 1: as Doug durn puts it, life and death on the

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Speaker 1: farm or life and death on the asphalt with bird eggs,

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Speaker 1: this is this is a phenomenal book to get your

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Speaker 1: kids involved with. It's just projects. There's things they do

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Speaker 1: and ways to engage them with discussions about ecology and

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Speaker 1: biology and everything outdoors. Yep. May the gold being if

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Speaker 1: you see a mouse and you say to your kid,

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Speaker 1: grab that, it'll grab it.

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Speaker 5: And then ideally turn it loose.

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Speaker 1: Well, I don't mean just the fact that they'll, Yeah,

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Speaker 1: you know, that's why I let my kids tell them

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Speaker 1: grab my mouse, You're probably gonna grab. It's doing a.

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Speaker 5: Lot of spiders.

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Speaker 1: We have an Iraq noophobia problem.

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Speaker 3: But I've accepted that it's true.

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Speaker 1: It's not just I think it's a psychological like. I

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Speaker 1: think it's a legitimate psychological thing, the Iraq and noophobia. Now,

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Speaker 1: this is harrowing. In Florida, a sixty one year old

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Speaker 1: woman and her husband were in a canoe in central Florida.

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Speaker 1: They went over they passed over an eleven foot alligator.

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Speaker 1: It's it seems like it's spooked and that's the noise

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Speaker 1: of it spooking in shallow water, flipped the canoe and

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Speaker 1: then decided and then killed the woman Tiger Creek near

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Speaker 1: Lake Kassimi, south of Orlando.

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Speaker 6: How harrowing be a rough way to go? I think

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Speaker 6: be worse than getting eaten by a grizzly.

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Speaker 1: Bear her husband? How hairing for her husband? Oh way

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Speaker 1: worse than getting killed by grizzly bear. There's just not

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Speaker 1: a lot of romance in now.

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Speaker 6: No hopefully drowned first, I guess.

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Speaker 5: Yeah. Just the question is is.

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Speaker 6: Are more people getting killed by alligators these days and

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Speaker 6: in days past.

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Speaker 1: Well, here's the real question. You're right, but there's a

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Speaker 1: way to arrive at that that. I like, I can't

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Speaker 1: remember who introduced this idea. Oh, I think it was

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Speaker 1: a podcast guest. Remember we had Adam Pancrats on the show,

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Speaker 1: and well, there's there's there's so like, so grizzly bears

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Speaker 1: dust off, Like, that's that's crass. Grizzly bears kill a

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Speaker 1: person or two every year in Montana. Right, So you'd

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Speaker 1: look be like, as grizzly bears have recovered and hit

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Speaker 1: recovery objectives, and there's like more bears at higher populations,

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Speaker 1: you'd be like, are bears more likely to kill a person?

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Speaker 1: Be like, well, let's look at what an individual's bear.

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Speaker 1: An individual bears chance of having a violent encounter with

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Speaker 1: a human.

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Speaker 4: We're a run in with a human to begin with,

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Speaker 4: and you look at human populations.

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Speaker 1: Yeah, but the thing but what I'm getting at is

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Speaker 1: like a bear today, a grizzly today in Idaho, Wyoming, Montana,

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Speaker 1: a grizzy today an individual bear is not that bear

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Speaker 1: is not more likely to maul a person. No, it's

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Speaker 1: not like their psychology has changed. There's more of them. Ye,

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Speaker 1: So if you went point me, if you went and

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Speaker 1: looked at Florida, like you know, alligators were had endangered

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Speaker 1: species protections in the seventies. Now they got one point

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Speaker 1: five million.

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Speaker 6: Golf course ponds and canals in people's backyards.

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Speaker 1: So don't Yeah, it's not like gators are like individual

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Speaker 1: gators are like, man, I wouldn't have before, but now

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Speaker 1: I'm gonna bite a person. It's just like there's you know,

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Speaker 1: so it might even be that alligator, an individual alligator

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Speaker 1: still has a point oh whatever percent chance of doing it,

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Speaker 1: and an individual alligator is not like more likely to

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Speaker 1: attack a person, but you just have more opportunities. But

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Speaker 1: still be like, what a couple of people. I don't

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Speaker 1: know how many people get attacked every year in Florida.

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Speaker 3: Couple die.

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Speaker 1: We've talked about a bunch that died, But I mean,

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Speaker 1: what is it like five six people per year? That's

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Speaker 1: easy in March get bit in Florida. How many people

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Speaker 1: get bit in Florida by an alligator? Divided?

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Speaker 4: I think Spencer is checking up on that. But in March,

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Speaker 4: this same area where this woman got was killed, another

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Speaker 4: woman was killed brilliantly. Yeah in March.

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Speaker 6: Same Did that one have something to do with a

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Speaker 6: dog rescuing a.

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Speaker 5: Dog or something?

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Speaker 6: Oh, yeah, there's something about a dog.

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Speaker 1: Because yeah, eighteen hundred grizzly bears and the lower forty

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Speaker 1: eight eighteen hundred grizzly bears kill a couple people a year,

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Speaker 1: so and so they're more dangerous per bear than gators.

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Speaker 3: If you have one thousand gators.

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Speaker 1: Yeah, but the other thing is you got tons of

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Speaker 1: dinky gators that you can't really count.

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Speaker 3: You have to start counting them out a certain size.

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Speaker 2: I guess Since nineteen forty eight, there have been four

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Speaker 2: hundred and fifty documented alligator bites in Florida. Thirty of

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Speaker 2: them were fatal. It's a eighty years seven years span.

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Speaker 4: I think we should finally get you on a gator

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Speaker 4: hunt for an episode.

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Speaker 1: Yeah, you know, I'd like to. I just never had

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Speaker 1: not that I don't want to. There's a lot of

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Speaker 1: stuff to do in this life.

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Speaker 3: There's a lot of stuff to.

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Speaker 1: Do in this life.

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Speaker 4: The gator nuggets are really good.

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Speaker 1: Yeah, I've had friends give me sax a gator me.

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Speaker 3: I just have never like.

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Speaker 1: Planned it, you know what I mean? This got us

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Speaker 1: thinking about this. Did you know that there used to

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Speaker 1: be it's so crazy and eleven thousand pound alligator?

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Speaker 6: You mean used to be like ten years ago, like

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Speaker 6: ten million years.

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Speaker 1: Ago, seventy eighty million years ago.

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Speaker 4: My colossal probably doesn't want to work on this.

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Speaker 7: Yeah, that one's a little too old. It's old.

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Speaker 1: Six to eight foot long, teeth.

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Speaker 4: Six to eight inch long?

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Speaker 1: What am I saying that would be? But do you

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Speaker 1: maybe mean inches? Yeah? Yeah, that's right, six to eight

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Speaker 1: inch teeth. There's a picture of one eating. There's like

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Speaker 1: a like a fanciful.

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Speaker 4: Painting of an artist rendering.

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Speaker 1: Oh yeah, but it's like it's not an actual photo.

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Speaker 3: R a t rex, which I love.

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Speaker 1: Yeah, you know what I mean.

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Speaker 3: But you can't rule out an eleven thousand pounder.

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Speaker 1: Uh. They found the bones from one in North Carolina

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Speaker 1: in eighteen fifty how long, I don't know.

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Speaker 7: I mean, that's an alligator the size of an orca

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Speaker 7: gown with.

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Speaker 1: Your kid, and they nudging up to the edge of

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Speaker 1: the pond.

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Speaker 5: And the head would be the size of this table.

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Speaker 4: Mm hmmm.

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Speaker 3: You'd seen him from so far away.

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Speaker 1: That's the thing I think about with dinosaurs is like,

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Speaker 1: what's that big ass dinosaur, the big plant eater. If

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Speaker 1: you're driving down the road, you'd be like, hey, look,

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Speaker 1: five miles away, there's one.

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Speaker 3: Do you know what I mean?

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Speaker 1: It'd be so obvious. Uh quick? How many other news

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Speaker 1: items we get?

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Speaker 7: It's even bigger than an orca.

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Speaker 1: This is an interesting deal because we had a wildlife

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Speaker 1: We had a retired US Fish and Wildlife Service agent

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Speaker 1: on the show not long ago talking about the smuggling

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Speaker 1: of wildlife parts, and there's this is reported that there

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Speaker 1: has been a post COVID decline in the smuggling of

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Speaker 1: panglin scales and elephant ivory, related to a Chinese encyclopedia

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Speaker 1: of medicine having said perhaps there is no health benefit

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Speaker 1: to panglin scales, I love it and ivory.

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Speaker 5: How was the eye every tied to medicine?

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Speaker 1: I don't know.

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Speaker 2: And I feel like the black market is always operated

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Speaker 2: outside of what traditional medicine would say is useful for

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Speaker 2: curing or doing something. So I can't believe that, like

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Speaker 2: they're attributing this to an official journal saying, hey, penguins

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Speaker 2: don't help your libido or whatever.

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Speaker 1: In twenty nineteen, smugglers crime syndicates were shipping vast quantities

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Speaker 1: of the two products from Africa into China. Says trafficking

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00:24:39,119 --> 00:24:45,760
Speaker 1: fell during the pandemic has remained low. Law enforcement efforts

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00:24:45,800 --> 00:24:48,439
Speaker 1: have helped. Falling prices have helped, and it goes on

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Speaker 1: to say another possible factor is that in twenty twenty,

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Speaker 1: Panglin scales were removed from an important encyclopedia of Chinese medicine.

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Speaker 6: We found out why.

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Speaker 4: Ivory quote ivory can purge the body of toxins and

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Speaker 4: enhance the complexion.

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Speaker 6: Sure, now do you just like rub a piece of

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Speaker 6: ivory on your face?

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Speaker 4: I think you grind didn't eat it? You know, obviously

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Speaker 4: not backed by science, but that's the.

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Speaker 1: My wife was getting off her toxins out last week,

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Speaker 1: she did a cleanse, and I was like, what toxins specifically,

444
00:25:30,280 --> 00:25:33,840
Speaker 1: the same way anybody answers it. They don't know what?

445
00:25:34,200 --> 00:25:36,600
Speaker 1: Tell me what toxins are coming out of you?

446
00:25:38,800 --> 00:25:41,679
Speaker 4: Pepass which we can't get out of us.

447
00:25:43,840 --> 00:25:45,760
Speaker 3: I had a social media hissy fit.

448
00:25:45,680 --> 00:25:50,920
Speaker 1: When and when uh, the Trump administration canceled some federal

449
00:25:50,960 --> 00:25:53,720
Speaker 1: spending on the lamp ray problem in the Great Lakes,

450
00:25:53,760 --> 00:25:56,400
Speaker 1: which I thought was a huge mistake and perhaps quite

451
00:25:56,400 --> 00:26:00,560
Speaker 1: expensive in the long run, and better news they're following

452
00:26:00,680 --> 00:26:06,879
Speaker 1: through now with trying to keep big head carp, silver carp,

453
00:26:06,960 --> 00:26:09,239
Speaker 1: the asiatic carp species out of the Great Lakes, And

454
00:26:09,240 --> 00:26:12,439
Speaker 1: in fact, which surprised me, is they've now working with

455
00:26:12,520 --> 00:26:18,640
Speaker 1: Michigan's Governor Jennifer Whitner. Whitmer, the administration is fast tracking

456
00:26:19,920 --> 00:26:24,720
Speaker 1: a barrier system to keep carp going through the Chicago

457
00:26:25,000 --> 00:26:28,960
Speaker 1: Canal and into the Great Lakes. There's often there's also

458
00:26:29,040 --> 00:26:32,879
Speaker 1: been a big argument among ecologists and fisheries people is

459
00:26:33,119 --> 00:26:35,800
Speaker 1: would those carp even like the Great Lakes anyways?

460
00:26:37,560 --> 00:26:37,600
Speaker 2: Do?

461
00:26:37,840 --> 00:26:38,200
Speaker 5: I mean?

462
00:26:39,640 --> 00:26:41,800
Speaker 6: The current thinking the only way to figure that out

463
00:26:41,920 --> 00:26:44,359
Speaker 6: is let them get in there, right, Like what you don't?

464
00:26:44,400 --> 00:26:46,679
Speaker 6: I mean, that could be devastating to the Great Lakes.

465
00:26:46,800 --> 00:26:49,320
Speaker 1: The current thinking is they would. Because some people said that,

466
00:26:49,359 --> 00:26:53,280
Speaker 1: well they probably wouldn't like it. The current thinking is

467
00:26:53,320 --> 00:26:57,119
Speaker 1: that they would like it and that it would be devastating.

468
00:26:57,200 --> 00:26:59,800
Speaker 7: Yeah. Well, the problem with carpe is they seem to

469
00:26:59,840 --> 00:27:01,720
Speaker 7: like almost everybody at water they've ever been in.

470
00:27:03,480 --> 00:27:10,399
Speaker 1: I liked everything. Yeah, all right, we're gonna do a

471
00:27:10,400 --> 00:27:12,119
Speaker 1: little background on dire wolves here.

472
00:27:13,119 --> 00:27:15,360
Speaker 2: You mentioned Game of Thrones twice earlier. Have you seen

473
00:27:15,400 --> 00:27:15,960
Speaker 2: Game of Throne.

474
00:27:16,080 --> 00:27:18,560
Speaker 3: Never I'm aware of it, have you?

475
00:27:19,200 --> 00:27:19,400
Speaker 1: Yes?

476
00:27:19,520 --> 00:27:21,080
Speaker 2: But I wanted your review on it.

477
00:27:21,600 --> 00:27:24,600
Speaker 1: Never watched it, Okay, I watched serial drama like it.

478
00:27:24,680 --> 00:27:26,159
Speaker 1: I don't watch serial dramas. I wouldn't like.

479
00:27:26,160 --> 00:27:27,440
Speaker 4: Did you watch Narcos?

480
00:27:27,800 --> 00:27:27,960
Speaker 1: Oh?

481
00:27:28,000 --> 00:27:30,000
Speaker 4: Wait, Narcos?

482
00:27:30,040 --> 00:27:31,240
Speaker 1: And then what's that?

483
00:27:31,800 --> 00:27:35,000
Speaker 4: There wasn't there a Danish serial dramas.

484
00:27:37,160 --> 00:27:40,080
Speaker 5: Fantasy stuff other than Star Wars.

485
00:27:39,960 --> 00:27:41,720
Speaker 1: A little bit of Star the Star Wars guy, a

486
00:27:41,760 --> 00:27:44,520
Speaker 1: little bit of Star Wars guy. I don't watch serial

487
00:27:44,560 --> 00:27:49,320
Speaker 1: dramas because I don't with the exception of what did

488
00:27:49,320 --> 00:27:49,879
Speaker 1: you just name that?

489
00:27:49,960 --> 00:27:53,560
Speaker 4: I did watch Danish drama and.

490
00:27:53,960 --> 00:27:58,520
Speaker 1: There's no date yet, okay, d Narcos, Yes, that's it.

491
00:27:59,000 --> 00:28:03,280
Speaker 4: Oh there's another one, push your one, Pushura or something.

492
00:28:03,280 --> 00:28:10,879
Speaker 4: An Italian an Italian series. I did watch that, Tina.

493
00:28:09,680 --> 00:28:13,840
Speaker 1: No Gomora well, because Gomora was a book, was a

494
00:28:13,880 --> 00:28:19,240
Speaker 1: nonfiction book about the Italian mafiosa. Out of that came

495
00:28:19,440 --> 00:28:23,919
Speaker 1: a film called Gomorra, which was a devastating film about

496
00:28:23,920 --> 00:28:26,720
Speaker 1: the Italian mafia. And then out of that so I

497
00:28:26,840 --> 00:28:30,520
Speaker 1: watched because I was liking Gomore of the book, Gamore

498
00:28:30,600 --> 00:28:33,840
Speaker 1: of the movie. I watched some of the Gomora the series.

499
00:28:35,000 --> 00:28:37,359
Speaker 1: What I not that it matters. What I don't like

500
00:28:37,400 --> 00:28:39,320
Speaker 1: about serial dramas is after a while, I want to

501
00:28:39,320 --> 00:28:40,120
Speaker 1: get out with my life.

502
00:28:40,720 --> 00:28:41,760
Speaker 4: Did you watch Breaking Bad?

503
00:28:41,880 --> 00:28:42,400
Speaker 1: Can you?

504
00:28:42,800 --> 00:28:44,880
Speaker 2: I've got the top five here. See if you've seen

505
00:28:44,920 --> 00:28:48,920
Speaker 2: any TV dramas of all time from the University of

506
00:28:48,920 --> 00:28:52,520
Speaker 2: Pennsylvania and the Sopranos. No The Wire, no Breaking Bad,

507
00:28:53,120 --> 00:28:57,680
Speaker 2: Mad Men, no Game of Thrones. Okay five, I trust

508
00:28:57,680 --> 00:28:57,959
Speaker 2: you now.

509
00:28:58,080 --> 00:28:59,200
Speaker 3: I'm not hacking on it.

510
00:28:59,280 --> 00:29:00,560
Speaker 2: I just can't sure.

511
00:29:00,640 --> 00:29:03,200
Speaker 1: I need to move on, like I can't give you

512
00:29:03,240 --> 00:29:04,000
Speaker 1: know me movies.

513
00:29:04,040 --> 00:29:06,680
Speaker 3: I could have watched in that amount of time and

514
00:29:06,680 --> 00:29:07,280
Speaker 3: then I could start.

515
00:29:07,280 --> 00:29:08,840
Speaker 1: Here's the other problem I have, and I don't want

516
00:29:08,840 --> 00:29:11,240
Speaker 1: to take up too much of our time. Here I

517
00:29:11,280 --> 00:29:14,360
Speaker 1: can start to smell the writers keeping it going. The

518
00:29:14,360 --> 00:29:16,320
Speaker 1: minute I get a whiff of them keeping it going,

519
00:29:16,600 --> 00:29:20,120
Speaker 1: getting extra season or two of yeah it is, Oh brother,

520
00:29:20,240 --> 00:29:22,000
Speaker 1: you know you can just smell them keeping it jump

521
00:29:22,040 --> 00:29:23,000
Speaker 1: the shark. Yeah.

522
00:29:23,080 --> 00:29:23,200
Speaker 4: Well.

523
00:29:23,200 --> 00:29:25,160
Speaker 7: The good news about Game of Thrones is it wasn't

524
00:29:25,160 --> 00:29:26,680
Speaker 7: that they were struggling to keep it going as they

525
00:29:26,720 --> 00:29:29,400
Speaker 7: were struggling to condense it into what they did. Oh,

526
00:29:29,440 --> 00:29:31,920
Speaker 7: because the books are just so the books kept it

527
00:29:31,920 --> 00:29:35,120
Speaker 7: going for them.

528
00:29:35,360 --> 00:29:38,280
Speaker 1: Oh, interesting thing here just about wolves in general. So

529
00:29:38,360 --> 00:29:40,520
Speaker 1: the two dire wolves were going to discuss today are

530
00:29:40,560 --> 00:29:44,120
Speaker 1: Romulus were named Romulus and Remus, which are fitting for

531
00:29:44,200 --> 00:29:47,480
Speaker 1: wolf names because Romulus killed his brother.

532
00:29:49,000 --> 00:29:51,000
Speaker 3: Was that part of your thinking, Well, we were.

533
00:29:50,880 --> 00:29:53,880
Speaker 1: Hoping nobody wolves are hipped to frat.

534
00:29:54,080 --> 00:29:56,480
Speaker 7: Sorry, yeah, exactly, it was fitting and plus the you

535
00:29:56,480 --> 00:29:59,000
Speaker 7: know race by a she wolf and h.

536
00:29:58,920 --> 00:30:01,440
Speaker 1: There's a statue in in sus Saint Marie Michigan of

537
00:30:01,520 --> 00:30:06,000
Speaker 1: Romulus and Remus suckling from the mother's the wolves teats.

538
00:30:06,080 --> 00:30:09,880
Speaker 4: Ye, do you feel like rolling pictures for the video audiences.

539
00:30:09,680 --> 00:30:12,680
Speaker 3: There on the little.

540
00:30:14,080 --> 00:30:16,239
Speaker 4: You want to start with the then adults? I mean

541
00:30:16,240 --> 00:30:18,640
Speaker 4: they're now what Matt, you said, seven.

542
00:30:18,360 --> 00:30:22,040
Speaker 7: And a half at seven point five months old, and

543
00:30:22,080 --> 00:30:24,080
Speaker 7: they're tipping the scales nearly your past.

544
00:30:24,120 --> 00:30:26,880
Speaker 4: Their little white fluffy pops.

545
00:30:28,440 --> 00:30:33,800
Speaker 1: Cute. Uh let me let me kick off a little bit.

546
00:30:34,720 --> 00:30:37,000
Speaker 1: Despite characters like that Game of Thrones are supposed to

547
00:30:37,040 --> 00:30:41,480
Speaker 1: be up north, right, it is a fictional Uh yeah,

548
00:30:41,880 --> 00:30:44,960
Speaker 1: it's like show in Canada.

549
00:30:46,560 --> 00:30:49,480
Speaker 5: It's a whole world. So there's like up north stuff and.

550
00:30:49,400 --> 00:30:52,960
Speaker 2: Down the family associated with dire Wolves. They're in the north.

551
00:30:53,080 --> 00:30:55,800
Speaker 1: Yes, despite because in my thing I have here. I

552
00:30:55,800 --> 00:30:59,520
Speaker 1: wrote this like, despite characteristics otherwise, dire wolves were Southern animals.

553
00:31:00,560 --> 00:31:04,080
Speaker 1: They ranged throughout the lower forty eight. They liked the

554
00:31:04,120 --> 00:31:06,440
Speaker 1: south and the hot so much that four thousand died

555
00:31:06,440 --> 00:31:11,880
Speaker 1: in La Have I told my story eighteen times about

556
00:31:11,880 --> 00:31:14,240
Speaker 1: my first date with my wife twenty Okay, yeah, at

557
00:31:14,320 --> 00:31:18,520
Speaker 1: least there were dire wolves in Mexico. There were dire

558
00:31:18,560 --> 00:31:22,280
Speaker 1: wolves in South America. I think there's one instance of

559
00:31:22,280 --> 00:31:26,040
Speaker 1: a dire wolf showing up in extreme southern Canada. A

560
00:31:26,080 --> 00:31:28,920
Speaker 1: way to think about it, which I realized looking at maps.

561
00:31:31,800 --> 00:31:36,520
Speaker 1: If you look where turkeys are, that's where dire wolves were.

562
00:31:37,320 --> 00:31:40,200
Speaker 1: Little teeny bit in Toronto, like a little teeny bit

563
00:31:40,320 --> 00:31:41,160
Speaker 1: in Ontario.

564
00:31:42,120 --> 00:31:44,240
Speaker 2: The National Park Service says they were found as far

565
00:31:44,280 --> 00:31:45,160
Speaker 2: north as Alaska.

566
00:31:45,400 --> 00:31:46,120
Speaker 1: That's not true.

567
00:31:46,240 --> 00:31:48,720
Speaker 2: I'm just telling you what the National Park Service says.

568
00:31:48,880 --> 00:31:50,960
Speaker 1: We've debated that and we haven't even talked about it

569
00:31:51,000 --> 00:31:52,600
Speaker 1: with our multiple people.

570
00:31:52,840 --> 00:31:56,120
Speaker 2: But the one guy who debated it said that they

571
00:31:56,120 --> 00:31:58,240
Speaker 2: weren't found at the Lebrea tar pits.

572
00:32:00,680 --> 00:32:03,000
Speaker 1: Who said that Angela Perry.

573
00:32:02,840 --> 00:32:07,280
Speaker 2: No, he was an older fella, got it got mildly

574
00:32:07,360 --> 00:32:09,640
Speaker 2: uncomfortable because I was like, no, I've I've seen him there.

575
00:32:09,840 --> 00:32:11,200
Speaker 3: We talked about that with the old person.

576
00:32:11,320 --> 00:32:17,960
Speaker 2: Yeah, we didn't, and we did. I could probably find it's.

577
00:32:17,680 --> 00:32:22,560
Speaker 1: Not talking about the guy that fishes muskies all the time.

578
00:32:23,560 --> 00:32:24,800
Speaker 2: When we can come back to this.

579
00:32:25,120 --> 00:32:27,480
Speaker 3: I think you're I think you're talking about a dream sequence.

580
00:32:28,520 --> 00:32:31,479
Speaker 7: They're definitely in lebre A lot of them, A lot

581
00:32:31,520 --> 00:32:31,719
Speaker 7: of them.

582
00:32:32,040 --> 00:32:33,760
Speaker 3: I have to tell my story about my first date and.

583
00:32:33,800 --> 00:32:34,560
Speaker 1: My wife all over.

584
00:32:35,000 --> 00:32:35,680
Speaker 2: I know, I know.

585
00:32:37,560 --> 00:32:40,880
Speaker 4: Oh, do you mean seem to have like Jack Carr? No,

586
00:32:41,000 --> 00:32:44,880
Speaker 4: I'm not, Jack said not. Sorry, we can visit this.

587
00:32:45,160 --> 00:32:45,800
Speaker 2: I will find it.

588
00:32:46,840 --> 00:32:48,920
Speaker 5: We know there'ner.

589
00:32:49,720 --> 00:32:53,640
Speaker 1: No Jack Corner. He was on the show Big Dinosaur Guy.

590
00:32:54,280 --> 00:32:55,680
Speaker 1: Spencer was talking about.

591
00:32:55,680 --> 00:32:57,480
Speaker 4: John Corner is composed.

592
00:32:57,560 --> 00:32:59,880
Speaker 1: They seem to have like water Florida and Texas has

593
00:33:00,200 --> 00:33:04,680
Speaker 1: a dire wolf. I don't believe we have good archaeological

594
00:33:04,720 --> 00:33:08,000
Speaker 1: sites showing humans and dire wolves together. How do you

595
00:33:08,040 --> 00:33:09,920
Speaker 1: feel about that statement, Matt.

596
00:33:10,320 --> 00:33:12,480
Speaker 7: I think that's directionally accurate.

597
00:33:15,680 --> 00:33:17,360
Speaker 1: If you took a dire wolf skull and a gray

598
00:33:17,360 --> 00:33:20,959
Speaker 1: wolf skull and you take fifteen measurements on it, this

599
00:33:21,040 --> 00:33:25,320
Speaker 1: is a good little tidbit. Four of those measurements are different.

600
00:33:27,160 --> 00:33:28,560
Speaker 1: I'd like to know what a black bear and a

601
00:33:28,600 --> 00:33:30,920
Speaker 1: grizzly bear. How many measurements are different on their skulls.

602
00:33:31,560 --> 00:33:34,040
Speaker 1: So if you take like somehow like you're doing like

603
00:33:34,600 --> 00:33:41,440
Speaker 1: skull morphology, they have four differences wider, wider, and burlier

604
00:33:41,440 --> 00:33:50,800
Speaker 1: than a gray wolf. Colossal says that ninety nine point

605
00:33:50,840 --> 00:33:55,240
Speaker 1: five percent similarity between a gray wolf and a dire wolf,

606
00:33:55,520 --> 00:33:57,480
Speaker 1: which always sounds like a lot until you think about

607
00:33:57,480 --> 00:34:00,760
Speaker 1: some other things. They say, we have sixty percent. We

608
00:34:01,040 --> 00:34:04,000
Speaker 1: humans have sixty percent genetic similarity with a carrot.

609
00:34:07,600 --> 00:34:08,280
Speaker 3: In this world.

610
00:34:08,280 --> 00:34:11,080
Speaker 1: What I'm pointing out is in this world little do

611
00:34:11,120 --> 00:34:13,440
Speaker 1: you know what I mean? Like in this world, little

612
00:34:13,480 --> 00:34:17,640
Speaker 1: things get different. I mean, like the differences of when

613
00:34:17,680 --> 00:34:22,040
Speaker 1: you start talking about these like numbers, what you're arguing

614
00:34:22,040 --> 00:34:24,600
Speaker 1: about is what comes after the last decimal point.

615
00:34:24,920 --> 00:34:27,759
Speaker 7: And we're talking about billions of base pairs in the genome. Okay, right,

616
00:34:27,840 --> 00:34:29,719
Speaker 7: So when you say ninety nine point five percent, you're

617
00:34:29,760 --> 00:34:32,239
Speaker 7: still looking at point five percent of three billion base pairs.

618
00:34:32,400 --> 00:34:34,200
Speaker 1: Yeah, a lot, but I mean like hearing it in

619
00:34:34,239 --> 00:34:37,000
Speaker 1: the percentage thing that gives you like a different idea.

620
00:34:36,719 --> 00:34:39,279
Speaker 7: And the percentage thing can be is one way to

621
00:34:39,280 --> 00:34:42,440
Speaker 7: explain it is a hard way to conceptualize it, because

622
00:34:42,440 --> 00:34:45,160
Speaker 7: at the same time, you could say African elephant and

623
00:34:45,200 --> 00:34:47,840
Speaker 7: an Asian elephant are early ninety eight percent similar.

624
00:34:48,680 --> 00:34:51,640
Speaker 1: Oh well, because I was just going to point out

625
00:34:51,680 --> 00:34:58,120
Speaker 1: that according and people debate these counts humans and chimpanzees

626
00:34:59,040 --> 00:35:01,880
Speaker 1: ninety eight point eight percent similar m.

627
00:35:03,760 --> 00:35:07,120
Speaker 7: And I think an important part of to remember when

628
00:35:07,120 --> 00:35:09,960
Speaker 7: we talk about percentages of genome is what part of

629
00:35:10,000 --> 00:35:13,120
Speaker 7: the genome is coding for very specific traits that end

630
00:35:13,239 --> 00:35:17,800
Speaker 7: up expressing as differences between us and a chimpanzee size, strength,

631
00:35:18,080 --> 00:35:24,279
Speaker 7: you know, musculature, hair, some pretty significant differences, so that

632
00:35:24,400 --> 00:35:26,600
Speaker 7: that doesn't require a huge piece of the genome to

633
00:35:26,640 --> 00:35:29,040
Speaker 7: make those changes. A lot of the genome actually doesn't

634
00:35:29,040 --> 00:35:31,760
Speaker 7: code for anything. It's just sort of, you know, it's

635
00:35:31,800 --> 00:35:34,440
Speaker 7: what we call non coding regions that we don't fully understand.

636
00:35:34,560 --> 00:35:36,040
Speaker 3: That's the part that's the part.

637
00:35:35,840 --> 00:35:38,279
Speaker 1: Of this that puzzles me that I don't get, And

638
00:35:38,320 --> 00:35:40,800
Speaker 1: that's probably why you can't look up how much genetic

639
00:35:40,840 --> 00:35:43,440
Speaker 1: similarity do we have with carrots, which I was doing

640
00:35:43,840 --> 00:35:46,480
Speaker 1: because it's like a lot of people areaying that's not

641
00:35:46,520 --> 00:35:47,920
Speaker 1: really a good way of looking at it.

642
00:35:48,080 --> 00:35:51,640
Speaker 7: I've seen a few people that look like a carrot was.

643
00:35:51,719 --> 00:35:52,319
Speaker 7: Do you know.

644
00:35:54,239 --> 00:35:57,360
Speaker 4: The form of former his genetics similarity might be like

645
00:35:57,440 --> 00:36:00,240
Speaker 4: sixty one percent sixty.

646
00:36:02,400 --> 00:36:09,439
Speaker 1: Okay, explain that point five percent? Like it like to you, Matt,

647
00:36:09,520 --> 00:36:13,760
Speaker 1: explain that if there's a gray wolf and a dire wolf,

648
00:36:13,880 --> 00:36:16,000
Speaker 1: and it seems like a gray wolf and a dire

649
00:36:16,000 --> 00:36:20,600
Speaker 1: wolf for ninety nine point five percent similar, what is

650
00:36:20,640 --> 00:36:22,960
Speaker 1: the point five and is it accurate to go and

651
00:36:23,000 --> 00:36:25,839
Speaker 1: look and say, like, I'll think about this with chimpanzees,

652
00:36:26,480 --> 00:36:28,360
Speaker 1: just to set up the question a little more specifically,

653
00:36:28,400 --> 00:36:31,399
Speaker 1: because everybody knows humans because you're one, we're all one,

654
00:36:32,040 --> 00:36:34,520
Speaker 1: and we know chimps because we've been watching nature documentaries

655
00:36:34,520 --> 00:36:38,200
Speaker 1: for a long time. So if humans and chimps are

656
00:36:38,280 --> 00:36:42,840
Speaker 1: ninety eight point eight percent similar, is it fair to

657
00:36:42,920 --> 00:36:48,279
Speaker 1: say two hundred and forty genes separate us from chimps?

658
00:36:48,320 --> 00:36:49,600
Speaker 3: Or is that a dumb way to look at it?

659
00:36:51,440 --> 00:36:54,279
Speaker 7: I mean, we're waiting into dangerous territory. Because I'm more

660
00:36:54,600 --> 00:36:57,400
Speaker 7: a conservation guy than a geneticist. I just play a

661
00:36:57,440 --> 00:36:59,840
Speaker 7: geneticis at back home, right. I work with really some

662
00:37:00,160 --> 00:37:03,359
Speaker 7: people like Beth who really explain these things really well.

663
00:37:03,400 --> 00:37:06,239
Speaker 7: But yes, I think it would be an oversimplification to

664
00:37:06,239 --> 00:37:08,799
Speaker 7: say two hundred and forty genes separate us from a chimpanzee.

665
00:37:09,239 --> 00:37:12,440
Speaker 7: Yeah that said, I mean when we talk about the

666
00:37:12,440 --> 00:37:15,080
Speaker 7: differences between dire wolves and gray wolves, well, we're really

667
00:37:15,080 --> 00:37:17,719
Speaker 7: looking for what are the core you know types, What

668
00:37:17,760 --> 00:37:20,480
Speaker 7: are those key areas of the genome in that point

669
00:37:20,480 --> 00:37:23,440
Speaker 7: five percent that we need to target in order to

670
00:37:23,560 --> 00:37:28,239
Speaker 7: confer the difference the primary difference, which is, like you said,

671
00:37:28,280 --> 00:37:31,600
Speaker 7: you know, sort of a robustness of a size, a musculature,

672
00:37:31,840 --> 00:37:35,560
Speaker 7: a hair difference, a coat difference that we've identified. And

673
00:37:36,000 --> 00:37:38,480
Speaker 7: how can we then identify those within that point five

674
00:37:38,520 --> 00:37:41,960
Speaker 7: percent and pick the few most impactful changes.

675
00:37:43,680 --> 00:37:45,799
Speaker 1: How do you decide look at.

676
00:37:48,480 --> 00:37:53,000
Speaker 3: Let's look at color? Why white?

677
00:37:53,880 --> 00:37:56,640
Speaker 7: So if you look at the genome of the dire wolf.

678
00:37:56,640 --> 00:37:58,600
Speaker 7: And this is a really great question and one that

679
00:37:58,680 --> 00:38:01,120
Speaker 7: kind of goes back to know when you talk about

680
00:38:01,160 --> 00:38:04,360
Speaker 7: having doctor Perry on earlier. Doctor Perry was part of

681
00:38:04,640 --> 00:38:06,520
Speaker 7: a paper that came out of twenty twenty one along

682
00:38:06,520 --> 00:38:11,920
Speaker 7: with Best Shapiro or chief scientist, who sequenced the genome

683
00:38:11,960 --> 00:38:14,359
Speaker 7: of the direwolf, and they only got what we would

684
00:38:14,400 --> 00:38:16,520
Speaker 7: say is about a zero point two five percent cover

685
00:38:16,640 --> 00:38:19,320
Speaker 7: or point twenty five x coverage, so sort of on average,

686
00:38:19,400 --> 00:38:21,400
Speaker 7: they were able to sequence a quarter of the genome

687
00:38:21,440 --> 00:38:24,879
Speaker 7: one time, so not a lot of data. We went

688
00:38:24,920 --> 00:38:29,560
Speaker 7: back and we started and found two samples of dire

689
00:38:29,640 --> 00:38:33,240
Speaker 7: wolf specimens that we were able to sample and sequence

690
00:38:33,239 --> 00:38:36,040
Speaker 7: their DNA, one from a seventy two thousand year old

691
00:38:36,040 --> 00:38:39,239
Speaker 7: skull and another from a from a thirteen thousand year

692
00:38:39,280 --> 00:38:42,520
Speaker 7: old tooth. We ended up producing what we would call

693
00:38:42,560 --> 00:38:46,120
Speaker 7: thirteen x coverage, so on average we sequenced every base

694
00:38:46,160 --> 00:38:48,439
Speaker 7: pair in that genome about thirteen times, so that gives

695
00:38:48,520 --> 00:38:51,000
Speaker 7: us a really robust data set as compared to the

696
00:38:51,040 --> 00:38:54,040
Speaker 7: twenty twenty one paper. What we identified when we have

697
00:38:54,239 --> 00:38:59,120
Speaker 7: this new robust data set is that they're between these

698
00:38:59,160 --> 00:39:02,200
Speaker 7: two investments that one was from Idaho, one from Ohio,

699
00:39:02,280 --> 00:39:04,520
Speaker 7: one from seventy two thousand years old, and one that's

700
00:39:04,520 --> 00:39:07,759
Speaker 7: thirteen thousand years old. Both code for the same light

701
00:39:07,920 --> 00:39:12,680
Speaker 7: coat color, not necessarily stark white like what we ended

702
00:39:12,760 --> 00:39:15,160
Speaker 7: up getting, but they could have and we don't know that.

703
00:39:15,680 --> 00:39:18,279
Speaker 1: But did you guys tweak it to make it white? No,

704
00:39:18,480 --> 00:39:20,600
Speaker 1: So it's because it's like such a wild I mean,

705
00:39:20,640 --> 00:39:24,359
Speaker 1: you have to admit, like the Game of Thrones thing

706
00:39:24,440 --> 00:39:27,000
Speaker 1: and people like the writer is involved with you guys,

707
00:39:27,239 --> 00:39:28,440
Speaker 1: like he makes them white.

708
00:39:28,680 --> 00:39:31,200
Speaker 7: Yep, well he made one of them white.

709
00:39:30,920 --> 00:39:31,680
Speaker 3: Like for the show.

710
00:39:31,800 --> 00:39:34,080
Speaker 7: Yeah, there's five of them that are sort of characters

711
00:39:34,080 --> 00:39:35,480
Speaker 7: in that show. Only one of them is white. The

712
00:39:35,560 --> 00:39:40,759
Speaker 7: other have very typical morph I'm sorry, wolf or coat colors.

713
00:39:40,960 --> 00:39:44,399
Speaker 1: So but when it was born, you didn't know what

714
00:39:44,440 --> 00:39:45,600
Speaker 1: color would be, knew it?

715
00:39:45,920 --> 00:39:48,440
Speaker 7: Okay, Yeah, we already knew because we had made that change.

716
00:39:48,520 --> 00:39:50,920
Speaker 1: So so you had like purposefully made it white.

717
00:39:51,360 --> 00:39:54,920
Speaker 7: Yes, Okay, So when we identified that definitely over this

718
00:39:55,600 --> 00:39:59,560
Speaker 7: sixty thousand years of divergence and this huge geographic distribution

719
00:39:59,640 --> 00:40:05,359
Speaker 7: between Ohio and Idaho, both animals coated for a very

720
00:40:05,440 --> 00:40:08,160
Speaker 7: light colored coat as compared to what we would have

721
00:40:08,200 --> 00:40:09,120
Speaker 7: seen in a gray wolf.

722
00:40:10,880 --> 00:40:13,040
Speaker 3: Like perhaps it was like kyot colored.

723
00:40:12,800 --> 00:40:14,279
Speaker 7: Could have been. Yeah, it could have been where it

724
00:40:14,320 --> 00:40:16,279
Speaker 7: could be even lighter like this white we saw.

725
00:40:17,200 --> 00:40:21,760
Speaker 3: We said, mountain pale if you look over your shoulder.

726
00:40:21,120 --> 00:40:23,600
Speaker 7: Yeah, I mean that could certainly be it, or it

727
00:40:23,640 --> 00:40:26,880
Speaker 7: could be white. You know, there there are reasons for that,

728
00:40:26,880 --> 00:40:30,640
Speaker 7: that white coat that could have persisted. But since we

729
00:40:30,680 --> 00:40:34,239
Speaker 7: saw this common trait between across space and time and

730
00:40:34,280 --> 00:40:37,080
Speaker 7: these two specimens We said, well, that's certainly interesting. We

731
00:40:37,120 --> 00:40:40,040
Speaker 7: only have two sequences, so that means one hundred percent

732
00:40:40,080 --> 00:40:44,000
Speaker 7: of what we sampled, you know, had this coat color.

733
00:40:44,040 --> 00:40:45,719
Speaker 7: So we said, well we should code for that. The

734
00:40:45,880 --> 00:40:49,080
Speaker 7: trick there is that we also identified very close to

735
00:40:49,120 --> 00:40:52,160
Speaker 7: that gene and what we had seen if you use

736
00:40:52,200 --> 00:40:54,920
Speaker 7: the genetic background of a wolf, wolfs that have that

737
00:40:54,960 --> 00:41:00,640
Speaker 7: same mutation also often can have an issue with deafness

738
00:41:00,719 --> 00:41:04,040
Speaker 7: or blindness. So we said, well, we don't want to

739
00:41:04,160 --> 00:41:06,799
Speaker 7: use the exact variant from the dire wolf genome that

740
00:41:06,840 --> 00:41:11,440
Speaker 7: we sequence because it could confer a deafness or blindness issue.

741
00:41:11,560 --> 00:41:14,600
Speaker 1: Can you explain that more? I don't understand. I don't

742
00:41:14,680 --> 00:41:16,279
Speaker 1: don't have the background to understand what you mean.

743
00:41:17,040 --> 00:41:20,600
Speaker 7: The dire wolf had this specific trait. We know when

744
00:41:20,600 --> 00:41:23,760
Speaker 7: we've seen that trait in the in a gray wolf,

745
00:41:24,280 --> 00:41:27,120
Speaker 7: like did it a naturally occur? It isn't naturally occurring.

746
00:41:28,200 --> 00:41:30,120
Speaker 7: It would be closely associated with.

747
00:41:30,120 --> 00:41:34,520
Speaker 6: So like a white gray wolf would be more likely to.

748
00:41:34,760 --> 00:41:38,440
Speaker 7: Yeah, exactly have that those issues. So we made a decision.

749
00:41:38,480 --> 00:41:40,880
Speaker 1: We said, well, so that's that's not something that was

750
00:41:40,920 --> 00:41:43,560
Speaker 1: found out. That's not something that people found out by

751
00:41:43,560 --> 00:41:45,840
Speaker 1: trying to insert that into.

752
00:41:46,200 --> 00:41:48,560
Speaker 7: Now that's just from people that had studied Kana genetics

753
00:41:48,600 --> 00:41:50,080
Speaker 7: across Yeah, across that.

754
00:41:50,400 --> 00:41:52,480
Speaker 1: I didn't understand. I was like, because I was like,

755
00:41:52,520 --> 00:41:55,160
Speaker 1: that would be a lot of experimentation to real exactly.

756
00:41:55,760 --> 00:41:58,279
Speaker 7: So our computational team is able to sort of run

757
00:41:58,320 --> 00:42:00,720
Speaker 7: these simulations that tell us what they think the gene

758
00:42:00,800 --> 00:42:03,520
Speaker 7: will code for, what it could also be associated with.

759
00:42:03,560 --> 00:42:05,919
Speaker 7: What could be an off target effect if you made

760
00:42:05,920 --> 00:42:09,000
Speaker 7: that edit and you know it had an unintended consequence.

761
00:42:09,480 --> 00:42:12,399
Speaker 7: So we elected. You'll you'll see in our press release

762
00:42:12,480 --> 00:42:14,240
Speaker 7: or in our news we talked about we made twenty

763
00:42:14,320 --> 00:42:18,480
Speaker 7: edits across fifteen genes with fifteen specific diabolf variants. I mean,

764
00:42:18,600 --> 00:42:21,040
Speaker 7: there are five variants that we edited for that were

765
00:42:21,040 --> 00:42:23,719
Speaker 7: not actually dire wolf. What we did is we went

766
00:42:23,719 --> 00:42:26,759
Speaker 7: and found analogous genes in closely related species. In the

767
00:42:26,760 --> 00:42:29,680
Speaker 7: case of the white coat, it was from domestic dogs.

768
00:42:30,680 --> 00:42:33,680
Speaker 7: We chose that specifically because we wanted to confer the

769
00:42:33,680 --> 00:42:39,000
Speaker 7: coat phenotype without risking welfare of the animal. So we

770
00:42:39,080 --> 00:42:43,279
Speaker 7: made a decision that said the safest way in order

771
00:42:43,320 --> 00:42:46,080
Speaker 7: to do this functional the extinction project we were working

772
00:42:46,120 --> 00:42:49,680
Speaker 7: on without risking the animal's health or welfare was took

773
00:42:49,719 --> 00:42:53,880
Speaker 7: it from a dog exactly. So that's why you know,

774
00:42:53,880 --> 00:42:55,880
Speaker 7: people go is it one hundred percent? Direbolf when you go, well,

775
00:42:55,920 --> 00:42:57,680
Speaker 7: number one, this whole hundred percent thing we just talked

776
00:42:57,719 --> 00:43:01,120
Speaker 7: about percentages of relatedness. It's kind of hard, it's not

777
00:43:01,160 --> 00:43:03,160
Speaker 7: a very clean way to explain it. On hundred percent

778
00:43:03,200 --> 00:43:05,080
Speaker 7: is kind of a bullshit argument.

779
00:43:06,040 --> 00:43:08,560
Speaker 1: But it's why is that not a good way looking

780
00:43:08,600 --> 00:43:08,960
Speaker 1: at it?

781
00:43:09,000 --> 00:43:11,680
Speaker 7: Well, just the variation between two individuals like you and

782
00:43:11,760 --> 00:43:14,880
Speaker 7: I have a significant variation. We're not one hundred percent identical.

783
00:43:15,280 --> 00:43:20,080
Speaker 7: So if aliens came and abducted you and I, right,

784
00:43:20,280 --> 00:43:23,120
Speaker 7: they would not then go and clone a human and

785
00:43:23,200 --> 00:43:26,399
Speaker 7: make current right. They would say, well, all humans look

786
00:43:26,480 --> 00:43:29,280
Speaker 7: like you and I. Well, we know that we represent

787
00:43:29,320 --> 00:43:32,120
Speaker 7: a very small portion of the genetic variability across the globe.

788
00:43:32,560 --> 00:43:34,439
Speaker 1: The one's got an honorary doctorate.

789
00:43:35,440 --> 00:43:36,839
Speaker 7: That's coded in there, I'm sure.

790
00:43:36,920 --> 00:43:39,680
Speaker 5: Yeah, so did.

791
00:43:39,600 --> 00:43:40,960
Speaker 1: You probably have a legit one right?

792
00:43:41,600 --> 00:43:44,799
Speaker 7: Not a doctor? No. I stopped at the master's level

793
00:43:44,840 --> 00:43:50,160
Speaker 7: and said this isn't for me. Yeah, So we we

794
00:43:50,320 --> 00:43:53,680
Speaker 7: tried to avoid this one hundred percent because that's really cloning, right,

795
00:43:53,960 --> 00:43:56,840
Speaker 7: We're not creating the identical individual we sequenced.

796
00:43:57,080 --> 00:44:01,000
Speaker 1: Yeah, because I think this is the thing that people

797
00:44:01,040 --> 00:44:03,840
Speaker 1: don't understand and it's hard for me to understand, is

798
00:44:03,880 --> 00:44:09,800
Speaker 1: that there's an idea from watching like Jurassic Park or whatever,

799
00:44:10,200 --> 00:44:14,640
Speaker 1: that you're able to pluck some living thing out of

800
00:44:14,680 --> 00:44:18,720
Speaker 1: a dire wolf bone and like add it into something.

801
00:44:19,239 --> 00:44:23,880
Speaker 1: It's more like you're looking at trying to think of

802
00:44:23,920 --> 00:44:27,879
Speaker 1: it like a I shouldn't do an analogy, but it's

803
00:44:27,960 --> 00:44:31,200
Speaker 1: like you're you're looking at a someone's got a paint

804
00:44:31,320 --> 00:44:36,040
Speaker 1: sample and you're like, oh, I could make something that color.

805
00:44:37,280 --> 00:44:38,880
Speaker 1: I don't know how they made that that color, but

806
00:44:38,960 --> 00:44:43,000
Speaker 1: I could make something that color, right, yep. And in

807
00:44:43,040 --> 00:44:44,759
Speaker 1: the end I'd look and be like, wow, that is

808
00:44:44,760 --> 00:44:46,520
Speaker 1: that kind of same color, but it might have different

809
00:44:46,520 --> 00:44:49,120
Speaker 1: constituent parts exactly. Is that a good analogy?

810
00:44:49,120 --> 00:44:50,759
Speaker 7: I think that's pretty good analogy. I might use it

811
00:44:50,800 --> 00:44:51,759
Speaker 7: later too. It's good.

812
00:44:52,920 --> 00:44:54,279
Speaker 3: Yeah, let me refine it for a day.

813
00:44:54,600 --> 00:44:58,239
Speaker 5: A doctor should have a more refined The way I.

814
00:44:58,200 --> 00:45:00,880
Speaker 7: Think about it is when we seek it's ancient DNA,

815
00:45:01,000 --> 00:45:03,960
Speaker 7: which is what best specialty is. Right when we go in,

816
00:45:04,200 --> 00:45:06,960
Speaker 7: what we're really sequencing is we're pulling out like a

817
00:45:07,000 --> 00:45:11,160
Speaker 7: two billion piece puzzle from that specimen. The DNA has

818
00:45:11,160 --> 00:45:14,000
Speaker 7: been fragmented over time and time, and you know, things

819
00:45:14,000 --> 00:45:16,319
Speaker 7: have been introduced like bacteria that chop up DNA into

820
00:45:16,360 --> 00:45:20,080
Speaker 7: small pieces. All we get out is we're able to

821
00:45:20,120 --> 00:45:22,320
Speaker 7: sequence all that, and we get these two billion puzzle

822
00:45:22,360 --> 00:45:23,799
Speaker 7: pieces and we have to figure out how to put

823
00:45:23,840 --> 00:45:26,799
Speaker 7: them back together. But we don't have the reference on

824
00:45:26,840 --> 00:45:29,400
Speaker 7: the cover of the puzzle, right, so we have to

825
00:45:29,480 --> 00:45:32,959
Speaker 7: use artificial intelligence machine learning algorithms in order to help

826
00:45:33,120 --> 00:45:34,600
Speaker 7: tell us how to do that. We also have to

827
00:45:34,600 --> 00:45:37,719
Speaker 7: align it to a close living relative, so then we go, well,

828
00:45:37,719 --> 00:45:39,080
Speaker 7: we know a dire wolf was sort of like a

829
00:45:39,120 --> 00:45:41,680
Speaker 7: gray wolf, so we can start building towards a gray wolf.

830
00:45:43,280 --> 00:45:45,319
Speaker 7: Then there are specific parts that we go, well, we

831
00:45:45,400 --> 00:45:47,440
Speaker 7: know it wasn't totally gray wolf, so we have to

832
00:45:47,480 --> 00:45:51,160
Speaker 7: start making educated decisions. Here almost guesses as to what

833
00:45:51,400 --> 00:45:55,000
Speaker 7: puzzle piece goes where, and so that's when you won't

834
00:45:55,080 --> 00:45:58,080
Speaker 7: end up with one hundred percent accurate representation of the

835
00:45:58,160 --> 00:46:01,560
Speaker 7: animal you sequence. Because there was non viable DNA in there,

836
00:46:01,560 --> 00:46:03,879
Speaker 7: you can't clone it. And now we're trying to put

837
00:46:03,880 --> 00:46:05,880
Speaker 7: something back together without a clear picture of what it

838
00:46:05,880 --> 00:46:06,399
Speaker 7: should have been.

839
00:46:07,400 --> 00:46:10,120
Speaker 1: So let me hate you with this one. Let's say

840
00:46:10,360 --> 00:46:13,000
Speaker 1: you took one of you took one of your animals

841
00:46:13,080 --> 00:46:19,839
Speaker 1: Romulus or Remus, and you uh have them forbid something

842
00:46:19,880 --> 00:46:22,520
Speaker 1: were to happen to it, and you boiled its skull

843
00:46:22,560 --> 00:46:26,080
Speaker 1: down and cleaned it up and threw it into.

844
00:46:25,880 --> 00:46:27,280
Speaker 3: A pile of la Brea tar pits.

845
00:46:28,520 --> 00:46:28,800
Speaker 1: Right.

846
00:46:28,880 --> 00:46:31,680
Speaker 3: Yeah, and then there's the person there sorting skulls.

847
00:46:32,160 --> 00:46:34,399
Speaker 1: What do you think the likelihood is that they would

848
00:46:34,400 --> 00:46:36,279
Speaker 1: grab that skull and they would throw in the dire

849
00:46:36,320 --> 00:46:36,839
Speaker 1: wolf pile.

850
00:46:37,320 --> 00:46:39,000
Speaker 7: That's a good question. I haven't thought about it that way.

851
00:46:39,000 --> 00:46:42,200
Speaker 7: I would do it in a much less uh fatalistic way.

852
00:46:42,520 --> 00:46:45,480
Speaker 7: I would. We can use uh you know, high tech

853
00:46:45,520 --> 00:46:48,600
Speaker 7: CT image in an actually three D print their skeleton,

854
00:46:48,640 --> 00:46:50,239
Speaker 7: which is one of the things we will be doing

855
00:46:50,360 --> 00:46:52,560
Speaker 7: is as a mature as they kind of come into

856
00:46:52,560 --> 00:46:55,640
Speaker 7: their full size, we're using CT imaging in order to

857
00:46:55,719 --> 00:46:58,560
Speaker 7: kind of get a clear picture of morphology from a skeleton.

858
00:46:58,680 --> 00:47:02,120
Speaker 6: Yeah, we're gonna like you mentioned that, we know, like

859
00:47:02,400 --> 00:47:06,239
Speaker 6: there's four skull measurements that are different, Like did you

860
00:47:06,400 --> 00:47:07,719
Speaker 6: build that into these?

861
00:47:08,760 --> 00:47:12,759
Speaker 7: Yeah? The diurbl farians we picked were specific to changes

862
00:47:12,840 --> 00:47:16,080
Speaker 7: that we knew would be associated with those few morphological

863
00:47:16,080 --> 00:47:18,760
Speaker 7: differences between the species, so you could.

864
00:47:18,560 --> 00:47:20,600
Speaker 1: You were you looked in at like what is with

865
00:47:20,760 --> 00:47:24,000
Speaker 1: the wide skull, the strong skull, or whatever you put it.

866
00:47:25,320 --> 00:47:27,080
Speaker 1: You know, we're talking about dogs, mingol this is one

867
00:47:27,160 --> 00:47:29,239
Speaker 1: and reading all the coverage about your projects, why did

868
00:47:29,239 --> 00:47:31,160
Speaker 1: the dogs get Why did the dog that birth?

869
00:47:31,280 --> 00:47:32,680
Speaker 3: Why was it a cesarean section?

870
00:47:33,680 --> 00:47:36,400
Speaker 7: Well, so it's a good question. I So one of

871
00:47:36,400 --> 00:47:38,840
Speaker 7: the things we did is while we were starting this project, I,

872
00:47:39,200 --> 00:47:40,759
Speaker 7: me and my team we put together this one hundred

873
00:47:40,800 --> 00:47:43,400
Speaker 7: and sixty page document that is the Animal Care Manual

874
00:47:43,480 --> 00:47:45,640
Speaker 7: of the Direwolf. Right, so we wrote an animal care

875
00:47:45,680 --> 00:47:48,319
Speaker 7: and management manual for species that nobody's ever worked with.

876
00:47:48,360 --> 00:47:52,439
Speaker 7: That's twelve thousand years extinct, super fun project. In there,

877
00:47:52,480 --> 00:47:55,799
Speaker 7: we had very specific uh protocols for how would we

878
00:47:55,880 --> 00:47:58,680
Speaker 7: birth this animal. Well, one of the fears was is

879
00:47:58,960 --> 00:48:02,160
Speaker 7: we don't know the fetal development rate and the size

880
00:48:02,200 --> 00:48:06,000
Speaker 7: of a newborn dire wolf. So we had one contingency

881
00:48:06,120 --> 00:48:09,839
Speaker 7: was cesarean section. The other plan was natural birth. As

882
00:48:09,880 --> 00:48:13,360
Speaker 7: we got closer, we were taking skull measurements via ultrasound

883
00:48:13,880 --> 00:48:16,520
Speaker 7: and comparing that to the pelvic opening of our surrogate.

884
00:48:16,719 --> 00:48:19,600
Speaker 7: Turns out would have been totally fine. We could have

885
00:48:19,719 --> 00:48:25,399
Speaker 7: passed those those pups naturally. That however, day sixty two

886
00:48:25,600 --> 00:48:27,360
Speaker 7: was sort of our cutoff. We said, if she doesn't

887
00:48:27,400 --> 00:48:29,959
Speaker 7: give birth by six day sixty two, they're gestations about

888
00:48:30,000 --> 00:48:34,080
Speaker 7: sixty to sixty three days. But using cloning technology, usually

889
00:48:34,080 --> 00:48:37,480
Speaker 7: you're a day or two ahead, right because the embryo

890
00:48:37,520 --> 00:48:43,239
Speaker 7: has developed in vitro before you've transferred it, So say

891
00:48:43,320 --> 00:48:45,279
Speaker 7: day sixty two might be more like day sixty four,

892
00:48:45,360 --> 00:48:48,760
Speaker 7: day sixty five. So we just had just like most humans,

893
00:48:48,800 --> 00:48:51,920
Speaker 7: sort of their doctor, if they're pregnant, will tell them, hey,

894
00:48:51,960 --> 00:48:53,799
Speaker 7: if you don't give birth by this date, we will

895
00:48:53,800 --> 00:48:57,600
Speaker 7: induce you. Well, we don't really do inductions with dogs.

896
00:48:57,840 --> 00:49:01,560
Speaker 7: The safest way to remove all the variables was just

897
00:49:01,600 --> 00:49:04,680
Speaker 7: a cesarean section, and it's a very common procedure dogs,

898
00:49:04,719 --> 00:49:07,000
Speaker 7: and we have you know, we had an amazing team

899
00:49:07,000 --> 00:49:09,360
Speaker 7: of surgeons that were able to do it, and you know,

900
00:49:09,600 --> 00:49:12,400
Speaker 7: I think the results speak for themselves. We remove those variables,

901
00:49:12,440 --> 00:49:15,279
Speaker 7: we give health, give birth to two healthy puppies, a

902
00:49:15,280 --> 00:49:19,120
Speaker 7: third on a second litter, and then and then all

903
00:49:19,120 --> 00:49:21,239
Speaker 7: the moms you know, ended up great and now they're

904
00:49:21,480 --> 00:49:24,040
Speaker 7: in their forever homes, you know, living life and.

905
00:49:24,080 --> 00:49:26,520
Speaker 6: When you say a second litter, like did you was

906
00:49:26,560 --> 00:49:29,960
Speaker 6: there only two puppies in? Like you weren't trying to

907
00:49:31,040 --> 00:49:32,000
Speaker 6: grow six of them?

908
00:49:32,120 --> 00:49:34,960
Speaker 7: It was like, well read, embryology is the numbers game.

909
00:49:35,120 --> 00:49:37,800
Speaker 7: If you've you know, know anybody it's gone through IVF,

910
00:49:38,200 --> 00:49:40,280
Speaker 7: you know years ago that used to put multiple embryos

911
00:49:40,440 --> 00:49:44,120
Speaker 7: into into a mom. The same idea with us is

912
00:49:44,200 --> 00:49:48,040
Speaker 7: when we're transferring embryos into a surrogate, we're transferring, you know,

913
00:49:48,560 --> 00:49:52,400
Speaker 7: twenty five embryos understanding that only two to six of

914
00:49:52,400 --> 00:49:54,719
Speaker 7: those would take, and in our case we got two

915
00:49:54,840 --> 00:50:01,239
Speaker 7: of them. We had another female that was had was

916
00:50:01,280 --> 00:50:03,719
Speaker 7: pregnant with a second letter and that's where Callisi, our third,

917
00:50:03,920 --> 00:50:07,880
Speaker 7: our female dire wolf was born in January. She actually

918
00:50:07,880 --> 00:50:10,400
Speaker 7: had a second puppy that she was born with. That

919
00:50:10,480 --> 00:50:14,319
Speaker 7: puppy unfortunately pass away in day ten of antritis, so

920
00:50:14,600 --> 00:50:17,880
Speaker 7: basically a perforated gut, and she got septically ill, which

921
00:50:17,920 --> 00:50:20,120
Speaker 7: is you know, we did a lot of looking and

922
00:50:20,200 --> 00:50:22,080
Speaker 7: we were able to determine it wasn't related to any

923
00:50:22,120 --> 00:50:25,480
Speaker 7: sort of effects of editing or cloning. It was sort

924
00:50:25,480 --> 00:50:28,360
Speaker 7: of an unfortunate event of puppy development that we see,

925
00:50:28,560 --> 00:50:32,279
Speaker 7: you know, a certain level of mortality. So that's how

926
00:50:32,280 --> 00:50:34,360
Speaker 7: we got to three. We originally thought we were going

927
00:50:34,400 --> 00:50:34,839
Speaker 7: to have four.

928
00:50:35,400 --> 00:50:37,160
Speaker 2: Will the dire wolves be fertile?

929
00:50:37,920 --> 00:50:41,799
Speaker 7: Yes, yeah, they would, They would be very fertile. We

930
00:50:42,000 --> 00:50:45,320
Speaker 7: have specific strategies we can deploy in order to ensure

931
00:50:45,440 --> 00:50:47,239
Speaker 7: that they would only breed when we intend to breed.

932
00:50:47,320 --> 00:50:49,960
Speaker 7: Right now, our interest is not in breeding at the moment,

933
00:50:50,560 --> 00:50:54,200
Speaker 7: but we could use things like contraceptive strategies or reproductive

934
00:50:54,239 --> 00:50:56,360
Speaker 7: management of you know, time in when the female is

935
00:50:56,400 --> 00:50:58,040
Speaker 7: with the pack or outside of the pack.

936
00:51:00,760 --> 00:51:02,719
Speaker 2: Can you give us some other examples of things that

937
00:51:02,760 --> 00:51:05,560
Speaker 2: are in this manual for raising a dire wolf? And

938
00:51:05,600 --> 00:51:07,000
Speaker 2: how do I get my hands on one?

939
00:51:08,200 --> 00:51:09,760
Speaker 7: The manual or the dire the manual?

940
00:51:10,280 --> 00:51:12,920
Speaker 2: I'm ment well both, but I'm I'm let's settle for

941
00:51:12,920 --> 00:51:13,400
Speaker 2: a manual.

942
00:51:13,480 --> 00:51:15,440
Speaker 7: The really cool thing is we published the manuals on

943
00:51:15,440 --> 00:51:17,440
Speaker 7: our website. So if you go to the fossil dot

944
00:51:17,480 --> 00:51:19,680
Speaker 7: com fors dire Well f you'll see there is a

945
00:51:19,800 --> 00:51:25,760
Speaker 7: downloadable animal care manual there. So it's everything from the

946
00:51:26,200 --> 00:51:28,640
Speaker 7: partuition issues. So how do you take care of a

947
00:51:29,800 --> 00:51:33,080
Speaker 7: of a whelping litter of puppies to how do we

948
00:51:33,120 --> 00:51:34,960
Speaker 7: manage them socially as they grow up. What is the

949
00:51:35,000 --> 00:51:38,120
Speaker 7: diet that they should be eating as they grow, What

950
00:51:38,200 --> 00:51:41,560
Speaker 7: are the expected milestones developmental milestones. These are things we're

951
00:51:41,600 --> 00:51:43,800
Speaker 7: sort of extrapolating from our worked with gray wolves and

952
00:51:43,840 --> 00:51:45,240
Speaker 7: other wild canids.

953
00:51:45,000 --> 00:51:46,759
Speaker 3: Who nursed them. The dog didn't nurse them.

954
00:51:46,800 --> 00:51:49,160
Speaker 7: So the dog did nurse them for the first few days,

955
00:51:49,600 --> 00:51:53,640
Speaker 7: and so Romulus and Remus there surrogate was a very

956
00:51:53,680 --> 00:51:56,440
Speaker 7: attentive mother, and she got to be so attentive that

957
00:51:56,480 --> 00:51:59,560
Speaker 7: she If you've ever had puppies with a mom, sometimes

958
00:51:59,600 --> 00:52:01,759
Speaker 7: they get to be a little nervous and they pick

959
00:52:01,800 --> 00:52:04,000
Speaker 7: them up and they just poke them too much and

960
00:52:04,160 --> 00:52:06,920
Speaker 7: starts to interrupt their feeding and sleeping cycles. So we

961
00:52:07,000 --> 00:52:10,200
Speaker 7: just said, you know, it's similar to the precautionary approach

962
00:52:10,239 --> 00:52:12,520
Speaker 7: we took with cesarean section. We said, we're gonna pull

963
00:52:12,560 --> 00:52:15,560
Speaker 7: them and we're gonna hand rear them on bottles. So

964
00:52:15,600 --> 00:52:17,520
Speaker 7: on day three we ended up pulling them.

965
00:52:17,760 --> 00:52:19,719
Speaker 1: Where's that dog. Now, that'd be a valuable dog.

966
00:52:19,760 --> 00:52:21,799
Speaker 7: It's a very valuable dog. And it was sort of.

967
00:52:21,760 --> 00:52:24,640
Speaker 3: Become a famous little like a famous little donor dog.

968
00:52:24,719 --> 00:52:28,560
Speaker 7: Yeah, we did this We worked with the American Humane Society,

969
00:52:28,640 --> 00:52:32,280
Speaker 7: is sort of the oldest global welfare society in the world.

970
00:52:32,960 --> 00:52:35,400
Speaker 7: We worked through them to do a double blind adoption,

971
00:52:35,640 --> 00:52:37,680
Speaker 7: so they vetted a home for them.

972
00:52:37,719 --> 00:52:38,640
Speaker 3: Whoever has it doesn't know.

973
00:52:38,680 --> 00:52:39,120
Speaker 1: They has no.

974
00:52:39,080 --> 00:52:44,440
Speaker 7: Idea what that whoa They don't know correct, nobody knows.

975
00:52:44,760 --> 00:52:46,840
Speaker 7: I don't know where they are and the home doesn't know.

976
00:52:47,040 --> 00:52:49,920
Speaker 1: Someday, that dog this is gonna do like a lot

977
00:52:49,920 --> 00:52:52,440
Speaker 1: of people do, where they start getting like I need

978
00:52:52,440 --> 00:52:55,480
Speaker 1: to find on my real parent. It's gonna look for

979
00:52:55,520 --> 00:52:59,280
Speaker 1: its incredible Disney movie. It's trying to find its children.

980
00:53:00,000 --> 00:53:03,279
Speaker 1: I'll be quite surprised when it finds them. You're so

981
00:53:04,600 --> 00:53:07,880
Speaker 1: big and white. What kind of dog was this?

982
00:53:08,320 --> 00:53:09,000
Speaker 3: You probably want to.

983
00:53:08,960 --> 00:53:10,080
Speaker 1: Say, this is give it up. Yeah, it was.

984
00:53:10,239 --> 00:53:11,840
Speaker 7: It was. It was a mutt, right, it was. It was.

985
00:53:11,960 --> 00:53:14,160
Speaker 7: It was a large hound that was mixed with a lot.

986
00:53:14,000 --> 00:53:16,920
Speaker 1: Of other rough big.

987
00:53:16,880 --> 00:53:19,040
Speaker 7: Somewhere between forty and seventy pounds.

988
00:53:19,080 --> 00:53:21,359
Speaker 3: Okay, you know.

989
00:53:21,280 --> 00:53:23,640
Speaker 1: I don't know if you're in a color you recently

990
00:53:23,680 --> 00:53:27,160
Speaker 1: got a dog. It's forty to seventy pounds.

991
00:53:29,440 --> 00:53:31,719
Speaker 7: I should have adopted the dog, like in Australia or

992
00:53:31,719 --> 00:53:32,760
Speaker 7: something where they would.

993
00:53:32,920 --> 00:53:34,880
Speaker 1: They'll never put together. Yeah, I know, it would be

994
00:53:34,920 --> 00:53:38,560
Speaker 1: like a real it's a little science project. Huh. So

995
00:53:38,680 --> 00:53:39,600
Speaker 1: that's what happened to it.

996
00:53:40,200 --> 00:53:41,560
Speaker 5: What do you feeding those things?

997
00:53:41,600 --> 00:53:41,719
Speaker 4: Now?

998
00:53:41,840 --> 00:53:43,319
Speaker 5: They're six months old?

999
00:53:43,600 --> 00:53:47,760
Speaker 7: So they started on a very typical milk replacer formula

1000
00:53:47,800 --> 00:53:49,440
Speaker 7: once we pulled them from mom, and then we started

1001
00:53:49,480 --> 00:53:53,200
Speaker 7: introducing ground meats, like really slurried meats, until they eventually

1002
00:53:53,239 --> 00:53:57,080
Speaker 7: startied in ground meats mostly beef and horse meat. Raw meat, yeah,

1003
00:53:57,160 --> 00:53:59,759
Speaker 7: rob me. And then we also had some dry kibble

1004
00:53:59,800 --> 00:54:02,000
Speaker 7: in there, you know, essential nutrients things that like that

1005
00:54:02,080 --> 00:54:05,520
Speaker 7: for development. Now that you know they're almost eight months old,

1006
00:54:05,520 --> 00:54:09,359
Speaker 7: we're they're they've transitioned from that sort of ground meat

1007
00:54:09,560 --> 00:54:12,320
Speaker 7: to whole prey item. So I get a whole rabbit,

1008
00:54:13,160 --> 00:54:15,520
Speaker 7: a whole chicken, they'll get a quarter of a deer,

1009
00:54:15,920 --> 00:54:17,719
Speaker 7: things like that. So we're starting to get them away

1010
00:54:17,719 --> 00:54:20,839
Speaker 7: from the two square meals a day to a sort

1011
00:54:20,880 --> 00:54:23,920
Speaker 7: of a gorge and fast more similar to a wild

1012
00:54:25,680 --> 00:54:26,520
Speaker 7: cadence of feeding.

1013
00:54:26,640 --> 00:54:29,560
Speaker 6: Are they in like a big outdoor enclosure?

1014
00:54:30,040 --> 00:54:30,279
Speaker 1: Yeah?

1015
00:54:30,400 --> 00:54:33,520
Speaker 7: So I heard a rumor where it is. Oh you did, Yeah,

1016
00:54:33,640 --> 00:54:34,279
Speaker 7: that should be good.

1017
00:54:34,320 --> 00:54:39,040
Speaker 4: Wait are you letting out private? Yeah?

1018
00:54:39,440 --> 00:54:43,160
Speaker 1: Okay, I'll tell private because it might be accurate.

1019
00:54:43,160 --> 00:54:44,000
Speaker 3: I just heard and I was like.

1020
00:54:44,000 --> 00:54:45,640
Speaker 1: Really, oh that would be good.

1021
00:54:45,800 --> 00:54:46,520
Speaker 7: I want to hear that off.

1022
00:54:47,280 --> 00:54:49,200
Speaker 1: I'll tell you what what I'll tell you what I heard.

1023
00:54:49,320 --> 00:54:51,960
Speaker 7: I will tell you they are in the northern continental

1024
00:54:52,080 --> 00:54:55,120
Speaker 7: United States. They live on about two thousand acres of

1025
00:54:55,680 --> 00:54:56,640
Speaker 7: a wildlife preserve.

1026
00:54:56,880 --> 00:54:58,600
Speaker 3: Oh yeah that fits.

1027
00:54:58,719 --> 00:55:01,920
Speaker 7: Yeah, we might be we might have to go, you know,

1028
00:55:02,000 --> 00:55:04,799
Speaker 7: snuff somebody out if they're telling our secrets. The real

1029
00:55:05,640 --> 00:55:09,719
Speaker 7: reason behind the secrecy is there. Did you guys see

1030
00:55:09,719 --> 00:55:11,560
Speaker 7: that we came out with this news about a month

1031
00:55:11,600 --> 00:55:14,040
Speaker 7: before dire Wolf that we made a wooly mouse. Yeah,

1032
00:55:14,320 --> 00:55:16,480
Speaker 7: I saw this. Kind of a silly thing, but a

1033
00:55:16,480 --> 00:55:19,120
Speaker 7: lot of fun. The idea was this was like a

1034
00:55:19,120 --> 00:55:24,000
Speaker 7: phenotype validation study. We were taking traits we knew existed

1035
00:55:24,160 --> 00:55:26,920
Speaker 7: in wooly mammoths and that we're targets for us to

1036
00:55:27,080 --> 00:55:29,879
Speaker 7: edit into wooly mammoth. But we wanted to say, oh,

1037
00:55:30,120 --> 00:55:33,160
Speaker 7: these edits are having the intended effect, So we didn't

1038
00:55:33,200 --> 00:55:35,480
Speaker 7: take the same wooly mammoth edit and edit that into

1039
00:55:35,719 --> 00:55:38,560
Speaker 7: a mouse. That just wouldn't work. We found the analogous

1040
00:55:38,640 --> 00:55:41,879
Speaker 7: genes in mice and conferred those changes and we made

1041
00:55:41,920 --> 00:55:45,640
Speaker 7: these little wooly mice. The idea that it showed that

1042
00:55:45,800 --> 00:55:48,720
Speaker 7: they had the same effects on coat length, coat color

1043
00:55:49,080 --> 00:55:50,960
Speaker 7: at a post tissue. Things like that that would be

1044
00:55:51,000 --> 00:55:54,799
Speaker 7: important to make an Asian elephant a wooly mammoth. We

1045
00:55:54,920 --> 00:55:57,160
Speaker 7: came out that news and I thought, I can't believe

1046
00:55:57,160 --> 00:55:59,279
Speaker 7: we're going to go public with this thing. And it

1047
00:55:59,320 --> 00:56:01,839
Speaker 7: broke the Internet and people just started showing up at

1048
00:56:01,840 --> 00:56:04,759
Speaker 7: our front door of our office building, not where the

1049
00:56:04,800 --> 00:56:05,600
Speaker 7: mice are.

1050
00:56:05,960 --> 00:56:06,120
Speaker 1: One.

1051
00:56:06,160 --> 00:56:08,000
Speaker 7: They wanted one, They wanted to see one. They wanted

1052
00:56:08,000 --> 00:56:10,360
Speaker 7: to talk to the people that did that. So we

1053
00:56:10,480 --> 00:56:13,840
Speaker 7: quickly said, well, this was a month before launch a direwolf.

1054
00:56:13,840 --> 00:56:16,719
Speaker 7: We knew we were about to launch a direwolf. We said,

1055
00:56:16,800 --> 00:56:19,600
Speaker 7: there's no way we could tell anybody even what state

1056
00:56:19,640 --> 00:56:22,360
Speaker 7: these animals are in, because suddenly people will be showing

1057
00:56:22,440 --> 00:56:24,760
Speaker 7: up and two thousand acres is a lot to protect.

1058
00:56:24,800 --> 00:56:27,520
Speaker 1: So if they're on two thousand acres, they there's no

1059
00:56:27,560 --> 00:56:30,320
Speaker 1: way you have a two thousand acre parcel that's free

1060
00:56:30,360 --> 00:56:32,399
Speaker 1: of other animals.

1061
00:56:32,800 --> 00:56:34,480
Speaker 7: No, yeah, it's so.

1062
00:56:34,440 --> 00:56:37,080
Speaker 1: Are they hunting whatever's on this two thousand acres.

1063
00:56:36,800 --> 00:56:40,560
Speaker 7: And they're fed so regularly it's like a zoo animal.

1064
00:56:40,280 --> 00:56:42,840
Speaker 1: But they're interacting. They're interacting with wildlife.

1065
00:56:42,840 --> 00:56:45,880
Speaker 7: I've seen them chase deer off things like that, so

1066
00:56:45,920 --> 00:56:48,239
Speaker 7: they definitely have that instinct and they're interested in it.

1067
00:56:49,000 --> 00:56:52,120
Speaker 7: But yeah, they also don't have some adult wolf that's

1068
00:56:52,200 --> 00:56:54,640
Speaker 7: showing this is how we sort of stalk a prey

1069
00:56:54,760 --> 00:56:56,200
Speaker 7: and then we get on it and this is the

1070
00:56:56,280 --> 00:56:59,600
Speaker 7: kill move. They don't have that yet. We could, over

1071
00:56:59,680 --> 00:57:02,960
Speaker 7: time and over generations do that similar to what they

1072
00:57:03,000 --> 00:57:05,040
Speaker 7: do with Mexican gray wolves and red wolves in terms

1073
00:57:05,040 --> 00:57:06,800
Speaker 7: of preparing them for introduction to the wild.

1074
00:57:07,120 --> 00:57:10,279
Speaker 6: And ye aren't you worried that like they could pick

1075
00:57:10,360 --> 00:57:14,359
Speaker 6: up like canine distemper or some disease from a wild canine.

1076
00:57:14,560 --> 00:57:18,800
Speaker 7: So we were pretty meticulous in our site selection process

1077
00:57:18,800 --> 00:57:20,880
Speaker 7: and one of the things we wanted to ensure was

1078
00:57:20,920 --> 00:57:24,200
Speaker 7: that it was an area that you know, gray wolves

1079
00:57:24,400 --> 00:57:29,520
Speaker 7: had been extirpated from, so there wasn't a potential issue there. Obviously,

1080
00:57:29,560 --> 00:57:31,680
Speaker 7: there's still coyotes in those areas, so we have to

1081
00:57:31,720 --> 00:57:34,680
Speaker 7: worry about that. We have coyote pre fencing, but coyotes

1082
00:57:34,720 --> 00:57:36,800
Speaker 7: are pretty widely they can get through some of that.

1083
00:57:37,000 --> 00:57:40,280
Speaker 1: So you got two thousand acres with no coyote on it.

1084
00:57:40,320 --> 00:57:41,800
Speaker 1: There's gonna be some people to want to talk.

1085
00:57:42,080 --> 00:57:47,760
Speaker 7: Yeah, yeah, but you know they undergo a pretty typical vaccination, right,

1086
00:57:48,000 --> 00:57:50,600
Speaker 7: and similar to what we would do with other animals.

1087
00:57:51,240 --> 00:57:55,160
Speaker 2: I'm looking at the care program milestone. There's a lot

1088
00:57:55,200 --> 00:57:58,760
Speaker 2: of things about social interaction in here. How does that

1089
00:57:58,800 --> 00:58:00,000
Speaker 2: work when you only have two of them?

1090
00:58:00,320 --> 00:58:01,360
Speaker 1: Oh? If three? Okay?

1091
00:58:01,480 --> 00:58:03,560
Speaker 7: So if three and there is an intention that will

1092
00:58:03,560 --> 00:58:06,280
Speaker 7: bring new litter on. Because our goal was to get

1093
00:58:06,280 --> 00:58:09,520
Speaker 7: to six to eight sort of a typical pack size. Obviously,

1094
00:58:09,720 --> 00:58:11,880
Speaker 7: you know we ended up at three, so we'll look

1095
00:58:11,920 --> 00:58:14,360
Speaker 7: at adding another litter to that to that group.

1096
00:58:15,920 --> 00:58:16,560
Speaker 1: Hit me with it.

1097
00:58:16,720 --> 00:58:19,760
Speaker 3: We're gonna get in this earlier. Tell me the why.

1098
00:58:20,760 --> 00:58:22,600
Speaker 1: I mean I get the why, like I get the

1099
00:58:22,600 --> 00:58:27,200
Speaker 1: why from a showman like that. You would build a park, right,

1100
00:58:27,280 --> 00:58:29,400
Speaker 1: you'd be able to use them for movies, and that'd

1101
00:58:29,440 --> 00:58:32,840
Speaker 1: be valuable. You'd build a theme park and that would

1102
00:58:32,880 --> 00:58:36,440
Speaker 1: be valuable. But what is like the what is the why? Like?

1103
00:58:36,560 --> 00:58:38,280
Speaker 1: Or maybe that is the why? Why?

1104
00:58:38,440 --> 00:58:40,280
Speaker 7: Yeah, you could do those things. That is not our why.

1105
00:58:40,360 --> 00:58:43,400
Speaker 7: That is not what we're doing. Our why is really

1106
00:58:43,400 --> 00:58:48,520
Speaker 7: built more around around this selection process that we had

1107
00:58:48,560 --> 00:58:51,320
Speaker 7: about two years ago we were looking at, you know,

1108
00:58:51,320 --> 00:58:54,360
Speaker 7: knowing that our big three projects Willie mammoth, Tasmani, and

1109
00:58:54,360 --> 00:58:56,360
Speaker 7: tiger ditto, those are going to take us a while.

1110
00:58:56,640 --> 00:58:58,680
Speaker 7: He said, well, we should start working on other projects

1111
00:58:58,680 --> 00:59:01,160
Speaker 7: that we could also help develop some of these de

1112
00:59:01,240 --> 00:59:04,120
Speaker 7: extinction pipelines, get through this process a little quicker and

1113
00:59:04,160 --> 00:59:07,240
Speaker 7: be able to see and learn and help inform the

1114
00:59:07,280 --> 00:59:10,960
Speaker 7: other projects. In addition to that, in twenty sixteen, the

1115
00:59:11,240 --> 00:59:15,200
Speaker 7: ICN came out with a Proxy Species Rewilding Guide or

1116
00:59:15,280 --> 00:59:19,080
Speaker 7: a creation guide, basically this guideline document that an independent

1117
00:59:19,080 --> 00:59:22,560
Speaker 7: group of international experts wrote something like thirty five key

1118
00:59:22,600 --> 00:59:26,520
Speaker 7: points in this guideline that talks about if you were

1119
00:59:26,640 --> 00:59:29,720
Speaker 7: to pursue de extinction or the creation of a proxy

1120
00:59:29,760 --> 00:59:32,720
Speaker 7: of an extinct species which is easier just called the extinction,

1121
00:59:34,560 --> 00:59:36,160
Speaker 7: here is how you should do it. And one of

1122
00:59:36,200 --> 00:59:38,760
Speaker 7: the most important things that it talks about there is,

1123
00:59:38,840 --> 00:59:41,320
Speaker 7: you know, there's a big welfare implication which sort of

1124
00:59:41,360 --> 00:59:44,040
Speaker 7: goes back to why we selected the specific edits we selected.

1125
00:59:44,440 --> 00:59:47,840
Speaker 7: There's also this process that these things should happen sort

1126
00:59:47,840 --> 00:59:51,640
Speaker 7: of in this contained, almost laboratory environment where we if

1127
00:59:51,640 --> 00:59:53,640
Speaker 7: you're going to pursue this the first time you do it.

1128
00:59:53,640 --> 00:59:56,560
Speaker 7: You should find a species that you know a lot about.

1129
00:59:56,760 --> 00:59:58,920
Speaker 7: You should do these things in a very controlled setting

1130
00:59:58,960 --> 01:00:01,880
Speaker 7: so you can study all the impacts from cradle to grave.

1131
01:00:02,600 --> 01:00:07,600
Speaker 1: Oh, just to be clear, the IUCN did this independent.

1132
01:00:07,080 --> 01:00:09,720
Speaker 7: Of you guys in twenty sixteen, way before Colossal was

1133
01:00:09,760 --> 01:00:10,040
Speaker 7: the thing.

1134
01:00:10,680 --> 01:00:13,200
Speaker 1: This was long. Yeah, so they were operating on like

1135
01:00:13,240 --> 01:00:15,960
Speaker 1: an assumption that we'll get there, but we weren't there yet.

1136
01:00:16,000 --> 01:00:18,560
Speaker 7: And that was when the Wooly Mammittee things and stuff

1137
01:00:18,600 --> 01:00:21,160
Speaker 7: was really bubbling up, and so it started. Biotechnology was

1138
01:00:21,160 --> 01:00:23,040
Speaker 7: getting to a point where they said we should come

1139
01:00:23,120 --> 01:00:25,520
Speaker 7: up with some sort of advantage. Yeah, so it was

1140
01:00:25,560 --> 01:00:28,480
Speaker 7: really it was great forethought. We use that as as

1141
01:00:28,760 --> 01:00:30,720
Speaker 7: a bit of a guiding light in the way that

1142
01:00:30,760 --> 01:00:34,960
Speaker 7: we designed this project. Because so we know a lot

1143
01:00:34,960 --> 01:00:38,160
Speaker 7: about canids and canad genetics. Right, we know more about

1144
01:00:38,240 --> 01:00:41,480
Speaker 7: canids than almost any other species, mostly because we all

1145
01:00:41,480 --> 01:00:44,200
Speaker 7: have you know, a gray wolf in our house, right,

1146
01:00:44,760 --> 01:00:47,040
Speaker 7: I mean it is dog days, right, we all have dogs.

1147
01:00:47,040 --> 01:00:49,600
Speaker 7: We all love our dogs. We study a lot about them,

1148
01:00:49,800 --> 01:00:53,480
Speaker 7: and we know a lot about North American canids. So

1149
01:00:53,560 --> 01:00:55,640
Speaker 7: that gave us a really strong foundation in order to

1150
01:00:55,680 --> 01:00:58,280
Speaker 7: be able to predict what were the effects of edits,

1151
01:00:58,320 --> 01:01:01,640
Speaker 7: what genes were responsible for, what physical trait, things like that.

1152
01:01:02,240 --> 01:01:06,640
Speaker 7: At the same time, there was this sort of you know,

1153
01:01:06,800 --> 01:01:10,240
Speaker 7: opportunity for us to show the de extinction pipeline is

1154
01:01:10,280 --> 01:01:12,320
Speaker 7: a real thing. I think when we talk about bringing

1155
01:01:12,360 --> 01:01:14,320
Speaker 7: back Willie Mammos, people sort of giggle and they go,

1156
01:01:14,360 --> 01:01:16,480
Speaker 7: that's never going to happen. So there was sort of

1157
01:01:16,480 --> 01:01:18,280
Speaker 7: this proof of principle need that we said, well, we

1158
01:01:18,280 --> 01:01:21,640
Speaker 7: could show how this would work. And finally, there was

1159
01:01:21,680 --> 01:01:24,360
Speaker 7: also the pop culture bit you touched on Direwolf, right,

1160
01:01:24,880 --> 01:01:28,200
Speaker 7: this idea that we could bring science into the pop culture,

1161
01:01:28,240 --> 01:01:31,480
Speaker 7: blend those, bring more eyes and attention onto de extinction,

1162
01:01:31,560 --> 01:01:34,880
Speaker 7: onto extinction conservation things like that. So there was this

1163
01:01:35,040 --> 01:01:37,720
Speaker 7: perfect ven diagram that sort of overlapped with gray Wolf

1164
01:01:37,760 --> 01:01:40,960
Speaker 7: and dire Wolf, that we could do all of those

1165
01:01:41,000 --> 01:01:44,720
Speaker 7: things while creating technologies that were responsible or could help

1166
01:01:45,040 --> 01:01:48,640
Speaker 7: save other endangered canids like the American red wolf, which

1167
01:01:48,880 --> 01:01:52,360
Speaker 7: you know lost in our bit of a media circus

1168
01:01:52,400 --> 01:01:54,400
Speaker 7: that occurred after Direwolf was this idea that we were

1169
01:01:54,400 --> 01:01:57,480
Speaker 7: also working on American red wolves and there's some cool

1170
01:01:57,480 --> 01:01:59,480
Speaker 7: stuff that we should talk about there. But so the

1171
01:01:59,520 --> 01:02:03,480
Speaker 7: why was sort of that perfect opportunity to blend in

1172
01:02:03,560 --> 01:02:07,640
Speaker 7: pop culture with a proof of principle of de extinction

1173
01:02:07,840 --> 01:02:11,200
Speaker 7: and taking that first passive de extinction that this twenty

1174
01:02:11,200 --> 01:02:13,400
Speaker 7: sixteen guideline sort of showed us how to do it,

1175
01:02:13,720 --> 01:02:17,040
Speaker 7: and that really outlined you know, know a lot about

1176
01:02:17,080 --> 01:02:18,920
Speaker 7: the animal, put them in an area where you can

1177
01:02:18,960 --> 01:02:21,840
Speaker 7: study them closely, and these would not be animals that

1178
01:02:21,880 --> 01:02:24,800
Speaker 7: would be a generation ready for release. We knew dire

1179
01:02:24,840 --> 01:02:27,439
Speaker 7: wolves were not going back into the wild. We made

1180
01:02:27,480 --> 01:02:31,240
Speaker 7: these as part of that pursuit of de extinction.

1181
01:02:31,560 --> 01:02:34,440
Speaker 1: So you do it knowing they won't go into the wild.

1182
01:02:34,480 --> 01:02:36,520
Speaker 1: Because those are our questions that have is how much

1183
01:02:36,560 --> 01:02:41,920
Speaker 1: do you like? How much how worried or wildlife professionals

1184
01:02:42,000 --> 01:02:43,960
Speaker 1: This is a question they hadn't had to grapple with before.

1185
01:02:44,960 --> 01:02:50,040
Speaker 1: How worried are wildlife professionals and conservationists that you would

1186
01:02:50,280 --> 01:02:55,840
Speaker 1: create a that you would like, create a creature that

1187
01:02:55,920 --> 01:03:01,960
Speaker 1: could potentially get out and then reproduce what imperiled species

1188
01:03:02,320 --> 01:03:04,920
Speaker 1: in a way that might be negative for those imperiled species,

1189
01:03:04,960 --> 01:03:07,080
Speaker 1: Like that's got to be a real concern. We're having

1190
01:03:07,120 --> 01:03:09,600
Speaker 1: a conversation right now where guys are like in the

1191
01:03:09,600 --> 01:03:12,920
Speaker 1: deer in the servid world, people are saying, oh, we

1192
01:03:13,000 --> 01:03:17,800
Speaker 1: can make We have some examples of deer that are

1193
01:03:17,920 --> 01:03:22,120
Speaker 1: very slow to develop CWD or they get CWD, but

1194
01:03:22,160 --> 01:03:25,880
Speaker 1: they're slow to be affected by it, And they say,

1195
01:03:25,960 --> 01:03:28,880
Speaker 1: what we'd like to do is cut these deer loose,

1196
01:03:29,320 --> 01:03:32,280
Speaker 1: just to introduce some of this besides it being, as

1197
01:03:32,280 --> 01:03:35,400
Speaker 1: it's been explained to me by many servid experts, is

1198
01:03:37,160 --> 01:03:39,400
Speaker 1: kind of like doesn't make sense when you understand the

1199
01:03:39,440 --> 01:03:43,320
Speaker 1: scale of the millions of deer out there and that

1200
01:03:43,400 --> 01:03:46,080
Speaker 1: you're going to try to like influence gene flow by

1201
01:03:46,120 --> 01:03:49,080
Speaker 1: cutting animals loose. But they also point to all these

1202
01:03:49,120 --> 01:03:53,040
Speaker 1: moral issues and other like issues like what if those

1203
01:03:53,080 --> 01:03:58,080
Speaker 1: things also carry a susceptibility to other diseases? What if

1204
01:03:58,120 --> 01:04:02,840
Speaker 1: that infers upon them a so sort of hidden weakness. Right, So,

1205
01:04:02,880 --> 01:04:06,000
Speaker 1: if you have these creatures and they get out and

1206
01:04:06,040 --> 01:04:09,240
Speaker 1: they start breeding with wolves, I assume they could breed

1207
01:04:09,280 --> 01:04:13,480
Speaker 1: with wolves. How nervous are people that that'll happen?

1208
01:04:13,640 --> 01:04:15,840
Speaker 7: I mean, that's a question we get almost every day

1209
01:04:16,000 --> 01:04:18,880
Speaker 7: when when people talk about it. So, first, the first

1210
01:04:19,320 --> 01:04:23,280
Speaker 7: step to protect against that is the facility itself, you know,

1211
01:04:23,360 --> 01:04:26,800
Speaker 7: extremely secure double fence actually triple fence if you include

1212
01:04:26,840 --> 01:04:30,320
Speaker 7: our perimeter fence. You know, we just we've had to

1213
01:04:30,360 --> 01:04:34,440
Speaker 7: have really thorough conversations with government agencies to explain all

1214
01:04:34,440 --> 01:04:36,160
Speaker 7: the measures that have been taken to ensure it's a

1215
01:04:36,160 --> 01:04:40,760
Speaker 7: biosecure facility. So there's that piece of it. The other

1216
01:04:40,840 --> 01:04:43,720
Speaker 7: side of it is is really sort of like the

1217
01:04:43,760 --> 01:04:47,520
Speaker 7: CWD example is really good is you know, we need

1218
01:04:47,560 --> 01:04:50,960
Speaker 7: to know more about these animals before you would ever

1219
01:04:51,000 --> 01:04:53,160
Speaker 7: cut something loose, whether it's a dire wolfer, it's a

1220
01:04:53,200 --> 01:04:55,160
Speaker 7: wooly mammoth, right, we need to know a lot about

1221
01:04:55,160 --> 01:04:57,520
Speaker 7: these animals and what are the effects of gene editing,

1222
01:04:57,520 --> 01:04:59,840
Speaker 7: what are the effects of cloning, what are the effects

1223
01:04:59,840 --> 01:05:03,040
Speaker 7: of the specific edits we chose, which is exactly why

1224
01:05:03,080 --> 01:05:05,040
Speaker 7: we chose dire wolf because we knew it'd stay in

1225
01:05:05,080 --> 01:05:07,240
Speaker 7: this area and we can do that cradle to grave

1226
01:05:07,960 --> 01:05:10,960
Speaker 7: sort of research with them. So this is a great

1227
01:05:10,960 --> 01:05:13,800
Speaker 7: opportunity for just to show what are those impacts. But

1228
01:05:13,880 --> 01:05:17,040
Speaker 7: you know, the CWD example is really interesting because you

1229
01:05:17,120 --> 01:05:19,480
Speaker 7: are you know, we get a question about playing God

1230
01:05:19,520 --> 01:05:21,200
Speaker 7: a lot, and that is sort of one of those

1231
01:05:21,240 --> 01:05:23,880
Speaker 7: sort of God decisions we're making. As we said, these

1232
01:05:23,920 --> 01:05:28,080
Speaker 7: animals have this specific preon gene that makes them more

1233
01:05:28,240 --> 01:05:32,760
Speaker 7: resistant or you know more can can bear the preonic

1234
01:05:32,800 --> 01:05:36,000
Speaker 7: disease more than others. But what are the what are

1235
01:05:36,040 --> 01:05:38,680
Speaker 7: the things that tag along with that? Or if all

1236
01:05:39,040 --> 01:05:42,360
Speaker 7: that's say, all the white tail in Texas were suddenly

1237
01:05:43,160 --> 01:05:45,840
Speaker 7: wiped out by CWD and only the animals that were

1238
01:05:45,840 --> 01:05:50,680
Speaker 7: released are remain you've created this sort of genetic bottleneck

1239
01:05:50,680 --> 01:05:53,400
Speaker 7: because I guess you didn't make ten thousand of those, right,

1240
01:05:53,440 --> 01:05:56,000
Speaker 7: you only made a hundred of them. So there is that,

1241
01:05:56,240 --> 01:05:58,440
Speaker 7: you know, there are a lot of these ethical decisions

1242
01:05:58,440 --> 01:06:01,560
Speaker 7: we have to have. Love the extinction, I love class.

1243
01:06:01,640 --> 01:06:04,000
Speaker 7: I love the Diary Project because it push puts that

1244
01:06:04,120 --> 01:06:06,600
Speaker 7: debate forward because we're gonna have to start having these

1245
01:06:06,600 --> 01:06:07,479
Speaker 7: conversations more.

1246
01:06:08,240 --> 01:06:12,200
Speaker 1: Right, That's that's like in my mind, that is the Yeah,

1247
01:06:12,920 --> 01:06:16,120
Speaker 1: Like that is the conversation. And and and I credit

1248
01:06:16,240 --> 01:06:19,960
Speaker 1: like to get somewhere, like, you know, let's let's say

1249
01:06:20,000 --> 01:06:22,440
Speaker 1: look at the people that are eager about the idea

1250
01:06:22,480 --> 01:06:27,480
Speaker 1: of colonizing Mars. Right, Yeah, there's all kinds of questions,

1251
01:06:27,920 --> 01:06:32,040
Speaker 1: huge questions about the possibility that but at some point

1252
01:06:32,120 --> 01:06:34,040
Speaker 1: you just go like, well we'll start going that direction.

1253
01:06:34,840 --> 01:06:37,880
Speaker 1: We'll solve what we can solve, understanding that there could

1254
01:06:37,920 --> 01:06:42,200
Speaker 1: possibly be a wall that we don't see. Like that

1255
01:06:42,480 --> 01:06:45,200
Speaker 1: you start before the pathway is clear, yep, right, you

1256
01:06:45,320 --> 01:06:47,080
Speaker 1: just like I don't know, we'll start walking that way

1257
01:06:47,120 --> 01:06:47,800
Speaker 1: and when.

1258
01:06:49,360 --> 01:06:51,240
Speaker 3: What happens, what happens, may we fall off the end

1259
01:06:51,280 --> 01:06:51,600
Speaker 3: of the earth.

1260
01:06:51,640 --> 01:06:53,320
Speaker 1: At some point it was all or not, but we

1261
01:06:53,400 --> 01:06:57,640
Speaker 1: got to start asking questions answer questions. I feel that

1262
01:06:57,680 --> 01:07:01,760
Speaker 1: with anything to do with the extinction, Like, because I

1263
01:07:01,800 --> 01:07:04,439
Speaker 1: don't understand the science, I'm not like train enough, smart

1264
01:07:04,520 --> 01:07:04,760
Speaker 1: enough to.

1265
01:07:04,800 --> 01:07:05,640
Speaker 3: Understand the science.

1266
01:07:05,840 --> 01:07:09,120
Speaker 1: You are a doctor, though I am.

1267
01:07:08,240 --> 01:07:09,400
Speaker 3: Other area of forestry.

1268
01:07:11,040 --> 01:07:14,280
Speaker 1: I can't understand the science, like I'm incapable of understanding science,

1269
01:07:14,800 --> 01:07:18,560
Speaker 1: but I can understand this. At some point, the de

1270
01:07:18,720 --> 01:07:24,320
Speaker 1: extinction process will be that we have, like we have

1271
01:07:24,520 --> 01:07:30,160
Speaker 1: a Tasmanian tiger, or a thiosine or an approximate we

1272
01:07:30,240 --> 01:07:37,320
Speaker 1: have an approximate ivory built woodpecker. We humbly ask your

1273
01:07:37,360 --> 01:07:42,200
Speaker 1: permission to let it go. And like I can just

1274
01:07:42,400 --> 01:07:49,360
Speaker 1: imagine the resistance, maybe even a knee jerk resistance, maybe

1275
01:07:49,400 --> 01:07:54,400
Speaker 1: an unwarranted resistance, but that will that to me not insurmountable,

1276
01:07:54,400 --> 01:07:57,800
Speaker 1: But that to me is the final argument.

1277
01:07:57,760 --> 01:08:00,000
Speaker 6: Because there's got to be a like, what's going to happen?

1278
01:08:00,000 --> 01:08:02,240
Speaker 6: And if we do, yes, we want to see what's

1279
01:08:02,280 --> 01:08:02,840
Speaker 6: going to happen?

1280
01:08:02,920 --> 01:08:05,680
Speaker 7: Right, But I think what's missed often in that sort

1281
01:08:05,720 --> 01:08:08,520
Speaker 7: of that very conservative approach to these things of well,

1282
01:08:08,560 --> 01:08:10,520
Speaker 7: we need to answer every question before we take the

1283
01:08:10,560 --> 01:08:13,160
Speaker 7: next step, is what is the opportunity cost of not

1284
01:08:13,240 --> 01:08:17,160
Speaker 7: acting right? If we could restore a diallyscene to Tasmania,

1285
01:08:17,200 --> 01:08:20,559
Speaker 7: could we start to reduce wildlife disease and level out

1286
01:08:20,600 --> 01:08:24,800
Speaker 7: prey populations in a way that helps support biodiversity of Tasmania? Yeah,

1287
01:08:24,880 --> 01:08:27,559
Speaker 7: I think we could. Are there some other negative effects

1288
01:08:27,960 --> 01:08:30,320
Speaker 7: there might be, and we should understand those. But we

1289
01:08:30,400 --> 01:08:33,559
Speaker 7: also can't say we need to tick one hundred percent

1290
01:08:33,560 --> 01:08:35,400
Speaker 7: of the boxes before we take the next step, because

1291
01:08:35,439 --> 01:08:36,599
Speaker 7: we will never get anywhere.

1292
01:08:37,000 --> 01:08:37,200
Speaker 1: Yeah.

1293
01:08:37,200 --> 01:08:39,160
Speaker 7: I think you know, Teddy Roosevelt had that really famous

1294
01:08:39,240 --> 01:08:41,920
Speaker 7: quote in the moment of any decision, the best thing

1295
01:08:41,960 --> 01:08:43,800
Speaker 7: to do is the right thing, The next best thing

1296
01:08:43,840 --> 01:08:45,280
Speaker 7: to do is the wrong thing, and the worst thing

1297
01:08:45,320 --> 01:08:48,240
Speaker 7: to do is nothing right. And so that's really our

1298
01:08:48,280 --> 01:08:50,200
Speaker 7: mentality is we need to push forward. We need to

1299
01:08:50,200 --> 01:08:53,639
Speaker 7: push technologies forward that give us an opportunity to solve

1300
01:08:53,640 --> 01:08:56,639
Speaker 7: for some issues, and could there be this butterfly effect

1301
01:08:56,640 --> 01:09:00,000
Speaker 7: that creates an unforeseen issue. Absolutely, should we be prepared

1302
01:09:00,280 --> 01:09:03,320
Speaker 7: as possible before we take those steps. Absolutely, I'm not

1303
01:09:03,400 --> 01:09:08,519
Speaker 7: debating that. But right now there is there is an

1304
01:09:08,520 --> 01:09:11,679
Speaker 7: issue in the conservation community that we are accepting status

1305
01:09:11,760 --> 01:09:15,080
Speaker 7: quo because we're afraid of the unknown, and we haven't

1306
01:09:15,120 --> 01:09:18,880
Speaker 7: acknowledged that status quo means a reduction of biodiversity over

1307
01:09:18,920 --> 01:09:21,040
Speaker 7: the next twenty five years of maybe up to fifty

1308
01:09:21,080 --> 01:09:25,040
Speaker 7: percent of the biodiversity that exists today. We need to

1309
01:09:25,120 --> 01:09:28,880
Speaker 7: start taking steps and bringing tools to the to the

1310
01:09:28,920 --> 01:09:33,719
Speaker 7: conservation game that create fundamental change, that create magnitudes of change,

1311
01:09:33,760 --> 01:09:37,360
Speaker 7: because we are getting our asses kicked on the biodiversity front.

1312
01:09:37,960 --> 01:09:42,880
Speaker 6: So another why that I've heard is like, why work

1313
01:09:42,920 --> 01:09:46,639
Speaker 6: with a species like a dire wolf that ultimately proved

1314
01:09:46,640 --> 01:09:51,479
Speaker 6: to be like unsuccessful. You know, it had its time

1315
01:09:51,560 --> 01:09:55,160
Speaker 6: and then was unable to cope with whatever changes led

1316
01:09:55,200 --> 01:10:00,439
Speaker 6: to its extinction. Why not work with like Siberian tis

1317
01:10:01,240 --> 01:10:04,840
Speaker 6: that are like imperiled and you could grow those.

1318
01:10:04,680 --> 01:10:06,080
Speaker 3: If they won't let them caught them loose.

1319
01:10:06,800 --> 01:10:09,639
Speaker 7: I'm just saying I think it's also not an either

1320
01:10:09,760 --> 01:10:11,920
Speaker 7: or proposition, that's not how we work. We are a

1321
01:10:11,960 --> 01:10:15,120
Speaker 7: mores more yes and type of group. So we loved

1322
01:10:15,200 --> 01:10:18,400
Speaker 7: using de extinction as this ability to bring new eyeballs,

1323
01:10:18,400 --> 01:10:21,040
Speaker 7: new funding to the table, bring attention and awareness and

1324
01:10:21,080 --> 01:10:23,640
Speaker 7: money to the conservation battles that we face on the

1325
01:10:23,720 --> 01:10:27,240
Speaker 7: endangered spaces front by using the extinction as an engine

1326
01:10:27,640 --> 01:10:32,400
Speaker 7: to fuel that that fight. So with the with the

1327
01:10:32,520 --> 01:10:35,479
Speaker 7: de extinction of the dire wolf, we have also been

1328
01:10:35,520 --> 01:10:38,880
Speaker 7: working on the genetic rescue of the American red wolf.

1329
01:10:39,000 --> 01:10:43,040
Speaker 7: Right red wolf declared extinct in the wild in nineteen eighty.

1330
01:10:43,120 --> 01:10:45,400
Speaker 7: In the sixties and seventies, US Fish and Wildlife realized

1331
01:10:45,439 --> 01:10:47,200
Speaker 7: that we had almost killed them all. So they went

1332
01:10:47,200 --> 01:10:51,040
Speaker 7: to Louisiana and Texas and they captured what they thought

1333
01:10:51,120 --> 01:10:54,160
Speaker 7: was the remaining group of red wolves using very specific

1334
01:10:54,640 --> 01:10:58,960
Speaker 7: morphological measurements similar to this dire wolf gray wolf debate right, Well,

1335
01:10:59,240 --> 01:11:01,719
Speaker 7: in reality with they did is they caught fourteen animals

1336
01:11:01,880 --> 01:11:04,000
Speaker 7: and they said this is now the founding population of

1337
01:11:04,000 --> 01:11:07,000
Speaker 7: a captive group that will then be kept in human

1338
01:11:07,040 --> 01:11:09,000
Speaker 7: care until we figure out how to get them back

1339
01:11:09,000 --> 01:11:09,479
Speaker 7: into the wild.

1340
01:11:09,600 --> 01:11:11,519
Speaker 1: Yeah. On what was the island they put them on?

1341
01:11:11,760 --> 01:11:14,720
Speaker 7: Oh? Yeah, I can't even was it off South Carolina? Yeah,

1342
01:11:14,760 --> 01:11:18,040
Speaker 7: it was off South Carolina. Now now they're in northeastern

1343
01:11:18,280 --> 01:11:22,840
Speaker 7: North Carolina, which is the experimental release site. Well, the

1344
01:11:22,880 --> 01:11:25,680
Speaker 7: problem with that is they were looking for a very

1345
01:11:25,720 --> 01:11:28,640
Speaker 7: specific phenotype and they didn't have the power genetics in

1346
01:11:28,680 --> 01:11:31,479
Speaker 7: the seventies when they were trying to understand what was

1347
01:11:31,479 --> 01:11:33,559
Speaker 7: a red wolf and what was a coyote in Louisiana

1348
01:11:33,600 --> 01:11:36,400
Speaker 7: and Texas. So what they did is they caught fourteen

1349
01:11:36,400 --> 01:11:41,679
Speaker 7: animals that fit a very specific bill. Wind the clock

1350
01:11:41,760 --> 01:11:44,320
Speaker 7: back about eight nine years ago Bridget von Holt from

1351
01:11:44,320 --> 01:11:48,640
Speaker 7: Princeton University in Chris Enbreski for Michigan Tech, they rediscovered

1352
01:11:48,800 --> 01:11:51,680
Speaker 7: that there is actually this extremely high level of red

1353
01:11:51,760 --> 01:11:55,200
Speaker 7: wolf ancestry in that canid population in Louisiana and Texas.

1354
01:11:55,439 --> 01:11:58,919
Speaker 7: And if you look at the genetics of those canids

1355
01:11:59,240 --> 01:12:01,559
Speaker 7: from this sort of high spot and it radiates out,

1356
01:12:01,800 --> 01:12:04,160
Speaker 7: you can see that the proportion of red wolf within

1357
01:12:04,280 --> 01:12:08,160
Speaker 7: these these Louisiana and Texas populations is about seventy percent

1358
01:12:08,240 --> 01:12:11,799
Speaker 7: red wolf thirty percent coyote. As you radiate out, you

1359
01:12:11,840 --> 01:12:13,479
Speaker 7: sort of flip it and by the time you get

1360
01:12:13,520 --> 01:12:16,800
Speaker 7: to like Dallas where I live, it's totally inverse. It's

1361
01:12:16,920 --> 01:12:19,320
Speaker 7: barely any red wolf in totally coyote. So this is

1362
01:12:19,360 --> 01:12:22,320
Speaker 7: a very unique group of canids that have persisted since

1363
01:12:22,360 --> 01:12:27,799
Speaker 7: the seventies. Meanwhile, the closed population that used the captive

1364
01:12:27,840 --> 01:12:31,479
Speaker 7: population that used for reintroduction to North Carolina has had

1365
01:12:31,520 --> 01:12:34,639
Speaker 7: the same fourteen founders. Actually only twelve of those bred

1366
01:12:34,640 --> 01:12:36,920
Speaker 7: and are represented. So now you have about two hundred

1367
01:12:36,920 --> 01:12:39,800
Speaker 7: and fiftywo hundred and seventy animals in captive population that

1368
01:12:40,160 --> 01:12:45,519
Speaker 7: stemmed from twelve individuals, huge genetic bottleneck and totally missed,

1369
01:12:46,840 --> 01:12:50,240
Speaker 7: you know, some of the phenotypic diversity of the red wolf.

1370
01:12:50,400 --> 01:12:52,880
Speaker 7: Going back to aliens abducted you and I, they would

1371
01:12:52,880 --> 01:12:55,559
Speaker 7: have missed all the people you liveing in Asia and

1372
01:12:55,760 --> 01:12:58,639
Speaker 7: Africa and South America, and they, you know, they would

1373
01:12:58,640 --> 01:13:01,600
Speaker 7: think everybody looks like like us, and that would be

1374
01:13:01,640 --> 01:13:05,760
Speaker 7: a horrible representation of what our species actually is. They

1375
01:13:05,760 --> 01:13:08,240
Speaker 7: did similar to that, they missed a lot of genetic

1376
01:13:08,240 --> 01:13:10,920
Speaker 7: diversity that still exists there. So we've created using the

1377
01:13:10,960 --> 01:13:14,000
Speaker 7: same technology used to make dire wolves, we now have

1378
01:13:14,080 --> 01:13:16,559
Speaker 7: this ability to be able to sequence all of these

1379
01:13:16,600 --> 01:13:19,080
Speaker 7: red wolves or these Gulf Coast canids, is what we

1380
01:13:19,120 --> 01:13:21,519
Speaker 7: call them or ghost wolves, because they have such high

1381
01:13:21,600 --> 01:13:25,519
Speaker 7: levels of genetic ancestry of the American red wolf. They

1382
01:13:25,560 --> 01:13:28,639
Speaker 7: also have a portion of their genome that's not assigned

1383
01:13:28,680 --> 01:13:32,200
Speaker 7: to any other canid. So it's likely that ghost portion

1384
01:13:32,920 --> 01:13:35,840
Speaker 7: is the ancestral red wolf, the pre extinction red wolf

1385
01:13:36,280 --> 01:13:38,599
Speaker 7: that we don't have our reference to yet, but we're creating.

1386
01:13:38,840 --> 01:13:42,000
Speaker 7: We're doing historic sequencing to understand before the extinction what

1387
01:13:42,680 --> 01:13:45,519
Speaker 7: was red wolf, and then that would assign how much

1388
01:13:45,560 --> 01:13:49,160
Speaker 7: red wolf are these coyotes. Now that's an opportunity, and

1389
01:13:49,240 --> 01:13:50,920
Speaker 7: this is one of the things we've been meeting with

1390
01:13:51,040 --> 01:13:53,479
Speaker 7: the US government and Secretary of Bergamon is now we

1391
01:13:53,520 --> 01:13:55,720
Speaker 7: have this opportunity to create a genetic rescue tool that

1392
01:13:55,760 --> 01:13:58,559
Speaker 7: could be an you know, sort of an opportunity to

1393
01:13:58,640 --> 01:14:01,920
Speaker 7: the US Fish and Wilde Recovery program to say, your

1394
01:14:02,040 --> 01:14:04,720
Speaker 7: species is sort of headed for extinction because of its

1395
01:14:04,960 --> 01:14:09,759
Speaker 7: extreme genetic inbreeding. We have a fresh set of genetics

1396
01:14:09,760 --> 01:14:13,439
Speaker 7: that we could plug into your population and help sort

1397
01:14:13,439 --> 01:14:16,800
Speaker 7: of revitalize. Similar to when Texas cougars went into the

1398
01:14:16,800 --> 01:14:20,880
Speaker 7: Florida panther population. We could do that type of genetic rescue.

1399
01:14:20,520 --> 01:14:22,719
Speaker 1: Effort, which was controversial at the time.

1400
01:14:22,760 --> 01:14:25,000
Speaker 7: And now people hail it as one of the biggest successes.

1401
01:14:25,080 --> 01:14:29,479
Speaker 1: What it was like, well, you're corrupting the Florida gene

1402
01:14:29,479 --> 01:14:33,200
Speaker 1: pool or it's not the same thing. Yeah, those Florida

1403
01:14:33,200 --> 01:14:36,840
Speaker 1: panthers will now be not quite Florida panthers.

1404
01:14:36,720 --> 01:14:38,960
Speaker 7: But they won't be cross eyed and can Yeah, And.

1405
01:14:38,920 --> 01:14:40,880
Speaker 3: People are like, well they're closer than none.

1406
01:14:41,040 --> 01:14:43,799
Speaker 7: Yes, would be the art. That's our focus is function

1407
01:14:44,000 --> 01:14:47,639
Speaker 7: within an ecosystem. Can we create a direwolf that would

1408
01:14:47,640 --> 01:14:50,920
Speaker 7: have performed the same function in its ecosystem or can

1409
01:14:50,960 --> 01:14:55,600
Speaker 7: we help rescue a species and it still performed the

1410
01:14:55,600 --> 01:14:57,960
Speaker 7: same function. The answer is yes, we absolutely can, and

1411
01:14:58,000 --> 01:15:00,000
Speaker 7: we need to be more focused on the pragmatic idea

1412
01:15:00,120 --> 01:15:02,720
Speaker 7: of what function does the species provide to its ecosystem

1413
01:15:02,960 --> 01:15:06,360
Speaker 7: versus genetic purity in this weird eugenics mentality we have

1414
01:15:06,479 --> 01:15:11,000
Speaker 7: about species. So that's why we sort of I think,

1415
01:15:11,520 --> 01:15:14,679
Speaker 7: raise eyebrowsers because we are more focused on that side

1416
01:15:14,760 --> 01:15:18,559
Speaker 7: than this idea of what is one hundred percent gray wolf?

1417
01:15:18,600 --> 01:15:19,639
Speaker 7: What is one hundred percent tire?

1418
01:15:20,080 --> 01:15:22,519
Speaker 6: I think there's some of the blowback is like you

1419
01:15:22,600 --> 01:15:25,280
Speaker 6: have an animal and that animal is that animal out

1420
01:15:25,320 --> 01:15:29,599
Speaker 6: in the wild and it becomes like less wild when

1421
01:15:29,600 --> 01:15:31,160
Speaker 6: you start tinkering with its.

1422
01:15:31,080 --> 01:15:34,280
Speaker 5: Genes and laying the hand of man on it. And

1423
01:15:34,600 --> 01:15:35,240
Speaker 5: you know what I mean.

1424
01:15:35,439 --> 01:15:37,800
Speaker 1: But in that case, the hand of man, I mean

1425
01:15:37,920 --> 01:15:42,599
Speaker 1: they were in a pen. Off of that, they were

1426
01:15:42,600 --> 01:15:45,280
Speaker 1: in an enclosure on an island, right to build up

1427
01:15:45,320 --> 01:15:48,840
Speaker 1: a reproducible numb I understand. I'm just saying it's very different.

1428
01:15:48,920 --> 01:15:51,400
Speaker 7: Yeah, Well, and the hand of man is why they're

1429
01:15:51,479 --> 01:15:54,519
Speaker 7: going extinct, right we were, Yeah, we were poaching them

1430
01:15:54,520 --> 01:15:55,440
Speaker 7: out of existence.

1431
01:15:58,479 --> 01:15:59,920
Speaker 3: But let me hit you with a critical let me

1432
01:16:00,160 --> 01:16:01,120
Speaker 3: red wolf criticism.

1433
01:16:01,240 --> 01:16:04,840
Speaker 1: Yeah, And in some ways I think this criticism was

1434
01:16:04,880 --> 01:16:06,759
Speaker 1: a little bit unfair because it's not like you're taking

1435
01:16:06,800 --> 01:16:10,320
Speaker 1: federal money that would normally go to red wolf, recovering

1436
01:16:10,360 --> 01:16:12,519
Speaker 1: it and using it for something else exactly. So it's

1437
01:16:12,520 --> 01:16:15,040
Speaker 1: like it's pure what the work is added. Well, there's

1438
01:16:15,040 --> 01:16:17,760
Speaker 1: the criticisms that would say this, like, guys that work

1439
01:16:17,760 --> 01:16:20,240
Speaker 1: on the red wolf problem are like, our problem is

1440
01:16:20,280 --> 01:16:23,719
Speaker 1: that people shoot them, yep, and they get hit by cars. Yes,

1441
01:16:24,439 --> 01:16:29,160
Speaker 1: So we're not looking for new animals. We're looking for

1442
01:16:29,280 --> 01:16:31,599
Speaker 1: how do we create an atmosphere in a place where

1443
01:16:31,600 --> 01:16:35,120
Speaker 1: they don't get shot and hit by cars. So it

1444
01:16:35,200 --> 01:16:38,599
Speaker 1: was like it's like, that's beside the point it is.

1445
01:16:38,840 --> 01:16:41,080
Speaker 7: It is not a silver bullet to the red wolf problem.

1446
01:16:41,200 --> 01:16:43,720
Speaker 7: It provides a genetic rescue and get buys them more time.

1447
01:16:43,760 --> 01:16:46,120
Speaker 7: Their captive population will begin to suffer as it's a

1448
01:16:46,120 --> 01:16:48,640
Speaker 7: closed population, right, so this gives us an opportunity to

1449
01:16:48,680 --> 01:16:53,679
Speaker 7: inject fresh genetics. Also, that North Carolina site, you know, whatever,

1450
01:16:54,120 --> 01:16:55,760
Speaker 7: you take it back eight years ago or so, the

1451
01:16:55,800 --> 01:16:58,000
Speaker 7: US Fishing Wildlife announced, Hey, we're gonna start finding a

1452
01:16:58,040 --> 01:17:01,680
Speaker 7: second site. A parallel left it because obviously, you know,

1453
01:17:01,880 --> 01:17:04,719
Speaker 7: if you're familiar with the red wolf issue, red wolf

1454
01:17:04,760 --> 01:17:07,080
Speaker 7: population in that area got north of one hundred and

1455
01:17:07,080 --> 01:17:12,479
Speaker 7: fifty individuals in around twenty thirteen. Basically people started shooting

1456
01:17:12,479 --> 01:17:14,479
Speaker 7: and they started getting hit by cars. Today their primary

1457
01:17:14,520 --> 01:17:18,400
Speaker 7: issues car strikes. What's interesting is it's a different area.

1458
01:17:18,439 --> 01:17:21,080
Speaker 7: In Louisiana and Texas, you have larger landscapes, but you

1459
01:17:21,080 --> 01:17:24,679
Speaker 7: don't you don't have as many public lands that protect

1460
01:17:24,720 --> 01:17:27,479
Speaker 7: the animals there, and those animals have persisted since we

1461
01:17:27,520 --> 01:17:29,439
Speaker 7: thought they would have gone extinct. I think there is

1462
01:17:29,479 --> 01:17:32,759
Speaker 7: this weird issue that people sort of say, well, if

1463
01:17:33,320 --> 01:17:36,280
Speaker 7: we acknowledge that these animals are predominantly red wolf or

1464
01:17:36,280 --> 01:17:38,679
Speaker 7: maybe even truly red wolf as we understand more about

1465
01:17:38,720 --> 01:17:41,360
Speaker 7: their genetics. That's also acknowledging the fact that we didn't

1466
01:17:41,400 --> 01:17:44,960
Speaker 7: need to intervene in nineteen seventy. They would have continued

1467
01:17:45,000 --> 01:17:46,799
Speaker 7: to persist in that area.

1468
01:17:46,920 --> 01:17:49,160
Speaker 3: Yeah, because where they do or they did bring them,

1469
01:17:49,200 --> 01:17:50,040
Speaker 3: they've tried.

1470
01:17:49,760 --> 01:17:53,599
Speaker 1: To keep coyotes. Yeah, right, like that understanding of that

1471
01:17:53,640 --> 01:17:56,000
Speaker 1: species with coyotes, and then here you have that other

1472
01:17:56,080 --> 01:18:00,639
Speaker 1: source population that probably has a bleeding edge where Kyle's

1473
01:18:00,720 --> 01:18:00,960
Speaker 1: roll in.

1474
01:18:01,040 --> 01:18:03,880
Speaker 7: People talk about admixture. You know, people will say call

1475
01:18:03,920 --> 01:18:06,480
Speaker 7: it hybridization, and hybridization is the wrong word, but admixture

1476
01:18:06,560 --> 01:18:09,160
Speaker 7: sort of this idea that two species have this you know,

1477
01:18:09,240 --> 01:18:12,280
Speaker 7: blending of genetics. They look at that as a bad thing,

1478
01:18:12,320 --> 01:18:15,919
Speaker 7: as it as diluting the gene stock of a species.

1479
01:18:16,200 --> 01:18:19,320
Speaker 7: But you know, hybridization and admixture of species is a

1480
01:18:19,400 --> 01:18:23,320
Speaker 7: natural process. It's actually evolutionary process that confers evolutionary advantages

1481
01:18:23,360 --> 01:18:26,320
Speaker 7: to animals, gives them the opportunity to thrive with a

1482
01:18:26,360 --> 01:18:29,360
Speaker 7: new within an evolving habitat. Right, they can look at

1483
01:18:29,360 --> 01:18:32,439
Speaker 7: a species and say, oh, that coyote actually is you know,

1484
01:18:33,080 --> 01:18:35,439
Speaker 7: much faster or stronger, whatever it is, and they breed

1485
01:18:35,479 --> 01:18:37,519
Speaker 7: with that, and that that it brings that into their

1486
01:18:37,560 --> 01:18:39,720
Speaker 7: gene pool. That's an adaptive strategy.

1487
01:18:40,560 --> 01:18:44,360
Speaker 1: That's a criticism you hear of the Linnaean system of

1488
01:18:44,720 --> 01:18:45,400
Speaker 1: know that we.

1489
01:18:48,360 --> 01:18:49,599
Speaker 3: What's wolf, canis lupus.

1490
01:18:49,680 --> 01:18:50,559
Speaker 7: Yeah, canis lupis.

1491
01:18:50,640 --> 01:18:55,439
Speaker 1: We're Homo sapien. The rainbow trout is on crinth, on corinth,

1492
01:18:55,520 --> 01:18:59,559
Speaker 1: this micas micas whatever the hell you go in and

1493
01:18:59,600 --> 01:19:02,280
Speaker 1: you say, ki, I'm gonna take all creatures on Earth

1494
01:19:02,439 --> 01:19:06,879
Speaker 1: and I'm going to say that they're all. Whatever causes

1495
01:19:06,920 --> 01:19:10,439
Speaker 1: you to lose sight of the long time, and the

1496
01:19:10,520 --> 01:19:11,440
Speaker 1: long time.

1497
01:19:11,280 --> 01:19:12,880
Speaker 3: Is that there's fluidity.

1498
01:19:13,000 --> 01:19:17,400
Speaker 1: Yes, things right, It's similar to things come together and

1499
01:19:17,400 --> 01:19:19,480
Speaker 1: fall apart, and it's not static. It's a snapshot.

1500
01:19:19,520 --> 01:19:21,840
Speaker 7: When we talk about species recovery, it's the same problem.

1501
01:19:21,880 --> 01:19:24,680
Speaker 7: We say recovered to what right are we recovering? You know,

1502
01:19:24,720 --> 01:19:28,960
Speaker 7: in North American centric conservation we say recover to fourteen

1503
01:19:29,040 --> 01:19:32,519
Speaker 7: ninety two, right, recovered before Europeans came in and ruined

1504
01:19:32,520 --> 01:19:32,880
Speaker 7: the place.

1505
01:19:33,040 --> 01:19:35,120
Speaker 3: Yeah, that's always my view.

1506
01:19:35,400 --> 01:19:39,200
Speaker 7: Yeah, but you know there was a there ebbs and

1507
01:19:39,240 --> 01:19:41,640
Speaker 7: flows before that, and there would have been ebbs and

1508
01:19:41,640 --> 01:19:44,200
Speaker 7: flows after that. At what point, why do we pick

1509
01:19:44,240 --> 01:19:46,080
Speaker 7: an arbitrary point in time. Why do we pick an

1510
01:19:46,200 --> 01:19:49,280
Speaker 7: arbitrary definition of a species in time to say that's

1511
01:19:49,320 --> 01:19:52,960
Speaker 7: the species it's it's I think it makes sense from

1512
01:19:53,000 --> 01:19:55,080
Speaker 7: a communication standpoint, we need to be able to talk

1513
01:19:55,120 --> 01:19:58,919
Speaker 7: about these things, classify things, have goals. That's very important.

1514
01:19:59,600 --> 01:20:02,679
Speaker 7: But to use that as this ideology that we cannot

1515
01:20:02,680 --> 01:20:04,519
Speaker 7: stray from, I think is naive.

1516
01:20:05,479 --> 01:20:08,400
Speaker 1: When I had many conversations with friends of mine in

1517
01:20:08,439 --> 01:20:11,320
Speaker 1: the wildlife world, when when Colossal made its announcement about

1518
01:20:11,320 --> 01:20:13,679
Speaker 1: the dire wolves, one of the conversations I can't remember

1519
01:20:13,680 --> 01:20:16,200
Speaker 1: who the hell is talking to was saying to me,

1520
01:20:16,520 --> 01:20:21,720
Speaker 1: you know where this would be really helpful. Blackfooted ferrets. Absolutely,

1521
01:20:23,360 --> 01:20:25,840
Speaker 1: blackfooted ferriests went through a ten ferret bottleneck.

1522
01:20:25,960 --> 01:20:26,320
Speaker 7: Mm hmm.

1523
01:20:27,160 --> 01:20:28,800
Speaker 1: Okay, but I want to return. I want to do

1524
01:20:28,800 --> 01:20:32,400
Speaker 1: the regulatory thing about ivory built woodpeckers and blackfooted ferrets

1525
01:20:33,479 --> 01:20:38,679
Speaker 1: who like who? What would ever have to say? Okay

1526
01:20:40,360 --> 01:20:44,519
Speaker 1: to be that like our population? Our global population of

1527
01:20:44,560 --> 01:20:48,200
Speaker 1: blackfooted ferrets was reduced down to ten animals, But we

1528
01:20:48,320 --> 01:20:53,280
Speaker 1: have all these specimens that show a level of genetic

1529
01:20:53,320 --> 01:20:58,320
Speaker 1: diversity that did not exist in those ten Those blackfooted

1530
01:20:58,360 --> 01:21:01,800
Speaker 1: farans aren't getting shot they're not getting run over, right,

1531
01:21:01,800 --> 01:21:05,000
Speaker 1: they're fairly easy to protect. Who has to say yes

1532
01:21:06,360 --> 01:21:12,240
Speaker 1: to start inferring lost genetic diversity into blackfooted ferrets, Like

1533
01:21:12,320 --> 01:21:14,960
Speaker 1: who would be the who is the one that goes okay,

1534
01:21:16,400 --> 01:21:17,439
Speaker 1: it's it's it's hard.

1535
01:21:17,520 --> 01:21:20,240
Speaker 7: So blackfoot affair, it's a great example because they've been

1536
01:21:20,320 --> 01:21:23,960
Speaker 7: using biotechnology to try to genetically rescue blackfooted ferret because

1537
01:21:24,000 --> 01:21:28,200
Speaker 7: people were banking tissues of blackfoot affairs back into the seventies.

1538
01:21:28,680 --> 01:21:31,280
Speaker 7: So if you heard Elizabeth Ann, she was the clone

1539
01:21:31,320 --> 01:21:34,639
Speaker 7: that they brought back. She was, you know, an unrepresented founder.

1540
01:21:34,760 --> 01:21:38,439
Speaker 7: They cloned her. Unfortunately that that single individual she didn't

1541
01:21:38,479 --> 01:21:41,320
Speaker 7: actually she wasn't able to reproduce, but they cloned her again,

1542
01:21:41,960 --> 01:21:45,120
Speaker 7: and now they've had the first ever clone breed and

1543
01:21:45,200 --> 01:21:48,360
Speaker 7: give give birth to offspring. Who's the US Fish and

1544
01:21:48,360 --> 01:21:51,920
Speaker 7: Wildlife Service recovery program. So that's where we would start.

1545
01:21:52,000 --> 01:21:54,040
Speaker 7: You start with the US Fish and Wildlife Service because

1546
01:21:54,040 --> 01:21:56,479
Speaker 7: they are the ones that are mandated by the Endangered

1547
01:21:56,520 --> 01:22:00,599
Speaker 7: Species Act to recover species listed by the Act, so

1548
01:22:00,680 --> 01:22:03,679
Speaker 7: they have the jurisdiction in that area. Now, if we're

1549
01:22:03,720 --> 01:22:05,720
Speaker 7: just using cloning like what they did, US Fish and

1550
01:22:05,720 --> 01:22:07,800
Speaker 7: Wildlife has been able to do that on their own.

1551
01:22:08,000 --> 01:22:10,720
Speaker 7: Now if we were to bring colossal type technology, which

1552
01:22:10,720 --> 01:22:14,840
Speaker 7: is cloning plus genetic editing, and we said one of

1553
01:22:14,840 --> 01:22:18,720
Speaker 7: the primary drivers is the blackfooted ferret extinction crisis we're

1554
01:22:18,760 --> 01:22:22,240
Speaker 7: facing is sylvatic plague. Sylvatic plague is present in their

1555
01:22:23,360 --> 01:22:30,200
Speaker 7: obligatory prey species, the paradog. Thanks, that's a hard one

1556
01:22:30,200 --> 01:22:33,680
Speaker 7: to remember. So parad dogs are carrying sylvatic plague, and

1557
01:22:33,720 --> 01:22:37,160
Speaker 7: that when they predate on an animal that has plague,

1558
01:22:37,200 --> 01:22:40,639
Speaker 7: they also get plague and they die. We know for

1559
01:22:40,840 --> 01:22:43,880
Speaker 7: a fact that the closest living relative of the blackfooted ferret,

1560
01:22:43,920 --> 01:22:48,320
Speaker 7: which is the domestic ferret, has a naturally occurring resistance

1561
01:22:48,360 --> 01:22:52,160
Speaker 7: to plague. And it's a very small change. So you

1562
01:22:52,160 --> 01:22:54,080
Speaker 7: could go in and make a genetic edit within the

1563
01:22:54,080 --> 01:22:58,479
Speaker 7: black footed fair genome and suddenly ferrets are resistant to

1564
01:22:58,520 --> 01:23:02,400
Speaker 7: blackfooted to sylvatic plaque. And now we can make a

1565
01:23:02,479 --> 01:23:06,200
Speaker 7: huge difference in the recovery of blackfoot affairs.

1566
01:23:06,240 --> 01:23:08,719
Speaker 1: But now it's like, now we're going to have whatever.

1567
01:23:08,880 --> 01:23:13,880
Speaker 7: Yeah, now someone's now we're dealing with multiple agencies, the USDA,

1568
01:23:14,479 --> 01:23:18,080
Speaker 7: the FDA, they all have they all have some jurisdiction

1569
01:23:18,200 --> 01:23:20,160
Speaker 7: in what we call intentionally altered genomes.

1570
01:23:20,240 --> 01:23:24,120
Speaker 6: Okay, there are examples of US Fish and Wildlife Service

1571
01:23:24,280 --> 01:23:29,120
Speaker 6: like saying this is an acceptable level of genetic representation.

1572
01:23:29,320 --> 01:23:33,320
Speaker 6: Like in Colorado, they like with cutthroat trout, they would

1573
01:23:33,360 --> 01:23:38,280
Speaker 6: find like an isolated like subspecies of cutthroat trout and

1574
01:23:38,320 --> 01:23:41,439
Speaker 6: be like, it's like pretty much there, So we're going

1575
01:23:41,479 --> 01:23:44,800
Speaker 6: to put it back into this place. And so I

1576
01:23:44,800 --> 01:23:47,679
Speaker 6: mean someone's capable of saying like it's close enough.

1577
01:23:48,400 --> 01:23:50,519
Speaker 7: And that's US Fish and wildlife typically. But once we

1578
01:23:50,520 --> 01:23:53,200
Speaker 7: start getting into gene editing and sure start inviting more

1579
01:23:53,280 --> 01:23:55,360
Speaker 7: regulators to the table. And that's what we're doing right now,

1580
01:23:55,400 --> 01:23:59,200
Speaker 7: and we're actually pushing the boundaries of the regulatory environment

1581
01:23:59,240 --> 01:24:02,120
Speaker 7: because this you know, these were all hypotheticals and now

1582
01:24:02,120 --> 01:24:04,600
Speaker 7: their realities and people are scrambling to say, how the

1583
01:24:04,600 --> 01:24:05,280
Speaker 7: hell do we do this?

1584
01:24:05,560 --> 01:24:09,120
Speaker 1: So could you run? Could you How long does a

1585
01:24:09,160 --> 01:24:10,120
Speaker 1: blackfooted fair it live?

1586
01:24:10,760 --> 01:24:11,519
Speaker 7: Ten twelve years?

1587
01:24:11,560 --> 01:24:11,840
Speaker 1: Okay?

1588
01:24:12,560 --> 01:24:13,400
Speaker 3: Could you run?

1589
01:24:13,560 --> 01:24:21,080
Speaker 1: Would it makes sense that you would run a generation? Right,

1590
01:24:22,560 --> 01:24:27,240
Speaker 1: you'd make a plag resistant blackfooted ferret if you ran

1591
01:24:27,320 --> 01:24:28,720
Speaker 1: it for a if you just let it go for

1592
01:24:28,760 --> 01:24:32,400
Speaker 1: a generation in captivity, would you then be able to

1593
01:24:32,439 --> 01:24:35,000
Speaker 1: be like would you then be able to dispel Would

1594
01:24:35,080 --> 01:24:39,280
Speaker 1: that be enough time to dispel some of the questions? Yeah,

1595
01:24:39,320 --> 01:24:41,680
Speaker 1: I think does it take like would you have to

1596
01:24:41,760 --> 01:24:45,120
Speaker 1: run five generations? It's a good question that puts you

1597
01:24:45,160 --> 01:24:46,240
Speaker 1: sixty years down the road.

1598
01:24:46,240 --> 01:24:48,280
Speaker 7: I mean, I certainly don't think Colossal has the answer

1599
01:24:48,320 --> 01:24:50,360
Speaker 7: for that, For that specific question. I think that is

1600
01:24:50,400 --> 01:24:52,760
Speaker 7: a regulatory question. You know, at what point do we

1601
01:24:52,800 --> 01:24:56,400
Speaker 7: feel comfortable with this now, knowing that they have a

1602
01:24:56,400 --> 01:25:00,080
Speaker 7: close living relative with this exact gene doing how how

1603
01:25:00,160 --> 01:25:02,200
Speaker 7: much do we need to do Because we've studied domestic

1604
01:25:02,200 --> 01:25:08,160
Speaker 7: ferrets for mm hmm hundreds of years, right, that might

1605
01:25:08,240 --> 01:25:10,680
Speaker 7: inform what's happening here. But I think you could set

1606
01:25:10,720 --> 01:25:13,280
Speaker 7: up an experimental population where you're able to do a

1607
01:25:13,280 --> 01:25:15,200
Speaker 7: lot of those things and within a generation or two

1608
01:25:15,240 --> 01:25:17,360
Speaker 7: begin to put animals back into the wild. Now, the

1609
01:25:17,439 --> 01:25:21,880
Speaker 7: trick with blackfooted ferret is that, you know, we probably

1610
01:25:21,920 --> 01:25:23,920
Speaker 7: need to find some resistance for prairie dogs as well.

1611
01:25:24,720 --> 01:25:26,599
Speaker 7: All right, if they're going to continue being the vector

1612
01:25:26,680 --> 01:25:30,040
Speaker 7: of the disease, you need to remove the vector, and

1613
01:25:30,120 --> 01:25:31,559
Speaker 7: they have to you know, you have to have parade

1614
01:25:31,600 --> 01:25:34,680
Speaker 7: dogs because parade dogs are blackfoot af fairts rely on

1615
01:25:34,720 --> 01:25:39,320
Speaker 7: their holes. Plus they eat them. So, you know, ranching

1616
01:25:39,520 --> 01:25:42,360
Speaker 7: folks don't really love parade dogs. They create a lot

1617
01:25:42,360 --> 01:25:45,320
Speaker 7: of problems for cattle, and so to go and convince

1618
01:25:45,360 --> 01:25:47,920
Speaker 7: people that we should do something protect parade dogs might

1619
01:25:47,960 --> 01:25:48,919
Speaker 7: be a harder sell.

1620
01:25:48,960 --> 01:25:55,920
Speaker 1: Like you feel that that the constant exposure could in time,

1621
01:25:56,920 --> 01:25:59,080
Speaker 1: that the susceptibility could could.

1622
01:25:58,880 --> 01:26:00,679
Speaker 7: Return potentially, I see.

1623
01:26:00,920 --> 01:26:02,400
Speaker 1: Yeah.

1624
01:26:02,680 --> 01:26:06,479
Speaker 2: A month ago, Time magazine reported that the Mandan and

1625
01:26:06,600 --> 01:26:09,960
Speaker 2: Ricara tribes have expressed a desire to have dire wolves

1626
01:26:10,000 --> 01:26:13,240
Speaker 2: live on their lands in North Dakota, a possibility Colossal

1627
01:26:13,320 --> 01:26:15,920
Speaker 2: is studying. So two things. One that seems to go

1628
01:26:15,960 --> 01:26:18,559
Speaker 2: against what you said earlier about the dire wolves living

1629
01:26:18,560 --> 01:26:21,519
Speaker 2: in the wild. And then two, what is that tribal

1630
01:26:21,520 --> 01:26:24,920
Speaker 2: communication been like with you guys about why they have

1631
01:26:25,040 --> 01:26:28,400
Speaker 2: a desire to have dire wolves on their reservation.

1632
01:26:28,760 --> 01:26:30,360
Speaker 7: Yeah. I wouldn't say it goes against what I was

1633
01:26:30,360 --> 01:26:32,479
Speaker 7: saying earlier. I think it would be replicating what we've

1634
01:26:32,479 --> 01:26:34,200
Speaker 7: done already, but on tribal lands.

1635
01:26:34,240 --> 01:26:37,760
Speaker 2: So in two thousand acre enclosure, Yeah, okay.

1636
01:26:37,720 --> 01:26:40,879
Speaker 7: So a year and a half ago we launched Colossal's

1637
01:26:40,880 --> 01:26:44,759
Speaker 7: Indigenous Council, which was bringing leaders in the conservation community

1638
01:26:44,880 --> 01:26:48,200
Speaker 7: from the indigenous world to the table to say, how

1639
01:26:48,200 --> 01:26:51,639
Speaker 7: can we enhance the conservation work you're already leading. How

1640
01:26:51,680 --> 01:26:56,040
Speaker 7: could we adopt you know, things like coexistence strategies that

1641
01:26:56,640 --> 01:26:59,360
Speaker 7: our Native American partners have been much better at than

1642
01:26:59,400 --> 01:27:02,559
Speaker 7: we have uh and and so part of that was,

1643
01:27:03,479 --> 01:27:06,280
Speaker 7: you know, one of the reasons for dire Wolf when

1644
01:27:06,280 --> 01:27:08,720
Speaker 7: we were launching is we were working with Chairman Fox

1645
01:27:08,760 --> 01:27:11,040
Speaker 7: at mh a nation in North Dakota, and we were

1646
01:27:11,040 --> 01:27:13,040
Speaker 7: talking to him about some bison genetic work that we

1647
01:27:13,040 --> 01:27:15,040
Speaker 7: were doing because Beth has a big background and by

1648
01:27:15,120 --> 01:27:18,760
Speaker 7: some genetics and and and Chairman Fox had started telling

1649
01:27:18,800 --> 01:27:21,040
Speaker 7: us an origin story of his of his people, and

1650
01:27:21,080 --> 01:27:24,439
Speaker 7: it talks about the great wolf, which was this large,

1651
01:27:24,600 --> 01:27:25,840
Speaker 7: extremely large wolf.

1652
01:27:26,600 --> 01:27:26,720
Speaker 4: UH.

1653
01:27:27,040 --> 01:27:28,840
Speaker 7: It was it was a white wolf or a light

1654
01:27:28,880 --> 01:27:32,120
Speaker 7: colored wolf, similar to what we're talking about, and it

1655
01:27:32,240 --> 01:27:34,879
Speaker 7: was part of their origin stories passed down through oral tradition,

1656
01:27:35,360 --> 01:27:39,640
Speaker 7: and their people believe that that that they had coexisted

1657
01:27:39,680 --> 01:27:42,040
Speaker 7: with dire wolves and that's part of their origin story.

1658
01:27:42,280 --> 01:27:43,800
Speaker 7: So there was a big interest. They said, if you

1659
01:27:43,840 --> 01:27:45,320
Speaker 7: guys are able to do this, we would love to

1660
01:27:45,560 --> 01:27:47,120
Speaker 7: bring the dire wolves back to our lands so we

1661
01:27:47,160 --> 01:27:50,799
Speaker 7: can also honor this UH this ancestral wolf that's important

1662
01:27:50,840 --> 01:27:51,360
Speaker 7: to our people.

1663
01:27:52,200 --> 01:27:54,720
Speaker 2: Who has done this is a global story? Who's done

1664
01:27:54,760 --> 01:27:57,640
Speaker 2: a good or a bad job? From your perspective of

1665
01:27:57,680 --> 01:27:58,280
Speaker 2: covering it?

1666
01:28:00,439 --> 01:28:01,679
Speaker 7: That's a that's a great question.

1667
01:28:02,040 --> 01:28:06,080
Speaker 1: I don't know. Yeah, there was one that was crazy.

1668
01:28:06,479 --> 01:28:07,080
Speaker 7: What's that one?

1669
01:28:08,439 --> 01:28:10,680
Speaker 1: It was like, I don't even want to say on

1670
01:28:10,760 --> 01:28:11,080
Speaker 1: the air.

1671
01:28:12,240 --> 01:28:13,719
Speaker 3: It was like a cult reporter.

1672
01:28:14,320 --> 01:28:19,400
Speaker 7: Oh yeah, we won't give them any clicks. That was wild.

1673
01:28:19,720 --> 01:28:22,599
Speaker 7: That was that was just looney tune stuff. I think

1674
01:28:22,600 --> 01:28:27,080
Speaker 7: there's been some bad stuff. A good example is what

1675
01:28:27,240 --> 01:28:30,639
Speaker 7: is the Cowboy Statesman. There's a Wyoming publication. They wrote

1676
01:28:30,680 --> 01:28:32,680
Speaker 7: an article about, you know, this is what's going to

1677
01:28:32,680 --> 01:28:35,400
Speaker 7: happen when direwolves introduced to Yellowstone, Like there's such a

1678
01:28:35,479 --> 01:28:39,360
Speaker 7: leap of logic, steps to the end. Yeah, yeah, And

1679
01:28:39,400 --> 01:28:40,680
Speaker 7: it was just you know, this is going to be

1680
01:28:40,680 --> 01:28:41,920
Speaker 7: horrible for Yellowstone. You know.

1681
01:28:41,960 --> 01:28:44,920
Speaker 2: Well, I like their stuff generally, but they are kind

1682
01:28:44,960 --> 01:28:47,040
Speaker 2: of bulldogs. I could see how they could take a

1683
01:28:47,080 --> 01:28:49,479
Speaker 2: story like this and and do exactly what you're saying.

1684
01:28:50,080 --> 01:28:51,920
Speaker 3: Yeah, I was skipping a lot of steps.

1685
01:28:52,120 --> 01:28:52,320
Speaker 1: Yeah.

1686
01:28:52,360 --> 01:28:54,400
Speaker 7: Another one that did a poor job that I was

1687
01:28:54,520 --> 01:28:56,439
Speaker 7: disappointed with because it's a friend of ours that we

1688
01:28:56,479 --> 01:28:59,960
Speaker 7: know pretty well. Was the Washington posted after Secretary Bergham

1689
01:29:00,040 --> 01:29:01,720
Speaker 7: had come out with the public statement of support for

1690
01:29:01,800 --> 01:29:05,840
Speaker 7: the extinction technologies as a tool for conservation. Was basically there.

1691
01:29:05,880 --> 01:29:10,160
Speaker 7: The headline was this really superficial stretch that said, oh,

1692
01:29:10,200 --> 01:29:13,080
Speaker 7: Secretary Bergham and the Trump administration will use the extinction

1693
01:29:13,160 --> 01:29:16,960
Speaker 7: to gut the Endanger Species Act. And they've said that. Well,

1694
01:29:17,000 --> 01:29:19,479
Speaker 7: they didn't say that. I mean his act, his quote,

1695
01:29:19,479 --> 01:29:21,519
Speaker 7: which is pretty powerful, and I was blown away that

1696
01:29:21,640 --> 01:29:25,040
Speaker 7: he took such a bold stance. Basically said, this is

1697
01:29:25,160 --> 01:29:29,160
Speaker 7: technology that could be fundamentally game changing to the recovery

1698
01:29:29,200 --> 01:29:34,040
Speaker 7: of species, which would lead to delisting. This wasn't just

1699
01:29:34,120 --> 01:29:35,080
Speaker 7: gutting the endangers.

1700
01:29:35,240 --> 01:29:37,519
Speaker 1: I read that, and that was but that's a very

1701
01:29:37,560 --> 01:29:43,479
Speaker 1: common kind of environmental reporting. Yeah, that it was. He

1702
01:29:43,880 --> 01:29:46,559
Speaker 1: he said something to the effect of in a perfect world,

1703
01:29:47,040 --> 01:29:48,040
Speaker 1: we would never get there.

1704
01:29:48,160 --> 01:29:50,800
Speaker 3: Yes, And then that was taken like, in.

1705
01:29:50,760 --> 01:29:54,360
Speaker 1: A proper role, we would never get to needing the

1706
01:29:54,680 --> 01:29:58,519
Speaker 1: ESA correct, which would wind up being if you if

1707
01:29:58,560 --> 01:30:01,479
Speaker 1: you pull a bunch of Americakinson said, would you like

1708
01:30:01,560 --> 01:30:04,960
Speaker 1: to live in a world where the Endangered Species Act

1709
01:30:05,000 --> 01:30:10,400
Speaker 1: was never necessary? Everyone will go Well, sure, he says that,

1710
01:30:10,680 --> 01:30:12,800
Speaker 1: and it's that he must mean he's going to get

1711
01:30:12,880 --> 01:30:14,479
Speaker 1: rid of it.

1712
01:30:13,840 --> 01:30:15,760
Speaker 7: Yeah, it's just funny and it happens.

1713
01:30:15,800 --> 01:30:18,320
Speaker 1: It was like it was it was a wild extrapolation,

1714
01:30:18,640 --> 01:30:20,519
Speaker 1: but I see what they were getting at. They're getting

1715
01:30:20,560 --> 01:30:22,880
Speaker 1: at that. It would be like, oh, they're going to

1716
01:30:23,080 --> 01:30:28,240
Speaker 1: use a squishy definition, right that, Like like, here's the fear.

1717
01:30:28,280 --> 01:30:31,479
Speaker 1: I'm guessing, like what what's driving the mentality? It would

1718
01:30:31,520 --> 01:30:36,599
Speaker 1: be that you get down to ten blackfooted ferrets and

1719
01:30:36,680 --> 01:30:38,240
Speaker 1: someone saying like, wow.

1720
01:30:38,400 --> 01:30:40,720
Speaker 3: They really should have ees a protection.

1721
01:30:40,760 --> 01:30:45,479
Speaker 7: And they'd be like, don't worry, we just ordered two hundred, right,

1722
01:30:45,560 --> 01:30:48,120
Speaker 7: But what's missing and like that's like I think that's

1723
01:30:48,120 --> 01:30:50,439
Speaker 7: what they're getting That would become the play in that

1724
01:30:50,600 --> 01:30:53,600
Speaker 7: sort of that just you know, menu of options and

1725
01:30:53,720 --> 01:30:56,000
Speaker 7: ordering the next species of interest. What's missing in that

1726
01:30:56,040 --> 01:30:58,960
Speaker 7: whole thing is habitat. And that's what I think a

1727
01:30:58,960 --> 01:31:01,400
Speaker 7: lot of these environmental reporters that have taken that very

1728
01:31:01,479 --> 01:31:05,280
Speaker 7: hardline stance are missing. Is this idea that recovery has

1729
01:31:05,560 --> 01:31:09,879
Speaker 7: very specific parameters around habitat requirement, right and sustainable population

1730
01:31:09,960 --> 01:31:12,639
Speaker 7: in the wild. So in order to achieve recovery, there's

1731
01:31:12,680 --> 01:31:16,840
Speaker 7: a habitat requirement. Also, I think de extinction technologies and

1732
01:31:16,920 --> 01:31:20,880
Speaker 7: using de extinction technologies to recover in danger species also

1733
01:31:21,000 --> 01:31:23,080
Speaker 7: has this really great sort of beacon of hope and

1734
01:31:23,160 --> 01:31:26,479
Speaker 7: sort of inspirational effort that if we said tomorrow we

1735
01:31:26,560 --> 01:31:30,479
Speaker 7: could bring back the I rebuild woodpecker, would we start

1736
01:31:30,520 --> 01:31:35,160
Speaker 7: suddenly protecting doing a better job of protecting habitat in Arkansas?

1737
01:31:35,760 --> 01:31:36,519
Speaker 5: Well, that's the thing.

1738
01:31:36,560 --> 01:31:36,840
Speaker 1: I mean.

1739
01:31:36,960 --> 01:31:41,160
Speaker 6: If the ESA comes with what many people would call

1740
01:31:41,479 --> 01:31:47,439
Speaker 6: like onerous regulations right regarding protecting habitat, other people would

1741
01:31:47,479 --> 01:31:48,120
Speaker 6: be like, oh.

1742
01:31:47,960 --> 01:31:49,679
Speaker 7: No, it's like we need them anymore.

1743
01:31:51,920 --> 01:31:55,840
Speaker 6: So bringing them back doesn't necessarily solve the problem exactly.

1744
01:31:56,160 --> 01:31:59,240
Speaker 7: It is a tool that could help accelerate and scale recovery.

1745
01:31:59,600 --> 01:32:00,360
Speaker 7: That's what it is.

1746
01:32:00,960 --> 01:32:02,400
Speaker 1: I want. I want a narrow one on the ivy

1747
01:32:02,439 --> 01:32:04,559
Speaker 1: build woodpecker. Maybe you can correct me on some of this,

1748
01:32:04,600 --> 01:32:07,000
Speaker 1: but here's why it excites me. It's like.

1749
01:32:09,960 --> 01:32:11,480
Speaker 3: There still is habitat.

1750
01:32:12,040 --> 01:32:15,080
Speaker 7: There is people more today than when they went extinct.

1751
01:32:15,160 --> 01:32:17,960
Speaker 1: Yeah, Like, I mean, there was a lot of habitat

1752
01:32:18,000 --> 01:32:22,720
Speaker 1: destruction that led to the problem, but there is habitat. Uh,

1753
01:32:24,080 --> 01:32:29,759
Speaker 1: this was so recent, right, there's people alive right now,

1754
01:32:29,960 --> 01:32:33,160
Speaker 1: there are people alive that saw Ivory build woodpeckers.

1755
01:32:33,240 --> 01:32:35,920
Speaker 7: Yeah, there's actually like photographs of them. There's a real

1756
01:32:35,960 --> 01:32:38,439
Speaker 7: to real video of what they think was the last

1757
01:32:38,479 --> 01:32:42,080
Speaker 7: Ivory build woodpecker leaving as singer was cutting down the tree.

1758
01:32:42,160 --> 01:32:42,599
Speaker 1: Yeah.

1759
01:32:43,040 --> 01:32:44,000
Speaker 7: It's pretty powerful.

1760
01:32:44,080 --> 01:32:46,519
Speaker 3: Yeah, there's like, yeah, that's the craziest part about it.

1761
01:32:46,600 --> 01:32:47,000
Speaker 7: Yeah.

1762
01:32:47,400 --> 01:32:51,599
Speaker 1: Dan Flores talks about that while New World is They're like, oh, yeah,

1763
01:32:51,800 --> 01:32:55,519
Speaker 1: there's some in that tree. Literally, I mean, I'm not

1764
01:32:55,640 --> 01:33:01,479
Speaker 1: joking literally that they actually cut the tree during a

1765
01:33:01,520 --> 01:33:05,599
Speaker 1: logging project. There was a contentious logging project cut the tree.

1766
01:33:06,600 --> 01:33:08,320
Speaker 1: No one really knew that it was the last ones,

1767
01:33:08,360 --> 01:33:10,320
Speaker 1: but they knew it was damn near and it turned

1768
01:33:10,320 --> 01:33:12,320
Speaker 1: out probably to be the last ones. They cut the

1769
01:33:12,360 --> 01:33:15,680
Speaker 1: tree down with the thing in there as like a

1770
01:33:15,680 --> 01:33:17,519
Speaker 1: hey man, don't tell me what to do kind of thing.

1771
01:33:18,360 --> 01:33:26,880
Speaker 1: So here you have where there's habitat. It's it like

1772
01:33:27,720 --> 01:33:30,599
Speaker 1: it's hard to put it a try, Like it might

1773
01:33:30,640 --> 01:33:33,479
Speaker 1: as well have not gone extinct, do you know what

1774
01:33:33,520 --> 01:33:35,800
Speaker 1: I mean? Like it's like it one extinct. It seems

1775
01:33:35,840 --> 01:33:37,920
Speaker 1: to almost have gone extinct by like a freak chance,

1776
01:33:38,439 --> 01:33:40,559
Speaker 1: or it might as well if it didn't, if it had,

1777
01:33:40,600 --> 01:33:44,920
Speaker 1: if it had stayed intact, we wouldn't think it was weird. Yeah,

1778
01:33:46,080 --> 01:33:49,439
Speaker 1: follow me, yep. If we had, if we had kept

1779
01:33:49,840 --> 01:33:53,920
Speaker 1: short faced bears and tact, it would be a thing

1780
01:33:53,920 --> 01:33:57,000
Speaker 1: that people remarked on every time they killed a person.

1781
01:33:57,160 --> 01:33:58,920
Speaker 3: You know, I'd be like, my god, can you believe?

1782
01:33:59,400 --> 01:34:03,080
Speaker 1: Right? The same way we marvel over alligators, we marvel

1783
01:34:03,120 --> 01:34:05,400
Speaker 1: over grizzly bears, we marvel over polar bears, and just

1784
01:34:05,439 --> 01:34:06,040
Speaker 1: be a bird.

1785
01:34:06,360 --> 01:34:08,160
Speaker 3: Yep, it'd be like a bird.

1786
01:34:08,439 --> 01:34:11,720
Speaker 1: It was kind of cool, but like a bird, you know,

1787
01:34:12,360 --> 01:34:16,280
Speaker 1: so like it's not an earth shattering environmental thing to

1788
01:34:16,360 --> 01:34:21,320
Speaker 1: have it around the ranch and farm community is not

1789
01:34:21,720 --> 01:34:23,880
Speaker 1: gonna have a be jeopardized by this.

1790
01:34:24,120 --> 01:34:24,200
Speaker 4: Ye.

1791
01:34:26,680 --> 01:34:30,080
Speaker 1: I can't think of anyone except affiliated woodpecker that would

1792
01:34:30,120 --> 01:34:31,040
Speaker 1: maybe be annoyed.

1793
01:34:31,640 --> 01:34:33,959
Speaker 3: They would like they're like, dude, we were the biggest woodpecker.

1794
01:34:33,960 --> 01:34:34,360
Speaker 3: Now we're not.

1795
01:34:34,960 --> 01:34:37,920
Speaker 1: Like they might be annoyed, but it's just there's no

1796
01:34:38,120 --> 01:34:39,639
Speaker 1: it doesn't seem like there's friction.

1797
01:34:39,920 --> 01:34:41,639
Speaker 7: No, there's not there.

1798
01:34:41,800 --> 01:34:44,720
Speaker 1: If we people you go like, how do you feel

1799
01:34:44,720 --> 01:34:47,000
Speaker 1: about bringing the irobialt woodpecker back, most people would be

1800
01:34:47,040 --> 01:34:49,400
Speaker 1: like the what Yeah, like it's the woodpecker is here,

1801
01:34:49,439 --> 01:34:51,200
Speaker 1: not long ago, We're gonna put it back.

1802
01:34:51,280 --> 01:34:55,320
Speaker 3: It's just it's a woodpecker. They'd probably be like, Okay,

1803
01:34:56,000 --> 01:34:56,280
Speaker 3: you know what I.

1804
01:34:56,320 --> 01:35:00,120
Speaker 1: Mean, Like like that seems to me, just from from

1805
01:35:00,160 --> 01:35:03,680
Speaker 1: a novice perspective, that seems to me like the thing.

1806
01:35:05,000 --> 01:35:08,679
Speaker 7: To do first, Well first is hard, right, there's technical

1807
01:35:08,760 --> 01:35:11,880
Speaker 7: challenges and the extinction of a bird. Right, So we

1808
01:35:12,040 --> 01:35:15,080
Speaker 7: have the Dodo project, which is our flagship avian species,

1809
01:35:16,080 --> 01:35:18,479
Speaker 7: and this is sort of leading the charge and overcoming

1810
01:35:18,520 --> 01:35:21,639
Speaker 7: the technical challenges, the biggest challenges. There is an important

1811
01:35:21,680 --> 01:35:25,439
Speaker 7: step in a d extinction where you've edited a cell

1812
01:35:25,479 --> 01:35:26,960
Speaker 7: line to the point where you go, this is now

1813
01:35:27,200 --> 01:35:29,720
Speaker 7: a cell of the animal of interest. This is now

1814
01:35:29,720 --> 01:35:33,000
Speaker 7: a direable cell. Now we'll use somatic cell nuclear transfer,

1815
01:35:33,040 --> 01:35:36,320
Speaker 7: which is most famously Dolly the Sheep cloning. That's called

1816
01:35:36,479 --> 01:35:40,599
Speaker 7: somatic cell nuclear transfer, and that's taking the DNA from

1817
01:35:40,640 --> 01:35:43,200
Speaker 7: that cell, removing the DNA of an egg cell and

1818
01:35:43,240 --> 01:35:46,000
Speaker 7: putting your DNA in and that fertilizes the cell and

1819
01:35:46,040 --> 01:35:47,600
Speaker 7: it becomes an embryo, and then you can transfer it

1820
01:35:47,600 --> 01:35:51,439
Speaker 7: into a circuit. With birds, very early on in the

1821
01:35:51,479 --> 01:35:55,479
Speaker 7: development of their egg they're calcifying the outside of it. Right,

1822
01:35:55,520 --> 01:35:58,559
Speaker 7: we can't access the eggs internally of the of a bird.

1823
01:35:58,680 --> 01:36:01,000
Speaker 7: We have to wait for them to lay those. So

1824
01:36:01,640 --> 01:36:05,880
Speaker 7: we are creating new platforms in order to edit germ cells,

1825
01:36:05,960 --> 01:36:09,400
Speaker 7: or the cells that eventually become sperm and egg. So

1826
01:36:09,760 --> 01:36:12,240
Speaker 7: as an egg is laid, you can window the egg,

1827
01:36:12,560 --> 01:36:15,479
Speaker 7: you can extract a few microleaders of blood. You can

1828
01:36:15,520 --> 01:36:18,880
Speaker 7: plate that blood and take out what we call them

1829
01:36:19,400 --> 01:36:23,240
Speaker 7: primordial germ cells. Those primordial germ cells are the cells

1830
01:36:23,240 --> 01:36:26,280
Speaker 7: that are circulating freely in the blood of this early fetus,

1831
01:36:27,160 --> 01:36:29,360
Speaker 7: and they eventually migrate to the gonads and they become

1832
01:36:29,400 --> 01:36:33,160
Speaker 7: sperm or egg, depending on the gender of that egg.

1833
01:36:34,520 --> 01:36:37,559
Speaker 7: If we then edit those while they're plated, you can

1834
01:36:37,600 --> 01:36:41,080
Speaker 7: create a stable line of these in vitro. Then now

1835
01:36:41,160 --> 01:36:44,439
Speaker 7: you have edited the germ cell that will eventually become

1836
01:36:44,479 --> 01:36:47,120
Speaker 7: Spermer egg, and then in a different egg, which is

1837
01:36:47,479 --> 01:36:50,600
Speaker 7: you know, sort of an egg that's been engineered to

1838
01:36:50,640 --> 01:36:52,840
Speaker 7: be sterile so they don't produce any of their own

1839
01:36:52,840 --> 01:36:55,840
Speaker 7: Spermer egg. At that same point of development, you can

1840
01:36:55,960 --> 01:36:59,240
Speaker 7: check you'll edited germ cells, they migrate to the gonads,

1841
01:36:59,439 --> 01:37:01,840
Speaker 7: and then you could have a pigeon or a chicken

1842
01:37:02,000 --> 01:37:04,680
Speaker 7: that's creating the sperm or egg of a dodo. When

1843
01:37:04,720 --> 01:37:08,200
Speaker 7: they breed, they give birth, they lay a Dodo egg

1844
01:37:08,840 --> 01:37:11,840
Speaker 7: or in this case, an I Rebuild woodpecker egg. So

1845
01:37:12,040 --> 01:37:14,640
Speaker 7: we're overcoming those challenges. Right now, we don't know a

1846
01:37:14,640 --> 01:37:18,080
Speaker 7: lot about woodpeckers, so we need to study more. Last

1847
01:37:18,160 --> 01:37:22,240
Speaker 7: year we launched Colossal's nonprofit, the Colossal Foundation. I'm the

1848
01:37:22,240 --> 01:37:24,800
Speaker 7: executive director of that foundation, which is meant to sort

1849
01:37:24,800 --> 01:37:27,760
Speaker 7: of be where the rubber of de extinction meets the

1850
01:37:27,840 --> 01:37:28,559
Speaker 7: road where.

1851
01:37:28,360 --> 01:37:30,599
Speaker 1: It meaning the road being the regulatory structure.

1852
01:37:30,720 --> 01:37:34,000
Speaker 7: Regulatory structure and critically endangered species. How do we literally

1853
01:37:34,080 --> 01:37:35,519
Speaker 7: keep species off our duty lists?

1854
01:37:35,800 --> 01:37:36,040
Speaker 1: Right?

1855
01:37:36,520 --> 01:37:39,320
Speaker 7: But also in some cases there's a North American conservation

1856
01:37:39,360 --> 01:37:41,800
Speaker 7: focus of that. So one of the flagship projects for

1857
01:37:41,880 --> 01:37:44,200
Speaker 7: the foundation is the I Rebuild Woodpecker.

1858
01:37:45,040 --> 01:37:47,680
Speaker 1: What is is it? Mauritius mauritious?

1859
01:37:47,720 --> 01:37:48,520
Speaker 7: Yeah?

1860
01:37:48,560 --> 01:37:51,240
Speaker 1: What is their attitude toward the Dodo?

1861
01:37:51,840 --> 01:37:57,080
Speaker 7: It's interesting it is a culturally significant icon of Mauritius.

1862
01:37:57,360 --> 01:38:03,120
Speaker 7: It is also closely associated with colonization. Right as colonizers

1863
01:38:03,120 --> 01:38:06,000
Speaker 7: showed up, they brought this species that wiped out this

1864
01:38:07,000 --> 01:38:09,439
Speaker 7: the Dodo. Now what's interesting there was not.

1865
01:38:09,520 --> 01:38:10,880
Speaker 1: Wasn't it people that wiped out the dodo.

1866
01:38:11,280 --> 01:38:13,200
Speaker 7: Yeah, it was the colonizers that came in, right. But

1867
01:38:13,360 --> 01:38:15,840
Speaker 7: what's interesting is Mauritius didn't have an indigenous people at

1868
01:38:15,840 --> 01:38:18,679
Speaker 7: that time, right, so they sort of came into the island.

1869
01:38:18,760 --> 01:38:22,720
Speaker 7: They introduced invasive species. So it was primarily rats and

1870
01:38:22,760 --> 01:38:25,520
Speaker 7: cats that were introduced to the island that started decimating

1871
01:38:25,520 --> 01:38:29,400
Speaker 7: a ground nesting bird and a flightless bird at that

1872
01:38:29,400 --> 01:38:30,679
Speaker 7: that's what led to the dodo.

1873
01:38:30,760 --> 01:38:32,920
Speaker 3: Plus sometimes I thought they just ate them all.

1874
01:38:33,160 --> 01:38:37,240
Speaker 7: There's there is a common misconception that they were a

1875
01:38:37,280 --> 01:38:40,519
Speaker 7: favorite food of sailors. There are also some really interesting

1876
01:38:40,560 --> 01:38:43,120
Speaker 7: funny logs you can read of, you know, sailors that

1877
01:38:43,200 --> 01:38:45,519
Speaker 7: said they would bring them on to provision the boat

1878
01:38:45,640 --> 01:38:48,200
Speaker 7: and then people would just resent having to eat it

1879
01:38:48,240 --> 01:38:51,160
Speaker 7: because it was all they had was this gross, greasy bird.

1880
01:38:51,240 --> 01:38:53,639
Speaker 7: So it was probably some combination thereof but the real

1881
01:38:53,680 --> 01:38:56,720
Speaker 7: issue was habitat was being destroyed as people came in

1882
01:38:56,760 --> 01:39:00,240
Speaker 7: to make room for colonies, and invasive species were just

1883
01:39:00,439 --> 01:39:04,439
Speaker 7: wiping out nests. And the dodo hadn't evolved with a

1884
01:39:04,439 --> 01:39:07,960
Speaker 7: mam million predator, so it didn't have this natural instinct

1885
01:39:08,040 --> 01:39:10,519
Speaker 7: to run or to do any so, you know, I

1886
01:39:10,520 --> 01:39:12,800
Speaker 7: think they said from the time Dodo was discovered to

1887
01:39:12,800 --> 01:39:15,720
Speaker 7: it when extinct was eighty years so just a really

1888
01:39:15,800 --> 01:39:20,120
Speaker 7: rapid extinction event. So Mauritius is really interested in recovering

1889
01:39:20,160 --> 01:39:24,519
Speaker 7: the Dodo. They on Mauritius, So we engage directly with

1890
01:39:24,800 --> 01:39:27,600
Speaker 7: the government of Mauritius with the Mursian Wildlife Foundation to

1891
01:39:27,640 --> 01:39:29,840
Speaker 7: talk about how can we use the extinction to drive

1892
01:39:29,880 --> 01:39:32,920
Speaker 7: conservation projects and habitat restoration projects on the island of

1893
01:39:33,000 --> 01:39:36,320
Speaker 7: Mauritius and the islets that surround Marcius. And then also

1894
01:39:36,600 --> 01:39:38,920
Speaker 7: in preparation for a Dodo, can we start to find

1895
01:39:38,960 --> 01:39:43,799
Speaker 7: habitat suitable habitat that would include us removing invasive species,

1896
01:39:43,840 --> 01:39:46,400
Speaker 7: not just invasive animals, but invasive plants have taken over.

1897
01:39:46,600 --> 01:39:49,839
Speaker 7: How can we restore the you know, the lost ebony trees,

1898
01:39:50,120 --> 01:39:52,240
Speaker 7: How can we start to bring back, you know, the

1899
01:39:53,040 --> 01:39:56,920
Speaker 7: forest of sixteen hundreds in order to prepare it for

1900
01:39:58,040 --> 01:40:00,360
Speaker 7: the Dodo. And so there's there's this amazing the interest,

1901
01:40:00,400 --> 01:40:03,639
Speaker 7: we have, this great underswellow support. We've launched a council

1902
01:40:03,680 --> 01:40:07,120
Speaker 7: of people, a stakeholders group that is about ten fifteen

1903
01:40:07,120 --> 01:40:10,200
Speaker 7: people that sort of represent a variety of industry from

1904
01:40:10,240 --> 01:40:15,000
Speaker 7: tourism to science to government, and regulatory so that we

1905
01:40:15,040 --> 01:40:18,559
Speaker 7: can begin to say, hey, we're making progress towards restoring

1906
01:40:18,600 --> 01:40:20,840
Speaker 7: the dota from extinction. What do we need to be

1907
01:40:20,920 --> 01:40:23,240
Speaker 7: doing here with the people of Mauritius, with the land

1908
01:40:23,280 --> 01:40:26,880
Speaker 7: of Mauritius, in order to make sure society, society is

1909
01:40:26,960 --> 01:40:29,120
Speaker 7: prepared and ecosystems are prepared.

1910
01:40:32,880 --> 01:40:36,880
Speaker 1: So crystal ball it for me? What ook at never

1911
01:40:37,520 --> 01:40:39,519
Speaker 1: not the science, the social component. If you had a

1912
01:40:39,520 --> 01:40:45,000
Speaker 1: crystal ball the social component, what are the odds socially,

1913
01:40:45,479 --> 01:40:50,599
Speaker 1: socially regulatory, whatever that you would get that you could

1914
01:40:50,680 --> 01:40:52,880
Speaker 1: get to where they would say, let's do it. Let's

1915
01:40:52,880 --> 01:40:56,600
Speaker 1: take this little islet and let's yeah cuss somehow, and

1916
01:40:56,600 --> 01:40:59,960
Speaker 1: they're just gonna They're gonna just live or die. They

1917
01:41:00,280 --> 01:41:02,280
Speaker 1: their ability to make a living here.

1918
01:41:04,479 --> 01:41:06,560
Speaker 7: I would never say anything's one hundred percent, because we

1919
01:41:06,560 --> 01:41:08,760
Speaker 7: could get wiped out by a night an asteroid. But

1920
01:41:08,880 --> 01:41:11,519
Speaker 7: right there there's I mean, I'm ruled out that. I

1921
01:41:12,160 --> 01:41:14,639
Speaker 7: like that the Earth kind of keeps going along. Yeah,

1922
01:41:14,640 --> 01:41:17,559
Speaker 7: I think it's ninety nine percent. Beth and I went

1923
01:41:17,560 --> 01:41:20,000
Speaker 7: to Murcius last year and had an incredible visit. We're

1924
01:41:20,000 --> 01:41:22,040
Speaker 7: going again this year, and the idea is to kind

1925
01:41:22,040 --> 01:41:25,400
Speaker 7: of keep these conversations. Moving forward, Mauritius has done an

1926
01:41:25,400 --> 01:41:28,320
Speaker 7: incredible job on their own of going and recovering some

1927
01:41:28,360 --> 01:41:30,759
Speaker 7: of the islets around there. There's this really famous island

1928
01:41:30,960 --> 01:41:34,000
Speaker 7: or islet on the north end of Mauritius called Round Island.

1929
01:41:35,000 --> 01:41:39,280
Speaker 7: It's this massive, round rock island. You know, you can't

1930
01:41:39,280 --> 01:41:41,160
Speaker 7: even you know, we landed a helicopter on it in

1931
01:41:41,240 --> 01:41:43,040
Speaker 7: order to get out there, and you're sort of at

1932
01:41:43,040 --> 01:41:45,479
Speaker 7: this twenty degree angle as you land, and you're landing

1933
01:41:45,520 --> 01:41:48,400
Speaker 7: on top of giant tortoises, and it was basically an

1934
01:41:48,439 --> 01:41:52,120
Speaker 7: island that was stripped down to just the rock. All

1935
01:41:52,160 --> 01:41:54,720
Speaker 7: the soil and plant life had been completely eradicated because

1936
01:41:54,720 --> 01:41:58,519
Speaker 7: they had introduced goats, and the goats were left there

1937
01:41:58,880 --> 01:42:00,960
Speaker 7: so that when you sailed buy you could pick up

1938
01:42:00,960 --> 01:42:04,320
Speaker 7: a few, you know, and you could provision the boat. Well,

1939
01:42:04,360 --> 01:42:06,639
Speaker 7: they just grazed the hell out of the whole thing,

1940
01:42:06,680 --> 01:42:10,000
Speaker 7: and basically every species native to that little islet was

1941
01:42:10,320 --> 01:42:13,080
Speaker 7: basically wiped out. So they've been recovering it for twenty years.

1942
01:42:13,479 --> 01:42:15,600
Speaker 7: And when you go and see where they were to

1943
01:42:15,600 --> 01:42:17,439
Speaker 7: where they are today, it's incredible work.

1944
01:42:17,479 --> 01:42:19,639
Speaker 1: So for all the reasons they've been recovering it Yeah.

1945
01:42:19,479 --> 01:42:21,400
Speaker 7: Yeah, they just wanted to bring background island. It has

1946
01:42:21,439 --> 01:42:24,160
Speaker 7: a lot of endemic species that we're important to try

1947
01:42:24,160 --> 01:42:28,680
Speaker 7: to save. So they're already doing this great work, I

1948
01:42:28,680 --> 01:42:32,280
Speaker 7: think colossal showing up with an opportunity to accelerate, enhance,

1949
01:42:32,439 --> 01:42:35,679
Speaker 7: bring more funding, try to bring technology to the table

1950
01:42:36,000 --> 01:42:38,280
Speaker 7: to do just that. So there are the reason why

1951
01:42:38,320 --> 01:42:40,000
Speaker 7: I think it's so high and imercious. The chances are

1952
01:42:40,000 --> 01:42:42,280
Speaker 7: so high and maercious. It's because there's all these islets

1953
01:42:42,280 --> 01:42:44,760
Speaker 7: that are sort of like living laboratories. You know, you

1954
01:42:44,880 --> 01:42:48,320
Speaker 7: can mitigate risk of some of these fears around GMOs

1955
01:42:49,160 --> 01:42:51,559
Speaker 7: or what is going to be the effect of this

1956
01:42:51,640 --> 01:42:54,000
Speaker 7: reintroduction by putting them on an island.

1957
01:42:54,160 --> 01:42:58,439
Speaker 6: Yeah, like if suddenly tomorrow all the grizzly bears in

1958
01:42:58,439 --> 01:43:02,759
Speaker 6: the lower forty eight when extinct us and you guys

1959
01:43:02,840 --> 01:43:07,200
Speaker 6: were like, oh that's okay, we can repopulate. There's still

1960
01:43:07,280 --> 01:43:12,920
Speaker 6: only so there's not a lot of appropriate habitat available

1961
01:43:13,000 --> 01:43:14,760
Speaker 6: for them, right, So.

1962
01:43:16,280 --> 01:43:17,440
Speaker 5: In an island.

1963
01:43:17,040 --> 01:43:22,360
Speaker 6: Situation, you have way more control. But it seems like

1964
01:43:22,680 --> 01:43:29,000
Speaker 6: your challenge is not like growing the animal, it's like

1965
01:43:29,280 --> 01:43:33,280
Speaker 6: cultivating a place where that animal can live successfully.

1966
01:43:33,840 --> 01:43:36,400
Speaker 7: Yeah, I think there's a lot of challenges around preparing

1967
01:43:36,439 --> 01:43:38,639
Speaker 7: the habitat for one of these animals and making sure

1968
01:43:38,680 --> 01:43:40,920
Speaker 7: the protections are in place, but also making sure that

1969
01:43:41,000 --> 01:43:42,920
Speaker 7: the people are ready for that.

1970
01:43:43,000 --> 01:43:43,200
Speaker 5: Yeah.

1971
01:43:43,200 --> 01:43:45,240
Speaker 7: I think this is a huge step for a society

1972
01:43:45,280 --> 01:43:48,679
Speaker 7: to say we want to take this next step. Whoever

1973
01:43:48,800 --> 01:43:54,080
Speaker 7: is will be the first government regulator culture to say

1974
01:43:54,400 --> 01:43:57,160
Speaker 7: we want to introduce a species return from extinction to

1975
01:43:57,320 --> 01:44:00,200
Speaker 7: the wild. I mean that is a massive step. I

1976
01:44:00,200 --> 01:44:02,680
Speaker 7: mean that is going to take somebody very bold, and

1977
01:44:02,680 --> 01:44:04,800
Speaker 7: I think that's why island nations are a little more.

1978
01:44:05,880 --> 01:44:08,160
Speaker 7: They have a little more opportunity because they can mitigate

1979
01:44:08,200 --> 01:44:10,200
Speaker 7: the risk because they don't have to worry about their

1980
01:44:10,200 --> 01:44:11,880
Speaker 7: neighbors say you no time out, you're doing what?

1981
01:44:12,160 --> 01:44:12,360
Speaker 1: Yeah.

1982
01:44:12,479 --> 01:44:14,599
Speaker 7: If we did this in the US, Canada would say

1983
01:44:14,640 --> 01:44:15,519
Speaker 7: what the hell are you doing?

1984
01:44:15,680 --> 01:44:20,639
Speaker 1: Yeah, we know it's a little bit interesting. A similar

1985
01:44:20,680 --> 01:44:27,360
Speaker 1: approach was we wound up introducing wild turkeys in a

1986
01:44:27,360 --> 01:44:31,000
Speaker 1: lot of places that wild turkeys weren't from. Yeah, so

1987
01:44:33,280 --> 01:44:36,080
Speaker 1: you know they think that historically he had turkeys and

1988
01:44:36,560 --> 01:44:39,320
Speaker 1: what's today thirty four states. Now we have turkeys in

1989
01:44:39,400 --> 01:44:43,519
Speaker 1: forty nine states. Very little friction there was I remember

1990
01:44:43,560 --> 01:44:45,680
Speaker 1: one state there was some friction in there and it

1991
01:44:45,840 --> 01:44:52,240
Speaker 1: was a worried about some rare annual and these guys

1992
01:44:52,280 --> 01:44:56,519
Speaker 1: wound up doing a big crop survey like bird crops,

1993
01:44:56,760 --> 01:44:58,960
Speaker 1: you know, and they just didn't find anything analogous to

1994
01:44:59,000 --> 01:45:02,679
Speaker 1: this and wild turkeys around the world or around the country,

1995
01:45:02,840 --> 01:45:04,880
Speaker 1: Like it just doesn't seem to think turkeys would pray on.

1996
01:45:05,520 --> 01:45:06,760
Speaker 3: But that was like one little.

1997
01:45:06,520 --> 01:45:10,160
Speaker 1: Test and they let them go, and it wound up

1998
01:45:10,160 --> 01:45:12,880
Speaker 1: being that we always talk about, well you have like invasives,

1999
01:45:12,880 --> 01:45:15,120
Speaker 1: which are bad, but it wound up being like it's

2000
01:45:15,160 --> 01:45:20,680
Speaker 1: like a species of no known negative. It's like no

2001
01:45:20,680 --> 01:45:25,559
Speaker 1: no negative. But the climates changed so much you might

2002
01:45:25,560 --> 01:45:27,400
Speaker 1: not get away with that what they were doing with

2003
01:45:27,400 --> 01:45:31,679
Speaker 1: turkeys into seventies, eighties, early nineties. You might not today

2004
01:45:33,120 --> 01:45:36,439
Speaker 1: be able to go into I'm not trying to pick

2005
01:45:36,479 --> 01:45:38,160
Speaker 1: on California today, it might not be able to go

2006
01:45:38,240 --> 01:45:40,120
Speaker 1: into California and say, hey, we're going to turn some

2007
01:45:40,960 --> 01:45:41,840
Speaker 1: wild turkeys loose.

2008
01:45:42,120 --> 01:45:44,080
Speaker 7: Oh like the cultural climate change.

2009
01:45:43,880 --> 01:45:45,519
Speaker 1: Yeah, you know what I mean, Like you might hit

2010
01:45:45,560 --> 01:45:48,240
Speaker 1: resistance for something that was just that happened. Because if

2011
01:45:48,280 --> 01:45:50,960
Speaker 1: you look at like the shift too in the fifties,

2012
01:45:51,120 --> 01:45:54,960
Speaker 1: they brought add ad into Texas, they brought Ibex into

2013
01:45:55,000 --> 01:45:55,679
Speaker 1: New Mexico.

2014
01:45:56,080 --> 01:45:58,360
Speaker 3: They bought oricx in New Mexico, and there.

2015
01:45:58,280 --> 01:46:00,439
Speaker 1: Was kind of a let's just see what happened, like

2016
01:46:00,479 --> 01:46:05,480
Speaker 1: they can't be bad attitude. That's that's built up resistance.

2017
01:46:05,920 --> 01:46:09,200
Speaker 1: And now even when people look like with ELK, we've

2018
01:46:09,200 --> 01:46:14,160
Speaker 1: only recovered ELK on fourteen percent of twenty percent of

2019
01:46:14,360 --> 01:46:16,960
Speaker 1: historic range. Fourteen percent of historic range, there's kind of

2020
01:46:16,960 --> 01:46:20,519
Speaker 1: mind boggling. Yeah, and that recovery effort is ground to

2021
01:46:20,560 --> 01:46:25,679
Speaker 1: a halt over chronic wasting disease. Just there's a less

2022
01:46:25,720 --> 01:46:29,559
Speaker 1: of an appetite to move servids from one area to

2023
01:46:29,640 --> 01:46:33,519
Speaker 1: another because like disease transmission. So it is in the

2024
01:46:33,560 --> 01:46:35,800
Speaker 1: conversation about what we get to a place where we're

2025
01:46:35,840 --> 01:46:41,479
Speaker 1: actually trying to do this, it's like the appetite seems

2026
01:46:41,520 --> 01:46:44,760
Speaker 1: to be just generally declining to like any kind of

2027
01:46:44,920 --> 01:46:45,679
Speaker 1: dice rolling.

2028
01:46:46,120 --> 01:46:49,080
Speaker 7: Well, yeah, I think you know there is a certain

2029
01:46:49,120 --> 01:46:51,040
Speaker 7: aspect of this that is you know better than you

2030
01:46:51,080 --> 01:46:54,720
Speaker 7: do better. Right, So we know that these animals, you know,

2031
01:46:55,120 --> 01:46:58,679
Speaker 7: a foreign animal or an invasive animal would have certain

2032
01:46:58,720 --> 01:47:01,640
Speaker 7: impacts on an ecosystem. So that is important that we

2033
01:47:02,400 --> 01:47:04,960
Speaker 7: take that precautionary approach because now we know better, so

2034
01:47:05,000 --> 01:47:07,679
Speaker 7: we must do better. However, I think the pendulum swunk

2035
01:47:07,680 --> 01:47:11,160
Speaker 7: all the way where now it's you know, any change

2036
01:47:11,760 --> 01:47:15,200
Speaker 7: is bad. Oh, and right now, if we do anything,

2037
01:47:15,240 --> 01:47:17,320
Speaker 7: if we try to change anything about the status quo,

2038
01:47:17,680 --> 01:47:21,519
Speaker 7: there are things we cannot account for. Yet therefore we

2039
01:47:21,560 --> 01:47:23,599
Speaker 7: should not take that step. And so we've moved into

2040
01:47:23,600 --> 01:47:27,720
Speaker 7: this fear based decision making, which is grinding recovery to

2041
01:47:27,720 --> 01:47:29,000
Speaker 7: a halt of a lot of species.

2042
01:47:29,120 --> 01:47:32,400
Speaker 1: No, and look, I was management help, Yeah, exactly.

2043
01:47:32,520 --> 01:47:34,719
Speaker 7: So I think we need to sort of the pendulum

2044
01:47:34,800 --> 01:47:36,560
Speaker 7: needs to come back to the center. I think, you know,

2045
01:47:36,640 --> 01:47:38,840
Speaker 7: on most things in life, from this moderate approach is

2046
01:47:38,840 --> 01:47:41,479
Speaker 7: probably the best approach. So how can we make the

2047
01:47:41,520 --> 01:47:44,760
Speaker 7: most informed decision while also having a certain appetite for risk,

2048
01:47:45,040 --> 01:47:47,160
Speaker 7: and with this idea that without risk there is no

2049
01:47:47,280 --> 01:47:50,200
Speaker 7: chance for a win out there, right, we need to

2050
01:47:50,240 --> 01:47:52,200
Speaker 7: take certain risks in order to recover species.

2051
01:47:53,479 --> 01:47:56,200
Speaker 2: You mentioned earlier that you didn't care for the reporting

2052
01:47:56,240 --> 01:47:59,160
Speaker 2: by Washington Posts and Cowboys Stay Daily, who did a

2053
01:47:59,160 --> 01:48:00,799
Speaker 2: good job of covering the story.

2054
01:48:01,360 --> 01:48:03,320
Speaker 7: Well, we had a lot of great folks, I mean,

2055
01:48:03,600 --> 01:48:06,960
Speaker 7: you know, obviously we were blown away that we got

2056
01:48:07,000 --> 01:48:09,639
Speaker 7: the cover Time magazine and Time did a great job.

2057
01:48:09,960 --> 01:48:14,680
Speaker 7: Jeff Jeff Pluger. He did this great article on the

2058
01:48:14,680 --> 01:48:19,040
Speaker 7: whole thing. The New Yorker broke our embargo and published

2059
01:48:19,040 --> 01:48:23,080
Speaker 7: the story. Oh yeah, so, although we're mad at them,

2060
01:48:23,120 --> 01:48:25,559
Speaker 7: if you read the story, it's pretty good. It's pretty

2061
01:48:25,560 --> 01:48:25,920
Speaker 7: good story.

2062
01:48:26,040 --> 01:48:27,240
Speaker 2: They know they were breaking it.

2063
01:48:27,439 --> 01:48:29,720
Speaker 7: Yeah they did. There's you know, there's I'll just put

2064
01:48:29,760 --> 01:48:31,439
Speaker 7: it this way, there's some emails going back and forth

2065
01:48:31,439 --> 01:48:33,760
Speaker 7: saying can we do this? We said no, and then

2066
01:48:33,880 --> 01:48:37,880
Speaker 7: they did it. Anyways, So that was heartbreaking because there's

2067
01:48:38,040 --> 01:48:39,519
Speaker 7: so many, you know, there's one hundred and seventy five

2068
01:48:39,560 --> 01:48:41,760
Speaker 7: people at Colossal that we're working their tails off to

2069
01:48:41,800 --> 01:48:44,280
Speaker 7: do this thing. Our marketing team was killing themselves, their

2070
01:48:44,320 --> 01:48:47,320
Speaker 7: PR teams were killing themselves to have this really ambitious

2071
01:48:47,400 --> 01:48:50,320
Speaker 7: launch event and then for somebody to selfishly try to

2072
01:48:50,360 --> 01:48:54,400
Speaker 7: go first was really frustrating. But despite all of that,

2073
01:48:54,439 --> 01:48:56,840
Speaker 7: New Yorker read a pretty cool piece now that said,

2074
01:48:56,920 --> 01:48:58,920
Speaker 7: he gave me like the dumbest quote ever and that thing,

2075
01:48:59,000 --> 01:49:00,400
Speaker 7: so I'm a little up about it.

2076
01:49:00,439 --> 01:49:01,280
Speaker 1: He gave you a bad quote.

2077
01:49:01,360 --> 01:49:03,080
Speaker 7: Yeah, well, you know, I was. I was out of

2078
01:49:03,080 --> 01:49:05,240
Speaker 7: the country when he was visiting, so I was zooming

2079
01:49:05,240 --> 01:49:06,960
Speaker 7: into a few calls and he was interviewing me over

2080
01:49:07,080 --> 01:49:09,040
Speaker 7: zoom and I just didn't have a lot of time.

2081
01:49:09,080 --> 01:49:11,880
Speaker 7: He basically said, what do you do here? You know,

2082
01:49:12,040 --> 01:49:14,479
Speaker 7: chief Animal Officers not a title. People really know what

2083
01:49:14,520 --> 01:49:16,360
Speaker 7: the hell it means. I'm not sure I know what

2084
01:49:16,400 --> 01:49:18,599
Speaker 7: it means. It's like you're a zoo keeper or something,

2085
01:49:18,920 --> 01:49:21,840
Speaker 7: chief zookeeper. One of my guys on my team calls

2086
01:49:21,840 --> 01:49:25,400
Speaker 7: me the chief pet officer. So I just told him

2087
01:49:25,439 --> 01:49:27,280
Speaker 7: I play with animals, just joking.

2088
01:49:27,560 --> 01:49:27,840
Speaker 1: Of course.

2089
01:49:27,920 --> 01:49:29,360
Speaker 7: That's the one thing he published.

2090
01:49:31,120 --> 01:49:32,200
Speaker 1: Like a cocky.

2091
01:49:33,360 --> 01:49:34,840
Speaker 7: I was like, there's a lot of thought that goes

2092
01:49:34,880 --> 01:49:37,240
Speaker 7: into what I do, right, But but it was fine.

2093
01:49:38,240 --> 01:49:40,760
Speaker 7: But yeah, I think there was a vast majority. I's

2094
01:49:40,800 --> 01:49:43,559
Speaker 7: a ninety five percent of the things that were published

2095
01:49:43,600 --> 01:49:47,400
Speaker 7: I thought were fair balanced, shed a positive light on

2096
01:49:47,439 --> 01:49:49,960
Speaker 7: what we were doing. Five percent of them were hyper

2097
01:49:49,960 --> 01:49:53,799
Speaker 7: negative to ass like almost just for the purpose of clickbait.

2098
01:49:54,720 --> 01:49:58,840
Speaker 1: Yeah, well, I mean you have to acknowledge that there's

2099
01:49:58,880 --> 01:50:02,120
Speaker 1: a real stir of the pot. Yeah yeah, I mean

2100
01:50:02,160 --> 01:50:03,000
Speaker 1: you're starting the pot.

2101
01:50:03,080 --> 01:50:05,560
Speaker 7: That's what I like about it. I think the conservation

2102
01:50:05,600 --> 01:50:08,080
Speaker 7: pot needs to be stirred. Like I said, I think

2103
01:50:08,080 --> 01:50:11,240
Speaker 7: we've gone to this fear based approach, where you know,

2104
01:50:11,320 --> 01:50:13,360
Speaker 7: in the Teddy Roosevelt quote, we're at we're at the

2105
01:50:13,400 --> 01:50:15,920
Speaker 7: third point where we said, you know, the worst, We're

2106
01:50:15,960 --> 01:50:17,920
Speaker 7: doing the worst thing, which is we're not making decisions.

2107
01:50:18,000 --> 01:50:22,040
Speaker 6: But do you guys feel an urgency to you're not

2108
01:50:22,080 --> 01:50:26,639
Speaker 6: going to stop messing around with ancient extinct species, but

2109
01:50:27,040 --> 01:50:31,600
Speaker 6: do you feel an urgency to like do something like

2110
01:50:31,720 --> 01:50:34,599
Speaker 6: more timely, like with a specie that is like.

2111
01:50:34,560 --> 01:50:36,360
Speaker 7: We I mean, the American red wolf is a good

2112
01:50:36,400 --> 01:50:39,440
Speaker 7: example of how what exactly what we're doing. But the Foundation,

2113
01:50:39,840 --> 01:50:42,960
Speaker 7: the Colossal Foundation, has forty five conservation partners. That's forty

2114
01:50:42,960 --> 01:50:46,160
Speaker 7: five separate projects that we're running for endangered species conservation.

2115
01:50:46,479 --> 01:50:51,759
Speaker 7: Everything from how can we use artificial intelligence to unlock

2116
01:50:51,800 --> 01:50:54,439
Speaker 7: the language of wolves? Right here in Yellowstone working with

2117
01:50:54,760 --> 01:50:57,600
Speaker 7: the Yellowstone Wolf Project Yellowstone Forever, Jeff Reed and the

2118
01:50:57,600 --> 01:51:01,160
Speaker 7: Grizzly Systems team trying to unlock language and in a

2119
01:51:01,160 --> 01:51:03,040
Speaker 7: way that we can track wolves over space and time

2120
01:51:03,080 --> 01:51:06,679
Speaker 7: without requiring collars and understand what are they talking about.

2121
01:51:07,000 --> 01:51:12,280
Speaker 7: To conferring resistance to toxins from invasive species. The Northern coal

2122
01:51:12,280 --> 01:51:15,880
Speaker 7: from the Northern Territory of Australia, is being decimated because

2123
01:51:15,880 --> 01:51:18,160
Speaker 7: one of the animals that predates on is the invasive

2124
01:51:18,200 --> 01:51:20,800
Speaker 7: cane toad, which has a buffotox in the boo. That

2125
01:51:20,880 --> 01:51:24,400
Speaker 7: neurotoxin kills the cane the coal when they eat them.

2126
01:51:24,600 --> 01:51:28,240
Speaker 7: So we've actually found one base pair change that animals

2127
01:51:28,280 --> 01:51:31,120
Speaker 7: that co evolved with the cane toad in South America

2128
01:51:31,280 --> 01:51:34,240
Speaker 7: have evolved that. So we're changing that one base pairent

2129
01:51:34,320 --> 01:51:37,000
Speaker 7: coal and we've already shown in the lab that it

2130
01:51:37,080 --> 01:51:40,840
Speaker 7: confers resistance to bufotoxin. So now we can give that

2131
01:51:40,920 --> 01:51:44,479
Speaker 7: to the government off Australia and say here is a

2132
01:51:44,520 --> 01:51:47,639
Speaker 7: tool for you guys to recover your northern coal and

2133
01:51:48,479 --> 01:51:50,680
Speaker 7: as a bonus, they'll eat all those cane toads that

2134
01:51:50,720 --> 01:51:52,439
Speaker 7: are invasive and are sort of one of the most

2135
01:51:52,560 --> 01:51:56,519
Speaker 7: famous examples of an invasive species. We also created a

2136
01:51:56,560 --> 01:51:59,599
Speaker 7: vaccine to help elephants with one of the most deadly

2137
01:51:59,680 --> 01:52:02,880
Speaker 7: virus as they face the elephant indo, thelotropic Herpes virushi,

2138
01:52:02,960 --> 01:52:05,200
Speaker 7: kills about thirty percent of elephants and human care and

2139
01:52:05,280 --> 01:52:09,160
Speaker 7: more and more, we understand that is an is decimating

2140
01:52:09,160 --> 01:52:12,960
Speaker 7: wild populations. So you know, we have forty five of

2141
01:52:12,960 --> 01:52:15,880
Speaker 7: these projects ongoing. Obviously, What makes it to the top

2142
01:52:16,200 --> 01:52:19,320
Speaker 7: of that of the storyline is always the exction. But

2143
01:52:19,400 --> 01:52:22,000
Speaker 7: that's the power of the extinction is it does bring awareness,

2144
01:52:22,000 --> 01:52:25,960
Speaker 7: It brings attention, it brings funding. Four hundred and fifty

2145
01:52:26,000 --> 01:52:29,120
Speaker 7: million dollars we've raised over four years to support the

2146
01:52:29,240 --> 01:52:32,240
Speaker 7: business of Colossal, and we've raised another seventy five million

2147
01:52:32,280 --> 01:52:35,320
Speaker 7: dollars for the Colossal Foundation. So you know, we talk

2148
01:52:35,400 --> 01:52:39,639
Speaker 7: about conservation is an under resourced fight. We're bringing resources

2149
01:52:39,680 --> 01:52:42,559
Speaker 7: to table. These are not a distraction of resources from

2150
01:52:42,680 --> 01:52:45,360
Speaker 7: places where you know you would have normally given money

2151
01:52:45,360 --> 01:52:48,200
Speaker 7: to WWF. Now we're going to Silicon Valley, We're going

2152
01:52:48,240 --> 01:52:51,200
Speaker 7: to capital you know, venture capital investors, and.

2153
01:52:51,160 --> 01:52:55,240
Speaker 1: We're right vay getting They like, what in the end,

2154
01:52:55,439 --> 01:52:57,439
Speaker 1: because you're not a nonprofit, you're not an.

2155
01:52:57,400 --> 01:52:59,519
Speaker 3: Environmental we have a non profit, you have oh not

2156
01:53:00,000 --> 01:53:00,679
Speaker 3: a foundation?

2157
01:53:00,800 --> 01:53:03,439
Speaker 1: Yep, Well, then there's a for profit component one in

2158
01:53:03,479 --> 01:53:05,080
Speaker 1: the end, what is the product?

2159
01:53:05,240 --> 01:53:07,839
Speaker 7: The product is the all of the technology we're creating.

2160
01:53:08,320 --> 01:53:10,840
Speaker 7: So it's more about process. Les's it's about product. We're

2161
01:53:10,880 --> 01:53:14,200
Speaker 7: creating processes that then we can license that IP to

2162
01:53:14,920 --> 01:53:19,960
Speaker 7: other fields of science, human healthcare research, you know, university research,

2163
01:53:20,000 --> 01:53:22,000
Speaker 7: things like that. We've already spun out two companies out

2164
01:53:22,000 --> 01:53:25,520
Speaker 7: of Colossal that are now standalone businesses, self sufficient businesses.

2165
01:53:25,760 --> 01:53:28,240
Speaker 7: One is a computational biology platform, So it's an AI

2166
01:53:28,320 --> 01:53:31,800
Speaker 7: tool that essentially helps you compare genes of animals and

2167
01:53:31,880 --> 01:53:34,639
Speaker 7: understand this gene codes for this trade god that type

2168
01:53:34,640 --> 01:53:34,840
Speaker 7: of thing.

2169
01:53:35,160 --> 01:53:38,960
Speaker 1: Now clients being universities and research facilities. Exactly, I gotcha.

2170
01:53:39,000 --> 01:53:42,560
Speaker 7: Another one is a plastics degradation company. So we've identified

2171
01:53:42,560 --> 01:53:46,680
Speaker 7: a microbe that's able to consume almost every plastic we've

2172
01:53:46,720 --> 01:53:49,320
Speaker 7: ever thrown at it, everything from really soft plastics to

2173
01:53:49,479 --> 01:53:52,960
Speaker 7: very hard, high density plastics, and we're using genetic engineering

2174
01:53:52,960 --> 01:53:55,479
Speaker 7: to try to enhance those types of qualities. And so

2175
01:53:55,560 --> 01:53:57,240
Speaker 7: this gives us, you know, a new tool in the

2176
01:53:57,280 --> 01:54:00,439
Speaker 7: way to recycle plastics. And it doesn't just recycle limit

2177
01:54:00,520 --> 01:54:03,400
Speaker 7: to you know, smaller pieces of plastic microplastic. This is

2178
01:54:03,439 --> 01:54:07,320
Speaker 7: breaking down the molecular bonds of this. So that's sort

2179
01:54:07,360 --> 01:54:09,640
Speaker 7: of what we're doing, is we're this incubator of technology

2180
01:54:09,800 --> 01:54:12,759
Speaker 7: that we can spin out and it funds the whole program.

2181
01:54:14,800 --> 01:54:18,400
Speaker 2: I've seen how wrapped or recovery centers when they're feeding,

2182
01:54:18,439 --> 01:54:20,400
Speaker 2: like an eagle chick or a hawk chick, they'll cover

2183
01:54:20,479 --> 01:54:22,160
Speaker 2: themselves in a bed sheet and they'll put like a

2184
01:54:22,200 --> 01:54:25,200
Speaker 2: puppet on their end to feed the chicks. To prevent

2185
01:54:25,280 --> 01:54:30,400
Speaker 2: human imprinting, And what kind of protocols that you guys

2186
01:54:30,439 --> 01:54:33,280
Speaker 2: have about dealing with your three dire wolves, Like how

2187
01:54:33,280 --> 01:54:34,480
Speaker 2: many humans have they seen?

2188
01:54:34,800 --> 01:54:40,160
Speaker 7: Oh, they've probably seen say fifteen or twenty, but they

2189
01:54:40,200 --> 01:54:43,880
Speaker 7: see four or five on a regular basis. These are

2190
01:54:44,200 --> 01:54:46,440
Speaker 7: not candidates for release into the wild. So we take

2191
01:54:46,440 --> 01:54:48,800
Speaker 7: a slightly different approach. Right if we're creating a stable

2192
01:54:48,840 --> 01:54:51,800
Speaker 7: population of captive animals that could eventually go back into

2193
01:54:51,800 --> 01:54:53,640
Speaker 7: the wild, which is not the case with the dire wolves,

2194
01:54:53,640 --> 01:54:56,600
Speaker 7: but with the red wolves that we cloned, you know,

2195
01:54:56,760 --> 01:54:59,760
Speaker 7: this generation is not a candidate for rewilding. We'll begin

2196
01:54:59,760 --> 01:55:01,800
Speaker 7: to take a more hands off approach as you create

2197
01:55:01,880 --> 01:55:05,560
Speaker 7: subsequent generations and model after very similar projects that are

2198
01:55:05,560 --> 01:55:09,280
Speaker 7: already going on with red wolf release into North Carolina,

2199
01:55:09,760 --> 01:55:14,280
Speaker 7: Mexican gray Wolf into the American Southwest. So mammals are

2200
01:55:14,280 --> 01:55:16,240
Speaker 7: a little different. They don't imprint the way birds do.

2201
01:55:16,480 --> 01:55:22,200
Speaker 7: Birds have a very strong imprint potential, so you can

2202
01:55:22,280 --> 01:55:27,000
Speaker 7: really mess a chick up if it imprints on a human,

2203
01:55:27,040 --> 01:55:30,400
Speaker 7: whereas with the mammals, they don't take that same level

2204
01:55:30,640 --> 01:55:31,560
Speaker 7: of their habituation.

2205
01:55:31,800 --> 01:55:34,760
Speaker 6: There's a certain degree of like learn behavior too, right,

2206
01:55:34,960 --> 01:55:37,920
Speaker 6: Like wolves need to be taught how to hunt by

2207
01:55:37,920 --> 01:55:40,440
Speaker 6: their pack, and.

2208
01:55:39,640 --> 01:55:41,760
Speaker 7: So there's a lot of opportunity for cross fostering. So

2209
01:55:42,480 --> 01:55:45,200
Speaker 7: you know, you can create puppies that then you would

2210
01:55:45,280 --> 01:55:48,520
Speaker 7: drop into dens of wild wolves if you wanted to,

2211
01:55:48,720 --> 01:55:51,760
Speaker 7: like say this was a gray wolf project. As you know,

2212
01:55:51,840 --> 01:55:53,920
Speaker 7: the pack it leaves the den for a bit, you

2213
01:55:53,920 --> 01:55:56,959
Speaker 7: can go and drop puppies in, and that's called cross fostering.

2214
01:55:57,000 --> 01:55:57,800
Speaker 7: So we could do that.

2215
01:55:57,800 --> 01:56:00,480
Speaker 1: Drop bunch nerd because someone we're going to get at.

2216
01:56:03,000 --> 01:56:06,000
Speaker 5: But as far as like you never.

2217
01:56:07,480 --> 01:56:09,720
Speaker 6: You would never be able to get to a situation

2218
01:56:09,760 --> 01:56:13,320
Speaker 6: where you would have real dial like diar Rols teaching

2219
01:56:13,400 --> 01:56:14,200
Speaker 6: diar Rolls how.

2220
01:56:14,120 --> 01:56:16,240
Speaker 1: To be direbls. You know that's impossible.

2221
01:56:16,280 --> 01:56:21,280
Speaker 7: Yeah, yeah, I mean, I wouldn't say it's impossible generations

2222
01:56:21,320 --> 01:56:22,720
Speaker 7: to get to, but you first have to.

2223
01:56:22,720 --> 01:56:24,840
Speaker 3: Know what there's no way to find out.

2224
01:56:25,200 --> 01:56:26,720
Speaker 7: Yeah, I mean this kind of goes back to some

2225
01:56:26,800 --> 01:56:28,600
Speaker 7: of the debate that you know, I thought people were

2226
01:56:28,600 --> 01:56:30,280
Speaker 7: going to say, you made a dire wolf, What the hell?

2227
01:56:30,400 --> 01:56:33,720
Speaker 7: This thing's an enormous hyper carnivore and really what they

2228
01:56:33,720 --> 01:56:37,320
Speaker 7: were going, well, that's not a direolf. You're so sure

2229
01:56:37,360 --> 01:56:38,920
Speaker 7: that's not a diarolf? Can you please tell me what

2230
01:56:38,960 --> 01:56:41,480
Speaker 7: a dire wolf is? Because if you can't tell me

2231
01:56:41,520 --> 01:56:43,240
Speaker 7: definitively what it is, you can't tell me what it

2232
01:56:43,280 --> 01:56:46,480
Speaker 7: isn't uh, you know, there's there's a lot of nuance

2233
01:56:46,560 --> 01:56:51,400
Speaker 7: to taxonomy, and we don't know how transgenics fall into

2234
01:56:51,520 --> 01:56:54,080
Speaker 7: taxonomy that you know, taxonomy wasn't built with this idea

2235
01:56:54,120 --> 01:56:58,080
Speaker 7: that suddenly along the evolutionary tree something just pops up

2236
01:56:58,600 --> 01:57:03,680
Speaker 7: on its own branch, right, So transgenics really challenge taxonomic classification.

2237
01:57:04,520 --> 01:57:07,600
Speaker 7: So you know, we're trying to help the world cope

2238
01:57:07,600 --> 01:57:09,960
Speaker 7: with that. We're trying to challenge taxonomists to figure out

2239
01:57:10,040 --> 01:57:12,400
Speaker 7: because transgenics are not going to stop a dire wolf.

2240
01:57:12,440 --> 01:57:16,000
Speaker 7: It won't stop with colossal they're synthetic. Biology is now

2241
01:57:16,000 --> 01:57:18,840
Speaker 7: a tool that conservation can use to cover a number

2242
01:57:18,880 --> 01:57:23,240
Speaker 7: of animals, a number of species issues. There will be

2243
01:57:23,360 --> 01:57:25,280
Speaker 7: the use of one gene from one animal put into

2244
01:57:25,320 --> 01:57:26,760
Speaker 7: another animal, and then they go, well, how do you

2245
01:57:26,800 --> 01:57:30,320
Speaker 7: now classify that animal? You know, taxonomy wasn't built to

2246
01:57:30,360 --> 01:57:31,000
Speaker 7: handle that.

2247
01:57:31,800 --> 01:57:34,560
Speaker 1: But and the thing and the behavior thing though this

2248
01:57:34,600 --> 01:57:35,560
Speaker 1: isn't the hack on it.

2249
01:57:35,840 --> 01:57:37,880
Speaker 3: But I'm saying, like you just.

2250
01:57:40,880 --> 01:57:43,720
Speaker 1: You might get you might get where you can win

2251
01:57:43,760 --> 01:57:48,480
Speaker 1: the debate and say, like, based on an agreed upon definition.

2252
01:57:48,040 --> 01:57:49,240
Speaker 7: This is a dire wolf.

2253
01:57:49,320 --> 01:57:54,320
Speaker 1: But what the problem is when you if you say like, behaviorally,

2254
01:57:55,760 --> 01:57:57,080
Speaker 1: there's no foundation for it.

2255
01:57:57,160 --> 01:57:59,560
Speaker 7: We can't say definitively no, you would never know what

2256
01:57:59,600 --> 01:58:01,920
Speaker 7: the animal what was doing. We haven't studied it. I mean,

2257
01:58:01,960 --> 01:58:04,480
Speaker 7: there's how many species across the world that are live

2258
01:58:04,560 --> 01:58:06,880
Speaker 7: today that we don't know behaviorally what they do, what

2259
01:58:06,960 --> 01:58:08,520
Speaker 7: is their function with an ecosystem?

2260
01:58:08,760 --> 01:58:08,960
Speaker 1: You know.

2261
01:58:09,040 --> 01:58:12,640
Speaker 7: So it's it's a valid point. It's one that it

2262
01:58:12,720 --> 01:58:15,640
Speaker 7: will challenge the extinction as we go forward. But I

2263
01:58:15,680 --> 01:58:17,880
Speaker 7: don't think it's a reason to not pursue the.

2264
01:58:17,840 --> 01:58:20,640
Speaker 2: Exc Has there been anything with their behavior that really

2265
01:58:20,720 --> 01:58:24,360
Speaker 2: surprises the people who watch them, Like they never howl

2266
01:58:24,720 --> 01:58:26,640
Speaker 2: or boy, they sure like digging holes.

2267
01:58:27,240 --> 01:58:30,080
Speaker 7: They love sticks, O big fans of sticks.

2268
01:58:30,320 --> 01:58:31,840
Speaker 2: Like wolf puppies love sticks.

2269
01:58:31,880 --> 01:58:33,480
Speaker 7: I don't know, yeah, I mean I don't think they

2270
01:58:33,560 --> 01:58:36,960
Speaker 7: get a lot of sticks to say, and tire.

2271
01:58:36,880 --> 01:58:38,440
Speaker 1: You guys carrying bones around it?

2272
01:58:38,600 --> 01:58:40,440
Speaker 6: Do you guys like go in there and cuddle with them,

2273
01:58:40,520 --> 01:58:41,600
Speaker 6: or will they rip your arm off.

2274
01:58:42,960 --> 01:58:44,720
Speaker 7: There's no cuddling at this point. You know, we were

2275
01:58:44,760 --> 01:58:49,200
Speaker 7: pretty specific early on. Contact was only what was required

2276
01:58:49,200 --> 01:58:51,920
Speaker 7: for the care and health of the animal. Now contact

2277
01:58:51,960 --> 01:58:54,800
Speaker 7: is basically not required. We'll drive in in order to

2278
01:58:54,880 --> 01:58:57,520
Speaker 7: drop feed and things like that. They also have an

2279
01:58:57,600 --> 01:58:59,760
Speaker 7: indoor building that they have access to, and it's sort

2280
01:58:59,760 --> 01:59:01,840
Speaker 7: of a smaller pen where they we can coax them

2281
01:59:01,880 --> 01:59:04,000
Speaker 7: into that so that we can do some basic management

2282
01:59:04,000 --> 01:59:07,720
Speaker 7: stuff if we ever had to, you know, anesthetize an

2283
01:59:07,720 --> 01:59:11,000
Speaker 7: animal because it was sick or injured. We use these areas,

2284
01:59:11,040 --> 01:59:13,520
Speaker 7: so there is some habituation to those areas because it's

2285
01:59:13,520 --> 01:59:16,000
Speaker 7: a management advantage for us to be able to not

2286
01:59:16,120 --> 01:59:17,680
Speaker 7: have to go out there and dart one.

2287
01:59:17,880 --> 01:59:19,880
Speaker 2: So what about their behavior is surprised you though?

2288
01:59:20,960 --> 01:59:23,080
Speaker 7: I know it is so early. It's hard to say, right,

2289
01:59:23,120 --> 01:59:25,360
Speaker 7: because they were just really goofy puppies that were that

2290
01:59:25,400 --> 01:59:28,040
Speaker 7: were around people and now they're really developing into their

2291
01:59:28,080 --> 01:59:32,040
Speaker 7: own I think the level of despite the habituation early

2292
01:59:32,120 --> 01:59:35,360
Speaker 7: on in their life, the level of flight distance that

2293
01:59:35,360 --> 01:59:38,760
Speaker 7: they still maintain with people outside of like two people

2294
01:59:38,800 --> 01:59:43,080
Speaker 7: on my team is pretty startling. Whereas I've worked with

2295
01:59:43,120 --> 01:59:46,560
Speaker 7: a lot of other captive wild canids and and they

2296
01:59:46,680 --> 01:59:49,120
Speaker 7: are go, oh, person equals food and they come running

2297
01:59:49,160 --> 01:59:51,520
Speaker 7: over all right. That these guys do not have that.

2298
01:59:51,560 --> 01:59:55,720
Speaker 7: There is a real fear of new people, new things.

2299
01:59:56,920 --> 02:00:00,480
Speaker 7: They how like they do, Yeah, they do. They They

2300
02:00:00,560 --> 02:00:02,960
Speaker 7: have a good howl. There's really you know, famous video

2301
02:00:02,960 --> 02:00:04,920
Speaker 7: that we put on YouTube that was their first howl,

2302
02:00:04,960 --> 02:00:08,400
Speaker 7: which was pretty powerful moment for the team, you know,

2303
02:00:08,800 --> 02:00:10,680
Speaker 7: sitting there holding them in one of the vetexs that

2304
02:00:10,720 --> 02:00:12,640
Speaker 7: works in the clinic we were giving we were doing

2305
02:00:12,640 --> 02:00:14,720
Speaker 7: a check up on them. It was like singing a song,

2306
02:00:15,120 --> 02:00:18,840
Speaker 7: a little mermaid song, and there's a part where it's like, uh,

2307
02:00:18,880 --> 02:00:20,880
Speaker 7: and she starts doing that, and all of a sudden,

2308
02:00:21,040 --> 02:00:24,880
Speaker 7: both boys just popped into a howl. That was pretty

2309
02:00:24,960 --> 02:00:25,760
Speaker 7: pretty incredible.

2310
02:00:25,920 --> 02:00:29,760
Speaker 2: Is their body size on track with what you'd expect

2311
02:00:29,880 --> 02:00:31,680
Speaker 2: the body signs of a dire wolf to be.

2312
02:00:32,120 --> 02:00:34,840
Speaker 7: Yeah, so we're looking you know, fossil record kind of

2313
02:00:34,880 --> 02:00:37,520
Speaker 7: suggests dire wolves were somewhere between that one hundred and

2314
02:00:37,520 --> 02:00:39,800
Speaker 7: thirty hundred forty pounds up to one hundred and sixty pounds.

2315
02:00:40,280 --> 02:00:42,600
Speaker 7: I think we're on pace for one hundred and forty pounds.

2316
02:00:42,640 --> 02:00:45,120
Speaker 7: Where we stand today, we're seven and a half months

2317
02:00:45,120 --> 02:00:48,640
Speaker 7: as shy of one hundred pounds. As is, their growth

2318
02:00:48,680 --> 02:00:51,880
Speaker 7: curve is definitely leveling out. But you know what we

2319
02:00:51,920 --> 02:00:53,640
Speaker 7: know from gray wolves is we could expect them to

2320
02:00:53,680 --> 02:00:56,840
Speaker 7: continue to grow until they're about twelve fifteen months and

2321
02:00:56,920 --> 02:00:59,000
Speaker 7: then from there they'll continue to put on weight. But

2322
02:00:59,000 --> 02:01:01,960
Speaker 7: they're they're pretty hall leggy animals as they are today.

2323
02:01:03,040 --> 02:01:08,320
Speaker 1: M I'm excited for this Ivory build woodpecker situation.

2324
02:01:09,800 --> 02:01:12,120
Speaker 7: Well yeah, I mean we gotta we got to get

2325
02:01:12,160 --> 02:01:13,560
Speaker 7: you down to the lab and we can show you

2326
02:01:13,560 --> 02:01:15,720
Speaker 7: what we're doing with with the woodpecker stuff.

2327
02:01:16,480 --> 02:01:17,720
Speaker 2: You know who else wouldn't like it to see? For

2328
02:01:17,760 --> 02:01:19,960
Speaker 2: the people who claim they see them now because they

2329
02:01:20,080 --> 02:01:21,280
Speaker 2: kind of they're like special.

2330
02:01:21,440 --> 02:01:23,200
Speaker 7: We get that on the thighlasting project too.

2331
02:01:23,280 --> 02:01:26,480
Speaker 1: Oh don't need Yeah, there's one behind my own.

2332
02:01:27,240 --> 02:01:29,560
Speaker 7: We were at a town hall meeting because we we

2333
02:01:29,600 --> 02:01:32,120
Speaker 7: will visit Tasmani and have town halls and try to

2334
02:01:32,160 --> 02:01:34,120
Speaker 7: hear from from the locals, and we have it at

2335
02:01:34,120 --> 02:01:38,160
Speaker 7: that Thilasting stakeholder group and one of them said, you know,

2336
02:01:38,280 --> 02:01:41,000
Speaker 7: I this is ten years ago. I was pumpkin gas

2337
02:01:41,040 --> 02:01:43,560
Speaker 7: down at this intersection. I saw one run by you.

2338
02:01:43,960 --> 02:01:45,720
Speaker 7: It's like, I don't want to be rude, but if

2339
02:01:45,760 --> 02:01:48,440
Speaker 7: they do exist, I don't think they're visiting gas stations.

2340
02:01:48,560 --> 02:01:52,680
Speaker 1: Yea, yeah, do you guys use that term Lazarus? We haven't.

2341
02:01:53,120 --> 02:01:55,520
Speaker 7: Yeah, but it's good. It's a good analogy.

2342
02:01:58,200 --> 02:02:00,600
Speaker 1: I was going to get to this before we're running

2343
02:02:00,640 --> 02:02:05,560
Speaker 1: out of time, but uh, Crystal ballt on Crystal Ball

2344
02:02:05,640 --> 02:02:11,920
Speaker 1: at on Tasmania saying hell, yeah, let's cut some thiosines loose.

2345
02:02:13,120 --> 02:02:15,520
Speaker 7: I think the chances will go much higher once we

2346
02:02:15,640 --> 02:02:17,600
Speaker 7: have a thioasine in hand, because I think there will

2347
02:02:17,600 --> 02:02:19,560
Speaker 7: suddenly be oh, this is a real thing that we

2348
02:02:19,600 --> 02:02:23,640
Speaker 7: need to be very serious about. Australia has a very

2349
02:02:23,640 --> 02:02:28,240
Speaker 7: conservative approach to wildlife management, and they've been burned.

2350
02:02:28,680 --> 02:02:29,880
Speaker 3: They don't have any non natives.

2351
02:02:29,960 --> 02:02:33,120
Speaker 7: Yeah, yeah, they've been burned in the past. Right, Cane

2352
02:02:33,120 --> 02:02:34,120
Speaker 7: toads a great example.

2353
02:02:34,920 --> 02:02:35,480
Speaker 1: I can see that.

2354
02:02:35,720 --> 02:02:38,480
Speaker 7: So I think there will be an onerous owner's process

2355
02:02:38,760 --> 02:02:40,560
Speaker 7: in order to get there. But I would say, yeah,

2356
02:02:40,840 --> 02:02:43,160
Speaker 7: you know, you know, that's sort of seventy five eighty

2357
02:02:43,240 --> 02:02:46,280
Speaker 7: five percent chance that they will definitely come along. I

2358
02:02:46,280 --> 02:02:49,400
Speaker 7: think Mauritius is much more willing to take that.

2359
02:02:49,440 --> 02:02:52,400
Speaker 1: What was the percent chance on Tasmania seventy five eighty five?

2360
02:02:52,680 --> 02:02:53,640
Speaker 1: Really optimistic.

2361
02:02:53,840 --> 02:02:56,680
Speaker 7: I'm telling you, what's these animals are back? I think, well,

2362
02:02:56,800 --> 02:03:00,480
Speaker 7: he'll be fighting people to say why is it in

2363
02:03:00,760 --> 02:03:03,160
Speaker 7: Texas in the United States and not in Tasmania.

2364
02:03:05,960 --> 02:03:09,840
Speaker 1: I love Willem Dafoe. One of the worst movies ever made.

2365
02:03:09,880 --> 02:03:12,960
Speaker 1: His style of scene movie, it's really bad. Just the logic.

2366
02:03:13,520 --> 02:03:15,280
Speaker 1: They had magical backpack syndrome.

2367
02:03:17,400 --> 02:03:18,000
Speaker 2: What is that?

2368
02:03:18,960 --> 02:03:22,760
Speaker 1: Magical backpack syndrome is a person has a little knapsack.

2369
02:03:24,280 --> 02:03:26,880
Speaker 2: Inducing like all sorts of ships.

2370
02:03:28,400 --> 02:03:30,320
Speaker 7: It's like a Harry Potter bag and I just keep

2371
02:03:30,400 --> 02:03:31,640
Speaker 7: pulling stuff out of it.

2372
02:03:31,600 --> 02:03:33,600
Speaker 3: And it's it's just the logic.

2373
02:03:33,680 --> 02:03:35,839
Speaker 1: It was like a great They're like, we should do something,

2374
02:03:35,920 --> 02:03:38,720
Speaker 1: or the idea is we should do something around Tasmanian times,

2375
02:03:39,720 --> 02:03:42,880
Speaker 1: and then that's just what came out in the end. Terrible.

2376
02:03:43,280 --> 02:03:46,320
Speaker 3: I assume on the show.

2377
02:03:46,360 --> 02:03:46,880
Speaker 1: I'd love to have.

2378
02:03:48,080 --> 02:03:52,480
Speaker 2: I assume your company has competitors. Have they Have they

2379
02:03:52,520 --> 02:03:55,440
Speaker 2: been excited for you guys? Have they been critical of

2380
02:03:55,440 --> 02:03:58,000
Speaker 2: what you've done? Are they like, damn, we thought we'd

2381
02:03:58,080 --> 02:03:59,120
Speaker 2: land on the moon first.

2382
02:03:59,360 --> 02:04:01,720
Speaker 7: I think it's I'm aware we're the only the extinction

2383
02:04:01,800 --> 02:04:02,800
Speaker 7: species preservations.

2384
02:04:02,840 --> 02:04:04,560
Speaker 2: Nobody else, the only people.

2385
02:04:04,280 --> 02:04:06,800
Speaker 7: We truly compete with the other nation states. You know,

2386
02:04:06,880 --> 02:04:09,040
Speaker 7: Korea has pursued some of the stuff. We know China

2387
02:04:09,080 --> 02:04:11,240
Speaker 7: has an interest in it, but it's not really at

2388
02:04:11,280 --> 02:04:14,840
Speaker 7: a company level. I think you're acutey. I think you're

2389
02:04:14,840 --> 02:04:17,040
Speaker 7: about to see people come in, but they're about four

2390
02:04:17,120 --> 02:04:18,920
Speaker 7: hundred and fifty million dollars in five years late.

2391
02:04:19,400 --> 02:04:21,640
Speaker 1: Are you guys constantly suns discouraged?

2392
02:04:22,160 --> 02:04:27,040
Speaker 6: Are you guys constantly getting like petitioned by people or

2393
02:04:27,360 --> 02:04:32,080
Speaker 6: or organizations about like their pet the extinction project or

2394
02:04:32,120 --> 02:04:36,560
Speaker 6: their pet project to prevent something from their animal.

2395
02:04:36,800 --> 02:04:38,320
Speaker 7: You know what we get. We get a lot of

2396
02:04:38,320 --> 02:04:40,080
Speaker 7: that where it's you know, hey, THI listen, is a

2397
02:04:40,080 --> 02:04:43,000
Speaker 7: really cool project. But did you consider about like the

2398
02:04:43,400 --> 02:04:47,400
Speaker 7: red pilliated northwest woodpecker. Right, it's like some random bird

2399
02:04:47,440 --> 02:04:49,080
Speaker 7: that you got well I didn't actually let me look

2400
02:04:49,120 --> 02:04:50,800
Speaker 7: it up, and you go, well, it's not really just

2401
02:04:50,880 --> 02:04:53,360
Speaker 7: extirpaated from that area. It's still lounge. Yeah, right there,

2402
02:04:53,400 --> 02:04:54,600
Speaker 7: you get a lot of that type of stuff.

2403
02:04:56,520 --> 02:04:57,440
Speaker 3: Dude, Thanks for coming on.

2404
02:04:57,560 --> 02:04:59,800
Speaker 7: Man, Hey, always great to be here. You guys, come

2405
02:04:59,800 --> 02:05:02,040
Speaker 7: on time you want I appreciate that, because I'll take

2406
02:05:02,080 --> 02:05:03,840
Speaker 7: you up on that, because you know, we're working in

2407
02:05:03,920 --> 02:05:06,000
Speaker 7: Yellowstone on a pretty regular basis now, so we're up

2408
02:05:06,000 --> 02:05:06,880
Speaker 7: in your neck of the woods.

2409
02:05:07,480 --> 02:05:08,720
Speaker 2: People aren't gonna like to hear that.

2410
02:05:08,760 --> 02:05:09,240
Speaker 1: I don't think.

2411
02:05:09,320 --> 02:05:11,560
Speaker 7: Well, if you're not dire related.

2412
02:05:11,320 --> 02:05:13,040
Speaker 1: Let me give you my end, let me give you

2413
02:05:13,080 --> 02:05:15,320
Speaker 1: my end sentiment about the whole thing. I think there's

2414
02:05:15,360 --> 02:05:19,480
Speaker 1: things that happen in American culture, global culture, whatever. There's

2415
02:05:19,520 --> 02:05:22,920
Speaker 1: things that happen to make everyone dumber, and there's things

2416
02:05:22,920 --> 02:05:26,920
Speaker 1: that happen to make everyone smarter. Right, this is definitely

2417
02:05:27,280 --> 02:05:30,440
Speaker 1: whatever ends up happening, like whatever happens with romulus and remissing,

2418
02:05:30,800 --> 02:05:36,720
Speaker 1: like whatever happened. This made everybody smarter because everybody said, wow,

2419
02:05:36,760 --> 02:05:41,160
Speaker 1: I never really thought about what is a species? What

2420
02:05:41,360 --> 02:05:45,080
Speaker 1: is de extinction? How do you define a blank? Is

2421
02:05:45,160 --> 02:05:48,960
Speaker 1: this okay or not okay? Like everyone got smarter and

2422
02:05:48,960 --> 02:05:50,560
Speaker 1: normally stuff happens. Every big is dumber.

2423
02:05:50,680 --> 02:05:52,720
Speaker 7: I love that. I'm going to steal that from you

2424
02:05:52,760 --> 02:05:53,480
Speaker 7: as well, because.

2425
02:05:53,240 --> 02:05:55,280
Speaker 3: I'm trying to get things that made everybody dumber.

2426
02:05:55,360 --> 02:05:56,360
Speaker 7: This isn't that positive.

2427
02:05:56,880 --> 02:05:59,920
Speaker 1: I think, Yeah, everybody got dumber.

2428
02:06:00,080 --> 02:06:03,240
Speaker 7: Yeah, people got a lot dumber TikTok. Yeah, net positive

2429
02:06:03,280 --> 02:06:05,320
Speaker 7: for the world. I think that's you know, if we're

2430
02:06:05,360 --> 02:06:08,160
Speaker 7: boiling down the story, this is a net positive because

2431
02:06:08,200 --> 02:06:11,720
Speaker 7: no one got hurt. Nobody got hurt. We're injecting, you know,

2432
02:06:11,760 --> 02:06:14,840
Speaker 7: we're injecting science into pop culture. We're injecting conservation where

2433
02:06:14,920 --> 02:06:16,960
Speaker 7: you know, if you look at Google search terms and

2434
02:06:16,960 --> 02:06:20,920
Speaker 7: hashtag trends from when we launched that first week, we

2435
02:06:20,960 --> 02:06:23,600
Speaker 7: saw all time highs of Google searches for red Wolf,

2436
02:06:23,640 --> 02:06:25,560
Speaker 7: for conservation, for the term extinction.

2437
02:06:26,120 --> 02:06:26,640
Speaker 1: Yeah.

2438
02:06:26,880 --> 02:06:30,120
Speaker 7: You know, if we can push this into a dinner

2439
02:06:30,160 --> 02:06:33,280
Speaker 7: table conversation for families to have with their kids talk

2440
02:06:33,280 --> 02:06:36,400
Speaker 7: about the biodiversity crisis, that is a win in and

2441
02:06:36,440 --> 02:06:36,960
Speaker 7: of itself.

2442
02:06:37,120 --> 02:06:37,680
Speaker 3: Yeah, you didn't.

2443
02:06:37,720 --> 02:06:39,840
Speaker 1: But and the other thing is you didn't siphon off

2444
02:06:39,880 --> 02:06:44,920
Speaker 1: of limited federal spending. No, it was like willing seller,

2445
02:06:45,000 --> 02:06:47,880
Speaker 1: willing buyer, willing investor, willing investment.

2446
02:06:48,040 --> 02:06:50,040
Speaker 7: Ben loves to say, we spared the world in another

2447
02:06:50,080 --> 02:06:51,200
Speaker 7: shitty software company.

2448
02:06:52,000 --> 02:06:56,440
Speaker 5: I like that, right, I like that.

2449
02:06:56,920 --> 02:07:00,720
Speaker 1: Yeah, it's a potster, dude, but it made everybody smarter exactly. No,

2450
02:07:00,760 --> 02:07:02,640
Speaker 1: I like I like it. It's like it's like a

2451
02:07:02,680 --> 02:07:06,680
Speaker 1: real mental It causes a lot of mental wrestling. Yeah,

2452
02:07:06,760 --> 02:07:09,560
Speaker 1: you know, what was that quote we had recently? That's

2453
02:07:09,560 --> 02:07:11,760
Speaker 1: the last thing I say. But it's not really applicable here,

2454
02:07:11,800 --> 02:07:14,520
Speaker 1: but I think we're Dan Floyda. No, it's a great quote.

2455
02:07:15,000 --> 02:07:15,840
Speaker 1: Dann Floyes was.

2456
02:07:15,840 --> 02:07:17,320
Speaker 3: Saying about historians.

2457
02:07:17,920 --> 02:07:21,200
Speaker 1: He goes, the reason the arguments are so the reason,

2458
02:07:21,360 --> 02:07:24,480
Speaker 1: it's like, the reason the arguments are so vicious is

2459
02:07:24,480 --> 02:07:25,560
Speaker 1: there's nothing at stake.

2460
02:07:29,520 --> 02:07:31,480
Speaker 3: But he was just making a joke about his own.

2461
02:07:31,320 --> 02:07:33,800
Speaker 1: Discipline as a historian, you know, like, how could people

2462
02:07:33,840 --> 02:07:38,080
Speaker 1: get so worked up about Clovis first or whatever? Right,

2463
02:07:39,560 --> 02:07:41,560
Speaker 1: But there's something at steak here. But I think the

2464
02:07:41,600 --> 02:07:43,840
Speaker 1: main thing is it's like, not the main thing. It's

2465
02:07:43,840 --> 02:07:44,480
Speaker 1: just fun to watch.

2466
02:07:44,640 --> 02:07:46,680
Speaker 3: Yeah, I appreciate that. It's fun to watch.

2467
02:07:46,440 --> 02:07:49,040
Speaker 1: People like all of a sudden arguing about stuff that

2468
02:07:49,080 --> 02:07:50,200
Speaker 1: the day before they didn't know.

2469
02:07:50,880 --> 02:07:51,800
Speaker 7: Yeah, it's perfect.

2470
02:07:51,920 --> 02:07:53,760
Speaker 6: You guys should do an Irish el connect.

2471
02:07:55,120 --> 02:07:56,440
Speaker 7: I won't tell you we're not going.

2472
02:07:58,560 --> 02:08:01,640
Speaker 5: Hunters would love that. Diarls could eat him.

2473
02:08:01,960 --> 02:08:02,600
Speaker 7: It works for me.

2474
02:08:02,840 --> 02:08:06,320
Speaker 1: He's got the amusement part figured out, all right. Thanks man,

2475
02:08:06,480 --> 02:08:07,400
Speaker 1: I really appreciate you coming.

2476
02:08:07,400 --> 02:08:08,000
Speaker 7: Thank you so much.

2477
02:08:08,200 --> 02:08:08,440
Speaker 4: Thank you