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Speaker 1: How the extraordinaries.

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Speaker 2: The pop science community was a light recently after a

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Speaker 2: paper came out that found evidence suggesting that raccoons are

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Speaker 2: showing signs of domestication. Now, if you've been listening to

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Speaker 2: our recent Listener Questions episode, you know that I'm wary

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Speaker 2: of raccoons as pets, in large part because they carry

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Speaker 2: a nasty parasite called raccoon roundworm. But what if raccoons

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Speaker 2: are starting down the road to domestication and we can

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Speaker 2: get rid of that parasite In domesticated raccoons. I don't

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Speaker 2: see raccoons producing any valuable product. We probably won't be

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Speaker 2: raising meat raccoons or dairy raccoons anytime soon, for example.

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Speaker 2: So that probably leaves raccoons as pets, which sounds super cute.

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Speaker 2: So will our children or grandchildren be taking their pet

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Speaker 2: raccoons on walks when we're older?

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Speaker 1: And just how far down.

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Speaker 2: The domestication trail I have raccoons actually traveled? And you know,

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Speaker 2: while we're at it, taking another step back, just how

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Speaker 2: well do we actually understand how domestication works in the

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Speaker 2: first place. We're gonna dig into all of those questions today.

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Speaker 2: Welcome to Daniel and Kelly's domesticated universe.

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Speaker 3: Hi, I'm Daniel. I study particles and aliens, and I

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Speaker 3: consider myself only partially domesticated.

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Speaker 1: Hi, I'm Kelly Waidersmith.

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Speaker 2: I study parasites and space, and I consider myself mostly feral.

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Speaker 3: You do round up to a wild animal, don't you can?

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Speaker 1: I think so? Yeah? Yeah?

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Speaker 3: Has that changed since you've lived on the farm.

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Speaker 2: You know, for a long time it has been the

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Speaker 2: case that you probably shouldn't bring me nice places because

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Speaker 2: I will say inappropriate things.

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Speaker 1: I don't know that it's gotten word.

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Speaker 3: You don't like poop on the furniture, No, no.

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Speaker 1: No, no.

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Speaker 2: But I guess, I guess the probability that I talk

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Speaker 2: about things like prolapses has gone up since I moved

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Speaker 2: to a farm.

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Speaker 1: So do with that what you will.

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Speaker 3: And we just had a record for zero to prolapse

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Speaker 3: in like thirty seconds on the fuck.

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Speaker 1: I don't think that's the record, Daniel.

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Speaker 2: I think I probably talked about prolapses in the intro already.

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Speaker 3: What is the pearl of prolapse? Anyway? Is it prolapses? Really?

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Speaker 1: It's not good, don't I don't know, I don't know.

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Speaker 3: It feels like you should upgrade from prolapse to like,

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Speaker 3: you know, what's beyond professional expert lapses or something.

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Speaker 2: Oh yeah, well, well you know what is good. What

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Speaker 2: is good is our discord community.

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Speaker 3: Yes, and their curiosity about the universe.

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Speaker 1: That's right.

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Speaker 2: And what's even better than good is our discord moderators.

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Speaker 3: Oh my gosh, heavenly.

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Speaker 1: Yes, they're amazing.

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Speaker 2: They keep the conversation civil and our community asks the

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Speaker 2: most amazing questions. And one of our amazing moderators is

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Speaker 2: Matt McCain. And he noted that there's this POPSI article

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Speaker 2: going around the webisphere or whatever about how about how

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Speaker 2: raccoons appear to be becoming domesticated? And then there was

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Speaker 2: this follow up where there was a video where this

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Speaker 2: woman was very angrily talking about how no, raccoons are

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Speaker 2: not becoming domesticated. This paper was junk, and Matt was like, Kelly,

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Speaker 2: we need the dKu treatment.

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Speaker 3: Hit us with it, which means Kelly does like twenty

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Speaker 3: hours of research and nerds out about this in fear

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Speaker 3: of not being able to answer a question.

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Speaker 1: So here's the thing.

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Speaker 2: When I got that request, I was like, oh, finally

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Speaker 2: something easy, because because the background for this paper is

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Speaker 2: like the Russian farmed fox experiment, which I learned about

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Speaker 2: in grad school and I was like, Bam, I know

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Speaker 2: all about this.

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Speaker 3: This is ironic foreshadowing.

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Speaker 1: Oh my gosh.

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Speaker 2: Yeah, it took twenty hours. It took twenty hours.

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Speaker 1: This was a lot.

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Speaker 3: I have a theory that it's always twenty hours of

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Speaker 3: research with you, Yeah, because everything has loose ends and

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Speaker 3: you're like, wait, but what about this? And I wonder,

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Speaker 3: and this is my question for you today, how much

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Speaker 3: of that is driven by curiosity? You're like, wait, I

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Speaker 3: need to understand how this thing works, and how much

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Speaker 3: of it is driven by fear?

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Speaker 1: Ah, fifty to fifty? I think. I think, yeah, it's

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Speaker 1: a little bit of both.

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Speaker 2: I mean, part of why I love this show is

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Speaker 2: that it's like I have an excuse to research something

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Speaker 2: and dig in deeper. And then another part of it,

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Speaker 2: especially when it's something that like overlaps with what I

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Speaker 2: studied in grad school.

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Speaker 3: Yeah, that you're supposed to.

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Speaker 2: Know, that's right, Like, if I get it wrong, it's

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Speaker 2: going to be particularly embarrassing because they're going to be

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Speaker 2: like doctor Wiersmith, you have an excuse for not knowing

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Speaker 2: the medical stuff, but this this well do.

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Speaker 3: You ever get emails from your colleagues other parasitologists who

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Speaker 3: are like, actually, Kelly, not once? Really? Yeah, all right,

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Speaker 3: hey that's good, it's good.

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Speaker 1: I mean maybe they don't disappointed.

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Speaker 2: Maybe none of my friends listen, but no, So you know,

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Speaker 2: I should worry less, but I just that's not that's

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Speaker 2: not my mo o.

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Speaker 3: Man.

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Speaker 1: I'm anxious, but feral anxious. But I need a T shirt.

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Speaker 3: Well, today we are going to worry our way into

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Speaker 3: an understanding of domestication and whether raccoons are on the

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Speaker 3: path to being the next dog.

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Speaker 2: That's right, and we are going to start with confusion

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Speaker 2: and end with confusion.

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Speaker 3: That's where twenty hours of research got you.

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Speaker 2: That is pretty well, well, that's where the field seems

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Speaker 2: to be right now. And so what's amazing is that,

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Speaker 2: like the confusion starts at what is domestication?

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Speaker 3: Okay, so let's start with that because I have a

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Speaker 3: naive understanding the domestication is like my cat leaves in

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Speaker 3: my house and couldn't survive in the wild. Is that

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Speaker 3: how scientists see domestication?

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Speaker 1: Yeah, so that that is one definition.

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Speaker 2: So one definition I came across was essentially, like a

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Speaker 2: domesticated animal is an animal where essentially, if you kicked

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Speaker 2: it out of human habitations or humans weren't taking care

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Speaker 2: of it anymore, it would die. And then trying to

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Speaker 2: figure out the process that led to that is what

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Speaker 2: you're studying when you study domestication.

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Speaker 3: All right, well, does that definition actually make sense, because

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Speaker 3: now that I think about it, I don't even think

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Speaker 3: it applies to like cats. We've had cats that like

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Speaker 3: just disappeared for a year and they seem.

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Speaker 1: Fine, exactly right.

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Speaker 2: And so that is why this stuff gets complicated because

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Speaker 2: there's plenty of cats that can live on their own,

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Speaker 2: and there are feral cat colonies. My goats would probably

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Speaker 2: kick the bucket if they were out there on their own,

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Speaker 2: but there have been, for example, pigs that get out

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Speaker 2: and then they start wild war groups, and so the

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Speaker 2: question is were they ever domesticated if that was the definition,

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Speaker 2: And so you can argue about that definition.

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Speaker 3: Yeah, And I remember visiting Tahiti for a Boondoggle conference

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Speaker 3: with my daughter, and Hazel's favorite thing about Tahiti was

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Speaker 3: not the beaches, was not the coral reefs, was not

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Speaker 3: the sting rays that would like swim up to the water.

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Speaker 3: It was the wild dog there were dogs everywhere, and

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Speaker 3: she would pet them and they would love her, and

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Speaker 3: they were friendly, but they were also wild, like they

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Speaker 3: weren't living specifically in a human house. But they were

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Speaker 3: also definitely connected to human society, right, so they were

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Speaker 3: dependent indirectly on humanity. Does that count as domesticated?

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Speaker 2: Well, first check out our Rabies episode for why that

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Speaker 2: story is giving Kelly a heart for why that episode

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Speaker 2: is increasing Kelly's blood pressure. But uh yeah, I mean,

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Speaker 2: so they came from domesticated animals and they are, you know,

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Speaker 2: maybe becoming less domesticated, but I think they would still

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Speaker 2: a lot of people would say they still count as

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Speaker 2: domesticated dogs.

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Speaker 1: But so let's go ahead to the definition that I'm

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Speaker 1: going to use today, right, what's that? So this is

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Speaker 1: like a two.

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Speaker 2: Part definition, and this is a definition that was sort

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Speaker 2: of forwarded by Darwin and is still popular today because

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Speaker 2: Darwin was one of the first people to start thinking

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Speaker 2: critically about this domestication process. And essentially, the first step

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Speaker 2: of the process is that the animals start spending time

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Speaker 2: near humans, and they start to develop traits that allow

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Speaker 2: them to spend time near us. So, for example, they

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Speaker 2: become less likely to run away, they become more adapted to,

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Speaker 2: for example, eating our garbage. They become less aggressive because

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Speaker 2: if they're attacking us, we're going to shoot and kill

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Speaker 2: them all, for example. And so the first step is

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Speaker 2: essentially them domesticating themselves and acquiring traits to spend more

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Speaker 2: time in proximity to us because there's something about living

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Speaker 2: near humans that they benefit from. Does that make sense?

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Speaker 3: It makes sense. But the whole thing makes me wonder, like,

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Speaker 3: why do we worry so much about the definition. Is

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Speaker 3: it important that we draw a dotted line and say

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Speaker 3: this thing is domesticated, this thing is not. We have

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Speaker 3: a super crisp definition of domestication. And I know every

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Speaker 3: philosophy conversation starts with definitions, but in this case, why

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Speaker 3: can't we just treat it as a spectrum and like

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Speaker 3: there's more domesticated and less domesticated. Why do we have

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Speaker 3: to say, like, this is domestication. Why is it important?

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Speaker 3: Does it inform the science we do later?

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Speaker 2: So this definition isn't a clean gradient, but it does

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Speaker 2: have steps, and so you can ask where along.

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Speaker 1: This process is a species?

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Speaker 2: Okay, And it becomes important to nerds because, for example,

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Speaker 2: initially I thought this episode was going to start with

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Speaker 2: a deep dive into the story of dog domestication, and

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Speaker 2: so I spent a week reading about that before I

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Speaker 2: went back and read the Raccoon paper and I was like, oh,

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Speaker 2: of course I need to start with the foxes. And

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Speaker 2: then I was like, you idiot. And so anyway, turns

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Speaker 2: out dogs domesticated we think about fifteen thousand years ago,

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Speaker 2: and I'm so glad I get to use some of

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Speaker 2: that information now. So we're sure that fifteen thousand years

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Speaker 2: ago during the Place to See, we started domesticating dogs,

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Speaker 2: but we think we might have started domesticating them as

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Speaker 2: early as forty thousand years ago.

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Speaker 1: That's a big difference, huge difference, right.

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Speaker 2: But so then the reason that we're having that debate

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Speaker 2: is because the question is like, well, they kind of

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Speaker 2: looked like wolves, and so at what point, like what

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Speaker 2: combination of traits and behaviors do we need before we

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Speaker 2: say that's a wolf and that's a dog, and like

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Speaker 2: what is the cutoff? And so we use things like

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Speaker 2: what does their face look like? And how were they

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Speaker 2: interacting with people? So, for example, if a dog is

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Speaker 2: buried with a person, or you know, if an animal

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Speaker 2: that is you know, somewhere on the gradient between dog

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Speaker 2: and wolf is buried with a person. Do we go

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Speaker 2: ahead and say that's a dog because of how closely

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Speaker 2: they're associated.

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Speaker 1: With the person. Trying to like nail down when an animal.

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Speaker 2: Is domesticated sort of helps us, I don't know, categorize

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Speaker 2: these things and help with these debates about like when

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Speaker 2: did a wolf become a dog? Which we as humans

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Speaker 2: care about. But you know, you can say, should I

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Speaker 2: care as a non scientist, I don't know, maybe not,

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Speaker 2: But scientists care as they're trying to like get clean stories.

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Speaker 3: Well, it makes sense to have crisp definitions so we

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Speaker 3: can communicate effectively about the bigger question of like what

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Speaker 3: happened What we call it? I guess doesn't really matter

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Speaker 3: as we agree on the labels, so we can talk

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Speaker 3: about their real science.

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Speaker 2: Yeah right, cool, Okay, So step one, we think what

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Speaker 2: happens typically is that the animals come into contact with

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Speaker 2: us more often, selection changes them in a variety of

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Speaker 2: ways to make it so that they can live near

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Speaker 2: us a little bit better. And then the next step

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Speaker 2: tends to be more like human purpose driven, So we say, okay,

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Speaker 2: the wolves have been living with us for a really

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Speaker 2: long time, they've become tamer, Like, they don't attack us anymore.

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Speaker 2: Wouldn't it be great if we could hunt with them?

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Speaker 2: And so then we like start bringing them into our

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Speaker 2: communities and we start breeding the ones that are nicer

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Speaker 2: or the ones that like retrieve the you know, deer

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Speaker 2: we just killed and bring it back to us. And

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Speaker 2: so the second step is humans like purposefully breeding them

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Speaker 2: for things that we want.

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Speaker 3: All right, So first you have unintentional selection, where their

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Speaker 3: evolution is being driven by being near humans and some

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Speaker 3: of them benefiting from that and therefore having more kids,

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Speaker 3: et cetera. And then intention selection where humans are like,

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Speaker 3: we like the ones that snuckle at night or whatever.

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Speaker 1: That's right, Yes, exactly, Okay, cool, that makes sense.

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Speaker 2: Yeah, but again, lots of debate about the best way

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Speaker 2: to define these things. And there are people who are like,

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Speaker 2: if your definition includes a process, if the process is

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Speaker 2: different than you know, you are already confusing yourself because

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Speaker 2: you've included a process in your definition. And what if

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Speaker 2: it worked differently, for example, for cats, have you excluded

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Speaker 2: cats now, because maybe that's not how it worked with cats,

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Speaker 2: And so anyway, lots of debate, but that's what we're

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Speaker 2: going with today, all right. So then there's been this

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Speaker 2: long observation going back again to Darwin, where when animals

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Speaker 2: are going through this like first step, and maybe even

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Speaker 2: partly through this second step where they are spending more

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Speaker 2: times with humans and becoming more docile and just sort

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Speaker 2: of generally becoming a little less wild, they often but

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Speaker 2: not always, start to acquire some similar looking traits. So,

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Speaker 2: for example, they often start to have some like white spots.

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Speaker 2: So for example, if you picture a horse or a

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Speaker 2: cow or some dogs, you might imagine like a lot

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Speaker 2: of them have this little white spot at the top

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Speaker 2: of their forehead, and that shows up less often in

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Speaker 2: wild animals, but you see it a little bit more

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Speaker 2: often in domestic.

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Speaker 3: Animals across different species, across different SPECIs cows, horses, dogs, cats. Yeah,

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Speaker 3: the idea is that as they get more domesticated, they

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Speaker 3: get more white spots on their forehead. And I hope

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Speaker 3: you're picking up on my tone of skepticism.

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Speaker 1: Uh huh, that's yep.

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Speaker 2: I am delivering the information right now, and then we

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Speaker 2: will dig into the skepticism.

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Speaker 1: I'm going to address that, all right.

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Speaker 2: You're also more likely to get floppy ears in some

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Speaker 2: cases reduced ears. In other cases shorter muzzles, so like

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Speaker 2: shorter mouths, smaller teeth, more docile animals, smaller brains, or

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Speaker 2: smaller cranial capacities. The reproductive cycles change so often. In nature,

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Speaker 2: animals will breth once a year, but when you have

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Speaker 2: animals that are domesticated, you can often get them to

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Speaker 2: breed any time of year. Or maybe they'll have like

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Speaker 2: two reproductive seasons instead of just one, so they tend

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Speaker 2: to have more babies, or they can have babies at

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Speaker 2: a greater range of times throughout the year. And finally,

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Speaker 2: they tend to have like juvenile traits that they retain

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Speaker 2: into adulthood.

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Speaker 1: So you know how babies are like super cute and

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Speaker 1: you're like, ah, puppies are the cutest.

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Speaker 2: Those traits that made them so cute when they were

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Speaker 2: younger just tend to stick around when they're adults, which

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Speaker 2: make us humans look at domesticated animals and still say

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Speaker 2: even when they're adults, oh, you're so cute, And partly

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Speaker 2: that's because they still have some of those baby traits.

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Speaker 3: I see, And I could imagine a mechanism where we

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Speaker 3: select for animals that we find cuter or dumber or

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Speaker 3: more convenient in some ways. So it's not totally implausible.

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Speaker 3: The white dots on their forehead, though I struggle with

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Speaker 3: it because is that like universally seen to be cute?

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Speaker 3: Is that the idea?

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Speaker 4: No?

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Speaker 2: And so first of all, I should I used white

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Speaker 2: dots in their forehead as one example of what they

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Speaker 2: call depigmentation, which is just generally you tend to have

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Speaker 2: have more white or more brown regions where you just

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Speaker 2: tend to have.

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Speaker 1: Like less dark colors.

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Speaker 2: Okay, so we use the word syndrome, it's called domestication

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Speaker 2: syndrome to describe this. And essentially the idea here is

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Speaker 2: that it's a package of traits that you tend to get.

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Speaker 2: You don't always get all of them, but you get

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Speaker 2: some of them as domestication is happening. So say, what

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Speaker 2: you're selecting for is wolves that don't bite people anymore.

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Speaker 2: This is just a thought experiment. Okay, So the idea

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Speaker 2: is when you get wolves that don't bite people anymore

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Speaker 2: for some reason, maybe we don't really understand it, a

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Speaker 2: bunch of these other things seem to change as well.

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Speaker 2: They also end up becoming lighter in color, and also

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Speaker 2: their mouths become a little bit shorter and their teeth

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Speaker 2: get smaller. Now, the question is why would that happen,

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Speaker 2: And the going hypothesis for why it happens is called

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Speaker 2: the neural crest cell hypothesis.

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Speaker 1: So here's the idea.

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Speaker 2: Very early in mammalian development, we have a set of

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Speaker 2: cells called the neural crest cells. These cells are stem cells,

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Speaker 2: and they are going to turn into the cells that

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Speaker 2: make your melanocytes. These are the cells that give us pigmentation.

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Speaker 2: We've talked about these in a bunch of listener Question episodes.

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Speaker 1: Right, They're going to.

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Speaker 2: Go on to produce the cells that make the bones

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Speaker 2: that make our head and our teeth. They're going to

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Speaker 2: go on to make cells that make up our nervous system,

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Speaker 2: and they essentially go on to make a bunch of

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Speaker 2: cell types that are related to the features that we

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Speaker 2: were just talking about.

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Speaker 3: All right, So there is a connection between those features

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Speaker 3: that have like a common origin something upstream where if

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Speaker 3: you tweaked it, it might affect all of those features downstream.

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Speaker 2: Yes, right, And so the going idea right now is

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Speaker 2: that something about selecting for being less aggressive changes something

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Speaker 2: about what the neural crest cells are doing.

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Speaker 1: And that changes all of these other traits.

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Speaker 3: All right, now that there's a mechanism, I'm a little

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Speaker 3: bit less skeptical.

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Speaker 1: All right, You shouldn't be, because here's the thing. All right.

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Speaker 2: So first of all, sometimes you see this package of

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Speaker 2: traits come together, sometimes you don't.

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Speaker 3: Oh you mean in which animals you see these together?

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Speaker 3: And which animals you don't?

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Speaker 1: Yeah?

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Speaker 2: Right, And we don't really have a good framework yet

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Speaker 2: for predicting, Like say you were going to take lions

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Speaker 2: and domesticate them. I'm being ridiculous, right, but like, say

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Speaker 2: you decide it's not ridiculous.

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Speaker 3: That sounds awesome. I would love to have a house lion.

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Speaker 1: Yeah right, Yeah, maybe.

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Speaker 3: Why can't biology make that happen for me?

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Speaker 1: I don't know. I feel like that's something like people

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Speaker 1: in the mafia.

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Speaker 3: Try to do. So that in Mike Tyson and Mike

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Speaker 3: Tyson has a house lion.

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Speaker 2: All right, So Daniel becomes a mafia boss and he says, oh, hey,

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Speaker 2: you know I'm not going to do a mafia voice.

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Speaker 3: Okay.

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Speaker 2: So Daniel becomes a mafia boss and he starts a

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Speaker 2: breeding program for lions. Yes, And so the question is

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Speaker 2: can we predict if the lion is going to have

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Speaker 2: floppy ears and shorter tea, or if he's gonna end

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Speaker 2: up with a smaller brain and a shorter muzzle.

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Speaker 1: Like, which of those traits that.

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Speaker 2: We were talking about are we going to predictable change

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Speaker 2: in that lion. We're not at the point yet where

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Speaker 2: we can predict are all of them going to change

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Speaker 2: or just a subset of them. But it kind of

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Speaker 2: makes sense because you might expect a bunch of them

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Speaker 2: to change initially, but then when once you've got that

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Speaker 2: lion domesticated, you could say, Okay, well what I really

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Speaker 2: want now is a lion that is completely white, because

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Speaker 2: then I can sell them to the circus. Right, And

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Speaker 2: so now you're like selecting for certain traits, and so

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Speaker 2: now instead of just like selecting for a docile animal

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Speaker 2: and seeing what you get, now you're selecting for like

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Speaker 2: very particular traits, and so you might expect something totally

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Speaker 2: different to happen. And so once humans start getting involved,

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Speaker 2: you might not expect to see this particular package of traits,

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Speaker 2: and so that might confuse a lot of things.

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Speaker 3: Also, for example, I might want my lions to look

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Speaker 3: ferocious instead of cute, because that's the whole point to

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Speaker 3: intimidate all my colleagues exactly, sound your little.

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Speaker 2: Syndrome there, that's right, Yes, you are selecting for longer

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Speaker 2: tea and yeah, stuff like that. So anyway, amazing example

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Speaker 2: we've come up with here, and so we're really good

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Speaker 2: at this.

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Speaker 1: Okay.

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Speaker 2: So one problem is this syndrome quote unquote is highly variable.

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Speaker 2: You don't always see it showing up. We can't predict

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Speaker 2: which traits are going to show up in which animals.

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Speaker 2: And this neural crest hypothesis is really hard to test.

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Speaker 2: And a lot of times when you find evidence that

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Speaker 2: a gene is associated with domestication, people will say, oh, okay,

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Speaker 2: that gene is associated with what's happening with the neural

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Speaker 2: crest cells, but it's also associated with a bunch of

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Speaker 2: other stuff, because, as Benjamin de Bevore told us, genes

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Speaker 2: do lots of things. Yeah, and so just because a

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Speaker 2: gene is associated with what the neural crest cells we're doing,

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Speaker 2: doesn't mean it's not associated with a bunch of other stuff.

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Speaker 2: And so maybe it's not the neural quest cells that mattered.

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Speaker 2: Maybe it's something that it happens at a completely different

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Speaker 2: stage of development. And so we have not really pinned

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Speaker 2: down that neural cress cells are what's important. It could

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Speaker 2: be something totally different. So even though we have a

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Speaker 2: mechanism that sounds convincing and there's some data to support it,

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Speaker 2: there's plenty of people who are saying, we really haven't

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Speaker 2: pinned this down, we don't really know what the mechanism is.

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Speaker 2: And there's a bunch of people who are like, and

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Speaker 2: there's so much variability. I don't even believe this domestication

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Speaker 2: syndrome thing exists.

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Speaker 3: And there's another danger there also, isn't there because I imagine

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Speaker 3: that people will try to use these domestication markers as

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Speaker 3: a way to argue that an animal is or isn't domesticated.

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Speaker 3: And just because there are consequence of being domesticated doesn't

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Speaker 3: mean that they're necessarily a signal of domestication. In the

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Speaker 3: same way that like, you might get a cough if

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Speaker 3: you have COVID, but having a cough doesn't mean you

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Speaker 3: have COVID. Right, There's lots of potential ways to get

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Speaker 3: a cough. Yes, so you have to like do a

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Speaker 3: basy and disentangling if you really want to be careful

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Speaker 3: about drawing your conclusions.

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Speaker 1: Right, exactly.

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Speaker 2: Okay, so now we've dug into like where we are now,

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Speaker 2: but I think it's important to look at a classical

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Speaker 2: experiment that sort of set up our belief in the

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Speaker 2: domestication syndrome and ends up being a really important cornerstone

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Speaker 2: for the raccoon experiment, which I promise we're gonna talk

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Speaker 2: about eventually, because that's what the episode's supposed to be about.

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Speaker 3: All right, let's take a break and we come back.

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Speaker 3: We're gonna hear all about the foxes, and I want

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Speaker 3: to hear about the domestication of dogs because I'm fascinated

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Speaker 3: by ancient human history and you did all that research,

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Speaker 3: so we're definitely gonna have to talk about it.

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Speaker 1: I don't have any notes ready for that.

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Speaker 3: Did Okay, we're back, and we're talking about domestication of animals,

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Speaker 3: making them cute, making them live next to you, making

441
00:21:57,280 --> 00:21:59,320
Speaker 3: them snuggle up to you at night, making them feel

442
00:21:59,320 --> 00:22:01,240
Speaker 3: like they are part of the human family.

443
00:22:01,560 --> 00:22:03,160
Speaker 1: Yay. All right.

444
00:22:03,280 --> 00:22:07,600
Speaker 2: So we talked about the domestication syndrome in the last segment,

445
00:22:08,160 --> 00:22:11,400
Speaker 2: and a bunch of our thinking about the domestication syndrome

446
00:22:11,480 --> 00:22:16,080
Speaker 2: came from one epic experiment that started in nineteen fifty

447
00:22:16,160 --> 00:22:19,040
Speaker 2: nine in the Soviet Union. It was started by a

448
00:22:19,080 --> 00:22:23,119
Speaker 2: geneticist named Dimitri Bliev at the Institute of Cytology and

449
00:22:23,160 --> 00:22:26,760
Speaker 2: Genetics and Nova sever Siberia. Just to as an asside

450
00:22:26,800 --> 00:22:29,399
Speaker 2: anyone who knows about Soviet history, he was starting this

451
00:22:29,480 --> 00:22:33,280
Speaker 2: experiment like around the time when you could get killed

452
00:22:33,520 --> 00:22:36,760
Speaker 2: for doing genetics experiments that were not like in line

453
00:22:37,040 --> 00:22:39,240
Speaker 2: with what Lysenko believed.

454
00:22:38,880 --> 00:22:40,080
Speaker 1: For how genetics should work.

455
00:22:40,119 --> 00:22:42,760
Speaker 2: And his brother was a geneticist who got killed for

456
00:22:42,880 --> 00:22:47,000
Speaker 2: like disagreeing with Lisenko. So anyway, this is intense, an

457
00:22:47,040 --> 00:22:49,359
Speaker 2: intense period to be a geneticist in the Soviet Union.

458
00:22:49,640 --> 00:22:51,359
Speaker 1: But he had this goal.

459
00:22:51,880 --> 00:22:54,560
Speaker 2: He knew that since Darwin, there was this package of

460
00:22:54,600 --> 00:22:59,040
Speaker 2: traits that people associated with domestication. He had this idea

461
00:22:59,119 --> 00:23:04,640
Speaker 2: that if you took foxes and selected specifically for tameness,

462
00:23:05,320 --> 00:23:07,600
Speaker 2: the genetics and the hormones that made that animal tame

463
00:23:07,640 --> 00:23:10,040
Speaker 2: would also change a bunch of stuff about that animal

464
00:23:10,440 --> 00:23:13,240
Speaker 2: alongside it reasonable, and so you would get those other

465
00:23:13,320 --> 00:23:16,320
Speaker 2: traits that would change too, and you'd get these tamer animals.

466
00:23:16,800 --> 00:23:20,679
Speaker 2: And so he said, let's go ahead and claim that

467
00:23:20,720 --> 00:23:23,440
Speaker 2: we are breeding animals to try to get better fur

468
00:23:23,680 --> 00:23:26,760
Speaker 2: for coats and stuff, so that Lysanko doesn't get us

469
00:23:26,760 --> 00:23:30,920
Speaker 2: all killed. But what we're actually doing is breeding for

470
00:23:31,160 --> 00:23:33,320
Speaker 2: animals that are going to be tamer. And over time

471
00:23:33,320 --> 00:23:35,480
Speaker 2: he was able to become a lot more upfront about

472
00:23:35,480 --> 00:23:36,800
Speaker 2: what the experiment was actually about.

473
00:23:37,040 --> 00:23:39,040
Speaker 3: So just to make sure I understand, he's got a

474
00:23:39,080 --> 00:23:42,119
Speaker 3: bunch of foxes, he's going to choose the tameest ones

475
00:23:42,240 --> 00:23:44,359
Speaker 3: every generation and breed them, and then he's going to

476
00:23:44,400 --> 00:23:48,280
Speaker 3: pay attention to other traits that change alongside tameness, even

477
00:23:48,320 --> 00:23:50,920
Speaker 3: if they're not directly the things he's selecting for.

478
00:23:51,240 --> 00:23:52,480
Speaker 1: Yes, exactly cool.

479
00:23:52,680 --> 00:23:54,480
Speaker 2: He has in his mind the kind of traits that

480
00:23:54,520 --> 00:23:58,000
Speaker 2: he's going to be looking for, because this idea of

481
00:23:58,040 --> 00:24:00,879
Speaker 2: a domestication syndrome, it wasn't called that a time, but

482
00:24:01,040 --> 00:24:03,800
Speaker 2: this idea of a domestication syndrome was already in people's

483
00:24:03,800 --> 00:24:06,399
Speaker 2: heads because Darwin had been describing it for a while.

484
00:24:06,640 --> 00:24:09,160
Speaker 3: But this seems very impractical to me. Like dumb biologists,

485
00:24:09,160 --> 00:24:11,400
Speaker 3: he usually choose to do experiments on animals with very

486
00:24:11,400 --> 00:24:13,560
Speaker 3: short life cycles so they can have like lots and

487
00:24:13,560 --> 00:24:16,960
Speaker 3: lots of generations in an afternoon, whereas like this experiment

488
00:24:17,040 --> 00:24:19,520
Speaker 3: might take five thousand years to show any results.

489
00:24:19,720 --> 00:24:20,560
Speaker 1: No, this is amazing.

490
00:24:20,600 --> 00:24:23,840
Speaker 2: But I mean, I think everybody's excited about domestic dogs, right,

491
00:24:23,880 --> 00:24:26,000
Speaker 2: and so he wanted to pick an animal that was

492
00:24:26,119 --> 00:24:29,080
Speaker 2: kind of close to dogs and see, like, can you

493
00:24:29,160 --> 00:24:33,040
Speaker 2: make foxes that are like dogs? Cause that would be amazing,

494
00:24:33,080 --> 00:24:35,159
Speaker 2: Like if you could do that, everybody would pay attention,

495
00:24:35,240 --> 00:24:36,639
Speaker 2: right because everyone loves dogs.

496
00:24:36,680 --> 00:24:37,479
Speaker 3: And I'd want one too.

497
00:24:37,520 --> 00:24:41,040
Speaker 2: That sounds really yes, right, me too, Me too. And

498
00:24:41,119 --> 00:24:44,440
Speaker 2: so in nineteen fifty nine, this experiment starts. And another

499
00:24:44,480 --> 00:24:46,879
Speaker 2: thing I love about the experiment is he found a

500
00:24:46,920 --> 00:24:48,520
Speaker 2: woman to run it, because at this time he was

501
00:24:48,600 --> 00:24:51,040
Speaker 2: running an institute, and so he got a woman named

502
00:24:51,119 --> 00:24:54,400
Speaker 2: Ludmilla Trutz to start it. They start with thirty male

503
00:24:54,480 --> 00:24:57,520
Speaker 2: foxes and one hundred vixens, which are female foxes from

504
00:24:57,640 --> 00:25:01,520
Speaker 2: a fur farm in Estonia, and and they knew that

505
00:25:01,640 --> 00:25:05,479
Speaker 2: these foxes were already tamer than wild relatives because they

506
00:25:05,520 --> 00:25:08,240
Speaker 2: had a history of being in fur farms for a while,

507
00:25:08,760 --> 00:25:11,560
Speaker 2: and so domestication had already essentially started. They knew that,

508
00:25:12,080 --> 00:25:14,679
Speaker 2: so they started breeding them. The pups stayed with just

509
00:25:14,800 --> 00:25:17,879
Speaker 2: mom for about two months. Then they were caged with

510
00:25:18,080 --> 00:25:20,040
Speaker 2: just their littermates and then at three months they were

511
00:25:20,040 --> 00:25:23,320
Speaker 2: caged alone and periodically they would take them out and

512
00:25:23,320 --> 00:25:25,720
Speaker 2: they would try to like not have humans interact with them.

513
00:25:25,920 --> 00:25:29,440
Speaker 2: Outside of these observation periods, they would take them out

514
00:25:29,480 --> 00:25:32,560
Speaker 2: and they would essentially interact with them briefly, and when

515
00:25:32,600 --> 00:25:35,080
Speaker 2: they were interacting with them, they would collect data on

516
00:25:35,240 --> 00:25:38,560
Speaker 2: how the fox pups interacted with the people.

517
00:25:38,920 --> 00:25:41,639
Speaker 3: This is like them trying to assess the tameness.

518
00:25:41,280 --> 00:25:44,080
Speaker 2: Of the fox and this this is not learned tameness.

519
00:25:44,119 --> 00:25:47,000
Speaker 2: This is not like I handle you every day from

520
00:25:47,000 --> 00:25:48,960
Speaker 2: when you're a puppy and see if you eventually get

521
00:25:49,040 --> 00:25:51,600
Speaker 2: used to me. This is like you're encountering me for

522
00:25:51,640 --> 00:25:55,120
Speaker 2: the first time outside of your cage. Do you a

523
00:25:55,160 --> 00:25:59,639
Speaker 2: bite me? Making you a Class three fox? Do you

524
00:26:00,200 --> 00:26:04,040
Speaker 2: b allow me to pet and handle you but show

525
00:26:04,119 --> 00:26:07,240
Speaker 2: no like emotionally friendly response to experimenters. That was a

526
00:26:07,280 --> 00:26:09,639
Speaker 2: quote so essentially like you will put up with me,

527
00:26:09,960 --> 00:26:14,320
Speaker 2: but you'd really rather not. Or Class one is quote

528
00:26:14,359 --> 00:26:18,399
Speaker 2: friendly towards experimenters, wagging their tails and whining like like

529
00:26:18,480 --> 00:26:19,639
Speaker 2: your dog does when you get.

530
00:26:19,480 --> 00:26:22,960
Speaker 3: Home, whining okay, yeah, let's yeah.

531
00:26:22,960 --> 00:26:24,040
Speaker 1: So like excited to see you.

532
00:26:24,640 --> 00:26:27,520
Speaker 2: They'd get Class one foxes and then the class one

533
00:26:27,560 --> 00:26:30,840
Speaker 2: foxes would breed with other class one foxes because that's

534
00:26:30,880 --> 00:26:33,600
Speaker 2: what they were trying to get more of. And very

535
00:26:33,680 --> 00:26:37,000
Speaker 2: quickly they started to see more and more tame animals,

536
00:26:37,080 --> 00:26:40,240
Speaker 2: and in fact, by the sixth generation, they were becoming

537
00:26:40,359 --> 00:26:44,119
Speaker 2: so friendly that they added an elite class. And the

538
00:26:44,280 --> 00:26:48,040
Speaker 2: elite classes were animals that were like really excited about

539
00:26:48,040 --> 00:26:50,680
Speaker 2: engaging with the humans. They would like whimper to get

540
00:26:50,680 --> 00:26:53,000
Speaker 2: their attention, they would sniff and lick them, and they

541
00:26:53,040 --> 00:26:56,000
Speaker 2: were essentially starting to act like dogs by the age

542
00:26:56,000 --> 00:26:56,560
Speaker 2: of one month.

543
00:26:56,760 --> 00:26:59,280
Speaker 3: And how long does six generations take in foxes? This

544
00:26:59,400 --> 00:27:00,640
Speaker 3: must have been like a decade.

545
00:27:00,880 --> 00:27:02,840
Speaker 2: I don't remember reading that in the papers. I'm gonna

546
00:27:02,840 --> 00:27:04,560
Speaker 2: admit I just looked it up on Google. How long

547
00:27:04,640 --> 00:27:06,400
Speaker 2: until they're able to breed? And it looks like they're

548
00:27:06,400 --> 00:27:08,320
Speaker 2: able to breed at nine to ten months old? So

549
00:27:08,760 --> 00:27:11,040
Speaker 2: what Yeah, so they're able to breed pretty easy. But

550
00:27:11,080 --> 00:27:13,280
Speaker 2: it does look like it's at least a year per cycle,

551
00:27:13,480 --> 00:27:14,720
Speaker 2: maybe a little bit more than that.

552
00:27:14,960 --> 00:27:18,080
Speaker 3: Another awkward fox question you probably didn't look up awesome

553
00:27:18,240 --> 00:27:21,199
Speaker 3: is well, foxes just breed with anybody we choose for

554
00:27:21,240 --> 00:27:23,159
Speaker 3: them to breed, Like you put two foxes in a

555
00:27:23,200 --> 00:27:26,280
Speaker 3: cage together and there's no question that's going to happen,

556
00:27:26,560 --> 00:27:29,280
Speaker 3: or some foxes like, eh, not feeling it, or I

557
00:27:29,359 --> 00:27:32,359
Speaker 3: prefer a different style of fox or whatever.

558
00:27:32,480 --> 00:27:35,720
Speaker 2: I did read that foxes that are particularly not friendly

559
00:27:35,840 --> 00:27:38,879
Speaker 2: are difficult to breed in captivity at all, and so

560
00:27:38,920 --> 00:27:41,320
Speaker 2: I think it could be if you like, massively stress

561
00:27:41,320 --> 00:27:43,960
Speaker 2: a fox out, they just have no interest in breeding period.

562
00:27:44,359 --> 00:27:44,879
Speaker 3: Yeah.

563
00:27:44,880 --> 00:27:48,640
Speaker 2: But the more calm of foxes, the more receptive they are.

564
00:27:49,520 --> 00:27:52,040
Speaker 2: It could be that they put some pairs together and

565
00:27:52,240 --> 00:27:55,320
Speaker 2: the female or the male were like, no thanks, and

566
00:27:55,480 --> 00:27:57,440
Speaker 2: maybe they would just try a new pair.

567
00:27:57,560 --> 00:28:00,080
Speaker 3: They didn't find each other particularly foxy.

568
00:28:00,760 --> 00:28:04,760
Speaker 2: Oh I didn't see that coming, and I should have.

569
00:28:05,000 --> 00:28:07,480
Speaker 1: Amazing, amazing, But this.

570
00:28:07,520 --> 00:28:12,840
Speaker 3: Result already says something interesting. If by selecting for friendliness

571
00:28:13,200 --> 00:28:16,359
Speaker 3: you get more and more friendly foxes, that tells you

572
00:28:16,440 --> 00:28:18,200
Speaker 3: that like friendliness is genetics.

573
00:28:18,280 --> 00:28:19,480
Speaker 1: Yes, yep, it does.

574
00:28:19,600 --> 00:28:20,040
Speaker 3: That's cool.

575
00:28:20,119 --> 00:28:23,720
Speaker 2: And by the tenth generation, eighteen percent of the babies

576
00:28:24,080 --> 00:28:28,280
Speaker 2: qualified as elite. By the twentieth generation, thirty five percent

577
00:28:28,359 --> 00:28:32,359
Speaker 2: were elite, and forty years into the experiment, which they

578
00:28:32,400 --> 00:28:33,520
Speaker 2: are still doing.

579
00:28:33,320 --> 00:28:35,879
Speaker 3: Thirty years wait, they're still doing this today. It's an

580
00:28:35,920 --> 00:28:36,760
Speaker 3: ongoing experiment.

581
00:28:36,880 --> 00:28:40,320
Speaker 2: It's an ongoing experiment forty years in Seventy to eighty

582
00:28:40,360 --> 00:28:42,680
Speaker 2: percent of the pups each generation are elite.

583
00:28:42,960 --> 00:28:47,200
Speaker 3: Wow. I love super long running experiments like knowing how

584
00:28:47,240 --> 00:28:51,000
Speaker 3: science works and how funding works and organization and recruiting

585
00:28:51,080 --> 00:28:54,040
Speaker 3: students whenever. Having anything run for more than a few

586
00:28:54,120 --> 00:28:58,280
Speaker 3: years is incredible. So like decades long experiments.

587
00:28:57,760 --> 00:28:59,240
Speaker 1: Yeah, absolutely incredible.

588
00:28:59,440 --> 00:29:01,440
Speaker 2: And they went through some lean periods, like when the

589
00:29:01,440 --> 00:29:05,200
Speaker 2: Soviet Union fell apart and they just went to Russia.

590
00:29:05,280 --> 00:29:05,440
Speaker 1: You know.

591
00:29:05,480 --> 00:29:07,880
Speaker 2: I read some papers where they were talking about, like

592
00:29:08,200 --> 00:29:10,600
Speaker 2: we had to sell some of our elite pups to

593
00:29:10,720 --> 00:29:13,320
Speaker 2: like rich people to try to get funding. We had

594
00:29:13,360 --> 00:29:15,480
Speaker 2: to it was it was tough, but anyway, they're still

595
00:29:15,480 --> 00:29:19,200
Speaker 2: going okay. So they also noted some physical changes. For example,

596
00:29:19,800 --> 00:29:22,040
Speaker 2: right when a fox is born, it tends to like

597
00:29:22,280 --> 00:29:24,560
Speaker 2: not be afraid of much of anything, Like its mom

598
00:29:24,640 --> 00:29:25,800
Speaker 2: is just kind of taking care of it.

599
00:29:25,800 --> 00:29:28,320
Speaker 1: It's exploring the world. It's hard to scare a fox.

600
00:29:28,920 --> 00:29:32,360
Speaker 2: And in the wild, the fear response kicks in around

601
00:29:32,360 --> 00:29:35,200
Speaker 2: six weeks of age, but for the foxes that had

602
00:29:35,200 --> 00:29:38,239
Speaker 2: been bred to be tame, fear responses didn't kick in

603
00:29:38,360 --> 00:29:41,000
Speaker 2: until about nine weeks of age, and so the thought

604
00:29:41,080 --> 00:29:43,200
Speaker 2: was that was giving them like a longer window to

605
00:29:43,320 --> 00:29:46,160
Speaker 2: engage with people and learn that people are okay, so

606
00:29:46,200 --> 00:29:49,440
Speaker 2: that maybe they wouldn't become afraid of people. That last

607
00:29:49,440 --> 00:29:51,960
Speaker 2: part is conjecture, but fear responses were taking longer to

608
00:29:52,040 --> 00:29:54,960
Speaker 2: kick in. Stress hormone levels if you measured them in

609
00:29:55,000 --> 00:29:58,120
Speaker 2: the foxes were lower, and so it looks like just

610
00:29:58,120 --> 00:30:00,920
Speaker 2: sort of in general, life was life stressful for the

611
00:30:00,960 --> 00:30:04,600
Speaker 2: foxes that were calmer. By the eighth to the tenth generation,

612
00:30:04,720 --> 00:30:07,480
Speaker 2: they had a star shaped white pattern on some of

613
00:30:07,520 --> 00:30:08,120
Speaker 2: their faces.

614
00:30:08,720 --> 00:30:10,080
Speaker 1: Yep, oh my gosh.

615
00:30:10,120 --> 00:30:13,200
Speaker 2: Some of them had floppy ears, some of them had

616
00:30:13,240 --> 00:30:15,720
Speaker 2: tails that were starting to roll, not all of them.

617
00:30:16,120 --> 00:30:18,960
Speaker 2: They had different amounts of serotonin in their brain, so

618
00:30:19,000 --> 00:30:22,080
Speaker 2: like even their brain chemistry was starting to change, and

619
00:30:22,240 --> 00:30:25,920
Speaker 2: by about fifteen to twenty generation, they had shorter tails

620
00:30:25,960 --> 00:30:28,840
Speaker 2: and shorter legs. Some of them was showing some like

621
00:30:28,920 --> 00:30:32,640
Speaker 2: muzzle changes they had like overbites or underbites. Not all

622
00:30:32,720 --> 00:30:35,400
Speaker 2: of the foxes had these changes, but some of them did.

623
00:30:35,960 --> 00:30:39,200
Speaker 2: And they were starting to reach sexual maturity about one

624
00:30:39,280 --> 00:30:42,480
Speaker 2: month earlier, and they were on average giving birth to

625
00:30:42,560 --> 00:30:45,480
Speaker 2: one additional pup, and they had longer mating seasons, so

626
00:30:45,520 --> 00:30:48,680
Speaker 2: you were starting to see changes in reproduction as well.

627
00:30:48,960 --> 00:30:52,600
Speaker 2: So we're hitting on some of those domestication syndrome traits

628
00:30:52,680 --> 00:30:55,440
Speaker 2: that we were talking about. And this is like within

629
00:30:55,480 --> 00:30:58,480
Speaker 2: a human lifetime. You were able to start seeing these traits,

630
00:30:58,480 --> 00:30:59,880
Speaker 2: which is amazing. Like, we don't know how long it

631
00:30:59,880 --> 00:31:05,240
Speaker 2: was in dogs, but like with very focused selection, you

632
00:31:05,280 --> 00:31:07,240
Speaker 2: were able to start seeing some of these traits show

633
00:31:07,320 --> 00:31:07,920
Speaker 2: up already.

634
00:31:08,120 --> 00:31:10,840
Speaker 3: Wow, that's amazing. But I guess one concern I have

635
00:31:10,880 --> 00:31:13,840
Speaker 3: with the whole experimental design is, you know, it's not

636
00:31:14,080 --> 00:31:17,320
Speaker 3: like blind in any way. If these folks are invested

637
00:31:17,320 --> 00:31:20,440
Speaker 3: in this outcome, they could have chosen for foxes that

638
00:31:20,480 --> 00:31:24,120
Speaker 3: were tame and for foxes that like had white patches.

639
00:31:23,720 --> 00:31:24,480
Speaker 1: On the foreheads.

640
00:31:24,640 --> 00:31:27,440
Speaker 2: Right, yeah, totally Okay, So first of all, the white

641
00:31:27,440 --> 00:31:29,720
Speaker 2: patches on their forehead would have to show up, though

642
00:31:30,280 --> 00:31:32,040
Speaker 2: you don't usually get those in the wild. So the

643
00:31:32,080 --> 00:31:35,040
Speaker 2: fact that it shows up might say something about what

644
00:31:35,080 --> 00:31:37,280
Speaker 2: the neural crest cells are doing, because part of the

645
00:31:37,360 --> 00:31:39,480
Speaker 2: idea is that the neural crest cells just like aren't

646
00:31:39,480 --> 00:31:41,960
Speaker 2: getting where they need to go, and so you start

647
00:31:41,960 --> 00:31:44,040
Speaker 2: getting white patches because you don't get milana sites where

648
00:31:44,040 --> 00:31:46,960
Speaker 2: you're supposed to have them. So the white patch showing

649
00:31:47,040 --> 00:31:49,200
Speaker 2: up at all, like, even if someone was selecting for it,

650
00:31:49,240 --> 00:31:51,640
Speaker 2: the fact that it shows up at all suggests something

651
00:31:51,720 --> 00:31:52,800
Speaker 2: about the mechanism working.

652
00:31:52,920 --> 00:31:53,200
Speaker 3: Cool.

653
00:31:53,240 --> 00:31:57,400
Speaker 2: But there have been actually a lot of critiques recently

654
00:31:57,520 --> 00:32:00,800
Speaker 2: about this study. And there's this called How to Tame

655
00:32:00,840 --> 00:32:03,840
Speaker 2: a Fox, And while I was reading the book, I

656
00:32:03,920 --> 00:32:06,560
Speaker 2: kept thinking, man, it sounds like they're trying to be

657
00:32:06,640 --> 00:32:10,400
Speaker 2: really careful, but also there's like a stage where they're like, well,

658
00:32:10,400 --> 00:32:12,400
Speaker 2: what if we take one of them and it lives

659
00:32:12,400 --> 00:32:15,840
Speaker 2: with us in our house and it's like and then

660
00:32:15,840 --> 00:32:17,680
Speaker 2: at one stage they say, well, maybe this is an

661
00:32:17,720 --> 00:32:21,520
Speaker 2: experiment that is on humans too and our response to

662
00:32:21,600 --> 00:32:24,480
Speaker 2: these things over time, because it is a two way thing.

663
00:32:24,680 --> 00:32:26,400
Speaker 2: And I'm like, it is a two way thing, but

664
00:32:27,160 --> 00:32:28,960
Speaker 2: it just I think it. To me, it just showed

665
00:32:29,000 --> 00:32:33,360
Speaker 2: how impossible it is to disentangle humans wanting to snuggle

666
00:32:33,400 --> 00:32:37,600
Speaker 2: cute foxes and dogs like, and so I can easily

667
00:32:37,840 --> 00:32:40,360
Speaker 2: imagine that there was a little bit of bias where

668
00:32:40,560 --> 00:32:43,400
Speaker 2: not only are you selecting four docile animals, but if

669
00:32:43,400 --> 00:32:45,160
Speaker 2: you start to see a little curl of the tail,

670
00:32:45,680 --> 00:32:48,920
Speaker 2: maybe you're willing to like imagine that an animal is

671
00:32:48,920 --> 00:32:50,720
Speaker 2: a little bit more docile if you also see the

672
00:32:50,760 --> 00:32:52,040
Speaker 2: curly tale, because that is.

673
00:32:51,960 --> 00:32:55,080
Speaker 1: So cute and like you need to breed that animal.

674
00:32:55,160 --> 00:32:57,640
Speaker 2: And so not that I want to cast dispersions on

675
00:32:57,680 --> 00:33:00,560
Speaker 2: any of these researchers like they do seem amaz okay,

676
00:33:00,600 --> 00:33:03,960
Speaker 2: but recently there was a big brew haha about this

677
00:33:04,080 --> 00:33:07,400
Speaker 2: paper because okay, so Lord at All twenty twenty wrote

678
00:33:07,440 --> 00:33:11,720
Speaker 2: this paper being like the domestication syndrome is like couput

679
00:33:11,800 --> 00:33:14,080
Speaker 2: and we should stop talking about the farmed fox experiment

680
00:33:14,200 --> 00:33:18,600
Speaker 2: because it turns out that actually the farmed foxes came

681
00:33:18,840 --> 00:33:22,240
Speaker 2: from fox farms in Canada that were started in the

682
00:33:22,280 --> 00:33:26,760
Speaker 2: eighteen eighties. And on these farms, they were selected for

683
00:33:26,880 --> 00:33:30,600
Speaker 2: things like white spots in the fur because they wanted

684
00:33:30,640 --> 00:33:34,320
Speaker 2: to sell that fur. And it's been known that some

685
00:33:34,360 --> 00:33:36,320
Speaker 2: of these animals were like a little bit tame, because

686
00:33:36,320 --> 00:33:38,480
Speaker 2: they showed some photos of the foxes like sitting on

687
00:33:38,520 --> 00:33:41,120
Speaker 2: the lap of someone. And so the people who wrote

688
00:33:41,120 --> 00:33:44,320
Speaker 2: this paper were like, maybe the researchers didn't even know.

689
00:33:44,560 --> 00:33:48,200
Speaker 1: The foxes came from Canada. But a couple things.

690
00:33:48,240 --> 00:33:50,800
Speaker 2: I'm not sure if the people who wrote this paper

691
00:33:51,280 --> 00:33:54,360
Speaker 2: were frustrated that people seem to have not understood the

692
00:33:54,440 --> 00:33:57,720
Speaker 2: farmed fox experiment or what. But like when you read

693
00:33:57,960 --> 00:34:02,560
Speaker 2: Lude Militrutz papers, she mentions, like, you know, these animals

694
00:34:02,640 --> 00:34:05,960
Speaker 2: had been domesticated before we got them, and she references

695
00:34:06,000 --> 00:34:08,840
Speaker 2: papers where they had mentioned that they came from farmed

696
00:34:09,040 --> 00:34:12,399
Speaker 2: foxes in Canada, and like, I think they knew all

697
00:34:12,440 --> 00:34:14,000
Speaker 2: of this, and they had mentioned all of this in

698
00:34:14,040 --> 00:34:16,600
Speaker 2: the papers, and so none of this felt new to me.

699
00:34:16,640 --> 00:34:18,680
Speaker 2: I was like, I'm pretty sure all of the papers

700
00:34:18,719 --> 00:34:20,480
Speaker 2: that I read had said all of this stuff before,

701
00:34:20,520 --> 00:34:23,680
Speaker 2: but maybe people weren't reading the original papers. Certainly that

702
00:34:23,760 --> 00:34:26,480
Speaker 2: happens in science a lot, where people read references of

703
00:34:26,520 --> 00:34:30,000
Speaker 2: the original papers instead of reading the original papers. But anyway,

704
00:34:30,120 --> 00:34:33,120
Speaker 2: so it is the case that these animals were a

705
00:34:33,200 --> 00:34:36,759
Speaker 2: little bit tamer than you would expect wild animals to

706
00:34:36,840 --> 00:34:40,520
Speaker 2: be and had already been selected for some white pigmentation,

707
00:34:41,080 --> 00:34:43,680
Speaker 2: although Lude Milla points out that the white spot on

708
00:34:43,719 --> 00:34:46,320
Speaker 2: the head is a totally different mutation that hadn't really

709
00:34:46,320 --> 00:34:49,920
Speaker 2: been seen in the Canadian foxes before, and just because

710
00:34:49,920 --> 00:34:52,960
Speaker 2: the animals had been a little bit tame already doesn't

711
00:34:53,040 --> 00:34:55,719
Speaker 2: change the fact that when you selected for it, even

712
00:34:55,800 --> 00:34:58,120
Speaker 2: more they got all of these additional changes.

713
00:34:58,600 --> 00:35:01,280
Speaker 3: Yeah, the experiment is about the changes, right, the input

714
00:35:01,320 --> 00:35:04,839
Speaker 3: foxes will affect that, but they don't control the changes, right.

715
00:35:04,880 --> 00:35:07,560
Speaker 2: And so that was Ludmilla's counter arguments because you know,

716
00:35:07,600 --> 00:35:10,919
Speaker 2: I read this chain of arguments and counter arguments, oh wow,

717
00:35:10,920 --> 00:35:11,920
Speaker 2: And so it was it was a.

718
00:35:11,840 --> 00:35:15,439
Speaker 1: Whole thing drama in biology, that's right.

719
00:35:15,760 --> 00:35:18,799
Speaker 2: And so one interesting point that the Lord at All

720
00:35:18,840 --> 00:35:20,919
Speaker 2: paper made though, was that like when people talk about

721
00:35:20,920 --> 00:35:23,840
Speaker 2: the domestication syndrome, they talk about it as though it

722
00:35:23,880 --> 00:35:27,560
Speaker 2: is a thing where when you domesticate animals you get

723
00:35:27,880 --> 00:35:32,040
Speaker 2: all of these packages of traits that change, but there's

724
00:35:32,239 --> 00:35:35,120
Speaker 2: tons of variability and you don't always see it. And

725
00:35:35,160 --> 00:35:39,200
Speaker 2: they were really highlighting that we talk about it like

726
00:35:39,239 --> 00:35:41,560
Speaker 2: it's a thing that definitely happens, but it's a thing

727
00:35:41,640 --> 00:35:44,000
Speaker 2: that happens in an inconsistent way, and we don't have

728
00:35:44,120 --> 00:35:48,120
Speaker 2: a good framework for predicting it. And people talk about

729
00:35:48,160 --> 00:35:50,880
Speaker 2: the farmed fox experiment as if it proved that domestication

730
00:35:50,960 --> 00:35:55,040
Speaker 2: syndromes happen, but this is one example in one place

731
00:35:55,120 --> 00:35:57,279
Speaker 2: and it hasn't been sort of like, we don't have

732
00:35:57,320 --> 00:35:59,760
Speaker 2: a consistent framework that looks at a bunch of different animals,

733
00:36:00,280 --> 00:36:02,239
Speaker 2: and that is important to keep in mind when we

734
00:36:02,280 --> 00:36:05,319
Speaker 2: are about to jump into the raccoon experiment. All right,

735
00:36:05,600 --> 00:36:07,880
Speaker 2: one more thing to keep in mind before we finally

736
00:36:07,880 --> 00:36:11,040
Speaker 2: talk about the raccoon experiment is one of the things

737
00:36:11,040 --> 00:36:13,960
Speaker 2: that we've been talking about is how the domestication syndrome

738
00:36:14,040 --> 00:36:16,719
Speaker 2: is often associated with changes in the shape of the head,

739
00:36:17,239 --> 00:36:20,680
Speaker 2: and in particular like shorter snouts. There was a group

740
00:36:20,719 --> 00:36:25,040
Speaker 2: of researchers that looked at wild foxes by looking at

741
00:36:25,320 --> 00:36:30,160
Speaker 2: fox heads that were in museums and were collected from

742
00:36:30,200 --> 00:36:33,200
Speaker 2: around the time when foxes were probably collected to go

743
00:36:33,360 --> 00:36:38,200
Speaker 2: into the Canadian fox farms that would have subsequently provided

744
00:36:38,239 --> 00:36:40,120
Speaker 2: the animals that would have ended up in the Russian

745
00:36:40,160 --> 00:36:43,720
Speaker 2: farmed fox experiment. And they looked at measurements on those

746
00:36:43,800 --> 00:36:48,760
Speaker 2: heads and compared them to the domesticated foxes from Russia.

747
00:36:48,880 --> 00:36:51,120
Speaker 3: So this is like an effort to sample foxes before

748
00:36:51,200 --> 00:36:53,880
Speaker 3: they were even selected for the fur farms.

749
00:36:54,040 --> 00:36:56,439
Speaker 2: That's right, Yes, this is an effort to really get

750
00:36:56,480 --> 00:36:59,759
Speaker 2: like what do wild foxes look like? What do their

751
00:36:59,800 --> 00:37:02,359
Speaker 2: heads look like? They looked at the heads of the

752
00:37:02,400 --> 00:37:06,719
Speaker 2: domesticated foxes from today, from the Russian farmed foxes, and

753
00:37:07,280 --> 00:37:10,200
Speaker 2: the Russian farmed fox experiment also has what they call

754
00:37:10,280 --> 00:37:15,120
Speaker 2: a control treatment where essentially they randomly breed the animals.

755
00:37:14,719 --> 00:37:15,680
Speaker 1: That are still aggressive.

756
00:37:16,000 --> 00:37:18,520
Speaker 2: Oh okay, so those are supposed to look like the

757
00:37:18,560 --> 00:37:22,960
Speaker 2: wild animals. And what they found was that there controlled

758
00:37:23,040 --> 00:37:27,560
Speaker 2: animals that were bred in Russia have very similar head

759
00:37:27,600 --> 00:37:30,960
Speaker 2: shapes to the animals that are super tame and are

760
00:37:31,000 --> 00:37:35,440
Speaker 2: super friendly that were bred. So it looks like selecting

761
00:37:35,560 --> 00:37:39,920
Speaker 2: for being super friendly is not necessarily what's changing the

762
00:37:39,920 --> 00:37:40,960
Speaker 2: shape of the head.

763
00:37:41,040 --> 00:37:43,920
Speaker 3: Right, because selecting for tameness or not both led to

764
00:37:44,000 --> 00:37:44,880
Speaker 3: changes in the shape.

765
00:37:44,719 --> 00:37:45,799
Speaker 1: Of the head exactly.

766
00:37:45,960 --> 00:37:49,280
Speaker 2: So something about being in captivity does seem to change

767
00:37:49,280 --> 00:37:51,759
Speaker 2: the shape of the head because the head was very

768
00:37:51,800 --> 00:37:55,360
Speaker 2: different than the wild foxes. But the difference between the

769
00:37:55,480 --> 00:37:59,640
Speaker 2: very docile and the aggressive farmed foxes there was some difference,

770
00:37:59,680 --> 00:38:02,080
Speaker 2: but it was very small, and the difference between the

771
00:38:02,080 --> 00:38:05,520
Speaker 2: wild ones was huge. So something about being in captivity

772
00:38:05,640 --> 00:38:10,000
Speaker 2: changes head shape and it's not necessarily selection for being

773
00:38:10,040 --> 00:38:11,200
Speaker 2: super friendly and snugly.

774
00:38:11,600 --> 00:38:15,480
Speaker 3: So it turns out it's complicated and it depends exactly.

775
00:38:15,680 --> 00:38:19,080
Speaker 2: Yes, it's complicated and it depends. Okay, So there was

776
00:38:19,120 --> 00:38:21,000
Speaker 2: a lot more complication we could have talked about. We

777
00:38:21,040 --> 00:38:23,279
Speaker 2: only have an hour sometime. I have to get to

778
00:38:23,320 --> 00:38:26,680
Speaker 2: the raccoons. Daniel's being very patient. Let's take a break,

779
00:38:26,680 --> 00:38:29,840
Speaker 2: and when we get back, I will actually address Matt's question.

780
00:38:49,680 --> 00:38:52,600
Speaker 3: Okay, we're back, and we are talking about domestication, how

781
00:38:52,600 --> 00:38:56,440
Speaker 3: it influences animals, if it influences animals, and what it

782
00:38:56,440 --> 00:38:59,160
Speaker 3: can tell us about raccoons. But before we move on

783
00:38:59,200 --> 00:39:01,560
Speaker 3: to the raccoons, Lly, I have to blow your mind

784
00:39:01,600 --> 00:39:04,200
Speaker 3: by asking a question that will really really surprise you.

785
00:39:04,600 --> 00:39:05,640
Speaker 1: Oh, okay, go ahead.

786
00:39:06,040 --> 00:39:08,839
Speaker 3: What is the Latin name of the silver.

787
00:39:08,560 --> 00:39:10,600
Speaker 1: Fox vope's vopies?

788
00:39:11,320 --> 00:39:13,120
Speaker 3: Yeah? And as you know, I'm not a big fan

789
00:39:13,160 --> 00:39:15,959
Speaker 3: of Latin names, but this one is super cool because

790
00:39:16,000 --> 00:39:17,600
Speaker 3: it's doubled. Why is it doubled?

791
00:39:18,239 --> 00:39:18,680
Speaker 1: Do you know?

792
00:39:18,840 --> 00:39:19,279
Speaker 3: I don't know.

793
00:39:19,360 --> 00:39:21,160
Speaker 1: Are you just trying to trick You're just asking to

794
00:39:21,200 --> 00:39:23,759
Speaker 1: trick me. I specifically didn't look it up because I

795
00:39:23,760 --> 00:39:26,040
Speaker 1: was like, I'm not going to bring it up because

796
00:39:26,120 --> 00:39:27,279
Speaker 1: Daniel doesn't want me to.

797
00:39:27,760 --> 00:39:29,719
Speaker 3: I'm not asking a trick you. I assumed you have

798
00:39:29,800 --> 00:39:32,279
Speaker 3: this deep well of knowledge about silver foxes and their

799
00:39:32,360 --> 00:39:35,880
Speaker 3: Latin names, and you rejoice in the Latin inscrutability of it.

800
00:39:36,040 --> 00:39:39,920
Speaker 2: Oh no, thank you, But I've stopped looking it up

801
00:39:39,960 --> 00:39:42,319
Speaker 2: because I was like, well, Daniel's what's the point in

802
00:39:42,360 --> 00:39:43,960
Speaker 2: looking it up If Daniel's not gonna.

803
00:39:43,760 --> 00:39:44,319
Speaker 1: Let me say it.

804
00:39:44,880 --> 00:39:47,160
Speaker 3: This is part of my Kelly domestication experiment.

805
00:39:51,120 --> 00:39:53,160
Speaker 2: Al right, all right, so now I'm going to google

806
00:39:53,200 --> 00:39:54,279
Speaker 2: what does volpees mean?

807
00:39:54,920 --> 00:39:56,680
Speaker 1: Vopees is Latin for fox.

808
00:39:58,239 --> 00:40:01,120
Speaker 3: Latin. So the Latin name of silver foxes is fox

809
00:40:01,160 --> 00:40:02,080
Speaker 3: fox fox fox.

810
00:40:02,200 --> 00:40:03,040
Speaker 1: Yes, it's a.

811
00:40:03,000 --> 00:40:05,120
Speaker 3: Pretty foxy fox. So yeah that makes sense.

812
00:40:05,200 --> 00:40:06,800
Speaker 1: Yeah, yeah, they studied fox foxes.

813
00:40:07,040 --> 00:40:09,200
Speaker 3: All right, so let's move on to raccoons.

814
00:40:09,280 --> 00:40:14,840
Speaker 2: Raccoons, Oh no, it's prion loader. Come on, Oh no,

815
00:40:15,600 --> 00:40:18,000
Speaker 2: frock prokon loader.

816
00:40:18,440 --> 00:40:20,600
Speaker 1: I can't say it. I didn't practice it. You're saying

817
00:40:20,600 --> 00:40:22,000
Speaker 1: me up for failure.

818
00:40:23,680 --> 00:40:26,600
Speaker 3: Now every time. You're not gonna know. But tell us

819
00:40:26,640 --> 00:40:30,319
Speaker 3: about the raccoons and Math's question and whether raccoons are

820
00:40:30,360 --> 00:40:31,399
Speaker 3: becoming domesticated.

821
00:40:31,480 --> 00:40:33,720
Speaker 2: All right, So we're specifically talking about a twenty twenty

822
00:40:33,719 --> 00:40:37,200
Speaker 2: five paper in Frontiers and Zoology by Apostolov.

823
00:40:37,440 --> 00:40:39,480
Speaker 1: At all. This is a paper out of RAFAELA.

824
00:40:39,560 --> 00:40:43,800
Speaker 2: Lesha's lab, and it is called tracking Domestication Signals across

825
00:40:43,840 --> 00:40:48,560
Speaker 2: populations of North American raccoons scientific name via citizen science

826
00:40:48,640 --> 00:40:52,960
Speaker 2: driven image repositories. So all right, so here's what they did.

827
00:40:53,320 --> 00:40:57,960
Speaker 2: Have you heard of I naturalist, Daniel Ein naturalist? I

828
00:40:57,960 --> 00:40:59,399
Speaker 2: I naturalist.

829
00:40:58,920 --> 00:41:01,080
Speaker 3: I naturalist. You know, I have no idea what that is?

830
00:41:01,160 --> 00:41:01,719
Speaker 1: Oh man? Okay.

831
00:41:01,719 --> 00:41:04,520
Speaker 2: So it's this amazing image repository where you can take

832
00:41:04,520 --> 00:41:06,920
Speaker 2: a picture of something, load it up, and if you

833
00:41:07,120 --> 00:41:09,759
Speaker 2: know what it is, you can say like, I am

834
00:41:09,840 --> 00:41:12,400
Speaker 2: at this location and here is what the species is,

835
00:41:12,440 --> 00:41:14,279
Speaker 2: and other people can like weigh in about whether you

836
00:41:14,360 --> 00:41:16,400
Speaker 2: got the species ID right or not, or if you

837
00:41:16,440 --> 00:41:18,680
Speaker 2: want to know what it is, you can like experts

838
00:41:18,680 --> 00:41:20,799
Speaker 2: can weigh in to tell you what it is. And

839
00:41:20,840 --> 00:41:26,040
Speaker 2: it's just essentially location information, photos and species identification information,

840
00:41:26,160 --> 00:41:27,920
Speaker 2: so you can try to figure out what it is

841
00:41:27,920 --> 00:41:30,040
Speaker 2: that you're seeing no matter where you are around the world.

842
00:41:30,960 --> 00:41:31,280
Speaker 3: Cool.

843
00:41:31,600 --> 00:41:33,640
Speaker 1: It's not just cool, Daniel, this is amazing.

844
00:41:33,800 --> 00:41:37,960
Speaker 3: This is really cool, mind blowing. Thank you taking international

845
00:41:38,000 --> 00:41:40,120
Speaker 3: network of Latin names all around the world.

846
00:41:41,239 --> 00:41:41,439
Speaker 1: Yeah.

847
00:41:41,560 --> 00:41:43,839
Speaker 2: I thought you were getting the attitude right, but then

848
00:41:43,880 --> 00:41:45,440
Speaker 2: you went to Latin names and now I think you're

849
00:41:45,480 --> 00:41:48,840
Speaker 2: being sarcastic. But anyway, all right, so there is a

850
00:41:48,920 --> 00:41:52,560
Speaker 2: database of something like twenty thousand photos of raccoons from

851
00:41:52,600 --> 00:41:56,960
Speaker 2: all over the United States. Okay, And the authors were wondering, okay,

852
00:41:57,040 --> 00:42:00,319
Speaker 2: are raccoons in urban areas, So essentially raccoon that are

853
00:42:00,320 --> 00:42:04,400
Speaker 2: living closer to us, eating our trash, for example, showing

854
00:42:04,560 --> 00:42:09,319
Speaker 2: signs of a domestication syndrome relative to raccoons that.

855
00:42:09,280 --> 00:42:10,280
Speaker 1: Live in the woods.

856
00:42:10,400 --> 00:42:13,279
Speaker 2: All right, So like a raccoon that lives near me

857
00:42:13,920 --> 00:42:17,640
Speaker 2: might look different than a raccoon that lives in Irvine

858
00:42:18,200 --> 00:42:21,759
Speaker 2: because it is in a higher density area and might

859
00:42:21,760 --> 00:42:24,920
Speaker 2: be eating from a trash can, whereas there's the lower

860
00:42:24,920 --> 00:42:26,839
Speaker 2: density of people out here. So a raccoon out here

861
00:42:26,920 --> 00:42:28,320
Speaker 2: might need to forage for its own food.

862
00:42:28,680 --> 00:42:29,399
Speaker 1: Does that make sense?

863
00:42:29,760 --> 00:42:33,000
Speaker 3: Yeah, So an urban area is the interact with humanity more.

864
00:42:33,480 --> 00:42:38,520
Speaker 3: And the question is whether this unintentional interaction with raccoons

865
00:42:38,719 --> 00:42:42,000
Speaker 3: is changing raccoons in the way we might expect from

866
00:42:42,040 --> 00:42:43,839
Speaker 3: domestication syndrome hypothesis.

867
00:42:43,920 --> 00:42:44,400
Speaker 1: Exactly.

868
00:42:44,560 --> 00:42:47,120
Speaker 3: Yes, And it's very cool when you come up with

869
00:42:47,160 --> 00:42:49,839
Speaker 3: a really grand hypothesis that's hard to test and then

870
00:42:49,840 --> 00:42:52,200
Speaker 3: figure out a way to test it with existing data.

871
00:42:52,440 --> 00:42:55,680
Speaker 2: Yes, super cool, Yes, amazing, amazing, And so they were like,

872
00:42:55,680 --> 00:42:58,000
Speaker 2: all right, we've got all of these photos that people

873
00:42:58,040 --> 00:43:02,720
Speaker 2: have taken, and we could ask if raccoons in urban

874
00:43:03,160 --> 00:43:08,600
Speaker 2: and rural environments have different snout lengths, and so we

875
00:43:08,640 --> 00:43:11,400
Speaker 2: would guess that the raccoons that are eating your trash

876
00:43:11,920 --> 00:43:15,440
Speaker 2: have a shorter snout based on the domestication syndrome idea,

877
00:43:16,000 --> 00:43:19,240
Speaker 2: then the raccoons that are out in the woods near Kelly.

878
00:43:20,120 --> 00:43:22,839
Speaker 2: And so they took the pictures and these pictures don't

879
00:43:22,840 --> 00:43:26,640
Speaker 2: have like rulers on them unfortunately, so you can't get

880
00:43:26,680 --> 00:43:30,600
Speaker 2: like absolute measurements, but they wanted to get ratios. So

881
00:43:30,719 --> 00:43:33,279
Speaker 2: essentially what they did was they took pictures and they

882
00:43:33,640 --> 00:43:37,560
Speaker 2: measured the relative distance between the tip of the nose

883
00:43:38,280 --> 00:43:39,960
Speaker 2: and the tear ducts, so they had to be able

884
00:43:39,960 --> 00:43:42,880
Speaker 2: to see the eye, and then the distance between the

885
00:43:42,920 --> 00:43:45,799
Speaker 2: tip of the nose and where the bottom and the

886
00:43:45,840 --> 00:43:48,600
Speaker 2: top of the ear connected to the rest of the skull.

887
00:43:49,280 --> 00:43:52,279
Speaker 2: And so if you have a shorter snout, then the

888
00:43:52,320 --> 00:43:55,000
Speaker 2: distance between the tear duct and the eye and the

889
00:43:55,040 --> 00:43:58,560
Speaker 2: nose is going to be shorter relative to like the

890
00:43:58,600 --> 00:43:59,439
Speaker 2: whole head length.

891
00:43:59,800 --> 00:44:00,680
Speaker 1: Does that make sense.

892
00:44:01,280 --> 00:44:04,960
Speaker 3: I think you're saying that they measured the nose to

893
00:44:05,080 --> 00:44:07,960
Speaker 3: ear distance and the nose to eye distance, and they

894
00:44:08,080 --> 00:44:11,000
Speaker 3: use that to get a ratio to say, like, how

895
00:44:11,040 --> 00:44:14,719
Speaker 3: snouty are you? Because they couldn't get absolute measurements on

896
00:44:14,760 --> 00:44:17,040
Speaker 3: these raccoons. They're just pictures of raccoons, and you can't

897
00:44:17,040 --> 00:44:20,200
Speaker 3: tell is this a really huge raccoon or is it

898
00:44:20,239 --> 00:44:22,120
Speaker 3: really just close to the camera beautiful?

899
00:44:22,200 --> 00:44:24,160
Speaker 1: Yes, exactly, thank you, that's what they did.

900
00:44:24,640 --> 00:44:27,400
Speaker 3: But they can't use anything in the images to like calibrate.

901
00:44:27,440 --> 00:44:29,439
Speaker 3: I mean, like if you see a raccoon that looks

902
00:44:29,480 --> 00:44:31,279
Speaker 3: like the size of a house, you know it's not

903
00:44:31,320 --> 00:44:33,799
Speaker 3: actually the size of a house. And usually there's something

904
00:44:33,840 --> 00:44:35,920
Speaker 3: else in the image that can tell you, oh, this

905
00:44:36,000 --> 00:44:38,640
Speaker 3: raccoon is really close up or something. But anyway, I'm

906
00:44:38,640 --> 00:44:40,120
Speaker 3: sure they thought of that and tried it, and it's

907
00:44:40,120 --> 00:44:40,560
Speaker 3: too hard.

908
00:44:40,719 --> 00:44:43,200
Speaker 2: Yeah, yeah, I mean some of these images don't really

909
00:44:43,239 --> 00:44:47,520
Speaker 2: have any helpful landmarks, and so so anyway, they didn't

910
00:44:47,560 --> 00:44:49,440
Speaker 2: do that for whatever reason. Okay, And so then the

911
00:44:49,440 --> 00:44:51,960
Speaker 2: next step they had these twenty thousand images, but they

912
00:44:52,000 --> 00:44:54,480
Speaker 2: wanted to make sure they weren't re using images of

913
00:44:54,520 --> 00:44:57,959
Speaker 2: the same raccoon. So just in case, one person every

914
00:44:58,040 --> 00:45:00,440
Speaker 2: day took fifty pictures of the same raccoon and loaded

915
00:45:00,440 --> 00:45:04,880
Speaker 2: them all up. They only used one image from each

916
00:45:05,160 --> 00:45:08,560
Speaker 2: person who uses I naturalist oh okay, And then they

917
00:45:08,600 --> 00:45:12,319
Speaker 2: made sure that they were only using images of raccoons

918
00:45:12,320 --> 00:45:16,759
Speaker 2: that were alive or freshly dead, or raccoons that were

919
00:45:16,800 --> 00:45:19,399
Speaker 2: I know, or raccoons that were facing the right way,

920
00:45:19,480 --> 00:45:22,040
Speaker 2: because you know, if a raccoon is sort of facing

921
00:45:22,040 --> 00:45:23,759
Speaker 2: at the wrong angle, you might not get the right

922
00:45:23,840 --> 00:45:26,600
Speaker 2: ratio that you're looking for. And so after they'd passed

923
00:45:26,600 --> 00:45:30,520
Speaker 2: the images by a bunch of different criteria, they ended

924
00:45:30,640 --> 00:45:34,040
Speaker 2: up with two hundred and forty nine images, which is

925
00:45:34,600 --> 00:45:37,440
Speaker 2: pretty small samples. That's the sound I made. That's the

926
00:45:37,480 --> 00:45:38,120
Speaker 2: sound I made.

927
00:45:38,360 --> 00:45:40,560
Speaker 3: It's not just that it's a small sample size, but

928
00:45:40,600 --> 00:45:44,160
Speaker 3: it's a huge selection effect, right, Yeah, one of these

929
00:45:44,200 --> 00:45:47,480
Speaker 3: criteria could easily be biasing their subsample.

930
00:45:47,920 --> 00:45:48,520
Speaker 1: I agree.

931
00:45:48,640 --> 00:45:53,120
Speaker 2: And they ended up with thirty eight rural raccoons and

932
00:45:53,120 --> 00:45:57,280
Speaker 2: two hundred and eleven urban raccoons. Wow, so pretty small

933
00:45:57,320 --> 00:45:59,840
Speaker 2: subset for the rural raccoons, which are supposed to be

934
00:45:59,840 --> 00:46:02,560
Speaker 2: like baseline two hundred and eleven urban ones.

935
00:46:02,640 --> 00:46:02,960
Speaker 1: Okay.

936
00:46:03,440 --> 00:46:05,279
Speaker 2: And so then they made those measurements that we talked

937
00:46:05,280 --> 00:46:07,280
Speaker 2: about making the ratios data.

938
00:46:07,719 --> 00:46:08,359
Speaker 1: There were a lot of.

939
00:46:08,280 --> 00:46:11,400
Speaker 2: Different authors, and it looks like a bunch of different

940
00:46:11,440 --> 00:46:15,720
Speaker 2: authors were making these measurements and they weren't all getting

941
00:46:15,760 --> 00:46:22,160
Speaker 2: the same measurements. So on one author, one author looked

942
00:46:22,239 --> 00:46:25,319
Speaker 2: at the different measurements made by the different members of

943
00:46:25,360 --> 00:46:29,960
Speaker 2: the team thirteen images and found sixty eight percent inter

944
00:46:30,160 --> 00:46:34,440
Speaker 2: radar reliability, which doesn't sound great to me. So the

945
00:46:34,480 --> 00:46:37,160
Speaker 2: way I've used these measures in the past has been like,

946
00:46:37,719 --> 00:46:43,759
Speaker 2: if I analyze the same photo five times, this is

947
00:46:43,800 --> 00:46:47,879
Speaker 2: a measure of how similar those analyzes are. So if

948
00:46:47,880 --> 00:46:50,480
Speaker 2: I get a one, that means every time I analyze

949
00:46:50,520 --> 00:46:53,520
Speaker 2: the photo, I get the exact same answer. Or you

950
00:46:53,560 --> 00:46:57,879
Speaker 2: can have five different people analyze one photo and if

951
00:46:57,880 --> 00:47:00,279
Speaker 2: they all get the same answer, then the answer would

952
00:47:00,320 --> 00:47:02,800
Speaker 2: be one, and if they all get a different answer,

953
00:47:03,280 --> 00:47:06,040
Speaker 2: then the answer would be zero. And so I looked

954
00:47:06,120 --> 00:47:10,480
Speaker 2: up the paper that was cited in this paper for

955
00:47:10,800 --> 00:47:16,080
Speaker 2: how these reliability analyses should be understood, and the paper

956
00:47:16,120 --> 00:47:18,920
Speaker 2: says that if you get a value less than point five,

957
00:47:19,120 --> 00:47:22,759
Speaker 2: you've done like a poor job of being reliable. If

958
00:47:22,760 --> 00:47:27,600
Speaker 2: it's between point five and point seventy five, that's moderate good.

959
00:47:27,719 --> 00:47:30,640
Speaker 2: Is between point seventy five and point nine and excellent

960
00:47:30,760 --> 00:47:34,080
Speaker 2: is greater than point nine. So this paper had point

961
00:47:34,120 --> 00:47:39,080
Speaker 2: six y' eight, which puts it at moderate reliability or

962
00:47:39,200 --> 00:47:41,839
Speaker 2: moderate consistency in answers.

963
00:47:42,400 --> 00:47:44,920
Speaker 3: That tells me, like, why are you writing this paper? Like,

964
00:47:44,960 --> 00:47:47,120
Speaker 3: go back to your study design and start over.

965
00:47:47,680 --> 00:47:50,239
Speaker 2: What frustrated me is that two hundred and forty nine

966
00:47:50,280 --> 00:47:52,279
Speaker 2: images is not a lot of images. And so I

967
00:47:52,320 --> 00:47:55,200
Speaker 2: would have just been like, Okay, three people are going

968
00:47:55,239 --> 00:47:57,920
Speaker 2: to each measure every single image, and we're going to

969
00:47:58,080 --> 00:48:01,480
Speaker 2: like average, or we're going to like, I don't know,

970
00:48:01,480 --> 00:48:03,520
Speaker 2: we're gonna do something like their two hundred and forty

971
00:48:03,600 --> 00:48:05,480
Speaker 2: nine images is not a lot. We're gonna find some

972
00:48:05,520 --> 00:48:07,360
Speaker 2: way to make sure that we're all getting the same answer.

973
00:48:07,520 --> 00:48:09,560
Speaker 3: Why can't they just write a computer program to analyze

974
00:48:09,560 --> 00:48:10,120
Speaker 3: these images?

975
00:48:10,440 --> 00:48:13,960
Speaker 2: Yeah bias way, Yeah, that or that, I don't know.

976
00:48:14,000 --> 00:48:15,759
Speaker 2: That might have been hard, but they just needed to

977
00:48:15,800 --> 00:48:21,160
Speaker 2: find a daniel. So anyway, that amount of reliability sounded

978
00:48:21,400 --> 00:48:24,839
Speaker 2: a little low to me personally. Yeah, anyway, So then

979
00:48:24,880 --> 00:48:27,960
Speaker 2: they looked up USDA plant hardiness zone, so they essentially

980
00:48:28,000 --> 00:48:31,279
Speaker 2: were looking for a possible impact of like weather on

981
00:48:31,360 --> 00:48:33,799
Speaker 2: snout length. Because these were raccoons from all over the

982
00:48:33,840 --> 00:48:37,200
Speaker 2: United States. And then they looked up some census information

983
00:48:37,239 --> 00:48:40,160
Speaker 2: to figure out if these raccoons were rural raccoons or

984
00:48:40,280 --> 00:48:44,080
Speaker 2: urban raccoons. So they looked at where these raccoons were

985
00:48:44,160 --> 00:48:46,160
Speaker 2: tagged on e naturalists to figure out if they were

986
00:48:46,160 --> 00:48:47,840
Speaker 2: found in the middle of a city or out in

987
00:48:47,880 --> 00:48:50,640
Speaker 2: the middle of nowhere. And then they did some statistical

988
00:48:50,640 --> 00:48:55,440
Speaker 2: modeling and essentially what they found was that in warmer areas,

989
00:48:56,440 --> 00:49:01,200
Speaker 2: snouts tend to be shorter, so there's an impact of climate.

990
00:49:02,520 --> 00:49:06,200
Speaker 2: And they found that there was an impact on urban

991
00:49:06,640 --> 00:49:12,240
Speaker 2: areas where urban raccoons did tend to have shorter snouts,

992
00:49:13,600 --> 00:49:16,920
Speaker 2: and so that is in the direction that we expected.

993
00:49:17,040 --> 00:49:20,320
Speaker 2: They found that they had three point six ish percent

994
00:49:20,680 --> 00:49:24,560
Speaker 2: shorter snouts between rural and urban raccoons, and.

995
00:49:24,520 --> 00:49:27,560
Speaker 3: I assume they did some statistics and found this is

996
00:49:27,600 --> 00:49:29,400
Speaker 3: a meaningful difference for these samples.

997
00:49:29,760 --> 00:49:32,359
Speaker 2: Yeah, but it's worth noting that they had initially done

998
00:49:32,360 --> 00:49:36,160
Speaker 2: a model that included a bunch of years. But when

999
00:49:36,440 --> 00:49:39,120
Speaker 2: they did that more complicated model that included a bunch

1000
00:49:39,160 --> 00:49:42,759
Speaker 2: of years, it had what's called a high variance inflation factor.

1001
00:49:42,800 --> 00:49:44,560
Speaker 2: We're not going to get into statistics. They ended up

1002
00:49:44,600 --> 00:49:47,120
Speaker 2: deciding that it was better to restrict the data to

1003
00:49:47,520 --> 00:49:49,520
Speaker 2: what they saw from twenty twenty to twenty twenty four.

1004
00:49:49,920 --> 00:49:53,000
Speaker 2: So after looking at an even smaller subset of the data,

1005
00:49:53,360 --> 00:49:56,160
Speaker 2: that's where they got that three point six percent snout reduction.

1006
00:49:56,560 --> 00:49:59,880
Speaker 2: So it ends up being a pretty small data set.

1007
00:50:00,400 --> 00:50:06,360
Speaker 2: But I mean their conclusion, to be fair is pretty conservative, Okay. Essentially,

1008
00:50:06,400 --> 00:50:09,359
Speaker 2: what they say is, we want to highlight raccoons as

1009
00:50:09,400 --> 00:50:12,840
Speaker 2: a new opportunity for observing early stage domestication patterns in

1010
00:50:12,880 --> 00:50:16,320
Speaker 2: a mammalian model system with no possibility of introgression or

1011
00:50:16,400 --> 00:50:20,560
Speaker 2: hybridization with other already domesticated mammalian species. Basically, they're like,

1012
00:50:21,040 --> 00:50:24,640
Speaker 2: maybe early domestication is happening here. The raccoons aren't going

1013
00:50:24,719 --> 00:50:29,320
Speaker 2: to be like mating with our cats or wild pigs

1014
00:50:29,520 --> 00:50:32,280
Speaker 2: or something like that. And so this is a system.

1015
00:50:32,320 --> 00:50:35,840
Speaker 3: Why not why can't we have a cat raccoon hybrid

1016
00:50:35,880 --> 00:50:36,920
Speaker 3: that sounds super cute?

1017
00:50:37,120 --> 00:50:37,600
Speaker 1: Sure?

1018
00:50:37,719 --> 00:50:40,680
Speaker 2: Yeah, well, I you know, we're gonna have Scott Egan

1019
00:50:40,719 --> 00:50:42,400
Speaker 2: on the show to talk about what is the species

1020
00:50:42,440 --> 00:50:45,520
Speaker 2: and he'll give you an answer, Daniel. And so there's

1021
00:50:45,560 --> 00:50:47,640
Speaker 2: a lot of room for further work. So, for example,

1022
00:50:47,680 --> 00:50:49,719
Speaker 2: this study also didn't look at whether or not the

1023
00:50:49,760 --> 00:50:53,040
Speaker 2: raccoons were males or females, and sexual dimorphism, which is,

1024
00:50:53,080 --> 00:50:55,879
Speaker 2: you know, when males and females look different, that could

1025
00:50:55,920 --> 00:50:58,680
Speaker 2: have also explained some of this variability. There's also some

1026
00:50:58,800 --> 00:51:02,239
Speaker 2: raccoons subspecies throughout the United States that look a little

1027
00:51:02,239 --> 00:51:04,560
Speaker 2: bit different that could have explained some of the variability too.

1028
00:51:05,120 --> 00:51:07,480
Speaker 2: I think you could do an experiment, and this is gross,

1029
00:51:07,480 --> 00:51:10,280
Speaker 2: but hear me out. Uh, there's a lot of raccoon

1030
00:51:10,400 --> 00:51:14,040
Speaker 2: roadkill around the United States. You could have people go

1031
00:51:14,160 --> 00:51:18,120
Speaker 2: out and collect raccoons from urban areas and rural areas

1032
00:51:18,160 --> 00:51:21,160
Speaker 2: all across the United States, get those actual measurements or

1033
00:51:21,200 --> 00:51:24,200
Speaker 2: actually even you know, collect the craniums and make a

1034
00:51:24,200 --> 00:51:26,720
Speaker 2: lot of different kinds of measurements and you know, really

1035
00:51:26,719 --> 00:51:29,360
Speaker 2: start to get at some of these values in a

1036
00:51:29,400 --> 00:51:31,200
Speaker 2: more concrete way. You could know if their males, know

1037
00:51:31,239 --> 00:51:33,520
Speaker 2: if they're females, know where they came from. But this

1038
00:51:33,640 --> 00:51:37,520
Speaker 2: is like an early attempt to answer this question using

1039
00:51:37,560 --> 00:51:41,520
Speaker 2: citizen science data and and like, I love citizen science data,

1040
00:51:41,560 --> 00:51:45,759
Speaker 2: but it also comes with so many complications that come

1041
00:51:45,800 --> 00:51:49,400
Speaker 2: with using data collected, you know, for purposes that it

1042
00:51:49,440 --> 00:51:53,040
Speaker 2: wasn't necessarily intended for by people who, like, you know,

1043
00:51:53,080 --> 00:51:55,560
Speaker 2: one of the filters they had to use was is

1044
00:51:55,600 --> 00:51:58,200
Speaker 2: this even a raccoon? You know, so like there might

1045
00:51:58,200 --> 00:52:00,360
Speaker 2: have been people who were like, it's a raccoon, but

1046
00:52:00,400 --> 00:52:04,120
Speaker 2: it's a bobcat or like you know, and so you know,

1047
00:52:04,160 --> 00:52:06,560
Speaker 2: they were trying to see what the best they could

1048
00:52:06,600 --> 00:52:09,400
Speaker 2: do with this data set was, and you know, they

1049
00:52:09,719 --> 00:52:12,759
Speaker 2: got an interesting answer consistent with the hypothesis. I don't

1050
00:52:12,800 --> 00:52:15,239
Speaker 2: feel like I would say we've got a slam dunk here,

1051
00:52:15,920 --> 00:52:18,600
Speaker 2: but good reason maybe to go out and try to

1052
00:52:18,640 --> 00:52:21,319
Speaker 2: collect the data in a bit more of a hands on,

1053
00:52:21,440 --> 00:52:22,120
Speaker 2: rigorous way.

1054
00:52:22,760 --> 00:52:25,760
Speaker 3: And there's lots of interesting issues there, like maybe people

1055
00:52:25,800 --> 00:52:28,840
Speaker 3: in urban versus rural environments are taking pictures of raccoons

1056
00:52:28,840 --> 00:52:32,200
Speaker 3: for different reasons. Right, Urban people are more scared of this,

1057
00:52:32,239 --> 00:52:34,719
Speaker 3: they're showing it to their landlord or something, and royal

1058
00:52:34,760 --> 00:52:36,920
Speaker 3: people are like, hey, cool, you're my friend the raccoon.

1059
00:52:37,480 --> 00:52:39,560
Speaker 3: And that could be influenced by how cute or how

1060
00:52:39,600 --> 00:52:43,120
Speaker 3: scary they look. I know, there's all sorts of possible complications.

1061
00:52:42,520 --> 00:52:44,720
Speaker 2: There, yeah, right, yeah, And you'd have to think about,

1062
00:52:44,760 --> 00:52:47,719
Speaker 2: you know, could the roadkill ones be biased in any way,

1063
00:52:47,800 --> 00:52:50,800
Speaker 2: like are the rural raccoons that are on the road,

1064
00:52:51,000 --> 00:52:53,759
Speaker 2: like the more aggressive raccoons because they're the ones that

1065
00:52:53,800 --> 00:52:55,920
Speaker 2: are willing to get to the road. And so, you know,

1066
00:52:56,040 --> 00:52:59,400
Speaker 2: any experiment where you're taking advantage of a sample of

1067
00:52:59,400 --> 00:53:02,040
Speaker 2: animals that you can easily get your hands on, you've

1068
00:53:02,080 --> 00:53:04,920
Speaker 2: got to wonder how those data are biased in some

1069
00:53:05,000 --> 00:53:06,839
Speaker 2: way that made it easy for you to get those data.

1070
00:53:07,280 --> 00:53:09,279
Speaker 3: All right, So then zoom out and big picture it

1071
00:53:09,280 --> 00:53:13,120
Speaker 3: for as Kelly, do you think that this domestication syndrome

1072
00:53:13,200 --> 00:53:15,960
Speaker 3: thing is real? Is it useful? What do you think

1073
00:53:16,000 --> 00:53:17,319
Speaker 3: the future is going to tell us?

1074
00:53:18,160 --> 00:53:18,520
Speaker 1: All right?

1075
00:53:18,560 --> 00:53:21,480
Speaker 2: So here is my gut feeling. I do feel like

1076
00:53:21,560 --> 00:53:25,200
Speaker 2: when you select for one trait in an animal, those

1077
00:53:25,239 --> 00:53:29,120
Speaker 2: traits are usually controlled by like genes and hormones that

1078
00:53:29,160 --> 00:53:32,080
Speaker 2: influence lots of other traits. And so I think most

1079
00:53:32,120 --> 00:53:34,320
Speaker 2: of the time when you domesticate an animal, you should

1080
00:53:34,320 --> 00:53:36,960
Speaker 2: expect a lot of things to change. And it wouldn't

1081
00:53:36,960 --> 00:53:39,480
Speaker 2: surprise me if a lot of the time when you

1082
00:53:39,560 --> 00:53:43,560
Speaker 2: are selecting for something like animals that are less likely

1083
00:53:43,600 --> 00:53:46,760
Speaker 2: to bite, a lot of the same traits would change

1084
00:53:47,160 --> 00:53:50,560
Speaker 2: in animals that we domesticate. And so I imagine that

1085
00:53:50,600 --> 00:53:53,319
Speaker 2: we will find a domestication syndrome has like I don't know,

1086
00:53:53,320 --> 00:53:56,160
Speaker 2: maybe two or three traits that are pretty consistently changed

1087
00:53:56,160 --> 00:53:58,920
Speaker 2: across species. I'm not going to bet my life on that,

1088
00:54:00,120 --> 00:54:02,239
Speaker 2: but like, it wouldn't surprise me if we found that,

1089
00:54:02,880 --> 00:54:04,720
Speaker 2: and so, yeah, I guess that's what I would guess.

1090
00:54:05,120 --> 00:54:08,120
Speaker 3: All right, that makes sense. Let's send this back to

1091
00:54:08,320 --> 00:54:11,120
Speaker 3: Matt to see if we have answered his question.

1092
00:54:11,560 --> 00:54:13,440
Speaker 4: Thank you, Kelly and Daniel for giving us a more

1093
00:54:13,480 --> 00:54:16,160
Speaker 4: grounded look beyond the pop science articles. I came for

1094
00:54:16,200 --> 00:54:19,480
Speaker 4: the raccoons, stayed for the Fox drama. I am disappointed, though,

1095
00:54:19,520 --> 00:54:22,200
Speaker 4: that we don't have raccoons as pets anytime soon. I

1096
00:54:22,280 --> 00:54:24,680
Speaker 4: was a bit skeptical too of the small sample sizes

1097
00:54:24,719 --> 00:54:26,719
Speaker 4: and the pop science takes that made it seem like

1098
00:54:26,840 --> 00:54:29,759
Speaker 4: raccoon domestication was happening and we were witnessing it in

1099
00:54:29,800 --> 00:54:32,000
Speaker 4: practically real time. So I'm really happy I brought it

1100
00:54:32,040 --> 00:54:34,760
Speaker 4: to you, guys. Thanks for giving it the Dkeu treatment.

1101
00:54:35,160 --> 00:54:37,720
Speaker 4: Fun fact that none of you asked for. Though Vulpus

1102
00:54:37,800 --> 00:54:40,920
Speaker 4: vulpus isn't the only animal to get the double Latin treatment.

1103
00:54:41,120 --> 00:54:44,600
Speaker 4: For the western gorilla, we have gorilla gorilla plus if

1104
00:54:44,680 --> 00:54:47,720
Speaker 4: Daniel goes full mafia like villain. He's got a wicked, smart,

1105
00:54:47,760 --> 00:54:51,080
Speaker 4: friendly and classy Discord community to build up his future empire.

1106
00:54:51,440 --> 00:54:55,719
Speaker 4: So come join us as we talk about the universe, space, geology, biology,

1107
00:54:56,280 --> 00:54:58,960
Speaker 4: other things that end in ology. The secret to a

1108
00:54:58,960 --> 00:55:00,319
Speaker 4: great Discord obviously is.

1109
00:55:00,320 --> 00:55:01,280
Speaker 3: Having a pet section.

1110
00:55:01,600 --> 00:55:05,120
Speaker 4: Our members are high energy and enthusiastic, intelligent people from

1111
00:55:05,120 --> 00:55:07,120
Speaker 4: all walks of life, and we'd love for more of

1112
00:55:07,160 --> 00:55:09,759
Speaker 4: you to join in on the conversations and obviously share

1113
00:55:09,800 --> 00:55:13,000
Speaker 4: your pets. Honestly, is one of the moderators too. We

1114
00:55:13,120 --> 00:55:16,080
Speaker 4: have a rarity when it comes to Discord communities. Everyone

1115
00:55:16,120 --> 00:55:19,160
Speaker 4: here is wicked, friendly and smart and extremely civil without

1116
00:55:19,200 --> 00:55:22,440
Speaker 4: needing constant intervention. So really the biggest credit goes to

1117
00:55:22,480 --> 00:55:24,400
Speaker 4: our Discord community for keeping.

1118
00:55:24,160 --> 00:55:24,800
Speaker 3: It that way.

1119
00:55:25,120 --> 00:55:27,799
Speaker 4: We hope to see you and your pets soon. Thanks

1120
00:55:27,880 --> 00:55:28,520
Speaker 4: Daniel and Kelly.

1121
00:55:28,880 --> 00:55:31,480
Speaker 3: All right, thank you very much everybody on the Discord

1122
00:55:31,600 --> 00:55:35,120
Speaker 3: for creating such a fun and inclusive community. If you

1123
00:55:35,160 --> 00:55:37,400
Speaker 3: think the Internet is toxic, you have not been to

1124
00:55:37,480 --> 00:55:42,120
Speaker 3: the dKu Discord. It's a bastion of creativity and support.

1125
00:55:42,120 --> 00:55:43,000
Speaker 3: Come join us.

1126
00:55:42,880 --> 00:55:45,520
Speaker 1: For conversation and pet photos.

1127
00:55:46,960 --> 00:55:50,680
Speaker 3: Short snouts and long snouts all are welcome and goats.

1128
00:55:50,920 --> 00:55:53,319
Speaker 3: When I finally do become a mafia villain and get

1129
00:55:53,320 --> 00:55:56,200
Speaker 3: my own pet lions, I'll post pictures on the discord.

1130
00:55:56,360 --> 00:55:56,959
Speaker 1: I can't wait.

1131
00:56:03,760 --> 00:56:06,160
Speaker 3: Thanks everybody for listening. Please go and do us a

1132
00:56:06,200 --> 00:56:09,439
Speaker 3: favor and rate the show on whatever podcast app you're using.

1133
00:56:09,520 --> 00:56:11,120
Speaker 3: It really helps people find us.

1134
00:56:11,640 --> 00:56:15,560
Speaker 2: Daniel and Kelly's Extraordinary Universe is edited by the amazing

1135
00:56:15,600 --> 00:56:16,320
Speaker 2: Matt Kesselman.

1136
00:56:16,560 --> 00:56:19,799
Speaker 3: He really is a wizard. You can also find us

1137
00:56:19,920 --> 00:56:24,960
Speaker 3: online on Blue Sky, Instagram, and x D and K Universe.

1138
00:56:25,040 --> 00:56:26,279
Speaker 3: Come engage with us.

1139
00:56:26,520 --> 00:56:29,879
Speaker 2: You can email us at questions at Danielankelly dot org.

1140
00:56:29,960 --> 00:56:31,319
Speaker 1: We really do want to.

1141
00:56:31,239 --> 00:56:33,799
Speaker 3: Hear from you, and you can find our website www

1142
00:56:33,880 --> 00:56:37,600
Speaker 3: dot danieland Kelly dot org, where you'll also find an

1143
00:56:37,640 --> 00:56:41,200
Speaker 3: invitation to join our discord where everybody comes and talks

1144
00:56:41,239 --> 00:56:42,600
Speaker 3: about the amazing.

1145
00:56:42,320 --> 00:56:46,600
Speaker 2: Universe, and we also have the most amazing moderators. This

1146
00:56:47,000 --> 00:56:49,640
Speaker 2: is an iHeart podcast. Thanks for joining us.